diff --git a/Kick-Off.md b/Kick-Off.md deleted file mode 100644 index 5694243e..00000000 --- a/Kick-Off.md +++ /dev/null @@ -1,104 +0,0 @@ -Ironhack Logo - -# Welcome to your Final Project! - -## Content -- [Project Description](#project-description) -- [Project Goals](#project-goals) -- [Requirements](#requirements) -- [Deliverables](#deliverables) -- [Schedule](#schedule) -- [Presentation](#presentation) -- [Tips & Tricks](#tips-&-tricks) - - - -## Project Description -In this project, you will pick a topic of your choosing and perform an end-to-end analysis using what you have learned. You will apply the statistical or machine learning techniques we have learned over the last few weeks and present your results to all of us as well as to the jury for the HackShow. - - - -## Project Goals -* Ask interesting and thoughful questions and find the data to answer them. -* Focus on improving in areas that are hard for you or learning more about something with which you feel comfortable. -* Apply the statistical and/or machine learning techniques we have learned. -* Create useful and clear graphs. -* Present your insights in a thoughtful, clear and accurate way. - - - -## Requirements -* You must plan your project. That is why creating a Kanban or Trello Board is mandatory. -* You **CAN'T CODE** until you project is planned. -* Create a *.gitignore* file and include it in your repository. -* You may be on team (3 people max) or work on your own. -* You can include ML or statistics, but it is not required to do so, as long as you have a rigorous analysis. -* You may use the data from your last project or from past projects. - - - -## Deliverables -* A well-commented notebook with your analysis (Jupyter or Zeppelin). -* A 5 minute presentation in the auditorium (+2 minutes of questions) -* A 5 minute presentation for the jury (+5 minutes of questions) -* Repository with your workflow + documentation + code. Even if you are working alone, you need to keep good practices! -* The database where you have kept your data. - - - -## Schedule -*Wednesday* -* Choose your topic and to work as a team or individually. - -*Thursday* -* Create your repository and README overview (template provided). -* Choose the dataset you would like to use. -* Teacher's validation - -**NO CODE UNTIL HERE** - -*Monday morning* -* Database schema validation. - -*Monday* -* Data in the database. - -*Tuesday evening* -* Analysis validation. - -*Wednesday* -* First rehearsal. - -*Thursday morning* -* General rehearsal. - -*Thursday evening* -* Presentation! - -*Friday morning* -* Jury presentation: they will select their two prefered projects. -* We will vote for the third project to be presented at Friday's HackShow. - -*Friday evening. HACKSHOW!* -* Auditorium presentation at 18:00. -* Vote for the winner of the HackShow. - - - -## Presentation -* 5 minute presentation in the auditorium (+2 minutes of questions) -* 5 minute presentation for the jury (+5 minutes of questions) - - - -## Tips & Tricks -* Organize yourself (don't get lost!). Respect deadlines. -* Ask for help vs Google is your friend. -* Define a simple approach first. You never know how the data can betray you 😉. -* Document yourself. Learn about the problem and what research has been done before you. -* Before making a graph, think what you want to represent. -* Don't force yourself to use tecniques if they are not helpful for your objective. -* If using machine learning, remember: - * This is an iterative process. Try your best to improve your model performance by: - * Try different models and select one that is the simplest yet produce the best result. - * Try different hyperparameters and see if they improve the result. diff --git a/README.md b/README.md new file mode 100644 index 00000000..a2c721d5 --- /dev/null +++ b/README.md @@ -0,0 +1,106 @@ +Ironhack Logo + +# Personality Detector +*Victoria Zauner* + +*DAFT BCN 03/20]* + +## Content +- [Project Description](#project-description) +- [Hypotheses / Questions](#hypotheses-questions) +- [Dataset](#dataset) +- [Cleaning](#cleaning) +- [Analysis](#analysis) +- [Model Training and Evaluation](#model-training-and-evaluation) +- [Conclusion](#conclusion) +- [Future Work](#future-work) +- [Workflow](#workflow) +- [Organization](#organization) +- [Links](#links) + +## Project Description +Out of personal interest for the topic, I chose to determine personality types according to the Myer-Briggs-Type-Indicator Theory from the text of comments on Social-Media. I applied two different approaches to the problem: First, I used the NLTK library to determine the personality types with Natural Language Processing. Second, I tried a quantitative approach by taking factors into account with include: the average length of a comment, the variance in length per comment, the average number of links used, the average number of question-marks used, the average number of exclamation-marks used, the average number of dots used, the average number of hashtags used and the average number of the words “me”, “myself” or “I” used per comment. A variety of Algorithms was tested to maximize accuracy. In the end, I assessed the best model on real-life data. + +## Hypotheses / Questions +* Can a person's personality type be determined merely by the language they use when posting on social media? +* In what ways can this determination or singular traits be useful for HR, Consulting and team-building purposes? +* What are the highly correlated properties/features/words of posts with personality types/traits? +* What are the main indicators to determine the personality traits of a person? + +## Dataset + +[Dataset](https://www.kaggle.com/datasnaek/mbti-type?select=mbti_1.csv) + +* The data was retrieved publicly from Kaggle. +* The original table contained 8675 clean rows with two columns: The personality Type classified with the four-letter indicator frame and the text from 50 comments of the same person. +* From this data, I extracted quantitative characteristics for the second part of my analysis. +* To retrieve the real-life data, I chose not to scrape Instagram but to build the data frame manually due to the network's protection. +* To improve the model's success, data retrieved particularly from the network it would be applied to later to accurately regard the vibe of the context reflected in the tone of the posts would have been very helpful. Also, exciting insights could be gained with further in-depth analysis of images matching the text and general activity and behaviour in the form of dates, comments, likes, follows, followers, fluctuations etc. + +## Cleaning +* For the natural language processing, I cleaned the Data using regular expressions and the nltk library to apply the PorterStemmer, lemmatize and remove stopwords from the text. +* For the quantitative data, I used string operations to extend the data frame by desired features. +* In both cases, the target in the form of the four-letter indicator was separated to allow decisions and determinations by 2 each and not by 16. + +## Analysis + +Description is in the [Workflow](#workflow). + +## Model Training and Evaluation + +Description is in the [Workflow](#workflow). + +## Conclusion + +* Personality Types and traits can be roughly determined by language only. +* The accuracy can still be improved and would significantly profit from the model being trained on data from the same social network as it is being applied to. +* The model could be used on profiles of companies, on the character of their social media marketing, as well as on the companies’ followers to determine buyer personas, which would allow adapting and consequently improving successful marketing. +* According to experience and by analysing the personality type of current employees and their performance and the way they flourish in the company culture can allow HR departments to determine a new employee's desired personality type. + +## Future Work + +Adding further data would help to make the model more universally applicable: Images, body language, behaviour on the platform would certainly be of interest. Also, the model could be further improved by finding and cleaning misleading words. + +## Workflow + +1. After the initial analysis of the data (Code 1), I cleaned and modified the data frame to prepare it for NLP (Code 2). + +2. In the following step, I vectorized the text using the Count Vectorizer for feature extraction, and I tried a variety of algorithms on the data, including Multinomial Naive Bayes, the Decision Tree and K-Nearest Neighbor. In this case, the Decision Tree Algorithm gave me the highest overall accuracy of 49% for determining one of the 16 personalities correctly. (Code 2) + +3. Next, I decided to split the target into the four traits concluding the personality type to choose between two options each rather than 16 at the same time. (Code 3) +Following this, I tried a variety of algorithms on the four traits separately, including Multinomial Naive Bayes, the Decision Tree, K- Nearest Neighbor and Random Forests. + +4. At first, I looked for the best performing algorithm for every trait and pieced a model together accordingly. (Code 4): +IE: K-Nearest Neighbors +NS: Random Forests +TF: Multinomial Naive Bayes +JP: Multinomial Naive Bayes + +5. To increase accuracy, I used PCA on the features extracted by the vectorizer and also used MinMaxScaler to scale the reduced features. Additionally, I tested every algorithm on different parameters (n_neighbors, max_depth etc.) to ensure optimized modelling. After this process, I decided to use the Random Forest Model on the fully reduced and scaled training data to maximize accuracy. + +6. Parallel to this process, I was working on my extended data frame, which featured quantitative characteristics of the posts such as the average length of a comment, the variance in length per comment, the average number of links used, the average number of question-marks used, the average number of exclamation-marks used, the average number of dots used, the average number of hashtags used and the average number of the words "me", "myself" or "I" used per comment and inspected the data for correlations with the personality types. (Code 4) + +7. In the following step, I created a Neural Network Model for the Quantitative Data, which resulted in decent accuracy for two of the four traits, IE and JP. (Code 5) + +8. When trying the other algorithms on the quantitative data, the Random Forests and Multinomial Naive Bayes Models performed best but not significantly better - if even - than when run on NLP. (Code 8) + +9. To test the model on real-life data, I asked friends for permission to analyse their posts on Instagram and take the test [online](https://www.16personalities.com) and retrieved a data frame from it, which I tested my algorithms on. (Code 7) + +## Organization + +My repository is organized in two folders: data and code. + +*Data* +* The remote Repo does not contain the larger files. +* The file quant_personalities.csv holds the data frame with the quantitative measures I created. +* The file reallife_data_insta.csv includes the caption of the friends that allowed me to use them. + +*Code* + +Includes the 9 Jupyter Notebooks described above. +Additionally, you find the saved model (random forests for each of the four personality features on the reduced and scaled nlp data) + +## Links + +[Repository](https://github.com/VickyZauner/Project-Week-8-Final-Project) + diff --git a/your-project/README.md b/your-project/README.md deleted file mode 100644 index 9563cd70..00000000 --- a/your-project/README.md +++ /dev/null @@ -1,72 +0,0 @@ -Ironhack Logo - -# Title of My Project -*[Your Name]* - -*[Your Cohort, Campus & Date]* - -## Content -- [Project Description](#project-description) -- [Hypotheses / Questions](#hypotheses-questions) -- [Dataset](#dataset) -- [Cleaning](#cleaning) -- [Analysis](#analysis) -- [Model Training and Evaluation](#model-training-and-evaluation) -- [Conclusion](#conclusion) -- [Future Work](#future-work) -- [Workflow](#workflow) -- [Organization](#organization) -- [Links](#links) - -## Project Description -Write a short description of your project: 3-5 sentences about what your project is about, why you chose this topic (if relevant), and what you are trying to show. - -## Hypotheses / Questions -* What data/business/research/personal question you would like to answer? -* What is the context for the question and the possible scientific or business application? -* What are the hypotheses you would like to test in order to answer your question? -Frame your hypothesis with statistical/data languages (i.e. define Null and Alternative Hypothesis). You can use formulas if you want but that is not required. - -## Dataset -* Where did you get your data? If you downloaded a dataset (either public or private), describe where you downloaded it and include the command to load the dataset. -* Did you build your own datset? If so, did you use an API or a web scraper? PRovide the relevant scripts in your repo. -* For all types of datasets, provide a description of the size, complexity, and data types included in your dataset, as well as a schema of the tables if necessary. -* If the question cannot be answered with the available data, why not? What data would you need to answer it better? - -## Cleaning -Describe your full process of data wrangling and cleaning. Document why you chose to fill missing values, extract outliers, or create the variables you did as well as your reasoning behind the process. - -## Analysis -* Overview the general steps you went through to analyze your data in order to test your hypothesis. -* Document each step of your data exploration and analysis. -* Include charts to demonstrate the effect of your work. -* If you used Machine Learning in your final project, describe your feature selection process. - -## Model Training and Evaluation -*Include this section only if you chose to include ML in your project.* -* Describe how you trained your model, the results you obtained, and how you evaluated those results. - -## Conclusion -* Summarize your results. What do they mean? -* What can you say about your hypotheses? -* Interpret your findings in terms of the questions you try to answer. - -## Future Work -Address any questions you were unable to answer, or any next steps or future extensions to your project. - -## Workflow -Outline the workflow you used in your project. What were the steps? -How did you test the accuracy of your analysis and/or machine learning algorithm? - -## Organization -How did you organize your work? Did you use any tools like a trello or kanban board? - -What does your repository look like? Explain your folder and file structure. - -## Links -Include links to your repository, slides and trello/kanban board. Feel free to include any other links associated with your project. - - -[Repository](https://github.com/) -[Slides](https://slides.com/) -[Trello](https://trello.com/en) diff --git a/your-project/code/Code 1 - Exploring the Dataset.ipynb b/your-project/code/Code 1 - Exploring the Dataset.ipynb new file mode 100644 index 00000000..47417afb --- /dev/null +++ b/your-project/code/Code 1 - Exploring the Dataset.ipynb @@ -0,0 +1,230 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exploring the Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv(r'../data/mbti_1.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
typeposts
0INFJ'http://www.youtube.com/watch?v=qsXHcwe3krw|||...
1ENTP'I'm finding the lack of me in these posts ver...
2INTP'Good one _____ https://www.youtube.com/wat...
3INTJ'Dear INTP, I enjoyed our conversation the o...
4ENTJ'You're fired.|||That's another silly misconce...
\n", + "
" + ], + "text/plain": [ + " type posts\n", + "0 INFJ 'http://www.youtube.com/watch?v=qsXHcwe3krw|||...\n", + "1 ENTP 'I'm finding the lack of me in these posts ver...\n", + "2 INTP 'Good one _____ https://www.youtube.com/wat...\n", + "3 INTJ 'Dear INTP, I enjoyed our conversation the o...\n", + "4 ENTJ 'You're fired.|||That's another silly misconce..." + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8675" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "INFP 1832\n", + "INFJ 1470\n", + "INTP 1304\n", + "INTJ 1091\n", + "ENTP 685\n", + "ENFP 675\n", + "ISTP 337\n", + "ISFP 271\n", + "ENTJ 231\n", + "ISTJ 205\n", + "ENFJ 190\n", + "ISFJ 166\n", + "ESTP 89\n", + "ESFP 48\n", + "ESFJ 42\n", + "ESTJ 39\n", + "Name: type, dtype: int64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.type.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "type 0\n", + "posts 0\n", + "dtype: int64" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/code/Code 2 - Creating Bags of Words | NLP Analysis 1.ipynb b/your-project/code/Code 2 - Creating Bags of Words | NLP Analysis 1.ipynb new file mode 100644 index 00000000..7b185e5c --- /dev/null +++ b/your-project/code/Code 2 - Creating Bags of Words | NLP Analysis 1.ipynb @@ -0,0 +1,934 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## General NLP" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import re\n", + "\n", + "import nltk\n", + "from nltk.stem import PorterStemmer\n", + "from nltk.stem import SnowballStemmer\n", + "from nltk import wordnet\n", + "from nltk.corpus import stopwords\n", + "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n", + "from sklearn.model_selection import train_test_split, cross_val_score\n", + "from sklearn.metrics import classification_report\n", + "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
typeposts
0INFJ'http://www.youtube.com/watch?v=qsXHcwe3krw|||...
1ENTP'I'm finding the lack of me in these posts ver...
2INTP'Good one _____ https://www.youtube.com/wat...
3INTJ'Dear INTP, I enjoyed our conversation the o...
4ENTJ'You're fired.|||That's another silly misconce...
\n", + "
" + ], + "text/plain": [ + " type posts\n", + "0 INFJ 'http://www.youtube.com/watch?v=qsXHcwe3krw|||...\n", + "1 ENTP 'I'm finding the lack of me in these posts ver...\n", + "2 INTP 'Good one _____ https://www.youtube.com/wat...\n", + "3 INTJ 'Dear INTP, I enjoyed our conversation the o...\n", + "4 ENTJ 'You're fired.|||That's another silly misconce..." + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv(r'../data/mbti_1.csv')\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def processed(x):\n", + " pattern = r'''(?i)\\b((?:https?://|www\\d{0,3}[.]|[a-z0-9.\\-]+[.][a-z]{2,4}/)(?:[^\\s()<>]+|\\(([^\\s()<>]+|(\\([^\\s()<>]+\\)))*\\))+(?:\\(([^\\s()<>]+|(\\([^\\s()<>]+\\)))*\\)|[^\\s`!()\\[\\]{};:'\".,<>?«»“”‘’]))'''\n", + " cleaned = re.sub(pattern, ' ', str(x)) \n", + " cleaned = re.sub(r\"[^A-Za-z0-9]+\", \" \", cleaned)\n", + " cleaned = re.sub(\" \\d+\", \" \", cleaned)\n", + " cleaned = cleaned.lower().strip()\n", + "\n", + " pattern2 = '\\w{1,}'\n", + " bag = re.findall(pattern2, cleaned)\n", + "\n", + " porter = PorterStemmer()\n", + " porter_bag = [porter.stem(word) for word in bag]\n", + "\n", + " lemmatizer = wordnet.WordNetLemmatizer()\n", + " bag_of_words = [lemmatizer.lemmatize(word) for word in porter_bag]\n", + "\n", + " stopwords_e = stopwords.words('english')\n", + " bag_of_words = [word for word in bag_of_words if word not in stopwords_e]\n", + "\n", + " return bag_of_words" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "text_processed = [processed(i) for i in data.posts]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "data['text_processed'] = text_processed" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
typepoststext_processed
0INFJ'http://www.youtube.com/watch?v=qsXHcwe3krw|||...[intj, moment, sportscent, top, ten, play, pra...
1ENTP'I'm finding the lack of me in these posts ver...[find, lack, post, veri, alarm, sex, bore, pos...
2INTP'Good one _____ https://www.youtube.com/wat...[good, one, cours, say, know, bless, cur, doe,...
3INTJ'Dear INTP, I enjoyed our conversation the o...[dear, intp, enjoy, convers, day, esoter, gab,...
4ENTJ'You're fired.|||That's another silly misconce...[fire, anoth, silli, misconcept, approach, log...
\n", + "
" + ], + "text/plain": [ + " type posts \\\n", + "0 INFJ 'http://www.youtube.com/watch?v=qsXHcwe3krw|||... \n", + "1 ENTP 'I'm finding the lack of me in these posts ver... \n", + "2 INTP 'Good one _____ https://www.youtube.com/wat... \n", + "3 INTJ 'Dear INTP, I enjoyed our conversation the o... \n", + "4 ENTJ 'You're fired.|||That's another silly misconce... \n", + "\n", + " text_processed \n", + "0 [intj, moment, sportscent, top, ten, play, pra... \n", + "1 [find, lack, post, veri, alarm, sex, bore, pos... \n", + "2 [good, one, cours, say, know, bless, cur, doe,... \n", + "3 [dear, intp, enjoy, convers, day, esoter, gab,... \n", + "4 [fire, anoth, silli, misconcept, approach, log... " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "data['text_ready'] = data.text_processed.apply(' '.join)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
typepoststext_processedtext_ready
0INFJ'http://www.youtube.com/watch?v=qsXHcwe3krw|||...[intj, moment, sportscent, top, ten, play, pra...intj moment sportscent top ten play prank ha l...
1ENTP'I'm finding the lack of me in these posts ver...[find, lack, post, veri, alarm, sex, bore, pos...find lack post veri alarm sex bore posit often...
2INTP'Good one _____ https://www.youtube.com/wat...[good, one, cours, say, know, bless, cur, doe,...good one cours say know bless cur doe absolut ...
3INTJ'Dear INTP, I enjoyed our conversation the o...[dear, intp, enjoy, convers, day, esoter, gab,...dear intp enjoy convers day esoter gab natur u...
4ENTJ'You're fired.|||That's another silly misconce...[fire, anoth, silli, misconcept, approach, log...fire anoth silli misconcept approach logic go ...
\n", + "
" + ], + "text/plain": [ + " type posts \\\n", + "0 INFJ 'http://www.youtube.com/watch?v=qsXHcwe3krw|||... \n", + "1 ENTP 'I'm finding the lack of me in these posts ver... \n", + "2 INTP 'Good one _____ https://www.youtube.com/wat... \n", + "3 INTJ 'Dear INTP, I enjoyed our conversation the o... \n", + "4 ENTJ 'You're fired.|||That's another silly misconce... \n", + "\n", + " text_processed \\\n", + "0 [intj, moment, sportscent, top, ten, play, pra... \n", + "1 [find, lack, post, veri, alarm, sex, bore, pos... \n", + "2 [good, one, cours, say, know, bless, cur, doe,... \n", + "3 [dear, intp, enjoy, convers, day, esoter, gab,... \n", + "4 [fire, anoth, silli, misconcept, approach, log... \n", + "\n", + " text_ready \n", + "0 intj moment sportscent top ten play prank ha l... \n", + "1 find lack post veri alarm sex bore posit often... \n", + "2 good one cours say know bless cur doe absolut ... \n", + "3 dear intp enjoy convers day esoter gab natur u... \n", + "4 fire anoth silli misconcept approach logic go ... " + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "data.to_csv(r'../data/personalities_cleaned.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "x = data['text_ready']\n", + "y = data['type']" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.feature_extraction.text import CountVectorizer\n", + "\n", + "vector = CountVectorizer(ngram_range=(2, 2)).fit(x) \n", + "X = vector.transform(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of the sparse matrix: (8675, 1986297)\n", + "Non-Zero occurences: 5339470\n", + "Density of the matrix = 0.03098735307727465\n" + ] + } + ], + "source": [ + "print(\"Shape of the sparse matrix: \", X.shape)\n", + "print(\"Non-Zero occurences: \", X.nnz)\n", + "\n", + "density = (X.nnz/(X.shape[0]*X.shape[1]))*100\n", + "print(\"Density of the matrix = \", density)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(6940, 1986297)" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_train.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[ 0 0 0 0 0 0 0 0 8 22 0 1 0 0 0 0]\n", + " [ 0 1 0 0 0 0 0 0 14 116 1 1 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 12 41 1 1 0 0 0 0]\n", + " [ 0 1 0 0 0 0 0 0 29 88 0 6 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 2 7 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 3 6 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 4 13 0 0 0 0 0 0]\n", + " [ 0 2 0 0 0 0 0 0 118 173 0 2 0 0 0 0]\n", + " [ 0 1 0 1 0 0 0 0 16 361 2 1 0 0 0 0]\n", + " [ 0 1 0 1 0 0 0 0 52 144 18 17 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 26 164 2 40 0 1 0 0]\n", + " [ 0 0 0 0 0 0 0 0 4 23 0 0 0 0 0 0]\n", + " [ 0 1 0 0 0 0 0 0 6 53 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 9 30 0 1 0 0 0 0]\n", + " [ 0 0 0 1 0 0 0 0 8 62 1 6 0 0 0 0]]\n", + "Score: 31.01\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " ENFJ 0.00 0.00 0.00 31\n", + " ENFP 0.14 0.01 0.01 133\n", + " ENTJ 0.00 0.00 0.00 55\n", + " ENTP 0.00 0.00 0.00 124\n", + " ESFJ 0.00 0.00 0.00 9\n", + " ESFP 0.00 0.00 0.00 9\n", + " ESTJ 0.00 0.00 0.00 9\n", + " ESTP 0.00 0.00 0.00 17\n", + " INFJ 0.38 0.40 0.39 295\n", + " INFP 0.28 0.95 0.43 382\n", + " INTJ 0.72 0.08 0.14 233\n", + " INTP 0.53 0.17 0.26 233\n", + " ISFJ 0.00 0.00 0.00 27\n", + " ISFP 0.00 0.00 0.00 60\n", + " ISTJ 0.00 0.00 0.00 40\n", + " ISTP 0.00 0.00 0.00 78\n", + "\n", + " accuracy 0.31 1735\n", + " macro avg 0.13 0.10 0.08 1735\n", + "weighted avg 0.30 0.31 0.21 1735\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + } + ], + "source": [ + "from sklearn.naive_bayes import MultinomialNB\n", + "\n", + "mnb = MultinomialNB()\n", + "mnb.fit(x_train, y_train)\n", + "\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[ 0 0 0 0 0 0 0 0 0 31 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 133 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 55 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 124 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 17 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 1 294 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 382 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 1 231 0 1 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 233 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 27 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 60 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 40 0 0 0 0 0 0]\n", + " [ 0 0 0 0 0 0 0 0 0 78 0 0 0 0 0 0]]\n", + "Score: 22.07\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " ENFJ 0.00 0.00 0.00 31\n", + " ENFP 0.00 0.00 0.00 133\n", + " ENTJ 0.00 0.00 0.00 55\n", + " ENTP 0.00 0.00 0.00 124\n", + " ESFJ 0.00 0.00 0.00 9\n", + " ESFP 0.00 0.00 0.00 9\n", + " ESTJ 0.00 0.00 0.00 9\n", + " ESTP 0.00 0.00 0.00 17\n", + " INFJ 0.50 0.00 0.01 295\n", + " INFP 0.22 1.00 0.36 382\n", + " INTJ 0.00 0.00 0.00 233\n", + " INTP 0.00 0.00 0.00 233\n", + " ISFJ 0.00 0.00 0.00 27\n", + " ISFP 0.00 0.00 0.00 60\n", + " ISTJ 0.00 0.00 0.00 40\n", + " ISTP 0.00 0.00 0.00 78\n", + "\n", + " accuracy 0.22 1735\n", + " macro avg 0.05 0.06 0.02 1735\n", + "weighted avg 0.13 0.22 0.08 1735\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + } + ], + "source": [ + "from sklearn.feature_extraction.text import TfidfVectorizer\n", + "\n", + "vectorizer = TfidfVectorizer()\n", + "X = vectorizer.fit_transform(x)\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=10)\n", + "mnb = MultinomialNB()\n", + "mnb.fit(x_train, y_train)\n", + "\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion Matrix for Decision Tree:\n", + "[[ 6 2 0 1 0 1 0 1 4 7 3 2 0 3 0 1]\n", + " [ 4 58 3 9 0 2 0 0 16 12 12 11 1 2 3 0]\n", + " [ 0 4 10 9 0 0 0 0 5 5 4 8 1 2 3 4]\n", + " [ 0 5 2 56 1 0 0 0 15 13 8 12 3 5 4 0]\n", + " [ 0 1 0 1 0 0 0 0 1 0 0 5 1 0 0 0]\n", + " [ 0 1 1 0 0 0 0 0 3 0 1 3 0 0 0 0]\n", + " [ 0 0 1 0 0 0 0 0 2 3 0 0 0 2 1 0]\n", + " [ 0 0 1 1 0 0 0 4 3 1 4 1 0 2 0 0]\n", + " [ 3 9 2 13 0 1 0 1 162 38 26 22 8 5 2 3]\n", + " [ 9 14 7 15 2 2 1 3 31 228 17 21 7 15 4 6]\n", + " [ 3 14 9 11 0 5 1 0 24 14 110 21 0 10 7 4]\n", + " [ 1 4 1 13 2 0 0 1 21 15 26 140 2 1 1 5]\n", + " [ 1 0 0 1 1 0 0 0 4 2 2 1 10 2 3 0]\n", + " [ 1 4 0 1 1 2 0 2 5 11 4 2 2 18 5 2]\n", + " [ 0 1 0 3 0 1 0 0 4 7 3 6 3 2 9 1]\n", + " [ 1 4 0 4 0 2 0 0 2 9 7 6 0 4 2 37]]\n", + "Score: 48.88\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " ENFJ 0.21 0.19 0.20 31\n", + " ENFP 0.48 0.44 0.46 133\n", + " ENTJ 0.27 0.18 0.22 55\n", + " ENTP 0.41 0.45 0.43 124\n", + " ESFJ 0.00 0.00 0.00 9\n", + " ESFP 0.00 0.00 0.00 9\n", + " ESTJ 0.00 0.00 0.00 9\n", + " ESTP 0.33 0.24 0.28 17\n", + " INFJ 0.54 0.55 0.54 295\n", + " INFP 0.62 0.60 0.61 382\n", + " INTJ 0.48 0.47 0.48 233\n", + " INTP 0.54 0.60 0.57 233\n", + " ISFJ 0.26 0.37 0.31 27\n", + " ISFP 0.25 0.30 0.27 60\n", + " ISTJ 0.20 0.23 0.21 40\n", + " ISTP 0.59 0.47 0.52 78\n", + "\n", + " accuracy 0.49 1735\n", + " macro avg 0.32 0.32 0.32 1735\n", + "weighted avg 0.49 0.49 0.49 1735\n", + "\n" + ] + } + ], + "source": [ + "from sklearn.tree import DecisionTreeClassifier\n", + "\n", + "dt = DecisionTreeClassifier()\n", + "dt.fit(x_train,y_train)\n", + "preddt = dt.predict(x_test)\n", + "print(\"Confusion Matrix for Decision Tree:\")\n", + "print(confusion_matrix(y_test,preddt))\n", + "print(\"Score:\",round(accuracy_score(y_test,preddt)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,preddt))" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion Matrix for Random Forest Classifier:\n", + "[[ 8 6 0 1 0 0 0 0 7 9 0 0 0 0 0 0]\n", + " [ 1 69 1 5 0 0 0 0 10 45 1 0 0 1 0 0]\n", + " [ 2 7 11 2 0 0 0 0 8 22 2 0 0 0 0 1]\n", + " [ 0 11 1 40 1 0 0 0 24 39 5 3 0 0 0 0]\n", + " [ 0 0 0 0 8 0 0 0 0 1 0 0 0 0 0 0]\n", + " [ 0 1 1 0 1 1 0 0 3 2 0 0 0 0 0 0]\n", + " [ 0 1 1 0 0 0 2 0 1 4 0 0 0 0 0 0]\n", + " [ 0 2 0 0 0 0 0 7 3 3 0 1 0 1 0 0]\n", + " [ 3 18 0 3 2 0 0 1 174 86 4 3 0 1 0 0]\n", + " [ 2 26 0 2 1 0 0 1 58 284 5 1 0 1 1 0]\n", + " [ 1 31 0 8 2 0 0 0 37 81 57 10 5 0 1 0]\n", + " [ 1 14 2 9 2 0 0 0 45 95 10 55 0 0 0 0]\n", + " [ 0 1 0 0 0 0 0 0 6 3 1 0 16 0 0 0]\n", + " [ 0 7 0 1 0 0 0 1 10 32 0 0 0 9 0 0]\n", + " [ 0 3 0 1 0 0 0 0 11 14 2 0 1 0 8 0]\n", + " [ 0 8 0 3 0 0 0 0 13 39 5 2 1 0 0 7]]\n", + "Score: 23.05\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " ENFJ 0.00 0.00 0.00 31\n", + " ENFP 0.00 0.00 0.00 133\n", + " ENTJ 0.00 0.00 0.00 55\n", + " ENTP 0.00 0.00 0.00 124\n", + " ESFJ 0.00 0.00 0.00 9\n", + " ESFP 0.00 0.00 0.00 9\n", + " ESTJ 0.00 0.00 0.00 9\n", + " ESTP 0.00 0.00 0.00 17\n", + " INFJ 0.62 0.03 0.06 295\n", + " INFP 0.22 1.00 0.37 382\n", + " INTJ 0.00 0.00 0.00 233\n", + " INTP 0.73 0.03 0.07 233\n", + " ISFJ 0.00 0.00 0.00 27\n", + " ISFP 0.00 0.00 0.00 60\n", + " ISTJ 0.00 0.00 0.00 40\n", + " ISTP 0.00 0.00 0.00 78\n", + "\n", + " accuracy 0.23 1735\n", + " macro avg 0.10 0.07 0.03 1735\n", + "weighted avg 0.25 0.23 0.10 1735\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + } + ], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "rfn = RandomForestClassifier(max_depth=5, random_state=0)\n", + "rfn.fit(x_train,y_train)\n", + "predrf = rfn.predict(x_test)\n", + "print(\"Confusion Matrix for Random Forest Classifier:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score: \",round(accuracy_score(y_test,predrf)*100,2))\n", + "print(\"Classification Report:\")\n", + "print(classification_report(y_test,predrf))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion Matrix for K Neighbors Classifier:\n", + "[[ 8 6 0 1 0 0 0 0 7 9 0 0 0 0 0 0]\n", + " [ 1 69 1 5 0 0 0 0 10 45 1 0 0 1 0 0]\n", + " [ 2 7 11 2 0 0 0 0 8 22 2 0 0 0 0 1]\n", + " [ 0 11 1 40 1 0 0 0 24 39 5 3 0 0 0 0]\n", + " [ 0 0 0 0 8 0 0 0 0 1 0 0 0 0 0 0]\n", + " [ 0 1 1 0 1 1 0 0 3 2 0 0 0 0 0 0]\n", + " [ 0 1 1 0 0 0 2 0 1 4 0 0 0 0 0 0]\n", + " [ 0 2 0 0 0 0 0 7 3 3 0 1 0 1 0 0]\n", + " [ 3 18 0 3 2 0 0 1 174 86 4 3 0 1 0 0]\n", + " [ 2 26 0 2 1 0 0 1 58 284 5 1 0 1 1 0]\n", + " [ 1 31 0 8 2 0 0 0 37 81 57 10 5 0 1 0]\n", + " [ 1 14 2 9 2 0 0 0 45 95 10 55 0 0 0 0]\n", + " [ 0 1 0 0 0 0 0 0 6 3 1 0 16 0 0 0]\n", + " [ 0 7 0 1 0 0 0 1 10 32 0 0 0 9 0 0]\n", + " [ 0 3 0 1 0 0 0 0 11 14 2 0 1 0 8 0]\n", + " [ 0 8 0 3 0 0 0 0 13 39 5 2 1 0 0 7]]\n", + "Score: 43.57\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " ENFJ 0.44 0.26 0.33 31\n", + " ENFP 0.34 0.52 0.41 133\n", + " ENTJ 0.65 0.20 0.31 55\n", + " ENTP 0.53 0.32 0.40 124\n", + " ESFJ 0.47 0.89 0.62 9\n", + " ESFP 1.00 0.11 0.20 9\n", + " ESTJ 1.00 0.22 0.36 9\n", + " ESTP 0.70 0.41 0.52 17\n", + " INFJ 0.42 0.59 0.49 295\n", + " INFP 0.37 0.74 0.50 382\n", + " INTJ 0.62 0.24 0.35 233\n", + " INTP 0.73 0.24 0.36 233\n", + " ISFJ 0.70 0.59 0.64 27\n", + " ISFP 0.69 0.15 0.25 60\n", + " ISTJ 0.80 0.20 0.32 40\n", + " ISTP 0.88 0.09 0.16 78\n", + "\n", + " accuracy 0.44 1735\n", + " macro avg 0.65 0.36 0.39 1735\n", + "weighted avg 0.54 0.44 0.41 1735\n", + "\n" + ] + } + ], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier\n", + "\n", + "knn = KNeighborsClassifier(n_neighbors=10)\n", + "knn.fit(x_train,y_train)\n", + "predknn = knn.predict(x_test)\n", + "print(\"Confusion Matrix for K Neighbors Classifier:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score: \",round(accuracy_score(y_test,predknn)*100,2))\n", + "print(\"Classification Report:\")\n", + "print(classification_report(y_test,predknn))" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already up-to-date: xgboost in /usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages (1.1.0)\n", + "Requirement already satisfied, skipping upgrade: scipy in /usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages (from xgboost) (1.4.1)\n", + "Requirement already satisfied, skipping upgrade: numpy in /usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages (from xgboost) (1.18.2)\n", + "\u001b[33mWARNING: You are using pip version 19.3.1; however, version 20.1.1 is available.\n", + "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n" + ] + } + ], + "source": [ + "import sys\n", + "!{sys.executable} -m pip install --upgrade xgboost" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "ename": "XGBoostError", + "evalue": "XGBoost Library (libxgboost.dylib) could not be loaded.\nLikely causes:\n * OpenMP runtime is not installed (vcomp140.dll or libgomp-1.dll for Windows, libomp.dylib for Mac OSX, libgomp.so for Linux and other UNIX-like OSes). Mac OSX users: Run `brew install libomp` to install OpenMP runtime.\n * You are running 32-bit Python on a 64-bit OS\nError message(s): ['dlopen(/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/xgboost/lib/libxgboost.dylib, 6): Library not loaded: /usr/local/opt/libomp/lib/libomp.dylib\\n Referenced from: /usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/xgboost/lib/libxgboost.dylib\\n Reason: image not found']\n", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mXGBoostError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mxgboost\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/xgboost/__init__.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDMatrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDeviceQuantileDMatrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBooster\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mtraining\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrabit\u001b[0m \u001b[0;31m# noqa\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/xgboost/core.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 173\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 174\u001b[0m \u001b[0;31m# load the XGBoost library globally\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 175\u001b[0;31m \u001b[0m_LIB\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_load_lib\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 176\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 177\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/xgboost/core.py\u001b[0m in \u001b[0;36m_load_lib\u001b[0;34m()\u001b[0m\n\u001b[1;32m 164\u001b[0m \u001b[0;34m'`brew install libomp` to install OpenMP runtime.\\n'\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 165\u001b[0m \u001b[0;34m' * You are running 32-bit Python on a 64-bit OS\\n'\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 166\u001b[0;31m 'Error message(s): {}\\n'.format(os_error_list))\n\u001b[0m\u001b[1;32m 167\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mXGBGetLastError\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrestype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mctypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mc_char_p\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 168\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_log_callback_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mXGBoostError\u001b[0m: XGBoost Library (libxgboost.dylib) could not be loaded.\nLikely causes:\n * OpenMP runtime is not installed (vcomp140.dll or libgomp-1.dll for Windows, libomp.dylib for Mac OSX, libgomp.so for Linux and other UNIX-like OSes). Mac OSX users: Run `brew install libomp` to install OpenMP runtime.\n * You are running 32-bit Python on a 64-bit OS\nError message(s): ['dlopen(/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/xgboost/lib/libxgboost.dylib, 6): Library not loaded: /usr/local/opt/libomp/lib/libomp.dylib\\n Referenced from: /usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/xgboost/lib/libxgboost.dylib\\n Reason: image not found']\n" + ] + } + ], + "source": [ + "import xgboost\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'XGBClassifier' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mxgb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mXGBClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mxgb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mpredxgb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxgb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Confusion Matrix for XGBoost Classifier:\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpredxgb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'XGBClassifier' is not defined" + ] + } + ], + "source": [ + "xgb = XGBClassifier()\n", + "xgb.fit(x_train,y_train)\n", + "predxgb = xgb.predict(x_test)\n", + "print(\"Confusion Matrix for XGBoost Classifier:\")\n", + "print(confusion_matrix(y_test,predxgb))\n", + "print(\"Score: \",round(accuracy_score(y_test,predxgb)*100,2))\n", + "print(\"Classification Report:\")\n", + "print(classification_report(y_test,predxgb))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/code/Code 3 - Splitting Targets | NLP Analysis 2.ipynb b/your-project/code/Code 3 - Splitting Targets | NLP Analysis 2.ipynb new file mode 100644 index 00000000..07fbc7fa --- /dev/null +++ b/your-project/code/Code 3 - Splitting Targets | NLP Analysis 2.ipynb @@ -0,0 +1,2226 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Splitting the target" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from sklearn.model_selection import train_test_split, cross_val_score\n", + "from sklearn.model_selection import cross_validate\n", + "from sklearn.model_selection import StratifiedKFold\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import learning_curve\n", + "from sklearn.ensemble import ExtraTreesClassifier\n", + "from sklearn.decomposition import TruncatedSVD\n", + "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.naive_bayes import MultinomialNB\n", + "from sklearn.metrics import classification_report\n", + "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv(r'../data/personalities_cleaned.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "data2 = data.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "data2['I-E'] = data2['type'].astype(str).str[0]\n", + "data2['N-S'] = data2['type'].astype(str).str[1]\n", + "data2['T-F'] = data2['type'].astype(str).str[2]\n", + "data2['J-P'] = data2['type'].astype(str).str[3]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "data2 = data2.drop(columns=['Unnamed: 0', 'posts', 'text_processed'])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "type 0\n", + "text_ready 1\n", + "I-E 0\n", + "N-S 0\n", + "T-F 0\n", + "J-P 0\n", + "dtype: int64" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data2.isnull().sum()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
typetext_readyI-EN-ST-FJ-P
3559INFPNaNINFP
\n", + "
" + ], + "text/plain": [ + " type text_ready I-E N-S T-F J-P\n", + "3559 INFP NaN I N F P" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1 = data2[data2.isna().any(axis=1)]\n", + "df1" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "data2.drop(data2.index[3559], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "type 0\n", + "text_ready 0\n", + "I-E 0\n", + "N-S 0\n", + "T-F 0\n", + "J-P 0\n", + "dtype: int64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data2.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
typetext_readyI-EN-ST-FJ-P
0INFJintj moment sportscent top ten play prank ha l...INFJ
1ENTPfind lack post veri alarm sex bore posit often...ENTP
2INTPgood one cours say know bless cur doe absolut ...INTP
3INTJdear intp enjoy convers day esoter gab natur u...INTJ
4ENTJfire anoth silli misconcept approach logic go ...ENTJ
\n", + "
" + ], + "text/plain": [ + " type text_ready I-E N-S T-F J-P\n", + "0 INFJ intj moment sportscent top ten play prank ha l... I N F J\n", + "1 ENTP find lack post veri alarm sex bore posit often... E N T P\n", + "2 INTP good one cours say know bless cur doe absolut ... I N T P\n", + "3 INTJ dear intp enjoy convers day esoter gab natur u... I N T J\n", + "4 ENTJ fire anoth silli misconcept approach logic go ... E N T J" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data2.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# learning with the model" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# defining learnings for each \n", + "x = data2['text_ready']\n", + "\n", + "y_IE = data2['I-E']\n", + "y_NS = data2['N-S']\n", + "y_TF = data2['T-F']\n", + "y_JP = data2['J-P']" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# for vector functions\n", + "from sklearn.feature_extraction.text import CountVectorizer\n", + "\n", + "vector = CountVectorizer(ngram_range=(2, 2)).fit(x) \n", + "X = vector.transform(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.decomposition import TruncatedSVD\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "\n", + "svd = TruncatedSVD(n_components = 100)\n", + "reduced = svd.fit_transform(X)\n", + "\n", + "scaler = MinMaxScaler()\n", + "scaled = scaler.fit_transform(reduced)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Optimizing Parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "I 6675\n", + "E 1999\n", + "Name: I-E, dtype: int64" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_IE.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E Accuracy for 1 neigbours.\n", + "Label I-E test score is : 0.6610951008645534\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 88 293]\n", + " [ 295 1059]]\n", + "I-E Accuracy for 3 neigbours.\n", + "Label I-E test score is : 0.7066282420749279\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 64 373]\n", + " [ 136 1162]]\n", + "I-E Accuracy for 5 neigbours.\n", + "Label I-E test score is : 0.7360230547550433\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 45 352]\n", + " [ 106 1232]]\n", + "I-E Accuracy for 7 neigbours.\n", + "Label I-E test score is : 0.7636887608069164\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 17 363]\n", + " [ 47 1308]]\n", + "I-E Accuracy for 9 neigbours.\n", + "Label I-E test score is : 0.7688760806916427\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 18 357]\n", + " [ 44 1316]]\n", + "I-E Accuracy for 11 neigbours.\n", + "Label I-E test score is : 0.7475504322766571\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 14 403]\n", + " [ 35 1283]]\n", + "I-E Accuracy for 13 neigbours.\n", + "Label I-E test score is : 0.7734870317002882\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 7 375]\n", + " [ 18 1335]]\n", + "I-E Accuracy for 15 neigbours.\n", + "Label I-E test score is : 0.7798270893371758\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 3 367]\n", + " [ 15 1350]]\n", + "I-E Accuracy for 17 neigbours.\n", + "Label I-E test score is : 0.7613832853025937\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 3 403]\n", + " [ 11 1318]]\n", + "I-E Accuracy for 19 neigbours.\n", + "Label I-E test score is : 0.7642651296829971\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 6 401]\n", + " [ 8 1320]]\n", + "I-E Accuracy for 21 neigbours.\n", + "Label I-E test score is : 0.7688760806916427\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 6 393]\n", + " [ 8 1328]]\n", + "I-E Accuracy for 23 neigbours.\n", + "Label I-E test score is : 0.7717579250720461\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 2 393]\n", + " [ 3 1337]]\n", + "I-E Accuracy for 25 neigbours.\n", + "Label I-E test score is : 0.7659942363112392\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 2 404]\n", + " [ 2 1327]]\n" + ] + } + ], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier\n", + "\n", + "\n", + "for x in range(1, 26, 2):\n", + " x_train, x_test, y_train, y_test = train_test_split(reduced, y_IE, test_size=0.2)\n", + " knn = KNeighborsClassifier(n_neighbors = x)\n", + " clf = knn.fit(x_train, y_train)\n", + " predknn = clf.predict(x_test)\n", + " print(\"I-E Accuracy for \", x, \" neigbours.\")\n", + " print('Label I-E test score is :',accuracy_score(y_test, predknn))\n", + " print(\"Confusion Matrix for KNeighbors:\")\n", + " print(confusion_matrix(y_test, predknn))\n", + " #print(\"Score:\",round(accuracy_score(predknn, y_test)*100,2))\n", + "\n", + "#print(\"*\" * 100)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E Accuracy for 1 neigbours.\n", + "Label I-E test score is : 0.6570605187319885\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 91 308]\n", + " [ 287 1049]]\n", + "I-E Accuracy for 3 neigbours.\n", + "Label I-E test score is : 0.7066282420749279\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 63 356]\n", + " [ 153 1163]]\n", + "I-E Accuracy for 5 neigbours.\n", + "Label I-E test score is : 0.729106628242075\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 38 374]\n", + " [ 96 1227]]\n", + "I-E Accuracy for 7 neigbours.\n", + "Label I-E test score is : 0.7596541786743516\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 25 367]\n", + " [ 50 1293]]\n", + "I-E Accuracy for 9 neigbours.\n", + "Label I-E test score is : 0.7613832853025937\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 17 382]\n", + " [ 32 1304]]\n", + "I-E Accuracy for 11 neigbours.\n", + "Label I-E test score is : 0.7538904899135447\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 19 394]\n", + " [ 33 1289]]\n", + "I-E Accuracy for 13 neigbours.\n", + "Label I-E test score is : 0.768299711815562\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 10 381]\n", + " [ 21 1323]]\n", + "I-E Accuracy for 15 neigbours.\n", + "Label I-E test score is : 0.7654178674351585\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 10 386]\n", + " [ 21 1318]]\n", + "I-E Accuracy for 17 neigbours.\n", + "Label I-E test score is : 0.7780979827089337\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 7 376]\n", + " [ 9 1343]]\n", + "I-E Accuracy for 19 neigbours.\n", + "Label I-E test score is : 0.7642651296829971\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 5 400]\n", + " [ 9 1321]]\n", + "I-E Accuracy for 21 neigbours.\n", + "Label I-E test score is : 0.7757925072046109\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 5 383]\n", + " [ 6 1341]]\n", + "I-E Accuracy for 23 neigbours.\n", + "Label I-E test score is : 0.7694524495677233\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 0 395]\n", + " [ 5 1335]]\n", + "I-E Accuracy for 25 neigbours.\n", + "Label I-E test score is : 0.777521613832853\n", + "Confusion Matrix for KNeighbors:\n", + "[[ 5 384]\n", + " [ 2 1344]]\n" + ] + } + ], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier\n", + "\n", + "\n", + "for x in range(1, 26, 2):\n", + " x_train, x_test, y_train, y_test = train_test_split(scaled, y_IE, test_size=0.2)\n", + " knn = KNeighborsClassifier(n_neighbors = x)\n", + " clf = knn.fit(x_train, y_train)\n", + " predknn = clf.predict(x_test)\n", + " print(\"I-E Accuracy for \", x, \" neigbours.\")\n", + " print('Label I-E test score is :',accuracy_score(y_test, predknn))\n", + " print(\"Confusion Matrix for KNeighbors:\")\n", + " print(confusion_matrix(y_test, predknn))" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E Accuracy for 1 max_depth.\n", + "[[1492 0]\n", + " [ 243 0]]\n", + "Score: 85.99\n", + "I-E Accuracy for 2 max_depth.\n", + "[[1510 0]\n", + " [ 225 0]]\n", + "Score: 87.03\n", + "I-E Accuracy for 3 max_depth.\n", + "[[1503 0]\n", + " [ 232 0]]\n", + "Score: 86.63\n", + "I-E Accuracy for 4 max_depth.\n", + "[[1487 0]\n", + " [ 248 0]]\n", + "Score: 85.71\n", + "I-E Accuracy for 5 max_depth.\n", + "[[1502 0]\n", + " [ 233 0]]\n", + "Score: 86.57\n", + "I-E Accuracy for 6 max_depth.\n", + "[[1499 0]\n", + " [ 236 0]]\n", + "Score: 86.4\n", + "I-E Accuracy for 7 max_depth.\n", + "[[1498 0]\n", + " [ 237 0]]\n", + "Score: 86.34\n", + "I-E Accuracy for 8 max_depth.\n", + "[[1525 0]\n", + " [ 210 0]]\n", + "Score: 87.9\n", + "I-E Accuracy for 9 max_depth.\n", + "[[1511 0]\n", + " [ 224 0]]\n", + "Score: 87.09\n", + "I-E Accuracy for 10 max_depth.\n", + "[[1490 0]\n", + " [ 244 1]]\n", + "Score: 85.94\n", + "I-E Accuracy for 11 max_depth.\n", + "[[1504 2]\n", + " [ 228 1]]\n", + "Score: 86.74\n", + "I-E Accuracy for 12 max_depth.\n", + "[[1470 3]\n", + " [ 262 0]]\n", + "Score: 84.73\n", + "I-E Accuracy for 13 max_depth.\n", + "[[1494 1]\n", + " [ 240 0]]\n", + "Score: 86.11\n", + "I-E Accuracy for 14 max_depth.\n", + "[[1471 5]\n", + " [ 257 2]]\n", + "Score: 84.9\n", + "I-E Accuracy for 15 max_depth.\n", + "[[1501 1]\n", + " [ 232 1]]\n", + "Score: 86.57\n" + ] + } + ], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "\n", + "for x in range(1, 16):\n", + " rfn = RandomForestClassifier(max_depth= x, random_state=0)\n", + "\n", + " # NS\n", + " x_train, x_test, y_train, y_test = train_test_split(reduced, y_NS, test_size=0.2)\n", + " rfn.fit(x_train, y_train)\n", + "# ieb_train = knn.score(x_train,y_train)\n", + "# ieb_test = knn.score (x_train,y_train)\n", + " predrfn = rfn.predict(x_test)\n", + " print(\"I-E Accuracy for \", x, \" max_depth.\")\n", + "# print('Label I-E train score is :',ieb_train)\n", + " print(confusion_matrix(y_test,predrfn))\n", + " print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", + " #print(\"Score:\",round(accuracy_score(predknn, y_test)*100,2))\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E Accuracy for 1 max_depth.\n", + "[[1490 0]\n", + " [ 245 0]]\n", + "Score: 85.88\n", + "I-E Accuracy for 2 max_depth.\n", + "[[1500 0]\n", + " [ 235 0]]\n", + "Score: 86.46\n", + "I-E Accuracy for 3 max_depth.\n", + "[[1502 0]\n", + " [ 233 0]]\n", + "Score: 86.57\n", + "I-E Accuracy for 4 max_depth.\n", + "[[1503 0]\n", + " [ 232 0]]\n", + "Score: 86.63\n", + "I-E Accuracy for 5 max_depth.\n", + "[[1478 0]\n", + " [ 257 0]]\n", + "Score: 85.19\n", + "I-E Accuracy for 6 max_depth.\n", + "[[1503 0]\n", + " [ 232 0]]\n", + "Score: 86.63\n", + "I-E Accuracy for 7 max_depth.\n", + "[[1497 0]\n", + " [ 238 0]]\n", + "Score: 86.28\n", + "I-E Accuracy for 8 max_depth.\n", + "[[1507 0]\n", + " [ 228 0]]\n", + "Score: 86.86\n", + "I-E Accuracy for 9 max_depth.\n", + "[[1510 1]\n", + " [ 224 0]]\n", + "Score: 87.03\n", + "I-E Accuracy for 10 max_depth.\n", + "[[1492 0]\n", + " [ 243 0]]\n", + "Score: 85.99\n", + "I-E Accuracy for 11 max_depth.\n", + "[[1484 1]\n", + " [ 250 0]]\n", + "Score: 85.53\n", + "I-E Accuracy for 12 max_depth.\n", + "[[1493 2]\n", + " [ 240 0]]\n", + "Score: 86.05\n", + "I-E Accuracy for 13 max_depth.\n", + "[[1505 2]\n", + " [ 227 1]]\n", + "Score: 86.8\n", + "I-E Accuracy for 14 max_depth.\n", + "[[1516 1]\n", + " [ 216 2]]\n", + "Score: 87.49\n", + "I-E Accuracy for 15 max_depth.\n", + "[[1492 4]\n", + " [ 239 0]]\n", + "Score: 85.99\n" + ] + } + ], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "\n", + "for x in range(1, 16):\n", + " rfn = RandomForestClassifier(max_depth= x, random_state=0)\n", + "\n", + " # NS\n", + " x_train, x_test, y_train, y_test = train_test_split(scaled, y_NS, test_size=0.2)\n", + " rfn.fit(x_train, y_train)\n", + "# ieb_train = knn.score(x_train,y_train)\n", + "# ieb_test = knn.score (x_train,y_train)\n", + " predrfn = rfn.predict(x_test)\n", + " print(\"I-E Accuracy for \", x, \" max_depth.\")\n", + "# print('Label I-E train score is :',ieb_train)\n", + " print(confusion_matrix(y_test,predrfn))\n", + " print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", + " #print(\"Score:\",round(accuracy_score(predknn, y_test)*100,2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### MultinomialNB" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E RESULTS\n", + "Label I-E train score is : 0.7685545467646635\n", + "Label I-E test score is : 0.7685545467646635\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[ 0 393]\n", + " [ 0 1342]]\n", + "Score: 77.35\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " E 0.00 0.00 0.00 393\n", + " I 0.77 1.00 0.87 1342\n", + "\n", + " accuracy 0.77 1735\n", + " macro avg 0.39 0.50 0.44 1735\n", + "weighted avg 0.60 0.77 0.67 1735\n", + "\n", + "****************************************************************************************************\n", + "N-S RESULTS\n", + "Label N-S train score is : 0.8617956477878657\n", + "Label N-S test score is : 0.8617956477878657\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[1497 0]\n", + " [ 238 0]]\n", + "Score: 86.28\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " N 0.86 1.00 0.93 1497\n", + " S 0.00 0.00 0.00 238\n", + "\n", + " accuracy 0.86 1735\n", + " macro avg 0.43 0.50 0.46 1735\n", + "weighted avg 0.74 0.86 0.80 1735\n", + "\n", + "****************************************************************************************************\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n", + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "T-F Results\n", + "Label T-F train score is : 0.5379737714368065\n", + "Label T-F test score is : 0.5379737714368065\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[960 0]\n", + " [775 0]]\n", + "Score: 55.33\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " F 0.55 1.00 0.71 960\n", + " T 0.00 0.00 0.00 775\n", + "\n", + " accuracy 0.55 1735\n", + " macro avg 0.28 0.50 0.36 1735\n", + "weighted avg 0.31 0.55 0.39 1735\n", + "\n", + "****************************************************************************************************\n", + "J-P Results\n", + "Label J-P train score is : 0.603112840466926\n", + "Label J-P test score is : 0.603112840466926\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[ 0 680]\n", + " [ 0 1055]]\n", + "Score: 60.81\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " J 0.00 0.00 0.00 680\n", + " P 0.61 1.00 0.76 1055\n", + "\n", + " accuracy 0.61 1735\n", + " macro avg 0.30 0.50 0.38 1735\n", + "weighted avg 0.37 0.61 0.46 1735\n", + "\n", + "****************************************************************************************************\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n", + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + } + ], + "source": [ + "from sklearn.naive_bayes import MultinomialNB\n", + "\n", + "mnb = MultinomialNB()\n", + "mnb.fit(x_train, y_train)\n", + "\n", + "\n", + "# IE\n", + "x_train, x_test, y_train, y_test = train_test_split(scaled, y_IE, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "ieb_train = mnb.score (x_train,y_train)\n", + "ieb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "print('Label I-E train score is :',ieb_train)\n", + "print('Label I-E test score is :',ieb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", + "x_train, x_test, y_train, y_test = train_test_split(scaled, y_NS, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "nsb_train = mnb.score (x_train,y_train)\n", + "nsb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "print('Label N-S train score is :',nsb_train)\n", + "print('Label N-S test score is :',nsb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "x_train, x_test, y_train, y_test = train_test_split(scaled, y_TF, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "tfb_train = mnb.score (x_train,y_train)\n", + "tfb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"T-F Results\")\n", + "print('Label T-F train score is :',tfb_train)\n", + "print('Label T-F test score is :',tfb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "x_train, x_test, y_train, y_test = train_test_split(scaled, y_JP, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "jpb_train = mnb.score (x_train,y_train)\n", + "jpb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"J-P Results\")\n", + "print('Label J-P train score is :',jpb_train)\n", + "print('Label J-P test score is :',jpb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Decision Tree" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E RESULTS\n", + "Label I-E train score is : 1.0\n", + "Label I-E test score is : 1.0\n", + "Confusion Matrix for Decision Tree:\n", + "[[ 121 272]\n", + " [ 333 1009]]\n", + "Score: 65.13\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " E 0.27 0.31 0.29 393\n", + " I 0.79 0.75 0.77 1342\n", + "\n", + " accuracy 0.65 1735\n", + " macro avg 0.53 0.53 0.53 1735\n", + "weighted avg 0.67 0.65 0.66 1735\n", + "\n", + "****************************************************************************************************\n", + "N-S RESULTS\n", + "Label N-S train score is : 1.0\n", + "Label N-S test score is : 1.0\n", + "Confusion Matrix for Decision Tree:\n", + "[[1239 258]\n", + " [ 196 42]]\n", + "Score: 73.83\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " N 0.86 0.83 0.85 1497\n", + " S 0.14 0.18 0.16 238\n", + "\n", + " accuracy 0.74 1735\n", + " macro avg 0.50 0.50 0.50 1735\n", + "weighted avg 0.76 0.74 0.75 1735\n", + "\n", + "****************************************************************************************************\n", + "T-F Results\n", + "Label T-F train score is : 1.0\n", + "Label T-F test score is : 1.0\n", + "Confusion Matrix for Decision Tree:\n", + "[[579 381]\n", + " [373 402]]\n", + "Score: 56.54\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " F 0.61 0.60 0.61 960\n", + " T 0.51 0.52 0.52 775\n", + "\n", + " accuracy 0.57 1735\n", + " macro avg 0.56 0.56 0.56 1735\n", + "weighted avg 0.57 0.57 0.57 1735\n", + "\n", + "****************************************************************************************************\n", + "J-P Results\n", + "Label J-P train score is : 1.0\n", + "Label J-P test score is : 1.0\n", + "Confusion Matrix for Decision Tree:\n", + "[[306 374]\n", + " [438 617]]\n", + "Score: 53.2\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " J 0.41 0.45 0.43 680\n", + " P 0.62 0.58 0.60 1055\n", + "\n", + " accuracy 0.53 1735\n", + " macro avg 0.52 0.52 0.52 1735\n", + "weighted avg 0.54 0.53 0.54 1735\n", + "\n", + "****************************************************************************************************\n" + ] + } + ], + "source": [ + "from sklearn.tree import DecisionTreeClassifier\n", + "\n", + "dt = DecisionTreeClassifier()\n", + "dt.fit(x_train, y_train)\n", + "\n", + "\n", + "# IE\n", + "x_train, x_test, y_train, y_test = train_test_split(reduced, y_IE, test_size=0.2, random_state=10)\n", + "dt.fit(x_train, y_train)\n", + "ieb_train = dt.score (x_train,y_train)\n", + "ieb_test = dt.score (x_train,y_train)\n", + "preddt = dt.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "print('Label I-E train score is :',ieb_train)\n", + "print('Label I-E test score is :',ieb_test)\n", + "print(\"Confusion Matrix for Decision Tree:\")\n", + "print(confusion_matrix(y_test,preddt))\n", + "print(\"Score:\",round(accuracy_score(y_test,preddt)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,preddt))\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", + "x_train, x_test, y_train, y_test = train_test_split(reduced, y_NS, test_size=0.2, random_state=10)\n", + "dt.fit(x_train, y_train)\n", + "nsb_train = dt.score (x_train,y_train)\n", + "nsb_test = dt.score (x_train,y_train)\n", + "preddt = dt.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "print('Label N-S train score is :',nsb_train)\n", + "print('Label N-S test score is :',nsb_test)\n", + "print(\"Confusion Matrix for Decision Tree:\")\n", + "print(confusion_matrix(y_test,preddt))\n", + "print(\"Score:\",round(accuracy_score(y_test,preddt)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,preddt))\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "x_train, x_test, y_train, y_test = train_test_split(reduced, y_TF, test_size=0.2, random_state=10)\n", + "dt.fit(x_train, y_train)\n", + "tfb_train = dt.score (x_train,y_train)\n", + "tfb_test = dt.score (x_train,y_train)\n", + "preddt = dt.predict(x_test)\n", + "print(\"T-F Results\")\n", + "print('Label T-F train score is :',tfb_train)\n", + "print('Label T-F test score is :',tfb_test)\n", + "print(\"Confusion Matrix for Decision Tree:\")\n", + "print(confusion_matrix(y_test,preddt))\n", + "print(\"Score:\",round(accuracy_score(y_test,preddt)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,preddt))\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "x_train, x_test, y_train, y_test = train_test_split(reduced, y_JP, test_size=0.2, random_state=10)\n", + "dt.fit(x_train, y_train)\n", + "jpb_train = dt.score (x_train,y_train)\n", + "jpb_test = dt.score (x_train,y_train)\n", + "preddt = dt.predict(x_test)\n", + "print(\"J-P Results\")\n", + "print('Label J-P train score is :',jpb_train)\n", + "print('Label J-P test score is :',jpb_test)\n", + "print(\"Confusion Matrix for Decision Tree:\")\n", + "print(confusion_matrix(y_test,preddt))\n", + "print(\"Score:\",round(accuracy_score(y_test,preddt)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,preddt))\n", + "print(\"*\" * 100)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E RESULTS\n", + "Label I-E train score is : 1.0\n", + "Label I-E test score is : 1.0\n", + "Confusion Matrix for Decision Tree:\n", + "[[116 277]\n", + " [357 985]]\n", + "Score: 63.46\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " E 0.25 0.30 0.27 393\n", + " I 0.78 0.73 0.76 1342\n", + "\n", + " accuracy 0.63 1735\n", + " macro avg 0.51 0.51 0.51 1735\n", + "weighted avg 0.66 0.63 0.65 1735\n", + "\n", + "****************************************************************************************************\n", + "N-S RESULTS\n", + "Label N-S train score is : 1.0\n", + "Label N-S test score is : 1.0\n", + "Confusion Matrix for Decision Tree:\n", + "[[1253 244]\n", + " [ 198 40]]\n", + "Score: 74.52\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " N 0.86 0.84 0.85 1497\n", + " S 0.14 0.17 0.15 238\n", + "\n", + " accuracy 0.75 1735\n", + " macro avg 0.50 0.50 0.50 1735\n", + "weighted avg 0.76 0.75 0.75 1735\n", + "\n", + "****************************************************************************************************\n", + "T-F Results\n", + "Label T-F train score is : 1.0\n", + "Label T-F test score is : 1.0\n", + "Confusion Matrix for Decision Tree:\n", + "[[589 371]\n", + " [357 418]]\n", + "Score: 58.04\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " F 0.62 0.61 0.62 960\n", + " T 0.53 0.54 0.53 775\n", + "\n", + " accuracy 0.58 1735\n", + " macro avg 0.58 0.58 0.58 1735\n", + "weighted avg 0.58 0.58 0.58 1735\n", + "\n", + "****************************************************************************************************\n", + "J-P Results\n", + "Label J-P train score is : 1.0\n", + "Label J-P test score is : 1.0\n", + "Confusion Matrix for Decision Tree:\n", + "[[309 371]\n", + " [432 623]]\n", + "Score: 53.72\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " J 0.42 0.45 0.43 680\n", + " P 0.63 0.59 0.61 1055\n", + "\n", + " accuracy 0.54 1735\n", + " macro avg 0.52 0.52 0.52 1735\n", + "weighted avg 0.54 0.54 0.54 1735\n", + "\n", + "****************************************************************************************************\n" + ] + } + ], + "source": [ + "from sklearn.tree import DecisionTreeClassifier\n", + "\n", + "dt = DecisionTreeClassifier()\n", + "dt.fit(x_train, y_train)\n", + "\n", + "\n", + "# IE\n", + "x_train, x_test, y_train, y_test = train_test_split(scaled, y_IE, test_size=0.2, random_state=10)\n", + "dt.fit(x_train, y_train)\n", + "ieb_train = dt.score (x_train,y_train)\n", + "ieb_test = dt.score (x_train,y_train)\n", + "preddt = dt.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "print('Label I-E train score is :',ieb_train)\n", + "print('Label I-E test score is :',ieb_test)\n", + "print(\"Confusion Matrix for Decision Tree:\")\n", + "print(confusion_matrix(y_test,preddt))\n", + "print(\"Score:\",round(accuracy_score(y_test,preddt)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,preddt))\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", + "x_train, x_test, y_train, y_test = train_test_split(scaled, y_NS, test_size=0.2, random_state=10)\n", + "dt.fit(x_train, y_train)\n", + "nsb_train = dt.score (x_train,y_train)\n", + "nsb_test = dt.score (x_train,y_train)\n", + "preddt = dt.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "print('Label N-S train score is :',nsb_train)\n", + "print('Label N-S test score is :',nsb_test)\n", + "print(\"Confusion Matrix for Decision Tree:\")\n", + "print(confusion_matrix(y_test,preddt))\n", + "print(\"Score:\",round(accuracy_score(y_test,preddt)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,preddt))\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "x_train, x_test, y_train, y_test = train_test_split(scaled, y_TF, test_size=0.2, random_state=10)\n", + "dt.fit(x_train, y_train)\n", + "tfb_train = dt.score (x_train,y_train)\n", + "tfb_test = dt.score (x_train,y_train)\n", + "preddt = dt.predict(x_test)\n", + "print(\"T-F Results\")\n", + "print('Label T-F train score is :',tfb_train)\n", + "print('Label T-F test score is :',tfb_test)\n", + "print(\"Confusion Matrix for Decision Tree:\")\n", + "print(confusion_matrix(y_test,preddt))\n", + "print(\"Score:\",round(accuracy_score(y_test,preddt)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,preddt))\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "x_train, x_test, y_train, y_test = train_test_split(scaled, y_JP, test_size=0.2, random_state=10)\n", + "dt.fit(x_train, y_train)\n", + "jpb_train = dt.score (x_train,y_train)\n", + "jpb_test = dt.score (x_train,y_train)\n", + "preddt = dt.predict(x_test)\n", + "print(\"J-P Results\")\n", + "print('Label J-P train score is :',jpb_train)\n", + "print('Label J-P test score is :',jpb_test)\n", + "print(\"Confusion Matrix for Decision Tree:\")\n", + "print(confusion_matrix(y_test,preddt))\n", + "print(\"Score:\",round(accuracy_score(y_test,preddt)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,preddt))\n", + "print(\"*\" * 100)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### K Nearest Neighbour" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E RESULTS\n", + "Label I-E train score is : 0.7731661622712206\n", + "Label I-E test score is : 0.7731661622712206\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[ 8 385]\n", + " [ 14 1328]]\n", + "Score: 77.0\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " E 0.36 0.02 0.04 393\n", + " I 0.78 0.99 0.87 1342\n", + "\n", + " accuracy 0.77 1735\n", + " macro avg 0.57 0.50 0.45 1735\n", + "weighted avg 0.68 0.77 0.68 1735\n", + "\n", + "****************************************************************************************************\n", + "N-S RESULTS\n", + "Label N-S train score is : 0.8622279867416054\n", + "Label N-S test score is : 0.8622279867416054\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[1496 1]\n", + " [ 238 0]]\n", + "Score: 86.22\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " N 0.86 1.00 0.93 1497\n", + " S 0.00 0.00 0.00 238\n", + "\n", + " accuracy 0.86 1735\n", + " macro avg 0.43 0.50 0.46 1735\n", + "weighted avg 0.74 0.86 0.80 1735\n", + "\n", + "****************************************************************************************************\n", + "T-F Results\n", + "Label T-F train score is : 0.6813661910938176\n", + "Label T-F test score is : 0.6813661910938176\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[496 464]\n", + " [257 518]]\n", + "Score: 58.44\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " F 0.66 0.52 0.58 960\n", + " T 0.53 0.67 0.59 775\n", + "\n", + " accuracy 0.58 1735\n", + " macro avg 0.59 0.59 0.58 1735\n", + "weighted avg 0.60 0.58 0.58 1735\n", + "\n", + "****************************************************************************************************\n", + "J-P Results\n", + "Label J-P train score is : 0.6536964980544747\n", + "Label J-P test score is : 0.6536964980544747\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[147 533]\n", + " [188 867]]\n", + "Score: 58.44\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " J 0.44 0.22 0.29 680\n", + " P 0.62 0.82 0.71 1055\n", + "\n", + " accuracy 0.58 1735\n", + " macro avg 0.53 0.52 0.50 1735\n", + "weighted avg 0.55 0.58 0.54 1735\n", + "\n", + "****************************************************************************************************\n" + ] + } + ], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier\n", + "\n", + "knn = KNeighborsClassifier(n_neighbors=15)\n", + "\n", + "\n", + "\n", + "# IE\n", + "x_train, x_test, y_train, y_test = train_test_split(reduced, y_IE, test_size=0.2, random_state=10)\n", + "knn.fit(x_train, y_train)\n", + "ieb_train = knn.score (x_train,y_train)\n", + "ieb_test = knn.score (x_train,y_train)\n", + "predknn = knn.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "print('Label I-E train score is :',ieb_train)\n", + "print('Label I-E test score is :',ieb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predknn))\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", + "x_train, x_test, y_train, y_test = train_test_split(reduced, y_NS, test_size=0.2, random_state=10)\n", + "knn.fit(x_train, y_train)\n", + "nsb_train = knn.score (x_train,y_train)\n", + "nsb_test = knn.score (x_train,y_train)\n", + "predknn = knn.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "print('Label N-S train score is :',nsb_train)\n", + "print('Label N-S test score is :',nsb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predknn))\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "x_train, x_test, y_train, y_test = train_test_split(reduced, y_TF, test_size=0.2, random_state=10)\n", + "knn.fit(x_train, y_train)\n", + "tfb_train = knn.score (x_train,y_train)\n", + "tfb_test = knn.score (x_train,y_train)\n", + "predknn = knn.predict(x_test)\n", + "print(\"T-F Results\")\n", + "print('Label T-F train score is :',tfb_train)\n", + "print('Label T-F test score is :',tfb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predknn))\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "x_train, x_test, y_train, y_test = train_test_split(reduced, y_JP, test_size=0.2, random_state=10)\n", + "knn.fit(x_train, y_train)\n", + "jpb_train = knn.score (x_train,y_train)\n", + "jpb_test = knn.score (x_train,y_train)\n", + "predknn = knn.predict(x_test)\n", + "print(\"J-P Results\")\n", + "print('Label J-P train score is :',jpb_train)\n", + "print('Label J-P test score is :',jpb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predknn))\n", + "print(\"*\" * 100)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E RESULTS\n", + "Label I-E train score is : 0.788586251621271\n", + "Label I-E test score is : 0.788586251621271\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[ 27 366]\n", + " [ 73 1269]]\n", + "Score: 74.7\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " E 0.27 0.07 0.11 393\n", + " I 0.78 0.95 0.85 1342\n", + "\n", + " accuracy 0.75 1735\n", + " macro avg 0.52 0.51 0.48 1735\n", + "weighted avg 0.66 0.75 0.68 1735\n", + "\n", + "****************************************************************************************************\n", + "N-S RESULTS\n", + "Label N-S train score is : 0.8642455685257242\n", + "Label N-S test score is : 0.8642455685257242\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[1479 18]\n", + " [ 234 4]]\n", + "Score: 85.48\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " N 0.86 0.99 0.92 1497\n", + " S 0.18 0.02 0.03 238\n", + "\n", + " accuracy 0.85 1735\n", + " macro avg 0.52 0.50 0.48 1735\n", + "weighted avg 0.77 0.85 0.80 1735\n", + "\n", + "****************************************************************************************************\n", + "T-F Results\n", + "Label T-F train score is : 0.7114858048710189\n", + "Label T-F test score is : 0.7114858048710189\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[510 450]\n", + " [295 480]]\n", + "Score: 57.06\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " F 0.63 0.53 0.58 960\n", + " T 0.52 0.62 0.56 775\n", + "\n", + " accuracy 0.57 1735\n", + " macro avg 0.57 0.58 0.57 1735\n", + "weighted avg 0.58 0.57 0.57 1735\n", + "\n", + "****************************************************************************************************\n", + "J-P Results\n", + "Label J-P train score is : 0.6931834558293702\n", + "Label J-P test score is : 0.6931834558293702\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[201 479]\n", + " [286 769]]\n", + "Score: 55.91\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " J 0.41 0.30 0.34 680\n", + " P 0.62 0.73 0.67 1055\n", + "\n", + " accuracy 0.56 1735\n", + " macro avg 0.51 0.51 0.51 1735\n", + "weighted avg 0.54 0.56 0.54 1735\n", + "\n", + "****************************************************************************************************\n" + ] + } + ], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier\n", + "\n", + "knn = KNeighborsClassifier(n_neighbors=7)\n", + "\n", + "\n", + "\n", + "# IE\n", + "x_train, x_test, y_train, y_test = train_test_split(reduced, y_IE, test_size=0.2, random_state=10)\n", + "knn.fit(x_train, y_train)\n", + "ieb_train = knn.score (x_train,y_train)\n", + "ieb_test = knn.score (x_train,y_train)\n", + "predknn = knn.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "print('Label I-E train score is :',ieb_train)\n", + "print('Label I-E test score is :',ieb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predknn))\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", + "x_train, x_test, y_train, y_test = train_test_split(reduced, y_NS, test_size=0.2, random_state=10)\n", + "knn.fit(x_train, y_train)\n", + "nsb_train = knn.score (x_train,y_train)\n", + "nsb_test = knn.score (x_train,y_train)\n", + "predknn = knn.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "print('Label N-S train score is :',nsb_train)\n", + "print('Label N-S test score is :',nsb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predknn))\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "x_train, x_test, y_train, y_test = train_test_split(reduced, y_TF, test_size=0.2, random_state=10)\n", + "knn.fit(x_train, y_train)\n", + "tfb_train = knn.score (x_train,y_train)\n", + "tfb_test = knn.score (x_train,y_train)\n", + "predknn = knn.predict(x_test)\n", + "print(\"T-F Results\")\n", + "print('Label T-F train score is :',tfb_train)\n", + "print('Label T-F test score is :',tfb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predknn))\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "x_train, x_test, y_train, y_test = train_test_split(reduced, y_JP, test_size=0.2, random_state=10)\n", + "knn.fit(x_train, y_train)\n", + "jpb_train = knn.score (x_train,y_train)\n", + "jpb_test = knn.score (x_train,y_train)\n", + "predknn = knn.predict(x_test)\n", + "print(\"J-P Results\")\n", + "print('Label J-P train score is :',jpb_train)\n", + "print('Label J-P test score is :',jpb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predknn))\n", + "print(\"*\" * 100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Random Forest" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E RESULTS\n", + "Label I-E train score is : 0.9136763222366335\n", + "Label I-E test score is : 0.9136763222366335\n", + "Confusion Matrix for Random Forest:\n", + "[[ 2 391]\n", + " [ 10 1332]]\n", + "Score: 76.89\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " E 0.17 0.01 0.01 393\n", + " I 0.77 0.99 0.87 1342\n", + "\n", + " accuracy 0.77 1735\n", + " macro avg 0.47 0.50 0.44 1735\n", + "weighted avg 0.64 0.77 0.67 1735\n", + "\n", + "****************************************************************************************************\n", + "N-S RESULTS\n", + "Label N-S train score is : 0.8962386511024644\n", + "Label N-S test score is : 0.8962386511024644\n", + "Confusion Matrix for Random Forest:\n", + "[[1495 2]\n", + " [ 238 0]]\n", + "Score: 86.17\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " N 0.86 1.00 0.93 1497\n", + " S 0.00 0.00 0.00 238\n", + "\n", + " accuracy 0.86 1735\n", + " macro avg 0.43 0.50 0.46 1735\n", + "weighted avg 0.74 0.86 0.80 1735\n", + "\n", + "****************************************************************************************************\n", + "T-F Results\n", + "Label T-F train score is : 0.9979824182158813\n", + "Label T-F test score is : 0.9979824182158813\n", + "Confusion Matrix for Random Forest:\n", + "[[727 233]\n", + " [339 436]]\n", + "Score: 67.03\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " F 0.68 0.76 0.72 960\n", + " T 0.65 0.56 0.60 775\n", + "\n", + " accuracy 0.67 1735\n", + " macro avg 0.67 0.66 0.66 1735\n", + "weighted avg 0.67 0.67 0.67 1735\n", + "\n", + "****************************************************************************************************\n", + "J-P Results\n", + "Label J-P train score is : 0.9816976509583514\n", + "Label J-P test score is : 0.9816976509583514\n", + "Confusion Matrix for Random Forest:\n", + "[[ 85 595]\n", + " [ 64 991]]\n", + "Score: 62.02\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " J 0.57 0.12 0.21 680\n", + " P 0.62 0.94 0.75 1055\n", + "\n", + " accuracy 0.62 1735\n", + " macro avg 0.60 0.53 0.48 1735\n", + "weighted avg 0.60 0.62 0.54 1735\n", + "\n", + "****************************************************************************************************\n" + ] + } + ], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "import joblib\n", + "\n", + "rfn = RandomForestClassifier(max_depth=14, random_state=0)\n", + "\n", + "\n", + "# IE\n", + "x_train, x_test, y_train, y_test = train_test_split(scaled, y_IE, test_size=0.2, random_state=10)\n", + "rfn.fit(x_train, y_train)\n", + "model = rfn.fit(x_train, y_train)\n", + "ieb_train = rfn.score (x_train,y_train)\n", + "ieb_test = rfn.score (x_train,y_train)\n", + "predrfn = rfn.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "print('Label I-E train score is :',ieb_train)\n", + "print('Label I-E test score is :',ieb_test)\n", + "print(\"Confusion Matrix for Random Forest:\")\n", + "print(confusion_matrix(y_test,predrfn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predrfn))\n", + "filename = 'RF_IE.sav'\n", + "joblib.dump(model, filename)\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", + "x_train, x_test, y_train, y_test = train_test_split(scaled, y_NS, test_size=0.2, random_state=10)\n", + "rfn.fit(x_train, y_train)\n", + "model = rfn.fit(x_train, y_train)\n", + "nsb_train = rfn.score (x_train,y_train)\n", + "nsb_test = rfn.score (x_train,y_train)\n", + "predrfn = rfn.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "print('Label N-S train score is :',nsb_train)\n", + "print('Label N-S test score is :',nsb_test)\n", + "print(\"Confusion Matrix for Random Forest:\")\n", + "print(confusion_matrix(y_test,predrfn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predrfn))\n", + "filename = 'RF_NS.sav'\n", + "joblib.dump(model, filename)\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "x_train, x_test, y_train, y_test = train_test_split(scaled, y_TF, test_size=0.2, random_state=10)\n", + "rfn.fit(x_train, y_train)\n", + "model = rfn.fit(x_train, y_train)\n", + "tfb_train = rfn.score (x_train,y_train)\n", + "tfb_test = rfn.score (x_train,y_train)\n", + "predrfn = rfn.predict(x_test)\n", + "print(\"T-F Results\")\n", + "print('Label T-F train score is :',tfb_train)\n", + "print('Label T-F test score is :',tfb_test)\n", + "print(\"Confusion Matrix for Random Forest:\")\n", + "print(confusion_matrix(y_test,predrfn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predrfn))\n", + "filename = 'RF_TF.sav'\n", + "joblib.dump(model, filename)\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "x_train, x_test, y_train, y_test = train_test_split(scaled, y_JP, test_size=0.2, random_state=10)\n", + "rfn.fit(x_train, y_train)\n", + "model = rfn.fit(x_train, y_train)\n", + "jpb_train = rfn.score (x_train,y_train)\n", + "jpb_test = rfn.score (x_train,y_train)\n", + "predrfn = rfn.predict(x_test)\n", + "print(\"J-P Results\")\n", + "print('Label J-P train score is :',jpb_train)\n", + "print('Label J-P test score is :',jpb_test)\n", + "print(\"Confusion Matrix for Random Forest:\")\n", + "print(confusion_matrix(y_test,predrfn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predrfn))\n", + "filename = 'RF_JP.sav'\n", + "joblib.dump(model, filename)\n", + "print(\"*\" * 100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Neural Network" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import random\n", + "import re\n", + "\n", + "import keras\n", + "from keras import models\n", + "from keras.models import Sequential\n", + "from keras.layers import Conv2D\n", + "from keras.layers import MaxPooling2D\n", + "from keras.layers import Flatten\n", + "from keras.layers import Dense\n", + "from sklearn import preprocessing\n", + "from sklearn.model_selection import train_test_split, cross_val_score" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
typetext_readyI-EN-ST-FJ-P
0INFJintj moment sportscent top ten play prank ha l...1000
1ENTPfind lack post veri alarm sex bore posit often...0011
2INTPgood one cours say know bless cur doe absolut ...1011
3INTJdear intp enjoy convers day esoter gab natur u...1010
4ENTJfire anoth silli misconcept approach logic go ...0010
.....................
8670ISFPbecaus alway think cat fi dom reason websit be...1101
8671ENFPthi thread alreadi exist someplac el doe heck ...0001
8672INTPmani question thing would take purpl pill pick...1011
8673INFPveri conflict right come want child honestli m...1001
8674INFPha long sinc personalitycaf although seem chan...1001
\n", + "

8674 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " type text_ready I-E N-S T-F \\\n", + "0 INFJ intj moment sportscent top ten play prank ha l... 1 0 0 \n", + "1 ENTP find lack post veri alarm sex bore posit often... 0 0 1 \n", + "2 INTP good one cours say know bless cur doe absolut ... 1 0 1 \n", + "3 INTJ dear intp enjoy convers day esoter gab natur u... 1 0 1 \n", + "4 ENTJ fire anoth silli misconcept approach logic go ... 0 0 1 \n", + "... ... ... ... ... ... \n", + "8670 ISFP becaus alway think cat fi dom reason websit be... 1 1 0 \n", + "8671 ENFP thi thread alreadi exist someplac el doe heck ... 0 0 0 \n", + "8672 INTP mani question thing would take purpl pill pick... 1 0 1 \n", + "8673 INFP veri conflict right come want child honestli m... 1 0 0 \n", + "8674 INFP ha long sinc personalitycaf although seem chan... 1 0 0 \n", + "\n", + " J-P \n", + "0 0 \n", + "1 1 \n", + "2 1 \n", + "3 0 \n", + "4 0 \n", + "... ... \n", + "8670 1 \n", + "8671 1 \n", + "8672 1 \n", + "8673 1 \n", + "8674 1 \n", + "\n", + "[8674 rows x 6 columns]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_neural = data2.copy()\n", + "\n", + "data_neural['I-E'] = data_neural['I-E'].astype('category')\n", + "data_neural['N-S'] = data_neural['N-S'].astype('category')\n", + "data_neural['T-F'] = data_neural['T-F'].astype('category')\n", + "data_neural['J-P'] = data_neural['J-P'].astype('category')\n", + "\n", + "cat_columns = data_neural.select_dtypes(['category']).columns\n", + "\n", + "data_neural[cat_columns] = data_neural[cat_columns].apply(lambda x: x.cat.codes)\n", + "\n", + "data_neural" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "x = data_neural['text_ready']\n", + "\n", + "y_IE = data_neural['I-E']\n", + "y_NS = data_neural['N-S']\n", + "y_TF = data_neural['T-F']\n", + "y_JP = data_neural['J-P']\n", + "\n", + "from sklearn.feature_extraction.text import CountVectorizer\n", + "\n", + "vector = CountVectorizer(ngram_range=(2, 2)).fit(x) \n", + "X = vector.transform(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "model = tf.keras.models.Sequential()\n", + "model.add(tf.keras.layers.Dense(units=256, kernel_initializer='uniform', activation='relu', input_dim=1986297))\n", + "model.add(tf.keras.layers.Dense(units=128, kernel_initializer='uniform', activation='relu'))\n", + "model.add(tf.keras.layers.Dense(units=64, kernel_initializer='uniform', activation='relu'))\n", + "model.add(tf.keras.layers.Dense(activation = 'sigmoid', units = 1, kernel_initializer = 'uniform')) # output layer has number of outputs not number of neurons\n", + "\n", + "model.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])\n", + "# calculating loss, model is always trying to minimize loss, adam is the default optimize\n", + "\n", + "\n", + "# model.fit(X_train, y_train, epochs = 10, batch_size= 25)\n", + "\n", + "# val_loss, val_acc = model.evaluate(X_test, y_test, verbose=0)\n", + "# print(f'Test loss: {val_loss}')\n", + "# print(f'Test accuracy: {val_acc}')" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(6939, 1986297)" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Keras is training/fitting/evaluating on array-like data. Keras may not be optimized for this format, so if your input data format is supported by TensorFlow I/O (https://github.com/tensorflow/io) we recommend using that to load a Dataset instead.\n", + "Epoch 1/10\n" + ] + }, + { + "ename": "InvalidArgumentError", + "evalue": " TypeError: 'SparseTensor' object is not subscriptable\nTraceback (most recent call last):\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/ops/script_ops.py\", line 241, in __call__\n return func(device, token, args)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/ops/script_ops.py\", line 130, in __call__\n ret = self._func(*args)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/autograph/impl/api.py\", line 309, in wrapper\n return func(*args, **kwargs)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 513, in py_method\n return [slice_array(inp) for inp in flat_inputs]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 513, in \n return [slice_array(inp) for inp in flat_inputs]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 512, in slice_array\n contiguous=contiguous)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in slice_arrays\n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in \n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in \n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\nTypeError: 'SparseTensor' object is not subscriptable\n\n\n\t [[{{node EagerPyFunc}}]]\n\t [[IteratorGetNext]] [Op:__inference_train_function_1612]\n\nFunction call stack:\ntrain_function\n", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mInvalidArgumentError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# IE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_IE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;36m25\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mpredmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"I-E RESULTS\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_method_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_in_multi_worker_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[0;31m# Running inside `run_distribute_coordinator` already.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m 846\u001b[0m batch_size=batch_size):\n\u001b[1;32m 847\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 848\u001b[0;31m \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 849\u001b[0m \u001b[0;31m# Catch OutOfRangeError for Datasets of unknown size.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 850\u001b[0m \u001b[0;31m# This blocks until the batch has finished executing.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 578\u001b[0m \u001b[0mxla_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 579\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 580\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 581\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 582\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 642\u001b[0m \u001b[0;31m# Lifting succeeded, so variables are initialized and we can run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 643\u001b[0m \u001b[0;31m# stateless function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 644\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 645\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 646\u001b[0m \u001b[0mcanon_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcanon_kwds\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 2418\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2419\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2420\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2421\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2422\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m 1663\u001b[0m if isinstance(t, (ops.Tensor,\n\u001b[1;32m 1664\u001b[0m resource_variable_ops.BaseResourceVariable))),\n\u001b[0;32m-> 1665\u001b[0;31m self.captured_inputs)\n\u001b[0m\u001b[1;32m 1666\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1667\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_call_flat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m 1744\u001b[0m \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1745\u001b[0m return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1746\u001b[0;31m ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m 1747\u001b[0m forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m 1748\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m 596\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 597\u001b[0m \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 598\u001b[0;31m ctx=ctx)\n\u001b[0m\u001b[1;32m 599\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 600\u001b[0m outputs = execute.execute_with_cancellation(\n", + "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 59\u001b[0m tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m 61\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mInvalidArgumentError\u001b[0m: TypeError: 'SparseTensor' object is not subscriptable\nTraceback (most recent call last):\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/ops/script_ops.py\", line 241, in __call__\n return func(device, token, args)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/ops/script_ops.py\", line 130, in __call__\n ret = self._func(*args)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/autograph/impl/api.py\", line 309, in wrapper\n return func(*args, **kwargs)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 513, in py_method\n return [slice_array(inp) for inp in flat_inputs]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 513, in \n return [slice_array(inp) for inp in flat_inputs]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py\", line 512, in slice_array\n contiguous=contiguous)\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in slice_arrays\n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in \n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\n File \"/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_utils.py\", line 391, in \n entries = [[x[i:i + 1] for i in indices] for x in arrays]\n\nTypeError: 'SparseTensor' object is not subscriptable\n\n\n\t [[{{node EagerPyFunc}}]]\n\t [[IteratorGetNext]] [Op:__inference_train_function_1612]\n\nFunction call stack:\ntrain_function\n" + ] + } + ], + "source": [ + "\n", + "\n", + "# IE\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y_IE, test_size=0.2, random_state=10)\n", + "model.fit(X_train, y_train, epochs = 10, batch_size= 25)\n", + "predmodel = model.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "# print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "# print(confusion_matrix(y_test,predmodel))\n", + "# print(\"Score:\",round(accuracy_score(y_test,predmodel)*100,2))\n", + "# print(\"Classification Report:\",classification_report(y_test,predmodel))\n", + "val_loss, val_acc = model.evaluate(X_test, y_test, verbose=0)\n", + "print(f'Test loss: {val_loss}')\n", + "print(f'Test accuracy: {val_acc}')\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y_NS, test_size=0.2, random_state=10)\n", + "model.fit(X_train, y_train, epochs = 10, batch_size= 25)\n", + "predmodel = model.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "# print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "# print(confusion_matrix(y_test,predmodel))\n", + "# print(\"Score:\",round(accuracy_score(y_test,predmodel)*100,2))\n", + "# print(\"Classification Report:\",classification_report(y_test,predmodel))\n", + "val_loss, val_acc = model.evaluate(X_test, y_test, verbose=0)\n", + "print(f'Test loss: {val_loss}')\n", + "print(f'Test accuracy: {val_acc}')\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y_TF, test_size=0.2, random_state=10)\n", + "model.fit(X_train, y_train, epochs = 10, batch_size= 25)\n", + "predmodel = model.predict(x_test)\n", + "print(\"T-F Results\")\n", + "# print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "# print(confusion_matrix(y_test,predmodel))\n", + "# print(\"Score:\",round(accuracy_score(y_test,predmodel)*100,2))\n", + "# print(\"Classification Report:\",classification_report(y_test,predmodel))\n", + "val_loss, val_acc = model.evaluate(X_test, y_test, verbose=0)\n", + "print(f'Test loss: {val_loss}')\n", + "print(f'Test accuracy: {val_acc}')\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y_JP, test_size=0.2, random_state=10)\n", + "model.fit(X_train, y_train, epochs = 10, batch_size= 25)\n", + "predmodel = model.predict(x_test)\n", + "print(\"J-P Results\")\n", + "# print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "# print(confusion_matrix(y_test,predmodel))\n", + "# print(\"Score:\",round(accuracy_score(y_test,predmodel)*100,2))\n", + "# print(\"Classification Report:\",classification_report(y_test,predmodel))\n", + "val_loss, val_acc = model.evaluate(X_test, y_test, verbose=0)\n", + "print(f'Test loss: {val_loss}')\n", + "print(f'Test accuracy: {val_acc}')\n", + "print(\"*\" * 100)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "XG BOOST" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.naive_bayes import MultinomialNB\n", + "\n", + "mnb = MultinomialNB()\n", + "mnb.fit(x_train, y_train)\n", + "\n", + "\n", + "# IE\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_IE, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "ieb_train = mnb.score (x_train,y_train)\n", + "ieb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "print('Label I-E train score is :',ieb_train)\n", + "print('Label I-E test score is :',ieb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_NS, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "nsb_train = mnb.score (x_train,y_train)\n", + "nsb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "print('Label N-S train score is :',nsb_train)\n", + "print('Label N-S test score is :',nsb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_TF, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "tfb_train = mnb.score (x_train,y_train)\n", + "tfb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"T-F Results\")\n", + "print('Label T-F train score is :',tfb_train)\n", + "print('Label T-F test score is :',tfb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_JP, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "jpb_train = mnb.score (x_train,y_train)\n", + "jpb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"J-P Results\")\n", + "print('Label J-P train score is :',jpb_train)\n", + "print('Label J-P test score is :',jpb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/code/Code 4 - Extending Data.ipynb b/your-project/code/Code 4 - Extending Data.ipynb new file mode 100644 index 00000000..9221454a --- /dev/null +++ b/your-project/code/Code 4 - Extending Data.ipynb @@ -0,0 +1,892 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Extending the Dataframe by Quantitative Data" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import re\n", + "\n", + "from sklearn.model_selection import cross_validate\n", + "from sklearn.model_selection import StratifiedKFold\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import learning_curve\n", + "from sklearn.ensemble import ExtraTreesClassifier\n", + "from sklearn.decomposition import TruncatedSVD\n", + "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.naive_bayes import MultinomialNB" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [], + "source": [ + "# clean data differently" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv(r'../data/mbti_1.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
typeposts
0INFJ'http://www.youtube.com/watch?v=qsXHcwe3krw|||...
1ENTP'I'm finding the lack of me in these posts ver...
2INTP'Good one _____ https://www.youtube.com/wat...
3INTJ'Dear INTP, I enjoyed our conversation the o...
4ENTJ'You're fired.|||That's another silly misconce...
\n", + "
" + ], + "text/plain": [ + " type posts\n", + "0 INFJ 'http://www.youtube.com/watch?v=qsXHcwe3krw|||...\n", + "1 ENTP 'I'm finding the lack of me in these posts ver...\n", + "2 INTP 'Good one _____ https://www.youtube.com/wat...\n", + "3 INTJ 'Dear INTP, I enjoyed our conversation the o...\n", + "4 ENTJ 'You're fired.|||That's another silly misconce..." + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "\n", + "def var_posts(string):\n", + " lengths = []\n", + " for post in string:\n", + " pattern2 = '\\w{1,}'\n", + " bag = re.findall(pattern2, post)\n", + " count_post = len(bag)\n", + " lengths.append(count_post)\n", + " return np.var(lengths)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
typepostslength_commentvar_lengthlinks_countqm_countexm_countimg_countmusic_countdots_countht_countme_count
0INFJ'http://www.youtube.com/watch?v=qsXHcwe3krw|||...11.120.1797500.480.360.060.120.020.300.01.18
1ENTP'I'm finding the lack of me in these posts ver...23.400.1844690.200.100.000.020.000.380.03.06
2INTP'Good one _____ https://www.youtube.com/wat...16.720.1840100.100.240.080.000.000.260.01.44
3INTJ'Dear INTP, I enjoyed our conversation the o...21.280.1866400.040.220.060.000.020.520.02.18
4ENTJ'You're fired.|||That's another silly misconce...19.340.1787230.120.200.020.040.020.420.01.48
\n", + "
" + ], + "text/plain": [ + " type posts length_comment \\\n", + "0 INFJ 'http://www.youtube.com/watch?v=qsXHcwe3krw|||... 11.12 \n", + "1 ENTP 'I'm finding the lack of me in these posts ver... 23.40 \n", + "2 INTP 'Good one _____ https://www.youtube.com/wat... 16.72 \n", + "3 INTJ 'Dear INTP, I enjoyed our conversation the o... 21.28 \n", + "4 ENTJ 'You're fired.|||That's another silly misconce... 19.34 \n", + "\n", + " var_length links_count qm_count exm_count img_count music_count \\\n", + "0 0.179750 0.48 0.36 0.06 0.12 0.02 \n", + "1 0.184469 0.20 0.10 0.00 0.02 0.00 \n", + "2 0.184010 0.10 0.24 0.08 0.00 0.00 \n", + "3 0.186640 0.04 0.22 0.06 0.00 0.02 \n", + "4 0.178723 0.12 0.20 0.02 0.04 0.02 \n", + "\n", + " dots_count ht_count me_count \n", + "0 0.30 0.0 1.18 \n", + "1 0.38 0.0 3.06 \n", + "2 0.26 0.0 1.44 \n", + "3 0.52 0.0 2.18 \n", + "4 0.42 0.0 1.48 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# average number of words per scentence\n", + "\n", + "# std number of words per scentence\n", + "\n", + "# number of adjectives used, dictionairy import, english dict of adj\n", + "\n", + "# number of commas in a scentence\n", + "\n", + "\n", + "data['length_comment'] = data['posts'].apply(lambda x: len(x.split())/50)\n", + "data['var_length'] = data['posts'].apply(lambda x: var_posts(x))\n", + "data['links_count'] = data['posts'].apply(lambda x: x.count('http')/50)\n", + "data['qm_count'] = data['posts'].apply(lambda x: x.count('?')/50)\n", + "data['exm_count'] = data['posts'].apply(lambda x: x.count('!')/50) # normalize by the amount of scentences/ percent of number of scentences\n", + "data['img_count'] = data['posts'].apply(lambda x: x.count('jpg')/50)\n", + "# data['music_count'] = data['posts'].apply(lambda x: x.count('music')/50)\n", + "data['dots_count'] = data['posts'].apply(lambda x: x.count('...')/50)\n", + "data['ht_count'] = data['posts'].apply(lambda x: x.count('#')/50)\n", + "data['me_count'] = data['posts'].apply(lambda x: (x.lower.count('me') + x.lower.count('i') + x.lower.count('myself'))/50) # add uppercase\n", + "\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(15,10))\n", + "sns.swarmplot(\"type\", \"length_comment\", data=data)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(15,10))\n", + "sns.violinplot(x='type', y='length_comment', data=data, inner=None, color='lightgray')\n", + "sns.stripplot(x='type', y='length_comment', data=data, size=4, jitter=True)\n", + "plt.ylabel('Words per comment')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(15,10))\n", + "sns.jointplot(x='length_comment', y='dots_count', data=data, kind='kde')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(15,10))\n", + "sns.jointplot(\"var_length\", \"length_comment\", data=data, kind=\"hex\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "data = data.drop(columns = 'posts')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
typelength_commentvar_lengthlinks_countqm_countexm_countimg_countmusic_countdots_countht_countme_count
0INFJ11.120.1797500.480.360.060.120.020.300.01.18
1ENTP23.400.1844690.200.100.000.020.000.380.03.06
2INTP16.720.1840100.100.240.080.000.000.260.01.44
3INTJ21.280.1866400.040.220.060.000.020.520.02.18
4ENTJ19.340.1787230.120.200.020.040.020.420.01.48
\n", + "
" + ], + "text/plain": [ + " type length_comment var_length links_count qm_count exm_count \\\n", + "0 INFJ 11.12 0.179750 0.48 0.36 0.06 \n", + "1 ENTP 23.40 0.184469 0.20 0.10 0.00 \n", + "2 INTP 16.72 0.184010 0.10 0.24 0.08 \n", + "3 INTJ 21.28 0.186640 0.04 0.22 0.06 \n", + "4 ENTJ 19.34 0.178723 0.12 0.20 0.02 \n", + "\n", + " img_count music_count dots_count ht_count me_count \n", + "0 0.12 0.02 0.30 0.0 1.18 \n", + "1 0.02 0.00 0.38 0.0 3.06 \n", + "2 0.00 0.00 0.26 0.0 1.44 \n", + "3 0.00 0.02 0.52 0.0 2.18 \n", + "4 0.04 0.02 0.42 0.0 1.48 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.00 7818\n", + "0.02 641\n", + "0.04 123\n", + "0.06 43\n", + "0.08 29\n", + "0.12 8\n", + "0.10 8\n", + "0.14 2\n", + "0.20 1\n", + "0.16 1\n", + "0.34 1\n", + "Name: ht_count, dtype: int64" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.ht_count.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "data['I-E'] = data['type'].astype(str).str[0]\n", + "data['N-S'] = data['type'].astype(str).str[1]\n", + "data['T-F'] = data['type'].astype(str).str[2]\n", + "data['J-P'] = data['type'].astype(str).str[3]\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "data['I-E'] = data['I-E'].astype('category')\n", + "data['N-S'] = data['N-S'].astype('category')\n", + "data['T-F'] = data['T-F'].astype('category')\n", + "data['J-P'] = data['J-P'].astype('category')\n", + "\n", + "cat_columns = data.select_dtypes(['category']).columns\n", + "\n", + "data[cat_columns] = data[cat_columns].apply(lambda x: x.cat.codes)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
typelength_commentvar_lengthlinks_countqm_countexm_countimg_countmusic_countdots_countht_countme_countI-EN-ST-FJ-P
0INFJ11.120.1797500.480.360.060.120.020.300.01.181000
1ENTP23.400.1844690.200.100.000.020.000.380.03.060011
2INTP16.720.1840100.100.240.080.000.000.260.01.441011
3INTJ21.280.1866400.040.220.060.000.020.520.02.181010
4ENTJ19.340.1787230.120.200.020.040.020.420.01.480010
\n", + "
" + ], + "text/plain": [ + " type length_comment var_length links_count qm_count exm_count \\\n", + "0 INFJ 11.12 0.179750 0.48 0.36 0.06 \n", + "1 ENTP 23.40 0.184469 0.20 0.10 0.00 \n", + "2 INTP 16.72 0.184010 0.10 0.24 0.08 \n", + "3 INTJ 21.28 0.186640 0.04 0.22 0.06 \n", + "4 ENTJ 19.34 0.178723 0.12 0.20 0.02 \n", + "\n", + " img_count music_count dots_count ht_count me_count I-E N-S T-F J-P \n", + "0 0.12 0.02 0.30 0.0 1.18 1 0 0 0 \n", + "1 0.02 0.00 0.38 0.0 3.06 0 0 1 1 \n", + "2 0.00 0.00 0.26 0.0 1.44 1 0 1 1 \n", + "3 0.00 0.02 0.52 0.0 2.18 1 0 1 0 \n", + "4 0.04 0.02 0.42 0.0 1.48 0 0 1 0 " + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "data.to_csv(r'../data/quant_personalities.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/code/Code 5 - Neural Network.ipynb b/your-project/code/Code 5 - Neural Network.ipynb new file mode 100644 index 00000000..3d5d638f --- /dev/null +++ b/your-project/code/Code 5 - Neural Network.ipynb @@ -0,0 +1,1011 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Neural Network learning " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import random\n", + "import re\n", + "\n", + "import keras\n", + "from keras import models\n", + "from keras.models import Sequential\n", + "from keras.layers import Conv2D\n", + "from keras.layers import MaxPooling2D\n", + "from keras.layers import Flatten\n", + "from keras.layers import Dense\n", + "from sklearn import preprocessing\n", + "from tensorflow.keras.models import load_model\n", + "import tensorflow as tf\n", + "from sklearn.model_selection import train_test_split, cross_val_score" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "quant_pers = pd.read_csv(r'../data/quant_personalities.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Unnamed: 0typelength_commentvar_lengthlinks_countqm_countexm_countimg_countmusic_countdots_countht_countme_countI-EN-ST-FJ-P
00INFJ11.120.1797500.480.360.060.120.020.300.01.181000
11ENTP23.400.1844690.200.100.000.020.000.380.03.060011
22INTP16.720.1840100.100.240.080.000.000.260.01.441011
33INTJ21.280.1866400.040.220.060.000.020.520.02.181010
44ENTJ19.340.1787230.120.200.020.040.020.420.01.480010
\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 type length_comment var_length links_count qm_count \\\n", + "0 0 INFJ 11.12 0.179750 0.48 0.36 \n", + "1 1 ENTP 23.40 0.184469 0.20 0.10 \n", + "2 2 INTP 16.72 0.184010 0.10 0.24 \n", + "3 3 INTJ 21.28 0.186640 0.04 0.22 \n", + "4 4 ENTJ 19.34 0.178723 0.12 0.20 \n", + "\n", + " exm_count img_count music_count dots_count ht_count me_count I-E \\\n", + "0 0.06 0.12 0.02 0.30 0.0 1.18 1 \n", + "1 0.00 0.02 0.00 0.38 0.0 3.06 0 \n", + "2 0.08 0.00 0.00 0.26 0.0 1.44 1 \n", + "3 0.06 0.00 0.02 0.52 0.0 2.18 1 \n", + "4 0.02 0.04 0.02 0.42 0.0 1.48 0 \n", + "\n", + " N-S T-F J-P \n", + "0 0 0 0 \n", + "1 0 1 1 \n", + "2 0 1 1 \n", + "3 0 1 0 \n", + "4 0 1 0 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "quant_pers.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "sep_targets = quant_pers.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# build model" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# neural network and accuracy in whole" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "quant_pers = quant_pers.drop(columns = \"Unnamed: 0\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "quant_pers['type'] = quant_pers['type'].astype('category')\n", + "quant_pers['type'] = quant_pers['type'].cat.codes" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
typelength_commentvar_lengthlinks_countqm_countexm_countimg_countmusic_countdots_countht_countme_countI-EN-ST-FJ-P
0811.12135.29000.480.360.060.120.020.300.01.18INFJ
1323.40187.47560.200.100.000.020.000.380.03.06ENTP
21116.72180.69000.100.240.080.000.000.260.01.44INTP
31021.28181.83240.040.220.060.000.020.520.02.18INTJ
4219.34196.45760.120.200.020.040.020.420.01.48ENTJ
\n", + "
" + ], + "text/plain": [ + " type length_comment var_length links_count qm_count exm_count \\\n", + "0 8 11.12 135.2900 0.48 0.36 0.06 \n", + "1 3 23.40 187.4756 0.20 0.10 0.00 \n", + "2 11 16.72 180.6900 0.10 0.24 0.08 \n", + "3 10 21.28 181.8324 0.04 0.22 0.06 \n", + "4 2 19.34 196.4576 0.12 0.20 0.02 \n", + "\n", + " img_count music_count dots_count ht_count me_count I-E N-S T-F J-P \n", + "0 0.12 0.02 0.30 0.0 1.18 I N F J \n", + "1 0.02 0.00 0.38 0.0 3.06 E N T P \n", + "2 0.00 0.00 0.26 0.0 1.44 I N T P \n", + "3 0.00 0.02 0.52 0.0 2.18 I N T J \n", + "4 0.04 0.02 0.42 0.0 1.48 E N T J " + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "y = pd.DataFrame(quant_pers['type'])\n", + "X = quant_pers.drop(columns=['type','I-E', 'N-S', 'J-P', 'T-F']).copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=124)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# X_train = tf.keras.utils.normalize(X_train, axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 6940 entries, 7502 to 4558\n", + "Data columns (total 10 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 length_comment 6940 non-null float64\n", + " 1 var_length 6940 non-null float64\n", + " 2 links_count 6940 non-null float64\n", + " 3 qm_count 6940 non-null float64\n", + " 4 exm_count 6940 non-null float64\n", + " 5 img_count 6940 non-null float64\n", + " 6 music_count 6940 non-null float64\n", + " 7 dots_count 6940 non-null float64\n", + " 8 ht_count 6940 non-null float64\n", + " 9 me_count 6940 non-null float64\n", + "dtypes: float64(10)\n", + "memory usage: 596.4 KB\n" + ] + } + ], + "source": [ + "X_train.info()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "val_loss, val_acc = voices_new_model.evaluate(X_new_test, y_new_test, verbose=0)\n", + "print(f'Test loss: {val_loss}')\n", + "print(f'Test accuracy: {val_acc}')" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 9.7968e-07 - accuracy: 0.0225\n", + "Epoch 2/10\n", + "278/278 [==============================] - 1s 2ms/step - loss: 9.7968e-07 - accuracy: 0.0225\n", + "Epoch 3/10\n", + "278/278 [==============================] - 1s 2ms/step - loss: 9.7968e-07 - accuracy: 0.0225\n", + "Epoch 4/10\n", + "278/278 [==============================] - 1s 2ms/step - loss: 9.7968e-07 - accuracy: 0.0225\n", + "Epoch 5/10\n", + "278/278 [==============================] - 1s 2ms/step - loss: 9.7968e-07 - accuracy: 0.0225\n", + "Epoch 6/10\n", + "278/278 [==============================] - 1s 2ms/step - loss: 9.7968e-07 - accuracy: 0.0225: 0s - loss: 9.8123e-07 - accuracy: \n", + "Epoch 7/10\n", + "278/278 [==============================] - 1s 2ms/step - loss: 9.7968e-07 - accuracy: 0.0225\n", + "Epoch 8/10\n", + "278/278 [==============================] - 1s 2ms/step - loss: 9.7968e-07 - accuracy: 0.0225\n", + "Epoch 9/10\n", + "278/278 [==============================] - 1s 2ms/step - loss: 9.7968e-07 - accuracy: 0.0225\n", + "Epoch 10/10\n", + "278/278 [==============================] - 1s 3ms/step - loss: 9.7968e-07 - accuracy: 0.0225\n", + "Test loss: 9.869979749055346e-07\n", + "Test accuracy: 0.019596541300415993\n" + ] + } + ], + "source": [ + "model = tf.keras.models.Sequential()\n", + "model.add(tf.keras.layers.Dense(units=256, kernel_initializer='uniform', activation='relu', input_dim=10))\n", + "model.add(tf.keras.layers.Dense(units=128, kernel_initializer='uniform', activation='relu'))\n", + "model.add(tf.keras.layers.Dense(units=64, kernel_initializer='uniform', activation='relu'))\n", + "model.add(tf.keras.layers.Dense(activation = 'sigmoid', units = 1, kernel_initializer = 'uniform')) # output layer has number of outputs not number of neurons\n", + "\n", + "model.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])\n", + "# calculating loss, model is always trying to minimize loss, adam is the default optimize\n", + "\n", + "\n", + "model.fit(X_train, y_train, epochs = 10, batch_size= 25)\n", + "\n", + "val_loss, val_acc = model.evaluate(X_test, y_test, verbose=0)\n", + "print(f'Test loss: {val_loss}')\n", + "print(f'Test accuracy: {val_acc}')" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "55/55 [==============================] - 0s 2ms/step - loss: 9.8700e-07 - accuracy: 0.0202\n", + "9.869979749055346e-07 0.020172910764813423\n" + ] + } + ], + "source": [ + "val_loss, val_acc = model.evaluate(X_test, y_test)\n", + "print(val_loss, val_acc)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# neural network on seperate targets" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
length_commentvar_lengthlinks_countqm_countexm_countimg_countmusic_countdots_countht_countme_countI-EN-ST-FJ-P
011.120.1797500.480.360.060.120.020.300.01.181000
123.400.1844690.200.100.000.020.000.380.03.060011
216.720.1840100.100.240.080.000.000.260.01.441011
321.280.1866400.040.220.060.000.020.520.02.181010
419.340.1787230.120.200.020.040.020.420.01.480010
\n", + "
" + ], + "text/plain": [ + " length_comment var_length links_count qm_count exm_count img_count \\\n", + "0 11.12 0.179750 0.48 0.36 0.06 0.12 \n", + "1 23.40 0.184469 0.20 0.10 0.00 0.02 \n", + "2 16.72 0.184010 0.10 0.24 0.08 0.00 \n", + "3 21.28 0.186640 0.04 0.22 0.06 0.00 \n", + "4 19.34 0.178723 0.12 0.20 0.02 0.04 \n", + "\n", + " music_count dots_count ht_count me_count I-E N-S T-F J-P \n", + "0 0.02 0.30 0.0 1.18 1 0 0 0 \n", + "1 0.00 0.38 0.0 3.06 0 0 1 1 \n", + "2 0.00 0.26 0.0 1.44 1 0 1 1 \n", + "3 0.02 0.52 0.0 2.18 1 0 1 0 \n", + "4 0.02 0.42 0.0 1.48 0 0 1 0 " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data2 = sep_targets.drop(columns = ['type'])\n", + "data2.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "#data2['I-E'] = data2['I-E'].astype('category')\n", + "#data2['N-S'] = data2['N-S'].astype('category')\n", + "#data2['T-F'] = data2['T-F'].astype('category')\n", + "#data2['J-P'] = data2['J-P'].astype('category')\n", + "\n", + "#cat_columns = data2.select_dtypes(['category']).columns\n", + "\n", + "# data2[cat_columns] = data2[cat_columns].apply(lambda x: x.cat.codes)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "X = data2.drop(columns = ['I-E', 'N-S' , 'T-F', 'J-P'])\n", + "\n", + "y_IE = data2['I-E']\n", + "y_NS = data2['N-S']\n", + "y_TF = data2['T-F']\n", + "y_JP = data2['J-P']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + " # X = tf.keras.utils.normalize(X, axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "model = tf.keras.models.Sequential()\n", + "model.add(tf.keras.layers.Dense(units=256, kernel_initializer='uniform', activation='relu', input_dim=10))\n", + "model.add(tf.keras.layers.Dense(units=128, kernel_initializer='uniform', activation='relu'))\n", + "model.add(tf.keras.layers.Dense(units=64, kernel_initializer='uniform', activation='relu'))\n", + "model.add(tf.keras.layers.Dense(activation = 'sigmoid', units = 1, kernel_initializer = 'uniform')) # output layer has number of outputs not number of neurons\n", + "\n", + "model.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])\n", + "# calculating loss, model is always trying to minimize loss, adam is the default optimize\n", + "\n", + "\n", + "# model.fit(X_train, y_train, epochs = 10, batch_size= 25)\n", + "\n", + "# val_loss, val_acc = model.evaluate(X_test, y_test, verbose=0)\n", + "# print(f'Test loss: {val_loss}')\n", + "# print(f'Test accuracy: {val_acc}')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "278/278 [==============================] - 1s 3ms/step - loss: 9.1520e-08 - accuracy: 0.7677\n", + "Epoch 2/10\n", + "278/278 [==============================] - 1s 3ms/step - loss: 9.1520e-08 - accuracy: 0.7677\n", + "Epoch 3/10\n", + "278/278 [==============================] - 1s 3ms/step - loss: 9.1520e-08 - accuracy: 0.7677\n", + "Epoch 4/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 9.1520e-08 - accuracy: 0.7677\n", + "Epoch 5/10\n", + "278/278 [==============================] - 1s 3ms/step - loss: 9.1520e-08 - accuracy: 0.7677: 0s - loss: 9.287\n", + "Epoch 6/10\n", + "278/278 [==============================] - 1s 3ms/step - loss: 9.1520e-08 - accuracy: 0.7677\n", + "Epoch 7/10\n", + "278/278 [==============================] - 1s 3ms/step - loss: 9.1520e-08 - accuracy: 0.7677\n", + "Epoch 8/10\n", + "278/278 [==============================] - 1s 3ms/step - loss: 9.1520e-08 - accuracy: 0.7677\n", + "Epoch 9/10\n", + "278/278 [==============================] - 1s 5ms/step - loss: 9.1520e-08 - accuracy: 0.7677\n", + "Epoch 10/10\n", + "278/278 [==============================] - 2s 8ms/step - loss: 9.1520e-08 - accuracy: 0.7677\n", + "I-E RESULTS\n", + "Test loss: 9.261908928692719e-08\n", + "Test accuracy: 0.7769452333450317\n", + "****************************************************************************************************\n", + "Epoch 1/10\n", + "278/278 [==============================] - 1s 3ms/step - loss: 1.6284e-08 - accuracy: 0.1366\n", + "Epoch 2/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 1.6284e-08 - accuracy: 0.1366\n", + "Epoch 3/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 1.6284e-08 - accuracy: 0.1366\n", + "Epoch 4/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 1.6284e-08 - accuracy: 0.1366\n", + "Epoch 5/10\n", + "278/278 [==============================] - 2s 6ms/step - loss: 1.6284e-08 - accuracy: 0.1366\n", + "Epoch 6/10\n", + "278/278 [==============================] - 1s 3ms/step - loss: 1.6284e-08 - accuracy: 0.1366\n", + "Epoch 7/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 1.6284e-08 - accuracy: 0.1366: 0s - loss: 1.677\n", + "Epoch 8/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 1.6284e-08 - accuracy: 0.1366\n", + "Epoch 9/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 1.6284e-08 - accuracy: 0.1366\n", + "Epoch 10/10\n", + "278/278 [==============================] - 2s 6ms/step - loss: 1.6284e-08 - accuracy: 0.1366\n", + "N-S RESULTS\n", + "Test loss: 1.710842312263594e-08\n", + "Test accuracy: 0.14351585507392883\n", + "****************************************************************************************************\n", + "Epoch 1/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 5.4829e-08 - accuracy: 0.4599: 0s - loss:\n", + "Epoch 2/10\n", + " 1/278 [..............................] - ETA: 0s - loss: 6.6757e-08 - accuracy: 0.5600WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.119151). Check your callbacks.\n", + "278/278 [==============================] - 2s 7ms/step - loss: 5.4829e-08 - accuracy: 0.4599\n", + "Epoch 3/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 5.4829e-08 - accuracy: 0.4599\n", + "Epoch 4/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 5.4829e-08 - accuracy: 0.4599\n", + "Epoch 5/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 5.4829e-08 - accuracy: 0.4599\n", + "Epoch 6/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 5.4829e-08 - accuracy: 0.4599: \n", + "Epoch 7/10\n", + "278/278 [==============================] - 1s 5ms/step - loss: 5.4829e-08 - accuracy: 0.4599\n", + "Epoch 8/10\n", + "278/278 [==============================] - 1s 5ms/step - loss: 5.4829e-08 - accuracy: 0.4599\n", + "Epoch 9/10\n", + "278/278 [==============================] - 1s 5ms/step - loss: 5.4829e-08 - accuracy: 0.4599\n", + "Epoch 10/10\n", + "278/278 [==============================] - 1s 5ms/step - loss: 5.4829e-08 - accuracy: 0.4599\n", + "T-F Results\n", + "Test loss: 5.421102500235975e-08\n", + "Test accuracy: 0.454755038022995\n", + "****************************************************************************************************\n", + "Epoch 1/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 7.2230e-08 - accuracy: 0.6059\n", + "Epoch 2/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 7.2230e-08 - accuracy: 0.6059\n", + "Epoch 3/10\n", + "278/278 [==============================] - 1s 5ms/step - loss: 7.2230e-08 - accuracy: 0.6059\n", + "Epoch 4/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 7.2230e-08 - accuracy: 0.6059\n", + "Epoch 5/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 7.2230e-08 - accuracy: 0.6059\n", + "Epoch 6/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 7.2230e-08 - accuracy: 0.6059\n", + "Epoch 7/10\n", + "278/278 [==============================] - 1s 5ms/step - loss: 7.2230e-08 - accuracy: 0.6059\n", + "Epoch 8/10\n", + "278/278 [==============================] - ETA: 0s - loss: 7.2296e-08 - accuracy: 0.60 - 1s 4ms/step - loss: 7.2230e-08 - accuracy: 0.6059\n", + "Epoch 9/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 7.2230e-08 - accuracy: 0.6059\n", + "Epoch 10/10\n", + "278/278 [==============================] - 1s 4ms/step - loss: 7.2230e-08 - accuracy: 0.6059\n", + "J-P Results\n", + "Test loss: 7.118202915989968e-08\n", + "Test accuracy: 0.5971181392669678\n", + "****************************************************************************************************\n" + ] + } + ], + "source": [ + "\n", + "\n", + "# IE\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y_IE, test_size=0.2, random_state=10)\n", + "model.fit(X_train, y_train, epochs = 10, batch_size= 25)\n", + "predmodel = model.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "# print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "# print(confusion_matrix(y_test,predmodel))\n", + "# print(\"Score:\",round(accuracy_score(y_test,predmodel)*100,2))\n", + "# print(\"Classification Report:\",classification_report(y_test,predmodel))\n", + "val_loss, val_acc = model.evaluate(X_test, y_test, verbose=0)\n", + "print(f'Test loss: {val_loss}')\n", + "print(f'Test accuracy: {val_acc}')\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y_NS, test_size=0.2, random_state=10)\n", + "model.fit(X_train, y_train, epochs = 10, batch_size= 25)\n", + "predmodel = model.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "# print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "# print(confusion_matrix(y_test,predmodel))\n", + "# print(\"Score:\",round(accuracy_score(y_test,predmodel)*100,2))\n", + "# print(\"Classification Report:\",classification_report(y_test,predmodel))\n", + "val_loss, val_acc = model.evaluate(X_test, y_test, verbose=0)\n", + "print(f'Test loss: {val_loss}')\n", + "print(f'Test accuracy: {val_acc}')\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y_TF, test_size=0.2, random_state=10)\n", + "model.fit(X_train, y_train, epochs = 10, batch_size= 25)\n", + "predmodel = model.predict(x_test)\n", + "print(\"T-F Results\")\n", + "# print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "# print(confusion_matrix(y_test,predmodel))\n", + "# print(\"Score:\",round(accuracy_score(y_test,predmodel)*100,2))\n", + "# print(\"Classification Report:\",classification_report(y_test,predmodel))\n", + "val_loss, val_acc = model.evaluate(X_test, y_test, verbose=0)\n", + "print(f'Test loss: {val_loss}')\n", + "print(f'Test accuracy: {val_acc}')\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y_JP, test_size=0.2, random_state=10)\n", + "model.fit(X_train, y_train, epochs = 10, batch_size= 25)\n", + "predmodel = model.predict(x_test)\n", + "print(\"J-P Results\")\n", + "# print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "# print(confusion_matrix(y_test,predmodel))\n", + "# print(\"Score:\",round(accuracy_score(y_test,predmodel)*100,2))\n", + "# print(\"Classification Report:\",classification_report(y_test,predmodel))\n", + "val_loss, val_acc = model.evaluate(X_test, y_test, verbose=0)\n", + "print(f'Test loss: {val_loss}')\n", + "print(f'Test accuracy: {val_acc}')\n", + "print(\"*\" * 100)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/code/Code 6 - Piecing together by Accuracy.ipynb b/your-project/code/Code 6 - Piecing together by Accuracy.ipynb new file mode 100644 index 00000000..c085e09e --- /dev/null +++ b/your-project/code/Code 6 - Piecing together by Accuracy.ipynb @@ -0,0 +1,453 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating new Model by Algorithm's Accuracy" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import re\n", + "import random" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from sklearn.model_selection import train_test_split, cross_val_score\n", + "from sklearn.model_selection import cross_validate\n", + "from sklearn.model_selection import StratifiedKFold\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import learning_curve\n", + "from sklearn.ensemble import ExtraTreesClassifier\n", + "from sklearn.decomposition import TruncatedSVD\n", + "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.naive_bayes import MultinomialNB\n", + "from sklearn.metrics import classification_report\n", + "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv(r'../data/personalities_cleaned.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "data['I-E'] = data['type'].astype(str).str[0]\n", + "data['N-S'] = data['type'].astype(str).str[1]\n", + "data['T-F'] = data['type'].astype(str).str[2]\n", + "data['J-P'] = data['type'].astype(str).str[3]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "data = data.drop(columns = ['Unnamed: 0', 'posts', 'text_processed'])\n", + "data.drop(data.index[3559], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
typetext_readyI-EN-ST-FJ-P
0INFJintj moment sportscent top ten play prank ha l...INFJ
1ENTPfind lack post veri alarm sex bore posit often...ENTP
2INTPgood one cours say know bless cur doe absolut ...INTP
3INTJdear intp enjoy convers day esoter gab natur u...INTJ
4ENTJfire anoth silli misconcept approach logic go ...ENTJ
\n", + "
" + ], + "text/plain": [ + " type text_ready I-E N-S T-F J-P\n", + "0 INFJ intj moment sportscent top ten play prank ha l... I N F J\n", + "1 ENTP find lack post veri alarm sex bore posit often... E N T P\n", + "2 INTP good one cours say know bless cur doe absolut ... I N T P\n", + "3 INTJ dear intp enjoy convers day esoter gab natur u... I N T J\n", + "4 ENTJ fire anoth silli misconcept approach logic go ... E N T J" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "x = data['text_ready']\n", + "\n", + "y_IE = data['I-E']\n", + "y_NS = data['N-S']\n", + "y_TF = data['T-F']\n", + "y_JP = data['J-P']" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.feature_extraction.text import CountVectorizer\n", + "\n", + "vector = CountVectorizer(ngram_range=(2, 2)).fit(x) \n", + "X = vector.transform(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split, cross_val_score" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E RESULTS\n", + "Label I-E train score is : 0.7685545467646635\n", + "Label I-E test score is : 0.7685545467646635\n", + "Confusion Matrix for K-Nearest Neighbors:\n", + "[[ 0 393]\n", + " [ 0 1342]]\n", + "Score: 77.35\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " E 0.00 0.00 0.00 393\n", + " I 0.77 1.00 0.87 1342\n", + "\n", + " accuracy 0.77 1735\n", + " macro avg 0.39 0.50 0.44 1735\n", + "weighted avg 0.60 0.77 0.67 1735\n", + "\n", + "****************************************************************************************************\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N-S RESULTS\n", + "Label N-S train score is : 0.8617956477878657\n", + "Label N-S test score is : 0.8617956477878657\n", + "Confusion Matrix for Random Forests:\n", + "[[1497 0]\n", + " [ 238 0]]\n", + "Score: 86.28\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " N 0.86 1.00 0.93 1497\n", + " S 0.00 0.00 0.00 238\n", + "\n", + " accuracy 0.86 1735\n", + " macro avg 0.43 0.50 0.46 1735\n", + "weighted avg 0.74 0.86 0.80 1735\n", + "\n", + "****************************************************************************************************\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "T-F Results\n", + "Label T-F train score is : 1.0\n", + "Label T-F test score is : 1.0\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[895 65]\n", + " [363 412]]\n", + "Score: 75.33\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " F 0.71 0.93 0.81 960\n", + " T 0.86 0.53 0.66 775\n", + "\n", + " accuracy 0.75 1735\n", + " macro avg 0.79 0.73 0.73 1735\n", + "weighted avg 0.78 0.75 0.74 1735\n", + "\n", + "****************************************************************************************************\n", + "J-P Results\n", + "Label J-P train score is : 1.0\n", + "Label J-P test score is : 1.0\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[311 369]\n", + " [193 862]]\n", + "Score: 67.61\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " J 0.62 0.46 0.53 680\n", + " P 0.70 0.82 0.75 1055\n", + "\n", + " accuracy 0.68 1735\n", + " macro avg 0.66 0.64 0.64 1735\n", + "weighted avg 0.67 0.68 0.66 1735\n", + "\n", + "****************************************************************************************************\n" + ] + } + ], + "source": [ + "# making the model according to Accuracies in Code \n", + "\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "knn = KNeighborsClassifier(n_neighbors=9) # see if can tweak accuracy\n", + "\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "rfn = RandomForestClassifier(max_depth=5, random_state=0) # see if can tweak accuracy\n", + "\n", + "from sklearn.naive_bayes import MultinomialNB\n", + "mnb = MultinomialNB()\n", + "\n", + "\n", + "\n", + "# IE\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_IE, test_size=0.2, random_state=10)\n", + "knn.fit(x_train, y_train)\n", + "ieb_train = knn.score (x_train,y_train)\n", + "ieb_test = knn.score (x_train,y_train)\n", + "predknn = knn.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "print('Label I-E train score is :',ieb_train)\n", + "print('Label I-E test score is :',ieb_test)\n", + "print(\"Confusion Matrix for K-Nearest Neighbors:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predknn))\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_NS, test_size=0.2, random_state=10)\n", + "rfn.fit(x_train, y_train)\n", + "nsb_train = rfn.score (x_train,y_train)\n", + "nsb_test = rfn.score (x_train,y_train)\n", + "predrfn = rfn.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "print('Label N-S train score is :',nsb_train)\n", + "print('Label N-S test score is :',nsb_test)\n", + "print(\"Confusion Matrix for Random Forests:\")\n", + "print(confusion_matrix(y_test,predrfn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predrfn))\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_TF, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "tfb_train = mnb.score (x_train,y_train)\n", + "tfb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"T-F Results\")\n", + "print('Label T-F train score is :',tfb_train)\n", + "print('Label T-F test score is :',tfb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_JP, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "jpb_train = mnb.score (x_train,y_train)\n", + "jpb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"J-P Results\")\n", + "print('Label J-P train score is :',jpb_train)\n", + "print('Label J-P test score is :',jpb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/code/Code 7 - Real Life Data.ipynb b/your-project/code/Code 7 - Real Life Data.ipynb new file mode 100644 index 00000000..25b02141 --- /dev/null +++ b/your-project/code/Code 7 - Real Life Data.ipynb @@ -0,0 +1,2613 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import re" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [], + "source": [ + "Darya = [\"Quarantine expectations. Swipe for reality\", \n", + " \"22 in 2020 - swipe to see a photo from yesterday. Age affects me.\", \n", + " \"hello? Santa? All I wish for Christmas is my mind back... \", \n", + " \"girl with a hat - a series. \", \n", + " \"casual day at uni. swipe for more idiots\", \n", + " \"thanking Steve for a nice iPhone front camera.\", \n", + " \"Missing this\", \n", + " \"*edit: still can‘t believe a sunscreen for 7.95 prevented me from having a huge sunburn\",\n", + " \"where I‘d rather be part 2\",\n", + " \"when people ask me if I‘ve got free time - I do. Except I don’t.\",\n", + " \n", + " \"just a small town girl, living in a capitalist world...\",\n", + " \"clueless\",\n", + " \"Might be serious, might be hungry. \",\n", + " \"you want blue? you want yellow?\",\n", + " \"While the wall fell nearly 30 years ago, I constantly fall on my face...\",\n", + " \"Im pretty ok with exams being over... \",\n", + " \"unpopular opinion: university can be fun. exhibit a\",\n", + " \"christmas is around the corner but all I‘m wishing for is an extention of deadlines...\",\n", + " \"on some occasions I even get out of the valley and LA to go sightseeing \",\n", + " \"diamonds are a girls best friend. Especially when you trade ‘em for plane tickets and uber rides\",\n", + " \n", + " \"not a threat, it’s a warning. (talking about the beginning of semester tomorrow)\",\n", + " \"come fly with me, let’s fly let’s fly away...\",\n", + " \"saltier than the Pacific\",\n", + " \"me, when I’m a) hangry or b) am studying @ nyfalosangeles @ williamroyal98\",\n", + " \"this whole you-gotta-study-while-the-sun-is-shining gets on my nerve. here’s an example of inner rage\",\n", + " \"you know you might have been spontaneous when while booking your flights they asked you if ou would want to check in for the flight back aswell.\",\n", + " \"Vitamins\",\n", + " \"finished the first semerster at HSG and I still can’t afford a porsche 911.\",\n", + " \"Me: you should be studying. Also me: enjoy the holidays\",\n", + " \"just kidding - I’m still a short ass little human\",\n", + " \n", + " \"week 2 at uni and my icloud and google drive are asking me to pay for more storage.\",\n", + " \"uni starts tomorrow and I already see myself either a) being lost cause I can't find the classroom or b) looking like Annabelle cause sleep is for the weak\",\n", + " \"oh boy\",\n", + " \"this should be my last week of summer but I think the weather decided to give me an early taste of fall already.\",\n", + " \"water falls, darya falls and just casually sit on floors cause standing is overrated \",\n", + " \"I promise I counted all the lights, but then I saw a squirrel and lost count \",\n", + " \"is it a set? Is it a real house? guess.\",\n", + " \"quiet on set\",\n", + " \"Baywatch, are you hiring?\",\n", + " \"What if perfume is actually bugspray but they want us to buy the nasty smelly stuff just to have a good laugh?\",\n", + " \n", + " \"I'm starting to wonder if spotlight can give you a tan cause the sun isn't coming to just yet. All jokes asid tho, I am beyond grateful to share the stage with so many talented people. Do what you love and you'll feel limitless \",\n", + " \"that second when the person you've been straing at looks at you and you quickly turn away, trying to look as casual as possible\",\n", + " \"at that moment I probably thought out a plan to convince my mom to paint one of my walls pink. even though I know it won't happen, happy #internationalwomensday ladies\",\n", + " \"Tell me about it, stud\",\n", + " \"nice to be back. bring it on 2017\"\n", + " \"2016. A year that had probably more faces than facebook. Victories and dissapointment, new friendships and new experiences; I was lucky enough to travel the several times and can't thank the people that made 2016 the year it was enough for all their kind words, laughter and happy tears.\",\n", + " \"don't mess with us - we have RG clubs\",\n", + " \"Summer is over and you're like: pardon me, what?\",\n", + " \"Looking back at those 5 weeks full of new friends, experiences and emotions puts a smile on my face. The teachers I've worked with inspired me every day, the people made my summer the best one yet. I love all of you and you'll always have a special place in my heart #obgottheheat and happy National Dance Day\",\n", + " \"everybody needs some attitude in their life #punintended #obgottheheat\"\n", + "]\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [], + "source": [ + "Theresa = [\"light times\", \n", + " \"simplicité.\", \n", + " \"chapter 1\", \n", + " \"*waiter pours up wine without being asked for*\", \n", + " \"what a year\", \n", + " \"it‘s friday i‘m in loved.\", \n", + " \"herzallerliebst.\", \n", + " \"wie sommerferien für erwachsene irgendwie.\",\n", + " \"sommer und sonne und zitroneneis und stella und ach man was soll ich sagen - das beste leben @stellamarieluise\",\n", + " \"das ist einfach Glück, wenn man euch hat\",\n", + " \n", + " \"an diesem Bild ist so Vieles richtig.\",\n", + " \"ernst gucken geht also doch. @shishaclothing #supportyourlocalbrand #shishaclothing\",\n", + " \"casual durch die gegend düsen halt\",\n", + " \"Liebe für alle Frauen. Aber für Diese am meisten! #happyweltfrauentag\",\n", + " \"meine entscheidungsfreude captured by @stellamarieluise\",\n", + " \"keine termine und leicht im dispo\",\n", + " \"future\",\n", + " \"Zukunftsprognose 2080: alle glücklich.\",\n", + " \"marilyn monroe momente beim brötchen holen\",\n", + " \"reflections.\",\n", + " \n", + " \"keine sorgen, keine wellen, kein conditioner\",\n", + " \"liebenswichtig\",\n", + " \"alles wie immer\",\n", + " \"gute gespräche und blöde ideen\",\n", + " \"klassisches arbeiten halt\",\n", + " \"mag ich\",\n", + " \"lieb ich\",\n", + " \"viel zu schön\",\n", + " \"kaltes bier who dis\",\n", + " \"guess i‘m just in love with sunrises and moonlight and rainstorms and everything that has soul\",\n", + " \n", + " \"semi zufrieden mit dem frühstück\",\n", + " \"ich hab das mit dem ernst gucken nicht mitbekommen\",\n", + " \"verliebtverliebt\",\n", + " \"trouble never looked so goddamn fine\",\n", + " \"und wenn das abendlicht in genau dieser farbe ist\",\n", + " \"say something in dutch\",\n", + " \"superlekker\",\n", + " \"Pass auf deine Füße auf \",\n", + " \"herz euch behalt ich\",\n", + " \"find ich gut\",\n", + " \n", + " \"wenn @lisbz meine wäsche aufhängt\",\n", + " \"find your fire\",\n", + " \"umziehen mit lieblingspizza und lieblingswg\",\n", + " \"lieb ich\",\n", + " \"ist das Kunst oder kann das weg?\"\n", + " \"kennt ihr diese Menschen, die stehen bleiben um den Himmel zu fotografieren?\",\n", + " \"no one is you and that is your power\",\n", + " \"HAPPY WELTFRAUENTAG\",\n", + " \"gründe warum ich kein Blogger bin\",\n", + " \"sommer\"\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [], + "source": [ + "Nico = [\"Haven't posted in a while, so this random picture is now filling the void. Been going within a lot in the past year and sorted out some things for myself. Happy to say, that I am refocused, re-energized and working on great things.\", \n", + " \"Impressions from Serfaus. 2020 is off to a good start. Swipe right for the best drinking game\", \n", + " \"Dogs of Berlin\", \n", + " \"Not my boat, still flexing \", \n", + " \"Lads\", \n", + " \"A weekend in Madrid is always a good idea! Hanging out the hangover at some historical sites \", \n", + " \"Drip too hard, don’t stand too close\", \n", + " \"I miss you bro. We pushed each other to be the best every day. There never was a better duo. „The Golden Boys“ Rest Easy\",\n", + " \"Getting settled in Berlin\",\n", + " \"It’s been three tough but fun years at WHU. Last weekend I finally graduated with a degree from the best business school in Germany, but more importantly with new friends for life and another place I can call home.\",\n", + " \n", + " \"Graduated\",\n", + " \"WHU boys - Class of 2017. Had so much fun with you. Now let's get the moolah\",\n", + " \"The pic is blurry but the day was sharp\",\n", + " \"Probably the most spectacular view I ever had was driving along the highway no. 1 between LA and SF\",\n", + " \"He acting all loyal in this pic, but that lil b* bit me the next minute\",\n", + " \"Probably the most spectacular view I ever had was driving along the highway no. 1 between LA and SF.\",\n", + " \"Hey @google, Hire me, if you want your bike back! #mountainview #google #siliconvalley\",\n", + " \"Throwback to an amazing road trip this fall. What a view! Def. worth the hike #hike #nature #travel #roadtrip #yosemite\",\n", + " \"Keep your eyes on the stars and keep your feet on the ground\",\n", + " \"You can clearly see I enjoyed being back in SoCal. That place is so beautiful...\",\n", + " \n", + " \"he Pacific #beers #beach #chilling #california #home #uglyfeet @goldclapmusic good day dude\",\n", + " \"Never hold back #weekendmood\",\n", + " \"Thank you @kaleandme for this awesome party! #crewlove @reiseminister_coco\",\n", + " \"tb to one of my best games ever Still holding the club record for most receptions per game 2013 vs Cottbus W31:15 13 receptions, 193 yards, 3 TDs Thank you @zbreez_ for the trust and the passes !\",\n", + " \"Throwback to the best winter ever. My kids group. I gave us the name the lil crackheads, because they got me cracking up all the time! #fuckhashtags\",\n", + " \"Won this one big time 60:20, but it cost us some good men... #hospitalreunion\",\n", + " \"#Repost @luebeckcougars Much way for improvement though personally. #getbettereveryday Gamestats #cougars #cougarnation #cougarspride #luebeck #luebeckcougars #football #americanfootball #gfl #amgid #afvd #gridiron #awaygame #ritterhude #badgers\",\n", + " \"100 days credits @gridiron_design_s\",\n", + " \"New Year's Eve in the alps with the crew. #illuminati\",\n", + " \"#tb to the good old times when sunday meant gameday and mondays newspaper Sitting in the library studying right now I wish I could go back in time\",\n", + " \n", + " \"What you believe becomes reality. Create your own world #sundaymood\",\n", + " \"Mom, can we have one?\",\n", + " \"Trying to get my surf game up. I think I have a much better form on the beach than in the water though haha #germansaintmermaids @lennartmhr\",\n", + " \"Table Mountain from Lion's Head @lennartmhr\",\n", + " \"I hate off-season... \",\n", + " \"This shoutout is overdue. Best musical speaker @realcoleworld!\",\n", + " \"#tb to when my bro @enzeocha_11 was making the Swedish defense look like 8th graders.\",\n", + " \"Hoffentlich liegt das in der Familie! #crossingfingers #toodamnhot\",\n", + " \"Touchdown 70:46 crazy game!\",\n", + " \"Some work by @dani_piedrabuena. Trying to look smart in that suit haha \",\n", + " \n", + " \"Back at it\",\n", + " \"Received this at work. Now my colleagues think I'm famous lol Thank you so much Colin!\",\n", + " \"#tb to the toughest job I ever had to do good times\",\n", + " \"office break \",\n", + " \"Beach is better. #barcelona #barca #beach #playa #sun #smile #spain #sleepyface\"\n", + " \"Thank you for such a lovely birthday night.\",\n", + " \"My new car... I wish. M4 Test Drive. Still having goosebumps #bmw #m4 #madrid #racing\",\n", + " \"Don't mess with NFL Runningbacks\",\n", + " \"perfect day at el Retiro\",\n", + " \"Morning routine. Scooter Ride #madrid #work #watch #scooter #sun #suit\"\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [], + "source": [ + "Dylan = [\n", + "'Happy golden Birthday Kayla! 22 years on Earth. The photos say it all... you’re one of a kind, and I feel lucky to call you sister. I’m so tremendously proud of you and all that you’ve accomplished this past year. Your unceasing desire to travel and explore is inspiring, and I can’t wait to see what this coming year brings! I know you’ll finish college strong and jump into your career with all the passion you have for everything you do. Happy Kayla Day, love you',\n", + "'Blood is thicker than water. This mud on the other hand... Alabama',\n", + "'A rocky start to the weekend. Alabam',\n", + "'DANGER - Propellers chop heads off.',\n", + "'Memorial Day marked the first Winter —> Summer change at the cabin.',\n", + "\"You're not the only one with great pics of us happy national sibling day squirt, love you and so proud of you.\",\n", + "\"You're not the only one with great pics of us happy national sibling day squirt, love you and so proud of you. #siblingsforthewin #finelookingduo #mynumerouno #lilsismayhem #lookitthempearlywhites\",\n", + "\"Missing my little beast. Sleep tight #audi #tt #3.2 #quattro #purrofakitty #growlofatiger #mapetiteblanchevoiture\",\n", + "\"Officially my view for the next year! #switzerland #valais #balconyview #lesrochesstudent #hospitality #naturalswissbeauty #lifeinthemountains #lastyear\",\n", + "\"Officially my view for the next year! #switzerland #valais #balconyview #lesrochesstudent #hospitality #naturalswissbeauty #lifeinthemountains #lastyear\",\n", + "\"I had the great fortune today of spotting the incredibly rare Bentley Continental GTZ Zagato! No. 1 of 9 in the world! #Lausanne #Switzerland #Bentley #Continental #Zagato #rare #1of9 #dreamcar #themcurves #iwanttoseetheinsurancebill #supercarsofswitzerland #carlove\",\n", + "\"The best respite from finals? Deep in the Swiss Alps will suffice #switzerland #finalsweek #audi #tt #3.2 #quattro #swissalps #freedom #squintyeyes\",\n", + "\"Welcome to my crib #castlehopping #neverstopexploring #switzerland #sion #notreallymycrib #schoolstartstomorrow #lovinlife #naturalbeauty\",\n", + "\"The Matterhorn in all its glory #matterhorn #zermatt #swissalps #neverstopexploring #lovinlife #myhandwasfreezing\",\n", + "\"Bienvenue Suisse #Switzerland #Mountaindriving #Somuchspace #Sierre #lesrochesswitzerland #lovinlife\",\n", + "\"A great day on the water comes to a close, as my summer day count winds down to 3 #Chevytrucks #2500HD #Mastercraft #Lakedays #Minnesota #Summerbummin #mastercraft2015\",\n", + "\"Stormy days at the lake #Stormydays #IronRange #NorthernMN #Nimbusamongus #Summertime\",\n", + "\"The land of the free and the home of the brave. Murica! #freedom #summer #homesweethome #sightforsoreeyes #freedomwithachanceofliberty #letskickit\",\n", + "\"It's a bit late, but it was great to have you here bro! Missing you already @bryce.gordon, until next time keep it real! :P #Shanghai #Xintiandi #Brotime #WhenImetyouinthesummer #Keepitreal\",\n", + "\"The PuLi hotel, Jingan Temple area #Shanghai #PuLi #Hotelschooldays #Urbanresort #LHW #Shouldhavebroughtmyswimsuit\",\n", + "\"Fun in the sun #Boracay #TheDistrict #letsgodiving #architectureonpoint #paradise\",\n", + "\"Beautiful Boracay sunset #Boracay#Philippines#Missionwork#AGIF#nobeachtimejustpreachtime\",\n", + "\"Have a great Valentines Day everyone! @lovelivalove this is waiting for you when you get back ;) #Valentinesday #chocolates #loveisintheair #wouldyoubemyvalentine? #girlslovechocolate\",\n", + "\"#tbt to being cute and devious :P now I'm just devious #youngme #grinchonesie #santahats #christmascheer #kaylathatface #attemptedsmile #saocute #ihategrowingup #takemeback\",\n", + "\"New addition to the family, ready to log some dive time. #Guam #G-Shock #divewatch #adventuretime\",\n", + "\"Speed is a blessing. #Chevrolet #Camaro #ifyourenotfirstyourelast #Guam #lastday #iwannagofast #transformer #niceparkingjob…\",\n", + "\"Time to go wreck diving! #Guam #diving #wreckdiving #WWII #deepblue #dontworrygotmytetanusshot\",\n", + "\"The smiles of two PADI certified advanced open water divers :D #success #guamdayfive #growinggills #GTDS #firstachievementof2015 #crazyeyes\",\n", + "\"A successful first day of diving on the beautiful island of Guam! #GTDS #icanbreatheunderwater #60ftclub #buoyancyisanissue #Guam #Padicertification #SCUBA #getoffthegrass\",\n", + "\"TBT to New Years. My first semester of college has been real with you guys! #bratansandbratishkas#russianmob#TheNest#firstmomentsof2015#livinglarge#americanamongstrussians\",\n", + "\"A big car for a big city #Cadillac #Escalade #Shanghai #JinQiao #Bigblackride #blueskies #weekendendeavours\",\n", + "\"Just another night in Hanoi. #Hanoi #bikes #people #cityviewcafe #yourebeingwatched #eyesinthesky #driving #youngandfree\",\n", + "\"I guess work isnt so bad ;p #shanghai #sunny #internship #Hyattonthebund #livingthedream #showmethemoney\",\n", + "\"About to start my internship! #Internship #firstdayofwork #livingthelife #gettingthingsdone #pathtosuccess #idkwhatimdoing #noobthings #wishmeluck\",\n", + "\"Florida in the summertime #Florida #boatsnhoes #bromance @johnnygmcdonald\",\n", + "\"Rainy dayz\",\n", + "\"Risk... Back to the old days #Risk#theguys#boardgames#Sweden#strategy#hipster\",\n", + "\"Finally get to see this kid again #powerranger #superfly #southafrican #hashtag #18621613390 #hmu #oneandonly\",\n", + "\"No he's not stealing it. #Latino#diy\",\n", + "\"My main bitchz #sosexy #immaluckyguy\",\n", + "\"#sunnyday#shanghaiskyline\",\n", + "\"#tbt #lastyr #swagdawg #flathatsnsunglasses #selfie #swaggy'd before justin bieber was even born.\",\n", + "\"Happy golden Birthday Kayla! 22 years on Earth. The photos say it all... you’re one of a kind, and I feel lucky to call you sister. I’m so tremendously proud of you and all that you’ve accomplished this past year. Your unceasing desire to travel and explore is inspiring, and I can’t wait to see what this coming year brings! I know you’ll finish college strong and jump into your career with all the passion you have for everything you do. Happy Kayla Day, love you\",\n", + "\"Lol #bluefrog#openwide#whatisthischeese#schooluniformreppin\",\n", + "\"A great day on the water comes to a close, as my summer day count winds down to 3 #Chevytrucks #2500HD #Mastercraft #Lakedays #Minnesota #Summerbummin #mastercraft2015\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [], + "source": [ + "Katie = [\n", + "\"Sonndorf VI #autumn #fall #colors #igersaustria #outdoors #loveaustria #beautiful #nature #naturelovers #naturephotography #memories #sun #afternoon #garden\",\n", + "\"Sonndorf V #autumn #fall #colors #igersaustria #outdoors #loveaustria #beautiful #nature #naturelovers #naturephotography #loveaustria #window #sun #afternoon\",\n", + "\"Sonndorf IV #autumn #fall #colors #igersaustria #outdoors #loveaustria #beautiful #nature #naturelovers #naturephotography #loveaustria #window #sun #afternoon\",\n", + "\"Sonndorf III #autumn #fall #colors #igersaustria #outdoors #loveaustria #beautiful #nature #naturelovers #naturephotography #loveaustria #window #sun #afternoon\",\n", + "\"Sonndorf II #autumn #fall #colors #igersaustria #outdoors #loveaustria #beautiful #nature #naturelovers #naturephotography #loveaustria #window #sun #afternoon\",\n", + "\"Sonndorf I #autumn #fall #colors #igersaustria #outdoors #loveaustria #beautiful #nature #naturelovers #naturephotography #loveaustria #window #sun #afternoon\",\n", + "\"he beauty of fall | Lech am Arlberg\",\n", + "\"Mystic Fog | Lech am Arlberg #fog #igersaustria #outdoors #loveaustria #beautiful #nature #naturelovers #naturephotography #mountains #fall #autumn #colors #arlberg\",\n", + "\"Naxos, Greece\",\n", + "\"vom Winde verweht | #schoten #fieren #raumerwind #mannlee #mannluv #übervorn #schotenlos #reffen #fock #groß #bugmann #heckmann\",\n", + "\"sibling goals\",\n", + "\"lovely team #ferienhort #ferienhortamwolfgangsee #campelicious #love #my #team\",\n", + "\"wishing for another awesome breakfast like today | @the_motto_is_diversity #themottoisdiversity #mottoamfluss #lovely #breakfast #acceptance #liveyourlife\",\n", + "\"the beauty of books & architecture | Stift Admont #austria #igersaustria #beautiful #monastery #stiftadmont #library #books #architecture #aesthetic #symmetry #colors #rose #blue #hiddengems\", \n", + "\"excited for spring #igersaustria #outdoors #beautiful #nature #naturelovers #naturephotography #outdoors #colors #spring #exploreaustria #austria #hallstatt #nofilterneeded\", \n", + "\"excited for spring #igersaustria #outdoors #beautiful #nature #naturelovers #naturephotography #outdoors #colors #spring #exploreaustria #austria #hallstatt #nofilterneeded\", \n", + "\"I think I've found Willy Wonka's chocolate mountain\", \n", + "\"beautiful switzerland\",\n", + "\"#mountains #colors #blue #white #snow #beautiful #nature #naturelovers #naturephotography #outdoors #moretocome #igersaustria #nofilter #travellover #switzerland #jungfraujoch #topofeurope #chocolatemountain #charlieandthechocolatefactory #willywonka\",\n", + "\"#switzerland #suisse #schweiz #beautiful #nature #lake #mountains #colors #blue #autumn #fall #favouriteseason #igersswitzerland #igersaustria #outdoor #walk\",\n", + "\"summer | Attersee, Austria #igersaustria #outdoors #naturephotography #macrophotography #naturelovers #plants #green #sun #details #leaf #beautiful #garden ##memories #missingsummer #anticipating #autumn\",\n", + "\"Kinkaku-ji, Kyoto, Japan #japan #kyoto #asia #travel #temple #traditional #throwback #memories #beautiful #garden #goldenhour #igersaustria #outdoors #travellover #travelwithfriends #pond #nature #naturelovers #naturephotography #reflections #japan_focus\",\n", + "\"Kyoto, Japan 2016 #igersaustria #japan #kyoto #asia #travel #faraway #autumn #fall #temple #traditional #awesome #architecture #wood #ancient #travelwithfriends #island #travellover #throwback #memories\", \"spring love #spring #flower #pink #beautiful #nature #love #instagood #instaspring #garden #tree #enjoy#love #warm #weather #nofilterneeded\",\n", + "\"on top of the world #mountains #Arlberg #Austria #skiing #snowboarding #enjoy #life #holidays #fun #family #friends #fog #clouds #beautiful #blue #white #naturephotography #outdoors\",\n", + "\"autumn 2017 III #fall #autumn #leaves #falltime #season #seasons #instafall #instagood #instaautumn #photooftheday #sunset #colorful #orange #red #autumnweather #fallweather #nature #nofilter #brotherandsister #family #fun\",\n", + "\"autumn 2017 II #autumn #fall #enjoy #light #sunset #beautiful #family #brother #photography #model #nature #instagood #instafall\",\n", + "\"autumn 2017 I #fall #autumn #leaves #falltime #season #seasons #instafall #instagood #instaautumn #photooftheday #colorful #orange #blue #autumnweather #fallweather #nature #sunset #family #brother\",\n", + "\"beautiful sunset at the Acropolis\",\n", + "\"spring #beautiful #nature #Butterfly #red #blue #black #white #rose #pink #colors #spring #concrete #animals #love #cute #fly #away #springtime #instagood #instaspring #instalove161w\",\n", + "\"I need more sunshine right now #travel #traveling #vacation #visiting #traveler #instatravel #instago #instagood #trip #holiday #photooftheday #fun #travelling #tourism #tourist #instapassport #instatraveling\",\n", + "\"missing the beach & the warm weather #japan #fukuoka #temple #bigcity #skyscraper #red #traditional #travel #3weeks #japanrailpass #jrpass #friends #park #beautiful #sightseeing #love #asia\",\n", + "\"Fukuoka, Japan #japan #fukuoka #temple #bigcity #skyscraper #red #traditional #travel #3weeks #japanrailpass #jrpass #friends #park #beautiful #sightseeing #love #asia\",\n", + "\"finally I've put all the pics from japan on my phone this was taken in Nara by @fabiakailphotography #Asia #asian # #Tokyo #Japan #Nara #nature #deer #cute #cheeky #animal #woods #temple #famous #throwback #travel #summer\",\n", + "\"early morning train ride #japan #landscape #woods #river #mirror #clouds #water #green #blue #nofilter #early #morning #train #beautiful #nature #love #backpacking\",\n", + "\"Okinawan beauty #okinawa #beach #sand #sea #blue #skies #summer #hot #sesokojima #beautiful #photography #nature\",\n", + "\"favourite #nofiltr #outdoors #ferienhort #green #fog #mountains #morning #happy #friends #love #holidays\",\n", + "\"miss my little tiger #nature #sky #sun #summer #garden #beautiful #pretty #sunset #cat #cats #catsagram #catstagram #instagood #kitten #kitty #kittens #pet #love #home\",\n", + "\"I am already looking forward to spending my summer holidays there\",\n", + "\"spring at home #nature #sky #sun #spring #beautiful #pretty #sunset #landscape #amazing #view #trip #tree #mountains #landscape_lovers #landscapelovers #landscape_lover #igersaustria #austria #canon\",\n", + "\"already miss this see you next year\",\n", + "\"favourite place\",\n", + "\"#arlberg #austria\",\n", + "\"need this again... #summer #austria #brother #fun\",\n", + "\"cloudy day in the mountains\",\n", + "\"one of these days\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [], + "source": [ + "Gustav= [\"optiimal GNTM watching with the best #teammaureen #lovemaureen #marryme? #throwbackto2013 #gntm #nureinekann #men's night #goodphoto #rockclimbing #heclimbs #climber #climberlife #boulderer #bouldering #mountaineering #mountains #mountainlover #slowandsteadywinstherace #hashtag #like4like #follow4follow #instalife #igerslinz #instaboi\",\n", + "\"Thanks mom für 20 years child support\",\n", + "\"Finally, the lockdown is over. And even though I haven't really been productive, at least my GI tract has. Litterally.\",\n", + "\"#hepoops #pooper #pooping #humanneeds #humanwaste #humansaregross #gross #toiletpaper #corona #lockdown #climber #heclimbs #boulderer #bouldering #rockclimbing #climberlife\",\n", + "\"Can you believe this? Well, it's true either way. I can't believe so many people out there live their lives unaware of this fact! #facts #spreadtheknowledge #educated #educateyourself #climber #climbing #rockclimber #heclimbs #bouldering #boulderersofinstagram #water #bamboo\",\n", + "\"Gotta get those PROUDEINS in, man! Oh yeah, and with all that proudein getting I nearly forgot to take a #foodporn picture, hence the half finished serving\",\n", + "\"#proudeins #nocarb #highprotein #limitierendeaminosäurewas #lowcarb #proteins #food #foodporn #eggs #protein #nutrition #heclimbs #climber #rockclimber #rockclimbing #boulderer #bouldering\",\n", + "\"Who would of thought that? I think this is a great point for eating more of this delicious food so many of us crave during times like these\",\n", + "\"#chocolate #science #research #facts #truth #food #nobodytellsyouthat #heclimbs #rockclimber #rockclimbing #mountaineering #bouldering #boulderer #climber #climberlife\",\n", + "\"You better start working on that beach body now to be in shape in time, and this also involves the right diet. Btw, this avocado I just ate has about 500 000 calories of fat in it. Yeah, you read that right, 500 000. That's more fat than @dkm14 has in his entire body, so Kudos to him!!\",\n", + "\"Come to think of it, I really don't understand how people still think this is a healthy food given these stats...\",\n", + "\"Oh yeah, and I managed to soil myself twice during the process of devouring this delicious, yet sinful treat\",\n", + "\"#helsi #avocado #facts #truth #nofakenews #food #foodporn #beachbody #nocarb #lowprotein #dietfood #diet #veggie #heclimbs #climber #rockclimbing #bouldering #workout de\",\n", + "\"I joyfully did my part today. Thanks wikipedia for carrying me and probably almost every other student of the last 15 years through school and some even through university, too!\",\n", + "\"#wikipedia #education #knowledge #enzyklopedia #thankful #climber #heclimbs #rockclimbing #rockclimber #wikipediaisavalidsource #wikipediaisnotacrediblesource #serious\",\n", + "\"#1 Offseason-Snack: Schwedenbombensemmerl #yummy #niemetz #snack #climbing #heclimbs #mountaineering #gottagetdemcaloriesin\",\n", + "\"Less tape than last time, but also less skin #heclimbs #climbing #bouldering #mountaineering #mountain #outdoor #training #nopainnogain #nohandsphotography\",\n", + "\"One of the #salzkammergut classics: The Mahdlgupf via ferrata. It was nice to take this quite long and thus exhausting challenge again! The struggle paid off, as the view on top was breathtaking\",\n", + "\"#viaferrata #mountain #heclimbs #mountaineering #beautiful\",\n", + "\"Did I use enough tape, @niemanddaheim #heclimbs #climbing #sportsclimbing #hands #training #nopainnogain\",\n", + "\"Hard routes being sent in Gloxwald\"\n", + "\"Beautiful climbs with even more beautiful facial expressions\"\n", + "\"Keep rolling @niemanddaheim @bauki_flo @sonnleitnerjulia\"\n", + "\"#heclimbs #climber #rockclimbing #mountain #mountaineering #granitecrew\",\n", + "\"5th via ferrata done! #geilerhengst #wilderritt #heclimbs #climber #mountain #mountaineering\",\n", + "\"@misternemo and me having some fun dressed up as romans at #petrinum\"]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [], + "source": [ + "Jonas= [\"Hanging Tree\",\n", + "\"When in Rome @vivi.duenwald\",\n", + "\"Shoutout to the best family I have ever had so far #proudmember #loveyou\",\n", + "\"Yep, the weather was nice #imshreddingonsunshine @janabrahms\",\n", + "\"I will find you, and I will kiss you #80s\",\n", + "\"Real or fake?\",\n", + "\"Top of Santiago #trek #manquehue\",\n", + "\"START Summit 2018 is over now for a week already and it was amazing! During my last two years at START, I have met so many inspiring and interesting people and gained so much of experience, which you could never get through studying. being a supporter two years ago, responsible for speakers and content last year and leading this year's project and bringing 2'500 students, founders, investors and more to st Gallen was the the best experience I've had in my life so far\",\n", + "\"Thanks for the whole START Team who made this event possible and now I'm looking forward for a new episode of my life (even though there is still enough to do for START) #STARTSummit\",\n", + "\"AUFSTIEG!!! Erste Liga here we come #wasfüreinteam #ersteliga #grizzlies #stgallen\",\n", + "\"Guck mal, da ist Wasser im Stein #washabichdaeigentlichfüreinenhutauf? #hutcrew #nachuntengucker #lachdochmal #nö #suchen\",\n", + "\"It was a blast!! At START Summit 2017 we welcomed 2'200 founders, students, investors and corporates. \",\n", + "\"Thanks a lot to all partners, that made this event possible, to all participants, that made this event such a great success (and contributed in this lit after show party) and to the rest of the team, that made me having such a good time preparing this!! Now it's time to be #mobile for a little. \",\n", + "\"It was awesome to be part of this team! #hoibese #maschinebrätscher\",\n", + "\"Visiting my old love Boston #boston #goodtobeback #möchtegernmodel #newburystreet #spontan\",\n", + "\"Climbing up the cristo in the sunset was for sure one of the best moments of the Magellan-posttrip in brasil #cristo #rio #brasil #sunset #posttrip #magellan\",\n", + "\"Light house in colonia de suiza #magellan #lighthouse #uruguay #exchange #view\",\n", + "\"It was an amazing time @peterescardo ! 10 days of exploring Switzerland and showing the culture, having fun and meeting awesome people from Uruguay! Looking forward to the second part in Uruguay #exchange #switzerland #uruguay #10days #zurich #nosleep #2minutes\",\n", + "\"Milano ! #tuttobene #ac\",\n", + "\"HSG-Ball 2015 @annamaria2662 #warnice #staytilltheend #hsg #prom\",\n", + "\"Bali #incredible #horizon #infinity #pool #gettingsometanon #bali #seminyak\",\n", + "\"Cruising in a nutshell around Mallorca with the banana crew #yachtlife\",\n", + "\"Step 2 on the journey: Washington, D.C. @scaryder #washington #dc #mamorial #national #mall #lincoln #whitehouse #capital #usa #travel #eastcoast\",\n", + "\"Nice days in Philadelphia with @scaryder #philadelphia #libertybell #rocky #skyline #philly #usa #travel #fun #steps\",\n", + "\"Memorial holidays in cape cod with the kings friends @matzee49 @cesar77muse @shion_wtnb @tiziana_cristina @lenajmay @scaryder @niqqi18 @lilita2824 #cape #cod #capecod #massachusetts #memorial #holidays #nice #wheather #beach #kingsboston #sky\",\n", + "\"Baseball lessons with our pro @jdagreda and @matzee49 #baseball #lessons #bestteacherever #jesus #kingscollege #boston\",\n", + "\"Sunset and sunrise at the Acadia national park, Maine #sunset #sunrise #acadia #national #park #maine #usa #beautiful #sun #sky #horizon #barharbor\",\n", + "\"Boston celtics - Cleveland Cavaliers playoffs game 3 #basketball #nba #boston #celtics #vs #cleveland #cavaliers #playoffs #third #game #lost #tdgarden #letsgoceltics\",\n", + "\"Kings College Boston !!! #kings #college #boston #friends\",\n", + "\"Fucking freezing at the rainy boston marathon #bostonmarathon #boston #marathon #rainy #shitwheather #cold #lastmile\",\n", + "\"Kings Friends at stanway stadium for Boston red sox - Washington nationals #kings #college #stanway #boston #redsox #vs #washington #nationals #baseball #stadium #sunglassesmusthave\",\n", + "\"Season is over #vergingwieimflug #freestyle #ski #funpark #kicker #valdifassa #italy\",\n", + "\"War ne geile ausbildungs zeit in #alpbach !\",\n", + "\"Skilehrer in Obertauern #Traumjob #bluecrew\",\n", + "\"Los hombres alemanes en Costa Rica ;D #christmastree\",\n", + "\"having a great time in costa rica and nicaragua with @alemurillov\",\n", + "\"Party, Palmen Weiber und n Bier @janabrahms #sister #mallorca #malle #chill #pool #mar #sol #Beer #sun #españa #nais #holiday\",\n", + "\"Deutschland 2 - Ghana 2 - good luck today vs #castelao #fortaleza #wm #wc #2014 #deutschland #germany #alemanha #ghana #gana #todomundo #good #luck #jogapramim #heissaufheuteabend #football #stadium #viertelfinale #vs #france\",\n", + "\"Ich bin all das wovor deine Eltern dich immer gewarnt haben #mottowoche #tag #3 #sido #kiffer #harzer #penner #fun #sörenderlund #sidosmaske\",\n", + "\"Die Moral von der geschicht... Bremen ist geil, Hamburg nicht #Derbysieg #einszunull #werder #heftigste #choreo #ultras #bremen #stadion #hundertstes #derby #sitdownhsv #stimmung #fans #happy #nice #day #schadehamburg\",\n", + "\"#skifoan #saalbach #hinterglemm #snow #mountains #oleoleole #saalbacherschneekatzen #fun\",\n", + "\"#fehmarn #isschongeil #sea #occean #sky #clouds #sailing #follow #your #instinct #vacations\",\n", + "\"#chil #sandiego #beach #sun #blue #sky #follow #sea #waves ##vacation #good #time\",\n", + "\"#sandiego #missionbeach #beach #hot #sun #sunset #follow #sea #pacific #cali #california #red #sky #clouds #cloudporn #boy\",\n", + "\"#baseball #game #losangeles #dodgers #vs #Cincinnati #reds #stadium #sky #clouds #la #california #cali #fun #dodgerstadium\",\n", + "\"Highway No. 1 #california #highway #sun #vacation #happy\",\n", + "\"@trolflex #party #friends #old #but #gold #fun #fact #club #night #music #sunglasses #möve #l4l\",\n", + "\"#saalbach#ski#schnee#snow#fun#mountains\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
nametypetext
0DaryaESFPQuarantine expectations. Swipe for reality 22 ...
\n", + "
" + ], + "text/plain": [ + " name type text\n", + "0 Darya ESFP Quarantine expectations. Swipe for reality 22 ..." + ] + }, + "execution_count": 140, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "darya_test = {'name': ['Darya'], 'type': ['ESFP']}\n", + "darya_row = pd.DataFrame(darya_test)\n", + "darya_row['text'] = ' '.join(Darya)\n", + "darya_row\n" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
nametypetext
0TheresaENFPlight times simplicité. chapter 1 *waiter pour...
\n", + "
" + ], + "text/plain": [ + " name type text\n", + "0 Theresa ENFP light times simplicité. chapter 1 *waiter pour..." + ] + }, + "execution_count": 139, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "theresa_test = {'name': ['Theresa'], 'type': ['ENFP']}\n", + "theresa_row = pd.DataFrame(theresa_test)\n", + "theresa_row['text'] = ' '.join(Theresa)\n", + "theresa_row" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
nametypetext
0NicoENFPHaven't posted in a while, so this random pict...
\n", + "
" + ], + "text/plain": [ + " name type text\n", + "0 Nico ENFP Haven't posted in a while, so this random pict..." + ] + }, + "execution_count": 141, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nico_test = {'name': ['Nico'], 'type': ['ENFP']}\n", + "nico_row = pd.DataFrame(nico_test)\n", + "nico_row['text'] = ' '.join(Nico)\n", + "nico_row" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
nametypetext
0DylanENTJHappy golden Birthday Kayla! 22 years on Earth...
\n", + "
" + ], + "text/plain": [ + " name type text\n", + "0 Dylan ENTJ Happy golden Birthday Kayla! 22 years on Earth..." + ] + }, + "execution_count": 130, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dylan_test = {'name': ['Dylan'], 'type': ['ENTJ']}\n", + "dylan_row = pd.DataFrame(dylan_test)\n", + "dylan_row['text'] = ' '.join(Dylan)\n", + "dylan_row" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
nametypetext
0KatieINFJSonndorf VI #autumn #fall #colors #igersaustri...
\n", + "
" + ], + "text/plain": [ + " name type text\n", + "0 Katie INFJ Sonndorf VI #autumn #fall #colors #igersaustri..." + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "katie_test = {'name': ['Katie'], 'type': ['INFJ']}\n", + "katie_row = pd.DataFrame(katie_test)\n", + "katie_row['text'] = ' '.join(Katie)\n", + "katie_row" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
nametypetext
0GustavISFJoptiimal GNTM watching with the best #teammaur...
\n", + "
" + ], + "text/plain": [ + " name type text\n", + "0 Gustav ISFJ optiimal GNTM watching with the best #teammaur..." + ] + }, + "execution_count": 132, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gustav_test = {'name': ['Gustav'], 'type': ['ISFJ']}\n", + "gustav_row = pd.DataFrame(gustav_test)\n", + "gustav_row['text'] = ' '.join(Gustav)\n", + "gustav_row" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
nametypetext
0JonasESFJHanging Tree When in Rome @vivi.duenwald Shou...
\n", + "
" + ], + "text/plain": [ + " name type text\n", + "0 Jonas ESFJ Hanging Tree When in Rome @vivi.duenwald Shou..." + ] + }, + "execution_count": 133, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "jonas_test = {'name': ['Jonas'], 'type': ['ESFJ']}\n", + "jonas_row = pd.DataFrame(jonas_test)\n", + "jonas_row['text'] = ' '.join(Jonas)\n", + "jonas_row" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
nametypetext
0DaryaESFPQuarantine expectations. Swipe for reality 22 ...
0TheresaENFPlight times simplicité. chapter 1 *waiter pour...
0NicoENFPHaven't posted in a while, so this random pict...
0KatieINFJSonndorf VI #autumn #fall #colors #igersaustri...
0DylanENTJHappy golden Birthday Kayla! 22 years on Earth...
0GustavISFJoptiimal GNTM watching with the best #teammaur...
0JonasESFJHanging Tree When in Rome @vivi.duenwald Shou...
\n", + "
" + ], + "text/plain": [ + " name type text\n", + "0 Darya ESFP Quarantine expectations. Swipe for reality 22 ...\n", + "0 Theresa ENFP light times simplicité. chapter 1 *waiter pour...\n", + "0 Nico ENFP Haven't posted in a while, so this random pict...\n", + "0 Katie INFJ Sonndorf VI #autumn #fall #colors #igersaustri...\n", + "0 Dylan ENTJ Happy golden Birthday Kayla! 22 years on Earth...\n", + "0 Gustav ISFJ optiimal GNTM watching with the best #teammaur...\n", + "0 Jonas ESFJ Hanging Tree When in Rome @vivi.duenwald Shou..." + ] + }, + "execution_count": 142, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rldata = pd.concat([darya_row,theresa_row,nico_row,katie_row,dylan_row,gustav_row,jonas_row])\n", + "rldata" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [], + "source": [ + "rlquant = rldata.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "import nltk\n", + "from nltk.stem import PorterStemmer\n", + "from nltk.stem import SnowballStemmer\n", + "from nltk import wordnet\n", + "from nltk.corpus import stopwords\n", + "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n", + "from sklearn.model_selection import train_test_split, cross_val_score\n", + "from sklearn.metrics import classification_report\n", + "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve\n", + "\n", + "from sklearn.model_selection import train_test_split, cross_val_score\n", + "from sklearn.model_selection import cross_validate\n", + "from sklearn.model_selection import StratifiedKFold\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import learning_curve\n", + "from sklearn.ensemble import ExtraTreesClassifier\n", + "from sklearn.decomposition import TruncatedSVD\n", + "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.naive_bayes import MultinomialNB\n", + "from sklearn.metrics import classification_report\n", + "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "def processed(x):\n", + " pattern = r'''(?i)\\b((?:https?://|www\\d{0,3}[.]|[a-z0-9.\\-]+[.][a-z]{2,4}/)(?:[^\\s()<>]+|\\(([^\\s()<>]+|(\\([^\\s()<>]+\\)))*\\))+(?:\\(([^\\s()<>]+|(\\([^\\s()<>]+\\)))*\\)|[^\\s`!()\\[\\]{};:'\".,<>?«»“”‘’]))'''\n", + " cleaned = re.sub(pattern, ' ', str(x)) \n", + " cleaned = re.sub(r\"[^A-Za-z0-9]+\", \" \", cleaned)\n", + " cleaned = re.sub(\" \\d+\", \" \", cleaned)\n", + " cleaned = cleaned.lower().strip()\n", + "\n", + " pattern2 = '\\w{1,}'\n", + " bag = re.findall(pattern2, cleaned)\n", + "\n", + " porter = PorterStemmer()\n", + " porter_bag = [porter.stem(word) for word in bag]\n", + "\n", + " lemmatizer = wordnet.WordNetLemmatizer()\n", + " bag_of_words = [lemmatizer.lemmatize(word) for word in porter_bag]\n", + "\n", + " stopwords_e = stopwords.words('english')\n", + " bag_of_words = [word for word in bag_of_words if word not in stopwords_e]\n", + "\n", + " return bag_of_words" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "text_processed = [processed(i) for i in rldata.text]" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "rldata['text_processed'] = text_processed\n" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
nametypetext_ready
0DaryaESFPquarantin expect swipe realiti swipe see photo...
0TheresaENFPlight time simplicit chapter waiter pour wine ...
0NicoENFPpost thi random pictur fill void go within lot...
0KatieINFJsonndorf vi autumn fall color igersaustria out...
0DylanENTJhappi golden birthday kayla year earth photo s...
0GustavISFJoptiim gntm watch best teammaureen lovemaureen...
0JonasESFJhang tree rome vivi duenwald shoutout best fam...
\n", + "
" + ], + "text/plain": [ + " name type text_ready\n", + "0 Darya ESFP quarantin expect swipe realiti swipe see photo...\n", + "0 Theresa ENFP light time simplicit chapter waiter pour wine ...\n", + "0 Nico ENFP post thi random pictur fill void go within lot...\n", + "0 Katie INFJ sonndorf vi autumn fall color igersaustria out...\n", + "0 Dylan ENTJ happi golden birthday kayla year earth photo s...\n", + "0 Gustav ISFJ optiim gntm watch best teammaureen lovemaureen...\n", + "0 Jonas ESFJ hang tree rome vivi duenwald shoutout best fam..." + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rldata['text_ready'] = rldata.text_processed.apply(' '.join)\n", + "rldata = rldata.drop(columns = ['text', 'text_processed'])\n", + "rldata" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# rldata.to_csv(r'../data/reallife_data_insta.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "rldata['I-E'] = rldata['type'].astype(str).str[0]\n", + "rldata['N-S'] = rldata['type'].astype(str).str[1]\n", + "rldata['T-F'] = rldata['type'].astype(str).str[2]\n", + "rldata['J-P'] = rldata['type'].astype(str).str[3]" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 False\n", + "0 False\n", + "0 False\n", + "0 False\n", + "0 False\n", + "0 False\n", + "0 False\n", + "Name: text_ready, dtype: bool" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rldata.text_ready.isnull()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv(r'../data/personalities_cleaned.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "data.drop(data.index[3559], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Unnamed: 0typepoststext_processedtext_ready
00INFJ'http://www.youtube.com/watch?v=qsXHcwe3krw|||...['intj', 'moment', 'sportscent', 'top', 'ten',...intj moment sportscent top ten play prank ha l...
11ENTP'I'm finding the lack of me in these posts ver...['find', 'lack', 'post', 'veri', 'alarm', 'sex...find lack post veri alarm sex bore posit often...
22INTP'Good one _____ https://www.youtube.com/wat...['good', 'one', 'cours', 'say', 'know', 'bless...good one cours say know bless cur doe absolut ...
33INTJ'Dear INTP, I enjoyed our conversation the o...['dear', 'intp', 'enjoy', 'convers', 'day', 'e...dear intp enjoy convers day esoter gab natur u...
44ENTJ'You're fired.|||That's another silly misconce...['fire', 'anoth', 'silli', 'misconcept', 'appr...fire anoth silli misconcept approach logic go ...
\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 type posts \\\n", + "0 0 INFJ 'http://www.youtube.com/watch?v=qsXHcwe3krw|||... \n", + "1 1 ENTP 'I'm finding the lack of me in these posts ver... \n", + "2 2 INTP 'Good one _____ https://www.youtube.com/wat... \n", + "3 3 INTJ 'Dear INTP, I enjoyed our conversation the o... \n", + "4 4 ENTJ 'You're fired.|||That's another silly misconce... \n", + "\n", + " text_processed \\\n", + "0 ['intj', 'moment', 'sportscent', 'top', 'ten',... \n", + "1 ['find', 'lack', 'post', 'veri', 'alarm', 'sex... \n", + "2 ['good', 'one', 'cours', 'say', 'know', 'bless... \n", + "3 ['dear', 'intp', 'enjoy', 'convers', 'day', 'e... \n", + "4 ['fire', 'anoth', 'silli', 'misconcept', 'appr... \n", + "\n", + " text_ready \n", + "0 intj moment sportscent top ten play prank ha l... \n", + "1 find lack post veri alarm sex bore posit often... \n", + "2 good one cours say know bless cur doe absolut ... \n", + "3 dear intp enjoy convers day esoter gab natur u... \n", + "4 fire anoth silli misconcept approach logic go ... " + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
typetext_ready
0INFJintj moment sportscent top ten play prank ha l...
1ENTPfind lack post veri alarm sex bore posit often...
2INTPgood one cours say know bless cur doe absolut ...
3INTJdear intp enjoy convers day esoter gab natur u...
4ENTJfire anoth silli misconcept approach logic go ...
\n", + "
" + ], + "text/plain": [ + " type text_ready\n", + "0 INFJ intj moment sportscent top ten play prank ha l...\n", + "1 ENTP find lack post veri alarm sex bore posit often...\n", + "2 INTP good one cours say know bless cur doe absolut ...\n", + "3 INTJ dear intp enjoy convers day esoter gab natur u...\n", + "4 ENTJ fire anoth silli misconcept approach logic go ..." + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data2 = data.drop(columns = ['posts', 'Unnamed: 0', 'text_processed'])\n", + "data2.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [], + "source": [ + "data2['I-E'] = data2['type'].astype(str).str[0]\n", + "data2['N-S'] = data2['type'].astype(str).str[1]\n", + "data2['T-F'] = data2['type'].astype(str).str[2]\n", + "data2['J-P'] = data2['type'].astype(str).str[3]" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
typetext_readyI-EN-ST-FJ-P
0ESFPquarantin expect swipe realiti swipe see photo...ESFP
0ENFPlight time simplicit chapter waiter pour wine ...ENFP
0ENFPpost thi random pictur fill void go within lot...ENFP
0INFJsonndorf vi autumn fall color igersaustria out...INFJ
0ENTJhappi golden birthday kayla year earth photo s...ENTJ
\n", + "
" + ], + "text/plain": [ + " type text_ready I-E N-S T-F J-P\n", + "0 ESFP quarantin expect swipe realiti swipe see photo... E S F P\n", + "0 ENFP light time simplicit chapter waiter pour wine ... E N F P\n", + "0 ENFP post thi random pictur fill void go within lot... E N F P\n", + "0 INFJ sonndorf vi autumn fall color igersaustria out... I N F J\n", + "0 ENTJ happi golden birthday kayla year earth photo s... E N T J" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rldata.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [], + "source": [ + "# rldata = rldata.drop(columns = ['name'])" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
typetext_readyI-EN-ST-FJ-P
8676ENFPpost thi random pictur fill void go within lot...ENFP
8677INFJsonndorf vi autumn fall color igersaustria out...INFJ
8678ENTJhappi golden birthday kayla year earth photo s...ENTJ
8679ISFJoptiim gntm watch best teammaureen lovemaureen...ISFJ
8680ESFJhang tree rome vivi duenwald shoutout best fam...ESFJ
\n", + "
" + ], + "text/plain": [ + " type text_ready I-E N-S T-F J-P\n", + "8676 ENFP post thi random pictur fill void go within lot... E N F P\n", + "8677 INFJ sonndorf vi autumn fall color igersaustria out... I N F J\n", + "8678 ENTJ happi golden birthday kayla year earth photo s... E N T J\n", + "8679 ISFJ optiim gntm watch best teammaureen lovemaureen... I S F J\n", + "8680 ESFJ hang tree rome vivi duenwald shoutout best fam... E S F J" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data2 = pd.concat([data2, rldata]).reset_index(drop=True)\n", + "data2.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [], + "source": [ + "data2['I-E'] = data2['type'].astype(str).str[0]\n", + "data2['N-S'] = data2['type'].astype(str).str[1]\n", + "data2['T-F'] = data2['type'].astype(str).str[2]\n", + "data2['J-P'] = data2['type'].astype(str).str[3]" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
typetext_readyI-EN-ST-FJ-P
8674ESFPquarantin expect swipe realiti swipe see photo...ESFP
8675ENFPlight time simplicit chapter waiter pour wine ...ENFP
8676ENFPpost thi random pictur fill void go within lot...ENFP
8677INFJsonndorf vi autumn fall color igersaustria out...INFJ
8678ENTJhappi golden birthday kayla year earth photo s...ENTJ
8679ISFJoptiim gntm watch best teammaureen lovemaureen...ISFJ
8680ESFJhang tree rome vivi duenwald shoutout best fam...ESFJ
\n", + "
" + ], + "text/plain": [ + " type text_ready I-E N-S T-F J-P\n", + "8674 ESFP quarantin expect swipe realiti swipe see photo... E S F P\n", + "8675 ENFP light time simplicit chapter waiter pour wine ... E N F P\n", + "8676 ENFP post thi random pictur fill void go within lot... E N F P\n", + "8677 INFJ sonndorf vi autumn fall color igersaustria out... I N F J\n", + "8678 ENTJ happi golden birthday kayla year earth photo s... E N T J\n", + "8679 ISFJ optiim gntm watch best teammaureen lovemaureen... I S F J\n", + "8680 ESFJ hang tree rome vivi duenwald shoutout best fam... E S F J" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data2.tail(7)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### NLP Model " + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [], + "source": [ + "x = data2['text_ready']\n", + "\n", + "y_IE = data2['I-E']\n", + "y_NS = data2['N-S']\n", + "y_TF = data2['T-F']\n", + "y_JP = data2['J-P']" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.feature_extraction.text import CountVectorizer\n", + "\n", + "vector = CountVectorizer(ngram_range=(2, 2)).fit(x) \n", + "X = vector.transform(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.decomposition import TruncatedSVD\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "\n", + "svd = TruncatedSVD(n_components = 100)\n", + "reduced = svd.fit_transform(X)\n", + "\n", + "scaler = MinMaxScaler()\n", + "scaled = scaler.fit_transform(reduced)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [], + "source": [ + "# import joblib" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [], + "source": [ + "# IE = joblib.load('RF_IE.sav')\n", + "# NS = joblib.load('RF_NS.sav')\n", + "# TF = joblib.load('RF_TF.sav')\n", + "# JP = joblib.load('RF_JP.sav')" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [], + "source": [ + "# IE_result = IE.score(X, y_IE)\n", + "# print(\"IE result is: \", IE_result)\n", + "\n", + "# NS_result = NS.score(X, y_NS)\n", + "# print(\"NS result is: \", NS_result)\n", + "\n", + "# TF_result = TF.score(X, y_TF)\n", + "# print(\"TF result is: \", TF_result)\n", + "\n", + "# JP_result = JP.score(X, y_JP)\n", + "# print(\"JP result is: \", JF_result)" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(7, 100)" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scaled[8674:].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E RESULTS\n", + "Label I-E train score is : 0.9219416580191399\n", + "Label I-E test score is : 0.9219416580191399\n", + "Confusion Matrix for Random Forest:\n", + "[[0 4]\n", + " [0 3]]\n", + "Score: 42.86\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " J 0.00 0.00 0.00 4\n", + " P 0.43 1.00 0.60 3\n", + "\n", + " accuracy 0.43 7\n", + " macro avg 0.21 0.50 0.30 7\n", + "weighted avg 0.18 0.43 0.26 7\n", + "\n", + "****************************************************************************************************\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N-S RESULTS\n", + "Label N-S train score is : 0.8833160382797187\n", + "Label N-S test score is : 0.8833160382797187\n", + "Confusion Matrix for Random Forest:\n", + "[[0 4]\n", + " [0 3]]\n", + "Score: 42.86\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " J 0.00 0.00 0.00 4\n", + " P 0.43 1.00 0.60 3\n", + "\n", + " accuracy 0.43 7\n", + " macro avg 0.21 0.50 0.30 7\n", + "weighted avg 0.18 0.43 0.26 7\n", + "\n", + "****************************************************************************************************\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "T-F Results\n", + "Label T-F train score is : 0.9960797878473423\n", + "Label T-F test score is : 0.9960797878473423\n", + "Confusion Matrix for Random Forest:\n", + "[[0 4]\n", + " [0 3]]\n", + "Score: 42.86\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " J 0.00 0.00 0.00 4\n", + " P 0.43 1.00 0.60 3\n", + "\n", + " accuracy 0.43 7\n", + " macro avg 0.21 0.50 0.30 7\n", + "weighted avg 0.18 0.43 0.26 7\n", + "\n", + "****************************************************************************************************\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "J-P Results\n", + "Label J-P train score is : 0.9674852992044275\n", + "Label J-P test score is : 0.9674852992044275\n", + "Confusion Matrix for Random Forest:\n", + "[[0 4]\n", + " [0 3]]\n", + "Score: 42.86\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " J 0.00 0.00 0.00 4\n", + " P 0.43 1.00 0.60 3\n", + "\n", + " accuracy 0.43 7\n", + " macro avg 0.21 0.50 0.30 7\n", + "weighted avg 0.18 0.43 0.26 7\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + } + ], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "rfn = RandomForestClassifier(max_depth=14, random_state=0)\n", + "\n", + "\n", + "# IE\n", + "x_train = scaled[0:8673]\n", + "x_test = scaled[8674:]\n", + "y_train = y_IE[0:8673]\n", + "y_test_IE = y_IE[8674:]\n", + "rfn.fit(x_train, y_train)\n", + "ieb_train = rfn.score (x_train,y_train)\n", + "ieb_test = rfn.score (x_train,y_train)\n", + "predrfn_IE = rfn.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "print('Label I-E train score is :',ieb_train)\n", + "print('Label I-E test score is :',ieb_test)\n", + "print(\"Confusion Matrix for Random Forest:\")\n", + "print(confusion_matrix(y_test,predrfn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predrfn))\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", + "x_train = scaled[0:8673]\n", + "x_test = scaled[8674:]\n", + "y_train = y_NS[0:8673]\n", + "y_test_NS = y_NS[8674:]\n", + "rfn.fit(x_train, y_train)\n", + "nsb_train = rfn.score (x_train,y_train)\n", + "nsb_test = rfn.score (x_train,y_train)\n", + "predrfn_NS = rfn.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "print('Label N-S train score is :',nsb_train)\n", + "print('Label N-S test score is :',nsb_test)\n", + "print(\"Confusion Matrix for Random Forest:\")\n", + "print(confusion_matrix(y_test,predrfn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predrfn))\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "x_train = scaled[0:8673]\n", + "x_test = scaled[8674:]\n", + "y_train = y_TF[0:8673]\n", + "y_test_TF = y_TF[8674:]\n", + "rfn.fit(x_train, y_train)\n", + "tfb_train = rfn.score (x_train,y_train)\n", + "tfb_test = rfn.score (x_train,y_train)\n", + "predrfn_TF = rfn.predict(x_test)\n", + "print(\"T-F Results\")\n", + "print('Label T-F train score is :',tfb_train)\n", + "print('Label T-F test score is :',tfb_test)\n", + "print(\"Confusion Matrix for Random Forest:\")\n", + "print(confusion_matrix(y_test,predrfn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predrfn))\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "x_train = scaled[0:8673]\n", + "x_test = scaled[8674:]\n", + "y_train = y_JP[0:8673]\n", + "y_test_JP = y_JP[8674:]\n", + "rfn.fit(x_train, y_train)\n", + "jpb_train = rfn.score (x_train,y_train)\n", + "jpb_test = rfn.score (x_train,y_train)\n", + "predrfn_JP = rfn.predict(x_test)\n", + "print(\"J-P Results\")\n", + "print('Label J-P train score is :',jpb_train)\n", + "print('Label J-P test score is :',jpb_test)\n", + "print(\"Confusion Matrix for Random Forest:\")\n", + "print(confusion_matrix(y_test,predrfn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predrfn))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [], + "source": [ + "rldata2 = rldata.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [], + "source": [ + "rldata2['y_test_IE'] = list(y_test_IE)\n", + "rldata2['predrfn_IE'] = predrfn_IE\n", + "rldata2['y_test_NS'] = list(y_test_NS)\n", + "rldata2['predrfn_NS'] = predrfn_NS\n", + "rldata2['y_test_TF'] = list(y_test_TF)\n", + "rldata2['predrfn_TF'] = predrfn_TF\n", + "rldata2['y_test_JP'] = list(y_test_JP)\n", + "rldata2['predrfn_JP'] = predrfn_JP" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [], + "source": [ + "rldata2 = rldata2.drop(columns = ['y_test_IE', 'y_test_NS', 'y_test_TF', 'y_test_JP'])" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
typetext_readyI-EN-ST-FJ-Ppredrfn_IEpredrfn_NSpredrfn_TFpredrfn_JP
0ESFPquarantin expect swipe realiti swipe see photo...ESFPINTP
0ENFPlight time simplicit chapter waiter pour wine ...ENFPINFP
0ENFPpost thi random pictur fill void go within lot...ENFPINTP
0INFJsonndorf vi autumn fall color igersaustria out...INFJINFP
0ENTJhappi golden birthday kayla year earth photo s...ENTJINTP
0ISFJoptiim gntm watch best teammaureen lovemaureen...ISFJINTP
0ESFJhang tree rome vivi duenwald shoutout best fam...ESFJINTP
\n", + "
" + ], + "text/plain": [ + " type text_ready I-E N-S T-F J-P \\\n", + "0 ESFP quarantin expect swipe realiti swipe see photo... E S F P \n", + "0 ENFP light time simplicit chapter waiter pour wine ... E N F P \n", + "0 ENFP post thi random pictur fill void go within lot... E N F P \n", + "0 INFJ sonndorf vi autumn fall color igersaustria out... I N F J \n", + "0 ENTJ happi golden birthday kayla year earth photo s... E N T J \n", + "0 ISFJ optiim gntm watch best teammaureen lovemaureen... I S F J \n", + "0 ESFJ hang tree rome vivi duenwald shoutout best fam... E S F J \n", + "\n", + " predrfn_IE predrfn_NS predrfn_TF predrfn_JP \n", + "0 I N T P \n", + "0 I N F P \n", + "0 I N T P \n", + "0 I N F P \n", + "0 I N T P \n", + "0 I N T P \n", + "0 I N T P " + ] + }, + "execution_count": 117, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rldata2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Quant Model" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [], + "source": [ + "quant = pd.read_csv(r'../data/quant_personalities.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Unnamed: 0typelength_commentvar_lengthlinks_countqm_countexm_countimg_countmusic_countdots_countht_countme_countI-EN-ST-FJ-P
00INFJ11.120.1797500.480.360.060.120.020.300.01.181000
11ENTP23.400.1844690.200.100.000.020.000.380.03.060011
22INTP16.720.1840100.100.240.080.000.000.260.01.441011
33INTJ21.280.1866400.040.220.060.000.020.520.02.181010
44ENTJ19.340.1787230.120.200.020.040.020.420.01.480010
\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 type length_comment var_length links_count qm_count \\\n", + "0 0 INFJ 11.12 0.179750 0.48 0.36 \n", + "1 1 ENTP 23.40 0.184469 0.20 0.10 \n", + "2 2 INTP 16.72 0.184010 0.10 0.24 \n", + "3 3 INTJ 21.28 0.186640 0.04 0.22 \n", + "4 4 ENTJ 19.34 0.178723 0.12 0.20 \n", + "\n", + " exm_count img_count music_count dots_count ht_count me_count I-E \\\n", + "0 0.06 0.12 0.02 0.30 0.0 1.18 1 \n", + "1 0.00 0.02 0.00 0.38 0.0 3.06 0 \n", + "2 0.08 0.00 0.00 0.26 0.0 1.44 1 \n", + "3 0.06 0.00 0.02 0.52 0.0 2.18 1 \n", + "4 0.02 0.04 0.02 0.42 0.0 1.48 0 \n", + "\n", + " N-S T-F J-P \n", + "0 0 0 0 \n", + "1 0 1 1 \n", + "2 0 1 1 \n", + "3 0 1 0 \n", + "4 0 1 0 " + ] + }, + "execution_count": 119, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "quant.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
nametypetext
0DaryaESFPQuarantine expectations. Swipe for reality 22 ...
0TheresaENFPlight times simplicité. chapter 1 *waiter pour...
0NicoENFPHaven't posted in a while, so this random pict...
0KatieINFJSonndorf VI #autumn #fall #colors #igersaustri...
0DylanENTJHappy golden Birthday Kayla! 22 years on Earth...
0GustavISFJoptiimal GNTM watching with the best #teammaur...
0JonasESFJHanging Tree When in Rome @vivi.duenwald Shou...
\n", + "
" + ], + "text/plain": [ + " name type text\n", + "0 Darya ESFP Quarantine expectations. Swipe for reality 22 ...\n", + "0 Theresa ENFP light times simplicité. chapter 1 *waiter pour...\n", + "0 Nico ENFP Haven't posted in a while, so this random pict...\n", + "0 Katie INFJ Sonndorf VI #autumn #fall #colors #igersaustri...\n", + "0 Dylan ENTJ Happy golden Birthday Kayla! 22 years on Earth...\n", + "0 Gustav ISFJ optiimal GNTM watching with the best #teammaur...\n", + "0 Jonas ESFJ Hanging Tree When in Rome @vivi.duenwald Shou..." + ] + }, + "execution_count": 144, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rlquant" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'builtin_function_or_method' object has no attribute 'count'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mrlquant\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dots_count'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrlquant\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'text'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'...'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mrlquant\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ht_count'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrlquant\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'text'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'#'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mrlquant\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'me_count'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrlquant\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'text'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'me'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'i'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'myself'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# add uppercase\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0mrlquant\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, convert_dtype, args, **kwds)\u001b[0m\n\u001b[1;32m 3846\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3847\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3848\u001b[0;31m \u001b[0mmapped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_infer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconvert_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3849\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3850\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32mpandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mrlquant\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dots_count'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrlquant\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'text'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'...'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mrlquant\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ht_count'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrlquant\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'text'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'#'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mrlquant\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'me_count'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrlquant\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'text'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'me'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'i'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'myself'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# add uppercase\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0mrlquant\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: 'builtin_function_or_method' object has no attribute 'count'" + ] + } + ], + "source": [ + "import re\n", + "\n", + "# rldata['length_comment'] = rldata['text'].apply(lambda x: len(x.split())/50)\n", + "# rldata['var_length'] = data['posts'].apply(lambda x: var_posts(x))\n", + "# rldata['links_count'] = rldata['posts'].apply(lambda x: x.count('http')/50)\n", + "rlquant['qm_count'] = rlquant['text'].apply(lambda x: x.count('?')/50)\n", + "rlquant['exm_count'] = rlquant['text'].apply(lambda x: x.count('!')/50) # normalize by the amount of scentences/ percent of number of scentences\n", + "# rldata['img_count'] = rldata['posts'].apply(lambda x: x.count('jpg')/50)\n", + "# data['music_count'] = data['posts'].apply(lambda x: x.count('music')/50)\n", + "rlquant['dots_count'] = rlquant['text'].apply(lambda x: x.count('...')/50)\n", + "rlquant['ht_count'] = rlquant['text'].apply(lambda x: x.count('#')/50)\n", + "rlquant['me_count'] = rlquant['text'].apply(lambda x: (x.lower.count('me') + x.lower.count('i') + x.lower.count('myself'))/50) # add uppercase\n", + "\n", + "rlquant" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/code/Code 8 - Trying Models on Quant Data.ipynb b/your-project/code/Code 8 - Trying Models on Quant Data.ipynb new file mode 100644 index 00000000..ee2b4d7c --- /dev/null +++ b/your-project/code/Code 8 - Trying Models on Quant Data.ipynb @@ -0,0 +1,1071 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Splitting the target" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from sklearn.model_selection import train_test_split, cross_val_score\n", + "from sklearn.model_selection import cross_validate\n", + "from sklearn.model_selection import StratifiedKFold\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import learning_curve\n", + "from sklearn.ensemble import ExtraTreesClassifier\n", + "from sklearn.decomposition import TruncatedSVD\n", + "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.naive_bayes import MultinomialNB\n", + "from sklearn.metrics import classification_report\n", + "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv(r'../data/quant_personalities.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "data2 = data.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Unnamed: 0typelength_commentvar_lengthlinks_countqm_countexm_countimg_countmusic_countdots_countht_countme_countI-EN-ST-FJ-P
00INFJ11.120.1797500.480.360.060.120.020.300.01.181000
11ENTP23.400.1844690.200.100.000.020.000.380.03.060011
22INTP16.720.1840100.100.240.080.000.000.260.01.441011
33INTJ21.280.1866400.040.220.060.000.020.520.02.181010
44ENTJ19.340.1787230.120.200.020.040.020.420.01.480010
\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 type length_comment var_length links_count qm_count \\\n", + "0 0 INFJ 11.12 0.179750 0.48 0.36 \n", + "1 1 ENTP 23.40 0.184469 0.20 0.10 \n", + "2 2 INTP 16.72 0.184010 0.10 0.24 \n", + "3 3 INTJ 21.28 0.186640 0.04 0.22 \n", + "4 4 ENTJ 19.34 0.178723 0.12 0.20 \n", + "\n", + " exm_count img_count music_count dots_count ht_count me_count I-E \\\n", + "0 0.06 0.12 0.02 0.30 0.0 1.18 1 \n", + "1 0.00 0.02 0.00 0.38 0.0 3.06 0 \n", + "2 0.08 0.00 0.00 0.26 0.0 1.44 1 \n", + "3 0.06 0.00 0.02 0.52 0.0 2.18 1 \n", + "4 0.02 0.04 0.02 0.42 0.0 1.48 0 \n", + "\n", + " N-S T-F J-P \n", + "0 0 0 0 \n", + "1 0 1 1 \n", + "2 0 1 1 \n", + "3 0 1 0 \n", + "4 0 1 0 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data2.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "data2 = data2.drop(columns=['Unnamed: 0'])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# learning with the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# defining learnings for each \n", + "\n", + "\n", + "X = data.drop(columns = ['type', 'I-E', 'N-S', 'T-F', 'J-P'])\n", + "\n", + "y_IE = data['I-E']\n", + "y_NS = data['N-S']\n", + "y_TF = data['T-F']\n", + "y_JP = data['J-P']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### MultinomialNB" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E RESULTS\n", + "Label I-E train score is : 0.7678674351585014\n", + "Label I-E test score is : 0.7678674351585014\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[ 0 387]\n", + " [ 0 1348]]\n", + "Score: 77.69\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " 0 0.00 0.00 0.00 387\n", + " 1 0.78 1.00 0.87 1348\n", + "\n", + " accuracy 0.78 1735\n", + " macro avg 0.39 0.50 0.44 1735\n", + "weighted avg 0.60 0.78 0.68 1735\n", + "\n", + "****************************************************************************************************\n", + "N-S RESULTS\n", + "Label N-S train score is : 0.8570605187319885\n", + "Label N-S test score is : 0.8570605187319885\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[1470 16]\n", + " [ 244 5]]\n", + "Score: 85.01\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " 0 0.86 0.99 0.92 1486\n", + " 1 0.24 0.02 0.04 249\n", + "\n", + " accuracy 0.85 1735\n", + " macro avg 0.55 0.50 0.48 1735\n", + "weighted avg 0.77 0.85 0.79 1735\n", + "\n", + "****************************************************************************************************\n", + "T-F Results\n", + "Label T-F train score is : 0.5236311239193083\n", + "Label T-F test score is : 0.5236311239193083\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[528 418]\n", + " [417 372]]\n", + "Score: 51.87\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " 0 0.56 0.56 0.56 946\n", + " 1 0.47 0.47 0.47 789\n", + "\n", + " accuracy 0.52 1735\n", + " macro avg 0.51 0.51 0.51 1735\n", + "weighted avg 0.52 0.52 0.52 1735\n", + "\n", + "****************************************************************************************************\n", + "J-P Results\n", + "Label J-P train score is : 0.570028818443804\n", + "Label J-P test score is : 0.570028818443804\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[126 573]\n", + " [194 842]]\n", + "Score: 55.79\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " 0 0.39 0.18 0.25 699\n", + " 1 0.60 0.81 0.69 1036\n", + "\n", + " accuracy 0.56 1735\n", + " macro avg 0.49 0.50 0.47 1735\n", + "weighted avg 0.51 0.56 0.51 1735\n", + "\n", + "****************************************************************************************************\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + } + ], + "source": [ + "from sklearn.naive_bayes import MultinomialNB\n", + "\n", + "mnb = MultinomialNB()\n", + "\n", + "\n", + "# IE\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_IE, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "ieb_train = mnb.score (x_train,y_train)\n", + "ieb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "print('Label I-E train score is :',ieb_train)\n", + "print('Label I-E test score is :',ieb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_NS, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "nsb_train = mnb.score (x_train,y_train)\n", + "nsb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "print('Label N-S train score is :',nsb_train)\n", + "print('Label N-S test score is :',nsb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_TF, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "tfb_train = mnb.score (x_train,y_train)\n", + "tfb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"T-F Results\")\n", + "print('Label T-F train score is :',tfb_train)\n", + "print('Label T-F test score is :',tfb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_JP, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "jpb_train = mnb.score (x_train,y_train)\n", + "jpb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"J-P Results\")\n", + "print('Label J-P train score is :',jpb_train)\n", + "print('Label J-P test score is :',jpb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Decision Tree" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E RESULTS\n", + "Label I-E train score is : 1.0\n", + "Label I-E test score is : 1.0\n", + "Confusion Matrix for Decision Tree:\n", + "[[ 104 283]\n", + " [ 320 1028]]\n", + "Score: 65.24\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " 0 0.25 0.27 0.26 387\n", + " 1 0.78 0.76 0.77 1348\n", + "\n", + " accuracy 0.65 1735\n", + " macro avg 0.51 0.52 0.51 1735\n", + "weighted avg 0.66 0.65 0.66 1735\n", + "\n", + "****************************************************************************************************\n", + "N-S RESULTS\n", + "Label N-S train score is : 1.0\n", + "Label N-S test score is : 1.0\n", + "Confusion Matrix for Decision Tree:\n", + "[[1225 261]\n", + " [ 203 46]]\n", + "Score: 73.26\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " 0 0.86 0.82 0.84 1486\n", + " 1 0.15 0.18 0.17 249\n", + "\n", + " accuracy 0.73 1735\n", + " macro avg 0.50 0.50 0.50 1735\n", + "weighted avg 0.76 0.73 0.74 1735\n", + "\n", + "****************************************************************************************************\n", + "T-F Results\n", + "Label T-F train score is : 1.0\n", + "Label T-F test score is : 1.0\n", + "Confusion Matrix for Decision Tree:\n", + "[[588 358]\n", + " [389 400]]\n", + "Score: 56.95\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " 0 0.60 0.62 0.61 946\n", + " 1 0.53 0.51 0.52 789\n", + "\n", + " accuracy 0.57 1735\n", + " macro avg 0.56 0.56 0.56 1735\n", + "weighted avg 0.57 0.57 0.57 1735\n", + "\n", + "****************************************************************************************************\n", + "J-P Results\n", + "Label J-P train score is : 1.0\n", + "Label J-P test score is : 1.0\n", + "Confusion Matrix for Decision Tree:\n", + "[[299 400]\n", + " [441 595]]\n", + "Score: 51.53\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " 0 0.40 0.43 0.42 699\n", + " 1 0.60 0.57 0.59 1036\n", + "\n", + " accuracy 0.52 1735\n", + " macro avg 0.50 0.50 0.50 1735\n", + "weighted avg 0.52 0.52 0.52 1735\n", + "\n", + "****************************************************************************************************\n" + ] + } + ], + "source": [ + "from sklearn.tree import DecisionTreeClassifier\n", + "\n", + "dt = DecisionTreeClassifier()\n", + "\n", + "\n", + "# IE\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_IE, test_size=0.2, random_state=10)\n", + "dt.fit(x_train, y_train)\n", + "ieb_train = dt.score (x_train,y_train)\n", + "ieb_test = dt.score (x_train,y_train)\n", + "preddt = dt.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "print('Label I-E train score is :',ieb_train)\n", + "print('Label I-E test score is :',ieb_test)\n", + "print(\"Confusion Matrix for Decision Tree:\")\n", + "print(confusion_matrix(y_test,preddt))\n", + "print(\"Score:\",round(accuracy_score(y_test,preddt)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,preddt))\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_NS, test_size=0.2, random_state=10)\n", + "dt.fit(x_train, y_train)\n", + "nsb_train = dt.score (x_train,y_train)\n", + "nsb_test = dt.score (x_train,y_train)\n", + "preddt = dt.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "print('Label N-S train score is :',nsb_train)\n", + "print('Label N-S test score is :',nsb_test)\n", + "print(\"Confusion Matrix for Decision Tree:\")\n", + "print(confusion_matrix(y_test,preddt))\n", + "print(\"Score:\",round(accuracy_score(y_test,preddt)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,preddt))\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_TF, test_size=0.2, random_state=10)\n", + "dt.fit(x_train, y_train)\n", + "tfb_train = dt.score (x_train,y_train)\n", + "tfb_test = dt.score (x_train,y_train)\n", + "preddt = dt.predict(x_test)\n", + "print(\"T-F Results\")\n", + "print('Label T-F train score is :',tfb_train)\n", + "print('Label T-F test score is :',tfb_test)\n", + "print(\"Confusion Matrix for Decision Tree:\")\n", + "print(confusion_matrix(y_test,preddt))\n", + "print(\"Score:\",round(accuracy_score(y_test,preddt)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,preddt))\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_JP, test_size=0.2, random_state=10)\n", + "dt.fit(x_train, y_train)\n", + "jpb_train = dt.score (x_train,y_train)\n", + "jpb_test = dt.score (x_train,y_train)\n", + "preddt = dt.predict(x_test)\n", + "print(\"J-P Results\")\n", + "print('Label J-P train score is :',jpb_train)\n", + "print('Label J-P test score is :',jpb_test)\n", + "print(\"Confusion Matrix for Decision Tree:\")\n", + "print(confusion_matrix(y_test,preddt))\n", + "print(\"Score:\",round(accuracy_score(y_test,preddt)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,preddt))\n", + "print(\"*\" * 100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### K Nearest Neighbour" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E RESULTS\n", + "Label I-E train score is : 0.7791066282420749\n", + "Label I-E test score is : 0.7791066282420749\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[ 30 357]\n", + " [ 78 1270]]\n", + "Score: 74.93\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " 0 0.28 0.08 0.12 387\n", + " 1 0.78 0.94 0.85 1348\n", + "\n", + " accuracy 0.75 1735\n", + " macro avg 0.53 0.51 0.49 1735\n", + "weighted avg 0.67 0.75 0.69 1735\n", + "\n", + "****************************************************************************************************\n", + "N-S RESULTS\n", + "Label N-S train score is : 0.8636887608069165\n", + "Label N-S test score is : 0.8636887608069165\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[1483 3]\n", + " [ 249 0]]\n", + "Score: 85.48\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " 0 0.86 1.00 0.92 1486\n", + " 1 0.00 0.00 0.00 249\n", + "\n", + " accuracy 0.85 1735\n", + " macro avg 0.43 0.50 0.46 1735\n", + "weighted avg 0.73 0.85 0.79 1735\n", + "\n", + "****************************************************************************************************\n", + "T-F Results\n", + "Label T-F train score is : 0.632420749279539\n", + "Label T-F test score is : 0.632420749279539\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[699 247]\n", + " [539 250]]\n", + "Score: 54.7\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " 0 0.56 0.74 0.64 946\n", + " 1 0.50 0.32 0.39 789\n", + "\n", + " accuracy 0.55 1735\n", + " macro avg 0.53 0.53 0.51 1735\n", + "weighted avg 0.54 0.55 0.53 1735\n", + "\n", + "****************************************************************************************************\n", + "J-P Results\n", + "Label J-P train score is : 0.6478386167146974\n", + "Label J-P test score is : 0.6478386167146974\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[248 451]\n", + " [373 663]]\n", + "Score: 52.51\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " 0 0.40 0.35 0.38 699\n", + " 1 0.60 0.64 0.62 1036\n", + "\n", + " accuracy 0.53 1735\n", + " macro avg 0.50 0.50 0.50 1735\n", + "weighted avg 0.52 0.53 0.52 1735\n", + "\n", + "****************************************************************************************************\n" + ] + } + ], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier\n", + "\n", + "knn = KNeighborsClassifier(n_neighbors=10)\n", + "\n", + "\n", + "# IE\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_IE, test_size=0.2, random_state=10)\n", + "knn.fit(x_train, y_train)\n", + "ieb_train = knn.score (x_train,y_train)\n", + "ieb_test = knn.score (x_train,y_train)\n", + "predknn = knn.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "print('Label I-E train score is :',ieb_train)\n", + "print('Label I-E test score is :',ieb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predknn))\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_NS, test_size=0.2, random_state=10)\n", + "knn.fit(x_train, y_train)\n", + "nsb_train = knn.score (x_train,y_train)\n", + "nsb_test = knn.score (x_train,y_train)\n", + "predknn = knn.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "print('Label N-S train score is :',nsb_train)\n", + "print('Label N-S test score is :',nsb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predknn))\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_TF, test_size=0.2, random_state=10)\n", + "knn.fit(x_train, y_train)\n", + "tfb_train = knn.score (x_train,y_train)\n", + "tfb_test = knn.score (x_train,y_train)\n", + "predknn = knn.predict(x_test)\n", + "print(\"T-F Results\")\n", + "print('Label T-F train score is :',tfb_train)\n", + "print('Label T-F test score is :',tfb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predknn))\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_JP, test_size=0.2, random_state=10)\n", + "knn.fit(x_train, y_train)\n", + "jpb_train = knn.score (x_train,y_train)\n", + "jpb_test = knn.score (x_train,y_train)\n", + "predknn = knn.predict(x_test)\n", + "print(\"J-P Results\")\n", + "print('Label J-P train score is :',jpb_train)\n", + "print('Label J-P test score is :',jpb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predknn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predknn))\n", + "print(\"*\" * 100)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### XGBoost" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.naive_bayes import MultinomialNB\n", + "\n", + "mnb = MultinomialNB()\n", + "mnb.fit(x_train, y_train)\n", + "\n", + "\n", + "# IE\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_IE, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "ieb_train = mnb.score (x_train,y_train)\n", + "ieb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "print('Label I-E train score is :',ieb_train)\n", + "print('Label I-E test score is :',ieb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_NS, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "nsb_train = mnb.score (x_train,y_train)\n", + "nsb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "print('Label N-S train score is :',nsb_train)\n", + "print('Label N-S test score is :',nsb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_TF, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "tfb_train = mnb.score (x_train,y_train)\n", + "tfb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"T-F Results\")\n", + "print('Label T-F train score is :',tfb_train)\n", + "print('Label T-F test score is :',tfb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_JP, test_size=0.2, random_state=10)\n", + "mnb.fit(x_train, y_train)\n", + "jpb_train = mnb.score (x_train,y_train)\n", + "jpb_test = mnb.score (x_train,y_train)\n", + "predmnb = mnb.predict(x_test)\n", + "print(\"J-P Results\")\n", + "print('Label J-P train score is :',jpb_train)\n", + "print('Label J-P test score is :',jpb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predmnb))\n", + "print(\"Score:\",round(accuracy_score(y_test,predmnb)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predmnb))\n", + "print(\"*\" * 100)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Random Forest" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "rfn = RandomForestClassifier(max_depth=5, random_state=0)\n", + "rfn.fit(x_train,y_train)\n", + "predrf = rfn.predict(x_test)\n", + "print(\"Confusion Matrix for Random Forest Classifier:\")\n", + "print(confusion_matrix(y_test,predknn))\n", + "print(\"Score: \",round(accuracy_score(y_test,predrf)*100,2))\n", + "print(\"Classification Report:\")\n", + "print(classification_report(y_test,predrf))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I-E RESULTS\n", + "Label I-E train score is : 0.7688760806916427\n", + "Label I-E test score is : 0.7688760806916427\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[ 0 387]\n", + " [ 0 1348]]\n", + "Score: 77.69\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " 0 0.00 0.00 0.00 387\n", + " 1 0.78 1.00 0.87 1348\n", + "\n", + " accuracy 0.78 1735\n", + " macro avg 0.39 0.50 0.44 1735\n", + "weighted avg 0.60 0.78 0.68 1735\n", + "\n", + "****************************************************************************************************\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N-S RESULTS\n", + "Label N-S train score is : 0.8636887608069165\n", + "Label N-S test score is : 0.8636887608069165\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[1486 0]\n", + " [ 249 0]]\n", + "Score: 85.65\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " 0 0.86 1.00 0.92 1486\n", + " 1 0.00 0.00 0.00 249\n", + "\n", + " accuracy 0.86 1735\n", + " macro avg 0.43 0.50 0.46 1735\n", + "weighted avg 0.73 0.86 0.79 1735\n", + "\n", + "****************************************************************************************************\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/Cellar/jupyterlab/1.2.4/libexec/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "T-F Results\n", + "Label T-F train score is : 0.6605187319884727\n", + "Label T-F test score is : 0.6605187319884727\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[667 279]\n", + " [370 419]]\n", + "Score: 62.59\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " 0 0.64 0.71 0.67 946\n", + " 1 0.60 0.53 0.56 789\n", + "\n", + " accuracy 0.63 1735\n", + " macro avg 0.62 0.62 0.62 1735\n", + "weighted avg 0.62 0.63 0.62 1735\n", + "\n", + "****************************************************************************************************\n", + "J-P Results\n", + "Label J-P train score is : 0.6131123919308358\n", + "Label J-P test score is : 0.6131123919308358\n", + "Confusion Matrix for Multinomial Naive Bayes:\n", + "[[ 7 692]\n", + " [ 9 1027]]\n", + "Score: 59.6\n", + "Classification Report: precision recall f1-score support\n", + "\n", + " 0 0.44 0.01 0.02 699\n", + " 1 0.60 0.99 0.75 1036\n", + "\n", + " accuracy 0.60 1735\n", + " macro avg 0.52 0.50 0.38 1735\n", + "weighted avg 0.53 0.60 0.45 1735\n", + "\n", + "****************************************************************************************************\n" + ] + } + ], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "rfn = RandomForestClassifier(max_depth=5, random_state=0)\n", + "\n", + "\n", + "# IE\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_IE, test_size=0.2, random_state=10)\n", + "rfn.fit(x_train, y_train)\n", + "ieb_train = rfn.score (x_train,y_train)\n", + "ieb_test = rfn.score (x_train,y_train)\n", + "predrfn = rfn.predict(x_test)\n", + "print(\"I-E RESULTS\")\n", + "print('Label I-E train score is :',ieb_train)\n", + "print('Label I-E test score is :',ieb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predrfn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predrfn))\n", + "print(\"*\" * 100)\n", + "\n", + "# NS\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_NS, test_size=0.2, random_state=10)\n", + "rfn.fit(x_train, y_train)\n", + "nsb_train = rfn.score (x_train,y_train)\n", + "nsb_test = rfn.score (x_train,y_train)\n", + "predrfn = rfn.predict(x_test)\n", + "print(\"N-S RESULTS\")\n", + "print('Label N-S train score is :',nsb_train)\n", + "print('Label N-S test score is :',nsb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predrfn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predrfn))\n", + "print(\"*\" * 100)\n", + "\n", + "# TF\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_TF, test_size=0.2, random_state=10)\n", + "rfn.fit(x_train, y_train)\n", + "tfb_train = rfn.score (x_train,y_train)\n", + "tfb_test = rfn.score (x_train,y_train)\n", + "predrfn = rfn.predict(x_test)\n", + "print(\"T-F Results\")\n", + "print('Label T-F train score is :',tfb_train)\n", + "print('Label T-F test score is :',tfb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predrfn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predrfn))\n", + "print(\"*\" * 100)\n", + "\n", + "# JP\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y_JP, test_size=0.2, random_state=10)\n", + "rfn.fit(x_train, y_train)\n", + "jpb_train = rfn.score (x_train,y_train)\n", + "jpb_test = rfn.score (x_train,y_train)\n", + "predrfn = rfn.predict(x_test)\n", + "print(\"J-P Results\")\n", + "print('Label J-P train score is :',jpb_train)\n", + "print('Label J-P test score is :',jpb_test)\n", + "print(\"Confusion Matrix for Multinomial Naive Bayes:\")\n", + "print(confusion_matrix(y_test,predrfn))\n", + "print(\"Score:\",round(accuracy_score(y_test,predrfn)*100,2))\n", + "print(\"Classification Report:\",classification_report(y_test,predrfn))\n", + "print(\"*\" * 100)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/code/Code 9 - Build Tool.ipynb b/your-project/code/Code 9 - Build Tool.ipynb new file mode 100644 index 00000000..ad599c18 --- /dev/null +++ b/your-project/code/Code 9 - Build Tool.ipynb @@ -0,0 +1,260 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Build Ultimate Tool!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Scrape Instagram" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "import click\n", + "\n", + "import requests\n", + "from bs4 import BeautifulSoup\n", + "\n", + "\n", + "def save_captions(prefix, user, caption_all):\n", + " \"\"\"\n", + " Write a list of captions to files (one file per caption).\n", + " :param prefix: The directory in which the files will be saved\n", + " :param prefix: str\n", + " :param user: Username of the account the posts' captions come from\n", + " (will be used as a prefix in the filenames)\n", + " :param user: str\n", + " :param caption_all: A list of captions\n", + " :param caption_all: list\n", + " :return: None\n", + " \"\"\"\n", + " for idx, caption in enumerate(caption_all):\n", + " filename = '{0}_{1}.txt'.format(user, idx)\n", + " filename = os.path.join(prefix, filename)\n", + " print('saving caption {0}'.format(filename))\n", + " text_file = open(filename, 'w')\n", + " text_file.write(caption)\n", + " text_file.close()\n", + "\n", + "\n", + "def download_and_save_images(prefix, user, img_url_all):\n", + " \"\"\"\n", + " Download and save a list of images from their urls.\n", + " :param prefix: The directory in which the images will be saved\n", + " :param prefix: str\n", + " :param user: Username of the account the posts' images come from\n", + " (will be used as a prefix in the filenames)\n", + " :param user: str\n", + " :param img_url_all: A list of image urls\n", + " :param img_url_all: list\n", + " :return: None\n", + " \"\"\"\n", + " for idx, img_url in enumerate(img_url_all):\n", + " img_data = requests.get(img_url, verify=False).content\n", + " filename = '{0}_{1}.jpg'.format(user, idx)\n", + " filename = os.path.join(prefix, filename)\n", + " print('saving image {0}'.format(filename))\n", + " with open(filename, 'wb') as handler:\n", + " handler.write(img_data)\n", + "\n", + "\n", + "@click.command()\n", + "@click.option('--images/--no-images', default=True,\n", + " help='Scrap also images.')\n", + "@click.option('--captions/--no-captions', default=True,\n", + " help='Scrap also captions.')\n", + "@click.option('--user', '-u', required=True,\n", + " help='The account to scrap.')\n", + "@click.option('--number', '-n', default=10,\n", + " help='Number of posts to scrap. (newer posts are scraped first).')\n", + "def scrap(images, captions, user, number):\n", + " \"\"\"\n", + " Scrap photos and captions from posts of a single user.\n", + " :param images: Include images in the data scraped\n", + " :param images: boolean\n", + " :param captions: Include captions in the data scraped\n", + " :param captions: boolean\n", + " :param user: The account to scrap\n", + " :param user: str\n", + " :return: None\n", + " \"\"\"\n", + " BASE_URL = 'https://deskgram.org'\n", + " SIG = \"?_nc_ht=scontent-dfw5-1.cdninstagram.com\"\n", + " USER = user\n", + " PATTERN_FOR_IMAGES = {'name': \"div\", 'attrs': {\"class\": \"post-img\"}}\n", + " PATTERN_FOR_CAPTIONS = {'name': \"div\", 'attrs': {\"class\": \"post-caption\"}}\n", + " DEST_FOLDER = './{0}'.format(USER)\n", + "\n", + " url = BASE_URL + '/' + USER\n", + " img_url_all = list() if images else None\n", + " caption_all = list() if captions else None\n", + "\n", + " if not os.path.exists(DEST_FOLDER):\n", + " os.makedirs(DEST_FOLDER)\n", + "\n", + " while True:\n", + " print('fetching {0}'.format(url))\n", + " r = requests.get(url, verify=False)\n", + " soup = BeautifulSoup(r.text, 'html.parser')\n", + "\n", + " if images:\n", + " images = soup.findAll(**PATTERN_FOR_IMAGES)\n", + " for image in images:\n", + " img_url = image.img['src'].split('?')[0]\n", + " img_url += SIG\n", + " img_url_all.append(img_url)\n", + " if len(img_url_all) == number and not captions:\n", + " break\n", + " print('Found {0} images.'.format(len(images)))\n", + " if len(img_url_all) == number and not captions:\n", + " break\n", + "\n", + " if captions:\n", + " captions = soup.findAll(**PATTERN_FOR_CAPTIONS)\n", + " for caption in captions:\n", + " caption_all.append(caption.text)\n", + " print(len(caption_all))\n", + " if len(caption_all) == number:\n", + " break\n", + " print('Found {0} captions.'.format(len(captions)))\n", + " if len(caption_all) == number:\n", + " break\n", + "\n", + " links = soup.findAll('a')\n", + " next_link = list(filter(lambda x: 'next_id' in x['href'], links))\n", + " if len(next_link) == 0:\n", + " break\n", + " else:\n", + " dest = next_link[0]['href']\n", + " url = BASE_URL + dest\n", + "\n", + " if images:\n", + " download_and_save_images(DEST_FOLDER, USER, img_url_all)\n", + "\n", + " if captions:\n", + " save_captions(DEST_FOLDER, USER, caption_all)\n", + "\n", + "\n", + "if __name__ == '__main__':\n", + " scrap()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Read Captions & Clean" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Prepare Data for Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Run Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Show Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-project/code/RF_IE.sav b/your-project/code/RF_IE.sav new file mode 100644 index 00000000..8546dd26 Binary files /dev/null and b/your-project/code/RF_IE.sav differ diff --git a/your-project/code/RF_JP.sav b/your-project/code/RF_JP.sav new file mode 100644 index 00000000..7f4c7d54 Binary files /dev/null and b/your-project/code/RF_JP.sav differ diff --git a/your-project/code/RF_NS.sav b/your-project/code/RF_NS.sav new file mode 100644 index 00000000..38e3e90c Binary files /dev/null and b/your-project/code/RF_NS.sav differ diff --git a/your-project/code/RF_TF.sav b/your-project/code/RF_TF.sav new file mode 100644 index 00000000..70dbe5a7 Binary files /dev/null and b/your-project/code/RF_TF.sav differ diff --git a/your-project/data/quant_personalities.csv b/your-project/data/quant_personalities.csv new file mode 100644 index 00000000..38776a3a --- /dev/null +++ b/your-project/data/quant_personalities.csv @@ -0,0 +1,8676 @@ +,type,length_comment,var_length,links_count,qm_count,exm_count,img_count,music_count,dots_count,ht_count,me_count,I-E,N-S,T-F,J-P +0,INFJ,11.12,0.17974993327512315,0.48,0.36,0.06,0.12,0.02,0.3,0.0,1.18,1,0,0,0 +1,ENTP,23.4,0.184468936246192,0.2,0.1,0.0,0.02,0.0,0.38,0.0,3.06,0,0,1,1 +2,INTP,16.72,0.1840100504234724,0.1,0.24,0.08,0.0,0.0,0.26,0.0,1.44,1,0,1,1 +3,INTJ,21.28,0.18663994130415476,0.04,0.22,0.06,0.0,0.02,0.52,0.0,2.18,1,0,1,0 +4,ENTJ,19.34,0.17872297102416185,0.12,0.2,0.02,0.04,0.02,0.42,0.0,1.48,0,0,1,0 +5,INTJ,29.82,0.18442131148125424,0.02,0.2,0.0,0.0,0.0,0.78,0.0,2.7,1,0,1,0 +6,INFJ,26.58,0.18430194339707912,0.04,0.26,0.06,0.0,0.02,0.74,0.0,2.82,1,0,0,0 +7,INTJ,24.46,0.18134081075404326,0.02,0.7,0.0,0.0,0.04,0.56,0.0,1.14,1,0,1,0 +8,INFJ,14.76,0.18395341312349842,0.46,0.44,0.02,0.02,0.02,0.34,0.0,1.3,1,0,0,0 +9,INTP,24.66,0.1768294999667744,0.14,0.26,0.06,0.0,0.02,0.48,0.0,1.26,1,0,1,1 +10,INFJ,29.08,0.18519722360511665,0.02,0.46,0.2,0.0,0.0,0.88,0.0,2.68,1,0,0,0 +11,ENFJ,15.94,0.18844895290304214,0.06,0.14,0.56,0.0,0.0,0.38,0.0,3.58,0,0,0,0 +12,INFJ,25.0,0.18579377767128796,0.0,0.28,0.16,0.0,0.04,0.64,0.0,2.82,1,0,0,0 +13,INTJ,31.9,0.181568682817145,0.0,0.18,0.12,0.0,0.0,0.84,0.0,3.36,1,0,1,0 +14,INTP,17.68,0.17669835209052753,0.06,0.04,0.04,0.0,0.0,0.3,0.0,2.1,1,0,1,1 +15,INTP,24.24,0.18264670988782278,0.02,0.22,0.08,0.0,0.0,0.6,0.0,2.74,1,0,1,1 +16,INFJ,27.52,0.18279510739435095,0.08,0.2,0.4,0.06,0.0,0.86,0.02,3.46,1,0,0,0 +17,INFP,32.74,0.18358067754398572,0.0,0.06,0.2,0.0,0.04,0.94,0.0,4.24,1,0,0,1 +18,INFJ,30.6,0.17664165469692616,0.0,0.12,0.12,0.0,0.02,0.76,0.0,2.96,1,0,0,0 +19,INFP,22.78,0.1875,0.0,0.2,0.1,0.0,0.0,0.62,0.06,1.9,1,0,0,1 +20,INTP,21.7,0.1777654954968171,0.02,0.16,0.04,0.0,0.0,0.42,0.0,1.34,1,0,1,1 +21,INFJ,22.02,0.18064215372064565,0.26,0.4,0.02,0.0,0.0,0.72,0.0,2.3,1,0,0,0 +22,ENTJ,23.84,0.18549902212654262,0.1,0.26,0.4,0.0,0.0,0.6,0.0,3.54,0,0,1,0 +23,INFP,13.04,0.19379993229316733,0.0,0.18,0.08,0.02,0.06,0.6,0.0,1.22,1,0,0,1 +24,ENTJ,21.72,0.1840306188040887,0.1,0.24,0.1,0.02,0.0,0.78,0.0,1.86,0,0,1,0 +25,INFP,21.94,0.18997462021331962,0.06,0.22,0.22,0.0,0.02,0.6,0.0,2.46,1,0,0,1 +26,ENFP,35.16,0.18950028551639353,0.0,0.02,0.24,0.0,0.0,1.46,0.0,4.22,0,0,0,1 +27,ISFP,18.1,0.19236020627323874,0.02,0.16,0.24,0.0,0.02,0.24,0.0,1.32,1,1,0,1 +28,INFP,29.08,0.18813057117559367,0.0,0.2,0.12,0.0,0.02,0.96,0.0,3.44,1,0,0,1 +29,INFJ,31.4,0.18187203670133642,0.06,0.12,0.14,0.0,0.0,0.84,0.02,3.7,1,0,0,0 +30,INFP,32.12,0.19151256682178394,0.0,0.12,0.14,0.0,0.0,0.92,0.0,4.74,1,0,0,1 +31,INFJ,19.68,0.1857978112793261,0.0,0.5,0.48,0.0,0.0,0.1,0.0,2.36,1,0,0,0 +32,INTP,23.16,0.17743735144435235,0.0,0.28,0.08,0.0,0.0,0.5,0.0,2.76,1,0,1,1 +33,INFJ,27.32,0.18444324699332784,0.02,0.2,0.06,0.0,0.0,0.62,0.04,3.72,1,0,0,0 +34,ENFJ,20.54,0.18617950321403504,0.06,0.22,0.24,0.0,0.06,0.4,0.0,2.08,0,0,0,0 +35,INFJ,7.14,0.18482593075085985,0.56,0.52,0.06,0.02,0.0,0.2,0.02,0.86,1,0,0,0 +36,INTJ,29.3,0.18152692628878617,0.08,0.16,0.18,0.04,0.02,0.9,0.0,3.2,1,0,1,0 +37,INFP,29.76,0.1802715039053254,0.08,0.1,0.0,0.0,0.02,0.62,0.02,2.8,1,0,0,1 +38,ENFP,27.88,0.18605046959906746,0.06,0.44,0.0,0.0,0.02,0.82,0.0,2.22,0,0,0,1 +39,ENTJ,33.12,0.19097706087580218,0.0,0.12,0.52,0.0,0.0,1.5,0.0,3.7,0,0,1,0 +40,ISTP,31.34,0.18695169372606996,0.08,0.1,0.04,0.0,0.02,0.74,0.0,3.68,1,1,1,1 +41,INFJ,28.26,0.19580625435453677,0.08,0.14,0.16,0.0,0.0,1.12,0.0,3.74,1,0,0,0 +42,INTP,20.7,0.18442225239372861,0.0,0.02,0.08,0.0,0.0,0.4,0.0,2.52,1,0,1,1 +43,INFP,27.72,0.1875,0.04,0.2,0.14,0.0,0.0,0.64,0.0,3.46,1,0,0,1 +44,INTJ,19.26,0.18398802885462287,0.2,0.24,0.02,0.04,0.0,0.24,0.02,2.32,1,0,1,0 +45,INTP,30.96,0.18438243306095836,0.08,0.16,0.04,0.0,0.1,0.9,0.0,3.52,1,0,1,1 +46,ENTJ,30.7,0.17831575298979205,0.02,0.12,0.18,0.0,0.02,0.88,0.0,2.08,0,0,1,0 +47,INFP,29.84,0.18844830298846837,0.0,0.06,0.48,0.0,0.02,0.68,0.0,3.7,1,0,0,1 +48,ISFJ,32.7,0.18458819973623947,0.0,0.02,0.04,0.0,0.0,0.92,0.0,3.86,1,1,0,0 +49,INTJ,21.96,0.18626946463567018,0.14,0.24,0.0,0.0,0.04,0.38,0.0,2.3,1,0,1,0 +50,INTP,29.64,0.1821216192081273,0.0,0.2,0.02,0.0,0.0,0.56,0.0,3.04,1,0,1,1 +51,INFP,27.92,0.18580233678052652,0.12,0.24,0.08,0.02,0.06,0.66,0.0,3.42,1,0,0,1 +52,ISFJ,27.22,0.1852237727298424,0.06,0.32,0.94,0.0,0.08,0.7,0.0,3.32,1,1,0,0 +53,INTP,29.24,0.18699824586654645,0.02,0.26,0.14,0.0,0.0,0.68,0.02,3.26,1,0,1,1 +54,INFP,25.42,0.19053782605391356,0.0,0.1,0.14,0.0,0.0,0.64,0.0,2.86,1,0,0,1 +55,INFP,19.2,0.1852877808556479,0.06,0.04,0.1,0.0,0.0,0.36,0.04,2.94,1,0,0,1 +56,INFP,26.58,0.18556003811529895,0.02,0.02,0.4,0.0,0.02,0.6,0.0,3.5,1,0,0,1 +57,ENFJ,25.58,0.18257324587451026,0.06,0.22,0.16,0.0,0.0,0.58,0.0,2.48,0,0,0,0 +58,INTP,26.2,0.18027058163039555,0.14,0.12,0.14,0.04,0.0,0.54,0.06,2.92,1,0,1,1 +59,INTJ,23.92,0.1757823367955337,0.0,0.16,0.06,0.0,0.0,0.5,0.0,2.22,1,0,1,0 +60,INFJ,21.52,0.18387538568031878,0.2,0.24,0.46,0.06,0.0,0.44,0.0,2.84,1,0,0,0 +61,INTP,29.4,0.18690926604278932,0.08,0.32,0.06,0.08,0.0,0.82,0.0,3.76,1,0,1,1 +62,INFP,20.9,0.1825625754378666,0.0,0.04,0.14,0.0,0.0,0.54,0.0,2.56,1,0,0,1 +63,INFP,31.86,0.1867578343182373,0.0,0.06,0.14,0.0,0.02,0.88,0.0,3.62,1,0,0,1 +64,ENTP,8.62,0.19923601585124218,0.0,0.0,0.0,0.0,0.02,0.08,0.0,1.02,0,0,1,1 +65,INTP,32.82,0.17923818567290753,0.0,0.16,0.04,0.0,0.0,0.76,0.0,3.72,1,0,1,1 +66,ISFP,24.62,0.18213442664947768,0.06,0.38,0.4,0.0,0.0,1.02,0.02,2.66,1,1,0,1 +67,ENFJ,34.46,0.18824501347256778,0.0,0.32,0.52,0.0,0.0,1.42,0.0,4.14,0,0,0,0 +68,INFJ,27.16,0.18373289896025682,0.08,0.36,0.14,0.0,0.14,0.82,0.0,3.22,1,0,0,0 +69,ENFJ,24.12,0.18613234685335422,0.04,0.26,0.16,0.0,0.02,0.46,0.02,3.08,0,0,0,0 +70,INFJ,34.14,0.18055406435332738,0.0,0.12,0.04,0.0,0.04,0.86,0.0,1.96,1,0,0,0 +71,INFP,19.42,0.17620088839351572,0.22,0.12,0.0,0.02,0.0,0.44,0.0,2.32,1,0,0,1 +72,ENFP,25.72,0.1776404195181042,0.14,0.06,0.0,0.12,0.0,0.54,0.0,2.84,0,0,0,1 +73,ISTP,20.54,0.1832459628078461,0.04,0.06,0.02,0.02,0.02,0.4,0.0,1.8,1,1,1,1 +74,INFJ,27.7,0.18290267464098156,0.02,0.14,0.22,0.02,0.0,0.92,0.0,3.02,1,0,0,0 +75,INTP,21.32,0.1896820240970872,0.0,0.12,0.1,0.0,0.02,0.52,0.0,2.48,1,0,1,1 +76,INTJ,31.36,0.17966428556521363,0.0,0.14,0.04,0.0,0.0,0.8,0.0,2.92,1,0,1,0 +77,INTJ,22.26,0.17897020518997078,0.02,0.12,0.06,0.02,0.0,0.44,0.0,2.38,1,0,1,0 +78,INTJ,32.54,0.17748131823283342,0.04,0.18,0.06,0.02,0.0,0.98,0.02,3.5,1,0,1,0 +79,INFP,33.46,0.18559703153007479,0.0,0.32,0.26,0.0,0.0,0.82,0.0,4.06,1,0,0,1 +80,INFP,18.44,0.18529110761994635,0.0,0.12,0.02,0.0,0.02,0.42,0.0,1.38,1,0,0,1 +81,INFJ,27.1,0.18959505650666686,0.06,0.36,0.3,0.0,0.0,0.56,0.0,3.72,1,0,0,0 +82,ENTP,31.44,0.1835096540927576,0.02,0.18,0.0,0.0,0.0,0.88,0.0,2.6,0,0,1,1 +83,ENTJ,24.48,0.18562815176249878,0.18,0.46,0.04,0.0,0.0,0.52,0.0,2.54,0,0,1,0 +84,INFP,18.18,0.19422172721533518,0.02,0.34,0.04,0.0,0.04,0.8,0.0,1.28,1,0,0,1 +85,INTP,15.62,0.18454432261894474,0.04,0.16,0.04,0.0,0.02,0.42,0.0,1.44,1,0,1,1 +86,INFP,26.12,0.18463446570605477,0.04,0.18,0.04,0.0,0.0,0.6,0.0,3.3,1,0,0,1 +87,ISFJ,15.78,0.19009813856414862,0.04,0.12,0.12,0.02,0.02,0.82,0.0,1.06,1,1,0,0 +88,ISTJ,16.8,0.17855357030294808,0.62,0.22,0.12,0.44,0.0,0.28,0.0,2.24,1,1,1,0 +89,INTP,29.02,0.17788304907148955,0.02,0.14,0.34,0.0,0.04,0.8,0.0,3.44,1,0,1,1 +90,ENTP,27.1,0.17778522715257866,0.06,0.12,0.04,0.06,0.04,0.66,0.02,3.28,0,0,1,1 +91,INTP,29.98,0.18517105083355792,0.0,0.34,0.14,0.0,0.12,0.86,0.0,3.26,1,0,1,1 +92,ISFP,20.26,0.18715421919355332,0.0,0.3,0.26,0.0,0.12,0.54,0.0,2.82,1,1,0,1 +93,ENFJ,9.42,0.19502163572894338,0.02,0.04,0.5,0.0,0.0,0.32,0.0,1.42,0,0,0,0 +94,INFJ,17.58,0.19220962864770497,0.0,0.22,0.24,0.0,0.0,0.26,0.0,2.3,1,0,0,0 +95,INFP,29.2,0.17947017061093226,0.02,0.32,0.1,0.0,0.02,0.78,0.0,2.48,1,0,0,1 +96,INFJ,28.22,0.1829437080875385,0.08,0.08,0.1,0.0,0.02,0.66,0.0,3.6,1,0,0,0 +97,INTJ,22.86,0.176076645595972,0.24,0.68,0.0,0.1,0.06,0.6,0.0,1.46,1,0,1,0 +98,INFJ,24.02,0.18828620830084183,0.16,0.34,0.28,0.1,0.0,0.7,0.0,2.54,1,0,0,0 +99,ENFP,23.66,0.19279124979174,0.08,0.1,0.6,0.02,0.0,0.9,0.04,2.46,0,0,0,1 +100,INFP,25.1,0.1846913580246914,0.22,0.26,0.24,0.04,0.0,0.64,0.0,3.3,1,0,0,1 +101,INFP,22.48,0.18352620307171386,0.22,0.3,0.04,0.0,0.0,0.48,0.02,2.46,1,0,0,1 +102,ENFP,20.04,0.1848350513628514,0.12,0.3,0.16,0.02,0.0,0.5,0.0,1.94,0,0,0,1 +103,INFJ,15.64,0.18233163661316582,0.28,0.12,0.12,0.16,0.0,0.46,0.0,1.8,1,0,0,0 +104,ISTP,20.18,0.1879269650424933,0.0,0.14,0.08,0.0,0.0,0.64,0.0,1.88,1,1,1,1 +105,INFJ,29.76,0.18817023644351952,0.04,0.18,0.44,0.0,0.06,0.72,0.0,4.02,1,0,0,0 +106,ISTJ,23.72,0.18475782868303042,0.04,0.24,0.28,0.0,0.0,0.5,0.02,2.22,1,1,1,0 +107,ENFJ,29.54,0.17705528515702584,0.04,0.28,0.1,0.0,0.02,0.88,0.0,2.82,0,0,0,0 +108,INTP,14.9,0.1800439703968616,0.22,0.24,0.0,0.0,0.0,0.12,0.0,1.42,1,0,1,1 +109,INTJ,23.24,0.18566982624671094,0.0,0.38,0.04,0.0,0.0,0.52,0.0,2.28,1,0,1,0 +110,ENTP,31.9,0.18794681321161716,0.0,0.34,0.04,0.0,0.0,0.74,0.0,4.06,0,0,1,1 +111,INFP,31.08,0.18919530728764414,0.0,0.36,0.62,0.0,0.0,1.24,0.0,4.06,1,0,0,1 +112,INFP,29.88,0.18004657256910947,0.04,0.3,0.04,0.0,0.0,0.76,0.0,3.5,1,0,0,1 +113,ESTP,13.72,0.18166106763093579,0.16,0.4,0.78,0.06,0.02,0.32,0.0,1.8,0,1,1,1 +114,ISTP,26.52,0.18649339195252446,0.0,0.1,0.0,0.0,0.0,0.86,0.0,1.94,1,1,1,1 +115,ENFP,26.12,0.1806785874435421,0.14,0.4,0.02,0.0,0.0,0.42,0.0,2.64,0,0,0,1 +116,ENTP,25.54,0.1808638485356236,0.0,0.66,0.0,0.0,0.0,0.3,0.0,2.06,0,0,1,1 +117,INTP,31.88,0.1812850383569353,0.02,0.22,0.0,0.0,0.0,0.96,0.02,3.68,1,0,1,1 +118,INFJ,22.08,0.1830136633300244,0.06,0.28,0.16,0.0,0.0,0.58,0.0,2.82,1,0,0,0 +119,ENFP,20.96,0.181933337828093,0.14,0.22,0.48,0.0,0.06,0.42,0.0,2.32,0,0,0,1 +120,ENTP,31.82,0.18340448313760807,0.02,0.12,0.34,0.02,0.0,1.08,0.0,2.64,0,0,1,1 +121,INTP,9.3,0.18043887673343947,0.02,0.08,0.0,0.0,0.02,0.18,0.0,1.04,1,0,1,1 +122,INFJ,25.26,0.1820917732994107,0.02,0.12,0.06,0.0,0.0,0.56,0.0,2.84,1,0,0,0 +123,ISTP,21.3,0.18512378484967779,0.16,0.18,0.02,0.1,0.0,0.3,0.0,2.78,1,1,1,1 +124,INTP,18.18,0.21378735804848134,0.22,0.3,0.04,0.02,0.02,1.1,0.0,1.0,1,0,1,1 +125,INTJ,24.34,0.18769930078685335,0.24,0.22,0.1,0.18,0.0,0.72,0.0,3.22,1,0,1,0 +126,ENTP,17.98,0.18931018609404665,0.04,0.28,0.0,0.02,0.0,0.22,0.0,1.26,0,0,1,1 +127,INFJ,30.2,0.17591710027523982,0.1,0.24,0.0,0.06,0.0,0.9,0.0,2.7,1,0,0,0 +128,INFJ,21.54,0.18650643562483593,0.06,0.32,0.1,0.04,0.0,0.46,0.0,2.52,1,0,0,0 +129,INFJ,25.82,0.18005860632536447,0.18,0.26,0.1,0.0,0.0,0.8,0.0,3.16,1,0,0,0 +130,ENTJ,20.26,0.18770934502127315,0.0,0.2,0.3,0.0,0.06,0.66,0.0,2.18,0,0,1,0 +131,ISFJ,33.44,0.1906338231015208,0.0,0.26,0.32,0.0,0.02,1.68,0.0,4.4,1,1,0,0 +132,INFP,29.76,0.1824784590766577,0.04,0.3,0.12,0.02,0.0,0.84,0.0,2.42,1,0,0,1 +133,INFJ,19.2,0.18549094286559326,0.08,0.4,0.0,0.0,0.0,0.3,0.0,1.6,1,0,0,0 +134,INTP,32.02,0.18806817886997265,0.0,0.16,0.0,0.0,0.0,0.82,0.0,3.84,1,0,1,1 +135,INFJ,22.0,0.18361305322291172,0.0,0.16,0.26,0.0,0.0,0.72,0.0,2.56,1,0,0,0 +136,INFP,26.94,0.19000668973868912,0.16,0.38,0.12,0.08,0.0,0.74,0.02,3.16,1,0,0,1 +137,INTP,27.18,0.1838580271557535,0.04,0.2,0.1,0.0,0.1,0.6,0.0,3.2,1,0,1,1 +138,INFP,23.62,0.18811060991359932,0.0,0.16,0.14,0.0,0.0,0.72,0.0,3.1,1,0,0,1 +139,ISFP,26.38,0.1851577684120442,0.0,0.24,0.12,0.0,0.02,0.7,0.0,2.6,1,1,0,1 +140,INTP,23.96,0.18296333712069043,0.06,0.12,0.0,0.0,0.0,0.56,0.0,2.26,1,0,1,1 +141,INFP,17.46,0.19117295211371302,0.14,0.3,0.12,0.02,0.16,0.34,0.08,2.02,1,0,0,1 +142,ENFP,17.12,0.19320741116443668,0.02,0.16,0.16,0.0,0.0,0.6,0.06,1.46,0,0,0,1 +143,ENFP,26.18,0.18764756996229454,0.18,0.16,0.12,0.08,0.06,0.66,0.0,2.96,0,0,0,1 +144,ENTP,28.58,0.18713609974986478,0.02,0.16,0.18,0.0,0.0,0.78,0.02,2.68,0,0,1,1 +145,INFJ,25.52,0.19388818193102964,0.0,1.1,0.34,0.0,0.06,0.5,0.0,2.78,1,0,0,0 +146,INFJ,21.4,0.17820657309057286,0.04,0.2,0.12,0.0,0.0,0.56,0.0,1.62,1,0,0,0 +147,ENTP,13.62,0.18405077717135318,0.02,0.2,0.08,0.02,0.02,0.44,0.0,1.42,0,0,1,1 +148,INFP,28.84,0.182889964410334,0.1,0.12,0.28,0.08,0.02,0.68,0.02,2.72,1,0,0,1 +149,INFP,12.1,0.18632333074760934,0.12,0.24,0.1,0.0,0.02,0.16,0.0,1.4,1,0,0,1 +150,INTP,27.42,0.17623852101460935,0.06,0.1,0.0,0.0,0.12,0.82,0.0,2.98,1,0,1,1 +151,INFJ,32.66,0.18372740140711846,0.1,0.3,0.0,0.0,0.02,1.16,0.0,2.94,1,0,0,0 +152,ENFP,26.12,0.1891823794780048,0.12,0.16,0.2,0.06,0.0,0.64,0.0,3.02,0,0,0,1 +153,ENFP,23.96,0.18729056144247247,0.08,0.16,0.32,0.02,0.04,0.88,0.0,2.56,0,0,0,1 +154,INTP,19.3,0.18134294276345705,0.04,0.2,0.12,0.0,0.0,0.48,0.02,1.4,1,0,1,1 +155,ENTP,7.54,0.18448410504967847,0.0,0.1,0.0,0.0,0.0,0.18,0.0,0.8,0,0,1,1 +156,INTJ,28.14,0.17951394374078183,0.0,0.22,0.02,0.0,0.0,0.7,0.0,3.48,1,0,1,0 +157,INTP,13.82,0.18639003817928923,0.0,0.14,0.0,0.0,0.0,0.28,0.0,2.12,1,0,1,1 +158,ENTP,14.16,0.17508005817566719,0.48,0.24,0.08,0.3,0.0,0.24,0.0,0.98,0,0,1,1 +159,ENFP,13.84,0.18166567983603238,0.52,0.5,0.04,0.02,0.0,0.34,0.0,1.36,0,0,0,1 +160,ESTP,28.22,0.19068610505132563,0.0,0.4,0.0,0.0,0.0,0.6,0.0,3.38,0,1,1,1 +161,INTJ,22.84,0.17910131189518447,0.18,0.16,0.04,0.04,0.0,0.52,0.06,1.76,1,0,1,0 +162,INTP,19.24,0.17811644206701785,0.02,0.22,0.0,0.0,0.0,0.46,0.0,1.94,1,0,1,1 +163,ENTP,25.56,0.18968001616983826,0.16,0.36,0.16,0.0,0.02,0.96,0.0,3.1,0,0,1,1 +164,INFP,27.88,0.187636009147877,0.2,0.36,0.3,0.08,0.0,0.86,0.0,3.4,1,0,0,1 +165,INFJ,23.44,0.19032877645875634,0.16,0.56,0.34,0.0,0.04,0.68,0.0,2.08,1,0,0,0 +166,INFJ,29.04,0.1805232245991535,0.14,0.28,0.08,0.02,0.02,0.82,0.0,3.04,1,0,0,0 +167,INTJ,19.86,0.18943595061217472,0.1,0.3,0.12,0.06,0.0,0.34,0.0,2.7,1,0,1,0 +168,ENFP,24.46,0.18715402778503532,0.0,0.16,0.24,0.0,0.0,0.66,0.0,2.72,0,0,0,1 +169,INTJ,30.7,0.18652316753641707,0.02,0.24,0.04,0.0,0.0,0.68,0.0,3.44,1,0,1,0 +170,INTJ,8.5,0.20083047600016976,0.06,0.16,0.08,0.02,0.0,0.04,0.0,1.06,1,0,1,0 +171,INTP,21.5,0.18043127944190435,0.02,0.16,0.08,0.02,0.0,0.46,0.0,1.44,1,0,1,1 +172,ISFJ,10.6,0.18770621377202829,0.12,0.2,0.06,0.02,0.04,0.2,0.0,1.44,1,1,0,0 +173,INTP,27.68,0.1885445293666564,0.04,0.4,0.5,0.0,0.0,1.14,0.0,2.74,1,0,1,1 +174,ISTP,23.84,0.18519678508481294,0.06,0.22,0.24,0.02,0.02,0.54,0.0,2.76,1,1,1,1 +175,INTP,16.16,0.18360380201777698,0.24,0.32,0.02,0.0,0.0,0.26,0.0,1.9,1,0,1,1 +176,INTP,23.3,0.18426563672058968,0.02,0.1,0.04,0.0,0.0,0.48,0.0,2.66,1,0,1,1 +177,INTP,23.64,0.1816278661023239,0.18,0.5,0.04,0.0,0.08,0.58,0.0,3.1,1,0,1,1 +178,INTP,22.36,0.17617558885659657,0.04,0.22,0.06,0.04,0.0,0.46,0.0,2.06,1,0,1,1 +179,INFP,24.7,0.1920191083202421,0.12,0.2,0.12,0.02,0.0,0.74,0.04,2.96,1,0,0,1 +180,ENTP,28.2,0.18033038179803604,0.06,0.18,0.3,0.02,0.04,0.68,0.0,2.5,0,0,1,1 +181,INFP,17.9,0.18127100073046024,0.22,0.08,0.18,0.14,0.0,0.48,0.02,1.74,1,0,0,1 +182,INTP,32.62,0.18443730081943624,0.1,0.22,0.14,0.1,0.02,1.0,0.0,4.1,1,0,1,1 +183,INTJ,22.02,0.19137405986560088,0.0,0.38,0.06,0.0,0.0,0.5,0.0,3.56,1,0,1,0 +184,ENTP,29.88,0.18851992848691548,0.04,0.4,0.12,0.0,0.0,0.78,0.0,1.9,0,0,1,1 +185,INFJ,28.16,0.18106099834310377,0.12,0.16,0.18,0.08,0.0,0.8,0.0,2.42,1,0,0,0 +186,INTJ,7.16,0.1941609416619957,0.0,0.0,0.08,0.0,0.0,0.08,0.0,1.02,1,0,1,0 +187,INTP,24.74,0.17813536451648548,0.0,0.08,0.0,0.0,0.04,0.4,0.0,2.1,1,0,1,1 +188,ISTP,24.42,0.18884450422366422,0.08,0.22,0.02,0.02,0.0,0.6,0.0,2.6,1,1,1,1 +189,ISTJ,28.46,0.18663579775083997,0.0,0.32,0.14,0.0,0.0,0.78,0.0,3.34,1,1,1,0 +190,INFP,25.46,0.18487689924634043,0.12,0.14,0.04,0.0,0.0,0.6,0.0,3.8,1,0,0,1 +191,INTJ,24.38,0.1892201009125869,0.0,0.12,0.12,0.0,0.04,0.48,0.0,2.76,1,0,1,0 +192,ENFP,19.86,0.1900078192846027,0.04,0.14,0.0,0.0,0.0,0.44,0.0,2.08,0,0,0,1 +193,INFP,15.86,0.18581056077960711,0.3,0.08,0.02,0.24,0.04,0.18,0.0,2.56,1,0,0,1 +194,INTP,30.24,0.1777347504120768,0.0,0.24,0.06,0.0,0.02,0.78,0.0,3.04,1,0,1,1 +195,INTJ,23.14,0.18348273743762822,0.12,0.12,0.18,0.0,0.0,0.54,0.0,2.42,1,0,1,0 +196,INTJ,14.62,0.1903208130447283,0.04,0.36,0.06,0.04,0.02,0.44,0.0,1.18,1,0,1,0 +197,INFP,25.98,0.18667252323874287,0.0,0.2,0.2,0.0,0.04,1.08,0.0,3.2,1,0,0,1 +198,INTP,30.9,0.18349200155097414,0.04,0.36,0.28,0.0,0.02,1.12,0.02,3.96,1,0,1,1 +199,INTP,24.48,0.1807668528216652,0.06,0.16,0.0,0.0,0.02,0.42,0.0,2.28,1,0,1,1 +200,ESFP,16.88,0.17519304871278701,0.22,0.26,0.1,0.1,0.0,0.38,0.0,1.26,0,1,0,1 +201,INTJ,17.24,0.17615386154343518,0.26,0.22,0.02,0.04,0.0,0.32,0.0,1.82,1,0,1,0 +202,ISFP,26.48,0.18761315872234968,0.02,0.18,0.2,0.02,0.04,0.8,0.0,2.88,1,1,0,1 +203,INFJ,19.66,0.18851481206984777,0.06,0.4,0.34,0.02,0.0,0.4,0.0,2.48,1,0,0,0 +204,INTJ,9.22,0.1791499147849783,0.66,0.26,0.0,0.22,0.02,0.18,0.0,1.94,1,0,1,0 +205,INTJ,20.34,0.18617388357220485,0.12,0.3,0.28,0.12,0.0,0.42,0.0,2.26,1,0,1,0 +206,INTP,28.88,0.1740914950939135,0.0,0.3,0.02,0.0,0.0,0.78,0.0,2.28,1,0,1,1 +207,INTP,19.46,0.1857254486990951,0.16,0.24,0.04,0.06,0.0,0.62,0.0,1.88,1,0,1,1 +208,INFP,21.42,0.18165639248965357,0.0,0.2,0.02,0.0,0.0,0.26,0.02,2.38,1,0,0,1 +209,ENFP,17.66,0.18896793275343818,0.02,0.36,0.3,0.02,0.0,0.24,0.0,2.08,0,0,0,1 +210,INFP,27.32,0.1805894498974653,0.02,0.16,0.1,0.0,0.02,0.5,0.0,2.6,1,0,0,1 +211,INFP,21.64,0.1966448557432774,0.0,0.22,0.08,0.0,0.0,0.64,0.0,2.22,1,0,0,1 +212,INFP,24.72,0.18139252061036878,0.06,0.24,0.02,0.02,0.0,0.98,0.0,2.48,1,0,0,1 +213,INFP,10.26,0.1817682277318641,0.0,0.12,0.02,0.0,0.0,0.16,0.0,0.96,1,0,0,1 +214,INTP,11.3,0.18297152027336352,0.24,0.4,0.98,0.14,0.0,0.18,0.0,1.28,1,0,1,1 +215,INTP,27.46,0.1762713238180874,0.06,0.52,0.04,0.0,0.02,0.6,0.0,2.44,1,0,1,1 +216,INFP,23.76,0.18482790321767661,0.18,0.42,0.1,0.0,0.0,0.64,0.0,2.86,1,0,0,1 +217,INFP,25.96,0.18027222622092218,0.0,0.04,0.0,0.0,0.0,0.56,0.0,2.5,1,0,0,1 +218,INFP,16.06,0.18291625321744817,0.26,0.26,0.06,0.06,0.06,0.28,0.02,1.38,1,0,0,1 +219,INFJ,21.14,0.187540617383168,0.08,0.12,0.06,0.0,0.0,0.56,0.0,3.04,1,0,0,0 +220,ENFP,21.84,0.18710975341951822,0.06,0.16,0.02,0.02,0.0,0.46,0.0,1.56,0,0,0,1 +221,INTJ,21.12,0.17907734203070963,0.06,0.26,0.14,0.02,0.0,0.42,0.0,1.98,1,0,1,0 +222,INFJ,27.32,0.18849026161597623,0.04,0.38,0.18,0.02,0.02,0.94,0.02,2.62,1,0,0,0 +223,INFJ,20.08,0.18293921934395616,0.34,0.44,0.18,0.0,0.0,0.6,0.0,1.92,1,0,0,0 +224,ENTP,12.14,0.1758102891413628,0.02,0.06,0.12,0.0,0.0,0.22,0.0,0.94,0,0,1,1 +225,INTP,18.62,0.20907929503953093,0.0,0.12,0.14,0.0,0.0,1.74,0.0,2.38,1,0,1,1 +226,INTP,29.74,0.1756600502974748,0.06,0.22,0.0,0.0,0.02,0.62,0.0,3.46,1,0,1,1 +227,INFP,16.86,0.19500511658185768,0.02,0.22,0.54,0.0,0.04,0.56,0.08,2.24,1,0,0,1 +228,INFP,24.3,0.1768805962350663,0.0,0.1,0.2,0.0,0.0,0.56,0.0,2.64,1,0,0,1 +229,INFJ,24.58,0.18339807032207184,0.0,0.14,0.06,0.0,0.0,0.44,0.0,2.76,1,0,0,0 +230,INTP,26.12,0.18122283781706344,0.22,0.22,0.14,0.0,0.08,0.68,0.02,2.6,1,0,1,1 +231,INFP,29.94,0.18614318743500508,0.0,0.24,0.56,0.0,0.0,0.94,0.0,3.04,1,0,0,1 +232,INTP,17.66,0.18846329602367035,0.44,0.62,0.0,0.04,0.04,0.34,0.0,1.68,1,0,1,1 +233,INFP,31.7,0.18693668603786034,0.0,0.48,0.34,0.0,0.0,1.02,0.0,3.68,1,0,0,1 +234,INFJ,15.84,0.19076705062342927,0.1,0.48,0.0,0.0,0.0,0.28,0.0,2.46,1,0,0,0 +235,ENFJ,28.76,0.18149922992144493,0.0,0.34,0.1,0.0,0.0,0.7,0.0,2.88,0,0,0,0 +236,ISTP,10.2,0.19182701464133384,0.02,0.08,0.0,0.02,0.0,0.12,0.0,0.6,1,1,1,1 +237,INTJ,27.96,0.18185464383662733,0.0,0.1,0.0,0.0,0.0,1.06,0.0,3.56,1,0,1,0 +238,INFP,29.46,0.18562356539730734,0.0,0.28,0.2,0.0,0.0,0.66,0.0,3.74,1,0,0,1 +239,ISFP,29.18,0.1797716998366958,0.0,0.12,0.0,0.0,0.0,0.68,0.0,3.26,1,1,0,1 +240,ENTJ,25.08,0.18058747207802697,0.0,0.02,0.9,0.0,0.0,0.58,0.0,3.04,0,0,1,0 +241,ENTP,22.36,0.18447379867677022,0.0,0.14,0.1,0.0,0.0,0.74,0.02,1.94,0,0,1,1 +242,ISFP,31.18,0.18319328734808446,0.0,0.1,0.1,0.0,0.02,0.82,0.0,3.66,1,1,0,1 +243,ENFP,26.18,0.18861692888340773,0.08,0.42,0.18,0.0,0.0,0.56,0.0,2.8,0,0,0,1 +244,INFP,29.16,0.18368394837734744,0.0,0.02,0.02,0.0,0.0,0.58,0.0,3.44,1,0,0,1 +245,ENFP,25.24,0.1897908342906051,0.0,0.08,0.06,0.0,0.02,0.52,0.0,3.02,0,0,0,1 +246,ENTP,23.82,0.19467041061227733,0.02,0.38,0.0,0.0,0.0,0.82,0.0,2.84,0,0,1,1 +247,INFP,29.64,0.18016789456435356,0.0,0.1,0.0,0.0,0.0,0.62,0.0,3.46,1,0,0,1 +248,INFP,26.1,0.18577216791310128,0.04,0.18,0.62,0.0,0.0,0.6,0.02,3.06,1,0,0,1 +249,ISFP,33.28,0.1867842810952036,0.04,0.14,0.38,0.0,0.06,1.2,0.0,4.92,1,1,0,1 +250,INFP,33.62,0.17968379275382532,0.0,0.22,0.44,0.0,0.0,1.06,0.0,3.66,1,0,0,1 +251,INFJ,23.8,0.18635777568484274,0.0,0.18,0.62,0.0,0.0,0.42,0.0,2.96,1,0,0,0 +252,ENTP,28.14,0.19167297064482156,0.0,0.1,0.16,0.0,0.0,0.78,0.02,2.48,0,0,1,1 +253,INFP,28.78,0.1841390379658193,0.02,0.06,0.16,0.02,0.0,0.54,0.0,3.62,1,0,0,1 +254,ISTP,21.46,0.18498180836583047,0.08,0.4,0.0,0.0,0.0,0.32,0.02,2.56,1,1,1,1 +255,INTJ,25.24,0.1824053621004183,0.18,0.22,0.02,0.06,0.0,0.56,0.0,2.96,1,0,1,0 +256,INTP,33.32,0.18398389144959784,0.0,0.12,0.14,0.0,0.0,1.04,0.0,3.2,1,0,1,1 +257,INFJ,23.52,0.18936148085712345,0.02,0.16,0.56,0.02,0.02,0.9,0.0,2.7,1,0,0,0 +258,ISTJ,26.44,0.19123337998898937,0.02,0.36,0.08,0.02,0.0,1.02,0.02,3.12,1,1,1,0 +259,INTJ,12.92,0.18516694204316544,0.2,0.26,0.06,0.0,0.0,0.16,0.0,1.66,1,0,1,0 +260,INFP,26.26,0.18261278406128686,0.16,0.32,0.18,0.0,0.02,0.8,0.06,2.7,1,0,0,1 +261,INFJ,10.5,0.18219094335428118,0.08,0.18,0.04,0.02,0.0,0.06,0.0,0.68,1,0,0,0 +262,INTJ,25.44,0.19702695893172084,0.0,0.58,0.36,0.0,0.0,1.2,0.0,3.58,1,0,1,0 +263,ENFJ,30.66,0.18226097338510686,0.04,0.12,0.08,0.02,0.06,0.92,0.0,3.74,0,0,0,0 +264,INFP,28.86,0.18402153018069972,0.0,0.06,0.04,0.0,0.0,0.62,0.0,3.44,1,0,0,1 +265,INFP,25.4,0.18247195316148032,0.0,0.2,0.12,0.0,0.02,0.84,0.02,3.44,1,0,0,1 +266,ISTJ,28.74,0.1857801411806633,0.0,0.28,0.16,0.0,0.0,0.84,0.0,2.62,1,1,1,0 +267,ISTJ,31.84,0.18365138309088502,0.04,0.18,0.14,0.0,0.0,0.92,0.0,3.38,1,1,1,0 +268,ISFP,12.04,0.18573893220642157,0.36,0.5,0.1,0.02,0.04,0.22,0.0,1.2,1,1,0,1 +269,INFP,29.44,0.18616438150665743,0.08,0.2,0.06,0.0,0.0,1.3,0.0,3.26,1,0,0,1 +270,INFJ,29.64,0.19035289655986398,0.0,0.2,0.88,0.0,0.0,0.76,0.0,3.18,1,0,0,0 +271,INFP,26.6,0.17598737601797262,0.0,0.18,0.02,0.0,0.02,0.62,0.02,2.74,1,0,0,1 +272,INTP,16.36,0.18291158504605529,0.2,0.32,0.04,0.02,0.0,0.38,0.0,1.74,1,0,1,1 +273,INFP,25.56,0.17855652748621473,0.08,0.06,0.04,0.08,0.0,0.62,0.02,3.14,1,0,0,1 +274,INTP,25.08,0.18985088527479566,0.0,0.18,0.04,0.0,0.0,0.6,0.0,3.92,1,0,1,1 +275,INTJ,7.28,0.17520162938232484,0.0,0.02,0.02,0.0,0.0,0.02,0.0,0.56,1,0,1,0 +276,INTJ,27.26,0.19766969968815415,0.08,0.26,0.06,0.0,0.0,0.62,0.0,3.04,1,0,1,0 +277,INFJ,30.5,0.18367660628513366,0.04,0.24,0.02,0.0,0.04,0.72,0.0,4.04,1,0,0,0 +278,INFP,31.2,0.181898691305051,0.0,0.2,0.12,0.0,0.0,0.94,0.0,2.92,1,0,0,1 +279,INTP,28.34,0.1816327719944484,0.14,0.18,0.02,0.02,0.12,0.56,0.0,2.88,1,0,1,1 +280,INTJ,22.98,0.18412375959969582,0.0,0.08,0.06,0.0,0.0,0.44,0.02,2.26,1,0,1,0 +281,ENFP,30.44,0.18466259043438332,0.02,0.32,0.38,0.0,0.0,0.82,0.0,3.74,0,0,0,1 +282,INFP,18.7,0.19097005563980105,0.06,0.4,0.04,0.0,0.0,0.16,0.0,2.44,1,0,0,1 +283,INTP,27.3,0.18836974713205507,0.04,0.28,0.04,0.0,0.0,0.64,0.0,2.54,1,0,1,1 +284,ENTJ,32.92,0.182055564035338,0.0,0.08,0.06,0.0,0.0,0.92,0.0,2.5,0,0,1,0 +285,INTJ,18.24,0.1834456507304703,0.3,0.24,0.04,0.16,0.0,0.52,0.0,2.04,1,0,1,0 +286,INFJ,23.7,0.1811113847330209,0.02,0.08,0.02,0.0,0.0,0.36,0.0,2.3,1,0,0,0 +287,INFP,25.04,0.17949958186063233,0.04,0.4,0.02,0.0,0.02,0.58,0.0,2.54,1,0,0,1 +288,ENTJ,30.24,0.18668577277847587,0.06,0.12,0.12,0.0,0.0,0.68,0.0,2.42,0,0,1,0 +289,INTP,26.56,0.1827506300820076,0.22,0.2,0.28,0.08,0.0,0.92,0.0,3.14,1,0,1,1 +290,INTP,26.86,0.18117832977238932,0.0,0.04,0.02,0.0,0.0,0.6,0.0,3.46,1,0,1,1 +291,INFP,29.0,0.18948695639854377,0.02,0.26,0.1,0.02,0.06,0.6,0.0,1.32,1,0,0,1 +292,INTP,25.72,0.17639201857835052,0.06,0.04,0.0,0.04,0.0,0.48,0.0,2.98,1,0,1,1 +293,ENTP,21.86,0.19181889652496192,0.06,0.22,0.06,0.04,0.0,0.54,0.08,2.4,0,0,1,1 +294,INTJ,27.0,0.18684794390165463,0.0,0.24,0.02,0.0,0.0,0.7,0.0,4.1,1,0,1,0 +295,INTP,31.68,0.18245383460179568,0.06,0.38,0.3,0.04,0.0,0.82,0.02,3.42,1,0,1,1 +296,INTJ,23.8,0.18056767550181774,0.02,0.12,0.1,0.0,0.02,0.58,0.0,2.4,1,0,1,0 +297,INFP,11.6,0.18638891064296342,0.08,0.12,0.08,0.0,0.0,0.16,0.02,0.94,1,0,0,1 +298,INFJ,17.34,0.19392740016116639,0.0,0.02,0.02,0.0,0.0,0.22,0.02,2.28,1,0,0,0 +299,INFJ,28.42,0.18248944344945184,0.0,0.12,0.06,0.0,0.0,0.86,0.0,3.46,1,0,0,0 +300,INFP,32.9,0.1790835042908779,0.04,0.06,0.06,0.02,0.08,0.94,0.0,3.5,1,0,0,1 +301,ENFP,28.96,0.17878315049801735,0.06,0.12,0.0,0.02,0.0,0.88,0.02,2.08,0,0,0,1 +302,INTP,29.22,0.18737238390254057,0.0,0.12,0.3,0.0,0.0,0.54,0.0,1.48,1,0,1,1 +303,ISTP,28.08,0.18719199225282496,0.2,0.3,0.26,0.2,0.12,0.8,0.02,3.04,1,1,1,1 +304,INTJ,26.18,0.18971027120257258,0.02,0.42,0.2,0.0,0.0,0.9,0.0,2.86,1,0,1,0 +305,ENFP,16.24,0.18889660396414595,0.2,0.2,0.16,0.0,0.02,0.24,0.0,1.54,0,0,0,1 +306,ENFP,24.92,0.18292666574293007,0.26,0.42,0.04,0.0,0.04,0.68,0.02,2.54,0,0,0,1 +307,ENTP,25.24,0.18904025690019693,0.04,0.24,0.2,0.0,0.0,1.12,0.02,2.12,0,0,1,1 +308,INTP,22.02,0.17906763744017745,0.18,0.22,0.1,0.08,0.0,0.66,0.0,2.78,1,0,1,1 +309,INFJ,28.8,0.18638841036896683,0.14,0.32,0.08,0.1,0.0,0.84,0.0,4.02,1,0,0,0 +310,INTJ,28.0,0.17946107394502717,0.0,0.42,0.02,0.0,0.0,0.74,0.02,2.24,1,0,1,0 +311,INFP,32.84,0.18352060684619284,0.0,0.08,0.18,0.0,0.0,0.82,0.0,4.38,1,0,0,1 +312,ENFP,33.2,0.1869328585465899,0.0,0.1,0.12,0.0,0.0,0.84,0.0,3.94,0,0,0,1 +313,ENFJ,22.56,0.1803575217191255,0.24,0.24,0.12,0.12,0.0,0.4,0.0,2.02,0,0,0,0 +314,ENTP,28.26,0.18504516454843872,0.0,0.52,0.26,0.0,0.02,0.84,0.0,3.46,0,0,1,1 +315,ISFP,27.68,0.18525345941515836,0.0,0.04,0.18,0.0,0.0,0.54,0.0,3.44,1,1,0,1 +316,INFP,32.36,0.189949696128996,0.04,0.24,0.08,0.0,0.04,1.56,0.0,3.8,1,0,0,1 +317,INFP,23.58,0.19092799184202236,0.04,0.2,0.44,0.0,0.02,0.44,0.0,3.28,1,0,0,1 +318,INFP,24.92,0.1840122885279017,0.1,0.1,0.06,0.0,0.0,0.54,0.0,3.48,1,0,0,1 +319,INTJ,33.88,0.1788833557038297,0.0,0.04,0.0,0.0,0.0,0.92,0.0,3.46,1,0,1,0 +320,ENTP,28.32,0.1895777600025138,0.0,0.38,0.24,0.0,0.0,0.56,0.0,3.16,0,0,1,1 +321,ENTP,23.76,0.18719329626765693,0.1,0.42,0.14,0.0,0.0,0.64,0.0,2.46,0,0,1,1 +322,INTP,21.42,0.17963922453017042,0.12,0.34,0.0,0.0,0.0,0.5,0.0,1.76,1,0,1,1 +323,INTP,29.58,0.19047279559590738,0.0,0.14,0.12,0.0,0.04,1.74,0.0,4.0,1,0,1,1 +324,INFJ,27.24,0.19456181835007172,0.04,0.14,0.26,0.0,0.0,0.62,0.0,3.64,1,0,0,0 +325,INFJ,18.56,0.18368326470932766,0.14,0.32,0.18,0.0,0.0,0.82,0.02,1.9,1,0,0,0 +326,INTP,27.86,0.18437100209121768,0.02,0.08,0.0,0.0,0.0,0.72,0.0,3.26,1,0,1,1 +327,ENFP,11.9,0.1905235967174534,0.04,0.12,0.12,0.04,0.0,0.34,0.0,1.28,0,0,0,1 +328,INFP,35.04,0.1891946170485074,0.0,0.14,0.12,0.0,0.0,0.98,0.0,4.32,1,0,0,1 +329,INTJ,31.24,0.18251345520069223,0.08,0.16,0.06,0.0,0.08,0.92,0.0,3.32,1,0,1,0 +330,INTJ,27.58,0.18320957353750145,0.0,0.1,0.08,0.0,0.0,0.78,0.0,2.44,1,0,1,0 +331,INFP,26.82,0.18155923974909938,0.02,0.16,0.1,0.0,0.02,0.7,0.0,2.56,1,0,0,1 +332,INFP,26.0,0.18911034199544605,0.04,0.08,0.12,0.0,0.0,0.9,0.0,2.94,1,0,0,1 +333,INFP,28.7,0.18734187229594634,0.02,0.24,0.68,0.0,0.0,0.66,0.0,2.88,1,0,0,1 +334,ISFP,27.1,0.1834401394761789,0.06,0.2,0.96,0.0,0.0,0.66,0.02,2.4,1,1,0,1 +335,ISFJ,30.46,0.18526203834413665,0.08,0.2,0.14,0.04,0.0,0.78,0.0,3.34,1,1,0,0 +336,INFP,23.44,0.18330222645013222,0.0,0.18,0.02,0.0,0.0,0.74,0.0,3.08,1,0,0,1 +337,INTP,30.6,0.17961622423961415,0.0,0.16,0.28,0.0,0.04,1.08,0.0,4.04,1,0,1,1 +338,INFP,24.22,0.18130175222477082,0.12,0.16,0.0,0.02,0.0,0.46,0.0,2.88,1,0,0,1 +339,INTJ,24.8,0.1816021591226856,0.0,0.28,0.02,0.0,0.0,0.44,0.0,2.74,1,0,1,0 +340,INTJ,22.64,0.17959822222222221,0.04,0.04,0.34,0.04,0.02,0.7,0.0,2.54,1,0,1,0 +341,ENTP,28.06,0.18925140375183808,0.08,0.14,0.1,0.0,0.02,0.9,0.0,2.74,0,0,1,1 +342,ENFJ,29.88,0.18685065370882437,0.0,0.1,0.3,0.0,0.04,0.82,0.0,1.7,0,0,0,0 +343,ISFJ,22.0,0.18447196624503404,0.12,0.16,0.14,0.1,0.02,0.44,0.0,1.72,1,1,0,0 +344,INTP,30.38,0.18490402664068895,0.02,0.2,0.12,0.0,0.0,0.84,0.0,3.84,1,0,1,1 +345,ISFP,32.28,0.18909282990570553,0.0,0.08,0.02,0.0,0.02,0.84,0.0,4.04,1,1,0,1 +346,ESTP,20.26,0.17728912522180332,0.02,0.12,0.04,0.02,0.02,0.22,0.0,2.24,0,1,1,1 +347,INFP,16.2,0.18739228782695722,0.0,0.14,0.02,0.0,0.04,0.2,0.0,2.02,1,0,0,1 +348,INTP,22.74,0.1890114313791654,0.06,0.3,0.22,0.04,0.02,0.58,0.0,3.08,1,0,1,1 +349,INTJ,25.34,0.18166795506740654,0.0,0.12,0.0,0.0,0.0,0.66,0.0,3.48,1,0,1,0 +350,INFP,30.9,0.17919644529439055,0.0,0.04,0.08,0.0,0.0,0.96,0.0,3.54,1,0,0,1 +351,INTJ,20.3,0.18279329463582253,0.16,0.24,0.4,0.08,0.0,0.54,0.0,2.36,1,0,1,0 +352,INFP,26.9,0.18412266517025094,0.0,0.18,0.18,0.0,0.0,0.62,0.0,3.58,1,0,0,1 +353,INFP,28.7,0.20116713463058453,0.04,0.4,0.8,0.02,0.04,1.5,0.0,3.8,1,0,0,1 +354,INFJ,24.66,0.18457715714249642,0.0,0.16,0.42,0.0,0.0,0.62,0.0,1.2,1,0,0,0 +355,INFP,13.3,0.19438144752736275,0.02,0.12,0.06,0.02,0.0,0.32,0.0,1.68,1,0,0,1 +356,ENTP,15.86,0.18482918881289082,0.12,0.14,0.08,0.04,0.0,0.26,0.0,1.92,0,0,1,1 +357,INTP,26.46,0.18255503678013402,0.08,0.28,0.02,0.04,0.04,0.64,0.0,3.22,1,0,1,1 +358,INTP,22.74,0.18614621424262326,0.0,0.02,0.0,0.0,0.06,0.7,0.0,2.38,1,0,1,1 +359,INFJ,26.38,0.1847662575448514,0.02,0.24,0.22,0.0,0.0,0.74,0.0,3.4,1,0,0,0 +360,ENFP,28.58,0.19271337405976466,0.0,0.12,0.36,0.0,0.0,1.54,0.0,3.38,0,0,0,1 +361,INTJ,28.38,0.18362229040011935,0.02,0.1,0.12,0.0,0.02,0.86,0.0,2.5,1,0,1,0 +362,ENTP,18.38,0.18754596433012427,0.0,0.14,0.06,0.0,0.0,0.36,0.0,0.76,0,0,1,1 +363,ENFP,29.46,0.17877472041955325,0.02,0.12,0.1,0.0,0.02,0.7,0.0,3.04,0,0,0,1 +364,INFJ,33.8,0.17608097812955711,0.04,0.02,0.0,0.0,0.06,0.92,0.0,4.84,1,0,0,0 +365,ISTJ,35.32,0.18489223031473792,0.0,0.06,0.0,0.0,0.02,0.96,0.0,3.82,1,1,1,0 +366,INFP,26.96,0.1962413530314116,0.06,0.4,0.16,0.0,0.0,1.32,0.0,3.12,1,0,0,1 +367,INFP,28.42,0.18231270928211665,0.1,0.22,0.04,0.04,0.02,0.7,0.0,2.58,1,0,0,1 +368,INFP,28.14,0.1851487190323272,0.06,0.04,0.12,0.0,0.02,1.14,0.0,3.9,1,0,0,1 +369,INTJ,28.02,0.1900195046119105,0.06,0.32,0.12,0.0,0.0,0.66,0.0,3.04,1,0,1,0 +370,INTJ,29.22,0.17503489217575782,0.02,0.16,0.02,0.0,0.0,0.82,0.0,2.82,1,0,1,0 +371,INFP,29.98,0.18761781335741176,0.02,0.08,0.08,0.0,0.02,0.8,0.0,3.98,1,0,0,1 +372,INFJ,31.34,0.1851857842515283,0.0,0.1,0.08,0.0,0.04,0.9,0.02,3.42,1,0,0,0 +373,INFP,31.24,0.18846200672516839,0.02,0.42,0.26,0.0,0.02,1.2,0.0,4.74,1,0,0,1 +374,INFP,15.44,0.19229007932934944,0.0,0.0,0.18,0.0,0.0,0.34,0.0,1.48,1,0,0,1 +375,ENTP,15.04,0.19088047649488069,0.0,0.1,0.1,0.0,0.0,0.24,0.0,1.22,0,0,1,1 +376,INTJ,27.48,0.18509180456742694,0.0,0.1,0.2,0.0,0.0,0.56,0.0,3.32,1,0,1,0 +377,INFP,28.94,0.18781697105228504,0.0,0.2,0.22,0.0,0.0,0.92,0.0,3.74,1,0,0,1 +378,INTJ,25.66,0.17868216181415444,0.02,0.14,0.12,0.0,0.0,0.42,0.0,2.2,1,0,1,0 +379,INTJ,5.54,0.18689320744097698,0.02,0.02,0.02,0.0,0.0,0.12,0.0,0.4,1,0,1,0 +380,INFP,29.82,0.1903640508107879,0.0,0.32,0.22,0.0,0.0,1.06,0.0,4.08,1,0,0,1 +381,ENFP,35.72,0.18690461445793935,0.0,0.18,0.5,0.0,0.0,1.12,0.0,4.62,0,0,0,1 +382,ISTP,1.02,0.18544973749423233,1.82,1.82,0.0,0.0,0.0,0.0,0.0,0.36,1,1,1,1 +383,INFP,30.16,0.18012556276828023,0.0,0.16,0.2,0.0,0.02,0.84,0.0,3.34,1,0,0,1 +384,ISFP,3.88,0.17503851480112245,0.02,0.04,0.04,0.0,0.0,0.12,0.02,0.18,1,1,0,1 +385,INFP,29.66,0.18785971148336084,0.0,0.22,0.48,0.0,0.02,1.0,0.0,3.38,1,0,0,1 +386,ENFP,22.38,0.18764611333401732,0.02,0.16,0.6,0.0,0.0,0.32,0.0,3.24,0,0,0,1 +387,ENTJ,28.8,0.18392087509509686,0.0,0.18,0.06,0.0,0.0,0.68,0.0,3.12,0,0,1,0 +388,ENFP,32.58,0.18470255875218877,0.0,0.14,0.0,0.0,0.02,0.8,0.0,4.02,0,0,0,1 +389,INFJ,21.8,0.19266062298362133,0.08,0.26,1.2,0.02,0.0,0.66,0.0,2.66,1,0,0,0 +390,INFJ,24.46,0.19137811613894143,0.02,0.14,0.74,0.0,0.0,0.74,0.0,2.06,1,0,0,0 +391,ISFP,16.62,0.1856556991920227,0.18,0.38,0.12,0.0,0.0,0.44,0.0,1.44,1,1,0,1 +392,INFP,21.36,0.17959629776059707,0.06,0.2,0.08,0.0,0.0,0.36,0.0,2.3,1,0,0,1 +393,INTP,21.5,0.18671496629953074,0.2,0.54,0.4,0.06,0.02,0.52,0.0,2.44,1,0,1,1 +394,ESTP,21.14,0.18835685292982618,0.08,0.32,0.0,0.0,0.04,0.34,0.0,2.86,0,1,1,1 +395,ENTP,23.48,0.18289758841797374,0.02,0.34,0.54,0.0,0.08,1.12,0.0,0.88,0,0,1,1 +396,ENFP,28.02,0.19139095434053474,0.06,0.12,0.28,0.0,0.0,1.46,0.0,3.06,0,0,0,1 +397,INFP,18.1,0.17684495427600833,0.08,0.3,0.02,0.06,0.0,0.24,0.0,1.9,1,0,0,1 +398,INFP,31.02,0.18008780426541618,0.0,0.3,0.16,0.0,0.0,1.06,0.0,3.6,1,0,0,1 +399,INFJ,26.32,0.19111645669967223,0.02,0.32,0.58,0.0,0.0,1.54,0.0,2.42,1,0,0,0 +400,INTJ,31.76,0.18304503881413686,0.0,0.08,0.02,0.0,0.0,0.92,0.0,3.82,1,0,1,0 +401,ISFP,25.94,0.18261845016244924,0.02,0.24,0.18,0.0,0.0,0.6,0.0,2.16,1,1,0,1 +402,INFJ,30.94,0.18373341493460127,0.06,0.3,0.0,0.0,0.04,0.84,0.0,2.98,1,0,0,0 +403,ISFP,28.02,0.18257708569779496,0.04,0.12,0.08,0.0,0.0,0.76,0.0,3.2,1,1,0,1 +404,INFP,23.6,0.1847064208492911,0.02,0.1,0.06,0.0,0.04,0.56,0.0,3.38,1,0,0,1 +405,ENFP,17.5,0.1921927807404979,0.06,0.26,0.08,0.0,0.0,0.74,0.0,2.28,0,0,0,1 +406,INTP,28.38,0.18668637638528515,0.0,0.3,0.0,0.0,0.0,0.86,0.02,2.54,1,0,1,1 +407,INFP,7.66,0.18616313942960616,0.02,0.0,0.04,0.0,0.02,0.1,0.0,0.92,1,0,0,1 +408,INFJ,28.98,0.19029713902863923,0.2,0.1,0.12,0.0,0.02,0.78,0.0,4.18,1,0,0,0 +409,ENFP,26.28,0.18341377214093818,0.02,0.76,0.2,0.0,0.0,0.64,0.02,2.06,0,0,0,1 +410,INFP,28.16,0.1900911609390141,0.08,0.18,0.24,0.0,0.0,0.88,0.0,3.44,1,0,0,1 +411,INFJ,26.7,0.18982461895177255,0.08,0.18,0.4,0.02,0.08,1.1,0.0,3.16,1,0,0,0 +412,INTP,26.5,0.1855420627171549,0.0,0.18,0.18,0.0,0.0,0.96,0.0,3.1,1,0,1,1 +413,INFJ,24.6,0.1868948441125384,0.02,0.24,0.54,0.0,0.02,0.56,0.0,3.14,1,0,0,0 +414,INFJ,27.56,0.18528309652877525,0.0,0.18,0.14,0.0,0.0,0.66,0.0,2.02,1,0,0,0 +415,ISFJ,29.64,0.18454344758725655,0.0,0.32,0.44,0.0,0.0,0.6,0.0,3.14,1,1,0,0 +416,INFJ,24.18,0.17804616359881084,0.0,0.28,0.1,0.0,0.02,0.54,0.0,2.52,1,0,0,0 +417,INFP,31.58,0.1823816989704817,0.0,0.22,0.44,0.0,0.0,0.9,0.0,3.08,1,0,0,1 +418,INFJ,22.74,0.1870706083865789,0.0,0.06,0.06,0.0,0.0,0.7,0.0,3.34,1,0,0,0 +419,INFP,19.84,0.18591129334741638,0.08,0.08,0.04,0.06,0.02,0.44,0.0,1.86,1,0,0,1 +420,INTP,26.54,0.18300433485566464,0.0,0.04,0.02,0.0,0.0,0.62,0.0,3.92,1,0,1,1 +421,ENTP,28.76,0.18269170916143096,0.0,0.2,0.02,0.0,0.0,0.7,0.0,2.22,0,0,1,1 +422,INTP,18.72,0.18259815216122385,0.26,0.02,0.04,0.0,0.02,0.44,0.0,1.98,1,0,1,1 +423,INFJ,19.3,0.19122492967836288,0.08,0.16,0.0,0.0,0.0,0.4,0.0,1.0,1,0,0,0 +424,INFJ,31.24,0.1840878816844425,0.06,0.38,0.28,0.0,0.08,1.08,0.0,3.96,1,0,0,0 +425,INTJ,23.0,0.17952320928852947,0.02,0.24,0.04,0.0,0.0,0.44,0.0,2.12,1,0,1,0 +426,INFP,18.32,0.19331816586991799,0.26,0.32,0.06,0.08,0.0,0.52,0.0,2.4,1,0,0,1 +427,INFP,23.7,0.18968609189309088,0.0,0.26,0.26,0.0,0.0,0.74,0.0,3.0,1,0,0,1 +428,ENFP,21.22,0.17595290466460564,0.06,0.08,0.08,0.0,0.02,0.52,0.0,1.84,0,0,0,1 +429,INFP,8.7,0.1841424876309396,0.44,0.46,0.02,0.02,0.0,0.06,0.0,1.34,1,0,0,1 +430,ESFP,4.46,0.2035336651892515,0.12,0.16,0.02,0.0,0.0,0.0,0.0,0.48,0,1,0,1 +431,ISTP,16.26,0.18509713343159573,0.0,0.12,0.02,0.0,0.0,0.26,0.0,1.62,1,1,1,1 +432,INFP,24.6,0.19295714736233677,0.0,0.3,0.38,0.0,0.0,0.48,0.0,2.68,1,0,0,1 +433,INTP,26.44,0.1799673378049664,0.0,0.08,0.0,0.0,0.0,0.52,0.02,3.0,1,0,1,1 +434,INFJ,25.38,0.17742292319113478,0.02,0.1,0.04,0.0,0.0,0.6,0.0,2.8,1,0,0,0 +435,ENFJ,31.5,0.18759589565139545,0.0,0.2,0.42,0.0,0.0,1.14,0.0,3.92,0,0,0,0 +436,INFP,21.16,0.18826250213687726,0.04,0.24,0.44,0.0,0.0,0.46,0.0,2.8,1,0,0,1 +437,ENTP,29.1,0.18368301651020266,0.02,0.04,0.32,0.0,0.0,0.72,0.0,2.62,0,0,1,1 +438,INTJ,18.7,0.18289227577528008,0.24,0.16,0.16,0.12,0.06,0.5,0.0,2.52,1,0,1,0 +439,INFJ,25.34,0.1893126023599576,0.0,0.6,0.0,0.0,0.0,0.54,0.0,2.58,1,0,0,0 +440,INFJ,25.52,0.18296454125125713,0.02,0.14,0.04,0.0,0.0,0.58,0.0,2.32,1,0,0,0 +441,INTP,27.32,0.19009088660329898,0.02,0.02,0.02,0.0,0.06,0.48,0.0,4.14,1,0,1,1 +442,ISTP,25.02,0.18383052552498153,0.04,0.16,0.12,0.02,0.02,0.54,0.0,2.78,1,1,1,1 +443,ENFP,18.0,0.18833611098599162,0.14,0.24,0.18,0.0,0.0,0.48,0.02,2.2,0,0,0,1 +444,ENFP,24.64,0.1858290183758487,0.1,0.02,0.14,0.0,0.02,0.78,0.0,3.74,0,0,0,1 +445,ENFP,31.24,0.18944568597706626,0.0,0.12,0.58,0.0,0.02,0.74,0.0,4.7,0,0,0,1 +446,ENFJ,26.52,0.18798897094194614,0.0,0.12,0.24,0.0,0.0,0.62,0.0,3.22,0,0,0,0 +447,ESTP,22.12,0.18888248213353334,0.04,0.22,0.22,0.0,0.0,0.34,0.02,2.06,0,1,1,1 +448,INFJ,33.58,0.18810111860237677,0.0,0.32,0.02,0.0,0.02,0.8,0.0,3.66,1,0,0,0 +449,INTJ,29.28,0.185109265185475,0.04,0.36,0.06,0.02,0.02,0.9,0.0,3.56,1,0,1,0 +450,ENTP,26.9,0.17841476099646308,0.08,0.16,0.08,0.04,0.0,0.62,0.0,2.26,0,0,1,1 +451,INFP,25.44,0.17895663449122,0.0,0.04,1.0,0.0,0.02,0.52,0.0,1.62,1,0,0,1 +452,INFP,27.24,0.18560940147389035,0.14,0.32,0.04,0.0,0.12,0.74,0.0,2.88,1,0,0,1 +453,INTP,22.46,0.18040293622653925,0.08,0.12,0.06,0.0,0.0,0.54,0.0,2.18,1,0,1,1 +454,INFP,15.9,0.18747421883084028,0.06,0.28,0.04,0.0,0.02,0.22,0.0,1.8,1,0,0,1 +455,INTJ,18.76,0.18668762610449136,0.18,0.64,0.5,0.08,0.0,0.48,0.0,1.82,1,0,1,0 +456,ENTP,25.22,0.1789727216091136,0.02,0.22,0.08,0.0,0.0,0.6,0.0,2.7,0,0,1,1 +457,INTP,22.72,0.19088370637556173,0.0,0.3,0.12,0.0,0.0,0.44,0.0,2.66,1,0,1,1 +458,ISTJ,25.7,0.1906935850850786,0.0,0.16,0.18,0.0,0.0,0.78,0.0,2.96,1,1,1,0 +459,INTJ,21.36,0.19090265240526003,0.16,0.46,0.16,0.02,0.02,0.36,0.02,1.52,1,0,1,0 +460,INTJ,28.22,0.18646544274877533,0.02,0.08,0.08,0.0,0.02,0.46,0.0,2.16,1,0,1,0 +461,INTJ,19.42,0.18294711679478057,0.0,0.2,0.12,0.0,0.0,0.28,0.0,1.5,1,0,1,0 +462,ENFP,32.16,0.183396279885113,0.04,0.16,0.26,0.0,0.02,0.92,0.0,3.38,0,0,0,1 +463,ISTP,19.96,0.1904601806007382,0.1,0.14,0.24,0.04,0.0,0.86,0.0,2.24,1,1,1,1 +464,INTJ,29.52,0.17144784455053425,0.02,0.1,0.02,0.0,0.0,0.72,0.0,2.64,1,0,1,0 +465,INFJ,19.12,0.1854740635988774,0.1,0.24,0.04,0.02,0.0,0.32,0.0,1.52,1,0,0,0 +466,INFJ,29.42,0.182294751570651,0.0,0.14,0.32,0.0,0.0,0.8,0.0,3.14,1,0,0,0 +467,INFP,22.34,0.1848215985677372,0.08,0.2,0.36,0.08,0.0,0.74,0.0,3.1,1,0,0,1 +468,INFJ,22.44,0.1880363137945294,0.0,0.04,0.04,0.0,0.02,0.5,0.0,2.94,1,0,0,0 +469,INFP,9.12,0.18574911193978152,0.0,0.0,0.0,0.0,0.0,0.24,0.0,0.52,1,0,0,1 +470,ENFP,24.9,0.192536239063261,0.0,0.62,0.58,0.0,0.0,0.62,0.0,2.98,0,0,0,1 +471,ENTP,20.68,0.17600103167010503,0.0,0.04,0.0,0.0,0.02,0.56,0.0,2.06,0,0,1,1 +472,INFJ,27.78,0.19116873779166507,0.0,0.16,0.1,0.0,0.0,0.78,0.0,4.22,1,0,0,0 +473,INFP,32.66,0.18605852020085342,0.06,0.06,0.08,0.0,0.06,0.7,0.0,4.32,1,0,0,1 +474,ISTP,25.84,0.19001163970615093,0.0,0.66,0.18,0.0,0.02,0.68,0.0,2.68,1,1,1,1 +475,INFJ,31.46,0.18302568116966741,0.0,0.12,0.18,0.0,0.0,0.84,0.0,3.38,1,0,0,0 +476,ESTP,28.9,0.1904722760560516,0.0,0.28,0.08,0.0,0.0,0.72,0.0,3.92,0,1,1,1 +477,ENFP,28.4,0.18616177640619794,0.02,0.1,0.24,0.0,0.0,0.74,0.0,3.28,0,0,0,1 +478,ISTP,26.4,0.1793640480548155,0.0,0.12,0.0,0.0,0.04,0.46,0.0,2.74,1,1,1,1 +479,INFP,33.4,0.18335538794278802,0.0,0.12,0.14,0.0,0.0,0.98,0.0,4.1,1,0,0,1 +480,INFJ,29.0,0.1853424058433186,0.0,0.28,0.42,0.0,0.0,0.64,0.0,3.12,1,0,0,0 +481,INFP,32.12,0.1903535616099539,0.0,0.58,0.22,0.0,0.0,0.9,0.0,3.28,1,0,0,1 +482,INTJ,17.74,0.1899501136200613,0.26,0.22,0.1,0.22,0.0,0.42,0.0,1.86,1,0,1,0 +483,ENFP,25.12,0.18176137244189258,0.2,0.3,0.0,0.16,0.08,0.78,0.0,1.9,0,0,0,1 +484,INFJ,19.86,0.18566798504381174,0.0,0.24,0.26,0.0,0.0,0.68,0.0,2.32,1,0,0,0 +485,INTP,30.66,0.18039457682426033,0.1,0.16,0.18,0.02,0.04,0.76,0.0,2.3,1,0,1,1 +486,INTJ,33.14,0.19058268572124984,0.0,0.16,0.18,0.0,0.02,1.04,0.0,4.26,1,0,1,0 +487,INTP,31.38,0.18732447937495236,0.0,0.1,0.06,0.0,0.0,0.78,0.0,4.58,1,0,1,1 +488,ENFP,24.82,0.18744801414240084,0.0,0.12,0.38,0.0,0.02,0.54,0.0,2.58,0,0,0,1 +489,INFP,21.58,0.18142331309884213,0.52,0.52,0.0,0.14,0.02,0.5,0.0,2.66,1,0,0,1 +490,ENTJ,25.94,0.1855006822386919,0.0,0.34,0.08,0.0,0.0,0.66,0.0,2.62,0,0,1,0 +491,INTP,18.16,0.1790729498641118,0.16,0.3,0.1,0.12,0.02,0.38,0.0,1.54,1,0,1,1 +492,INFP,31.98,0.18474788853526244,0.02,0.2,0.38,0.0,0.0,1.2,0.0,3.9,1,0,0,1 +493,INTP,29.54,0.18398160790345394,0.0,0.36,0.08,0.0,0.0,0.8,0.0,3.42,1,0,1,1 +494,INTP,24.16,0.1808906366245085,0.04,0.28,0.0,0.02,0.0,0.56,0.0,2.78,1,0,1,1 +495,INFP,28.64,0.18376900173584457,0.04,0.04,0.16,0.0,0.0,0.54,0.0,3.7,1,0,0,1 +496,INFJ,19.78,0.197044927467621,0.12,0.26,0.0,0.0,0.0,0.84,0.0,2.3,1,0,0,0 +497,ENTP,13.78,0.18644999927713288,0.0,0.14,0.02,0.0,0.0,0.18,0.0,1.4,0,0,1,1 +498,INFJ,30.32,0.18636280906179778,0.02,0.28,0.32,0.0,0.06,1.08,0.0,3.32,1,0,0,0 +499,INTJ,23.1,0.17960071908779598,0.08,0.1,0.0,0.02,0.0,0.64,0.0,2.16,1,0,1,0 +500,INTJ,16.48,0.17540965036340636,0.0,0.02,0.0,0.0,0.0,0.2,0.0,1.4,1,0,1,0 +501,INFJ,29.62,0.18989649846164441,0.02,0.42,0.3,0.0,0.0,0.84,0.0,2.92,1,0,0,0 +502,ENTP,25.34,0.17753085877337696,0.1,0.18,0.02,0.06,0.0,0.5,0.0,2.8,0,0,1,1 +503,INTJ,31.66,0.18670994012092476,0.06,0.44,0.0,0.0,0.0,0.98,0.0,3.66,1,0,1,0 +504,INFP,29.9,0.18591294818324744,0.02,0.22,0.02,0.02,0.0,0.66,0.0,4.04,1,0,0,1 +505,INTP,20.82,0.18215164800299657,0.0,0.22,0.02,0.0,0.0,0.46,0.0,2.06,1,0,1,1 +506,INFP,27.8,0.18481280012065485,0.04,0.18,0.04,0.02,0.0,0.64,0.0,3.7,1,0,0,1 +507,ISFP,31.56,0.18929041712165648,0.02,0.16,0.5,0.0,0.14,0.84,0.0,3.32,1,1,0,1 +508,ISFP,17.2,0.19919040580489114,0.02,0.06,0.42,0.02,0.0,0.36,0.0,1.98,1,1,0,1 +509,ISTP,18.48,0.18707509738570227,0.08,0.12,0.28,0.04,0.0,0.22,0.0,1.86,1,1,1,1 +510,INTP,29.08,0.19264571018597587,0.0,0.16,0.26,0.0,0.0,0.82,0.0,3.78,1,0,1,1 +511,INTJ,21.9,0.16848167036577302,0.04,0.24,0.0,0.0,0.0,0.52,0.0,1.38,1,0,1,0 +512,INFJ,31.34,0.18907793452590732,0.0,0.1,0.38,0.0,0.02,0.9,0.0,4.42,1,0,0,0 +513,ENTJ,18.36,0.18716950010394431,0.0,0.14,0.4,0.0,0.04,0.3,0.0,2.22,0,0,1,0 +514,INTJ,32.0,0.17938120862652623,0.06,0.3,0.08,0.0,0.0,1.02,0.0,1.18,1,0,1,0 +515,INFJ,32.28,0.1746256356065621,0.02,0.22,0.04,0.0,0.0,0.94,0.0,2.86,1,0,0,0 +516,ENFJ,27.88,0.1849622633549298,0.08,0.3,0.04,0.0,0.0,0.78,0.0,2.66,0,0,0,0 +517,INTJ,27.26,0.18726952880469275,0.04,0.4,0.0,0.0,0.02,1.16,0.0,2.6,1,0,1,0 +518,INFJ,15.22,0.18552404804343525,0.06,0.16,0.04,0.04,0.02,0.14,0.0,1.08,1,0,0,0 +519,ENTJ,28.32,0.17600925527046302,0.06,0.18,0.06,0.0,0.0,0.74,0.0,2.36,0,0,1,0 +520,INTP,23.56,0.18003994320658684,0.04,0.08,0.0,0.02,0.0,0.38,0.0,2.32,1,0,1,1 +521,INTJ,11.26,0.18004113755869483,1.7,1.34,0.1,0.0,0.0,0.46,0.0,1.2,1,0,1,0 +522,INTJ,26.46,0.17728152134196315,0.0,0.02,0.0,0.0,0.0,0.5,0.0,2.58,1,0,1,0 +523,ISTP,21.94,0.18751916149295095,0.02,0.06,0.08,0.0,0.0,0.52,0.0,2.1,1,1,1,1 +524,INFJ,32.76,0.18135459935545417,0.0,0.14,0.12,0.0,0.0,0.98,0.0,3.8,1,0,0,0 +525,INFP,19.38,0.1893310827194435,0.02,0.22,0.24,0.0,0.06,0.56,0.0,2.62,1,0,0,1 +526,INFP,28.36,0.1856071235657688,0.0,0.02,0.3,0.0,0.06,0.68,0.0,3.92,1,0,0,1 +527,INTP,26.44,0.17091925835786484,0.22,0.12,1.0,0.22,0.0,0.52,0.0,2.8,1,0,1,1 +528,INTP,19.4,0.18655101208616515,0.06,0.22,0.02,0.02,0.0,0.52,0.0,2.12,1,0,1,1 +529,ESFP,25.22,0.19992687130198028,0.0,0.28,1.94,0.0,0.0,0.74,0.0,2.8,0,1,0,1 +530,INFJ,20.92,0.18490271271451753,0.1,0.08,0.08,0.06,0.0,0.38,0.0,1.44,1,0,0,0 +531,INTP,23.3,0.18164270723173537,0.0,0.2,0.08,0.0,0.0,0.54,0.0,2.26,1,0,1,1 +532,INFJ,20.84,0.18249908995186992,0.12,0.3,0.1,0.0,0.08,0.58,0.0,2.02,1,0,0,0 +533,ISFJ,32.58,0.18610026735978494,0.02,0.22,0.12,0.0,0.02,0.82,0.0,4.34,1,1,0,0 +534,ENTJ,20.62,0.17605870102264887,0.04,0.24,0.04,0.0,0.0,0.34,0.02,1.76,0,0,1,0 +535,INFP,32.08,0.176833213732675,0.0,0.06,0.0,0.0,0.0,0.8,0.0,2.54,1,0,0,1 +536,ENTP,20.8,0.18786132893628635,0.02,0.24,0.36,0.0,0.0,0.46,0.0,2.54,0,0,1,1 +537,INFP,10.82,0.18831685756669936,0.02,0.06,0.06,0.02,0.0,0.26,0.0,1.38,1,0,0,1 +538,ENFJ,21.26,0.1820038256474693,0.02,0.02,0.16,0.0,0.08,0.36,0.0,2.5,0,0,0,0 +539,INFP,20.26,0.18719115663615662,0.46,0.5,0.04,0.0,0.02,0.58,0.0,2.76,1,0,0,1 +540,INFP,23.66,0.19498321316013426,0.0,1.08,0.36,0.0,0.0,1.02,0.0,2.2,1,0,0,1 +541,ENFP,31.0,0.1832629123474268,0.16,0.24,0.22,0.0,0.0,0.72,0.0,3.2,0,0,0,1 +542,INFP,24.72,0.18124632325053253,0.0,0.04,0.48,0.0,0.0,0.68,0.0,2.64,1,0,0,1 +543,ISFJ,25.22,0.18215745790878438,0.08,0.26,0.0,0.04,0.0,0.98,0.0,1.98,1,1,0,0 +544,INTJ,24.28,0.18296480170299412,0.14,0.32,0.02,0.0,0.04,0.64,0.0,2.78,1,0,1,0 +545,INFJ,18.24,0.1823232254462766,0.04,0.24,0.38,0.0,0.0,0.24,0.0,1.24,1,0,0,0 +546,ESTJ,23.02,0.18710338544773747,0.0,0.28,0.42,0.0,0.02,0.62,0.0,2.68,0,1,1,0 +547,INFJ,24.32,0.17975507377466218,0.02,0.04,0.16,0.0,0.02,0.58,0.0,3.22,1,0,0,0 +548,INTP,28.48,0.17923095561601152,0.06,0.1,0.0,0.0,0.0,0.72,0.0,2.82,1,0,1,1 +549,INFP,29.0,0.18087610765325707,0.04,0.26,0.04,0.0,0.02,0.64,0.0,3.3,1,0,0,1 +550,ISFJ,28.02,0.18885354928655482,0.04,0.3,0.86,0.02,0.0,0.86,0.0,2.94,1,1,0,0 +551,INFJ,24.38,0.18585976304609111,0.02,0.12,0.02,0.0,0.02,0.86,0.0,3.28,1,0,0,0 +552,ENTP,26.96,0.18006046270140846,0.08,0.18,0.02,0.0,0.0,0.92,0.0,1.78,0,0,1,1 +553,ENTJ,17.52,0.18963107467873574,0.02,0.22,0.66,0.0,0.0,0.58,0.06,1.42,0,0,1,0 +554,ENTP,21.56,0.18327058476377986,0.0,0.3,0.32,0.0,0.0,0.4,0.0,1.58,0,0,1,1 +555,INTJ,28.88,0.1826646131990751,0.0,0.12,0.0,0.0,0.0,0.7,0.02,3.8,1,0,1,0 +556,INFP,29.46,0.17774356253560356,0.0,0.54,0.46,0.0,0.04,0.82,0.0,1.84,1,0,0,1 +557,INFP,34.24,0.18703219755923267,0.0,0.08,0.4,0.0,0.0,0.98,0.0,4.62,1,0,0,1 +558,INFJ,25.32,0.18786531826293867,0.1,0.06,0.08,0.0,0.0,0.54,0.0,3.52,1,0,0,0 +559,INTJ,30.98,0.18085438752454844,0.02,0.18,0.08,0.0,0.02,0.78,0.02,3.14,1,0,1,0 +560,INTJ,24.42,0.19571022719459638,0.02,0.48,0.04,0.0,0.0,0.52,0.04,3.14,1,0,1,0 +561,INTP,26.78,0.18393953987325673,0.0,0.42,0.08,0.0,0.0,0.8,0.0,2.56,1,0,1,1 +562,INTJ,22.96,0.19487865363518836,0.0,0.24,0.32,0.0,0.0,0.9,0.04,3.06,1,0,1,0 +563,INFJ,21.24,0.18863643740340147,0.04,0.04,0.22,0.04,0.0,0.36,0.0,1.02,1,0,0,0 +564,INFP,24.88,0.185217159161597,0.04,0.22,0.3,0.0,0.0,0.86,0.0,3.0,1,0,0,1 +565,ENTP,28.1,0.190822305549765,0.0,0.32,0.08,0.0,0.0,0.64,0.0,2.88,0,0,1,1 +566,ENTJ,21.78,0.17895518093283033,0.18,0.26,0.16,0.02,0.0,0.48,0.02,2.22,0,0,1,0 +567,ISTP,14.66,0.18953526108861773,0.52,0.54,0.06,0.0,0.04,0.48,0.0,1.4,1,1,1,1 +568,INFP,31.38,0.18348911782814822,0.06,0.36,0.2,0.0,0.0,0.94,0.0,3.3,1,0,0,1 +569,ENFP,27.08,0.18614794766189963,0.0,0.2,0.14,0.0,0.0,0.92,0.0,3.32,0,0,0,1 +570,ISTP,33.54,0.19137864725027778,0.0,0.08,0.08,0.0,0.0,1.0,0.0,4.0,1,1,1,1 +571,INTP,27.48,0.18786008829099235,0.0,0.16,0.0,0.0,0.0,0.6,0.0,3.88,1,0,1,1 +572,INFP,20.68,0.18589301100365863,0.0,0.02,0.02,0.0,0.0,0.42,0.0,2.44,1,0,0,1 +573,INFJ,21.94,0.18152279911565325,0.0,0.16,0.18,0.0,0.0,0.32,0.0,3.22,1,0,0,0 +574,INFJ,31.36,0.18702654401593394,0.02,0.36,0.42,0.0,0.0,0.86,0.0,2.98,1,0,0,0 +575,ENTP,29.74,0.18020750508505914,0.0,0.12,0.12,0.0,0.04,0.8,0.0,3.26,0,0,1,1 +576,INFJ,28.78,0.182021420133739,0.02,0.38,0.18,0.0,0.0,0.9,0.0,3.0,1,0,0,0 +577,INTP,26.18,0.1780587700175384,0.0,0.18,0.02,0.0,0.02,0.6,0.0,2.22,1,0,1,1 +578,INTJ,22.64,0.18234785193446215,0.02,0.3,0.06,0.0,0.0,0.7,0.0,2.36,1,0,1,0 +579,INFP,25.14,0.17698708801743893,0.12,0.4,0.02,0.04,0.0,0.72,0.0,2.8,1,0,0,1 +580,INFP,20.08,0.18395022421372068,0.06,0.08,0.04,0.04,0.0,0.48,0.0,3.28,1,0,0,1 +581,INFJ,33.0,0.18273413603470018,0.04,0.38,0.48,0.0,0.0,1.08,0.0,3.72,1,0,0,0 +582,ENFP,25.54,0.187970302011136,0.08,0.26,0.34,0.0,0.02,0.6,0.0,2.96,0,0,0,1 +583,ISFP,11.26,0.1930582944511164,0.06,0.04,0.1,0.0,0.0,0.1,0.0,1.64,1,1,0,1 +584,INFJ,21.58,0.18813228768654383,0.0,0.02,0.22,0.0,0.04,0.46,0.0,3.16,1,0,0,0 +585,INTJ,30.72,0.1787901675407092,0.0,0.18,0.06,0.0,0.02,0.96,0.0,3.18,1,0,1,0 +586,INTP,31.54,0.1897132172585017,0.0,0.12,0.34,0.0,0.02,1.44,0.0,4.36,1,0,1,1 +587,ENTP,14.84,0.18410998694755337,0.12,0.24,0.26,0.02,0.0,0.36,0.0,1.8,0,0,1,1 +588,ENFJ,21.74,0.17922740283964445,0.08,0.14,0.06,0.0,0.04,0.54,0.0,3.0,0,0,0,0 +589,ENTP,28.54,0.18603999744369598,0.0,0.34,0.1,0.0,0.04,0.8,0.0,3.48,0,0,1,1 +590,INTJ,31.4,0.1808755809711968,0.04,0.04,0.0,0.0,0.0,0.88,0.0,3.06,1,0,1,0 +591,INFP,28.1,0.18397823024212304,0.08,0.14,0.2,0.0,0.02,0.62,0.0,3.18,1,0,0,1 +592,ENFP,28.9,0.17779412378506843,0.08,0.06,0.02,0.02,0.0,0.84,0.0,3.1,0,0,0,1 +593,ESTP,16.38,0.19330604673869847,0.1,0.3,0.3,0.0,0.02,0.36,0.0,1.24,0,1,1,1 +594,INFP,29.56,0.18257537351176756,0.06,0.1,0.08,0.0,0.0,0.78,0.0,2.38,1,0,0,1 +595,ESTJ,16.34,0.18025524580433172,0.02,0.06,0.0,0.0,0.0,0.2,0.02,1.18,0,1,1,0 +596,INFP,18.92,0.19246455198226917,0.02,0.08,0.02,0.0,0.0,0.46,0.0,2.66,1,0,0,1 +597,ENFP,19.46,0.19381224536515732,0.18,0.2,0.2,0.06,0.0,0.34,0.0,2.58,0,0,0,1 +598,INFJ,29.18,0.18184285106993833,0.0,0.08,0.0,0.0,0.0,1.0,0.0,3.28,1,0,0,0 +599,INFP,20.44,0.1837913586956189,0.06,0.42,0.52,0.0,0.06,0.74,0.0,1.88,1,0,0,1 +600,INFP,31.14,0.190146301028468,0.0,0.2,0.14,0.0,0.0,0.6,0.0,4.5,1,0,0,1 +601,INTJ,11.46,0.1802269147099694,0.02,0.04,0.0,0.02,0.0,0.0,0.0,0.82,1,0,1,0 +602,INFJ,32.62,0.18710000102073102,0.02,0.14,0.02,0.02,0.0,1.0,0.0,3.76,1,0,0,0 +603,INFP,12.7,0.1892371641786778,0.02,0.12,0.16,0.0,0.0,0.34,0.0,1.52,1,0,0,1 +604,INTP,32.0,0.18339897243057368,0.06,0.32,0.06,0.0,0.0,0.74,0.02,3.7,1,0,1,1 +605,INFP,31.68,0.17808185668776028,0.02,0.04,0.02,0.0,0.0,0.94,0.0,2.88,1,0,0,1 +606,INTJ,27.4,0.17974266701240985,0.04,0.04,0.06,0.02,0.02,0.52,0.0,3.86,1,0,1,0 +607,ISTP,29.08,0.1831645261763223,0.0,0.04,0.02,0.0,0.02,0.56,0.0,3.48,1,1,1,1 +608,INFP,13.14,0.18930920884379188,0.24,0.38,0.02,0.0,0.0,0.26,0.0,2.08,1,0,0,1 +609,INFJ,31.36,0.18128503835693532,0.06,0.2,0.12,0.0,0.0,0.88,0.0,3.58,1,0,0,0 +610,INFP,32.16,0.18566390798663351,0.02,0.46,0.02,0.02,0.02,1.18,0.0,3.58,1,0,0,1 +611,INTP,25.48,0.18600241409108612,0.0,0.2,0.04,0.0,0.0,0.5,0.0,2.96,1,0,1,1 +612,INTP,22.44,0.18755542008151388,0.02,0.18,0.46,0.0,0.0,0.72,0.0,2.48,1,0,1,1 +613,ENFJ,27.92,0.18527637500768207,0.0,0.3,0.3,0.0,0.0,0.78,0.0,3.24,0,0,0,0 +614,INFP,24.58,0.18329119405868508,0.02,0.18,0.1,0.0,0.0,0.54,0.0,2.86,1,0,0,1 +615,INFJ,21.1,0.18608304096296813,0.08,0.12,0.08,0.02,0.0,0.54,0.0,1.9,1,0,0,0 +616,ENTP,35.7,0.1867629930507273,0.0,0.06,0.22,0.0,0.0,1.0,0.0,4.62,0,0,1,1 +617,ESFP,19.56,0.185871682867569,0.0,0.36,0.22,0.0,0.0,0.42,0.0,1.86,0,1,0,1 +618,INTP,23.28,0.18516268633219185,0.08,0.34,0.1,0.0,0.02,0.56,0.0,2.88,1,0,1,1 +619,INFP,17.32,0.18464123026411264,0.16,0.22,0.0,0.0,0.0,0.32,0.0,2.0,1,0,0,1 +620,INFJ,25.78,0.17920277850342867,0.2,0.16,0.0,0.04,0.02,0.66,0.0,3.12,1,0,0,0 +621,ISTP,26.68,0.17817117350571232,0.02,0.32,0.18,0.0,0.0,0.78,0.0,2.62,1,1,1,1 +622,INTJ,29.24,0.18630977136094673,0.0,0.22,0.0,0.0,0.0,0.7,0.0,2.78,1,0,1,0 +623,INFJ,24.34,0.18111146267035236,0.04,0.16,0.1,0.0,0.0,0.6,0.0,2.76,1,0,0,0 +624,ENFP,27.66,0.18398123183391005,0.04,0.2,0.28,0.02,0.0,1.08,0.0,3.08,0,0,0,1 +625,INFJ,22.06,0.19026548053000505,0.0,0.1,0.12,0.0,0.0,0.56,0.0,1.98,1,0,0,0 +626,INFP,14.92,0.17518354569435104,0.2,0.34,0.14,0.06,0.0,0.26,0.02,1.04,1,0,0,1 +627,ESFJ,23.9,0.1923164413651085,0.0,0.2,0.3,0.0,0.02,0.46,0.0,3.52,0,1,0,0 +628,INFP,21.66,0.19243460837571041,0.04,0.18,0.18,0.0,0.0,0.8,0.0,2.64,1,0,0,1 +629,INFJ,26.58,0.18740166515221526,0.08,0.24,0.2,0.0,0.0,0.68,0.0,2.0,1,0,0,0 +630,ISTJ,19.6,0.1729254908368152,0.24,0.12,0.2,0.12,0.0,0.5,0.0,1.2,1,1,1,0 +631,INTP,31.56,0.184327334375794,0.0,0.16,0.12,0.0,0.0,0.9,0.0,3.66,1,0,1,1 +632,INFP,18.38,0.1831183516709277,0.22,0.14,0.04,0.08,0.02,0.32,0.0,2.12,1,0,0,1 +633,ISFJ,27.3,0.18306576046977346,0.0,0.06,1.14,0.0,0.02,1.0,0.0,3.52,1,1,0,0 +634,ESFP,16.12,0.19148396442960597,0.0,0.24,0.14,0.0,0.0,0.52,0.0,2.44,0,1,0,1 +635,ISTP,10.9,0.18593824122656666,0.08,0.16,0.26,0.02,0.02,0.1,0.0,1.1,1,1,1,1 +636,ENFP,29.74,0.1787106853267005,0.02,0.48,0.0,0.0,0.02,0.68,0.0,2.62,0,0,0,1 +637,INTJ,31.18,0.18507437019460996,0.02,0.16,0.0,0.0,0.04,0.88,0.0,3.14,1,0,1,0 +638,ISTP,28.76,0.1837576848481465,0.0,0.12,0.08,0.0,0.0,0.6,0.0,3.68,1,1,1,1 +639,ENTP,24.1,0.18879010289928494,0.06,0.3,0.34,0.04,0.0,0.56,0.0,3.38,0,0,1,1 +640,ENTP,27.52,0.18099606660668296,0.0,0.3,0.22,0.0,0.0,0.72,0.0,2.48,0,0,1,1 +641,INFP,19.36,0.19351403081240826,0.12,0.24,0.04,0.0,0.0,0.48,0.0,2.78,1,0,0,1 +642,INTJ,26.8,0.18289619983909505,0.02,0.06,0.28,0.04,0.0,0.74,0.0,3.04,1,0,1,0 +643,ENFP,29.66,0.18970060761204133,0.0,0.48,0.16,0.0,0.02,1.0,0.0,3.54,0,0,0,1 +644,INFP,34.18,0.18715793138279915,0.0,0.18,0.08,0.0,0.04,1.0,0.0,4.18,1,0,0,1 +645,INTJ,10.8,0.19069811591154853,0.06,0.1,0.22,0.04,0.0,0.1,0.0,1.68,1,0,1,0 +646,INTJ,22.52,0.183875407788696,0.06,0.12,0.34,0.04,0.0,0.48,0.0,2.26,1,0,1,0 +647,ENTP,24.9,0.17794863777411796,0.0,0.4,0.02,0.0,0.0,0.52,0.0,1.14,0,0,1,1 +648,INFP,8.06,0.18348101548339227,0.0,0.04,0.04,0.0,0.0,0.14,0.0,1.2,1,0,0,1 +649,INFP,14.64,0.1899693154106621,0.0,0.34,0.24,0.0,0.02,0.38,0.0,1.38,1,0,0,1 +650,ENFP,33.12,0.18616542306917713,0.0,0.34,0.72,0.0,0.0,1.16,0.0,3.58,0,0,0,1 +651,ENFP,26.02,0.19242108061833474,0.1,0.28,0.04,0.02,0.0,0.7,0.0,2.6,0,0,0,1 +652,INTP,24.58,0.17929113430387816,0.0,0.0,0.02,0.0,0.0,0.4,0.0,2.46,1,0,1,1 +653,INFJ,29.04,0.18476589708117427,0.02,0.4,0.3,0.0,0.06,0.74,0.0,3.42,1,0,0,0 +654,INTP,30.76,0.1871085377543689,0.0,0.16,0.02,0.0,0.0,0.76,0.0,3.32,1,0,1,1 +655,INFP,27.98,0.18497597414635794,0.0,0.16,0.06,0.0,0.02,0.8,0.0,3.68,1,0,0,1 +656,INFJ,29.22,0.1807610315337703,0.0,0.5,0.1,0.0,0.0,0.8,0.0,2.62,1,0,0,0 +657,INTJ,22.64,0.18507303994082838,0.0,0.04,0.02,0.0,0.0,0.34,0.0,3.32,1,0,1,0 +658,ENTP,29.04,0.18420207788758156,0.0,0.24,0.02,0.0,0.0,0.64,0.0,3.54,0,0,1,1 +659,INFJ,24.84,0.17902341265123445,0.04,0.1,0.2,0.02,0.02,0.46,0.02,2.92,1,0,0,0 +660,INTP,24.48,0.18516541696868036,0.0,0.14,0.18,0.0,0.0,0.64,0.0,3.3,1,0,1,1 +661,INTP,33.72,0.18507790432482554,0.02,0.08,0.02,0.0,0.0,0.78,0.0,4.66,1,0,1,1 +662,INFJ,27.46,0.1747498445283205,0.04,0.26,0.58,0.0,0.0,0.58,0.0,2.74,1,0,0,0 +663,INFP,28.5,0.19118008556104035,0.0,0.48,0.2,0.0,0.0,0.84,0.0,3.68,1,0,0,1 +664,INTP,29.5,0.18683398387360264,0.0,0.12,0.04,0.0,0.0,0.64,0.0,3.12,1,0,1,1 +665,ISTP,5.44,0.1833036965425295,0.1,0.24,0.08,0.06,0.0,0.04,0.0,0.34,1,1,1,1 +666,ENTP,20.06,0.1862874747550966,0.0,0.12,0.0,0.0,0.02,0.38,0.0,1.34,0,0,1,1 +667,INFP,19.3,0.18849462173593962,0.04,0.26,0.26,0.0,0.0,0.44,0.0,2.6,1,0,0,1 +668,INFP,26.48,0.19272014759690353,0.08,0.22,0.38,0.02,0.0,0.86,0.0,3.46,1,0,0,1 +669,INTP,28.26,0.17697912377318464,0.0,0.12,0.0,0.0,0.0,0.9,0.0,1.92,1,0,1,1 +670,ISTP,22.68,0.1935928743882845,0.04,0.34,0.68,0.0,0.0,0.62,0.0,2.78,1,1,1,1 +671,INTP,27.24,0.18517233342779332,0.16,0.4,0.12,0.0,0.06,0.72,0.0,3.92,1,0,1,1 +672,INFP,25.82,0.17769844409220728,0.02,0.38,0.08,0.02,0.0,0.54,0.0,2.28,1,0,0,1 +673,INFP,25.3,0.19233969113055874,0.0,0.28,0.72,0.0,0.0,0.82,0.02,2.96,1,0,0,1 +674,ENTP,25.42,0.18620270611160056,0.04,0.2,0.0,0.02,0.0,1.22,0.0,2.32,0,0,1,1 +675,INFP,26.26,0.1857344577500422,0.14,0.24,0.6,0.02,0.0,1.14,0.0,2.54,1,0,0,1 +676,INFJ,25.26,0.17578204406049516,0.0,0.16,0.04,0.0,0.0,0.64,0.0,2.32,1,0,0,0 +677,ENTP,11.52,0.1852954554546858,0.06,0.06,0.0,0.0,0.0,0.16,0.0,0.56,0,0,1,1 +678,ISTP,28.64,0.18119182175482154,0.04,0.22,0.08,0.0,0.0,0.74,0.0,2.66,1,1,1,1 +679,INFP,29.7,0.18242623899479515,0.04,0.14,0.04,0.0,0.02,0.84,0.0,2.38,1,0,0,1 +680,INFP,25.22,0.19534611627997056,0.12,0.12,0.08,0.0,0.0,0.76,0.0,3.16,1,0,0,1 +681,INTJ,25.46,0.18391785729742338,0.16,0.22,0.06,0.02,0.02,0.58,0.0,1.54,1,0,1,0 +682,ENTJ,23.94,0.18651576316801227,0.02,0.62,0.2,0.0,0.0,0.8,0.0,1.78,0,0,1,0 +683,ESFJ,19.62,0.18577241309259104,0.08,0.28,0.14,0.02,0.02,0.26,0.0,2.06,0,1,0,0 +684,ENFP,18.64,0.18846727348263587,0.1,0.4,0.3,0.0,0.0,0.4,0.0,2.3,0,0,0,1 +685,INFP,26.3,0.181676750725495,0.14,0.12,0.08,0.08,0.0,0.66,0.02,3.52,1,0,0,1 +686,INFJ,8.1,0.18891361239512605,0.26,0.38,0.04,0.1,0.0,0.14,0.0,1.0,1,0,0,0 +687,INFP,23.72,0.1777049413298125,0.04,0.1,0.06,0.02,0.02,0.4,0.0,2.48,1,0,0,1 +688,INFJ,21.22,0.18879239036307047,0.12,0.56,0.34,0.0,0.0,0.78,0.02,2.58,1,0,0,0 +689,INFJ,25.46,0.18307978232615293,0.2,0.24,0.1,0.0,0.0,1.04,0.0,3.2,1,0,0,0 +690,ESFP,21.8,0.18438827069471667,0.04,0.18,0.02,0.04,0.0,0.42,0.0,2.7,0,1,0,1 +691,INFP,24.04,0.18097479231821187,0.0,0.1,0.1,0.0,0.0,0.42,0.0,2.86,1,0,0,1 +692,INTJ,23.68,0.18314029933777046,0.14,0.1,0.0,0.04,0.0,0.54,0.0,2.64,1,0,1,0 +693,ENTP,22.54,0.18823832783967115,0.02,0.08,0.32,0.0,0.0,0.68,0.0,2.04,0,0,1,1 +694,ENTP,21.78,0.18655370213964806,0.02,0.12,0.24,0.0,0.0,0.48,0.0,1.8,0,0,1,1 +695,INFP,25.9,0.19387268612401445,0.0,0.08,0.62,0.0,0.02,0.84,0.0,2.54,1,0,0,1 +696,INTP,25.8,0.17474084744944235,0.14,0.2,0.14,0.04,0.0,0.66,0.0,2.38,1,0,1,1 +697,INFP,16.66,0.18103558333321715,0.24,0.24,0.14,0.1,0.06,0.58,0.0,1.76,1,0,0,1 +698,ISTJ,31.3,0.1883478442339088,0.02,0.14,0.1,0.0,0.0,0.82,0.02,3.68,1,1,1,0 +699,INFP,26.82,0.18528404614765145,0.0,0.12,0.12,0.0,0.0,0.36,0.0,3.78,1,0,0,1 +700,ENTP,21.82,0.18121319018486268,0.22,0.22,0.0,0.04,0.0,0.52,0.0,2.94,0,0,1,1 +701,INFJ,23.14,0.18982858164849437,0.02,0.16,0.16,0.0,0.0,0.66,0.0,2.46,1,0,0,0 +702,ENFP,23.36,0.19407264858453252,0.08,0.28,0.0,0.0,0.0,0.62,0.0,2.7,0,0,0,1 +703,ENFJ,10.78,0.19947302224379623,0.0,0.02,0.6,0.0,0.0,0.64,0.0,1.5,0,0,0,0 +704,INTJ,35.08,0.18046594206694933,0.0,0.08,0.02,0.0,0.0,1.08,0.0,3.66,1,0,1,0 +705,ENTP,24.54,0.18145062976023926,0.3,0.24,0.14,0.2,0.02,0.54,0.0,2.4,0,0,1,1 +706,INTP,32.42,0.17641848235727145,0.0,0.16,0.06,0.0,0.0,0.82,0.04,3.12,1,0,1,1 +707,INFP,23.34,0.18418100674708263,0.02,0.14,0.02,0.0,0.0,0.44,0.0,2.02,1,0,0,1 +708,INFP,19.46,0.1876117368150598,0.04,0.5,0.44,0.0,0.0,0.38,0.0,1.76,1,0,0,1 +709,ENFP,26.44,0.1924349226123305,0.02,2.42,0.58,0.0,0.08,0.54,0.0,3.84,0,0,0,1 +710,INFJ,29.86,0.1828776770434093,0.08,0.3,0.02,0.0,0.12,0.78,0.0,3.8,1,0,0,0 +711,INTJ,25.7,0.18159548544448936,0.12,0.16,0.02,0.04,0.0,0.66,0.0,2.62,1,0,1,0 +712,INTJ,19.42,0.18276733978990314,0.16,0.18,0.0,0.02,0.02,0.36,0.02,2.36,1,0,1,0 +713,INFP,25.68,0.18399473743775463,0.12,0.26,0.22,0.08,0.0,0.66,0.0,2.46,1,0,0,1 +714,INFJ,30.24,0.18191760661208037,0.02,0.32,0.12,0.0,0.0,1.08,0.0,2.96,1,0,0,0 +715,INFJ,12.76,0.1848093774300656,0.06,0.18,0.16,0.0,0.0,0.26,0.0,1.28,1,0,0,0 +716,INTJ,30.22,0.17815747612761174,0.04,0.1,0.0,0.02,0.0,0.88,0.0,3.08,1,0,1,0 +717,INTJ,23.46,0.18838229674279894,0.04,0.36,0.92,0.0,0.0,0.6,0.0,2.5,1,0,1,0 +718,INTP,22.42,0.18253348637449046,0.1,0.28,0.08,0.0,0.04,0.5,0.0,3.06,1,0,1,1 +719,INFJ,29.02,0.18487057214185562,0.0,0.34,0.32,0.0,0.0,0.72,0.0,3.06,1,0,0,0 +720,INFJ,34.8,0.18334547309517413,0.0,0.12,0.44,0.0,0.0,1.42,0.0,2.96,1,0,0,0 +721,INFP,19.82,0.1947527606609642,0.0,0.08,0.26,0.0,0.06,0.6,0.0,2.64,1,0,0,1 +722,INFJ,27.72,0.1883685649785073,0.24,0.4,0.14,0.0,0.0,0.84,0.0,2.92,1,0,0,0 +723,INTJ,27.66,0.18557667433045855,0.08,0.16,0.02,0.02,0.0,0.7,0.0,2.98,1,0,1,0 +724,INTP,29.28,0.1936848436526444,0.16,0.28,0.0,0.08,0.02,1.8,0.0,3.6,1,0,1,1 +725,INFJ,14.76,0.19098207430462266,0.14,0.32,0.14,0.02,0.0,0.36,0.0,1.76,1,0,0,0 +726,INFP,9.86,0.17787398257377462,0.06,0.2,0.0,0.02,0.0,0.16,0.0,0.76,1,0,0,1 +727,INTJ,30.0,0.17927699878413844,0.02,0.18,0.08,0.0,0.0,0.72,0.02,2.58,1,0,1,0 +728,INTJ,26.86,0.17920894780408986,0.0,0.2,0.04,0.0,0.02,0.68,0.0,2.96,1,0,1,0 +729,INTP,29.7,0.18298572262382753,0.0,0.08,0.12,0.0,0.02,0.76,0.0,3.18,1,0,1,1 +730,INTP,30.66,0.18656980644629828,0.0,0.12,0.16,0.0,0.0,0.78,0.0,3.44,1,0,1,1 +731,ISFP,24.0,0.18896427291706175,0.22,0.7,0.26,0.0,0.0,0.8,0.0,2.82,1,1,0,1 +732,ESFP,22.44,0.1836357135545167,0.0,0.0,0.0,0.0,0.08,0.6,0.0,2.7,0,1,0,1 +733,INFJ,23.32,0.18288333744934834,0.08,0.2,0.0,0.0,0.0,0.58,0.0,3.64,1,0,0,0 +734,ISFP,24.62,0.18825085956999865,0.02,0.14,0.0,0.0,0.04,0.38,0.0,2.22,1,1,0,1 +735,INTP,26.96,0.18704565544019036,0.08,0.34,0.2,0.06,0.0,0.96,0.0,1.8,1,0,1,1 +736,ISTP,3.7,0.16747612995182498,0.02,0.04,0.02,0.0,0.0,0.08,0.0,0.34,1,1,1,1 +737,ENTJ,29.48,0.18173086992331489,0.0,0.1,0.0,0.0,0.0,0.7,0.0,2.1,0,0,1,0 +738,ENFP,29.7,0.19042944631529485,0.02,0.6,1.28,0.0,0.0,0.66,0.0,3.34,0,0,0,1 +739,ENTP,24.94,0.1874818115679391,0.04,0.2,0.1,0.0,0.0,0.4,0.02,2.9,0,0,1,1 +740,INTJ,16.06,0.18327011964873993,0.0,0.24,0.12,0.0,0.0,0.24,0.0,1.54,1,0,1,0 +741,INFP,23.76,0.1813619937098969,0.02,0.36,0.32,0.02,0.0,0.44,0.0,2.28,1,0,0,1 +742,ISFP,32.4,0.18797719867883028,0.02,0.54,0.0,0.0,0.0,1.7,0.0,3.78,1,1,0,1 +743,ESTP,27.6,0.1865468635075498,0.0,0.22,0.16,0.0,0.02,0.74,0.0,2.72,0,1,1,1 +744,ISTP,13.12,0.1968005060913073,0.16,0.18,0.42,0.06,0.0,0.48,0.0,1.48,1,1,1,1 +745,INFP,23.26,0.18759276436569705,0.1,0.3,0.1,0.0,0.02,0.6,0.0,1.56,1,0,0,1 +746,INFJ,20.5,0.18435725181049825,0.02,0.04,0.14,0.0,0.0,0.42,0.0,2.42,1,0,0,0 +747,ENFJ,26.76,0.188828875623389,0.12,0.16,0.34,0.06,0.0,0.94,0.0,2.96,0,0,0,0 +748,INTP,31.54,0.1824601601868754,0.0,0.22,0.18,0.0,0.0,0.96,0.0,3.18,1,0,1,1 +749,ENFP,28.24,0.18945874168874718,0.04,0.24,0.66,0.0,0.0,0.94,0.0,3.3,0,0,0,1 +750,INTP,19.94,0.17998365083933784,0.32,0.38,0.06,0.1,0.0,0.42,0.0,2.72,1,0,1,1 +751,ISTP,26.6,0.17693718378979656,0.26,0.2,0.02,0.0,0.02,0.56,0.0,2.94,1,1,1,1 +752,INFJ,15.98,0.1825391011182742,0.0,0.2,0.1,0.0,0.04,0.38,0.04,1.54,1,0,0,0 +753,INFP,11.08,0.19451979806521674,0.0,0.0,0.16,0.0,0.0,0.8,0.0,1.5,1,0,0,1 +754,INTP,23.52,0.1796330325185714,0.04,0.12,0.0,0.0,0.02,0.52,0.0,2.8,1,0,1,1 +755,INFJ,24.3,0.191494379875216,0.06,0.32,0.22,0.0,0.0,0.86,0.0,3.62,1,0,0,0 +756,INTJ,17.36,0.18221114741745545,0.04,0.22,0.0,0.04,0.0,0.42,0.0,2.4,1,0,1,0 +757,INFJ,26.02,0.18154329339729156,0.0,0.08,0.18,0.0,0.02,0.46,0.0,3.14,1,0,0,0 +758,INFJ,27.42,0.19576765934985482,0.02,0.04,0.66,0.0,0.0,2.66,0.0,1.18,1,0,0,0 +759,INFJ,23.54,0.18318833145241273,0.0,0.1,0.1,0.0,0.0,0.48,0.02,2.72,1,0,0,0 +760,ENTJ,19.08,0.18852878901714362,0.12,0.22,0.04,0.0,0.0,0.88,0.0,0.62,0,0,1,0 +761,ISTP,26.9,0.1861601402857371,0.2,0.42,0.06,0.0,0.04,0.58,0.0,3.68,1,1,1,1 +762,INFP,30.14,0.19086222174773065,0.0,0.04,0.1,0.0,0.0,1.18,0.0,3.32,1,0,0,1 +763,INFJ,29.7,0.17832637734687593,0.06,0.1,0.24,0.0,0.0,0.82,0.0,3.98,1,0,0,0 +764,ESTJ,20.54,0.18756431694968995,0.0,0.18,0.26,0.0,0.0,0.48,0.0,2.48,0,1,1,0 +765,INFJ,22.56,0.19506382977095932,0.02,0.26,0.16,0.0,0.0,0.56,0.0,3.08,1,0,0,0 +766,INTP,8.84,0.17910667970685715,0.34,0.3,0.04,0.1,0.0,0.08,0.0,0.94,1,0,1,1 +767,INFP,20.12,0.1853925239830665,0.0,0.14,0.02,0.0,0.0,0.4,0.0,2.32,1,0,0,1 +768,INFP,31.78,0.18482573392296972,0.0,0.34,0.18,0.0,0.04,1.06,0.0,4.12,1,0,0,1 +769,INFP,27.46,0.18943859918905587,0.02,0.32,0.46,0.0,0.0,0.66,0.0,3.74,1,0,0,1 +770,INTP,29.24,0.18477392096016163,0.02,0.24,0.38,0.0,0.02,1.1,0.02,3.6,1,0,1,1 +771,ENFP,33.46,0.1830097483574961,0.04,0.18,0.26,0.0,0.02,0.92,0.0,3.74,0,0,0,1 +772,INFP,30.7,0.18317517753830648,0.0,0.16,0.14,0.0,0.02,0.82,0.0,2.52,1,0,0,1 +773,ENTP,15.94,0.1885721952455748,0.0,0.06,0.22,0.0,0.0,0.4,0.0,1.54,0,0,1,1 +774,ENFP,0.76,0.1937772345626528,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.02,0,0,0,1 +775,ENFJ,21.58,0.17858896450441972,0.0,0.16,0.1,0.0,0.02,0.4,0.0,1.78,0,0,0,0 +776,ISTP,25.46,0.17431634186763043,0.02,0.14,0.02,0.0,0.0,0.54,0.0,2.12,1,1,1,1 +777,INFP,28.52,0.1827060358243978,0.0,0.32,0.04,0.0,0.0,0.62,0.0,2.92,1,0,0,1 +778,INTJ,32.2,0.18237722851504629,0.0,0.06,0.04,0.0,0.0,0.86,0.0,4.08,1,0,1,0 +779,INTP,24.68,0.18643164327750117,0.08,0.24,0.16,0.08,0.0,0.7,0.0,2.86,1,0,1,1 +780,ISTP,26.98,0.18469846993168543,0.04,0.14,0.02,0.02,0.04,0.92,0.0,3.42,1,1,1,1 +781,INTJ,27.44,0.17848999800465457,0.02,0.24,0.0,0.0,0.0,0.68,0.0,3.36,1,0,1,0 +782,INFP,29.08,0.1772114617867927,0.02,0.14,0.04,0.0,0.04,0.7,0.0,2.72,1,0,0,1 +783,ISFJ,24.54,0.18597084327712068,0.02,0.18,0.24,0.02,0.02,0.92,0.0,3.42,1,1,0,0 +784,INFP,26.46,0.1882596705749396,0.06,0.16,0.38,0.0,0.0,0.56,0.02,3.28,1,0,0,1 +785,INTP,26.48,0.18063899272180686,0.02,0.36,0.0,0.02,0.0,0.56,0.0,2.12,1,0,1,1 +786,ENFJ,25.72,0.18660147682687123,0.0,0.34,0.2,0.0,0.0,0.4,0.0,3.3,0,0,0,0 +787,INFJ,29.52,0.19117903152546425,0.0,0.38,0.02,0.0,0.0,1.08,0.0,3.88,1,0,0,0 +788,ENFP,30.68,0.1922230707577582,0.04,0.66,0.46,0.0,0.0,0.78,0.02,4.16,0,0,0,1 +789,INTP,23.94,0.18820089456307126,0.0,0.3,0.04,0.0,0.0,0.5,0.0,2.98,1,0,1,1 +790,INTJ,24.44,0.18029928957750893,0.04,0.06,0.34,0.0,0.0,0.64,0.0,2.68,1,0,1,0 +791,INFJ,28.54,0.17476658939847375,0.0,0.06,0.16,0.0,0.0,0.68,0.0,2.42,1,0,0,0 +792,ENTP,28.22,0.1825239183443518,0.02,0.2,0.16,0.02,0.0,0.7,0.0,2.5,0,0,1,1 +793,ENFP,31.44,0.1875,0.0,0.3,0.16,0.0,0.02,0.86,0.0,2.4,0,0,0,1 +794,INFP,20.72,0.1846876413406979,0.0,0.18,0.18,0.0,0.02,0.46,0.0,2.22,1,0,0,1 +795,INTJ,31.32,0.17584905534899073,0.06,0.18,0.02,0.02,0.04,0.88,0.0,3.48,1,0,1,0 +796,INFP,30.3,0.18313167861682184,0.0,0.04,0.04,0.0,0.0,0.98,0.0,2.72,1,0,0,1 +797,INTJ,17.34,0.183173284644063,0.32,0.24,0.1,0.12,0.0,0.4,0.0,1.7,1,0,1,0 +798,INTP,27.2,0.18447534005810304,0.08,0.1,0.14,0.0,0.0,0.68,0.0,3.7,1,0,1,1 +799,ENTJ,31.92,0.18242501271890163,0.0,0.14,0.02,0.0,0.0,0.76,0.0,1.3,0,0,1,0 +800,INTP,24.66,0.18010032068287507,0.0,0.2,0.14,0.0,0.0,0.46,0.0,1.98,1,0,1,1 +801,INTP,27.68,0.18481625376196523,0.06,0.06,0.0,0.0,0.04,0.6,0.0,3.82,1,0,1,1 +802,INFJ,29.46,0.18868953986505807,0.02,0.42,0.22,0.0,0.0,0.88,0.0,3.72,1,0,0,0 +803,INFJ,27.94,0.18879105215096334,0.02,0.08,0.16,0.0,0.02,0.7,0.0,3.44,1,0,0,0 +804,INFJ,27.92,0.18000030894283028,0.0,0.14,0.04,0.0,0.02,0.78,0.0,3.06,1,0,0,0 +805,INTP,32.08,0.1780711923554595,0.0,0.1,0.02,0.0,0.0,0.92,0.0,3.54,1,0,1,1 +806,ENTP,18.48,0.176311902218068,0.28,0.32,0.0,0.06,0.0,0.36,0.0,2.08,0,0,1,1 +807,INFJ,14.18,0.1910102707291423,0.0,0.2,0.08,0.0,0.0,0.42,0.0,2.06,1,0,0,0 +808,ISTP,23.4,0.18234535595422408,0.0,0.08,0.02,0.0,0.02,0.38,0.0,2.1,1,1,1,1 +809,INFP,26.04,0.178618247416553,0.08,0.14,0.06,0.0,0.0,0.56,0.0,3.14,1,0,0,1 +810,ESTP,30.56,0.1852216161179727,0.02,0.22,0.04,0.0,0.0,1.02,0.0,3.32,0,1,1,1 +811,INTJ,24.54,0.1791939090609708,0.2,0.32,0.14,0.04,0.02,0.66,0.0,1.86,1,0,1,0 +812,ENTP,29.38,0.18030645294008937,0.02,0.1,0.0,0.0,0.0,0.66,0.0,2.4,0,0,1,1 +813,INFP,26.82,0.19320290775911803,0.02,0.58,0.52,0.0,0.0,1.62,0.0,3.16,1,0,0,1 +814,ESFJ,26.28,0.19466633012560608,0.0,0.28,0.3,0.0,0.0,0.46,0.0,2.8,0,1,0,0 +815,INFJ,26.82,0.1871985938730853,0.0,0.28,0.06,0.0,0.0,0.66,0.0,3.86,1,0,0,0 +816,INTP,30.58,0.1787818916923934,0.04,0.14,0.0,0.0,0.04,0.92,0.0,3.84,1,0,1,1 +817,INFJ,18.56,0.1937305545577988,0.06,0.46,0.14,0.02,0.0,0.82,0.0,1.9,1,0,0,0 +818,INTJ,18.76,0.18260415374590538,0.04,0.22,0.02,0.02,0.0,0.36,0.06,1.36,1,0,1,0 +819,ENTJ,19.08,0.1778677510299863,0.0,0.06,0.0,0.0,0.0,0.44,0.0,2.3,0,0,1,0 +820,ISTP,29.22,0.1820280612244898,0.04,0.16,0.14,0.04,0.0,0.58,0.0,3.08,1,1,1,1 +821,INFJ,27.94,0.18301076034329922,0.06,0.08,0.18,0.02,0.0,0.7,0.0,3.48,1,0,0,0 +822,INFJ,30.32,0.19119872301313806,0.12,0.04,0.18,0.1,0.0,1.04,0.0,3.28,1,0,0,0 +823,ISTP,21.52,0.18960458296310842,0.08,0.32,0.12,0.0,0.04,0.46,0.0,2.52,1,1,1,1 +824,INTP,32.3,0.1798313716613852,0.02,0.1,0.16,0.0,0.0,0.88,0.0,3.44,1,0,1,1 +825,ENTP,29.02,0.1826225912990576,0.0,0.3,0.48,0.0,0.0,0.64,0.02,2.94,0,0,1,1 +826,INFP,21.48,0.18503891186443308,0.26,0.28,0.18,0.0,0.02,0.66,0.0,2.22,1,0,0,1 +827,ENFP,31.38,0.1861944764567206,0.0,0.12,0.1,0.0,0.0,0.74,0.0,4.1,0,0,0,1 +828,ENTJ,28.48,0.18364509017438335,0.0,0.42,0.02,0.0,0.0,0.72,0.0,2.56,0,0,1,0 +829,INTP,28.02,0.1712885802469135,0.0,0.38,0.0,0.0,0.0,0.7,0.0,2.54,1,0,1,1 +830,ENTJ,23.46,0.19422541010029037,0.02,0.34,0.12,0.0,0.0,1.02,0.0,2.26,0,0,1,0 +831,INTP,28.94,0.1876701065212728,0.06,0.68,0.06,0.02,0.0,0.82,0.02,2.48,1,0,1,1 +832,ISTP,25.46,0.18268366084636975,0.0,0.08,0.0,0.0,0.02,0.54,0.0,2.78,1,1,1,1 +833,INFP,17.06,0.18132536649522618,0.28,0.36,0.04,0.02,0.08,0.34,0.0,2.2,1,0,0,1 +834,ENTJ,24.64,0.1804756977707138,0.04,0.1,0.08,0.04,0.0,0.6,0.0,2.3,0,0,1,0 +835,INTJ,19.02,0.17838591056217892,0.04,0.18,0.04,0.0,0.0,0.42,0.0,1.4,1,0,1,0 +836,INFP,22.74,0.18346284122423065,0.14,0.34,0.04,0.0,0.0,0.48,0.0,2.84,1,0,0,1 +837,INFP,30.26,0.18329417479456117,0.06,0.12,0.0,0.0,0.02,0.92,0.0,3.6,1,0,0,1 +838,ENTP,25.48,0.19047736911240384,0.04,0.2,0.08,0.0,0.02,0.56,0.0,2.5,0,0,1,1 +839,ENFJ,25.0,0.1877395617234768,0.04,0.08,0.36,0.0,0.0,0.68,0.04,3.04,0,0,0,0 +840,INTJ,21.8,0.19238637787986868,0.02,0.32,0.1,0.0,0.04,0.68,0.0,2.16,1,0,1,0 +841,INTP,30.16,0.1816804548730045,0.0,0.16,0.04,0.0,0.0,0.72,0.0,3.02,1,0,1,1 +842,INTP,29.04,0.18278346247724886,0.04,0.1,0.04,0.0,0.0,0.6,0.0,3.1,1,0,1,1 +843,INTP,26.44,0.17919479471218772,0.02,0.2,0.02,0.0,0.0,0.58,0.0,2.62,1,0,1,1 +844,INTP,12.14,0.19135144891151823,0.0,0.06,0.0,0.0,0.02,0.24,0.0,1.28,1,0,1,1 +845,INFP,24.92,0.18576310110577604,0.02,0.18,0.14,0.0,0.08,0.6,0.0,2.94,1,0,0,1 +846,ENTJ,21.34,0.18346610957401951,0.16,0.18,0.08,0.08,0.0,0.62,0.02,2.48,0,0,1,0 +847,INTP,27.44,0.1825992054429105,0.0,0.14,0.04,0.0,0.02,0.6,0.02,4.04,1,0,1,1 +848,INTP,23.5,0.1745645116218823,0.24,0.34,0.0,0.0,0.0,0.82,0.0,1.98,1,0,1,1 +849,INTJ,19.36,0.18374545767423978,0.02,0.08,0.02,0.0,0.0,0.3,0.0,1.98,1,0,1,0 +850,INTJ,17.22,0.17685583985950284,0.0,0.1,0.0,0.0,0.0,0.48,0.0,1.74,1,0,1,0 +851,INTJ,27.1,0.1809775904710653,0.0,0.32,0.02,0.0,0.0,0.44,0.0,3.08,1,0,1,0 +852,INTJ,24.98,0.18701294367283955,0.0,0.48,0.22,0.0,0.0,0.48,0.04,2.72,1,0,1,0 +853,INFP,12.52,0.19629434225786227,0.0,0.24,0.66,0.0,0.0,0.58,0.0,1.52,1,0,0,1 +854,ESTP,15.88,0.18332426296561188,0.02,0.22,0.24,0.0,0.0,0.3,0.0,2.08,0,1,1,1 +855,INTP,29.22,0.17914999588449085,0.04,0.12,0.08,0.02,0.06,0.74,0.0,3.1,1,0,1,1 +856,INFP,26.82,0.1851367619144557,0.02,0.26,0.0,0.0,0.08,0.68,0.0,3.68,1,0,0,1 +857,INFJ,21.4,0.17734548955775106,0.32,0.3,0.02,0.08,0.0,0.62,0.0,2.16,1,0,0,0 +858,ISTP,22.92,0.18544283745096343,0.04,0.16,0.08,0.0,0.04,0.54,0.0,2.7,1,1,1,1 +859,INTP,26.12,0.17464968223893815,0.02,0.32,0.02,0.0,0.0,0.7,0.0,1.66,1,0,1,1 +860,INFP,25.34,0.1866803366226665,0.08,0.4,0.0,0.0,0.0,0.48,0.0,3.12,1,0,0,1 +861,INFP,33.9,0.18616638731094332,0.0,0.08,0.32,0.0,0.04,1.0,0.02,4.46,1,0,0,1 +862,INFJ,21.44,0.19129939258452586,0.0,0.04,0.14,0.0,0.0,1.6,0.0,3.16,1,0,0,0 +863,INTJ,29.12,0.1783819482807539,0.0,0.12,0.0,0.0,0.02,0.74,0.0,2.94,1,0,1,0 +864,INTP,19.22,0.18242487384335557,0.08,0.2,0.02,0.0,0.0,0.34,0.0,1.5,1,0,1,1 +865,INFJ,8.82,0.19249379393983176,0.12,0.2,0.1,0.02,0.0,0.06,0.0,0.78,1,0,0,0 +866,ENFP,24.32,0.18375729056653356,0.04,0.2,0.48,0.0,0.02,0.46,0.0,2.68,0,0,0,1 +867,ENTJ,27.44,0.18242835246089237,0.06,0.26,0.36,0.0,0.0,0.66,0.0,3.36,0,0,1,0 +868,INTP,25.92,0.18481142291152683,0.0,0.22,0.02,0.0,0.0,0.64,0.0,2.14,1,0,1,1 +869,INTJ,30.2,0.1874281485372877,0.0,0.12,0.2,0.0,0.0,1.16,0.0,3.44,1,0,1,0 +870,INTP,6.78,0.17818518734009733,0.34,0.14,0.02,0.16,0.0,0.04,0.0,0.54,1,0,1,1 +871,ISTP,26.98,0.19312843046217257,0.02,0.42,0.3,0.0,0.02,0.62,0.0,3.36,1,1,1,1 +872,INFP,11.38,0.1882540914578137,0.22,0.2,0.18,0.02,0.0,0.18,0.0,1.64,1,0,0,1 +873,INFP,6.22,0.19846454482154488,0.0,0.08,0.0,0.0,0.0,0.16,0.0,0.9,1,0,0,1 +874,INFP,33.8,0.18440603523986715,0.0,0.04,0.16,0.0,0.0,0.9,0.0,4.78,1,0,0,1 +875,ENTP,28.54,0.18673709991805965,0.04,0.42,0.0,0.0,0.02,0.64,0.0,2.64,0,0,1,1 +876,INFP,24.56,0.1867774626481409,0.04,0.26,0.04,0.04,0.0,0.48,0.0,2.1,1,0,0,1 +877,INTJ,24.92,0.17822452442460204,0.08,0.08,0.08,0.02,0.04,0.62,0.0,2.22,1,0,1,0 +878,INFP,20.72,0.18998332018094158,0.1,0.34,0.12,0.0,0.0,1.32,0.0,2.44,1,0,0,1 +879,INTJ,34.44,0.18989911280866217,0.02,0.08,0.1,0.02,0.0,0.78,0.0,3.54,1,0,1,0 +880,INFJ,30.0,0.18637682549625656,0.04,0.1,0.36,0.04,0.0,1.06,0.0,3.78,1,0,0,0 +881,INFP,26.8,0.18221158876671914,0.36,0.04,0.14,0.34,0.08,0.68,0.0,3.24,1,0,0,1 +882,INTJ,17.22,0.19293706000524058,0.06,0.06,1.42,0.04,0.0,0.28,0.0,2.12,1,0,1,0 +883,INFP,27.06,0.1861881197139365,0.04,0.08,0.42,0.0,0.04,0.78,0.0,3.5,1,0,0,1 +884,ENFP,20.06,0.1952704995554928,0.0,0.14,0.08,0.0,0.0,0.52,0.0,2.22,0,0,0,1 +885,ESTP,20.92,0.18565734681737064,0.02,0.64,0.02,0.0,0.02,0.4,0.0,1.76,0,1,1,1 +886,ISFP,22.6,0.1826367854795925,0.02,0.22,0.14,0.0,0.04,0.8,0.0,1.92,1,1,0,1 +887,INTP,21.3,0.1819938114437525,0.1,0.22,0.06,0.0,0.06,0.54,0.02,1.92,1,0,1,1 +888,ENFP,18.7,0.18386941462530346,0.4,0.42,0.32,0.02,0.0,0.4,0.0,0.76,0,0,0,1 +889,INTJ,20.98,0.18170539351423404,0.26,0.34,0.18,0.16,0.0,0.56,0.0,2.1,1,0,1,0 +890,INFP,23.4,0.17895554588067747,0.28,0.46,0.16,0.0,0.46,0.4,0.0,2.64,1,0,0,1 +891,INTP,26.04,0.18598933002211113,0.2,0.38,0.06,0.0,0.0,0.62,0.0,3.8,1,0,1,1 +892,INFP,27.52,0.1894285386109305,0.02,0.12,0.22,0.0,0.02,0.92,0.0,2.6,1,0,0,1 +893,INTP,26.96,0.1855191909048545,0.02,0.18,0.0,0.0,0.0,0.86,0.0,3.28,1,0,1,1 +894,INFJ,24.38,0.18065052783851945,0.02,0.24,0.12,0.02,0.02,0.96,0.0,1.84,1,0,0,0 +895,INTP,30.7,0.18076036051004038,0.0,0.24,0.06,0.0,0.02,0.72,0.0,2.26,1,0,1,1 +896,INFJ,28.62,0.18033092546074964,0.06,0.24,0.0,0.0,0.0,0.72,0.0,2.86,1,0,0,0 +897,INFP,34.06,0.17856309036862913,0.04,0.16,0.04,0.02,0.0,0.94,0.0,4.06,1,0,0,1 +898,ISTP,26.4,0.18462987589888147,0.02,0.24,0.02,0.0,0.0,0.88,0.0,3.8,1,1,1,1 +899,INTJ,24.94,0.18228774844961257,0.02,0.02,0.08,0.02,0.0,0.6,0.0,2.36,1,0,1,0 +900,INFJ,14.88,0.1857204541221829,0.28,0.38,0.04,0.0,0.02,0.28,0.0,1.66,1,0,0,0 +901,INTP,24.52,0.1879347812890772,0.02,0.2,0.12,0.0,0.0,0.68,0.0,2.5,1,0,1,1 +902,INTP,24.7,0.17907829115161322,0.02,0.26,0.0,0.0,0.0,0.46,0.0,2.72,1,0,1,1 +903,INTP,24.04,0.1854741989847086,0.1,0.38,0.26,0.02,0.02,0.66,0.0,2.36,1,0,1,1 +904,INTJ,26.36,0.18882423329863215,0.06,0.3,0.14,0.0,0.02,0.9,0.0,2.68,1,0,1,0 +905,INFP,33.7,0.18539452430330852,0.0,0.12,0.18,0.0,0.02,0.86,0.0,3.88,1,0,0,1 +906,INFJ,31.94,0.18225353852529377,0.08,0.3,0.04,0.0,0.0,0.84,0.0,4.16,1,0,0,0 +907,INTP,28.26,0.1822102566514405,0.04,0.1,0.06,0.02,0.0,0.7,0.0,4.24,1,0,1,1 +908,ENTP,26.74,0.1872941606307943,0.0,0.2,0.1,0.0,0.02,0.86,0.0,2.68,0,0,1,1 +909,INFP,19.24,0.20107371381542063,0.0,0.28,0.46,0.0,0.0,0.68,0.0,2.66,1,0,0,1 +910,INFP,29.96,0.1904235671019922,0.06,0.2,0.06,0.04,0.0,0.56,0.0,3.42,1,0,0,1 +911,INFJ,30.4,0.18951747465950072,0.0,0.2,0.1,0.0,0.0,0.66,0.0,3.88,1,0,0,0 +912,INFP,24.52,0.1873754891806226,0.08,0.22,0.18,0.0,0.0,1.1,0.0,3.24,1,0,0,1 +913,INFP,27.82,0.19084054347743126,0.0,0.12,0.12,0.0,0.02,0.6,0.0,4.0,1,0,0,1 +914,ESFJ,26.74,0.1867282988125672,0.08,0.26,0.0,0.08,0.0,0.52,0.0,3.68,0,1,0,0 +915,INFP,24.1,0.18012087987474168,0.08,0.08,0.34,0.04,0.06,0.54,0.0,2.4,1,0,0,1 +916,INTJ,22.8,0.18066105875531882,0.16,0.3,0.08,0.0,0.0,0.42,0.0,2.08,1,0,1,0 +917,INFP,29.64,0.17657822114854846,0.06,0.12,0.0,0.04,0.0,0.88,0.0,2.46,1,0,0,1 +918,ENFP,24.7,0.18927450005870922,0.0,0.1,0.56,0.0,0.0,0.54,0.0,3.16,0,0,0,1 +919,INFP,34.7,0.18625317996950927,0.0,0.62,0.14,0.0,0.0,1.12,0.0,4.18,1,0,0,1 +920,INFP,18.2,0.18557722233891108,0.0,0.0,0.02,0.0,0.02,0.3,0.0,2.16,1,0,0,1 +921,INFP,26.18,0.18341042658884502,0.0,0.28,0.36,0.0,0.0,0.62,0.0,2.5,1,0,0,1 +922,ENTP,15.34,0.18123696153772811,0.04,0.1,0.0,0.04,0.02,0.36,0.0,1.84,0,0,1,1 +923,ISTP,18.26,0.18207060146218967,0.08,0.4,0.0,0.0,0.0,0.36,0.0,1.94,1,1,1,1 +924,INFJ,32.06,0.18646638248116412,0.0,0.18,0.24,0.0,0.0,0.86,0.0,3.78,1,0,0,0 +925,INFP,18.52,0.18668271844904544,0.0,0.38,0.18,0.0,0.0,0.34,0.0,1.76,1,0,0,1 +926,ISFP,34.22,0.18420177216728956,0.0,0.1,0.08,0.0,0.0,1.0,0.0,4.4,1,1,0,1 +927,INFP,27.32,0.18649968791195945,0.02,0.12,0.14,0.0,0.02,0.68,0.08,3.96,1,0,0,1 +928,ISTP,28.4,0.18514013576156776,0.0,0.48,0.04,0.0,0.0,0.8,0.0,2.98,1,1,1,1 +929,INFJ,31.96,0.18066771178162627,0.0,0.16,0.08,0.0,0.0,0.92,0.0,3.72,1,0,0,0 +930,ENFJ,21.2,0.19103873298730736,0.06,0.44,0.32,0.0,0.0,0.32,0.0,1.96,0,0,0,0 +931,INTJ,29.56,0.18534254830385824,0.0,0.28,0.12,0.0,0.04,0.72,0.0,4.26,1,0,1,0 +932,INFJ,20.98,0.18852406918894993,0.0,0.18,0.02,0.0,0.02,0.38,0.0,2.12,1,0,0,0 +933,INFP,19.0,0.18765263840792368,0.12,0.12,0.64,0.08,0.0,0.38,0.0,2.58,1,0,0,1 +934,INFP,22.76,0.18105581125329945,0.16,0.26,0.14,0.02,0.0,0.48,0.0,2.58,1,0,0,1 +935,ENTP,27.6,0.1902104791995332,0.0,0.16,0.08,0.0,0.04,0.94,0.02,3.66,0,0,1,1 +936,INFJ,22.3,0.1874073593842377,0.2,0.42,0.22,0.0,0.0,0.48,0.0,2.6,1,0,0,0 +937,INFP,17.5,0.1857126419388562,0.22,0.28,0.18,0.0,0.04,0.38,0.0,2.28,1,0,0,1 +938,INFP,19.36,0.1866174183638787,0.02,0.52,0.0,0.0,0.0,0.24,0.0,2.32,1,0,0,1 +939,INTP,25.12,0.17395489238215822,0.02,0.14,0.02,0.0,0.0,0.72,0.02,2.2,1,0,1,1 +940,ISTP,21.4,0.19107152373304262,0.06,0.28,0.04,0.0,0.0,0.48,0.0,2.86,1,1,1,1 +941,INTJ,17.5,0.19140285532246967,0.22,0.42,0.46,0.0,0.0,0.4,0.0,2.2,1,0,1,0 +942,INFP,20.86,0.19245085932078382,0.34,0.38,0.02,0.02,0.08,0.84,0.0,2.72,1,0,0,1 +943,ISTJ,18.68,0.19399602792735612,0.14,0.32,1.22,0.0,0.0,0.54,0.0,1.24,1,1,1,0 +944,INFJ,24.42,0.18550721056581862,0.16,0.06,0.18,0.0,0.0,0.48,0.0,3.06,1,0,0,0 +945,INTJ,28.46,0.18654794938581853,0.0,0.18,0.02,0.0,0.0,0.72,0.0,2.76,1,0,1,0 +946,INFP,29.16,0.18199945837559045,0.06,0.12,0.12,0.06,0.04,0.56,0.0,3.06,1,0,0,1 +947,ISFJ,29.72,0.18956396573043463,0.0,0.1,0.1,0.0,0.0,0.86,0.0,3.56,1,1,0,0 +948,INFJ,23.42,0.19058015750024665,0.0,0.28,0.46,0.0,0.0,0.74,0.0,2.84,1,0,0,0 +949,INTP,30.88,0.17842583122666278,0.0,0.18,0.0,0.0,0.0,0.78,0.0,2.74,1,0,1,1 +950,INFP,19.02,0.19492562968197216,0.06,0.26,0.08,0.02,0.06,0.5,0.0,2.72,1,0,0,1 +951,INTJ,29.74,0.18455367666409866,0.0,0.22,0.08,0.0,0.02,0.68,0.0,3.34,1,0,1,0 +952,INFP,22.08,0.19344791550335144,0.04,0.32,0.44,0.0,0.0,0.32,0.0,2.12,1,0,0,1 +953,ISFP,29.42,0.19191764754611015,0.0,0.44,1.36,0.0,0.0,0.66,0.0,1.48,1,1,0,1 +954,INFP,11.18,0.1813394969013207,0.5,0.4,0.04,0.08,0.02,0.36,0.0,1.66,1,0,0,1 +955,ENTJ,23.96,0.1797888966351009,0.04,0.18,0.0,0.04,0.0,0.48,0.0,2.44,0,0,1,0 +956,INFP,31.8,0.18611122989105006,0.12,0.3,0.14,0.04,0.0,0.8,0.0,2.94,1,0,0,1 +957,INTJ,23.66,0.18416431229724323,0.04,0.24,0.0,0.02,0.02,0.7,0.02,2.82,1,0,1,0 +958,INFJ,33.7,0.1819707912704899,0.0,0.1,0.14,0.0,0.0,0.84,0.0,4.1,1,0,0,0 +959,ISFP,27.4,0.18180418269167664,0.02,0.18,0.14,0.0,0.02,0.82,0.0,3.64,1,1,0,1 +960,ENFP,17.14,0.19426298876890613,0.1,0.32,0.2,0.0,0.04,0.36,0.02,2.02,0,0,0,1 +961,INFJ,33.1,0.1901419749252071,0.0,0.22,0.52,0.0,0.02,1.6,0.02,4.28,1,0,0,0 +962,ISFJ,24.82,0.19084685791206185,0.12,0.22,0.72,0.08,0.02,0.56,0.0,3.46,1,1,0,0 +963,INFJ,33.04,0.18290512993525965,0.0,0.1,0.1,0.0,0.0,0.76,0.0,4.76,1,0,0,0 +964,INTP,4.46,0.17323217980445454,0.0,0.02,0.0,0.0,0.0,0.1,0.0,0.34,1,0,1,1 +965,INFP,20.3,0.19040282502437128,0.02,0.5,0.18,0.0,0.0,0.28,0.0,2.18,1,0,0,1 +966,ENFP,24.38,0.18362683756397574,0.06,0.18,0.0,0.0,0.0,0.5,0.0,2.6,0,0,0,1 +967,INFP,25.36,0.18138024464548874,0.12,0.2,0.22,0.02,0.0,0.62,0.02,2.54,1,0,0,1 +968,INFP,28.2,0.18724683570212727,0.02,0.34,0.2,0.02,0.0,0.82,0.0,3.9,1,0,0,1 +969,ENFP,23.52,0.18928514964964382,0.16,0.2,0.28,0.0,0.0,0.74,0.02,2.16,0,0,0,1 +970,INFP,29.54,0.1919119254750979,0.06,0.22,0.74,0.04,0.0,1.16,0.0,3.5,1,0,0,1 +971,INTP,31.4,0.18423056186029774,0.0,0.26,0.06,0.0,0.0,0.84,0.0,3.78,1,0,1,1 +972,INFP,25.6,0.1868830583040857,0.02,0.4,0.16,0.0,0.0,0.54,0.0,3.68,1,0,0,1 +973,ENFP,23.58,0.19091606857900284,0.0,0.06,0.48,0.0,0.0,0.36,0.0,3.46,0,0,0,1 +974,ENFP,18.84,0.18712630875473377,0.24,0.18,0.04,0.12,0.0,0.38,0.0,2.1,0,0,0,1 +975,INTP,21.04,0.1814864512893167,0.02,0.1,0.02,0.0,0.0,0.36,0.0,2.94,1,0,1,1 +976,INFP,31.46,0.1876959904718072,0.0,0.16,0.16,0.0,0.06,1.06,0.0,3.32,1,0,0,1 +977,INTP,24.92,0.19148627321555448,0.04,0.24,0.0,0.0,0.0,0.56,0.0,3.0,1,0,1,1 +978,INTJ,19.7,0.1713752596108306,0.0,0.04,0.08,0.0,0.0,0.62,0.0,1.54,1,0,1,0 +979,INTJ,27.28,0.17898465681180045,0.0,0.08,0.16,0.0,0.0,0.64,0.0,2.16,1,0,1,0 +980,ENTP,17.64,0.18494588598682657,0.02,0.2,0.0,0.0,0.02,0.38,0.0,1.9,0,0,1,1 +981,ENTP,26.64,0.18725860981586212,0.0,0.4,0.34,0.0,0.0,0.96,0.02,1.7,0,0,1,1 +982,INFP,25.84,0.17887513897620075,0.06,0.1,0.06,0.04,0.02,0.7,0.0,2.0,1,0,0,1 +983,ENTP,23.98,0.179191450830107,0.0,0.08,0.1,0.0,0.0,0.38,0.0,2.36,0,0,1,1 +984,INTP,20.24,0.18337977760682198,0.16,0.32,0.1,0.06,0.04,0.58,0.0,2.32,1,0,1,1 +985,ENTP,29.4,0.18351674478152316,0.02,0.24,0.4,0.0,0.0,0.96,0.02,3.16,0,0,1,1 +986,ENFJ,32.72,0.1882324531975723,0.02,0.22,0.42,0.02,0.0,1.3,0.0,2.82,0,0,0,0 +987,ISFP,31.4,0.18196017336720258,0.04,0.12,0.04,0.02,0.02,1.04,0.0,3.38,1,1,0,1 +988,INFP,16.9,0.19461673695009304,0.08,0.16,0.06,0.02,0.0,0.28,0.0,1.08,1,0,0,1 +989,ENTP,26.16,0.18568767144962367,0.02,0.52,0.44,0.0,0.0,1.02,0.0,2.58,0,0,1,1 +990,INFJ,30.3,0.1883750033184303,0.06,0.42,0.3,0.0,0.0,1.06,0.0,3.36,1,0,0,0 +991,INTP,33.02,0.1904297457512608,0.02,0.34,0.1,0.0,0.0,0.84,0.0,4.24,1,0,1,1 +992,ISTP,26.58,0.1822009465794739,0.0,0.3,0.04,0.0,0.0,0.5,0.0,2.16,1,1,1,1 +993,INTJ,28.36,0.18571636235047442,0.0,0.68,0.12,0.0,0.0,0.58,0.0,3.9,1,0,1,0 +994,INFP,24.5,0.18178567998080902,0.0,0.2,0.6,0.0,0.0,0.64,0.0,0.92,1,0,0,1 +995,ENFP,17.02,0.1843471482886994,0.02,0.04,0.02,0.02,0.02,0.22,0.0,2.56,0,0,0,1 +996,ENTP,24.76,0.1949973806284985,0.16,0.28,0.26,0.04,0.04,0.46,0.04,2.38,0,0,1,1 +997,INFP,23.62,0.17639480628343615,0.22,0.24,0.18,0.04,0.02,0.66,0.0,2.06,1,0,0,1 +998,ENTJ,26.46,0.17936728604492835,0.02,0.08,0.1,0.0,0.0,0.48,0.0,2.74,0,0,1,0 +999,ISTJ,17.3,0.19453463745437855,0.02,0.12,0.26,0.02,0.02,0.42,0.0,2.1,1,1,1,0 +1000,ISFJ,28.36,0.17653326402081146,0.0,0.08,0.24,0.0,0.0,0.66,0.0,2.66,1,1,0,0 +1001,INTJ,20.86,0.19249753490712115,0.08,0.22,0.14,0.0,0.0,0.42,0.0,2.22,1,0,1,0 +1002,INFJ,27.0,0.1944516867078496,0.0,0.16,0.6,0.0,0.0,0.82,0.0,3.56,1,0,0,0 +1003,INTJ,17.16,0.18706643948594814,0.32,0.46,0.38,0.0,0.0,0.46,0.02,1.82,1,0,1,0 +1004,INFP,30.34,0.18297871267982874,0.0,0.12,0.08,0.0,0.0,0.84,0.0,3.76,1,0,0,1 +1005,INTJ,26.9,0.18670748672632417,0.04,0.32,0.12,0.0,0.0,0.6,0.0,2.9,1,0,1,0 +1006,INTJ,24.86,0.17778949309993067,0.0,0.08,0.02,0.0,0.0,0.56,0.02,1.54,1,0,1,0 +1007,ISTP,26.16,0.18099576743149925,0.0,0.16,0.0,0.0,0.0,0.46,0.0,2.72,1,1,1,1 +1008,INFJ,22.52,0.18416039564038528,0.0,0.16,0.04,0.0,0.04,0.56,0.0,2.92,1,0,0,0 +1009,INFJ,15.2,0.19732258112774176,0.0,0.36,1.5,0.0,0.0,0.36,0.0,1.82,1,0,0,0 +1010,INFP,28.64,0.18962163217947037,0.12,0.12,0.0,0.0,0.02,0.8,0.0,3.42,1,0,0,1 +1011,INTJ,27.56,0.1786561926893352,0.0,0.04,0.0,0.0,0.0,0.68,0.0,2.04,1,0,1,0 +1012,INFP,25.8,0.18408496103644115,0.1,0.16,0.06,0.0,0.0,0.62,0.0,3.72,1,0,0,1 +1013,ENFP,26.78,0.19645913074062976,0.02,0.12,0.4,0.0,0.02,0.52,0.0,4.2,0,0,0,1 +1014,INFP,10.16,0.1858528328713888,0.0,0.06,0.04,0.0,0.0,0.24,0.0,1.48,1,0,0,1 +1015,INFP,30.74,0.1874424626037368,0.02,0.12,0.2,0.0,0.0,0.88,0.02,4.1,1,0,0,1 +1016,INFP,32.12,0.19367034934297986,0.02,0.34,0.78,0.0,0.04,1.16,0.0,3.5,1,0,0,1 +1017,ENFJ,31.5,0.18691018189925782,0.02,0.32,0.14,0.0,0.0,0.82,0.02,3.68,0,0,0,0 +1018,INTJ,21.04,0.1892798437502656,0.1,0.28,0.24,0.0,0.0,0.58,0.0,2.92,1,0,1,0 +1019,INFJ,33.86,0.1809343097151019,0.0,0.08,0.12,0.0,0.06,0.96,0.0,4.72,1,0,0,0 +1020,INFP,24.9,0.18153239956475128,0.0,0.06,0.34,0.0,0.02,0.56,0.0,2.76,1,0,0,1 +1021,INFP,27.56,0.1837979235189769,0.1,0.12,0.28,0.04,0.0,0.64,0.0,3.42,1,0,0,1 +1022,ENTP,11.02,0.1860999189724047,0.0,0.08,0.06,0.0,0.0,0.24,0.02,1.0,0,0,1,1 +1023,ENTP,27.54,0.19251833955380038,0.0,0.38,0.06,0.0,0.0,0.8,0.0,2.68,0,0,1,1 +1024,INFP,19.04,0.18722087865010154,0.0,0.34,0.12,0.0,0.0,0.38,0.0,2.66,1,0,0,1 +1025,INTP,23.04,0.1870296878648894,0.04,0.14,0.18,0.02,0.0,0.5,0.0,2.9,1,0,1,1 +1026,ISTP,24.76,0.18293458705739885,0.0,0.14,0.0,0.0,0.0,0.62,0.0,2.74,1,1,1,1 +1027,INFP,30.0,0.18230477197869807,0.0,0.08,0.1,0.0,0.0,0.86,0.0,3.54,1,0,0,1 +1028,ENTP,23.7,0.184216175784214,0.1,0.14,0.04,0.04,0.06,0.56,0.04,2.44,0,0,1,1 +1029,ENFP,27.52,0.18512050960948195,0.04,0.3,0.4,0.0,0.02,0.8,0.0,2.96,0,0,0,1 +1030,INTJ,1.38,0.17796489866114099,0.7,0.66,0.02,0.04,0.0,0.04,0.0,0.28,1,0,1,0 +1031,ISFP,26.16,0.18852080083310926,0.04,0.18,0.2,0.0,0.0,0.52,0.0,3.38,1,1,0,1 +1032,ENTP,21.6,0.18318563680875374,0.0,0.0,0.02,0.0,0.0,0.58,0.0,2.18,0,0,1,1 +1033,INFP,28.26,0.18609467227344625,0.0,0.2,0.16,0.0,0.0,0.68,0.0,3.02,1,0,0,1 +1034,INFP,15.58,0.1825504112892981,0.3,0.44,0.34,0.0,0.02,0.38,0.02,1.76,1,0,0,1 +1035,INFJ,31.76,0.1910712645934638,0.04,0.06,0.38,0.02,0.0,1.3,0.0,3.6,1,0,0,0 +1036,INTJ,29.18,0.1875622354952699,0.0,0.18,0.12,0.0,0.0,0.7,0.0,3.64,1,0,1,0 +1037,ENFP,26.04,0.1857053474412242,0.12,0.32,0.44,0.0,0.02,0.66,0.0,2.48,0,0,0,1 +1038,INTP,30.5,0.18502382315334973,0.0,0.14,0.18,0.0,0.0,0.76,0.0,3.36,1,0,1,1 +1039,ENFP,30.68,0.18558325972921078,0.0,0.08,1.34,0.0,0.0,1.24,0.0,3.52,0,0,0,1 +1040,INTP,28.88,0.1865336623854414,0.0,0.2,0.0,0.0,0.0,0.56,0.0,3.88,1,0,1,1 +1041,INTJ,26.4,0.17752000485030367,0.02,0.24,0.02,0.02,0.02,0.58,0.0,2.62,1,0,1,0 +1042,INTP,29.56,0.17613278432214366,0.02,0.04,0.12,0.0,0.0,0.6,0.0,1.18,1,0,1,1 +1043,INTJ,28.32,0.18141811626958476,0.02,0.14,0.0,0.0,0.04,0.64,0.0,1.68,1,0,1,0 +1044,INFJ,26.64,0.1861797000004089,0.1,0.12,0.16,0.02,0.0,0.98,0.0,2.82,1,0,0,0 +1045,INFP,27.4,0.18861068325403588,0.04,0.22,0.48,0.0,0.02,0.78,0.0,3.56,1,0,0,1 +1046,INTJ,23.66,0.1788735359205931,0.0,0.06,0.12,0.0,0.0,0.56,0.0,2.04,1,0,1,0 +1047,ENFP,32.28,0.1877113269831454,0.02,0.14,0.26,0.02,0.02,0.72,0.0,4.38,0,0,0,1 +1048,ENTP,15.88,0.18347596583019243,0.0,0.04,0.0,0.0,0.0,0.14,0.02,1.48,0,0,1,1 +1049,ISTJ,5.98,0.18920990521291048,0.2,0.2,0.02,0.0,0.04,0.1,0.0,0.84,1,1,1,0 +1050,ISFP,20.34,0.18555840662894293,0.1,0.1,0.18,0.0,0.06,0.4,0.0,1.24,1,1,0,1 +1051,INFJ,29.46,0.17980797013445515,0.0,0.06,0.24,0.0,0.0,0.62,0.0,1.04,1,0,0,0 +1052,ISFJ,18.12,0.17689672369571835,0.0,0.06,0.0,0.0,0.12,0.4,0.0,2.1,1,1,0,0 +1053,ENFJ,28.32,0.1895617130804241,0.0,0.22,0.4,0.0,0.0,0.78,0.0,3.7,0,0,0,0 +1054,INFP,14.26,0.17930324897349126,0.0,0.02,0.04,0.0,0.0,0.36,0.0,1.2,1,0,0,1 +1055,ISTP,7.38,0.2009428020908243,0.0,0.08,0.56,0.0,0.04,0.06,0.0,1.34,1,1,1,1 +1056,INFJ,31.64,0.18564966949609313,0.04,0.16,0.02,0.0,0.0,0.98,0.0,3.36,1,0,0,0 +1057,ENTP,10.72,0.1825985274151076,0.2,0.28,0.0,0.04,0.0,0.14,0.0,0.82,0,0,1,1 +1058,ENTP,29.22,0.17849258336973978,0.08,0.16,0.12,0.04,0.0,0.88,0.0,2.46,0,0,1,1 +1059,ISTP,25.86,0.1936200122299609,0.02,0.44,0.1,0.0,0.0,1.28,0.0,2.9,1,1,1,1 +1060,ISFP,31.36,0.17916753005469224,0.04,0.18,0.06,0.0,0.0,0.7,0.0,3.36,1,1,0,1 +1061,ISFJ,34.12,0.1837988600014397,0.02,0.24,0.16,0.0,0.06,0.86,0.0,4.04,1,1,0,0 +1062,ISFP,28.76,0.17886996839201388,0.1,0.18,0.18,0.0,0.02,0.68,0.0,3.6,1,1,0,1 +1063,ISTJ,34.52,0.18851847245913836,0.02,0.18,0.08,0.02,0.0,0.9,0.02,3.64,1,1,1,0 +1064,INFP,15.06,0.19484695127576193,0.0,0.4,0.7,0.0,0.0,0.62,0.0,1.64,1,0,0,1 +1065,INFP,33.72,0.18617303040000002,0.0,0.14,0.08,0.0,0.0,0.86,0.0,3.52,1,0,0,1 +1066,ENFP,19.32,0.19695237528580775,0.02,0.22,0.44,0.0,0.0,0.42,0.0,2.26,0,0,0,1 +1067,ISFP,17.44,0.17699196,0.0,0.18,0.02,0.0,0.0,0.24,0.0,2.3,1,1,0,1 +1068,INFP,29.7,0.1834046791044994,0.04,0.16,0.1,0.02,0.0,1.04,0.0,4.1,1,0,0,1 +1069,INFP,25.58,0.1877006956157018,0.06,0.2,0.0,0.0,0.02,0.6,0.0,1.24,1,0,0,1 +1070,ISTP,29.82,0.18515043719693622,0.0,0.28,0.0,0.0,0.0,1.24,0.0,1.54,1,1,1,1 +1071,INTP,29.22,0.1836565000165692,0.06,0.16,0.04,0.0,0.02,1.06,0.0,2.5,1,0,1,1 +1072,INFP,32.94,0.18711426458769748,0.02,0.24,0.34,0.0,0.06,0.96,0.0,3.48,1,0,0,1 +1073,INTJ,24.62,0.18055915852499665,0.0,0.14,0.0,0.0,0.0,0.5,0.0,2.96,1,0,1,0 +1074,INFJ,25.66,0.18396961150208327,0.0,0.18,0.44,0.0,0.0,0.46,0.0,2.52,1,0,0,0 +1075,ENFP,21.32,0.1864493574407417,0.06,0.26,0.72,0.02,0.0,0.46,0.0,1.9,0,0,0,1 +1076,INTP,21.06,0.1856216072816849,0.06,0.1,0.12,0.0,0.0,0.78,0.0,2.24,1,0,1,1 +1077,ENFJ,23.36,0.1884511023438744,0.1,0.2,0.32,0.04,0.04,0.64,0.0,1.04,0,0,0,0 +1078,INFP,25.68,0.18959138656108956,0.22,0.32,0.16,0.0,0.06,1.0,0.0,2.78,1,0,0,1 +1079,INFP,25.24,0.19182461442903703,0.02,0.2,0.08,0.0,0.0,0.9,0.0,3.9,1,0,0,1 +1080,INFP,24.12,0.18014789627237016,0.08,0.4,0.02,0.0,0.08,0.48,0.0,2.38,1,0,0,1 +1081,INFP,23.24,0.18294856864919654,0.04,0.04,0.08,0.04,0.0,0.52,0.0,2.54,1,0,0,1 +1082,ENTP,14.66,0.18150083734603475,0.16,0.36,0.0,0.08,0.0,0.34,0.0,1.44,0,0,1,1 +1083,ENTP,24.86,0.17975807525462556,0.2,0.3,0.04,0.04,0.04,0.66,0.0,1.76,0,0,1,1 +1084,INFJ,15.48,0.18192924220610543,0.16,0.28,0.2,0.04,0.0,0.42,0.0,1.76,1,0,0,0 +1085,INFP,27.76,0.17955287839031908,0.0,0.28,0.26,0.0,0.0,0.7,0.0,2.46,1,0,0,1 +1086,INFP,19.82,0.1855115112715146,0.06,0.14,0.7,0.0,0.02,0.44,0.0,2.14,1,0,0,1 +1087,INTJ,24.52,0.17505750491697658,0.02,0.08,0.0,0.02,0.0,0.44,0.0,2.48,1,0,1,0 +1088,ISFP,7.26,0.19979822755032264,0.14,0.22,0.0,0.0,0.02,0.1,0.0,1.12,1,1,0,1 +1089,ISTJ,30.14,0.1791847469162271,0.0,0.04,0.0,0.0,0.0,0.82,0.0,3.3,1,1,1,0 +1090,ENFP,25.2,0.19378120328052528,0.18,0.32,0.3,0.0,0.0,1.62,0.14,2.9,0,0,0,1 +1091,INFP,23.2,0.18788990405358655,0.06,0.06,0.24,0.0,0.0,0.72,0.0,2.56,1,0,0,1 +1092,ENTP,26.92,0.1797723041450332,0.02,0.08,0.0,0.0,0.04,0.6,0.0,2.92,0,0,1,1 +1093,ENFJ,28.92,0.19052859975676628,0.02,0.38,0.92,0.02,0.0,0.72,0.0,2.8,0,0,0,0 +1094,INFP,32.2,0.18837833778555935,0.0,0.12,1.0,0.0,0.16,1.04,0.0,4.32,1,0,0,1 +1095,INFP,26.44,0.18511157177600768,0.02,0.12,0.2,0.0,0.0,0.74,0.0,3.42,1,0,0,1 +1096,INFP,29.98,0.1858368403834851,0.02,0.44,0.36,0.0,0.0,1.04,0.0,3.56,1,0,0,1 +1097,INFP,30.36,0.1825579480022949,0.02,0.18,0.12,0.0,0.02,0.82,0.0,3.28,1,0,0,1 +1098,INFJ,32.08,0.18599911000796007,0.0,0.2,0.14,0.0,0.02,0.9,0.0,4.06,1,0,0,0 +1099,INTJ,11.5,0.17942344140558342,0.02,0.24,0.04,0.0,0.0,0.12,0.0,1.14,1,0,1,0 +1100,INFP,26.8,0.18592443155320978,0.02,0.08,0.08,0.0,0.0,0.64,0.0,2.88,1,0,0,1 +1101,INFJ,32.3,0.18416986576178307,0.0,0.06,0.12,0.0,0.02,0.74,0.0,3.84,1,0,0,0 +1102,INTP,12.56,0.18392881204230485,0.08,0.02,0.0,0.06,0.0,0.34,0.0,1.56,1,0,1,1 +1103,ISTP,29.4,0.1815948600765365,0.1,0.28,0.06,0.0,0.04,0.8,0.0,2.62,1,1,1,1 +1104,INFP,31.92,0.18284192821850032,0.0,0.02,0.06,0.0,0.0,0.82,0.0,3.2,1,0,0,1 +1105,INFJ,30.28,0.180302060592569,0.0,0.04,0.08,0.0,0.02,0.68,0.0,4.16,1,0,0,0 +1106,INTP,26.42,0.18421440552029672,0.0,0.22,0.1,0.0,0.0,0.7,0.0,3.06,1,0,1,1 +1107,INFJ,22.82,0.1842939684597929,0.12,0.26,0.3,0.0,0.0,0.62,0.0,2.46,1,0,0,0 +1108,INFP,30.68,0.1818357139380613,0.0,0.04,0.04,0.0,0.02,0.98,0.0,3.3,1,0,0,1 +1109,INTP,19.86,0.19275637607567753,0.06,0.14,0.02,0.0,0.0,0.74,0.0,2.56,1,0,1,1 +1110,INFJ,12.98,0.18125861028994597,0.28,0.04,0.1,0.12,0.0,0.26,0.0,2.14,1,0,0,0 +1111,INTP,29.86,0.18391730598657993,0.04,0.2,0.02,0.0,0.02,0.64,0.0,3.14,1,0,1,1 +1112,ESTJ,17.28,0.17639344185986972,0.1,0.08,0.0,0.08,0.0,0.46,0.0,1.3,0,1,1,0 +1113,ENTP,28.06,0.18413245235832662,0.0,0.22,0.02,0.0,0.0,0.62,0.0,2.5,0,0,1,1 +1114,INFJ,29.74,0.18378069613132933,0.0,0.14,0.16,0.0,0.0,0.62,0.0,3.62,1,0,0,0 +1115,ENTJ,25.98,0.183607459274165,0.02,0.22,0.04,0.02,0.0,0.54,0.0,2.7,0,0,1,0 +1116,INTJ,9.52,0.17802577528978905,0.0,0.1,0.06,0.0,0.0,0.3,0.0,0.7,1,0,1,0 +1117,INTJ,29.38,0.18155152177274086,0.04,0.18,0.28,0.04,0.02,0.98,0.0,1.42,1,0,1,0 +1118,ISTP,18.68,0.1877874031338235,0.16,0.1,0.02,0.12,0.0,0.42,0.0,2.14,1,1,1,1 +1119,ISTP,18.62,0.18615353954081634,0.0,0.12,0.04,0.0,0.0,0.86,0.0,2.26,1,1,1,1 +1120,ENTP,29.22,0.1828710744742713,0.06,0.1,0.12,0.04,0.0,0.88,0.0,3.12,0,0,1,1 +1121,INFJ,21.46,0.1906780245507863,0.02,0.0,0.08,0.0,0.02,0.4,0.0,2.72,1,0,0,0 +1122,INFP,29.78,0.18307313845719142,0.0,0.24,0.36,0.0,0.0,1.34,0.02,3.58,1,0,0,1 +1123,ENFP,20.88,0.18554632883627387,0.0,0.22,0.0,0.0,0.0,0.46,0.0,0.8,0,0,0,1 +1124,INTP,27.32,0.18462644696697483,0.02,0.08,0.08,0.0,0.04,0.68,0.0,3.38,1,0,1,1 +1125,INTP,26.64,0.1811365494232322,0.06,0.24,0.14,0.0,0.0,0.8,0.02,3.0,1,0,1,1 +1126,INFJ,17.04,0.1780413360413441,0.2,0.26,0.0,0.0,0.02,0.28,0.0,1.66,1,0,0,0 +1127,ISFJ,30.88,0.18659911071429722,0.04,0.18,0.12,0.0,0.0,0.82,0.0,4.18,1,1,0,0 +1128,ISTJ,33.26,0.19028698573442085,0.0,0.1,0.06,0.0,0.0,1.8,0.0,3.02,1,1,1,0 +1129,INFJ,26.24,0.18635468154601081,0.0,0.32,0.02,0.0,0.0,0.92,0.0,2.2,1,0,0,0 +1130,INFJ,25.22,0.18159355522958653,0.02,0.18,0.16,0.0,0.02,0.44,0.0,1.5,1,0,0,0 +1131,INFP,28.86,0.18588440998773553,0.08,0.14,0.14,0.02,0.0,0.58,0.0,2.8,1,0,0,1 +1132,INFP,27.5,0.1875485594025052,0.0,0.18,0.08,0.0,0.0,0.8,0.0,3.26,1,0,0,1 +1133,ENTP,28.04,0.19037169987266425,0.0,0.18,0.24,0.0,0.0,0.94,0.0,2.78,0,0,1,1 +1134,ESFP,30.66,0.1942631180448948,0.0,0.24,0.12,0.0,0.0,0.78,0.02,3.4,0,1,0,1 +1135,INTJ,33.76,0.18068852654586387,0.0,0.08,0.0,0.0,0.0,0.98,0.0,3.16,1,0,1,0 +1136,INTJ,8.38,0.1847573763595938,0.04,0.04,0.12,0.0,0.0,0.1,0.0,1.18,1,0,1,0 +1137,INFJ,27.86,0.18384112416528703,0.0,0.06,0.02,0.0,0.0,0.64,0.0,3.66,1,0,0,0 +1138,INTP,17.78,0.1910731416137198,0.0,0.28,0.12,0.0,0.0,0.56,0.0,2.18,1,0,1,1 +1139,ISFP,13.8,0.1874729700511322,0.12,0.34,0.16,0.04,0.0,0.38,0.0,1.56,1,1,0,1 +1140,INFJ,33.28,0.17957449399036293,0.06,0.12,0.18,0.0,0.02,0.88,0.0,3.9,1,0,0,0 +1141,ISTJ,10.24,0.1989804659030487,0.0,0.12,0.12,0.0,0.0,0.12,0.0,1.28,1,1,1,0 +1142,INTJ,20.32,0.18112115251121974,0.0,0.24,0.32,0.0,0.0,0.42,0.0,2.6,1,0,1,0 +1143,INFP,21.98,0.1859897206004338,0.08,0.3,0.4,0.02,0.0,0.52,0.0,2.8,1,0,0,1 +1144,ENTP,25.38,0.18224533297023382,0.12,0.3,0.06,0.12,0.0,0.72,0.0,1.72,0,0,1,1 +1145,INTP,27.74,0.1778577553098172,0.0,0.24,0.04,0.0,0.0,0.86,0.0,2.82,1,0,1,1 +1146,INTJ,25.74,0.17744372897514518,0.04,0.14,0.0,0.02,0.0,0.42,0.0,3.04,1,0,1,0 +1147,INFP,32.56,0.18204804731169086,0.0,0.22,0.38,0.0,0.0,0.9,0.0,2.94,1,0,0,1 +1148,ENFP,23.92,0.19257635049016425,0.02,0.24,0.36,0.0,0.14,0.8,0.0,3.04,0,0,0,1 +1149,INTP,20.5,0.18972673056767114,0.16,0.34,0.14,0.0,0.04,0.8,0.0,2.74,1,0,1,1 +1150,INFP,21.96,0.18622206439777056,0.0,0.22,0.32,0.0,0.04,0.38,0.0,2.26,1,0,0,1 +1151,INFP,25.66,0.19145446176005465,0.12,0.28,0.32,0.0,0.02,0.8,0.0,3.04,1,0,0,1 +1152,INFP,13.32,0.17830520003641556,0.02,0.06,0.16,0.0,0.0,0.28,0.0,1.08,1,0,0,1 +1153,INFJ,9.98,0.21205329973141032,0.02,0.18,1.3,0.0,0.0,0.9,0.0,0.56,1,0,0,0 +1154,ISFJ,32.94,0.17894011245006602,0.0,0.1,0.2,0.0,0.0,0.9,0.0,4.0,1,1,0,0 +1155,ISFJ,22.24,0.19188524974203827,0.02,0.06,0.48,0.0,0.14,0.48,0.0,3.56,1,1,0,0 +1156,INFP,26.94,0.18729898163605527,0.0,0.34,0.4,0.0,0.0,0.68,0.0,3.24,1,0,0,1 +1157,INFP,12.44,0.1920014090513847,0.02,0.22,0.06,0.0,0.04,1.02,0.0,0.78,1,0,0,1 +1158,INTJ,20.94,0.18953635660768645,0.08,0.22,0.28,0.0,0.04,0.36,0.0,2.62,1,0,1,0 +1159,ENFP,29.16,0.1884749504599462,0.06,0.12,0.14,0.0,0.04,0.58,0.0,4.0,0,0,0,1 +1160,ISFP,20.88,0.18094570692949852,0.0,0.2,0.04,0.0,0.0,0.36,0.0,2.46,1,1,0,1 +1161,ENTJ,31.78,0.19169514393210826,0.0,0.22,0.14,0.0,0.0,0.76,0.0,4.22,0,0,1,0 +1162,INTP,25.7,0.18324362871749914,0.0,0.14,0.22,0.0,0.0,0.62,0.0,3.02,1,0,1,1 +1163,ENTP,19.14,0.18808921590102062,0.24,0.36,0.12,0.08,0.0,0.48,0.0,1.72,0,0,1,1 +1164,INTJ,28.5,0.1897114250778956,0.06,0.56,0.32,0.0,0.04,1.06,0.02,3.02,1,0,1,0 +1165,INFP,30.54,0.18314089777017356,0.0,0.14,0.12,0.0,0.0,0.8,0.0,3.54,1,0,0,1 +1166,ISTJ,12.92,0.1805449860554054,0.06,0.2,0.02,0.0,0.0,0.26,0.0,1.42,1,1,1,0 +1167,INFP,26.02,0.1861253356933594,0.16,0.34,0.14,0.0,0.0,0.68,0.0,3.48,1,0,0,1 +1168,INFP,30.48,0.1920981998947728,0.0,0.1,1.34,0.0,0.04,0.84,0.0,3.78,1,0,0,1 +1169,INFP,20.34,0.18381564896851535,0.0,0.08,0.1,0.0,0.0,0.34,0.0,2.12,1,0,0,1 +1170,INTJ,26.48,0.18786409296308038,0.06,0.16,0.46,0.0,0.0,0.52,0.02,3.86,1,0,1,0 +1171,INTP,15.2,0.19287900000000002,0.28,0.26,0.0,0.1,0.04,1.16,0.0,1.04,1,0,1,1 +1172,ISTP,25.3,0.18618015011784467,0.04,0.28,0.1,0.02,0.0,0.54,0.0,2.76,1,1,1,1 +1173,ENFP,18.86,0.1917375639312313,0.0,0.2,0.16,0.0,0.0,0.48,0.0,2.38,0,0,0,1 +1174,INTJ,18.22,0.18311533888228299,0.16,0.16,0.12,0.1,0.04,0.3,0.0,2.3,1,0,1,0 +1175,INTJ,32.34,0.18366839199613122,0.02,0.22,0.12,0.0,0.0,0.84,0.0,3.54,1,0,1,0 +1176,ISFP,23.5,0.17785210726284228,0.24,0.12,0.04,0.06,0.02,0.54,0.0,2.58,1,1,0,1 +1177,ENTP,23.1,0.19068641138842043,0.02,0.26,0.24,0.0,0.0,0.56,0.0,2.78,0,0,1,1 +1178,INFJ,30.1,0.18409159087145482,0.08,0.18,0.02,0.0,0.0,1.14,0.0,3.16,1,0,0,0 +1179,INFP,32.44,0.1833457184576906,0.0,0.14,0.06,0.0,0.02,0.92,0.0,4.34,1,0,0,1 +1180,INTP,12.7,0.18447164660320797,0.16,0.28,0.0,0.0,0.04,0.24,0.0,0.9,1,0,1,1 +1181,ENTP,16.38,0.18998145111286552,0.0,0.2,0.6,0.0,0.0,0.34,0.0,1.56,0,0,1,1 +1182,INFP,16.38,0.19299548327084784,0.02,0.2,0.1,0.0,0.0,0.3,0.0,2.42,1,0,0,1 +1183,INFP,32.58,0.184761496032974,0.02,0.26,0.24,0.0,0.04,0.9,0.0,4.32,1,0,0,1 +1184,ISFP,26.18,0.18595623999359945,0.02,0.32,0.22,0.0,0.02,0.62,0.0,2.16,1,1,0,1 +1185,ENFP,23.68,0.19253791058747866,0.04,0.14,0.36,0.0,0.02,1.3,0.0,2.3,0,0,0,1 +1186,INTP,26.12,0.18081898664803972,0.0,0.16,0.02,0.0,0.02,0.54,0.0,2.82,1,0,1,1 +1187,ENFP,19.04,0.195571180306356,0.16,0.18,0.34,0.14,0.02,0.26,0.0,1.54,0,0,0,1 +1188,INFP,25.78,0.1927608961366943,0.02,0.4,0.08,0.0,0.04,0.68,0.0,2.92,1,0,0,1 +1189,INFP,25.4,0.18842147010570495,0.06,0.22,0.3,0.0,0.0,0.7,0.0,3.16,1,0,0,1 +1190,INTJ,20.7,0.18932058497065168,0.0,0.12,0.24,0.0,0.0,0.62,0.04,2.06,1,0,1,0 +1191,INFP,27.78,0.18350425224471575,0.0,0.28,0.46,0.0,0.0,0.66,0.0,3.5,1,0,0,1 +1192,ESTP,27.56,0.17791778856173482,0.08,0.2,0.02,0.0,0.0,0.72,0.0,3.04,0,1,1,1 +1193,INTJ,28.42,0.18187117278555362,0.0,0.3,0.12,0.0,0.0,0.92,0.0,2.34,1,0,1,0 +1194,INFP,24.3,0.19216126166327477,0.0,0.16,0.24,0.0,0.0,0.5,0.0,3.5,1,0,0,1 +1195,INFJ,16.46,0.19303849966061343,0.0,0.22,1.14,0.0,0.0,0.32,0.0,1.96,1,0,0,0 +1196,INTJ,17.16,0.1805516477086177,0.0,0.1,0.0,0.0,0.0,0.24,0.02,1.74,1,0,1,0 +1197,INFJ,29.46,0.18292654118931848,0.02,0.18,0.24,0.0,0.0,1.16,0.0,3.34,1,0,0,0 +1198,INFP,21.8,0.18494826119341182,0.06,0.02,0.2,0.06,0.0,0.48,0.0,2.36,1,0,0,1 +1199,ENTP,23.56,0.17172458297238213,0.02,0.2,0.3,0.02,0.0,0.42,0.0,0.9,0,0,1,1 +1200,INTP,22.7,0.18619401382697548,0.24,0.36,0.22,0.14,0.0,0.5,0.0,2.2,1,0,1,1 +1201,ISTJ,31.78,0.17614116742765326,0.04,0.1,0.32,0.0,0.02,1.14,0.0,3.58,1,1,1,0 +1202,INFP,19.4,0.18439288164782353,0.04,0.24,0.2,0.0,0.02,0.38,0.0,1.52,1,0,0,1 +1203,ENFP,27.86,0.1879977580985193,0.0,0.3,0.74,0.0,0.04,1.54,0.0,2.2,0,0,0,1 +1204,ENTP,25.34,0.18236281628233109,0.0,0.3,0.02,0.0,0.0,0.62,0.0,2.66,0,0,1,1 +1205,INFJ,17.38,0.1916414586551462,0.1,0.2,0.18,0.0,0.0,0.5,0.0,2.12,1,0,0,0 +1206,ISTJ,28.26,0.18655060773318063,0.02,0.36,0.34,0.0,0.0,0.68,0.1,3.64,1,1,1,0 +1207,ESTP,22.48,0.1902774270925388,0.0,0.32,0.12,0.0,0.0,0.6,0.0,2.8,0,1,1,1 +1208,INTJ,9.52,0.16920817868185778,0.56,0.92,0.42,0.0,0.04,0.42,0.0,0.6,1,0,1,0 +1209,ENTP,23.02,0.18406774925063046,0.0,0.26,0.16,0.0,0.0,0.56,0.0,2.8,0,0,1,1 +1210,INFJ,30.76,0.18448874320782618,0.0,0.24,0.06,0.0,0.0,0.76,0.0,3.44,1,0,0,0 +1211,ESTP,24.76,0.17987151385262992,0.02,0.26,0.06,0.0,0.04,0.52,0.0,1.56,0,1,1,1 +1212,ENFP,30.52,0.18289663407534087,0.0,0.22,0.44,0.0,0.0,1.0,0.0,2.56,0,0,0,1 +1213,INTJ,24.6,0.1853426606422832,0.14,0.26,0.06,0.0,0.0,0.58,0.0,2.64,1,0,1,0 +1214,ENTP,24.02,0.18422375587572307,0.0,0.28,0.02,0.0,0.0,0.62,0.0,1.5,0,0,1,1 +1215,ENTP,20.06,0.18797301388721527,0.0,0.08,0.02,0.0,0.02,0.32,0.0,1.48,0,0,1,1 +1216,ISTJ,16.42,0.17732331283967612,0.02,0.1,0.04,0.0,0.0,0.44,0.0,2.12,1,1,1,0 +1217,ENTP,25.22,0.18274113139982737,0.02,0.14,0.22,0.02,0.02,0.56,0.0,2.48,0,0,1,1 +1218,ESFP,12.24,0.1774654062444769,0.34,0.18,0.04,0.14,0.02,0.22,0.0,1.58,0,1,0,1 +1219,INFP,20.52,0.17844583007819584,0.16,0.12,0.02,0.06,0.0,0.4,0.0,1.12,1,0,0,1 +1220,ENTP,24.34,0.17960980264471033,0.02,0.12,0.18,0.0,0.0,0.68,0.06,2.16,0,0,1,1 +1221,ENFJ,18.54,0.19443032159801124,0.12,0.14,1.04,0.04,0.02,0.72,0.0,2.5,0,0,0,0 +1222,INFJ,29.98,0.19081519616885356,0.04,0.26,0.66,0.0,0.0,1.02,0.0,3.14,1,0,0,0 +1223,INFP,30.62,0.1903526070659127,0.0,0.26,0.38,0.0,0.04,0.7,0.0,3.62,1,0,0,1 +1224,ENFP,28.2,0.1903428533951754,0.02,0.16,0.16,0.0,0.04,0.62,0.0,4.04,0,0,0,1 +1225,INTJ,24.3,0.18840201426121775,0.06,0.34,0.14,0.0,0.0,0.64,0.0,3.14,1,0,1,0 +1226,INFP,23.76,0.18579014289594117,0.1,0.32,0.42,0.0,0.0,0.54,0.0,2.62,1,0,0,1 +1227,ESFJ,30.66,0.18579394818789302,0.0,0.3,0.24,0.0,0.0,0.68,0.0,3.98,0,1,0,0 +1228,INFP,16.78,0.1963400847520914,0.0,0.2,0.04,0.0,0.0,0.36,0.0,1.56,1,0,0,1 +1229,INTJ,27.9,0.18314153990489632,0.02,0.2,0.12,0.0,0.0,0.66,0.0,2.9,1,0,1,0 +1230,INFJ,28.6,0.18583851823631967,0.0,0.12,0.18,0.0,0.0,1.0,0.0,3.08,1,0,0,0 +1231,INTJ,15.44,0.17784044956688475,0.24,0.36,0.02,0.0,0.0,0.28,0.0,1.42,1,0,1,0 +1232,INTJ,19.52,0.18729444184241015,0.1,0.32,0.36,0.0,0.02,0.44,0.0,1.6,1,0,1,0 +1233,INFP,16.64,0.1833587326437815,0.34,0.12,0.04,0.22,0.04,0.24,0.0,2.06,1,0,0,1 +1234,ENTP,24.46,0.18367470710847603,0.0,0.22,0.04,0.0,0.04,0.44,0.0,3.08,0,0,1,1 +1235,INTJ,23.48,0.1916954193932183,0.12,0.14,0.0,0.04,0.0,0.78,0.02,1.68,1,0,1,0 +1236,ENFP,26.5,0.1902229812338683,0.2,0.22,0.14,0.1,0.0,1.64,0.0,3.06,0,0,0,1 +1237,INFJ,33.36,0.18698319227672322,0.0,0.06,0.04,0.0,0.0,0.9,0.0,4.44,1,0,0,0 +1238,INFP,21.44,0.18030581910957685,0.2,0.18,0.04,0.12,0.02,0.54,0.0,2.62,1,0,0,1 +1239,ISFP,20.12,0.1946693940054435,0.26,0.12,0.08,0.16,0.04,0.4,0.0,2.44,1,1,0,1 +1240,ESTJ,27.8,0.18055839445968044,0.06,0.16,0.0,0.0,0.0,0.62,0.02,2.7,0,1,1,0 +1241,INFP,23.54,0.19630222330684166,0.04,0.14,0.74,0.02,0.0,1.2,0.0,2.58,1,0,0,1 +1242,ENFP,24.18,0.19555555555555557,0.04,0.14,0.68,0.0,0.0,1.04,0.0,3.08,0,0,0,1 +1243,INTP,30.36,0.19378253824966127,0.0,0.22,0.12,0.0,0.0,0.84,0.0,4.86,1,0,1,1 +1244,INTJ,25.2,0.18724120110492157,0.08,0.2,0.18,0.04,0.0,0.66,0.02,2.7,1,0,1,0 +1245,INTJ,20.42,0.17914956424140446,0.06,0.2,0.16,0.0,0.0,0.34,0.04,1.52,1,0,1,0 +1246,INFP,19.5,0.17325784092882254,0.04,0.06,0.04,0.02,0.0,0.58,0.0,2.0,1,0,0,1 +1247,ENTJ,28.2,0.18539561091292578,0.0,0.08,0.14,0.0,0.0,0.6,0.0,3.7,0,0,1,0 +1248,ENFP,32.1,0.18711563894659897,0.0,0.16,0.24,0.0,0.0,0.86,0.0,4.34,0,0,0,1 +1249,ENFJ,32.12,0.1897749403725831,0.02,0.12,0.3,0.0,0.06,1.42,0.0,3.02,0,0,0,0 +1250,INFP,8.34,0.17954112119645133,0.06,0.12,0.14,0.0,0.0,0.14,0.0,0.8,1,0,0,1 +1251,ISFP,27.68,0.18187893602155625,0.02,0.3,0.22,0.0,0.0,0.88,0.0,3.16,1,1,0,1 +1252,INTP,23.0,0.18162471397252922,0.0,0.18,0.0,0.0,0.0,0.46,0.02,2.28,1,0,1,1 +1253,INFP,32.14,0.18531633246312937,0.04,0.04,0.2,0.0,0.0,1.0,0.0,4.18,1,0,0,1 +1254,INFJ,23.74,0.18601896372260132,0.0,0.12,0.04,0.0,0.0,0.5,0.0,2.78,1,0,0,0 +1255,ENFP,21.16,0.18877778985044313,0.0,0.36,0.02,0.0,0.0,0.46,0.0,2.1,0,0,0,1 +1256,INTJ,30.8,0.18045565695267213,0.0,0.22,0.12,0.0,0.0,0.82,0.0,3.38,1,0,1,0 +1257,INFP,24.22,0.1787556596701065,0.34,0.26,0.0,0.02,0.08,0.92,0.02,2.36,1,0,0,1 +1258,INFP,17.92,0.18657570820242417,0.0,0.14,0.14,0.0,0.0,0.36,0.02,2.24,1,0,0,1 +1259,INFJ,31.06,0.18527160254813896,0.02,0.22,0.12,0.0,0.0,0.76,0.0,4.38,1,0,0,0 +1260,INFJ,30.7,0.18755554320987655,0.0,0.18,0.34,0.0,0.04,0.92,0.0,3.32,1,0,0,0 +1261,ENTP,27.3,0.17967457098172335,0.02,0.34,0.02,0.0,0.02,0.78,0.0,2.58,0,0,1,1 +1262,INFJ,18.58,0.19469103954081635,0.08,0.16,0.42,0.0,0.0,0.4,0.0,2.8,1,0,0,0 +1263,INTP,29.66,0.184028220685191,0.1,0.22,0.0,0.0,0.0,0.84,0.0,3.34,1,0,1,1 +1264,ENTP,28.46,0.1806671033488603,0.02,0.0,0.0,0.02,0.0,0.66,0.0,2.38,0,0,1,1 +1265,INFP,31.78,0.18030736480272244,0.0,0.18,0.0,0.0,0.0,0.82,0.0,2.52,1,0,0,1 +1266,ENFP,24.4,0.19022630965990814,0.12,0.14,0.96,0.02,0.06,0.74,0.02,2.7,0,0,0,1 +1267,ENTJ,17.44,0.18000038229162738,0.1,0.38,0.0,0.04,0.02,0.26,0.0,1.62,0,0,1,0 +1268,ISFJ,26.78,0.1847165118030822,0.06,0.46,0.24,0.04,0.0,1.12,0.0,0.84,1,1,0,0 +1269,INFJ,30.26,0.17798312345856976,0.04,0.22,0.02,0.0,0.0,0.72,0.0,2.96,1,0,0,0 +1270,INTP,24.6,0.18906592525027557,0.0,0.08,0.14,0.0,0.0,0.46,0.0,2.94,1,0,1,1 +1271,INFP,30.56,0.18842692282182305,0.16,0.2,0.54,0.0,0.0,1.04,0.0,3.84,1,0,0,1 +1272,INTP,31.98,0.1806114508231556,0.02,0.32,0.0,0.0,0.0,0.82,0.0,3.98,1,0,1,1 +1273,INFP,32.28,0.18427498951419855,0.0,0.08,0.02,0.0,0.04,0.82,0.0,4.62,1,0,0,1 +1274,INTJ,15.14,0.18317224988682665,0.12,0.34,0.24,0.0,0.0,0.42,0.0,1.5,1,0,1,0 +1275,INTJ,30.1,0.1854741031294575,0.0,0.16,0.04,0.0,0.02,0.68,0.0,3.68,1,0,1,0 +1276,INFJ,27.94,0.18365278141538263,0.1,0.14,0.06,0.0,0.02,0.82,0.0,3.38,1,0,0,0 +1277,INFJ,34.6,0.18727930120508418,0.0,0.24,0.08,0.0,0.0,0.98,0.0,4.22,1,0,0,0 +1278,ENTP,31.76,0.18877884639538256,0.0,0.22,0.28,0.0,0.04,0.76,0.0,4.34,0,0,1,1 +1279,ESTP,32.9,0.18363631904459984,0.02,0.32,0.16,0.0,0.0,0.94,0.0,4.46,0,1,1,1 +1280,ISTP,25.18,0.18218315972222227,0.0,0.24,0.02,0.0,0.0,0.48,0.0,2.62,1,1,1,1 +1281,ENTP,28.38,0.1769174489774804,0.02,0.16,0.04,0.0,0.02,0.64,0.0,3.02,0,0,1,1 +1282,INFJ,31.04,0.18065867512163505,0.04,0.22,0.04,0.04,0.0,0.86,0.0,2.14,1,0,0,0 +1283,INTP,20.04,0.18790413731744476,0.06,0.08,0.08,0.04,0.0,0.92,0.02,2.74,1,0,1,1 +1284,INFJ,36.66,0.1877590953410509,0.0,0.06,0.04,0.0,0.02,0.78,0.0,3.38,1,0,0,0 +1285,ENTP,31.92,0.18251615118129896,0.0,0.0,0.1,0.0,0.02,1.28,0.0,2.28,0,0,1,1 +1286,INTJ,27.28,0.18690617910648905,0.04,0.34,0.22,0.0,0.0,0.92,0.0,2.82,1,0,1,0 +1287,ENTP,30.34,0.1869695724503182,0.02,0.14,0.18,0.0,0.0,1.02,0.02,3.96,0,0,1,1 +1288,INTJ,21.76,0.1860437381672903,0.08,0.18,0.22,0.02,0.02,1.2,0.0,3.06,1,0,1,0 +1289,INTP,10.98,0.18372863425561103,0.2,0.18,0.04,0.0,0.0,0.12,0.0,1.4,1,0,1,1 +1290,ISTP,24.38,0.1816390967200234,0.04,0.28,0.36,0.04,0.0,0.74,0.0,2.4,1,1,1,1 +1291,INTP,22.56,0.18682371998084984,0.1,0.14,0.06,0.04,0.0,0.76,0.0,2.72,1,0,1,1 +1292,INFJ,29.74,0.18403194241285104,0.0,0.18,0.18,0.0,0.02,1.04,0.0,2.88,1,0,0,0 +1293,ENFP,29.38,0.1898056615173732,0.02,0.04,0.56,0.0,0.04,0.9,0.0,3.28,0,0,0,1 +1294,ISTJ,26.38,0.18758265554871856,0.02,0.18,0.0,0.0,0.0,0.8,0.02,2.88,1,1,1,0 +1295,INTP,29.74,0.18503376172733843,0.0,0.26,0.08,0.0,0.02,0.78,0.0,4.12,1,0,1,1 +1296,INFP,15.82,0.18330691315057293,0.36,0.38,0.14,0.0,0.02,0.26,0.0,1.98,1,0,0,1 +1297,INFJ,23.28,0.18705994659629516,0.16,0.38,0.4,0.1,0.0,1.28,0.0,2.44,1,0,0,0 +1298,INFJ,29.14,0.1817658662467784,0.0,0.3,1.0,0.0,0.0,0.72,0.0,3.4,1,0,0,0 +1299,INFJ,17.98,0.18539005607359804,0.0,0.16,0.2,0.0,0.0,0.34,0.0,2.38,1,0,0,0 +1300,INFP,22.56,0.19227255721650652,0.04,0.26,0.48,0.02,0.0,0.72,0.0,2.62,1,0,0,1 +1301,INFJ,22.2,0.1864230967356306,0.02,0.32,0.16,0.0,0.0,0.38,0.0,2.16,1,0,0,0 +1302,INFP,5.88,0.18989755403637515,0.0,0.14,0.02,0.0,0.0,0.2,0.0,0.66,1,0,0,1 +1303,ESTP,29.66,0.1830431259427083,0.0,0.38,0.42,0.0,0.02,0.9,0.0,2.28,0,1,1,1 +1304,INFJ,29.32,0.18554454367535406,0.0,0.1,0.12,0.0,0.0,0.72,0.0,3.66,1,0,0,0 +1305,INFP,31.86,0.19103887523387023,0.0,0.14,0.36,0.0,0.0,0.92,0.0,4.6,1,0,0,1 +1306,ISFJ,28.72,0.18746845425917721,0.12,0.02,0.0,0.12,0.0,0.72,0.0,1.56,1,1,0,0 +1307,INFP,21.92,0.18329294926258388,0.06,0.22,0.6,0.0,0.02,0.62,0.0,0.82,1,0,0,1 +1308,ENTP,27.8,0.18354795971846677,0.12,0.5,0.0,0.0,0.04,0.74,0.0,2.8,0,0,1,1 +1309,ENTP,29.06,0.18233610865945632,0.0,0.32,0.02,0.0,0.0,0.88,0.02,2.28,0,0,1,1 +1310,INFP,20.4,0.18482111902452159,0.44,0.46,0.18,0.0,0.02,0.62,0.0,2.82,1,0,0,1 +1311,INFP,29.88,0.18570608516148612,0.0,0.16,0.12,0.0,0.0,0.84,0.0,2.78,1,0,0,1 +1312,ENFP,23.38,0.19121344092678222,0.04,0.32,2.1,0.0,0.04,0.56,0.0,2.74,0,0,0,1 +1313,ISTJ,25.7,0.19386354176738574,0.06,0.12,0.0,0.02,0.0,0.64,0.0,2.12,1,1,1,0 +1314,INFP,32.22,0.19584402491110528,0.06,0.12,0.16,0.0,0.02,1.52,0.0,3.92,1,0,0,1 +1315,INTJ,37.22,0.18529516880686658,0.02,0.02,0.04,0.0,0.0,0.88,0.02,4.7,1,0,1,0 +1316,INFP,36.6,0.18534432923590094,0.0,0.18,0.14,0.0,0.02,1.56,0.0,4.6,1,0,0,1 +1317,INFP,25.3,0.19023065565905428,0.04,0.08,0.0,0.04,0.0,0.48,0.0,4.26,1,0,0,1 +1318,INFJ,32.2,0.17407066857490647,0.0,0.06,0.0,0.0,0.04,0.94,0.0,3.76,1,0,0,0 +1319,ISFP,24.7,0.19443379880111378,0.02,0.26,0.38,0.0,0.02,1.84,0.0,3.9,1,1,0,1 +1320,INFP,6.02,0.19688509944182128,0.0,0.0,0.0,0.0,0.0,0.28,0.0,0.54,1,0,0,1 +1321,INFJ,31.92,0.1796332157394448,0.04,0.08,0.02,0.0,0.0,0.84,0.0,3.74,1,0,0,0 +1322,ENTP,22.56,0.19531816998810236,0.0,0.2,0.1,0.0,0.0,0.6,0.0,2.24,0,0,1,1 +1323,INTP,33.22,0.18350667834159792,0.0,0.16,0.02,0.0,0.02,0.84,0.0,4.2,1,0,1,1 +1324,INTP,24.66,0.18204458526016606,0.02,0.32,0.02,0.0,0.0,0.66,0.0,3.18,1,0,1,1 +1325,ISTP,34.64,0.18517333243693987,0.0,0.26,0.3,0.0,0.0,1.02,0.0,2.62,1,1,1,1 +1326,ENTP,29.22,0.17822118258752032,0.02,0.3,0.16,0.0,0.04,0.8,0.0,2.9,0,0,1,1 +1327,INTP,23.64,0.17834563884580704,0.2,0.3,0.02,0.0,0.02,0.6,0.0,1.94,1,0,1,1 +1328,ISTP,27.02,0.1863871389930719,0.02,0.06,0.06,0.02,0.0,0.46,0.02,2.98,1,1,1,1 +1329,INFJ,31.28,0.186145637519208,0.04,0.2,0.12,0.02,0.0,0.86,0.02,4.22,1,0,0,0 +1330,ISTP,29.16,0.19398236510505548,0.0,0.24,0.22,0.0,0.0,1.08,0.0,3.96,1,1,1,1 +1331,INFP,25.72,0.18640533005664903,0.0,0.12,0.3,0.0,0.0,0.62,0.0,3.04,1,0,0,1 +1332,INTP,10.84,0.17805514292807584,0.0,0.04,0.02,0.0,0.0,0.24,0.0,1.26,1,0,1,1 +1333,INTP,21.32,0.18198362903906337,0.0,0.16,0.06,0.0,0.0,0.48,0.0,2.12,1,0,1,1 +1334,ENFJ,27.38,0.18916554417788498,0.06,0.18,0.3,0.0,0.0,0.62,0.0,3.28,0,0,0,0 +1335,ENFP,27.94,0.18670789668300447,0.0,0.2,0.16,0.0,0.04,0.64,0.0,3.18,0,0,0,1 +1336,ENFJ,34.64,0.1845674754759199,0.0,0.22,0.24,0.0,0.0,1.12,0.0,3.28,0,0,0,0 +1337,INTP,28.72,0.1924360394172315,0.0,0.16,0.04,0.0,0.0,0.6,0.0,3.4,1,0,1,1 +1338,INFJ,7.54,0.18137110284189875,0.78,0.88,0.16,0.02,0.06,0.22,0.0,0.9,1,0,0,0 +1339,ENTJ,14.16,0.18297043423438114,0.12,0.12,0.02,0.02,0.0,0.22,0.0,1.42,0,0,1,0 +1340,INTP,29.1,0.19280144129900076,0.02,0.26,0.18,0.02,0.02,0.74,0.0,2.64,1,0,1,1 +1341,ENTJ,22.42,0.1801180984224966,0.02,0.18,0.0,0.02,0.0,0.42,0.0,2.5,0,0,1,0 +1342,INTJ,8.58,0.18783960736694505,0.0,0.02,0.1,0.0,0.0,0.18,0.0,0.94,1,0,1,0 +1343,INFP,13.64,0.15973174381489968,0.78,0.0,0.04,0.78,0.0,0.28,0.04,0.76,1,0,0,1 +1344,ISTP,26.58,0.19740145787133265,0.26,0.12,0.22,0.24,0.0,1.64,0.0,3.32,1,1,1,1 +1345,ENFP,24.2,0.2027155560767553,0.0,0.2,0.34,0.0,0.0,0.48,0.0,1.54,0,0,0,1 +1346,INFP,17.12,0.1913275579333848,0.26,0.52,0.04,0.1,0.02,0.22,0.0,2.48,1,0,0,1 +1347,ENFP,3.66,0.19625243726241076,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.44,0,0,0,1 +1348,INFJ,27.82,0.18710061447377588,0.0,0.08,0.04,0.0,0.0,0.64,0.0,3.26,1,0,0,0 +1349,ISFJ,34.68,0.18583145359385048,0.0,0.28,0.34,0.0,0.0,1.08,0.0,4.1,1,1,0,0 +1350,INFJ,20.32,0.1945046116576442,0.02,0.24,0.44,0.0,0.02,0.34,0.0,2.5,1,0,0,0 +1351,INFJ,30.96,0.18434723230205094,0.0,0.08,0.16,0.0,0.08,1.12,0.0,3.38,1,0,0,0 +1352,INFJ,22.36,0.1900272923996325,0.0,0.12,0.84,0.0,0.0,0.7,0.0,1.22,1,0,0,0 +1353,INFJ,32.38,0.1797065745377361,0.02,0.22,0.42,0.0,0.02,0.92,0.0,3.4,1,0,0,0 +1354,INFJ,25.3,0.17975241199896028,0.04,0.16,0.1,0.02,0.04,0.56,0.0,3.44,1,0,0,0 +1355,INTJ,22.76,0.18311284746229078,0.16,0.18,0.04,0.04,0.0,0.54,0.0,2.5,1,0,1,0 +1356,INFP,26.54,0.19118351879563186,0.04,0.36,0.32,0.0,0.04,0.86,0.0,2.58,1,0,0,1 +1357,INFP,25.62,0.18626700490475603,0.02,0.16,0.22,0.0,0.04,0.58,0.0,3.5,1,0,0,1 +1358,INFJ,27.5,0.18918784398645505,0.04,0.46,0.26,0.0,0.0,0.96,0.02,2.34,1,0,0,0 +1359,INFJ,31.68,0.17669227230884083,0.0,0.06,0.14,0.0,0.0,0.82,0.02,2.9,1,0,0,0 +1360,INFP,24.54,0.18412513224299545,0.0,0.12,0.14,0.0,0.06,0.96,0.0,3.1,1,0,0,1 +1361,ISTJ,28.54,0.18117552102819418,0.06,0.08,0.04,0.0,0.02,0.74,0.0,3.02,1,1,1,0 +1362,ENFP,29.8,0.1799850867544992,0.02,0.38,0.88,0.0,0.0,1.26,0.0,2.36,0,0,0,1 +1363,INFP,29.32,0.18723663039640634,0.06,0.16,0.1,0.0,0.02,0.62,0.02,3.68,1,0,0,1 +1364,ENTJ,15.54,0.18935778040762657,0.06,0.12,0.08,0.0,0.0,0.18,0.0,2.3,0,0,1,0 +1365,ENFP,28.02,0.18982197426153455,0.04,0.4,1.38,0.0,0.02,0.52,0.0,2.88,0,0,0,1 +1366,ENFP,20.96,0.18568220033582264,0.14,0.28,0.66,0.0,0.04,0.56,0.0,2.56,0,0,0,1 +1367,ENTP,23.54,0.18779832892976994,0.0,0.3,0.14,0.0,0.0,0.74,0.0,2.28,0,0,1,1 +1368,ESTJ,25.5,0.18496932058868754,0.1,0.3,0.12,0.0,0.0,0.66,0.0,2.42,0,1,1,0 +1369,INFJ,16.44,0.1877484263855371,0.5,0.62,0.18,0.0,0.02,0.46,0.0,1.62,1,0,0,0 +1370,INFJ,23.14,0.19895291898231715,0.12,0.22,0.34,0.04,0.0,1.76,0.02,2.32,1,0,0,0 +1371,INTP,32.08,0.19638022221539042,0.0,0.26,1.02,0.0,0.0,1.28,0.0,1.18,1,0,1,1 +1372,INFJ,16.8,0.18161402184328088,0.02,0.24,0.04,0.0,0.0,0.2,0.02,1.46,1,0,0,0 +1373,INTP,20.08,0.18104843820918184,0.02,0.32,0.08,0.0,0.0,0.34,0.0,2.6,1,0,1,1 +1374,ISTP,27.42,0.1832649517223653,0.1,0.36,0.04,0.0,0.0,0.66,0.0,1.86,1,1,1,1 +1375,INFP,25.64,0.18456616106271334,0.1,0.14,0.06,0.0,0.0,0.64,0.0,2.66,1,0,0,1 +1376,INFP,31.46,0.18897320298865788,0.0,0.18,0.2,0.0,0.0,0.74,0.0,4.74,1,0,0,1 +1377,INTP,28.18,0.18134352324263037,0.02,0.2,0.12,0.02,0.0,0.84,0.0,3.26,1,0,1,1 +1378,INFJ,31.5,0.18717148545408205,0.0,0.18,0.08,0.0,0.02,0.84,0.0,4.34,1,0,0,0 +1379,INTP,28.66,0.18252901639756547,0.08,0.22,0.06,0.04,0.06,0.84,0.0,3.26,1,0,1,1 +1380,ENFP,27.24,0.1882891237332136,0.06,0.18,0.4,0.0,0.1,1.0,0.0,2.84,0,0,0,1 +1381,INFJ,28.04,0.1841144559822536,0.0,0.34,0.02,0.0,0.0,0.68,0.0,3.82,1,0,0,0 +1382,INFJ,30.36,0.18343356519512635,0.02,0.04,0.06,0.0,0.0,0.7,0.0,3.1,1,0,0,0 +1383,INTP,14.26,0.18957799197470016,0.1,0.18,0.1,0.02,0.04,0.12,0.0,1.38,1,0,1,1 +1384,INFJ,23.68,0.19879877696340797,0.12,0.16,0.12,0.0,0.0,2.0,0.0,2.08,1,0,0,0 +1385,ENFJ,22.48,0.18988744042846822,0.0,0.14,0.46,0.0,0.02,0.54,0.0,2.6,0,0,0,0 +1386,INFJ,9.98,0.19592234722193905,0.02,0.1,0.06,0.0,0.02,0.16,0.0,1.44,1,0,0,0 +1387,ENFP,18.52,0.18800993214486078,0.2,0.14,0.44,0.02,0.02,0.38,0.0,1.1,0,0,0,1 +1388,ENTP,21.96,0.18933364070955377,0.06,0.3,0.06,0.04,0.0,0.5,0.0,2.12,0,0,1,1 +1389,ISTP,18.94,0.18273654620738722,0.4,0.48,0.18,0.06,0.0,0.5,0.02,2.5,1,1,1,1 +1390,ENTJ,31.52,0.17856787884345743,0.0,0.34,0.32,0.0,0.0,1.22,0.0,2.46,0,0,1,0 +1391,INFP,30.06,0.18578089105264065,0.0,0.02,0.06,0.0,0.0,0.7,0.0,3.42,1,0,0,1 +1392,ENFP,31.72,0.18531536019811323,0.08,0.3,0.18,0.0,0.1,0.76,0.0,3.12,0,0,0,1 +1393,INTP,23.88,0.18119450078677826,0.02,0.4,0.0,0.02,0.0,0.46,0.0,2.22,1,0,1,1 +1394,INTP,15.18,0.1915225010628343,0.08,0.38,0.26,0.0,0.0,0.2,0.0,1.58,1,0,1,1 +1395,INFP,22.96,0.194894552678881,0.0,0.3,0.0,0.0,0.0,0.78,0.0,3.46,1,0,0,1 +1396,INFP,32.66,0.18980652985639243,0.0,0.0,0.86,0.0,0.0,1.66,0.0,4.3,1,0,0,1 +1397,ENTP,27.64,0.17858857391694893,0.02,0.08,0.42,0.0,0.04,0.56,0.0,3.24,0,0,1,1 +1398,ISFP,29.14,0.17512314049586775,0.04,0.16,0.02,0.0,0.06,0.68,0.0,2.54,1,1,0,1 +1399,INFJ,22.24,0.18668727699981094,0.1,0.28,0.02,0.08,0.06,0.38,0.0,2.36,1,0,0,0 +1400,INFJ,22.12,0.18803549931745983,0.0,0.22,0.0,0.0,0.0,0.48,0.0,2.94,1,0,0,0 +1401,INFP,18.98,0.18896799713722903,0.14,0.16,0.04,0.0,0.02,0.46,0.0,2.58,1,0,0,1 +1402,ISTP,22.18,0.17899433179485325,0.06,0.06,0.0,0.06,0.0,0.46,0.0,1.94,1,1,1,1 +1403,INFP,31.02,0.18054667568849941,0.0,0.0,0.02,0.0,0.02,0.72,0.0,1.56,1,0,0,1 +1404,INFP,29.72,0.18798191887521412,0.04,0.54,0.2,0.0,0.0,0.86,0.0,3.4,1,0,0,1 +1405,INFJ,29.24,0.1853096330725746,0.0,0.3,0.04,0.0,0.0,0.96,0.0,2.54,1,0,0,0 +1406,INFP,32.66,0.17905175533313722,0.0,0.12,0.0,0.0,0.04,1.04,0.0,3.58,1,0,0,1 +1407,INFP,33.04,0.18980012284066627,0.0,0.18,0.14,0.0,0.0,0.82,0.0,3.58,1,0,0,1 +1408,ENTP,26.06,0.18587038507570142,0.02,0.24,0.62,0.0,0.0,0.78,0.02,2.66,0,0,1,1 +1409,ISTP,31.82,0.18764496110161624,0.0,0.24,0.28,0.0,0.0,0.82,0.0,3.28,1,1,1,1 +1410,INTP,15.86,0.18223935216182555,0.0,0.18,0.0,0.0,0.08,0.36,0.0,1.6,1,0,1,1 +1411,INTJ,23.04,0.18528914169974062,0.0,0.64,0.02,0.0,0.0,0.48,0.0,1.88,1,0,1,0 +1412,ISTJ,27.02,0.1803953147625517,0.12,0.18,0.06,0.0,0.08,0.5,0.0,2.96,1,1,1,0 +1413,INFP,20.32,0.18520047539690293,0.1,0.24,0.02,0.04,0.02,0.32,0.04,2.58,1,0,0,1 +1414,INFJ,29.76,0.189608508928825,0.0,0.18,0.14,0.0,0.0,0.7,0.0,3.68,1,0,0,0 +1415,ENFJ,27.92,0.1820687034822026,0.04,0.4,0.12,0.0,0.0,0.82,0.0,1.88,0,0,0,0 +1416,INFP,34.12,0.17628083809802383,0.02,0.08,0.0,0.0,0.0,0.94,0.0,3.92,1,0,0,1 +1417,INFP,28.34,0.1908714933622753,0.0,0.28,0.28,0.0,0.02,0.54,0.0,1.1,1,0,0,1 +1418,ENTP,25.04,0.185258024691358,0.02,0.32,0.04,0.02,0.0,0.44,0.0,3.38,0,0,1,1 +1419,INTP,12.32,0.18689155691510506,0.0,0.1,0.02,0.0,0.02,0.26,0.0,1.4,1,0,1,1 +1420,INTP,26.74,0.18472921747309937,0.0,0.14,0.12,0.0,0.0,0.84,0.0,3.12,1,0,1,1 +1421,ISFP,25.62,0.18619679283695742,0.2,0.18,0.26,0.02,0.04,0.88,0.0,2.94,1,1,0,1 +1422,INFJ,10.04,0.1816942373333842,0.66,0.82,0.04,0.0,0.0,0.32,0.0,0.86,1,0,0,0 +1423,INFP,28.7,0.18765297999404829,0.0,0.2,0.7,0.0,0.0,0.78,0.02,3.3,1,0,0,1 +1424,INFJ,32.92,0.18859388664654267,0.02,0.04,0.38,0.0,0.02,0.96,0.0,4.68,1,0,0,0 +1425,INFP,8.16,0.19049556454835406,0.02,0.04,0.08,0.0,0.0,0.18,0.0,0.76,1,0,0,1 +1426,INTP,28.54,0.18155299228745003,0.04,0.28,0.08,0.0,0.0,0.7,0.0,2.62,1,0,1,1 +1427,INFJ,26.5,0.1902949131279499,0.0,0.28,0.08,0.0,0.0,0.9,0.0,2.56,1,0,0,0 +1428,ENTP,22.38,0.18869773151005503,0.0,0.32,0.0,0.0,0.0,0.9,0.0,2.64,0,0,1,1 +1429,ESFP,8.96,0.18996322866110593,0.02,0.16,0.0,0.0,0.0,0.18,0.0,0.8,0,1,0,1 +1430,INTP,16.3,0.1888468315972222,0.02,0.1,0.04,0.0,0.0,0.4,0.0,2.0,1,0,1,1 +1431,ENTP,27.42,0.19139627978346535,0.0,0.04,0.52,0.0,0.0,0.98,0.0,1.78,0,0,1,1 +1432,ENTP,22.18,0.17858863297917274,0.06,0.16,0.16,0.0,0.0,0.52,0.0,2.32,0,0,1,1 +1433,ENFP,19.3,0.18429342836064785,0.0,0.04,0.16,0.0,0.02,0.5,0.0,2.12,0,0,0,1 +1434,INFP,11.72,0.17983940266249965,0.62,0.76,0.0,0.0,0.0,0.18,0.0,1.26,1,0,0,1 +1435,ENFP,26.84,0.18554972680041376,0.04,0.1,0.04,0.0,0.0,0.58,0.0,3.34,0,0,0,1 +1436,INFP,25.54,0.18185019282616538,0.18,0.38,0.28,0.0,0.06,0.8,0.0,2.32,1,0,0,1 +1437,INFJ,33.76,0.18070205254155516,0.02,0.1,0.34,0.04,0.0,0.98,0.0,3.14,1,0,0,0 +1438,INFP,29.0,0.18718459046774208,0.08,0.24,0.1,0.02,0.02,0.66,0.02,4.44,1,0,0,1 +1439,INFP,27.54,0.18413791197861545,0.14,0.38,0.32,0.0,0.0,0.66,0.0,3.32,1,0,0,1 +1440,INFP,28.44,0.1979850095814013,0.0,0.22,0.38,0.0,0.0,2.06,0.0,3.94,1,0,0,1 +1441,ENFJ,25.08,0.19198872516154353,0.22,0.34,0.02,0.02,0.0,0.56,0.0,2.86,0,0,0,0 +1442,INTP,30.12,0.18473301786870783,0.04,0.2,0.0,0.0,0.0,0.84,0.02,4.14,1,0,1,1 +1443,INFP,23.2,0.1841732902650925,0.1,0.32,0.2,0.0,0.0,0.5,0.0,1.96,1,0,0,1 +1444,INTJ,29.08,0.18008303934279069,0.0,0.42,0.08,0.0,0.0,0.74,0.0,2.62,1,0,1,0 +1445,INFJ,27.22,0.19177774799674185,0.0,0.32,0.0,0.0,0.0,0.6,0.0,4.48,1,0,0,0 +1446,ISFJ,25.38,0.18617166303876567,0.0,0.06,0.26,0.0,0.0,0.48,0.0,3.42,1,1,0,0 +1447,ENFJ,23.98,0.17427618622432453,0.0,0.12,0.08,0.0,0.02,0.52,0.0,2.42,0,0,0,0 +1448,INFP,28.2,0.1831906291597975,0.0,0.26,0.26,0.0,0.0,0.62,0.02,3.58,1,0,0,1 +1449,INTP,8.48,0.1910204081632653,0.06,0.04,0.02,0.06,0.02,0.24,0.0,1.44,1,0,1,1 +1450,ESTJ,28.26,0.17771400666263026,0.0,0.2,0.0,0.0,0.0,0.62,0.0,2.88,0,1,1,0 +1451,INTP,13.14,0.17956738541437928,0.04,0.08,0.04,0.04,0.0,0.36,0.0,1.16,1,0,1,1 +1452,ENFP,22.52,0.18314953315756038,0.0,0.14,0.56,0.0,0.0,0.58,0.0,1.94,0,0,0,1 +1453,INFJ,24.54,0.18742943833471767,0.0,0.12,0.54,0.0,0.0,1.0,0.0,3.02,1,0,0,0 +1454,ENTP,27.46,0.18401028718701234,0.0,0.14,0.14,0.0,0.02,0.76,0.0,2.32,0,0,1,1 +1455,ENFP,34.8,0.18767632233030346,0.0,0.1,0.4,0.0,0.0,1.2,0.0,3.76,0,0,0,1 +1456,INTJ,24.62,0.18304923643750945,0.0,0.18,0.06,0.0,0.02,0.64,0.0,2.52,1,0,1,0 +1457,INFP,30.92,0.18155396557274447,0.0,0.04,0.0,0.0,0.0,0.72,0.0,4.24,1,0,0,1 +1458,INFJ,28.16,0.17922785419405446,0.0,0.02,0.02,0.0,0.04,0.64,0.0,3.52,1,0,0,0 +1459,INTJ,14.7,0.18485293802644565,0.16,0.24,0.0,0.1,0.0,0.2,0.0,1.42,1,0,1,0 +1460,INFP,21.7,0.18438502406152293,0.2,0.28,0.04,0.02,0.02,0.5,0.0,3.2,1,0,0,1 +1461,ENTP,17.06,0.19309375125576608,0.28,0.48,0.5,0.16,0.0,0.36,0.0,2.18,0,0,1,1 +1462,ENFP,31.04,0.18464281589904105,0.0,0.12,0.32,0.0,0.02,0.74,0.0,3.64,0,0,0,1 +1463,INFJ,8.82,0.19019450242590336,0.0,0.06,0.08,0.0,0.0,0.16,0.0,0.84,1,0,0,0 +1464,INTP,24.6,0.17182015835087408,0.0,0.18,0.02,0.0,0.0,0.46,0.0,2.22,1,0,1,1 +1465,INFJ,26.58,0.18636003972985046,0.12,0.22,0.22,0.02,0.04,0.88,0.06,2.68,1,0,0,0 +1466,INFJ,29.14,0.1844901645430819,0.06,0.18,0.3,0.0,0.0,0.74,0.0,3.08,1,0,0,0 +1467,INTP,16.52,0.18646020797591117,0.06,0.36,0.24,0.0,0.0,0.32,0.0,1.56,1,0,1,1 +1468,ISTJ,24.1,0.19018824762578013,0.0,0.1,0.3,0.0,0.0,0.6,0.0,3.26,1,1,1,0 +1469,INFP,31.46,0.1814382647606142,0.02,0.16,0.0,0.02,0.0,0.8,0.0,3.38,1,0,0,1 +1470,INFP,26.64,0.1848452313036095,0.2,0.3,0.22,0.02,0.02,0.56,0.04,2.6,1,0,0,1 +1471,ISTP,16.86,0.1870175625770119,0.14,0.28,0.28,0.0,0.0,0.4,0.0,2.56,1,1,1,1 +1472,INFP,22.06,0.17944082785450416,0.0,0.04,0.04,0.0,0.02,0.3,0.0,2.7,1,0,0,1 +1473,INFP,25.26,0.1906692394450775,0.0,0.5,0.1,0.0,0.0,0.62,0.0,3.02,1,0,0,1 +1474,INFJ,32.16,0.18991221810065437,0.0,0.38,0.04,0.0,0.02,1.1,0.0,2.98,1,0,0,0 +1475,INFJ,28.74,0.18056308459626916,0.18,0.16,0.16,0.0,0.02,0.86,0.0,2.92,1,0,0,0 +1476,INFJ,22.32,0.18295304417979472,0.08,0.18,0.06,0.02,0.02,0.24,0.0,2.92,1,0,0,0 +1477,INTJ,26.22,0.18175137506209418,0.04,0.08,0.28,0.04,0.0,0.86,0.0,3.08,1,0,1,0 +1478,INTJ,32.48,0.17718613576770517,0.0,0.16,0.0,0.0,0.0,0.9,0.02,2.6,1,0,1,0 +1479,ENTJ,33.54,0.1882799168322623,0.0,0.02,0.22,0.0,0.0,1.36,0.0,3.68,0,0,1,0 +1480,ISTJ,27.46,0.18708623085495218,0.0,0.26,0.04,0.0,0.0,0.5,0.0,4.0,1,1,1,0 +1481,ESTP,31.18,0.18604895771636876,0.0,0.24,0.18,0.0,0.0,0.7,0.0,4.28,0,1,1,1 +1482,INFP,26.68,0.18612317269991008,0.0,0.18,0.08,0.0,0.0,1.16,0.0,3.14,1,0,0,1 +1483,INTP,26.78,0.18968808257562947,0.12,0.28,0.06,0.06,0.0,0.78,0.0,3.92,1,0,1,1 +1484,INFP,32.14,0.1807870168973205,0.0,0.08,0.14,0.0,0.0,0.82,0.0,3.44,1,0,0,1 +1485,ISTP,24.58,0.18582753232469076,0.12,0.22,0.1,0.02,0.0,0.56,0.06,3.04,1,1,1,1 +1486,INFJ,25.7,0.18984174930203526,0.06,0.14,0.32,0.02,0.0,0.76,0.0,2.82,1,0,0,0 +1487,INFP,24.36,0.17989216894432103,0.0,0.12,0.02,0.0,0.0,0.48,0.0,2.12,1,0,0,1 +1488,INTP,15.38,0.19901617929501156,0.02,0.18,0.38,0.0,0.0,0.22,0.02,1.64,1,0,1,1 +1489,INFP,17.32,0.19204389574759947,0.0,0.04,0.1,0.0,0.0,0.54,0.0,2.34,1,0,0,1 +1490,INFP,29.84,0.1857483330632507,0.0,0.06,0.12,0.0,0.0,0.8,0.0,3.94,1,0,0,1 +1491,INTP,17.46,0.1786704217023234,0.0,0.06,0.02,0.0,0.0,0.42,0.0,1.56,1,0,1,1 +1492,INFP,27.5,0.18852978611303506,0.22,0.34,0.2,0.0,0.06,0.68,0.0,2.54,1,0,0,1 +1493,INFP,20.26,0.18284027019940316,0.32,0.34,0.1,0.16,0.02,0.62,0.02,2.7,1,0,0,1 +1494,INFJ,22.22,0.18036378850280202,0.0,0.08,0.0,0.0,0.0,0.44,0.0,1.58,1,0,0,0 +1495,ENFP,29.64,0.18822682252162853,0.06,0.26,0.2,0.0,0.02,0.96,0.0,3.22,0,0,0,1 +1496,INFJ,22.32,0.18596472608802056,0.0,0.16,0.1,0.0,0.0,0.74,0.0,2.44,1,0,0,0 +1497,ENTP,24.24,0.17864514918371174,0.02,0.06,0.0,0.0,0.02,0.48,0.0,2.12,0,0,1,1 +1498,ISFP,22.84,0.18484861623102458,0.1,0.48,0.46,0.0,0.02,0.74,0.0,2.68,1,1,0,1 +1499,ISTJ,25.5,0.17997031598467061,0.0,0.16,0.0,0.0,0.02,0.5,0.0,2.46,1,1,1,0 +1500,ISFJ,30.54,0.17786468251689025,0.04,0.14,0.22,0.02,0.0,0.88,0.0,2.94,1,1,0,0 +1501,INFP,33.34,0.17994399932512628,0.0,0.0,0.2,0.0,0.02,1.06,0.0,2.98,1,0,0,1 +1502,ENFP,27.24,0.2047027657408729,0.0,0.5,1.06,0.0,0.02,1.1,0.04,2.72,0,0,0,1 +1503,ISTJ,23.42,0.1820723931579434,0.1,0.04,0.08,0.02,0.0,0.58,0.0,3.04,1,1,1,0 +1504,INFP,28.6,0.18502156700235606,0.26,0.4,0.16,0.04,0.16,1.08,0.0,3.04,1,0,0,1 +1505,INFJ,8.9,0.19195492073977494,0.38,0.02,0.24,0.14,0.0,0.1,0.0,1.22,1,0,0,0 +1506,INTJ,31.12,0.1856347544067222,0.0,0.42,0.06,0.0,0.04,0.72,0.0,4.72,1,0,1,0 +1507,ISFJ,22.08,0.18845967864687163,0.04,0.14,0.16,0.0,0.0,0.46,0.0,3.68,1,1,0,0 +1508,ISFJ,16.76,0.1893081462371795,0.34,0.12,0.22,0.2,0.04,0.6,0.02,1.3,1,1,0,0 +1509,ESFJ,34.68,0.17919796168314453,0.0,0.0,0.0,0.0,0.0,1.18,0.0,3.66,0,1,0,0 +1510,INTJ,18.42,0.2001996023454415,0.04,0.18,0.0,0.0,0.0,2.32,0.0,0.92,1,0,1,0 +1511,INFP,30.72,0.18523612135960352,0.02,0.26,0.18,0.0,0.04,0.68,0.0,3.52,1,0,0,1 +1512,INTJ,34.12,0.17380303159088326,0.0,0.14,0.04,0.0,0.0,1.08,0.0,2.78,1,0,1,0 +1513,ISTP,24.2,0.17617006242186573,0.08,0.34,0.02,0.0,0.0,0.68,0.0,2.06,1,1,1,1 +1514,INFP,22.36,0.1860934701464065,0.1,0.14,0.24,0.06,0.0,0.52,0.0,2.44,1,0,0,1 +1515,ENTP,14.86,0.19336754604477513,0.0,0.16,0.32,0.0,0.0,0.16,0.0,1.96,0,0,1,1 +1516,INTP,12.96,0.19132645445404622,0.38,0.36,0.0,0.0,0.0,0.18,0.0,1.72,1,0,1,1 +1517,INTP,32.84,0.1786838899799535,0.02,0.18,0.04,0.0,0.0,0.92,0.0,2.76,1,0,1,1 +1518,ENFP,31.24,0.18998201985205376,0.02,0.36,0.08,0.0,0.1,0.88,0.0,4.26,0,0,0,1 +1519,INFP,23.6,0.1864811208997699,0.0,0.26,0.18,0.0,0.0,0.52,0.0,2.7,1,0,0,1 +1520,INFP,27.22,0.1834639182698096,0.06,0.12,0.22,0.02,0.08,0.6,0.0,3.34,1,0,0,1 +1521,ENTP,24.28,0.18280255755240152,0.0,0.08,0.28,0.0,0.0,0.88,0.0,2.74,0,0,1,1 +1522,INFJ,21.94,0.1850114923131641,0.5,0.56,0.02,0.0,0.0,0.66,0.0,2.18,1,0,0,0 +1523,INTJ,31.8,0.18763902787376205,0.04,0.2,0.18,0.02,0.02,1.36,0.0,4.4,1,0,1,0 +1524,ENFP,13.72,0.1935668802850065,0.02,0.94,0.44,0.0,0.0,0.24,0.0,1.46,0,0,0,1 +1525,INTJ,15.48,0.1707297013929549,0.56,0.24,0.04,0.34,0.02,0.32,0.0,1.6,1,0,1,0 +1526,INFJ,22.84,0.187806080345402,0.16,0.38,0.04,0.0,0.0,1.08,0.0,1.84,1,0,0,0 +1527,INTJ,28.68,0.19050543866984238,0.0,0.08,0.24,0.0,0.0,0.64,0.0,4.12,1,0,1,0 +1528,ESFP,15.22,0.19177413322330905,0.1,0.1,0.1,0.0,0.0,0.22,0.0,1.1,0,1,0,1 +1529,INTP,27.76,0.17368091994738913,0.04,0.18,0.02,0.0,0.0,0.68,0.0,1.82,1,0,1,1 +1530,INTJ,32.6,0.18678301607362982,0.04,0.06,0.14,0.02,0.0,1.06,0.0,3.42,1,0,1,0 +1531,INFJ,32.5,0.1853180651410987,0.02,0.26,0.22,0.0,0.0,1.02,0.0,4.36,1,0,0,0 +1532,INFJ,5.46,0.19784209125456026,0.86,0.9,0.0,0.0,0.0,0.04,0.0,0.54,1,0,0,0 +1533,ESTP,31.92,0.1766629894002094,0.02,0.1,0.1,0.0,0.0,0.88,0.0,3.64,0,1,1,1 +1534,ISTP,22.06,0.17979689911891267,0.02,0.02,0.0,0.0,0.02,0.28,0.0,0.78,1,1,1,1 +1535,ENTP,29.8,0.18327988987453211,0.0,0.12,0.16,0.0,0.0,0.64,0.0,3.12,0,0,1,1 +1536,INTJ,24.82,0.19054124333324432,0.0,0.2,0.06,0.0,0.02,0.46,0.0,3.36,1,0,1,0 +1537,INTJ,24.4,0.18132656176236084,0.18,0.2,0.3,0.08,0.0,0.72,0.0,2.94,1,0,1,0 +1538,ENTP,33.42,0.1914573412350727,0.0,0.34,0.02,0.0,0.0,1.04,0.0,4.26,0,0,1,1 +1539,ISTJ,24.62,0.18276829133343392,0.0,0.12,0.06,0.0,0.0,0.56,0.0,2.7,1,1,1,0 +1540,INFP,5.44,0.19541340301958302,0.06,0.04,0.14,0.0,0.0,0.12,0.0,0.66,1,0,0,1 +1541,INFP,25.58,0.17325555840328108,0.04,0.1,0.0,0.0,0.0,0.68,0.0,1.8,1,0,0,1 +1542,INTP,32.74,0.18588077473465606,0.0,0.16,0.1,0.0,0.0,0.98,0.0,3.8,1,0,1,1 +1543,INTP,22.38,0.17050597073687332,0.18,0.48,0.0,0.0,0.0,0.62,0.0,1.88,1,0,1,1 +1544,ISTP,16.8,0.18446886985648367,0.28,0.22,0.02,0.0,0.0,0.38,0.0,1.44,1,1,1,1 +1545,INFP,27.28,0.18296113163704233,0.0,0.06,0.04,0.0,0.0,0.68,0.0,2.98,1,0,0,1 +1546,INFP,9.36,0.1776721982972671,0.18,0.2,0.02,0.02,0.0,0.14,0.0,1.34,1,0,0,1 +1547,INTP,26.02,0.18247402134676313,0.02,0.34,0.42,0.0,0.06,0.64,0.0,3.1,1,0,1,1 +1548,INFP,28.54,0.18593339202618242,0.06,0.14,0.0,0.02,0.02,0.76,0.0,2.9,1,0,0,1 +1549,INFJ,29.84,0.18428316955017304,0.08,0.2,0.4,0.0,0.0,1.12,0.0,3.94,1,0,0,0 +1550,INFP,32.64,0.17999565972222223,0.02,0.12,0.04,0.02,0.0,0.88,0.0,3.42,1,0,0,1 +1551,INFJ,20.1,0.18219425404804354,0.0,0.06,0.0,0.0,0.02,0.42,0.0,2.88,1,0,0,0 +1552,ENTP,11.76,0.19189146123189885,0.04,0.04,0.16,0.02,0.0,0.3,0.0,1.52,0,0,1,1 +1553,INTP,25.34,0.18258666441185573,0.1,0.12,0.1,0.04,0.0,0.64,0.0,2.82,1,0,1,1 +1554,INTP,10.7,0.19214103564966173,0.0,0.28,0.06,0.0,0.02,0.22,0.0,0.92,1,0,1,1 +1555,INTJ,8.8,0.17387400140094747,0.08,0.18,0.02,0.0,0.02,0.22,0.0,0.96,1,0,1,0 +1556,INTJ,28.86,0.1789133404011679,0.06,0.16,0.14,0.0,0.02,0.94,0.0,3.02,1,0,1,0 +1557,INFP,15.64,0.18221623012085042,0.18,0.1,0.1,0.0,0.0,0.24,0.0,1.92,1,0,0,1 +1558,INTJ,30.86,0.18526169627630668,0.02,0.36,0.04,0.0,0.0,1.08,0.02,2.82,1,0,1,0 +1559,ESTJ,26.76,0.179157817090607,0.0,0.12,0.0,0.0,0.06,0.66,0.0,2.5,0,1,1,0 +1560,ISFJ,27.2,0.187516766145723,0.04,0.16,0.36,0.0,0.0,0.6,0.0,3.82,1,1,0,0 +1561,INFP,20.82,0.19375389785126818,0.02,0.3,0.5,0.0,0.0,0.6,0.0,3.02,1,0,0,1 +1562,ENTP,19.98,0.1882035402678756,0.12,0.26,0.2,0.0,0.02,0.74,0.06,2.44,0,0,1,1 +1563,ISFJ,31.34,0.18055775407932007,0.0,0.02,0.22,0.0,0.02,0.7,0.0,3.46,1,1,0,0 +1564,ISTJ,29.7,0.18233952136289888,0.06,0.2,0.14,0.0,0.0,0.68,0.06,3.24,1,1,1,0 +1565,INFJ,29.5,0.18074631285068982,0.0,0.16,0.0,0.0,0.0,0.9,0.0,2.6,1,0,0,0 +1566,ENTP,16.94,0.18161188387656135,0.04,0.2,0.18,0.0,0.0,0.28,0.0,1.6,0,0,1,1 +1567,INTP,32.38,0.18746041589796783,0.02,0.34,0.14,0.02,0.02,1.06,0.0,2.92,1,0,1,1 +1568,INTJ,34.28,0.18078713672944519,0.0,0.24,0.0,0.0,0.0,0.84,0.0,4.18,1,0,1,0 +1569,INFP,21.46,0.18925041597952058,0.0,0.1,0.18,0.0,0.16,0.8,0.0,2.64,1,0,0,1 +1570,INFP,18.14,0.19560393851915328,0.0,0.3,0.52,0.0,0.0,0.18,0.0,2.36,1,0,0,1 +1571,INFP,29.82,0.18272840579392188,0.02,0.18,0.16,0.0,0.0,0.8,0.0,3.36,1,0,0,1 +1572,INFP,28.56,0.17486662265024397,0.02,0.18,0.0,0.0,0.0,0.8,0.04,3.08,1,0,0,1 +1573,INFP,23.02,0.18852175584651149,0.12,0.08,0.32,0.04,0.0,0.54,0.0,2.62,1,0,0,1 +1574,INTP,17.42,0.18122469952338763,0.22,0.16,0.28,0.14,0.0,0.36,0.0,2.06,1,0,1,1 +1575,INFJ,17.22,0.18814377253296438,0.0,0.04,0.06,0.0,0.0,0.48,0.0,2.66,1,0,0,0 +1576,ISTP,30.98,0.1822441049811331,0.08,0.16,0.02,0.0,0.0,0.88,0.0,3.78,1,1,1,1 +1577,INTP,21.58,0.1842819687201724,0.1,0.08,0.0,0.0,0.0,0.66,0.0,2.08,1,0,1,1 +1578,INTP,23.1,0.17653501019048942,0.02,0.04,0.02,0.0,0.02,0.36,0.02,2.42,1,0,1,1 +1579,ISTP,29.72,0.18507012800662914,0.0,0.18,0.16,0.0,0.02,0.76,0.0,3.26,1,1,1,1 +1580,INFP,13.78,0.18087686707615142,0.2,0.32,0.06,0.0,0.0,0.22,0.02,1.4,1,0,0,1 +1581,INFJ,28.26,0.1872072783938452,0.0,0.12,0.0,0.0,0.04,0.6,0.0,4.3,1,0,0,0 +1582,INTP,30.34,0.18712283943074967,0.0,0.12,0.06,0.0,0.0,0.82,0.02,2.6,1,0,1,1 +1583,INFJ,17.72,0.18783390514593373,0.02,0.22,0.14,0.0,0.0,0.26,0.0,1.82,1,0,0,0 +1584,INTP,20.12,0.18974205779702802,0.04,0.26,0.26,0.02,0.0,0.42,0.0,2.34,1,0,1,1 +1585,INFP,13.56,0.1873318009282429,0.2,0.2,0.0,0.0,0.06,0.3,0.04,1.46,1,0,0,1 +1586,ENFP,23.8,0.1909695264058616,0.08,0.16,1.02,0.0,0.0,0.7,0.0,3.0,0,0,0,1 +1587,ENFJ,27.02,0.1881265624605439,0.0,0.1,0.18,0.0,0.02,0.54,0.0,3.76,0,0,0,0 +1588,ENFP,24.98,0.18248801065812081,0.06,0.26,0.14,0.02,0.0,0.4,0.02,1.38,0,0,0,1 +1589,INTJ,19.04,0.17837483041698832,0.0,0.1,0.0,0.0,0.0,0.46,0.0,2.0,1,0,1,0 +1590,INFJ,30.2,0.1880441605449745,0.02,0.2,0.0,0.02,0.0,1.04,0.0,3.96,1,0,0,0 +1591,INTP,23.44,0.18751816992504391,0.12,0.26,0.0,0.0,0.0,0.66,0.0,2.62,1,0,1,1 +1592,INTP,27.14,0.17895481964641508,0.04,0.24,0.12,0.0,0.02,0.76,0.0,2.54,1,0,1,1 +1593,INFJ,28.48,0.18660901947100722,0.06,0.34,0.22,0.0,0.04,0.66,0.0,3.58,1,0,0,0 +1594,ENFP,33.68,0.18658387221635472,0.02,0.28,0.16,0.0,0.0,1.14,0.0,4.28,0,0,0,1 +1595,INFJ,26.12,0.1870386253462604,0.06,0.18,0.26,0.08,0.0,0.64,0.0,2.36,1,0,0,0 +1596,INTP,4.42,0.17125879446189937,0.0,0.02,0.0,0.0,0.0,0.16,0.0,0.38,1,0,1,1 +1597,INFP,22.06,0.1801304515495466,0.18,0.22,0.08,0.1,0.02,0.52,0.0,2.86,1,0,0,1 +1598,INTJ,24.12,0.18884878413529949,0.02,0.12,0.14,0.0,0.0,0.6,0.0,2.9,1,0,1,0 +1599,INFP,17.76,0.18448029606906402,0.02,0.08,0.06,0.0,0.02,0.42,0.0,2.1,1,0,0,1 +1600,INFJ,27.44,0.17241567077946623,0.08,0.14,0.04,0.0,0.0,0.72,0.0,2.34,1,0,0,0 +1601,INTJ,16.98,0.18237600248513877,0.06,0.08,0.0,0.0,0.0,0.32,0.0,1.32,1,0,1,0 +1602,INFP,23.66,0.1875366837849766,0.02,0.16,0.24,0.0,0.0,0.6,0.0,2.52,1,0,0,1 +1603,INFJ,18.14,0.19125011676432976,0.18,0.38,0.06,0.0,0.0,0.38,0.0,2.48,1,0,0,0 +1604,ENTP,26.12,0.18343667939260483,0.02,0.22,0.0,0.0,0.04,0.62,0.0,2.76,0,0,1,1 +1605,INTP,27.28,0.18517821521321837,0.12,0.34,0.0,0.0,0.06,0.84,0.0,2.48,1,0,1,1 +1606,INTP,26.08,0.17999922280085026,0.0,0.1,0.02,0.0,0.0,0.66,0.02,1.92,1,0,1,1 +1607,INTP,30.48,0.1798992615528519,0.04,0.14,0.06,0.0,0.0,0.82,0.0,3.12,1,0,1,1 +1608,INTP,20.28,0.18592824127282792,0.14,0.38,0.06,0.0,0.0,0.56,0.02,2.1,1,0,1,1 +1609,ENFP,30.74,0.1772744774169814,0.0,0.08,0.06,0.0,0.0,0.78,0.0,3.02,0,0,0,1 +1610,ENTP,29.2,0.18005839820666414,0.02,0.1,0.08,0.0,0.04,0.74,0.0,3.02,0,0,1,1 +1611,INFJ,32.82,0.18494119672529816,0.0,0.02,0.0,0.0,0.0,1.26,0.0,5.14,1,0,0,0 +1612,ENTP,7.82,0.18109138851881068,0.04,0.06,0.16,0.04,0.0,0.06,0.0,0.98,0,0,1,1 +1613,INFP,27.34,0.18144605120004334,0.0,0.34,0.12,0.0,0.0,0.64,0.0,2.62,1,0,0,1 +1614,INFP,28.36,0.18833550133020047,0.02,0.12,0.16,0.0,0.0,0.96,0.0,3.52,1,0,0,1 +1615,ENFP,34.0,0.18573462565263318,0.0,0.28,0.16,0.0,0.0,0.94,0.0,3.22,0,0,0,1 +1616,ENTP,22.22,0.1894005076299299,0.0,0.08,0.52,0.0,0.0,0.66,0.0,2.14,0,0,1,1 +1617,INTP,30.24,0.182095746780606,0.0,0.24,0.0,0.0,0.02,0.92,0.0,2.28,1,0,1,1 +1618,INTP,28.0,0.1850281476952889,0.02,0.16,0.0,0.0,0.02,0.72,0.0,3.64,1,0,1,1 +1619,INFP,30.52,0.18001430016404735,0.0,0.06,0.1,0.0,0.0,1.28,0.0,2.36,1,0,0,1 +1620,INTP,27.5,0.18528095143038278,0.0,0.22,0.52,0.0,0.0,0.7,0.0,3.34,1,0,1,1 +1621,ENTP,24.46,0.1799047223732466,0.1,0.24,0.54,0.04,0.02,0.5,0.0,2.62,0,0,1,1 +1622,INTP,28.74,0.1882259746887212,0.02,0.02,0.18,0.0,0.0,0.58,0.0,3.78,1,0,1,1 +1623,INTP,26.74,0.17774334056543908,0.08,0.4,0.22,0.0,0.04,0.62,0.0,2.5,1,0,1,1 +1624,INTP,27.8,0.19179218657897631,0.02,0.14,0.14,0.02,0.0,0.96,0.04,2.62,1,0,1,1 +1625,ENFJ,33.2,0.180219089522778,0.0,0.3,0.7,0.0,0.0,1.1,0.0,3.9,0,0,0,0 +1626,INFP,32.6,0.18278791700683167,0.0,0.3,0.04,0.0,0.0,0.84,0.0,4.2,1,0,0,1 +1627,ENFP,30.12,0.1856657001464657,0.0,0.26,0.36,0.0,0.0,0.84,0.0,3.06,0,0,0,1 +1628,INFP,26.6,0.18097785239748151,0.02,0.02,0.04,0.0,0.0,1.02,0.0,3.06,1,0,0,1 +1629,ENFP,30.56,0.20245310560463675,0.0,0.26,0.8,0.0,0.0,2.54,0.0,2.84,0,0,0,1 +1630,INFJ,18.4,0.18071008752609996,0.1,0.34,0.16,0.1,0.0,0.3,0.0,1.46,1,0,0,0 +1631,INTP,28.98,0.18401910291769483,0.0,0.2,0.0,0.0,0.0,0.76,0.02,3.06,1,0,1,1 +1632,INTJ,25.72,0.18463558542729622,0.04,0.12,0.22,0.0,0.0,0.58,0.0,3.02,1,0,1,0 +1633,ISTP,26.44,0.179968421946765,0.0,0.18,0.1,0.0,0.02,0.48,0.0,1.52,1,1,1,1 +1634,INFP,27.64,0.18082170009914572,0.1,0.42,0.38,0.0,0.0,0.8,0.0,3.26,1,0,0,1 +1635,INTJ,29.88,0.1871046861976175,0.02,0.08,0.2,0.02,0.06,1.26,0.0,2.7,1,0,1,0 +1636,ISFP,11.8,0.17834914804611776,0.66,0.2,0.0,0.44,0.0,0.42,0.0,1.54,1,1,0,1 +1637,INFJ,32.64,0.18925799902093754,0.0,0.2,0.08,0.0,0.0,1.2,0.0,4.26,1,0,0,0 +1638,INTP,24.52,0.18198207209266878,0.02,0.24,0.04,0.0,0.0,0.62,0.0,2.8,1,0,1,1 +1639,ENFP,25.58,0.1978260222863608,0.02,0.18,0.82,0.0,0.0,0.58,0.0,3.76,0,0,0,1 +1640,ENFJ,11.98,0.19223481820142346,0.22,0.42,1.0,0.0,0.0,0.28,0.02,0.98,0,0,0,0 +1641,INFP,23.06,0.17712946170347105,0.0,0.02,0.08,0.0,0.14,0.58,0.0,2.44,1,0,0,1 +1642,INTJ,22.12,0.17831129517461752,0.1,0.38,0.0,0.02,0.0,0.52,0.0,2.64,1,0,1,0 +1643,INFJ,32.18,0.18285679810108055,0.04,0.66,0.12,0.0,0.0,1.0,0.0,3.72,1,0,0,0 +1644,INTP,28.44,0.1872465564052153,0.0,0.32,0.62,0.0,0.0,1.06,0.0,2.5,1,0,1,1 +1645,INTJ,26.64,0.17991034126934732,0.0,0.28,0.26,0.0,0.0,0.54,0.0,2.78,1,0,1,0 +1646,INTP,30.12,0.18760240503868741,0.02,0.12,0.06,0.0,0.0,0.82,0.0,4.2,1,0,1,1 +1647,ENFP,33.24,0.1870517719422048,0.0,0.34,0.02,0.0,0.0,0.9,0.0,5.0,0,0,0,1 +1648,INTP,21.66,0.1876179059633738,0.0,0.24,0.28,0.0,0.0,0.44,0.0,1.82,1,0,1,1 +1649,INTP,28.88,0.18444554825095366,0.02,0.14,0.02,0.0,0.0,1.04,0.0,2.96,1,0,1,1 +1650,ENFP,29.06,0.1848072787131199,0.0,0.0,0.14,0.0,0.06,0.66,0.0,3.22,0,0,0,1 +1651,ENFP,29.74,0.18792117810258616,0.06,0.28,0.78,0.0,0.02,0.84,0.0,3.04,0,0,0,1 +1652,ESFJ,25.44,0.18369253716980052,0.0,0.34,0.2,0.0,0.02,0.56,0.0,2.12,0,1,0,0 +1653,INFP,25.88,0.18933963723608965,0.0,0.16,0.18,0.0,0.02,0.68,0.0,3.38,1,0,0,1 +1654,INFJ,15.26,0.18663803709260957,0.06,0.2,0.06,0.0,0.0,0.32,0.0,1.2,1,0,0,0 +1655,INFP,21.62,0.18831979437357965,0.04,0.46,0.04,0.0,0.04,0.46,0.0,2.24,1,0,0,1 +1656,INTP,26.7,0.17661608169812323,0.0,0.16,0.12,0.0,0.04,0.52,0.0,2.62,1,0,1,1 +1657,INTJ,25.32,0.18704080448242053,0.02,0.22,0.18,0.0,0.04,0.64,0.0,2.94,1,0,1,0 +1658,INFJ,33.56,0.1826259673765983,0.04,0.22,0.16,0.04,0.02,1.42,0.0,3.9,1,0,0,0 +1659,ENTP,28.74,0.18332289898270637,0.02,0.28,0.0,0.0,0.0,0.72,0.0,2.64,0,0,1,1 +1660,ISTJ,8.74,0.18810009254104365,0.08,0.14,0.0,0.0,0.02,0.3,0.0,0.4,1,1,1,0 +1661,INFP,28.92,0.178857420314825,0.04,0.04,0.26,0.02,0.0,0.8,0.0,2.9,1,0,0,1 +1662,INFP,27.38,0.18360100746758376,0.1,0.18,0.08,0.0,0.0,0.62,0.02,3.48,1,0,0,1 +1663,INFP,30.46,0.1809001749530488,0.02,0.06,0.16,0.0,0.0,1.02,0.0,3.16,1,0,0,1 +1664,INFP,29.08,0.1812052963646094,0.0,0.16,0.16,0.0,0.0,0.9,0.0,2.5,1,0,0,1 +1665,INTJ,32.96,0.17646856273984737,0.0,0.12,0.02,0.0,0.02,0.82,0.0,3.16,1,0,1,0 +1666,INTJ,18.38,0.19256920071323722,0.04,0.24,0.64,0.0,0.0,0.86,0.0,2.0,1,0,1,0 +1667,ENFP,22.62,0.18516892034711457,0.0,0.02,0.0,0.0,0.0,0.48,0.0,2.88,0,0,0,1 +1668,INTP,28.52,0.18087835384835843,0.02,0.3,0.06,0.0,0.0,1.06,0.0,2.34,1,0,1,1 +1669,INFJ,25.38,0.18903871816359946,0.0,0.28,0.22,0.0,0.02,0.64,0.0,2.62,1,0,0,0 +1670,INTJ,30.84,0.1802507748629976,0.04,0.12,0.16,0.02,0.04,0.7,0.0,3.3,1,0,1,0 +1671,INTP,33.42,0.18182098765432098,0.02,0.24,0.04,0.0,0.0,0.98,0.0,3.78,1,0,1,1 +1672,INTJ,25.58,0.18276557560012038,0.02,0.24,0.02,0.0,0.0,0.92,0.02,2.5,1,0,1,0 +1673,INTP,18.12,0.18661279565319766,0.08,0.18,0.06,0.04,0.0,0.38,0.02,2.26,1,0,1,1 +1674,INTJ,28.38,0.1815824342989315,0.0,0.12,0.12,0.0,0.0,1.26,0.02,2.92,1,0,1,0 +1675,INFP,26.94,0.1776423141304302,0.0,0.12,0.26,0.0,0.02,0.52,0.0,2.54,1,0,0,1 +1676,INTP,25.42,0.18559557168642207,0.04,0.14,0.12,0.04,0.04,0.58,0.0,3.44,1,0,1,1 +1677,INTP,20.1,0.17922565398335313,0.0,0.08,0.0,0.0,0.0,0.42,0.0,1.82,1,0,1,1 +1678,ENTP,32.32,0.19098289953029182,0.0,0.44,0.2,0.0,0.0,0.78,0.0,3.1,0,0,1,1 +1679,ENFJ,21.9,0.191356693088074,0.12,0.26,0.44,0.1,0.0,0.62,0.0,1.9,0,0,0,0 +1680,INFP,30.88,0.1800140938020827,0.0,0.1,0.04,0.0,0.0,0.86,0.0,3.1,1,0,0,1 +1681,INTJ,28.74,0.18487949144166077,0.0,0.16,0.34,0.0,0.02,0.96,0.04,3.06,1,0,1,0 +1682,ISFP,23.74,0.18585674341633235,0.14,0.22,0.18,0.0,0.02,0.76,0.04,3.16,1,1,0,1 +1683,INTJ,24.48,0.19217388062164598,0.0,0.24,0.1,0.0,0.04,0.76,0.0,2.56,1,0,1,0 +1684,INFJ,25.12,0.18372583871926884,0.08,0.1,0.08,0.0,0.0,0.66,0.02,3.12,1,0,0,0 +1685,ENTP,4.72,0.1910502485836513,0.26,0.38,0.18,0.02,0.0,0.04,0.0,0.56,0,0,1,1 +1686,INTJ,26.06,0.18792209644918223,0.0,0.16,0.04,0.0,0.0,0.52,0.0,3.26,1,0,1,0 +1687,INFJ,30.22,0.18808109554752764,0.04,0.22,0.26,0.0,0.0,0.96,0.0,2.84,1,0,0,0 +1688,ISTJ,21.4,0.1906319302420685,0.0,0.1,1.44,0.0,0.0,0.42,0.0,1.86,1,1,1,0 +1689,ENFP,31.18,0.18503013763720497,0.02,0.2,0.56,0.0,0.0,0.88,0.0,3.76,0,0,0,1 +1690,INFJ,31.12,0.18567779157636705,0.02,0.26,0.02,0.0,0.0,0.74,0.0,3.66,1,0,0,0 +1691,INFJ,28.52,0.18332031881611047,0.0,0.12,0.22,0.0,0.0,0.62,0.0,3.6,1,0,0,0 +1692,INFP,13.44,0.18048356296331258,0.6,0.64,0.02,0.0,0.08,0.3,0.0,1.54,1,0,0,1 +1693,ISFP,32.46,0.1869084782368436,0.02,0.2,0.22,0.0,0.1,1.24,0.0,3.12,1,1,0,1 +1694,ESTJ,14.16,0.1936934399566618,0.1,0.28,0.04,0.0,0.02,0.4,0.0,1.88,0,1,1,0 +1695,INFJ,16.54,0.1964180041154951,0.04,0.32,0.48,0.0,0.02,0.56,0.0,1.5,1,0,0,0 +1696,INTJ,22.28,0.1840532713652313,0.02,0.28,0.1,0.0,0.02,0.56,0.0,2.42,1,0,1,0 +1697,ENTP,15.62,0.18880048464482357,0.12,0.26,0.12,0.02,0.0,0.38,0.0,1.3,0,0,1,1 +1698,INTJ,16.68,0.1896580375104106,0.02,0.3,0.04,0.0,0.0,0.28,0.0,1.82,1,0,1,0 +1699,INTP,27.32,0.18382830835537398,0.04,0.16,0.14,0.04,0.0,0.66,0.0,3.2,1,0,1,1 +1700,INTP,18.16,0.1830984701153594,0.0,0.2,0.16,0.0,0.04,0.24,0.0,2.14,1,0,1,1 +1701,INTP,25.7,0.1843635117308605,0.06,0.08,0.02,0.04,0.02,0.8,0.0,1.88,1,0,1,1 +1702,INFJ,19.36,0.18747868531007048,0.18,0.28,0.04,0.0,0.02,0.4,0.0,2.34,1,0,0,0 +1703,INTJ,21.92,0.17842258368214808,0.14,0.34,0.0,0.02,0.02,0.48,0.0,2.1,1,0,1,0 +1704,INTP,32.88,0.18681391689750695,0.04,0.1,0.02,0.0,0.02,1.0,0.0,4.34,1,0,1,1 +1705,ISFJ,17.22,0.17893003519852158,0.1,0.08,0.0,0.02,0.04,0.24,0.0,1.94,1,1,0,0 +1706,INFP,27.3,0.1848988011791775,0.0,0.32,0.54,0.0,0.04,0.52,0.0,1.58,1,0,0,1 +1707,INTP,27.56,0.18307641517918355,0.02,0.14,0.0,0.0,0.04,0.6,0.0,3.52,1,0,1,1 +1708,INFP,33.1,0.18023068509988696,0.04,0.14,0.16,0.0,0.0,0.82,0.0,3.4,1,0,0,1 +1709,INFJ,21.3,0.189148608398233,0.1,0.22,0.18,0.02,0.0,0.76,0.02,2.72,1,0,0,0 +1710,INFP,31.4,0.17956829364111154,0.14,0.2,0.1,0.0,0.0,0.76,0.0,3.48,1,0,0,1 +1711,ENFP,26.06,0.18995217274156362,0.04,0.38,0.12,0.04,0.0,1.2,0.0,2.44,0,0,0,1 +1712,ISTP,31.22,0.1895023690101638,0.04,0.28,0.14,0.0,0.0,1.34,0.0,3.82,1,1,1,1 +1713,ENFJ,21.54,0.18611011634207839,0.0,0.1,0.2,0.0,0.06,0.38,0.0,2.26,0,0,0,0 +1714,INFJ,30.2,0.18967844813335633,0.0,0.14,0.16,0.0,0.06,0.62,0.0,3.32,1,0,0,0 +1715,ENTP,17.3,0.17811534713860608,0.02,0.1,0.02,0.02,0.0,0.34,0.0,2.1,0,0,1,1 +1716,ENFP,12.1,0.18250803034630647,0.62,0.66,0.02,0.08,0.02,0.26,0.0,1.38,0,0,0,1 +1717,INFP,23.14,0.18852457281241006,0.06,0.1,0.22,0.0,0.04,0.52,0.0,2.96,1,0,0,1 +1718,INFP,16.36,0.17962850948731532,0.28,0.34,0.0,0.04,0.02,0.36,0.0,0.82,1,0,0,1 +1719,ESTP,28.3,0.186512616372626,0.02,0.16,0.2,0.02,0.02,0.7,0.0,3.28,0,1,1,1 +1720,ENTP,24.14,0.18164043142496672,0.02,0.3,0.0,0.0,0.0,0.62,0.0,2.22,0,0,1,1 +1721,ENTP,28.16,0.18331453287197236,0.08,0.16,0.12,0.0,0.0,0.78,0.0,3.7,0,0,1,1 +1722,INTP,24.34,0.18672165316156697,0.02,0.16,0.16,0.0,0.0,0.62,0.0,2.8,1,0,1,1 +1723,INFP,28.8,0.1866281842824149,0.08,0.04,0.04,0.08,0.0,0.76,0.0,3.78,1,0,0,1 +1724,INFJ,21.82,0.17885135545609177,0.06,0.18,0.02,0.0,0.0,0.5,0.0,1.58,1,0,0,0 +1725,INTJ,24.32,0.18579323718689983,0.12,0.34,0.1,0.04,0.0,0.84,0.0,2.84,1,0,1,0 +1726,INFP,30.76,0.17870657516029836,0.0,0.06,0.12,0.0,0.02,0.8,0.0,4.18,1,0,0,1 +1727,ENTJ,27.08,0.17536809493803826,0.08,0.14,0.22,0.04,0.0,0.62,0.0,2.3,0,0,1,0 +1728,ISFJ,11.94,0.18805073898084415,0.06,0.08,0.02,0.04,0.0,0.3,0.0,0.6,1,1,0,0 +1729,INTJ,33.12,0.18542963640434956,0.02,0.06,0.1,0.0,0.0,0.9,0.0,4.32,1,0,1,0 +1730,INFP,30.38,0.1806683081104991,0.0,0.16,0.0,0.0,0.0,0.96,0.0,3.62,1,0,0,1 +1731,INTJ,24.54,0.18484372575809896,0.0,0.16,0.28,0.0,0.0,0.54,0.0,3.12,1,0,1,0 +1732,INFP,26.72,0.1871499052040571,0.06,0.16,0.18,0.04,0.0,0.68,0.0,3.16,1,0,0,1 +1733,ENFJ,28.78,0.18800242141690549,0.04,0.12,0.44,0.0,0.02,1.06,0.0,2.64,0,0,0,0 +1734,INTJ,25.86,0.1863907309110729,0.0,0.4,0.0,0.0,0.02,0.6,0.0,3.78,1,0,1,0 +1735,INFJ,24.72,0.1821754596069509,0.0,0.12,0.06,0.0,0.0,0.52,0.0,2.78,1,0,0,0 +1736,INTJ,28.66,0.182523955282998,0.0,0.2,0.38,0.0,0.0,1.08,0.0,3.24,1,0,1,0 +1737,INFJ,16.98,0.18546761186264307,0.42,0.48,0.12,0.0,0.0,0.3,0.0,2.12,1,0,0,0 +1738,INTJ,24.48,0.18666366512531732,0.02,0.46,0.16,0.02,0.0,0.8,0.0,2.94,1,0,1,0 +1739,INFP,19.28,0.18604542427511764,0.06,0.46,0.18,0.0,0.0,0.32,0.0,2.9,1,0,0,1 +1740,ISTJ,26.52,0.17874164157830133,0.0,0.06,0.7,0.0,0.0,0.38,0.0,1.54,1,1,1,0 +1741,INTJ,19.04,0.19911187620405815,0.0,0.26,0.14,0.0,0.0,1.14,0.04,1.94,1,0,1,0 +1742,INTJ,20.8,0.18031711739516218,0.0,0.2,0.0,0.0,0.02,0.38,0.02,1.94,1,0,1,0 +1743,INTJ,34.78,0.17972163416062342,0.0,0.0,0.0,0.0,0.0,0.94,0.0,4.68,1,0,1,0 +1744,INFJ,29.96,0.18485758302550725,0.0,0.42,0.18,0.0,0.0,0.72,0.0,3.56,1,0,0,0 +1745,INTP,29.08,0.1832158239760464,0.0,0.6,0.0,0.0,0.0,2.2,0.0,2.08,1,0,1,1 +1746,INTP,27.26,0.18249935984113938,0.0,0.14,0.14,0.0,0.0,0.82,0.0,2.76,1,0,1,1 +1747,ENTP,30.62,0.1824790370086288,0.0,0.04,0.16,0.0,0.0,0.7,0.0,2.7,0,0,1,1 +1748,ENTP,23.14,0.18368585099175733,0.02,0.1,0.04,0.0,0.0,0.4,0.0,2.1,0,0,1,1 +1749,INTP,18.58,0.17965886061224054,0.0,0.1,0.04,0.0,0.0,0.18,0.0,1.72,1,0,1,1 +1750,INTP,15.26,0.19137953715322645,0.02,0.3,0.06,0.0,0.0,0.38,0.0,1.78,1,0,1,1 +1751,INFJ,27.98,0.18981843484374403,0.0,0.22,0.06,0.0,0.06,0.66,0.0,3.14,1,0,0,0 +1752,INFJ,28.12,0.18199743389438208,0.06,0.12,0.04,0.02,0.0,0.62,0.0,3.1,1,0,0,0 +1753,INTP,20.84,0.18554840685313195,0.1,0.26,0.06,0.08,0.0,0.48,0.0,2.74,1,0,1,1 +1754,INTJ,17.76,0.18849871724644993,0.02,0.16,0.0,0.0,0.02,0.64,0.0,1.84,1,0,1,0 +1755,INTP,17.84,0.1844442110667694,0.02,0.2,0.0,0.0,0.02,0.42,0.08,1.6,1,0,1,1 +1756,INTP,20.22,0.19333365296877647,0.26,0.54,2.44,0.2,0.0,0.46,0.04,2.78,1,0,1,1 +1757,ENTJ,18.64,0.1739146460196371,0.06,0.14,0.0,0.02,0.0,0.44,0.0,1.34,0,0,1,0 +1758,ISTJ,29.84,0.18908407075993,0.16,0.28,0.0,0.02,0.02,0.66,0.0,4.22,1,1,1,0 +1759,ISFP,6.8,0.18115345647419903,0.0,0.0,0.0,0.0,0.0,0.08,0.0,0.34,1,1,0,1 +1760,INTJ,23.3,0.18114442630563726,0.2,0.4,0.0,0.1,0.04,0.76,0.0,2.44,1,0,1,0 +1761,INFP,21.36,0.19619370635512415,0.14,0.22,0.04,0.08,0.02,0.46,0.0,3.1,1,0,0,1 +1762,ENTP,30.02,0.18935682664861378,0.04,0.38,0.16,0.0,0.06,0.9,0.0,3.44,0,0,1,1 +1763,ISTJ,23.1,0.18157795890973089,0.18,0.28,0.04,0.0,0.0,0.44,0.0,2.82,1,1,1,0 +1764,INTJ,23.32,0.19267320467939014,0.0,0.34,0.0,0.0,0.02,0.4,0.0,3.0,1,0,1,0 +1765,ISTP,29.98,0.18054553131272702,0.0,0.16,0.02,0.0,0.04,0.78,0.0,2.34,1,1,1,1 +1766,INFJ,27.42,0.17597931270890282,0.08,0.22,0.1,0.0,0.0,0.92,0.0,2.12,1,0,0,0 +1767,ISTJ,14.8,0.18840310799730065,0.0,0.06,0.1,0.0,0.0,0.68,0.0,1.16,1,1,1,0 +1768,ISTJ,30.48,0.182538546967527,0.02,0.16,0.04,0.0,0.0,0.72,0.0,3.28,1,1,1,0 +1769,INTP,7.42,0.186665284702977,0.0,0.06,0.0,0.0,0.0,0.08,0.0,0.9,1,0,1,1 +1770,ENFP,25.52,0.18981656225415341,0.02,0.18,0.4,0.0,0.02,0.64,0.0,4.36,0,0,0,1 +1771,INTJ,15.5,0.18280004843750405,0.38,0.52,0.06,0.02,0.0,0.32,0.0,1.42,1,0,1,0 +1772,INFJ,33.64,0.18330209160047345,0.0,0.1,0.02,0.0,0.06,0.9,0.0,3.66,1,0,0,0 +1773,INTP,17.5,0.17937661456543755,0.0,0.3,0.0,0.0,0.0,0.4,0.0,1.76,1,0,1,1 +1774,ENTP,24.88,0.17918122613799087,0.0,0.54,0.02,0.0,0.0,0.6,0.0,2.48,0,0,1,1 +1775,ISFJ,25.22,0.19100772233400976,0.02,0.28,0.26,0.0,0.0,0.78,0.0,2.92,1,1,0,0 +1776,INFP,24.24,0.18578250973472274,0.16,0.38,0.22,0.0,0.0,0.52,0.0,2.72,1,0,0,1 +1777,INFJ,18.6,0.18379186390235072,0.12,0.1,0.26,0.08,0.0,0.28,0.0,1.68,1,0,0,0 +1778,ISFP,28.58,0.18707184321693662,0.06,0.32,0.26,0.0,0.02,0.94,0.0,3.4,1,1,0,1 +1779,INTJ,26.74,0.17955019591330657,0.04,0.3,0.3,0.0,0.0,0.44,0.0,1.94,1,0,1,0 +1780,ENFP,33.1,0.18688465937872764,0.0,0.14,0.06,0.0,0.0,1.1,0.0,3.0,0,0,0,1 +1781,INFP,18.46,0.18366953212602186,0.42,0.56,0.0,0.0,0.06,0.48,0.0,2.26,1,0,0,1 +1782,INTJ,32.26,0.18621798209079554,0.0,0.1,0.18,0.0,0.0,0.84,0.0,3.5,1,0,1,0 +1783,ENTP,19.66,0.18704160635519473,0.08,0.28,0.24,0.02,0.0,0.42,0.0,1.28,0,0,1,1 +1784,ENFP,5.02,0.18357752173095934,0.04,0.18,0.12,0.0,0.0,0.1,0.0,0.5,0,0,0,1 +1785,ENTJ,31.6,0.18302754754425501,0.06,0.38,1.46,0.0,0.02,1.52,0.0,3.24,0,0,1,0 +1786,INFJ,25.66,0.18274622222222225,0.0,0.22,0.22,0.0,0.02,0.64,0.0,3.74,1,0,0,0 +1787,INFP,28.14,0.18263515581705678,0.0,0.38,0.0,0.0,0.0,0.54,0.0,3.46,1,0,0,1 +1788,INTJ,30.04,0.18455458006944297,0.12,0.22,0.14,0.04,0.02,1.0,0.0,3.96,1,0,1,0 +1789,ENFP,25.56,0.18880548913433823,0.12,0.6,0.86,0.0,0.0,0.9,0.0,1.82,0,0,0,1 +1790,INFJ,27.08,0.17673335755998099,0.0,0.08,0.16,0.0,0.0,0.66,0.0,2.66,1,0,0,0 +1791,ENTJ,21.76,0.18801491122488442,0.18,0.28,0.18,0.08,0.02,0.82,0.0,2.34,0,0,1,0 +1792,INFP,28.08,0.18975529492322005,0.02,0.16,0.32,0.0,0.02,0.64,0.0,3.26,1,0,0,1 +1793,INFJ,32.8,0.17961084952979134,0.02,0.2,0.0,0.0,0.02,0.72,0.0,3.92,1,0,0,0 +1794,INTJ,30.34,0.17994896166851665,0.0,0.26,0.1,0.0,0.0,0.66,0.0,3.22,1,0,1,0 +1795,INFP,23.06,0.1791871336169221,0.02,0.04,0.0,0.0,0.04,0.38,0.0,0.94,1,0,0,1 +1796,INTJ,27.9,0.1866587118460355,0.0,0.32,0.0,0.0,0.0,0.52,0.0,2.62,1,0,1,0 +1797,INFJ,34.82,0.17554865995564423,0.02,0.06,0.02,0.0,0.04,0.92,0.0,3.3,1,0,0,0 +1798,INTP,21.54,0.17263159018350868,0.0,0.2,0.04,0.0,0.04,0.5,0.0,2.1,1,0,1,1 +1799,INTP,22.72,0.18154103306595,0.04,0.08,0.06,0.02,0.0,0.46,0.0,2.82,1,0,1,1 +1800,INTJ,25.54,0.17821894609557168,0.0,0.16,0.0,0.0,0.0,0.6,0.0,1.96,1,0,1,0 +1801,INFP,29.02,0.18946182730783404,0.04,0.18,0.26,0.0,0.02,0.9,0.0,4.14,1,0,0,1 +1802,INFP,25.56,0.18378733121648963,0.0,0.1,0.08,0.0,0.0,0.6,0.0,3.2,1,0,0,1 +1803,ISTP,25.08,0.18539175536999847,0.0,0.26,0.2,0.0,0.0,0.44,0.0,2.42,1,1,1,1 +1804,ISTP,29.6,0.18878111108657314,0.0,0.3,0.1,0.0,0.02,1.52,0.0,3.22,1,1,1,1 +1805,INFP,25.78,0.1812485717352963,0.0,0.16,0.1,0.0,0.0,0.48,0.0,3.72,1,0,0,1 +1806,INTP,21.24,0.18277679004692066,0.08,0.24,0.08,0.0,0.02,0.34,0.0,1.92,1,0,1,1 +1807,ISTJ,26.68,0.187368073915327,0.02,0.28,0.28,0.0,0.0,0.5,0.0,3.26,1,1,1,0 +1808,ESFJ,29.4,0.18686709264360776,0.14,0.32,0.28,0.0,0.06,0.76,0.0,3.82,0,1,0,0 +1809,ISTJ,21.0,0.194107548469317,0.22,0.52,0.12,0.02,0.0,1.12,0.02,2.94,1,1,1,0 +1810,INFJ,28.76,0.18619974187313446,0.04,0.08,0.08,0.02,0.0,0.92,0.0,3.18,1,0,0,0 +1811,ISTJ,28.18,0.18275568668550163,0.02,0.2,0.12,0.0,0.02,0.78,0.0,2.04,1,1,1,0 +1812,INTP,30.06,0.1813749974468178,0.06,0.1,0.02,0.02,0.02,0.88,0.0,3.6,1,0,1,1 +1813,INFJ,25.56,0.1910184750716466,0.0,0.1,0.16,0.0,0.0,0.74,0.0,4.26,1,0,0,0 +1814,ISFJ,13.82,0.1889941449724373,0.06,0.18,0.06,0.04,0.0,0.28,0.0,1.48,1,1,0,0 +1815,INTP,24.5,0.18254915277917994,0.06,0.26,0.14,0.02,0.16,0.72,0.0,2.74,1,0,1,1 +1816,ISTP,21.04,0.18479551852163587,0.08,0.16,0.04,0.02,0.02,0.5,0.0,2.52,1,1,1,1 +1817,ENTP,28.56,0.18839960360354766,0.02,0.08,0.12,0.0,0.0,0.76,0.0,3.18,0,0,1,1 +1818,INTP,24.82,0.19424063969028077,0.0,0.24,0.08,0.0,0.06,0.4,0.0,4.68,1,0,1,1 +1819,ENTJ,27.54,0.1762438598096344,0.1,0.2,0.18,0.02,0.0,0.76,0.0,2.36,0,0,1,0 +1820,ENTP,9.88,0.1791375946727408,0.04,0.04,0.08,0.04,0.0,0.16,0.0,0.9,0,0,1,1 +1821,ENTP,15.94,0.18815216945156196,0.0,0.2,0.06,0.0,0.0,0.36,0.0,1.86,0,0,1,1 +1822,INFP,25.8,0.17944037094257081,0.14,0.1,0.08,0.1,0.06,0.76,0.0,3.12,1,0,0,1 +1823,INTJ,12.2,0.1812597577515743,0.7,0.72,0.0,0.0,0.0,0.68,0.0,1.0,1,0,1,0 +1824,ENTP,25.04,0.1786627711099429,0.06,0.24,0.04,0.04,0.0,0.6,0.0,2.14,0,0,1,1 +1825,INFJ,31.36,0.19125779276588795,0.0,0.08,0.16,0.0,0.0,0.94,0.0,5.12,1,0,0,0 +1826,INFJ,14.18,0.18781978693974619,0.38,0.38,0.04,0.02,0.0,0.14,0.12,1.42,1,0,0,0 +1827,INFP,29.76,0.18818738270050975,0.0,0.28,0.16,0.0,0.0,1.0,0.0,3.38,1,0,0,1 +1828,INFP,20.9,0.18555689153926466,0.0,0.62,0.52,0.0,0.0,0.28,0.0,1.82,1,0,0,1 +1829,INTP,25.58,0.18179294986011074,0.1,0.16,0.02,0.02,0.02,0.7,0.0,3.22,1,0,1,1 +1830,INFJ,19.14,0.189404227923393,0.02,0.34,0.04,0.0,0.02,0.3,0.0,2.42,1,0,0,0 +1831,INTJ,27.12,0.18719049372922286,0.02,0.28,0.0,0.0,0.02,0.64,0.0,2.68,1,0,1,0 +1832,INFP,19.96,0.18432065938565514,0.18,0.24,0.1,0.12,0.0,0.4,0.0,2.46,1,0,0,1 +1833,ISFP,22.32,0.17777393837508212,0.04,0.06,0.06,0.02,0.04,0.64,0.0,2.5,1,1,0,1 +1834,INFP,29.8,0.1850359594847056,0.0,0.36,0.24,0.0,0.08,0.86,0.0,2.58,1,0,0,1 +1835,INFJ,22.2,0.18405689645125575,0.0,0.1,0.04,0.0,0.0,0.42,0.0,2.78,1,0,0,0 +1836,INFP,23.92,0.19449766674021884,0.0,0.04,0.32,0.0,0.1,0.7,0.0,3.18,1,0,0,1 +1837,INTP,10.54,0.18774793363996034,0.0,0.08,0.0,0.0,0.0,0.14,0.0,1.16,1,0,1,1 +1838,INFJ,26.78,0.18845255471506778,0.0,0.08,0.12,0.0,0.0,1.08,0.0,2.98,1,0,0,0 +1839,INFP,27.3,0.18153886218190976,0.04,0.2,0.04,0.0,0.02,0.92,0.02,2.64,1,0,0,1 +1840,INFP,28.52,0.17167523532973025,0.12,0.5,0.0,0.0,0.0,0.72,0.0,1.68,1,0,0,1 +1841,INTJ,16.08,0.18107922166334478,0.04,0.12,0.12,0.0,0.0,0.18,0.0,2.06,1,0,1,0 +1842,INTJ,14.1,0.19240000000000002,0.1,0.22,0.06,0.04,0.0,0.24,0.0,1.62,1,0,1,0 +1843,INTP,13.06,0.1906316870402796,0.0,0.1,0.02,0.0,0.02,0.48,0.0,1.92,1,0,1,1 +1844,ENFP,15.02,0.19036564795016075,0.04,0.18,0.06,0.0,0.0,0.18,0.0,1.92,0,0,0,1 +1845,ENTP,19.76,0.18210298572495912,0.02,0.28,0.02,0.0,0.0,0.26,0.02,1.48,0,0,1,1 +1846,ENTP,24.74,0.18845160905568523,0.02,0.18,0.06,0.0,0.0,0.7,0.0,2.94,0,0,1,1 +1847,ENTP,25.3,0.18220417493428528,0.0,0.24,0.1,0.0,0.0,0.52,0.0,3.02,0,0,1,1 +1848,INTJ,28.82,0.18954790462077392,0.02,0.22,0.2,0.0,0.0,0.94,0.0,3.92,1,0,1,0 +1849,INFP,32.9,0.18075082544969584,0.0,0.2,0.04,0.0,0.0,0.94,0.0,3.14,1,0,0,1 +1850,INFP,27.02,0.1893605289890267,0.0,0.08,0.02,0.0,0.0,0.78,0.02,3.36,1,0,0,1 +1851,INFP,24.68,0.18697116298258312,0.04,0.2,0.3,0.0,0.0,0.58,0.0,3.18,1,0,0,1 +1852,INTJ,24.52,0.1816955484285297,0.02,0.24,0.1,0.0,0.02,0.58,0.0,2.46,1,0,1,0 +1853,ENFP,30.64,0.1813278172284219,0.08,0.04,0.14,0.04,0.02,0.7,0.0,2.84,0,0,0,1 +1854,ISFP,24.26,0.1916891335239321,0.02,0.02,0.3,0.0,0.0,0.66,0.0,2.76,1,1,0,1 +1855,INFJ,16.66,0.1819530050075015,0.32,0.34,0.08,0.0,0.0,0.56,0.0,1.62,1,0,0,0 +1856,ENFP,27.72,0.18546200459487003,0.04,0.54,0.18,0.0,0.0,1.26,0.0,2.24,0,0,0,1 +1857,ISTJ,18.88,0.18526867451549178,0.08,0.0,0.0,0.08,0.0,0.24,0.0,2.8,1,1,1,0 +1858,INFJ,34.28,0.1904990360266233,0.0,0.24,0.48,0.0,0.0,1.14,0.0,3.42,1,0,0,0 +1859,ISTP,25.76,0.17928575728936202,0.1,0.16,0.04,0.0,0.02,0.56,0.04,2.28,1,1,1,1 +1860,ISFP,34.08,0.18329868311162054,0.0,0.06,0.08,0.0,0.02,1.08,0.0,5.14,1,1,0,1 +1861,INFP,20.3,0.19348429235227907,0.0,0.08,0.12,0.0,0.0,0.32,0.0,2.2,1,0,0,1 +1862,INFJ,28.0,0.1809358095161069,0.14,0.3,0.0,0.0,0.02,0.72,0.0,3.54,1,0,0,0 +1863,INTJ,25.9,0.18001409380208272,0.0,0.22,0.0,0.0,0.02,0.5,0.0,2.78,1,0,1,0 +1864,INFP,25.8,0.19088816696746724,0.02,0.16,0.02,0.0,0.04,0.52,0.0,2.76,1,0,0,1 +1865,INFP,25.48,0.18471051819606096,0.08,0.1,0.02,0.08,0.0,0.5,0.0,2.94,1,0,0,1 +1866,INFJ,17.1,0.1766533400807776,0.02,0.02,0.0,0.0,0.04,0.22,0.0,1.74,1,0,0,0 +1867,ISTJ,31.92,0.16962907407342573,0.0,0.34,0.02,0.0,0.0,0.9,0.0,2.26,1,1,1,0 +1868,ENTP,19.16,0.1843535188625571,0.18,0.28,0.08,0.04,0.02,0.4,0.0,1.84,0,0,1,1 +1869,INFP,13.9,0.19115407491061603,0.04,0.28,0.14,0.02,0.0,0.2,0.0,1.4,1,0,0,1 +1870,INFJ,28.56,0.17971335857220122,0.0,0.16,0.12,0.0,0.0,0.54,0.0,2.6,1,0,0,0 +1871,INFP,15.06,0.19112159145212645,0.02,0.1,0.0,0.0,0.0,0.38,0.0,2.18,1,0,0,1 +1872,ISFJ,28.3,0.18397825334631265,0.0,0.2,0.12,0.0,0.0,0.76,0.02,2.78,1,1,0,0 +1873,INTJ,11.5,0.1880944347499262,0.42,0.36,0.0,0.1,0.0,0.8,0.0,1.28,1,0,1,0 +1874,ISFP,19.46,0.1828505735676113,0.08,0.1,0.02,0.04,0.0,0.46,0.0,2.76,1,1,0,1 +1875,INTJ,32.94,0.18039721184834007,0.04,0.06,0.06,0.04,0.02,0.9,0.0,3.3,1,0,1,0 +1876,ENTP,29.36,0.17989907956247075,0.06,0.12,0.0,0.02,0.0,1.26,0.0,2.36,0,0,1,1 +1877,INTP,31.32,0.170565204274421,0.0,0.04,0.0,0.0,0.04,0.82,0.0,2.4,1,0,1,1 +1878,INFP,25.14,0.1893284604388355,0.04,0.1,0.2,0.0,0.26,1.02,0.0,3.56,1,0,0,1 +1879,ENTP,19.6,0.19244004866904854,0.06,0.14,0.14,0.0,0.0,0.36,0.0,1.86,0,0,1,1 +1880,ENFP,18.42,0.19046879513067702,0.12,0.16,0.0,0.02,0.0,0.7,0.0,1.5,0,0,0,1 +1881,ENTP,24.04,0.17829272370769436,0.02,0.3,0.06,0.0,0.0,0.54,0.0,1.88,0,0,1,1 +1882,INFJ,19.86,0.1840243821898934,0.0,0.1,0.1,0.0,0.0,0.5,0.0,2.52,1,0,0,0 +1883,INTP,11.28,0.179263494492695,0.0,0.06,0.0,0.0,0.0,0.14,0.0,1.26,1,0,1,1 +1884,ENTP,22.62,0.18189173111243304,0.02,0.24,0.26,0.0,0.0,0.4,0.0,2.72,0,0,1,1 +1885,INTJ,22.28,0.18848470179057655,0.0,0.26,0.02,0.0,0.0,0.52,0.0,2.26,1,0,1,0 +1886,INTJ,29.82,0.1813448960465059,0.02,0.1,0.02,0.0,0.06,0.74,0.0,2.44,1,0,1,0 +1887,INTJ,29.8,0.17957761229982927,0.0,0.12,0.26,0.0,0.0,0.9,0.0,3.2,1,0,1,0 +1888,INTP,24.9,0.18451737100790513,0.02,0.24,0.06,0.02,0.0,0.68,0.0,2.36,1,0,1,1 +1889,INFP,20.62,0.18899412654222078,0.0,0.06,0.58,0.0,0.0,0.36,0.02,2.34,1,0,0,1 +1890,ISTJ,28.62,0.1851582450269707,0.0,0.04,0.04,0.0,0.0,0.7,0.0,3.52,1,1,1,0 +1891,INTP,16.34,0.18721623191982767,0.42,0.34,0.04,0.0,0.0,0.74,0.0,1.84,1,0,1,1 +1892,INTP,27.66,0.18199448119678857,0.02,0.1,0.0,0.0,0.0,0.6,0.0,3.1,1,0,1,1 +1893,INTP,24.88,0.18416460277176694,0.32,0.1,0.14,0.24,0.02,0.52,0.0,3.12,1,0,1,1 +1894,ISTP,11.36,0.18135329957522245,0.26,0.14,0.06,0.04,0.02,0.42,0.0,1.26,1,1,1,1 +1895,INTP,23.3,0.17965908689097873,0.16,0.04,0.1,0.12,0.0,0.48,0.0,2.54,1,0,1,1 +1896,ENTP,18.44,0.19090193098676303,0.04,0.28,0.04,0.0,0.0,0.2,0.0,1.76,0,0,1,1 +1897,INTP,24.74,0.18401450197074715,0.06,0.28,0.16,0.0,0.02,0.56,0.02,2.12,1,0,1,1 +1898,INTJ,9.44,0.1789713751693465,0.0,0.0,0.02,0.0,0.0,0.28,0.0,0.92,1,0,1,0 +1899,ISTJ,10.08,0.18376053741878778,0.02,0.02,0.2,0.0,0.02,0.1,0.0,0.76,1,1,1,0 +1900,ENTP,32.94,0.18614235672266324,0.02,0.06,0.02,0.0,0.0,1.0,0.0,3.78,0,0,1,1 +1901,ISTJ,25.92,0.1909076321554606,0.02,0.24,0.08,0.0,0.0,0.78,0.0,3.0,1,1,1,0 +1902,INFP,10.2,0.18091739322766537,0.7,0.52,0.14,0.18,0.02,0.64,0.0,1.72,1,0,0,1 +1903,ENTP,27.26,0.17980326557494974,0.04,0.22,0.0,0.02,0.0,0.78,0.02,2.7,0,0,1,1 +1904,ISFP,34.14,0.19154343619119035,0.0,0.1,0.14,0.0,0.0,1.2,0.0,4.12,1,1,0,1 +1905,ENFJ,25.86,0.1928569420353285,0.08,0.38,0.22,0.02,0.02,1.0,0.0,3.08,0,0,0,0 +1906,ISFP,19.58,0.18005612221766593,0.1,0.34,0.12,0.0,0.0,0.32,0.0,2.16,1,1,0,1 +1907,INFJ,18.04,0.19468994032835113,0.0,0.16,0.92,0.0,0.0,0.3,0.0,2.44,1,0,0,0 +1908,INTJ,30.02,0.18141958057392937,0.06,0.22,0.24,0.02,0.0,0.88,0.0,2.46,1,0,1,0 +1909,INTJ,30.56,0.18340553027706197,0.02,0.26,0.04,0.0,0.0,0.98,0.02,2.5,1,0,1,0 +1910,INFP,18.52,0.18512485534343256,0.0,0.16,0.22,0.0,0.02,0.48,0.0,1.56,1,0,0,1 +1911,ENTP,29.4,0.18699888185449862,0.06,0.08,0.02,0.0,0.0,0.74,0.0,4.48,0,0,1,1 +1912,INFJ,26.5,0.18562481858204097,0.0,0.14,0.24,0.0,0.04,0.84,0.02,2.94,1,0,0,0 +1913,ENTP,26.16,0.18617728531855954,0.0,0.46,0.2,0.0,0.02,0.62,0.0,1.68,0,0,1,1 +1914,INFP,23.9,0.18743111050398806,0.06,0.3,0.06,0.0,0.06,0.84,0.04,2.4,1,0,0,1 +1915,ENTJ,24.66,0.1826174926252532,0.02,0.5,0.0,0.0,0.0,0.54,0.0,2.86,0,0,1,0 +1916,ESTJ,25.4,0.18670847900249807,0.0,0.26,0.9,0.0,0.02,0.58,0.0,2.82,0,1,1,0 +1917,ENTP,21.48,0.18626788528816507,0.06,0.44,0.04,0.0,0.0,0.38,0.0,2.34,0,0,1,1 +1918,INFJ,30.48,0.17444568546485215,0.0,0.02,0.0,0.0,0.0,0.8,0.0,2.5,1,0,0,0 +1919,INTP,23.7,0.17729501381743823,0.0,0.36,0.04,0.0,0.0,0.52,0.0,1.54,1,0,1,1 +1920,INFP,18.92,0.18473085822306237,0.08,0.1,0.24,0.02,0.0,0.32,0.0,1.92,1,0,0,1 +1921,INFJ,21.46,0.18207217736077755,0.0,0.0,0.32,0.0,0.0,0.34,0.0,2.42,1,0,0,0 +1922,INFP,35.32,0.18791837703824468,0.0,0.16,0.0,0.0,0.0,1.26,0.0,4.46,1,0,0,1 +1923,ESFP,27.94,0.18351879313078628,0.0,0.12,0.26,0.0,0.02,0.54,0.0,2.6,0,1,0,1 +1924,INFJ,30.44,0.1904858870197225,0.02,0.14,0.04,0.02,0.02,0.84,0.0,4.18,1,0,0,0 +1925,ENFP,32.06,0.18482281361495853,0.0,0.14,0.16,0.0,0.0,0.8,0.0,3.08,0,0,0,1 +1926,INTJ,29.36,0.18589970578947992,0.02,0.2,0.16,0.0,0.06,0.8,0.0,3.34,1,0,1,0 +1927,INTP,33.62,0.18103929604245836,0.0,0.12,0.12,0.0,0.0,0.88,0.0,3.66,1,0,1,1 +1928,ENTP,26.74,0.18564609905037757,0.04,0.28,0.06,0.0,0.0,0.68,0.0,2.92,0,0,1,1 +1929,ISTJ,23.62,0.18971251652403545,0.04,0.32,0.12,0.02,0.04,0.58,0.0,2.46,1,1,1,0 +1930,ESFP,29.6,0.1875772224550973,0.0,0.36,0.2,0.0,0.0,0.86,0.0,3.48,0,1,0,1 +1931,ISFP,19.6,0.17560632271380733,0.42,0.4,0.06,0.06,0.04,0.6,0.0,1.84,1,1,0,1 +1932,INTJ,22.08,0.18364736191468903,0.0,0.22,0.14,0.0,0.0,0.62,0.0,1.96,1,0,1,0 +1933,ISTP,20.16,0.1703253750569573,0.04,0.0,0.04,0.0,0.0,0.32,0.02,1.9,1,1,1,1 +1934,ENFP,31.18,0.18534926305963723,0.04,0.1,0.16,0.02,0.0,1.06,0.0,4.22,0,0,0,1 +1935,INTJ,32.9,0.17685802516055021,0.0,0.0,0.0,0.0,0.0,0.92,0.02,3.26,1,0,1,0 +1936,INFJ,16.9,0.17397080100051357,0.12,0.26,0.1,0.0,0.0,0.3,0.0,1.62,1,0,0,0 +1937,INFJ,23.04,0.1777107669886725,0.0,0.16,0.24,0.0,0.02,0.5,0.0,2.78,1,0,0,0 +1938,INTJ,31.72,0.18133601621248355,0.02,0.2,0.04,0.0,0.0,0.96,0.0,3.32,1,0,1,0 +1939,ENFP,32.3,0.20130848852017375,0.0,0.24,0.28,0.0,0.0,0.7,0.0,3.02,0,0,0,1 +1940,ISTP,23.9,0.1857808317081712,0.04,0.3,0.14,0.0,0.02,0.76,0.0,3.28,1,1,1,1 +1941,ENTP,12.94,0.19033333869408695,0.0,0.1,0.0,0.0,0.0,0.18,0.0,1.0,0,0,1,1 +1942,INFP,19.66,0.18727009399580566,0.0,0.24,0.0,0.0,0.0,0.34,0.0,2.36,1,0,0,1 +1943,INFP,20.04,0.18430139096880216,0.14,0.22,0.04,0.0,0.02,0.38,0.0,2.08,1,0,0,1 +1944,INTJ,31.54,0.18875348075761986,0.02,0.1,0.14,0.0,0.04,0.8,0.0,3.56,1,0,1,0 +1945,INTP,29.54,0.18727375584106415,0.0,0.16,0.04,0.0,0.0,1.6,0.0,2.68,1,0,1,1 +1946,ESTP,25.5,0.17963227745414487,0.06,0.14,0.2,0.04,0.0,0.6,0.0,2.8,0,1,1,1 +1947,INFJ,28.32,0.18758102676272823,0.0,0.38,0.34,0.0,0.02,0.7,0.0,3.68,1,0,0,0 +1948,ENFP,16.68,0.18188366758025148,0.16,0.18,0.0,0.0,0.1,0.42,0.0,1.1,0,0,0,1 +1949,INTP,27.96,0.17397238173608257,0.0,0.02,0.04,0.0,0.0,0.74,0.0,2.14,1,0,1,1 +1950,ENTP,13.9,0.18498622357769098,0.32,0.36,0.0,0.04,0.0,0.4,0.0,1.12,0,0,1,1 +1951,ENTP,26.98,0.1849323884364371,0.02,0.1,0.48,0.0,0.0,0.62,0.02,3.08,0,0,1,1 +1952,INTJ,21.34,0.19252214168678938,0.0,0.2,0.1,0.0,0.0,0.82,0.0,2.7,1,0,1,0 +1953,INFJ,27.54,0.18206418463170196,0.02,0.1,0.4,0.0,0.0,0.68,0.0,4.32,1,0,0,0 +1954,INFJ,23.8,0.18179227490234168,0.04,0.22,0.18,0.02,0.0,0.62,0.0,1.96,1,0,0,0 +1955,ISTP,23.3,0.18248350833051083,0.26,0.3,0.0,0.0,0.0,0.52,0.0,2.22,1,1,1,1 +1956,INFP,28.84,0.1855077372544788,0.04,0.38,0.46,0.0,0.0,0.94,0.02,3.02,1,0,0,1 +1957,INTP,32.18,0.18651994758239637,0.04,0.28,0.12,0.0,0.02,0.9,0.0,3.8,1,0,1,1 +1958,INFJ,16.48,0.18484022391836952,0.12,0.4,0.34,0.02,0.04,0.28,0.0,1.78,1,0,0,0 +1959,INFJ,21.4,0.18174565984672153,0.22,0.24,0.04,0.04,0.0,0.48,0.0,2.28,1,0,0,0 +1960,INTJ,24.38,0.18649821963378338,0.02,0.18,0.02,0.0,0.0,0.44,0.04,3.04,1,0,1,0 +1961,INFP,28.58,0.18726580816765642,0.08,0.28,0.08,0.0,0.0,0.8,0.0,3.3,1,0,0,1 +1962,ENTP,24.46,0.18133937294340402,0.08,0.26,0.08,0.02,0.06,0.68,0.0,1.96,0,0,1,1 +1963,INTJ,28.52,0.17871743358978082,0.02,0.18,0.08,0.0,0.0,0.7,0.0,3.04,1,0,1,0 +1964,INFP,22.34,0.18932471885813143,0.04,0.18,0.32,0.0,0.0,0.64,0.0,2.18,1,0,0,1 +1965,INFJ,30.04,0.18169644674970636,0.04,0.1,0.2,0.04,0.02,1.12,0.0,3.5,1,0,0,0 +1966,ENTP,30.22,0.18485897073143331,0.0,0.32,0.04,0.0,0.0,0.74,0.0,2.96,0,0,1,1 +1967,INTJ,35.16,0.1823601714830373,0.0,0.08,0.08,0.0,0.02,1.0,0.0,4.14,1,0,1,0 +1968,INTP,23.14,0.17769376508736814,0.02,0.3,0.0,0.0,0.02,0.5,0.0,2.12,1,0,1,1 +1969,ISFP,22.98,0.18386067982089022,0.12,0.18,0.6,0.04,0.0,0.52,0.0,1.88,1,1,0,1 +1970,ISFP,19.34,0.18246061675679154,0.04,0.16,0.12,0.0,0.0,0.34,0.0,1.44,1,1,0,1 +1971,INTJ,32.58,0.1903274472134444,0.02,0.34,0.08,0.0,0.0,1.0,0.0,3.48,1,0,1,0 +1972,ENFP,17.56,0.17236937477700434,0.56,0.32,0.02,0.16,0.02,0.58,0.02,2.38,0,0,0,1 +1973,INFJ,29.48,0.17803161982248517,0.0,0.18,0.24,0.0,0.12,0.8,0.0,3.6,1,0,0,0 +1974,INTP,28.8,0.18614746928351666,0.06,0.28,0.0,0.0,0.0,0.92,0.0,3.68,1,0,1,1 +1975,INFP,20.3,0.20076662477554524,0.06,0.2,0.42,0.0,0.0,0.56,0.0,2.32,1,0,0,1 +1976,ISFP,27.02,0.1788867056745665,0.04,0.18,0.08,0.0,0.0,0.68,0.0,3.06,1,1,0,1 +1977,ISTP,30.46,0.17967993293213297,0.0,0.36,0.0,0.0,0.0,0.68,0.0,2.64,1,1,1,1 +1978,ENTP,27.2,0.18911747273713517,0.02,0.18,0.08,0.0,0.0,0.58,0.0,2.4,0,0,1,1 +1979,ENFP,27.56,0.19795199681940054,0.0,0.6,0.42,0.0,0.0,0.48,0.0,2.7,0,0,0,1 +1980,INFJ,26.72,0.18614216394841246,0.0,0.22,0.04,0.0,0.0,0.52,0.0,3.34,1,0,0,0 +1981,ISTJ,20.0,0.1961122582814157,0.18,0.42,0.1,0.0,0.0,0.78,0.0,2.26,1,1,1,0 +1982,ENFJ,23.32,0.18370779338440232,0.0,0.06,0.04,0.0,0.02,0.72,0.0,1.62,0,0,0,0 +1983,INTJ,13.42,0.19515023817938623,0.04,0.16,0.2,0.0,0.0,0.16,0.0,1.0,1,0,1,0 +1984,INTP,23.66,0.18587038507570144,0.26,0.7,0.3,0.0,0.0,0.54,0.02,2.84,1,0,1,1 +1985,ISTJ,13.08,0.18679541934943128,0.0,0.08,0.02,0.0,0.0,0.1,0.0,2.18,1,1,1,0 +1986,ISTP,24.66,0.1906592869959407,0.04,0.12,0.06,0.04,0.0,0.8,0.04,2.82,1,1,1,1 +1987,ENTP,28.8,0.18881966430855146,0.0,0.18,0.58,0.0,0.0,0.7,0.0,2.12,0,0,1,1 +1988,INFP,31.34,0.18306930280957337,0.02,0.12,0.08,0.04,0.02,0.74,0.0,2.44,1,0,0,1 +1989,ENFJ,18.94,0.17665118226435989,0.08,0.16,0.0,0.0,0.0,0.54,0.0,1.5,0,0,0,0 +1990,INTJ,18.24,0.1935774306022821,0.0,0.12,0.24,0.0,0.0,0.4,0.0,1.96,1,0,1,0 +1991,ENTP,20.38,0.18407596785975158,0.08,0.02,0.22,0.0,0.0,0.28,0.0,2.72,0,0,1,1 +1992,INFJ,19.58,0.1975043591898503,0.16,0.14,0.8,0.16,0.0,0.72,0.0,2.52,1,0,0,0 +1993,INFP,23.48,0.17936571457012443,0.0,0.14,0.04,0.0,0.0,0.54,0.0,3.34,1,0,0,1 +1994,INTP,13.94,0.18232701385554323,0.02,0.08,1.02,0.0,0.0,0.24,0.0,1.5,1,0,1,1 +1995,INTP,31.26,0.17806468957172408,0.0,0.14,0.0,0.0,0.0,0.8,0.0,2.26,1,0,1,1 +1996,INTJ,16.7,0.20383093102638167,0.1,0.18,0.08,0.04,0.0,0.4,0.0,1.46,1,0,1,0 +1997,INTP,21.32,0.1799359093617629,0.0,0.24,0.0,0.0,0.0,0.28,0.0,2.22,1,0,1,1 +1998,INTP,22.3,0.19040702496975234,0.02,0.14,0.24,0.02,0.0,0.86,0.0,2.06,1,0,1,1 +1999,INTJ,12.58,0.18131232638888892,0.54,0.34,0.02,0.12,0.0,0.22,0.0,1.54,1,0,1,0 +2000,ENTP,25.8,0.1928555061317481,0.1,0.54,0.38,0.02,0.02,0.7,0.02,2.12,0,0,1,1 +2001,INFP,29.74,0.1736596927740906,0.0,0.14,0.14,0.0,0.0,0.64,0.0,3.7,1,0,0,1 +2002,INFJ,31.34,0.18909246764300486,0.0,0.2,0.12,0.0,0.04,0.94,0.0,3.64,1,0,0,0 +2003,INTP,23.98,0.18708257829233052,0.08,0.44,0.64,0.0,0.0,0.62,0.0,2.28,1,0,1,1 +2004,INFJ,27.6,0.17683530088301147,0.0,0.3,0.08,0.0,0.06,0.56,0.0,3.34,1,0,0,0 +2005,INFP,20.5,0.18843166398218913,0.0,0.18,0.9,0.0,0.0,0.2,0.0,1.14,1,0,0,1 +2006,INTJ,19.32,0.17967910164987416,0.16,0.32,0.06,0.04,0.0,0.36,0.0,1.66,1,0,1,0 +2007,ISFJ,19.1,0.1833717028286124,0.06,0.26,0.22,0.0,0.0,0.56,0.0,1.34,1,1,0,0 +2008,ESFP,28.04,0.18658833640701838,0.04,0.26,0.06,0.02,0.02,0.64,0.0,2.9,0,1,0,1 +2009,INTJ,27.0,0.18002679682863265,0.12,0.32,0.16,0.0,0.0,0.8,0.0,2.92,1,0,1,0 +2010,INFP,26.04,0.1812268190432914,0.1,0.18,0.1,0.1,0.02,0.9,0.0,2.76,1,0,0,1 +2011,ENTP,22.16,0.18309195740626458,0.06,0.3,0.06,0.0,0.0,0.56,0.0,2.3,0,0,1,1 +2012,INFP,29.44,0.1820362457231997,0.08,0.16,0.1,0.04,0.0,0.7,0.0,3.14,1,0,0,1 +2013,ISTJ,24.64,0.1854545473639992,0.04,0.08,0.04,0.0,0.0,0.46,0.0,2.66,1,1,1,0 +2014,INTP,18.7,0.18122059074623792,0.14,0.22,0.0,0.0,0.0,0.34,0.0,1.46,1,0,1,1 +2015,INTJ,27.78,0.17608069764842546,0.04,0.36,0.02,0.0,0.0,0.74,0.0,2.5,1,0,1,0 +2016,INTJ,26.58,0.1847020252628715,0.0,0.26,0.28,0.0,0.0,0.68,0.0,1.94,1,0,1,0 +2017,INTP,26.28,0.18425878002509155,0.04,0.34,0.26,0.0,0.0,0.74,0.0,1.94,1,0,1,1 +2018,INTP,27.04,0.18897750833559437,0.04,0.06,0.4,0.0,0.02,0.7,0.12,3.62,1,0,1,1 +2019,INTJ,15.64,0.1806528167522957,0.88,0.88,0.02,0.0,0.0,0.34,0.0,1.86,1,0,1,0 +2020,ESFP,24.86,0.19117765376595655,0.06,0.4,0.1,0.0,0.04,0.9,0.0,2.78,0,1,0,1 +2021,ENFP,33.3,0.17991129222468616,0.02,0.12,0.04,0.0,0.0,0.96,0.0,3.38,0,0,0,1 +2022,INFJ,28.02,0.18843725241053166,0.0,0.22,0.3,0.0,0.0,0.76,0.0,2.66,1,0,0,0 +2023,ISTP,22.62,0.18211020724716895,0.04,0.24,0.06,0.0,0.04,0.3,0.0,3.14,1,1,1,1 +2024,INTJ,24.76,0.18165297293861885,0.14,0.54,0.36,0.0,0.02,0.68,0.0,2.02,1,0,1,0 +2025,INFP,28.46,0.1774298395443595,0.08,0.14,0.06,0.1,0.04,0.7,0.0,2.86,1,0,0,1 +2026,INTJ,25.24,0.18293549314166951,0.0,0.2,0.26,0.0,0.02,0.62,0.0,2.58,1,0,1,0 +2027,ENTP,21.54,0.1804088622648973,0.04,0.1,0.02,0.0,0.0,0.4,0.0,2.52,0,0,1,1 +2028,ISFP,17.68,0.18610892687610478,0.02,0.08,0.14,0.0,0.02,0.38,0.0,2.12,1,1,0,1 +2029,INTP,28.62,0.1772193133826096,0.02,0.14,0.18,0.0,0.0,0.72,0.0,2.72,1,0,1,1 +2030,ENFP,20.74,0.1932500313405296,0.0,0.2,1.4,0.0,0.0,0.58,0.0,2.46,0,0,0,1 +2031,INTJ,30.26,0.18290512993525965,0.0,0.34,0.02,0.0,0.0,1.08,0.0,2.9,1,0,1,0 +2032,INTJ,18.48,0.19040412805562892,0.02,0.34,0.52,0.0,0.0,0.42,0.0,1.88,1,0,1,0 +2033,INFJ,19.16,0.18303027396470467,0.12,0.44,0.02,0.06,0.18,0.46,0.0,1.56,1,0,0,0 +2034,INTP,29.32,0.18511471692854156,0.0,0.12,0.2,0.0,0.0,0.7,0.0,2.82,1,0,1,1 +2035,INTP,28.72,0.17797530766216438,0.06,0.16,0.0,0.02,0.0,0.68,0.0,3.24,1,0,1,1 +2036,ISTP,28.66,0.18624755529911918,0.02,0.18,0.1,0.02,0.0,1.06,0.0,2.52,1,1,1,1 +2037,INTP,22.5,0.18236690127964383,0.02,0.08,0.08,0.0,0.02,0.48,0.0,1.9,1,0,1,1 +2038,ENTP,25.74,0.18380950631281318,0.04,0.12,0.06,0.04,0.02,0.7,0.0,2.88,0,0,1,1 +2039,INFP,27.02,0.18564869783964996,0.08,0.28,0.0,0.08,0.0,0.78,0.04,3.18,1,0,0,1 +2040,INTJ,34.44,0.17571637457866995,0.0,0.02,0.0,0.0,0.0,0.96,0.0,3.2,1,0,1,0 +2041,INFP,28.76,0.1864142924171979,0.0,0.3,0.1,0.0,0.02,0.76,0.0,2.88,1,0,0,1 +2042,ISTP,15.5,0.17553434846327465,0.24,0.5,0.0,0.0,0.02,0.2,0.0,1.12,1,1,1,1 +2043,ENTJ,23.3,0.17885504226299592,0.1,0.08,0.2,0.1,0.0,0.34,0.0,1.7,0,0,1,0 +2044,ESFJ,7.3,0.1832600064559092,0.0,0.16,0.0,0.0,0.0,0.08,0.0,0.84,0,1,0,0 +2045,INTJ,9.62,0.1814754791817549,0.02,0.12,0.0,0.0,0.02,0.18,0.0,1.12,1,0,1,0 +2046,INTP,32.34,0.17809828637420205,0.0,0.24,0.2,0.0,0.0,0.86,0.0,3.42,1,0,1,1 +2047,INTP,25.02,0.19563921165682047,0.1,0.48,0.42,0.0,0.02,1.1,0.0,2.74,1,0,1,1 +2048,INTJ,21.92,0.1845929443858888,0.02,0.28,0.14,0.0,0.0,0.6,0.0,1.58,1,0,1,0 +2049,INFJ,18.96,0.19714106345663263,0.02,0.24,0.5,0.02,0.0,0.48,0.02,1.32,1,0,0,0 +2050,INFJ,28.36,0.18017952582845045,0.0,0.32,0.34,0.0,0.02,0.74,0.0,2.34,1,0,0,0 +2051,INFP,20.4,0.18639114221950623,0.02,0.06,0.16,0.02,0.0,0.34,0.0,3.0,1,0,0,1 +2052,INFP,32.12,0.18769866033524743,0.06,0.08,0.12,0.0,0.0,0.88,0.0,4.86,1,0,0,1 +2053,INFP,35.14,0.18727288534714656,0.0,0.1,0.16,0.0,0.0,1.56,0.0,5.04,1,0,0,1 +2054,INFP,27.4,0.18254317114830443,0.0,0.22,0.38,0.0,0.02,0.6,0.12,3.4,1,0,0,1 +2055,INFP,27.38,0.18917900492542877,0.0,0.14,0.2,0.0,0.0,1.14,0.0,2.84,1,0,0,1 +2056,INFP,25.56,0.1853330993652344,0.14,0.16,0.06,0.08,0.02,0.64,0.0,3.06,1,0,0,1 +2057,ENFP,28.24,0.19968423937728316,0.04,0.3,1.04,0.0,0.0,1.0,0.0,1.44,0,0,0,1 +2058,INTP,24.42,0.18352732783929407,0.18,0.34,0.24,0.0,0.02,0.58,0.0,2.6,1,0,1,1 +2059,ENFP,28.74,0.18915881022641776,0.0,0.16,0.78,0.0,0.0,1.0,0.0,4.2,0,0,0,1 +2060,INFP,13.44,0.19942933323998224,0.0,0.08,0.86,0.0,0.02,0.62,0.02,1.44,1,0,0,1 +2061,INFJ,30.48,0.19969092014847364,0.02,0.1,0.26,0.02,0.02,0.82,0.0,4.5,1,0,0,0 +2062,INFJ,30.16,0.20005204750117808,0.06,0.12,3.68,0.0,0.16,1.38,0.0,4.18,1,0,0,0 +2063,INFJ,22.18,0.19532739679259825,0.02,0.16,0.02,0.0,0.0,0.38,0.0,2.98,1,0,0,0 +2064,INFP,30.22,0.18776080880330503,0.0,0.14,0.46,0.0,0.0,0.78,0.02,3.32,1,0,0,1 +2065,INFP,29.8,0.18243071680069403,0.06,0.2,0.04,0.0,0.16,0.76,0.0,4.44,1,0,0,1 +2066,INFP,23.56,0.18466854523092496,0.12,0.12,0.04,0.0,0.0,0.48,0.0,0.68,1,0,0,1 +2067,INFJ,28.08,0.18628139715373185,0.14,0.3,0.32,0.04,0.0,1.02,0.04,3.16,1,0,0,0 +2068,INFP,21.06,0.19035983908373744,0.02,0.18,0.32,0.0,0.0,0.44,0.0,2.6,1,0,0,1 +2069,ENFP,17.52,0.1876420185112088,0.04,0.16,0.18,0.0,0.04,0.48,0.0,1.9,0,0,0,1 +2070,INTJ,12.02,0.17930926804172684,0.62,0.44,0.16,0.24,0.0,0.58,0.0,1.04,1,0,1,0 +2071,INFJ,27.04,0.19372506179206994,0.0,0.66,1.48,0.0,0.0,0.58,0.0,3.26,1,0,0,0 +2072,INFP,35.02,0.18723942075363922,0.02,0.14,0.08,0.0,0.0,1.28,0.0,3.72,1,0,0,1 +2073,INTP,32.98,0.18664080674384081,0.0,0.32,0.08,0.0,0.02,1.0,0.0,3.94,1,0,1,1 +2074,INFJ,32.46,0.18175736858940364,0.0,0.06,0.0,0.0,0.0,0.88,0.0,3.34,1,0,0,0 +2075,ISTP,19.58,0.18222042731704888,0.32,0.44,0.0,0.02,0.0,0.44,0.0,2.36,1,1,1,1 +2076,ENFP,27.18,0.18499317591908943,0.02,0.22,0.52,0.0,0.0,0.7,0.0,2.56,0,0,0,1 +2077,INTP,32.96,0.1789346710290906,0.02,0.08,0.02,0.02,0.0,0.82,0.0,3.4,1,0,1,1 +2078,INFJ,30.92,0.17778211779473718,0.06,0.22,0.02,0.0,0.02,0.9,0.02,3.46,1,0,0,0 +2079,ENFJ,18.64,0.18823382566339197,0.0,0.16,0.28,0.0,0.02,0.44,0.0,2.52,0,0,0,0 +2080,INFP,29.54,0.18256734037394898,0.0,0.08,0.12,0.0,0.0,0.88,0.0,2.72,1,0,0,1 +2081,ISTJ,31.0,0.18920172978536634,0.02,0.24,0.18,0.0,0.0,0.74,0.0,2.46,1,1,1,0 +2082,ISTJ,28.76,0.18815665585840205,0.0,0.18,0.02,0.0,0.0,0.88,0.0,3.68,1,1,1,0 +2083,ENTJ,14.7,0.19045681009601528,0.3,0.56,0.18,0.0,0.06,0.34,0.02,1.62,0,0,1,0 +2084,INTP,17.34,0.18729854968740356,0.16,0.24,0.28,0.04,0.02,0.36,0.0,2.74,1,0,1,1 +2085,INFJ,26.26,0.1842537804076266,0.0,0.26,0.0,0.0,0.0,0.62,0.0,2.9,1,0,0,0 +2086,INTJ,11.84,0.18144468229208338,0.38,0.26,0.0,0.04,0.0,0.04,0.0,1.98,1,0,1,0 +2087,ENFP,29.66,0.1846650508332024,0.0,0.16,0.14,0.0,0.0,0.8,0.0,3.64,0,0,0,1 +2088,INTP,15.28,0.1885255735331712,0.14,0.28,0.22,0.12,0.0,0.32,0.0,1.64,1,0,1,1 +2089,ENFP,12.64,0.184152967988057,0.1,0.08,0.1,0.08,0.02,0.24,0.0,0.6,0,0,0,1 +2090,INFJ,31.14,0.18464126215270432,0.12,0.32,0.14,0.0,0.04,0.92,0.0,3.92,1,0,0,0 +2091,INFP,32.46,0.19410207743514185,0.02,0.1,0.0,0.02,0.02,0.8,0.0,2.86,1,0,0,1 +2092,ENTP,31.28,0.17888888888888893,0.0,0.14,0.12,0.0,0.02,0.8,0.0,2.88,0,0,1,1 +2093,INTP,11.46,0.17730862285499274,0.32,0.22,0.02,0.08,0.0,0.22,0.0,0.84,1,0,1,1 +2094,ENFP,28.56,0.1786825006325332,0.02,0.22,0.04,0.0,0.0,0.8,0.02,2.64,0,0,0,1 +2095,INFJ,32.34,0.18030595977589217,0.08,0.02,0.14,0.06,0.02,1.04,0.0,3.52,1,0,0,0 +2096,INTP,10.34,0.18448738453677657,0.02,0.16,0.02,0.0,0.0,0.28,0.0,0.96,1,0,1,1 +2097,INFJ,12.06,0.18459224172248526,0.02,0.2,0.26,0.02,0.02,0.16,0.0,1.24,1,0,0,0 +2098,INFJ,27.46,0.19242149713758924,0.04,0.26,0.64,0.0,0.0,0.62,0.04,3.8,1,0,0,0 +2099,INTJ,24.26,0.18402350416766458,0.02,0.16,0.02,0.0,0.02,0.44,0.0,2.64,1,0,1,0 +2100,ENTJ,24.48,0.17591295822975386,0.04,0.32,0.06,0.0,0.0,0.54,0.0,1.52,0,0,1,0 +2101,INTP,24.88,0.175036752063002,0.08,0.12,0.0,0.02,0.04,0.68,0.04,1.92,1,0,1,1 +2102,ISTP,20.1,0.18649805468016944,0.14,0.46,0.0,0.08,0.0,0.32,0.0,0.78,1,1,1,1 +2103,ISFP,18.54,0.18664369124744723,0.0,0.08,0.0,0.0,0.0,0.24,0.0,2.12,1,1,0,1 +2104,INFP,27.82,0.18660119283960988,0.08,0.18,0.46,0.02,0.0,0.78,0.02,3.5,1,0,0,1 +2105,ESTP,27.1,0.18838523472289884,0.04,0.3,0.02,0.0,0.0,0.56,0.0,3.48,0,1,1,1 +2106,INTP,18.62,0.18887285450681907,0.06,0.5,0.14,0.0,0.0,0.44,0.0,2.06,1,0,1,1 +2107,ISTP,27.14,0.18016013570531533,0.0,0.32,0.08,0.0,0.02,0.58,0.0,2.88,1,1,1,1 +2108,INFP,14.92,0.17820491888066825,0.48,0.26,0.4,0.26,0.0,0.36,0.0,1.84,1,0,0,1 +2109,ENTP,19.12,0.1814265905382462,0.12,0.18,0.06,0.04,0.0,0.26,0.0,1.24,0,0,1,1 +2110,INFP,34.78,0.18134457657115793,0.0,0.08,0.0,0.0,0.0,0.82,0.0,4.3,1,0,0,1 +2111,INFJ,30.28,0.18015605051174866,0.0,0.14,0.0,0.0,0.0,0.72,0.0,3.06,1,0,0,0 +2112,ENTJ,23.42,0.1870894920720236,0.0,0.14,0.0,0.0,0.02,0.38,0.0,2.88,0,0,1,0 +2113,ENFP,24.42,0.1818682710162784,0.04,0.16,0.04,0.0,0.02,0.42,0.0,2.52,0,0,0,1 +2114,ESTP,26.16,0.18411452448773852,0.04,0.16,0.32,0.04,0.0,0.68,0.02,2.4,0,1,1,1 +2115,INTP,29.76,0.17785256134535113,0.0,0.26,0.1,0.0,0.0,0.78,0.0,3.36,1,0,1,1 +2116,INFJ,24.24,0.18866276530701367,0.08,0.28,0.0,0.02,0.0,0.44,0.0,2.34,1,0,0,0 +2117,INFP,28.98,0.18081742189585012,0.04,0.04,0.04,0.0,0.0,0.82,0.0,3.08,1,0,0,1 +2118,INFJ,13.66,0.18508864456975946,0.08,0.2,0.12,0.0,0.0,0.3,0.02,1.66,1,0,0,0 +2119,INFJ,17.94,0.18788507524307815,0.28,0.06,0.34,0.22,0.02,0.5,0.0,2.22,1,0,0,0 +2120,INTP,25.38,0.18101739079676055,0.0,0.06,0.06,0.0,0.02,0.56,0.0,3.3,1,0,1,1 +2121,INTJ,14.5,0.1871292078868436,0.0,0.08,0.58,0.0,0.0,0.1,0.0,2.02,1,0,1,0 +2122,ISTP,31.88,0.18751448183972433,0.0,0.2,0.02,0.0,0.02,0.76,0.0,2.54,1,1,1,1 +2123,INFP,20.18,0.1894390685860964,0.16,0.24,0.66,0.08,0.02,0.54,0.0,2.1,1,0,0,1 +2124,ISFP,20.78,0.1935266113394879,0.06,0.16,0.04,0.0,0.0,0.42,0.0,2.78,1,1,0,1 +2125,INTP,14.66,0.18380340193850672,0.26,0.54,0.12,0.0,0.0,0.46,0.0,1.46,1,0,1,1 +2126,INFJ,36.22,0.18651881276566915,0.0,0.02,0.12,0.0,0.0,1.14,0.0,4.22,1,0,0,0 +2127,INFP,25.22,0.186338643293336,0.0,0.06,0.16,0.0,0.0,0.92,0.0,2.7,1,0,0,1 +2128,INFP,30.28,0.18628446930280956,0.04,0.14,0.86,0.0,0.08,0.64,0.0,3.68,1,0,0,1 +2129,ISFJ,23.54,0.18871130188388108,0.04,0.2,0.32,0.04,0.0,0.72,0.0,3.44,1,1,0,0 +2130,INTP,11.42,0.20343909514248318,0.0,0.18,0.2,0.0,0.02,0.28,0.02,1.24,1,0,1,1 +2131,ISFJ,30.88,0.18289810546636978,0.0,0.14,0.26,0.0,0.04,0.76,0.0,4.0,1,1,0,0 +2132,INFP,20.92,0.18348048337062636,0.22,0.36,0.0,0.02,0.0,0.44,0.0,2.62,1,0,0,1 +2133,ENTP,12.82,0.19475032814420054,0.1,0.4,0.08,0.0,0.0,0.38,0.0,1.56,0,0,1,1 +2134,INFJ,15.98,0.19174321607440617,0.04,0.32,0.12,0.0,0.0,0.26,0.0,2.08,1,0,0,0 +2135,ENTJ,28.34,0.18418218650523366,0.02,0.28,0.06,0.0,0.0,0.76,0.0,2.32,0,0,1,0 +2136,INFJ,31.46,0.18697004588359778,0.02,0.12,0.14,0.0,0.0,1.1,0.0,3.9,1,0,0,0 +2137,INTJ,33.32,0.1844127530865798,0.04,0.02,0.06,0.0,0.0,0.98,0.02,3.18,1,0,1,0 +2138,INFP,23.06,0.1910325879478108,0.06,0.24,0.06,0.02,0.0,0.48,0.0,3.32,1,0,0,1 +2139,ENFP,30.92,0.19153513787018783,0.0,0.26,0.08,0.0,0.04,0.88,0.0,3.5,0,0,0,1 +2140,ENTP,21.5,0.18792562946938282,0.16,0.26,0.0,0.02,0.0,0.54,0.0,2.08,0,0,1,1 +2141,ENFJ,31.28,0.18174282312953102,0.04,0.18,0.48,0.04,0.0,0.66,0.0,3.92,0,0,0,0 +2142,ENFP,22.3,0.1849184830786414,0.2,0.34,0.16,0.02,0.02,0.68,0.0,2.42,0,0,0,1 +2143,INFJ,19.78,0.18434075414099158,0.4,0.74,0.0,0.0,0.0,0.72,0.0,2.02,1,0,0,0 +2144,ISFJ,27.16,0.18379872525530452,0.02,0.14,0.14,0.0,0.02,0.74,0.0,3.16,1,1,0,0 +2145,INTP,20.24,0.18436959577667572,0.04,0.12,0.08,0.04,0.06,0.42,0.0,2.36,1,0,1,1 +2146,ENTP,23.68,0.18125822274336484,0.0,0.12,0.04,0.0,0.0,0.56,0.0,2.4,0,0,1,1 +2147,ISTP,30.04,0.1858466935337305,0.02,0.2,0.08,0.0,0.0,0.62,0.02,3.82,1,1,1,1 +2148,INTJ,18.32,0.18065736726256174,0.02,0.14,0.04,0.0,0.0,0.42,0.0,1.42,1,0,1,0 +2149,INTJ,22.64,0.18784547900965928,0.06,0.08,0.0,0.02,0.02,0.4,0.0,3.08,1,0,1,0 +2150,INFP,28.52,0.1855649870042581,0.08,0.2,0.14,0.0,0.02,0.74,0.0,2.32,1,0,0,1 +2151,INTJ,28.24,0.1848160225478853,0.0,0.14,0.14,0.0,0.0,0.76,0.02,3.32,1,0,1,0 +2152,INTJ,32.54,0.17739072752698198,0.04,0.04,0.06,0.0,0.02,0.8,0.0,3.8,1,0,1,0 +2153,INFJ,29.06,0.1855397865622169,0.12,0.28,0.28,0.06,0.12,0.84,0.0,3.48,1,0,0,0 +2154,ISFP,22.88,0.18722973004492252,0.0,0.32,0.1,0.0,0.04,0.5,0.0,3.44,1,1,0,1 +2155,ENTJ,30.14,0.18235482157744046,0.08,0.32,0.16,0.0,0.0,0.8,0.0,2.2,0,0,1,0 +2156,INFP,26.36,0.18406304818356717,0.0,0.22,0.06,0.0,0.08,0.64,0.0,3.44,1,0,0,1 +2157,INFP,26.96,0.18096243750000002,0.12,0.32,0.1,0.0,0.0,0.64,0.0,2.58,1,0,0,1 +2158,INTJ,24.32,0.18266396200188353,0.02,0.08,0.12,0.0,0.0,0.54,0.0,2.02,1,0,1,0 +2159,ENFJ,23.2,0.19749268504567838,0.0,0.18,0.46,0.0,0.0,1.4,0.0,2.0,0,0,0,0 +2160,INTJ,17.42,0.1813296729683753,0.4,0.06,0.04,0.06,0.02,0.32,0.02,2.16,1,0,1,0 +2161,ENFP,23.64,0.19446379013046197,0.02,0.58,0.44,0.0,0.04,0.5,0.02,2.48,0,0,0,1 +2162,ENFJ,29.16,0.18802804269409162,0.0,0.34,1.28,0.0,0.08,0.7,0.0,3.46,0,0,0,0 +2163,INFJ,23.42,0.1892853718362142,0.14,0.2,0.02,0.1,0.0,0.66,0.0,3.06,1,0,0,0 +2164,INFP,13.16,0.18731879690588174,0.02,0.02,0.52,0.02,0.0,0.26,0.0,2.14,1,0,0,1 +2165,ENFJ,22.06,0.17311876466549353,0.08,0.28,0.04,0.02,0.0,0.5,0.0,1.86,0,0,0,0 +2166,INTJ,29.86,0.18187688863460372,0.0,0.12,0.0,0.0,0.0,0.66,0.0,3.34,1,0,1,0 +2167,INTJ,15.92,0.17790364829653646,0.02,0.18,0.12,0.0,0.0,0.24,0.0,2.3,1,0,1,0 +2168,INFP,25.14,0.1915894698493752,0.0,0.1,0.0,0.0,0.0,0.58,0.0,2.5,1,0,0,1 +2169,ISFP,23.48,0.18818907963102002,0.08,0.2,0.12,0.04,0.06,1.28,0.0,2.74,1,1,0,1 +2170,INTP,17.4,0.17262572828370204,0.14,0.1,0.08,0.08,0.0,0.26,0.0,1.32,1,0,1,1 +2171,ISTP,17.78,0.18629538614477634,0.02,0.3,0.02,0.0,0.0,0.28,0.02,2.0,1,1,1,1 +2172,INFJ,31.74,0.18160729873806472,0.0,0.2,0.42,0.0,0.0,0.94,0.0,2.96,1,0,0,0 +2173,ENTJ,23.48,0.18471705238540478,0.0,0.26,0.2,0.0,0.0,0.52,0.0,2.58,0,0,1,0 +2174,INTJ,15.92,0.1911251688235481,0.38,0.04,0.18,0.24,0.0,0.5,0.0,2.06,1,0,1,0 +2175,INTP,28.64,0.19167219510322483,0.0,0.56,0.04,0.0,0.0,0.7,0.0,2.42,1,0,1,1 +2176,ESTJ,32.98,0.1797427305447016,0.0,0.2,0.04,0.0,0.0,0.98,0.0,3.5,0,1,1,0 +2177,INFJ,17.26,0.18854517369381715,0.02,0.18,0.04,0.0,0.0,0.24,0.0,2.24,1,0,0,0 +2178,ENFP,13.5,0.19137210440877112,0.1,0.2,0.12,0.04,0.0,0.22,0.0,1.24,0,0,0,1 +2179,INTP,30.52,0.18485990056754706,0.02,0.04,0.06,0.02,0.0,0.78,0.0,3.28,1,0,1,1 +2180,INTJ,31.16,0.18394728204532898,0.0,0.04,0.04,0.0,0.0,0.86,0.0,3.32,1,0,1,0 +2181,ESTP,29.1,0.18206254286694107,0.08,0.2,0.06,0.02,0.02,0.7,0.0,2.76,0,1,1,1 +2182,INFJ,25.92,0.18524996861186147,0.18,0.18,0.08,0.02,0.08,0.66,0.0,3.32,1,0,0,0 +2183,INFP,36.38,0.18590290152782213,0.0,0.06,0.08,0.0,0.04,1.12,0.0,5.2,1,0,0,1 +2184,INTP,27.28,0.18396186154142602,0.0,0.18,0.22,0.0,0.02,0.62,0.0,3.32,1,0,1,1 +2185,ENFP,29.86,0.1855593097382919,0.02,0.1,0.26,0.0,0.0,0.76,0.0,4.0,0,0,0,1 +2186,INFP,24.0,0.1840084688551547,0.02,0.16,0.02,0.0,0.0,0.46,0.0,3.06,1,0,0,1 +2187,INFP,32.22,0.1884261691121498,0.0,0.22,0.24,0.0,0.0,0.9,0.0,4.02,1,0,0,1 +2188,INFP,7.42,0.20508058572493088,0.0,0.22,0.56,0.0,0.0,0.16,0.0,0.76,1,0,0,1 +2189,INFP,24.22,0.17647885909451203,0.0,0.32,0.0,0.0,0.0,0.48,0.0,2.36,1,0,0,1 +2190,INTP,30.44,0.17735082245644102,0.0,0.2,0.04,0.0,0.06,0.94,0.0,3.28,1,0,1,1 +2191,ENFP,23.26,0.18324839418712943,0.24,0.2,0.0,0.06,0.08,0.62,0.0,2.34,0,0,0,1 +2192,INFP,27.02,0.18586104138224632,0.12,0.28,0.0,0.0,0.0,0.68,0.0,3.56,1,0,0,1 +2193,ENTP,29.6,0.1784287141540207,0.0,0.1,0.06,0.0,0.0,0.84,0.0,3.02,0,0,1,1 +2194,ENTP,29.56,0.1816249830482919,0.0,0.24,0.04,0.0,0.0,0.96,0.0,2.08,0,0,1,1 +2195,INFJ,34.24,0.18047429854346975,0.0,0.12,0.34,0.0,0.04,0.9,0.02,3.92,1,0,0,0 +2196,INTP,24.26,0.18413832072525868,0.04,0.06,0.06,0.0,0.0,0.62,0.0,3.16,1,0,1,1 +2197,INFJ,28.7,0.18410432622798556,0.02,0.14,0.06,0.02,0.04,0.76,0.0,3.34,1,0,0,0 +2198,INFJ,27.18,0.18448992148694118,0.08,0.26,0.0,0.0,0.02,0.68,0.0,2.82,1,0,0,0 +2199,INFJ,31.22,0.1765805487568815,0.0,0.18,0.02,0.0,0.0,0.76,0.02,3.64,1,0,0,0 +2200,INFP,24.98,0.18065783844386082,0.22,0.32,0.24,0.0,0.0,0.52,0.0,2.52,1,0,0,1 +2201,INFP,24.3,0.19286222683902735,0.02,0.04,0.5,0.0,0.02,0.86,0.0,2.86,1,0,0,1 +2202,INTP,32.18,0.1870340666362361,0.02,0.16,0.12,0.0,0.0,1.04,0.06,3.38,1,0,1,1 +2203,ENTP,30.84,0.17839300926066354,0.0,0.38,0.06,0.0,0.0,0.8,0.0,2.46,0,0,1,1 +2204,ISTP,14.88,0.19354509246506119,0.22,0.3,0.06,0.0,0.02,0.24,0.0,2.18,1,1,1,1 +2205,ENTP,8.08,0.18049184303490845,0.0,0.2,0.0,0.0,0.04,0.04,0.0,0.58,0,0,1,1 +2206,ENTP,19.84,0.17928242479268672,0.14,0.26,0.0,0.08,0.0,0.36,0.0,2.2,0,0,1,1 +2207,INTP,15.32,0.186704463056585,0.22,0.42,0.02,0.0,0.0,0.26,0.02,2.02,1,0,1,1 +2208,INFJ,25.14,0.19641810464450912,0.06,0.5,0.48,0.0,0.0,0.62,0.0,3.76,1,0,0,0 +2209,INTP,19.5,0.19351727559910842,0.02,0.24,0.1,0.0,0.04,0.7,0.0,1.16,1,0,1,1 +2210,INFJ,33.82,0.18369361634478887,0.04,0.24,0.0,0.0,0.0,1.06,0.0,3.56,1,0,0,0 +2211,INTJ,11.92,0.17708487316495228,0.06,0.06,0.0,0.02,0.02,0.3,0.0,1.4,1,0,1,0 +2212,ENTJ,24.56,0.18522861334323396,0.0,0.8,0.0,0.0,0.0,0.48,0.0,2.02,0,0,1,0 +2213,ENTJ,29.48,0.18603381676071515,0.04,0.26,0.06,0.08,0.0,0.88,0.0,2.88,0,0,1,0 +2214,INTJ,13.42,0.18857732162037352,0.12,0.26,0.1,0.06,0.0,0.7,0.0,0.86,1,0,1,0 +2215,ENFP,28.22,0.17957221600094733,0.12,0.24,0.1,0.0,0.06,0.86,0.0,3.02,0,0,0,1 +2216,ENFP,33.94,0.1876999678769757,0.0,0.22,0.28,0.0,0.0,0.94,0.0,3.98,0,0,0,1 +2217,ENFP,33.34,0.19764273830087273,0.0,0.12,1.06,0.0,0.02,1.56,0.0,3.24,0,0,0,1 +2218,INFP,10.36,0.18254030957041503,0.5,0.5,0.04,0.04,0.02,0.26,0.0,1.22,1,0,0,1 +2219,INFP,31.06,0.18441689761444008,0.02,0.06,0.26,0.02,0.04,0.9,0.0,4.46,1,0,0,1 +2220,INFJ,18.84,0.18717669680621854,0.1,0.12,0.1,0.0,0.0,0.48,0.0,2.48,1,0,0,0 +2221,INTJ,23.26,0.1878703695550722,0.06,0.28,0.14,0.0,0.02,0.6,0.0,2.74,1,0,1,0 +2222,ENTP,23.22,0.18845850793397395,0.18,0.22,0.08,0.06,0.04,1.04,0.0,2.86,0,0,1,1 +2223,INTJ,28.14,0.18472857337211476,0.02,0.34,0.4,0.0,0.0,0.76,0.0,3.12,1,0,1,0 +2224,INFP,25.16,0.18330307544799204,0.12,0.26,0.06,0.04,0.0,1.14,0.0,2.12,1,0,0,1 +2225,ISTP,21.56,0.18682832293113413,0.04,0.32,0.24,0.0,0.0,0.56,0.0,2.64,1,1,1,1 +2226,INTJ,21.1,0.1882709713593653,0.0,0.22,0.0,0.0,0.0,0.42,0.0,2.38,1,0,1,0 +2227,ENFP,27.04,0.18609607523415034,0.02,0.26,0.3,0.02,0.0,0.68,0.0,3.14,0,0,0,1 +2228,ISFP,31.82,0.18215452207640642,0.0,0.02,0.04,0.0,0.06,0.76,0.0,4.04,1,1,0,1 +2229,INTP,21.2,0.18009030764322692,0.06,0.26,0.16,0.0,0.0,0.42,0.0,2.24,1,0,1,1 +2230,INFJ,21.08,0.19091029878001572,0.0,0.14,0.08,0.0,0.02,0.54,0.0,0.88,1,0,0,0 +2231,ENTP,26.64,0.17771475199617318,0.0,0.28,0.1,0.0,0.0,0.68,0.02,2.16,0,0,1,1 +2232,ISFJ,6.42,0.20284507450287567,0.14,0.1,0.24,0.02,0.06,0.6,0.0,0.9,1,1,0,0 +2233,ENFJ,7.8,0.18318312166092224,0.6,0.38,0.44,0.2,0.02,0.24,0.0,1.16,0,0,0,0 +2234,ISFJ,29.52,0.1869114764217754,0.0,0.04,0.04,0.0,0.0,0.68,0.0,5.24,1,1,0,0 +2235,ISFP,19.02,0.1874776805644132,0.08,0.12,0.28,0.0,0.02,0.34,0.0,2.36,1,1,0,1 +2236,INTP,20.7,0.17703791782269782,0.0,0.1,0.04,0.0,0.0,0.54,0.0,2.34,1,0,1,1 +2237,ENTP,29.14,0.17843210843630108,0.0,0.24,0.08,0.0,0.0,0.66,0.0,2.82,0,0,1,1 +2238,INFJ,23.5,0.17984522690592591,0.02,0.12,0.5,0.0,0.0,0.74,0.02,2.7,1,0,0,0 +2239,ESTP,35.64,0.1827430537448907,0.02,0.16,0.02,0.0,0.0,1.0,0.0,2.76,0,1,1,1 +2240,INFJ,29.92,0.18049801460608922,0.0,0.04,0.02,0.0,0.0,0.9,0.02,2.96,1,0,0,0 +2241,INTP,25.02,0.16741398817949096,0.14,0.3,0.04,0.02,0.0,0.62,0.0,1.74,1,0,1,1 +2242,INTJ,23.78,0.18421263334700635,0.12,0.26,0.04,0.02,0.0,1.02,0.0,2.62,1,0,1,0 +2243,ENTP,25.04,0.18645005457114333,0.04,0.12,0.0,0.0,0.02,0.46,0.0,2.8,0,0,1,1 +2244,ISTJ,30.12,0.17628871276281366,0.1,0.18,0.0,0.0,0.02,0.76,0.0,3.16,1,1,1,0 +2245,ENFP,32.72,0.18550762574364524,0.0,0.08,0.16,0.0,0.0,0.7,0.0,4.28,0,0,0,1 +2246,INTJ,26.1,0.18657439832376782,0.0,0.48,0.0,0.0,0.0,0.44,0.0,3.22,1,0,1,0 +2247,INFJ,28.36,0.18731421858151162,0.0,0.14,0.08,0.0,0.0,0.96,0.0,3.18,1,0,0,0 +2248,INFP,32.82,0.1832342055246396,0.0,0.22,0.02,0.0,0.02,0.8,0.0,4.06,1,0,0,1 +2249,INFP,27.9,0.18394557822679006,0.02,0.12,0.08,0.0,0.0,0.62,0.0,3.62,1,0,0,1 +2250,INTP,19.22,0.18518996266213145,0.22,0.4,0.12,0.0,0.02,0.48,0.0,2.1,1,0,1,1 +2251,INFJ,25.02,0.18187920816147865,0.02,0.24,0.04,0.02,0.0,0.62,0.0,2.58,1,0,0,0 +2252,INFJ,27.84,0.17862875464644024,0.04,0.2,0.38,0.02,0.0,0.82,0.0,1.44,1,0,0,0 +2253,ENFP,19.24,0.19069822289055047,0.32,0.46,0.44,0.02,0.1,0.52,0.0,2.06,0,0,0,1 +2254,INFJ,24.04,0.19189013040739802,0.02,0.34,0.02,0.0,0.0,1.34,0.0,1.84,1,0,0,0 +2255,INTJ,9.86,0.20222478621141665,0.0,0.0,0.0,0.0,0.0,0.22,0.0,1.62,1,0,1,0 +2256,INTP,30.22,0.17898129097261647,0.0,0.3,0.0,0.0,0.0,0.8,0.0,4.0,1,0,1,1 +2257,INTJ,18.76,0.18448262644361418,0.04,0.2,0.04,0.0,0.0,0.52,0.0,1.76,1,0,1,0 +2258,ENFP,32.9,0.18735172809436432,0.0,0.28,0.02,0.0,0.0,0.86,0.0,3.0,0,0,0,1 +2259,INTP,13.7,0.18647048443071346,0.14,0.28,0.02,0.0,0.02,0.14,0.0,1.42,1,0,1,1 +2260,ENFP,24.3,0.1815563903901618,0.0,0.28,0.8,0.0,0.0,0.46,0.0,3.54,0,0,0,1 +2261,ISFJ,30.44,0.1847633072479773,0.02,0.28,0.1,0.0,0.0,1.06,0.0,3.24,1,1,0,0 +2262,INFP,23.22,0.18556324216743375,0.1,0.18,0.26,0.0,0.0,0.46,0.0,2.84,1,0,0,1 +2263,ISFJ,32.88,0.18274340133485956,0.06,0.08,0.04,0.06,0.0,0.9,0.0,4.3,1,1,0,0 +2264,ENFJ,24.5,0.1810445048702511,0.14,0.2,0.24,0.0,0.02,0.52,0.0,2.6,0,0,0,0 +2265,INFP,26.34,0.19422208308005043,0.08,0.26,0.2,0.04,0.04,1.02,0.0,3.6,1,0,0,1 +2266,ESTJ,30.66,0.18665362184684106,0.02,0.22,0.08,0.0,0.0,0.9,0.0,4.16,0,1,1,0 +2267,ESTP,28.12,0.18259985207100593,0.0,0.2,0.0,0.0,0.0,0.64,0.0,2.92,0,1,1,1 +2268,ENTJ,32.3,0.18378620510890892,0.04,0.22,0.1,0.0,0.04,1.32,0.0,2.88,0,0,1,0 +2269,ENTP,21.96,0.18491317921485034,0.0,0.18,0.02,0.0,0.0,0.42,0.0,2.48,0,0,1,1 +2270,INFJ,30.58,0.18331715736878512,0.02,0.1,0.16,0.0,0.0,0.82,0.0,4.02,1,0,0,0 +2271,ESFJ,20.42,0.18806652346146707,0.08,0.26,0.08,0.0,0.0,0.46,0.0,2.56,0,1,0,0 +2272,INFJ,26.2,0.17953806297693817,0.04,0.12,0.02,0.0,0.0,0.58,0.02,2.68,1,0,0,0 +2273,INFJ,30.28,0.18223157438445087,0.0,0.12,0.1,0.0,0.04,0.64,0.0,4.0,1,0,0,0 +2274,ENFP,32.9,0.18517125525872546,0.0,0.12,0.26,0.0,0.0,0.96,0.0,3.64,0,0,0,1 +2275,ENTP,23.44,0.17717266967946996,0.0,0.08,0.08,0.0,0.0,0.48,0.0,2.8,0,0,1,1 +2276,INTP,22.2,0.18406275563799654,0.0,0.02,0.0,0.0,0.02,0.36,0.0,2.94,1,0,1,1 +2277,INFP,10.06,0.19479162610693304,0.12,0.1,0.08,0.06,0.0,0.42,0.0,1.24,1,0,0,1 +2278,INTP,19.3,0.18231808543617084,0.08,0.26,0.04,0.02,0.02,0.56,0.0,1.92,1,0,1,1 +2279,ENTP,30.24,0.17864514918371174,0.02,0.16,0.06,0.0,0.0,0.88,0.0,2.4,0,0,1,1 +2280,INFJ,22.7,0.18188619197840178,0.02,0.18,0.54,0.0,0.0,0.72,0.02,2.62,1,0,0,0 +2281,INFP,24.26,0.18618193432441685,0.02,0.1,0.12,0.0,0.0,0.66,0.0,3.14,1,0,0,1 +2282,INTP,29.24,0.1868281260093324,0.02,0.2,0.24,0.0,0.02,0.82,0.0,3.68,1,0,1,1 +2283,ENTP,26.62,0.18466874178194145,0.14,0.46,0.06,0.0,0.12,0.7,0.0,3.6,0,0,1,1 +2284,INFJ,18.1,0.19427190647462902,0.12,0.24,0.6,0.06,0.0,0.86,0.0,2.02,1,0,0,0 +2285,INFP,29.04,0.1895783758448927,0.06,0.2,0.18,0.04,0.06,1.06,0.0,3.04,1,0,0,1 +2286,ENFJ,28.98,0.18394845841680296,0.0,0.06,0.06,0.0,0.02,0.72,0.0,2.02,0,0,0,0 +2287,INFP,26.78,0.18788208429316597,0.0,0.24,0.12,0.0,0.04,0.76,0.0,3.72,1,0,0,1 +2288,INTP,24.82,0.18707520682280032,0.0,0.08,0.32,0.0,0.0,0.88,0.0,2.14,1,0,1,1 +2289,INTP,12.52,0.1849714368398708,0.1,0.22,0.06,0.02,0.0,0.28,0.0,0.88,1,0,1,1 +2290,ENFP,28.48,0.19241241847676013,0.06,0.3,0.84,0.04,0.04,1.08,0.0,3.52,0,0,0,1 +2291,INFP,16.56,0.17969816073164455,0.26,0.42,0.06,0.0,0.0,0.3,0.04,1.46,1,0,0,1 +2292,ENTP,23.46,0.18260752098272537,0.04,0.14,0.1,0.0,0.1,0.48,0.0,2.24,0,0,1,1 +2293,ENFP,8.18,0.19448387659661404,0.08,0.18,0.56,0.0,0.02,0.4,0.0,0.76,0,0,0,1 +2294,ENFJ,30.58,0.18395892269110964,0.0,0.5,0.22,0.0,0.0,0.92,0.0,3.24,0,0,0,0 +2295,INFJ,25.76,0.19320464669403434,0.06,0.08,0.42,0.04,0.08,0.52,0.0,4.68,1,0,0,0 +2296,ENFP,36.3,0.18753918290484145,0.0,0.16,0.16,0.0,0.0,0.86,0.0,5.0,0,0,0,1 +2297,ENFP,34.3,0.1912158129546263,0.0,0.06,0.08,0.0,0.0,0.8,0.0,4.42,0,0,0,1 +2298,ENFP,33.72,0.18388976735699872,0.0,0.2,0.24,0.0,0.0,0.94,0.0,3.32,0,0,0,1 +2299,ENFP,33.82,0.1877695705291119,0.0,0.24,0.16,0.0,0.0,1.2,0.0,3.84,0,0,0,1 +2300,ENFP,25.22,0.18864511382263296,0.02,0.32,0.38,0.02,0.0,1.38,0.0,2.34,0,0,0,1 +2301,ENFP,20.7,0.19002071120497557,0.02,0.08,0.28,0.0,0.0,0.54,0.0,2.14,0,0,0,1 +2302,ENFP,28.68,0.185948035240784,0.0,0.3,0.5,0.0,0.02,1.06,0.0,4.02,0,0,0,1 +2303,ENFP,28.36,0.1970433218926853,0.0,0.22,0.76,0.0,0.0,1.44,0.08,3.1,0,0,0,1 +2304,ENTP,27.42,0.18150947303328743,0.0,0.1,0.18,0.0,0.0,0.6,0.0,2.82,0,0,1,1 +2305,INFJ,21.6,0.1977983421779746,0.0,0.44,0.7,0.0,0.0,1.18,0.02,2.04,1,0,0,0 +2306,ENFJ,26.76,0.19578249275109455,0.02,0.14,0.28,0.0,0.06,2.14,0.0,2.86,0,0,0,0 +2307,ESTP,19.02,0.19336677176507303,0.06,0.3,0.28,0.0,0.0,0.72,0.0,2.34,0,1,1,1 +2308,INFJ,2.74,0.17823045618676298,0.92,0.1,0.0,0.04,0.0,0.08,0.0,0.38,1,0,0,0 +2309,INTP,11.36,0.17733042261369533,0.0,0.06,0.02,0.0,0.0,0.22,0.0,1.34,1,0,1,1 +2310,INFP,31.52,0.17256886954890244,0.02,0.08,0.0,0.0,0.0,0.78,0.0,2.58,1,0,0,1 +2311,ENFP,32.74,0.17780888638539744,0.04,0.46,0.0,0.0,0.0,0.82,0.0,3.82,0,0,0,1 +2312,INFP,15.96,0.18394705386000368,0.46,0.5,0.06,0.0,0.06,0.34,0.04,2.06,1,0,0,1 +2313,INFP,21.68,0.1803421199029116,0.06,0.1,0.06,0.0,0.0,0.48,0.0,1.82,1,0,0,1 +2314,INTP,20.96,0.18526855161899397,0.02,0.1,0.04,0.0,0.0,0.6,0.0,1.86,1,0,1,1 +2315,ENTJ,28.3,0.19021611103604819,0.08,0.38,0.34,0.04,0.0,1.2,0.0,3.44,0,0,1,0 +2316,ENTP,16.76,0.18152583508935133,0.0,0.16,0.24,0.0,0.0,0.66,0.02,1.52,0,0,1,1 +2317,ENTP,24.1,0.17793245553485917,0.02,0.08,0.06,0.0,0.12,0.42,0.0,2.6,0,0,1,1 +2318,ENTP,27.78,0.18349298188283167,0.0,0.26,0.08,0.0,0.02,0.98,0.0,2.68,0,0,1,1 +2319,ENTP,30.72,0.1850750616765634,0.0,0.14,0.16,0.0,0.0,0.84,0.0,3.04,0,0,1,1 +2320,ENTP,25.62,0.182778254444799,0.02,0.3,0.46,0.0,0.0,0.52,0.06,2.82,0,0,1,1 +2321,ENTP,26.58,0.17646390382004665,0.0,0.34,0.3,0.0,0.0,0.68,0.0,1.24,0,0,1,1 +2322,ENTP,22.76,0.18381213183174988,0.0,0.32,0.02,0.0,0.0,0.56,0.0,1.98,0,0,1,1 +2323,ENTP,28.16,0.18270399902511777,0.02,0.18,0.0,0.0,0.0,1.46,0.0,2.34,0,0,1,1 +2324,ENTP,27.74,0.1873027554785395,0.04,0.2,0.14,0.0,0.0,0.82,0.0,3.32,0,0,1,1 +2325,ENTP,24.32,0.18560211998893336,0.0,0.08,0.12,0.0,0.0,0.66,0.0,2.32,0,0,1,1 +2326,ENTP,25.0,0.18558266581858462,0.02,0.18,0.08,0.0,0.0,0.52,0.0,2.34,0,0,1,1 +2327,INTJ,26.72,0.176513282043838,0.06,0.3,0.04,0.0,0.0,0.54,0.0,2.38,1,0,1,0 +2328,ENTP,17.68,0.1821453862893584,0.14,0.28,0.08,0.04,0.0,0.34,0.0,1.68,0,0,1,1 +2329,INFJ,21.32,0.18517821521321837,0.18,0.28,0.26,0.1,0.04,0.42,0.08,2.06,1,0,0,0 +2330,INFP,16.26,0.17940633559779812,0.46,0.52,0.06,0.0,0.02,0.4,0.02,1.08,1,0,0,1 +2331,INFJ,28.16,0.17668789015058672,0.0,0.3,0.02,0.0,0.0,0.64,0.0,2.24,1,0,0,0 +2332,INFP,22.56,0.18521943792269932,0.38,0.6,0.04,0.02,0.0,0.52,0.02,2.44,1,0,0,1 +2333,INFP,31.4,0.18207494343306596,0.04,0.12,0.02,0.0,0.0,0.9,0.0,3.52,1,0,0,1 +2334,INFP,33.04,0.18293282462766638,0.0,0.18,0.0,0.0,0.04,0.94,0.0,2.86,1,0,0,1 +2335,ENTJ,32.04,0.17888500128321427,0.0,0.08,0.46,0.0,0.0,0.84,0.0,3.44,0,0,1,0 +2336,INFP,22.26,0.1785081071806457,0.42,0.32,0.08,0.1,0.0,0.64,0.0,2.24,1,0,0,1 +2337,INTJ,29.66,0.17851722014249485,0.06,0.42,0.02,0.0,0.0,0.8,0.0,3.38,1,0,1,0 +2338,ISFP,29.3,0.18188407255711608,0.08,0.12,0.16,0.0,0.0,0.76,0.0,3.36,1,1,0,1 +2339,INTP,24.68,0.17918324294808533,0.1,0.36,0.08,0.0,0.0,0.56,0.0,1.54,1,0,1,1 +2340,ISTP,22.84,0.17908974523652432,0.0,0.1,0.06,0.0,0.0,0.46,0.0,2.44,1,1,1,1 +2341,INFJ,27.86,0.18851821719546327,0.0,0.5,0.06,0.0,0.0,0.96,0.02,3.3,1,0,0,0 +2342,INTP,27.58,0.1857165558469209,0.02,0.02,0.24,0.02,0.02,0.94,0.0,2.82,1,0,1,1 +2343,ENTP,19.52,0.18376249368320735,0.34,0.56,0.1,0.02,0.02,0.5,0.0,1.9,0,0,1,1 +2344,ESTJ,28.24,0.18342643729021763,0.04,0.26,0.02,0.0,0.0,0.8,0.0,1.7,0,1,1,0 +2345,INFJ,16.3,0.18692356194783666,0.08,0.26,0.08,0.02,0.0,0.48,0.04,1.64,1,0,0,0 +2346,INTP,25.26,0.2020075415958825,0.04,0.56,0.92,0.0,0.0,0.7,0.0,3.42,1,0,1,1 +2347,INTJ,16.58,0.17730597714267138,0.0,0.16,0.08,0.0,0.0,0.26,0.0,1.44,1,0,1,0 +2348,INTP,26.12,0.18655411234405614,0.0,0.3,0.08,0.0,0.0,0.64,0.0,3.52,1,0,1,1 +2349,INTJ,21.24,0.1808517229650893,0.0,0.48,0.04,0.0,0.02,0.48,0.0,1.46,1,0,1,0 +2350,INFJ,20.36,0.18477799283801077,0.3,0.4,0.06,0.06,0.0,0.5,0.02,2.12,1,0,0,0 +2351,ENFJ,15.28,0.18519452719699497,0.06,0.12,0.02,0.0,0.0,0.18,0.02,1.66,0,0,0,0 +2352,INFP,25.42,0.1849682055440189,0.0,0.12,0.16,0.0,0.0,0.5,0.0,2.04,1,0,0,1 +2353,ISTP,27.76,0.18174593508242687,0.04,0.12,0.04,0.0,0.0,0.62,0.02,1.86,1,1,1,1 +2354,INTP,26.26,0.18144556122509473,0.0,0.12,0.0,0.0,0.0,0.64,0.0,3.16,1,0,1,1 +2355,INTJ,15.06,0.18747579630190175,0.14,0.22,0.04,0.0,0.04,0.28,0.08,1.38,1,0,1,0 +2356,INTP,29.96,0.17957144979993903,0.02,0.14,0.06,0.0,0.0,0.9,0.0,2.14,1,0,1,1 +2357,INTJ,33.68,0.19043764696656776,0.0,0.12,0.02,0.0,0.0,0.86,0.02,2.46,1,0,1,0 +2358,ENFP,26.9,0.1868287351551208,0.04,0.32,0.3,0.0,0.0,0.72,0.0,3.12,0,0,0,1 +2359,INTP,9.36,0.18482625182891543,0.0,0.04,0.0,0.0,0.0,0.16,0.0,0.88,1,0,1,1 +2360,INTJ,29.26,0.18264873703616438,0.0,0.12,0.04,0.0,0.02,0.74,0.0,3.36,1,0,1,0 +2361,ENTP,11.4,0.19113569971086297,0.08,0.2,0.04,0.04,0.0,0.22,0.0,1.02,0,0,1,1 +2362,INTJ,33.28,0.19084556682720014,0.0,0.04,0.1,0.0,0.0,0.82,0.02,3.78,1,0,1,0 +2363,INFP,28.52,0.18503386766529215,0.0,0.32,0.24,0.0,0.02,0.6,0.02,4.16,1,0,0,1 +2364,INTP,23.08,0.1713398231876358,0.4,0.16,0.0,0.2,0.02,0.68,0.0,2.48,1,0,1,1 +2365,INFP,32.2,0.18108167952661972,0.0,0.16,0.02,0.0,0.0,0.84,0.0,3.32,1,0,0,1 +2366,INFP,25.22,0.1866225762825078,0.0,0.28,0.82,0.0,0.02,0.78,0.0,2.1,1,0,0,1 +2367,INTP,26.8,0.18113216964626783,0.0,0.34,0.0,0.0,0.0,0.68,0.0,2.66,1,0,1,1 +2368,INFP,22.7,0.1802758788207696,0.0,0.3,0.18,0.0,0.0,0.58,0.0,2.36,1,0,0,1 +2369,INTP,25.96,0.18174200110940789,0.5,0.1,0.08,0.3,0.0,0.64,0.0,2.8,1,0,1,1 +2370,INTP,25.74,0.1812953848277632,0.04,0.34,0.14,0.0,0.02,0.6,0.0,2.28,1,0,1,1 +2371,ISFJ,26.6,0.19570742283011985,0.08,0.52,0.5,0.0,0.0,0.46,0.0,3.88,1,1,0,0 +2372,ENTP,17.7,0.18684204566810944,0.02,0.18,0.1,0.0,0.0,0.38,0.0,2.04,0,0,1,1 +2373,INTP,27.12,0.1722436209510929,0.0,0.32,0.0,0.0,0.0,0.7,0.0,1.76,1,0,1,1 +2374,INFP,27.32,0.18527736928993793,0.0,0.02,0.0,0.0,0.02,0.64,0.0,3.34,1,0,0,1 +2375,ENFP,34.02,0.18372480123486828,0.0,0.18,0.18,0.0,0.1,1.22,0.0,3.7,0,0,0,1 +2376,INFP,24.86,0.17662100538383557,0.1,0.26,0.02,0.02,0.0,0.54,0.0,2.76,1,0,0,1 +2377,ENTP,19.24,0.18210298572495912,0.04,0.1,0.0,0.04,0.02,1.3,0.0,0.88,0,0,1,1 +2378,INFP,30.32,0.1868549186525528,0.0,0.32,0.42,0.0,0.02,0.74,0.0,3.44,1,0,0,1 +2379,INTJ,25.48,0.18521390545349775,0.02,0.08,0.06,0.02,0.0,1.44,0.0,3.08,1,0,1,0 +2380,INFP,27.3,0.1852855415429165,0.06,0.36,0.02,0.0,0.0,0.64,0.0,2.78,1,0,0,1 +2381,INFJ,1.44,0.18871356335514572,0.06,0.0,0.0,0.04,0.0,0.08,0.0,0.16,1,0,0,0 +2382,INTP,25.12,0.18050409298800543,0.0,0.32,0.24,0.0,0.0,0.46,0.0,1.96,1,0,1,1 +2383,INFP,20.7,0.1843554581423038,0.08,0.24,0.14,0.0,0.0,0.48,0.0,1.98,1,0,0,1 +2384,ISTP,29.06,0.17805619019839217,0.14,0.26,0.0,0.0,0.0,0.8,0.0,2.08,1,1,1,1 +2385,ENFP,29.32,0.18120031977397721,0.0,0.3,0.06,0.0,0.0,0.86,0.0,2.94,0,0,0,1 +2386,INFJ,17.74,0.1816043378445453,0.1,0.18,0.02,0.04,0.0,0.42,0.0,1.54,1,0,0,0 +2387,INTJ,20.96,0.18930076960745726,0.16,0.22,0.02,0.02,0.02,0.64,0.0,2.74,1,0,1,0 +2388,INFP,30.08,0.18813365343865668,0.0,0.28,0.38,0.0,0.1,1.22,0.0,3.9,1,0,0,1 +2389,INFP,32.08,0.1865487857182551,0.0,0.08,0.38,0.0,0.0,0.86,0.0,4.48,1,0,0,1 +2390,ENFJ,28.1,0.1869283231164593,0.34,0.44,0.14,0.04,0.02,0.7,0.0,3.22,0,0,0,0 +2391,INTJ,26.24,0.18635085209327448,0.08,0.24,0.06,0.06,0.0,1.08,0.0,2.4,1,0,1,0 +2392,INFP,19.3,0.1792028724480798,0.28,0.1,0.02,0.02,0.0,0.66,0.0,2.88,1,0,0,1 +2393,INFJ,25.24,0.18420072752720845,0.04,0.32,0.52,0.0,0.0,0.72,0.0,3.02,1,0,0,0 +2394,INFP,22.34,0.17984072042545887,0.02,0.14,0.06,0.0,0.08,0.46,0.0,2.32,1,0,0,1 +2395,INFJ,31.62,0.18419936599274653,0.28,0.28,0.28,0.0,0.0,1.18,0.0,3.82,1,0,0,0 +2396,INTP,26.32,0.18092989835016365,0.06,0.2,0.06,0.0,0.08,0.46,0.0,3.52,1,0,1,1 +2397,INFP,24.78,0.18919826680209473,0.02,0.1,0.44,0.0,0.02,0.58,0.04,3.04,1,0,0,1 +2398,ENTP,21.7,0.18339884728132727,0.02,0.62,0.38,0.0,0.0,0.54,0.0,1.7,0,0,1,1 +2399,ENFP,31.36,0.18146193662416363,0.0,0.14,0.1,0.0,0.0,0.78,0.0,3.82,0,0,0,1 +2400,ISTP,24.86,0.19004844557691886,0.28,0.32,0.34,0.08,0.02,0.54,0.0,2.56,1,1,1,1 +2401,INTP,9.28,0.18119654489358258,0.58,0.32,0.04,0.18,0.0,0.18,0.0,1.2,1,0,1,1 +2402,INFP,30.0,0.18664451376184252,0.06,0.08,0.32,0.06,0.06,0.72,0.0,3.96,1,0,0,1 +2403,ISFP,25.56,0.18572804355242062,0.0,0.28,0.48,0.0,0.04,0.54,0.0,2.68,1,1,0,1 +2404,INTP,26.06,0.18862277966225155,0.02,0.22,0.58,0.0,0.0,1.4,0.0,2.18,1,0,1,1 +2405,INTJ,27.08,0.1844341172669381,0.0,0.42,0.0,0.0,0.0,0.76,0.0,2.8,1,0,1,0 +2406,INFJ,27.5,0.18448095750009075,0.02,0.14,0.28,0.0,0.0,0.68,0.0,3.24,1,0,0,0 +2407,ENTP,32.86,0.18101577480741904,0.02,0.1,0.02,0.0,0.0,0.9,0.0,3.98,0,0,1,1 +2408,INTP,10.7,0.17410665652811505,0.82,0.74,0.02,0.12,0.0,0.34,0.0,0.84,1,0,1,1 +2409,INFJ,20.32,0.18319951022661926,0.3,0.38,0.04,0.0,0.0,0.56,0.0,1.76,1,0,0,0 +2410,INFJ,27.66,0.17663493939166952,0.14,0.32,0.08,0.02,0.0,0.76,0.0,3.1,1,0,0,0 +2411,ENFP,25.42,0.1875171809496817,0.04,0.16,0.12,0.0,0.0,0.6,0.0,2.52,0,0,0,1 +2412,INFP,18.96,0.17915317706840073,0.5,0.48,0.08,0.0,0.08,0.58,0.0,2.26,1,0,0,1 +2413,INFP,30.22,0.18626539102040818,0.08,0.36,0.02,0.0,0.06,0.88,0.0,3.94,1,0,0,1 +2414,INTJ,31.02,0.1821531347154306,0.0,0.5,0.22,0.0,0.02,1.2,0.0,3.1,1,0,1,0 +2415,INTP,16.84,0.1893619100474433,0.04,0.28,0.16,0.0,0.0,0.38,0.0,2.0,1,0,1,1 +2416,INFP,16.32,0.18712734853496962,0.06,0.18,0.06,0.0,0.02,0.26,0.0,1.86,1,0,0,1 +2417,ENTP,13.56,0.18741158789269532,0.1,0.28,0.16,0.04,0.0,0.16,0.0,1.76,0,0,1,1 +2418,INFP,15.1,0.19234459022846573,0.0,0.08,0.36,0.0,0.0,0.4,0.0,1.6,1,0,0,1 +2419,INFJ,27.82,0.18122423765591006,0.04,0.08,0.08,0.0,0.02,1.1,0.0,2.84,1,0,0,0 +2420,INFP,18.66,0.17811925341982138,0.0,0.06,0.0,0.0,0.0,0.76,0.0,1.28,1,0,0,1 +2421,ENTJ,27.56,0.17859779298305511,0.04,0.66,0.0,0.0,0.0,0.56,0.06,2.48,0,0,1,0 +2422,INTJ,24.34,0.18339826206664112,0.06,0.26,0.1,0.0,0.02,0.58,0.0,2.32,1,0,1,0 +2423,INTP,13.7,0.1933768447972721,0.0,0.12,0.1,0.0,0.0,0.66,0.0,1.38,1,0,1,1 +2424,INFP,27.74,0.1868322614003052,0.08,0.2,0.1,0.02,0.0,0.62,0.0,3.52,1,0,0,1 +2425,ENFP,29.7,0.19018688625186037,0.0,0.18,0.14,0.0,0.02,1.08,0.0,4.18,0,0,0,1 +2426,ENFJ,7.94,0.18353382855772235,0.52,0.1,0.06,0.12,0.0,0.12,0.0,1.1,0,0,0,0 +2427,ENTP,28.38,0.19015285440668125,0.18,0.12,0.7,0.0,0.0,0.62,0.0,3.52,0,0,1,1 +2428,INFP,28.66,0.18668956134004258,0.0,0.06,0.42,0.0,0.02,0.76,0.0,3.76,1,0,0,1 +2429,INTJ,22.86,0.18087637380556232,0.14,0.28,0.04,0.0,0.06,0.56,0.0,1.72,1,0,1,0 +2430,INFJ,28.46,0.18161882109001293,0.0,0.14,0.28,0.0,0.02,0.8,0.0,2.92,1,0,0,0 +2431,ESFJ,15.7,0.19511885961652795,0.0,0.06,0.48,0.0,0.0,0.16,0.0,1.76,0,1,0,0 +2432,ENTP,26.68,0.1867017024513059,0.08,0.24,0.0,0.02,0.0,0.6,0.0,3.66,0,0,1,1 +2433,INFJ,28.28,0.1861683334333026,0.06,0.14,0.18,0.0,0.0,0.62,0.0,3.6,1,0,0,0 +2434,ENTP,26.24,0.1885603086735838,0.06,0.2,0.08,0.0,0.0,0.78,0.0,2.64,0,0,1,1 +2435,INTP,25.78,0.202259286223445,0.0,0.06,0.3,0.0,0.0,3.12,0.0,2.68,1,0,1,1 +2436,INTJ,16.82,0.18436278456357694,0.32,0.44,0.0,0.06,0.0,0.3,0.0,1.6,1,0,1,0 +2437,INFJ,30.78,0.1921921589364371,0.0,0.22,0.4,0.0,0.04,0.76,0.0,3.8,1,0,0,0 +2438,ENTP,16.72,0.1872327765168857,0.14,0.12,0.14,0.0,0.0,0.32,0.0,1.66,0,0,1,1 +2439,INTJ,27.32,0.18330703218952887,0.04,0.2,0.04,0.02,0.02,0.78,0.0,2.84,1,0,1,0 +2440,INTP,28.42,0.1774495496919988,0.02,0.18,0.46,0.0,0.0,0.68,0.0,1.26,1,0,1,1 +2441,INTJ,31.8,0.17347831533694405,0.0,0.08,0.04,0.0,0.0,0.96,0.0,3.44,1,0,1,0 +2442,INTP,24.12,0.18157367442999356,0.1,0.42,0.14,0.08,0.0,1.7,0.0,2.28,1,0,1,1 +2443,ENTP,23.5,0.18567420148618916,0.02,0.26,0.06,0.0,0.0,0.66,0.02,2.36,0,0,1,1 +2444,ENTP,23.4,0.1762734402706743,0.1,0.28,0.1,0.02,0.0,0.46,0.0,2.74,0,0,1,1 +2445,INFP,26.7,0.1834323273514773,0.08,0.18,0.1,0.0,0.0,0.82,0.0,2.38,1,0,0,1 +2446,INTJ,26.82,0.17916213017751476,0.02,0.1,0.02,0.0,0.0,0.68,0.0,2.7,1,0,1,0 +2447,ISFJ,28.64,0.1898953642576546,0.02,0.18,0.52,0.02,0.0,0.94,0.0,4.0,1,1,0,0 +2448,INTJ,29.5,0.18833972174785443,0.02,0.24,0.16,0.0,0.0,0.8,0.0,3.96,1,0,1,0 +2449,INTJ,16.52,0.18449182943455658,0.12,0.18,0.14,0.04,0.0,0.32,0.0,1.8,1,0,1,0 +2450,ENTJ,21.24,0.18822581830839008,0.1,0.26,0.2,0.02,0.0,0.5,0.0,2.12,0,0,1,0 +2451,INFJ,21.92,0.18507777887556098,0.12,0.38,0.12,0.0,0.0,0.6,0.0,2.14,1,0,0,0 +2452,INFJ,21.62,0.18168904958677684,0.1,0.26,0.14,0.0,0.0,0.66,0.0,1.62,1,0,0,0 +2453,ISTP,11.14,0.18966121890210927,0.06,0.22,0.32,0.0,0.0,0.36,0.0,1.04,1,1,1,1 +2454,ESTP,30.46,0.18320481667978303,0.02,0.28,0.12,0.0,0.04,0.86,0.0,2.5,0,1,1,1 +2455,INFJ,27.1,0.1867854537972419,0.18,0.22,0.02,0.0,0.04,0.6,0.0,2.74,1,0,0,0 +2456,ISTP,21.68,0.18071462416789938,0.16,0.32,0.18,0.08,0.0,0.56,0.0,2.22,1,1,1,1 +2457,ISFP,18.28,0.18609903294511346,0.06,0.1,0.46,0.0,0.0,0.36,0.0,2.0,1,1,0,1 +2458,INFP,24.62,0.17951980440091397,0.4,0.44,0.04,0.02,0.06,0.68,0.0,2.4,1,0,0,1 +2459,INFJ,24.48,0.189650978996084,0.26,0.16,0.26,0.12,0.0,1.04,0.0,3.26,1,0,0,0 +2460,ENFP,27.18,0.19159121941853857,0.04,0.28,0.72,0.0,0.0,0.88,0.0,2.9,0,0,0,1 +2461,INFP,34.22,0.17901542913145033,0.06,0.06,0.02,0.06,0.0,1.06,0.0,3.66,1,0,0,1 +2462,INFJ,24.98,0.18806821800303686,0.0,0.16,0.16,0.0,0.02,0.78,0.0,2.88,1,0,0,0 +2463,INFJ,6.44,0.17377091619483362,0.78,0.42,0.0,0.4,0.0,0.32,0.0,1.1,1,0,0,0 +2464,INTJ,29.3,0.18310701186792797,0.02,0.24,0.02,0.0,0.0,0.84,0.0,3.52,1,0,1,0 +2465,ENFP,24.52,0.1803337810893324,0.22,0.24,0.08,0.0,0.02,0.7,0.0,2.94,0,0,0,1 +2466,INFP,33.7,0.18311922803412575,0.0,0.04,0.18,0.0,0.08,0.9,0.0,3.8,1,0,0,1 +2467,INFP,35.02,0.1847613424997145,0.02,0.1,0.32,0.0,0.0,1.0,0.0,4.02,1,0,0,1 +2468,INFP,26.38,0.19083785787974286,0.0,0.04,0.08,0.0,0.0,1.16,0.0,1.48,1,0,0,1 +2469,ENFP,37.62,0.18682601585589084,0.0,0.2,0.12,0.0,0.0,1.2,0.0,5.02,0,0,0,1 +2470,INFP,19.88,0.18118517963906122,0.44,0.46,0.0,0.0,0.0,0.5,0.0,2.44,1,0,0,1 +2471,INFP,26.38,0.18343762348249723,0.0,0.32,0.06,0.0,0.04,0.72,0.0,2.26,1,0,0,1 +2472,ISTP,20.1,0.18230734740917712,0.16,0.26,0.04,0.0,0.02,0.3,0.0,2.28,1,1,1,1 +2473,INFJ,25.8,0.19318368089689605,0.16,0.26,0.18,0.06,0.0,1.0,0.0,2.9,1,0,0,0 +2474,INTJ,23.96,0.18129729177066337,0.44,0.36,0.06,0.2,0.0,0.86,0.12,2.2,1,0,1,0 +2475,INTJ,17.3,0.1875,0.52,0.24,0.18,0.42,0.0,0.54,0.0,1.3,1,0,1,0 +2476,ESTP,34.62,0.18889751349916076,0.02,0.1,0.04,0.02,0.02,1.16,0.0,3.42,0,1,1,1 +2477,INFJ,24.3,0.1806992339464975,0.24,0.24,0.02,0.04,0.04,0.44,0.0,2.7,1,0,0,0 +2478,INFP,33.36,0.1821479713246937,0.0,0.06,0.08,0.0,0.02,0.84,0.0,4.34,1,0,0,1 +2479,ISTP,33.26,0.18402730809367765,0.0,0.32,0.2,0.0,0.0,1.0,0.0,3.28,1,1,1,1 +2480,ENFP,30.2,0.18382493717806553,0.04,0.34,0.12,0.0,0.0,1.12,0.0,3.84,0,0,0,1 +2481,INTJ,27.08,0.187453250640542,0.08,0.26,0.08,0.0,0.0,0.62,0.02,2.62,1,0,1,0 +2482,INFJ,31.76,0.18277451148161183,0.04,0.2,0.04,0.02,0.0,0.9,0.02,2.94,1,0,0,0 +2483,INTJ,13.24,0.18299153728985054,0.66,0.6,0.04,0.06,0.12,0.26,0.0,1.72,1,0,1,0 +2484,ISTP,25.28,0.1838387042412193,0.02,0.4,0.02,0.0,0.0,0.58,0.0,2.56,1,1,1,1 +2485,INTJ,27.98,0.17994593632964972,0.0,0.48,0.0,0.0,0.0,0.66,0.0,3.56,1,0,1,0 +2486,ENFP,22.36,0.1876537514792899,0.02,0.44,0.24,0.0,0.02,0.8,0.02,2.58,0,0,0,1 +2487,INFP,23.26,0.18361246530873265,0.24,0.3,0.06,0.0,0.02,0.5,0.0,2.64,1,0,0,1 +2488,ENTP,26.72,0.18525051399025666,0.06,0.24,0.04,0.04,0.02,0.56,0.0,2.2,0,0,1,1 +2489,INFJ,6.7,0.2041502443242374,0.3,0.16,0.06,0.08,0.0,0.06,0.0,1.46,1,0,0,0 +2490,INTJ,26.32,0.18387850060195118,0.08,0.2,0.1,0.0,0.0,0.72,0.0,2.64,1,0,1,0 +2491,INFJ,16.64,0.17692471057489703,0.62,0.06,0.12,0.58,0.02,0.84,0.0,1.36,1,0,0,0 +2492,ESTP,11.18,0.19339846705746713,0.16,0.2,0.08,0.0,0.02,0.22,0.0,1.16,0,1,1,1 +2493,ESFP,18.5,0.18780229701762927,0.1,0.16,0.14,0.0,0.0,0.3,0.0,2.16,0,1,0,1 +2494,INFJ,35.2,0.18113785726669454,0.0,0.12,0.1,0.0,0.0,0.96,0.0,4.44,1,0,0,0 +2495,INTP,20.96,0.1760926044515545,0.02,0.24,0.0,0.0,0.0,0.32,0.0,2.38,1,0,1,1 +2496,INTJ,22.14,0.18925034085504308,0.0,0.36,0.0,0.0,0.0,0.3,0.0,2.18,1,0,1,0 +2497,ENFJ,30.5,0.18277363444010283,0.06,0.2,0.1,0.0,0.0,0.86,0.0,3.42,0,0,0,0 +2498,INFJ,30.06,0.18448664206543688,0.02,0.22,0.1,0.02,0.0,0.84,0.0,1.92,1,0,0,0 +2499,INFJ,22.74,0.18788880154501603,0.02,0.06,0.24,0.0,0.06,0.6,0.0,2.88,1,0,0,0 +2500,INFP,27.12,0.18318740580547638,0.06,0.22,0.08,0.0,0.0,1.08,0.02,2.36,1,0,0,1 +2501,ENTP,24.84,0.1835897229688554,0.0,0.2,0.18,0.0,0.0,0.84,0.0,3.1,0,0,1,1 +2502,INFP,29.4,0.1861968959236617,0.08,0.22,0.12,0.0,0.0,0.8,0.0,2.68,1,0,0,1 +2503,INFP,27.18,0.18618513376992338,0.0,0.18,0.5,0.0,0.04,0.56,0.0,3.68,1,0,0,1 +2504,ISFP,8.5,0.16234502015364508,0.62,0.18,0.16,0.02,0.0,0.14,0.04,1.62,1,1,0,1 +2505,INTJ,29.84,0.18140868262538623,0.04,0.2,0.02,0.02,0.0,0.9,0.0,2.8,1,0,1,0 +2506,INTP,30.8,0.18496218529835107,0.0,0.2,0.02,0.0,0.0,0.7,0.0,3.66,1,0,1,1 +2507,ISFP,15.22,0.18617575669485684,0.14,0.4,0.2,0.0,0.0,0.3,0.0,1.62,1,1,0,1 +2508,ISTP,24.74,0.188103664152269,0.04,0.18,0.0,0.02,0.0,0.86,0.0,1.98,1,1,1,1 +2509,INFJ,27.56,0.18577013405104764,0.12,0.1,0.32,0.02,0.0,0.8,0.0,3.64,1,0,0,0 +2510,ENFP,32.42,0.18149591419150515,0.0,0.04,0.06,0.0,0.0,0.86,0.02,3.32,0,0,0,1 +2511,INFJ,23.34,0.1855819526398516,0.06,0.06,0.0,0.06,0.0,0.74,0.0,3.02,1,0,0,0 +2512,INTP,30.86,0.17979915847340028,0.06,0.54,0.02,0.0,0.0,0.84,0.0,2.28,1,0,1,1 +2513,INTP,30.72,0.17790317157635038,0.04,0.06,0.02,0.02,0.0,1.06,0.0,3.64,1,0,1,1 +2514,INTP,20.88,0.18017885959495852,0.0,0.26,0.1,0.0,0.0,0.36,0.0,1.98,1,0,1,1 +2515,ISTP,14.92,0.1812093103170555,0.18,0.04,0.0,0.1,0.04,0.2,0.0,1.62,1,1,1,1 +2516,INFJ,25.78,0.18346903439830906,0.02,0.1,0.14,0.0,0.0,0.58,0.0,3.32,1,0,0,0 +2517,ENTP,25.22,0.18114409246407334,0.04,0.24,0.16,0.0,0.0,0.8,0.0,2.7,0,0,1,1 +2518,INTJ,23.44,0.18116971394359585,0.0,0.14,0.04,0.0,0.0,0.54,0.0,1.86,1,0,1,0 +2519,ISTP,24.02,0.18448568929078615,0.0,0.08,0.0,0.0,0.02,0.44,0.0,3.6,1,1,1,1 +2520,INFJ,28.92,0.18135576601900133,0.02,0.24,0.14,0.0,0.02,1.0,0.02,3.24,1,0,0,0 +2521,INFP,20.92,0.18519768333730388,0.32,0.22,0.02,0.1,0.04,0.44,0.0,2.76,1,0,0,1 +2522,INTJ,31.18,0.184264009472654,0.02,0.56,0.08,0.0,0.0,1.08,0.0,4.2,1,0,1,0 +2523,ENTP,23.54,0.17973933134087558,0.0,0.24,0.0,0.0,0.0,0.42,0.0,2.84,0,0,1,1 +2524,INFJ,33.02,0.18005679200984046,0.0,0.08,0.04,0.0,0.02,0.8,0.0,4.04,1,0,0,0 +2525,INTJ,33.08,0.18451066047811587,0.0,0.08,0.0,0.0,0.0,0.92,0.0,3.9,1,0,1,0 +2526,INTP,18.36,0.18152164677512347,0.18,0.34,0.02,0.06,0.0,0.44,0.02,1.72,1,0,1,1 +2527,INFP,24.0,0.19173723991589098,0.06,0.16,0.2,0.0,0.0,0.84,0.02,3.4,1,0,0,1 +2528,ENFP,10.94,0.18980945192874352,0.0,0.06,0.12,0.0,0.0,0.32,0.0,1.12,0,0,0,1 +2529,INFJ,34.4,0.18597079760989393,0.0,0.14,0.02,0.0,0.04,1.84,0.0,2.0,1,0,0,0 +2530,ENTP,32.86,0.1901087545019438,0.04,0.26,0.34,0.0,0.0,1.26,0.0,1.84,0,0,1,1 +2531,INFJ,24.2,0.17796188640263486,0.0,0.14,0.0,0.0,0.0,0.56,0.0,1.82,1,0,0,0 +2532,INFJ,18.06,0.1934741699793544,0.02,0.18,0.1,0.0,0.0,0.68,0.0,2.38,1,0,0,0 +2533,ENFJ,34.62,0.1723038351335312,0.04,0.06,0.0,0.0,0.02,0.94,0.0,3.8,0,0,0,0 +2534,ENTJ,7.04,0.17921651393635707,0.14,0.0,0.0,0.04,0.0,0.12,0.0,0.66,0,0,1,0 +2535,ENFP,25.42,0.19057236667668986,0.08,0.18,0.14,0.0,0.0,1.04,0.0,3.74,0,0,0,1 +2536,ENFJ,14.72,0.18424076070973433,0.02,0.1,0.02,0.0,0.0,0.3,0.0,1.22,0,0,0,0 +2537,ESFP,7.96,0.18352008628059868,0.08,0.2,0.0,0.0,0.0,0.2,0.0,1.02,0,1,0,1 +2538,ISTP,24.9,0.1814995963619793,0.06,0.1,0.0,0.02,0.0,0.56,0.0,3.46,1,1,1,1 +2539,INTJ,19.72,0.1901651160724767,0.0,0.14,0.06,0.0,0.04,0.94,0.02,0.84,1,0,1,0 +2540,INFJ,23.8,0.18081943481246374,0.0,0.24,0.08,0.0,0.02,0.42,0.0,1.28,1,0,0,0 +2541,INFJ,29.82,0.18854753109700267,0.0,0.3,0.12,0.0,0.08,0.66,0.0,2.48,1,0,0,0 +2542,INTP,25.26,0.1815451730248397,0.02,0.14,0.02,0.0,0.0,0.66,0.0,3.04,1,0,1,1 +2543,INFJ,30.24,0.1874129425526274,0.0,0.22,0.22,0.0,0.0,0.82,0.0,2.7,1,0,0,0 +2544,INTP,18.42,0.17810144837282307,0.38,0.44,0.14,0.0,0.1,0.4,0.0,1.98,1,0,1,1 +2545,INTP,23.8,0.19661125584610625,0.04,0.8,0.4,0.0,0.0,0.98,0.0,3.5,1,0,1,1 +2546,INFJ,29.48,0.17925855725236498,0.04,0.18,0.0,0.0,0.12,0.72,0.0,3.2,1,0,0,0 +2547,ISFP,23.72,0.18654840955056115,0.16,0.22,0.18,0.0,0.0,0.5,0.0,2.96,1,1,0,1 +2548,INFJ,31.36,0.183975304440493,0.0,0.08,0.08,0.0,0.0,1.1,0.0,3.66,1,0,0,0 +2549,INTP,20.22,0.17874217465998565,0.08,0.18,0.04,0.04,0.02,0.44,0.0,2.64,1,0,1,1 +2550,INFJ,26.1,0.1896374429228153,0.1,0.16,0.56,0.02,0.0,0.82,0.0,3.2,1,0,0,0 +2551,INFJ,21.6,0.1827198553504012,0.28,0.3,0.02,0.0,0.06,0.42,0.0,2.72,1,0,0,0 +2552,INTJ,33.2,0.1851377684528616,0.0,0.04,0.12,0.0,0.02,1.26,0.0,4.18,1,0,1,0 +2553,INTP,22.68,0.183510806765181,0.0,0.36,0.02,0.0,0.0,0.56,0.02,2.54,1,0,1,1 +2554,INTP,22.12,0.18039624443490423,0.0,0.08,0.0,0.0,0.0,0.4,0.0,2.5,1,0,1,1 +2555,ENFP,21.42,0.18311248966509813,0.04,0.12,0.14,0.0,0.02,0.54,0.0,2.78,0,0,0,1 +2556,INTJ,33.06,0.1868790961569678,0.02,0.04,0.02,0.0,0.0,0.94,0.06,4.62,1,0,1,0 +2557,ISTJ,27.74,0.18380869857650645,0.0,0.34,0.02,0.0,0.0,0.62,0.0,3.38,1,1,1,0 +2558,INFP,13.32,0.17838587720205354,0.74,0.72,0.0,0.18,0.0,0.28,0.02,1.76,1,0,0,1 +2559,INTP,31.24,0.18241438706011823,0.0,0.12,0.1,0.0,0.04,0.78,0.0,4.26,1,0,1,1 +2560,INTJ,32.86,0.17900125707292233,0.0,0.06,0.0,0.0,0.02,0.92,0.04,2.56,1,0,1,0 +2561,ENFP,33.48,0.18593407703164477,0.0,0.22,0.0,0.0,0.0,1.4,0.0,3.74,0,0,0,1 +2562,INFJ,20.86,0.1866143655780062,0.48,0.46,0.08,0.06,0.0,0.46,0.0,2.8,1,0,0,0 +2563,INTP,28.74,0.18128814536915958,0.0,0.16,0.18,0.0,0.0,0.68,0.0,3.08,1,0,1,1 +2564,INFP,24.64,0.1875534606861279,0.02,0.22,0.02,0.0,0.0,0.6,0.0,3.7,1,0,0,1 +2565,INTJ,23.86,0.19355313534697305,0.02,0.4,0.34,0.0,0.0,0.44,0.0,2.68,1,0,1,0 +2566,INTP,25.78,0.17748195081996518,0.1,0.22,0.0,0.1,0.0,0.44,0.0,2.6,1,0,1,1 +2567,INTP,27.28,0.180942570405069,0.04,0.1,0.12,0.04,0.02,0.66,0.0,2.36,1,0,1,1 +2568,INTP,29.6,0.181599527505577,0.0,0.16,0.18,0.0,0.02,0.8,0.0,4.18,1,0,1,1 +2569,INFJ,15.76,0.18797910900399012,0.16,0.2,0.04,0.0,0.04,0.42,0.0,2.24,1,0,0,0 +2570,INFP,18.94,0.18296296165492082,0.16,0.28,0.02,0.0,0.0,0.28,0.0,1.7,1,0,0,1 +2571,INTP,27.36,0.1778450661191521,0.0,0.16,0.1,0.0,0.02,0.66,0.0,2.58,1,0,1,1 +2572,INFJ,28.58,0.18342447014492297,0.08,0.16,0.48,0.0,0.0,0.74,0.0,2.88,1,0,0,0 +2573,INFP,22.3,0.1864905950592615,0.04,0.22,0.04,0.0,0.02,0.64,0.0,1.76,1,0,0,1 +2574,INFJ,33.04,0.18347855232231247,0.0,0.08,0.32,0.0,0.0,0.82,0.0,4.76,1,0,0,0 +2575,INFJ,24.2,0.1899739115899128,0.0,0.12,0.14,0.0,0.0,0.48,0.0,3.44,1,0,0,0 +2576,ENFP,19.16,0.18427569690983023,0.48,0.2,0.32,0.3,0.02,0.88,0.0,1.82,0,0,0,1 +2577,INFJ,30.0,0.19057942226437702,0.08,0.32,0.1,0.0,0.0,0.98,0.0,4.56,1,0,0,0 +2578,ISTJ,32.74,0.18061107535598067,0.0,0.02,0.12,0.0,0.0,1.12,0.0,3.78,1,1,1,0 +2579,ISFP,22.32,0.1838505540016102,0.02,0.2,0.0,0.02,0.0,0.36,0.0,2.74,1,1,0,1 +2580,INFP,17.58,0.18134200550593918,0.16,0.28,0.14,0.0,0.0,0.28,0.0,1.78,1,0,0,1 +2581,ISFP,27.46,0.18790279164705034,0.0,0.1,1.08,0.0,0.02,0.48,0.0,3.64,1,1,0,1 +2582,INFP,27.64,0.18168904958677687,0.36,0.22,0.0,0.3,0.0,0.84,0.0,3.48,1,0,0,1 +2583,INFJ,27.28,0.1950035241600752,0.1,0.24,0.0,0.08,0.0,0.62,0.0,3.06,1,0,0,0 +2584,INFJ,22.4,0.18795530554336934,0.0,0.32,0.12,0.0,0.0,0.58,0.0,2.72,1,0,0,0 +2585,ENTP,16.56,0.2036701798040794,0.02,0.18,0.46,0.0,0.0,0.42,0.0,2.52,0,0,1,1 +2586,INTJ,22.48,0.18092437907665457,0.04,0.4,0.0,0.0,0.04,0.54,0.0,2.12,1,0,1,0 +2587,INFJ,24.1,0.18380691892787548,0.14,0.14,0.02,0.06,0.0,0.54,0.0,3.12,1,0,0,0 +2588,INFJ,24.12,0.18082317598013292,0.0,0.36,0.04,0.0,0.0,0.5,0.0,2.8,1,0,0,0 +2589,ISFJ,21.52,0.18759560228046382,0.04,0.2,0.1,0.02,0.0,0.58,0.0,3.7,1,1,0,0 +2590,INTJ,25.28,0.18702355195534026,0.04,0.3,0.0,0.0,0.04,0.76,0.0,3.08,1,0,1,0 +2591,INTP,25.62,0.18376946366782013,0.04,0.12,0.06,0.0,0.0,0.72,0.0,3.4,1,0,1,1 +2592,INFP,15.86,0.18142257916578117,0.02,0.06,0.02,0.0,0.02,0.22,0.0,1.56,1,0,0,1 +2593,INTP,25.94,0.18217743061789077,0.02,0.26,0.08,0.02,0.0,0.5,0.0,3.1,1,0,1,1 +2594,INTP,19.82,0.17981144834270302,0.02,0.2,0.16,0.0,0.0,0.54,0.0,1.92,1,0,1,1 +2595,ISTJ,29.8,0.17862082795041823,0.02,0.46,0.02,0.0,0.0,0.76,0.0,1.92,1,1,1,0 +2596,INTJ,32.18,0.18587551897968133,0.0,0.3,0.66,0.0,0.0,1.16,0.0,3.82,1,0,1,0 +2597,ENFP,23.56,0.18495577308201655,0.08,0.04,0.0,0.0,0.02,0.5,0.06,2.8,0,0,0,1 +2598,INTP,22.84,0.18851027692363673,0.02,0.22,0.14,0.0,0.02,0.52,0.0,3.36,1,0,1,1 +2599,INTP,29.64,0.17089656088750338,0.02,0.12,0.0,0.0,0.0,0.86,0.0,2.0,1,0,1,1 +2600,INFP,30.26,0.18313453447999367,0.1,0.24,0.12,0.0,0.0,0.96,0.0,3.62,1,0,0,1 +2601,ENTP,21.98,0.18996769093782068,0.0,0.22,0.82,0.0,0.08,0.76,0.0,2.44,0,0,1,1 +2602,INTJ,29.58,0.18117512369150873,0.0,0.18,0.0,0.0,0.0,0.74,0.0,2.96,1,0,1,0 +2603,INTJ,19.18,0.17883783730050803,0.1,0.32,0.04,0.0,0.0,0.58,0.02,2.02,1,0,1,0 +2604,INTJ,30.52,0.1802066042955586,0.0,0.34,0.02,0.0,0.0,0.8,0.0,2.9,1,0,1,0 +2605,INTJ,21.42,0.18312356046871917,0.0,0.22,0.06,0.0,0.02,0.54,0.0,2.3,1,0,1,0 +2606,INFP,19.78,0.18205888742581972,0.02,0.18,0.02,0.02,0.02,0.44,0.0,1.14,1,0,0,1 +2607,INFP,29.5,0.18949599940511597,0.0,0.04,0.06,0.0,0.0,0.82,0.0,4.36,1,0,0,1 +2608,ESTP,19.42,0.17881417272791475,0.14,0.18,0.02,0.08,0.0,0.42,0.0,2.1,0,1,1,1 +2609,INFP,26.4,0.18583340642954257,0.08,0.28,0.36,0.04,0.04,0.76,0.0,2.9,1,0,0,1 +2610,ENFJ,8.04,0.17192906574394462,0.04,0.06,0.02,0.02,0.0,0.16,0.0,0.88,0,0,0,0 +2611,INTP,15.36,0.2106937254798917,0.0,0.06,0.0,0.0,0.0,3.62,0.0,0.62,1,0,1,1 +2612,ENFJ,16.96,0.19225274640392037,0.24,0.5,0.12,0.0,0.0,0.28,0.0,1.54,0,0,0,0 +2613,ISFJ,33.88,0.1842250854731478,0.0,0.0,0.18,0.0,0.02,0.92,0.0,5.2,1,1,0,0 +2614,ENFP,24.8,0.19017452593663844,0.04,0.46,0.62,0.0,0.06,0.6,0.0,3.48,0,0,0,1 +2615,ISTJ,31.96,0.17816468557027587,0.0,0.08,0.06,0.0,0.06,0.86,0.0,3.24,1,1,1,0 +2616,INFP,28.46,0.18900860703343716,0.0,0.18,0.16,0.0,0.0,0.86,0.0,4.56,1,0,0,1 +2617,INFP,19.52,0.17909762014707045,0.28,0.34,0.36,0.0,0.06,0.34,0.0,2.02,1,0,0,1 +2618,ISTP,15.0,0.17791665730778736,0.0,0.2,0.0,0.0,0.0,0.1,0.0,1.3,1,1,1,1 +2619,INFP,24.16,0.18668884690115461,0.0,0.14,0.02,0.0,0.0,0.44,0.0,2.36,1,0,0,1 +2620,ENTP,29.84,0.18073505269647017,0.0,0.14,0.02,0.0,0.0,1.04,0.0,2.5,0,0,1,1 +2621,INFP,27.8,0.18052839827194367,0.02,0.08,0.02,0.0,0.06,0.72,0.0,3.38,1,0,0,1 +2622,INFJ,12.14,0.18389571278616185,0.5,0.7,0.02,0.02,0.0,0.2,0.0,0.94,1,0,0,0 +2623,INFP,32.3,0.18481631772815238,0.0,0.18,0.56,0.0,0.0,1.0,0.0,3.62,1,0,0,1 +2624,INFP,27.72,0.19089525625443673,0.02,0.16,1.26,0.0,0.0,0.64,0.0,3.32,1,0,0,1 +2625,INTJ,27.94,0.18714811329916614,0.0,0.46,0.04,0.0,0.0,0.66,0.0,3.62,1,0,1,0 +2626,INFP,26.02,0.18530556724457642,0.0,0.14,0.0,0.0,0.06,0.74,0.02,2.86,1,0,0,1 +2627,INFP,33.18,0.18022006603838972,0.0,0.14,0.1,0.0,0.0,0.86,0.0,3.9,1,0,0,1 +2628,ENFP,25.56,0.18927094980166173,0.06,0.26,0.82,0.0,0.02,0.54,0.0,2.84,0,0,0,1 +2629,INTP,24.52,0.18428239873358077,0.02,0.22,0.0,0.0,0.0,0.48,0.0,3.04,1,0,1,1 +2630,ENTP,33.32,0.18474165278128196,0.0,0.24,0.06,0.0,0.0,1.08,0.0,2.98,0,0,1,1 +2631,INFJ,22.24,0.1942236306020824,0.0,0.18,0.14,0.0,0.0,1.88,0.0,2.2,1,0,0,0 +2632,INFJ,31.56,0.18589002817271158,0.02,0.06,0.36,0.0,0.0,0.96,0.02,2.78,1,0,0,0 +2633,ISTP,20.62,0.18537479680349708,0.02,0.04,0.14,0.0,0.0,0.52,0.02,2.22,1,1,1,1 +2634,ISTP,13.3,0.18896251752226578,0.4,0.34,0.02,0.08,0.0,0.22,0.0,1.38,1,1,1,1 +2635,INFP,12.94,0.19137739204769966,0.0,0.04,0.56,0.0,0.0,0.3,0.0,2.12,1,0,0,1 +2636,INFP,27.9,0.18312649283225402,0.04,0.16,0.16,0.0,0.0,0.7,0.0,2.8,1,0,0,1 +2637,INTP,28.62,0.18546156423234908,0.0,0.04,0.04,0.0,0.02,0.7,0.0,3.62,1,0,1,1 +2638,INFJ,19.78,0.18332647517768133,0.0,0.12,0.02,0.0,0.0,0.52,0.0,1.78,1,0,0,0 +2639,ENFP,31.84,0.18883868741381055,0.0,0.12,0.44,0.0,0.0,0.8,0.0,3.9,0,0,0,1 +2640,INFJ,6.94,0.1989847395688586,0.02,0.12,0.02,0.0,0.0,0.3,0.0,0.9,1,0,0,0 +2641,INFJ,25.98,0.18169867661004127,0.06,0.06,0.02,0.0,0.02,0.96,0.02,2.74,1,0,0,0 +2642,ESTP,29.88,0.18432380389028785,0.0,0.42,0.12,0.0,0.06,0.9,0.0,3.98,0,1,1,1 +2643,INFJ,24.9,0.18195929483751283,0.06,0.08,0.24,0.0,0.04,0.6,0.0,2.78,1,0,0,0 +2644,INTP,25.16,0.17869940804977633,0.0,0.2,0.0,0.0,0.0,0.6,0.0,3.18,1,0,1,1 +2645,ENTP,22.4,0.1849136086292115,0.0,0.32,0.02,0.0,0.02,0.44,0.0,2.4,0,0,1,1 +2646,INFP,27.16,0.18055554690840273,0.0,0.24,0.0,0.0,0.0,0.46,0.0,3.4,1,0,0,1 +2647,INFJ,14.04,0.18726481676465595,0.0,0.14,0.22,0.0,0.0,0.18,0.0,1.92,1,0,0,0 +2648,INFJ,25.54,0.18311298117351754,0.06,0.24,0.0,0.0,0.0,0.62,0.0,2.42,1,0,0,0 +2649,INTJ,25.16,0.18043103333987065,0.0,0.14,0.06,0.0,0.04,0.54,0.0,2.36,1,0,1,0 +2650,ISFP,25.6,0.18550922552471608,0.04,0.24,0.12,0.0,0.04,0.36,0.0,3.36,1,1,0,1 +2651,INFP,32.2,0.17923130876101953,0.02,0.28,0.22,0.0,0.0,0.78,0.0,3.94,1,0,0,1 +2652,INFP,34.04,0.18383539821128195,0.02,0.08,0.18,0.0,0.0,0.8,0.04,4.96,1,0,0,1 +2653,ENTP,27.5,0.1800934110306808,0.0,0.16,0.08,0.0,0.0,0.74,0.0,2.6,0,0,1,1 +2654,INTJ,11.46,0.18960532926734983,0.0,0.06,0.06,0.0,0.0,0.12,0.0,1.42,1,0,1,0 +2655,ENTP,19.44,0.18331326078448354,0.02,0.18,0.04,0.0,0.0,0.16,0.0,1.96,0,0,1,1 +2656,INTP,27.68,0.17545535485035124,0.08,0.32,0.02,0.02,0.0,0.84,0.0,2.94,1,0,1,1 +2657,INTJ,25.78,0.18636433292319024,0.02,0.14,0.18,0.0,0.04,0.82,0.0,3.32,1,0,1,0 +2658,INTP,27.02,0.1859020077076723,0.06,0.32,0.06,0.0,0.0,1.06,0.0,2.78,1,0,1,1 +2659,ENFJ,27.98,0.18015927473772758,0.02,0.16,0.0,0.0,0.02,0.9,0.0,3.22,0,0,0,0 +2660,ISTP,16.48,0.18367238704276234,0.1,0.36,0.04,0.0,0.0,0.08,0.0,1.74,1,1,1,1 +2661,ENTP,23.86,0.1797564673089149,0.1,0.12,0.04,0.04,0.0,0.62,0.0,1.98,0,0,1,1 +2662,INFJ,26.18,0.18414365166652258,0.1,0.16,0.5,0.0,0.04,0.86,0.0,3.12,1,0,0,0 +2663,INFJ,28.14,0.18307227194128212,0.02,0.16,0.14,0.0,0.0,0.78,0.0,2.62,1,0,0,0 +2664,INTJ,24.98,0.18793623793557207,0.02,0.22,0.0,0.02,0.02,0.72,0.0,2.98,1,0,1,0 +2665,ISFP,19.76,0.18870362078523728,0.12,0.2,0.0,0.02,0.02,0.28,0.0,2.8,1,1,0,1 +2666,INFP,23.68,0.1777060111856766,0.08,0.18,0.06,0.0,0.02,0.48,0.0,2.2,1,0,0,1 +2667,ENTP,29.98,0.18780703503490068,0.0,0.18,0.12,0.0,0.04,0.72,0.0,3.24,0,0,1,1 +2668,INTJ,26.66,0.18252604956032328,0.06,0.1,0.0,0.02,0.0,0.88,0.0,2.96,1,0,1,0 +2669,INFP,26.56,0.1832354567786116,0.02,0.38,0.2,0.0,0.0,0.68,0.0,2.92,1,0,0,1 +2670,INTP,23.86,0.18039445219531244,0.02,0.28,0.04,0.02,0.0,0.6,0.0,2.28,1,0,1,1 +2671,INTJ,25.24,0.18155364285788744,0.12,0.32,0.12,0.0,0.04,0.56,0.0,2.84,1,0,1,0 +2672,ENFP,29.32,0.18547559076786552,0.0,0.14,0.12,0.0,0.0,0.8,0.0,2.12,0,0,0,1 +2673,INTP,20.9,0.18600115768983122,0.1,0.16,0.26,0.02,0.08,0.5,0.0,2.54,1,0,1,1 +2674,ISTP,32.32,0.17675226536134564,0.0,0.14,0.36,0.0,0.0,1.04,0.0,3.8,1,1,1,1 +2675,INTP,29.42,0.18216837595415283,0.04,0.2,0.2,0.0,0.0,0.84,0.0,2.94,1,0,1,1 +2676,INTP,28.18,0.1812583475341378,0.0,0.1,0.14,0.0,0.08,0.82,0.02,2.5,1,0,1,1 +2677,INTP,30.16,0.18227901568415356,0.02,0.16,0.02,0.0,0.02,0.78,0.0,3.08,1,0,1,1 +2678,INTP,15.86,0.17850654564551938,0.3,0.44,0.04,0.0,0.0,0.36,0.0,1.5,1,0,1,1 +2679,INTJ,31.0,0.18274191531262263,0.02,0.18,0.02,0.0,0.02,0.84,0.02,3.2,1,0,1,0 +2680,INFP,20.32,0.18730641176217586,0.26,0.28,0.1,0.0,0.0,0.58,0.0,2.54,1,0,0,1 +2681,INFJ,25.6,0.18409627305038095,0.0,0.14,0.18,0.0,0.0,0.56,0.0,1.94,1,0,0,0 +2682,INTJ,17.7,0.17768584937572973,0.32,0.46,0.04,0.04,0.06,0.52,0.0,1.48,1,0,1,0 +2683,INFJ,27.1,0.18004633700969602,0.2,0.34,0.06,0.08,0.0,0.76,0.0,1.98,1,0,0,0 +2684,INFP,22.44,0.1838796917195081,0.02,0.1,0.04,0.0,0.02,0.46,0.0,3.02,1,0,0,1 +2685,INFP,4.74,0.18070864347969023,0.14,0.06,0.02,0.14,0.0,0.08,0.0,0.4,1,0,0,1 +2686,ESFP,27.04,0.18658610817863125,0.06,0.2,0.1,0.0,0.0,0.72,0.0,3.32,0,1,0,1 +2687,ENFJ,25.14,0.18495378123359202,0.0,0.38,0.08,0.0,0.0,0.5,0.0,3.08,0,0,0,0 +2688,INTJ,26.42,0.18877084554318288,0.0,0.18,0.18,0.0,0.0,0.88,0.0,3.04,1,0,1,0 +2689,INFJ,19.84,0.18883599382083063,0.2,0.28,0.36,0.02,0.04,0.36,0.04,1.8,1,0,0,0 +2690,INTP,32.56,0.17913707967351766,0.0,0.04,0.14,0.0,0.04,0.86,0.0,3.62,1,0,1,1 +2691,ENTP,23.88,0.17821341427002182,0.0,0.06,0.04,0.0,0.0,0.6,0.0,1.68,0,0,1,1 +2692,ISTP,17.2,0.1858490618557972,0.1,0.14,0.02,0.04,0.0,0.32,0.08,1.92,1,1,1,1 +2693,INTJ,23.14,0.18651187302374603,0.14,0.36,0.04,0.0,0.02,0.58,0.0,2.64,1,0,1,0 +2694,ESTJ,18.22,0.18402438218989342,0.06,0.2,0.02,0.02,0.0,0.26,0.0,1.94,0,1,1,0 +2695,INTJ,19.74,0.1802343406110689,0.0,0.02,0.02,0.0,0.0,0.26,0.0,2.06,1,0,1,0 +2696,ENTP,20.66,0.1956882052871721,0.22,0.76,0.66,0.0,0.0,0.48,0.0,2.34,0,0,1,1 +2697,INFJ,27.34,0.1841742537992431,0.26,0.4,0.06,0.0,0.08,0.58,0.0,3.64,1,0,0,0 +2698,INFP,31.1,0.18467311929929434,0.08,0.24,0.22,0.02,0.02,0.76,0.0,3.18,1,0,0,1 +2699,ENFJ,27.14,0.18041275441646576,0.2,0.26,0.02,0.0,0.0,0.7,0.02,2.82,0,0,0,0 +2700,INFP,35.16,0.18829946752515506,0.04,0.16,0.02,0.02,0.02,1.02,0.0,4.48,1,0,0,1 +2701,INFJ,32.74,0.17490498592552256,0.0,0.06,0.0,0.0,0.0,0.78,0.0,3.26,1,0,0,0 +2702,INTJ,22.1,0.18486993677194968,0.06,0.26,0.06,0.0,0.04,0.44,0.0,3.0,1,0,1,0 +2703,ENFJ,29.02,0.1881053984816057,0.0,0.26,0.12,0.0,0.0,0.64,0.02,3.46,0,0,0,0 +2704,INFJ,26.94,0.18370862663923432,0.02,0.16,0.0,0.0,0.0,0.5,0.0,3.16,1,0,0,0 +2705,INFJ,29.9,0.17465420060794837,0.04,0.28,0.0,0.0,0.0,0.78,0.0,2.86,1,0,0,0 +2706,ENFP,28.22,0.18333914331924486,0.04,0.22,0.92,0.02,0.02,0.74,0.0,2.94,0,0,0,1 +2707,INFJ,28.54,0.18437471052770435,0.04,0.06,0.04,0.0,0.0,0.78,0.0,3.58,1,0,0,0 +2708,ISTP,26.4,0.18703716106216597,0.02,0.22,0.1,0.0,0.02,0.54,0.0,3.18,1,1,1,1 +2709,INFJ,24.3,0.1865077447752662,0.08,0.18,0.06,0.0,0.02,0.6,0.0,1.6,1,0,0,0 +2710,ISTP,21.34,0.1905340890005396,0.02,0.06,0.0,0.0,0.02,0.4,0.0,2.52,1,1,1,1 +2711,INTP,28.26,0.18683093151097688,0.04,0.18,0.02,0.0,0.06,0.78,0.0,3.6,1,0,1,1 +2712,INFP,26.8,0.19006432649043586,0.06,0.24,0.16,0.0,0.02,0.9,0.0,3.84,1,0,0,1 +2713,INTP,24.82,0.1845257416193561,0.04,0.24,0.14,0.0,0.02,0.36,0.0,3.24,1,0,1,1 +2714,INTJ,21.16,0.18130922377457387,0.34,0.34,0.24,0.0,0.02,0.62,0.0,2.52,1,0,1,0 +2715,ENTP,32.9,0.18924816504661776,0.0,0.24,0.14,0.0,0.0,0.82,0.0,3.38,0,0,1,1 +2716,ENTJ,28.44,0.18419094813314146,0.0,0.16,0.1,0.0,0.02,0.86,0.0,2.54,0,0,1,0 +2717,INTJ,27.94,0.17801903628792384,0.0,0.24,0.22,0.0,0.0,0.68,0.0,2.54,1,0,1,0 +2718,INFJ,14.22,0.17967433103669284,0.08,0.34,0.28,0.0,0.02,0.42,0.0,1.4,1,0,0,0 +2719,INFP,24.06,0.1859490562225099,0.0,0.06,0.08,0.0,0.0,0.54,0.0,2.94,1,0,0,1 +2720,ENTP,25.2,0.18337059551184265,0.02,0.2,0.22,0.0,0.0,0.62,0.0,2.96,0,0,1,1 +2721,ENTJ,26.1,0.18507466357147592,0.0,0.24,0.2,0.0,0.0,0.74,0.0,2.66,0,0,1,0 +2722,INFJ,29.04,0.18351464939974663,0.0,0.18,0.04,0.0,0.0,0.82,0.0,3.1,1,0,0,0 +2723,ENFP,29.88,0.18214742482631438,0.0,0.08,0.08,0.0,0.0,0.76,0.0,2.04,0,0,0,1 +2724,ISTP,32.24,0.17395637203809483,0.02,0.3,0.0,0.0,0.04,0.88,0.0,2.54,1,1,1,1 +2725,ENFP,23.56,0.1860996700180822,0.12,0.2,0.5,0.04,0.02,0.88,0.0,2.56,0,0,0,1 +2726,INFP,30.58,0.18124978939560374,0.0,0.28,0.26,0.0,0.0,0.94,0.0,3.46,1,0,0,1 +2727,ENFP,28.08,0.18652217911958174,0.06,0.32,0.14,0.04,0.0,0.88,0.0,3.06,0,0,0,1 +2728,ISFP,13.3,0.18523978366156651,0.16,0.22,0.04,0.04,0.0,0.2,0.0,1.02,1,1,0,1 +2729,ENTJ,24.2,0.18155420536075503,0.04,0.22,0.02,0.0,0.0,0.54,0.0,2.58,0,0,1,0 +2730,INTJ,11.52,0.19025935048029022,0.02,0.04,0.28,0.0,0.0,0.4,0.0,1.4,1,0,1,0 +2731,ENTP,13.4,0.18819155761088488,0.56,0.44,0.14,0.1,0.0,0.48,0.0,1.46,0,0,1,1 +2732,ENTP,32.12,0.18782202608517568,0.0,0.18,0.2,0.0,0.0,1.24,0.0,3.56,0,0,1,1 +2733,ENFP,34.14,0.18850988875910835,0.0,0.16,0.0,0.0,0.06,0.8,0.0,4.06,0,0,0,1 +2734,ISFP,24.7,0.18448088535805793,0.0,0.06,0.28,0.0,0.04,0.54,0.0,2.66,1,1,0,1 +2735,INFP,25.94,0.18800126786032215,0.08,0.2,0.06,0.02,0.0,0.58,0.0,3.58,1,0,0,1 +2736,ENTP,26.12,0.18175940609000402,0.0,0.1,0.18,0.0,0.0,0.58,0.0,3.24,0,0,1,1 +2737,ISFJ,30.4,0.1793143491274346,0.02,0.1,0.14,0.0,0.02,0.98,0.0,3.16,1,1,0,0 +2738,ISFJ,28.12,0.1860928525096477,0.0,0.2,0.08,0.0,0.0,0.52,0.0,3.04,1,1,0,0 +2739,INFJ,29.64,0.19071132235508712,0.04,0.3,0.4,0.04,0.0,0.96,0.0,3.74,1,0,0,0 +2740,INTP,22.0,0.17992143800272745,0.0,0.18,0.0,0.0,0.0,0.46,0.0,2.18,1,0,1,1 +2741,INFJ,16.04,0.19554296688090889,0.04,0.74,1.18,0.02,0.0,0.2,0.02,2.1,1,0,0,0 +2742,INFP,30.9,0.1859242797147402,0.04,0.1,0.02,0.0,0.04,0.8,0.0,4.36,1,0,0,1 +2743,ESTP,19.5,0.18645306106702547,0.04,0.12,0.12,0.0,0.02,0.54,0.0,2.12,0,1,1,1 +2744,INFP,32.02,0.179639448119704,0.0,0.14,0.06,0.0,0.02,1.02,0.0,3.72,1,0,0,1 +2745,INFJ,9.84,0.18825480000597516,0.06,0.0,0.4,0.06,0.0,0.14,0.0,1.24,1,0,0,0 +2746,INTP,16.58,0.18599080410360547,0.14,0.4,0.0,0.0,0.0,0.54,0.0,1.96,1,0,1,1 +2747,INFP,24.1,0.1840268738649787,0.0,0.2,0.32,0.0,0.02,0.48,0.02,1.96,1,0,0,1 +2748,INTP,23.8,0.18254305020716294,0.08,0.16,0.04,0.06,0.0,0.5,0.0,2.44,1,0,1,1 +2749,ENFP,29.24,0.18853379389906333,0.0,0.36,0.1,0.0,0.02,1.1,0.0,2.78,0,0,0,1 +2750,INFP,20.62,0.19193741054627952,0.0,0.26,0.16,0.0,0.0,0.58,0.0,2.68,1,0,0,1 +2751,ISFP,27.4,0.18161943028449964,0.0,0.14,0.0,0.0,0.0,0.62,0.0,3.06,1,1,0,1 +2752,ISTP,19.12,0.19062473032305124,0.28,0.48,0.0,0.04,0.0,1.72,0.0,2.5,1,1,1,1 +2753,INFJ,29.08,0.19006190687562802,0.0,0.14,0.14,0.0,0.0,0.9,0.0,3.28,1,0,0,0 +2754,INTJ,17.08,0.18753651234039231,0.88,0.54,0.22,0.04,0.0,0.46,0.06,1.88,1,0,1,0 +2755,INFJ,33.02,0.18139059688671547,0.0,0.04,0.02,0.0,0.0,0.92,0.0,3.44,1,0,0,0 +2756,INFJ,26.42,0.18059818440536088,0.06,0.3,0.04,0.0,0.08,0.74,0.0,2.42,1,0,0,0 +2757,INFJ,27.62,0.1853516288018361,0.02,0.1,0.12,0.0,0.0,0.62,0.0,3.42,1,0,0,0 +2758,ENTJ,31.6,0.18659590215040692,0.04,0.2,0.08,0.0,0.02,0.82,0.02,2.68,0,0,1,0 +2759,INTP,26.68,0.1834703684972435,0.0,0.3,0.02,0.0,0.0,0.54,0.0,3.28,1,0,1,1 +2760,ISFP,29.4,0.18614009848393856,0.04,0.06,0.32,0.0,0.0,0.7,0.0,4.02,1,1,0,1 +2761,ISFP,12.58,0.18882319670061448,0.78,0.8,0.2,0.0,0.06,0.2,0.02,1.78,1,1,0,1 +2762,INFJ,24.74,0.18828456576478492,0.06,0.16,0.28,0.0,0.0,0.54,0.0,3.5,1,0,0,0 +2763,INFP,31.5,0.19415730389139046,0.04,0.2,0.16,0.0,0.08,1.32,0.0,3.84,1,0,0,1 +2764,INFP,24.44,0.18307640612053971,0.02,0.14,0.0,0.0,0.0,0.76,0.02,2.66,1,0,0,1 +2765,ISTJ,9.8,0.18466453585053996,0.1,0.22,0.0,0.0,0.0,0.18,0.0,0.92,1,1,1,0 +2766,INFJ,25.4,0.18155768353933058,0.0,0.18,0.08,0.0,0.0,0.56,0.0,2.92,1,0,0,0 +2767,ENFP,31.8,0.18468271294435387,0.0,0.18,0.62,0.0,0.0,1.44,0.02,2.78,0,0,0,1 +2768,INTJ,34.32,0.18282229465176425,0.0,0.24,0.0,0.0,0.02,1.06,0.0,2.86,1,0,1,0 +2769,ENTP,18.82,0.1922006015602063,0.02,0.38,0.34,0.0,0.0,0.56,0.0,1.74,0,0,1,1 +2770,INFP,31.46,0.1807296489163957,0.0,0.1,0.22,0.0,0.0,0.96,0.02,3.34,1,0,0,1 +2771,ISTJ,31.24,0.18046446056961418,0.0,0.08,0.02,0.0,0.04,0.7,0.0,3.84,1,1,1,0 +2772,INFP,22.62,0.2006670953977664,0.0,0.12,0.64,0.0,0.0,2.52,0.0,2.06,1,0,0,1 +2773,INTJ,22.92,0.1973944115023199,0.0,0.32,0.12,0.0,0.0,1.0,0.0,2.44,1,0,1,0 +2774,INFP,29.42,0.1843049529245198,0.04,0.1,0.04,0.0,0.0,0.72,0.0,3.54,1,0,0,1 +2775,INTJ,18.66,0.19157003058691685,0.3,0.38,0.06,0.04,0.12,0.64,0.0,2.3,1,0,1,0 +2776,INFP,26.2,0.1824506506804526,0.04,0.16,0.02,0.0,0.0,0.62,0.02,2.9,1,0,0,1 +2777,ISTP,28.0,0.181290484375,0.0,0.06,0.04,0.0,0.0,0.8,0.0,2.84,1,1,1,1 +2778,ENFP,27.0,0.19105498775612217,0.04,0.22,1.22,0.0,0.04,0.62,0.0,2.78,0,0,0,1 +2779,ISTJ,27.1,0.1876644952570224,0.02,0.32,0.0,0.0,0.04,0.44,0.0,3.7,1,1,1,0 +2780,INFP,21.74,0.19202764618282386,0.3,0.42,0.02,0.02,0.08,1.08,0.0,1.84,1,0,0,1 +2781,ISTJ,17.42,0.18160907436939872,0.0,0.14,0.04,0.0,0.0,0.42,0.02,1.9,1,1,1,0 +2782,ISFJ,31.94,0.18417676164148036,0.02,0.2,0.08,0.02,0.0,0.88,0.02,3.76,1,1,0,0 +2783,INFJ,24.18,0.18491874792563748,0.12,0.24,0.18,0.08,0.0,0.54,0.0,3.18,1,0,0,0 +2784,INFP,25.02,0.18949818746353525,0.04,0.12,0.62,0.04,0.0,0.72,0.0,2.86,1,0,0,1 +2785,ENTP,18.08,0.1908464485423268,0.0,0.1,0.5,0.0,0.0,0.76,0.0,2.0,0,0,1,1 +2786,INFJ,34.8,0.1823555956987092,0.0,0.12,0.06,0.0,0.02,0.88,0.0,4.36,1,0,0,0 +2787,INTP,17.0,0.18574446739766848,0.32,0.36,0.08,0.02,0.02,0.36,0.0,1.86,1,0,1,1 +2788,INFJ,11.42,0.17755199182473375,0.0,0.0,0.0,0.0,0.0,0.28,0.0,1.06,1,0,0,0 +2789,ENFP,24.92,0.1807235983513662,0.16,0.26,0.08,0.02,0.06,0.58,0.0,2.58,0,0,0,1 +2790,ISTJ,10.38,0.18132264071189746,0.26,0.34,0.02,0.0,0.0,0.16,0.0,0.8,1,1,1,0 +2791,ENTP,32.36,0.1806769219783472,0.0,0.16,0.08,0.0,0.0,0.88,0.0,3.4,0,0,1,1 +2792,ENTP,16.48,0.184320883870355,0.36,0.5,0.3,0.26,0.0,0.38,0.02,1.44,0,0,1,1 +2793,ENTP,30.4,0.17573466536234483,0.0,0.26,0.08,0.0,0.0,0.78,0.0,3.14,0,0,1,1 +2794,INTP,29.94,0.1756310100351151,0.1,0.18,0.04,0.0,0.02,0.8,0.0,2.82,1,0,1,1 +2795,INFJ,28.64,0.19029413988657842,0.06,0.14,0.04,0.0,0.0,0.76,0.0,3.28,1,0,0,0 +2796,INFJ,35.2,0.18056354342789657,0.0,0.12,0.22,0.0,0.0,0.92,0.0,3.84,1,0,0,0 +2797,INTP,15.44,0.1753418739674123,0.06,0.16,0.12,0.06,0.0,0.14,0.0,1.26,1,0,1,1 +2798,INFJ,20.36,0.1937807366113579,0.14,0.24,0.54,0.02,0.0,0.6,0.0,2.5,1,0,0,0 +2799,ENTP,34.34,0.17833494369787883,0.0,0.04,0.02,0.0,0.0,0.88,0.0,2.94,0,0,1,1 +2800,ISTJ,22.46,0.18742366412925027,0.06,0.08,0.02,0.0,0.0,0.68,0.0,2.9,1,1,1,0 +2801,ENTP,3.04,0.19992318220882982,0.22,0.06,0.0,0.0,0.0,0.02,0.0,0.3,0,0,1,1 +2802,INTP,23.94,0.18117330776812607,0.0,0.22,0.08,0.0,0.0,0.54,0.0,2.98,1,0,1,1 +2803,INTJ,26.68,0.18197305508833136,0.04,0.16,0.0,0.0,0.04,0.62,0.0,2.9,1,0,1,0 +2804,ENTP,22.66,0.18924405726125162,0.02,0.22,0.08,0.0,0.0,0.62,0.0,2.66,0,0,1,1 +2805,INFJ,22.58,0.18285811361039211,0.06,0.26,0.54,0.0,0.02,0.42,0.0,1.5,1,0,0,0 +2806,INTP,23.78,0.18234028206039568,0.2,0.2,0.04,0.0,0.02,0.6,0.0,2.92,1,0,1,1 +2807,INFP,26.38,0.18916159639805802,0.04,0.16,0.14,0.0,0.2,0.58,0.0,3.64,1,0,0,1 +2808,INFP,24.92,0.1808860656348939,0.0,0.26,0.02,0.0,0.02,0.62,0.0,2.94,1,0,0,1 +2809,INFP,17.26,0.17920741530204823,0.08,0.14,0.16,0.0,0.02,0.36,0.0,1.14,1,0,0,1 +2810,INFJ,24.38,0.18760133764040912,0.14,0.2,0.02,0.06,0.02,0.86,0.02,2.62,1,0,0,0 +2811,INTP,31.68,0.18214365465475105,0.0,0.76,0.32,0.0,0.0,0.86,0.0,3.76,1,0,1,1 +2812,INFJ,22.96,0.18518993766966807,0.0,0.1,0.16,0.0,0.04,0.52,0.0,2.94,1,0,0,0 +2813,ENFP,30.12,0.18446424210877274,0.0,0.16,0.04,0.0,0.0,0.72,0.0,3.76,0,0,0,1 +2814,INFJ,29.82,0.1822309031876267,0.04,0.38,0.18,0.0,0.04,0.96,0.0,2.26,1,0,0,0 +2815,INFP,31.64,0.19005832663180838,0.04,0.14,0.24,0.02,0.04,1.08,0.0,3.92,1,0,0,1 +2816,INFJ,25.06,0.1820086209526632,0.04,0.14,0.08,0.04,0.0,0.48,0.0,2.88,1,0,0,0 +2817,INFP,11.22,0.18901322367799525,0.24,0.22,0.02,0.06,0.0,0.22,0.0,1.36,1,0,0,1 +2818,INTJ,26.64,0.1844079637106088,0.08,0.58,0.18,0.04,0.0,0.7,0.0,2.54,1,0,1,0 +2819,INFP,33.04,0.18156776962766893,0.0,0.02,0.0,0.0,0.08,1.08,0.02,3.0,1,0,0,1 +2820,ENTP,8.98,0.1967762802317333,0.06,0.1,0.08,0.02,0.0,0.16,0.0,1.14,0,0,1,1 +2821,ENFJ,26.52,0.19656072162536462,0.0,0.12,0.14,0.0,0.04,0.82,0.0,2.2,0,0,0,0 +2822,INTJ,21.78,0.17932582248520712,0.08,0.26,0.08,0.02,0.02,0.56,0.0,2.26,1,0,1,0 +2823,INFP,29.44,0.1809720977639531,0.08,0.12,0.02,0.0,0.02,0.6,0.0,3.44,1,0,0,1 +2824,INFJ,32.56,0.18333042016155748,0.0,0.24,0.14,0.0,0.0,0.86,0.0,3.48,1,0,0,0 +2825,INFJ,27.18,0.17931373515058135,0.06,0.14,0.1,0.0,0.02,0.62,0.0,3.38,1,0,0,0 +2826,INFJ,23.66,0.1891601360945368,0.14,0.18,0.72,0.0,0.0,1.1,0.0,3.12,1,0,0,0 +2827,INTP,29.18,0.18112097760024795,0.02,0.18,0.06,0.0,0.02,0.84,0.0,3.2,1,0,1,1 +2828,INFJ,29.7,0.18306295017815985,0.04,0.16,0.08,0.0,0.0,0.68,0.02,2.66,1,0,0,0 +2829,ISFP,15.86,0.1910557446796245,0.58,0.76,0.28,0.06,0.1,0.28,0.0,1.9,1,1,0,1 +2830,INTJ,21.16,0.17993079584775087,0.12,0.24,0.1,0.0,0.0,0.42,0.0,2.38,1,0,1,0 +2831,INTJ,26.92,0.17546765026911057,0.02,0.26,0.06,0.0,0.0,0.82,0.0,2.46,1,0,1,0 +2832,INTP,20.74,0.18420415507949434,0.14,0.26,0.08,0.0,0.02,0.48,0.0,2.58,1,0,1,1 +2833,ENTP,23.78,0.17665583214386907,0.0,0.22,0.02,0.0,0.0,0.54,0.0,2.32,0,0,1,1 +2834,INFP,21.72,0.18603233962664084,0.02,0.2,0.48,0.0,0.0,0.48,0.0,1.98,1,0,0,1 +2835,INTP,9.44,0.17428957216156782,0.06,0.06,0.0,0.0,0.0,0.14,0.0,0.84,1,0,1,1 +2836,ENFP,32.0,0.1809785448955449,0.04,0.18,0.22,0.0,0.0,0.82,0.0,3.4,0,0,0,1 +2837,ISTP,18.4,0.188645013926004,0.04,0.16,0.04,0.02,0.0,0.14,0.02,2.26,1,1,1,1 +2838,INTP,30.84,0.18034289484157257,0.1,0.22,0.0,0.0,0.0,0.7,0.0,4.28,1,0,1,1 +2839,ENFP,24.66,0.19079639781743551,0.0,0.0,0.08,0.0,0.02,0.74,0.0,3.22,0,0,0,1 +2840,ISFJ,23.08,0.18764775408544415,0.02,0.36,0.08,0.0,0.0,0.72,0.0,2.0,1,1,0,0 +2841,INFP,14.48,0.1918314539426789,0.12,0.0,0.36,0.08,0.0,0.2,0.0,0.96,1,0,0,1 +2842,INFP,26.42,0.18393736283700088,0.0,0.12,0.16,0.0,0.02,0.7,0.0,3.16,1,0,0,1 +2843,ENTP,27.0,0.17622944614070224,0.0,0.2,0.02,0.0,0.02,0.62,0.0,1.76,0,0,1,1 +2844,INFP,21.38,0.1875,0.02,0.32,0.04,0.0,0.0,0.7,0.0,2.28,1,0,0,1 +2845,ESFP,27.96,0.1831491190136219,0.0,0.18,0.24,0.0,0.0,0.84,0.0,2.96,0,1,0,1 +2846,ENTP,31.04,0.18347102889379135,0.04,0.52,0.18,0.02,0.0,0.82,0.0,3.04,0,0,1,1 +2847,INFP,27.7,0.17936676694562406,0.0,0.22,0.02,0.0,0.0,0.66,0.0,3.32,1,0,0,1 +2848,INFJ,20.24,0.18447503624803333,0.02,0.3,0.5,0.0,0.0,0.36,0.0,1.86,1,0,0,0 +2849,ENTP,17.12,0.18109375,0.1,0.22,0.3,0.02,0.0,0.42,0.0,1.42,0,0,1,1 +2850,INFJ,26.5,0.1881745768210866,0.04,0.3,0.12,0.02,0.02,0.58,0.0,3.24,1,0,0,0 +2851,ENFJ,26.3,0.1833639805300162,0.02,0.08,0.16,0.0,0.0,0.5,0.0,3.6,0,0,0,0 +2852,INFP,22.64,0.18660768529102043,0.02,0.14,0.1,0.0,0.02,1.0,0.0,2.8,1,0,0,1 +2853,ENTP,29.44,0.18874871977871327,0.0,0.14,0.12,0.0,0.0,0.8,0.0,3.26,0,0,1,1 +2854,INFJ,29.62,0.18784653100232404,0.0,0.12,0.04,0.0,0.0,0.78,0.04,3.94,1,0,0,0 +2855,INFJ,28.92,0.1784451852497482,0.0,0.18,0.0,0.0,0.0,0.72,0.0,2.94,1,0,0,0 +2856,INTJ,27.24,0.18537021023238898,0.2,0.08,0.0,0.02,0.0,0.72,0.0,2.64,1,0,1,0 +2857,INFP,20.28,0.18526342433960125,0.0,0.12,0.32,0.0,0.02,0.4,0.0,2.78,1,0,0,1 +2858,INFJ,17.9,0.19571744297636134,0.08,0.18,0.42,0.0,0.0,1.68,0.0,1.62,1,0,0,0 +2859,INTP,20.8,0.17731512345338354,0.0,0.12,0.34,0.0,0.0,0.44,0.0,1.98,1,0,1,1 +2860,INTP,20.94,0.18040532585219768,0.02,0.16,0.08,0.0,0.02,0.5,0.0,2.44,1,0,1,1 +2861,INFP,27.18,0.18927341649746088,0.12,0.22,0.18,0.0,0.02,0.8,0.0,3.52,1,0,0,1 +2862,ENFJ,11.78,0.18090096857195556,0.46,0.08,0.42,0.36,0.0,0.68,0.0,1.72,0,0,0,0 +2863,INFJ,28.36,0.1812344499753431,0.06,0.06,0.12,0.0,0.04,0.72,0.0,3.5,1,0,0,0 +2864,ENTP,24.26,0.17868088671258628,0.0,0.08,0.0,0.0,0.0,0.3,0.0,2.6,0,0,1,1 +2865,ISTP,18.24,0.18311869713604786,0.08,0.2,0.02,0.0,0.02,0.22,0.0,2.14,1,1,1,1 +2866,INTP,29.32,0.1876673868625929,0.0,0.18,0.1,0.0,0.0,0.82,0.0,4.22,1,0,1,1 +2867,ISFP,19.4,0.18524234485975447,0.04,0.18,0.0,0.0,0.1,0.32,0.0,2.98,1,1,0,1 +2868,ENTP,30.92,0.18293373442056224,0.0,0.2,0.0,0.0,0.02,0.82,0.0,2.76,0,0,1,1 +2869,INFP,29.7,0.18314131292577218,0.1,0.3,0.06,0.08,0.1,0.92,0.0,3.34,1,0,0,1 +2870,INFP,19.56,0.18284912862805844,0.16,0.38,0.26,0.0,0.02,0.28,0.0,1.92,1,0,0,1 +2871,ENTP,26.32,0.18324812465656112,0.02,0.32,0.1,0.0,0.04,0.8,0.0,2.68,0,0,1,1 +2872,ENTP,33.92,0.18702536107514525,0.02,0.06,0.08,0.0,0.02,0.92,0.0,2.22,0,0,1,1 +2873,INTJ,15.4,0.18747369805394704,0.04,0.22,0.0,0.0,0.0,0.18,0.0,1.64,1,0,1,0 +2874,ENTP,22.62,0.18632444593156397,0.0,0.14,0.6,0.0,0.0,0.54,0.0,2.36,0,0,1,1 +2875,INFJ,29.76,0.1817804928065318,0.06,0.12,0.0,0.02,0.0,0.68,0.0,3.7,1,0,0,0 +2876,INTJ,1.96,0.17876325883506552,0.16,0.04,0.0,0.06,0.0,0.02,0.0,0.42,1,0,1,0 +2877,ENTP,28.06,0.18780704940058154,0.06,0.24,0.28,0.04,0.04,0.8,0.0,4.02,0,0,1,1 +2878,INFJ,30.36,0.18120800440079718,0.0,0.06,0.1,0.0,0.0,0.72,0.0,3.48,1,0,0,0 +2879,ESTJ,29.4,0.19288343742869396,0.04,0.56,0.22,0.0,0.0,0.7,0.0,3.62,0,1,1,0 +2880,ENTP,24.18,0.18443601054213582,0.06,0.3,0.36,0.0,0.02,0.58,0.0,2.34,0,0,1,1 +2881,ENFP,23.84,0.19032817614312636,0.02,0.3,0.24,0.0,0.0,1.02,0.0,3.18,0,0,0,1 +2882,INFJ,24.82,0.18404204396032867,0.22,0.08,0.04,0.04,0.06,0.6,0.0,3.26,1,0,0,0 +2883,INTP,27.56,0.18691374862400048,0.04,0.54,0.04,0.0,0.0,0.86,0.0,3.18,1,0,1,1 +2884,INFP,32.14,0.1825106788479794,0.04,0.1,0.38,0.0,0.0,0.9,0.0,4.28,1,0,0,1 +2885,ENFP,28.56,0.18112037505573497,0.02,0.18,0.04,0.0,0.0,0.64,0.0,2.54,0,0,0,1 +2886,ISFP,19.26,0.1847476633427316,0.1,0.24,0.06,0.0,0.06,0.36,0.0,2.18,1,1,0,1 +2887,INTJ,32.74,0.1809638811737713,0.0,0.2,0.0,0.0,0.0,0.76,0.0,3.26,1,0,1,0 +2888,ISTP,13.8,0.1856473629948221,0.12,0.34,0.08,0.0,0.0,0.2,0.0,1.46,1,1,1,1 +2889,ISFP,32.1,0.19332862426844152,0.0,0.08,0.34,0.0,0.0,1.14,0.0,3.6,1,1,0,1 +2890,INFJ,32.58,0.18761842725904634,0.02,0.26,0.2,0.0,0.0,0.92,0.02,3.82,1,0,0,0 +2891,INFP,18.96,0.18770591182127686,0.06,0.18,0.46,0.0,0.0,0.42,0.0,2.14,1,0,0,1 +2892,INFJ,29.66,0.1878897340300829,0.0,0.12,0.6,0.0,0.0,0.68,0.0,3.56,1,0,0,0 +2893,INFP,30.48,0.18880248062289648,0.02,0.18,0.2,0.02,0.02,1.02,0.0,2.86,1,0,0,1 +2894,ISFJ,16.2,0.20076904722928335,0.06,0.24,0.0,0.0,0.0,0.22,0.0,2.18,1,1,0,0 +2895,INTP,4.82,0.17816005859509956,0.0,0.0,0.02,0.0,0.0,0.1,0.0,0.62,1,0,1,1 +2896,INTP,26.0,0.17889813821234168,0.02,0.16,0.18,0.0,0.0,0.56,0.02,3.08,1,0,1,1 +2897,ISFP,25.1,0.1915264387047645,0.02,0.4,1.0,0.02,0.0,1.38,0.0,3.34,1,1,0,1 +2898,INTP,20.72,0.18302806660647036,0.3,0.22,0.62,0.2,0.02,0.78,0.0,2.36,1,0,1,1 +2899,INFJ,10.76,0.19415421144550343,0.08,0.22,0.76,0.0,0.0,0.1,0.0,1.48,1,0,0,0 +2900,ISTP,31.2,0.18484284742255164,0.0,0.1,0.56,0.0,0.0,0.96,0.0,3.7,1,1,1,1 +2901,INTJ,9.6,0.19029170198293482,0.0,0.1,0.16,0.0,0.0,0.24,0.0,1.24,1,0,1,0 +2902,INTJ,24.76,0.18359360422332524,0.0,0.06,0.0,0.0,0.0,0.82,0.0,2.86,1,0,1,0 +2903,ESFP,12.96,0.18431240771672597,0.0,0.08,0.22,0.0,0.0,0.42,0.0,1.56,0,1,0,1 +2904,ENTP,20.8,0.18758098476635854,0.0,0.16,0.06,0.0,0.02,0.5,0.0,2.44,0,0,1,1 +2905,ENFP,32.8,0.18969924022268128,0.0,0.24,0.18,0.0,0.0,1.24,0.0,3.64,0,0,0,1 +2906,INFJ,30.82,0.18020173043658042,0.0,0.24,0.32,0.0,0.02,0.84,0.0,3.68,1,0,0,0 +2907,INFJ,27.86,0.18616788822435043,0.04,0.1,0.2,0.0,0.0,0.88,0.0,3.5,1,0,0,0 +2908,INFJ,24.24,0.18724924799498796,0.18,0.26,0.08,0.0,0.0,0.56,0.0,2.78,1,0,0,0 +2909,INFJ,22.7,0.1801502288550382,0.0,0.28,0.02,0.0,0.02,0.36,0.02,1.8,1,0,0,0 +2910,INTP,29.58,0.17475859055585033,0.0,0.04,0.0,0.0,0.0,0.94,0.0,2.44,1,0,1,1 +2911,ISFJ,27.52,0.1850801955152431,0.0,0.12,0.28,0.0,0.04,0.8,0.0,3.34,1,1,0,0 +2912,INFP,5.74,0.2127577716220376,0.0,0.12,0.22,0.06,0.02,0.46,0.0,0.22,1,0,0,1 +2913,INFP,27.74,0.18582093446213,0.04,0.1,0.02,0.0,0.02,0.64,0.02,3.4,1,0,0,1 +2914,INTP,17.42,0.17889232283966217,0.04,0.02,0.0,0.0,0.0,0.24,0.02,2.02,1,0,1,1 +2915,INFP,17.1,0.18752271281892763,0.16,0.12,0.34,0.14,0.0,0.38,0.0,2.4,1,0,0,1 +2916,ISFP,27.48,0.1888590895462206,0.04,0.16,0.2,0.0,0.0,0.78,0.0,2.88,1,1,0,1 +2917,INFP,27.26,0.18926506563533538,0.0,0.38,0.44,0.0,0.02,1.1,0.0,1.56,1,0,0,1 +2918,INTJ,28.7,0.18299221846136718,0.0,0.14,0.04,0.0,0.02,0.8,0.04,2.2,1,0,1,0 +2919,INFJ,26.22,0.1934535073576139,0.0,0.3,0.38,0.0,0.0,0.6,0.0,2.86,1,0,0,0 +2920,ENTP,28.56,0.18383961254719552,0.0,0.3,0.02,0.0,0.0,0.78,0.0,2.6,0,0,1,1 +2921,INFJ,26.84,0.19136277592461054,0.02,0.44,0.44,0.0,0.04,0.72,0.0,2.78,1,0,0,0 +2922,INFP,29.04,0.17939483161569025,0.1,0.12,0.0,0.0,0.06,1.3,0.0,1.66,1,0,0,1 +2923,ISFJ,20.58,0.18245828702545683,0.22,0.18,0.0,0.0,0.02,0.38,0.0,2.02,1,1,0,0 +2924,INFJ,21.1,0.1868334457733067,0.02,0.32,0.2,0.0,0.0,0.48,0.0,2.32,1,0,0,0 +2925,INFJ,26.34,0.17615708617390372,0.0,0.06,0.18,0.0,0.0,0.52,0.0,3.28,1,0,0,0 +2926,ENFP,25.94,0.18867577138066416,0.0,0.48,0.88,0.0,0.02,1.44,0.0,3.04,0,0,0,1 +2927,INFP,28.96,0.18822204887943497,0.0,0.56,0.42,0.0,0.02,1.02,0.0,1.96,1,0,0,1 +2928,ISFJ,26.48,0.18678349098040883,0.08,0.38,0.36,0.0,0.0,0.92,0.0,2.94,1,1,0,0 +2929,ENFP,27.26,0.1886883641829964,0.0,0.14,0.06,0.0,0.0,0.76,0.0,3.36,0,0,0,1 +2930,INTJ,28.34,0.18101788564817464,0.0,0.08,0.36,0.0,0.0,0.64,0.0,3.58,1,0,1,0 +2931,INTJ,31.54,0.18797248301967912,0.0,0.18,0.18,0.0,0.0,0.74,0.0,3.1,1,0,1,0 +2932,INTJ,29.68,0.18547000344361614,0.0,0.1,0.28,0.0,0.0,0.92,0.0,3.64,1,0,1,0 +2933,INFJ,25.44,0.1821569477830417,0.0,0.22,0.08,0.0,0.04,0.54,0.06,3.38,1,0,0,0 +2934,INFP,24.06,0.18560280995577455,0.12,0.18,0.02,0.0,0.16,0.52,0.0,2.98,1,0,0,1 +2935,INFP,23.28,0.19508064077692353,0.0,0.2,0.88,0.0,0.0,1.74,0.0,2.46,1,0,0,1 +2936,INFP,34.72,0.18586811340470885,0.02,0.02,0.16,0.0,0.0,0.92,0.0,4.86,1,0,0,1 +2937,INFJ,22.66,0.18994941678530436,0.0,0.2,0.12,0.0,0.02,0.42,0.0,2.82,1,0,0,0 +2938,INTP,21.72,0.18382550193651323,0.0,0.06,0.12,0.0,0.0,0.7,0.0,2.18,1,0,1,1 +2939,INFP,19.0,0.18228614456864067,0.26,0.2,0.52,0.14,0.0,0.7,0.0,1.14,1,0,0,1 +2940,INTJ,25.68,0.17824744888888894,0.0,0.12,0.0,0.0,0.0,0.52,0.0,1.82,1,0,1,0 +2941,ENTP,25.16,0.18302672852574206,0.08,0.1,0.0,0.02,0.02,0.56,0.0,2.28,0,0,1,1 +2942,ENFP,18.78,0.18852770924407272,0.02,0.28,0.42,0.0,0.0,0.44,0.0,2.06,0,0,0,1 +2943,ENTP,23.24,0.17901514028185725,0.3,0.08,0.02,0.0,0.0,0.66,0.0,2.32,0,0,1,1 +2944,INTJ,32.04,0.18129520091613088,0.0,0.14,0.28,0.0,0.0,1.04,0.0,3.04,1,0,1,0 +2945,INFJ,28.32,0.17915683524078477,0.0,0.12,0.02,0.0,0.04,0.7,0.0,3.2,1,0,0,0 +2946,INFJ,14.82,0.17513495450463304,0.28,0.2,0.0,0.02,0.0,0.38,0.0,2.04,1,0,0,0 +2947,ISTP,21.3,0.18787036955507225,0.0,0.18,0.34,0.0,0.02,0.42,0.0,2.6,1,1,1,1 +2948,INTJ,22.1,0.18417929114347634,0.08,0.32,0.1,0.02,0.02,0.6,0.0,2.24,1,0,1,0 +2949,INTP,15.48,0.17477609905114488,0.02,0.1,0.12,0.0,0.0,0.4,0.0,1.82,1,0,1,1 +2950,INTJ,25.58,0.1828384370535716,0.02,0.24,0.1,0.0,0.0,0.7,0.0,2.36,1,0,1,0 +2951,INTP,19.48,0.18610411485518447,0.08,0.16,0.04,0.0,0.02,0.72,0.0,2.28,1,0,1,1 +2952,INFJ,19.3,0.1863793183525715,0.28,0.32,0.28,0.0,0.08,0.36,0.0,2.68,1,0,0,0 +2953,ISFJ,14.2,0.1895735876369868,0.12,0.28,0.02,0.0,0.0,0.22,0.02,1.94,1,1,0,0 +2954,ENFP,18.38,0.18418780563671355,0.08,0.26,0.28,0.04,0.02,0.38,0.0,2.3,0,0,0,1 +2955,ISTJ,29.3,0.18671365399298026,0.0,0.4,0.02,0.0,0.0,0.64,0.0,3.14,1,1,1,0 +2956,ESFP,31.08,0.19138890596835118,0.0,0.3,0.56,0.0,0.0,1.32,0.08,3.3,0,1,0,1 +2957,INTP,25.86,0.17745573962988942,0.06,0.26,0.0,0.0,0.02,0.62,0.02,1.86,1,0,1,1 +2958,INTP,14.74,0.1825424255139725,0.0,0.06,0.02,0.0,0.0,0.28,0.02,1.68,1,0,1,1 +2959,INFJ,30.36,0.1827232915592039,0.0,0.04,0.18,0.0,0.04,0.76,0.0,3.6,1,0,0,0 +2960,ISTP,30.86,0.1792948181304363,0.0,0.18,0.06,0.0,0.0,0.74,0.0,2.52,1,1,1,1 +2961,ENFJ,31.08,0.18650306522403706,0.0,0.28,0.2,0.0,0.04,0.86,0.0,2.56,0,0,0,0 +2962,ENTP,25.98,0.1890996433966798,0.04,0.34,0.28,0.0,0.0,0.58,0.02,2.48,0,0,1,1 +2963,INFJ,17.76,0.193518481385245,0.08,0.26,0.08,0.0,0.02,0.3,0.0,2.52,1,0,0,0 +2964,INFJ,15.44,0.19417726971067423,0.04,0.1,0.32,0.02,0.0,0.4,0.0,1.88,1,0,0,0 +2965,INFP,27.18,0.18237807528077982,0.1,0.14,0.68,0.1,0.02,0.8,0.0,3.26,1,0,0,1 +2966,ENFP,23.9,0.18165422637707893,0.02,0.08,0.34,0.0,0.02,0.6,0.0,2.08,0,0,0,1 +2967,INFP,28.86,0.18686354789227957,0.0,0.36,0.18,0.0,0.02,0.8,0.0,1.44,1,0,0,1 +2968,INTP,19.64,0.1830909665040646,0.02,0.34,0.08,0.0,0.02,0.44,0.0,1.48,1,0,1,1 +2969,INFP,28.42,0.1934534527260568,0.0,0.02,0.2,0.0,0.04,0.84,0.02,4.38,1,0,0,1 +2970,ENFP,29.66,0.18810468053468446,0.0,0.14,0.22,0.0,0.0,0.78,0.0,3.38,0,0,0,1 +2971,ESTP,24.02,0.18924333734199844,0.1,0.3,0.08,0.04,0.02,0.56,0.04,2.58,0,1,1,1 +2972,INFJ,27.62,0.18679457066929825,0.02,0.28,0.16,0.0,0.02,0.76,0.02,3.52,1,0,0,0 +2973,INFP,15.48,0.1914571769784125,0.16,0.32,0.14,0.0,0.02,0.24,0.0,1.8,1,0,0,1 +2974,INFJ,19.64,0.1941166407624183,0.02,0.2,0.4,0.0,0.02,0.76,0.0,1.68,1,0,0,0 +2975,ESTP,11.76,0.18772736112121022,0.14,0.12,0.04,0.0,0.0,0.16,0.02,1.24,0,1,1,1 +2976,INFJ,24.42,0.17880356897064428,0.0,0.08,0.02,0.0,0.0,0.64,0.0,2.78,1,0,0,0 +2977,INTP,32.76,0.17728744790430767,0.0,0.2,0.0,0.0,0.0,0.8,0.0,3.46,1,0,1,1 +2978,INTP,19.44,0.18767301029764966,0.02,0.34,0.06,0.02,0.02,0.3,0.02,1.68,1,0,1,1 +2979,INTP,25.66,0.1780609435506772,0.02,0.14,0.08,0.02,0.0,0.54,0.0,2.5,1,0,1,1 +2980,INFP,26.14,0.1917686648462747,0.08,0.28,0.12,0.0,0.04,0.58,0.0,1.4,1,0,0,1 +2981,INTP,26.82,0.18014675319971216,0.02,0.26,0.04,0.0,0.0,0.66,0.0,2.0,1,0,1,1 +2982,ENFP,26.0,0.1844912451183192,0.02,0.14,0.0,0.02,0.0,0.52,0.0,2.8,0,0,0,1 +2983,INTJ,20.9,0.18113264669806028,0.06,0.14,0.06,0.0,0.0,0.54,0.02,1.7,1,0,1,0 +2984,ISTJ,18.4,0.1911457250124571,0.0,0.22,0.04,0.0,0.0,0.34,0.02,2.32,1,1,1,0 +2985,INFJ,28.02,0.1861400002516278,0.04,0.48,0.22,0.02,0.0,0.68,0.0,3.1,1,0,0,0 +2986,INFJ,27.56,0.18154144038727843,0.04,0.16,0.04,0.04,0.0,0.98,0.0,2.92,1,0,0,0 +2987,INTJ,19.02,0.17807399940907329,0.32,0.22,0.14,0.16,0.0,0.64,0.0,1.34,1,0,1,0 +2988,ISFJ,24.98,0.18675185210826706,0.16,0.22,0.04,0.0,0.14,0.64,0.0,2.5,1,1,0,0 +2989,INFP,18.74,0.18938817448508108,0.16,0.08,0.1,0.1,0.04,0.46,0.0,2.54,1,0,0,1 +2990,INFJ,11.64,0.177497679673651,0.56,0.84,0.0,0.0,0.0,0.28,0.0,1.24,1,0,0,0 +2991,INFP,29.78,0.18125785623037918,0.08,0.36,0.28,0.04,0.06,0.88,0.0,3.22,1,0,0,1 +2992,ISFJ,23.78,0.1872700419404762,0.04,0.12,0.08,0.02,0.0,0.54,0.0,3.72,1,1,0,0 +2993,ENTJ,26.38,0.18921314984606094,0.0,0.26,0.12,0.0,0.02,0.6,0.0,2.78,0,0,1,0 +2994,ENTP,17.08,0.18704429607664907,0.24,0.34,0.2,0.06,0.0,0.68,0.04,1.8,0,0,1,1 +2995,INFP,31.04,0.1801538941163823,0.0,0.08,0.18,0.0,0.0,0.76,0.0,3.52,1,0,0,1 +2996,ISTP,20.42,0.18101146718918892,0.0,0.34,0.2,0.0,0.0,0.32,0.0,2.26,1,1,1,1 +2997,INFJ,29.38,0.18873698079261225,0.0,0.0,0.24,0.0,0.0,0.6,0.0,4.06,1,0,0,0 +2998,ISTP,28.02,0.18292598234450616,0.02,0.04,0.06,0.0,0.0,0.72,0.0,2.84,1,1,1,1 +2999,INTP,18.92,0.1775868475753413,0.02,0.1,0.02,0.0,0.0,0.46,0.0,0.98,1,0,1,1 +3000,INTP,28.7,0.1834760740238512,0.04,0.12,0.16,0.02,0.0,0.7,0.0,2.42,1,0,1,1 +3001,INFJ,29.9,0.18641463677964276,0.02,0.08,0.16,0.0,0.0,1.3,0.0,3.7,1,0,0,0 +3002,ISFP,22.44,0.18031209171914683,0.28,0.32,0.14,0.08,0.0,0.82,0.0,1.78,1,1,0,1 +3003,INFJ,18.38,0.18541629326202838,0.34,0.42,0.1,0.06,0.0,0.46,0.0,2.28,1,0,0,0 +3004,ENTP,27.0,0.19523279210781477,0.0,0.24,0.02,0.0,0.0,0.56,0.0,3.02,0,0,1,1 +3005,INFJ,32.88,0.18171693445245776,0.04,0.2,0.2,0.0,0.02,0.96,0.06,2.58,1,0,0,0 +3006,INFJ,32.36,0.20210201416136062,0.12,0.42,0.12,0.0,0.0,3.32,0.0,4.4,1,0,0,0 +3007,INTP,12.9,0.18361087171771662,0.12,0.18,0.02,0.0,0.02,0.12,0.0,1.18,1,0,1,1 +3008,INFJ,26.9,0.18066718126397968,0.0,0.1,0.4,0.0,0.0,0.6,0.0,2.12,1,0,0,0 +3009,ESTP,27.58,0.18895130804433743,0.1,0.4,1.0,0.12,0.0,0.84,0.02,2.24,0,1,1,1 +3010,INFJ,14.78,0.18518563208626038,0.04,0.16,0.14,0.0,0.0,0.28,0.0,1.62,1,0,0,0 +3011,INFJ,30.06,0.18849310231392136,0.06,0.18,0.3,0.06,0.0,1.0,0.02,3.46,1,0,0,0 +3012,INFJ,23.72,0.1820812885260619,0.0,0.04,0.04,0.0,0.04,0.58,0.0,2.56,1,0,0,0 +3013,INTJ,25.18,0.18124677807450815,0.0,0.26,0.06,0.0,0.0,0.7,0.02,2.12,1,0,1,0 +3014,INFP,25.96,0.18109210070639137,0.1,0.3,0.4,0.0,0.0,0.74,0.0,2.52,1,0,0,1 +3015,INFJ,21.48,0.1941656839858035,0.04,0.24,0.02,0.0,0.0,0.4,0.0,3.4,1,0,0,0 +3016,INFP,23.02,0.18550185388678694,0.1,0.32,0.0,0.0,0.02,0.6,0.0,3.0,1,0,0,1 +3017,ENFP,17.56,0.18472933920561277,0.02,0.16,0.2,0.0,0.0,0.3,0.0,2.42,0,0,0,1 +3018,INTP,23.36,0.18960316961598725,0.02,0.14,0.24,0.0,0.0,0.58,0.0,3.48,1,0,1,1 +3019,INFJ,33.24,0.1861265626026559,0.02,0.06,0.1,0.0,0.0,0.96,0.0,2.36,1,0,0,0 +3020,ENTJ,25.94,0.1737752124638118,0.02,0.3,0.0,0.02,0.0,0.42,0.0,2.16,0,0,1,0 +3021,INTJ,9.54,0.18472213495262194,0.06,0.08,0.62,0.04,0.0,0.1,0.0,1.2,1,0,1,0 +3022,INTJ,15.74,0.1846190533249102,0.08,0.2,0.0,0.02,0.0,0.28,0.0,1.5,1,0,1,0 +3023,ESTP,6.3,0.1884588898933898,0.0,0.1,0.06,0.0,0.0,0.16,0.0,0.62,0,1,1,1 +3024,ENFP,24.3,0.19459986136260757,0.14,0.22,0.08,0.04,0.0,1.4,0.0,3.04,0,0,0,1 +3025,INFP,28.54,0.1843329127956693,0.02,0.18,0.12,0.0,0.02,0.86,0.0,3.66,1,0,0,1 +3026,ESFJ,29.74,0.19160928803928545,0.0,0.18,0.56,0.0,0.0,0.82,0.0,3.16,0,1,0,0 +3027,ENTP,20.6,0.18731176284394535,0.0,0.24,0.0,0.0,0.0,0.74,0.0,1.1,0,0,1,1 +3028,ENFJ,25.34,0.17608909371685721,0.0,0.16,0.0,0.0,0.0,0.68,0.0,2.42,0,0,0,0 +3029,INFP,26.46,0.1880604613667066,0.1,0.3,0.28,0.0,0.0,0.62,0.04,3.6,1,0,0,1 +3030,ENFP,29.42,0.18714230600114565,0.0,0.28,0.4,0.0,0.0,0.82,0.0,3.1,0,0,0,1 +3031,INTP,27.94,0.18177971368578386,0.0,0.14,0.0,0.0,0.0,0.54,0.0,3.66,1,0,1,1 +3032,INTP,29.18,0.1879718400175833,0.04,0.12,0.22,0.0,0.04,1.14,0.0,3.64,1,0,1,1 +3033,INTJ,15.42,0.18189772941197027,0.42,0.12,0.08,0.1,0.02,0.34,0.02,1.9,1,0,1,0 +3034,INFJ,28.26,0.18827092414151342,0.08,0.24,0.18,0.02,0.0,0.92,0.02,3.16,1,0,0,0 +3035,INFP,31.06,0.17335272368983654,0.06,0.12,0.38,0.02,0.02,1.12,0.0,1.76,1,0,0,1 +3036,ISFJ,16.72,0.1954150084841197,0.06,0.32,0.16,0.0,0.0,0.22,0.0,1.0,1,1,0,0 +3037,INFP,5.94,0.18288258236023053,0.0,0.04,0.2,0.0,0.0,0.16,0.0,0.64,1,0,0,1 +3038,INFP,24.3,0.18088831393299232,0.0,0.14,0.24,0.0,0.02,0.6,0.0,2.68,1,0,0,1 +3039,INFP,29.26,0.1808344025903195,0.18,0.08,0.04,0.12,0.0,0.74,0.0,3.08,1,0,0,1 +3040,INTP,28.28,0.1901519302923672,0.02,0.34,0.22,0.0,0.0,0.62,0.0,3.48,1,0,1,1 +3041,INTJ,20.16,0.19345026927199765,0.0,0.24,0.14,0.0,0.0,1.34,0.0,2.34,1,0,1,0 +3042,ISFP,27.9,0.1920438957475995,0.0,0.26,0.34,0.0,0.0,0.66,0.08,4.16,1,1,0,1 +3043,ENFP,32.14,0.1904898943645509,0.0,0.22,0.34,0.0,0.0,1.48,0.0,3.92,0,0,0,1 +3044,ISTJ,21.8,0.18738903686161015,0.14,0.22,0.02,0.02,0.0,0.52,0.02,2.26,1,1,1,0 +3045,ISTP,16.28,0.18845136032410836,0.0,0.2,0.0,0.0,0.0,0.2,0.0,1.82,1,1,1,1 +3046,INTJ,25.52,0.17825612717267886,0.04,0.14,0.08,0.02,0.0,0.86,0.02,2.26,1,0,1,0 +3047,INFP,26.82,0.1839726868177003,0.02,0.1,0.14,0.0,0.0,0.8,0.0,3.16,1,0,0,1 +3048,INFJ,33.82,0.1726002640083938,0.0,0.06,0.02,0.0,0.0,0.94,0.0,4.12,1,0,0,0 +3049,ISFP,15.18,0.18785561805504827,0.0,0.18,0.06,0.0,0.0,0.24,0.0,2.08,1,1,0,1 +3050,ISTP,28.28,0.18833387991949946,0.02,0.42,0.22,0.0,0.14,0.6,0.0,3.36,1,1,1,1 +3051,INFJ,26.46,0.1787874234904702,0.02,0.06,0.02,0.02,0.0,0.56,0.0,3.6,1,0,0,0 +3052,ESTP,26.22,0.19212158182381273,0.0,0.36,0.28,0.0,0.0,0.78,0.0,2.56,0,1,1,1 +3053,INFP,30.58,0.18264359488302812,0.0,0.06,0.02,0.0,0.02,0.74,0.0,3.48,1,0,0,1 +3054,INFP,20.68,0.19329559466501714,0.0,0.22,0.54,0.0,0.0,0.38,0.0,2.44,1,0,0,1 +3055,INTP,27.88,0.18297478920863902,0.0,0.1,0.0,0.0,0.02,0.64,0.0,2.72,1,0,1,1 +3056,INFP,25.98,0.18101712365062306,0.0,0.22,0.16,0.0,0.0,0.66,0.0,2.1,1,0,0,1 +3057,INFP,26.58,0.1851606729607678,0.04,0.16,0.42,0.0,0.06,0.62,0.02,2.96,1,0,0,1 +3058,INFP,27.26,0.185462950744159,0.18,0.22,0.1,0.14,0.06,0.58,0.0,3.76,1,0,0,1 +3059,INFP,30.66,0.18355773311214846,0.04,0.3,0.34,0.0,0.0,0.9,0.0,2.86,1,0,0,1 +3060,INFJ,30.88,0.18503696742743203,0.02,0.14,0.38,0.02,0.04,0.7,0.0,3.74,1,0,0,0 +3061,INTJ,29.72,0.18434333480897544,0.0,0.14,0.1,0.0,0.0,0.84,0.0,3.1,1,0,1,0 +3062,INFP,27.98,0.18165022000085446,0.0,0.24,0.0,0.0,0.0,0.72,0.0,3.84,1,0,0,1 +3063,INFP,26.64,0.18680855353323064,0.08,0.28,0.5,0.0,0.1,0.72,0.0,2.78,1,0,0,1 +3064,ENFP,31.68,0.18466089379654893,0.02,0.26,0.24,0.02,0.0,0.84,0.0,3.8,0,0,0,1 +3065,INFJ,23.26,0.19018200449640035,0.0,0.12,0.02,0.0,0.02,0.66,0.0,2.98,1,0,0,0 +3066,ISFP,25.42,0.18042090207853664,0.14,0.24,0.06,0.0,0.12,0.62,0.0,2.52,1,1,0,1 +3067,ENTP,19.96,0.1979659198414465,0.06,0.42,0.02,0.0,0.02,0.54,0.0,2.5,0,0,1,1 +3068,ENFP,22.12,0.19695386747291505,0.02,0.4,0.36,0.0,0.0,0.76,0.0,2.78,0,0,0,1 +3069,INFP,9.98,0.1816395399305555,0.34,0.08,0.08,0.06,0.0,0.14,0.0,0.92,1,0,0,1 +3070,ENFP,15.72,0.19190409182697418,0.04,0.28,0.24,0.0,0.1,0.44,0.0,1.36,0,0,0,1 +3071,INTJ,21.74,0.17700698614579571,0.18,0.26,0.06,0.0,0.0,0.56,0.0,2.36,1,0,1,0 +3072,INTJ,5.56,0.17600965584067865,0.0,0.02,0.02,0.0,0.0,0.18,0.0,0.38,1,0,1,0 +3073,INTP,29.92,0.18478548799587072,0.0,0.22,0.3,0.0,0.0,0.72,0.0,2.88,1,0,1,1 +3074,INTP,29.5,0.1869539899611065,0.0,0.44,0.34,0.0,0.0,1.02,0.0,3.42,1,0,1,1 +3075,ENTJ,15.7,0.17818968359824983,0.22,0.14,0.02,0.04,0.04,0.26,0.0,1.5,0,0,1,0 +3076,INFJ,32.78,0.1847020828201368,0.0,0.18,0.28,0.0,0.0,1.12,0.0,3.78,1,0,0,0 +3077,INFP,26.46,0.18282105583741579,0.0,0.22,0.08,0.0,0.02,0.62,0.06,3.04,1,0,0,1 +3078,INFJ,14.72,0.18862682815394735,0.2,0.14,0.06,0.02,0.0,0.22,0.0,1.72,1,0,0,0 +3079,ISTP,26.6,0.18533537339703157,0.02,0.24,0.04,0.0,0.0,0.58,0.0,3.02,1,1,1,1 +3080,ENTJ,25.06,0.19021968191309674,0.0,0.4,0.04,0.0,0.0,0.66,0.0,2.66,0,0,1,0 +3081,ESTP,35.06,0.18537772570779637,0.02,0.24,0.04,0.0,0.0,0.86,0.0,3.28,0,1,1,1 +3082,ENFP,34.0,0.18475854798112426,0.0,0.28,0.22,0.0,0.0,0.98,0.0,4.6,0,0,0,1 +3083,INTP,28.26,0.17906391054184434,0.0,0.08,0.02,0.0,0.0,0.66,0.0,3.24,1,0,1,1 +3084,INFP,13.68,0.19022607917091433,0.0,0.26,0.38,0.0,0.0,0.5,0.0,1.52,1,0,0,1 +3085,INFJ,20.72,0.1857398109590852,0.16,0.2,0.58,0.0,0.02,0.4,0.0,1.26,1,0,0,0 +3086,INTP,17.52,0.1817273673257024,0.06,0.04,0.02,0.06,0.04,0.42,0.0,2.46,1,0,1,1 +3087,INFP,21.22,0.19016844279279796,0.06,0.14,0.42,0.0,0.0,0.44,0.0,3.16,1,0,0,1 +3088,ENTP,31.3,0.18142204973437023,0.02,0.16,0.02,0.02,0.0,0.94,0.0,3.04,0,0,1,1 +3089,INTP,16.28,0.18724114751238854,0.0,0.34,0.02,0.0,0.0,0.22,0.0,2.22,1,0,1,1 +3090,ENTJ,30.28,0.18131199247855587,0.0,0.12,0.16,0.0,0.02,0.72,0.0,3.66,0,0,1,0 +3091,ENTP,29.04,0.18058175880040211,0.0,0.3,0.34,0.0,0.0,0.58,0.0,3.2,0,0,1,1 +3092,INTJ,3.68,0.18198962297026636,0.0,0.04,0.0,0.0,0.0,0.06,0.0,0.34,1,0,1,0 +3093,ENTJ,8.06,0.17375424692837244,0.42,0.42,0.1,0.18,0.0,0.2,0.0,0.78,0,0,1,0 +3094,INTP,24.32,0.17679161091182963,0.04,0.18,0.02,0.0,0.0,0.52,0.0,1.86,1,0,1,1 +3095,INTJ,25.24,0.18204154218140514,0.06,0.1,0.02,0.04,0.0,0.42,0.02,2.18,1,0,1,0 +3096,INFJ,26.5,0.1777481768330601,0.24,0.2,0.06,0.04,0.0,0.74,0.0,2.48,1,0,0,0 +3097,INTJ,27.94,0.19504119806616008,0.04,0.22,0.18,0.02,0.02,0.78,0.02,3.92,1,0,1,0 +3098,INFP,28.24,0.1841977323817122,0.0,0.16,0.0,0.0,0.0,0.66,0.0,3.08,1,0,0,1 +3099,INFJ,30.14,0.1888979621116564,0.04,0.2,0.0,0.0,0.06,0.64,0.0,3.24,1,0,0,0 +3100,INFP,33.42,0.18264124375359247,0.0,0.38,0.0,0.0,0.0,1.08,0.0,4.02,1,0,0,1 +3101,ENTJ,9.34,0.18138798191130348,0.0,0.04,0.04,0.0,0.0,0.22,0.0,1.24,0,0,1,0 +3102,ISTJ,26.66,0.18801334484919302,0.04,0.28,0.2,0.02,0.0,0.58,0.0,3.56,1,1,1,0 +3103,ENTP,30.32,0.1829607995134598,0.0,0.18,0.38,0.0,0.06,0.72,0.0,3.18,0,0,1,1 +3104,INTJ,20.18,0.18821691336577343,0.06,0.1,0.22,0.02,0.0,0.84,0.0,2.34,1,0,1,0 +3105,INFJ,31.5,0.17659618846163425,0.02,0.1,0.1,0.0,0.0,0.86,0.0,3.32,1,0,0,0 +3106,INFJ,28.42,0.1777052509921137,0.18,0.08,0.0,0.16,0.0,0.9,0.0,3.56,1,0,0,0 +3107,INTP,19.02,0.19156038844271128,0.0,0.2,0.16,0.0,0.0,0.18,0.0,2.14,1,0,1,1 +3108,ESTP,23.58,0.18580185502134192,0.0,0.18,0.08,0.0,0.02,0.58,0.0,2.22,0,1,1,1 +3109,INTJ,30.46,0.17715614682037872,0.02,0.14,0.0,0.02,0.0,0.78,0.0,2.92,1,0,1,0 +3110,ISFJ,25.36,0.18703700686369504,0.0,0.34,0.14,0.0,0.0,0.64,0.0,3.16,1,1,0,0 +3111,ISTP,12.46,0.19410253086967155,0.28,0.02,0.24,0.0,0.0,0.26,0.02,1.56,1,1,1,1 +3112,INFP,20.2,0.195669383560261,0.14,0.28,0.26,0.04,0.02,0.5,0.0,1.12,1,0,0,1 +3113,INFP,28.84,0.1832282316944848,0.04,0.12,0.14,0.0,0.0,0.7,0.0,3.42,1,0,0,1 +3114,INTJ,26.4,0.18348453327075404,0.02,0.2,0.0,0.0,0.0,0.46,0.0,3.9,1,0,1,0 +3115,ESFP,14.92,0.18634576843066972,0.0,0.22,0.32,0.0,0.02,0.32,0.0,1.58,0,1,0,1 +3116,INFP,32.22,0.178573070981197,0.04,0.08,0.1,0.0,0.02,0.9,0.0,3.6,1,0,0,1 +3117,ISFP,26.06,0.18810402330248507,0.04,0.18,0.0,0.0,0.04,0.54,0.0,3.22,1,1,0,1 +3118,INTJ,22.52,0.17860053744178586,0.06,0.08,0.18,0.0,0.02,0.44,0.0,2.06,1,0,1,0 +3119,INFJ,25.56,0.18472799167533818,0.08,0.22,0.0,0.04,0.0,0.66,0.0,2.86,1,0,0,0 +3120,ENTP,22.32,0.18455631583141277,0.0,0.18,0.04,0.0,0.02,0.44,0.0,1.38,0,0,1,1 +3121,ENFP,29.96,0.1819763953459329,0.02,0.1,0.26,0.0,0.0,0.76,0.0,2.5,0,0,0,1 +3122,ESFP,27.1,0.18719712471815972,0.0,0.14,0.34,0.0,0.0,0.64,0.0,2.2,0,1,0,1 +3123,INTP,20.44,0.18113660942781168,0.26,0.28,0.04,0.04,0.04,0.46,0.0,2.52,1,0,1,1 +3124,INFP,8.68,0.1801420748268281,0.68,0.6,0.0,0.0,0.0,0.2,0.0,1.04,1,0,0,1 +3125,INTJ,4.24,0.18271242014811465,0.0,0.06,0.0,0.0,0.0,0.08,0.0,0.46,1,0,1,0 +3126,ESFP,20.24,0.187601304789252,0.02,0.16,0.16,0.0,0.0,0.5,0.0,1.66,0,1,0,1 +3127,INTP,24.52,0.17949812830890272,0.14,0.3,0.1,0.04,0.0,0.54,0.0,2.42,1,0,1,1 +3128,ENTJ,31.42,0.19017825724302417,0.0,0.24,0.5,0.0,0.0,1.34,0.02,3.38,0,0,1,0 +3129,INTP,12.32,0.1835877675873492,0.02,0.1,0.12,0.0,0.0,0.26,0.0,1.2,1,0,1,1 +3130,INFJ,28.94,0.19502104922080002,0.0,0.76,1.3,0.0,0.02,1.48,0.0,3.64,1,0,0,0 +3131,INTJ,21.26,0.1824867421202095,0.18,0.16,0.16,0.08,0.0,0.54,0.02,2.3,1,0,1,0 +3132,ENFP,28.2,0.18479551852163587,0.04,0.04,0.36,0.04,0.0,0.74,0.0,3.1,0,0,0,1 +3133,INTP,28.06,0.18417456332993457,0.02,0.4,0.16,0.02,0.02,0.9,0.02,2.88,1,0,1,1 +3134,INFP,23.02,0.18890189263719487,0.02,0.26,0.1,0.0,0.0,0.64,0.0,2.28,1,0,0,1 +3135,INTP,33.8,0.1877846492523289,0.0,0.04,0.04,0.0,0.0,0.88,0.0,4.02,1,0,1,1 +3136,INFP,22.56,0.1850557157908295,0.02,0.34,0.34,0.0,0.02,0.46,0.0,2.24,1,0,0,1 +3137,INTJ,24.88,0.1867130910925033,0.0,0.36,0.06,0.0,0.1,0.58,0.0,2.24,1,0,1,0 +3138,INFJ,17.78,0.18826451880010536,0.28,0.4,0.16,0.04,0.0,0.32,0.0,2.76,1,0,0,0 +3139,ENTJ,22.84,0.1840413377420355,0.02,0.12,0.36,0.02,0.0,0.96,0.0,2.62,0,0,1,0 +3140,INTP,10.74,0.17895423334775085,0.0,0.02,0.02,0.0,0.0,0.22,0.0,1.44,1,0,1,1 +3141,INTP,27.18,0.18550907579201098,0.0,0.2,0.02,0.0,0.0,0.56,0.0,3.26,1,0,1,1 +3142,ENFP,22.34,0.18645648791758115,0.12,0.18,0.36,0.0,0.0,0.6,0.0,3.34,0,0,0,1 +3143,INTJ,10.56,0.18431510693249736,0.0,0.08,0.0,0.0,0.0,0.06,0.0,1.1,1,0,1,0 +3144,INFJ,25.72,0.18441234557408445,0.0,0.16,0.08,0.0,0.0,0.66,0.02,2.86,1,0,0,0 +3145,ISFJ,20.16,0.19435260467168,0.0,0.2,0.1,0.0,0.0,0.96,0.0,2.36,1,1,0,0 +3146,ISFP,9.2,0.18612263088759617,0.1,0.22,0.02,0.0,0.0,0.22,0.0,0.94,1,1,0,1 +3147,ENFJ,26.72,0.18729300695651976,0.02,0.16,0.02,0.0,0.0,1.16,0.02,2.06,0,0,0,0 +3148,ISTJ,24.88,0.1827289923303587,0.1,0.06,0.04,0.08,0.0,0.76,0.0,1.92,1,1,1,0 +3149,INTP,18.54,0.18114118159384526,0.32,0.42,0.08,0.08,0.06,0.4,0.0,1.96,1,0,1,1 +3150,INFP,26.76,0.18218458107899324,0.02,0.06,0.02,0.0,0.06,0.54,0.0,3.84,1,0,0,1 +3151,INFP,23.6,0.18940604583382237,0.0,0.26,0.04,0.0,0.0,0.38,0.0,2.12,1,0,0,1 +3152,INFP,23.26,0.1903799198526903,0.08,0.38,0.28,0.0,0.1,0.78,0.0,2.68,1,0,0,1 +3153,INFJ,15.26,0.18493576331360945,0.2,0.42,0.0,0.02,0.0,0.18,0.0,1.2,1,0,0,0 +3154,INTP,18.58,0.1895766742744543,0.04,0.14,0.0,0.0,0.06,0.36,0.0,2.3,1,0,1,1 +3155,ISTJ,25.12,0.1887236962756665,0.04,0.14,0.36,0.0,0.0,0.36,0.0,2.48,1,1,1,0 +3156,INFP,22.48,0.1840454481813843,0.16,0.18,0.16,0.04,0.04,0.34,0.0,1.52,1,0,0,1 +3157,ISFJ,25.9,0.18607821165279428,0.04,0.38,0.06,0.02,0.0,0.66,0.0,3.24,1,1,0,0 +3158,INTJ,31.4,0.18016043667376241,0.08,0.24,0.04,0.0,0.0,0.78,0.0,2.5,1,0,1,0 +3159,INFJ,22.38,0.18753786157720193,0.06,0.12,0.22,0.0,0.0,0.38,0.0,2.1,1,0,0,0 +3160,ESTP,24.36,0.1852235775461273,0.1,0.16,0.06,0.06,0.0,0.52,0.0,2.28,0,1,1,1 +3161,INFP,32.22,0.1773164966300273,0.0,0.22,0.08,0.0,0.0,0.84,0.0,2.92,1,0,0,1 +3162,INFP,27.04,0.17447811941710056,0.0,0.16,0.08,0.0,0.0,0.72,0.0,2.64,1,0,0,1 +3163,INFJ,15.14,0.18194164556212195,0.0,0.18,0.0,0.0,0.0,0.36,0.0,1.68,1,0,0,0 +3164,ISTJ,27.08,0.1907474014489593,0.06,0.58,0.2,0.0,0.0,0.58,0.02,2.88,1,1,1,0 +3165,INTJ,32.18,0.19014390239506493,0.02,0.24,0.0,0.0,0.0,0.86,0.0,3.86,1,0,1,0 +3166,INTP,24.8,0.18130205571206023,0.14,0.14,0.26,0.0,0.0,0.58,0.0,2.96,1,0,1,1 +3167,INFP,21.76,0.18630084513164735,0.04,0.06,0.06,0.02,0.04,0.42,0.0,2.94,1,0,0,1 +3168,INFP,30.62,0.1898269416684834,0.02,0.16,0.14,0.0,0.02,0.68,0.0,3.96,1,0,0,1 +3169,INFJ,14.2,0.18940559882569818,0.02,0.08,0.06,0.0,0.0,0.24,0.0,2.08,1,0,0,0 +3170,INFP,26.78,0.1877971862122455,0.0,0.04,0.0,0.0,0.0,1.26,0.0,3.26,1,0,0,1 +3171,INTJ,17.82,0.18051052567591586,0.38,0.26,0.04,0.12,0.0,0.56,0.0,1.74,1,0,1,0 +3172,INFJ,29.1,0.18551440814488113,0.04,0.12,0.1,0.0,0.0,0.68,0.0,3.5,1,0,0,0 +3173,INFJ,20.9,0.18109867301784185,0.06,0.16,0.66,0.0,0.0,0.48,0.0,2.26,1,0,0,0 +3174,ENFJ,30.66,0.18853209235428442,0.0,0.24,0.18,0.0,0.06,0.7,0.0,3.54,0,0,0,0 +3175,INTP,21.98,0.19446046949833987,0.0,0.74,0.46,0.0,0.0,0.32,0.0,2.72,1,0,1,1 +3176,INFJ,21.06,0.18396769930534737,0.26,0.4,0.06,0.0,0.0,0.72,0.0,2.22,1,0,0,0 +3177,INTP,19.22,0.17853821608429823,0.04,0.24,0.0,0.0,0.04,0.22,0.0,1.58,1,0,1,1 +3178,ENFP,30.84,0.19007424700294756,0.0,0.06,0.14,0.0,0.0,0.8,0.0,3.78,0,0,0,1 +3179,ISTJ,6.84,0.18125808811701888,0.0,0.04,0.0,0.0,0.0,0.0,0.0,0.8,1,1,1,0 +3180,INTP,27.0,0.18402285907227042,0.0,0.26,0.06,0.0,0.0,0.52,0.0,3.28,1,0,1,1 +3181,INFP,27.18,0.1828632902156468,0.16,0.26,0.18,0.0,0.02,0.62,0.0,2.8,1,0,0,1 +3182,ISFP,25.24,0.18053275874199384,0.18,0.5,0.02,0.0,0.0,0.6,0.0,2.38,1,1,0,1 +3183,INFJ,30.08,0.18097053813367447,0.0,0.14,0.32,0.0,0.0,0.62,0.02,2.94,1,0,0,0 +3184,INTP,25.76,0.18489420872257353,0.04,0.06,0.04,0.02,0.0,0.66,0.0,3.26,1,0,1,1 +3185,ENTJ,25.82,0.18274453066059088,0.1,0.36,0.56,0.02,0.04,0.84,0.0,2.78,0,0,1,0 +3186,INTJ,26.82,0.1747626483484876,0.0,0.48,0.06,0.0,0.04,0.68,0.0,2.44,1,0,1,0 +3187,INTJ,22.46,0.18078607259674126,0.0,0.12,0.12,0.0,0.0,0.52,0.0,2.38,1,0,1,0 +3188,INFP,22.48,0.18453613534646124,0.04,0.12,0.08,0.0,0.02,0.4,0.06,2.46,1,0,0,1 +3189,INFJ,32.28,0.17836745973982726,0.0,0.24,0.08,0.0,0.0,0.82,0.0,2.76,1,0,0,0 +3190,INTJ,27.32,0.17799883299559346,0.02,0.1,0.12,0.0,0.06,0.6,0.0,3.04,1,0,1,0 +3191,ENTP,21.16,0.18971577764558134,0.0,0.16,0.08,0.0,0.0,0.42,0.0,1.94,0,0,1,1 +3192,INFP,24.1,0.17725323169700166,0.08,0.12,0.02,0.0,0.0,0.44,0.0,2.52,1,0,0,1 +3193,INTP,27.86,0.180439719714363,0.1,0.24,0.0,0.04,0.0,0.76,0.0,2.86,1,0,1,1 +3194,INTP,16.34,0.18704288354273949,0.4,0.6,0.06,0.0,0.02,0.38,0.0,1.8,1,0,1,1 +3195,ENTJ,23.86,0.18794850949489922,0.0,0.12,0.14,0.0,0.0,1.38,0.0,2.72,0,0,1,0 +3196,INTP,20.0,0.18637140094558113,0.08,0.2,0.16,0.02,0.02,0.38,0.0,2.7,1,0,1,1 +3197,INTJ,11.6,0.19067463157961204,0.06,0.14,0.12,0.06,0.0,0.2,0.0,1.28,1,0,1,0 +3198,ISTJ,26.04,0.18521943792269935,0.02,0.2,0.18,0.02,0.0,0.52,0.0,2.86,1,1,1,0 +3199,INTJ,31.1,0.17286051508808545,0.0,0.08,0.12,0.0,0.04,0.7,0.0,3.34,1,0,1,0 +3200,ISTP,30.84,0.1811396230949751,0.02,0.16,0.0,0.0,0.0,0.68,0.0,3.78,1,1,1,1 +3201,INTJ,17.8,0.18100034459897346,0.04,0.24,0.1,0.0,0.0,0.28,0.0,1.22,1,0,1,0 +3202,ENFP,24.88,0.18345571828471743,0.1,0.12,0.3,0.0,0.08,0.42,0.0,2.4,0,0,0,1 +3203,ESTP,15.96,0.19181027694840433,0.12,0.34,0.4,0.02,0.02,0.32,0.0,1.12,0,1,1,1 +3204,INFJ,24.16,0.19316016250193963,0.02,0.08,0.3,0.0,0.0,0.86,0.0,2.88,1,0,0,0 +3205,INTJ,22.48,0.18647550612443842,0.0,0.34,0.08,0.0,0.0,0.44,0.0,3.18,1,0,1,0 +3206,ISFP,28.26,0.18080625498362987,0.16,0.18,0.18,0.06,0.06,0.86,0.0,3.48,1,1,0,1 +3207,INTP,23.18,0.18052822324313905,0.08,0.12,0.04,0.02,0.0,0.48,0.0,2.32,1,0,1,1 +3208,INFJ,28.54,0.19115023571301668,0.02,0.44,0.16,0.0,0.0,0.66,0.0,3.36,1,0,0,0 +3209,INTJ,24.4,0.17769582552096308,0.06,0.54,0.0,0.0,0.0,0.5,0.0,1.76,1,0,1,0 +3210,INFP,28.68,0.18481481743928993,0.0,0.24,0.28,0.0,0.0,0.7,0.0,3.16,1,0,0,1 +3211,INTP,13.6,0.1927180431706386,0.04,0.24,0.02,0.04,0.02,0.2,0.0,1.9,1,0,1,1 +3212,INFJ,16.92,0.1948522030461328,0.0,1.16,0.0,0.0,0.02,1.1,0.0,1.14,1,0,0,0 +3213,INFP,26.34,0.18514231647280893,0.08,0.38,0.18,0.0,0.1,0.7,0.0,2.68,1,0,0,1 +3214,INFJ,27.86,0.1789317313213035,0.04,0.14,0.1,0.02,0.0,0.68,0.0,3.04,1,0,0,0 +3215,ENFP,33.84,0.17901659786903013,0.02,0.08,0.26,0.02,0.0,0.98,0.02,3.88,0,0,0,1 +3216,INTJ,18.18,0.1888945265889037,0.0,0.32,0.06,0.0,0.0,0.46,0.0,2.14,1,0,1,0 +3217,ENFJ,24.82,0.19430613120580076,0.04,0.4,0.38,0.02,0.04,0.52,0.0,2.88,0,0,0,0 +3218,ISTP,30.18,0.18077620494531643,0.02,0.08,0.04,0.0,0.0,0.92,0.0,3.48,1,1,1,1 +3219,INFP,13.64,0.1784301936147507,0.12,0.08,0.0,0.04,0.0,0.36,0.02,1.26,1,0,0,1 +3220,INTP,26.2,0.182402708658893,0.06,0.18,0.04,0.0,0.04,0.7,0.0,2.8,1,0,1,1 +3221,INTP,28.34,0.17859909675701466,0.02,0.08,0.04,0.0,0.0,0.6,0.0,3.38,1,0,1,1 +3222,INTJ,26.56,0.18575454474229647,0.0,0.24,0.32,0.0,0.0,0.64,0.0,3.4,1,0,1,0 +3223,INTP,19.04,0.18084284866002007,0.1,0.28,0.16,0.08,0.0,0.46,0.0,1.42,1,0,1,1 +3224,INTP,26.5,0.18177277130751104,0.08,0.26,0.1,0.0,0.1,0.76,0.0,3.48,1,0,1,1 +3225,INFJ,24.92,0.1950933316335138,0.04,0.02,0.08,0.04,0.02,0.94,0.0,3.32,1,0,0,0 +3226,ESTP,28.62,0.18661563191645353,0.06,0.3,0.12,0.0,0.0,0.86,0.0,3.04,0,1,1,1 +3227,ISFJ,23.6,0.1768499639062322,0.0,0.28,0.1,0.0,0.0,0.48,0.0,3.12,1,1,0,0 +3228,INTP,25.62,0.18826720009529926,0.06,0.16,0.02,0.02,0.02,1.5,0.0,3.42,1,0,1,1 +3229,ESTP,18.92,0.18008516513187758,0.38,0.38,0.06,0.0,0.02,0.46,0.0,1.8,0,1,1,1 +3230,INTJ,24.42,0.1804104160580137,0.2,0.14,0.0,0.02,0.0,0.6,0.04,2.78,1,0,1,0 +3231,INTP,6.72,0.1766568234989687,0.28,0.26,0.0,0.04,0.0,0.06,0.0,0.52,1,0,1,1 +3232,ISTP,27.28,0.18037063042219564,0.02,0.14,0.02,0.0,0.06,1.18,0.0,3.68,1,1,1,1 +3233,ENTP,13.74,0.19506004553179046,0.1,0.26,0.16,0.02,0.02,0.24,0.0,1.48,0,0,1,1 +3234,ENTP,27.88,0.1970627784511596,0.0,0.6,0.18,0.0,0.0,0.98,0.06,1.84,0,0,1,1 +3235,ENTP,28.12,0.18103804104128385,0.02,0.16,0.06,0.0,0.0,0.7,0.0,2.98,0,0,1,1 +3236,ENTP,16.6,0.17641651630962568,0.0,0.06,0.0,0.0,0.0,0.4,0.0,0.5,0,0,1,1 +3237,ENFP,26.8,0.1909626947717877,0.06,0.26,0.44,0.0,0.0,0.78,0.0,2.72,0,0,0,1 +3238,INTP,26.74,0.18110684817988773,0.02,0.48,0.0,0.0,0.02,0.82,0.0,2.1,1,0,1,1 +3239,INFJ,27.0,0.18726663575436836,0.02,0.2,0.38,0.0,0.0,0.68,0.0,1.54,1,0,0,0 +3240,INFJ,34.9,0.18252202543340706,0.0,0.1,0.12,0.0,0.0,1.12,0.0,4.22,1,0,0,0 +3241,INTJ,20.2,0.18737947937877789,0.1,0.22,0.4,0.0,0.0,0.64,0.0,2.3,1,0,1,0 +3242,INTP,23.78,0.18571692253368535,0.08,0.32,0.1,0.0,0.0,0.74,0.0,2.16,1,0,1,1 +3243,INFP,25.9,0.1854517806839882,0.0,0.32,0.56,0.0,0.0,0.76,0.0,3.12,1,0,0,1 +3244,INTP,29.42,0.18591271360907494,0.0,0.06,0.04,0.0,0.02,0.7,0.0,4.06,1,0,1,1 +3245,ENFJ,3.6,0.1879273491743291,0.0,0.0,0.0,0.0,0.0,0.14,0.0,0.5,0,0,0,0 +3246,INTJ,16.02,0.18474859358740897,0.06,0.34,0.06,0.02,0.0,0.34,0.0,1.82,1,0,1,0 +3247,INTJ,20.48,0.1915938403279439,0.46,0.56,0.24,0.0,0.12,0.56,0.0,2.68,1,0,1,0 +3248,INFP,27.94,0.18831269453121044,0.06,0.12,0.28,0.04,0.02,0.92,0.0,3.26,1,0,0,1 +3249,ENFJ,31.86,0.1753093265668166,0.02,0.14,0.06,0.0,0.02,0.84,0.0,3.02,0,0,0,0 +3250,INFP,23.02,0.18417787095261107,0.1,0.22,0.0,0.02,0.0,0.44,0.02,2.54,1,0,0,1 +3251,ENTP,21.62,0.1774300644354524,0.0,0.04,0.04,0.0,0.0,0.68,0.0,2.02,0,0,1,1 +3252,ISTJ,35.7,0.18604058970374157,0.0,0.12,0.08,0.0,0.0,0.82,0.0,1.94,1,1,1,0 +3253,INTP,28.0,0.18097681111564337,0.02,0.38,0.26,0.02,0.0,0.52,0.0,2.52,1,0,1,1 +3254,ISTP,27.82,0.18688773019571062,0.0,0.3,0.0,0.0,0.0,0.58,0.0,3.1,1,1,1,1 +3255,INFP,28.42,0.18570754782857063,0.16,0.16,0.22,0.14,0.0,0.88,0.0,3.68,1,0,0,1 +3256,INFJ,19.54,0.18447687987025835,0.0,0.0,0.06,0.0,0.0,0.42,0.0,2.2,1,0,0,0 +3257,INFP,30.1,0.17896872719783666,0.02,0.14,0.02,0.0,0.06,0.8,0.0,3.36,1,0,0,1 +3258,ENFJ,31.28,0.1863244549616592,0.0,0.16,0.24,0.0,0.0,0.76,0.0,4.1,0,0,0,0 +3259,INTJ,15.96,0.1745670765986799,0.14,0.16,0.08,0.02,0.0,0.3,0.0,1.56,1,0,1,0 +3260,INFJ,7.48,0.1766167811376226,0.0,0.06,0.0,0.0,0.0,0.06,0.0,0.5,1,0,0,0 +3261,INFJ,22.36,0.19250728649381985,0.1,0.06,0.0,0.0,0.08,0.4,0.06,2.04,1,0,0,0 +3262,ISTP,19.52,0.18212114158163265,0.0,0.16,0.0,0.0,0.0,0.36,0.0,2.06,1,1,1,1 +3263,INFP,29.76,0.18549526662475863,0.0,0.04,0.06,0.0,0.0,0.74,0.0,3.62,1,0,0,1 +3264,ENFJ,9.1,0.19536093750000003,0.0,0.0,0.02,0.0,0.0,0.04,0.0,1.18,0,0,0,0 +3265,ENTJ,26.7,0.18137702308146628,0.08,0.42,0.06,0.04,0.04,0.6,0.0,2.96,0,0,1,0 +3266,INFP,28.58,0.18831866914958711,0.08,0.24,0.26,0.0,0.0,0.98,0.0,3.58,1,0,0,1 +3267,INTP,25.98,0.18640133945213094,0.02,0.08,0.08,0.0,0.0,0.68,0.0,3.96,1,0,1,1 +3268,INTP,26.74,0.184141537628922,0.08,0.24,0.04,0.08,0.02,0.62,0.0,3.44,1,0,1,1 +3269,INFJ,19.04,0.18874896125942314,0.2,0.04,0.3,0.2,0.0,0.54,0.0,2.22,1,0,0,0 +3270,INFP,30.82,0.18645029459603124,0.02,0.18,0.36,0.0,0.04,1.16,0.0,3.16,1,0,0,1 +3271,ISTJ,11.74,0.18714893188087686,0.0,0.22,0.0,0.0,0.02,0.14,0.0,1.76,1,1,1,0 +3272,INFP,18.12,0.18161008521213748,0.16,0.24,0.08,0.0,0.0,0.4,0.0,2.14,1,0,0,1 +3273,INTP,23.66,0.18696921681987935,0.0,0.14,0.1,0.0,0.02,0.3,0.0,2.94,1,0,1,1 +3274,INFJ,28.18,0.18746777104604273,0.0,0.28,0.5,0.0,0.02,0.62,0.0,2.74,1,0,0,0 +3275,ISFP,9.72,0.18389743298646272,0.62,0.56,0.06,0.06,0.04,0.4,0.0,1.0,1,1,0,1 +3276,INFP,25.66,0.1884320202149764,0.0,0.08,0.18,0.0,0.04,0.42,0.0,2.78,1,0,0,1 +3277,INFJ,28.96,0.18561568336017814,0.0,0.14,0.2,0.0,0.0,0.72,0.0,3.54,1,0,0,0 +3278,INFP,30.92,0.18458689492386116,0.02,0.02,0.3,0.02,0.0,0.88,0.0,3.8,1,0,0,1 +3279,INFP,19.04,0.18421534075656285,0.06,0.18,0.02,0.0,0.0,0.32,0.02,2.2,1,0,0,1 +3280,INFP,15.72,0.19165966730794662,0.46,0.24,0.12,0.16,0.0,0.42,0.34,1.82,1,0,0,1 +3281,INFP,32.72,0.19074967781270916,0.0,0.22,0.14,0.0,0.0,1.0,0.0,4.14,1,0,0,1 +3282,ESTJ,28.98,0.18061242406611727,0.04,0.08,0.16,0.0,0.2,0.72,0.0,3.42,0,1,1,0 +3283,INTP,18.54,0.18268026342443056,0.0,0.4,0.02,0.0,0.0,0.34,0.0,1.84,1,0,1,1 +3284,INTP,25.34,0.17765954614756696,0.08,0.36,0.0,0.0,0.02,0.48,0.0,1.92,1,0,1,1 +3285,ISTP,15.66,0.1882098480670155,0.06,0.22,0.12,0.0,0.0,0.28,0.0,2.0,1,1,1,1 +3286,INFJ,28.08,0.18560107090752298,0.0,0.38,0.08,0.0,0.0,1.18,0.0,2.68,1,0,0,0 +3287,ENTP,9.14,0.18859663579787825,0.16,0.32,0.06,0.02,0.02,0.16,0.0,0.8,0,0,1,1 +3288,INFJ,30.92,0.1900096648050908,0.0,0.1,0.48,0.0,0.0,1.46,0.0,3.82,1,0,0,0 +3289,INTP,22.56,0.1877102927315826,0.0,0.06,0.3,0.0,0.06,0.54,0.0,2.52,1,0,1,1 +3290,INTP,21.92,0.18415973409289485,0.0,0.14,0.0,0.0,0.02,0.3,0.0,2.98,1,0,1,1 +3291,ENFP,31.52,0.18354091715053322,0.1,0.2,0.16,0.08,0.0,0.98,0.0,3.82,0,0,0,1 +3292,INFP,25.88,0.17751479289940827,0.0,0.18,0.0,0.0,0.04,0.62,0.0,2.52,1,0,0,1 +3293,ENFP,31.3,0.18582830679365336,0.04,0.18,0.5,0.04,0.0,0.84,0.0,3.14,0,0,0,1 +3294,INFJ,32.62,0.18427287279276194,0.0,0.28,0.12,0.0,0.0,0.86,0.0,3.52,1,0,0,0 +3295,INFP,21.64,0.18411264475972133,0.08,0.42,0.24,0.04,0.0,0.36,0.0,3.18,1,0,0,1 +3296,ESTP,29.8,0.1887355028344649,0.0,0.28,0.38,0.0,0.02,0.72,0.0,1.48,0,1,1,1 +3297,INTJ,18.56,0.18503891186443308,0.04,0.08,0.08,0.0,0.0,0.24,0.0,2.88,1,0,1,0 +3298,INFP,28.62,0.1868863561582861,0.06,0.2,0.16,0.0,0.06,0.78,0.0,3.82,1,0,0,1 +3299,INFP,20.72,0.18165077049732967,0.06,0.12,0.12,0.0,0.0,0.58,0.0,1.84,1,0,0,1 +3300,INTJ,29.98,0.18114452011670654,0.04,0.24,0.1,0.04,0.02,0.78,0.0,3.3,1,0,1,0 +3301,ISTP,23.5,0.19001030332867266,0.06,0.2,0.22,0.0,0.0,0.76,0.0,2.52,1,1,1,1 +3302,INFP,26.72,0.18454133594803127,0.0,0.1,0.1,0.0,0.0,0.6,0.02,2.88,1,0,0,1 +3303,INFP,31.44,0.18478955402990546,0.02,0.1,0.1,0.0,0.04,0.8,0.0,4.38,1,0,0,1 +3304,INTP,23.7,0.1771095260831332,0.16,0.16,0.0,0.06,0.02,0.62,0.04,1.7,1,0,1,1 +3305,ENTP,27.28,0.17969164194454337,0.0,0.2,0.22,0.0,0.0,0.6,0.0,3.28,0,0,1,1 +3306,INFJ,13.28,0.1756000882011635,0.0,0.2,0.0,0.0,0.0,0.08,0.0,0.92,1,0,0,0 +3307,ENTJ,29.3,0.1815670540360768,0.08,0.34,0.02,0.0,0.0,0.84,0.0,3.0,0,0,1,0 +3308,INFJ,29.18,0.18886737837755624,0.08,0.32,0.12,0.0,0.0,0.96,0.0,3.86,1,0,0,0 +3309,INFP,27.44,0.1817389571287922,0.0,0.04,0.18,0.0,0.06,0.66,0.0,3.06,1,0,0,1 +3310,INTJ,18.28,0.19623814473111884,0.0,0.12,0.18,0.0,0.0,0.38,0.0,2.2,1,0,1,0 +3311,INTJ,11.6,0.1908085143592578,0.02,0.1,0.02,0.0,0.0,0.2,0.0,1.3,1,0,1,0 +3312,ENFP,29.5,0.18492673777303179,0.0,0.24,0.38,0.0,0.0,0.8,0.0,3.22,0,0,0,1 +3313,INFP,32.74,0.18799425400490807,0.04,0.22,0.08,0.0,0.0,0.96,0.0,4.28,1,0,0,1 +3314,INFJ,6.7,0.19733110182639624,0.04,0.08,0.18,0.0,0.0,0.06,0.0,0.68,1,0,0,0 +3315,ENFP,16.34,0.19569936015321412,0.18,0.8,4.38,0.08,0.0,0.5,0.0,0.82,0,0,0,1 +3316,INTJ,30.48,0.1842418015027425,0.0,0.06,0.12,0.0,0.0,1.0,0.0,4.18,1,0,1,0 +3317,INFP,19.3,0.18160536083454515,0.14,0.18,0.0,0.04,0.0,0.68,0.0,2.2,1,0,0,1 +3318,INFP,32.88,0.17934147797968583,0.02,0.14,0.02,0.0,0.02,0.9,0.0,4.08,1,0,0,1 +3319,INFJ,29.48,0.1875602845395361,0.0,0.2,0.12,0.0,0.0,0.62,0.0,3.9,1,0,0,0 +3320,ISFP,21.3,0.1783406997241902,0.02,0.06,0.02,0.0,0.02,0.54,0.0,2.5,1,1,0,1 +3321,INFJ,31.28,0.1887635053010084,0.06,0.2,0.14,0.0,0.0,0.84,0.0,3.84,1,0,0,0 +3322,ISFP,21.8,0.19490516953334375,0.08,0.08,0.06,0.04,0.02,0.9,0.0,2.58,1,1,0,1 +3323,INFJ,28.32,0.18923466459201535,0.02,0.42,0.14,0.0,0.0,0.86,0.0,1.46,1,0,0,0 +3324,INFJ,33.1,0.17551248692748458,0.0,0.02,0.0,0.0,0.02,1.26,0.0,4.16,1,0,0,0 +3325,INTP,12.14,0.18477597916946653,0.1,0.12,0.0,0.0,0.0,0.34,0.0,1.38,1,0,1,1 +3326,INFJ,23.84,0.19110033884798636,0.02,0.1,0.22,0.0,0.0,1.12,0.0,2.7,1,0,0,0 +3327,INTP,31.12,0.17949242406097457,0.0,0.14,0.22,0.0,0.02,0.76,0.0,4.46,1,0,1,1 +3328,INFP,28.2,0.18826109883604764,0.04,0.12,0.12,0.0,0.0,0.66,0.0,3.28,1,0,0,1 +3329,ENTJ,26.18,0.195394431569232,0.0,0.26,1.8,0.0,0.0,1.22,0.02,3.64,0,0,1,0 +3330,ISTP,32.54,0.1814492732180758,0.02,0.18,0.0,0.02,0.0,0.76,0.0,3.54,1,1,1,1 +3331,ISTP,13.46,0.19244358408318663,0.02,0.12,0.18,0.02,0.0,0.34,0.0,1.58,1,1,1,1 +3332,INTJ,24.44,0.179501084404641,0.02,0.2,0.26,0.0,0.0,0.54,0.0,2.1,1,0,1,0 +3333,INFP,31.7,0.1930520712424505,0.02,0.16,0.18,0.02,0.04,0.98,0.0,4.02,1,0,0,1 +3334,INFJ,28.26,0.17851358800652678,0.0,0.08,0.08,0.0,0.04,0.68,0.0,3.24,1,0,0,0 +3335,INFP,33.9,0.18633294236304518,0.0,0.2,0.26,0.0,0.0,0.86,0.0,4.56,1,0,0,1 +3336,ISTP,26.3,0.18637521517004116,0.08,0.38,0.18,0.04,0.0,0.8,0.0,2.52,1,1,1,1 +3337,INTJ,31.78,0.18170294768041836,0.02,0.36,0.0,0.0,0.02,0.86,0.0,3.42,1,0,1,0 +3338,INFP,20.94,0.18476140374011518,0.08,0.14,0.12,0.0,0.02,0.26,0.02,2.34,1,0,0,1 +3339,ISFJ,6.34,0.18983624065503735,0.02,0.04,0.0,0.0,0.0,0.04,0.0,1.08,1,1,0,0 +3340,INFP,28.26,0.1878785346512988,0.06,0.26,0.18,0.02,0.0,0.88,0.0,3.3,1,0,0,1 +3341,INTJ,15.66,0.1840789422676083,0.0,0.02,0.02,0.0,0.0,0.48,0.0,1.66,1,0,1,0 +3342,INFP,15.24,0.19252595732049999,0.12,0.22,0.4,0.04,0.02,0.4,0.0,1.9,1,0,0,1 +3343,INFP,21.26,0.18812953235287658,0.12,0.2,0.34,0.1,0.02,0.56,0.0,2.42,1,0,0,1 +3344,ENFP,17.5,0.18836555488984455,0.02,0.28,0.12,0.0,0.0,0.44,0.0,2.0,0,0,0,1 +3345,ENTJ,21.5,0.18162926209406274,0.02,0.38,0.08,0.0,0.0,0.58,0.0,1.98,0,0,1,0 +3346,INFP,22.32,0.1815010207652329,0.0,0.02,0.34,0.0,0.02,0.56,0.0,2.56,1,0,0,1 +3347,INTP,22.08,0.17783137836541393,0.08,0.36,0.08,0.04,0.02,0.5,0.0,2.14,1,0,1,1 +3348,INTP,10.78,0.18587367389411957,0.54,0.54,0.02,0.02,0.0,0.14,0.02,1.22,1,0,1,1 +3349,ISFJ,26.56,0.18417292353404094,0.0,0.04,0.34,0.0,0.0,0.78,0.0,3.18,1,1,0,0 +3350,INFJ,21.02,0.18595407013777482,0.18,0.12,0.02,0.02,0.0,0.48,0.0,2.62,1,0,0,0 +3351,INTP,15.62,0.19080867043426528,0.06,0.18,0.12,0.0,0.0,0.3,0.0,2.14,1,0,1,1 +3352,ISTP,26.26,0.18265212717892512,0.08,0.16,0.16,0.04,0.02,0.66,0.0,2.66,1,1,1,1 +3353,ESTP,25.96,0.17598451796009812,0.02,0.16,0.02,0.0,0.0,0.52,0.0,1.92,0,1,1,1 +3354,ENTP,26.94,0.1835543735554958,0.04,0.26,0.2,0.0,0.02,0.94,0.0,2.9,0,0,1,1 +3355,INFJ,29.56,0.1849327378761686,0.04,0.12,0.0,0.0,0.12,0.6,0.0,3.92,1,0,0,0 +3356,ISTP,18.56,0.18115504651549896,0.04,0.26,0.0,0.0,0.04,0.66,0.0,1.82,1,1,1,1 +3357,INFP,13.04,0.1871201948671725,0.14,0.2,0.04,0.02,0.0,0.22,0.0,1.66,1,0,0,1 +3358,INFP,20.9,0.18704448380102126,0.08,0.12,0.38,0.0,0.04,0.4,0.0,2.54,1,0,0,1 +3359,INFP,26.38,0.18787569336657553,0.06,0.16,0.18,0.02,0.02,0.9,0.02,3.34,1,0,0,1 +3360,INFJ,11.14,0.18876865441608365,0.12,0.12,0.0,0.0,0.02,0.2,0.0,1.24,1,0,0,0 +3361,INFP,25.76,0.19386775907340292,0.0,0.08,0.22,0.0,0.02,0.94,0.0,3.54,1,0,0,1 +3362,ISTJ,32.44,0.1867607193683523,0.0,0.2,0.02,0.0,0.0,0.94,0.0,4.2,1,1,1,0 +3363,INFJ,18.18,0.18877733857115708,0.0,0.42,0.86,0.0,0.0,0.36,0.0,1.86,1,0,0,0 +3364,INTJ,25.28,0.18045362885602892,0.0,0.1,0.2,0.0,0.04,0.64,0.0,2.96,1,0,1,0 +3365,ENTP,18.42,0.19030652431662065,0.0,0.24,0.28,0.0,0.0,0.66,0.0,1.92,0,0,1,1 +3366,ISFP,26.5,0.18496873287189522,0.08,0.4,0.18,0.0,0.0,0.64,0.0,2.96,1,1,0,1 +3367,INFP,30.16,0.17979329121571097,0.04,0.26,0.18,0.02,0.04,0.9,0.0,3.2,1,0,0,1 +3368,INFP,33.0,0.18123938468502407,0.04,0.18,0.18,0.0,0.0,0.94,0.02,2.94,1,0,0,1 +3369,INTJ,24.72,0.18302147075235012,0.04,0.28,0.08,0.0,0.0,0.54,0.0,2.4,1,0,1,0 +3370,INTJ,30.42,0.1818876739783596,0.02,0.24,0.02,0.02,0.0,0.94,0.0,2.8,1,0,1,0 +3371,INTJ,30.62,0.18335418466537348,0.0,0.06,0.08,0.0,0.0,0.82,0.0,4.32,1,0,1,0 +3372,INTP,24.28,0.1850395315521441,0.0,0.06,0.1,0.0,0.0,0.98,0.0,2.7,1,0,1,1 +3373,INTP,30.5,0.18973337661753756,0.04,0.22,0.24,0.02,0.02,0.92,0.0,4.56,1,0,1,1 +3374,INFJ,25.42,0.18759633909936285,0.08,0.16,0.08,0.0,0.0,0.62,0.0,2.36,1,0,0,0 +3375,INFJ,26.24,0.1821482610529025,0.06,0.12,0.26,0.0,0.02,0.78,0.0,3.3,1,0,0,0 +3376,ISFJ,27.12,0.18743541720916435,0.1,0.42,0.34,0.06,0.0,0.56,0.0,3.08,1,1,0,0 +3377,INFJ,32.34,0.18217468424418484,0.0,0.1,0.02,0.0,0.0,0.84,0.0,4.22,1,0,0,0 +3378,INFP,25.68,0.1835621478147107,0.08,0.28,0.02,0.0,0.1,0.44,0.0,0.98,1,0,0,1 +3379,INFP,19.74,0.18488545524219388,0.0,0.1,0.16,0.0,0.04,0.32,0.0,2.48,1,0,0,1 +3380,INTP,28.78,0.18018623706849707,0.02,0.32,0.06,0.0,0.0,1.18,0.0,1.44,1,0,1,1 +3381,INTP,23.36,0.17445880634499972,0.0,0.3,0.18,0.0,0.0,0.4,0.02,2.52,1,0,1,1 +3382,INFJ,29.02,0.19029226139701483,0.0,0.04,0.7,0.0,0.0,1.26,0.02,3.34,1,0,0,0 +3383,INFJ,24.02,0.1844424525936777,0.06,0.34,0.24,0.0,0.04,1.0,0.0,2.62,1,0,0,0 +3384,INFJ,31.48,0.18230742104996156,0.04,0.2,0.28,0.0,0.14,0.84,0.0,3.68,1,0,0,0 +3385,INFJ,22.46,0.1895471761636226,0.04,0.16,0.04,0.04,0.0,0.92,0.0,2.52,1,0,0,0 +3386,INFJ,29.54,0.18363277138283518,0.04,0.08,0.26,0.0,0.02,0.94,0.0,3.32,1,0,0,0 +3387,INFJ,32.1,0.18136336907560388,0.0,0.22,0.36,0.0,0.02,1.18,0.0,3.42,1,0,0,0 +3388,INTJ,27.1,0.17606528628732415,0.0,0.56,0.0,0.0,0.0,0.44,0.0,2.38,1,0,1,0 +3389,INFJ,26.4,0.19225605146633024,0.14,0.16,0.14,0.04,0.02,0.74,0.0,3.36,1,0,0,0 +3390,INFJ,19.14,0.19798116531192425,0.06,0.58,0.32,0.0,0.02,0.4,0.0,2.14,1,0,0,0 +3391,INFJ,16.2,0.20153926513283132,0.0,0.12,0.74,0.0,0.0,0.38,0.04,1.72,1,0,0,0 +3392,INFJ,15.8,0.18401981451294408,0.0,0.06,0.0,0.0,0.0,0.16,0.0,1.7,1,0,0,0 +3393,INFJ,31.36,0.18689887389451088,0.0,0.26,0.28,0.0,0.0,0.78,0.0,3.18,1,0,0,0 +3394,INFJ,20.88,0.19444526763469713,0.0,0.12,0.2,0.0,0.0,0.46,0.0,2.18,1,0,0,0 +3395,INFJ,28.0,0.1849889475364363,0.02,0.4,0.22,0.0,0.0,1.0,0.0,3.34,1,0,0,0 +3396,INFJ,20.32,0.18215278542143717,0.02,0.12,0.02,0.0,0.04,0.42,0.06,2.24,1,0,0,0 +3397,INFJ,30.26,0.1872369346996483,0.04,0.4,0.06,0.0,0.02,0.74,0.0,3.22,1,0,0,0 +3398,INFJ,33.02,0.18524867376268092,0.0,0.22,0.02,0.0,0.02,0.86,0.0,4.0,1,0,0,0 +3399,INFJ,28.98,0.19201487779624407,0.02,0.52,0.08,0.02,0.0,0.64,0.0,3.3,1,0,0,0 +3400,ENTP,24.34,0.19008770854851983,0.02,0.58,0.24,0.0,0.0,1.16,0.0,2.52,0,0,1,1 +3401,INFJ,27.18,0.1848700245975642,0.06,0.32,0.16,0.0,0.02,0.66,0.02,2.86,1,0,0,0 +3402,INFP,22.12,0.18538963370701655,0.0,0.32,0.38,0.0,0.0,0.92,0.0,2.36,1,0,0,1 +3403,INFP,27.3,0.18366035242382217,0.1,0.16,0.48,0.08,0.02,1.14,0.0,2.26,1,0,0,1 +3404,INFP,29.44,0.17947057242676703,0.0,0.1,0.66,0.0,0.0,0.72,0.0,3.64,1,0,0,1 +3405,INFP,22.14,0.19120198201062877,0.0,0.14,0.16,0.0,0.0,0.46,0.0,2.08,1,0,0,1 +3406,INFP,26.2,0.18694320987654314,0.06,0.24,0.68,0.0,0.0,0.58,0.0,2.96,1,0,0,1 +3407,INFP,26.66,0.19347001776217387,0.0,0.18,0.78,0.0,0.02,0.82,0.0,3.38,1,0,0,1 +3408,INFJ,20.72,0.19540245879660648,0.02,0.34,0.26,0.0,0.0,0.68,0.0,2.26,1,0,0,0 +3409,INFP,25.74,0.18275278252690297,0.0,0.14,0.72,0.0,0.0,0.76,0.02,2.02,1,0,0,1 +3410,INFP,22.6,0.1871790277915296,0.0,0.44,0.22,0.0,0.0,0.54,0.0,2.7,1,0,0,1 +3411,INFP,23.54,0.18951999095823863,0.0,0.08,0.2,0.0,0.04,0.36,0.0,2.96,1,0,0,1 +3412,INFP,16.3,0.1785552369224752,0.0,0.14,0.06,0.0,0.0,0.32,0.0,1.34,1,0,0,1 +3413,INFP,31.92,0.18720737457087677,0.0,0.18,0.06,0.0,0.02,1.2,0.0,4.36,1,0,0,1 +3414,INFP,26.02,0.17678455157536643,0.0,0.12,0.04,0.0,0.0,0.64,0.0,2.22,1,0,0,1 +3415,INFP,28.92,0.18764833268772108,0.04,0.26,0.12,0.0,0.02,0.82,0.0,3.08,1,0,0,1 +3416,INFP,8.52,0.18512232942007173,0.2,0.26,0.0,0.0,0.0,0.14,0.0,0.76,1,0,0,1 +3417,INFJ,24.36,0.18901593034226247,0.1,0.26,0.32,0.02,0.04,0.66,0.0,2.8,1,0,0,0 +3418,INTJ,16.26,0.17554227458536614,0.1,0.12,0.06,0.0,0.02,0.26,0.0,1.7,1,0,1,0 +3419,INFP,16.34,0.18618822146412087,0.6,0.62,0.18,0.0,0.0,0.5,0.0,1.94,1,0,0,1 +3420,INTJ,27.42,0.1869199567043896,0.0,0.12,0.02,0.0,0.0,0.62,0.0,3.8,1,0,1,0 +3421,INFP,30.56,0.18657126306971308,0.0,0.1,0.02,0.0,0.02,0.9,0.0,2.8,1,0,0,1 +3422,INTJ,26.88,0.18227830785540852,0.0,0.22,0.3,0.0,0.0,0.64,0.0,3.34,1,0,1,0 +3423,INTP,20.42,0.18183465203415716,0.1,0.26,0.08,0.0,0.0,0.48,0.0,2.26,1,0,1,1 +3424,ENTP,24.52,0.18361913137905475,0.08,0.04,0.22,0.08,0.0,0.66,0.0,2.52,0,0,1,1 +3425,INFP,17.32,0.18004358328082337,0.36,0.14,0.18,0.16,0.02,0.46,0.0,2.12,1,0,0,1 +3426,INFJ,28.44,0.1893063954173936,0.0,0.34,0.28,0.0,0.0,1.02,0.0,3.3,1,0,0,0 +3427,INFJ,32.08,0.18900161368337784,0.02,0.22,0.1,0.0,0.0,0.82,0.0,3.9,1,0,0,0 +3428,INFP,32.32,0.18488780553753054,0.02,0.1,0.56,0.02,0.0,1.0,0.0,3.88,1,0,0,1 +3429,INFP,25.5,0.18491480221712805,0.2,0.52,0.18,0.0,0.0,0.58,0.0,3.12,1,0,0,1 +3430,INFJ,26.18,0.18281674701625264,0.16,0.22,0.18,0.1,0.0,0.62,0.0,2.82,1,0,0,0 +3431,ENTP,27.04,0.18776288088642662,0.0,0.04,0.0,0.0,0.04,0.58,0.0,4.36,0,0,1,1 +3432,INTJ,26.6,0.17024673279455893,0.0,0.02,0.0,0.0,0.0,0.72,0.0,2.92,1,0,1,0 +3433,INTJ,27.68,0.18399414570598596,0.0,0.54,0.28,0.0,0.0,0.8,0.0,2.34,1,0,1,0 +3434,INFP,29.52,0.179614555191102,0.0,0.12,0.0,0.0,0.0,0.78,0.0,3.38,1,0,0,1 +3435,ENTP,19.98,0.18107713507450732,0.04,0.12,0.0,0.02,0.02,0.36,0.0,1.72,0,0,1,1 +3436,INFP,26.98,0.18446846179769416,0.0,0.28,0.04,0.0,0.0,0.56,0.0,4.12,1,0,0,1 +3437,ISFP,25.18,0.1869857695345688,0.14,0.14,0.12,0.0,0.02,0.5,0.0,3.22,1,1,0,1 +3438,INFJ,25.22,0.18497988095844295,0.14,0.3,0.08,0.02,0.0,0.68,0.02,2.16,1,0,0,0 +3439,INFJ,32.56,0.18747391757983525,0.02,0.18,0.2,0.0,0.0,1.48,0.04,3.88,1,0,0,0 +3440,ISTP,28.64,0.18597281531334406,0.02,0.16,0.0,0.0,0.0,0.62,0.02,1.26,1,1,1,1 +3441,INFP,33.98,0.18197772096420745,0.0,0.1,0.08,0.0,0.0,0.86,0.0,3.72,1,0,0,1 +3442,INTJ,9.62,0.17976585125556005,0.58,0.6,0.0,0.02,0.0,0.16,0.0,1.2,1,0,1,0 +3443,INFP,28.1,0.17968895917809322,0.0,0.22,0.4,0.0,0.0,0.44,0.0,3.08,1,0,0,1 +3444,ENFJ,23.4,0.17890480364542413,0.0,0.18,0.0,0.0,0.0,0.66,0.0,2.44,0,0,0,0 +3445,INFP,22.26,0.18665970334417292,0.12,0.1,0.14,0.1,0.04,0.68,0.0,2.86,1,0,0,1 +3446,ENFP,21.96,0.1806677463777435,0.16,0.36,0.0,0.1,0.04,0.46,0.0,2.46,0,0,0,1 +3447,ENFP,29.26,0.1966979720018315,0.02,0.42,0.2,0.0,0.0,0.74,0.0,3.06,0,0,0,1 +3448,INFJ,17.8,0.18207950234848871,0.2,0.24,0.12,0.0,0.0,0.28,0.0,2.0,1,0,0,0 +3449,INTJ,25.52,0.18141588864119915,0.04,0.12,0.18,0.0,0.0,0.68,0.0,3.08,1,0,1,0 +3450,INTP,16.54,0.18885829205230725,0.18,0.32,0.1,0.0,0.02,0.26,0.0,1.78,1,0,1,1 +3451,INTP,31.42,0.17682143313019175,0.0,0.0,0.06,0.0,0.0,0.78,0.0,4.04,1,0,1,1 +3452,INTP,14.74,0.1861021149085639,0.32,0.46,0.16,0.22,0.0,0.84,0.0,1.52,1,0,1,1 +3453,INFP,19.68,0.19127140540498602,0.02,0.4,0.06,0.0,0.06,0.3,0.0,2.8,1,0,0,1 +3454,ENTJ,25.24,0.18434023692626458,0.0,0.54,0.44,0.0,0.0,0.52,0.0,2.12,0,0,1,0 +3455,INFP,20.4,0.1881138447012556,0.04,0.12,0.12,0.0,0.02,0.54,0.0,2.08,1,0,0,1 +3456,INTP,31.38,0.18008680849994657,0.0,0.2,0.18,0.0,0.0,0.96,0.0,3.26,1,0,1,1 +3457,INFP,16.48,0.18614978929699327,0.02,0.12,0.08,0.0,0.06,0.34,0.0,1.72,1,0,0,1 +3458,INFJ,6.6,0.1934698261137,0.06,0.1,0.02,0.02,0.0,0.06,0.0,0.68,1,0,0,0 +3459,ENTP,26.66,0.1822983913045546,0.02,0.18,0.06,0.0,0.0,0.64,0.0,2.74,0,0,1,1 +3460,INTJ,30.46,0.1803438619286349,0.08,0.4,0.02,0.0,0.02,0.9,0.0,3.12,1,0,1,0 +3461,INTJ,25.68,0.1916579596952673,0.02,0.12,0.32,0.0,0.02,0.6,0.0,4.0,1,0,1,0 +3462,INTJ,17.16,0.18470278410908103,0.0,0.24,0.08,0.0,0.0,0.42,0.0,2.04,1,0,1,0 +3463,INTJ,24.08,0.1785929892067862,0.0,0.34,0.04,0.0,0.0,0.56,0.0,1.78,1,0,1,0 +3464,INTJ,29.42,0.1712255645368034,0.0,0.22,0.04,0.0,0.0,0.6,0.0,2.66,1,0,1,0 +3465,INTJ,25.88,0.19509075531427394,0.0,0.12,0.08,0.0,0.0,1.42,0.0,3.1,1,0,1,0 +3466,ISTP,25.3,0.18292246163598885,0.0,0.12,0.02,0.0,0.0,0.46,0.0,2.48,1,1,1,1 +3467,INTP,8.52,0.18135914239282155,0.02,0.06,0.0,0.02,0.0,0.12,0.0,0.82,1,0,1,1 +3468,INTP,7.68,0.18345426423369096,0.0,0.06,0.0,0.0,0.02,0.24,0.0,0.78,1,0,1,1 +3469,INTP,25.18,0.19194901571515946,0.0,0.22,0.02,0.0,0.0,0.68,0.0,2.9,1,0,1,1 +3470,INTP,22.72,0.17878709499771855,0.04,0.18,0.02,0.0,0.06,0.58,0.0,2.72,1,0,1,1 +3471,INTP,19.48,0.18973176826192095,0.0,0.26,0.02,0.0,0.0,0.5,0.0,1.74,1,0,1,1 +3472,INTP,27.26,0.18122635233521386,0.02,0.18,0.08,0.0,0.0,0.66,0.06,3.52,1,0,1,1 +3473,INTP,28.04,0.18073533057851235,0.1,0.22,0.16,0.08,0.0,0.88,0.0,2.48,1,0,1,1 +3474,INTP,24.9,0.19291004657689817,0.0,0.16,0.14,0.0,0.0,0.82,0.0,2.12,1,0,1,1 +3475,INTP,26.48,0.1847298842621851,0.08,0.18,0.1,0.06,0.0,0.66,0.0,3.12,1,0,1,1 +3476,INFP,31.74,0.17939231524925797,0.02,0.16,0.0,0.0,0.02,0.92,0.02,3.76,1,0,0,1 +3477,INFJ,23.58,0.1961424589207959,0.0,0.1,0.38,0.0,0.02,0.94,0.0,2.48,1,0,0,0 +3478,INFJ,30.84,0.17315020112312243,0.0,0.12,0.08,0.0,0.0,0.72,0.0,2.94,1,0,0,0 +3479,INTJ,27.24,0.1933985821157575,0.0,0.22,0.84,0.0,0.0,1.52,0.0,1.88,1,0,1,0 +3480,INFJ,27.58,0.1859104591607546,0.0,0.2,0.0,0.0,0.0,0.68,0.0,2.8,1,0,0,0 +3481,ISFJ,16.96,0.1808906366245085,0.0,0.1,0.08,0.0,0.0,0.28,0.0,1.78,1,1,0,0 +3482,INFJ,22.24,0.19330717646571954,0.0,0.12,0.56,0.0,0.0,0.76,0.0,2.8,1,0,0,0 +3483,ENFP,29.22,0.1828586863085672,0.04,0.2,0.58,0.02,0.0,0.84,0.0,4.04,0,0,0,1 +3484,ENTP,14.38,0.18518199168315938,0.0,0.06,0.0,0.0,0.0,0.34,0.0,1.26,0,0,1,1 +3485,INFP,24.44,0.1840797717636574,0.0,0.24,0.24,0.0,0.02,1.52,0.0,2.3,1,0,0,1 +3486,INFP,35.76,0.18597088663927372,0.0,0.0,0.02,0.0,0.02,1.02,0.0,4.04,1,0,0,1 +3487,ENTP,23.56,0.18837718210672488,0.0,0.18,0.54,0.0,0.0,0.7,0.0,2.58,0,0,1,1 +3488,INTP,24.56,0.18250469541443665,0.02,0.16,0.12,0.0,0.02,0.48,0.0,3.16,1,0,1,1 +3489,ENFP,25.42,0.18670217263629915,0.0,0.2,0.06,0.0,0.0,0.72,0.0,2.54,0,0,0,1 +3490,INFP,28.24,0.18250222778118744,0.18,0.12,0.04,0.1,0.04,0.74,0.0,3.44,1,0,0,1 +3491,INTJ,22.62,0.18049323906897802,0.16,0.22,0.08,0.1,0.0,0.58,0.0,2.14,1,0,1,0 +3492,INTJ,30.94,0.18168571429472177,0.0,0.12,0.02,0.0,0.04,1.22,0.0,3.52,1,0,1,0 +3493,INTP,4.26,0.17662336137167686,0.0,0.04,0.0,0.0,0.0,0.12,0.0,0.22,1,0,1,1 +3494,INFP,16.4,0.176409008601177,0.4,0.34,0.1,0.08,0.0,0.28,0.0,2.06,1,0,0,1 +3495,ESFJ,23.96,0.19031386050258697,0.16,0.22,0.08,0.16,0.0,0.42,0.0,3.46,0,1,0,0 +3496,INFJ,34.5,0.17866148075238897,0.0,0.18,0.04,0.0,0.0,1.12,0.0,4.1,1,0,0,0 +3497,INFJ,28.22,0.1829403203395693,0.0,0.26,0.0,0.0,0.0,0.76,0.0,3.36,1,0,0,0 +3498,ENFJ,25.76,0.1901645692615291,0.0,0.16,0.04,0.0,0.0,0.58,0.02,2.98,0,0,0,0 +3499,INTP,15.64,0.19292995470572422,0.08,0.68,0.4,0.04,0.0,0.28,0.0,1.8,1,0,1,1 +3500,INFJ,15.16,0.18473404094893378,0.04,0.1,0.08,0.0,0.0,0.26,0.0,0.7,1,0,0,0 +3501,INTP,15.3,0.18384222448224855,0.06,0.1,0.02,0.02,0.0,0.32,0.0,1.44,1,0,1,1 +3502,INFJ,20.72,0.19114587841932135,0.0,0.04,0.14,0.0,0.0,0.82,0.2,2.9,1,0,0,0 +3503,ENTP,30.2,0.1876639539030399,0.0,0.14,0.38,0.0,0.06,0.8,0.0,4.1,0,0,1,1 +3504,INTP,19.64,0.17680221926599424,0.08,0.16,0.08,0.04,0.0,0.34,0.0,2.5,1,0,1,1 +3505,ISFP,30.62,0.18309077848483024,0.04,0.18,0.28,0.0,0.02,0.66,0.0,3.56,1,1,0,1 +3506,INTJ,24.78,0.18338711117638462,0.16,0.5,0.08,0.08,0.0,0.58,0.0,2.48,1,0,1,0 +3507,INFJ,20.32,0.18611553147409002,0.0,0.42,0.06,0.0,0.0,0.32,0.0,1.86,1,0,0,0 +3508,INFJ,2.82,0.18928817613301327,1.0,1.06,0.0,0.0,0.0,0.04,0.0,0.86,1,0,0,0 +3509,INFJ,25.36,0.1887314969120674,0.0,0.2,0.14,0.0,0.02,0.66,0.0,3.64,1,0,0,0 +3510,INFP,26.22,0.18967031497220982,0.0,0.2,0.08,0.0,0.0,0.6,0.0,3.32,1,0,0,1 +3511,INFP,25.86,0.18478851767200907,0.0,0.1,0.14,0.0,0.06,0.72,0.0,1.92,1,0,0,1 +3512,INFJ,20.32,0.18376763799094736,0.2,0.34,0.18,0.02,0.02,0.7,0.0,2.0,1,0,0,0 +3513,INFP,14.12,0.18601663102142518,0.08,0.12,0.04,0.0,0.0,0.26,0.0,1.66,1,0,0,1 +3514,INTJ,24.54,0.1908618143153585,0.02,0.34,0.04,0.0,0.02,0.48,0.0,2.42,1,0,1,0 +3515,ISFJ,12.92,0.18322291537554292,0.16,0.22,0.0,0.06,0.0,0.26,0.0,1.44,1,1,0,0 +3516,INFP,11.46,0.18882785434933455,0.14,0.16,0.12,0.0,0.0,0.26,0.0,1.72,1,0,0,1 +3517,INTJ,16.2,0.19275458387682912,0.06,0.28,0.06,0.06,0.0,0.86,0.0,1.54,1,0,1,0 +3518,INTP,30.08,0.18783853337813963,0.0,0.08,0.08,0.0,0.02,0.72,0.0,3.8,1,0,1,1 +3519,INTJ,23.86,0.1787054947390796,0.12,0.42,0.02,0.06,0.02,0.52,0.0,2.4,1,0,1,0 +3520,INFP,24.88,0.18274539395107658,0.0,0.12,0.24,0.0,0.0,0.5,0.0,3.46,1,0,0,1 +3521,ISTJ,29.66,0.18633600071936812,0.06,0.26,0.28,0.02,0.0,0.84,0.0,3.58,1,1,1,0 +3522,ISTP,31.14,0.18868952686516632,0.0,0.14,0.3,0.0,0.0,0.76,0.0,3.42,1,1,1,1 +3523,ISTP,33.66,0.19980735234027272,0.0,0.14,0.18,0.0,0.0,1.48,0.0,4.36,1,1,1,1 +3524,ISTP,23.9,0.18527626315180945,0.0,0.14,0.12,0.0,0.0,0.68,0.0,2.9,1,1,1,1 +3525,ISTP,18.86,0.18664221361360298,0.0,0.14,0.02,0.0,0.0,0.32,0.02,2.32,1,1,1,1 +3526,INFJ,24.42,0.18419334330332227,0.0,0.2,0.02,0.0,0.02,0.44,0.0,3.02,1,0,0,0 +3527,ISTP,26.9,0.18616718682909542,0.0,0.08,0.0,0.0,0.06,0.82,0.0,3.32,1,1,1,1 +3528,ENTP,30.84,0.18445272353424033,0.02,0.28,0.22,0.0,0.0,0.72,0.0,3.62,0,0,1,1 +3529,ESFJ,26.24,0.19155953192635197,0.0,0.04,0.64,0.0,0.0,0.66,0.0,2.38,0,1,0,0 +3530,INFJ,28.22,0.18162023874011202,0.14,0.36,0.08,0.0,0.0,0.86,0.0,1.94,1,0,0,0 +3531,INFJ,33.26,0.1832224828957101,0.0,0.04,0.0,0.0,0.02,0.84,0.0,3.6,1,0,0,0 +3532,INTP,21.2,0.1821658410468846,0.1,0.06,0.62,0.06,0.0,0.38,0.0,2.6,1,0,1,1 +3533,INFJ,19.88,0.18897660455545245,0.02,0.3,0.26,0.0,0.0,0.66,0.0,2.54,1,0,0,0 +3534,INFP,29.3,0.18489674060922057,0.04,0.14,0.02,0.0,0.08,0.76,0.0,2.7,1,0,0,1 +3535,INFP,28.1,0.17538032378765966,0.0,0.12,0.22,0.0,0.0,0.7,0.0,2.62,1,0,0,1 +3536,ISFP,26.12,0.19604152863997917,0.0,0.24,1.32,0.0,0.0,0.82,0.04,3.94,1,1,0,1 +3537,ENTP,15.44,0.1807005624954343,0.08,0.18,0.12,0.0,0.0,0.3,0.0,1.68,0,0,1,1 +3538,ESTP,25.12,0.18848947529969695,0.0,0.54,0.06,0.0,0.0,0.66,0.0,2.12,0,1,1,1 +3539,INFP,29.14,0.1812177519509946,0.04,0.1,0.4,0.0,0.08,0.68,0.0,2.88,1,0,0,1 +3540,INFP,24.5,0.18313462476907716,0.0,0.12,0.16,0.0,0.0,0.62,0.08,2.76,1,0,0,1 +3541,INFJ,32.62,0.18456376098149638,0.0,0.18,0.44,0.0,0.04,0.94,0.0,3.84,1,0,0,0 +3542,INFP,14.96,0.17879192140698713,0.06,0.14,0.16,0.0,0.0,0.5,0.0,1.9,1,0,0,1 +3543,INTP,28.36,0.17881606316502638,0.02,0.14,0.04,0.0,0.0,0.94,0.0,2.16,1,0,1,1 +3544,INFJ,29.82,0.17764929225822734,0.0,0.4,0.0,0.0,0.0,0.62,0.0,2.9,1,0,0,0 +3545,INFP,29.5,0.1833977813368746,0.0,0.22,0.36,0.0,0.0,0.78,0.0,2.26,1,0,0,1 +3546,INTP,26.66,0.1853548749093487,0.0,0.02,0.4,0.0,0.02,0.68,0.0,3.16,1,0,1,1 +3547,INFJ,32.74,0.18019726011708959,0.02,0.22,0.0,0.0,0.0,0.98,0.0,4.44,1,0,0,0 +3548,ENFP,27.42,0.18735945410146254,0.0,0.36,0.28,0.0,0.0,0.88,0.0,2.78,0,0,0,1 +3549,ISFP,25.16,0.18657090538210935,0.06,0.28,0.34,0.0,0.02,0.42,0.0,2.78,1,1,0,1 +3550,ENFP,19.34,0.21446497594445693,0.04,0.2,4.0,0.0,0.0,0.66,0.0,2.32,0,0,0,1 +3551,INFP,15.6,0.18785063044438835,0.5,0.5,0.04,0.0,0.02,0.7,0.0,1.9,1,0,0,1 +3552,INFP,17.8,0.18743338662876097,0.12,0.18,0.22,0.04,0.02,0.44,0.0,0.84,1,0,0,1 +3553,INTJ,29.2,0.18366502257874548,0.1,0.3,0.0,0.0,0.02,0.88,0.0,3.14,1,0,1,0 +3554,INTJ,16.88,0.1920404584091513,0.14,0.42,0.16,0.0,0.02,0.3,0.0,2.16,1,0,1,0 +3555,INTP,24.74,0.19468489358860738,0.02,0.26,0.06,0.0,0.06,0.82,0.02,3.44,1,0,1,1 +3556,INTP,17.18,0.18074792243767313,0.0,0.12,0.18,0.0,0.0,0.48,0.0,1.42,1,0,1,1 +3557,INTJ,20.3,0.1858774618072498,0.16,0.16,0.08,0.04,0.0,0.4,0.0,2.6,1,0,1,0 +3558,INTP,19.74,0.1839765090494136,0.0,0.34,0.0,0.0,0.0,0.58,0.02,2.68,1,0,1,1 +3559,INFP,0.08,0.2092951677439212,0.02,0.02,0.0,0.0,0.0,0.0,0.0,0.0,1,0,0,1 +3560,ENTP,17.64,0.18344384767742775,0.0,0.06,0.0,0.0,0.0,0.24,0.0,1.94,0,0,1,1 +3561,ENFP,2.48,0.18626328085282312,0.0,0.0,0.04,0.0,0.0,0.1,0.0,0.18,0,0,0,1 +3562,ISFP,24.12,0.1888738785352976,0.04,0.14,0.56,0.0,0.02,0.6,0.0,3.3,1,1,0,1 +3563,INFP,14.5,0.19211675308992868,0.1,0.38,0.26,0.02,0.0,0.52,0.0,1.68,1,0,0,1 +3564,INTJ,12.22,0.1861607523133177,0.34,0.24,0.14,0.16,0.0,0.24,0.02,0.64,1,0,1,0 +3565,INTP,23.38,0.17942271942407256,0.5,0.64,0.48,0.02,0.02,0.56,0.0,2.92,1,0,1,1 +3566,ISFP,28.52,0.186552595218746,0.14,0.22,0.12,0.02,0.06,1.16,0.0,2.98,1,1,0,1 +3567,INFJ,33.38,0.18062720114423825,0.0,0.28,0.1,0.0,0.0,1.04,0.02,3.9,1,0,0,0 +3568,INTP,31.5,0.18718503085031268,0.02,0.16,0.68,0.0,0.0,0.76,0.0,4.42,1,0,1,1 +3569,INTJ,27.18,0.18265561331039049,0.02,0.08,0.08,0.0,0.0,0.68,0.0,2.62,1,0,1,0 +3570,ENFP,32.0,0.17720435418039981,0.0,0.04,0.18,0.0,0.0,0.7,0.0,3.44,0,0,0,1 +3571,ISTP,14.18,0.18696236806719593,0.16,0.26,0.08,0.04,0.02,0.14,0.0,1.22,1,1,1,1 +3572,INFJ,26.94,0.18713457338732656,0.02,0.16,0.46,0.0,0.0,1.06,0.0,3.26,1,0,0,0 +3573,ESTP,10.74,0.1948692030010259,0.02,0.22,0.06,0.0,0.0,0.46,0.0,1.42,0,1,1,1 +3574,INTP,24.22,0.17889847485741656,0.14,0.14,0.0,0.04,0.06,0.42,0.0,2.8,1,0,1,1 +3575,INFJ,25.34,0.18556213017751477,0.1,0.14,0.26,0.02,0.02,1.08,0.0,1.8,1,0,0,0 +3576,INFJ,27.46,0.18855241134797868,0.0,0.06,0.1,0.0,0.02,0.82,0.0,3.72,1,0,0,0 +3577,INTP,23.98,0.17861832097029234,0.06,0.2,0.04,0.0,0.0,0.62,0.02,2.54,1,0,1,1 +3578,INFJ,32.3,0.1830882651614364,0.06,0.14,0.34,0.06,0.0,0.8,0.0,3.56,1,0,0,0 +3579,ISTP,26.62,0.18403234537191857,0.06,0.16,0.1,0.0,0.0,0.58,0.0,3.76,1,1,1,1 +3580,INFP,16.54,0.191497460723187,0.0,0.02,0.0,0.0,0.02,0.46,0.0,1.94,1,0,0,1 +3581,ISFJ,11.84,0.19493687365189427,0.0,0.14,0.1,0.0,0.0,0.22,0.0,1.4,1,1,0,0 +3582,ISTP,32.18,0.1842152139209401,0.04,0.08,0.0,0.0,0.0,0.96,0.0,2.6,1,1,1,1 +3583,INFP,30.96,0.18569883561795747,0.04,0.14,0.34,0.02,0.02,1.24,0.0,4.22,1,0,0,1 +3584,INFJ,23.06,0.17551981386520074,0.06,0.16,0.0,0.0,0.0,0.62,0.0,2.52,1,0,0,0 +3585,INTJ,18.44,0.17753666807181115,0.02,0.2,0.02,0.02,0.02,0.2,0.0,1.56,1,0,1,0 +3586,INTP,27.72,0.18994766423128667,0.02,0.12,0.06,0.02,0.0,1.06,0.0,3.34,1,0,1,1 +3587,INFP,12.68,0.19046735006728838,0.02,0.08,0.08,0.02,0.02,0.26,0.0,1.54,1,0,0,1 +3588,INFJ,30.04,0.18336142955163756,0.06,0.32,0.14,0.0,0.02,0.86,0.0,3.44,1,0,0,0 +3589,ENTP,25.12,0.1790743489383732,0.16,0.24,0.1,0.12,0.0,0.84,0.04,2.52,0,0,1,1 +3590,ENTP,23.34,0.17969349759587044,0.04,0.28,0.1,0.0,0.0,0.46,0.0,2.0,0,0,1,1 +3591,INTP,29.28,0.1777637085301607,0.04,0.18,0.02,0.0,0.0,0.8,0.0,3.24,1,0,1,1 +3592,ENTJ,29.82,0.17929181746358544,0.04,0.18,0.16,0.0,0.04,0.78,0.02,3.74,0,0,1,0 +3593,ISTP,11.58,0.18330283521200846,0.4,0.18,0.04,0.1,0.0,0.28,0.0,1.44,1,1,1,1 +3594,INTP,21.24,0.17852914285938357,0.04,0.3,0.02,0.0,0.0,0.36,0.0,2.72,1,0,1,1 +3595,ENTP,21.22,0.17232056358234954,0.24,0.3,0.04,0.12,0.04,0.34,0.0,1.48,0,0,1,1 +3596,INFJ,28.14,0.18399034134033543,0.04,0.22,0.1,0.0,0.0,0.76,0.0,3.98,1,0,0,0 +3597,ENFJ,27.86,0.19760600847200338,0.0,0.38,0.28,0.0,0.0,1.6,0.0,2.9,0,0,0,0 +3598,ISTP,20.94,0.18458416289791627,0.12,0.28,0.26,0.0,0.0,0.64,0.0,2.56,1,1,1,1 +3599,INFP,30.68,0.18663573012026435,0.0,0.08,0.22,0.0,0.0,1.36,0.0,3.88,1,0,0,1 +3600,ENTP,21.22,0.1895517119069964,0.02,0.18,0.28,0.0,0.04,0.52,0.0,1.26,0,0,1,1 +3601,ENFP,29.54,0.18530853691970947,0.0,0.16,0.02,0.0,0.06,0.62,0.0,4.26,0,0,0,1 +3602,INTP,19.54,0.17937306244684587,0.2,0.38,0.0,0.0,0.02,0.52,0.0,2.4,1,0,1,1 +3603,ISFJ,31.4,0.1858115812491515,0.0,0.38,0.16,0.0,0.0,0.64,0.0,3.94,1,1,0,0 +3604,INTP,25.54,0.1868495867430689,0.0,0.22,0.08,0.0,0.0,0.66,0.0,3.46,1,0,1,1 +3605,INFP,25.36,0.1827930224230829,0.02,0.58,0.08,0.0,0.0,0.42,0.0,2.12,1,0,0,1 +3606,INTP,32.08,0.18583349674705157,0.0,0.16,0.02,0.0,0.0,0.88,0.0,2.86,1,0,1,1 +3607,INFP,17.84,0.18841257180438778,0.04,0.24,0.04,0.0,0.0,0.2,0.0,2.26,1,0,0,1 +3608,INFJ,34.1,0.18014514588452105,0.0,0.18,0.08,0.0,0.0,1.28,0.0,3.96,1,0,0,0 +3609,ISTP,29.2,0.1818871462150286,0.08,0.26,0.04,0.02,0.02,0.72,0.04,2.94,1,1,1,1 +3610,INFP,24.4,0.18841840437090848,0.12,0.22,0.02,0.02,0.08,0.52,0.0,3.24,1,0,0,1 +3611,INFJ,21.62,0.1821388840256447,0.12,0.16,0.02,0.0,0.0,0.5,0.0,2.98,1,0,0,0 +3612,INFJ,20.64,0.17978958325058444,0.04,0.22,0.04,0.0,0.06,0.5,0.0,1.88,1,0,0,0 +3613,INFJ,28.1,0.1848878571819357,0.16,0.12,0.34,0.06,0.0,1.02,0.0,3.14,1,0,0,0 +3614,INTP,26.02,0.1771958711300693,0.0,0.12,0.28,0.0,0.0,0.76,0.0,2.8,1,0,1,1 +3615,INTP,32.4,0.1798503027996448,0.02,0.04,0.0,0.0,0.0,1.06,0.0,3.6,1,0,1,1 +3616,INTJ,3.9,0.16836734693877556,0.84,0.64,0.1,0.14,0.0,0.1,0.0,0.52,1,0,1,0 +3617,INTJ,3.94,0.18988073224852067,0.18,0.3,0.08,0.02,0.0,0.1,0.0,0.86,1,0,1,0 +3618,INTJ,26.54,0.18695698932096577,0.02,0.4,0.08,0.0,0.0,0.7,0.02,2.8,1,0,1,0 +3619,ISTP,30.88,0.18517714998671142,0.0,0.12,0.08,0.0,0.02,0.8,0.0,3.86,1,1,1,1 +3620,ENTP,23.42,0.187882345618126,0.0,0.38,0.08,0.0,0.0,0.66,0.0,2.14,0,0,1,1 +3621,INTP,24.72,0.18685136307669176,0.0,0.24,0.02,0.0,0.02,0.58,0.0,3.04,1,0,1,1 +3622,INTJ,31.06,0.1880014230776581,0.04,0.26,0.0,0.0,0.0,0.84,0.0,2.38,1,0,1,0 +3623,INTP,30.44,0.17809687419156695,0.0,0.14,0.02,0.0,0.0,0.76,0.0,2.48,1,0,1,1 +3624,ISFP,14.92,0.1794551330415975,0.56,0.48,0.14,0.06,0.06,0.5,0.0,1.7,1,1,0,1 +3625,ENTJ,29.2,0.18345049890494297,0.04,0.26,0.3,0.0,0.02,0.88,0.02,2.9,0,0,1,0 +3626,ENFP,21.62,0.19334340632456826,0.0,0.2,0.18,0.0,0.0,1.22,0.02,2.06,0,0,0,1 +3627,ENTJ,26.92,0.19431340002263447,0.0,0.34,0.36,0.0,0.0,1.3,0.0,2.82,0,0,1,0 +3628,ENTP,16.84,0.19106209077286,0.18,0.26,0.0,0.02,0.0,0.46,0.0,1.82,0,0,1,1 +3629,INFJ,13.0,0.200939820071622,0.1,0.28,0.96,0.0,0.0,0.54,0.0,1.84,1,0,0,0 +3630,INFP,23.98,0.18575899356545542,0.14,0.3,0.18,0.0,0.0,0.92,0.0,3.38,1,0,0,1 +3631,ENTP,32.86,0.18456833511327408,0.06,0.28,0.06,0.06,0.0,1.26,0.0,3.76,0,0,1,1 +3632,ISTP,23.52,0.18270592710025726,0.24,0.34,0.02,0.0,0.0,0.74,0.0,1.84,1,1,1,1 +3633,ENFP,22.2,0.1929385883632929,0.0,0.12,0.68,0.0,0.0,1.1,0.0,3.22,0,0,0,1 +3634,INFJ,32.3,0.17188475283126708,0.02,0.3,0.0,0.0,0.0,0.86,0.0,2.22,1,0,0,0 +3635,ISTJ,23.12,0.18674380603019902,0.58,0.58,0.14,0.02,0.06,1.52,0.0,1.68,1,1,1,0 +3636,INFP,24.88,0.1778964357307226,0.04,0.28,0.28,0.0,0.0,0.5,0.0,2.42,1,0,0,1 +3637,ENFP,32.68,0.18065135516137912,0.0,0.1,0.04,0.0,0.0,0.88,0.0,2.44,0,0,0,1 +3638,ENTP,33.82,0.18726506233510695,0.0,0.14,0.1,0.0,0.0,1.08,0.0,4.14,0,0,1,1 +3639,INFJ,33.5,0.1826182258479324,0.0,0.42,0.02,0.0,0.0,0.84,0.0,4.02,1,0,0,0 +3640,ENFJ,23.88,0.18030686953568048,0.02,0.04,0.12,0.0,0.04,0.56,0.0,2.26,0,0,0,0 +3641,ISTJ,23.32,0.18850227471056877,0.16,0.3,0.14,0.02,0.02,0.5,0.0,2.68,1,1,1,0 +3642,INTJ,22.32,0.18597071585310812,0.02,0.36,0.06,0.0,0.02,0.4,0.0,2.4,1,0,1,0 +3643,INFJ,25.66,0.18461492632851925,0.1,0.42,0.22,0.02,0.0,0.5,0.0,2.56,1,0,0,0 +3644,INTP,24.68,0.18716298254664634,0.16,0.36,0.34,0.0,0.0,0.92,0.0,1.72,1,0,1,1 +3645,INFP,27.18,0.1852843211879628,0.0,0.18,0.3,0.0,0.02,0.74,0.0,0.98,1,0,0,1 +3646,INTP,27.96,0.18200732614757661,0.02,0.18,0.12,0.02,0.0,0.84,0.02,2.0,1,0,1,1 +3647,ENTJ,28.86,0.18219888445471305,0.0,0.12,0.0,0.0,0.0,0.8,0.0,3.02,0,0,1,0 +3648,ISFJ,23.72,0.18115415433634804,0.08,0.14,0.1,0.0,0.0,0.5,0.0,3.3,1,1,0,0 +3649,ISFP,20.96,0.17905573168608466,0.02,0.2,0.02,0.0,0.0,0.44,0.0,2.86,1,1,0,1 +3650,ISFJ,33.34,0.184241498473019,0.0,0.12,0.28,0.0,0.0,0.96,0.0,4.64,1,1,0,0 +3651,INTJ,22.72,0.19218578847012047,0.02,0.14,0.14,0.0,0.0,0.56,0.0,2.58,1,0,1,0 +3652,INFJ,30.2,0.185794104464845,0.02,0.18,0.28,0.0,0.0,0.8,0.0,3.18,1,0,0,0 +3653,ENTP,27.58,0.18318463409517757,0.16,0.34,0.48,0.0,0.0,0.8,0.0,2.6,0,0,1,1 +3654,INFJ,31.12,0.18859276928650054,0.0,0.12,0.04,0.0,0.0,1.0,0.0,3.9,1,0,0,0 +3655,INFJ,34.74,0.18882298838946796,0.04,0.04,0.22,0.02,0.0,1.22,0.0,4.54,1,0,0,0 +3656,ENTP,21.74,0.18325055832989867,0.1,0.08,0.2,0.0,0.02,0.56,0.0,2.6,0,0,1,1 +3657,INFJ,31.28,0.18758552855652388,0.06,0.16,0.04,0.0,0.04,0.88,0.0,4.04,1,0,0,0 +3658,ENFJ,31.56,0.18960068945705258,0.0,0.22,0.56,0.0,0.0,1.5,0.0,4.0,0,0,0,0 +3659,INTP,23.54,0.18415862381167947,0.04,0.22,0.02,0.0,0.0,0.72,0.0,1.92,1,0,1,1 +3660,ENFP,22.18,0.1890160139089856,0.1,0.28,0.84,0.02,0.0,0.46,0.0,2.78,0,0,0,1 +3661,ISFJ,20.22,0.18390407160531322,0.0,0.18,0.1,0.0,0.0,0.38,0.0,1.26,1,1,0,0 +3662,ENFP,20.68,0.18885589187993615,0.0,0.1,0.56,0.0,0.0,0.76,0.0,2.9,0,0,0,1 +3663,INFP,19.92,0.18466665607058602,0.04,0.1,0.08,0.02,0.0,0.62,0.0,3.18,1,0,0,1 +3664,ISTP,22.78,0.19204952371494402,0.0,0.22,0.3,0.0,0.0,0.68,0.0,2.66,1,1,1,1 +3665,ENTP,20.86,0.19694920034979102,0.02,0.06,0.06,0.0,0.0,0.28,0.0,1.6,0,0,1,1 +3666,INTJ,25.62,0.1770331115988843,0.0,0.14,0.08,0.0,0.0,0.7,0.0,2.26,1,0,1,0 +3667,INTJ,30.44,0.1844632874850169,0.0,0.08,0.1,0.0,0.0,0.76,0.0,3.32,1,0,1,0 +3668,INFP,27.6,0.18558328186791556,0.0,0.14,0.22,0.0,0.0,0.7,0.0,3.8,1,0,0,1 +3669,INTP,26.88,0.17717683170993923,0.04,0.46,0.1,0.02,0.0,0.74,0.0,2.38,1,0,1,1 +3670,INFP,35.82,0.18202978321133156,0.02,0.2,0.06,0.0,0.0,0.88,0.0,3.38,1,0,0,1 +3671,INTJ,20.56,0.18100599101409176,0.06,0.28,0.0,0.02,0.0,0.3,0.0,2.26,1,0,1,0 +3672,INFJ,31.88,0.18719877833515328,0.0,0.14,0.14,0.0,0.0,0.84,0.0,3.72,1,0,0,0 +3673,ENFP,30.36,0.18142348505093464,0.04,0.12,0.18,0.02,0.0,0.72,0.0,1.44,0,0,0,1 +3674,ENFP,24.44,0.18074208564570265,0.06,0.08,0.16,0.04,0.02,0.4,0.0,2.78,0,0,0,1 +3675,ENTJ,28.28,0.17457240380290115,0.02,0.22,0.14,0.0,0.0,0.56,0.0,3.38,0,0,1,0 +3676,ENFP,20.06,0.1962335511248746,0.0,0.04,0.14,0.0,0.02,0.74,0.0,2.9,0,0,0,1 +3677,ENFP,31.04,0.19204389574759947,0.0,0.12,0.48,0.0,0.0,0.84,0.0,3.32,0,0,0,1 +3678,INFP,25.02,0.18820441331603813,0.04,0.1,0.52,0.0,0.08,0.78,0.0,2.82,1,0,0,1 +3679,ENTJ,26.64,0.18473304858841572,0.0,0.32,0.02,0.0,0.04,0.66,0.0,3.06,0,0,1,0 +3680,ENTP,21.86,0.19020356450743034,0.0,0.24,0.58,0.0,0.0,0.72,0.0,2.48,0,0,1,1 +3681,ESTP,28.6,0.19063886813241376,0.14,0.38,1.44,0.0,0.0,0.66,0.0,3.32,0,1,1,1 +3682,INTP,21.58,0.17831625697410824,0.2,0.46,0.04,0.02,0.0,0.46,0.02,1.72,1,0,1,1 +3683,INFP,28.62,0.20034793531083817,0.0,0.18,0.72,0.0,0.0,2.28,0.02,3.58,1,0,0,1 +3684,ISFP,29.82,0.1855506602825887,0.0,0.36,0.06,0.0,0.0,0.8,0.02,2.66,1,1,0,1 +3685,INFP,22.1,0.18446811683653586,0.0,0.18,0.14,0.0,0.02,0.74,0.0,2.3,1,0,0,1 +3686,INFP,28.78,0.18908211893058083,0.04,0.36,0.86,0.0,0.0,0.82,0.02,3.0,1,0,0,1 +3687,INFP,27.84,0.18654687145461235,0.1,0.18,0.04,0.02,0.0,0.9,0.0,3.1,1,0,0,1 +3688,INFJ,26.28,0.18666923602428714,0.06,0.48,0.04,0.02,0.0,0.72,0.0,2.4,1,0,0,0 +3689,ENFP,24.36,0.1866455266041086,0.02,0.22,0.62,0.0,0.08,0.56,0.0,2.94,0,0,0,1 +3690,INFJ,26.82,0.1867486799341059,0.06,0.3,0.1,0.0,0.02,0.58,0.0,2.72,1,0,0,0 +3691,INFP,10.98,0.1822614162830546,0.72,0.58,0.04,0.14,0.0,0.5,0.0,1.78,1,0,0,1 +3692,INTP,26.22,0.20036991603619617,0.02,0.1,0.14,0.02,0.0,3.92,0.02,3.14,1,0,1,1 +3693,INTP,28.56,0.17790771851178266,0.04,0.04,0.02,0.0,0.02,0.76,0.0,3.66,1,0,1,1 +3694,ENFP,28.1,0.18734992201343836,0.12,0.22,0.96,0.04,0.0,0.72,0.0,2.96,0,0,0,1 +3695,INTJ,7.28,0.19068227159441392,0.02,0.06,0.02,0.0,0.0,0.2,0.0,1.04,1,0,1,0 +3696,ISTP,29.8,0.18317620586088332,0.0,0.3,0.02,0.0,0.0,0.6,0.0,2.98,1,1,1,1 +3697,INTP,33.8,0.18612570231931858,0.02,0.16,0.1,0.0,0.0,0.98,0.0,4.58,1,0,1,1 +3698,ENTJ,30.82,0.19145430589787005,0.0,0.12,0.14,0.0,0.0,1.36,0.0,3.26,0,0,1,0 +3699,INFJ,24.26,0.18560643830793216,0.04,0.56,0.04,0.0,0.0,0.5,0.04,2.66,1,0,0,0 +3700,ENFP,3.74,0.1869027745340108,0.0,0.02,0.04,0.0,0.02,0.08,0.0,0.3,0,0,0,1 +3701,ISFJ,2.34,0.19333268784678223,0.68,0.68,0.0,0.04,0.0,0.12,0.0,1.2,1,1,0,0 +3702,ISFP,22.8,0.18173797066546254,0.02,0.32,0.04,0.0,0.04,0.52,0.0,2.66,1,1,0,1 +3703,INFP,35.38,0.18355573660730845,0.0,0.02,0.0,0.0,0.0,0.9,0.0,4.0,1,0,0,1 +3704,INTP,28.78,0.18550556746951988,0.0,0.4,0.1,0.0,0.0,0.56,0.0,3.42,1,0,1,1 +3705,INTP,29.54,0.18238320928959986,0.06,0.24,0.12,0.0,0.0,0.74,0.02,3.34,1,0,1,1 +3706,INFP,21.86,0.19369119614925903,0.02,0.64,0.36,0.0,0.0,1.0,0.0,2.66,1,0,0,1 +3707,INFP,31.2,0.1886912965634597,0.06,0.18,0.18,0.04,0.02,1.12,0.0,3.8,1,0,0,1 +3708,ISFP,30.34,0.18457047208669355,0.02,0.16,0.04,0.0,0.02,0.68,0.0,2.88,1,1,0,1 +3709,ISTJ,28.48,0.18849918454946676,0.02,0.16,0.26,0.0,0.04,0.86,0.0,3.56,1,1,1,0 +3710,ENFP,15.6,0.18849851242685306,0.04,0.16,0.04,0.04,0.04,0.54,0.0,1.64,0,0,0,1 +3711,INTJ,13.46,0.19497213772109664,0.38,0.48,0.02,0.0,0.0,0.78,0.02,1.32,1,0,1,0 +3712,ENFJ,29.84,0.1822031779761986,0.06,0.16,0.16,0.0,0.0,0.76,0.0,3.28,0,0,0,0 +3713,INTP,17.94,0.18141436802191635,0.18,0.28,0.26,0.08,0.0,0.38,0.0,2.12,1,0,1,1 +3714,INTP,19.96,0.18165361706500557,0.1,0.06,0.0,0.06,0.0,0.44,0.0,2.16,1,0,1,1 +3715,ISFP,31.88,0.17688981107005727,0.0,0.3,0.08,0.0,0.0,0.92,0.0,4.38,1,1,0,1 +3716,INFJ,27.56,0.18371023190593452,0.02,0.14,0.16,0.02,0.0,0.82,0.0,2.76,1,0,0,0 +3717,INFP,20.04,0.19605470096707922,0.22,0.38,0.28,0.0,0.0,0.32,0.0,2.2,1,0,0,1 +3718,ENTP,25.64,0.18512454289021096,0.04,0.24,0.02,0.0,0.0,0.54,0.0,3.2,0,0,1,1 +3719,INTP,27.98,0.19678803147564758,0.02,0.18,0.12,0.02,0.0,0.72,0.0,2.6,1,0,1,1 +3720,INTP,32.94,0.1813078194774247,0.0,0.16,0.1,0.0,0.1,1.14,0.0,3.38,1,0,1,1 +3721,INTJ,12.4,0.18937361561300525,0.16,0.14,0.04,0.06,0.0,0.4,0.02,1.46,1,0,1,0 +3722,INTP,23.84,0.18493930529988534,0.1,0.1,0.0,0.02,0.0,0.52,0.0,3.04,1,0,1,1 +3723,INFP,16.24,0.18812611412098168,0.18,0.14,0.64,0.1,0.0,0.58,0.0,2.16,1,0,0,1 +3724,INFJ,26.76,0.18266565937894105,0.02,0.16,0.0,0.0,0.0,0.56,0.0,3.12,1,0,0,0 +3725,ENTP,20.5,0.18107818588011063,0.08,0.42,0.14,0.06,0.02,0.46,0.0,2.4,0,0,1,1 +3726,ISTJ,32.08,0.18631773900503684,0.0,0.18,0.08,0.0,0.0,1.1,0.0,2.86,1,1,1,0 +3727,INFJ,17.62,0.19123048668503217,0.02,0.24,0.1,0.0,0.1,0.46,0.0,1.74,1,0,0,0 +3728,INTJ,20.42,0.1820359185708145,0.24,0.36,0.06,0.0,0.06,0.44,0.0,2.0,1,0,1,0 +3729,INTJ,27.38,0.19540171702539452,0.0,0.28,0.1,0.0,0.02,1.34,0.0,3.22,1,0,1,0 +3730,INTP,17.16,0.1872183246423394,0.02,0.04,0.08,0.0,0.0,0.56,0.0,1.96,1,0,1,1 +3731,INFJ,9.18,0.17570776318138376,0.0,0.02,0.0,0.0,0.04,0.14,0.0,1.02,1,0,0,0 +3732,INFP,27.24,0.18514231647280893,0.06,0.14,0.18,0.0,0.0,0.6,0.0,3.2,1,0,0,1 +3733,ENTP,26.04,0.1828211417767433,0.0,0.28,0.14,0.0,0.0,0.7,0.0,2.08,0,0,1,1 +3734,INFP,18.58,0.1867847664541605,0.0,0.04,0.16,0.0,0.0,0.44,0.0,2.36,1,0,0,1 +3735,ENTP,25.6,0.17347317230652792,0.1,0.34,0.02,0.0,0.0,0.7,0.0,1.6,0,0,1,1 +3736,INFP,23.88,0.1927733448844092,0.0,0.1,0.32,0.0,0.06,0.44,0.0,3.44,1,0,0,1 +3737,ISTJ,32.16,0.1842626288608439,0.0,0.26,0.46,0.0,0.02,0.86,0.0,3.1,1,1,1,0 +3738,ENFP,25.4,0.18255648142901545,0.0,0.18,0.3,0.0,0.0,0.34,0.0,3.14,0,0,0,1 +3739,INFJ,21.54,0.1798099452248873,0.0,0.32,0.0,0.0,0.0,0.82,0.0,1.34,1,0,0,0 +3740,INTJ,23.14,0.18009810330481982,0.16,0.2,0.14,0.04,0.02,0.78,0.0,2.66,1,0,1,0 +3741,ENFP,26.78,0.18822363277151966,0.0,0.2,0.16,0.0,0.0,0.64,0.0,3.22,0,0,0,1 +3742,INFJ,14.82,0.1795969595700196,0.02,0.08,0.08,0.0,0.0,0.32,0.02,1.3,1,0,0,0 +3743,ENTP,26.56,0.1841899852601612,0.0,0.16,0.1,0.0,0.0,0.64,0.0,2.82,0,0,1,1 +3744,ENFJ,15.92,0.18765954045823688,0.0,0.06,0.9,0.0,0.0,0.44,0.0,1.66,0,0,0,0 +3745,ISTJ,28.14,0.1837255913789233,0.0,0.18,0.1,0.0,0.04,0.76,0.0,3.18,1,1,1,0 +3746,ENTP,29.92,0.18698478821385875,0.02,0.06,0.38,0.0,0.02,0.96,0.0,3.26,0,0,1,1 +3747,INFP,30.44,0.18296833284556394,0.02,0.2,0.32,0.0,0.0,0.68,0.0,4.2,1,0,0,1 +3748,INFJ,10.88,0.20326222669230953,0.0,0.0,0.02,0.0,0.0,0.86,0.0,1.36,1,0,0,0 +3749,INTP,27.96,0.18585199766510793,0.02,0.42,0.08,0.0,0.06,0.68,0.0,3.0,1,0,1,1 +3750,INTJ,21.04,0.18476613075225953,0.0,0.04,0.12,0.0,0.0,0.54,0.0,2.18,1,0,1,0 +3751,ISTP,32.24,0.18108532548064485,0.02,0.34,0.18,0.0,0.0,0.8,0.0,3.38,1,1,1,1 +3752,INFP,25.06,0.18533229102139828,0.0,0.04,0.0,0.0,0.0,0.76,0.0,2.36,1,0,0,1 +3753,ESFP,23.04,0.1841424876309396,0.18,0.22,0.02,0.0,0.0,0.4,0.0,2.34,0,1,0,1 +3754,INTP,17.8,0.17399579417490801,0.14,0.14,0.12,0.06,0.02,0.3,0.0,1.82,1,0,1,1 +3755,INTP,25.62,0.17945428966175078,0.0,0.3,0.12,0.0,0.0,0.68,0.0,2.66,1,0,1,1 +3756,INFP,35.06,0.1854752086004024,0.0,0.16,0.14,0.0,0.0,1.26,0.0,4.74,1,0,0,1 +3757,INFP,34.54,0.18809526619995298,0.02,0.18,0.12,0.0,0.0,1.08,0.0,4.16,1,0,0,1 +3758,ENTP,27.4,0.19171684623534638,0.02,0.2,0.28,0.02,0.02,1.16,0.08,2.88,0,0,1,1 +3759,INTP,24.34,0.1828903375253574,0.1,0.12,0.06,0.0,0.0,0.56,0.0,3.1,1,0,1,1 +3760,INTP,24.2,0.18621875504923016,0.02,0.2,0.16,0.0,0.0,0.58,0.0,1.84,1,0,1,1 +3761,ENTP,27.72,0.18402648303123875,0.02,0.18,0.06,0.02,0.02,0.56,0.0,3.16,0,0,1,1 +3762,ENTJ,27.82,0.18239484308995216,0.0,0.28,0.04,0.0,0.0,0.62,0.0,2.72,0,0,1,0 +3763,INFJ,23.66,0.189216806479549,0.02,0.02,0.08,0.0,0.0,0.78,0.0,2.92,1,0,0,0 +3764,INFJ,24.88,0.18661972914742997,0.04,0.1,0.12,0.02,0.0,0.82,0.0,0.62,1,0,0,0 +3765,INFP,25.84,0.19057532642487715,0.1,0.12,0.06,0.06,0.02,0.6,0.0,3.5,1,0,0,1 +3766,ENTJ,25.52,0.18936923668073052,0.1,0.26,0.18,0.0,0.0,0.54,0.0,2.08,0,0,1,0 +3767,ESTP,18.18,0.18768181808550982,0.0,0.12,0.24,0.0,0.04,0.24,0.0,1.58,0,1,1,1 +3768,ENFP,12.36,0.1919269013705993,0.0,0.22,0.18,0.0,0.0,0.36,0.0,1.66,0,0,0,1 +3769,INTP,27.2,0.17790027664529717,0.06,0.28,0.02,0.0,0.04,0.64,0.0,3.18,1,0,1,1 +3770,INTJ,14.4,0.1847007619707145,0.26,0.42,0.08,0.0,0.04,0.36,0.0,1.18,1,0,1,0 +3771,INFP,21.94,0.18201216836862127,0.16,0.26,0.08,0.0,0.0,0.38,0.0,2.24,1,0,0,1 +3772,INTJ,23.6,0.18453237204968137,0.02,0.44,0.04,0.0,0.0,0.5,0.0,2.06,1,0,1,0 +3773,ENFJ,28.48,0.1973178786414982,0.0,0.34,1.32,0.0,0.04,1.28,0.0,2.86,0,0,0,0 +3774,ENFP,28.58,0.18123893918937992,0.0,0.08,0.14,0.0,0.02,0.7,0.0,3.22,0,0,0,1 +3775,INFJ,29.14,0.18367526785101077,0.02,0.4,0.18,0.0,0.04,0.72,0.0,3.28,1,0,0,0 +3776,INFJ,24.9,0.1836363695657159,0.02,0.04,0.04,0.02,0.0,0.76,0.0,3.66,1,0,0,0 +3777,ENFJ,31.98,0.18651764163606901,0.0,0.04,0.4,0.0,0.0,1.2,0.0,3.86,0,0,0,0 +3778,ISTP,27.88,0.18307274290021447,0.08,0.22,0.02,0.06,0.0,0.54,0.0,3.4,1,1,1,1 +3779,ISTJ,27.12,0.18460362182369905,0.02,0.34,0.04,0.0,0.0,0.6,0.0,3.56,1,1,1,0 +3780,INTP,26.84,0.17421434316196902,0.02,0.16,0.06,0.0,0.02,0.74,0.0,1.76,1,0,1,1 +3781,ENTP,28.06,0.1771409809562559,0.06,0.24,0.08,0.0,0.0,0.7,0.0,2.58,0,0,1,1 +3782,ISTP,29.0,0.19056024696393267,0.02,0.18,0.4,0.0,0.0,0.6,0.0,1.4,1,1,1,1 +3783,INTP,24.88,0.18979806568179408,0.1,0.3,0.34,0.02,0.0,0.76,0.0,2.68,1,0,1,1 +3784,INTJ,30.54,0.18143136301145182,0.02,0.22,0.06,0.02,0.0,0.86,0.0,3.48,1,0,1,0 +3785,INTP,22.12,0.17401516296590872,0.0,0.48,0.06,0.0,0.0,0.62,0.0,1.66,1,0,1,1 +3786,INFP,22.9,0.18547072432472156,0.12,0.26,0.14,0.0,0.02,0.4,0.02,2.48,1,0,0,1 +3787,INTJ,24.22,0.18190815208835937,0.02,0.26,0.28,0.0,0.02,0.6,0.0,2.42,1,0,1,0 +3788,INTP,16.98,0.17322338863136216,0.06,0.06,0.0,0.0,0.0,0.36,0.0,1.44,1,0,1,1 +3789,INFP,21.98,0.19114236987707997,0.06,0.14,0.04,0.0,0.02,0.42,0.0,3.02,1,0,0,1 +3790,ENFP,31.08,0.18282382340691378,0.02,0.48,0.0,0.0,0.0,0.72,0.0,2.86,0,0,0,1 +3791,INTP,19.2,0.18880886426592797,0.0,0.42,0.0,0.0,0.0,0.58,0.0,1.32,1,0,1,1 +3792,INFJ,26.08,0.1815410897544596,0.12,0.24,0.06,0.08,0.0,0.62,0.0,2.36,1,0,0,0 +3793,INTJ,26.22,0.18543509794874832,0.04,0.06,0.08,0.02,0.0,0.68,0.0,2.62,1,0,1,0 +3794,INFP,23.0,0.18982189052863138,0.08,0.32,0.3,0.04,0.0,0.68,0.0,2.86,1,0,0,1 +3795,ENTP,23.9,0.180003548954768,0.04,0.22,0.0,0.0,0.0,0.4,0.0,1.7,0,0,1,1 +3796,INTP,31.86,0.18276485386621133,0.08,0.28,0.06,0.02,0.02,0.8,0.0,3.2,1,0,1,1 +3797,INFJ,14.48,0.1775239665002471,0.1,0.12,0.02,0.0,0.04,0.3,0.02,1.5,1,0,0,0 +3798,ENTP,30.1,0.1813043249686357,0.0,0.2,0.02,0.0,0.0,1.08,0.0,2.58,0,0,1,1 +3799,INFJ,27.58,0.18616537500066518,0.02,0.2,0.02,0.0,0.0,0.94,0.0,3.14,1,0,0,0 +3800,INFJ,23.94,0.1843009762853583,0.0,0.2,0.08,0.0,0.0,0.64,0.0,2.6,1,0,0,0 +3801,ENFP,34.12,0.18838195648888117,0.0,0.2,0.02,0.0,0.06,1.22,0.0,4.0,0,0,0,1 +3802,ENTP,25.32,0.17999929805961892,0.0,0.22,0.06,0.0,0.0,0.44,0.0,2.88,0,0,1,1 +3803,INTJ,26.5,0.1798427172118267,0.2,0.26,0.0,0.14,0.04,0.82,0.0,2.22,1,0,1,0 +3804,INFP,1.72,0.179993441534519,0.0,0.0,0.02,0.0,0.0,0.04,0.0,0.18,1,0,0,1 +3805,INTP,16.4,0.17878157714749277,0.24,0.34,0.04,0.0,0.06,0.18,0.0,2.06,1,0,1,1 +3806,ISFJ,16.12,0.19189342005539653,0.0,0.18,0.52,0.0,0.0,0.18,0.0,2.26,1,1,0,0 +3807,INFP,29.7,0.18574072862967644,0.0,0.0,0.02,0.0,0.04,0.62,0.0,4.34,1,0,0,1 +3808,ENFJ,33.36,0.18446400000000004,0.0,0.04,0.06,0.0,0.0,0.84,0.0,3.78,0,0,0,0 +3809,INTJ,27.86,0.18163395718989206,0.0,0.26,0.02,0.0,0.0,0.74,0.0,2.54,1,0,1,0 +3810,INFJ,33.58,0.18349265201349768,0.02,0.1,0.0,0.02,0.02,0.88,0.0,3.28,1,0,0,0 +3811,INFJ,27.78,0.1854484075245654,0.02,0.12,0.1,0.0,0.06,0.56,0.0,3.5,1,0,0,0 +3812,ESFP,16.22,0.19250024124984555,0.0,0.04,0.3,0.0,0.0,0.34,0.0,1.98,0,1,0,1 +3813,INTP,25.5,0.18222853526226424,0.04,0.34,0.0,0.02,0.0,0.62,0.0,1.96,1,0,1,1 +3814,ENTP,14.18,0.1961691421419259,0.04,0.54,0.62,0.0,0.02,1.2,0.0,1.22,0,0,1,1 +3815,INTP,23.68,0.19042831113418368,0.0,0.12,0.06,0.0,0.02,1.34,0.0,1.58,1,0,1,1 +3816,ISTP,28.22,0.18490691237050372,0.02,0.1,0.26,0.0,0.0,0.62,0.0,2.58,1,1,1,1 +3817,INFP,30.64,0.1840853588032482,0.0,0.28,0.4,0.0,0.0,1.04,0.0,3.3,1,0,0,1 +3818,ENTP,27.8,0.18602140927943164,0.08,0.22,0.14,0.02,0.0,1.26,0.0,2.06,0,0,1,1 +3819,INTJ,22.06,0.18965950237928764,0.08,0.74,0.12,0.0,0.02,0.42,0.0,1.96,1,0,1,0 +3820,ENFP,25.92,0.18282325601899285,0.02,0.18,0.02,0.0,0.0,0.52,0.04,1.48,0,0,0,1 +3821,INFP,20.58,0.18061213091315767,0.14,0.06,0.14,0.06,0.02,0.44,0.0,2.04,1,0,0,1 +3822,INFP,30.6,0.18854409170981531,0.0,0.38,0.18,0.0,0.0,0.72,0.0,3.06,1,0,0,1 +3823,INTP,21.66,0.17161433585433597,0.16,0.16,0.02,0.0,0.0,0.4,0.02,1.88,1,0,1,1 +3824,ESTJ,24.96,0.18787831801945096,0.0,0.42,0.14,0.0,0.0,0.52,0.0,2.92,0,1,1,0 +3825,INFP,21.86,0.19147674458860275,0.02,0.24,0.2,0.0,0.0,0.42,0.1,2.1,1,0,0,1 +3826,ENFP,18.1,0.18677871341343102,0.3,0.14,0.22,0.18,0.06,0.58,0.0,2.56,0,0,0,1 +3827,INFP,33.12,0.18184365166558164,0.04,0.16,0.18,0.02,0.0,0.9,0.0,3.56,1,0,0,1 +3828,ESFP,28.06,0.18553976486480384,0.0,0.22,0.46,0.0,0.0,0.8,0.0,2.56,0,1,0,1 +3829,INTP,21.66,0.18604734840065457,0.14,0.1,0.12,0.02,0.0,0.54,0.0,2.96,1,0,1,1 +3830,ENFP,27.84,0.19617361436945302,0.0,0.14,2.12,0.0,0.04,0.98,0.0,2.78,0,0,0,1 +3831,INFP,20.28,0.19086020070075888,0.0,0.06,0.32,0.0,0.0,0.5,0.0,2.24,1,0,0,1 +3832,ENFP,25.36,0.18823177066195557,0.0,0.16,0.42,0.0,0.0,0.44,0.0,2.64,0,0,0,1 +3833,INFP,27.54,0.18381697265985478,0.02,0.12,0.08,0.0,0.0,0.56,0.0,3.3,1,0,0,1 +3834,ENFP,27.72,0.18595684820655653,0.0,0.48,0.38,0.0,0.0,0.74,0.0,2.92,0,0,0,1 +3835,INFP,28.12,0.18679392388341698,0.0,0.12,0.04,0.0,0.02,0.66,0.0,2.3,1,0,0,1 +3836,INFJ,28.52,0.19126174290748965,0.0,0.2,0.18,0.0,0.0,0.62,0.0,4.34,1,0,0,0 +3837,ENTJ,17.4,0.1783070920207544,0.02,0.26,0.04,0.02,0.0,0.4,0.0,1.88,0,0,1,0 +3838,INTJ,28.36,0.1853064138008851,0.08,0.08,0.0,0.0,0.0,1.08,0.0,3.06,1,0,1,0 +3839,INTJ,24.82,0.19033655090046794,0.14,0.2,0.32,0.0,0.04,1.06,0.0,3.06,1,0,1,0 +3840,INTP,25.54,0.1822267962703981,0.02,0.4,0.12,0.0,0.02,0.58,0.0,1.84,1,0,1,1 +3841,INFJ,23.76,0.18510974776203643,0.04,0.12,0.12,0.0,0.04,0.42,0.0,3.04,1,0,0,0 +3842,INFJ,32.26,0.18567117131175687,0.0,0.08,0.1,0.0,0.0,0.9,0.0,4.14,1,0,0,0 +3843,ENTP,24.68,0.17913574821026632,0.16,0.2,0.04,0.0,0.02,0.64,0.0,0.6,0,0,1,1 +3844,INTP,29.58,0.17979329121571094,0.0,0.4,0.08,0.0,0.04,0.94,0.0,2.56,1,0,1,1 +3845,INFJ,29.04,0.18422798909854984,0.16,0.24,0.06,0.0,0.0,0.8,0.0,3.02,1,0,0,0 +3846,INFJ,27.66,0.18562791172804516,0.02,0.06,0.1,0.0,0.0,0.82,0.0,3.04,1,0,0,0 +3847,INFJ,14.26,0.19137834658831251,0.0,0.04,0.1,0.0,0.0,0.34,0.0,1.48,1,0,0,0 +3848,INFP,28.84,0.18696889464784844,0.04,0.14,0.0,0.06,0.12,0.7,0.0,3.44,1,0,0,1 +3849,INTP,30.42,0.1852908271758812,0.04,0.1,0.04,0.0,0.04,1.34,0.0,2.04,1,0,1,1 +3850,INTJ,13.76,0.1869501255622535,0.0,0.06,0.16,0.0,0.02,0.3,0.0,1.58,1,0,1,0 +3851,INTP,29.86,0.1862244442574045,0.0,0.18,0.02,0.0,0.0,0.88,0.0,3.96,1,0,1,1 +3852,ENTP,31.22,0.18650539825365,0.0,0.18,0.0,0.0,0.02,0.68,0.0,4.04,0,0,1,1 +3853,INFP,28.3,0.1821734260625731,0.04,0.26,0.04,0.04,0.0,1.1,0.0,3.62,1,0,0,1 +3854,ENTP,29.88,0.18239999999999998,0.18,0.3,0.1,0.0,0.0,0.78,0.0,3.02,0,0,1,1 +3855,ISFJ,6.48,0.18323810462906523,0.6,0.6,0.1,0.02,0.02,0.08,0.0,0.7,1,1,0,0 +3856,INFP,18.24,0.17872808636307308,0.56,0.58,0.06,0.02,0.0,0.44,0.0,1.8,1,0,0,1 +3857,INTP,14.08,0.18806352779164157,0.06,0.12,0.0,0.0,0.04,0.08,0.0,1.96,1,0,1,1 +3858,ENFP,23.22,0.18884911382521852,0.0,0.3,0.06,0.0,0.0,0.48,0.0,2.74,0,0,0,1 +3859,ENFP,21.96,0.1834699391234077,0.08,0.32,0.1,0.02,0.0,0.4,0.04,2.36,0,0,0,1 +3860,INFP,20.16,0.17834821537466797,0.0,0.06,0.02,0.0,0.0,0.5,0.0,2.18,1,0,0,1 +3861,ENTP,16.64,0.19288200624975282,0.0,0.12,0.76,0.02,0.0,0.32,0.0,1.78,0,0,1,1 +3862,INTJ,16.96,0.1887009327859505,0.0,0.2,0.02,0.0,0.02,0.48,0.0,2.6,1,0,1,0 +3863,INTP,19.64,0.19168046256816584,0.06,0.16,0.16,0.02,0.02,0.56,0.02,1.96,1,0,1,1 +3864,INFP,32.12,0.18955846144566063,0.0,0.22,0.08,0.0,0.0,1.22,0.0,4.56,1,0,0,1 +3865,ENFJ,27.82,0.1947666451893635,0.02,0.12,2.0,0.02,0.0,0.94,0.0,2.84,0,0,0,0 +3866,ISTP,25.08,0.19228562591564388,0.0,0.44,0.12,0.0,0.0,0.46,0.02,2.24,1,1,1,1 +3867,INTJ,31.1,0.1845951845878739,0.02,0.16,0.04,0.0,0.0,0.82,0.02,3.3,1,0,1,0 +3868,ENFJ,23.1,0.17865330889048067,0.06,0.2,0.04,0.06,0.0,0.56,0.02,2.36,0,0,0,0 +3869,INTJ,27.86,0.1850757379019939,0.0,0.2,0.12,0.0,0.02,1.22,0.0,3.26,1,0,1,0 +3870,ISFJ,26.76,0.18394365305139307,0.0,0.1,0.1,0.0,0.0,0.5,0.0,3.58,1,1,0,0 +3871,INFP,26.24,0.1751748880610103,0.22,0.38,0.04,0.02,0.06,0.72,0.0,2.84,1,0,0,1 +3872,INFJ,35.54,0.18707229633804476,0.0,0.16,0.3,0.0,0.0,1.48,0.0,4.7,1,0,0,0 +3873,ISFP,27.14,0.18461667298632034,0.0,0.08,0.02,0.0,0.02,0.46,0.0,3.04,1,1,0,1 +3874,ISTJ,17.58,0.18797506840735195,0.28,0.26,0.06,0.0,0.02,0.56,0.0,1.98,1,1,1,0 +3875,INFJ,8.12,0.1819389739459069,0.6,0.66,0.04,0.02,0.06,0.14,0.0,0.88,1,0,0,0 +3876,ISTJ,26.82,0.1867869046081221,0.06,0.22,0.02,0.0,0.0,0.62,0.0,3.1,1,1,1,0 +3877,INTJ,28.02,0.18048225918648286,0.0,0.46,0.18,0.0,0.0,0.78,0.0,2.72,1,0,1,0 +3878,INTP,29.18,0.18941097679933122,0.0,0.3,0.0,0.0,0.02,0.98,0.0,3.24,1,0,1,1 +3879,INTJ,34.54,0.18306348025959984,0.0,0.06,0.04,0.0,0.02,0.88,0.0,4.3,1,0,1,0 +3880,ENFJ,29.06,0.18869761381682487,0.0,0.12,0.26,0.0,0.02,0.78,0.0,3.56,0,0,0,0 +3881,INFP,32.76,0.1813256686758822,0.02,0.44,0.02,0.0,0.04,0.86,0.0,3.22,1,0,0,1 +3882,INTP,21.84,0.18293228139381987,0.16,0.4,0.08,0.04,0.02,0.5,0.0,2.3,1,0,1,1 +3883,ENFP,20.1,0.17524826057852244,0.04,0.34,0.06,0.0,0.0,0.36,0.0,1.4,0,0,0,1 +3884,ISTJ,30.48,0.17699617813032234,0.06,0.06,0.04,0.04,0.0,0.68,0.0,3.14,1,1,1,0 +3885,INFJ,33.48,0.18561356581961366,0.02,0.16,0.08,0.0,0.0,1.0,0.0,5.08,1,0,0,0 +3886,ISFP,13.52,0.18850839650558318,0.06,0.14,0.14,0.0,0.02,0.2,0.0,1.92,1,1,0,1 +3887,ENFJ,29.66,0.18711606705748152,0.02,0.06,0.32,0.0,0.0,1.54,0.0,3.26,0,0,0,0 +3888,ISTP,29.86,0.18074625390388943,0.0,0.08,0.24,0.0,0.04,0.62,0.04,3.6,1,1,1,1 +3889,INTJ,28.24,0.185029191898825,0.02,0.26,0.02,0.0,0.0,0.68,0.0,3.72,1,0,1,0 +3890,ENTJ,29.32,0.1777521221769907,0.0,0.2,0.46,0.0,0.02,0.64,0.0,2.58,0,0,1,0 +3891,INTJ,20.14,0.17706057861367144,0.04,0.12,0.0,0.0,0.0,0.4,0.0,1.88,1,0,1,0 +3892,INFP,24.14,0.1884149342220987,0.0,0.04,0.56,0.0,0.02,0.52,0.0,2.86,1,0,0,1 +3893,INFP,27.42,0.18069897239321253,0.04,0.16,0.16,0.02,0.0,0.72,0.0,3.5,1,0,0,1 +3894,INFP,24.58,0.186339650677299,0.1,0.1,0.06,0.06,0.02,0.48,0.0,3.4,1,0,0,1 +3895,INFP,16.32,0.19345866074786852,0.02,0.06,0.0,0.0,0.0,0.14,0.0,0.56,1,0,0,1 +3896,INTJ,17.42,0.1830696970057321,0.08,0.14,0.02,0.0,0.0,0.32,0.0,2.32,1,0,1,0 +3897,INFP,24.42,0.18963785330748073,0.0,0.04,0.34,0.0,0.02,0.7,0.0,3.04,1,0,0,1 +3898,INFJ,25.4,0.18291981063917845,0.18,0.16,0.02,0.02,0.0,0.64,0.0,2.72,1,0,0,0 +3899,INFP,33.38,0.19427200967432434,0.0,0.56,0.48,0.0,0.0,1.02,0.0,3.64,1,0,0,1 +3900,INTJ,11.08,0.18621917627020523,0.02,0.08,0.02,0.0,0.02,0.14,0.0,1.02,1,0,1,0 +3901,ENFP,31.68,0.1819470699432892,0.04,0.12,0.04,0.04,0.0,0.86,0.0,3.66,0,0,0,1 +3902,ENFP,33.94,0.18262864632377107,0.0,0.22,0.06,0.0,0.02,0.78,0.0,3.92,0,0,0,1 +3903,ENTP,22.76,0.18906855044041168,0.0,0.18,0.0,0.0,0.0,0.44,0.0,1.7,0,0,1,1 +3904,INFP,36.5,0.1810234641483102,0.02,0.14,0.02,0.0,0.02,0.98,0.02,4.54,1,0,0,1 +3905,INFJ,29.32,0.18478778121645456,0.0,0.18,0.48,0.0,0.0,0.86,0.0,4.02,1,0,0,0 +3906,INFP,34.1,0.18759304055279047,0.0,0.24,0.1,0.0,0.02,0.96,0.0,4.24,1,0,0,1 +3907,INFJ,34.46,0.18294677527518413,0.04,0.1,0.02,0.04,0.02,0.96,0.08,4.28,1,0,0,0 +3908,ENTJ,27.94,0.18416519543274093,0.06,0.28,0.1,0.02,0.0,0.64,0.0,3.0,0,0,1,0 +3909,INTJ,29.56,0.17532693316937306,0.02,0.04,0.1,0.0,0.0,0.68,0.1,2.42,1,0,1,0 +3910,INFP,20.24,0.1865291808498027,0.2,0.22,0.06,0.0,0.0,0.5,0.0,1.66,1,0,0,1 +3911,INTJ,32.52,0.18601745110436632,0.0,0.14,0.12,0.0,0.0,0.88,0.0,4.76,1,0,1,0 +3912,ISFJ,32.54,0.18487950443103893,0.0,0.2,0.36,0.0,0.0,0.76,0.0,3.7,1,1,0,0 +3913,INTP,33.84,0.17818042739692144,0.0,0.14,0.0,0.0,0.0,0.88,0.0,4.58,1,0,1,1 +3914,ENTP,29.0,0.1877761749414635,0.0,0.16,0.14,0.0,0.0,0.86,0.0,2.76,0,0,1,1 +3915,INTP,13.76,0.18269841321003455,0.0,0.1,0.08,0.0,0.0,0.24,0.0,0.94,1,0,1,1 +3916,ISFP,15.7,0.18442907458584382,0.16,0.24,0.04,0.04,0.02,0.18,0.0,1.84,1,1,0,1 +3917,ENTP,30.44,0.18198552987643105,0.02,0.1,0.2,0.0,0.0,0.98,0.02,3.38,0,0,1,1 +3918,ISTP,25.18,0.18094462072107215,0.08,0.1,0.0,0.0,0.02,0.6,0.0,2.38,1,1,1,1 +3919,ENTP,19.4,0.18007499965708343,0.0,0.04,0.0,0.0,0.0,0.38,0.0,1.74,0,0,1,1 +3920,ENFJ,31.38,0.18537420309720135,0.0,0.12,0.02,0.0,0.0,0.68,0.0,3.44,0,0,0,0 +3921,ENFP,26.0,0.18940776857650624,0.1,0.26,0.24,0.06,0.0,0.56,0.0,3.04,0,0,0,1 +3922,ENFP,20.68,0.18296001896680883,0.0,0.06,0.18,0.0,0.0,0.28,0.0,1.92,0,0,0,1 +3923,ENTP,22.22,0.1814762657084069,0.08,0.22,0.18,0.02,0.0,0.44,0.0,2.82,0,0,1,1 +3924,ISTJ,25.94,0.1860617716055041,0.06,0.12,0.34,0.06,0.0,0.7,0.0,2.94,1,1,1,0 +3925,ENTJ,24.48,0.182402726411099,0.04,0.28,0.0,0.04,0.0,0.6,0.0,2.68,0,0,1,0 +3926,ENTP,28.04,0.18794612496646626,0.02,0.36,0.2,0.0,0.0,0.78,0.0,3.04,0,0,1,1 +3927,INTP,23.16,0.18053846913580243,0.0,0.06,0.0,0.0,0.04,0.54,0.0,2.86,1,0,1,1 +3928,INFP,23.58,0.175056794386986,0.12,0.32,0.16,0.0,0.08,0.48,0.0,2.22,1,0,0,1 +3929,ENFJ,22.96,0.19328014254247478,0.26,0.42,0.16,0.02,0.0,1.2,0.0,2.2,0,0,0,0 +3930,INFJ,29.38,0.18795437871890994,0.02,0.12,0.04,0.0,0.0,0.74,0.0,3.32,1,0,0,0 +3931,INFP,25.34,0.18563206384544842,0.0,0.08,0.52,0.0,0.02,0.56,0.0,3.44,1,0,0,1 +3932,ENFP,32.84,0.1808643388019661,0.0,0.2,0.02,0.0,0.02,0.92,0.0,3.3,0,0,0,1 +3933,ENFP,31.34,0.18794878275137542,0.0,0.22,0.02,0.0,0.02,1.84,0.0,2.72,0,0,0,1 +3934,INFJ,12.34,0.1880499568035104,0.24,0.54,0.54,0.02,0.0,0.28,0.0,1.5,1,0,0,0 +3935,ENFP,26.2,0.18136054306564853,0.0,0.3,0.18,0.0,0.0,0.42,0.0,2.6,0,0,0,1 +3936,ISTJ,29.84,0.1846074623776017,0.0,0.08,0.02,0.0,0.0,0.58,0.0,3.82,1,1,1,0 +3937,INFJ,22.66,0.18498135894677273,0.02,0.14,0.12,0.0,0.0,0.66,0.0,2.02,1,0,0,0 +3938,ISTP,19.52,0.18651291339912102,0.0,0.34,0.2,0.0,0.0,0.54,0.0,1.94,1,1,1,1 +3939,ESFJ,34.52,0.1856486345331737,0.0,0.2,0.54,0.0,0.0,1.1,0.0,3.66,0,1,0,0 +3940,INFJ,28.12,0.17987464615519447,0.0,0.2,0.14,0.0,0.02,1.02,0.0,1.12,1,0,0,0 +3941,INFJ,32.98,0.18889172583896535,0.02,0.12,0.14,0.0,0.02,1.06,0.0,3.98,1,0,0,0 +3942,INFJ,9.96,0.19485755698231066,0.0,0.5,0.04,0.0,0.0,0.18,0.0,1.58,1,0,0,0 +3943,INTJ,25.08,0.18193303382911952,0.1,0.36,0.08,0.06,0.02,0.64,0.02,2.68,1,0,1,0 +3944,ISTP,27.58,0.18692293201673282,0.0,0.08,0.12,0.0,0.02,0.8,0.0,2.3,1,1,1,1 +3945,INFJ,25.06,0.18996083332019087,0.0,0.36,0.44,0.0,0.0,0.64,0.0,3.5,1,0,0,0 +3946,ENFP,31.68,0.1805969218034291,0.0,0.12,0.26,0.0,0.0,0.84,0.0,1.22,0,0,0,1 +3947,INFJ,29.7,0.1915962279504572,0.0,0.08,0.2,0.0,0.0,0.72,0.04,3.52,1,0,0,0 +3948,INFP,13.08,0.18248838766726785,0.38,0.34,0.1,0.06,0.0,0.2,0.0,1.7,1,0,0,1 +3949,ENFP,27.68,0.186699279656097,0.02,0.28,0.32,0.0,0.04,0.78,0.0,4.04,0,0,0,1 +3950,INFP,33.7,0.18546546396550626,0.0,0.2,0.02,0.0,0.0,0.92,0.0,3.62,1,0,0,1 +3951,INTP,16.04,0.19231893381936055,0.0,0.28,0.38,0.0,0.0,0.34,0.0,1.5,1,0,1,1 +3952,INFP,27.34,0.18518827208875757,0.08,0.26,0.22,0.04,0.04,0.56,0.0,3.0,1,0,0,1 +3953,INTJ,27.26,0.18304694966270274,0.0,0.24,0.0,0.0,0.0,1.34,0.0,3.14,1,0,1,0 +3954,INTP,28.24,0.18030298898628092,0.12,0.2,0.0,0.04,0.1,0.58,0.0,2.78,1,0,1,1 +3955,ISFP,29.42,0.1818027049602614,0.02,0.14,0.22,0.0,0.0,0.82,0.0,3.86,1,1,0,1 +3956,ISFJ,20.62,0.1795723280742436,0.08,0.06,0.14,0.04,0.0,0.38,0.0,2.26,1,1,0,0 +3957,INFP,31.22,0.18691392052545244,0.0,0.34,0.3,0.0,0.0,0.9,0.0,2.8,1,0,0,1 +3958,INFJ,23.54,0.18396059207781829,0.14,0.42,0.16,0.0,0.0,0.66,0.0,3.08,1,0,0,0 +3959,INTJ,25.48,0.18530612280140324,0.02,0.28,0.08,0.0,0.0,0.56,0.0,1.44,1,0,1,0 +3960,INFP,36.06,0.18184190123255142,0.0,0.0,0.06,0.0,0.02,0.92,0.0,4.6,1,0,0,1 +3961,ENFP,31.24,0.1806121832637295,0.02,0.06,0.24,0.0,0.0,0.98,0.0,3.84,0,0,0,1 +3962,ESTJ,36.8,0.1860328389244245,0.0,0.18,0.1,0.0,0.02,1.16,0.0,4.4,0,1,1,0 +3963,ISFJ,30.26,0.17372831654263707,0.04,0.12,0.02,0.0,0.0,0.86,0.02,2.96,1,1,0,0 +3964,ENFP,23.86,0.1904049663364052,0.12,0.2,0.2,0.0,0.0,0.94,0.0,3.34,0,0,0,1 +3965,INTJ,19.08,0.17915206701314434,0.02,0.08,0.1,0.0,0.0,0.48,0.0,1.6,1,0,1,0 +3966,ISFP,18.62,0.18735824990376942,0.0,0.08,0.16,0.0,0.06,0.34,0.0,1.92,1,1,0,1 +3967,INFJ,18.06,0.18769642065551237,0.08,0.26,0.2,0.0,0.0,0.5,0.0,1.94,1,0,0,0 +3968,INTP,30.06,0.18171421149900138,0.04,0.28,0.08,0.0,0.02,0.76,0.0,3.08,1,0,1,1 +3969,ENTJ,32.4,0.18244899518848115,0.0,0.42,0.02,0.0,0.0,1.04,0.0,3.2,0,0,1,0 +3970,INTP,25.08,0.18480297626562303,0.0,0.2,0.06,0.0,0.0,0.46,0.0,2.42,1,0,1,1 +3971,ISFJ,31.14,0.18697170261767443,0.04,0.14,0.06,0.0,0.0,0.88,0.0,4.06,1,1,0,0 +3972,INTJ,22.94,0.18524485467900906,0.08,0.22,0.06,0.02,0.02,0.4,0.0,3.28,1,0,1,0 +3973,ENTJ,15.26,0.18244243269528618,0.04,0.12,0.14,0.0,0.0,0.44,0.0,1.7,0,0,1,0 +3974,INFP,22.0,0.18509388078646405,0.08,0.14,0.1,0.0,0.06,0.72,0.0,2.02,1,0,0,1 +3975,INFJ,21.04,0.18443245754239745,0.0,0.14,0.0,0.0,0.0,0.54,0.0,1.6,1,0,0,0 +3976,INFJ,14.16,0.18112655078496914,0.0,0.02,0.1,0.0,0.0,0.32,0.0,1.26,1,0,0,0 +3977,INFP,16.84,0.18636738751333293,0.38,0.06,0.0,0.2,0.0,0.34,0.0,2.06,1,0,0,1 +3978,INTJ,34.24,0.17870373160017206,0.0,0.04,0.3,0.0,0.0,1.02,0.0,3.18,1,0,1,0 +3979,INFJ,25.82,0.1754016773859617,0.04,0.26,0.28,0.02,0.02,0.86,0.0,2.6,1,0,0,0 +3980,INFP,31.92,0.18270732882370386,0.02,0.22,0.06,0.0,0.02,1.0,0.0,3.72,1,0,0,1 +3981,INFJ,24.72,0.1950509899286813,0.0,0.16,0.16,0.0,0.0,1.34,0.0,3.46,1,0,0,0 +3982,INFJ,19.22,0.1821990579289229,0.02,0.18,0.06,0.02,0.0,0.44,0.0,2.08,1,0,0,0 +3983,ENFP,30.62,0.18038295823389935,0.0,0.26,0.14,0.0,0.0,0.78,0.0,3.7,0,0,0,1 +3984,ENTJ,10.24,0.18746345563592165,0.2,0.38,0.14,0.02,0.0,0.34,0.0,1.08,0,0,1,0 +3985,INTP,30.7,0.18812481444432652,0.0,0.08,0.0,0.0,0.02,0.78,0.0,4.54,1,0,1,1 +3986,ENTP,24.42,0.18079695219121628,0.0,0.36,0.06,0.0,0.0,0.44,0.0,2.5,0,0,1,1 +3987,INTJ,20.72,0.18406026698880035,0.1,0.24,0.06,0.04,0.0,0.48,0.0,2.0,1,0,1,0 +3988,INFP,27.68,0.18656257475626842,0.02,0.18,0.04,0.0,0.02,0.58,0.0,3.54,1,0,0,1 +3989,INTP,30.5,0.18132277083181145,0.06,0.22,0.0,0.0,0.0,0.8,0.0,3.3,1,0,1,1 +3990,INFJ,30.4,0.1839864174528565,0.04,0.1,0.1,0.02,0.06,0.78,0.0,3.74,1,0,0,0 +3991,INFJ,9.18,0.18596742076797573,0.02,0.18,0.14,0.0,0.0,0.24,0.0,0.7,1,0,0,0 +3992,INFJ,25.66,0.19444759628125374,0.04,0.34,0.2,0.0,0.0,1.18,0.0,2.62,1,0,0,0 +3993,ENFP,28.06,0.1859678811615863,0.0,0.1,0.04,0.0,0.0,1.38,0.0,2.56,0,0,0,1 +3994,INTP,27.9,0.1834863593705945,0.06,0.22,0.08,0.04,0.0,0.8,0.0,3.02,1,0,1,1 +3995,ISTP,13.32,0.17938911573638477,0.4,0.22,0.08,0.0,0.0,0.34,0.02,1.58,1,1,1,1 +3996,INTJ,13.04,0.1846803410473197,0.44,0.32,0.04,0.14,0.0,0.28,0.0,1.7,1,0,1,0 +3997,ISTP,24.64,0.1850737033764378,0.1,0.24,0.16,0.04,0.0,0.56,0.0,2.7,1,1,1,1 +3998,INTJ,18.34,0.18851212902372924,0.02,0.3,0.04,0.0,0.0,0.6,0.0,1.5,1,0,1,0 +3999,INTJ,25.24,0.1859222454914381,0.1,0.16,0.06,0.0,0.02,0.56,0.0,2.62,1,0,1,0 +4000,INFP,25.14,0.18956683334194202,0.04,0.12,0.16,0.02,0.0,0.54,0.0,2.96,1,0,0,1 +4001,ESFP,26.54,0.19300149505538433,0.0,1.24,0.54,0.0,0.0,0.78,0.0,2.22,0,1,0,1 +4002,INTP,18.06,0.1822495270359725,0.0,0.14,0.48,0.0,0.02,0.46,0.0,1.72,1,0,1,1 +4003,ESTP,25.14,0.17880627670160226,0.02,0.18,0.04,0.02,0.0,0.36,0.0,1.72,0,1,1,1 +4004,INTJ,25.12,0.1887043284077181,0.0,0.2,0.1,0.0,0.02,1.0,0.0,1.62,1,0,1,0 +4005,INTJ,24.72,0.18556318367346342,0.14,0.08,0.08,0.1,0.02,0.54,0.0,2.86,1,0,1,0 +4006,INFP,35.62,0.18581411692066707,0.0,0.3,0.04,0.0,0.0,1.14,0.0,3.86,1,0,0,1 +4007,INFJ,14.8,0.1814503147316035,0.46,0.28,0.06,0.26,0.0,0.36,0.0,1.78,1,0,0,0 +4008,INFP,19.94,0.18358594189679595,0.04,0.26,0.1,0.0,0.0,0.26,0.0,2.16,1,0,0,1 +4009,INFP,26.98,0.18306236355750002,0.12,0.14,0.18,0.06,0.02,0.84,0.0,3.34,1,0,0,1 +4010,INTP,29.68,0.18599447013778606,0.0,0.14,0.18,0.0,0.04,0.68,0.0,3.52,1,0,1,1 +4011,ISTJ,23.92,0.1837080425687388,0.0,0.12,0.0,0.0,0.06,0.4,0.0,2.56,1,1,1,0 +4012,ENTP,11.8,0.19185788534021034,0.0,0.26,0.08,0.0,0.0,0.12,0.0,1.2,0,0,1,1 +4013,ENTP,12.62,0.1862001122252505,0.2,0.3,0.04,0.04,0.0,0.22,0.02,2.88,0,0,1,1 +4014,INFP,23.5,0.19034599243408595,0.04,0.42,0.42,0.0,0.02,0.7,0.0,1.36,1,0,0,1 +4015,ENTP,24.5,0.18305247054443985,0.06,0.04,0.02,0.04,0.0,0.54,0.0,2.84,0,0,1,1 +4016,ISTJ,20.92,0.1812307575465781,0.1,0.46,0.0,0.0,0.0,0.34,0.0,1.12,1,1,1,0 +4017,INTP,14.92,0.18862288444986106,0.14,0.42,0.1,0.0,0.0,0.3,0.0,1.76,1,0,1,1 +4018,INFJ,26.16,0.18148176120869325,0.36,0.46,0.46,0.0,0.04,0.74,0.02,3.3,1,0,0,0 +4019,ESFJ,26.1,0.18032439099267347,0.0,0.18,0.78,0.0,0.02,0.66,0.0,3.16,0,1,0,0 +4020,INTJ,31.16,0.1813159905685861,0.0,0.32,0.02,0.0,0.0,0.74,0.0,3.62,1,0,1,0 +4021,INTP,28.96,0.17168812563702482,0.0,0.24,0.18,0.0,0.0,1.02,0.0,3.3,1,0,1,1 +4022,ENFP,23.06,0.18442090973492795,0.14,0.32,0.14,0.0,0.0,0.64,0.0,2.26,0,0,0,1 +4023,INFP,33.82,0.18188020858230514,0.04,0.06,0.16,0.04,0.0,0.88,0.0,3.68,1,0,0,1 +4024,INTP,22.68,0.18552900574684547,0.16,0.24,0.0,0.08,0.02,0.34,0.0,3.06,1,0,1,1 +4025,INTP,16.52,0.18896457284139773,0.3,0.34,0.48,0.04,0.0,0.3,0.0,1.7,1,0,1,1 +4026,INTP,33.72,0.18541804006099954,0.0,0.34,0.04,0.0,0.0,0.98,0.0,3.6,1,0,1,1 +4027,ENTP,14.54,0.18174358252735132,0.0,0.18,0.14,0.0,0.0,0.12,0.0,1.94,0,0,1,1 +4028,INFJ,19.82,0.17521589095255177,0.08,0.1,0.08,0.08,0.04,0.36,0.0,2.0,1,0,0,0 +4029,ENFP,31.54,0.19331587869120037,0.02,0.08,1.06,0.02,0.02,1.48,0.0,3.98,0,0,0,1 +4030,INFP,14.68,0.20070159074881802,0.02,0.16,0.1,0.0,0.0,0.98,0.0,1.68,1,0,0,1 +4031,INFJ,30.7,0.18487111238508627,0.0,0.2,0.08,0.0,0.0,1.2,0.0,3.56,1,0,0,0 +4032,INFP,26.2,0.1838495394801226,0.08,0.18,0.28,0.06,0.0,0.52,0.0,3.06,1,0,0,1 +4033,ENTJ,26.32,0.18382258939926988,0.0,0.16,0.08,0.0,0.0,1.18,0.0,2.88,0,0,1,0 +4034,INFP,29.74,0.18557940318660937,0.12,0.42,0.22,0.0,0.02,0.72,0.0,3.36,1,0,0,1 +4035,ENTP,19.86,0.19295892441908988,0.18,0.36,0.12,0.0,0.0,0.52,0.0,2.62,0,0,1,1 +4036,ISTP,28.54,0.18476992231772413,0.04,0.28,0.4,0.0,0.0,0.72,0.02,2.94,1,1,1,1 +4037,INFP,26.56,0.17662850000995337,0.02,0.22,0.04,0.0,0.02,0.66,0.0,3.04,1,0,0,1 +4038,ENTP,25.02,0.18285419960245117,0.0,0.18,0.12,0.0,0.08,0.64,0.0,2.16,0,0,1,1 +4039,INFP,14.28,0.1929725443198201,0.0,0.38,0.1,0.0,0.06,0.28,0.0,1.46,1,0,0,1 +4040,ENFP,32.1,0.18236548232046362,0.0,0.14,0.14,0.0,0.0,0.82,0.0,3.18,0,0,0,1 +4041,ISFP,24.66,0.18032210501874657,0.04,0.18,0.0,0.0,0.02,0.56,0.0,2.22,1,1,0,1 +4042,ENFP,25.58,0.18452833393054435,0.02,0.14,0.12,0.0,0.02,0.66,0.0,2.88,0,0,0,1 +4043,INFP,21.92,0.1883260014676268,0.24,0.2,0.28,0.1,0.08,0.44,0.0,2.74,1,0,0,1 +4044,ENFP,15.58,0.18327474859636664,0.0,0.06,0.04,0.0,0.0,0.34,0.0,1.68,0,0,0,1 +4045,INFP,22.32,0.180877739477317,0.18,0.32,0.18,0.06,0.0,0.52,0.0,2.16,1,0,0,1 +4046,INFP,22.8,0.18071846824069251,0.02,0.02,0.04,0.02,0.14,0.5,0.0,2.52,1,0,0,1 +4047,ENFP,14.34,0.1822011829339632,0.12,0.06,0.96,0.1,0.02,0.18,0.02,0.82,0,0,0,1 +4048,ISTJ,14.78,0.19132546366171072,0.04,0.42,0.02,0.0,0.0,0.18,0.0,1.66,1,1,1,0 +4049,ISFJ,31.38,0.19073030299397242,0.0,0.12,0.24,0.0,0.02,0.68,0.0,3.36,1,1,0,0 +4050,INTJ,21.4,0.17971651325115473,0.0,0.12,0.32,0.0,0.0,0.6,0.0,2.38,1,0,1,0 +4051,INTJ,30.48,0.17744778348237303,0.0,0.12,0.02,0.0,0.0,0.86,0.0,2.72,1,0,1,0 +4052,INFP,30.96,0.18226946893431561,0.0,0.1,0.02,0.0,0.0,0.7,0.0,3.44,1,0,0,1 +4053,ENTP,24.36,0.19322075151457205,0.04,0.34,0.52,0.02,0.0,0.58,0.0,2.78,0,0,1,1 +4054,INTP,29.16,0.18351082516863082,0.02,0.04,0.16,0.02,0.04,0.88,0.0,3.86,1,0,1,1 +4055,INFP,26.36,0.18281929698203325,0.06,0.04,0.32,0.0,0.0,0.58,0.0,2.82,1,0,0,1 +4056,INTJ,20.16,0.19598007202148438,0.1,0.26,0.0,0.0,0.0,0.62,0.0,2.06,1,0,1,0 +4057,ENFJ,29.32,0.18170293406735835,0.08,0.2,0.04,0.0,0.0,0.78,0.0,2.96,0,0,0,0 +4058,INTP,29.54,0.18480311236403119,0.02,0.1,0.08,0.0,0.0,0.72,0.04,3.52,1,0,1,1 +4059,ISFP,19.28,0.17909005409381445,0.0,0.38,0.24,0.0,0.0,0.6,0.0,1.88,1,1,0,1 +4060,ISTJ,31.56,0.18717592907834366,0.0,0.48,0.22,0.0,0.02,0.9,0.0,3.86,1,1,1,0 +4061,ENFP,15.58,0.1964897511651581,0.0,0.06,0.12,0.0,0.0,0.76,0.0,1.54,0,0,0,1 +4062,ESTJ,27.26,0.18978248284316646,0.02,0.12,0.62,0.02,0.0,0.66,0.0,2.9,0,1,1,0 +4063,INFJ,9.3,0.18007624304082406,0.16,0.06,0.0,0.16,0.0,0.16,0.0,1.2,1,0,0,0 +4064,INFP,19.08,0.1814779305132571,0.0,0.28,0.1,0.0,0.0,0.4,0.0,2.06,1,0,0,1 +4065,INFJ,27.26,0.1844381046068372,0.08,0.26,0.02,0.02,0.0,0.58,0.0,2.6,1,0,0,0 +4066,INTP,23.72,0.18487790495803452,0.0,0.24,0.08,0.0,0.04,0.52,0.0,2.24,1,0,1,1 +4067,ISTJ,29.52,0.1849990543787825,0.04,0.06,0.06,0.02,0.04,0.74,0.0,3.46,1,1,1,0 +4068,INTP,18.56,0.17968842590017153,0.0,0.24,0.08,0.0,0.0,0.46,0.0,1.82,1,0,1,1 +4069,ENTP,11.28,0.19086796495757738,0.16,0.34,0.08,0.02,0.0,0.24,0.0,1.02,0,0,1,1 +4070,INTP,28.28,0.1809004830865723,0.02,0.2,0.06,0.02,0.0,0.84,0.0,1.86,1,0,1,1 +4071,INFJ,28.08,0.1855212302294345,0.02,0.1,0.2,0.0,0.0,0.7,0.0,3.92,1,0,0,0 +4072,ENFP,30.18,0.18531669605597703,0.0,0.24,0.18,0.0,0.02,0.98,0.02,3.42,0,0,0,1 +4073,INFJ,25.62,0.1945921411064552,0.0,0.28,0.32,0.0,0.0,1.68,0.0,3.2,1,0,0,0 +4074,INFP,31.98,0.18351719481724757,0.0,0.12,0.0,0.0,0.0,0.86,0.0,4.02,1,0,0,1 +4075,ENTJ,23.5,0.18533836370248466,0.02,0.22,0.1,0.0,0.0,0.52,0.0,2.54,0,0,1,0 +4076,INTP,24.22,0.17650328953621605,0.0,0.46,0.04,0.0,0.0,0.68,0.0,1.14,1,0,1,1 +4077,ENTP,31.12,0.18279267924923057,0.0,0.2,0.14,0.0,0.02,0.76,0.0,2.96,0,0,1,1 +4078,ENTP,31.74,0.18637894610969388,0.0,0.18,0.04,0.0,0.0,0.86,0.02,3.58,0,0,1,1 +4079,INTP,17.28,0.18742723392485708,0.04,0.1,0.0,0.0,0.02,0.36,0.0,2.28,1,0,1,1 +4080,INFJ,22.18,0.18206601535318506,0.12,0.14,0.02,0.0,0.02,0.62,0.0,2.94,1,0,0,0 +4081,ISTP,14.6,0.18616423830787526,0.26,0.26,0.02,0.08,0.12,0.18,0.02,1.56,1,1,1,1 +4082,INFJ,15.88,0.18219909560111497,0.0,0.12,0.2,0.0,0.0,0.46,0.0,2.14,1,0,0,0 +4083,INFP,24.46,0.18629294632551993,0.06,0.14,0.1,0.0,0.06,0.98,0.02,3.28,1,0,0,1 +4084,ENFP,24.8,0.20129050901460863,0.0,0.28,0.74,0.0,0.06,0.78,0.0,3.2,0,0,0,1 +4085,INFP,33.1,0.180992117066316,0.06,0.1,0.02,0.02,0.0,0.8,0.0,4.1,1,0,0,1 +4086,INFP,23.56,0.18683759549489043,0.2,0.2,0.36,0.06,0.0,0.74,0.0,1.46,1,0,0,1 +4087,INTP,28.42,0.19065628154728367,0.0,0.46,0.04,0.0,0.0,1.26,0.0,3.22,1,0,1,1 +4088,INTJ,26.1,0.18495201916549686,0.04,0.26,0.04,0.04,0.0,0.7,0.0,3.24,1,0,1,0 +4089,INTP,33.48,0.1866991314172728,0.0,0.16,0.08,0.0,0.0,0.82,0.0,3.66,1,0,1,1 +4090,INFP,29.96,0.17917126370862685,0.0,0.22,0.2,0.0,0.0,0.78,0.0,2.78,1,0,0,1 +4091,INFJ,30.34,0.18998833022741982,0.0,0.28,1.36,0.0,0.06,0.78,0.0,4.4,1,0,0,0 +4092,INFP,12.74,0.18303962341162577,0.14,0.12,0.3,0.04,0.0,0.44,0.0,1.32,1,0,0,1 +4093,INTP,19.7,0.19080912681443918,0.0,0.16,0.18,0.0,0.02,0.96,0.0,2.3,1,0,1,1 +4094,ISFP,17.34,0.18645891705460763,0.22,0.32,0.32,0.06,0.0,0.34,0.0,1.78,1,1,0,1 +4095,INFJ,27.8,0.18509084317446295,0.04,0.22,0.14,0.0,0.0,0.8,0.0,2.88,1,0,0,0 +4096,ENFP,14.6,0.18321426378990718,0.06,0.3,0.0,0.0,0.02,0.28,0.0,1.54,0,0,0,1 +4097,INFJ,21.42,0.18100459812816283,0.0,0.12,0.0,0.0,0.0,0.58,0.0,2.74,1,0,0,0 +4098,INTP,22.14,0.1885753344,0.02,0.22,0.1,0.0,0.0,0.5,0.0,2.84,1,0,1,1 +4099,ENFP,26.06,0.17772573555107077,0.14,0.24,0.08,0.0,0.02,0.66,0.0,2.36,0,0,0,1 +4100,INTJ,27.96,0.17846294727684806,0.04,0.14,0.02,0.0,0.04,0.9,0.0,1.82,1,0,1,0 +4101,ENFP,13.92,0.18816542750198584,0.02,0.14,0.02,0.0,0.0,0.3,0.0,1.62,0,0,0,1 +4102,INFP,25.62,0.1798034335035141,0.18,0.2,0.08,0.04,0.0,0.58,0.02,2.86,1,0,0,1 +4103,INFP,33.98,0.18238539832912018,0.04,0.3,0.14,0.0,0.0,1.16,0.0,3.62,1,0,0,1 +4104,INFP,17.24,0.18810925712739657,0.0,0.1,0.1,0.0,0.0,0.52,0.0,2.32,1,0,0,1 +4105,ISTJ,22.04,0.17536153570427368,0.0,0.06,0.02,0.0,0.0,0.42,0.0,1.52,1,1,1,0 +4106,INFJ,28.72,0.18071110803422571,0.0,0.18,0.08,0.0,0.0,0.82,0.0,2.76,1,0,0,0 +4107,ISTP,22.26,0.18472744299470495,0.04,0.14,0.02,0.0,0.0,0.6,0.0,1.92,1,1,1,1 +4108,INFJ,23.84,0.1815689690255886,0.02,0.04,0.54,0.0,0.04,0.7,0.0,2.84,1,0,0,0 +4109,INFP,20.22,0.1943118650234817,0.0,0.14,0.22,0.0,0.0,0.58,0.0,3.0,1,0,0,1 +4110,INTJ,34.02,0.18364327665912458,0.0,0.12,0.12,0.0,0.02,0.92,0.0,3.86,1,0,1,0 +4111,INFJ,28.38,0.18075617729686802,0.08,0.24,0.0,0.0,0.0,0.92,0.0,2.82,1,0,0,0 +4112,ENTJ,25.2,0.19092226128561135,0.26,0.32,0.26,0.06,0.02,0.62,0.0,2.92,0,0,1,0 +4113,ISFP,16.98,0.18504788274089423,0.66,0.88,0.08,0.02,0.08,0.54,0.0,1.82,1,1,0,1 +4114,INTP,16.1,0.1790647975688658,0.14,0.22,0.06,0.02,0.0,0.38,0.02,1.3,1,0,1,1 +4115,ISFJ,29.84,0.18685035373188086,0.08,0.14,0.52,0.0,0.0,0.88,0.0,3.44,1,1,0,0 +4116,INTJ,12.08,0.19294985203993495,0.02,0.12,0.02,0.0,0.0,0.22,0.0,1.36,1,0,1,0 +4117,ESTJ,25.86,0.1875174301051931,0.0,0.08,0.22,0.0,0.0,0.82,0.0,2.98,0,1,1,0 +4118,INFJ,28.68,0.19034017918317978,0.04,0.22,0.08,0.0,0.04,1.36,0.0,1.16,1,0,0,0 +4119,INFJ,25.4,0.18276451514029793,0.02,0.26,0.3,0.0,0.0,0.74,0.0,2.16,1,0,0,0 +4120,INFJ,33.72,0.1841163242492493,0.02,0.22,0.12,0.0,0.0,0.9,0.0,4.2,1,0,0,0 +4121,INTP,33.26,0.18459262856790018,0.0,0.18,0.04,0.0,0.0,0.92,0.0,4.4,1,0,1,1 +4122,INTP,27.98,0.17843209339422844,0.0,0.1,0.0,0.0,0.0,0.74,0.02,2.42,1,0,1,1 +4123,ISTJ,18.82,0.18528149933564148,0.02,0.1,0.24,0.02,0.04,0.34,0.0,1.36,1,1,1,0 +4124,INTP,20.24,0.1898803862010034,0.34,0.5,0.2,0.1,0.04,0.58,0.0,2.64,1,0,1,1 +4125,INTP,24.94,0.18426776822091445,0.12,0.86,0.12,0.0,0.06,0.56,0.0,2.74,1,0,1,1 +4126,INFJ,32.6,0.18465008931694615,0.02,0.24,0.18,0.0,0.04,1.32,0.0,4.64,1,0,0,0 +4127,ISTJ,30.66,0.1922019137829098,0.08,0.2,0.26,0.02,0.0,0.7,0.0,3.96,1,1,1,0 +4128,ENTP,23.28,0.18432497994484667,0.08,0.32,0.26,0.0,0.02,0.56,0.1,1.92,0,0,1,1 +4129,ESTJ,24.4,0.18433683699846404,0.0,0.18,0.24,0.0,0.0,0.52,0.0,3.42,0,1,1,0 +4130,ENFP,20.5,0.1952661984463274,0.02,0.38,0.32,0.0,0.0,1.3,0.0,2.62,0,0,0,1 +4131,ISFJ,14.1,0.19343904010598412,0.46,0.58,0.06,0.02,0.04,0.2,0.0,1.52,1,1,0,0 +4132,ENFP,20.98,0.18364452900998868,0.06,0.1,0.04,0.0,0.04,0.52,0.0,1.22,0,0,0,1 +4133,ENTP,27.16,0.19293040917877782,0.02,0.5,0.64,0.0,0.02,1.04,0.0,2.8,0,0,1,1 +4134,INFJ,33.6,0.18497692825620268,0.0,0.32,0.3,0.0,0.0,1.0,0.0,4.3,1,0,0,0 +4135,ENTP,27.66,0.190132600419911,0.08,0.16,0.0,0.04,0.0,0.96,0.0,2.78,0,0,1,1 +4136,INTJ,26.64,0.18493919163942849,0.04,0.36,0.26,0.02,0.0,1.2,0.12,2.96,1,0,1,0 +4137,INFP,26.68,0.1782264694456344,0.02,0.16,0.1,0.0,0.02,0.6,0.0,2.62,1,0,0,1 +4138,INFP,9.76,0.2012180413286424,0.02,0.32,0.08,0.0,0.0,0.08,0.0,0.98,1,0,0,1 +4139,INFJ,29.18,0.18594588525725467,0.12,0.18,0.04,0.02,0.1,1.02,0.0,3.56,1,0,0,0 +4140,INTP,16.64,0.19475811922097266,0.0,0.24,0.18,0.0,0.0,0.32,0.0,2.12,1,0,1,1 +4141,ENTP,16.54,0.1815127600492701,0.38,0.46,0.02,0.04,0.0,0.38,0.0,1.64,0,0,1,1 +4142,INTJ,20.98,0.1855659986934425,0.02,0.1,0.04,0.0,0.0,0.42,0.0,2.8,1,0,1,0 +4143,INFJ,31.48,0.17850564457540258,0.04,0.08,0.08,0.0,0.0,0.84,0.02,3.0,1,0,0,0 +4144,INTJ,8.24,0.1864308555484398,0.24,0.32,0.0,0.14,0.02,0.2,0.02,0.94,1,0,1,0 +4145,INTP,19.34,0.18225139751159633,0.1,0.16,0.14,0.04,0.0,0.38,0.0,1.9,1,0,1,1 +4146,INFJ,30.44,0.18370311386396898,0.0,0.1,0.04,0.0,0.0,0.6,0.0,4.14,1,0,0,0 +4147,INTP,23.86,0.17495814909035462,0.0,0.32,0.0,0.0,0.0,0.42,0.02,1.86,1,0,1,1 +4148,ESTJ,25.68,0.18611919385788142,0.0,0.3,0.1,0.0,0.0,0.86,0.0,2.32,0,1,1,0 +4149,ENFJ,29.82,0.18691203788387506,0.18,0.3,0.4,0.0,0.12,0.84,0.0,3.5,0,0,0,0 +4150,INFJ,29.7,0.18928995031151644,0.1,0.26,0.62,0.06,0.08,0.84,0.0,3.18,1,0,0,0 +4151,INTP,18.86,0.18119809392310432,0.34,0.62,0.06,0.02,0.02,0.46,0.0,1.5,1,0,1,1 +4152,ENTJ,21.16,0.18660180791861514,0.0,0.46,0.06,0.0,0.0,0.36,0.0,2.1,0,0,1,0 +4153,INFP,24.08,0.1887607210962871,0.0,0.02,0.04,0.0,0.04,0.54,0.0,3.22,1,0,0,1 +4154,INFP,18.6,0.18519148618052889,0.3,0.44,0.06,0.02,0.0,0.72,0.0,2.64,1,0,0,1 +4155,INTP,10.66,0.20402770621337707,0.12,0.24,0.0,0.0,0.0,0.12,0.0,1.14,1,0,1,1 +4156,INFP,15.32,0.19409636316859488,0.0,0.18,0.18,0.0,0.0,0.6,0.0,2.1,1,0,0,1 +4157,ISFJ,29.4,0.19112325830666066,0.08,0.12,0.22,0.04,0.06,0.74,0.0,4.04,1,1,0,0 +4158,INFJ,27.8,0.18409757565463353,0.0,0.26,0.0,0.0,0.0,0.6,0.0,3.26,1,0,0,0 +4159,INFP,26.34,0.19621340201020487,0.02,0.18,0.44,0.0,0.0,0.92,0.0,2.46,1,0,0,1 +4160,INTJ,25.38,0.18211661509498214,0.0,0.08,0.08,0.0,0.0,0.8,0.0,3.0,1,0,1,0 +4161,INTJ,21.9,0.1874639613673337,0.22,0.26,0.96,0.0,0.04,1.1,0.0,2.28,1,0,1,0 +4162,ENFJ,28.4,0.19691715388189032,0.0,0.12,0.08,0.0,0.06,1.72,0.0,1.46,0,0,0,0 +4163,INFJ,23.12,0.1873744890224946,0.02,0.22,0.14,0.0,0.0,0.78,0.0,2.26,1,0,0,0 +4164,ENFP,17.1,0.19489458152306333,0.02,0.32,0.38,0.02,0.04,0.46,0.0,1.88,0,0,0,1 +4165,ESTP,4.98,0.16931627218697484,1.06,0.42,0.0,0.54,0.02,0.14,0.0,1.0,0,1,1,1 +4166,INFJ,33.06,0.17987902336497844,0.02,0.24,0.04,0.0,0.0,0.94,0.0,3.92,1,0,0,0 +4167,ENFJ,28.92,0.18535383226285757,0.02,0.12,0.76,0.0,0.02,0.82,0.02,2.94,0,0,0,0 +4168,ENTP,25.52,0.1836338687686055,0.08,0.1,0.2,0.0,0.02,0.58,0.02,3.14,0,0,1,1 +4169,INFJ,31.28,0.18584444252077564,0.08,0.26,0.44,0.0,0.0,0.84,0.0,4.46,1,0,0,0 +4170,INFP,19.76,0.1869711033134834,0.04,0.08,0.44,0.0,0.0,0.42,0.0,2.02,1,0,0,1 +4171,ENTP,29.62,0.18025366961466838,0.0,0.18,0.22,0.0,0.0,0.78,0.0,2.98,0,0,1,1 +4172,ENTP,32.74,0.18530083100982347,0.0,0.5,0.42,0.0,0.0,1.02,0.0,3.76,0,0,1,1 +4173,ENFJ,20.92,0.18326317474606688,0.0,0.12,0.0,0.0,0.0,0.36,0.0,2.36,0,0,0,0 +4174,INTP,32.56,0.1774972558022486,0.0,0.14,0.28,0.0,0.0,1.44,0.0,3.98,1,0,1,1 +4175,INTJ,25.84,0.1885385980106798,0.0,0.18,0.46,0.0,0.0,0.78,0.0,3.06,1,0,1,0 +4176,INFP,11.82,0.19050543866984238,0.1,0.24,0.08,0.0,0.02,0.18,0.0,1.52,1,0,0,1 +4177,ENTP,25.58,0.1843649865912605,0.0,0.14,0.32,0.0,0.0,0.52,0.0,2.54,0,0,1,1 +4178,INFP,10.48,0.18599704766591002,0.0,0.06,0.0,0.0,0.02,0.18,0.0,1.2,1,0,0,1 +4179,INFP,31.12,0.18205820987979104,0.0,0.3,0.28,0.0,0.0,0.86,0.02,3.02,1,0,0,1 +4180,INTP,32.16,0.17938704523337784,0.0,0.14,0.06,0.0,0.02,0.92,0.0,3.1,1,0,1,1 +4181,ENTP,29.16,0.17676424310824557,0.0,0.1,0.38,0.0,0.0,0.64,0.0,3.18,0,0,1,1 +4182,ESTJ,28.34,0.18606650494660656,0.0,0.18,0.42,0.0,0.0,0.92,0.0,2.58,0,1,1,0 +4183,ENFP,25.38,0.20364290408079103,0.04,0.08,0.18,0.0,0.1,0.48,0.0,3.62,0,0,0,1 +4184,INTP,10.32,0.19335840604016127,0.18,0.28,0.18,0.02,0.0,0.14,0.0,0.9,1,0,1,1 +4185,INFP,22.06,0.1840437846309804,0.38,0.34,0.34,0.04,0.0,0.68,0.0,1.62,1,0,0,1 +4186,ENTJ,21.88,0.18698421104224702,0.0,0.1,0.02,0.0,0.0,0.34,0.0,2.5,0,0,1,0 +4187,INTP,29.48,0.17309559567399382,0.08,0.14,0.04,0.0,0.08,0.8,0.0,2.78,1,0,1,1 +4188,INFP,20.66,0.19038005156643906,0.28,0.44,0.18,0.1,0.02,0.64,0.02,2.56,1,0,0,1 +4189,INFJ,30.76,0.18266160878899618,0.1,0.08,0.06,0.04,0.06,0.86,0.0,3.76,1,0,0,0 +4190,INFJ,20.14,0.19224077409114493,0.0,0.2,0.14,0.0,0.02,0.6,0.0,2.94,1,0,0,0 +4191,ESTJ,30.52,0.1874205895512382,0.0,0.2,0.06,0.0,0.0,0.68,0.0,3.66,0,1,1,0 +4192,INFP,25.9,0.17905965699275705,0.0,0.12,0.62,0.0,0.0,0.48,0.0,3.04,1,0,0,1 +4193,INTJ,23.94,0.1854391447944552,0.28,0.36,0.34,0.08,0.02,0.96,0.02,2.54,1,0,1,0 +4194,ISTJ,20.26,0.18911064776526226,0.0,0.1,0.1,0.0,0.0,0.42,0.0,2.26,1,1,1,0 +4195,ENFP,22.2,0.1764757415362431,0.06,0.24,0.14,0.04,0.02,0.66,0.0,1.96,0,0,0,1 +4196,ISFJ,23.6,0.18767287736631522,0.0,0.06,0.2,0.0,0.0,0.34,0.0,2.86,1,1,0,0 +4197,INFJ,32.64,0.18942501474890971,0.0,0.12,0.32,0.0,0.0,0.96,0.0,3.52,1,0,0,0 +4198,INTP,23.3,0.18356905414556524,0.34,0.22,0.1,0.22,0.0,0.68,0.0,2.72,1,0,1,1 +4199,INTP,29.94,0.19309852412282794,0.1,0.16,0.1,0.06,0.06,1.16,0.0,3.58,1,0,1,1 +4200,INFP,31.86,0.18333422706872052,0.04,0.08,0.12,0.02,0.06,0.84,0.0,3.76,1,0,0,1 +4201,INTP,29.4,0.17908795155855511,0.08,0.28,0.08,0.0,0.0,0.72,0.0,2.92,1,0,1,1 +4202,INTP,28.24,0.1889362067832456,0.0,0.38,0.0,0.0,0.0,0.78,0.0,3.78,1,0,1,1 +4203,ENFP,21.28,0.18653289637326861,0.04,0.2,0.12,0.0,0.0,0.44,0.04,2.08,0,0,0,1 +4204,INTP,25.96,0.17741698601223424,0.0,0.24,0.0,0.0,0.0,0.66,0.0,1.88,1,0,1,1 +4205,INTP,13.0,0.1764543613628881,0.0,0.12,0.0,0.0,0.0,0.26,0.0,1.36,1,0,1,1 +4206,ENFJ,31.36,0.1819741183913817,0.0,0.04,0.18,0.0,0.04,1.06,0.02,4.18,0,0,0,0 +4207,INTP,23.02,0.19097234137373892,0.0,0.24,0.18,0.0,0.02,0.76,0.04,2.86,1,0,1,1 +4208,ENFJ,32.4,0.1939572090021665,0.0,0.14,0.52,0.0,0.0,1.66,0.0,4.36,0,0,0,0 +4209,ENTP,27.04,0.18997123650014977,0.06,0.06,0.08,0.0,0.0,0.96,0.0,3.48,0,0,1,1 +4210,ENFP,25.44,0.19028586695444774,0.08,0.1,0.38,0.06,0.0,0.98,0.0,3.42,0,0,0,1 +4211,INTP,31.8,0.18222108521276512,0.0,0.06,0.14,0.0,0.0,0.76,0.0,3.18,1,0,1,1 +4212,INTP,26.66,0.17680498261386082,0.02,0.18,0.16,0.0,0.0,0.48,0.0,2.58,1,0,1,1 +4213,INFP,32.08,0.17893930541447226,0.0,0.1,0.0,0.0,0.0,1.08,0.0,3.56,1,0,0,1 +4214,ISFP,19.62,0.19306016646848986,0.0,0.16,1.12,0.0,0.0,0.46,0.0,2.78,1,1,0,1 +4215,INFJ,27.6,0.18653404148271827,0.1,0.18,0.1,0.1,0.0,0.88,0.0,2.92,1,0,0,0 +4216,INFJ,29.8,0.1850302606621002,0.06,0.38,0.42,0.02,0.0,0.68,0.0,3.92,1,0,0,0 +4217,INFP,21.76,0.18613128052001032,0.02,0.06,0.1,0.0,0.02,0.42,0.0,3.12,1,0,0,1 +4218,INFP,27.24,0.18639663759461422,0.0,0.28,0.24,0.0,0.0,0.72,0.0,3.32,1,0,0,1 +4219,INFP,26.58,0.18717793440222247,0.12,0.12,0.08,0.04,0.0,0.94,0.0,2.9,1,0,0,1 +4220,INFJ,30.12,0.1794253645205725,0.0,0.2,0.0,0.0,0.0,0.72,0.0,3.38,1,0,0,0 +4221,INTP,23.94,0.17926023875161734,0.06,0.24,0.38,0.02,0.0,0.58,0.0,2.44,1,0,1,1 +4222,INFP,25.92,0.1840093817758932,0.0,0.26,0.9,0.0,0.0,0.6,0.0,2.96,1,0,0,1 +4223,INTP,15.46,0.178794795467341,0.14,0.24,0.02,0.14,0.0,0.28,0.0,2.12,1,0,1,1 +4224,INFJ,31.6,0.1846042108535647,0.1,0.28,0.02,0.0,0.02,1.0,0.0,3.46,1,0,0,0 +4225,INTJ,19.82,0.18204261146302306,0.24,0.44,0.16,0.0,0.0,0.46,0.0,2.02,1,0,1,0 +4226,ENTP,24.5,0.19113889370373668,0.0,0.16,0.1,0.0,0.0,0.58,0.0,3.14,0,0,1,1 +4227,INTJ,26.84,0.188628681880929,0.0,0.32,0.16,0.0,0.08,0.82,0.0,3.14,1,0,1,0 +4228,INFJ,27.92,0.1819132731281442,0.06,0.4,0.0,0.02,0.0,0.74,0.0,2.52,1,0,0,0 +4229,ESTP,26.02,0.19192943577818525,0.0,0.32,0.26,0.0,0.0,0.54,0.0,3.86,0,1,1,1 +4230,INTP,22.38,0.18095025977765145,0.04,0.14,0.04,0.0,0.0,0.46,0.0,1.22,1,0,1,1 +4231,INTP,24.1,0.17675691790753734,0.08,0.34,0.04,0.0,0.02,0.48,0.0,2.08,1,0,1,1 +4232,INFP,27.18,0.18975443771979356,0.02,0.12,0.14,0.0,0.02,0.56,0.0,4.18,1,0,0,1 +4233,ISTP,19.06,0.18706586585437354,0.1,0.44,0.16,0.0,0.0,0.48,0.0,1.76,1,1,1,1 +4234,ENFJ,21.56,0.19044075963718826,0.0,0.26,0.36,0.0,0.0,0.54,0.0,2.84,0,0,0,0 +4235,INFJ,32.78,0.1826364832346901,0.0,0.16,0.16,0.0,0.0,1.24,0.0,3.52,1,0,0,0 +4236,ENFJ,25.28,0.18606525550820924,0.06,0.26,0.2,0.0,0.0,0.68,0.0,3.0,0,0,0,0 +4237,INFJ,26.64,0.17898007124341245,0.02,0.3,0.04,0.0,0.0,0.82,0.0,2.12,1,0,0,0 +4238,INFP,29.4,0.18854446428902055,0.0,0.04,0.18,0.0,0.0,0.64,0.0,2.94,1,0,0,1 +4239,INTP,28.42,0.18560502844993912,0.04,0.22,0.16,0.0,0.0,0.68,0.0,2.86,1,0,1,1 +4240,INFP,24.72,0.18325080145231473,0.46,0.68,0.02,0.0,0.02,0.54,0.0,3.0,1,0,0,1 +4241,INFP,27.9,0.1927923121644267,0.0,0.08,0.38,0.0,0.1,0.66,0.0,4.34,1,0,0,1 +4242,INFJ,36.08,0.18198664694004701,0.0,0.18,0.1,0.0,0.0,1.26,0.04,4.2,1,0,0,0 +4243,INTP,17.62,0.1993010456821588,0.02,0.38,0.2,0.02,0.04,1.1,0.0,2.92,1,0,1,1 +4244,INFJ,21.94,0.18422747928994082,0.04,0.78,0.1,0.0,0.0,0.34,0.0,1.82,1,0,0,0 +4245,ENFP,18.56,0.18586608196997806,0.0,0.24,0.36,0.0,0.0,0.3,0.0,1.84,0,0,0,1 +4246,ISTJ,29.7,0.19003086519914927,0.0,0.2,0.34,0.0,0.04,0.96,0.0,4.2,1,1,1,0 +4247,ENFP,13.34,0.1941100770407092,0.0,0.38,0.08,0.0,0.0,0.28,0.08,1.62,0,0,0,1 +4248,INFP,29.02,0.18366569889610948,0.06,0.16,0.26,0.0,0.0,0.88,0.0,3.18,1,0,0,1 +4249,ISTJ,30.6,0.18687904007637904,0.0,0.18,0.08,0.0,0.04,1.02,0.1,3.62,1,1,1,0 +4250,ENTP,30.02,0.18085225938470584,0.02,0.2,0.0,0.02,0.0,0.76,0.0,2.14,0,0,1,1 +4251,INFJ,10.54,0.1904407596371882,0.36,0.16,0.12,0.02,0.0,0.28,0.0,1.0,1,0,0,0 +4252,ENTJ,21.8,0.1822596451868891,0.0,0.04,0.04,0.0,0.0,0.72,0.0,2.88,0,0,1,0 +4253,ENFP,19.14,0.19124557029441888,0.0,0.18,0.1,0.0,0.0,0.38,0.0,2.14,0,0,0,1 +4254,INTP,32.78,0.18121878692878268,0.06,0.12,0.12,0.02,0.02,0.8,0.0,3.32,1,0,1,1 +4255,ENTP,28.26,0.1792218740199845,0.0,0.14,0.06,0.0,0.0,0.7,0.0,3.18,0,0,1,1 +4256,ISFP,29.92,0.17871313548153753,0.0,0.08,0.0,0.0,0.02,0.74,0.0,3.84,1,1,0,1 +4257,INFP,10.36,0.1837622684114271,0.34,0.2,0.0,0.06,0.0,0.24,0.0,1.66,1,0,0,1 +4258,INFP,25.22,0.18269181170273413,0.18,0.2,0.12,0.08,0.02,0.66,0.0,2.96,1,0,0,1 +4259,INFP,24.26,0.17951462565885024,0.12,0.12,0.08,0.04,0.0,0.66,0.0,1.36,1,0,0,1 +4260,ENTP,28.74,0.167305172154467,0.0,0.2,0.02,0.0,0.0,0.8,0.0,1.68,0,0,1,1 +4261,INTJ,23.18,0.18512196328144626,0.0,0.1,0.0,0.0,0.0,0.5,0.0,2.26,1,0,1,0 +4262,INFP,23.26,0.19062518440395024,0.04,0.26,0.0,0.0,0.02,0.7,0.0,2.92,1,0,0,1 +4263,ENFP,28.02,0.188884029623134,0.02,0.24,0.14,0.0,0.0,0.92,0.0,3.02,0,0,0,1 +4264,INFJ,27.6,0.1859148335449865,0.04,0.28,0.1,0.0,0.04,0.78,0.0,3.3,1,0,0,0 +4265,ISTP,28.14,0.18820158471464926,0.0,0.24,0.62,0.0,0.0,0.84,0.02,3.6,1,1,1,1 +4266,INFP,25.64,0.1884490100809911,0.0,0.48,0.9,0.0,0.04,0.5,0.0,2.98,1,0,0,1 +4267,INFJ,22.4,0.18988637155792373,0.12,0.2,0.1,0.04,0.02,0.84,0.0,2.32,1,0,0,0 +4268,INFP,29.72,0.18487405542128246,0.0,0.1,0.14,0.0,0.02,1.04,0.0,2.4,1,0,0,1 +4269,ENTJ,31.62,0.1883338031985169,0.0,0.8,0.0,0.0,0.0,0.74,0.0,3.4,0,0,1,0 +4270,INTP,28.76,0.1853069198763381,0.06,0.3,0.16,0.0,0.0,0.78,0.0,3.66,1,0,1,1 +4271,INFJ,24.22,0.18059817558161234,0.1,0.18,0.16,0.0,0.02,0.68,0.0,2.6,1,0,0,0 +4272,INFP,29.22,0.18642061315398722,0.0,0.22,0.08,0.0,0.0,0.88,0.0,3.76,1,0,0,1 +4273,INFP,30.1,0.18245532622085991,0.02,0.02,0.0,0.02,0.02,0.88,0.0,4.62,1,0,0,1 +4274,INFP,26.9,0.17870378273804455,0.12,0.28,0.3,0.0,0.08,0.68,0.0,2.22,1,0,0,1 +4275,INTP,26.86,0.18504245628561652,0.08,0.32,0.08,0.04,0.0,0.86,0.0,2.92,1,0,1,1 +4276,INFJ,29.8,0.18220010698855385,0.06,0.2,0.14,0.0,0.02,0.66,0.0,3.44,1,0,0,0 +4277,ESFJ,33.54,0.18336243179609077,0.0,0.16,0.26,0.0,0.02,0.86,0.0,4.32,0,1,0,0 +4278,ENFP,21.28,0.18823323149726987,0.48,0.38,0.14,0.02,0.02,0.94,0.0,2.3,0,0,0,1 +4279,ENFJ,28.4,0.19316176903958746,0.02,0.14,0.02,0.0,0.0,0.7,0.0,3.62,0,0,0,0 +4280,INFP,27.68,0.19531715010422573,0.22,0.38,0.38,0.0,0.02,1.02,0.0,3.5,1,0,0,1 +4281,INTJ,29.0,0.18350256004268642,0.0,0.06,0.0,0.0,0.0,0.86,0.0,3.06,1,0,1,0 +4282,ENTP,24.62,0.17672685393886153,0.0,0.16,0.14,0.0,0.0,0.56,0.0,2.06,0,0,1,1 +4283,ISFP,27.26,0.19166521740273199,0.0,0.08,0.08,0.0,0.08,0.88,0.0,3.04,1,1,0,1 +4284,INFP,25.16,0.1866060900130331,0.04,0.12,0.08,0.02,0.06,1.12,0.0,2.74,1,0,0,1 +4285,INFJ,31.96,0.1798969087480987,0.0,0.04,0.22,0.0,0.0,0.98,0.0,3.84,1,0,0,0 +4286,INTJ,18.76,0.19052391179125774,0.04,0.26,0.14,0.0,0.0,0.36,0.0,2.12,1,0,1,0 +4287,INFP,25.58,0.18337589950677907,0.04,0.18,0.02,0.0,0.0,0.44,0.0,2.58,1,0,0,1 +4288,INTP,32.82,0.18488341228912902,0.0,0.2,0.0,0.0,0.02,0.74,0.0,5.0,1,0,1,1 +4289,INFP,29.5,0.1844306245991385,0.04,0.16,0.02,0.0,0.0,0.62,0.0,3.88,1,0,0,1 +4290,INFP,28.1,0.18514773979937527,0.06,0.2,0.02,0.04,0.0,0.74,0.0,3.02,1,0,0,1 +4291,INFJ,26.64,0.18233498437499998,0.02,0.1,0.1,0.0,0.0,0.78,0.0,3.2,1,0,0,0 +4292,INFJ,31.82,0.1851580877999161,0.0,0.2,0.08,0.0,0.0,0.96,0.0,3.78,1,0,0,0 +4293,INTJ,27.2,0.1838365202357799,0.1,0.36,0.12,0.0,0.04,0.84,0.0,3.08,1,0,1,0 +4294,ENTJ,22.94,0.19256948644632849,0.02,0.26,0.14,0.02,0.0,0.62,0.0,1.86,0,0,1,0 +4295,ESFJ,17.38,0.18840423327734696,0.04,0.02,0.06,0.0,0.02,0.22,0.0,0.86,0,1,0,0 +4296,INTJ,29.4,0.1809006001297578,0.0,0.28,0.08,0.0,0.02,0.84,0.0,3.62,1,0,1,0 +4297,INTP,22.14,0.18518330447288917,0.14,0.28,0.06,0.02,0.0,0.58,0.0,2.42,1,0,1,1 +4298,INTP,30.88,0.18104281289298732,0.02,0.2,0.06,0.0,0.0,0.78,0.0,3.08,1,0,1,1 +4299,INTP,29.46,0.18420588162045037,0.06,0.34,0.08,0.0,0.0,0.86,0.0,3.28,1,0,1,1 +4300,ENFP,30.72,0.18757403896761823,0.06,0.14,0.18,0.0,0.02,0.74,0.0,3.6,0,0,0,1 +4301,ENTP,16.68,0.18952275861501097,0.04,0.14,0.08,0.0,0.02,0.24,0.0,1.34,0,0,1,1 +4302,INTP,16.96,0.18075057219528232,0.1,0.08,0.1,0.08,0.0,0.3,0.0,2.06,1,0,1,1 +4303,ISFJ,31.54,0.18909813274123094,0.0,0.1,0.32,0.0,0.0,0.92,0.0,4.36,1,1,0,0 +4304,ENTP,19.18,0.19143270743671617,0.08,0.2,1.2,0.0,0.0,0.4,0.0,2.42,0,0,1,1 +4305,INTP,19.94,0.1833084808177648,0.18,0.24,0.08,0.06,0.0,0.44,0.0,2.08,1,0,1,1 +4306,ENTJ,31.72,0.1874859178730802,0.0,0.2,0.16,0.0,0.0,0.8,0.0,4.1,0,0,1,0 +4307,INTJ,26.4,0.18459547454782071,0.4,0.48,0.18,0.02,0.06,0.76,0.02,3.14,1,0,1,0 +4308,ENFP,18.34,0.17995967071780153,0.04,0.24,0.14,0.0,0.0,0.28,0.0,2.02,0,0,0,1 +4309,INTP,8.94,0.18469890968449776,0.8,0.86,0.16,0.0,0.0,0.26,0.0,1.32,1,0,1,1 +4310,INFP,28.44,0.18118111132243825,0.08,0.16,0.12,0.04,0.0,0.72,0.0,3.6,1,0,0,1 +4311,INTP,22.96,0.19258695038026813,0.02,0.28,0.06,0.02,0.02,0.66,0.0,2.4,1,0,1,1 +4312,ENTP,28.0,0.18510276534307163,0.04,0.08,0.14,0.02,0.0,0.62,0.0,3.88,0,0,1,1 +4313,INFJ,35.3,0.18362216561518974,0.0,0.02,0.2,0.0,0.0,0.9,0.06,4.88,1,0,0,0 +4314,INTP,27.56,0.17621334399254102,0.0,0.04,0.34,0.0,0.0,0.64,0.0,2.84,1,0,1,1 +4315,INFJ,32.14,0.1794045697681025,0.02,0.02,0.16,0.0,0.08,0.68,0.0,4.12,1,0,0,0 +4316,ISFP,31.66,0.19121410127333086,0.0,0.32,0.02,0.0,0.0,0.62,0.0,4.48,1,1,0,1 +4317,ENTP,32.2,0.1879658947605611,0.0,0.14,0.0,0.0,0.0,1.12,0.0,3.14,0,0,1,1 +4318,INFP,20.82,0.19018874380590994,0.02,0.16,0.02,0.02,0.02,0.46,0.0,2.04,1,0,0,1 +4319,INTP,30.4,0.1817596731101832,0.0,0.28,0.0,0.0,0.06,0.74,0.0,3.72,1,0,1,1 +4320,INFP,16.48,0.1894149445481116,0.0,0.16,0.08,0.0,0.0,0.56,0.0,1.46,1,0,0,1 +4321,INFP,23.06,0.18376394503131943,0.12,0.3,0.44,0.04,0.02,0.56,0.0,1.56,1,0,0,1 +4322,INTJ,14.18,0.18495702292685254,0.0,0.16,0.02,0.0,0.0,0.36,0.0,1.44,1,0,1,0 +4323,INTJ,19.0,0.18690260539516448,0.0,0.16,0.2,0.0,0.0,0.58,0.0,2.36,1,0,1,0 +4324,INTP,28.96,0.19578938969245196,0.02,0.2,0.06,0.0,0.0,0.9,0.0,3.78,1,0,1,1 +4325,INTP,24.9,0.18195624366080743,0.1,0.12,0.0,0.1,0.0,0.64,0.0,3.3,1,0,1,1 +4326,INFJ,31.5,0.19204784226610758,0.04,0.16,0.16,0.0,0.02,1.6,0.0,4.8,1,0,0,0 +4327,INTP,30.74,0.18485416690779663,0.04,0.02,0.02,0.02,0.0,2.0,0.0,1.7,1,0,1,1 +4328,ISTP,30.76,0.18678333122626284,0.02,0.22,0.0,0.0,0.0,0.74,0.0,3.12,1,1,1,1 +4329,INTJ,24.46,0.18666426156221408,0.0,0.14,0.0,0.0,0.0,0.46,0.02,3.12,1,0,1,0 +4330,ISFP,16.1,0.1789372201658757,0.02,0.06,0.18,0.0,0.0,0.42,0.0,2.4,1,1,0,1 +4331,INTJ,22.32,0.19095720955916914,0.12,0.14,0.28,0.08,0.0,0.68,0.0,3.04,1,0,1,0 +4332,INTP,15.94,0.184955540512652,0.06,0.34,0.12,0.06,0.0,0.52,0.0,1.7,1,0,1,1 +4333,INFJ,32.44,0.18239089086898239,0.1,0.24,0.2,0.02,0.06,0.82,0.0,4.04,1,0,0,0 +4334,ENFP,21.8,0.19555555555555557,0.02,0.34,0.04,0.0,0.02,0.34,0.0,2.8,0,0,0,1 +4335,INTJ,27.8,0.18662979664682872,0.0,0.34,0.24,0.0,0.02,0.6,0.0,3.96,1,0,1,0 +4336,ISTP,33.34,0.1834665758953417,0.0,0.26,0.06,0.0,0.04,0.94,0.0,4.26,1,1,1,1 +4337,INFJ,29.36,0.18595034396519075,0.02,0.16,0.0,0.0,0.02,0.7,0.0,3.16,1,0,0,0 +4338,ENFP,25.42,0.1817366933917957,0.0,0.26,0.4,0.0,0.0,0.6,0.0,2.18,0,0,0,1 +4339,ENFP,34.14,0.18395535586038492,0.0,0.2,0.1,0.0,0.04,1.1,0.0,3.64,0,0,0,1 +4340,INFP,35.12,0.18919196880740866,0.0,0.14,0.2,0.0,0.06,0.94,0.0,4.58,1,0,0,1 +4341,ENFP,29.0,0.1920675298145942,0.0,0.04,0.5,0.0,0.0,0.94,0.0,3.32,0,0,0,1 +4342,INFP,25.96,0.19202915708550572,0.02,0.04,0.06,0.02,0.02,0.58,0.0,3.4,1,0,0,1 +4343,INFJ,31.6,0.1881820859129399,0.04,0.24,0.16,0.04,0.04,1.04,0.0,4.04,1,0,0,0 +4344,ENTP,26.4,0.19276903544173044,0.0,0.1,0.5,0.0,0.04,0.84,0.0,3.24,0,0,1,1 +4345,INTJ,33.18,0.19386351337223073,0.02,0.18,0.14,0.0,0.0,0.9,0.02,1.2,1,0,1,0 +4346,ENTP,25.52,0.180719768325732,0.04,0.16,0.32,0.02,0.0,0.82,0.0,2.7,0,0,1,1 +4347,ENFP,22.92,0.20264816960259285,0.06,0.14,0.9,0.04,0.02,1.18,0.0,2.9,0,0,0,1 +4348,INTP,27.6,0.177978043504768,0.06,0.14,0.1,0.02,0.08,0.7,0.0,3.6,1,0,1,1 +4349,INFP,25.22,0.17540333019623608,0.04,0.08,0.02,0.0,0.0,0.72,0.04,2.12,1,0,0,1 +4350,INFJ,31.04,0.18671910618849816,0.04,0.28,0.06,0.04,0.04,0.82,0.0,3.72,1,0,0,0 +4351,ENFP,25.7,0.1874831638494938,0.08,0.16,0.08,0.0,0.02,0.86,0.0,3.38,0,0,0,1 +4352,INFJ,30.96,0.19325333403234307,0.0,0.2,0.24,0.0,0.0,1.58,0.02,3.72,1,0,0,0 +4353,INFP,20.26,0.17813257588790454,0.0,0.6,0.02,0.0,0.12,0.62,0.0,1.88,1,0,0,1 +4354,INFP,26.2,0.18469435958235186,0.0,0.02,0.06,0.0,0.0,0.72,0.0,3.32,1,0,0,1 +4355,ISFP,26.26,0.18419354793495893,0.0,0.16,0.18,0.0,0.02,0.48,0.0,3.8,1,1,0,1 +4356,INTJ,22.28,0.18439070002133698,0.04,0.1,0.16,0.0,0.02,0.58,0.0,2.28,1,0,1,0 +4357,INTP,12.62,0.18275075350790193,0.06,0.14,0.26,0.02,0.0,0.12,0.0,1.16,1,0,1,1 +4358,INFP,27.3,0.18251169595637673,0.0,0.18,0.24,0.0,0.06,0.72,0.0,1.98,1,0,0,1 +4359,ISFJ,25.42,0.1895583814116245,0.0,0.16,0.28,0.0,0.0,0.54,0.0,3.0,1,1,0,0 +4360,INFP,3.8,0.19729184027720592,0.1,0.06,0.04,0.0,0.0,0.06,0.0,0.42,1,0,0,1 +4361,INFJ,31.72,0.18406988255277795,0.0,0.2,0.44,0.0,0.0,0.98,0.0,3.9,1,0,0,0 +4362,INFJ,24.42,0.18213449317174085,0.04,0.12,0.36,0.0,0.02,0.56,0.0,3.2,1,0,0,0 +4363,ENFP,25.42,0.18802041981562326,0.0,0.32,0.34,0.0,0.02,0.58,0.0,2.34,0,0,0,1 +4364,INFJ,30.4,0.18225865016027287,0.02,0.18,0.16,0.0,0.0,1.58,0.0,2.94,1,0,0,0 +4365,ESFJ,29.04,0.1855563450945454,0.0,0.1,0.08,0.0,0.0,0.68,0.0,3.54,0,1,0,0 +4366,INTJ,24.46,0.17813340759147442,0.04,0.18,0.02,0.0,0.0,0.44,0.0,2.9,1,0,1,0 +4367,INFJ,30.42,0.17089461529140199,0.04,0.18,0.02,0.0,0.0,0.88,0.0,1.6,1,0,0,0 +4368,INFJ,29.6,0.1845399177463079,0.06,0.22,0.16,0.02,0.04,0.88,0.0,3.08,1,0,0,0 +4369,INFP,25.78,0.18507134390734872,0.0,0.1,0.22,0.0,0.0,0.56,0.0,3.42,1,0,0,1 +4370,INTJ,19.8,0.1806516892942769,0.02,0.12,0.08,0.02,0.04,0.52,0.0,1.94,1,0,1,0 +4371,ISFP,16.72,0.19027262356807303,0.02,0.2,0.18,0.0,0.0,0.12,0.0,2.82,1,1,0,1 +4372,INTP,24.1,0.18406414389334988,0.0,0.22,0.14,0.0,0.02,0.74,0.0,3.28,1,0,1,1 +4373,INFP,20.48,0.1865452220986784,0.0,0.08,0.24,0.0,0.0,0.7,0.0,2.8,1,0,0,1 +4374,INFP,25.78,0.172300865616919,0.32,0.3,0.0,0.02,0.24,0.62,0.0,2.06,1,0,0,1 +4375,INTJ,15.88,0.18458610123108957,0.02,0.14,0.0,0.0,0.0,0.28,0.0,1.66,1,0,1,0 +4376,ENFJ,14.16,0.19559303237044565,0.0,0.1,0.0,0.0,0.04,0.3,0.0,1.2,0,0,0,0 +4377,INFJ,29.26,0.188700164101934,0.0,0.32,0.12,0.0,0.0,0.7,0.0,3.1,1,0,0,0 +4378,ESFJ,23.54,0.17979119051273224,0.12,0.1,0.22,0.0,0.0,0.62,0.0,2.38,0,1,0,0 +4379,INFP,27.16,0.18471880743203836,0.02,0.2,0.0,0.0,0.0,0.74,0.0,2.42,1,0,0,1 +4380,ENTJ,22.08,0.1873127902800512,0.08,0.3,0.0,0.0,0.0,0.46,0.0,2.56,0,0,1,0 +4381,INFP,17.84,0.17951600743688287,0.18,0.26,0.04,0.0,0.02,0.42,0.0,2.0,1,0,0,1 +4382,INFP,31.88,0.18073480175104634,0.02,0.06,0.1,0.0,0.0,0.9,0.0,3.4,1,0,0,1 +4383,INFP,28.74,0.1915450060585939,0.0,0.14,0.2,0.0,0.0,0.64,0.0,3.82,1,0,0,1 +4384,ENTP,28.98,0.1857030773360684,0.0,0.12,0.06,0.0,0.02,1.12,0.0,2.06,0,0,1,1 +4385,INFP,12.7,0.1882552800734619,0.1,0.0,0.02,0.0,0.04,0.32,0.0,1.88,1,0,0,1 +4386,INFJ,32.86,0.18591013035526788,0.0,0.14,0.34,0.0,0.0,1.24,0.0,4.92,1,0,0,0 +4387,INTP,24.66,0.18145399393138548,0.02,0.26,0.08,0.0,0.0,0.5,0.0,2.6,1,0,1,1 +4388,ISFP,17.62,0.17252654752582913,0.44,0.5,0.12,0.02,0.02,0.56,0.02,1.56,1,1,0,1 +4389,INTJ,27.32,0.1830801636210741,0.08,0.3,0.18,0.0,0.02,0.78,0.0,2.88,1,0,1,0 +4390,INFJ,30.78,0.1820578522782067,0.02,0.06,0.02,0.0,0.0,0.88,0.04,3.42,1,0,0,0 +4391,ISFP,31.28,0.18878586980486572,0.0,0.36,0.28,0.0,0.0,0.82,0.0,4.86,1,1,0,1 +4392,INFJ,31.14,0.1823697775794543,0.0,0.2,0.4,0.0,0.0,0.98,0.0,3.68,1,0,0,0 +4393,INFJ,22.68,0.19079427309818361,0.0,0.24,0.68,0.0,0.0,0.66,0.0,2.4,1,0,0,0 +4394,INFP,24.24,0.1868891187723024,0.04,0.3,0.16,0.0,0.0,0.68,0.0,3.84,1,0,0,1 +4395,INFP,13.72,0.18265690497345005,0.02,0.12,0.04,0.0,0.02,0.2,0.0,1.12,1,0,0,1 +4396,ENTJ,34.58,0.1835001719251176,0.0,0.04,0.04,0.0,0.0,1.16,0.0,4.24,0,0,1,0 +4397,INTP,34.86,0.1817706410268648,0.0,0.08,0.12,0.0,0.02,1.18,0.0,4.58,1,0,1,1 +4398,ENFP,22.46,0.1883762662856806,0.04,0.28,0.66,0.0,0.0,0.86,0.0,0.96,0,0,0,1 +4399,ENFP,26.0,0.19121880694872376,0.06,0.38,0.1,0.0,0.08,0.84,0.0,3.08,0,0,0,1 +4400,INFJ,27.68,0.18632078070929486,0.0,0.04,0.2,0.0,0.02,0.58,0.0,4.26,1,0,0,0 +4401,ENFP,22.52,0.18641811593663982,0.0,0.1,0.94,0.0,0.04,0.5,0.0,2.54,0,0,0,1 +4402,INFJ,30.66,0.180510875159173,0.0,0.1,0.12,0.0,0.0,0.62,0.0,4.1,1,0,0,0 +4403,ENFP,22.5,0.19297058494964614,0.02,0.14,0.2,0.0,0.04,0.54,0.0,2.44,0,0,0,1 +4404,ENFP,17.08,0.1967489517037542,0.04,0.24,0.58,0.0,0.0,0.28,0.0,1.48,0,0,0,1 +4405,INTJ,23.22,0.18614712773039474,0.0,0.26,0.28,0.0,0.0,0.8,0.0,2.64,1,0,1,0 +4406,ENTP,18.46,0.1909208947118512,0.04,0.04,0.0,0.04,0.0,0.3,0.0,2.36,0,0,1,1 +4407,INFJ,24.6,0.18923650852751436,0.1,0.34,0.58,0.04,0.0,0.64,0.0,2.34,1,0,0,0 +4408,INFJ,26.34,0.19132457117475513,0.14,0.16,0.14,0.0,0.0,0.94,0.0,3.46,1,0,0,0 +4409,ENFJ,25.08,0.1854865921587346,0.04,0.22,0.5,0.0,0.0,0.68,0.0,2.6,0,0,0,0 +4410,INFJ,24.36,0.18105442608270045,0.0,0.1,0.08,0.0,0.04,0.7,0.0,2.28,1,0,0,0 +4411,INFJ,31.98,0.1813431882517954,0.0,0.1,0.18,0.0,0.0,1.02,0.0,3.98,1,0,0,0 +4412,INFJ,31.5,0.182607035481827,0.02,0.06,0.24,0.0,0.06,0.9,0.02,3.44,1,0,0,0 +4413,INFJ,26.64,0.1869163933855748,0.08,0.36,0.28,0.0,0.02,0.9,0.0,2.62,1,0,0,0 +4414,INTJ,26.3,0.1805458633617437,0.08,0.16,0.04,0.0,0.0,0.62,0.02,1.94,1,0,1,0 +4415,INTP,30.32,0.1857813388644133,0.0,0.44,0.2,0.0,0.0,1.12,0.0,3.32,1,0,1,1 +4416,ENTP,24.7,0.18520344377028847,0.0,0.46,0.02,0.0,0.0,0.62,0.0,2.28,0,0,1,1 +4417,INFP,22.82,0.19655598848757663,0.18,0.1,0.1,0.04,0.04,0.86,0.0,1.72,1,0,0,1 +4418,INTJ,25.92,0.18141267543999312,0.02,0.14,0.0,0.0,0.06,0.58,0.0,3.52,1,0,1,0 +4419,ISTP,13.96,0.18449247963294726,0.06,0.06,0.02,0.02,0.0,0.2,0.0,1.5,1,1,1,1 +4420,INFP,31.2,0.19103177334135563,0.0,0.68,0.26,0.0,0.0,0.8,0.0,1.6,1,0,0,1 +4421,INFJ,26.52,0.18323814484754092,0.04,0.22,0.08,0.0,0.0,0.58,0.0,2.98,1,0,0,0 +4422,ENFP,21.12,0.18567756665505913,0.14,0.16,0.14,0.0,0.0,1.3,0.0,1.66,0,0,0,1 +4423,ENTJ,32.58,0.18889075790488533,0.0,0.16,0.0,0.0,0.0,0.76,0.0,3.52,0,0,1,0 +4424,INTJ,19.14,0.1916442296207698,0.18,0.4,1.12,0.08,0.06,0.56,0.0,1.66,1,0,1,0 +4425,INFP,24.74,0.17977426039436856,0.0,0.5,0.12,0.0,0.0,0.64,0.0,2.38,1,0,0,1 +4426,ENTP,29.7,0.18269601996013116,0.0,0.14,0.02,0.0,0.0,0.72,0.0,2.76,0,0,1,1 +4427,ISTP,30.16,0.1875,0.02,0.22,0.1,0.0,0.0,0.78,0.04,3.7,1,1,1,1 +4428,INFP,31.66,0.18839444285074253,0.0,0.14,0.5,0.0,0.02,0.82,0.0,3.16,1,0,0,1 +4429,ENFP,22.1,0.18699601042646954,0.02,0.54,0.5,0.02,0.0,0.54,0.0,2.6,0,0,0,1 +4430,ISFP,19.08,0.18120258707961656,0.36,0.46,0.12,0.0,0.06,0.52,0.0,2.14,1,1,0,1 +4431,ISFP,32.66,0.18311341606664927,0.0,0.04,0.3,0.0,0.02,0.86,0.0,4.08,1,1,0,1 +4432,INTP,15.96,0.18392312153351648,0.12,0.16,0.06,0.0,0.0,0.26,0.0,2.22,1,0,1,1 +4433,ENTP,10.04,0.20660875707773646,0.0,0.26,0.28,0.0,0.0,0.06,0.0,0.96,0,0,1,1 +4434,INFP,19.02,0.18731231241793944,0.02,0.12,0.04,0.02,0.0,0.34,0.0,2.2,1,0,0,1 +4435,INFJ,13.28,0.1821975498002206,0.16,0.32,0.06,0.02,0.0,0.18,0.0,1.56,1,0,0,0 +4436,INFJ,25.8,0.18047796652593623,0.04,0.24,0.22,0.0,0.0,0.56,0.0,2.78,1,0,0,0 +4437,INTP,12.36,0.1808232344362881,0.0,0.16,0.02,0.0,0.0,0.1,0.0,1.6,1,0,1,1 +4438,INFJ,33.72,0.1919361014543616,0.02,0.12,0.38,0.0,0.0,0.72,0.0,4.82,1,0,0,0 +4439,INTP,29.06,0.18509437326385436,0.1,0.2,0.0,0.02,0.0,0.74,0.0,2.88,1,0,1,1 +4440,INFP,14.46,0.18157609861429178,0.5,0.5,0.2,0.02,0.08,0.32,0.0,1.74,1,0,0,1 +4441,INFP,20.76,0.18480594186273328,0.06,0.2,0.48,0.0,0.0,0.3,0.0,1.86,1,0,0,1 +4442,INTP,19.72,0.18333912174199726,0.04,0.16,0.06,0.04,0.0,0.36,0.0,2.08,1,0,1,1 +4443,INFJ,31.3,0.1800065044795299,0.0,0.26,0.02,0.0,0.0,0.8,0.0,2.6,1,0,0,0 +4444,ENFP,28.54,0.19607918928627766,0.0,0.36,0.7,0.0,0.0,0.98,0.0,3.1,0,0,0,1 +4445,INFJ,27.72,0.17934198159401132,0.0,0.14,0.22,0.0,0.02,0.68,0.0,2.84,1,0,0,0 +4446,INFP,20.22,0.18071257922680156,0.0,0.02,0.32,0.0,0.02,0.36,0.0,2.64,1,0,0,1 +4447,INFJ,21.3,0.18138289393822055,0.22,0.28,0.0,0.0,0.04,0.5,0.0,1.88,1,0,0,0 +4448,INFP,33.18,0.17954788218682627,0.04,0.12,0.02,0.0,0.0,0.86,0.02,4.32,1,0,0,1 +4449,INFJ,29.44,0.1851910052865857,0.0,0.3,0.18,0.0,0.0,0.72,0.0,3.14,1,0,0,0 +4450,INTP,30.08,0.1804606804012495,0.02,0.16,0.02,0.0,0.0,0.76,0.0,3.5,1,0,1,1 +4451,ISFP,20.76,0.18484095015299795,0.0,0.06,0.18,0.0,0.02,0.32,0.0,3.0,1,1,0,1 +4452,INTP,25.9,0.19056494456640202,0.0,0.18,0.08,0.0,0.0,0.6,0.02,2.66,1,0,1,1 +4453,INTP,30.28,0.18123833252019914,0.02,0.24,0.02,0.0,0.0,0.8,0.0,3.04,1,0,1,1 +4454,ISTP,25.92,0.19379343050757017,0.02,0.2,0.32,0.0,0.02,0.54,0.0,2.28,1,1,1,1 +4455,INFP,29.78,0.1817672242981923,0.02,0.14,0.02,0.0,0.02,0.68,0.0,2.94,1,0,0,1 +4456,ENFP,28.1,0.18636826220434302,0.12,0.34,0.2,0.02,0.0,0.94,0.0,2.86,0,0,0,1 +4457,INFJ,32.74,0.17975078301916944,0.12,0.14,0.1,0.0,0.04,0.94,0.0,4.82,1,0,0,0 +4458,INFP,22.2,0.18204538196373884,0.0,0.26,0.36,0.0,0.0,0.32,0.0,2.52,1,0,0,1 +4459,ENTP,23.76,0.18848610579160763,0.02,0.34,0.0,0.0,0.0,0.52,0.0,2.12,0,0,1,1 +4460,ENTJ,18.78,0.1862175578142867,0.14,0.26,0.0,0.0,0.0,1.0,0.0,0.82,0,0,1,0 +4461,ENFP,17.62,0.19264615916011668,0.1,0.18,0.54,0.0,0.04,0.82,0.02,1.14,0,0,0,1 +4462,INFP,19.28,0.19080220877916174,0.02,0.18,0.0,0.0,0.02,0.64,0.0,2.54,1,0,0,1 +4463,INFJ,15.82,0.18968672962675182,0.02,0.02,0.14,0.0,0.06,0.36,0.0,1.24,1,0,0,0 +4464,ISTP,28.0,0.19369082550266797,0.0,0.22,0.16,0.0,0.0,1.28,0.0,3.68,1,1,1,1 +4465,INFP,31.0,0.1836583785393854,0.0,0.12,0.04,0.0,0.0,0.86,0.0,3.56,1,0,0,1 +4466,INFP,33.08,0.18338274912486177,0.02,0.24,0.34,0.02,0.1,1.08,0.04,3.78,1,0,0,1 +4467,INFP,18.58,0.18928361048107092,0.0,0.36,0.36,0.0,0.02,0.44,0.0,2.74,1,0,0,1 +4468,INFJ,26.14,0.18704912977595572,0.0,0.06,0.28,0.0,0.0,1.02,0.0,2.76,1,0,0,0 +4469,INTP,27.46,0.18564751974231697,0.08,0.44,0.04,0.06,0.0,0.54,0.02,2.72,1,0,1,1 +4470,INFJ,18.04,0.18656351235833013,0.02,0.36,1.26,0.0,0.0,0.28,0.0,2.36,1,0,0,0 +4471,INTP,29.24,0.17624202620047547,0.0,0.18,0.06,0.0,0.0,0.78,0.0,3.64,1,0,1,1 +4472,INFP,30.96,0.181718895987475,0.0,0.22,0.0,0.0,0.0,0.72,0.0,3.64,1,0,0,1 +4473,INFJ,23.32,0.18182057827956116,0.1,0.22,0.06,0.0,0.02,0.66,0.0,2.42,1,0,0,0 +4474,ENFP,21.26,0.18323557419058564,0.02,0.1,0.24,0.0,0.0,0.54,0.0,1.58,0,0,0,1 +4475,INFJ,9.88,0.16160766259875092,0.34,0.06,0.0,0.28,0.02,0.3,0.0,0.36,1,0,0,0 +4476,INFP,21.42,0.18915654157731068,0.34,0.34,0.16,0.06,0.02,0.62,0.0,2.68,1,0,0,1 +4477,ISFP,25.88,0.18693280685672542,0.02,0.24,0.04,0.02,0.02,0.62,0.0,3.36,1,1,0,1 +4478,INTP,31.0,0.18519250074360502,0.0,0.18,0.02,0.0,0.0,0.74,0.0,2.52,1,0,1,1 +4479,INFP,28.02,0.18681198917036182,0.0,0.14,0.46,0.0,0.02,0.8,0.0,3.46,1,0,0,1 +4480,INFJ,27.06,0.19144156765731163,0.04,0.36,0.02,0.02,0.0,0.6,0.0,3.22,1,0,0,0 +4481,INFJ,24.38,0.1884999838712279,0.16,0.06,0.06,0.0,0.0,0.84,0.0,2.38,1,0,0,0 +4482,ENFP,23.34,0.192533462013478,0.0,0.16,0.16,0.0,0.04,0.36,0.0,2.98,0,0,0,1 +4483,INFJ,30.36,0.18491215958901577,0.0,0.24,1.06,0.0,0.02,0.96,0.0,4.46,1,0,0,0 +4484,INFP,28.7,0.18055398439240364,0.0,0.1,0.18,0.0,0.0,0.74,0.0,3.32,1,0,0,1 +4485,INFP,29.14,0.18486199478292908,0.16,0.28,0.24,0.04,0.02,0.98,0.0,3.8,1,0,0,1 +4486,INFJ,21.5,0.18005258118000297,0.06,0.12,0.24,0.02,0.0,0.42,0.0,2.42,1,0,0,0 +4487,ENTP,24.72,0.18744897612294903,0.02,0.24,0.66,0.0,0.0,0.66,0.0,2.46,0,0,1,1 +4488,INFP,29.76,0.16972019419901155,0.0,0.02,0.08,0.0,0.0,0.82,0.0,2.4,1,0,0,1 +4489,INFP,11.1,0.19120920379339096,0.0,0.3,0.44,0.0,0.0,0.24,0.0,1.4,1,0,0,1 +4490,INTJ,12.1,0.18458869492848176,0.0,0.04,0.04,0.0,0.0,0.14,0.02,1.32,1,0,1,0 +4491,ENFP,26.92,0.19110229711193866,0.0,0.32,0.46,0.0,0.02,0.6,0.0,3.12,0,0,0,1 +4492,ENTP,29.44,0.1859465219672649,0.08,0.14,0.06,0.0,0.02,0.92,0.0,3.84,0,0,1,1 +4493,ENFP,22.2,0.20373823221341228,0.02,0.28,0.46,0.0,0.0,1.84,0.0,2.48,0,0,0,1 +4494,ENFP,28.84,0.18298692901326238,0.0,0.4,0.16,0.0,0.0,0.74,0.02,3.1,0,0,0,1 +4495,INFJ,27.38,0.17735068169476442,0.0,0.16,0.0,0.0,0.02,0.54,0.0,2.96,1,0,0,0 +4496,INTP,24.46,0.1864954337861273,0.0,0.3,0.5,0.0,0.0,0.78,0.0,2.98,1,0,1,1 +4497,INFP,23.52,0.18364782766898685,0.06,0.12,0.14,0.0,0.0,0.64,0.0,3.16,1,0,0,1 +4498,ISFJ,26.06,0.18814044443550126,0.0,0.02,0.12,0.0,0.0,0.72,0.0,3.26,1,1,0,0 +4499,INTJ,30.58,0.1798458222875815,0.06,0.12,0.06,0.0,0.04,0.98,0.02,3.14,1,0,1,0 +4500,INFP,31.18,0.18910854136574803,0.02,0.36,0.34,0.0,0.02,1.46,0.0,4.02,1,0,0,1 +4501,INTJ,28.92,0.18335996731787013,0.02,0.34,0.1,0.0,0.0,0.66,0.0,3.66,1,0,1,0 +4502,INTJ,22.36,0.19252417542693354,0.04,0.1,0.28,0.0,0.06,0.52,0.0,2.48,1,0,1,0 +4503,INTP,31.56,0.1750377558768788,0.02,0.2,0.1,0.0,0.0,0.96,0.0,2.84,1,0,1,1 +4504,INFP,23.24,0.18257204990711912,0.0,0.06,0.04,0.0,0.04,0.58,0.0,3.1,1,0,0,1 +4505,ENFP,7.34,0.184642179832175,0.0,0.04,0.22,0.0,0.0,0.14,0.0,0.56,0,0,0,1 +4506,ENTP,29.6,0.1876960310029915,0.0,0.2,0.04,0.0,0.0,0.72,0.02,2.88,0,0,1,1 +4507,INFP,33.52,0.180164230926431,0.0,0.12,0.28,0.0,0.0,0.94,0.0,3.98,1,0,0,1 +4508,ISTP,32.72,0.1872526430579032,0.0,0.36,0.02,0.0,0.0,0.9,0.0,2.9,1,1,1,1 +4509,INTP,27.26,0.18446548872428098,0.1,0.54,0.06,0.0,0.02,0.76,0.0,2.3,1,0,1,1 +4510,ESTP,24.42,0.17803237077011413,0.02,0.12,0.16,0.0,0.04,0.52,0.0,2.14,0,1,1,1 +4511,ENTJ,26.66,0.18400565723024367,0.0,0.18,0.0,0.0,0.0,0.54,0.0,3.28,0,0,1,0 +4512,ENTP,33.84,0.17580066003314987,0.0,0.26,0.02,0.0,0.0,0.78,0.0,3.7,0,0,1,1 +4513,INFP,30.76,0.19015254981788132,0.0,0.18,0.08,0.0,0.0,1.06,0.0,3.8,1,0,0,1 +4514,INTJ,24.74,0.19255856840379756,0.02,0.66,0.36,0.0,0.0,0.7,0.0,2.52,1,0,1,0 +4515,ISTP,31.4,0.18494993548338626,0.02,0.12,0.16,0.0,0.0,0.9,0.0,3.5,1,1,1,1 +4516,ENFP,20.68,0.18914883649593459,0.04,0.2,0.28,0.0,0.0,0.5,0.02,2.5,0,0,0,1 +4517,INFP,21.6,0.1781697078592656,0.14,0.18,0.08,0.0,0.02,0.68,0.0,1.12,1,0,0,1 +4518,ENFP,22.38,0.18687874961483808,0.06,0.3,0.18,0.0,0.0,0.62,0.0,2.62,0,0,0,1 +4519,ENFP,32.08,0.19692200743559413,0.02,0.4,1.14,0.0,0.02,1.18,0.0,4.14,0,0,0,1 +4520,INFP,24.72,0.17720689148122729,0.06,0.26,0.04,0.04,0.02,0.48,0.0,1.98,1,0,0,1 +4521,INFP,26.16,0.18561534516107195,0.0,0.16,0.22,0.0,0.0,0.66,0.0,3.24,1,0,0,1 +4522,ENTP,17.58,0.18432273549703584,0.1,0.12,0.02,0.06,0.0,0.24,0.0,1.6,0,0,1,1 +4523,ISFP,19.42,0.18216905252162016,0.04,0.06,0.3,0.04,0.0,0.46,0.0,0.7,1,1,0,1 +4524,INTP,21.58,0.1824937701944363,0.14,0.26,0.18,0.06,0.0,0.52,0.0,1.72,1,0,1,1 +4525,INFJ,34.8,0.17773244646272682,0.0,0.1,0.08,0.0,0.0,0.94,0.0,3.98,1,0,0,0 +4526,ENTP,23.96,0.18853404999598403,0.06,0.28,0.02,0.02,0.0,0.5,0.0,2.58,0,0,1,1 +4527,INFJ,31.18,0.18674117297623424,0.0,0.36,0.0,0.0,0.0,1.18,0.0,2.9,1,0,0,0 +4528,INFP,30.48,0.1945098938702639,0.0,0.1,0.3,0.0,0.0,0.82,0.0,4.58,1,0,0,1 +4529,INFP,32.48,0.19291797916352302,0.02,0.2,0.08,0.0,0.02,1.46,0.0,3.9,1,0,0,1 +4530,INFP,9.7,0.19403368169375002,0.04,0.14,0.2,0.02,0.02,0.1,0.0,0.58,1,0,0,1 +4531,ENTP,18.72,0.19399058358904261,0.02,0.36,0.12,0.02,0.0,0.46,0.0,2.28,0,0,1,1 +4532,INTP,22.02,0.18115124590240195,0.12,0.38,0.0,0.0,0.0,0.72,0.0,1.9,1,0,1,1 +4533,INFJ,26.64,0.18605496102523453,0.06,0.12,0.34,0.02,0.02,0.58,0.0,3.54,1,0,0,0 +4534,INTP,26.96,0.18838943391073884,0.08,0.26,0.2,0.04,0.0,0.72,0.0,3.4,1,0,1,1 +4535,INTP,22.44,0.17601108795458828,0.2,0.3,0.0,0.08,0.0,0.46,0.0,2.12,1,0,1,1 +4536,INFP,33.42,0.18060841853970544,0.0,0.0,0.12,0.0,0.0,0.84,0.0,4.56,1,0,0,1 +4537,ISFP,32.34,0.18772919680857014,0.0,0.2,0.16,0.0,0.0,1.0,0.0,3.4,1,1,0,1 +4538,INFP,30.8,0.1985234458427294,0.02,0.2,0.62,0.0,0.0,0.84,0.0,3.3,1,0,0,1 +4539,INTP,19.86,0.1836572844377505,0.06,0.06,0.0,0.0,0.02,0.72,0.0,2.94,1,0,1,1 +4540,INFP,20.36,0.18204763663379506,0.12,0.18,0.02,0.0,0.0,0.3,0.0,1.66,1,0,0,1 +4541,INTJ,18.26,0.19046191932806414,0.08,0.38,0.04,0.0,0.04,0.28,0.0,2.06,1,0,1,0 +4542,ISFP,12.22,0.1887006402358009,0.54,0.6,0.04,0.0,0.06,0.4,0.0,1.54,1,1,0,1 +4543,INFJ,25.82,0.18700232445945353,0.36,0.52,0.3,0.04,0.0,0.96,0.0,2.46,1,0,0,0 +4544,INFP,24.42,0.19703999672138722,0.08,0.36,0.06,0.0,0.02,1.22,0.0,2.74,1,0,0,1 +4545,INTP,14.3,0.18958018193606524,0.02,0.16,0.0,0.0,0.02,0.44,0.0,1.28,1,0,1,1 +4546,INFJ,27.04,0.19897826301282961,0.0,0.22,0.36,0.0,0.0,0.72,0.0,2.6,1,0,0,0 +4547,INFJ,17.36,0.1747366973692507,0.02,0.18,0.02,0.0,0.04,0.38,0.0,1.28,1,0,0,0 +4548,INFP,33.78,0.1781033414340387,0.02,0.14,0.02,0.0,0.0,0.9,0.0,2.86,1,0,0,1 +4549,INTP,32.16,0.18391488105547776,0.0,0.14,0.04,0.0,0.02,0.76,0.02,3.8,1,0,1,1 +4550,ISFP,27.88,0.17931615664906228,0.26,0.18,0.06,0.14,0.08,1.14,0.0,1.62,1,1,0,1 +4551,INFJ,29.62,0.1827299153112613,0.02,0.2,0.2,0.0,0.0,0.72,0.0,4.16,1,0,0,0 +4552,ISFP,31.94,0.19372530972198207,0.0,0.34,0.38,0.0,0.0,0.82,0.0,2.84,1,1,0,1 +4553,INTP,29.94,0.18113259323429332,0.08,0.12,0.0,0.04,0.0,0.82,0.0,4.08,1,0,1,1 +4554,INFJ,31.9,0.1824729472495196,0.02,0.06,0.02,0.0,0.0,0.86,0.02,3.84,1,0,0,0 +4555,ENFP,19.62,0.19237141001073213,0.02,0.2,0.22,0.0,0.04,0.46,0.0,2.1,0,0,0,1 +4556,INFP,31.3,0.1863332636527155,0.0,0.12,0.4,0.0,0.0,0.8,0.0,3.98,1,0,0,1 +4557,ENTJ,25.78,0.18320658670401843,0.04,0.14,0.06,0.02,0.02,0.72,0.0,3.2,0,0,1,0 +4558,INFP,31.16,0.18748557997351514,0.0,0.18,0.48,0.0,0.02,0.78,0.0,3.98,1,0,0,1 +4559,INFP,25.82,0.1982166049822798,0.16,0.26,0.62,0.0,0.02,0.9,0.02,3.04,1,0,0,1 +4560,ENTP,31.58,0.19067373070928853,0.0,0.24,0.28,0.0,0.0,1.42,0.0,3.48,0,0,1,1 +4561,ENFP,20.22,0.18478136795568492,0.02,0.04,0.0,0.02,0.02,0.56,0.0,2.22,0,0,0,1 +4562,INFJ,29.02,0.18487607043377502,0.04,0.14,0.14,0.0,0.0,0.96,0.0,3.84,1,0,0,0 +4563,INFP,24.38,0.18006552548079274,0.14,0.1,0.24,0.0,0.02,0.46,0.0,3.42,1,0,0,1 +4564,INFP,19.4,0.1866355420186281,0.04,0.04,0.0,0.0,0.0,0.4,0.0,2.1,1,0,0,1 +4565,INFJ,29.4,0.18071470402943873,0.0,0.06,0.22,0.0,0.0,0.56,0.0,3.5,1,0,0,0 +4566,INFP,21.22,0.18426014660483267,0.44,0.58,0.04,0.04,0.12,0.54,0.0,2.36,1,0,0,1 +4567,INTP,28.64,0.18018375305704276,0.02,0.06,0.02,0.02,0.02,0.64,0.0,2.84,1,0,1,1 +4568,ISFP,10.58,0.18917066602012084,0.0,0.02,0.1,0.0,0.06,0.16,0.02,1.6,1,1,0,1 +4569,INFJ,22.96,0.19391962263700327,0.22,0.34,0.22,0.04,0.0,1.34,0.0,2.74,1,0,0,0 +4570,ESFJ,15.58,0.18014731451541713,0.06,0.22,0.02,0.0,0.0,0.28,0.04,1.86,0,1,0,0 +4571,INFJ,21.28,0.18502419337141066,0.18,0.18,0.08,0.0,0.0,0.46,0.0,2.88,1,0,0,0 +4572,INTJ,22.88,0.17940080978674477,0.1,0.18,0.04,0.0,0.0,0.64,0.0,2.18,1,0,1,0 +4573,ENTJ,22.7,0.18524593020825134,0.0,0.3,0.18,0.0,0.0,0.56,0.0,2.44,0,0,1,0 +4574,INTP,30.88,0.1858075923076911,0.08,0.2,0.1,0.02,0.06,1.14,0.0,3.64,1,0,1,1 +4575,INFP,34.06,0.17906812971714026,0.0,0.12,0.16,0.0,0.0,1.04,0.0,3.04,1,0,0,1 +4576,INTJ,26.88,0.18380431160592453,0.06,0.12,0.16,0.02,0.0,0.78,0.0,3.08,1,0,1,0 +4577,INFJ,19.6,0.18370045555962458,0.1,0.26,0.14,0.0,0.04,0.84,0.0,1.56,1,0,0,0 +4578,INFP,19.18,0.18305008348224985,0.24,0.28,0.12,0.06,0.0,0.22,0.0,2.74,1,0,0,1 +4579,INTP,29.6,0.177767250673226,0.0,0.26,0.06,0.0,0.0,0.78,0.0,2.66,1,0,1,1 +4580,ENTP,30.9,0.17912712176588932,0.02,0.26,0.02,0.02,0.0,0.84,0.0,2.44,0,0,1,1 +4581,INTP,17.88,0.18129085897687736,0.0,0.16,0.04,0.0,0.0,0.36,0.0,2.1,1,0,1,1 +4582,INTJ,23.1,0.1841431691793566,0.18,0.32,0.1,0.04,0.02,0.74,0.02,2.78,1,0,1,0 +4583,INTJ,30.4,0.18498608902135727,0.02,0.26,0.1,0.0,0.0,0.98,0.0,3.74,1,0,1,0 +4584,INTJ,23.1,0.18023294913808893,0.0,0.04,0.02,0.0,0.06,0.56,0.0,2.04,1,0,1,0 +4585,INTP,16.38,0.1905705326846065,0.0,0.06,0.5,0.0,0.0,0.4,0.0,2.42,1,0,1,1 +4586,ENFP,20.76,0.1906863523413879,0.1,0.36,0.38,0.0,0.0,0.48,0.0,2.24,0,0,0,1 +4587,ISFP,26.66,0.18723642622804443,0.0,0.2,0.46,0.0,0.04,0.78,0.0,3.0,1,1,0,1 +4588,INTJ,27.18,0.17811792805879617,0.0,0.06,0.02,0.0,0.04,0.52,0.0,3.2,1,0,1,0 +4589,ISFP,6.56,0.18062206680245274,0.12,0.04,0.0,0.08,0.02,0.06,0.0,0.36,1,1,0,1 +4590,INTP,25.0,0.17729324429440205,0.02,0.14,0.0,0.0,0.02,0.52,0.0,2.16,1,0,1,1 +4591,ISTP,26.84,0.19257569893173138,0.04,0.32,0.08,0.0,0.0,0.7,0.0,3.3,1,1,1,1 +4592,INTP,16.44,0.1821000470754316,0.56,0.42,0.12,0.24,0.0,0.4,0.0,1.28,1,0,1,1 +4593,INFP,27.46,0.18738659194866128,0.04,0.18,0.1,0.0,0.08,0.66,0.0,3.52,1,0,0,1 +4594,INFP,27.98,0.18145843690682426,0.06,0.2,0.26,0.0,0.06,1.02,0.0,2.84,1,0,0,1 +4595,INFP,21.62,0.18446655120531735,0.02,0.74,0.06,0.0,0.02,0.44,0.0,1.94,1,0,0,1 +4596,INTP,24.94,0.18450450864499518,0.1,0.18,0.08,0.0,0.04,0.44,0.0,3.38,1,0,1,1 +4597,INTP,12.88,0.18689159009656184,0.14,0.24,0.18,0.04,0.0,0.18,0.0,2.28,1,0,1,1 +4598,ISTP,29.24,0.17790063339848936,0.06,0.14,0.08,0.02,0.0,0.9,0.0,2.56,1,1,1,1 +4599,ESTP,19.0,0.17632914702020286,0.1,0.16,0.0,0.02,0.0,0.18,0.02,1.7,0,1,1,1 +4600,INTJ,20.92,0.1785979052674924,0.2,0.38,0.04,0.04,0.0,0.56,0.0,2.14,1,0,1,0 +4601,INFP,30.78,0.18450308500009918,0.02,0.02,0.08,0.0,0.0,1.0,0.0,3.52,1,0,0,1 +4602,ENTJ,15.88,0.1850532478514254,0.02,0.18,0.04,0.0,0.02,0.28,0.0,1.82,0,0,1,0 +4603,INFJ,26.7,0.1857091646300637,0.0,0.42,0.3,0.0,0.0,0.72,0.0,2.7,1,0,0,0 +4604,INFP,28.22,0.18892852424362536,0.04,0.1,0.14,0.0,0.02,0.68,0.0,3.2,1,0,0,1 +4605,INTJ,18.06,0.17489748650261952,0.04,0.16,0.06,0.02,0.0,0.56,0.0,2.06,1,0,1,0 +4606,INTP,26.5,0.1830107771560048,0.02,0.1,0.12,0.02,0.0,0.68,0.0,3.54,1,0,1,1 +4607,ENFP,15.02,0.19030798881699526,0.0,0.24,0.2,0.0,0.0,0.34,0.0,2.06,0,0,0,1 +4608,INFJ,12.34,0.18352018298628597,0.14,0.2,0.02,0.1,0.0,0.1,0.0,1.62,1,0,0,0 +4609,INFP,30.76,0.18522894130028847,0.0,0.2,0.1,0.0,0.0,0.84,0.0,3.66,1,0,0,1 +4610,INTP,31.5,0.17408433369364454,0.0,0.02,0.0,0.0,0.02,0.74,0.0,2.8,1,0,1,1 +4611,INTJ,25.94,0.18331044573004368,0.02,0.2,0.06,0.02,0.04,0.54,0.0,3.4,1,0,1,0 +4612,ENTP,20.74,0.17773179958942237,0.02,0.34,0.0,0.0,0.0,0.36,0.02,1.74,0,0,1,1 +4613,INFP,24.72,0.1857634891439947,0.18,0.34,0.06,0.0,0.04,0.92,0.0,2.78,1,0,0,1 +4614,INFP,22.2,0.2306939900926112,0.04,0.22,0.04,0.0,0.06,0.46,0.0,2.52,1,0,0,1 +4615,INTJ,28.38,0.1834821235993917,0.0,0.12,0.0,0.0,0.02,0.62,0.0,2.78,1,0,1,0 +4616,INFJ,26.62,0.18695436129890555,0.0,0.3,0.26,0.0,0.0,0.54,0.0,3.28,1,0,0,0 +4617,INTP,29.82,0.18357049859375052,0.02,0.16,0.08,0.0,0.04,1.02,0.0,2.14,1,0,1,1 +4618,ENTP,31.9,0.1926196053430374,0.0,0.1,0.04,0.0,0.0,1.0,0.0,3.86,0,0,1,1 +4619,INTP,15.4,0.1934477435400817,0.38,0.46,0.08,0.0,0.0,0.6,0.0,2.36,1,0,1,1 +4620,INFJ,26.8,0.1885572665881546,0.1,0.32,0.12,0.06,0.02,0.68,0.0,3.36,1,0,0,0 +4621,INFP,26.44,0.18606791694847438,0.02,0.16,0.06,0.0,0.04,0.64,0.0,2.6,1,0,0,1 +4622,INTP,9.32,0.17827414381209375,0.12,0.2,0.0,0.06,0.02,0.2,0.0,0.74,1,0,1,1 +4623,ENFP,31.32,0.19176878963296606,0.04,0.14,0.24,0.04,0.0,1.18,0.0,4.28,0,0,0,1 +4624,INFJ,23.76,0.17988096000000006,0.02,0.14,0.12,0.0,0.0,0.54,0.0,2.88,1,0,0,0 +4625,INTJ,27.5,0.18390337253689715,0.0,0.26,0.08,0.0,0.0,0.64,0.0,2.52,1,0,1,0 +4626,INTP,23.06,0.18768839476851193,0.0,0.3,0.02,0.0,0.0,0.4,0.0,3.12,1,0,1,1 +4627,INTJ,26.44,0.18105987256896997,0.0,0.2,0.18,0.0,0.0,0.5,0.0,2.62,1,0,1,0 +4628,INFP,29.32,0.18386786394305438,0.04,0.14,0.04,0.0,0.0,0.76,0.0,3.54,1,0,0,1 +4629,INTP,23.78,0.18395497323609866,0.08,0.22,0.34,0.02,0.02,0.64,0.0,3.06,1,0,1,1 +4630,ENFP,17.16,0.18223644342888903,0.0,0.24,0.0,0.0,0.0,0.46,0.0,1.78,0,0,0,1 +4631,ENFP,20.9,0.18770767555008824,0.04,0.08,0.22,0.0,0.02,0.26,0.0,2.46,0,0,0,1 +4632,INFJ,25.62,0.17854412463748034,0.1,0.32,0.0,0.0,0.06,0.68,0.0,1.76,1,0,0,0 +4633,INFJ,18.58,0.19247541418918365,0.32,0.22,0.04,0.0,0.02,0.48,0.0,2.24,1,0,0,0 +4634,INFP,30.14,0.18393222732410813,0.0,0.12,0.02,0.0,0.0,0.76,0.0,3.52,1,0,0,1 +4635,ENFJ,29.76,0.1867939722233365,0.0,0.1,0.3,0.0,0.04,0.58,0.0,3.9,0,0,0,0 +4636,INTP,27.0,0.18352013675307735,0.04,0.34,0.02,0.0,0.0,0.74,0.0,2.96,1,0,1,1 +4637,INTP,26.82,0.17450434039085355,0.0,0.12,0.0,0.0,0.0,0.66,0.0,1.96,1,0,1,1 +4638,ESTP,17.44,0.18239311468731348,0.48,0.44,0.06,0.06,0.06,0.32,0.0,2.42,0,1,1,1 +4639,INTP,21.46,0.18656559229787914,0.0,0.14,0.0,0.0,0.04,0.52,0.0,1.26,1,0,1,1 +4640,ISTJ,22.52,0.1873630137397144,0.08,0.22,0.06,0.0,0.02,0.48,0.0,3.6,1,1,1,0 +4641,INFP,18.44,0.19284071562567204,0.24,0.28,0.02,0.08,0.0,0.36,0.0,2.44,1,0,0,1 +4642,ENTP,32.9,0.1849370377626936,0.02,0.28,0.0,0.0,0.0,1.02,0.0,3.42,0,0,1,1 +4643,INFJ,26.36,0.1865267786255022,0.24,0.34,0.04,0.0,0.0,0.66,0.0,2.26,1,0,0,0 +4644,ENTP,17.56,0.18279219111066264,0.34,0.24,0.28,0.04,0.04,0.46,0.0,1.84,0,0,1,1 +4645,ENFP,24.52,0.18555996264134994,0.04,0.22,0.5,0.0,0.0,0.58,0.02,2.4,0,0,0,1 +4646,ISFJ,14.9,0.18847295243468787,0.16,0.18,0.06,0.02,0.02,0.34,0.0,1.86,1,1,0,0 +4647,ENTP,21.08,0.18609487286804732,0.0,0.16,0.14,0.0,0.02,0.76,0.0,1.52,0,0,1,1 +4648,INFP,20.46,0.19618723551706516,0.06,0.3,0.0,0.02,0.02,0.28,0.0,2.7,1,0,0,1 +4649,INFJ,30.84,0.18909193550564735,0.02,0.06,0.26,0.0,0.04,1.2,0.02,3.32,1,0,0,0 +4650,ISTP,11.24,0.1912059149226518,0.0,0.1,0.26,0.0,0.0,0.5,0.0,1.42,1,1,1,1 +4651,ENFJ,24.36,0.1936020538901658,0.0,0.28,0.22,0.0,0.0,0.68,0.02,2.64,0,0,0,0 +4652,INFJ,28.46,0.18494845105672242,0.0,0.1,0.14,0.0,0.0,0.66,0.0,2.82,1,0,0,0 +4653,INFP,28.52,0.18703029756539405,0.2,0.18,0.6,0.02,0.02,0.74,0.0,3.2,1,0,0,1 +4654,INFP,31.18,0.1825091661301899,0.0,0.06,0.34,0.0,0.0,1.12,0.02,4.14,1,0,0,1 +4655,INTP,30.18,0.18053785890769114,0.06,0.06,0.04,0.02,0.0,0.74,0.0,4.24,1,0,1,1 +4656,INTJ,23.0,0.1871545377375337,0.0,0.24,0.0,0.0,0.06,0.44,0.0,2.66,1,0,1,0 +4657,INTP,19.22,0.17873514157282708,0.1,0.2,0.18,0.0,0.0,0.34,0.0,1.82,1,0,1,1 +4658,ENFP,27.88,0.1834479512479526,0.08,0.2,0.66,0.04,0.02,0.78,0.0,3.14,0,0,0,1 +4659,INFP,24.94,0.18355046430228794,0.04,0.54,0.24,0.0,0.0,0.82,0.0,2.74,1,0,0,1 +4660,INTP,28.74,0.17536918563715456,0.0,0.14,0.06,0.0,0.0,0.82,0.0,2.82,1,0,1,1 +4661,INFJ,17.66,0.17852000217547878,0.4,0.14,0.2,0.26,0.0,0.36,0.0,2.86,1,0,0,0 +4662,INTJ,18.2,0.18364650839546162,0.04,0.3,1.2,0.02,0.0,0.18,0.0,2.2,1,0,1,0 +4663,INFJ,28.22,0.1865706447187929,0.08,0.08,0.08,0.0,0.02,0.82,0.0,3.2,1,0,0,0 +4664,ENTP,23.02,0.17666006862378011,0.12,0.16,0.02,0.0,0.08,0.58,0.0,2.68,0,0,1,1 +4665,ENTP,22.64,0.18323972386912393,0.08,0.26,0.08,0.02,0.0,0.52,0.0,2.3,0,0,1,1 +4666,INTP,26.8,0.187952545705925,0.0,0.16,0.02,0.0,0.0,0.64,0.0,3.56,1,0,1,1 +4667,INTP,32.1,0.17927015880184077,0.02,0.24,0.0,0.0,0.0,0.94,0.0,2.62,1,0,1,1 +4668,INFJ,25.46,0.18212566627558963,0.08,0.14,0.04,0.0,0.0,0.68,0.0,2.4,1,0,0,0 +4669,INTP,30.48,0.18035366587846363,0.06,0.16,0.14,0.0,0.06,0.82,0.0,2.86,1,0,1,1 +4670,ISTJ,27.56,0.18962563660367643,0.02,0.12,0.36,0.0,0.0,1.06,0.0,4.18,1,1,1,0 +4671,ENFP,19.58,0.18778521244057234,0.1,0.18,0.18,0.0,0.0,0.42,0.0,2.58,0,0,0,1 +4672,ENTP,17.76,0.18213880920601508,0.12,0.06,0.0,0.0,0.0,0.32,0.0,2.06,0,0,1,1 +4673,INFJ,29.04,0.19262935702434164,0.0,0.4,0.26,0.0,0.0,1.5,0.0,3.26,1,0,0,0 +4674,INFJ,28.24,0.18546415376695682,0.04,0.16,0.7,0.0,0.0,0.98,0.0,3.7,1,0,0,0 +4675,ENTP,24.94,0.18115452495414247,0.02,0.04,0.04,0.02,0.0,0.56,0.02,2.94,0,0,1,1 +4676,INFJ,27.1,0.1806682312432602,0.04,0.22,0.4,0.0,0.02,0.76,0.0,2.06,1,0,0,0 +4677,INFP,29.28,0.18262148358623928,0.06,0.14,0.04,0.0,0.04,0.74,0.0,2.7,1,0,0,1 +4678,ENFP,34.66,0.18767080982187268,0.0,0.2,0.28,0.0,0.0,1.34,0.02,3.66,0,0,0,1 +4679,INFJ,32.46,0.18339408043082012,0.0,0.06,0.16,0.0,0.0,0.94,0.0,3.68,1,0,0,0 +4680,INTJ,23.34,0.17675554975790547,0.1,0.24,0.12,0.04,0.0,0.5,0.0,2.68,1,0,1,0 +4681,ISFP,21.14,0.1861486877095618,0.06,0.24,0.32,0.02,0.02,0.6,0.0,2.18,1,1,0,1 +4682,INTJ,30.54,0.17289960001310503,0.0,0.32,0.08,0.0,0.0,0.96,0.0,1.44,1,0,1,0 +4683,ISTJ,22.58,0.1766808111687841,0.22,0.36,0.28,0.0,0.02,0.8,0.0,2.3,1,1,1,0 +4684,INFJ,29.58,0.1837690371369771,0.06,0.28,0.0,0.0,0.0,0.76,0.0,2.86,1,0,0,0 +4685,INTJ,11.94,0.17606562432896328,0.48,0.58,0.0,0.0,0.0,0.26,0.0,1.06,1,0,1,0 +4686,INTP,26.54,0.17751988002684604,0.12,0.06,0.06,0.1,0.12,0.6,0.0,3.64,1,0,1,1 +4687,INFJ,18.76,0.18940893886925336,0.18,0.22,0.12,0.06,0.04,0.34,0.0,2.8,1,0,0,0 +4688,INFP,12.0,0.19391719790189849,0.34,0.34,0.12,0.04,0.02,0.38,0.0,1.08,1,0,0,1 +4689,ENFP,27.28,0.1871544798641077,0.0,0.12,0.5,0.0,0.0,1.08,0.0,2.54,0,0,0,1 +4690,INFP,26.54,0.1784315838863425,0.0,0.22,0.04,0.0,0.0,0.46,0.0,1.96,1,0,0,1 +4691,INFP,25.08,0.19090277534509467,0.18,0.24,0.2,0.0,0.0,0.78,0.0,3.52,1,0,0,1 +4692,INFP,35.02,0.18922351861037323,0.0,0.16,0.02,0.0,0.0,1.14,0.0,3.78,1,0,0,1 +4693,INTP,28.1,0.18549497502397663,0.06,0.32,0.0,0.0,0.0,0.62,0.0,2.8,1,0,1,1 +4694,INFP,29.3,0.1815835307668792,0.0,0.28,0.16,0.0,0.0,0.88,0.0,3.52,1,0,0,1 +4695,INTP,18.04,0.18493944380992772,0.4,0.34,0.1,0.06,0.08,0.36,0.02,2.42,1,0,1,1 +4696,INTP,24.86,0.18007195764349046,0.06,0.2,0.04,0.0,0.0,0.62,0.0,2.16,1,0,1,1 +4697,ENTP,18.44,0.1869977189143771,0.02,0.14,0.06,0.02,0.0,0.36,0.0,2.42,0,0,1,1 +4698,INTP,18.7,0.18657899524641158,0.0,0.18,0.02,0.0,0.0,0.34,0.0,1.84,1,0,1,1 +4699,INFP,28.84,0.17700815321815475,0.12,0.22,0.08,0.02,0.0,0.78,0.0,2.02,1,0,0,1 +4700,INTP,25.08,0.1812426181058501,0.02,0.18,0.1,0.0,0.08,0.48,0.0,2.76,1,0,1,1 +4701,ENTP,23.66,0.18069870801463567,0.02,0.26,0.06,0.02,0.0,0.52,0.0,1.94,0,0,1,1 +4702,INFP,22.98,0.19276914330332892,0.06,0.22,0.08,0.0,0.0,0.56,0.0,3.2,1,0,0,1 +4703,INFP,28.06,0.1849730114222804,0.0,0.12,0.22,0.0,0.0,0.76,0.0,2.9,1,0,0,1 +4704,INFJ,16.84,0.19185114556638408,0.4,0.38,0.18,0.02,0.0,0.46,0.0,1.56,1,0,0,0 +4705,ISTJ,17.36,0.19105271757624806,0.04,0.2,0.1,0.02,0.0,0.8,0.0,1.88,1,1,1,0 +4706,INTP,20.1,0.1807643562481426,0.32,0.06,0.08,0.02,0.0,0.38,0.0,1.98,1,0,1,1 +4707,INFP,22.44,0.18139814790003292,0.02,0.14,0.04,0.0,0.06,0.52,0.0,2.58,1,0,0,1 +4708,ENFP,22.98,0.1829121193681611,0.0,0.4,0.58,0.0,0.02,0.54,0.0,2.14,0,0,0,1 +4709,ENTJ,20.72,0.18642294945206336,0.0,0.9,0.74,0.0,0.0,0.42,0.0,1.2,0,0,1,0 +4710,INTJ,33.52,0.1798753723148947,0.0,0.14,0.02,0.0,0.0,0.96,0.0,4.6,1,0,1,0 +4711,INFP,26.56,0.18203128864700635,0.18,0.4,0.3,0.0,0.04,0.68,0.02,3.58,1,0,0,1 +4712,ENFP,32.86,0.18775173539530984,0.0,0.32,0.14,0.0,0.0,1.02,0.0,3.98,0,0,0,1 +4713,INTP,16.7,0.18556819538201033,0.18,0.34,0.36,0.04,0.0,0.24,0.0,1.36,1,0,1,1 +4714,INFJ,27.28,0.18487892303029485,0.1,0.12,0.0,0.0,0.0,0.72,0.0,3.92,1,0,0,0 +4715,INFJ,32.06,0.17807428883269025,0.04,0.1,0.02,0.02,0.04,0.86,0.0,2.94,1,0,0,0 +4716,INFP,13.5,0.18975423198311736,0.02,0.22,0.44,0.0,0.02,0.16,0.02,1.46,1,0,0,1 +4717,INTJ,29.34,0.17964302639220076,0.02,0.14,0.0,0.0,0.04,0.78,0.0,3.7,1,0,1,0 +4718,ISFJ,29.76,0.18251339798164432,0.16,0.26,0.06,0.04,0.0,0.74,0.0,3.68,1,1,0,0 +4719,INTP,25.42,0.17725188275789808,0.06,0.28,0.26,0.0,0.02,0.68,0.0,2.44,1,0,1,1 +4720,ISFJ,32.04,0.1838039536929222,0.04,0.28,0.08,0.02,0.0,0.88,0.0,2.9,1,1,0,0 +4721,INTP,24.66,0.18113324979755485,0.0,0.04,0.02,0.0,0.0,0.58,0.0,3.46,1,0,1,1 +4722,INFP,25.04,0.1830919178333098,0.2,0.28,0.08,0.0,0.0,0.68,0.0,2.62,1,0,0,1 +4723,INFP,27.0,0.17873592656762194,0.04,0.36,0.1,0.0,0.0,0.64,0.0,2.98,1,0,0,1 +4724,INFJ,23.82,0.18212718557682314,0.14,0.2,0.06,0.0,0.0,0.54,0.0,2.32,1,0,0,0 +4725,INTP,22.72,0.19049635732048253,0.02,0.2,0.1,0.0,0.02,0.78,0.0,2.74,1,0,1,1 +4726,ISTJ,22.02,0.18447416720004017,0.18,0.16,0.06,0.02,0.0,0.4,0.02,2.14,1,1,1,0 +4727,INTJ,31.92,0.18087111282677396,0.0,0.1,0.04,0.0,0.0,0.98,0.0,3.58,1,0,1,0 +4728,ISTP,24.82,0.18347993161362294,0.1,0.24,0.0,0.0,0.0,0.58,0.0,2.58,1,1,1,1 +4729,INTP,32.18,0.17634184937652284,0.02,0.08,0.0,0.0,0.04,0.72,0.0,2.64,1,0,1,1 +4730,INTJ,30.52,0.1798539847273345,0.02,0.06,0.02,0.0,0.06,0.76,0.0,3.16,1,0,1,0 +4731,INTJ,20.22,0.17932994130587146,0.0,0.04,0.0,0.0,0.02,0.3,0.0,2.34,1,0,1,0 +4732,INFJ,23.16,0.17605977557585842,0.04,0.1,0.04,0.0,0.0,0.46,0.0,1.96,1,0,0,0 +4733,INTP,18.96,0.192061343208704,0.24,0.22,0.28,0.02,0.02,0.64,0.0,2.5,1,0,1,1 +4734,ISTP,30.32,0.18004138682288065,0.0,0.22,0.02,0.0,0.0,0.8,0.04,3.32,1,1,1,1 +4735,INTP,29.42,0.18378069613132933,0.06,0.28,0.1,0.04,0.0,0.66,0.0,3.44,1,0,1,1 +4736,INTP,12.5,0.19090858970316651,0.0,0.1,0.12,0.0,0.0,0.3,0.0,0.88,1,0,1,1 +4737,INFJ,30.22,0.17849336920093614,0.14,0.12,0.12,0.1,0.06,0.58,0.0,3.78,1,0,0,0 +4738,INFP,19.3,0.1890529725239802,0.24,0.2,0.1,0.12,0.0,0.68,0.0,2.46,1,0,0,1 +4739,INFP,34.28,0.18333743087295773,0.0,0.14,0.22,0.0,0.02,1.12,0.06,4.22,1,0,0,1 +4740,INTP,31.64,0.1810857723682927,0.0,0.32,0.02,0.0,0.02,0.92,0.0,2.88,1,0,1,1 +4741,INTJ,23.1,0.18482138051122926,0.0,0.2,0.04,0.0,0.0,0.44,0.0,2.78,1,0,1,0 +4742,INFP,22.76,0.1851055450566065,0.24,0.3,0.0,0.0,0.08,0.48,0.0,3.18,1,0,0,1 +4743,INTJ,27.54,0.1832319359042314,0.0,0.22,0.0,0.0,0.02,0.8,0.0,2.34,1,0,1,0 +4744,INFP,29.2,0.18344967152560626,0.04,0.24,0.04,0.0,0.0,0.7,0.0,2.84,1,0,0,1 +4745,ISFP,21.3,0.1812092599384492,0.12,0.12,0.16,0.0,0.1,0.36,0.0,1.94,1,1,0,1 +4746,INTJ,24.66,0.18149591419150513,0.02,0.22,0.12,0.02,0.0,0.6,0.02,2.44,1,0,1,0 +4747,INFP,30.52,0.1804019015644026,0.06,0.14,0.18,0.04,0.0,0.74,0.0,3.56,1,0,0,1 +4748,ENTP,29.82,0.18347517500762198,0.04,0.2,0.04,0.02,0.0,0.72,0.0,2.58,0,0,1,1 +4749,INFP,25.56,0.18835815266338696,0.02,0.06,0.44,0.02,0.02,0.64,0.0,3.9,1,0,0,1 +4750,ENFP,34.92,0.18874438821391268,0.0,0.28,0.34,0.0,0.02,1.32,0.0,4.72,0,0,0,1 +4751,ENTJ,29.96,0.18248719842907982,0.02,0.14,0.06,0.02,0.0,0.82,0.0,3.0,0,0,1,0 +4752,ENTJ,25.78,0.18671916425934793,0.16,0.3,0.2,0.0,0.0,0.64,0.0,2.6,0,0,1,0 +4753,INFP,30.16,0.19485631191571898,0.0,0.32,0.04,0.0,0.0,0.68,0.0,3.48,1,0,0,1 +4754,INTP,18.36,0.18173256813376187,0.0,0.26,0.1,0.0,0.0,0.3,0.0,1.78,1,0,1,1 +4755,INFJ,28.74,0.17692723483830508,0.02,0.26,0.2,0.0,0.0,0.7,0.0,2.88,1,0,0,0 +4756,INTP,27.18,0.1800353686618585,0.18,0.14,0.06,0.0,0.0,0.7,0.04,2.22,1,0,1,1 +4757,INFP,27.86,0.1820496555694823,0.02,0.12,0.02,0.0,0.0,0.62,0.0,3.16,1,0,0,1 +4758,INTP,25.44,0.179480648630814,0.08,0.12,0.1,0.0,0.02,0.44,0.0,2.44,1,0,1,1 +4759,INFP,32.6,0.18514519431291024,0.06,0.22,0.08,0.0,0.16,0.82,0.0,3.96,1,0,0,1 +4760,ENFP,28.34,0.1942597228075195,0.0,0.0,0.52,0.0,0.0,1.22,0.0,3.04,0,0,0,1 +4761,INTJ,20.2,0.18021871207618614,0.1,0.06,0.0,0.04,0.02,0.38,0.0,2.34,1,0,1,0 +4762,ENFP,27.62,0.18545617248519827,0.0,0.14,0.06,0.0,0.0,0.6,0.0,2.22,0,0,0,1 +4763,INFP,21.78,0.18679223590188426,0.0,0.28,0.08,0.0,0.02,0.5,0.0,2.78,1,0,0,1 +4764,INFP,26.52,0.18086410093403102,0.0,0.14,0.0,0.0,0.0,0.46,0.0,3.7,1,0,0,1 +4765,INTP,27.8,0.1787521728285655,0.14,0.2,0.08,0.0,0.08,0.6,0.0,3.38,1,0,1,1 +4766,ENTJ,17.58,0.1912782311932358,0.12,0.46,0.28,0.02,0.0,0.38,0.0,2.26,0,0,1,0 +4767,INFP,16.04,0.194606427499511,0.02,0.34,0.44,0.0,0.06,0.42,0.0,1.46,1,0,0,1 +4768,INFP,31.02,0.18482728775003035,0.0,0.12,0.08,0.0,0.02,0.74,0.0,3.44,1,0,0,1 +4769,INTP,19.86,0.19104625244647236,0.04,0.54,0.54,0.0,0.02,0.42,0.02,2.22,1,0,1,1 +4770,INFP,28.7,0.19225967240843309,0.08,0.2,0.34,0.0,0.0,0.92,0.0,4.22,1,0,0,1 +4771,INTJ,29.16,0.17739555617875585,0.02,0.08,0.02,0.0,0.0,0.66,0.0,3.54,1,0,1,0 +4772,ENTP,18.3,0.18771438764578582,0.0,0.12,0.04,0.0,0.0,0.36,0.0,1.44,0,0,1,1 +4773,ISTP,10.92,0.18732803695014627,0.22,0.28,0.02,0.0,0.02,0.3,0.0,1.36,1,1,1,1 +4774,INTJ,26.0,0.17737455553029657,0.0,0.12,0.16,0.0,0.0,0.58,0.0,2.92,1,0,1,0 +4775,INFP,32.46,0.1864128843640788,0.02,0.12,0.54,0.02,0.02,1.1,0.0,3.8,1,0,0,1 +4776,INFJ,16.52,0.18473759982979623,0.02,0.06,0.22,0.02,0.0,0.38,0.0,2.3,1,0,0,0 +4777,INTJ,25.28,0.18497811859285596,0.02,0.26,0.06,0.0,0.0,0.56,0.0,2.36,1,0,1,0 +4778,ENFP,24.24,0.1871268399788242,0.04,0.14,0.4,0.0,0.02,0.56,0.0,1.94,0,0,0,1 +4779,INTJ,25.38,0.18711061921212968,0.02,0.14,0.06,0.0,0.0,0.68,0.0,3.24,1,0,1,0 +4780,ISTJ,23.2,0.1833104800215449,0.26,0.38,0.48,0.12,0.0,0.52,0.04,1.78,1,1,1,0 +4781,ENTJ,26.26,0.18691647532170072,0.0,0.38,0.26,0.0,0.0,0.64,0.0,3.14,0,0,1,0 +4782,INTP,22.8,0.19494082528064532,0.18,0.28,0.06,0.02,0.04,0.58,0.0,2.4,1,0,1,1 +4783,INFJ,14.1,0.18401228852790175,0.12,0.18,0.1,0.0,0.0,0.34,0.0,1.56,1,0,0,0 +4784,ENTJ,21.92,0.18869378741729112,0.14,0.18,0.12,0.1,0.04,0.72,0.0,2.04,0,0,1,0 +4785,ISTP,12.76,0.18041416729328213,0.38,0.46,0.0,0.06,0.02,0.08,0.0,1.6,1,1,1,1 +4786,INTP,17.86,0.18164907010066134,0.18,0.32,0.1,0.04,0.0,0.38,0.0,1.72,1,0,1,1 +4787,ENTP,28.22,0.1852856188360568,0.04,0.14,0.04,0.04,0.0,1.3,0.0,3.02,0,0,1,1 +4788,ESFJ,30.82,0.18799039815313076,0.0,0.34,0.08,0.0,0.0,0.76,0.0,3.26,0,1,0,0 +4789,ISFJ,23.66,0.18871808486987407,0.02,0.14,0.22,0.02,0.04,0.52,0.0,2.62,1,1,0,0 +4790,INTJ,29.38,0.18405357056694555,0.0,0.14,0.1,0.0,0.0,0.74,0.0,3.24,1,0,1,0 +4791,INTJ,22.04,0.18688370154014786,0.06,0.12,0.78,0.0,0.02,0.5,0.0,2.38,1,0,1,0 +4792,ENTJ,31.34,0.18225906883601956,0.04,0.16,0.08,0.0,0.0,0.88,0.0,2.58,0,0,1,0 +4793,INTJ,19.98,0.19215409152398885,0.04,0.52,0.14,0.0,0.0,0.6,0.0,2.1,1,0,1,0 +4794,INTJ,30.76,0.176440952601408,0.04,0.16,0.06,0.0,0.0,0.9,0.0,2.68,1,0,1,0 +4795,ENTP,23.56,0.18676732077137656,0.0,0.08,0.0,0.0,0.0,0.4,0.0,2.92,0,0,1,1 +4796,INFJ,21.42,0.1834167904924688,0.24,0.42,0.68,0.02,0.02,0.6,0.0,2.02,1,0,0,0 +4797,ISTP,29.2,0.177358960061579,0.0,0.24,0.02,0.0,0.0,0.9,0.0,2.56,1,1,1,1 +4798,INFP,22.2,0.17753342702785174,0.02,0.14,0.06,0.0,0.06,0.5,0.0,1.62,1,0,0,1 +4799,ENTP,12.28,0.1888573956429155,0.06,0.04,0.08,0.02,0.0,0.28,0.0,0.98,0,0,1,1 +4800,INFP,22.42,0.18899292281443145,0.0,0.08,0.24,0.0,0.0,0.52,0.0,2.68,1,0,0,1 +4801,ESTP,23.9,0.18366163935517038,0.02,0.38,0.1,0.02,0.0,0.4,0.0,2.14,0,1,1,1 +4802,INFP,21.34,0.1878821698339243,0.0,0.14,0.28,0.0,0.06,0.72,0.0,2.48,1,0,0,1 +4803,ENFJ,28.02,0.18589463669715312,0.02,0.4,0.44,0.0,0.0,0.66,0.0,2.96,0,0,0,0 +4804,INFP,31.36,0.18011238553548928,0.04,0.12,0.0,0.04,0.02,1.06,0.0,3.0,1,0,0,1 +4805,INFJ,35.06,0.18423615289390663,0.02,0.14,0.4,0.02,0.0,1.0,0.0,4.38,1,0,0,0 +4806,INFJ,32.06,0.18508307528165086,0.02,0.16,0.58,0.0,0.0,0.84,0.0,4.28,1,0,0,0 +4807,ESTP,16.44,0.19005025838720216,0.0,0.3,0.4,0.0,0.02,0.28,0.0,0.88,0,1,1,1 +4808,INFJ,28.34,0.19199382518990307,0.04,0.16,0.48,0.0,0.02,0.72,0.0,3.8,1,0,0,0 +4809,INFP,22.14,0.19975038914170165,0.1,0.28,0.24,0.02,0.04,0.5,0.0,2.26,1,0,0,1 +4810,INFP,30.6,0.1826077928107166,0.04,0.26,0.42,0.02,0.08,0.8,0.0,4.04,1,0,0,1 +4811,INTJ,14.7,0.18539222156351717,0.12,0.16,0.2,0.02,0.02,0.16,0.0,2.54,1,0,1,0 +4812,INFJ,30.32,0.18883813181817433,0.0,0.12,0.06,0.0,0.0,0.78,0.0,3.92,1,0,0,0 +4813,ENTP,28.46,0.18486708619208173,0.0,0.08,0.1,0.0,0.0,1.0,0.02,2.68,0,0,1,1 +4814,ENFP,22.1,0.19351416957573797,0.02,0.18,1.36,0.02,0.0,0.56,0.0,2.54,0,0,0,1 +4815,INTP,26.32,0.1821695760143855,0.0,0.2,0.22,0.0,0.0,0.6,0.0,3.88,1,0,1,1 +4816,INFJ,22.6,0.19441609051524192,0.02,0.3,0.06,0.0,0.08,1.34,0.0,2.8,1,0,0,0 +4817,INFJ,28.7,0.19177352479084459,0.0,0.08,0.16,0.0,0.04,0.9,0.02,3.46,1,0,0,0 +4818,INTJ,21.52,0.18431579051461572,0.1,0.12,0.3,0.06,0.02,0.54,0.0,1.94,1,0,1,0 +4819,INTJ,24.98,0.18314846064102133,0.0,0.16,0.0,0.0,0.0,0.76,0.0,2.9,1,0,1,0 +4820,INFP,11.92,0.19030008313364954,0.06,0.08,0.14,0.0,0.0,0.26,0.0,2.2,1,0,0,1 +4821,ISFP,25.66,0.1827162331236821,0.02,0.14,0.12,0.0,0.0,0.56,0.0,3.42,1,1,0,1 +4822,INFJ,15.92,0.18697934018603676,0.04,0.2,0.06,0.0,0.0,0.5,0.0,2.1,1,0,0,0 +4823,ISFJ,21.4,0.18711095643252815,0.14,0.46,0.16,0.04,0.0,0.38,0.0,2.54,1,1,0,0 +4824,INFP,31.64,0.18271329809466938,0.0,0.28,0.18,0.0,0.0,0.84,0.0,4.44,1,0,0,1 +4825,INFP,20.5,0.18040602180907145,0.0,0.08,0.44,0.0,0.0,0.56,0.0,2.98,1,0,0,1 +4826,ENTP,32.28,0.18884914542326012,0.0,0.22,0.18,0.0,0.0,0.96,0.0,3.96,0,0,1,1 +4827,INTJ,17.56,0.18622541518291777,0.02,0.24,0.1,0.0,0.0,0.42,0.0,1.78,1,0,1,0 +4828,INTP,27.7,0.1822148831881795,0.06,0.18,0.08,0.02,0.12,0.7,0.0,3.44,1,0,1,1 +4829,INTP,24.34,0.18083369159783844,0.08,0.22,0.0,0.02,0.0,0.78,0.0,2.56,1,0,1,1 +4830,ENFP,33.06,0.19064506936109743,0.0,0.14,0.32,0.0,0.02,0.96,0.0,4.2,0,0,0,1 +4831,INFP,31.74,0.17195159816348035,0.04,0.1,0.02,0.0,0.04,0.88,0.0,3.58,1,0,0,1 +4832,ESFP,8.06,0.194337475307765,0.04,0.2,0.08,0.0,0.0,0.04,0.0,0.98,0,1,0,1 +4833,INFP,34.26,0.19470625106453668,0.0,0.04,0.66,0.0,0.0,2.02,0.0,4.38,1,0,0,1 +4834,INTJ,16.64,0.19441102805015398,0.08,0.22,0.08,0.04,0.0,0.62,0.0,2.4,1,0,1,0 +4835,INTP,25.62,0.1831656229438143,0.02,0.26,0.08,0.0,0.0,0.66,0.0,3.1,1,0,1,1 +4836,ISFJ,24.6,0.17397458322698178,0.0,0.14,0.06,0.0,0.0,0.68,0.0,1.88,1,1,0,0 +4837,INTP,25.14,0.17684137686979257,0.0,0.32,0.0,0.0,0.02,0.5,0.0,2.44,1,0,1,1 +4838,INTJ,27.34,0.17976895730604311,0.02,0.36,0.1,0.0,0.04,0.66,0.0,2.0,1,0,1,0 +4839,INTJ,30.1,0.18453435584611294,0.04,0.28,0.0,0.0,0.0,0.86,0.0,4.26,1,0,1,0 +4840,INFJ,32.42,0.1784985655229321,0.02,0.02,0.04,0.0,0.0,0.94,0.0,3.4,1,0,0,0 +4841,INFP,31.12,0.18349930832295128,0.02,0.1,0.12,0.0,0.0,0.84,0.0,3.62,1,0,0,1 +4842,ENTP,33.32,0.1795075764383307,0.0,0.02,0.3,0.0,0.0,0.72,0.0,4.0,0,0,1,1 +4843,ENTP,24.14,0.18437337067150372,0.0,0.26,0.08,0.0,0.0,0.7,0.0,3.08,0,0,1,1 +4844,INFP,18.82,0.18814481438018268,0.4,0.46,0.16,0.08,0.02,0.28,0.0,2.62,1,0,0,1 +4845,INFP,27.76,0.1895229867257839,0.0,0.34,0.34,0.0,0.0,0.9,0.0,1.96,1,0,0,1 +4846,INFP,10.52,0.18389062321905755,0.08,0.22,0.32,0.06,0.0,0.2,0.06,1.44,1,0,0,1 +4847,ENTP,22.76,0.17717503554631078,0.0,0.1,0.1,0.0,0.02,0.54,0.0,2.32,0,0,1,1 +4848,INTJ,26.8,0.18000619881458843,0.0,0.2,0.0,0.0,0.02,0.66,0.0,2.24,1,0,1,0 +4849,INFP,31.58,0.1896197477341176,0.0,0.28,0.62,0.0,0.02,1.24,0.0,3.32,1,0,0,1 +4850,INFJ,31.48,0.17464039010837906,0.0,0.14,0.02,0.0,0.0,0.96,0.0,2.86,1,0,0,0 +4851,INFJ,27.82,0.18503749123185909,0.02,0.14,0.32,0.02,0.02,0.56,0.0,3.46,1,0,0,0 +4852,INTP,26.98,0.1831791372915473,0.2,0.32,0.1,0.06,0.06,1.36,0.0,2.88,1,0,1,1 +4853,INTJ,26.62,0.18195864898619762,0.02,0.1,0.04,0.0,0.0,0.88,0.0,2.82,1,0,1,0 +4854,INTJ,29.54,0.18853461510935363,0.12,0.36,0.06,0.0,0.02,0.98,0.04,3.44,1,0,1,0 +4855,ISTP,24.9,0.17749202342249734,0.0,0.2,0.04,0.0,0.0,0.52,0.02,2.7,1,1,1,1 +4856,INTJ,26.24,0.1777772728385287,0.0,0.12,0.38,0.0,0.0,0.62,0.0,2.56,1,0,1,0 +4857,INFP,29.2,0.1838157976275744,0.0,0.02,0.36,0.0,0.04,0.8,0.0,3.16,1,0,0,1 +4858,ISTJ,27.24,0.18877440675487986,0.08,0.32,0.22,0.0,0.08,1.28,0.0,2.9,1,1,1,0 +4859,INTP,21.76,0.18952684039099355,0.0,0.3,0.1,0.0,0.0,0.76,0.0,2.76,1,0,1,1 +4860,INTP,28.24,0.18227792796576486,0.0,0.1,0.06,0.0,0.0,0.62,0.0,2.98,1,0,1,1 +4861,ISFJ,30.02,0.18671528751737515,0.04,0.16,0.14,0.02,0.0,0.74,0.0,4.42,1,1,0,0 +4862,ISTP,30.68,0.1860403970407528,0.0,0.08,0.02,0.0,0.0,0.68,0.0,3.62,1,1,1,1 +4863,INTJ,31.5,0.1760330062046992,0.0,0.24,0.12,0.0,0.0,0.88,0.0,3.1,1,0,1,0 +4864,INTP,19.84,0.18639433286096108,0.14,0.18,0.2,0.12,0.0,1.06,0.0,2.36,1,0,1,1 +4865,INFJ,25.72,0.1801092870137262,0.12,0.34,0.2,0.04,0.02,0.82,0.0,2.4,1,0,0,0 +4866,INTP,20.28,0.18928098281026678,0.0,0.24,0.06,0.0,0.0,0.48,0.02,2.16,1,0,1,1 +4867,INTJ,29.76,0.1785691928545196,0.02,0.14,0.02,0.02,0.0,0.88,0.0,2.54,1,0,1,0 +4868,ISTJ,21.2,0.1839365494104934,0.08,0.48,0.18,0.0,0.02,0.5,0.02,2.2,1,1,1,0 +4869,ENFJ,17.56,0.19060545798966821,0.66,0.7,0.06,0.02,0.04,0.42,0.02,2.86,0,0,0,0 +4870,INFJ,27.96,0.18046135465068996,0.1,0.26,0.1,0.02,0.0,0.86,0.0,3.02,1,0,0,0 +4871,INTJ,31.74,0.1805479831694546,0.0,0.02,0.06,0.0,0.0,0.76,0.0,4.7,1,0,1,0 +4872,ENFP,29.94,0.1777687694648746,0.0,0.12,0.04,0.0,0.02,0.58,0.02,3.22,0,0,0,1 +4873,INFP,30.04,0.1841608857253308,0.02,0.08,0.12,0.0,0.04,0.8,0.0,3.2,1,0,0,1 +4874,INFJ,33.38,0.17769950955330266,0.02,0.08,0.0,0.04,0.0,0.92,0.0,2.74,1,0,0,0 +4875,ISTP,28.3,0.18287069417558288,0.02,0.16,0.12,0.02,0.0,0.76,0.0,2.84,1,1,1,1 +4876,INTP,28.0,0.18598856728457844,0.0,0.22,0.26,0.0,0.06,1.6,0.0,2.56,1,0,1,1 +4877,ESTP,20.32,0.184250514285614,0.08,0.22,0.76,0.04,0.02,0.28,0.0,2.44,0,1,1,1 +4878,ENTP,25.26,0.18791736110334342,0.0,0.12,0.02,0.0,0.0,0.6,0.0,2.52,0,0,1,1 +4879,ISTJ,29.42,0.1820007223566104,0.0,0.16,0.0,0.0,0.0,0.54,0.0,3.7,1,1,1,0 +4880,INTP,26.12,0.19076331305630112,0.02,0.08,0.04,0.0,0.0,0.54,0.0,3.76,1,0,1,1 +4881,INFJ,26.96,0.1878003853238404,0.06,0.14,0.16,0.04,0.0,0.68,0.0,3.54,1,0,0,0 +4882,INFJ,21.22,0.18862841340275624,0.18,0.18,0.18,0.02,0.06,0.46,0.0,2.1,1,0,0,0 +4883,ENFP,11.38,0.18746152070277367,0.02,0.14,0.04,0.0,0.0,0.22,0.0,1.4,0,0,0,1 +4884,INTJ,32.34,0.18322676686979786,0.0,0.18,0.0,0.0,0.0,0.88,0.0,3.18,1,0,1,0 +4885,INFP,28.52,0.1883213908635259,0.04,0.18,0.0,0.0,0.06,0.96,0.0,3.54,1,0,0,1 +4886,INTJ,15.84,0.1823120132730193,0.02,0.12,0.0,0.02,0.04,0.22,0.0,1.18,1,0,1,0 +4887,INFP,22.92,0.18341902984324854,0.04,0.12,0.22,0.04,0.0,0.38,0.0,3.02,1,0,0,1 +4888,ENFP,20.92,0.17958146094586375,0.06,0.0,0.14,0.02,0.0,0.24,0.0,0.92,0,0,0,1 +4889,ENFP,33.26,0.18694889464918096,0.0,0.16,0.5,0.0,0.02,0.52,0.0,5.26,0,0,0,1 +4890,INFP,26.02,0.17470026230446478,0.0,0.0,0.0,0.0,0.0,0.44,0.0,2.72,1,0,0,1 +4891,INTJ,22.54,0.18332878707340297,0.14,0.34,0.02,0.0,0.04,0.52,0.0,2.9,1,0,1,0 +4892,ENFP,10.44,0.18545931747774916,0.36,0.38,0.02,0.0,0.0,0.14,0.02,1.34,0,0,0,1 +4893,ENTP,18.42,0.1903448429711469,0.0,0.12,0.24,0.0,0.0,0.22,0.0,2.14,0,0,1,1 +4894,INTP,19.3,0.18804695466725813,0.14,0.32,0.08,0.06,0.0,0.34,0.0,1.82,1,0,1,1 +4895,ENFP,27.48,0.19054060092303385,0.04,0.78,0.34,0.0,0.0,0.58,0.0,2.56,0,0,0,1 +4896,INFJ,22.08,0.18581530581111033,0.02,0.02,0.18,0.0,0.0,0.66,0.0,2.84,1,0,0,0 +4897,INFP,33.48,0.18099384816763261,0.0,0.06,0.12,0.0,0.0,0.92,0.0,4.02,1,0,0,1 +4898,INTJ,17.26,0.18373468890858924,0.14,0.28,0.08,0.06,0.0,0.28,0.0,1.78,1,0,1,0 +4899,ENTP,30.3,0.18359176800165117,0.0,0.3,0.26,0.0,0.0,0.64,0.0,4.28,0,0,1,1 +4900,ENTP,29.06,0.18595100020990915,0.08,0.12,0.06,0.02,0.0,0.64,0.0,3.34,0,0,1,1 +4901,ISTP,14.36,0.18672044408856617,0.02,0.24,0.0,0.0,0.0,0.1,0.0,1.42,1,1,1,1 +4902,INFJ,8.98,0.18155698398122638,0.72,0.76,0.06,0.0,0.0,0.26,0.04,0.82,1,0,0,0 +4903,INTJ,24.54,0.19205580580053644,0.08,0.4,0.28,0.08,0.0,0.7,0.0,2.26,1,0,1,0 +4904,ENTP,31.12,0.18398728633455957,0.0,0.28,0.18,0.0,0.0,0.72,0.0,4.0,0,0,1,1 +4905,INFJ,22.5,0.1843270148431499,0.12,0.16,0.3,0.0,0.0,0.56,0.0,2.74,1,0,0,0 +4906,INFP,13.4,0.188359961227863,0.88,0.9,0.36,0.0,0.0,0.56,0.04,1.5,1,0,0,1 +4907,ESTJ,26.06,0.18481418236122557,0.06,0.16,0.08,0.0,0.0,0.72,0.0,2.06,0,1,1,0 +4908,INTJ,25.52,0.18983002008902763,0.08,0.18,0.08,0.0,0.0,0.66,0.0,3.02,1,0,1,0 +4909,ENFP,19.38,0.18773360099169634,0.0,0.06,0.44,0.0,0.0,0.4,0.0,2.26,0,0,0,1 +4910,INTJ,23.26,0.19110539995857198,0.0,0.04,0.22,0.0,0.04,0.68,0.0,2.68,1,0,1,0 +4911,INTJ,14.18,0.19424515681279267,0.28,0.34,0.64,0.1,0.04,0.64,0.02,1.54,1,0,1,0 +4912,ISFP,32.86,0.1902061075908563,0.06,0.12,3.02,0.0,0.0,0.94,0.04,3.9,1,1,0,1 +4913,INFP,27.74,0.18239495258717417,0.1,0.08,0.16,0.0,0.0,0.84,0.0,2.36,1,0,0,1 +4914,ENTP,28.34,0.18405347463332947,0.0,0.12,0.28,0.0,0.0,0.72,0.0,3.08,0,0,1,1 +4915,INFJ,29.72,0.18809129996951246,0.1,0.24,0.16,0.08,0.0,0.74,0.0,3.78,1,0,0,0 +4916,INFP,31.24,0.18359001140513043,0.04,0.3,0.0,0.0,0.02,0.88,0.04,3.86,1,0,0,1 +4917,INFJ,31.72,0.18406995570465937,0.04,0.14,0.12,0.0,0.02,1.02,0.0,3.48,1,0,0,0 +4918,INTJ,32.12,0.18604567444949616,0.0,0.02,0.04,0.0,0.0,0.88,0.0,3.08,1,0,1,0 +4919,INFJ,30.7,0.18709134317732776,0.1,0.26,0.32,0.02,0.0,0.88,0.0,3.86,1,0,0,0 +4920,ISFJ,28.8,0.1865238759376057,0.0,0.26,0.02,0.0,0.06,0.7,0.0,4.36,1,1,0,0 +4921,INTJ,30.1,0.17660320597273826,0.0,0.1,0.0,0.02,0.02,0.64,0.0,2.64,1,0,1,0 +4922,ISFP,14.62,0.18409373714057128,0.16,0.26,0.1,0.0,0.06,0.32,0.0,1.74,1,1,0,1 +4923,INFP,22.92,0.18817885733658613,0.3,0.32,0.08,0.02,0.0,0.46,0.0,3.46,1,0,0,1 +4924,INFP,32.88,0.18367633723944773,0.0,0.04,0.16,0.0,0.0,0.86,0.0,3.54,1,0,0,1 +4925,INFJ,19.44,0.1843298509234802,0.04,0.06,0.0,0.0,0.0,0.32,0.0,1.88,1,0,0,0 +4926,ENFP,23.96,0.1889293727944511,0.06,0.3,0.32,0.0,0.0,0.42,0.0,3.06,0,0,0,1 +4927,INFP,32.26,0.18726049613373721,0.0,0.04,0.22,0.0,0.0,0.92,0.02,3.54,1,0,0,1 +4928,ENFP,8.24,0.18709283495273976,0.0,0.14,0.04,0.0,0.0,0.16,0.0,1.0,0,0,0,1 +4929,INFP,11.76,0.1732373865093829,0.58,0.3,0.02,0.22,0.0,0.18,0.0,1.26,1,0,0,1 +4930,INTJ,24.78,0.1838563358287649,0.02,0.1,0.0,0.0,0.0,0.74,0.0,2.52,1,0,1,0 +4931,INFJ,34.46,0.18588354344918043,0.0,0.14,0.14,0.0,0.0,1.08,0.0,4.52,1,0,0,0 +4932,INFP,26.06,0.1810824752557607,0.04,0.36,0.06,0.0,0.02,0.64,0.0,2.04,1,0,0,1 +4933,INFP,28.64,0.18383207143317426,0.08,0.26,0.12,0.02,0.0,0.58,0.0,3.32,1,0,0,1 +4934,INTP,30.3,0.17581461458785796,0.02,0.18,0.0,0.0,0.0,0.78,0.0,2.52,1,0,1,1 +4935,ISFP,20.18,0.19038370830062665,0.16,0.24,0.32,0.16,0.02,0.46,0.0,2.3,1,1,0,1 +4936,INFJ,33.24,0.1813640249411495,0.0,0.08,0.4,0.0,0.02,0.98,0.0,3.66,1,0,0,0 +4937,INTP,16.52,0.19087636232158536,0.0,0.04,0.04,0.0,0.0,0.22,0.0,2.12,1,0,1,1 +4938,INTP,33.18,0.1823681269895591,0.0,0.08,0.1,0.0,0.0,1.2,0.0,4.18,1,0,1,1 +4939,INTP,8.38,0.18143672450214585,0.0,0.0,0.1,0.0,0.0,0.18,0.0,0.92,1,0,1,1 +4940,INTJ,30.4,0.1843851615576692,0.0,0.22,0.3,0.0,0.0,0.76,0.0,3.02,1,0,1,0 +4941,INTJ,25.74,0.18583654475111222,0.14,0.18,0.24,0.02,0.0,1.08,0.0,2.5,1,0,1,0 +4942,INTP,18.18,0.17674919430883365,0.54,0.5,0.14,0.06,0.04,0.48,0.0,2.12,1,0,1,1 +4943,ISTJ,32.92,0.18458637140975365,0.0,0.1,0.1,0.0,0.02,0.78,0.0,3.92,1,1,1,0 +4944,INTP,23.34,0.1951190616019352,0.02,0.58,0.64,0.02,0.0,0.64,0.0,2.16,1,0,1,1 +4945,INFJ,26.72,0.18522308481455432,0.1,0.3,0.38,0.04,0.02,0.52,0.0,3.08,1,0,0,0 +4946,INFP,24.4,0.18655607946683617,0.08,0.2,0.24,0.02,0.02,0.4,0.0,2.8,1,0,0,1 +4947,INFJ,32.92,0.18845784023668635,0.0,0.2,0.08,0.0,0.0,1.0,0.0,3.04,1,0,0,0 +4948,ENFP,27.36,0.19092789047295947,0.02,0.3,0.24,0.02,0.02,0.74,0.04,3.56,0,0,0,1 +4949,ISFP,26.88,0.18474212028935733,0.04,0.16,0.88,0.02,0.0,0.58,0.0,3.12,1,1,0,1 +4950,ENFP,19.04,0.1838949596819478,0.08,0.24,0.6,0.0,0.0,0.6,0.0,2.08,0,0,0,1 +4951,ESTP,23.96,0.19274730098718754,0.06,0.14,0.28,0.02,0.02,0.48,0.0,1.68,0,1,1,1 +4952,INTJ,13.84,0.1923604527849903,0.04,0.14,0.14,0.04,0.0,0.42,0.0,1.84,1,0,1,0 +4953,INTJ,17.0,0.18159528146851897,0.0,0.22,0.06,0.0,0.0,0.34,0.0,2.24,1,0,1,0 +4954,INTJ,21.5,0.18507535495070987,0.0,0.24,0.06,0.0,0.0,0.5,0.0,3.0,1,0,1,0 +4955,INFP,29.96,0.1852039953204504,0.04,0.2,0.2,0.0,0.0,0.88,0.0,3.26,1,0,0,1 +4956,INTP,0.94,0.1675299512925074,0.14,0.06,0.0,0.08,0.0,0.02,0.0,0.18,1,0,1,1 +4957,INFP,27.98,0.18116297352011862,0.06,0.18,0.1,0.0,0.08,0.88,0.04,3.34,1,0,0,1 +4958,ENFJ,21.9,0.18444841610460228,0.0,0.2,0.46,0.0,0.0,0.58,0.0,1.64,0,0,0,0 +4959,INTP,25.08,0.18407211634141749,0.0,0.14,0.08,0.0,0.04,0.4,0.0,3.4,1,0,1,1 +4960,ENTP,20.72,0.20188442938020323,0.02,0.12,0.18,0.0,0.0,0.4,0.02,1.88,0,0,1,1 +4961,INFP,25.7,0.1861335372312541,0.04,0.18,0.0,0.02,0.02,0.68,0.0,1.8,1,0,0,1 +4962,INFJ,32.58,0.18697555498507684,0.04,0.26,0.6,0.0,0.04,0.86,0.02,4.1,1,0,0,0 +4963,INFJ,23.28,0.1934669454758366,0.06,0.24,1.1,0.02,0.0,0.48,0.0,2.94,1,0,0,0 +4964,INTJ,12.14,0.1974343881712145,0.02,0.12,0.32,0.0,0.0,0.24,0.0,1.64,1,0,1,0 +4965,INFJ,33.28,0.19294253615365967,0.04,0.28,0.08,0.0,0.0,1.4,0.0,3.3,1,0,0,0 +4966,ENFP,13.64,0.18554508349788934,0.04,0.04,0.0,0.02,0.04,0.16,0.0,1.62,0,0,0,1 +4967,INFP,33.44,0.18572252307070505,0.0,0.48,0.18,0.0,0.12,1.16,0.0,4.2,1,0,0,1 +4968,INFP,25.48,0.18339712661403787,0.08,0.12,0.08,0.0,0.02,0.5,0.0,1.14,1,0,0,1 +4969,INTJ,28.16,0.1847761429104047,0.02,0.26,0.12,0.0,0.0,0.86,0.0,3.28,1,0,1,0 +4970,INFJ,18.14,0.19061962644345115,0.02,0.1,0.06,0.0,0.0,0.5,0.0,2.46,1,0,0,0 +4971,INFP,17.98,0.18861261042440433,0.18,0.24,0.2,0.04,0.02,0.56,0.0,2.28,1,0,0,1 +4972,INFP,24.52,0.18808178498857173,0.02,0.26,0.02,0.0,0.0,0.5,0.0,1.9,1,0,0,1 +4973,INFJ,23.48,0.17860472461888613,0.4,0.32,0.16,0.18,0.0,0.7,0.0,2.4,1,0,0,0 +4974,INTP,18.5,0.18166492823313227,0.3,0.44,0.12,0.0,0.0,0.34,0.0,1.8,1,0,1,1 +4975,INTJ,30.58,0.18473994220098378,0.0,0.38,0.06,0.0,0.0,0.8,0.0,3.96,1,0,1,0 +4976,INFP,27.88,0.1898356625172977,0.0,0.08,0.5,0.0,0.0,0.84,0.0,3.58,1,0,0,1 +4977,INTJ,23.78,0.18963416161267319,0.0,0.24,0.16,0.0,0.0,0.42,0.0,2.5,1,0,1,0 +4978,INTJ,30.06,0.1841876206939828,0.0,0.12,0.2,0.0,0.0,0.8,0.0,4.36,1,0,1,0 +4979,INFJ,33.34,0.1863145610094144,0.0,0.28,0.08,0.0,0.02,1.12,0.0,4.32,1,0,0,0 +4980,INFJ,20.76,0.184319188772317,0.0,0.1,0.02,0.0,0.0,0.58,0.0,2.52,1,0,0,0 +4981,ENTJ,17.26,0.19004766560370845,0.24,0.28,1.34,0.04,0.02,0.5,0.0,1.78,0,0,1,0 +4982,ENTJ,24.38,0.1899703590826723,0.0,0.3,0.12,0.0,0.0,0.64,0.0,3.32,0,0,1,0 +4983,ISFJ,27.22,0.18182285578999002,0.0,0.08,0.14,0.0,0.0,0.6,0.0,3.24,1,1,0,0 +4984,INTJ,31.28,0.18389015237327483,0.02,0.18,0.1,0.02,0.0,0.96,0.0,4.22,1,0,1,0 +4985,INFJ,25.72,0.1844343315556844,0.02,0.2,0.04,0.0,0.0,0.62,0.0,2.48,1,0,0,0 +4986,INFP,30.22,0.18094841472577064,0.02,0.44,0.2,0.0,0.0,0.68,0.0,3.28,1,0,0,1 +4987,INFJ,30.32,0.1722153017422776,0.04,0.02,0.04,0.04,0.0,0.7,0.0,2.74,1,0,0,0 +4988,INFP,23.6,0.18345384021038993,0.04,0.2,0.1,0.02,0.0,0.52,0.0,3.1,1,0,0,1 +4989,INFJ,31.22,0.18733628035826871,0.02,0.26,0.32,0.02,0.0,1.0,0.0,2.94,1,0,0,0 +4990,ISTP,28.76,0.18799529274855456,0.0,0.08,0.2,0.0,0.02,0.56,0.08,3.16,1,1,1,1 +4991,INTJ,22.18,0.1911734916923463,0.14,0.34,0.26,0.0,0.0,0.84,0.08,2.44,1,0,1,0 +4992,INTP,20.0,0.18720593218209464,0.04,0.16,0.12,0.0,0.0,0.44,0.0,1.58,1,0,1,1 +4993,INTP,32.64,0.18544153092019783,0.02,0.24,0.26,0.0,0.0,0.74,0.0,4.7,1,0,1,1 +4994,INFP,21.72,0.18677349657456288,0.0,0.08,0.1,0.0,0.02,0.62,0.0,3.16,1,0,0,1 +4995,INFJ,9.26,0.19050580765296107,0.36,0.4,0.14,0.0,0.0,0.54,0.0,1.14,1,0,0,0 +4996,INFJ,13.94,0.2092202221537906,0.0,0.08,0.0,0.0,0.0,2.96,0.0,1.82,1,0,0,0 +4997,ENFP,31.34,0.18623682568037725,0.04,0.22,0.34,0.0,0.02,0.78,0.0,3.02,0,0,0,1 +4998,ENFP,30.52,0.18639665312722228,0.02,0.12,0.12,0.0,0.0,0.82,0.0,3.94,0,0,0,1 +4999,ENFJ,32.4,0.18381656651917153,0.0,0.24,0.42,0.0,0.0,0.9,0.0,3.32,0,0,0,0 +5000,INFJ,21.62,0.18056875189752636,0.12,0.3,0.26,0.0,0.0,0.4,0.0,2.46,1,0,0,0 +5001,ENFP,6.98,0.19520345012253465,0.14,0.12,0.1,0.02,0.0,0.22,0.0,0.66,0,0,0,1 +5002,INTP,18.2,0.19044976396221064,0.1,0.44,0.2,0.0,0.0,0.44,0.0,2.04,1,0,1,1 +5003,INFP,28.16,0.18500962259990528,0.02,0.22,0.2,0.0,0.0,0.68,0.0,3.02,1,0,0,1 +5004,ENFP,28.0,0.1836007642737983,0.0,0.3,0.7,0.0,0.0,1.04,0.0,1.34,0,0,0,1 +5005,ENFJ,26.98,0.18789780944078666,0.02,0.12,0.28,0.02,0.0,0.62,0.0,3.26,0,0,0,0 +5006,INTP,22.08,0.18373780161633102,0.02,0.22,0.08,0.0,0.02,0.4,0.0,2.32,1,0,1,1 +5007,ISTJ,21.14,0.1851726047480217,0.06,0.16,0.1,0.0,0.0,0.58,0.0,2.04,1,1,1,0 +5008,INTP,34.88,0.18739196199077293,0.0,0.06,0.1,0.0,0.02,0.92,0.0,4.44,1,0,1,1 +5009,INFJ,16.84,0.18893929016364605,0.02,0.04,0.1,0.0,0.02,0.64,0.0,1.78,1,0,0,0 +5010,INTJ,34.44,0.18463140746722734,0.0,0.3,0.12,0.0,0.0,0.88,0.0,4.26,1,0,1,0 +5011,INFJ,34.92,0.18654863946616282,0.0,0.16,0.02,0.0,0.0,0.82,0.0,4.46,1,0,0,0 +5012,ENFP,27.76,0.18614669277685897,0.1,0.18,0.14,0.08,0.0,0.76,0.0,2.62,0,0,0,1 +5013,INFJ,29.56,0.18520770387132604,0.0,0.4,0.14,0.0,0.02,1.0,0.0,2.84,1,0,0,0 +5014,INTJ,33.0,0.19331065759637184,0.0,0.62,0.12,0.0,0.0,1.56,0.0,3.64,1,0,1,0 +5015,INFJ,26.0,0.19157413524410977,0.18,0.38,0.52,0.02,0.06,0.74,0.0,3.02,1,0,0,0 +5016,ENTJ,23.0,0.19310620366760772,0.02,0.18,0.02,0.0,0.02,0.54,0.0,2.76,0,0,1,0 +5017,ENTP,31.72,0.17934989973951013,0.0,0.28,0.16,0.0,0.0,0.82,0.0,3.2,0,0,1,1 +5018,INTJ,22.2,0.18014578177351456,0.0,0.14,0.04,0.0,0.0,0.5,0.0,1.96,1,0,1,0 +5019,ISFP,16.46,0.1873004081816591,0.4,0.44,0.2,0.02,0.04,0.3,0.0,2.24,1,1,0,1 +5020,INFP,18.6,0.1821119321444296,0.04,0.12,0.06,0.0,0.02,0.32,0.0,2.12,1,0,0,1 +5021,INTJ,25.44,0.17816931984987444,0.04,0.1,0.04,0.04,0.0,0.54,0.0,3.08,1,0,1,0 +5022,INTP,30.94,0.1835436312527082,0.06,0.18,0.06,0.0,0.02,0.78,0.0,3.08,1,0,1,1 +5023,INFJ,22.56,0.19161931032229051,0.0,0.44,0.0,0.0,0.0,0.6,0.0,2.8,1,0,0,0 +5024,INFJ,24.78,0.18581475584694154,0.18,0.14,0.06,0.1,0.0,0.64,0.0,2.28,1,0,0,0 +5025,INFP,19.32,0.18523377955838904,0.02,0.2,0.02,0.0,0.02,0.26,0.02,2.24,1,0,0,1 +5026,ENFP,23.12,0.1884214085904435,0.04,0.06,0.36,0.0,0.02,0.78,0.0,2.68,0,0,0,1 +5027,ISFJ,33.08,0.1879606092284681,0.0,0.06,0.38,0.0,0.0,0.82,0.0,4.26,1,1,0,0 +5028,INTP,19.06,0.19154692132173604,0.02,0.24,0.26,0.0,0.02,0.38,0.0,2.68,1,0,1,1 +5029,INFJ,23.82,0.18646916284066015,0.08,0.42,0.04,0.08,0.0,0.58,0.0,2.46,1,0,0,0 +5030,INTP,26.26,0.1747993637153666,0.0,0.28,0.34,0.0,0.0,0.64,0.02,1.0,1,0,1,1 +5031,INTP,30.44,0.177423711066151,0.02,0.2,0.0,0.0,0.04,0.72,0.0,3.06,1,0,1,1 +5032,ENTP,35.24,0.1881698975857723,0.0,0.04,0.0,0.0,0.04,1.36,0.0,4.22,0,0,1,1 +5033,ESFJ,33.2,0.18514651868605062,0.0,0.06,0.16,0.0,0.0,0.88,0.0,2.9,0,1,0,0 +5034,INFJ,31.38,0.17767971936953378,0.0,0.12,0.0,0.0,0.0,0.72,0.0,2.4,1,0,0,0 +5035,ISTP,18.24,0.1805808935024656,0.12,0.26,0.14,0.06,0.02,0.3,0.02,1.92,1,1,1,1 +5036,ENFP,33.88,0.1821318771473058,0.0,0.08,0.06,0.0,0.0,0.98,0.0,4.1,0,0,0,1 +5037,INFJ,20.5,0.18696305234307056,0.08,0.3,0.38,0.0,0.0,0.48,0.02,1.76,1,0,0,0 +5038,INTJ,30.24,0.18341402084113057,0.04,0.18,0.3,0.0,0.02,0.76,0.0,3.42,1,0,1,0 +5039,INTJ,29.12,0.17407119261183832,0.0,0.04,0.0,0.0,0.0,0.66,0.0,3.84,1,0,1,0 +5040,ENFP,17.72,0.19516566707889094,0.08,0.24,0.22,0.0,0.04,0.52,0.0,2.46,0,0,0,1 +5041,INFP,22.68,0.1858273245041575,0.02,0.32,0.0,0.02,0.0,0.46,0.0,2.58,1,0,0,1 +5042,INFJ,21.82,0.18722270672606575,0.02,0.2,0.1,0.0,0.02,0.48,0.0,2.64,1,0,0,0 +5043,ENTP,24.98,0.18008734902441542,0.02,0.22,0.02,0.0,0.0,0.7,0.0,1.94,0,0,1,1 +5044,INFJ,22.06,0.1843496840980677,0.02,0.04,0.42,0.02,0.0,0.96,0.0,2.14,1,0,0,0 +5045,ENFJ,12.36,0.17553362183442112,0.0,0.04,0.0,0.0,0.04,0.06,0.0,0.66,0,0,0,0 +5046,INFP,18.88,0.18666123134605273,0.04,0.08,0.04,0.0,0.0,0.4,0.0,1.98,1,0,0,1 +5047,ENTP,24.58,0.1938794845821216,0.02,0.28,0.24,0.02,0.02,0.66,0.0,2.04,0,0,1,1 +5048,INFJ,28.26,0.18289719375954733,0.02,0.12,0.5,0.0,0.1,0.82,0.0,3.94,1,0,0,0 +5049,ENFP,28.72,0.19094352284023666,0.04,0.42,0.7,0.02,0.0,0.54,0.0,3.7,0,0,0,1 +5050,ISFP,25.84,0.1925610007223224,0.0,0.08,0.36,0.0,0.0,0.84,0.0,3.48,1,1,0,1 +5051,INTJ,29.1,0.18619160738632518,0.0,0.16,0.18,0.0,0.0,0.88,0.0,4.16,1,0,1,0 +5052,INFJ,25.72,0.2037470737055153,0.0,0.34,0.18,0.0,0.06,0.74,0.0,3.38,1,0,0,0 +5053,ENFP,27.42,0.18336515146135837,0.0,0.34,0.38,0.0,0.0,0.76,0.0,3.18,0,0,0,1 +5054,ENTJ,30.6,0.19068828139982943,0.0,0.38,0.22,0.0,0.0,0.72,0.0,2.9,0,0,1,0 +5055,ESFJ,30.18,0.18102996124542067,0.0,0.08,0.24,0.0,0.0,0.88,0.0,4.0,0,1,0,0 +5056,INFJ,25.98,0.18685207089638572,0.0,0.34,0.28,0.0,0.0,0.64,0.0,3.46,1,0,0,0 +5057,INFP,29.1,0.18924413346954316,0.1,0.26,0.74,0.0,0.0,1.08,0.0,4.04,1,0,0,1 +5058,INTJ,25.72,0.18480031469904043,0.16,0.18,0.12,0.0,0.0,0.72,0.0,3.06,1,0,1,0 +5059,INTP,23.74,0.18817515604895332,0.16,0.42,0.0,0.0,0.0,0.54,0.0,2.56,1,0,1,1 +5060,INTP,23.56,0.18022912792211027,0.06,0.02,0.22,0.06,0.08,0.54,0.02,3.5,1,0,1,1 +5061,ENFJ,30.44,0.18732973901189387,0.0,0.52,1.0,0.0,0.0,1.46,0.0,3.7,0,0,0,0 +5062,INFP,26.12,0.19475437011964744,0.08,0.28,1.06,0.04,0.0,0.92,0.0,3.72,1,0,0,1 +5063,ENTP,26.34,0.18997696155322974,0.0,0.1,0.04,0.0,0.0,0.56,0.0,3.54,0,0,1,1 +5064,INFP,27.02,0.17618114639621238,0.0,0.24,0.12,0.0,0.02,0.5,0.0,2.62,1,0,0,1 +5065,ENTP,24.96,0.18238869364333013,0.06,0.18,0.2,0.02,0.0,0.68,0.0,3.3,0,0,1,1 +5066,ESTP,24.0,0.18408764978630293,0.02,0.22,0.1,0.0,0.0,0.52,0.0,2.42,0,1,1,1 +5067,ISTJ,19.74,0.18790291692442734,0.1,0.32,0.08,0.02,0.0,0.32,0.0,2.48,1,1,1,0 +5068,INTP,24.82,0.18858581180875822,0.12,0.3,0.0,0.0,0.02,0.48,0.0,2.22,1,0,1,1 +5069,INTJ,24.46,0.18142530545504099,0.06,0.28,0.0,0.02,0.0,0.92,0.0,1.8,1,0,1,0 +5070,INFJ,31.5,0.1840649923458902,0.04,0.16,0.06,0.02,0.0,0.7,0.0,4.1,1,0,0,0 +5071,INFJ,30.7,0.18227341721256446,0.02,0.12,0.04,0.0,0.02,0.78,0.0,3.9,1,0,0,0 +5072,ENFP,31.2,0.19049921477730833,0.0,0.2,0.16,0.0,0.0,1.02,0.0,3.24,0,0,0,1 +5073,ISTP,29.74,0.18727709106437612,0.02,0.2,0.0,0.0,0.0,0.7,0.0,3.44,1,1,1,1 +5074,ENFP,23.86,0.1883388040752427,0.16,0.64,0.16,0.04,0.0,1.08,0.0,1.72,0,0,0,1 +5075,INFP,20.6,0.18247424225585723,0.18,0.18,0.02,0.06,0.0,0.52,0.0,2.18,1,0,0,1 +5076,ENTP,24.28,0.19025001228437663,0.0,0.4,0.12,0.0,0.0,0.7,0.0,2.62,0,0,1,1 +5077,INTJ,20.86,0.1721344715029655,0.12,0.68,0.06,0.0,0.0,0.34,0.0,1.98,1,0,1,0 +5078,INFJ,20.24,0.1853422228218466,0.0,0.18,0.12,0.0,0.0,0.5,0.0,2.02,1,0,0,0 +5079,INTP,27.06,0.17671987102661246,0.0,0.2,0.1,0.0,0.02,0.64,0.0,2.22,1,0,1,1 +5080,INFP,22.18,0.18596721021016066,0.14,0.14,0.06,0.02,0.04,0.4,0.0,2.82,1,0,0,1 +5081,INFJ,28.96,0.18837354567030032,0.02,0.16,0.38,0.02,0.0,1.04,0.0,2.54,1,0,0,0 +5082,INFP,19.46,0.18746009577116793,0.12,0.08,0.4,0.06,0.0,0.44,0.0,1.96,1,0,0,1 +5083,INFP,15.2,0.17967370950405806,0.68,0.56,0.1,0.2,0.08,0.36,0.02,1.54,1,0,0,1 +5084,INTP,9.8,0.19408166089965398,0.0,0.06,0.0,0.0,0.0,0.08,0.0,0.82,1,0,1,1 +5085,INFJ,26.56,0.1890697960263446,0.02,0.1,1.5,0.0,0.0,0.74,0.0,2.74,1,0,0,0 +5086,INTP,20.52,0.1844788024324443,0.04,0.06,0.02,0.0,0.0,0.5,0.0,2.5,1,0,1,1 +5087,INFJ,20.96,0.18630463036191097,0.06,0.26,0.14,0.0,0.0,0.6,0.02,2.36,1,0,0,0 +5088,INFJ,23.14,0.1884965671799388,0.22,0.22,0.02,0.02,0.02,0.42,0.0,3.0,1,0,0,0 +5089,INFJ,27.88,0.18246418228928515,0.0,0.58,0.04,0.0,0.0,1.08,0.0,2.34,1,0,0,0 +5090,INFP,11.24,0.18721267408046666,0.7,0.7,0.12,0.0,0.02,0.32,0.04,0.58,1,0,0,1 +5091,INFJ,28.0,0.17712771551736337,0.0,0.16,0.1,0.0,0.04,0.66,0.0,2.66,1,0,0,0 +5092,INFP,34.0,0.193103118533575,0.02,0.18,0.4,0.0,0.0,1.1,0.0,4.48,1,0,0,1 +5093,INFP,24.74,0.1760259464255593,0.08,0.2,0.0,0.0,0.0,0.6,0.0,2.64,1,0,0,1 +5094,ISFP,19.06,0.1922332482446138,0.08,0.3,0.9,0.04,0.02,0.52,0.0,1.76,1,1,0,1 +5095,ISFP,15.72,0.18850421773289494,0.0,0.26,0.0,0.0,0.02,0.32,0.0,1.92,1,1,0,1 +5096,ENFP,24.94,0.18954614908930828,0.0,0.08,0.44,0.0,0.0,0.6,0.0,3.26,0,0,0,1 +5097,INFJ,6.62,0.15528035529793774,0.72,0.56,0.1,0.56,0.0,0.54,0.0,0.7,1,0,0,0 +5098,INFP,32.76,0.18732116335556687,0.0,0.18,0.18,0.0,0.0,1.0,0.0,3.76,1,0,0,1 +5099,INFJ,26.72,0.18035192148153545,0.1,0.22,0.04,0.06,0.0,0.6,0.0,3.4,1,0,0,0 +5100,ENTP,27.62,0.18359858335205487,0.16,0.1,0.28,0.0,0.0,0.82,0.0,2.74,0,0,1,1 +5101,INTJ,31.74,0.1812917944362798,0.0,0.14,0.1,0.0,0.0,0.9,0.0,3.22,1,0,1,0 +5102,INTJ,26.32,0.18388029888069188,0.12,0.14,0.0,0.04,0.0,0.7,0.0,3.02,1,0,1,0 +5103,INFP,27.6,0.18595525908589478,0.02,0.18,0.38,0.0,0.04,0.78,0.0,3.72,1,0,0,1 +5104,INFJ,26.36,0.1868948350631608,0.04,0.22,0.04,0.0,0.02,0.6,0.0,3.3,1,0,0,0 +5105,INTJ,28.62,0.17864431782308987,0.12,0.34,0.06,0.02,0.0,0.88,0.0,2.5,1,0,1,0 +5106,ENTP,28.32,0.18523801512651994,0.0,0.26,0.18,0.0,0.02,0.72,0.0,2.28,0,0,1,1 +5107,ISTJ,28.76,0.18483452887497948,0.04,0.2,0.04,0.0,0.0,0.68,0.0,2.86,1,1,1,0 +5108,INFP,13.4,0.17978392227550477,0.16,0.16,0.06,0.02,0.04,0.14,0.0,1.38,1,0,0,1 +5109,ENTJ,25.1,0.18228621326509586,0.02,0.4,0.1,0.0,0.0,0.58,0.0,2.62,0,0,1,0 +5110,INFP,31.16,0.18355285929833107,0.0,0.2,0.28,0.0,0.0,0.88,0.0,3.5,1,0,0,1 +5111,INFP,27.88,0.18934233808372847,0.0,0.52,0.4,0.0,0.02,0.72,0.0,2.84,1,0,0,1 +5112,INFP,17.76,0.18369445214320573,0.02,0.2,0.16,0.0,0.0,0.52,0.0,1.9,1,0,0,1 +5113,ISFP,25.22,0.18167770998927707,0.02,0.16,0.0,0.0,0.0,0.66,0.0,2.64,1,1,0,1 +5114,INFP,21.98,0.19058047079050258,0.04,0.24,0.26,0.0,0.02,0.28,0.0,2.38,1,0,0,1 +5115,INFJ,28.64,0.18549012250628016,0.02,0.32,0.32,0.0,0.02,0.8,0.0,3.84,1,0,0,0 +5116,ENFJ,22.5,0.18067908080653505,0.0,0.16,0.36,0.0,0.0,0.56,0.0,2.56,0,0,0,0 +5117,ENTP,17.06,0.19549501127950458,0.04,0.16,0.02,0.0,0.0,0.46,0.0,2.1,0,0,1,1 +5118,ENFP,27.8,0.18962105678469163,0.02,0.32,1.08,0.0,0.0,0.92,0.02,3.18,0,0,0,1 +5119,ENFP,25.66,0.18304458954319233,0.0,0.22,0.04,0.0,0.08,0.76,0.02,3.3,0,0,0,1 +5120,ENTJ,28.88,0.1868500105615326,0.02,0.26,0.34,0.0,0.0,0.7,0.0,3.5,0,0,1,0 +5121,INTP,13.52,0.19102750281238365,0.0,0.02,0.28,0.0,0.0,0.3,0.0,1.52,1,0,1,1 +5122,INTJ,22.04,0.1875,0.06,0.26,0.0,0.0,0.0,0.48,0.0,2.02,1,0,1,0 +5123,ENTJ,21.9,0.18485396406174487,0.04,0.2,0.08,0.02,0.0,0.44,0.0,1.78,0,0,1,0 +5124,INFP,28.76,0.18252016266112922,0.02,0.2,0.06,0.0,0.0,0.68,0.0,2.6,1,0,0,1 +5125,INFP,30.28,0.18774931048072743,0.0,0.14,0.1,0.0,0.0,1.08,0.0,2.48,1,0,0,1 +5126,INFJ,28.14,0.18406436166204065,0.04,0.22,0.02,0.02,0.0,0.74,0.02,3.36,1,0,0,0 +5127,ENTP,24.66,0.17789197559284703,0.1,0.26,0.04,0.0,0.0,0.6,0.0,2.74,0,0,1,1 +5128,ENFJ,32.28,0.18635896726303433,0.04,0.28,0.42,0.0,0.04,1.02,0.0,3.36,0,0,0,0 +5129,INFP,24.2,0.17680885980053745,0.12,0.18,0.12,0.0,0.0,0.82,0.0,2.42,1,0,0,1 +5130,INFJ,28.32,0.17200308416524634,0.06,0.04,0.12,0.0,0.0,0.62,0.02,2.16,1,0,0,0 +5131,INTJ,29.26,0.18087174693028252,0.0,0.24,0.04,0.0,0.02,0.78,0.0,3.02,1,0,1,0 +5132,INTJ,29.74,0.18193333782809298,0.0,0.36,0.0,0.0,0.0,1.0,0.0,1.56,1,0,1,0 +5133,INTP,30.54,0.1850066222454698,0.04,0.2,0.08,0.0,0.0,0.82,0.0,3.52,1,0,1,1 +5134,INTJ,17.78,0.18948926191769855,0.0,0.14,0.02,0.0,0.0,0.2,0.0,2.68,1,0,1,0 +5135,INTP,16.48,0.17838841486680462,0.44,0.4,0.04,0.18,0.0,0.42,0.0,1.54,1,0,1,1 +5136,INTJ,21.26,0.18712045632560464,0.02,0.32,0.24,0.0,0.0,0.58,0.0,1.82,1,0,1,0 +5137,INTJ,26.54,0.18093514434409202,0.24,0.26,0.04,0.04,0.0,0.9,0.0,2.06,1,0,1,0 +5138,INTJ,27.44,0.18449538321516123,0.0,0.18,0.08,0.0,0.0,0.64,0.0,2.66,1,0,1,0 +5139,ENFJ,29.72,0.1834571763521019,0.02,0.14,0.0,0.02,0.0,0.8,0.0,2.32,0,0,0,0 +5140,ENTJ,20.96,0.17781280907487254,0.24,0.5,0.12,0.04,0.0,0.56,0.02,1.78,0,0,1,0 +5141,ENFP,22.18,0.1790597263313609,0.04,0.18,0.02,0.0,0.0,0.46,0.0,2.0,0,0,0,1 +5142,ENFP,27.46,0.18625941439604646,0.04,0.2,0.26,0.0,0.0,0.64,0.0,2.5,0,0,0,1 +5143,INFJ,33.06,0.17839758430521774,0.02,0.02,0.26,0.0,0.04,0.96,0.0,2.86,1,0,0,0 +5144,ENFP,24.94,0.1831223167556898,0.02,0.14,0.26,0.02,0.0,0.6,0.0,2.28,0,0,0,1 +5145,INTJ,22.96,0.18681715225949616,0.04,0.34,0.0,0.0,0.0,0.56,0.0,1.62,1,0,1,0 +5146,ENTP,30.28,0.18032010285946365,0.04,0.16,0.1,0.0,0.02,0.7,0.0,2.42,0,0,1,1 +5147,INTJ,18.68,0.18334585955048416,0.02,0.24,0.18,0.0,0.0,0.42,0.0,2.56,1,0,1,0 +5148,INFP,29.34,0.17922206269646607,0.02,0.04,0.02,0.0,0.0,0.76,0.0,3.38,1,0,0,1 +5149,INFP,31.1,0.18634950220030588,0.02,0.12,0.1,0.0,0.0,0.76,0.0,3.96,1,0,0,1 +5150,INFJ,27.24,0.18896373967097,0.06,0.18,0.32,0.0,0.02,0.96,0.0,2.96,1,0,0,0 +5151,INFP,15.12,0.19889437223070683,0.08,0.38,0.54,0.0,0.0,0.38,0.0,1.5,1,0,0,1 +5152,INFJ,33.84,0.1799909512758238,0.02,0.22,0.04,0.0,0.0,1.2,0.0,3.8,1,0,0,0 +5153,INTP,24.96,0.18476494729401152,0.04,0.24,0.02,0.02,0.0,0.68,0.0,2.66,1,0,1,1 +5154,INTP,20.6,0.18074560745668358,0.0,0.36,0.3,0.0,0.02,0.3,0.0,1.54,1,0,1,1 +5155,ENFJ,23.68,0.18521777658783456,0.0,0.04,0.12,0.0,0.0,0.46,0.0,2.3,0,0,0,0 +5156,INTJ,22.22,0.1762481754256646,0.0,0.24,0.0,0.0,0.0,0.44,0.0,2.3,1,0,1,0 +5157,INTP,23.64,0.18901160544410414,0.06,0.24,0.06,0.02,0.0,0.68,0.02,2.48,1,0,1,1 +5158,ISFJ,26.68,0.18825397845140884,0.12,0.16,0.02,0.0,0.0,0.74,0.08,3.4,1,1,0,0 +5159,INTJ,14.96,0.18882505071473865,0.06,0.26,0.16,0.04,0.0,0.24,0.0,1.14,1,0,1,0 +5160,INFP,29.7,0.19420367109236555,0.04,0.38,2.66,0.02,0.02,0.82,0.0,3.22,1,0,0,1 +5161,ISTP,23.14,0.18787458710975388,0.0,0.26,0.02,0.0,0.04,0.54,0.0,2.92,1,1,1,1 +5162,ESTJ,26.7,0.18876530733649796,0.0,0.22,0.02,0.0,0.0,0.66,0.0,3.76,0,1,1,0 +5163,INFJ,25.22,0.18771232100684912,0.04,0.28,0.12,0.0,0.0,1.26,0.0,3.04,1,0,0,0 +5164,ENTJ,24.7,0.19097383000765736,0.04,0.14,0.0,0.0,0.0,0.68,0.0,2.48,0,0,1,0 +5165,ENFJ,14.56,0.1821177783422848,0.2,0.42,0.08,0.0,0.0,0.18,0.0,0.94,0,0,0,0 +5166,INFJ,35.08,0.18460889655666252,0.0,0.14,0.14,0.0,0.0,1.08,0.02,4.68,1,0,0,0 +5167,INTP,23.86,0.18385946872091527,0.16,0.22,0.02,0.0,0.0,0.44,0.0,3.34,1,0,1,1 +5168,ESTJ,22.16,0.18524474765449708,0.0,0.06,0.08,0.0,0.0,0.48,0.0,2.48,0,1,1,0 +5169,INFP,28.7,0.18268436744355757,0.02,0.3,0.08,0.0,0.0,1.02,0.0,2.42,1,0,0,1 +5170,INTP,15.2,0.1846703424125912,0.46,0.46,0.0,0.02,0.08,0.3,0.0,2.1,1,0,1,1 +5171,ENTP,22.68,0.18740364604837267,0.04,0.2,0.2,0.0,0.0,0.46,0.02,2.28,0,0,1,1 +5172,INFP,23.74,0.1886101918779916,0.12,0.4,0.66,0.0,0.0,0.92,0.0,2.66,1,0,0,1 +5173,INFJ,23.66,0.17927993082643867,0.02,0.04,0.0,0.0,0.02,0.42,0.0,1.48,1,0,0,0 +5174,INTJ,22.46,0.19005381442736494,0.0,0.44,0.08,0.0,0.0,0.84,0.0,2.14,1,0,1,0 +5175,INTP,22.04,0.1787994382370502,0.06,0.36,0.02,0.0,0.0,0.36,0.0,1.74,1,0,1,1 +5176,ESFJ,26.46,0.1869048035636142,0.0,0.22,0.5,0.0,0.0,0.6,0.0,2.76,0,1,0,0 +5177,ENTP,26.56,0.18539044309448358,0.0,0.12,0.34,0.0,0.0,0.84,0.0,2.9,0,0,1,1 +5178,INFP,22.52,0.17989915781493038,0.06,0.08,0.04,0.06,0.02,0.42,0.0,2.7,1,0,0,1 +5179,ENTJ,14.5,0.20245034397227657,0.1,0.34,0.94,0.0,0.0,0.24,0.0,1.86,0,0,1,0 +5180,INFJ,23.04,0.1828436361462498,0.04,0.16,0.06,0.02,0.0,0.74,0.0,2.72,1,0,0,0 +5181,ENTJ,8.08,0.18868480725623582,0.04,0.08,1.66,0.04,0.02,0.18,0.0,0.5,0,0,1,0 +5182,INTJ,28.68,0.18148386823832638,0.0,0.12,0.0,0.0,0.0,0.58,0.0,3.76,1,0,1,0 +5183,INTP,23.14,0.1805935809304114,0.0,0.1,0.0,0.0,0.0,0.46,0.0,1.72,1,0,1,1 +5184,INFJ,28.92,0.18434877582844686,0.06,0.22,0.02,0.0,0.02,0.64,0.0,3.62,1,0,0,0 +5185,ENFP,29.72,0.18664635134921154,0.0,0.12,0.2,0.0,0.02,0.74,0.0,4.04,0,0,0,1 +5186,INFJ,32.48,0.18692185428198563,0.0,0.04,0.14,0.0,0.0,0.88,0.0,3.1,1,0,0,0 +5187,ENFP,28.34,0.18784893980809503,0.0,0.08,0.78,0.0,0.0,0.76,0.0,3.44,0,0,0,1 +5188,INFJ,30.66,0.19715286035143353,0.0,0.08,1.14,0.0,0.06,2.4,0.0,3.82,1,0,0,0 +5189,INFP,29.84,0.19170350677443934,0.0,0.18,0.42,0.0,0.08,0.84,0.02,4.22,1,0,0,1 +5190,ISTP,27.94,0.18504111799945283,0.02,0.04,0.12,0.02,0.02,1.02,0.0,4.14,1,1,1,1 +5191,INFP,28.82,0.186237069930853,0.0,0.14,0.08,0.0,0.04,0.76,0.0,3.56,1,0,0,1 +5192,ENFP,23.12,0.19035830144754462,0.0,0.22,0.32,0.0,0.0,0.64,0.0,1.94,0,0,0,1 +5193,INFJ,29.24,0.18280807158169335,0.0,0.4,0.02,0.0,0.0,0.76,0.0,3.34,1,0,0,0 +5194,ESFP,29.96,0.1841696432044266,0.0,0.38,0.06,0.0,0.0,0.68,0.0,3.44,0,1,0,1 +5195,INFJ,22.3,0.1926891743158385,0.02,0.14,0.22,0.0,0.0,0.88,0.0,3.48,1,0,0,0 +5196,ENTP,19.28,0.1907440853257118,0.02,0.4,0.02,0.0,0.0,0.6,0.0,2.08,0,0,1,1 +5197,ENTP,24.84,0.17702820750916498,0.5,0.02,0.02,0.5,0.0,1.02,0.0,2.52,0,0,1,1 +5198,INTP,12.96,0.18141094587414525,0.28,0.26,1.0,0.1,0.0,0.18,0.0,1.5,1,0,1,1 +5199,INTP,19.92,0.1850750518105808,0.02,0.38,0.02,0.0,0.0,0.48,0.0,1.88,1,0,1,1 +5200,INFP,26.58,0.1815414862561262,0.22,0.12,0.24,0.16,0.04,0.8,0.0,3.08,1,0,0,1 +5201,ESTP,5.88,0.1828869663258487,0.04,0.06,0.1,0.0,0.0,0.1,0.02,0.76,0,1,1,1 +5202,INFP,26.6,0.18606316816355772,0.04,0.0,0.12,0.02,0.0,0.66,0.0,3.72,1,0,0,1 +5203,ISFJ,27.44,0.18764226995071157,0.04,0.52,0.3,0.02,0.04,0.78,0.0,3.42,1,1,0,0 +5204,ENTP,21.38,0.18457755323467892,0.02,0.12,0.0,0.0,0.04,0.7,0.0,2.86,0,0,1,1 +5205,ENFP,21.34,0.1907327197096254,0.0,0.38,0.16,0.0,0.0,0.42,0.0,2.62,0,0,0,1 +5206,ENFP,26.84,0.18282882392545818,0.08,0.2,0.12,0.02,0.0,0.74,0.0,2.8,0,0,0,1 +5207,INFP,19.04,0.17821345545092465,0.54,0.74,0.0,0.14,0.08,0.46,0.0,2.04,1,0,0,1 +5208,ENFP,24.08,0.18738903686161013,0.02,0.16,0.1,0.0,0.0,0.4,0.0,2.22,0,0,0,1 +5209,ENTJ,16.76,0.1864305551555448,0.02,0.08,0.14,0.02,0.02,0.3,0.0,2.38,0,0,1,0 +5210,ENTP,27.08,0.1824769783119233,0.18,0.22,0.24,0.04,0.04,0.9,0.0,3.18,0,0,1,1 +5211,INTP,22.68,0.18452563742899145,0.0,0.22,0.04,0.0,0.0,0.44,0.0,2.82,1,0,1,1 +5212,ENTP,19.36,0.19128338076020335,0.08,0.26,0.2,0.02,0.0,0.7,0.02,2.1,0,0,1,1 +5213,INTP,28.5,0.18702237769916824,0.08,0.34,0.12,0.02,0.0,1.1,0.0,3.2,1,0,1,1 +5214,ENTJ,20.58,0.17931055661240586,0.16,0.18,0.02,0.02,0.0,0.48,0.0,1.16,0,0,1,0 +5215,INFJ,21.78,0.185607554942247,0.0,0.24,0.2,0.0,0.0,0.5,0.0,1.74,1,0,0,0 +5216,INFJ,25.28,0.18066074073893282,0.02,0.32,0.06,0.0,0.04,0.74,0.0,3.16,1,0,0,0 +5217,INFP,26.6,0.17927681881563035,0.0,0.12,0.0,0.0,0.0,0.68,0.0,2.76,1,0,0,1 +5218,INTJ,19.24,0.18179368502393967,0.06,0.18,0.02,0.0,0.0,0.2,0.0,2.18,1,0,1,0 +5219,INFJ,21.98,0.19197596185833532,0.18,0.38,0.18,0.04,0.0,0.9,0.0,2.58,1,0,0,0 +5220,INFP,23.24,0.18394482126363326,0.0,0.2,0.04,0.0,0.0,0.38,0.0,2.16,1,0,0,1 +5221,INFP,36.94,0.18162356823764064,0.0,0.0,0.02,0.0,0.02,1.02,0.0,4.38,1,0,0,1 +5222,ISFJ,33.98,0.1852830824740601,0.0,0.08,0.26,0.0,0.0,0.8,0.0,5.32,1,1,0,0 +5223,INFJ,23.3,0.1901282908638865,0.1,0.12,0.02,0.0,0.0,0.44,0.0,3.16,1,0,0,0 +5224,INFP,31.42,0.1784957652608637,0.0,0.16,0.1,0.0,0.0,0.72,0.0,3.88,1,0,0,1 +5225,INFJ,29.38,0.1852970750272863,0.04,0.26,0.6,0.0,0.0,0.74,0.0,3.48,1,0,0,0 +5226,ENTP,21.6,0.19617394460939233,0.06,0.28,0.68,0.04,0.0,0.88,0.0,1.88,0,0,1,1 +5227,ISFP,22.22,0.1849568239546843,0.04,0.42,0.22,0.02,0.0,0.58,0.0,1.7,1,1,0,1 +5228,INFP,21.72,0.18371585948533198,0.2,0.24,0.12,0.0,0.0,0.44,0.0,1.08,1,0,0,1 +5229,INFP,13.6,0.17783089420102777,0.86,0.44,0.0,0.42,0.02,0.28,0.0,0.66,1,0,0,1 +5230,INFJ,24.66,0.19398455425069433,0.14,0.3,0.38,0.0,0.0,0.88,0.0,2.68,1,0,0,0 +5231,INFP,15.24,0.18136384996282737,0.3,0.52,0.16,0.0,0.04,0.24,0.0,2.04,1,0,0,1 +5232,INTJ,15.04,0.199377430301357,0.0,0.3,0.1,0.0,0.0,0.4,0.0,1.3,1,0,1,0 +5233,INFP,32.04,0.17657775449201607,0.02,0.1,0.0,0.0,0.0,0.92,0.0,3.02,1,0,0,1 +5234,INFP,26.02,0.18189307753809483,0.12,0.28,0.12,0.0,0.0,0.54,0.0,3.26,1,0,0,1 +5235,INTJ,20.34,0.18597427571708286,0.54,0.46,0.08,0.18,0.0,0.52,0.02,1.08,1,0,1,0 +5236,INTP,29.6,0.1877108178235707,0.02,0.26,0.14,0.0,0.0,0.7,0.0,4.1,1,0,1,1 +5237,ENTJ,27.74,0.18379497538189618,0.0,0.24,0.12,0.0,0.0,0.94,0.0,2.92,0,0,1,0 +5238,INTJ,19.04,0.18862517218724342,0.04,0.26,0.02,0.02,0.0,0.28,0.08,2.26,1,0,1,0 +5239,INTP,32.2,0.18589795527961095,0.0,0.18,0.4,0.0,0.02,0.78,0.0,3.96,1,0,1,1 +5240,ENTP,23.66,0.18788391134049315,0.0,0.16,0.08,0.0,0.0,0.46,0.0,2.8,0,0,1,1 +5241,INFJ,10.82,0.18504925063625224,0.0,0.12,0.02,0.0,0.0,0.18,0.0,0.92,1,0,0,0 +5242,INFJ,10.68,0.1871440443835493,0.02,0.02,0.0,0.02,0.0,0.12,0.0,0.6,1,0,0,0 +5243,INFP,24.06,0.18477859041868644,0.16,0.2,0.04,0.02,0.0,0.54,0.0,3.32,1,0,0,1 +5244,ENFJ,29.56,0.18763387275266963,0.04,0.08,0.42,0.04,0.0,0.84,0.0,3.16,0,0,0,0 +5245,INFP,20.22,0.19015861941213238,0.02,0.14,0.04,0.02,0.0,0.46,0.0,2.52,1,0,0,1 +5246,ENFP,26.42,0.1854043364684243,0.0,0.06,0.34,0.0,0.04,0.66,0.0,2.24,0,0,0,1 +5247,INTP,27.8,0.17947577312930985,0.1,0.18,0.06,0.06,0.04,0.9,0.0,3.0,1,0,1,1 +5248,INTJ,14.64,0.19037317468902104,0.0,0.2,0.5,0.0,0.0,0.4,0.0,1.2,1,0,1,0 +5249,INTJ,6.1,0.19117247614650662,0.08,0.12,0.3,0.04,0.02,0.12,0.0,0.28,1,0,1,0 +5250,INFJ,33.32,0.18637606618873503,0.0,0.22,0.42,0.0,0.0,0.86,0.0,3.72,1,0,0,0 +5251,INFJ,23.88,0.17339972897211223,0.1,0.14,0.36,0.0,0.02,0.5,0.02,2.12,1,0,0,0 +5252,ISTP,33.08,0.1876055743054808,0.0,0.36,0.0,0.0,0.0,1.12,0.0,1.84,1,1,1,1 +5253,INTJ,32.96,0.18465346086987974,0.02,0.26,0.32,0.0,0.0,1.02,0.0,3.32,1,0,1,0 +5254,ENFP,22.54,0.18299161208069312,0.04,0.04,0.34,0.0,0.04,0.54,0.0,3.14,0,0,0,1 +5255,INFP,11.54,0.18679347578826194,0.02,0.02,0.14,0.0,0.0,0.26,0.0,1.72,1,0,0,1 +5256,INTP,21.46,0.1907133232707631,0.0,0.32,0.4,0.0,0.04,0.32,0.0,2.86,1,0,1,1 +5257,INFP,3.76,0.18680018658275832,0.8,0.82,0.08,0.0,0.02,0.06,0.0,0.52,1,0,0,1 +5258,ENTP,20.04,0.18268124632726673,0.1,0.14,0.0,0.04,0.0,0.52,0.0,2.4,0,0,1,1 +5259,INFJ,26.44,0.18714057060097386,0.02,0.22,0.42,0.02,0.0,0.68,0.0,2.66,1,0,0,0 +5260,INFP,26.94,0.19063459456071993,0.08,0.22,0.12,0.0,0.04,0.56,0.0,3.4,1,0,0,1 +5261,INTP,15.7,0.1853735620658708,0.36,0.5,0.04,0.08,0.02,0.42,0.0,1.82,1,0,1,1 +5262,INTP,18.8,0.18184742550532004,0.18,0.18,0.0,0.02,0.0,0.28,0.0,2.66,1,0,1,1 +5263,INFP,23.66,0.18438509297287334,0.06,0.08,0.14,0.02,0.02,0.4,0.0,2.08,1,0,0,1 +5264,ESTJ,17.58,0.1887014507240649,0.04,0.14,0.06,0.0,0.0,0.32,0.0,1.64,0,1,1,0 +5265,ISTJ,22.44,0.19161010477883098,0.08,0.16,1.08,0.04,0.0,0.76,0.0,2.72,1,1,1,0 +5266,ISTP,13.7,0.17584546832956757,0.02,0.12,0.0,0.02,0.0,0.14,0.0,0.74,1,1,1,1 +5267,ENFP,25.5,0.19861447159625967,0.02,0.54,0.18,0.0,0.0,1.06,0.02,2.98,0,0,0,1 +5268,INFP,27.88,0.19741532093247185,0.0,0.3,0.62,0.0,0.02,0.84,0.0,3.16,1,0,0,1 +5269,INTP,16.14,0.1831017799210391,0.12,0.22,0.04,0.02,0.0,0.34,0.0,1.56,1,0,1,1 +5270,INFP,18.02,0.18525459510278583,0.04,0.1,0.0,0.0,0.0,0.24,0.0,2.26,1,0,0,1 +5271,INTP,24.24,0.18110933700441773,0.04,0.22,0.02,0.0,0.0,0.38,0.0,2.64,1,0,1,1 +5272,ENTJ,19.48,0.1876290211250707,0.0,0.18,0.2,0.0,0.0,0.56,0.0,2.34,0,0,1,0 +5273,INFP,26.56,0.18899456530476672,0.0,0.32,0.82,0.0,0.0,0.82,0.02,3.18,1,0,0,1 +5274,INFP,16.9,0.19226010934112545,0.1,0.14,0.08,0.02,0.02,0.38,0.0,2.58,1,0,0,1 +5275,ENFP,27.08,0.18852984849570467,0.0,0.14,0.62,0.0,0.0,0.7,0.0,3.92,0,0,0,1 +5276,INFJ,27.86,0.1776940341935838,0.04,0.12,0.22,0.04,0.04,0.68,0.0,2.54,1,0,0,0 +5277,ENTP,33.18,0.18865003020156593,0.0,0.14,0.92,0.0,0.02,0.88,0.02,4.26,0,0,1,1 +5278,ENFP,30.06,0.18784641965631232,0.0,0.3,0.1,0.0,0.0,0.8,0.0,2.56,0,0,0,1 +5279,INTP,31.18,0.18274626169907202,0.0,0.14,0.0,0.0,0.0,1.08,0.0,4.02,1,0,1,1 +5280,ENFP,21.14,0.1830130003127681,0.36,0.22,0.42,0.24,0.0,1.0,0.0,2.1,0,0,0,1 +5281,INFP,28.28,0.17936436138935488,0.02,0.1,0.0,0.0,0.0,0.62,0.0,3.38,1,0,0,1 +5282,ESFJ,28.78,0.17827762140367234,0.0,0.4,0.6,0.0,0.02,0.76,0.0,3.54,0,1,0,0 +5283,INFJ,27.94,0.18853208528835494,0.08,0.26,0.18,0.0,0.02,0.74,0.0,3.36,1,0,0,0 +5284,INTP,18.86,0.19783671758576943,0.06,0.26,0.12,0.0,0.0,0.5,0.0,2.42,1,0,1,1 +5285,ENFP,25.52,0.1856674967531674,0.0,0.18,0.14,0.0,0.0,0.64,0.0,2.24,0,0,0,1 +5286,INTJ,28.78,0.180439719714363,0.0,0.2,0.0,0.0,0.0,0.68,0.0,2.86,1,0,1,0 +5287,ENTP,16.0,0.18587529286371585,0.5,0.2,0.04,0.16,0.0,0.36,0.0,1.88,0,0,1,1 +5288,ESTP,16.4,0.1834838243091444,0.06,0.38,0.1,0.0,0.0,0.32,0.02,1.4,0,1,1,1 +5289,INFJ,26.0,0.18292989642765853,0.06,0.1,0.0,0.04,0.0,0.48,0.0,2.8,1,0,0,0 +5290,ENFP,27.2,0.18880155165040952,0.04,0.72,0.52,0.0,0.02,0.78,0.02,3.06,0,0,0,1 +5291,INFJ,23.3,0.17870789531503514,0.04,0.18,0.0,0.0,0.0,0.5,0.0,2.34,1,0,0,0 +5292,INFP,26.26,0.1807260918514544,0.02,0.18,0.06,0.0,0.08,0.58,0.0,2.88,1,0,0,1 +5293,ISFP,30.0,0.19162216664500437,0.04,0.62,0.28,0.0,0.04,0.9,0.0,2.98,1,1,0,1 +5294,ISTP,18.76,0.18928307487779103,0.1,0.12,0.0,0.02,0.0,0.26,0.0,1.44,1,1,1,1 +5295,INTP,17.68,0.1829423926652224,0.16,0.44,0.18,0.1,0.0,0.44,0.0,1.38,1,0,1,1 +5296,ENTP,21.18,0.1810550223065112,0.16,0.2,0.14,0.02,0.06,0.68,0.0,2.7,0,0,1,1 +5297,INTP,28.38,0.18148672538476288,0.06,0.3,0.2,0.0,0.02,0.82,0.0,3.18,1,0,1,1 +5298,INTJ,27.28,0.18706105965195996,0.12,0.2,0.06,0.08,0.0,0.9,0.06,2.5,1,0,1,0 +5299,INTJ,28.22,0.18299137726390197,0.04,0.06,0.1,0.02,0.02,0.88,0.0,2.92,1,0,1,0 +5300,INFP,26.52,0.18817509219855583,0.0,0.14,0.24,0.0,0.16,0.78,0.0,3.22,1,0,0,1 +5301,INTP,29.16,0.1724455167378295,0.0,0.08,0.06,0.0,0.0,0.8,0.02,2.04,1,0,1,1 +5302,ENTP,16.82,0.1890355998493341,0.0,0.1,0.22,0.0,0.0,0.4,0.0,1.7,0,0,1,1 +5303,INTJ,16.56,0.17732279024673217,0.08,0.24,0.04,0.0,0.0,0.24,0.0,2.26,1,0,1,0 +5304,ENTP,27.06,0.17810991559672554,0.02,0.26,0.16,0.0,0.0,0.52,0.0,2.4,0,0,1,1 +5305,INFJ,21.16,0.18627938245636275,0.1,0.3,0.02,0.02,0.02,0.6,0.0,2.14,1,0,0,0 +5306,INTP,10.66,0.18391421950659048,1.14,1.1,0.0,0.06,0.0,0.6,0.0,1.12,1,0,1,1 +5307,INFP,13.92,0.1786363720444527,0.34,0.48,0.02,0.0,0.02,0.24,0.0,1.58,1,0,0,1 +5308,ENTP,29.9,0.18388436460677898,0.04,0.22,0.14,0.02,0.0,1.06,0.02,2.7,0,0,1,1 +5309,INTP,27.62,0.18300889778887133,0.0,0.3,0.08,0.0,0.02,0.6,0.04,2.32,1,0,1,1 +5310,ENTP,17.86,0.19004019141522932,0.1,0.16,0.2,0.0,0.0,0.32,0.0,2.38,0,0,1,1 +5311,INFJ,30.86,0.18325900167619863,0.0,0.08,0.0,0.0,0.08,0.72,0.0,4.26,1,0,0,0 +5312,ENFP,20.52,0.1812590918720429,0.0,0.3,0.14,0.0,0.0,0.48,0.0,1.74,0,0,0,1 +5313,INFP,24.22,0.1843334066947354,0.22,0.24,0.26,0.08,0.02,0.58,0.0,2.5,1,0,0,1 +5314,INTJ,30.96,0.1782712056990665,0.06,0.12,0.0,0.0,0.02,0.86,0.0,3.08,1,0,1,0 +5315,INFJ,25.34,0.18357028613169596,0.08,0.14,0.0,0.0,0.0,0.4,0.0,2.36,1,0,0,0 +5316,ISFP,20.84,0.1881040121197955,0.06,0.06,0.06,0.0,0.02,0.72,0.0,3.08,1,1,0,1 +5317,INFP,11.98,0.18662658946337438,0.0,0.0,0.04,0.0,0.0,0.22,0.0,0.92,1,0,0,1 +5318,ISFP,21.86,0.18574833306325073,0.08,0.3,0.08,0.04,0.0,0.6,0.0,2.2,1,1,0,1 +5319,INFJ,15.66,0.17788128411631463,0.02,0.1,0.0,0.0,0.0,0.3,0.0,1.32,1,0,0,0 +5320,INFJ,21.88,0.18374407784211463,0.0,0.12,0.12,0.0,0.0,0.48,0.0,1.0,1,0,0,0 +5321,ENTP,25.08,0.1960628707810716,0.0,0.18,0.2,0.0,0.02,0.66,0.0,3.08,0,0,1,1 +5322,ISTJ,32.62,0.18220607930437815,0.0,0.18,0.04,0.0,0.02,0.82,0.0,3.8,1,1,1,0 +5323,INFJ,7.58,0.1946014542017106,0.0,0.02,0.12,0.0,0.0,0.24,0.0,0.76,1,0,0,0 +5324,INFP,27.1,0.18355732552026083,0.02,0.06,0.0,0.0,0.02,0.72,0.0,3.12,1,0,0,1 +5325,INTP,17.62,0.1926642236707496,0.36,0.38,0.14,0.0,0.0,0.42,0.0,2.16,1,0,1,1 +5326,INFJ,25.36,0.18907648286282683,0.04,0.34,0.12,0.0,0.0,0.88,0.0,3.3,1,0,0,0 +5327,ENFP,11.5,0.18952369290398968,0.0,0.1,0.1,0.0,0.04,0.28,0.0,1.06,0,0,0,1 +5328,INFP,15.78,0.19623904121775396,0.0,0.18,0.16,0.0,0.06,0.16,0.0,2.38,1,0,0,1 +5329,INFP,22.76,0.18604163093336276,0.22,0.4,1.24,0.02,0.0,0.78,0.0,1.92,1,0,0,1 +5330,INFP,28.18,0.18689084180581197,0.08,0.44,0.12,0.0,0.02,0.74,0.0,3.52,1,0,0,1 +5331,INTP,18.18,0.186516316403933,0.08,0.04,0.02,0.04,0.0,0.36,0.06,2.32,1,0,1,1 +5332,ENTP,22.76,0.18238156136155242,0.6,0.16,0.34,0.0,0.0,0.6,0.0,4.68,0,0,1,1 +5333,ENFP,21.92,0.18363335025346803,0.04,0.06,0.0,0.0,0.0,0.44,0.0,2.72,0,0,0,1 +5334,INTP,31.78,0.19011644348699117,0.0,0.18,0.1,0.0,0.0,1.42,0.0,2.9,1,0,1,1 +5335,INFP,17.92,0.1834479098541799,0.16,0.3,0.02,0.0,0.02,0.34,0.0,1.9,1,0,0,1 +5336,INTJ,19.84,0.18619956418039946,0.02,0.36,0.18,0.0,0.0,0.6,0.0,1.98,1,0,1,0 +5337,INFP,19.66,0.18125661585695102,0.02,0.08,0.12,0.0,0.04,0.52,0.0,2.3,1,0,0,1 +5338,INFP,23.3,0.18753711952405144,0.0,0.34,0.04,0.0,0.0,0.4,0.0,3.4,1,0,0,1 +5339,INTJ,28.34,0.17149575080836052,0.0,0.14,0.0,0.0,0.0,0.98,0.0,1.42,1,0,1,0 +5340,INTJ,19.28,0.1795132643966283,0.22,0.38,0.0,0.04,0.02,0.38,0.02,1.62,1,0,1,0 +5341,INFP,33.4,0.18848812673217435,0.0,0.2,0.76,0.0,0.02,1.42,0.0,5.08,1,0,0,1 +5342,ENTP,11.2,0.1838525513406681,0.0,0.06,0.02,0.0,0.0,0.34,0.0,1.14,0,0,1,1 +5343,INFP,21.22,0.19117254908367007,0.0,0.14,0.34,0.0,0.0,0.44,0.0,1.76,1,0,0,1 +5344,INTJ,23.8,0.1820700522670956,0.0,0.06,0.0,0.0,0.0,0.68,0.0,3.04,1,0,1,0 +5345,ISFJ,26.6,0.1868074845090199,0.1,0.18,0.66,0.06,0.08,1.1,0.0,3.74,1,1,0,0 +5346,ESTP,27.76,0.1784435972649636,0.04,0.26,0.02,0.0,0.0,0.92,0.0,2.3,0,1,1,1 +5347,INFP,26.22,0.17922670575953836,0.22,0.18,0.04,0.16,0.0,0.64,0.0,3.12,1,0,0,1 +5348,ISTP,27.34,0.18922882348833422,0.0,0.1,0.0,0.0,0.0,0.62,0.0,3.88,1,1,1,1 +5349,INTP,28.74,0.18263164834273193,0.1,0.26,0.1,0.06,0.0,0.78,0.0,2.14,1,0,1,1 +5350,ENFP,29.3,0.18980346877381635,0.0,0.28,0.72,0.0,0.0,0.74,0.0,3.24,0,0,0,1 +5351,INFJ,18.4,0.18408089629874744,0.16,0.12,0.02,0.12,0.0,0.32,0.0,1.9,1,0,0,0 +5352,INTJ,22.32,0.18669590350212692,0.0,0.18,0.16,0.0,0.0,0.48,0.0,2.62,1,0,1,0 +5353,INFP,17.64,0.1777347504120768,0.46,0.16,0.04,0.4,0.0,0.46,0.0,2.8,1,0,0,1 +5354,INTP,13.28,0.1764049807425314,0.38,0.3,0.0,0.2,0.0,0.36,0.0,1.6,1,0,1,1 +5355,INTJ,20.8,0.18179136110974228,0.12,0.32,0.0,0.0,0.02,0.42,0.0,2.38,1,0,1,0 +5356,ENFP,30.92,0.1874029879851052,0.0,0.18,0.84,0.0,0.0,0.96,0.0,3.54,0,0,0,1 +5357,ENTP,22.8,0.17415929509463365,0.0,0.38,0.16,0.0,0.02,0.5,0.0,1.62,0,0,1,1 +5358,INFJ,16.52,0.1801127203800204,0.0,0.02,0.0,0.0,0.0,0.24,0.0,1.52,1,0,0,0 +5359,INFJ,25.84,0.1868074845090199,0.06,0.32,0.22,0.0,0.02,0.66,0.0,3.06,1,0,0,0 +5360,ISTJ,27.68,0.19581929573320458,0.04,0.24,0.02,0.04,0.0,0.9,0.0,3.4,1,1,1,0 +5361,INFP,19.98,0.1926156179869537,0.02,0.22,0.16,0.0,0.02,0.78,0.0,2.4,1,0,0,1 +5362,INTJ,27.08,0.17960586261986064,0.0,0.26,0.1,0.0,0.02,0.72,0.0,2.22,1,0,1,0 +5363,INTP,23.1,0.1868356663473748,0.04,0.32,0.06,0.0,0.0,0.6,0.0,2.46,1,0,1,1 +5364,INFP,30.32,0.1836991761903831,0.02,0.14,0.14,0.0,0.0,1.04,0.0,4.02,1,0,0,1 +5365,INFJ,31.98,0.18632027484592423,0.02,0.26,0.32,0.0,0.0,1.02,0.0,3.1,1,0,0,0 +5366,ENTP,31.06,0.175294328839143,0.02,0.06,0.08,0.0,0.0,0.84,0.0,3.16,0,0,1,1 +5367,ISTP,18.42,0.19540535691220565,0.12,0.2,0.08,0.0,0.0,0.92,0.02,2.12,1,1,1,1 +5368,INFP,23.58,0.18699479453112416,0.0,0.28,0.16,0.0,0.0,0.46,0.0,3.04,1,0,0,1 +5369,INTJ,26.7,0.17293638060626174,0.0,0.48,0.0,0.0,0.0,0.56,0.0,2.32,1,0,1,0 +5370,INFP,20.52,0.17134809280303887,0.1,0.2,0.0,0.02,0.0,0.58,0.0,1.62,1,0,0,1 +5371,INTP,30.32,0.17953078897044986,0.04,0.16,0.04,0.0,0.0,0.76,0.0,2.88,1,0,1,1 +5372,INFJ,25.86,0.18488843886807851,0.1,0.18,0.28,0.06,0.0,0.64,0.0,2.54,1,0,0,0 +5373,INFP,29.94,0.19056834189039293,0.0,0.02,0.04,0.0,0.0,0.92,0.0,4.1,1,0,0,1 +5374,INFJ,26.04,0.1914898646859751,0.0,0.18,0.1,0.0,0.0,0.94,0.0,3.32,1,0,0,0 +5375,ENFP,25.6,0.1892546467979618,0.02,0.3,0.36,0.0,0.0,0.68,0.0,2.16,0,0,0,1 +5376,ENTJ,29.2,0.17768194424953404,0.04,0.18,0.0,0.04,0.06,0.7,0.0,3.46,0,0,1,0 +5377,ENFP,29.02,0.18912168734695375,0.02,0.06,0.16,0.0,0.0,0.78,0.0,3.46,0,0,0,1 +5378,INFP,28.02,0.1907400727187972,0.2,0.24,0.12,0.14,0.0,0.9,0.0,3.54,1,0,0,1 +5379,ESTP,17.32,0.18662486479177934,0.02,0.24,0.04,0.0,0.0,0.5,0.0,1.8,0,1,1,1 +5380,INFP,27.56,0.19317874526771237,0.02,0.22,0.64,0.0,0.0,1.3,0.02,3.28,1,0,0,1 +5381,INFJ,25.88,0.1841880177514793,0.04,0.44,0.12,0.0,0.02,0.66,0.0,2.06,1,0,0,0 +5382,INFJ,29.94,0.1806557604282664,0.0,0.1,0.0,0.0,0.0,0.66,0.0,3.74,1,0,0,0 +5383,INFP,23.34,0.18191216653057574,0.06,0.22,0.18,0.06,0.04,0.56,0.0,1.96,1,0,0,1 +5384,ENFJ,27.78,0.18861024310118651,0.0,0.22,0.1,0.0,0.0,0.7,0.02,4.12,0,0,0,0 +5385,INFJ,29.44,0.18350469111328818,0.04,0.26,0.04,0.0,0.0,0.8,0.0,3.04,1,0,0,0 +5386,ENTJ,28.04,0.17785561202635597,0.0,0.04,0.04,0.0,0.0,0.7,0.0,2.18,0,0,1,0 +5387,INFP,27.3,0.18445605150544997,0.0,0.12,0.22,0.0,0.0,0.72,0.0,3.3,1,0,0,1 +5388,INFJ,10.76,0.18687343750000004,0.24,0.24,0.26,0.04,0.0,0.12,0.04,1.14,1,0,0,0 +5389,INFJ,32.74,0.18336216154931043,0.0,0.12,0.0,0.0,0.0,0.86,0.0,3.62,1,0,0,0 +5390,INFJ,25.48,0.1813274351840864,0.08,0.22,0.12,0.0,0.0,0.8,0.0,3.16,1,0,0,0 +5391,INFP,28.96,0.18697735419112851,0.04,0.04,0.12,0.02,0.0,0.66,0.0,4.12,1,0,0,1 +5392,INFP,29.6,0.1839795524691358,0.06,0.26,0.06,0.0,0.04,0.76,0.0,2.86,1,0,0,1 +5393,INFP,21.8,0.19045429721266083,0.02,0.24,0.14,0.0,0.0,0.52,0.0,2.9,1,0,0,1 +5394,INTJ,27.86,0.19370357592944756,0.0,0.24,0.1,0.0,0.0,1.04,0.0,4.1,1,0,1,0 +5395,INFJ,26.82,0.1886526392034009,0.02,0.24,0.08,0.02,0.0,0.74,0.0,2.96,1,0,0,0 +5396,ENFP,30.22,0.18286513623357478,0.1,0.1,0.04,0.02,0.04,0.72,0.0,4.04,0,0,0,1 +5397,INFJ,18.86,0.18640990426844212,0.0,0.36,0.14,0.0,0.02,0.36,0.0,1.84,1,0,0,0 +5398,ENFP,26.16,0.19004417391714923,0.06,0.1,0.04,0.0,0.02,0.44,0.0,2.18,0,0,0,1 +5399,ENTP,24.02,0.18485586335477816,0.18,0.38,0.22,0.06,0.0,0.52,0.0,2.28,0,0,1,1 +5400,ENFJ,18.64,0.18970939150594024,0.1,0.22,0.0,0.0,0.0,0.4,0.0,1.74,0,0,0,0 +5401,ENFP,29.48,0.19290292667582037,0.08,0.18,0.42,0.0,0.02,0.68,0.0,3.38,0,0,0,1 +5402,ENTJ,23.16,0.18795851213404677,0.06,0.1,0.2,0.0,0.02,0.56,0.0,3.0,0,0,1,0 +5403,INFP,20.9,0.18901786733425535,0.04,0.2,0.54,0.0,0.0,0.42,0.0,3.32,1,0,0,1 +5404,INTJ,23.98,0.18036060732272416,0.0,0.4,0.34,0.0,0.02,0.62,0.0,2.1,1,0,1,0 +5405,INFJ,27.02,0.18970212999361502,0.0,0.4,0.16,0.0,0.0,0.96,0.0,3.06,1,0,0,0 +5406,ENTP,30.8,0.18702359773284152,0.0,0.38,0.1,0.0,0.0,0.92,0.0,3.24,0,0,1,1 +5407,ENFP,29.6,0.18682845414821886,0.0,0.12,0.28,0.0,0.0,0.7,0.0,3.2,0,0,0,1 +5408,INFP,23.02,0.18478978160443207,0.08,0.1,0.02,0.0,0.0,0.44,0.0,3.1,1,0,0,1 +5409,ISFP,20.04,0.18985868114641213,0.08,0.14,0.06,0.0,0.0,0.42,0.0,2.86,1,1,0,1 +5410,INFJ,23.02,0.1892918305273705,0.12,0.08,0.34,0.02,0.0,0.86,0.0,2.76,1,0,0,0 +5411,INFJ,22.2,0.1900249895876718,0.0,0.16,0.22,0.0,0.0,0.6,0.0,2.04,1,0,0,0 +5412,INTJ,27.58,0.1822325065951173,0.1,0.14,0.0,0.04,0.0,0.96,0.0,2.98,1,0,1,0 +5413,ISTP,14.94,0.18897157543184165,0.0,0.08,0.08,0.0,0.0,0.38,0.0,1.38,1,1,1,1 +5414,ENTP,14.16,0.1954910351846271,0.02,0.06,0.3,0.02,0.0,0.52,0.0,1.18,0,0,1,1 +5415,INFJ,21.76,0.18781511776010812,0.12,0.2,0.0,0.06,0.0,0.56,0.0,2.76,1,0,0,0 +5416,ISFP,8.8,0.178356663639809,0.7,0.72,0.0,0.0,0.0,0.18,0.02,0.64,1,1,0,1 +5417,ENTP,32.14,0.17836949509234326,0.0,0.2,0.04,0.0,0.04,0.98,0.0,2.62,0,0,1,1 +5418,INFP,30.56,0.18505570670576463,0.0,0.0,0.06,0.0,0.0,0.78,0.0,2.86,1,0,0,1 +5419,INFP,29.04,0.17817746297446757,0.0,0.14,0.06,0.0,0.0,0.8,0.0,3.2,1,0,0,1 +5420,INTP,23.56,0.18592141692393346,0.08,0.3,0.04,0.02,0.02,0.6,0.0,2.06,1,0,1,1 +5421,INFJ,32.98,0.1878197103459615,0.02,0.1,0.0,0.0,0.0,1.5,0.0,3.76,1,0,0,0 +5422,ENTP,26.38,0.1853419091802284,0.0,0.08,0.04,0.0,0.0,0.56,0.0,2.72,0,0,1,1 +5423,INTP,18.2,0.18683346062911482,0.1,0.36,0.18,0.04,0.0,0.26,0.0,2.0,1,0,1,1 +5424,ISTJ,22.36,0.18675123028715807,0.12,0.2,0.06,0.04,0.0,0.86,0.0,2.52,1,1,1,0 +5425,INTP,29.18,0.1785510818184483,0.02,0.1,0.04,0.0,0.0,0.76,0.0,3.08,1,0,1,1 +5426,INTJ,27.44,0.18053829630529664,0.08,0.04,0.06,0.0,0.0,0.7,0.0,3.44,1,0,1,0 +5427,INFP,24.14,0.18405351834119896,0.04,0.28,0.02,0.0,0.1,0.42,0.0,2.58,1,0,0,1 +5428,INFP,27.0,0.1858235329558393,0.1,0.1,0.38,0.02,0.02,0.58,0.0,2.92,1,0,0,1 +5429,INTP,24.8,0.19055795010383317,0.0,0.2,0.18,0.0,0.0,0.6,0.0,4.04,1,0,1,1 +5430,INFP,18.14,0.18556985179579666,0.36,0.44,0.02,0.0,0.0,0.42,0.0,1.98,1,0,0,1 +5431,INFJ,34.04,0.17662005011883591,0.0,0.1,0.0,0.0,0.02,1.0,0.0,3.42,1,0,0,0 +5432,ESTP,30.2,0.18933877818943176,0.04,0.2,0.0,0.0,0.0,0.64,0.0,2.94,0,1,1,1 +5433,INFP,15.12,0.1821546082688903,0.1,0.12,0.04,0.0,0.02,0.22,0.0,1.88,1,0,0,1 +5434,ISTP,31.78,0.1890137361484901,0.0,0.14,0.1,0.0,0.0,1.04,0.0,4.24,1,1,1,1 +5435,ENTP,18.2,0.18224555195665718,0.04,0.26,0.0,0.0,0.02,0.26,0.0,1.54,0,0,1,1 +5436,INFP,12.86,0.18118007163261776,0.0,0.08,0.04,0.0,0.0,0.3,0.0,1.56,1,0,0,1 +5437,INTJ,27.8,0.17959876691366713,0.04,0.1,0.02,0.0,0.0,0.66,0.0,3.78,1,0,1,0 +5438,INFJ,29.76,0.19191042948152387,0.0,0.08,0.0,0.0,0.0,1.26,0.0,2.32,1,0,0,0 +5439,INFP,17.58,0.17638211225446743,0.06,0.18,0.12,0.04,0.0,0.36,0.0,1.6,1,0,0,1 +5440,INTP,22.72,0.17958989079174742,0.14,0.24,0.0,0.04,0.02,0.4,0.0,2.32,1,0,1,1 +5441,INTJ,22.84,0.17964168798706345,0.06,0.14,0.06,0.06,0.0,0.46,0.0,2.56,1,0,1,0 +5442,INFJ,26.72,0.18187743201396284,0.04,0.14,0.04,0.0,0.0,0.62,0.0,3.32,1,0,0,0 +5443,INFJ,33.64,0.19087792059573852,0.0,0.06,0.0,0.0,0.0,1.08,0.0,4.2,1,0,0,0 +5444,ENTP,22.88,0.17882310501064286,0.0,0.16,0.0,0.0,0.0,0.58,0.0,1.26,0,0,1,1 +5445,INFP,18.9,0.1842590073843178,0.2,0.08,0.02,0.1,0.02,0.56,0.0,2.38,1,0,0,1 +5446,INFP,24.38,0.18659142930300038,0.02,0.24,0.06,0.0,0.0,0.56,0.0,2.58,1,0,0,1 +5447,INTP,28.46,0.18528453624088517,0.04,0.2,0.0,0.02,0.0,0.68,0.0,4.06,1,0,1,1 +5448,INTJ,10.8,0.1903174394949287,0.16,0.2,0.08,0.0,0.02,0.16,0.0,1.98,1,0,1,0 +5449,INFJ,23.94,0.18830547519028315,0.22,0.14,0.06,0.2,0.0,0.8,0.0,2.26,1,0,0,0 +5450,ISTJ,18.72,0.1849108642705087,0.0,0.0,0.1,0.0,0.0,0.24,0.0,2.18,1,1,1,0 +5451,ENTP,24.56,0.1779052886983796,0.02,0.12,0.08,0.0,0.04,0.62,0.0,2.24,0,0,1,1 +5452,INFJ,5.88,0.18768076635510106,0.12,0.18,0.0,0.0,0.0,0.1,0.0,0.74,1,0,0,0 +5453,ISTP,20.82,0.1807045447067857,0.38,0.26,0.04,0.18,0.0,0.56,0.0,2.46,1,1,1,1 +5454,INTP,34.62,0.18391995079423298,0.0,0.02,0.06,0.0,0.0,0.92,0.0,3.96,1,0,1,1 +5455,INTJ,31.9,0.18225707050118853,0.0,0.12,0.06,0.0,0.0,0.92,0.0,3.56,1,0,1,0 +5456,INTP,31.54,0.1795435610505582,0.0,0.08,0.24,0.0,0.02,0.64,0.0,3.32,1,0,1,1 +5457,INTJ,20.04,0.17980974557970653,0.04,0.08,0.06,0.0,0.0,0.6,0.0,1.88,1,0,1,0 +5458,ENTJ,19.38,0.17973682123948573,0.0,0.3,0.22,0.0,0.0,0.56,0.0,1.4,0,0,1,0 +5459,INFP,32.84,0.18460034248894144,0.0,0.02,0.52,0.0,0.04,0.84,0.0,4.58,1,0,0,1 +5460,INFP,23.64,0.18238011326038556,0.16,0.2,0.16,0.04,0.02,0.48,0.0,2.4,1,0,0,1 +5461,INFJ,24.62,0.19101836552694293,0.0,0.14,0.14,0.0,0.02,0.42,0.0,2.64,1,0,0,0 +5462,ISTP,18.5,0.18759387907049205,0.0,0.28,0.0,0.0,0.02,0.3,0.0,2.2,1,1,1,1 +5463,ENFP,24.64,0.18053697819063724,0.02,0.06,0.3,0.0,0.02,0.72,0.0,3.06,0,0,0,1 +5464,INFJ,27.9,0.18340978670157812,0.12,0.2,0.06,0.0,0.0,0.66,0.0,3.88,1,0,0,0 +5465,ISTP,22.04,0.18774600687660156,0.0,0.18,0.14,0.0,0.0,0.46,0.0,2.74,1,1,1,1 +5466,ENFJ,27.58,0.17970730851624694,0.02,0.3,0.44,0.0,0.0,0.68,0.04,2.68,0,0,0,0 +5467,INTJ,21.62,0.18422543846611178,0.06,0.16,0.0,0.0,0.0,0.38,0.0,1.94,1,0,1,0 +5468,INTP,24.52,0.18429744908788973,0.04,0.22,0.0,0.0,0.0,0.5,0.0,1.92,1,0,1,1 +5469,ESFJ,32.42,0.18587322926377098,0.0,0.3,0.26,0.0,0.04,1.0,0.0,3.32,0,1,0,0 +5470,INTP,21.62,0.1826778389293061,0.22,0.12,0.06,0.08,0.0,0.36,0.0,2.14,1,0,1,1 +5471,ISFP,15.72,0.1956315614970353,0.04,0.54,0.22,0.0,0.06,0.28,0.0,1.38,1,1,0,1 +5472,ESFJ,31.36,0.18470385937981318,0.0,0.12,0.06,0.0,0.02,0.94,0.0,3.4,0,1,0,0 +5473,INFJ,29.74,0.18292877088050546,0.04,0.1,0.08,0.0,0.0,0.7,0.0,3.0,1,0,0,0 +5474,ISTP,33.96,0.1857759429536499,0.02,0.34,0.02,0.02,0.0,1.14,0.0,3.28,1,1,1,1 +5475,ISFJ,24.84,0.18157630064049543,0.0,0.32,0.12,0.0,0.0,0.64,0.0,2.16,1,1,0,0 +5476,ISTP,21.9,0.18140227226917308,0.08,0.32,0.18,0.0,0.02,0.6,0.0,2.14,1,1,1,1 +5477,ISTJ,32.72,0.19473656689807628,0.1,0.26,0.22,0.08,0.0,1.22,0.0,3.54,1,1,1,0 +5478,INTP,27.42,0.18216425475603684,0.12,0.3,0.0,0.0,0.0,0.64,0.0,3.04,1,0,1,1 +5479,INTP,21.38,0.18124093922052045,0.04,0.18,0.02,0.0,0.02,0.58,0.0,1.82,1,0,1,1 +5480,INTP,26.28,0.18290027580200321,0.22,0.64,0.14,0.02,0.1,0.6,0.02,2.78,1,0,1,1 +5481,INFJ,25.14,0.18457218522583557,0.18,0.36,0.36,0.1,0.0,0.48,0.0,3.0,1,0,0,0 +5482,INFP,33.26,0.17805427158202394,0.0,0.1,0.1,0.0,0.0,0.98,0.0,3.38,1,0,0,1 +5483,INFJ,24.8,0.1926514613086105,0.0,0.18,0.52,0.0,0.0,0.8,0.0,3.08,1,0,0,0 +5484,ISTP,26.48,0.18115597251168944,0.0,0.24,0.08,0.0,0.0,0.54,0.0,2.9,1,1,1,1 +5485,INFP,24.82,0.1781632110490091,0.04,0.18,0.0,0.0,0.04,0.58,0.02,2.76,1,0,0,1 +5486,INFJ,27.82,0.181560060076154,0.0,0.02,0.08,0.0,0.0,0.66,0.0,3.44,1,0,0,0 +5487,ENFP,11.66,0.18353720564895495,0.0,0.12,0.04,0.0,0.0,0.26,0.0,1.36,0,0,0,1 +5488,INTJ,20.64,0.17885083902981477,0.0,0.18,0.04,0.0,0.0,0.46,0.0,2.68,1,0,1,0 +5489,INTP,31.06,0.18288258236023053,0.0,0.2,0.02,0.0,0.0,0.64,0.0,2.9,1,0,1,1 +5490,INFP,25.72,0.1883017457423468,0.04,0.14,0.24,0.0,0.0,0.82,0.0,2.8,1,0,0,1 +5491,INFJ,26.86,0.18291448076849512,0.0,0.16,0.18,0.0,0.02,0.72,0.0,2.82,1,0,0,0 +5492,INFP,24.62,0.188628352682786,0.04,0.24,0.18,0.02,0.0,0.38,0.0,2.88,1,0,0,1 +5493,ENFP,29.96,0.18140589569160995,0.0,0.1,0.08,0.0,0.0,0.72,0.0,3.14,0,0,0,1 +5494,INFP,23.04,0.1808450161201804,0.0,0.12,0.12,0.0,0.0,0.26,0.0,2.74,1,0,0,1 +5495,ISFP,11.0,0.1812929836048586,0.6,0.72,0.1,0.0,0.0,0.18,0.0,1.2,1,1,0,1 +5496,INTP,27.86,0.1860812737420705,0.02,0.16,0.3,0.0,0.04,0.7,0.0,3.5,1,0,1,1 +5497,ISFP,10.88,0.17333617013019378,0.2,0.3,0.0,0.04,0.0,0.24,0.0,1.18,1,1,0,1 +5498,ISFP,18.34,0.19288445827090575,0.02,0.28,0.5,0.0,0.0,0.4,0.0,2.0,1,1,0,1 +5499,INFP,26.26,0.18371519828460628,0.06,0.1,0.16,0.04,0.0,0.64,0.0,3.1,1,0,0,1 +5500,INTP,21.9,0.18344632451624573,0.02,0.24,0.08,0.0,0.0,0.4,0.0,2.7,1,0,1,1 +5501,INFP,22.22,0.17657037018925129,0.08,0.14,0.02,0.02,0.02,0.38,0.0,2.08,1,0,0,1 +5502,INFJ,18.38,0.19310812032545446,0.48,0.0,0.74,0.0,0.0,0.58,0.0,2.06,1,0,0,0 +5503,INFP,26.02,0.18536518119626882,0.0,0.32,0.02,0.0,0.0,0.56,0.0,3.64,1,0,0,1 +5504,INTJ,32.62,0.18807779856547296,0.02,0.16,0.08,0.0,0.0,0.9,0.0,3.8,1,0,1,0 +5505,INTJ,28.44,0.17809146849975793,0.2,0.18,0.08,0.06,0.0,0.9,0.0,2.26,1,0,1,0 +5506,ENTP,26.34,0.18259627138499698,0.2,0.34,0.06,0.12,0.02,0.66,0.0,3.34,0,0,1,1 +5507,INFP,30.88,0.1817103576384001,0.04,0.08,0.04,0.0,0.08,0.86,0.0,3.74,1,0,0,1 +5508,ENTP,27.02,0.17977728474554988,0.0,0.38,0.2,0.0,0.0,0.72,0.0,2.34,0,0,1,1 +5509,INTJ,25.22,0.18066955855495334,0.0,0.1,0.02,0.0,0.0,0.5,0.0,1.26,1,0,1,0 +5510,INTJ,26.7,0.17874244808263617,0.0,0.08,0.16,0.0,0.04,0.6,0.0,2.84,1,0,1,0 +5511,ENFP,29.68,0.18945970126505104,0.02,0.1,0.04,0.0,0.0,0.78,0.0,3.52,0,0,0,1 +5512,INTP,27.1,0.17625719951658617,0.02,0.08,0.0,0.02,0.0,0.52,0.0,3.36,1,0,1,1 +5513,INTP,31.52,0.17919153871813973,0.02,0.18,0.08,0.0,0.0,0.78,0.0,3.08,1,0,1,1 +5514,ENTP,23.16,0.18669303611388557,0.06,0.32,0.08,0.0,0.02,0.56,0.0,2.74,0,0,1,1 +5515,INFP,15.32,0.18434922780255164,0.06,0.18,0.06,0.0,0.02,0.28,0.0,1.9,1,0,0,1 +5516,INFP,14.54,0.18637268752372774,0.2,0.24,0.1,0.0,0.0,0.16,0.0,1.58,1,0,0,1 +5517,INTP,31.56,0.18451795837573415,0.0,0.2,0.04,0.0,0.0,0.72,0.0,3.9,1,0,1,1 +5518,ENTP,24.16,0.17915969909903373,0.2,0.38,0.06,0.06,0.02,0.46,0.0,1.98,0,0,1,1 +5519,INTP,25.14,0.17740008600910456,0.06,0.32,0.08,0.0,0.04,0.52,0.0,3.2,1,0,1,1 +5520,ENTP,29.56,0.18076408300644425,0.04,0.32,0.24,0.0,0.0,0.72,0.0,3.78,0,0,1,1 +5521,INFJ,30.56,0.1867380700213052,0.02,0.18,0.2,0.0,0.0,0.94,0.02,3.68,1,0,0,0 +5522,INFP,11.06,0.1990838270575355,0.2,0.26,0.58,0.02,0.0,0.2,0.0,1.34,1,0,0,1 +5523,ENFP,22.64,0.203742000915479,0.02,0.3,1.82,0.02,0.06,0.68,0.0,1.42,0,0,0,1 +5524,INFJ,31.64,0.19346689940286982,0.06,0.22,0.32,0.0,0.0,1.46,0.0,3.58,1,0,0,0 +5525,INFJ,34.42,0.17974468495662588,0.02,0.18,0.06,0.0,0.0,1.12,0.0,4.52,1,0,0,0 +5526,INFP,17.36,0.18029654860523875,0.06,0.18,0.04,0.02,0.0,0.24,0.02,1.34,1,0,0,1 +5527,INTJ,28.78,0.18675780716936088,0.1,0.16,0.04,0.08,0.02,0.62,0.0,3.64,1,0,1,0 +5528,INFP,27.58,0.18818510509662265,0.04,0.14,0.08,0.0,0.04,0.76,0.0,4.06,1,0,0,1 +5529,INFP,25.92,0.18159868810545093,0.0,0.06,0.08,0.0,0.02,0.54,0.0,2.32,1,0,0,1 +5530,ENFJ,34.38,0.18626797477734694,0.02,0.06,0.4,0.0,0.02,0.88,0.0,4.2,0,0,0,0 +5531,INFP,25.6,0.18023036276527662,0.04,0.32,0.16,0.0,0.0,0.56,0.0,2.6,1,0,0,1 +5532,ESTP,23.26,0.18805479716962317,0.04,0.16,0.06,0.0,0.0,0.54,0.0,1.42,0,1,1,1 +5533,INFP,26.48,0.18605971804425686,0.06,0.14,0.06,0.02,0.0,1.02,0.0,2.78,1,0,0,1 +5534,INFP,13.28,0.18796816314605636,0.14,0.28,0.04,0.0,0.04,0.4,0.0,1.64,1,0,0,1 +5535,INTP,9.12,0.17949120970353188,0.06,0.24,0.04,0.02,0.0,0.1,0.0,0.64,1,0,1,1 +5536,INTJ,19.86,0.1884963378166654,0.0,0.14,0.18,0.0,0.0,0.36,0.02,2.26,1,0,1,0 +5537,ENTP,35.68,0.18720024023808668,0.0,0.22,0.1,0.0,0.0,1.06,0.0,3.18,0,0,1,1 +5538,ENFJ,24.6,0.18797487874114152,0.04,0.28,0.08,0.0,0.0,1.52,0.0,2.44,0,0,0,0 +5539,INTJ,25.52,0.18374542089999812,0.08,0.16,0.04,0.0,0.0,0.58,0.0,3.18,1,0,1,0 +5540,INTP,26.52,0.18466500264078872,0.0,0.28,0.06,0.0,0.0,0.66,0.0,2.92,1,0,1,1 +5541,ISFP,18.44,0.18991090387769885,0.06,0.28,0.0,0.02,0.04,0.26,0.0,2.22,1,1,0,1 +5542,INTP,23.76,0.18644796974834457,0.04,0.18,0.0,0.04,0.0,0.66,0.0,2.74,1,0,1,1 +5543,INTP,29.98,0.17628947928922067,0.04,0.14,0.02,0.0,0.0,0.8,0.0,3.08,1,0,1,1 +5544,ENFP,26.6,0.19516182025237663,0.02,0.38,0.46,0.0,0.0,1.04,0.0,2.66,0,0,0,1 +5545,INFJ,28.5,0.1915457964813346,0.04,0.1,0.52,0.02,0.0,0.82,0.0,3.38,1,0,0,0 +5546,INFP,20.96,0.1837277550729797,0.08,0.1,0.02,0.04,0.0,0.46,0.0,2.28,1,0,0,1 +5547,INTJ,32.72,0.18652106931734627,0.0,0.3,0.14,0.0,0.0,0.92,0.0,3.6,1,0,1,0 +5548,INTP,10.9,0.1855187705220352,0.02,0.02,0.08,0.0,0.02,0.14,0.0,1.68,1,0,1,1 +5549,INTP,13.82,0.18568685738960936,0.08,0.16,0.04,0.0,0.0,0.14,0.0,1.46,1,0,1,1 +5550,INTP,25.02,0.1875,0.06,0.18,0.0,0.0,0.02,0.46,0.0,2.48,1,0,1,1 +5551,INTJ,20.54,0.1872441149041698,0.0,0.32,0.12,0.0,0.0,0.32,0.0,2.72,1,0,1,0 +5552,ISTJ,26.08,0.1891337005110319,0.02,0.28,0.18,0.0,0.02,0.98,0.0,2.94,1,1,1,0 +5553,INTJ,27.34,0.18235378699539617,0.04,0.24,0.0,0.0,0.0,0.66,0.0,3.02,1,0,1,0 +5554,ENTP,26.4,0.18063510225419674,0.04,0.22,0.0,0.02,0.0,0.76,0.02,2.36,0,0,1,1 +5555,INFP,23.58,0.17916863781221706,0.12,0.24,0.12,0.0,0.02,0.42,0.0,3.32,1,0,0,1 +5556,INFJ,15.46,0.18553034164311838,0.06,0.12,0.04,0.0,0.02,0.14,0.0,1.94,1,0,0,0 +5557,INTP,30.78,0.1828732100873779,0.0,0.44,0.0,0.0,0.0,0.88,0.0,2.84,1,0,1,1 +5558,INTP,22.16,0.18655888991239358,0.02,0.28,0.78,0.0,0.0,0.62,0.0,2.82,1,0,1,1 +5559,INFP,21.42,0.18441223526958517,0.16,0.18,0.04,0.04,0.0,0.4,0.0,3.04,1,0,0,1 +5560,INFP,25.26,0.18190491046376858,0.08,0.2,0.02,0.02,0.02,0.64,0.0,1.8,1,0,0,1 +5561,INFP,28.96,0.18610811273832148,0.0,0.32,0.26,0.0,0.02,0.8,0.0,3.56,1,0,0,1 +5562,INTJ,30.9,0.17874510431140012,0.0,0.16,0.06,0.0,0.0,0.82,0.0,3.12,1,0,1,0 +5563,INTJ,26.6,0.18555904414653784,0.0,0.04,0.0,0.0,0.0,0.66,0.0,3.66,1,0,1,0 +5564,INTJ,28.4,0.17818012153164364,0.0,0.12,0.02,0.0,0.02,0.74,0.02,2.74,1,0,1,0 +5565,ENFP,17.12,0.184307549548369,0.22,0.3,1.48,0.0,0.08,0.22,0.0,1.82,0,0,0,1 +5566,ENTP,27.92,0.19051010516950612,0.02,0.4,0.8,0.0,0.0,1.02,0.02,3.32,0,0,1,1 +5567,ENFP,31.54,0.18098247629991382,0.02,0.14,0.12,0.0,0.0,1.0,0.02,3.76,0,0,0,1 +5568,INFP,25.42,0.1794969558278542,0.08,0.14,0.06,0.0,0.0,0.72,0.02,2.14,1,0,0,1 +5569,ENTP,24.64,0.18106345546817132,0.0,0.16,0.32,0.0,0.04,0.54,0.0,2.28,0,0,1,1 +5570,INFJ,11.48,0.18494351470812379,0.24,0.32,0.18,0.0,0.0,0.12,0.0,1.2,1,0,0,0 +5571,INFP,23.04,0.18478593247191358,0.02,0.16,0.14,0.0,0.04,0.38,0.0,2.6,1,0,0,1 +5572,INFP,25.58,0.17974622113901317,0.32,0.22,0.02,0.26,0.0,0.74,0.0,1.64,1,0,0,1 +5573,INFJ,24.74,0.18564403766340135,0.02,0.28,0.32,0.0,0.0,0.68,0.0,2.16,1,0,0,0 +5574,ENFP,30.24,0.18216874726387478,0.0,0.38,0.0,0.0,0.04,0.66,0.02,3.62,0,0,0,1 +5575,ISTP,18.7,0.1858944413633134,0.12,0.14,0.02,0.02,0.0,0.34,0.0,2.26,1,1,1,1 +5576,INFJ,25.36,0.18117414335883944,0.34,0.12,0.34,0.22,0.06,0.64,0.0,2.86,1,0,0,0 +5577,ENTP,32.66,0.1910908781948832,0.04,0.2,0.02,0.0,0.0,1.02,0.0,2.92,0,0,1,1 +5578,INTP,29.68,0.19178849224007274,0.0,0.36,0.02,0.0,0.0,0.8,0.0,2.26,1,0,1,1 +5579,ESFP,26.06,0.19036854833022923,0.0,0.54,0.24,0.0,0.0,0.92,0.0,2.98,0,1,0,1 +5580,ENFP,23.9,0.18477304487345372,0.04,0.14,0.68,0.0,0.0,0.5,0.0,3.0,0,0,0,1 +5581,ENTJ,30.84,0.1879703912448138,0.04,0.26,0.32,0.02,0.02,0.98,0.0,3.04,0,0,1,0 +5582,ESFJ,23.3,0.18746042425300985,0.0,0.26,0.06,0.0,0.02,0.6,0.0,2.68,0,1,0,0 +5583,INTJ,23.56,0.17676757315273256,0.02,0.08,0.02,0.0,0.02,0.54,0.0,1.88,1,0,1,0 +5584,INTJ,23.76,0.17790984481932617,0.0,0.16,0.1,0.0,0.0,0.56,0.02,1.86,1,0,1,0 +5585,INFJ,23.98,0.18905817107105272,0.2,0.1,0.08,0.12,0.0,0.52,0.02,2.76,1,0,0,0 +5586,INTP,29.94,0.17432587922521012,0.0,0.16,0.08,0.0,0.0,0.74,0.0,3.12,1,0,1,1 +5587,ENTP,28.36,0.1842144055202967,0.0,0.42,0.08,0.0,0.02,0.5,0.0,3.68,0,0,1,1 +5588,ISTP,35.62,0.18597628509947514,0.02,0.06,0.06,0.0,0.0,1.06,0.0,4.34,1,1,1,1 +5589,ISTP,31.26,0.17764361795207131,0.02,0.0,0.04,0.0,0.0,0.88,0.0,2.72,1,1,1,1 +5590,INFJ,26.1,0.19079461231688177,0.1,0.2,0.1,0.08,0.0,0.66,0.0,3.28,1,0,0,0 +5591,ENTP,25.86,0.18343914922638338,0.0,0.36,0.24,0.0,0.02,0.56,0.0,2.14,0,0,1,1 +5592,INFP,28.44,0.18918451875611844,0.08,0.2,0.34,0.0,0.0,1.08,0.0,3.5,1,0,0,1 +5593,ENFP,22.68,0.19197879482124783,0.0,0.14,0.3,0.0,0.02,0.58,0.0,2.82,0,0,0,1 +5594,INFJ,25.96,0.18999468681073856,0.04,0.34,0.06,0.0,0.04,0.6,0.0,2.84,1,0,0,0 +5595,INFP,30.18,0.1840871047509612,0.02,0.0,0.1,0.02,0.0,0.9,0.0,1.5,1,0,0,1 +5596,INFP,25.28,0.18284921824204498,0.12,0.22,0.22,0.04,0.0,0.58,0.0,3.24,1,0,0,1 +5597,ENTP,28.66,0.176097701545227,0.08,0.28,0.44,0.0,0.04,0.74,0.0,2.26,0,0,1,1 +5598,INFP,20.56,0.18110332995897493,0.2,0.1,0.36,0.16,0.0,0.58,0.02,2.38,1,0,0,1 +5599,ENTJ,14.82,0.18988183858709257,0.0,0.02,0.06,0.0,0.0,0.4,0.0,1.7,0,0,1,0 +5600,ENTP,30.48,0.185036090262034,0.04,0.48,0.06,0.0,0.0,0.76,0.0,2.8,0,0,1,1 +5601,ISTP,16.02,0.1772031818918595,0.52,0.34,0.0,0.06,0.22,0.28,0.0,2.14,1,1,1,1 +5602,INTJ,28.06,0.18183788414977717,0.0,0.1,0.1,0.0,0.0,0.52,0.0,3.16,1,0,1,0 +5603,ENTP,22.66,0.1912534310422756,0.04,0.32,0.42,0.0,0.02,0.86,0.0,2.5,0,0,1,1 +5604,INTJ,31.3,0.1821760860733415,0.0,0.28,0.24,0.0,0.04,1.34,0.0,2.52,1,0,1,0 +5605,INFP,29.02,0.17905916708759492,0.0,0.16,0.66,0.0,0.02,0.7,0.0,2.96,1,0,0,1 +5606,INFJ,27.9,0.1957856489353643,0.0,0.12,0.46,0.0,0.04,0.76,0.0,3.42,1,0,0,0 +5607,ISTJ,16.06,0.19266804083847325,0.0,0.22,0.78,0.0,0.0,0.52,0.0,1.24,1,1,1,0 +5608,INTJ,25.2,0.17567972613846222,0.02,0.22,0.06,0.02,0.0,0.54,0.0,2.46,1,0,1,0 +5609,INTJ,31.86,0.18463700210363518,0.0,0.04,0.02,0.0,0.0,0.86,0.0,2.6,1,0,1,0 +5610,ISTJ,28.64,0.19209979072132494,0.0,0.4,0.36,0.0,0.0,0.96,0.0,3.48,1,1,1,0 +5611,ENFP,20.88,0.17898289085283,0.02,0.18,0.18,0.0,0.0,0.5,0.0,2.12,0,0,0,1 +5612,ENTP,26.52,0.1839287942165486,0.0,0.26,0.12,0.0,0.0,0.54,0.0,2.72,0,0,1,1 +5613,INFJ,32.02,0.1811968503866543,0.0,0.12,0.06,0.0,0.0,0.86,0.0,3.34,1,0,0,0 +5614,INFP,27.28,0.17858961849848445,0.0,0.08,0.0,0.0,0.02,0.68,0.0,2.28,1,0,0,1 +5615,INTJ,24.8,0.17954489981752866,0.02,0.22,0.14,0.0,0.0,0.52,0.0,2.24,1,0,1,0 +5616,INTP,20.96,0.18136018914328045,0.0,0.24,0.0,0.0,0.08,0.34,0.0,2.2,1,0,1,1 +5617,INFP,21.72,0.18845403881538836,0.08,0.28,0.62,0.02,0.0,0.6,0.0,2.4,1,0,0,1 +5618,INTP,15.24,0.193766182603144,0.04,0.3,0.06,0.0,0.0,0.6,0.0,2.0,1,0,1,1 +5619,INFP,11.98,0.20277190396855907,0.02,0.18,0.44,0.0,0.0,0.54,0.0,1.34,1,0,0,1 +5620,INTJ,12.86,0.18898551567521685,0.14,0.22,0.04,0.02,0.0,0.36,0.0,1.78,1,0,1,0 +5621,INTJ,10.22,0.17475877675398518,0.04,0.04,0.02,0.0,0.0,0.26,0.0,1.04,1,0,1,0 +5622,ENTP,20.7,0.18163126827058557,0.02,0.1,0.04,0.0,0.0,0.26,0.0,1.68,0,0,1,1 +5623,INTP,23.58,0.1889199835549212,0.0,0.44,0.3,0.0,0.0,0.56,0.0,2.7,1,0,1,1 +5624,INFJ,21.52,0.1869835553491462,0.0,0.1,0.12,0.0,0.02,0.5,0.0,2.34,1,0,0,0 +5625,INFJ,31.82,0.17857911099369947,0.02,0.14,0.62,0.0,0.0,0.86,0.0,4.14,1,0,0,0 +5626,ENFP,24.12,0.18284671895833635,0.04,0.16,0.46,0.0,0.0,1.56,0.0,2.16,0,0,0,1 +5627,ISFP,27.14,0.18698400631707782,0.0,0.46,0.24,0.0,0.0,0.56,0.0,3.22,1,1,0,1 +5628,ENTP,22.56,0.18194543113050304,0.06,0.22,0.02,0.04,0.0,0.5,0.0,3.16,0,0,1,1 +5629,ENFP,27.5,0.18294510485499751,0.12,0.22,0.48,0.0,0.02,1.08,0.0,2.28,0,0,0,1 +5630,INFP,25.98,0.18686927766326034,0.18,0.36,0.18,0.02,0.02,0.44,0.0,3.24,1,0,0,1 +5631,INTP,22.7,0.1765452620304105,0.06,0.32,0.04,0.0,0.0,0.52,0.02,0.74,1,0,1,1 +5632,INFP,27.52,0.18113313512679985,0.06,0.24,0.12,0.0,0.0,0.8,0.0,3.66,1,0,0,1 +5633,ISFJ,20.12,0.18635388054238425,0.1,0.12,0.06,0.08,0.0,0.8,0.0,2.2,1,1,0,0 +5634,ENTP,23.18,0.18674055800799755,0.08,0.26,0.1,0.0,0.0,0.68,0.0,2.9,0,0,1,1 +5635,INTJ,30.88,0.1875139579029211,0.0,0.1,0.02,0.0,0.0,0.82,0.0,3.88,1,0,1,0 +5636,INTP,25.38,0.1799055023891888,0.04,0.12,0.0,0.0,0.04,0.48,0.02,2.36,1,0,1,1 +5637,INFP,21.48,0.19283189911834137,0.1,0.38,0.14,0.02,0.0,0.72,0.0,2.68,1,0,0,1 +5638,ENTP,28.88,0.1818902482252062,0.06,0.06,0.04,0.04,0.04,0.86,0.0,2.98,0,0,1,1 +5639,ENFJ,31.04,0.1823563464171523,0.0,0.1,0.04,0.0,0.0,0.74,0.0,3.88,0,0,0,0 +5640,INFJ,25.7,0.18235170039153367,0.02,0.16,0.14,0.0,0.0,0.62,0.0,3.16,1,0,0,0 +5641,INTP,8.78,0.1740100127504747,0.1,0.08,0.0,0.04,0.0,0.18,0.02,0.56,1,0,1,1 +5642,ISFP,25.72,0.1872575843804192,0.06,0.32,0.22,0.02,0.02,0.88,0.0,2.88,1,1,0,1 +5643,INFP,27.78,0.18850387939619653,0.06,0.06,0.02,0.0,0.0,1.06,0.0,1.22,1,0,0,1 +5644,INTP,27.4,0.19695981160018647,0.06,0.32,0.16,0.0,0.12,1.8,0.0,3.72,1,0,1,1 +5645,ENTP,22.42,0.18611115350569274,0.06,0.12,0.18,0.0,0.0,0.46,0.0,2.36,0,0,1,1 +5646,ISTJ,30.06,0.18559342462190628,0.0,0.18,0.16,0.0,0.02,0.92,0.0,2.68,1,1,1,0 +5647,INTP,18.76,0.18422526088288904,0.22,0.22,0.14,0.14,0.0,0.48,0.0,2.44,1,0,1,1 +5648,INFJ,26.28,0.18518817161632356,0.08,0.16,0.08,0.0,0.0,0.88,0.0,3.08,1,0,0,0 +5649,ISFP,29.74,0.18462714995313595,0.0,0.12,0.08,0.0,0.0,0.96,0.0,3.2,1,1,0,1 +5650,INTP,17.42,0.1852718494391645,0.1,0.14,0.02,0.0,0.0,0.36,0.0,1.58,1,0,1,1 +5651,INFJ,21.64,0.19522408329560886,0.04,0.2,0.0,0.0,0.0,0.58,0.04,2.54,1,0,0,0 +5652,ENTP,29.52,0.180659971183747,0.0,0.18,0.04,0.0,0.0,0.8,0.0,3.56,0,0,1,1 +5653,INTP,17.96,0.18747864160630712,0.16,0.42,0.06,0.06,0.04,0.36,0.0,2.16,1,0,1,1 +5654,INFJ,25.7,0.1820658839403583,0.0,0.06,0.06,0.0,0.0,0.6,0.0,2.34,1,0,0,0 +5655,INFJ,25.24,0.1817418988283855,0.0,0.08,0.16,0.0,0.0,0.54,0.0,3.14,1,0,0,0 +5656,ENTJ,28.46,0.18071286253312777,0.02,0.02,0.08,0.02,0.04,0.64,0.0,3.6,0,0,1,0 +5657,INTP,30.64,0.1865329234819476,0.0,0.28,0.16,0.0,0.04,0.84,0.0,2.96,1,0,1,1 +5658,INFJ,24.4,0.18207925192468283,0.02,0.12,0.1,0.0,0.02,0.6,0.0,2.38,1,0,0,0 +5659,INFP,33.48,0.18426498030873348,0.04,0.22,0.24,0.0,0.0,0.84,0.0,3.56,1,0,0,1 +5660,ESTJ,17.62,0.18409974425970507,0.0,0.32,0.0,0.0,0.0,0.28,0.0,1.3,0,1,1,0 +5661,ENFJ,26.18,0.17963874553507977,0.0,0.16,0.02,0.0,0.0,0.58,0.0,2.74,0,0,0,0 +5662,INFJ,28.92,0.19069273272518056,0.04,0.26,0.18,0.0,0.0,0.9,0.0,4.14,1,0,0,0 +5663,INFP,28.76,0.18269775667617563,0.04,0.08,0.04,0.04,0.0,1.02,0.0,3.24,1,0,0,1 +5664,INFP,26.48,0.18732329893798658,0.06,0.44,0.2,0.04,0.0,0.6,0.0,3.12,1,0,0,1 +5665,INFP,24.3,0.18956417675655457,0.06,0.26,0.22,0.0,0.0,0.58,0.0,2.82,1,0,0,1 +5666,INFP,26.4,0.18347696408229877,0.12,0.24,0.16,0.02,0.02,0.78,0.04,3.0,1,0,0,1 +5667,INFJ,25.42,0.1842789149906856,0.06,0.26,0.06,0.0,0.0,0.64,0.0,3.12,1,0,0,0 +5668,INFP,24.98,0.19980302687638563,0.32,0.58,1.42,0.0,0.04,1.12,0.0,2.96,1,0,0,1 +5669,INTP,17.04,0.1915232651532571,0.02,0.22,0.32,0.0,0.04,0.38,0.0,1.9,1,0,1,1 +5670,INTP,19.04,0.1823775012185228,0.24,0.22,0.02,0.1,0.0,0.42,0.0,2.24,1,0,1,1 +5671,ENTP,16.56,0.18384027775716802,0.22,0.46,0.04,0.02,0.04,0.22,0.0,1.36,0,0,1,1 +5672,ISTJ,22.34,0.18095405160706207,0.18,0.36,0.12,0.0,0.02,0.38,0.0,2.9,1,1,1,0 +5673,INFP,30.54,0.19087293820270182,0.02,0.26,0.86,0.0,0.0,1.08,0.04,4.06,1,0,0,1 +5674,INFJ,29.18,0.18620156866835655,0.0,0.4,0.02,0.0,0.0,0.64,0.0,2.78,1,0,0,0 +5675,ISTJ,30.78,0.18745605554714748,0.0,0.08,0.06,0.0,0.0,0.78,0.0,3.08,1,1,1,0 +5676,INFJ,30.32,0.18267334127649537,0.02,0.1,0.2,0.0,0.0,0.72,0.0,4.12,1,0,0,0 +5677,INFJ,30.58,0.17887300434968312,0.02,0.1,0.16,0.0,0.0,0.72,0.0,2.84,1,0,0,0 +5678,INFP,26.6,0.1921460036424094,0.02,0.26,0.04,0.0,0.0,0.8,0.0,3.54,1,0,0,1 +5679,INTJ,16.12,0.18239186999203968,0.1,0.28,0.0,0.0,0.0,0.36,0.0,1.5,1,0,1,0 +5680,INTJ,31.22,0.18588240310950485,0.0,0.12,0.0,0.0,0.0,0.78,0.0,3.6,1,0,1,0 +5681,INTP,26.36,0.17701508684977102,0.1,0.24,0.0,0.0,0.0,0.64,0.0,2.6,1,0,1,1 +5682,INTJ,21.32,0.1866475433748819,0.18,0.28,0.12,0.06,0.0,0.52,0.0,2.14,1,0,1,0 +5683,INTJ,20.1,0.17775947969085987,0.08,0.12,0.06,0.02,0.0,0.44,0.0,1.96,1,0,1,0 +5684,INFP,27.04,0.19693111956779805,0.0,0.28,0.3,0.0,0.0,0.7,0.02,1.78,1,0,0,1 +5685,INTJ,28.72,0.18165920364097202,0.0,0.36,0.02,0.0,0.02,0.82,0.0,2.54,1,0,1,0 +5686,ENTP,27.84,0.176401813502945,0.0,0.1,0.1,0.0,0.14,0.44,0.0,2.76,0,0,1,1 +5687,INTP,25.36,0.1783862488102617,0.0,0.48,0.06,0.0,0.0,0.78,0.0,1.62,1,0,1,1 +5688,INFJ,18.94,0.18926400185771547,0.04,0.16,0.58,0.0,0.02,0.6,0.0,2.12,1,0,0,0 +5689,INFP,24.86,0.1875335345399732,0.08,0.32,0.14,0.0,0.0,0.72,0.0,2.42,1,0,0,1 +5690,INTJ,21.08,0.1941106638451945,0.26,0.28,0.56,0.24,0.0,0.9,0.0,3.16,1,0,1,0 +5691,INTJ,25.9,0.1824,0.0,0.52,0.06,0.0,0.0,0.7,0.0,2.98,1,0,1,0 +5692,INTP,16.2,0.18801229292192487,0.38,0.34,0.02,0.06,0.02,0.4,0.0,2.3,1,0,1,1 +5693,INTP,19.28,0.17527332631871775,0.04,0.14,0.12,0.0,0.04,0.3,0.0,2.46,1,0,1,1 +5694,ENFJ,34.64,0.18915623047444916,0.0,0.04,0.02,0.0,0.0,1.04,0.0,3.92,0,0,0,0 +5695,INTP,27.18,0.1820968470869908,0.0,0.16,0.1,0.0,0.0,0.78,0.02,2.74,1,0,1,1 +5696,INTP,19.64,0.1894878560126733,0.02,0.34,0.12,0.0,0.04,0.52,0.0,2.24,1,0,1,1 +5697,ENTP,20.84,0.1829242154971505,0.02,0.24,0.3,0.0,0.0,0.34,0.0,1.8,0,0,1,1 +5698,INTP,27.34,0.18160690394784348,0.0,0.04,0.0,0.0,0.0,0.74,0.0,2.58,1,0,1,1 +5699,ISTJ,25.44,0.18344715701583658,0.04,0.14,0.16,0.02,0.02,0.52,0.0,2.76,1,1,1,0 +5700,INTJ,16.06,0.18350200152950533,0.2,0.18,0.9,0.18,0.0,0.28,0.0,2.08,1,0,1,0 +5701,ISFP,28.0,0.18473728155372987,0.06,0.08,0.04,0.0,0.04,0.48,0.0,3.72,1,1,0,1 +5702,INTP,30.54,0.18474705448999804,0.02,0.18,0.1,0.0,0.0,0.86,0.0,3.5,1,0,1,1 +5703,INFJ,27.54,0.17305246051610512,0.06,0.0,0.0,0.0,0.0,0.62,0.0,1.24,1,0,0,0 +5704,INFP,16.66,0.1921486824728475,0.02,0.2,0.12,0.0,0.0,0.24,0.0,2.52,1,0,0,1 +5705,ISFJ,27.8,0.17873052897998284,0.02,0.22,0.04,0.0,0.0,0.72,0.02,1.8,1,1,0,0 +5706,INTJ,27.82,0.18339961655095505,0.08,0.08,0.1,0.0,0.08,0.74,0.0,3.02,1,0,1,0 +5707,INFJ,20.98,0.1791357806104134,0.1,0.16,0.02,0.02,0.02,0.4,0.0,2.12,1,0,0,0 +5708,ENFP,11.98,0.18096529738358094,0.02,0.12,0.24,0.02,0.02,0.26,0.0,0.88,0,0,0,1 +5709,ENTJ,29.36,0.18506476639326427,0.02,0.12,0.04,0.0,0.0,0.76,0.0,3.46,0,0,1,0 +5710,ISFP,7.22,0.18941359524282558,0.18,0.24,0.0,0.02,0.0,0.08,0.0,1.24,1,1,0,1 +5711,INFJ,27.86,0.18385327179452549,0.08,0.2,0.1,0.04,0.02,0.78,0.0,2.82,1,0,0,0 +5712,INFP,24.34,0.17918726260853873,0.08,0.1,0.08,0.04,0.02,0.48,0.0,3.5,1,0,0,1 +5713,ENFJ,12.1,0.18623560457856395,0.06,0.26,0.48,0.0,0.0,0.1,0.0,1.58,0,0,0,0 +5714,ISTJ,24.62,0.1813128788645158,0.04,0.32,0.2,0.0,0.02,0.62,0.0,2.34,1,1,1,0 +5715,ENTP,19.12,0.18251858576018962,0.04,0.22,0.0,0.02,0.0,0.32,0.0,1.14,0,0,1,1 +5716,ISTJ,13.24,0.1807389844752751,0.26,0.24,0.02,0.08,0.0,0.18,0.0,1.18,1,1,1,0 +5717,INTP,18.66,0.18419338373771865,0.02,0.16,0.08,0.0,0.02,0.36,0.0,2.56,1,0,1,1 +5718,INTJ,31.62,0.1804662235138408,0.02,0.28,0.08,0.02,0.0,0.8,0.0,3.78,1,0,1,0 +5719,ISFP,11.38,0.1915961129418854,0.12,0.2,0.28,0.0,0.0,0.12,0.0,1.22,1,1,0,1 +5720,INFP,30.8,0.187617924502039,0.0,0.2,0.16,0.0,0.02,0.82,0.0,4.24,1,0,0,1 +5721,INTJ,11.8,0.18985209379816986,0.0,0.08,0.06,0.0,0.0,0.64,0.0,0.88,1,0,1,0 +5722,INTJ,21.02,0.1755485312922333,0.16,0.24,0.02,0.0,0.0,0.44,0.0,2.04,1,0,1,0 +5723,INFJ,14.82,0.1789785969782189,0.32,0.42,0.12,0.08,0.0,0.28,0.0,1.36,1,0,0,0 +5724,ENFJ,23.66,0.18515068075199187,0.02,0.68,0.12,0.0,0.0,0.56,0.0,2.12,0,0,0,0 +5725,ENTP,28.04,0.18357735556940685,0.0,0.14,0.36,0.0,0.0,0.66,0.02,3.28,0,0,1,1 +5726,ENTJ,24.7,0.18058581248127728,0.1,0.32,0.18,0.0,0.0,0.62,0.0,1.98,0,0,1,0 +5727,ENTJ,29.38,0.18382969688846257,0.04,0.14,0.0,0.0,0.0,0.64,0.0,3.82,0,0,1,0 +5728,INFJ,12.48,0.19006696079848148,0.12,0.28,0.6,0.0,0.0,0.2,0.0,1.7,1,0,0,0 +5729,INFP,31.64,0.18303087967429874,0.04,0.18,0.06,0.0,0.0,0.9,0.0,3.34,1,0,0,1 +5730,INTJ,29.02,0.18799677649750232,0.04,0.18,0.22,0.0,0.0,0.9,0.0,3.64,1,0,1,0 +5731,ENFP,14.32,0.20260140793289855,0.04,0.1,0.18,0.0,0.0,0.38,0.0,1.42,0,0,0,1 +5732,INFP,18.64,0.18887395569460572,0.02,0.2,0.2,0.02,0.0,0.26,0.0,2.22,1,0,0,1 +5733,ENTP,25.28,0.1770835310910485,0.02,0.3,0.08,0.0,0.02,0.28,0.0,2.5,0,0,1,1 +5734,INFP,34.9,0.186237076854103,0.02,0.08,0.02,0.02,0.0,1.12,0.0,4.58,1,0,0,1 +5735,INFJ,26.96,0.17918622666925105,0.04,0.4,0.24,0.0,0.0,0.72,0.0,1.58,1,0,0,0 +5736,ISTP,30.18,0.18282531759711446,0.02,0.14,0.02,0.0,0.02,0.68,0.0,3.62,1,1,1,1 +5737,INFJ,31.1,0.18848126924942296,0.0,0.12,0.76,0.0,0.0,0.9,0.0,3.26,1,0,0,0 +5738,INTP,31.9,0.17907344792198834,0.02,0.02,0.02,0.0,0.0,0.8,0.0,4.1,1,0,1,1 +5739,INFP,21.06,0.18918349259011505,0.0,0.22,1.72,0.0,0.0,0.42,0.0,2.82,1,0,0,1 +5740,INTP,17.56,0.18194512010037148,0.24,0.3,0.0,0.0,0.0,0.48,0.0,2.98,1,0,1,1 +5741,ENFP,28.44,0.19304284241488004,0.0,0.16,0.48,0.0,0.0,0.82,0.0,3.4,0,0,0,1 +5742,INTJ,31.06,0.17799712561948497,0.0,0.2,0.18,0.0,0.02,0.84,0.0,3.36,1,0,1,0 +5743,INFP,21.24,0.18456193149682698,0.36,0.38,0.12,0.0,0.04,0.52,0.0,2.58,1,0,0,1 +5744,INTP,25.3,0.18435807138441243,0.04,0.2,0.22,0.02,0.02,1.08,0.0,2.64,1,0,1,1 +5745,INFJ,22.52,0.18739203974949542,0.22,0.1,0.14,0.1,0.0,0.9,0.0,2.62,1,0,0,0 +5746,INFP,31.08,0.19470593598330968,0.06,0.22,0.2,0.0,0.0,1.46,0.0,3.5,1,0,0,1 +5747,ENTP,18.68,0.17804672840975758,0.04,0.44,0.12,0.0,0.0,0.38,0.02,1.94,0,0,1,1 +5748,INTP,24.68,0.17664212223378786,0.08,0.26,0.0,0.02,0.0,0.74,0.0,2.16,1,0,1,1 +5749,INTJ,22.96,0.17989907956247073,0.0,0.1,0.1,0.0,0.0,0.58,0.0,2.78,1,0,1,0 +5750,INFJ,28.02,0.18445587074767428,0.0,0.32,0.32,0.0,0.0,0.66,0.0,3.0,1,0,0,0 +5751,INFP,22.66,0.18770165712457443,0.04,0.12,0.72,0.04,0.02,0.86,0.0,2.7,1,0,0,1 +5752,INFP,26.74,0.18547786915721917,0.04,0.12,0.4,0.02,0.02,0.58,0.0,3.18,1,0,0,1 +5753,INTP,32.42,0.18136028300481727,0.02,0.22,0.28,0.0,0.0,0.92,0.0,3.7,1,0,1,1 +5754,ESFP,25.74,0.1782314973659681,0.08,0.24,0.08,0.0,0.0,0.58,0.0,2.56,0,1,0,1 +5755,INTP,36.08,0.187435530459698,0.0,0.12,0.08,0.0,0.0,1.02,0.0,5.56,1,0,1,1 +5756,INFP,18.08,0.18754579593178378,0.14,0.32,0.06,0.0,0.0,0.24,0.0,1.3,1,0,0,1 +5757,ENTP,28.92,0.1900445684695825,0.0,0.3,0.16,0.0,0.0,0.88,0.02,3.58,0,0,1,1 +5758,INTJ,26.78,0.18543512393900655,0.0,0.14,0.3,0.0,0.0,0.62,0.0,3.62,1,0,1,0 +5759,ISFP,26.18,0.18946322776923022,0.0,0.12,0.18,0.0,0.04,1.28,0.0,3.36,1,1,0,1 +5760,INFJ,31.42,0.1892651613400892,0.02,0.02,0.44,0.02,0.0,1.06,0.0,3.9,1,0,0,0 +5761,INFP,20.8,0.18300420583429225,0.26,0.38,0.24,0.0,0.04,0.9,0.0,2.08,1,0,0,1 +5762,ENFP,28.66,0.184982626894988,0.02,0.12,0.04,0.0,0.02,0.66,0.0,2.7,0,0,0,1 +5763,INFJ,26.32,0.1832518514216361,0.06,0.12,0.04,0.0,0.0,0.74,0.0,2.76,1,0,0,0 +5764,INFP,26.68,0.1837160908510218,0.0,0.14,0.0,0.0,0.0,0.9,0.0,2.92,1,0,0,1 +5765,INFP,18.72,0.17596378877155394,0.16,0.18,0.02,0.1,0.0,0.42,0.0,1.1,1,0,0,1 +5766,INTJ,27.3,0.17871746538342498,0.0,0.18,0.14,0.0,0.0,0.68,0.0,2.58,1,0,1,0 +5767,INFP,21.56,0.18294093306029777,0.04,0.18,0.2,0.0,0.0,0.58,0.0,2.88,1,0,0,1 +5768,INTJ,20.0,0.17494087734248004,0.0,0.18,0.0,0.0,0.0,0.38,0.0,1.54,1,0,1,0 +5769,ENFP,30.78,0.18134620972157403,0.0,0.22,0.96,0.0,0.06,0.64,0.0,3.56,0,0,0,1 +5770,ISTJ,21.18,0.19390993312145044,0.0,0.36,0.18,0.0,0.0,0.32,0.0,1.76,1,1,1,0 +5771,INFP,24.16,0.17653436768906974,0.3,0.06,0.06,0.08,0.0,0.68,0.0,2.38,1,0,0,1 +5772,INTJ,32.1,0.18270622837192324,0.0,0.32,0.02,0.0,0.0,0.74,0.0,3.54,1,0,1,0 +5773,INFP,28.32,0.18166731054605395,0.0,0.08,0.04,0.0,0.0,0.66,0.0,2.44,1,0,0,1 +5774,INTJ,27.46,0.18239999999999998,0.0,0.08,0.32,0.0,0.0,0.64,0.0,2.94,1,0,1,0 +5775,ENFP,30.06,0.18243623207760198,0.02,0.14,0.22,0.0,0.0,0.8,0.0,4.68,0,0,0,1 +5776,ENFP,23.7,0.18774478924589402,0.06,0.3,0.14,0.06,0.1,0.9,0.0,3.04,0,0,0,1 +5777,ENFJ,36.26,0.18718537678700933,0.04,0.12,0.04,0.0,0.02,1.06,0.0,4.96,0,0,0,0 +5778,INTJ,30.4,0.1894378662109375,0.04,0.12,0.02,0.0,0.0,1.02,0.0,3.16,1,0,1,0 +5779,INTJ,26.2,0.17444261635866212,0.02,0.18,0.0,0.0,0.0,0.68,0.0,2.16,1,0,1,0 +5780,INTJ,29.5,0.17715540566016882,0.04,0.0,0.0,0.02,0.0,0.76,0.0,3.3,1,0,1,0 +5781,ENFP,21.44,0.17777060574282627,0.38,0.34,0.06,0.0,0.0,0.6,0.0,2.14,0,0,0,1 +5782,INTP,19.24,0.1907481992118788,0.24,0.48,0.04,0.0,0.0,0.84,0.0,2.08,1,0,1,1 +5783,INTJ,19.64,0.1806300849274062,0.22,0.24,0.22,0.02,0.06,0.44,0.0,1.36,1,0,1,0 +5784,INFJ,27.82,0.1852713478844615,0.04,0.2,0.0,0.02,0.0,0.76,0.0,3.1,1,0,0,0 +5785,ISTP,23.26,0.19164811984356211,0.02,0.18,0.26,0.02,0.0,0.48,0.0,2.74,1,1,1,1 +5786,INFP,23.36,0.17540813068337116,0.02,0.16,0.06,0.0,0.0,0.52,0.0,1.98,1,0,0,1 +5787,ENTP,18.92,0.1695326803556621,0.12,0.14,0.08,0.0,0.0,0.42,0.0,1.04,0,0,1,1 +5788,INTP,27.88,0.1866246378137024,0.06,0.26,0.16,0.0,0.0,0.66,0.0,3.38,1,0,1,1 +5789,ENTP,21.64,0.1887160387304491,0.06,0.02,0.06,0.02,0.0,0.5,0.02,2.14,0,0,1,1 +5790,INFP,29.18,0.1863220074304085,0.0,0.06,0.1,0.0,0.06,0.84,0.0,3.4,1,0,0,1 +5791,ENFP,21.62,0.1900452412842582,0.0,0.38,0.44,0.0,0.02,0.28,0.0,2.72,0,0,0,1 +5792,ENFP,27.7,0.1951749271571856,0.14,0.14,0.12,0.02,0.0,2.16,0.0,3.18,0,0,0,1 +5793,INTP,25.32,0.18197661898879605,0.12,0.18,0.06,0.04,0.0,0.64,0.0,2.78,1,0,1,1 +5794,ISFJ,26.44,0.18839979848085595,0.02,0.12,0.04,0.0,0.0,0.52,0.0,3.12,1,1,0,0 +5795,INFP,31.94,0.1888980637615305,0.0,0.18,1.14,0.0,0.0,1.08,0.0,4.48,1,0,0,1 +5796,ENFP,14.96,0.17941596139812002,0.38,0.4,0.5,0.0,0.02,0.16,0.0,1.12,0,0,0,1 +5797,INTP,19.96,0.18254190497268463,0.0,0.84,0.1,0.0,0.06,0.38,0.02,1.82,1,0,1,1 +5798,INTJ,29.88,0.18359217885533208,0.0,0.12,0.06,0.0,0.02,1.14,0.0,3.58,1,0,1,0 +5799,INTJ,7.8,0.18153868521509714,0.0,0.0,0.0,0.0,0.0,0.26,0.0,0.94,1,0,1,0 +5800,INTP,19.3,0.17839509086596908,0.3,0.46,0.02,0.0,0.08,0.34,0.0,1.76,1,0,1,1 +5801,INFP,25.92,0.18347947532219575,0.16,0.18,0.12,0.12,0.06,0.8,0.0,2.98,1,0,0,1 +5802,INFP,20.82,0.19119044613888295,0.04,0.06,0.08,0.0,0.0,0.58,0.0,2.78,1,0,0,1 +5803,ENTP,28.38,0.19557375831184576,0.06,0.36,0.14,0.0,0.02,0.74,0.0,2.26,0,0,1,1 +5804,ENFP,17.74,0.18356001781384787,0.12,0.18,0.06,0.04,0.0,0.24,0.0,1.68,0,0,0,1 +5805,INTJ,9.0,0.18260722646832295,0.0,0.04,0.0,0.0,0.0,0.18,0.0,1.0,1,0,1,0 +5806,INTP,31.02,0.18607082139324563,0.02,0.3,0.12,0.0,0.0,0.94,0.0,3.08,1,0,1,1 +5807,INTJ,31.18,0.17588715876849942,0.0,0.06,0.02,0.0,0.0,0.86,0.0,3.98,1,0,1,0 +5808,ISFP,32.42,0.1869885285352301,0.04,0.22,0.0,0.0,0.0,0.92,0.0,3.66,1,1,0,1 +5809,INTP,20.06,0.1790046578743541,0.02,0.28,0.04,0.0,0.0,0.44,0.0,2.06,1,0,1,1 +5810,ENFJ,31.84,0.18562246605400448,0.14,0.22,0.14,0.08,0.0,0.86,0.0,3.78,0,0,0,0 +5811,INFP,24.66,0.18432879403864347,0.14,0.24,0.44,0.04,0.0,0.86,0.0,2.4,1,0,0,1 +5812,ISFJ,31.7,0.19154824924191477,0.0,0.06,0.42,0.0,0.04,0.78,0.0,4.06,1,1,0,0 +5813,INFP,12.04,0.1703045702004978,0.9,0.14,0.4,0.74,0.0,0.68,0.06,2.14,1,0,0,1 +5814,INFJ,28.0,0.18295366789010298,0.02,0.2,0.2,0.0,0.0,0.66,0.0,3.22,1,0,0,0 +5815,INTP,28.54,0.18737875851539335,0.0,0.18,0.02,0.0,0.0,1.26,0.0,3.94,1,0,1,1 +5816,INFP,27.26,0.18639686851265752,0.0,0.1,0.44,0.0,0.02,1.04,0.02,3.18,1,0,0,1 +5817,ESFP,19.8,0.19691702787735024,0.0,0.3,0.52,0.0,0.0,0.8,0.0,2.72,0,1,0,1 +5818,ENTP,21.22,0.17267460227931888,0.0,0.26,0.02,0.0,0.0,0.4,0.0,2.12,0,0,1,1 +5819,INFP,29.16,0.19094902390119053,0.04,0.36,0.12,0.0,0.1,0.6,0.02,4.1,1,0,0,1 +5820,INFJ,23.82,0.18882954715921887,0.0,0.14,0.1,0.0,0.0,0.44,0.0,3.46,1,0,0,0 +5821,INTP,25.58,0.1852890035653969,0.14,0.12,0.12,0.08,0.0,0.52,0.0,3.44,1,0,1,1 +5822,INTP,18.8,0.18523334366003613,0.28,0.38,0.0,0.0,0.02,0.48,0.04,1.22,1,0,1,1 +5823,INFJ,14.14,0.19170316828254844,0.14,0.42,0.0,0.0,0.02,0.28,0.02,1.58,1,0,0,0 +5824,INTJ,24.78,0.17894011245006602,0.1,0.1,0.16,0.0,0.0,0.78,0.0,2.3,1,0,1,0 +5825,INTP,30.3,0.17904877891388432,0.02,0.34,0.1,0.0,0.02,0.76,0.0,2.7,1,0,1,1 +5826,ENTP,26.22,0.18408993315137856,0.02,0.26,0.04,0.0,0.0,0.76,0.0,2.28,0,0,1,1 +5827,INFJ,26.0,0.18358477943626886,0.1,0.28,0.1,0.0,0.0,0.86,0.1,2.08,1,0,0,0 +5828,ENTJ,31.54,0.17959098281113464,0.0,0.16,0.06,0.0,0.04,0.74,0.0,3.44,0,0,1,0 +5829,INFP,22.54,0.18149300926317932,0.2,0.34,0.2,0.02,0.02,0.6,0.0,3.26,1,0,0,1 +5830,ENTP,20.88,0.18625863994978445,0.0,0.2,0.16,0.0,0.02,0.42,0.0,2.0,0,0,1,1 +5831,ISFJ,0.42,0.16029626126152505,0.86,0.4,0.0,0.14,0.0,0.0,0.0,0.52,1,1,0,0 +5832,INFP,30.74,0.18820894733065896,0.0,0.24,0.66,0.0,0.04,0.92,0.0,3.56,1,0,0,1 +5833,INFJ,26.58,0.18794346486334113,0.0,0.1,0.9,0.0,0.0,0.82,0.0,3.5,1,0,0,0 +5834,ENTJ,18.28,0.18587854883515612,0.02,0.14,0.02,0.0,0.0,0.32,0.02,1.82,0,0,1,0 +5835,ENFP,27.28,0.1946537516811695,0.0,0.5,2.24,0.0,0.0,1.02,0.04,2.92,0,0,0,1 +5836,ENFJ,29.38,0.18390872485813486,0.1,0.2,0.34,0.06,0.04,0.8,0.0,3.04,0,0,0,0 +5837,ISTP,4.38,0.17992093423780534,0.56,0.56,0.0,0.0,0.0,0.04,0.0,0.32,1,1,1,1 +5838,INFJ,32.54,0.19312963633024172,0.02,0.26,0.38,0.0,0.02,1.0,0.0,4.54,1,0,0,0 +5839,ISFJ,32.16,0.1843392552305621,0.0,0.08,0.16,0.0,0.0,0.6,0.0,4.44,1,1,0,0 +5840,ISFJ,32.98,0.1865247221401917,0.0,0.1,0.14,0.0,0.0,0.92,0.0,5.2,1,1,0,0 +5841,INFJ,30.04,0.18666195242786127,0.0,0.32,0.0,0.0,0.0,0.68,0.0,3.04,1,0,0,0 +5842,ISFP,12.92,0.19059378817177727,0.02,0.02,0.84,0.0,0.0,0.08,0.0,1.08,1,1,0,1 +5843,INTP,26.2,0.181351860811601,0.0,0.36,0.34,0.02,0.02,0.68,0.0,2.4,1,0,1,1 +5844,ENTJ,20.7,0.1819135488098631,0.24,0.3,0.06,0.08,0.0,0.4,0.0,1.26,0,0,1,0 +5845,ENFP,30.2,0.18876289743966268,0.04,0.48,0.2,0.0,0.0,0.68,0.0,3.46,0,0,0,1 +5846,INFJ,27.86,0.1874061444256823,0.02,0.14,0.06,0.0,0.0,0.8,0.0,2.62,1,0,0,0 +5847,INFJ,23.78,0.19597897153351704,0.0,0.14,0.7,0.0,0.02,0.7,0.0,3.08,1,0,0,0 +5848,ESTP,16.06,0.19161649229642178,0.02,0.18,0.02,0.0,0.0,0.42,0.0,1.56,0,1,1,1 +5849,INFP,27.82,0.18167089631317093,0.0,0.32,0.02,0.0,0.0,0.62,0.0,2.32,1,0,0,1 +5850,INTP,6.8,0.1786976015210493,0.6,0.72,0.0,0.02,0.0,0.06,0.02,0.94,1,0,1,1 +5851,INFJ,31.62,0.18652072000188907,0.0,0.18,0.26,0.0,0.0,0.88,0.0,3.4,1,0,0,0 +5852,INTP,25.66,0.18058339067158735,0.06,0.26,0.16,0.0,0.0,0.5,0.0,1.5,1,0,1,1 +5853,INTP,24.72,0.17356819718571972,0.04,0.14,0.0,0.02,0.02,0.56,0.0,1.98,1,0,1,1 +5854,ENFJ,24.6,0.1836896798637799,0.16,0.18,0.12,0.04,0.0,0.54,0.0,3.38,0,0,0,0 +5855,ISFP,28.26,0.18347914891518544,0.0,0.64,0.08,0.0,0.0,0.74,0.0,3.08,1,1,0,1 +5856,INTJ,32.02,0.18307054200505082,0.0,0.18,0.0,0.0,0.0,0.84,0.0,3.5,1,0,1,0 +5857,INTJ,17.18,0.18255336452367277,0.1,0.2,0.04,0.06,0.0,0.3,0.0,1.76,1,0,1,0 +5858,ENTP,19.36,0.18632204789779044,0.12,0.28,0.18,0.08,0.0,0.38,0.0,2.28,0,0,1,1 +5859,INFP,9.0,0.19114102079395082,0.0,0.14,0.04,0.0,0.0,0.26,0.0,1.32,1,0,0,1 +5860,ENFP,22.32,0.17962132972915668,0.0,0.18,0.06,0.0,0.0,0.1,0.0,2.66,0,0,0,1 +5861,ISFP,27.86,0.191630169815214,0.0,0.22,0.26,0.0,0.02,0.7,0.0,4.16,1,1,0,1 +5862,ENTP,30.44,0.18158954782959008,0.0,0.08,0.0,0.0,0.02,1.08,0.0,1.7,0,0,1,1 +5863,INFJ,31.72,0.1784242982933071,0.02,0.1,0.0,0.0,0.04,0.88,0.0,3.36,1,0,0,0 +5864,ENTP,15.36,0.1898074919949076,0.2,0.24,0.22,0.08,0.02,0.62,0.0,1.92,0,0,1,1 +5865,ISTP,17.46,0.18599124284091514,0.48,0.54,0.04,0.0,0.0,0.56,0.0,2.22,1,1,1,1 +5866,ISTP,25.44,0.18139001742677693,0.06,0.36,0.02,0.0,0.0,0.6,0.0,2.72,1,1,1,1 +5867,INFP,26.84,0.18899406008551878,0.1,0.16,0.54,0.0,0.0,0.78,0.0,3.38,1,0,0,1 +5868,INFJ,27.06,0.18911831878933388,0.02,0.66,0.3,0.0,0.0,0.64,0.0,2.84,1,0,0,0 +5869,INFP,26.82,0.18959514498043226,0.02,0.14,0.1,0.02,0.0,0.74,0.0,2.86,1,0,0,1 +5870,INTJ,26.88,0.18729673538964114,0.02,0.2,0.06,0.0,0.0,1.0,0.0,2.94,1,0,1,0 +5871,INTP,18.8,0.17609338686747175,0.4,0.46,0.08,0.24,0.0,0.38,0.0,1.8,1,0,1,1 +5872,INTJ,30.28,0.1809298826381144,0.04,0.38,0.0,0.0,0.0,1.08,0.0,1.68,1,0,1,0 +5873,INTJ,22.2,0.17616497502047784,0.04,0.14,0.02,0.0,0.02,0.36,0.0,2.6,1,0,1,0 +5874,INFP,28.2,0.18148285011957344,0.02,0.14,0.1,0.02,0.0,0.78,0.0,2.76,1,0,0,1 +5875,INTP,28.88,0.17158735840692185,0.0,0.12,0.14,0.0,0.02,0.64,0.0,1.88,1,0,1,1 +5876,INFP,26.34,0.18873534014524668,0.12,0.3,0.62,0.0,0.04,0.66,0.0,2.32,1,0,0,1 +5877,ENFP,28.68,0.17762099654850477,0.0,0.34,0.02,0.0,0.0,0.88,0.0,2.34,0,0,0,1 +5878,ISTP,33.48,0.18322306531717691,0.0,0.1,0.08,0.0,0.0,0.88,0.0,3.44,1,1,1,1 +5879,INTP,5.3,0.1870028522829993,0.96,1.0,0.08,0.0,0.0,0.12,0.0,1.1,1,0,1,1 +5880,INTJ,34.3,0.18036345117129132,0.0,0.14,0.0,0.0,0.0,0.82,0.0,1.86,1,0,1,0 +5881,INFP,27.44,0.18504391864941921,0.08,0.16,0.08,0.02,0.12,0.54,0.0,2.86,1,0,0,1 +5882,ISTP,16.96,0.18626825484924736,0.0,0.2,0.0,0.0,0.0,0.28,0.0,1.62,1,1,1,1 +5883,INTJ,27.86,0.18595380233999526,0.0,0.1,0.04,0.0,0.04,0.64,0.0,2.76,1,0,1,0 +5884,INFJ,26.86,0.1867289593203332,0.16,0.42,0.02,0.0,0.0,0.64,0.0,3.18,1,0,0,0 +5885,INFP,21.92,0.18484282929450008,0.02,0.12,0.1,0.02,0.0,0.38,0.0,2.88,1,0,0,1 +5886,INFP,25.9,0.18480844620529926,0.02,0.14,0.3,0.0,0.0,0.58,0.0,3.32,1,0,0,1 +5887,INFP,24.62,0.17993930662196292,0.02,0.2,0.18,0.0,0.0,0.58,0.0,3.34,1,0,0,1 +5888,ENTP,21.2,0.17561983471074386,0.14,0.18,0.0,0.0,0.0,0.48,0.0,1.52,0,0,1,1 +5889,INFJ,24.6,0.1846304051709704,0.06,0.4,0.02,0.02,0.0,0.56,0.0,2.76,1,0,0,0 +5890,INFJ,31.18,0.17979486099624833,0.0,0.1,0.08,0.0,0.0,0.98,0.0,3.42,1,0,0,0 +5891,INFP,26.78,0.18121344546895712,0.0,0.0,0.18,0.0,0.02,0.58,0.0,2.58,1,0,0,1 +5892,INTJ,13.42,0.1952948009970035,0.02,0.26,0.18,0.0,0.0,0.64,0.0,1.7,1,0,1,0 +5893,INFP,15.08,0.18786880846789678,0.18,0.18,0.24,0.1,0.0,0.28,0.0,1.44,1,0,0,1 +5894,INTP,31.88,0.18439641236387333,0.0,0.04,0.26,0.0,0.0,0.9,0.0,3.92,1,0,1,1 +5895,INFP,29.76,0.18950963397592993,0.04,0.28,0.06,0.0,0.0,0.68,0.0,3.8,1,0,0,1 +5896,ENTJ,24.64,0.1808692576356825,0.0,0.28,0.5,0.0,0.0,0.4,0.0,2.5,0,0,1,0 +5897,INTP,32.48,0.1727342439689998,0.0,0.28,0.02,0.0,0.0,0.9,0.0,4.32,1,0,1,1 +5898,INTJ,28.76,0.18265199188245915,0.06,0.12,0.0,0.0,0.0,0.64,0.0,3.02,1,0,1,0 +5899,INFP,20.78,0.17940366245767986,0.02,0.32,0.14,0.0,0.0,0.36,0.0,1.98,1,0,0,1 +5900,INTJ,32.1,0.17068091646283154,0.02,0.18,0.0,0.0,0.0,0.96,0.0,2.28,1,0,1,0 +5901,INTJ,32.82,0.18385026152462783,0.0,0.02,0.04,0.0,0.06,0.72,0.0,2.82,1,0,1,0 +5902,INTJ,26.4,0.18670168613026641,0.1,0.28,0.0,0.0,0.02,0.7,0.0,3.34,1,0,1,0 +5903,INFJ,32.94,0.18877442797623112,0.0,0.06,0.44,0.0,0.0,0.96,0.0,4.14,1,0,0,0 +5904,ENFP,28.7,0.18770547127109735,0.18,0.34,0.16,0.08,0.0,0.88,0.0,3.18,0,0,0,1 +5905,ENTP,25.2,0.1797555948939532,0.02,0.28,0.28,0.0,0.02,0.76,0.0,2.06,0,0,1,1 +5906,ENTP,21.3,0.19160854625931584,0.06,0.36,0.26,0.0,0.02,0.48,0.0,2.24,0,0,1,1 +5907,ENTP,20.64,0.19037127574553994,0.1,0.1,0.2,0.06,0.0,0.46,0.0,2.56,0,0,1,1 +5908,INFJ,25.52,0.18663397992142103,0.0,0.08,0.06,0.0,0.0,0.52,0.0,3.02,1,0,0,0 +5909,ENFP,27.06,0.1771383284879916,0.02,0.22,0.1,0.0,0.0,0.74,0.02,2.36,0,0,0,1 +5910,INFP,16.22,0.19090464489250816,0.26,0.04,0.06,0.04,0.1,1.44,0.0,1.68,1,0,0,1 +5911,ISTJ,26.82,0.17945791774916262,0.06,0.3,0.02,0.0,0.02,0.6,0.0,3.04,1,1,1,0 +5912,INFP,27.28,0.18641555990874317,0.0,0.04,0.64,0.0,0.04,0.66,0.0,3.32,1,0,0,1 +5913,ISTP,29.02,0.19114351738384516,0.0,0.32,0.24,0.0,0.0,0.8,0.0,3.16,1,1,1,1 +5914,INFJ,15.72,0.18336442218791948,0.4,0.48,0.12,0.0,0.02,0.5,0.0,1.16,1,0,0,0 +5915,INTJ,13.62,0.18617461398997653,0.0,0.02,0.26,0.0,0.0,0.32,0.0,1.72,1,0,1,0 +5916,INFP,24.28,0.18077979328786561,0.08,0.12,0.06,0.06,0.0,0.72,0.0,3.08,1,0,0,1 +5917,ISFP,21.44,0.17709753084329005,0.1,0.08,0.0,0.02,0.0,0.46,0.0,1.54,1,1,0,1 +5918,INTJ,26.56,0.1800298598334609,0.0,0.24,0.02,0.0,0.0,0.5,0.0,2.44,1,0,1,0 +5919,ENFP,31.5,0.18113358851716257,0.02,0.2,0.06,0.0,0.08,0.74,0.0,4.22,0,0,0,1 +5920,INTP,27.68,0.18062095575654113,0.0,0.12,0.24,0.0,0.02,0.82,0.0,3.06,1,0,1,1 +5921,ISFJ,29.32,0.1853008439699623,0.0,0.48,0.3,0.0,0.0,0.9,0.0,3.52,1,1,0,0 +5922,ESTP,28.88,0.1787678086478385,0.0,0.1,0.0,0.0,0.0,0.64,0.0,3.16,0,1,1,1 +5923,INFP,21.16,0.1975147935871072,0.22,0.34,0.12,0.12,0.02,1.96,0.0,1.82,1,0,0,1 +5924,INTP,21.44,0.184714437792512,0.02,0.24,0.26,0.0,0.0,0.42,0.0,2.2,1,0,1,1 +5925,INFP,21.08,0.18075835000439092,0.02,0.4,0.2,0.0,0.0,0.84,0.0,3.16,1,0,0,1 +5926,ENFP,33.74,0.18704907307980165,0.04,0.12,0.28,0.0,0.0,0.96,0.0,4.24,0,0,0,1 +5927,INFJ,31.62,0.18709097429302055,0.0,0.1,0.36,0.0,0.0,0.84,0.0,2.76,1,0,0,0 +5928,INTP,23.58,0.17632866757719515,0.12,0.12,0.02,0.02,0.0,0.46,0.0,2.34,1,0,1,1 +5929,INFJ,30.46,0.18544055055582845,0.04,0.2,0.02,0.0,0.0,0.82,0.0,4.02,1,0,0,0 +5930,ISTJ,15.44,0.18172292913857008,0.0,0.14,0.0,0.0,0.0,0.4,0.0,1.64,1,1,1,0 +5931,ISTP,16.16,0.19390086950679325,0.1,0.28,0.18,0.06,0.0,0.72,0.02,1.32,1,1,1,1 +5932,INFP,24.1,0.18671665123434936,0.04,0.18,0.12,0.0,0.0,0.64,0.0,0.88,1,0,0,1 +5933,INFP,22.52,0.18408958903006492,0.28,0.32,0.06,0.0,0.08,0.54,0.0,2.24,1,0,0,1 +5934,INTJ,27.02,0.17539920622255353,0.16,0.24,0.06,0.06,0.0,0.58,0.0,2.56,1,0,1,0 +5935,INFP,32.28,0.18506874902236217,0.02,0.1,0.56,0.0,0.06,0.84,0.0,3.86,1,0,0,1 +5936,INTJ,25.74,0.18812724225116356,0.04,0.38,0.22,0.02,0.0,0.62,0.0,3.34,1,0,1,0 +5937,ENTJ,24.74,0.18462282441352873,0.0,0.36,0.02,0.0,0.0,0.52,0.0,2.16,0,0,1,0 +5938,ISTJ,26.8,0.18063443515799155,0.0,0.16,0.06,0.0,0.02,0.66,0.0,2.72,1,1,1,0 +5939,ENFP,29.58,0.1827329462810666,0.0,0.16,0.08,0.0,0.02,0.66,0.0,2.84,0,0,0,1 +5940,INTJ,25.22,0.1868900822247332,0.04,0.12,0.08,0.02,0.0,0.72,0.0,3.12,1,0,1,0 +5941,INFP,25.44,0.18348960477654291,0.26,0.36,0.0,0.02,0.0,0.64,0.0,4.02,1,0,0,1 +5942,INFP,12.02,0.18269944592209478,0.38,0.38,0.04,0.0,0.0,0.44,0.0,1.46,1,0,0,1 +5943,ENFP,29.82,0.18685037248855943,0.24,0.18,0.5,0.14,0.02,1.18,0.02,3.94,0,0,0,1 +5944,INFJ,28.36,0.18335220438402908,0.02,0.12,0.36,0.0,0.0,0.6,0.0,3.74,1,0,0,0 +5945,INFJ,27.32,0.1866782234525483,0.02,0.12,0.1,0.0,0.02,0.72,0.0,3.68,1,0,0,0 +5946,INFP,20.48,0.18121875183296066,0.14,0.2,0.04,0.0,0.0,0.46,0.0,2.52,1,0,0,1 +5947,INFP,33.72,0.18929045273818973,0.0,0.14,0.84,0.0,0.0,1.26,0.0,5.0,1,0,0,1 +5948,ISFP,24.24,0.19434025424673362,0.02,0.22,1.0,0.02,0.04,0.54,0.0,3.2,1,1,0,1 +5949,INFP,30.46,0.18554267387944987,0.04,0.06,0.12,0.0,0.0,0.82,0.0,3.3,1,0,0,1 +5950,ENTP,28.38,0.18455922120064897,0.02,0.12,0.42,0.0,0.02,0.74,0.0,3.32,0,0,1,1 +5951,INFJ,27.04,0.18993759880396446,0.0,0.18,0.08,0.0,0.0,0.68,0.0,2.94,1,0,0,0 +5952,INFP,14.5,0.18441659761624976,0.3,0.42,0.1,0.0,0.02,0.34,0.0,1.94,1,0,0,1 +5953,INFP,19.46,0.1938617116714954,0.24,0.36,0.18,0.04,0.04,0.64,0.0,2.0,1,0,0,1 +5954,INFJ,23.74,0.18034968689060304,0.1,0.18,0.04,0.06,0.0,0.46,0.0,2.82,1,0,0,0 +5955,INTJ,32.24,0.1713894203738652,0.0,0.02,0.06,0.0,0.0,0.88,0.0,2.72,1,0,1,0 +5956,ISTP,8.74,0.18662608844224965,0.8,0.8,0.1,0.0,0.04,0.26,0.0,1.34,1,1,1,1 +5957,ISFP,25.78,0.18546405560933,0.12,0.14,0.38,0.0,0.02,0.62,0.0,3.56,1,1,0,1 +5958,ENTJ,30.02,0.1846023441253245,0.08,0.32,0.06,0.0,0.04,0.72,0.0,3.54,0,0,1,0 +5959,INFP,27.58,0.19054793272881762,0.0,0.3,0.12,0.0,0.0,0.62,0.0,4.58,1,0,0,1 +5960,INFP,18.82,0.19114719142806536,0.1,0.1,0.56,0.0,0.0,0.64,0.0,2.54,1,0,0,1 +5961,ENFP,25.54,0.18796087186256452,0.08,0.32,1.58,0.06,0.0,0.74,0.0,2.42,0,0,0,1 +5962,INFP,20.14,0.18800012845921948,0.02,0.12,0.46,0.02,0.04,0.28,0.0,2.22,1,0,0,1 +5963,INFP,25.46,0.17986337299463617,0.12,0.12,0.6,0.1,0.06,0.8,0.0,3.16,1,0,0,1 +5964,ENFP,22.5,0.18162492634578392,0.02,0.12,0.0,0.0,0.0,0.44,0.0,2.12,0,0,0,1 +5965,ISTJ,31.46,0.1854549681632653,0.02,0.18,0.08,0.0,0.0,0.84,0.0,3.4,1,1,1,0 +5966,ISTJ,25.76,0.19384244287331143,0.16,0.2,0.66,0.02,0.0,1.18,0.0,3.78,1,1,1,0 +5967,INTP,22.28,0.17901491653113805,0.0,0.1,0.04,0.0,0.02,0.56,0.0,2.62,1,0,1,1 +5968,INTJ,17.72,0.18509953934194529,0.06,0.24,0.34,0.0,0.02,0.34,0.02,1.9,1,0,1,0 +5969,INTJ,26.9,0.18489475349367682,0.0,0.14,0.42,0.0,0.0,0.68,0.0,3.44,1,0,1,0 +5970,INTP,28.44,0.18668306374385732,0.04,0.48,0.02,0.0,0.0,0.96,0.0,3.08,1,0,1,1 +5971,ENTJ,27.58,0.1834178090217227,0.02,0.12,0.36,0.0,0.0,0.58,0.0,2.74,0,0,1,0 +5972,ISFP,20.72,0.19489482489494947,0.04,0.2,0.24,0.04,0.0,0.62,0.0,2.34,1,1,0,1 +5973,INTP,22.98,0.18351932717833297,0.0,0.32,0.08,0.0,0.0,0.54,0.0,1.56,1,0,1,1 +5974,INTP,15.56,0.19046326241978248,0.36,0.44,0.1,0.06,0.02,0.36,0.0,1.6,1,0,1,1 +5975,INTP,23.26,0.1826550703460399,0.0,0.4,0.02,0.0,0.0,0.34,0.0,2.1,1,0,1,1 +5976,INTP,24.8,0.18477403590475824,0.02,0.18,0.02,0.02,0.0,0.94,0.0,2.62,1,0,1,1 +5977,INFP,21.46,0.18565892005468954,0.02,0.1,0.18,0.0,0.02,0.42,0.0,2.2,1,0,0,1 +5978,ENTP,25.9,0.17921596140803953,0.04,0.42,0.06,0.02,0.0,0.7,0.0,2.34,0,0,1,1 +5979,INFJ,20.74,0.18332004710812055,0.28,0.42,0.02,0.04,0.12,0.5,0.0,2.2,1,0,0,0 +5980,INTJ,4.12,0.17483191472796183,0.02,0.0,0.0,0.0,0.0,0.06,0.02,0.3,1,0,1,0 +5981,ENTP,20.06,0.18944090880799497,0.0,0.08,0.16,0.0,0.0,0.3,0.0,2.8,0,0,1,1 +5982,INFP,22.28,0.1926369692279835,0.06,0.28,0.22,0.0,0.02,0.5,0.0,2.5,1,0,0,1 +5983,INFJ,35.26,0.1877882924209557,0.0,0.26,0.48,0.0,0.0,1.22,0.02,4.12,1,0,0,0 +5984,INFP,26.7,0.17569374968913032,0.0,0.14,0.06,0.0,0.0,0.62,0.0,2.16,1,0,0,1 +5985,INFJ,24.3,0.19688054981155598,0.06,0.22,0.38,0.0,0.08,1.9,0.0,2.52,1,0,0,0 +5986,INFJ,26.38,0.19934967800464745,0.22,0.28,0.06,0.08,0.0,1.62,0.0,3.54,1,0,0,0 +5987,INFJ,28.98,0.18247757157842934,0.02,0.14,0.06,0.0,0.0,0.78,0.0,3.24,1,0,0,0 +5988,INFJ,20.16,0.19006567858663664,0.26,0.2,0.22,0.0,0.0,0.64,0.0,2.36,1,0,0,0 +5989,INFP,25.16,0.18678734897373409,0.02,0.16,0.22,0.0,0.02,0.6,0.0,2.4,1,0,0,1 +5990,INFP,32.46,0.18182161657863857,0.0,0.08,0.5,0.0,0.12,0.82,0.0,3.56,1,0,0,1 +5991,ESFJ,31.66,0.1754098844509097,0.0,0.04,0.24,0.0,0.0,0.9,0.0,3.08,0,1,0,0 +5992,INFP,30.98,0.1864942047958615,0.02,0.22,0.96,0.0,0.02,0.86,0.0,2.84,1,0,0,1 +5993,INFJ,27.9,0.18649840140671953,0.18,0.34,0.12,0.0,0.0,0.64,0.0,3.22,1,0,0,0 +5994,ENFP,25.6,0.1833587835170192,0.02,0.36,0.22,0.0,0.0,0.54,0.0,2.92,0,0,0,1 +5995,INFP,23.04,0.18502572702257847,0.08,0.22,0.02,0.08,0.02,0.4,0.0,3.04,1,0,0,1 +5996,INTP,16.4,0.18155634605880067,0.04,0.36,0.0,0.0,0.0,0.38,0.0,1.6,1,0,1,1 +5997,INTP,27.82,0.17648606889127855,0.0,0.06,0.0,0.0,0.0,0.62,0.0,2.12,1,0,1,1 +5998,INFP,17.14,0.20098512413059083,0.0,0.18,1.26,0.0,0.02,0.7,0.0,2.04,1,0,0,1 +5999,ISTP,29.0,0.18533552153768726,0.02,0.14,0.14,0.0,0.0,0.58,0.0,3.54,1,1,1,1 +6000,INFP,31.28,0.1973144523639024,0.0,0.24,0.1,0.0,0.0,1.96,0.0,1.6,1,0,0,1 +6001,INFJ,33.64,0.18573022567799838,0.0,0.08,0.24,0.0,0.0,0.92,0.0,4.26,1,0,0,0 +6002,INTP,27.22,0.1767250913452881,0.18,0.34,0.08,0.04,0.0,0.7,0.0,1.16,1,0,1,1 +6003,ENTP,20.54,0.1829229979016954,0.32,0.22,0.06,0.22,0.02,0.5,0.0,2.2,0,0,1,1 +6004,INTP,31.24,0.18682249817384955,0.0,0.12,0.12,0.0,0.0,0.76,0.0,4.06,1,0,1,1 +6005,INFJ,28.5,0.18808663378065352,0.0,0.16,0.54,0.0,0.0,1.1,0.0,3.74,1,0,0,0 +6006,INFP,24.6,0.19410783081374658,0.02,0.3,0.1,0.02,0.0,0.9,0.02,3.04,1,0,0,1 +6007,INTJ,32.96,0.18448870992171376,0.0,0.12,0.12,0.0,0.02,0.82,0.0,4.02,1,0,1,0 +6008,INFJ,23.14,0.18209441433839202,0.08,0.26,0.1,0.0,0.0,0.6,0.0,2.72,1,0,0,0 +6009,INFP,14.94,0.1763496706776942,0.5,0.26,0.02,0.28,0.04,0.36,0.0,1.86,1,0,0,1 +6010,INTP,31.38,0.1856986510498154,0.0,0.1,0.0,0.0,0.0,0.76,0.0,3.78,1,0,1,1 +6011,ENTP,26.92,0.17948544406203729,0.02,0.1,0.16,0.0,0.04,0.54,0.02,2.8,0,0,1,1 +6012,INTP,20.34,0.18459958085504527,0.0,0.12,0.06,0.0,0.0,0.4,0.0,2.06,1,0,1,1 +6013,INTP,27.88,0.17333189752332157,0.02,0.08,0.0,0.0,0.0,0.66,0.0,3.06,1,0,1,1 +6014,ISTJ,21.28,0.18957486233405046,0.14,0.2,0.0,0.02,0.02,0.62,0.02,2.08,1,1,1,0 +6015,INFP,17.44,0.18013785522543085,0.14,0.3,0.16,0.02,0.0,0.44,0.0,1.68,1,0,0,1 +6016,ENFP,26.92,0.1885827987768552,0.0,0.2,0.36,0.0,0.0,0.68,0.0,3.08,0,0,0,1 +6017,INFP,14.54,0.18975556151040535,0.52,0.32,0.08,0.4,0.0,0.54,0.0,1.54,1,0,0,1 +6018,ENTJ,17.6,0.19185370012172656,0.0,0.2,0.1,0.0,0.0,0.48,0.0,2.3,0,0,1,0 +6019,INTJ,31.06,0.18060890578898273,0.0,0.04,0.04,0.0,0.02,0.8,0.04,2.4,1,0,1,0 +6020,INFP,33.06,0.17759771418019105,0.06,0.1,0.0,0.0,0.0,0.78,0.0,3.8,1,0,0,1 +6021,INFJ,32.98,0.19190595785434186,0.0,0.34,0.12,0.0,0.0,0.98,0.0,4.28,1,0,0,0 +6022,ENFP,29.78,0.18710282535938993,0.0,0.5,0.86,0.0,0.0,0.96,0.04,2.94,0,0,0,1 +6023,ISFP,28.98,0.19299960554685378,0.0,0.32,0.7,0.0,0.08,0.72,0.0,3.44,1,1,0,1 +6024,INFJ,25.42,0.1934688837513793,0.0,0.32,0.58,0.0,0.0,0.76,0.0,3.76,1,0,0,0 +6025,INFJ,22.52,0.17603958455704347,0.18,0.2,0.0,0.04,0.02,0.8,0.02,1.56,1,0,0,0 +6026,INTP,24.76,0.17746176205866013,0.06,0.18,0.08,0.0,0.0,0.86,0.16,2.32,1,0,1,1 +6027,INTJ,27.78,0.1864778115032349,0.06,0.12,0.04,0.02,0.0,0.68,0.0,3.36,1,0,1,0 +6028,ISFP,18.32,0.1896081475431778,0.12,0.4,0.02,0.02,0.0,0.4,0.0,1.3,1,1,0,1 +6029,INFP,17.24,0.18900240364441961,0.2,0.26,0.08,0.04,0.06,0.44,0.0,2.36,1,0,0,1 +6030,ISTP,25.64,0.18072432517144607,0.1,0.24,0.06,0.04,0.0,0.68,0.0,2.52,1,1,1,1 +6031,INFJ,30.68,0.18321749964501038,0.06,0.34,0.04,0.0,0.0,0.74,0.0,4.14,1,0,0,0 +6032,ISFP,20.72,0.18693349009690421,0.06,0.18,0.08,0.0,0.06,0.34,0.0,2.84,1,1,0,1 +6033,INTJ,28.52,0.18392480145997947,0.04,0.26,0.08,0.0,0.0,0.74,0.0,3.64,1,0,1,0 +6034,ESTJ,28.62,0.18485495203448082,0.04,0.12,0.0,0.0,0.0,0.48,0.0,3.18,0,1,1,0 +6035,INFP,31.82,0.18326869947879282,0.0,0.14,0.1,0.0,0.04,0.8,0.08,3.64,1,0,0,1 +6036,ENTP,20.28,0.1883104603125735,0.12,0.12,0.24,0.02,0.0,0.66,0.02,2.38,0,0,1,1 +6037,INFP,25.54,0.1822457721875754,0.0,0.12,0.04,0.0,0.0,0.48,0.0,2.48,1,0,0,1 +6038,INFP,29.68,0.1815929867885582,0.02,0.16,0.02,0.0,0.0,0.66,0.0,2.16,1,0,0,1 +6039,INTP,26.82,0.1894914591045834,0.02,0.12,0.0,0.02,0.0,0.62,0.0,3.56,1,0,1,1 +6040,INTP,22.04,0.18311810754497337,0.0,0.16,0.08,0.0,0.0,0.44,0.0,2.74,1,0,1,1 +6041,ISFJ,19.72,0.1922070948338079,0.0,0.22,0.3,0.0,0.0,1.04,0.0,2.52,1,1,0,0 +6042,ENTJ,29.16,0.17666792163927336,0.0,0.16,0.0,0.0,0.0,0.62,0.0,2.84,0,0,1,0 +6043,INFP,26.42,0.1823130088999663,0.08,0.24,0.18,0.0,0.0,0.72,0.0,2.86,1,0,0,1 +6044,INTJ,8.32,0.1737223802051194,0.38,0.3,0.02,0.08,0.0,0.16,0.0,0.98,1,0,1,0 +6045,ISTJ,8.36,0.20655655436052411,0.0,0.1,0.14,0.0,0.0,0.32,0.0,1.0,1,1,1,0 +6046,ESFJ,19.58,0.18820571064661423,0.02,0.16,0.6,0.0,0.0,0.24,0.02,2.6,0,1,0,0 +6047,INTJ,25.4,0.184469512670071,0.04,0.06,0.04,0.04,0.02,0.62,0.0,2.78,1,0,1,0 +6048,ISTJ,22.12,0.18671786338818042,0.0,0.36,0.14,0.0,0.02,0.46,0.0,2.18,1,1,1,0 +6049,ENTP,26.28,0.17923491054633392,0.0,0.2,0.0,0.0,0.02,0.58,0.0,3.06,0,0,1,1 +6050,INTP,23.28,0.19154253370915597,0.0,0.28,1.52,0.0,0.0,0.66,0.0,2.3,1,0,1,1 +6051,ENFP,25.1,0.19184220834873025,0.06,0.1,0.4,0.0,0.02,1.58,0.0,2.54,0,0,0,1 +6052,ENTJ,15.6,0.18112760806891878,0.0,0.1,0.18,0.0,0.0,0.22,0.0,1.34,0,0,1,0 +6053,INFJ,26.64,0.19401870055081602,0.06,0.12,0.0,0.0,0.0,2.3,0.0,1.98,1,0,0,0 +6054,INFJ,8.64,0.19061028004302022,0.14,0.12,0.2,0.04,0.0,0.22,0.0,1.18,1,0,0,0 +6055,INFP,24.22,0.1856459381606141,0.1,0.2,0.1,0.0,0.02,1.02,0.02,2.84,1,0,0,1 +6056,ISFP,24.5,0.18036157395616167,0.0,0.02,0.02,0.0,0.0,0.54,0.0,3.62,1,1,0,1 +6057,INFP,21.22,0.18416671798542208,0.06,0.08,0.1,0.0,0.02,0.68,0.0,2.56,1,0,0,1 +6058,INTP,31.2,0.19165833607438731,0.0,0.18,0.36,0.0,0.02,1.0,0.0,4.0,1,0,1,1 +6059,INFJ,21.82,0.18114828563685517,0.06,0.16,0.16,0.02,0.0,1.26,0.02,2.44,1,0,0,0 +6060,INTJ,25.52,0.1854130529658652,0.0,0.24,0.12,0.0,0.02,0.82,0.0,2.58,1,0,1,0 +6061,INFJ,30.0,0.19017517123859543,0.06,0.16,0.24,0.02,0.08,0.92,0.0,3.18,1,0,0,0 +6062,ENFP,27.14,0.18302212217368427,0.0,0.06,0.58,0.0,0.0,0.8,0.0,3.18,0,0,0,1 +6063,INTP,16.76,0.1814587620513486,0.04,0.14,0.0,0.02,0.0,0.24,0.0,1.54,1,0,1,1 +6064,INFP,16.2,0.19848006206129923,0.0,0.24,0.28,0.0,0.0,0.28,0.0,1.56,1,0,0,1 +6065,INFP,26.16,0.18514556478771121,0.06,0.22,1.02,0.0,0.0,1.0,0.04,2.86,1,0,0,1 +6066,ENTP,28.28,0.1818778048790116,0.0,0.14,0.38,0.0,0.0,0.74,0.0,2.5,0,0,1,1 +6067,INFP,16.32,0.18586995578529997,0.2,0.34,0.0,0.0,0.0,0.28,0.0,2.0,1,0,0,1 +6068,INTJ,31.4,0.18503841440423927,0.06,0.1,0.42,0.0,0.0,0.72,0.0,4.68,1,0,1,0 +6069,ENTP,30.88,0.17836621112438905,0.0,0.12,0.0,0.0,0.0,0.94,0.0,2.18,0,0,1,1 +6070,ISFP,10.02,0.19187711567767407,0.0,0.06,0.86,0.0,0.0,0.14,0.0,1.34,1,1,0,1 +6071,INTJ,30.16,0.18816728081879922,0.0,0.22,0.5,0.0,0.0,1.04,0.0,3.94,1,0,1,0 +6072,INFP,27.54,0.19745126851921785,0.16,0.3,0.76,0.08,0.02,1.3,0.0,3.24,1,0,0,1 +6073,INTJ,18.28,0.1803730810244249,0.0,0.2,0.26,0.0,0.02,0.34,0.08,2.16,1,0,1,0 +6074,INTJ,2.7,0.1855448104974902,0.02,0.02,0.16,0.0,0.0,0.02,0.0,0.62,1,0,1,0 +6075,ENFP,21.9,0.19011294881967863,0.12,0.16,0.2,0.06,0.04,0.56,0.0,2.2,0,0,0,1 +6076,ISFJ,33.14,0.18458262357196295,0.04,0.12,0.24,0.0,0.0,1.12,0.0,3.62,1,1,0,0 +6077,ENTJ,20.84,0.18243855467418418,0.04,0.1,0.06,0.04,0.0,0.4,0.0,2.24,0,0,1,0 +6078,ENFP,20.42,0.18587626923356773,0.0,0.48,0.14,0.0,0.0,0.24,0.0,2.2,0,0,0,1 +6079,INTJ,26.48,0.1812261683193614,0.0,0.12,0.04,0.0,0.0,0.56,0.0,3.46,1,0,1,0 +6080,INFP,16.9,0.18279799075513517,0.46,0.46,0.06,0.08,0.0,0.48,0.0,1.92,1,0,0,1 +6081,INTP,27.18,0.18370224161040313,0.0,0.12,0.06,0.0,0.0,1.02,0.0,2.46,1,0,1,1 +6082,ISFJ,33.88,0.18647142775584388,0.02,0.22,0.12,0.0,0.02,1.16,0.0,5.2,1,1,0,0 +6083,INFP,23.54,0.18387652676734434,0.14,0.18,0.0,0.04,0.0,0.56,0.02,2.84,1,0,0,1 +6084,ENFP,26.18,0.18198988687685394,0.02,0.12,0.16,0.0,0.02,0.54,0.0,3.24,0,0,0,1 +6085,INTJ,14.42,0.1834140208411306,0.0,0.16,0.14,0.0,0.0,0.6,0.0,1.44,1,0,1,0 +6086,INFP,33.12,0.18846924212924074,0.0,0.14,0.22,0.0,0.04,1.22,0.0,4.8,1,0,0,1 +6087,ENTJ,16.82,0.1777193232316303,0.0,0.24,0.04,0.0,0.0,0.36,0.0,2.26,0,0,1,0 +6088,INTJ,22.96,0.1852890636637961,0.22,0.42,0.04,0.0,0.02,0.42,0.0,2.12,1,0,1,0 +6089,INFJ,17.06,0.18413646375658643,0.04,0.08,0.0,0.0,0.02,0.42,0.0,1.46,1,0,0,0 +6090,INTP,27.84,0.19116634485881703,0.0,0.12,0.24,0.0,0.04,1.54,0.0,3.06,1,0,1,1 +6091,ENTP,28.18,0.1848350513628514,0.0,0.34,0.02,0.0,0.0,0.76,0.0,3.46,0,0,1,1 +6092,ISFP,8.04,0.18692879200118254,0.02,0.02,0.76,0.0,0.0,0.08,0.02,1.4,1,1,0,1 +6093,INTP,29.5,0.1843198404080705,0.02,0.44,0.04,0.0,0.02,0.88,0.0,3.56,1,0,1,1 +6094,ENFP,31.22,0.18549299083718376,0.0,0.1,0.26,0.0,0.02,0.88,0.0,3.8,0,0,0,1 +6095,INFP,25.24,0.18875624936230997,0.0,0.18,0.14,0.0,0.0,0.68,0.0,2.96,1,0,0,1 +6096,INTP,19.08,0.1775147928994083,0.38,0.3,0.02,0.06,0.0,0.42,0.0,2.3,1,0,1,1 +6097,INTJ,29.02,0.18768652066199862,0.0,0.26,0.06,0.0,0.0,0.72,0.0,3.82,1,0,1,0 +6098,ENFP,28.5,0.1818388499350273,0.04,0.28,0.26,0.0,0.0,0.7,0.0,2.62,0,0,0,1 +6099,INFP,18.22,0.18535433613992253,0.04,0.18,0.0,0.02,0.0,0.26,0.0,1.12,1,0,0,1 +6100,INFP,28.58,0.1889034212189854,0.0,0.36,0.38,0.0,0.0,0.84,0.0,3.58,1,0,0,1 +6101,ISTP,28.14,0.1809504464039667,0.04,0.1,0.08,0.0,0.0,0.58,0.0,3.2,1,1,1,1 +6102,INTP,10.3,0.21238820328822725,0.04,0.5,0.46,0.0,0.0,0.2,0.0,1.4,1,0,1,1 +6103,INFP,28.3,0.180760857080447,0.06,0.12,0.04,0.02,0.0,0.74,0.0,3.4,1,0,0,1 +6104,INTP,19.66,0.19275703811955566,0.02,0.2,0.24,0.02,0.08,0.56,0.0,1.66,1,0,1,1 +6105,INFP,7.94,0.1829388477281999,0.02,0.02,0.0,0.0,0.0,0.3,0.0,0.82,1,0,0,1 +6106,ISFP,26.82,0.1820579179532624,0.02,0.06,0.28,0.0,0.0,0.44,0.0,3.72,1,1,0,1 +6107,INFP,32.88,0.19282349311826005,0.06,0.18,0.0,0.0,0.02,1.88,0.0,4.42,1,0,0,1 +6108,INFJ,16.88,0.1990369814715526,0.04,0.52,0.04,0.0,0.0,0.34,0.0,0.58,1,0,0,0 +6109,ENFP,21.92,0.18671656710476936,0.0,0.04,0.28,0.0,0.04,0.54,0.08,2.38,0,0,0,1 +6110,INTJ,17.02,0.18762845541770062,0.0,0.08,0.12,0.0,0.0,0.44,0.0,2.3,1,0,1,0 +6111,ISTP,25.28,0.18467453250380866,0.02,0.36,0.04,0.0,0.0,0.72,0.0,2.42,1,1,1,1 +6112,ENFP,24.98,0.18682475038080265,0.04,0.52,0.64,0.02,0.0,0.5,0.0,2.78,0,0,0,1 +6113,INTP,27.26,0.18628289127756745,0.02,0.26,0.24,0.0,0.02,0.86,0.0,2.68,1,0,1,1 +6114,INFP,23.34,0.17138277876616845,0.06,0.4,0.0,0.02,0.0,0.7,0.0,1.66,1,0,0,1 +6115,INTP,25.22,0.1879650528625268,0.0,0.24,0.0,0.0,0.0,0.8,0.0,3.36,1,0,1,1 +6116,INTP,32.0,0.18594064210741332,0.04,0.28,0.02,0.0,0.06,1.3,0.0,2.5,1,0,1,1 +6117,ENTP,20.06,0.1855984114843637,0.04,0.22,0.2,0.02,0.0,0.42,0.0,1.24,0,0,1,1 +6118,INTP,25.56,0.17443483582002958,0.0,0.22,0.16,0.0,0.04,0.5,0.0,2.6,1,0,1,1 +6119,INFJ,25.16,0.18264842300162254,0.02,0.14,0.16,0.0,0.0,0.56,0.0,2.38,1,0,0,0 +6120,ENTP,25.58,0.18208238642360106,0.04,0.2,0.0,0.02,0.0,0.8,0.02,2.28,0,0,1,1 +6121,INTP,33.78,0.17767838232729147,0.02,0.12,0.12,0.0,0.02,1.0,0.0,4.2,1,0,1,1 +6122,INFJ,20.06,0.17553078333854416,0.24,0.48,0.06,0.0,0.02,0.44,0.0,3.0,1,0,0,0 +6123,ENTP,11.0,0.1819195376567066,0.04,0.12,0.0,0.02,0.02,0.12,0.0,0.72,0,0,1,1 +6124,ENFJ,32.1,0.18371832149542702,0.02,0.18,0.48,0.0,0.0,1.0,0.0,3.92,0,0,0,0 +6125,ISTJ,3.56,0.1816517524985421,0.0,0.04,0.06,0.0,0.0,0.12,0.0,0.52,1,1,1,0 +6126,INFP,20.08,0.18199222033518433,0.1,0.18,0.22,0.0,0.0,0.38,0.0,2.28,1,0,0,1 +6127,INTP,30.5,0.18421452307481406,0.02,0.2,0.04,0.0,0.0,0.76,0.0,4.04,1,0,1,1 +6128,INFJ,13.82,0.1875271296795502,0.12,0.2,0.16,0.08,0.0,0.5,0.0,1.64,1,0,0,0 +6129,ISFP,24.78,0.18953799965080131,0.0,0.06,0.14,0.0,0.0,0.6,0.0,3.58,1,1,0,1 +6130,INFP,24.54,0.18561933836008873,0.1,0.18,0.3,0.0,0.02,0.54,0.0,1.36,1,0,0,1 +6131,INTP,11.2,0.1942147234846584,0.06,0.12,0.12,0.02,0.0,0.4,0.0,1.34,1,0,1,1 +6132,INTJ,24.92,0.18551762339882633,0.08,0.24,0.08,0.0,0.0,0.6,0.0,3.54,1,0,1,0 +6133,INTP,32.62,0.18579265402276285,0.0,0.06,0.26,0.0,0.0,0.88,0.0,4.68,1,0,1,1 +6134,ENTP,26.64,0.1827389803752496,0.02,0.4,0.28,0.0,0.0,1.4,0.0,2.36,0,0,1,1 +6135,INTJ,31.9,0.1831599309170371,0.0,0.3,0.02,0.0,0.0,0.96,0.0,3.54,1,0,1,0 +6136,INFJ,22.14,0.18883685975257253,0.22,0.16,0.02,0.12,0.0,0.44,0.0,3.04,1,0,0,0 +6137,ISFP,28.38,0.17843031103900608,0.06,0.46,0.14,0.04,0.0,0.76,0.0,3.3,1,1,0,1 +6138,INTP,18.9,0.1881499283500106,0.06,0.18,0.06,0.0,0.02,0.54,0.0,0.84,1,0,1,1 +6139,ENTP,32.9,0.1889468963012674,0.02,0.26,0.38,0.0,0.02,0.84,0.0,4.1,0,0,1,1 +6140,ENTJ,26.52,0.19529312442746263,0.1,0.08,0.36,0.0,0.06,0.72,0.0,3.04,0,0,1,0 +6141,ENFP,21.92,0.19438893923107212,0.0,0.26,2.34,0.0,0.0,0.8,0.0,2.84,0,0,0,1 +6142,ISFJ,23.72,0.19272773759953246,0.0,0.08,0.28,0.0,0.06,0.72,0.0,2.94,1,1,0,0 +6143,INTJ,31.68,0.19049043296578108,0.0,0.2,0.24,0.0,0.0,0.9,0.0,3.96,1,0,1,0 +6144,ISFJ,13.54,0.18426739175681475,0.7,0.64,0.16,0.08,0.0,0.36,0.0,1.74,1,1,0,0 +6145,ENFP,28.48,0.18706928505454307,0.06,0.12,0.08,0.04,0.0,0.86,0.0,3.66,0,0,0,1 +6146,INTP,8.8,0.18431617302057085,0.76,0.76,0.02,0.0,0.06,0.14,0.0,0.88,1,0,1,1 +6147,INFP,32.8,0.18026494401871088,0.06,0.18,0.1,0.0,0.0,0.96,0.0,3.44,1,0,0,1 +6148,INFP,10.34,0.18449641815359125,0.52,0.54,0.44,0.0,0.02,0.24,0.04,1.5,1,0,0,1 +6149,INTP,28.96,0.18853121389307098,0.02,0.28,0.32,0.0,0.0,1.54,0.0,3.32,1,0,1,1 +6150,ENFP,13.68,0.18457377149284626,0.3,0.44,0.66,0.02,0.0,0.32,0.0,1.4,0,0,0,1 +6151,ISTP,22.94,0.18662928727669612,0.0,0.24,0.04,0.0,0.0,0.78,0.0,2.62,1,1,1,1 +6152,INFJ,25.48,0.18590046278017341,0.0,0.12,0.1,0.0,0.0,0.84,0.0,1.22,1,0,0,0 +6153,INFJ,28.1,0.1862467790712317,0.02,0.16,0.02,0.0,0.0,0.76,0.0,3.58,1,0,0,0 +6154,INFP,28.7,0.18904276944026768,0.02,0.14,0.26,0.0,0.0,0.74,0.0,4.04,1,0,0,1 +6155,INTJ,20.06,0.17892976489369236,0.0,0.3,0.04,0.0,0.04,0.44,0.0,1.64,1,0,1,0 +6156,INFJ,25.98,0.18550286456282164,0.18,0.1,0.1,0.0,0.1,0.86,0.02,2.24,1,0,0,0 +6157,INFP,34.84,0.1785517226983059,0.0,0.04,0.1,0.0,0.0,1.0,0.0,4.22,1,0,0,1 +6158,ENTJ,26.66,0.18638832939995661,0.02,0.36,0.04,0.0,0.0,0.66,0.02,2.48,0,0,1,0 +6159,INTJ,32.34,0.18298075624663096,0.02,0.12,0.02,0.0,0.0,0.86,0.0,3.0,1,0,1,0 +6160,ISTP,20.88,0.18630173805618377,0.14,0.18,0.02,0.06,0.0,0.56,0.0,1.98,1,1,1,1 +6161,INFP,33.16,0.1813662218416579,0.04,0.12,0.04,0.0,0.06,0.84,0.0,4.82,1,0,0,1 +6162,INFP,21.36,0.17669003896831706,0.1,0.18,0.06,0.02,0.02,0.34,0.0,2.6,1,0,0,1 +6163,ISTJ,20.42,0.18641197222222217,0.0,0.08,0.08,0.0,0.0,0.4,0.0,1.96,1,1,1,0 +6164,INFJ,20.74,0.18333020045768397,0.02,0.14,0.02,0.0,0.04,0.62,0.0,2.3,1,0,0,0 +6165,INTJ,26.4,0.17735029335106411,0.0,0.3,0.02,0.0,0.0,0.66,0.0,2.46,1,0,1,0 +6166,ISFP,31.6,0.1837002924378064,0.12,0.44,0.08,0.0,0.02,1.08,0.0,3.38,1,1,0,1 +6167,INTJ,17.04,0.18617226771092915,0.16,0.26,0.0,0.04,0.02,0.22,0.0,1.68,1,0,1,0 +6168,ISTP,23.46,0.18545518120461713,0.08,0.24,0.06,0.02,0.0,0.62,0.0,2.98,1,1,1,1 +6169,INFJ,12.86,0.18915424757919277,0.64,0.66,0.26,0.0,0.04,0.38,0.0,1.4,1,0,0,0 +6170,ISTP,29.28,0.18403506470065073,0.0,0.04,0.04,0.0,0.02,0.72,0.0,3.46,1,1,1,1 +6171,ENTP,19.62,0.18770535553724227,0.02,0.4,0.26,0.0,0.0,0.6,0.0,1.86,0,0,1,1 +6172,ENTJ,17.06,0.1819907907057979,0.14,0.22,0.12,0.08,0.02,0.2,0.0,1.92,0,0,1,0 +6173,ESTP,24.7,0.18589807776138453,0.0,0.14,0.02,0.0,0.0,0.5,0.0,3.24,0,1,1,1 +6174,INTJ,22.62,0.18169614627538475,0.1,0.36,0.02,0.0,0.02,0.54,0.0,2.2,1,0,1,0 +6175,ISFJ,30.18,0.18250596070997543,0.0,0.24,0.04,0.0,0.0,0.88,0.0,3.3,1,1,0,0 +6176,INTP,26.88,0.1790040068635949,0.18,0.18,0.04,0.04,0.04,0.82,0.02,2.68,1,0,1,1 +6177,INFP,8.14,0.18614726103993928,0.58,0.52,0.0,0.0,0.02,0.18,0.0,0.9,1,0,0,1 +6178,INFJ,27.36,0.18547722481596007,0.0,0.0,0.56,0.0,0.02,0.86,0.0,2.54,1,0,0,0 +6179,INFJ,21.04,0.18834531542484556,0.0,0.18,0.2,0.0,0.0,0.64,0.0,2.38,1,0,0,0 +6180,INTP,25.72,0.18205339385573707,0.04,0.08,0.04,0.04,0.0,0.64,0.0,2.98,1,0,1,1 +6181,INFP,30.8,0.18073663920043825,0.0,0.34,0.22,0.0,0.0,0.56,0.0,3.82,1,0,0,1 +6182,ENTP,22.42,0.188009801792927,0.02,0.34,0.0,0.0,0.0,0.36,0.0,1.26,0,0,1,1 +6183,ENFP,24.94,0.18685065370882437,0.1,0.14,0.2,0.0,0.04,1.08,0.0,3.4,0,0,0,1 +6184,INFP,28.04,0.18400493557136008,0.0,0.12,0.02,0.0,0.02,1.22,0.0,2.82,1,0,0,1 +6185,ENFP,14.4,0.20245443434624996,0.04,0.32,0.5,0.04,0.0,0.26,0.02,2.12,0,0,0,1 +6186,INTJ,21.28,0.1847035741363568,0.02,0.34,0.06,0.0,0.0,0.42,0.04,2.38,1,0,1,0 +6187,INTJ,24.36,0.19142500543319138,0.04,0.3,0.04,0.02,0.0,0.68,0.0,2.48,1,0,1,0 +6188,INTP,24.02,0.1877506514419393,0.2,0.2,0.06,0.08,0.0,0.54,0.0,1.98,1,0,1,1 +6189,INFP,31.04,0.18598591362921416,0.0,0.18,0.06,0.0,0.0,1.2,0.0,3.28,1,0,0,1 +6190,INFP,22.8,0.1779398884971978,0.04,0.38,0.2,0.0,0.02,0.48,0.0,2.06,1,0,0,1 +6191,INFP,32.2,0.1838862434664034,0.08,0.24,0.26,0.0,0.04,1.06,0.0,3.72,1,0,0,1 +6192,INFP,25.04,0.18120247779371815,0.0,0.34,0.1,0.0,0.02,0.5,0.0,2.56,1,0,0,1 +6193,INFJ,25.14,0.18517731649277439,0.0,0.08,0.06,0.0,0.04,0.8,0.0,3.32,1,0,0,0 +6194,ENFP,27.56,0.18924184972294378,0.04,0.3,0.36,0.0,0.08,0.68,0.0,3.64,0,0,0,1 +6195,INFJ,32.0,0.1825645904696581,0.0,0.14,0.2,0.0,0.0,1.02,0.0,3.6,1,0,0,0 +6196,ISFJ,30.6,0.1853705377700093,0.02,0.2,0.5,0.0,0.0,0.92,0.02,3.58,1,1,0,0 +6197,ENTP,23.1,0.18447822145061726,0.16,0.82,0.24,0.0,0.0,0.62,0.0,2.14,0,0,1,1 +6198,INFP,32.16,0.1822294390253591,0.04,0.06,0.02,0.0,0.0,0.74,0.0,2.32,1,0,0,1 +6199,INFJ,31.54,0.1862129535990482,0.0,0.16,0.0,0.0,0.06,0.68,0.0,2.78,1,0,0,0 +6200,ENFP,28.92,0.18863258337380426,0.02,0.14,0.18,0.0,0.02,0.68,0.0,3.48,0,0,0,1 +6201,ENTP,25.48,0.17954498191248242,0.24,0.36,0.0,0.02,0.02,0.6,0.0,2.76,0,0,1,1 +6202,INFJ,3.26,0.18225745918558833,0.0,0.04,0.02,0.0,0.0,0.04,0.0,0.36,1,0,0,0 +6203,INFJ,31.64,0.19016189094754138,0.02,0.12,0.12,0.0,0.0,1.38,0.02,3.02,1,0,0,0 +6204,ESFP,9.68,0.17878651288737882,0.58,0.46,0.16,0.16,0.04,0.2,0.0,1.76,0,1,0,1 +6205,INFP,21.26,0.18544187252215855,0.1,0.22,0.18,0.0,0.02,0.58,0.0,2.66,1,0,0,1 +6206,INTJ,21.36,0.1803347798708798,0.06,0.06,0.0,0.04,0.0,0.38,0.0,2.12,1,0,1,0 +6207,ENFP,28.04,0.18793885177341124,0.06,0.18,0.34,0.0,0.0,1.08,0.0,3.04,0,0,0,1 +6208,INFP,23.84,0.17884063707612183,0.12,0.26,0.16,0.04,0.0,0.64,0.0,2.4,1,0,0,1 +6209,INFP,23.76,0.1758636166952175,0.02,0.14,0.38,0.02,0.0,0.6,0.0,1.86,1,0,0,1 +6210,ENFP,21.22,0.19086349545084724,0.0,0.4,0.16,0.0,0.0,0.36,0.0,2.62,0,0,0,1 +6211,ISTP,33.98,0.18231898226479787,0.0,0.02,0.0,0.0,0.0,0.92,0.0,4.52,1,1,1,1 +6212,ESFP,8.46,0.18703751442379982,0.0,0.12,0.1,0.0,0.0,0.04,0.0,1.02,0,1,0,1 +6213,ISTP,21.96,0.1867841292854106,0.02,0.08,0.26,0.0,0.0,0.68,0.02,2.42,1,1,1,1 +6214,INTP,22.16,0.19005534976355654,0.34,0.34,0.04,0.06,0.0,1.24,0.0,2.38,1,0,1,1 +6215,INTJ,5.28,0.1835567283901329,0.3,0.1,0.0,0.24,0.0,0.2,0.02,0.66,1,0,1,0 +6216,ENTJ,30.06,0.18669210892140792,0.0,0.5,0.14,0.0,0.0,0.88,0.0,3.26,0,0,1,0 +6217,INFP,18.56,0.19540280951256486,0.1,0.24,0.08,0.02,0.02,0.3,0.0,2.42,1,0,0,1 +6218,ISTP,20.02,0.19703931300734948,0.02,0.38,0.08,0.0,0.04,0.36,0.0,2.66,1,1,1,1 +6219,ENFJ,31.44,0.18745731603334218,0.0,0.24,0.22,0.0,0.0,0.84,0.0,2.9,0,0,0,0 +6220,INFP,27.24,0.17928034716035796,0.16,0.18,0.0,0.06,0.1,0.92,0.0,1.98,1,0,0,1 +6221,ENFJ,25.32,0.18497569843690845,0.06,0.16,0.08,0.06,0.0,0.58,0.0,2.18,0,0,0,0 +6222,INTJ,25.2,0.17245184308748837,0.0,0.0,0.0,0.0,0.0,0.34,0.0,1.54,1,0,1,0 +6223,ISTP,24.7,0.1872854078831619,0.0,0.12,0.12,0.0,0.0,0.54,0.0,3.84,1,1,1,1 +6224,INTP,22.2,0.17833158979048214,0.0,0.14,0.0,0.0,0.0,0.68,0.02,1.92,1,0,1,1 +6225,INTJ,23.66,0.174699265470986,0.0,0.16,0.14,0.0,0.02,0.58,0.0,3.18,1,0,1,0 +6226,INTP,21.58,0.1818973145583256,0.04,0.04,0.02,0.0,0.0,0.4,0.0,2.6,1,0,1,1 +6227,INTJ,13.18,0.17946341783622113,0.0,0.08,0.06,0.0,0.0,0.38,0.0,0.94,1,0,1,0 +6228,INFJ,32.32,0.1969614410392467,0.0,0.24,0.74,0.0,0.0,2.44,0.0,1.76,1,0,0,0 +6229,INTJ,14.9,0.19270432596304765,0.04,0.14,0.94,0.02,0.02,0.64,0.0,1.44,1,0,1,0 +6230,INTP,9.58,0.18281803957566733,0.22,0.28,0.02,0.06,0.02,0.24,0.0,1.44,1,0,1,1 +6231,ENFP,23.94,0.18839958609419796,0.02,0.18,0.48,0.02,0.0,0.5,0.0,3.16,0,0,0,1 +6232,INFP,31.94,0.18443458238091034,0.0,0.1,0.16,0.0,0.02,0.86,0.0,3.84,1,0,0,1 +6233,ENTP,28.48,0.18966843505435757,0.0,0.04,0.06,0.0,0.04,0.88,0.0,3.48,0,0,1,1 +6234,INFP,20.96,0.18616009980138898,0.1,0.28,0.1,0.0,0.02,0.4,0.0,2.84,1,0,0,1 +6235,INFP,31.02,0.1851076042842834,0.02,0.14,0.0,0.0,0.02,0.7,0.0,3.86,1,0,0,1 +6236,INTP,25.14,0.186034473776044,0.08,0.36,0.16,0.0,0.04,0.76,0.0,2.9,1,0,1,1 +6237,ISFP,20.1,0.1881384810628418,0.04,0.02,0.02,0.0,0.02,0.2,0.0,2.12,1,1,0,1 +6238,ISFP,25.4,0.18441087855027347,0.22,0.14,0.06,0.1,0.08,0.64,0.0,3.26,1,1,0,1 +6239,INTJ,19.16,0.18216355199096793,0.1,0.02,0.0,0.08,0.0,0.4,0.0,2.7,1,0,1,0 +6240,ENFP,25.94,0.1814234158642799,0.0,0.1,0.18,0.0,0.0,0.7,0.0,2.9,0,0,0,1 +6241,ENTP,19.26,0.1908105165432099,0.08,0.2,0.08,0.0,0.02,0.18,0.0,2.44,0,0,1,1 +6242,INFP,19.68,0.19361346729426518,0.0,0.36,0.4,0.0,0.0,0.56,0.0,1.86,1,0,0,1 +6243,INFP,19.22,0.19489665842709034,0.04,0.28,0.0,0.02,0.02,1.76,0.0,1.22,1,0,0,1 +6244,ENTP,20.38,0.19062555486687646,0.0,0.46,0.34,0.0,0.0,0.66,0.0,2.3,0,0,1,1 +6245,ENFP,10.7,0.2022375880395612,0.0,0.26,0.08,0.0,0.0,0.56,0.0,1.86,0,0,0,1 +6246,ENFP,27.74,0.1887171081605845,0.0,0.06,0.08,0.0,0.0,0.8,0.0,3.86,0,0,0,1 +6247,ENTP,25.18,0.18631959968592826,0.02,0.8,0.04,0.0,0.0,0.76,0.0,1.68,0,0,1,1 +6248,INFJ,21.7,0.1945370883652485,0.14,0.38,0.34,0.1,0.0,1.02,0.12,1.22,1,0,0,0 +6249,INTJ,12.94,0.17793715381764155,0.02,0.06,0.02,0.0,0.0,0.2,0.0,1.72,1,0,1,0 +6250,INTP,29.92,0.18039472508404264,0.0,0.22,0.0,0.0,0.0,0.74,0.0,3.22,1,0,1,1 +6251,INTP,15.48,0.18208894457185357,0.16,0.2,0.1,0.06,0.0,0.24,0.0,2.0,1,0,1,1 +6252,ISTP,20.96,0.18944339965242846,0.2,0.34,0.04,0.0,0.0,0.48,0.02,2.48,1,1,1,1 +6253,ISFJ,26.04,0.18904659482477348,0.18,0.24,0.5,0.06,0.04,0.74,0.0,3.4,1,1,0,0 +6254,INFJ,24.76,0.18319267475613615,0.2,0.3,0.3,0.0,0.0,0.5,0.0,2.7,1,0,0,0 +6255,INFJ,26.3,0.18728697520005688,0.3,0.04,0.18,0.06,0.0,0.9,0.02,2.56,1,0,0,0 +6256,INFP,21.36,0.1820216455723641,0.06,0.3,0.08,0.0,0.0,0.32,0.0,2.7,1,0,0,1 +6257,ENTJ,17.58,0.1891517511133352,0.0,0.14,0.2,0.0,0.0,0.46,0.0,1.18,0,0,1,0 +6258,INFJ,23.28,0.20072637394092793,0.0,0.58,0.94,0.0,0.0,0.72,0.0,2.62,1,0,0,0 +6259,INFP,31.82,0.19402712038910705,0.04,0.26,1.46,0.0,0.0,1.7,0.0,3.3,1,0,0,1 +6260,INTJ,20.42,0.1923645701992075,0.28,0.44,0.36,0.02,0.04,0.6,0.0,2.0,1,0,1,0 +6261,INFJ,19.74,0.18420754026966113,0.04,0.14,0.12,0.02,0.04,0.54,0.0,2.42,1,0,0,0 +6262,ENTJ,33.38,0.18439248547362636,0.04,0.3,0.04,0.0,0.0,1.24,0.0,2.98,0,0,1,0 +6263,INTP,20.34,0.17743192899408283,0.12,0.1,0.02,0.02,0.02,0.4,0.0,3.0,1,0,1,1 +6264,ISFJ,29.1,0.18632688156620889,0.0,0.1,0.1,0.0,0.02,0.76,0.0,3.94,1,1,0,0 +6265,ENTP,31.82,0.17975368470987355,0.04,0.32,0.06,0.0,0.0,0.8,0.02,3.18,0,0,1,1 +6266,INFP,15.48,0.18405132873498825,0.22,0.48,0.08,0.0,0.04,0.2,0.0,1.44,1,0,0,1 +6267,INTP,29.44,0.1822754502530946,0.02,0.14,0.04,0.0,0.0,0.7,0.0,3.04,1,0,1,1 +6268,INTP,30.88,0.1850318088692595,0.0,0.28,0.0,0.0,0.06,0.9,0.0,3.92,1,0,1,1 +6269,ENFP,31.26,0.183264368829566,0.04,0.56,0.32,0.02,0.04,0.78,0.0,3.5,0,0,0,1 +6270,INFJ,19.8,0.1829105079279764,0.24,0.36,0.06,0.0,0.1,0.36,0.0,2.32,1,0,0,0 +6271,ESTP,30.2,0.19581281102417972,0.0,0.3,0.08,0.0,0.0,2.0,0.0,3.46,0,1,1,1 +6272,INFJ,31.3,0.18386612449449014,0.02,0.2,0.46,0.0,0.06,0.92,0.0,3.68,1,0,0,0 +6273,INTP,21.98,0.1891758787529323,0.0,0.08,0.08,0.0,0.08,0.94,0.0,1.74,1,0,1,1 +6274,INFP,10.38,0.18738641528921363,0.08,0.12,0.08,0.0,0.0,0.18,0.0,1.58,1,0,0,1 +6275,ENTJ,26.12,0.1850439006574142,0.0,0.06,0.0,0.0,0.0,0.72,0.0,3.64,0,0,1,0 +6276,ENTP,23.04,0.183115338882283,0.06,0.32,0.06,0.0,0.0,0.6,0.0,2.0,0,0,1,1 +6277,ISTP,22.0,0.18387958786049582,0.38,0.4,0.04,0.02,0.02,0.6,0.0,2.62,1,1,1,1 +6278,INTP,0.84,0.1409878832456255,0.98,0.54,0.06,0.88,0.0,0.08,0.0,0.22,1,0,1,1 +6279,INFJ,20.58,0.17579131630279352,0.02,0.1,0.16,0.0,0.0,0.5,0.0,1.88,1,0,0,0 +6280,ISFP,12.98,0.18650446635335527,0.14,0.32,0.42,0.06,0.02,0.38,0.02,1.0,1,1,0,1 +6281,ISFJ,25.66,0.18882289108821093,0.0,0.18,0.02,0.0,0.0,0.74,0.0,2.42,1,1,0,0 +6282,ENTP,26.0,0.18092667051410063,0.0,0.12,0.0,0.0,0.0,0.72,0.02,1.92,0,0,1,1 +6283,INTJ,24.78,0.18861174775393472,0.0,0.22,0.1,0.0,0.04,0.5,0.0,3.52,1,0,1,0 +6284,ENTP,31.36,0.1811922070225538,0.0,0.08,0.04,0.0,0.0,0.78,0.0,2.28,0,0,1,1 +6285,INTP,20.88,0.18700955648305853,0.06,0.36,0.34,0.02,0.0,0.6,0.0,2.36,1,0,1,1 +6286,INFP,13.62,0.19581667961302165,0.02,0.14,0.22,0.0,0.0,0.46,0.0,1.6,1,0,0,1 +6287,INTJ,28.26,0.18142430280391358,0.0,0.22,0.12,0.0,0.02,0.58,0.0,3.22,1,0,1,0 +6288,ISTP,27.36,0.18478465863595023,0.0,0.08,0.02,0.0,0.0,0.68,0.0,2.28,1,1,1,1 +6289,INFJ,25.74,0.18484772767287472,0.02,0.28,0.02,0.0,0.0,0.62,0.0,3.08,1,0,0,0 +6290,INTP,33.22,0.1813778494466578,0.02,0.28,0.12,0.0,0.02,1.0,0.0,3.5,1,0,1,1 +6291,INFJ,24.46,0.18064163093397442,0.02,0.08,0.04,0.0,0.04,0.86,0.0,2.2,1,0,0,0 +6292,INTP,28.56,0.180240836193861,0.12,0.22,0.0,0.06,0.14,0.64,0.02,3.14,1,0,1,1 +6293,INFP,32.56,0.19165885118535173,0.0,0.32,0.76,0.0,0.0,1.32,0.0,3.3,1,0,0,1 +6294,INTJ,31.16,0.18375782353276768,0.0,0.18,0.16,0.0,0.0,0.88,0.0,3.18,1,0,1,0 +6295,INFJ,32.42,0.17771522504630594,0.0,0.26,0.22,0.0,0.0,0.92,0.02,2.24,1,0,0,0 +6296,INTJ,30.04,0.19145988475575149,0.02,0.12,0.04,0.0,0.02,0.74,0.0,3.72,1,0,1,0 +6297,ENTP,31.14,0.17882187638473424,0.04,0.1,0.06,0.02,0.0,0.78,0.0,4.06,0,0,1,1 +6298,INFP,27.82,0.18538303512907886,0.0,0.08,0.04,0.0,0.0,0.52,0.0,3.86,1,0,0,1 +6299,INTJ,31.58,0.1878355307005115,0.06,0.36,0.12,0.04,0.02,0.7,0.0,2.72,1,0,1,0 +6300,INTP,27.42,0.18317735046308128,0.04,0.16,0.06,0.0,0.02,0.54,0.0,3.2,1,0,1,1 +6301,INFP,21.06,0.1865595743369326,0.24,0.28,0.02,0.06,0.0,0.34,0.0,2.5,1,0,0,1 +6302,INTJ,18.98,0.1844894297506688,0.08,0.0,0.0,0.06,0.0,0.58,0.0,1.74,1,0,1,0 +6303,INFP,25.54,0.19449385799964308,0.06,0.08,0.36,0.02,0.02,0.92,0.02,3.62,1,0,0,1 +6304,INFP,25.96,0.18802430654976449,0.06,0.38,0.22,0.0,0.0,0.7,0.0,3.0,1,0,0,1 +6305,INTP,30.4,0.1872388885284133,0.0,0.3,0.02,0.0,0.0,0.74,0.04,3.46,1,0,1,1 +6306,INFJ,13.06,0.17082347748288335,0.0,0.0,0.02,0.0,0.02,0.4,0.0,1.26,1,0,0,0 +6307,INTJ,20.46,0.17448122233396793,0.04,0.28,0.08,0.0,0.0,0.38,0.0,1.1,1,0,1,0 +6308,INFP,34.3,0.18471422614427874,0.0,0.08,0.1,0.0,0.02,1.4,0.0,3.82,1,0,0,1 +6309,INFP,22.76,0.17952320928852947,0.1,0.18,0.04,0.0,0.0,0.46,0.0,2.8,1,0,0,1 +6310,ISTP,27.82,0.18746787045419974,0.04,0.28,0.02,0.0,0.08,0.7,0.0,3.34,1,1,1,1 +6311,INFP,25.6,0.185106140309881,0.04,0.18,0.08,0.02,0.02,0.56,0.02,2.66,1,0,0,1 +6312,ISTJ,28.96,0.1866248647917793,0.04,0.28,0.26,0.02,0.0,1.38,0.0,2.92,1,1,1,0 +6313,INTJ,16.98,0.18627956405187715,0.0,0.08,0.04,0.0,0.0,0.44,0.0,1.86,1,0,1,0 +6314,ESFJ,24.8,0.1781630003344708,0.08,0.12,0.06,0.02,0.0,0.56,0.0,3.12,0,1,0,0 +6315,INTP,11.62,0.1770954506451171,0.44,0.16,0.02,0.16,0.0,0.24,0.0,1.14,1,0,1,1 +6316,INFJ,20.9,0.17931563543559345,0.32,0.38,0.0,0.02,0.04,0.42,0.0,2.08,1,0,0,0 +6317,ISTP,27.04,0.17141272433386334,0.06,0.12,0.02,0.0,0.06,0.76,0.0,2.64,1,1,1,1 +6318,ENTJ,22.74,0.18289419369819593,0.02,0.24,0.34,0.02,0.0,0.48,0.0,2.12,0,0,1,0 +6319,INFP,32.28,0.18516375171467767,0.0,0.12,0.16,0.0,0.0,0.88,0.0,3.64,1,0,0,1 +6320,ENFP,25.94,0.1818662746728177,0.16,0.08,0.1,0.0,0.0,0.74,0.0,2.02,0,0,0,1 +6321,INTJ,27.66,0.18593366374679188,0.02,0.42,0.14,0.02,0.0,0.64,0.0,3.42,1,0,1,0 +6322,INTP,16.14,0.18293823293620415,0.64,0.58,0.1,0.08,0.04,0.3,0.0,2.02,1,0,1,1 +6323,INTJ,26.46,0.18379894559977153,0.0,0.14,0.2,0.0,0.0,0.6,0.0,2.28,1,0,1,0 +6324,INTJ,33.92,0.1792885385730925,0.0,0.08,0.22,0.0,0.08,1.42,0.0,2.38,1,0,1,0 +6325,INFP,20.42,0.18648198300412608,0.08,0.14,0.22,0.02,0.0,0.44,0.02,2.48,1,0,0,1 +6326,INFJ,28.84,0.18989872798822427,0.02,0.28,0.72,0.0,0.0,1.14,0.0,2.84,1,0,0,0 +6327,INTJ,25.9,0.1756416775019892,0.04,0.42,0.04,0.0,0.0,0.7,0.0,2.72,1,0,1,0 +6328,ESTP,24.8,0.18359702906044242,0.02,0.12,0.04,0.02,0.0,0.54,0.0,2.76,0,1,1,1 +6329,INFJ,13.08,0.19217831417123774,0.3,0.38,0.24,0.02,0.04,0.32,0.0,1.52,1,0,0,0 +6330,INTJ,34.86,0.18103012465712845,0.0,0.24,0.08,0.0,0.0,1.04,0.0,3.36,1,0,1,0 +6331,INTP,23.86,0.18117542427290678,0.08,0.16,0.06,0.02,0.0,0.44,0.0,2.64,1,0,1,1 +6332,INTP,29.56,0.1813219540161821,0.02,0.16,0.0,0.0,0.02,0.78,0.0,3.66,1,0,1,1 +6333,ISFP,19.52,0.19279824822676733,0.0,0.28,0.1,0.0,0.04,0.56,0.0,2.62,1,1,0,1 +6334,INTP,26.7,0.1849856913843949,0.0,0.04,0.02,0.0,0.0,0.52,0.0,3.18,1,0,1,1 +6335,ISFP,7.7,0.1898657146334537,0.06,0.02,0.0,0.0,0.0,0.04,0.02,0.72,1,1,0,1 +6336,INFP,14.34,0.16680881895418168,0.28,0.06,0.0,0.18,0.02,0.28,0.0,0.98,1,0,0,1 +6337,INFP,22.98,0.17696876690324378,0.02,0.18,0.02,0.0,0.0,0.48,0.0,1.92,1,0,0,1 +6338,INFJ,35.98,0.18198148251084842,0.0,0.38,0.0,0.0,0.0,1.18,0.0,4.28,1,0,0,0 +6339,INTP,27.5,0.1797239055569711,0.04,0.2,0.14,0.0,0.02,0.52,0.0,2.8,1,0,1,1 +6340,INTP,26.74,0.18869510497511485,0.0,0.34,0.32,0.0,0.0,0.9,0.0,2.38,1,0,1,1 +6341,INTJ,23.74,0.1808769263463159,0.08,0.14,0.06,0.0,0.0,0.86,0.0,1.86,1,0,1,0 +6342,ENTP,22.06,0.19038023540042998,0.0,0.32,0.26,0.0,0.0,0.42,0.0,2.04,0,0,1,1 +6343,ENTP,25.6,0.18787385090255201,0.0,0.18,0.04,0.0,0.0,0.5,0.0,3.76,0,0,1,1 +6344,ISTJ,18.54,0.19963535957244669,0.02,0.18,0.78,0.02,0.0,0.44,0.0,2.18,1,1,1,0 +6345,ISTP,26.34,0.19439906765048146,0.0,0.18,0.02,0.0,0.02,0.54,0.0,3.34,1,1,1,1 +6346,INFP,27.18,0.18371979741075822,0.1,0.32,0.06,0.0,0.04,0.8,0.0,3.38,1,0,0,1 +6347,ESFJ,14.18,0.18437230729550846,0.12,0.16,0.02,0.0,0.02,0.22,0.0,1.78,0,1,0,0 +6348,INFP,31.34,0.18778274109873844,0.0,0.46,0.1,0.0,0.0,0.86,0.0,3.42,1,0,0,1 +6349,ISTP,23.0,0.18611597168260616,0.06,0.32,0.06,0.0,0.0,0.62,0.0,3.82,1,1,1,1 +6350,ENFP,23.34,0.17405700966136214,0.0,0.3,0.04,0.0,0.0,0.46,0.0,1.98,0,0,0,1 +6351,INTP,28.0,0.1851273181696886,0.08,0.06,0.02,0.08,0.0,0.8,0.14,3.54,1,0,1,1 +6352,INFJ,21.32,0.18931225232909044,0.04,0.1,0.22,0.0,0.0,0.66,0.0,1.9,1,0,0,0 +6353,INTP,22.64,0.18188240060902502,0.16,0.2,0.0,0.06,0.02,0.5,0.0,1.86,1,0,1,1 +6354,INTJ,25.82,0.1835426972634842,0.0,0.16,0.08,0.0,0.02,0.62,0.0,2.32,1,0,1,0 +6355,ENTJ,29.06,0.18158814302242796,0.0,0.24,0.06,0.0,0.0,0.74,0.02,2.84,0,0,1,0 +6356,ENFP,25.02,0.19645463830942755,0.0,0.1,0.7,0.0,0.0,0.82,0.0,2.78,0,0,0,1 +6357,INFP,32.94,0.18313278591682414,0.0,0.2,0.2,0.0,0.0,0.88,0.0,4.56,1,0,0,1 +6358,INFJ,19.18,0.18461821298401612,0.14,0.28,0.02,0.02,0.02,0.36,0.0,2.06,1,0,0,0 +6359,ISTP,19.06,0.18696516846843267,0.0,0.1,0.02,0.0,0.0,0.52,0.0,1.86,1,1,1,1 +6360,INFP,22.72,0.18430901772473696,0.0,0.38,0.02,0.0,0.0,0.52,0.0,2.84,1,0,0,1 +6361,ENTP,21.72,0.18330391530321097,0.02,0.2,0.16,0.0,0.02,0.36,0.0,2.04,0,0,1,1 +6362,INTP,27.08,0.18542445045495143,0.02,0.1,0.1,0.0,0.02,0.88,0.0,3.68,1,0,1,1 +6363,INTJ,19.86,0.18145584815770363,0.0,0.38,0.1,0.0,0.02,0.42,0.0,1.78,1,0,1,0 +6364,ESFJ,19.52,0.17968312719857038,0.02,0.16,0.22,0.0,0.0,0.44,0.0,2.04,0,1,0,0 +6365,INFJ,26.76,0.18790046030060686,0.06,0.18,0.12,0.02,0.06,0.62,0.0,3.48,1,0,0,0 +6366,INFP,35.72,0.18243553569607104,0.02,0.08,0.26,0.0,0.0,0.78,0.0,4.68,1,0,0,1 +6367,ENFP,24.52,0.18984071081767745,0.02,0.24,0.22,0.0,0.1,0.5,0.0,2.7,0,0,0,1 +6368,INTJ,20.06,0.18182241471747218,0.12,0.16,0.04,0.02,0.0,0.46,0.0,2.04,1,0,1,0 +6369,ENTP,20.6,0.1849913176019724,0.08,0.12,0.08,0.02,0.02,0.42,0.0,1.66,0,0,1,1 +6370,INFJ,26.1,0.19475437855922192,0.0,0.44,1.08,0.0,0.04,0.84,0.0,3.16,1,0,0,0 +6371,INTJ,29.86,0.18559586507377462,0.0,0.14,0.06,0.0,0.0,0.72,0.0,2.78,1,0,1,0 +6372,ESFP,11.92,0.2037731978030548,0.0,0.2,0.36,0.0,0.0,0.16,0.0,1.22,0,1,0,1 +6373,INFP,32.58,0.17657629116498413,0.0,0.0,0.0,0.0,0.0,0.86,0.0,4.38,1,0,0,1 +6374,INTP,29.78,0.17862366161300877,0.04,0.2,0.0,0.0,0.02,0.84,0.0,2.58,1,0,1,1 +6375,INFP,3.42,0.18273937819011596,0.86,0.92,0.0,0.0,0.0,0.06,0.0,0.5,1,0,0,1 +6376,ISTP,16.7,0.18902153860035192,0.58,0.38,0.02,0.02,0.0,0.88,0.0,2.88,1,1,1,1 +6377,ENFP,24.26,0.18659427661919226,0.06,0.62,0.44,0.0,0.0,0.56,0.0,2.06,0,0,0,1 +6378,ENFJ,34.68,0.1809174155274637,0.0,0.26,0.14,0.0,0.02,0.84,0.0,3.6,0,0,0,0 +6379,INFJ,21.06,0.180532693791307,0.0,0.16,0.08,0.0,0.0,0.42,0.0,1.1,1,0,0,0 +6380,INFJ,13.82,0.19241947097087417,0.32,0.38,0.12,0.04,0.08,0.68,0.0,1.36,1,0,0,0 +6381,INTP,20.04,0.18597710431159548,0.06,0.2,0.02,0.0,0.02,0.38,0.0,2.4,1,0,1,1 +6382,ENFP,24.62,0.18071038235024078,0.0,0.26,0.36,0.0,0.02,0.6,0.0,2.7,0,0,0,1 +6383,INTP,17.8,0.1849092295658962,0.02,0.14,0.02,0.0,0.0,0.38,0.0,2.28,1,0,1,1 +6384,INFJ,23.74,0.1832137227784333,0.0,0.2,0.02,0.0,0.02,0.54,0.0,1.68,1,0,0,0 +6385,ISTP,17.12,0.18992147430260756,0.02,0.26,0.18,0.0,0.0,0.3,0.0,1.82,1,1,1,1 +6386,INFP,15.42,0.1853311676360465,0.3,0.3,0.12,0.06,0.0,0.48,0.0,2.0,1,0,0,1 +6387,INFP,29.72,0.1877069290290956,0.12,0.38,0.6,0.02,0.0,0.62,0.0,2.8,1,0,0,1 +6388,INFJ,20.98,0.1822894005093143,0.06,0.22,0.0,0.0,0.0,0.44,0.02,0.98,1,0,0,0 +6389,INTP,25.34,0.18603624854072168,0.08,0.34,0.22,0.04,0.0,0.76,0.0,2.92,1,0,1,1 +6390,ISFP,20.08,0.1884913920835702,0.0,0.24,0.16,0.0,0.0,1.06,0.0,2.38,1,1,0,1 +6391,INTJ,24.44,0.19404678452199925,0.0,0.4,0.0,0.0,0.02,0.54,0.0,2.56,1,0,1,0 +6392,ISTP,10.02,0.1914312157177791,0.0,0.12,0.0,0.0,0.0,0.24,0.0,1.28,1,1,1,1 +6393,INFP,34.26,0.1877786177076202,0.0,0.08,0.04,0.0,0.02,0.94,0.0,4.84,1,0,0,1 +6394,INTP,29.18,0.17716277717685236,0.08,0.14,0.02,0.0,0.04,0.72,0.0,3.5,1,0,1,1 +6395,INFJ,16.54,0.19586861276422898,0.22,0.34,0.16,0.04,0.0,0.6,0.04,1.92,1,0,0,0 +6396,ISFJ,23.18,0.18016372610041814,0.08,0.14,0.12,0.0,0.0,0.6,0.0,1.36,1,1,0,0 +6397,INTJ,21.42,0.18798293444328823,0.0,0.5,0.04,0.0,0.02,0.38,0.02,2.8,1,0,1,0 +6398,INFJ,9.98,0.19546560192455478,0.14,0.16,0.1,0.0,0.0,0.42,0.0,0.82,1,0,0,0 +6399,INTP,28.14,0.1829791501161265,0.0,0.22,0.14,0.0,0.0,0.88,0.06,2.18,1,0,1,1 +6400,INFJ,22.56,0.1895828706741424,0.0,0.3,0.1,0.0,0.0,0.52,0.0,3.46,1,0,0,0 +6401,INTP,12.0,0.18200913169433952,0.04,0.08,0.0,0.0,0.0,0.28,0.0,1.36,1,0,1,1 +6402,INTP,32.68,0.18047092594537648,0.02,0.26,0.0,0.0,0.02,0.94,0.0,3.96,1,0,1,1 +6403,INTJ,28.26,0.18084757929927364,0.02,0.08,0.04,0.02,0.02,0.68,0.0,3.04,1,0,1,0 +6404,INTP,24.68,0.1759572920288393,0.02,0.22,0.06,0.02,0.0,0.46,0.0,2.34,1,0,1,1 +6405,INFP,25.8,0.1943787972031171,0.0,0.34,0.3,0.0,0.0,0.86,0.0,3.16,1,0,0,1 +6406,INFJ,28.24,0.18926777183101032,0.0,0.18,0.34,0.0,0.02,0.86,0.0,3.9,1,0,0,0 +6407,ISFJ,5.38,0.20033757498822707,0.0,0.04,0.0,0.0,0.0,0.06,0.0,0.88,1,1,0,0 +6408,INFP,23.78,0.1832658016053283,0.0,0.26,0.0,0.0,0.02,0.4,0.0,2.78,1,0,0,1 +6409,ISFP,25.08,0.1863918508909619,0.02,0.14,0.14,0.0,0.0,0.8,0.0,3.16,1,1,0,1 +6410,ISFP,23.42,0.18316430134652012,0.06,0.16,0.12,0.02,0.0,0.46,0.0,2.02,1,1,0,1 +6411,INTJ,27.96,0.1744339451554893,0.02,0.2,0.12,0.0,0.0,0.8,0.0,2.4,1,0,1,0 +6412,INFJ,30.1,0.19005438889020554,0.02,0.28,0.14,0.0,0.0,0.86,0.0,3.86,1,0,0,0 +6413,ISTJ,14.86,0.17402282504016298,0.0,0.06,0.0,0.0,0.0,0.42,0.0,1.08,1,1,1,0 +6414,ENFP,18.98,0.1838844884576734,0.04,0.14,0.46,0.0,0.08,0.44,0.0,0.62,0,0,0,1 +6415,INTJ,29.9,0.18295114870343376,0.0,0.14,0.02,0.0,0.04,0.8,0.0,3.14,1,0,1,0 +6416,ENTP,24.02,0.18720947162910453,0.14,0.26,0.24,0.0,0.0,0.46,0.0,0.7,0,0,1,1 +6417,INFP,16.18,0.18811829793046925,0.08,0.3,0.18,0.0,0.0,0.14,0.0,1.76,1,0,0,1 +6418,INTJ,23.38,0.18252851029465042,0.04,0.44,0.08,0.0,0.02,0.72,0.0,1.42,1,0,1,0 +6419,ISTJ,15.58,0.1896602612741496,0.0,0.12,0.14,0.0,0.0,0.42,0.0,2.04,1,1,1,0 +6420,INTJ,30.24,0.18018375305704276,0.0,0.4,0.06,0.0,0.0,0.72,0.0,2.8,1,0,1,0 +6421,ENTP,27.68,0.1860754541186635,0.02,0.18,0.06,0.0,0.0,0.82,0.0,3.24,0,0,1,1 +6422,INFP,27.84,0.18054994181229947,0.08,0.16,0.14,0.0,0.0,0.94,0.0,2.46,1,0,0,1 +6423,ISTJ,23.5,0.1835789856786424,0.22,0.26,0.08,0.08,0.02,0.7,0.0,2.0,1,1,1,0 +6424,INFP,28.78,0.19082784151870094,0.02,0.14,0.62,0.02,0.0,0.54,0.0,4.02,1,0,0,1 +6425,ENTJ,33.36,0.18281693516168432,0.0,0.26,0.0,0.0,0.0,0.92,0.0,3.46,0,0,1,0 +6426,INFJ,27.74,0.18487919771186948,0.02,0.16,0.58,0.0,0.02,0.68,0.0,2.26,1,0,0,0 +6427,INFP,8.86,0.18561909002935467,0.02,0.14,0.0,0.0,0.0,0.04,0.0,0.58,1,0,0,1 +6428,INFP,30.28,0.18476839640765796,0.02,0.4,0.2,0.0,0.0,0.84,0.0,2.72,1,0,0,1 +6429,INTP,16.4,0.18526315069878688,0.0,0.16,0.1,0.0,0.0,0.12,0.0,2.04,1,0,1,1 +6430,INFP,25.04,0.19132170826742798,0.0,0.16,0.1,0.0,0.0,0.6,0.0,2.48,1,0,0,1 +6431,ISTP,31.24,0.1883775926661741,0.0,0.12,0.0,0.0,0.0,0.66,0.0,4.58,1,1,1,1 +6432,ENTP,25.66,0.19248810801078256,0.02,0.26,0.04,0.0,0.0,0.8,0.0,2.52,0,0,1,1 +6433,INTP,16.16,0.1769463824034657,0.3,0.18,0.0,0.22,0.02,0.28,0.0,2.12,1,0,1,1 +6434,INTJ,18.68,0.18658922660723706,0.04,0.24,0.32,0.0,0.0,0.3,0.02,2.34,1,0,1,0 +6435,INFP,34.28,0.1786156587681628,0.0,0.12,0.0,0.0,0.0,0.98,0.0,1.9,1,0,0,1 +6436,INTJ,20.86,0.18998829323137686,0.08,0.28,0.18,0.04,0.0,0.64,0.0,2.34,1,0,1,0 +6437,ISTJ,29.64,0.19330268973491035,0.0,0.26,0.26,0.0,0.0,1.24,0.02,3.34,1,1,1,0 +6438,INFP,17.42,0.1866435196911677,0.16,0.02,0.28,0.0,0.0,0.52,0.06,2.62,1,0,0,1 +6439,INTJ,32.62,0.18757126973592309,0.0,0.36,0.16,0.0,0.0,0.82,0.0,2.76,1,0,1,0 +6440,INTP,20.48,0.18811491837117655,0.36,0.56,0.0,0.0,0.0,1.46,0.0,1.26,1,0,1,1 +6441,ESFJ,15.18,0.19462711259718526,0.0,0.1,0.36,0.0,0.0,0.58,0.0,1.78,0,1,0,0 +6442,INTJ,7.52,0.18882739580299135,0.02,0.04,0.04,0.0,0.0,0.1,0.0,1.06,1,0,1,0 +6443,INFJ,26.08,0.1832136830519497,0.0,0.12,0.46,0.0,0.02,0.74,0.0,2.64,1,0,0,0 +6444,ENFJ,31.8,0.1886315776099293,0.0,0.2,0.28,0.0,0.04,1.02,0.0,1.78,0,0,0,0 +6445,ENFP,24.88,0.1948988886298938,0.0,0.14,0.62,0.0,0.0,0.5,0.0,2.4,0,0,0,1 +6446,ENFP,31.64,0.18133240529451342,0.0,0.46,0.06,0.0,0.04,1.02,0.0,2.62,0,0,0,1 +6447,INFP,17.0,0.18164929284388492,0.32,0.38,0.02,0.0,0.04,0.36,0.0,1.96,1,0,0,1 +6448,INFJ,30.98,0.18709926825785406,0.02,0.6,0.32,0.0,0.0,0.8,0.0,3.4,1,0,0,0 +6449,ENFJ,29.86,0.18590884361569585,0.1,0.3,0.22,0.0,0.0,0.72,0.0,3.62,0,0,0,0 +6450,ENTP,27.64,0.18078443472678846,0.06,0.34,0.02,0.02,0.0,0.62,0.0,2.76,0,0,1,1 +6451,ISTJ,15.4,0.21087252476472604,0.04,0.66,1.1,0.04,0.0,1.58,0.0,1.24,1,1,1,0 +6452,INFP,20.94,0.18578270996963922,0.0,0.16,0.18,0.0,0.02,0.34,0.0,2.58,1,0,0,1 +6453,INTJ,31.88,0.18017695481493345,0.04,0.1,0.08,0.0,0.0,0.68,0.02,3.46,1,0,1,0 +6454,INFP,27.4,0.19625924370451955,0.02,0.38,0.12,0.0,0.0,0.7,0.0,3.92,1,0,0,1 +6455,ISTP,28.16,0.18578125986310665,0.04,0.24,0.04,0.0,0.0,0.76,0.0,3.94,1,1,1,1 +6456,INTP,25.72,0.18943683087672172,0.0,0.16,0.14,0.0,0.0,0.6,0.0,3.82,1,0,1,1 +6457,INTJ,29.96,0.1854553166614636,0.08,0.22,0.02,0.0,0.0,0.82,0.0,3.66,1,0,1,0 +6458,INFP,33.7,0.1845811673586738,0.0,0.3,0.1,0.0,0.02,0.94,0.0,3.72,1,0,0,1 +6459,ENTP,18.96,0.17970117108193698,0.08,0.2,0.04,0.02,0.02,0.22,0.0,2.34,0,0,1,1 +6460,ENFP,29.82,0.1817830446914541,0.0,0.62,0.0,0.0,0.0,1.12,0.0,2.54,0,0,0,1 +6461,INTJ,25.72,0.18611070995343038,0.04,0.14,0.18,0.02,0.0,1.04,0.02,2.04,1,0,1,0 +6462,ISFP,20.62,0.182609125938138,0.14,0.34,0.08,0.02,0.04,0.56,0.0,3.0,1,1,0,1 +6463,INFP,29.78,0.1836379604322555,0.08,0.22,0.44,0.0,0.0,0.82,0.0,3.42,1,0,0,1 +6464,INTJ,18.7,0.17629922138464588,0.02,0.16,0.0,0.0,0.0,0.36,0.0,2.32,1,0,1,0 +6465,ENFP,18.64,0.18518605649734113,0.0,0.16,0.16,0.0,0.0,0.52,0.0,1.86,0,0,0,1 +6466,INFP,24.02,0.1788245397983467,0.04,0.14,0.3,0.0,0.0,0.5,0.0,2.92,1,0,0,1 +6467,INTP,29.48,0.18412202637554917,0.0,0.12,0.14,0.0,0.0,0.76,0.08,2.82,1,0,1,1 +6468,ENTP,32.94,0.18709800923148467,0.0,0.18,0.2,0.0,0.0,1.7,0.0,3.64,0,0,1,1 +6469,INFJ,24.7,0.18031617237914105,0.0,0.16,0.0,0.0,0.0,0.52,0.0,3.0,1,0,0,0 +6470,ENFP,9.04,0.1842932668523763,0.44,0.54,0.02,0.04,0.0,0.1,0.0,0.96,0,0,0,1 +6471,ENFP,10.8,0.18946468609222955,0.4,0.48,0.24,0.0,0.0,0.76,0.0,0.92,0,0,0,1 +6472,INFP,31.56,0.1942135997317914,0.0,0.16,0.12,0.0,0.0,1.1,0.0,3.34,1,0,0,1 +6473,ENFJ,28.18,0.18015493677583538,0.0,0.32,0.08,0.0,0.0,1.02,0.0,2.74,0,0,0,0 +6474,INTJ,26.98,0.17485519535679853,0.0,0.22,0.0,0.0,0.0,0.8,0.0,1.9,1,0,1,0 +6475,INFP,25.92,0.17918803214107848,0.04,0.16,0.38,0.02,0.0,0.76,0.0,2.2,1,0,0,1 +6476,INTP,29.42,0.17679736411046965,0.0,0.08,0.0,0.0,0.0,0.78,0.0,2.68,1,0,1,1 +6477,INTP,33.6,0.18077910078818762,0.0,0.36,0.04,0.0,0.0,0.9,0.0,4.38,1,0,1,1 +6478,INFP,30.28,0.1720123595744229,0.02,0.26,0.0,0.0,0.02,0.72,0.0,3.02,1,0,0,1 +6479,INTJ,32.32,0.18166781997776396,0.02,0.28,0.0,0.0,0.0,1.0,0.0,2.54,1,0,1,0 +6480,INFJ,17.22,0.18087319625717463,0.0,0.12,0.02,0.0,0.0,0.44,0.0,0.9,1,0,0,0 +6481,ISFP,13.82,0.18673870460424258,0.12,0.06,0.0,0.0,0.02,0.2,0.0,1.52,1,1,0,1 +6482,ISTP,14.78,0.1884580499841471,0.08,0.24,0.06,0.0,0.04,0.38,0.0,2.12,1,1,1,1 +6483,ENTP,22.66,0.18686931826626388,0.0,0.34,0.28,0.0,0.0,0.28,0.0,3.08,0,0,1,1 +6484,INTP,28.76,0.18960376356531092,0.02,0.16,0.18,0.0,0.06,0.76,0.0,4.02,1,0,1,1 +6485,INFP,17.36,0.1817720875215224,0.22,0.28,0.0,0.0,0.04,0.44,0.0,2.38,1,0,0,1 +6486,INFP,22.04,0.18757864107431477,0.0,0.08,0.16,0.0,0.02,0.4,0.0,1.88,1,0,0,1 +6487,INTJ,13.56,0.17254237819227197,0.0,0.18,0.02,0.0,0.0,0.16,0.0,1.14,1,0,1,0 +6488,INTJ,17.38,0.18335851878505152,0.06,0.28,0.02,0.0,0.0,0.22,0.0,2.16,1,0,1,0 +6489,INTJ,27.56,0.18893535883044427,0.14,0.32,0.28,0.06,0.0,0.78,0.0,3.08,1,0,1,0 +6490,ISTP,16.56,0.18136317789453932,0.38,0.2,0.02,0.14,0.02,0.7,0.0,1.58,1,1,1,1 +6491,INFJ,30.66,0.18280213365435005,0.0,0.34,0.26,0.0,0.0,0.82,0.0,3.46,1,0,0,0 +6492,INTJ,19.54,0.19930249185860086,0.04,0.32,0.76,0.04,0.0,1.08,0.0,2.42,1,0,1,0 +6493,ENTJ,22.82,0.1875,0.04,0.62,0.34,0.02,0.0,0.44,0.0,1.84,0,0,1,0 +6494,INFP,30.56,0.18901186149479735,0.0,0.08,0.52,0.0,0.02,0.74,0.0,3.68,1,0,0,1 +6495,ENFP,12.54,0.19102454323709625,0.0,0.02,0.16,0.0,0.0,0.2,0.0,1.44,0,0,0,1 +6496,INFP,22.92,0.17534127807039393,0.38,0.26,0.28,0.2,0.0,0.82,0.0,2.62,1,0,0,1 +6497,ENTP,17.94,0.1861938701855542,0.0,0.08,0.0,0.0,0.0,0.2,0.0,1.32,0,0,1,1 +6498,INFP,31.0,0.18849667109975915,0.06,0.18,0.06,0.0,0.08,1.0,0.02,3.48,1,0,0,1 +6499,ISTP,21.7,0.18737869968888796,0.06,0.2,0.02,0.0,0.0,0.54,0.0,2.04,1,1,1,1 +6500,INFJ,28.36,0.1806477627628292,0.0,0.12,0.1,0.0,0.04,0.52,0.0,3.54,1,0,0,0 +6501,INFP,22.94,0.19099202529946202,0.0,0.08,0.26,0.0,0.02,1.16,0.0,1.88,1,0,0,1 +6502,INTJ,20.58,0.18848829295229047,0.02,0.02,0.28,0.0,0.04,0.48,0.0,2.48,1,0,1,0 +6503,INFP,12.56,0.18775996505626588,0.22,0.16,0.42,0.08,0.06,0.3,0.0,1.42,1,0,0,1 +6504,INFP,21.58,0.18171518195727773,0.0,0.12,0.06,0.0,0.0,0.42,0.0,2.04,1,0,0,1 +6505,INFP,31.48,0.19041508625540723,0.0,0.2,0.26,0.0,0.0,0.9,0.0,3.52,1,0,0,1 +6506,INFP,27.36,0.18472998504852517,0.0,0.16,0.0,0.0,0.08,0.56,0.0,2.96,1,0,0,1 +6507,INFP,25.64,0.18365854024887085,0.26,0.34,0.14,0.12,0.0,0.74,0.0,3.18,1,0,0,1 +6508,INFJ,30.34,0.18323377671279772,0.04,0.18,0.32,0.0,0.0,0.84,0.0,2.98,1,0,0,0 +6509,INFP,33.52,0.1876185270765299,0.0,0.08,0.4,0.0,0.0,1.02,0.0,3.98,1,0,0,1 +6510,INFJ,32.58,0.18934540985703702,0.0,0.18,0.1,0.0,0.0,0.82,0.0,4.4,1,0,0,0 +6511,ENFP,31.92,0.19020276224187047,0.04,0.2,0.38,0.0,0.0,0.84,0.0,4.12,0,0,0,1 +6512,INFJ,30.08,0.17973551703405205,0.0,0.1,0.08,0.0,0.0,0.7,0.0,3.18,1,0,0,0 +6513,INFP,25.46,0.1830699289422382,0.1,0.16,0.22,0.06,0.02,0.82,0.0,3.26,1,0,0,1 +6514,INFP,5.2,0.19608356967786073,0.0,0.0,0.14,0.0,0.04,0.1,0.0,0.76,1,0,0,1 +6515,INTP,21.92,0.17484430249073457,0.06,0.24,0.04,0.0,0.0,0.46,0.02,2.64,1,0,1,1 +6516,INFP,16.92,0.18369450720230793,0.02,0.18,0.32,0.0,0.0,0.36,0.0,2.4,1,0,0,1 +6517,ENFP,26.74,0.18331439577522837,0.3,0.26,0.02,0.14,0.02,1.1,0.02,2.0,0,0,0,1 +6518,ENTP,30.62,0.192892233395821,0.0,0.2,0.26,0.0,0.02,2.18,0.0,3.28,0,0,1,1 +6519,INFP,30.12,0.1812200878192119,0.0,0.34,0.36,0.0,0.0,0.82,0.0,3.6,1,0,0,1 +6520,INTP,32.28,0.1758601045081621,0.08,0.16,0.0,0.0,0.1,0.88,0.0,3.86,1,0,1,1 +6521,INTJ,21.46,0.18978620218309078,0.0,0.1,0.14,0.0,0.0,0.42,0.0,1.5,1,0,1,0 +6522,INFP,12.42,0.19203213219066984,0.48,0.36,0.1,0.18,0.0,0.14,0.0,1.96,1,0,0,1 +6523,INFJ,33.9,0.18332227452403405,0.0,0.22,0.04,0.0,0.0,1.02,0.0,4.14,1,0,0,0 +6524,INFP,19.7,0.19034717424224026,0.28,0.38,0.4,0.04,0.02,0.34,0.0,2.28,1,0,0,1 +6525,ISFJ,21.12,0.19383351921800762,0.0,0.28,0.7,0.0,0.0,0.72,0.0,2.54,1,1,0,0 +6526,INFJ,31.9,0.18094158366264412,0.02,0.14,0.06,0.0,0.02,0.86,0.0,4.16,1,0,0,0 +6527,ENFJ,31.54,0.19236233810041078,0.0,0.28,0.38,0.0,0.0,1.16,0.0,4.08,0,0,0,0 +6528,ESFP,24.32,0.1865592961451998,0.04,0.18,0.0,0.04,0.0,0.6,0.0,2.64,0,1,0,1 +6529,INTP,23.08,0.18675341181955443,0.06,0.2,0.0,0.0,0.0,0.64,0.0,2.38,1,0,1,1 +6530,INFP,30.96,0.18452371299931797,0.0,0.1,0.02,0.0,0.0,1.0,0.0,3.58,1,0,0,1 +6531,INTJ,24.7,0.18200032336458163,0.1,0.14,0.14,0.06,0.0,0.76,0.0,2.0,1,0,1,0 +6532,INFP,28.78,0.17794678477401404,0.06,0.16,0.0,0.0,0.04,0.84,0.0,2.96,1,0,0,1 +6533,ESFP,25.04,0.1849460275299252,0.02,0.22,0.22,0.0,0.0,0.64,0.0,2.66,0,1,0,1 +6534,ENFP,26.44,0.18580437274997422,0.02,0.36,0.46,0.0,0.02,0.58,0.0,2.96,0,0,0,1 +6535,INFJ,25.56,0.18518741155657575,0.02,0.34,0.68,0.0,0.0,0.48,0.0,3.02,1,0,0,0 +6536,INFJ,30.58,0.1845017022860026,0.04,0.06,0.38,0.02,0.0,0.7,0.0,2.82,1,0,0,0 +6537,ENFP,25.16,0.18267846367621196,0.1,0.4,0.0,0.0,0.08,0.4,0.0,1.7,0,0,0,1 +6538,ENFP,26.56,0.18479907471752036,0.0,0.2,0.04,0.0,0.0,0.62,0.0,2.48,0,0,0,1 +6539,ENTP,18.38,0.1831900948545125,0.04,0.2,0.16,0.0,0.0,0.28,0.0,1.56,0,0,1,1 +6540,INFJ,14.12,0.1958201697177313,0.44,0.48,0.22,0.0,0.0,0.72,0.0,2.02,1,0,0,0 +6541,INFP,27.66,0.18896119472463727,0.0,0.16,0.26,0.0,0.12,0.7,0.0,3.4,1,0,0,1 +6542,INFP,29.7,0.1801068513118523,0.0,0.12,0.1,0.0,0.02,0.78,0.0,3.44,1,0,0,1 +6543,INFP,31.66,0.19135140539861514,0.0,0.06,0.5,0.0,0.0,2.04,0.0,2.48,1,0,0,1 +6544,INTJ,30.06,0.18841364691225815,0.0,0.28,0.12,0.0,0.0,1.02,0.0,2.44,1,0,1,0 +6545,ISFJ,32.24,0.18160203051748333,0.04,0.12,0.04,0.0,0.0,0.78,0.0,3.34,1,1,0,0 +6546,ENTP,28.1,0.18526563836535953,0.14,0.28,0.0,0.04,0.04,0.78,0.0,3.18,0,0,1,1 +6547,ISTP,23.44,0.18871783786128615,0.06,0.14,0.16,0.0,0.06,0.46,0.0,2.3,1,1,1,1 +6548,INFP,27.16,0.1801006137408667,0.04,0.18,0.18,0.0,0.04,0.56,0.0,3.46,1,0,0,1 +6549,INFP,16.54,0.194471511356105,0.22,0.26,0.2,0.1,0.02,0.72,0.0,2.3,1,0,0,1 +6550,ISTP,21.58,0.17604243220738516,0.04,0.08,0.0,0.0,0.02,0.78,0.0,2.12,1,1,1,1 +6551,INFP,27.72,0.18583987929318818,0.02,0.1,0.18,0.0,0.02,0.7,0.0,2.46,1,0,0,1 +6552,INFP,19.7,0.1962778619106813,0.02,0.48,0.02,0.0,0.0,0.52,0.0,1.84,1,0,0,1 +6553,INFP,25.34,0.18443336619924727,0.32,0.42,0.06,0.0,0.04,0.8,0.0,3.02,1,0,0,1 +6554,ENTP,28.42,0.19832705584947785,0.0,0.22,0.38,0.0,0.04,1.5,0.0,3.48,0,0,1,1 +6555,INTP,22.8,0.18982754112725422,0.08,0.18,0.1,0.0,0.08,0.38,0.0,3.1,1,0,1,1 +6556,INFJ,34.82,0.18068898390484098,0.0,0.2,0.02,0.0,0.0,0.9,0.0,3.36,1,0,0,0 +6557,INFP,8.76,0.18098247629991382,0.66,0.38,0.12,0.36,0.0,0.24,0.0,1.06,1,0,0,1 +6558,INFJ,24.62,0.17818396792341584,0.12,0.24,0.02,0.0,0.02,0.66,0.0,2.18,1,0,0,0 +6559,ISFP,24.16,0.1824404047294908,0.18,0.26,0.0,0.02,0.02,0.46,0.0,3.4,1,1,0,1 +6560,INFP,22.76,0.19175559914135681,0.0,0.2,0.18,0.0,0.0,0.5,0.0,2.36,1,0,0,1 +6561,ISTJ,33.12,0.18574821808837777,0.0,0.1,0.04,0.0,0.0,0.8,0.0,3.96,1,1,1,0 +6562,INTJ,28.98,0.18331525794453207,0.06,0.22,0.14,0.0,0.0,0.76,0.0,3.28,1,0,1,0 +6563,INFP,25.02,0.18185970499938864,0.1,0.24,0.08,0.1,0.02,0.62,0.0,1.54,1,0,0,1 +6564,ISFJ,22.86,0.20123066945386364,0.02,0.12,1.48,0.0,0.0,1.3,0.0,3.08,1,1,0,0 +6565,ISTJ,24.66,0.18407985051166248,0.08,0.26,0.02,0.0,0.0,0.54,0.0,2.6,1,1,1,0 +6566,INTJ,28.3,0.18284220677043614,0.0,0.04,0.02,0.0,0.02,0.64,0.0,3.54,1,0,1,0 +6567,ENFP,26.3,0.186425905802282,0.0,0.2,0.22,0.0,0.0,0.8,0.0,2.94,0,0,0,1 +6568,INTP,14.98,0.19281314677484396,0.46,0.22,0.12,0.28,0.0,0.3,0.0,2.04,1,0,1,1 +6569,INTJ,20.26,0.18825503347606834,0.0,0.34,0.16,0.0,0.0,0.92,0.0,1.92,1,0,1,0 +6570,INFJ,25.04,0.1908455963616803,0.02,0.16,0.14,0.0,0.0,0.58,0.0,3.18,1,0,0,0 +6571,INTJ,19.28,0.18270085084458695,0.04,0.12,0.02,0.02,0.0,0.36,0.0,2.06,1,0,1,0 +6572,INTP,15.46,0.17801651557095072,0.3,0.2,0.0,0.0,0.04,0.2,0.0,1.48,1,0,1,1 +6573,INTP,29.98,0.17524465852887566,0.02,0.08,0.0,0.0,0.06,0.74,0.0,3.8,1,0,1,1 +6574,ISFP,31.88,0.18342697345493794,0.04,0.06,0.04,0.02,0.06,0.74,0.0,3.88,1,1,0,1 +6575,INTP,29.44,0.18134108525272744,0.02,0.08,0.04,0.02,0.02,0.88,0.0,3.16,1,0,1,1 +6576,INFJ,23.3,0.18705655753707578,0.1,0.24,0.0,0.0,0.0,1.66,0.02,2.76,1,0,0,0 +6577,INFJ,24.32,0.18532506665891704,0.12,0.18,0.08,0.06,0.0,0.9,0.0,2.86,1,0,0,0 +6578,INTP,20.16,0.187154612603783,0.06,0.2,0.16,0.04,0.0,0.5,0.0,2.52,1,0,1,1 +6579,INTP,29.94,0.17425979703093913,0.06,0.24,0.04,0.02,0.0,0.84,0.0,2.66,1,0,1,1 +6580,INFJ,30.46,0.1787221118468636,0.0,0.3,0.02,0.0,0.0,0.8,0.0,2.56,1,0,0,0 +6581,INTP,22.5,0.18279171026331137,0.0,0.16,0.0,0.0,0.06,0.54,0.0,2.28,1,0,1,1 +6582,INTP,19.0,0.18786212472400815,0.28,0.26,0.16,0.18,0.0,0.5,0.0,2.46,1,0,1,1 +6583,INTP,31.16,0.17910896710203011,0.06,0.1,0.14,0.04,0.0,0.76,0.0,3.38,1,0,1,1 +6584,ENFJ,35.28,0.1829559948979592,0.0,0.08,0.06,0.0,0.06,0.98,0.0,1.26,0,0,0,0 +6585,INFP,17.76,0.17502286710990822,0.26,0.32,0.02,0.0,0.0,0.34,0.0,2.38,1,0,0,1 +6586,ENFP,22.7,0.19174935805125004,0.0,0.32,0.04,0.0,0.0,1.5,0.0,2.52,0,0,0,1 +6587,INFP,26.68,0.18544158286698656,0.08,0.06,0.0,0.02,0.0,0.66,0.0,2.8,1,0,0,1 +6588,INFJ,31.28,0.1874728290406426,0.04,0.1,0.02,0.04,0.0,0.86,0.0,3.64,1,0,0,0 +6589,INFJ,25.66,0.18359117848106232,0.06,0.06,0.32,0.02,0.04,0.6,0.0,3.08,1,0,0,0 +6590,ISTP,20.74,0.1818237692506548,0.12,0.12,0.0,0.1,0.02,0.32,0.0,2.08,1,1,1,1 +6591,INFJ,28.54,0.18512532983792995,0.0,0.16,0.52,0.0,0.02,0.7,0.0,3.54,1,0,0,0 +6592,INFP,30.36,0.17821269027990755,0.0,0.28,0.12,0.0,0.0,0.76,0.0,2.62,1,0,0,1 +6593,INFJ,23.56,0.1766360444130842,0.0,0.24,0.12,0.0,0.0,0.46,0.0,2.02,1,0,0,0 +6594,INFJ,26.14,0.17950623814169778,0.06,0.22,0.1,0.0,0.0,0.7,0.0,2.36,1,0,0,0 +6595,INFP,22.64,0.19325876026011438,0.0,0.08,0.16,0.0,0.0,0.4,0.0,3.04,1,0,0,1 +6596,INFP,15.3,0.18423643197585501,0.28,0.22,0.06,0.02,0.0,0.28,0.0,1.84,1,0,0,1 +6597,ENFP,24.4,0.1853047766827973,0.02,0.18,0.12,0.0,0.0,0.52,0.0,2.34,0,0,0,1 +6598,ENFP,31.74,0.1896840363993623,0.0,0.34,0.52,0.0,0.06,0.72,0.0,3.72,0,0,0,1 +6599,INTP,26.92,0.19341445904707938,0.0,0.14,0.18,0.0,0.0,0.86,0.0,4.58,1,0,1,1 +6600,INTP,16.34,0.18601741950799522,0.18,0.26,0.04,0.06,0.0,0.5,0.0,1.4,1,0,1,1 +6601,ENTP,17.48,0.1830522878910679,0.08,0.12,0.36,0.0,0.0,0.36,0.0,2.0,0,0,1,1 +6602,INFP,27.34,0.18490336543733502,0.14,0.16,0.08,0.08,0.04,0.7,0.0,3.34,1,0,0,1 +6603,INTJ,25.04,0.1874671268907721,0.04,0.66,0.2,0.0,0.04,0.92,0.0,2.76,1,0,1,0 +6604,INFJ,30.1,0.18597953593467997,0.06,0.26,0.06,0.02,0.0,0.88,0.0,3.96,1,0,0,0 +6605,INFP,30.84,0.1818487943195177,0.0,0.12,0.32,0.0,0.04,0.76,0.0,3.76,1,0,0,1 +6606,ENTP,12.86,0.2100808897669686,0.02,0.36,0.68,0.0,0.0,1.7,0.0,0.98,0,0,1,1 +6607,INTP,16.9,0.19490015266168545,0.04,0.22,0.18,0.0,0.0,0.36,0.0,2.56,1,0,1,1 +6608,ISTP,18.96,0.18013482085405752,0.08,0.24,0.04,0.04,0.04,0.3,0.0,1.86,1,1,1,1 +6609,INTP,29.94,0.1831278973184029,0.02,0.18,0.14,0.0,0.0,0.72,0.0,3.06,1,0,1,1 +6610,ENTP,29.72,0.17781604876711646,0.0,0.3,0.08,0.0,0.0,0.8,0.0,3.2,0,0,1,1 +6611,ENTP,24.22,0.17963114874255381,0.06,0.14,0.1,0.04,0.02,0.66,0.0,2.54,0,0,1,1 +6612,INTJ,27.32,0.1808638210675804,0.0,0.12,0.04,0.0,0.0,1.02,0.0,2.48,1,0,1,0 +6613,INFP,18.22,0.1935637802667576,0.0,0.32,0.16,0.0,0.06,0.9,0.0,1.76,1,0,0,1 +6614,INFP,20.22,0.1852776784786517,0.18,0.22,0.04,0.0,0.0,0.42,0.02,2.68,1,0,0,1 +6615,INFP,30.4,0.18854403786970353,0.0,0.24,0.06,0.0,0.0,0.7,0.0,3.26,1,0,0,1 +6616,ISTJ,17.76,0.1694778424496498,0.3,0.08,0.0,0.28,0.02,0.6,0.0,1.76,1,1,1,0 +6617,ENTP,32.2,0.19004060706313772,0.0,0.36,0.02,0.0,0.02,0.82,0.0,3.96,0,0,1,1 +6618,INTP,18.82,0.18678762409884725,0.02,0.34,0.04,0.0,0.0,0.34,0.0,2.06,1,0,1,1 +6619,INFP,30.7,0.18662082758670748,0.0,0.34,0.34,0.0,0.0,1.04,0.0,3.62,1,0,0,1 +6620,ENFP,24.54,0.1858013231332585,0.04,0.2,0.26,0.0,0.04,0.72,0.0,2.6,0,0,0,1 +6621,ISTP,15.12,0.17865706797950112,0.02,0.14,0.0,0.0,0.0,0.3,0.0,0.96,1,1,1,1 +6622,INTP,32.64,0.180764148409111,0.0,0.06,0.02,0.0,0.06,0.84,0.0,3.64,1,0,1,1 +6623,INTP,28.14,0.18335587691583544,0.02,0.22,0.14,0.0,0.0,0.64,0.0,3.44,1,0,1,1 +6624,ISFJ,31.72,0.17814965042555353,0.0,0.1,0.2,0.0,0.02,0.94,0.0,4.1,1,1,0,0 +6625,INTP,30.24,0.1845483298336379,0.02,0.26,0.1,0.0,0.02,1.0,0.0,3.1,1,0,1,1 +6626,ISTP,29.24,0.18713698043408641,0.06,0.06,0.02,0.0,0.0,0.88,0.0,3.74,1,1,1,1 +6627,ISTP,11.08,0.19089168846294727,0.02,0.04,0.02,0.0,0.02,0.14,0.0,1.88,1,1,1,1 +6628,INFJ,30.14,0.18641325801753256,0.0,0.14,0.22,0.0,0.0,0.88,0.0,3.56,1,0,0,0 +6629,INTP,12.26,0.17264053483816627,0.4,0.62,0.02,0.0,0.0,0.2,0.0,1.18,1,0,1,1 +6630,INFP,28.0,0.18103439048199205,0.0,0.08,0.16,0.0,0.0,0.74,0.0,2.52,1,0,0,1 +6631,INTP,25.06,0.1830267012773351,0.2,0.24,0.0,0.02,0.12,0.54,0.04,1.74,1,0,1,1 +6632,INTJ,5.3,0.19682425356807493,0.06,0.16,0.0,0.06,0.0,0.14,0.0,0.54,1,0,1,0 +6633,ISTP,19.84,0.18373442760869868,0.2,0.28,0.08,0.0,0.02,0.64,0.0,2.4,1,1,1,1 +6634,INFP,25.38,0.1775411425328016,0.08,0.12,0.1,0.04,0.0,0.62,0.0,2.02,1,0,0,1 +6635,INTJ,28.54,0.17514256377054682,0.02,0.3,0.06,0.0,0.0,0.76,0.0,2.42,1,0,1,0 +6636,INTP,20.1,0.18585519645475285,0.06,0.3,0.04,0.02,0.02,0.4,0.0,1.86,1,0,1,1 +6637,INTJ,26.02,0.18069166014634164,0.0,0.16,0.02,0.0,0.02,0.74,0.0,2.6,1,0,1,0 +6638,ENFP,22.0,0.19157486703435936,0.16,0.2,0.22,0.12,0.0,0.56,0.0,2.2,0,0,0,1 +6639,ENTJ,33.3,0.18177114586428478,0.0,0.22,0.02,0.0,0.02,1.12,0.0,4.18,0,0,1,0 +6640,INTP,15.02,0.17932241339639807,0.0,0.04,1.5,0.0,0.0,0.3,0.0,1.5,1,0,1,1 +6641,INFJ,24.8,0.18615022363990608,0.04,0.08,0.0,0.0,0.0,0.48,0.0,3.5,1,0,0,0 +6642,ENFJ,31.84,0.18487497227500682,0.1,0.36,0.42,0.0,0.06,0.96,0.0,3.8,0,0,0,0 +6643,ENFP,28.18,0.1877899854318419,0.0,0.24,0.08,0.0,0.0,0.86,0.0,3.42,0,0,0,1 +6644,ENTP,28.82,0.18526986751614932,0.0,0.16,0.1,0.0,0.0,0.8,0.02,3.42,0,0,1,1 +6645,INFJ,20.74,0.1848484496053836,0.06,0.3,0.04,0.04,0.0,0.58,0.0,1.84,1,0,0,0 +6646,INTP,24.18,0.18673786859112612,0.0,0.46,0.02,0.0,0.02,0.54,0.0,2.46,1,0,1,1 +6647,INTJ,26.96,0.18010847497187993,0.02,0.14,0.16,0.0,0.0,0.72,0.0,3.22,1,0,1,0 +6648,ENTP,28.52,0.18577023367734005,0.08,0.02,0.08,0.0,0.06,1.82,0.0,2.98,0,0,1,1 +6649,ENFP,30.18,0.1870110043085451,0.0,0.06,1.98,0.0,0.04,0.62,0.0,3.82,0,0,0,1 +6650,INFP,29.6,0.18068476505147701,0.0,0.34,0.08,0.0,0.0,1.14,0.0,2.66,1,0,0,1 +6651,INFP,31.12,0.18401537452933414,0.04,0.06,0.18,0.0,0.02,0.88,0.02,3.92,1,0,0,1 +6652,INTP,16.24,0.18235678728019875,0.42,0.44,0.2,0.08,0.02,0.4,0.0,2.24,1,0,1,1 +6653,ENFP,29.5,0.19013650395890982,0.0,0.12,0.22,0.0,0.0,0.6,0.0,3.7,0,0,0,1 +6654,INTJ,29.06,0.18683846984703859,0.08,0.04,0.24,0.0,0.0,0.98,0.0,1.62,1,0,1,0 +6655,INFJ,30.36,0.1899570780772813,0.0,0.18,0.28,0.0,0.02,0.94,0.0,2.9,1,0,0,0 +6656,INFP,22.74,0.18189099074883058,0.1,0.2,0.08,0.0,0.0,0.56,0.0,3.08,1,0,0,1 +6657,INTJ,20.7,0.18912616430780363,0.14,0.18,0.06,0.04,0.0,0.28,0.0,2.7,1,0,1,0 +6658,ISTJ,20.12,0.18327157591165227,0.14,0.14,0.12,0.04,0.0,0.36,0.0,1.72,1,1,1,0 +6659,INTJ,27.0,0.183546193739244,0.02,0.14,0.1,0.0,0.04,0.66,0.0,4.06,1,0,1,0 +6660,INTP,7.86,0.1851771693631896,0.28,0.32,0.02,0.0,0.02,0.12,0.0,0.66,1,0,1,1 +6661,INFP,30.68,0.18322054234851592,0.02,0.1,0.0,0.0,0.02,0.68,0.0,2.44,1,0,0,1 +6662,INFP,30.12,0.18642957544380004,0.02,0.02,0.2,0.0,0.0,0.88,0.0,3.7,1,0,0,1 +6663,INFJ,26.64,0.18720553633217993,0.02,0.1,0.16,0.0,0.0,0.84,0.0,3.26,1,0,0,0 +6664,INFP,23.72,0.1823467168807492,0.12,0.24,0.12,0.0,0.0,0.52,0.04,2.68,1,0,0,1 +6665,INFJ,28.34,0.188671601636609,0.06,0.28,0.2,0.0,0.0,0.56,0.0,3.12,1,0,0,0 +6666,INFP,33.64,0.18478619966849125,0.0,0.08,0.18,0.0,0.02,0.86,0.0,4.94,1,0,0,1 +6667,INTP,25.8,0.1868392684342738,0.12,0.16,0.18,0.12,0.0,0.7,0.0,2.94,1,0,1,1 +6668,ISFP,13.04,0.18203969416433574,0.06,0.32,0.04,0.0,0.0,0.12,0.0,1.38,1,1,0,1 +6669,INTJ,23.82,0.18475740444938044,0.02,0.1,0.16,0.0,0.08,0.78,0.02,2.8,1,0,1,0 +6670,ISTJ,22.88,0.1853993055555555,0.0,0.26,0.1,0.0,0.02,0.52,0.0,2.44,1,1,1,0 +6671,ISFP,23.12,0.18344174833675278,0.0,0.16,0.08,0.0,0.0,0.56,0.0,2.18,1,1,0,1 +6672,INTJ,32.84,0.18703271190688353,0.0,0.14,0.06,0.0,0.0,0.84,0.0,4.6,1,0,1,0 +6673,ENTP,29.6,0.17905052067761956,0.06,0.16,0.46,0.02,0.0,0.74,0.0,3.24,0,0,1,1 +6674,ENTJ,18.12,0.1836842523079171,0.0,0.08,0.16,0.0,0.0,0.42,0.0,1.82,0,0,1,0 +6675,INFP,20.66,0.19563854221693716,0.02,0.42,0.14,0.0,0.04,0.54,0.0,2.84,1,0,0,1 +6676,INTJ,29.7,0.1780671928846818,0.04,0.08,0.02,0.02,0.0,0.8,0.0,2.96,1,0,1,0 +6677,INTJ,34.34,0.18389038809481845,0.0,0.02,0.0,0.0,0.02,1.16,0.0,3.56,1,0,1,0 +6678,INTJ,23.12,0.1850393189493729,0.06,0.24,0.28,0.04,0.0,0.6,0.0,2.24,1,0,1,0 +6679,ENTP,26.06,0.180528013695917,0.0,0.1,0.06,0.0,0.0,0.52,0.0,2.78,0,0,1,1 +6680,INFP,27.1,0.18408139549289113,0.06,0.18,0.04,0.0,0.0,0.9,0.0,2.08,1,0,0,1 +6681,INTP,29.6,0.17915398133709562,0.0,0.02,0.3,0.0,0.0,0.66,0.0,3.36,1,0,1,1 +6682,ENTP,26.96,0.1858395556883756,0.02,0.14,0.06,0.0,0.0,0.82,0.0,3.76,0,0,1,1 +6683,INFP,26.96,0.1896573338101096,0.0,0.14,0.06,0.0,0.0,0.66,0.0,3.5,1,0,0,1 +6684,INFJ,29.74,0.182062309235687,0.0,0.02,0.12,0.0,0.0,0.62,0.0,3.54,1,0,0,0 +6685,ENTJ,11.7,0.17932390497197231,0.06,0.1,0.0,0.0,0.0,0.14,0.0,0.68,0,0,1,0 +6686,INTP,24.38,0.18493961631902717,0.14,0.08,0.2,0.0,0.0,0.58,0.0,2.1,1,0,1,1 +6687,INTJ,22.5,0.18433501679398442,0.04,0.28,0.16,0.02,0.0,0.66,0.0,2.4,1,0,1,0 +6688,INFP,21.48,0.183950466882907,0.16,0.22,0.0,0.02,0.04,0.58,0.0,1.44,1,0,0,1 +6689,ESTP,19.4,0.1874602733205642,0.3,0.42,0.14,0.0,0.0,0.48,0.0,1.8,0,1,1,1 +6690,INFP,22.82,0.17631769010534407,0.18,0.28,0.02,0.0,0.0,0.54,0.0,2.18,1,0,0,1 +6691,INTP,23.32,0.185667937183685,0.12,0.2,0.06,0.0,0.02,0.6,0.0,3.0,1,0,1,1 +6692,INTJ,19.84,0.18640040918587247,0.0,0.14,0.0,0.0,0.0,0.36,0.0,2.6,1,0,1,0 +6693,INFP,22.74,0.18346099210946834,0.02,0.1,0.14,0.0,0.0,0.64,0.0,2.66,1,0,0,1 +6694,ENTP,33.86,0.18382784774135308,0.0,0.14,0.14,0.0,0.0,0.88,0.0,3.44,0,0,1,1 +6695,ENTP,19.6,0.18967520052558606,0.06,0.24,0.3,0.06,0.0,1.14,0.0,1.46,0,0,1,1 +6696,INTP,19.7,0.18107234555810064,0.32,0.36,0.18,0.04,0.0,0.42,0.0,2.1,1,0,1,1 +6697,INTJ,28.64,0.1769720541965436,0.04,0.12,0.04,0.0,0.0,0.78,0.02,2.42,1,0,1,0 +6698,INFJ,26.62,0.17962334029698618,0.14,0.24,0.38,0.02,0.06,0.92,0.0,2.7,1,0,0,0 +6699,ISFP,18.96,0.19719891514189614,0.04,0.4,0.02,0.0,0.02,0.22,0.0,1.84,1,1,0,1 +6700,INFJ,28.2,0.1842656089902667,0.0,0.06,0.22,0.0,0.0,0.68,0.0,2.72,1,0,0,0 +6701,ESFP,12.0,0.18688495773772626,0.02,0.14,0.46,0.02,0.0,0.38,0.0,0.98,0,1,0,1 +6702,INFP,27.52,0.17742993714357472,0.0,0.02,0.0,0.0,0.02,0.52,0.0,2.42,1,0,0,1 +6703,ENTP,16.1,0.18418762069398278,0.14,0.12,0.02,0.06,0.0,0.18,0.0,2.32,0,0,1,1 +6704,INTP,27.18,0.18318730199813293,0.2,0.24,0.0,0.02,0.06,0.6,0.0,3.38,1,0,1,1 +6705,ISTP,22.94,0.18924292330072262,0.02,0.08,0.02,0.0,0.0,0.4,0.0,2.84,1,1,1,1 +6706,INTP,18.1,0.18446750680240198,0.2,0.2,0.02,0.02,0.0,0.38,0.0,1.66,1,0,1,1 +6707,ISFP,23.1,0.18392160167312696,0.06,0.1,0.14,0.02,0.0,0.84,0.0,3.08,1,1,0,1 +6708,ENTP,19.14,0.18628623292402474,0.16,0.06,0.1,0.14,0.0,0.38,0.0,1.92,0,0,1,1 +6709,INFP,26.66,0.18200198095804976,0.02,0.08,0.0,0.0,0.0,0.66,0.0,2.96,1,0,0,1 +6710,ESFJ,35.32,0.19018674356481657,0.0,0.1,0.5,0.0,0.0,0.94,0.0,4.78,0,1,0,0 +6711,INTJ,25.5,0.18467203770016385,0.08,0.22,0.16,0.0,0.0,0.7,0.0,3.28,1,0,1,0 +6712,ISFP,31.96,0.20210990072307644,0.02,0.1,0.76,0.0,0.02,1.0,0.0,3.22,1,1,0,1 +6713,INFP,27.62,0.17651483805101495,0.08,0.26,0.12,0.0,0.0,0.66,0.02,2.74,1,0,0,1 +6714,INTP,24.58,0.18694448699710586,0.0,0.58,0.34,0.0,0.0,0.46,0.0,2.94,1,0,1,1 +6715,INTP,29.42,0.18386832824915386,0.04,0.24,0.14,0.0,0.02,0.72,0.0,3.82,1,0,1,1 +6716,INFP,34.5,0.1858585296951342,0.02,0.2,0.24,0.0,0.0,1.16,0.0,3.3,1,0,0,1 +6717,INTP,25.48,0.18883001784528192,0.0,0.3,0.08,0.0,0.06,0.64,0.02,3.08,1,0,1,1 +6718,INFJ,32.18,0.19179830474073428,0.02,0.42,0.26,0.0,0.0,1.06,0.0,4.18,1,0,0,0 +6719,INTJ,29.42,0.18390128697964603,0.04,0.16,0.1,0.0,0.0,0.76,0.02,2.66,1,0,1,0 +6720,INFP,24.6,0.18088225164729704,0.08,0.26,0.0,0.0,0.1,0.74,0.04,1.8,1,0,0,1 +6721,ENFJ,31.42,0.18902659693158735,0.02,0.04,0.0,0.0,0.0,1.08,0.0,3.12,0,0,0,0 +6722,INTP,7.86,0.18360622113300032,0.08,0.06,0.02,0.04,0.0,0.2,0.0,0.8,1,0,1,1 +6723,INFP,23.06,0.18668592887396118,0.06,0.14,0.02,0.0,0.0,0.46,0.0,3.46,1,0,0,1 +6724,INTP,11.76,0.19531821023259344,0.08,0.44,0.34,0.04,0.04,0.2,0.0,2.02,1,0,1,1 +6725,ENFP,31.5,0.18691397413807437,0.0,0.14,1.1,0.0,0.1,0.8,0.0,3.82,0,0,0,1 +6726,INFP,20.9,0.1804417396162681,0.04,0.02,0.06,0.04,0.12,0.36,0.0,2.66,1,0,0,1 +6727,INTJ,36.44,0.18795946342088976,0.0,0.12,0.0,0.0,0.02,0.9,0.0,6.06,1,0,1,0 +6728,ENTP,11.82,0.19206206309336066,0.18,0.46,0.02,0.04,0.0,0.24,0.0,1.28,0,0,1,1 +6729,INTJ,17.86,0.18687192168897004,0.06,0.42,0.26,0.02,0.02,0.3,0.04,1.5,1,0,1,0 +6730,INTP,30.2,0.18165154474928075,0.04,0.22,0.0,0.0,0.0,0.82,0.0,2.98,1,0,1,1 +6731,INFJ,28.38,0.18538575869550397,0.08,0.22,0.0,0.0,0.0,0.9,0.0,3.1,1,0,0,0 +6732,INTP,23.96,0.18647273529080466,0.26,0.26,0.06,0.1,0.0,0.76,0.0,2.76,1,0,1,1 +6733,INFP,27.16,0.18823899541697992,0.16,0.48,0.38,0.06,0.04,1.08,0.08,1.36,1,0,0,1 +6734,ISTP,29.6,0.18086358887347184,0.18,0.22,0.06,0.02,0.02,0.66,0.0,3.34,1,1,1,1 +6735,ISTJ,18.16,0.18775518857330592,0.18,0.36,0.26,0.12,0.04,0.26,0.0,1.7,1,1,1,0 +6736,ESTP,25.38,0.1896967266655277,0.0,0.32,0.54,0.0,0.0,0.66,0.02,2.8,0,1,1,1 +6737,INFP,19.98,0.1870569532151285,0.26,0.32,0.02,0.0,0.08,0.4,0.0,2.8,1,0,0,1 +6738,INFJ,26.68,0.18629179092585627,0.0,0.0,0.6,0.0,0.0,0.48,0.02,3.66,1,0,0,0 +6739,INFP,22.76,0.1858787962323308,0.0,0.18,0.04,0.0,0.0,0.62,0.0,3.3,1,0,0,1 +6740,ENFP,27.54,0.19067377280329312,0.04,0.44,0.14,0.0,0.02,0.78,0.02,2.92,0,0,0,1 +6741,INTJ,28.38,0.17632473377505173,0.08,0.14,0.0,0.02,0.0,0.62,0.0,2.66,1,0,1,0 +6742,ISTJ,26.84,0.19051316119333328,0.0,0.14,0.06,0.0,0.0,0.84,0.0,2.96,1,1,1,0 +6743,INFJ,25.28,0.18317054701952482,0.12,0.18,0.12,0.0,0.02,0.76,0.0,3.52,1,0,0,0 +6744,INFP,14.58,0.1870905677503303,0.04,0.22,0.26,0.02,0.0,0.28,0.0,1.18,1,0,0,1 +6745,INTJ,17.94,0.18899919580316177,0.0,0.22,0.2,0.0,0.0,0.34,0.0,1.48,1,0,1,0 +6746,INFJ,26.26,0.18913371663571302,0.14,0.16,0.02,0.0,0.08,0.82,0.0,3.36,1,0,0,0 +6747,ENTP,26.98,0.18114077873960147,0.0,0.2,0.06,0.0,0.0,0.62,0.0,3.24,0,0,1,1 +6748,INFP,31.72,0.1810594183424315,0.0,0.14,0.14,0.0,0.02,0.76,0.0,2.12,1,0,0,1 +6749,ESTP,23.4,0.19048425624560056,0.02,0.24,0.06,0.0,0.0,0.48,0.0,3.02,0,1,1,1 +6750,ENTP,14.66,0.18119541028860517,0.0,0.36,0.0,0.0,0.0,0.22,0.0,0.86,0,0,1,1 +6751,INTP,30.0,0.18083364195584872,0.0,0.28,0.02,0.0,0.0,0.78,0.0,3.46,1,0,1,1 +6752,INFJ,21.54,0.17982763913863425,0.02,0.28,0.04,0.0,0.0,0.54,0.0,1.7,1,0,0,0 +6753,INFJ,30.6,0.1810366729678639,0.06,0.44,0.1,0.02,0.0,0.88,0.0,3.3,1,0,0,0 +6754,ENTJ,14.02,0.1924435551737043,0.0,0.22,0.02,0.0,0.0,0.18,0.0,1.52,0,0,1,0 +6755,ISTJ,25.36,0.18239134867650142,0.0,0.14,0.0,0.0,0.0,0.44,0.0,2.92,1,1,1,0 +6756,ENFJ,23.38,0.18427768595041327,0.06,0.2,0.22,0.0,0.02,0.58,0.02,2.58,0,0,0,0 +6757,INTP,30.14,0.18189173111243304,0.0,0.16,0.0,0.0,0.0,0.74,0.0,3.2,1,0,1,1 +6758,ISTJ,15.12,0.18692370060487368,0.02,0.04,0.12,0.0,0.0,0.34,0.0,1.62,1,1,1,0 +6759,ESTP,29.26,0.18010080477251156,0.08,0.0,0.02,0.0,0.0,0.68,0.0,2.72,0,1,1,1 +6760,INFP,21.24,0.18283400012671658,0.02,0.08,0.48,0.0,0.0,0.42,0.0,2.46,1,0,0,1 +6761,INFJ,33.24,0.18314424947676602,0.0,0.36,0.36,0.0,0.02,0.78,0.0,3.88,1,0,0,0 +6762,INFJ,23.38,0.18887387853529763,0.0,0.3,0.12,0.0,0.02,0.66,0.0,2.62,1,0,0,0 +6763,INFP,22.04,0.19257575771665356,0.0,0.4,0.0,0.0,0.0,0.92,0.0,2.58,1,0,0,1 +6764,INFJ,23.74,0.1729879987895331,0.02,0.02,0.0,0.0,0.0,0.5,0.0,1.64,1,0,0,0 +6765,INFJ,24.98,0.19360871330322732,0.0,0.12,1.04,0.0,0.0,1.1,0.0,3.02,1,0,0,0 +6766,INFP,20.24,0.1924376409073433,0.3,0.34,0.04,0.0,0.02,0.48,0.0,2.58,1,0,0,1 +6767,INFJ,29.54,0.1770050186766037,0.0,0.14,0.02,0.0,0.0,0.74,0.08,2.66,1,0,0,0 +6768,INFJ,27.62,0.18553415346223018,0.02,0.18,0.28,0.0,0.02,0.78,0.08,3.12,1,0,0,0 +6769,INFJ,30.18,0.18871104396656613,0.02,0.14,0.28,0.0,0.0,0.96,0.0,3.14,1,0,0,0 +6770,INTJ,31.48,0.18199352620914164,0.0,0.2,0.22,0.0,0.0,0.82,0.0,2.42,1,0,1,0 +6771,ISTP,31.68,0.1875276456924629,0.0,0.14,0.04,0.0,0.0,0.8,0.0,3.94,1,1,1,1 +6772,INFJ,30.44,0.18461020408163265,0.0,0.38,0.32,0.0,0.0,0.86,0.0,2.98,1,0,0,0 +6773,INTP,24.22,0.1860665603559351,0.1,0.14,0.06,0.0,0.0,0.6,0.02,3.08,1,0,1,1 +6774,INFJ,30.5,0.1858115812491515,0.0,0.16,0.4,0.0,0.02,0.76,0.0,2.98,1,0,0,0 +6775,INTJ,23.8,0.18598194265172674,0.14,0.12,0.06,0.06,0.0,0.64,0.0,3.36,1,0,1,0 +6776,INFJ,24.32,0.19206354979608023,0.1,0.34,0.96,0.0,0.04,0.76,0.0,3.56,1,0,0,0 +6777,ENFP,27.84,0.18812788762539134,0.0,0.16,0.42,0.0,0.0,0.58,0.0,3.14,0,0,0,1 +6778,INFP,28.24,0.19571511594012694,0.04,0.06,0.06,0.0,0.08,1.7,0.0,2.36,1,0,0,1 +6779,INTJ,30.0,0.18265920546637412,0.0,0.18,0.04,0.0,0.02,0.74,0.04,2.18,1,0,1,0 +6780,INFP,13.92,0.18454906287525727,0.46,0.5,0.22,0.0,0.04,0.32,0.0,1.08,1,0,0,1 +6781,ENFP,18.28,0.18752263057825216,0.1,0.3,0.16,0.0,0.0,0.4,0.0,2.16,0,0,0,1 +6782,ENFP,17.8,0.17038969897840442,0.34,0.1,0.28,0.34,0.0,0.68,0.0,1.7,0,0,0,1 +6783,INFP,20.34,0.18579089088088568,0.18,0.24,0.06,0.0,0.02,0.24,0.0,2.64,1,0,0,1 +6784,ENTP,32.64,0.18005876470099066,0.02,0.12,0.02,0.0,0.0,1.12,0.02,1.8,0,0,1,1 +6785,INFJ,33.72,0.1781421502324883,0.0,0.2,0.02,0.0,0.0,1.1,0.0,4.16,1,0,0,0 +6786,ENTJ,28.84,0.18104791823595348,0.02,0.22,0.0,0.0,0.0,0.62,0.0,2.86,0,0,1,0 +6787,INTJ,21.22,0.17493795126128092,0.12,0.16,0.14,0.04,0.0,0.5,0.0,2.52,1,0,1,0 +6788,INFP,32.9,0.1851896577171302,0.0,0.1,0.16,0.0,0.02,0.92,0.0,4.22,1,0,0,1 +6789,INFJ,34.22,0.1837333241479485,0.02,0.14,0.14,0.0,0.0,0.88,0.0,4.36,1,0,0,0 +6790,ENTJ,25.24,0.18700250659413062,0.04,0.24,0.0,0.0,0.0,0.48,0.0,3.16,0,0,1,0 +6791,INFJ,18.16,0.19367621608137775,0.08,0.16,0.1,0.0,0.0,0.32,0.0,2.42,1,0,0,0 +6792,INTP,25.28,0.17644517812559102,0.04,0.22,0.02,0.0,0.0,0.52,0.0,2.14,1,0,1,1 +6793,INTJ,33.0,0.18201174805788017,0.0,0.2,0.04,0.0,0.04,1.02,0.0,3.24,1,0,1,0 +6794,INFP,30.48,0.1761310274294801,0.0,0.26,0.06,0.0,0.0,0.8,0.0,2.64,1,0,0,1 +6795,INFJ,22.96,0.2002732438338182,0.0,0.12,0.32,0.0,0.0,1.48,0.0,2.76,1,0,0,0 +6796,INTP,30.32,0.18125477411522192,0.0,0.22,0.14,0.0,0.0,0.82,0.0,4.22,1,0,1,1 +6797,INTJ,10.3,0.19155838530727634,0.04,0.1,0.08,0.0,0.0,0.22,0.0,1.1,1,0,1,0 +6798,INTJ,20.94,0.18562465024060884,0.12,0.34,0.06,0.0,0.0,0.6,0.0,1.92,1,0,1,0 +6799,ENFJ,25.38,0.18390426446280994,0.0,0.12,0.12,0.0,0.04,0.58,0.0,3.46,0,0,0,0 +6800,ENTP,21.6,0.1815693608806059,0.04,0.36,0.08,0.0,0.0,0.66,0.0,2.58,0,0,1,1 +6801,INFP,29.74,0.17665433207176548,0.0,0.14,0.1,0.0,0.0,0.9,0.0,2.44,1,0,0,1 +6802,INFJ,16.52,0.1874434517106503,0.0,0.28,0.06,0.0,0.0,0.28,0.0,1.68,1,0,0,0 +6803,INFP,32.5,0.18620525664327964,0.0,0.1,0.02,0.0,0.02,0.86,0.0,3.4,1,0,0,1 +6804,ISFP,25.06,0.1810883068457868,0.0,0.14,0.1,0.0,0.0,0.5,0.0,3.16,1,1,0,1 +6805,ISFP,6.72,0.17375807529516596,0.74,0.22,0.26,0.06,0.0,0.1,0.0,1.0,1,1,0,1 +6806,INFJ,28.86,0.18754646552097654,0.02,0.54,0.12,0.02,0.0,0.98,0.0,4.06,1,0,0,0 +6807,INFP,17.24,0.18356817299206962,0.32,0.38,0.3,0.14,0.08,0.48,0.0,1.96,1,0,0,1 +6808,INTP,28.18,0.1863679592365076,0.02,0.2,0.08,0.02,0.0,0.84,0.0,3.36,1,0,1,1 +6809,INFP,27.88,0.18250840004905367,0.02,0.02,0.08,0.02,0.0,0.76,0.0,1.58,1,0,0,1 +6810,INTJ,24.04,0.17685663175650082,0.08,0.22,0.0,0.02,0.02,0.56,0.0,2.58,1,0,1,0 +6811,INTP,32.22,0.1825865178907171,0.06,0.24,0.1,0.0,0.04,1.14,0.0,3.52,1,0,1,1 +6812,INFP,19.96,0.18430825813774215,0.0,0.14,0.18,0.0,0.0,0.34,0.0,3.12,1,0,0,1 +6813,ENFJ,29.78,0.18347094728346697,0.0,0.18,0.22,0.0,0.0,0.82,0.0,3.26,0,0,0,0 +6814,ISTJ,34.56,0.19041727560576538,0.02,0.3,0.1,0.0,0.0,1.46,0.0,2.74,1,1,1,0 +6815,ISFJ,35.9,0.18108715436478873,0.0,0.1,0.0,0.0,0.0,0.98,0.0,5.02,1,1,0,0 +6816,INFJ,24.64,0.18281078296347042,0.0,0.18,0.04,0.0,0.0,0.5,0.0,3.2,1,0,0,0 +6817,INTJ,21.0,0.17503133376308422,0.12,0.28,0.0,0.0,0.06,0.44,0.0,2.38,1,0,1,0 +6818,INTP,17.22,0.18667244355567997,0.0,0.24,0.02,0.0,0.04,0.18,0.0,1.98,1,0,1,1 +6819,INFP,29.18,0.18541668037858355,0.02,0.16,0.08,0.02,0.0,0.82,0.0,4.08,1,0,0,1 +6820,INFP,31.82,0.18473194415034178,0.0,0.04,0.26,0.0,0.06,0.8,0.02,4.02,1,0,0,1 +6821,ENFJ,28.0,0.19129949101391827,0.02,0.1,0.38,0.02,0.02,1.38,0.0,3.3,0,0,0,0 +6822,INFP,4.82,0.1728804618482472,0.9,0.46,0.1,0.52,0.0,0.26,0.0,1.08,1,0,0,1 +6823,INFP,17.94,0.19497231764899603,0.08,0.32,0.44,0.02,0.02,0.38,0.0,2.46,1,0,0,1 +6824,INFP,17.04,0.18231321512567947,0.12,0.02,1.12,0.06,0.0,0.48,0.0,0.66,1,0,0,1 +6825,ENTJ,26.64,0.18978858304834142,0.02,0.26,0.38,0.0,0.0,0.72,0.0,3.62,0,0,1,0 +6826,INFJ,25.08,0.1824111062051216,0.04,0.22,0.26,0.0,0.02,0.58,0.0,3.42,1,0,0,0 +6827,INTP,27.06,0.17927200386839431,0.18,0.26,0.22,0.02,0.0,0.72,0.0,2.56,1,0,1,1 +6828,INFP,28.14,0.18536391917964318,0.08,0.26,0.22,0.04,0.06,0.92,0.0,3.06,1,0,0,1 +6829,INFJ,31.92,0.18925501431115543,0.02,0.08,0.08,0.0,0.06,0.9,0.0,4.38,1,0,0,0 +6830,ENTP,15.2,0.18923019082425224,0.12,0.22,0.06,0.02,0.02,0.16,0.0,1.96,0,0,1,1 +6831,INTP,19.5,0.1867192455440293,0.4,0.54,0.08,0.0,0.0,0.42,0.02,2.02,1,0,1,1 +6832,INFP,31.78,0.18742821455826134,0.02,0.14,0.04,0.0,0.0,0.7,0.0,3.22,1,0,0,1 +6833,INTJ,29.86,0.17730815979712777,0.04,0.04,0.02,0.02,0.0,0.64,0.0,2.1,1,0,1,0 +6834,INFJ,27.32,0.18742142813020254,0.0,0.42,0.24,0.0,0.0,0.64,0.0,3.16,1,0,0,0 +6835,ISFP,18.56,0.18045080982531517,0.0,0.04,0.14,0.0,0.2,0.54,0.0,2.36,1,1,0,1 +6836,INTJ,29.58,0.18489708991252177,0.0,0.16,0.18,0.0,0.02,0.66,0.0,3.12,1,0,1,0 +6837,ENFP,22.1,0.18895767580258166,0.16,0.22,0.72,0.02,0.1,0.44,0.0,2.32,0,0,0,1 +6838,INFJ,29.98,0.18341399201823366,0.0,0.14,0.08,0.0,0.04,0.9,0.0,3.44,1,0,0,0 +6839,INFJ,28.58,0.1820993525897395,0.02,0.04,0.02,0.0,0.02,0.74,0.0,2.16,1,0,0,0 +6840,INFP,30.18,0.18712699497456606,0.08,0.34,0.12,0.0,0.0,1.1,0.0,3.32,1,0,0,1 +6841,INTJ,24.44,0.18449309340848696,0.0,0.28,0.02,0.0,0.0,0.68,0.0,2.22,1,0,1,0 +6842,ENFP,24.78,0.18564083358888614,0.06,0.18,0.7,0.0,0.06,0.4,0.0,2.9,0,0,0,1 +6843,ENTP,30.1,0.18742409983090333,0.0,0.26,0.06,0.0,0.0,0.72,0.0,3.48,0,0,1,1 +6844,INFJ,28.3,0.17844358995443962,0.08,0.14,0.3,0.02,0.0,0.66,0.0,3.18,1,0,0,0 +6845,INFP,12.84,0.17698870557387592,0.0,0.06,0.02,0.0,0.04,0.22,0.0,1.76,1,0,0,1 +6846,ENFP,28.12,0.18350854465839997,0.0,0.14,0.48,0.0,0.0,0.76,0.0,2.28,0,0,0,1 +6847,INFJ,19.74,0.18827229859879968,0.02,0.22,0.32,0.0,0.0,0.3,0.0,2.78,1,0,0,0 +6848,ENFP,19.7,0.19040221380054972,0.22,0.38,0.7,0.0,0.02,0.56,0.0,1.76,0,0,0,1 +6849,INFJ,29.52,0.17582323559599541,0.04,0.28,0.06,0.0,0.04,0.94,0.0,3.54,1,0,0,0 +6850,ENFJ,31.3,0.18862604045129533,0.02,0.34,1.06,0.02,0.0,0.92,0.0,3.28,0,0,0,0 +6851,INTP,13.3,0.17656405633233543,0.3,0.32,0.06,0.0,0.0,0.14,0.0,1.74,1,0,1,1 +6852,INFJ,28.94,0.18521701383628547,0.04,0.18,0.1,0.0,0.0,1.04,0.0,4.1,1,0,0,0 +6853,ISTP,23.7,0.18664909079862801,0.06,0.18,0.0,0.02,0.0,0.36,0.0,3.04,1,1,1,1 +6854,ENFP,31.98,0.18270953496240414,0.04,0.1,0.62,0.02,0.04,0.9,0.04,3.46,0,0,0,1 +6855,INFJ,29.92,0.1833650661033521,0.16,0.2,0.04,0.04,0.06,0.76,0.0,2.8,1,0,0,0 +6856,INTJ,19.52,0.1868940695682731,0.06,0.18,0.06,0.0,0.0,0.5,0.0,1.74,1,0,1,0 +6857,ENFP,19.6,0.189469789779614,0.0,0.08,0.08,0.0,0.0,1.0,0.0,2.3,0,0,0,1 +6858,INTJ,15.08,0.1799112053177395,0.7,0.7,0.08,0.02,0.02,0.4,0.0,2.24,1,0,1,0 +6859,ENFP,11.3,0.1832460451007436,0.0,0.08,0.02,0.0,0.0,0.28,0.0,1.38,0,0,0,1 +6860,INFJ,30.96,0.18797812052790075,0.0,0.1,0.32,0.0,0.0,0.74,0.0,3.78,1,0,0,0 +6861,INFP,27.5,0.17905070727111802,0.18,0.16,0.06,0.08,0.02,0.62,0.0,2.86,1,0,0,1 +6862,ENFJ,22.44,0.19178093262923243,0.1,0.2,0.06,0.04,0.0,0.88,0.0,2.14,0,0,0,0 +6863,INFJ,26.08,0.18921177838914816,0.08,0.1,0.06,0.0,0.0,1.06,0.0,2.48,1,0,0,0 +6864,INFJ,27.62,0.18287389182765992,0.06,0.08,0.06,0.04,0.0,1.04,0.0,3.22,1,0,0,0 +6865,ENFP,21.12,0.21149578877760036,0.0,0.38,1.54,0.0,0.0,0.9,0.0,3.0,0,0,0,1 +6866,ISTP,25.26,0.18557758277169045,0.02,0.1,0.12,0.02,0.0,0.48,0.0,3.48,1,1,1,1 +6867,ENTP,25.4,0.1825819704904176,0.04,0.18,0.0,0.0,0.0,0.74,0.0,2.58,0,0,1,1 +6868,INTJ,30.84,0.18006539102748834,0.0,0.1,0.02,0.0,0.02,0.82,0.0,4.08,1,0,1,0 +6869,ENFJ,21.76,0.18618239090794414,0.14,0.38,0.8,0.0,0.0,0.52,0.0,3.12,0,0,0,0 +6870,INTJ,5.4,0.20291918832324668,0.14,0.14,0.02,0.04,0.0,0.06,0.02,0.5,1,0,1,0 +6871,INFJ,23.54,0.1826324889966244,0.0,0.26,0.42,0.0,0.0,0.68,0.0,2.42,1,0,0,0 +6872,INFP,27.48,0.17895883627427792,0.14,0.26,0.14,0.0,0.02,0.64,0.0,2.18,1,0,0,1 +6873,INFP,25.84,0.18299342873592422,0.12,0.26,0.16,0.02,0.02,0.66,0.0,2.72,1,0,0,1 +6874,INTJ,25.26,0.1853061856781994,0.02,0.56,0.04,0.0,0.0,1.04,0.0,2.6,1,0,1,0 +6875,INFJ,20.98,0.17867747869289433,0.1,0.16,0.0,0.02,0.02,0.56,0.0,2.0,1,0,0,0 +6876,ISFP,19.52,0.18440994394716212,0.28,0.48,0.04,0.0,0.06,0.42,0.0,2.66,1,1,0,1 +6877,INFJ,32.68,0.18393179856185327,0.0,0.16,0.04,0.0,0.02,0.94,0.0,3.18,1,0,0,0 +6878,INTP,32.6,0.17576053400059496,0.0,0.06,0.0,0.0,0.0,0.82,0.0,3.02,1,0,1,1 +6879,INFJ,27.18,0.17993079584775087,0.06,0.2,0.28,0.0,0.0,0.68,0.0,3.1,1,0,0,0 +6880,INFP,35.0,0.19106422684318933,0.02,0.2,0.46,0.0,0.0,1.04,0.0,3.0,1,0,0,1 +6881,ENFP,32.72,0.18419030762297753,0.0,0.24,0.12,0.0,0.0,0.94,0.0,3.98,0,0,0,1 +6882,INTP,17.5,0.186279058044212,0.0,0.04,0.06,0.0,0.12,0.42,0.0,2.08,1,0,1,1 +6883,INFP,17.76,0.18514939867951535,0.08,0.18,0.38,0.0,0.0,0.44,0.02,1.52,1,0,0,1 +6884,INTP,26.36,0.18572560021111745,0.0,0.1,0.02,0.0,0.0,0.46,0.0,3.0,1,0,1,1 +6885,INFP,21.42,0.18812140695833737,0.04,0.06,0.02,0.02,0.04,0.52,0.0,2.56,1,0,0,1 +6886,ISTJ,19.24,0.1834203351180162,0.06,0.2,0.02,0.0,0.02,0.32,0.0,2.72,1,1,1,0 +6887,INFJ,31.66,0.18432131054398365,0.0,0.22,0.58,0.0,0.04,0.76,0.0,3.76,1,0,0,0 +6888,INFJ,31.72,0.1868121507231154,0.0,0.08,0.2,0.0,0.0,0.9,0.0,3.48,1,0,0,0 +6889,ISTP,3.54,0.19323134408049494,0.0,0.1,0.0,0.0,0.0,0.14,0.0,0.38,1,1,1,1 +6890,ISFP,21.18,0.18261942948661075,0.26,0.4,0.04,0.0,0.04,0.48,0.0,2.36,1,1,0,1 +6891,INFP,22.9,0.18258325473751028,0.06,0.22,1.06,0.0,0.0,0.54,0.0,2.26,1,0,0,1 +6892,ISFP,32.86,0.18165095054379327,0.0,0.06,0.26,0.0,0.04,0.86,0.0,4.08,1,1,0,1 +6893,ENFP,22.38,0.19116304979170007,0.02,0.24,1.3,0.0,0.02,0.66,0.0,2.62,0,0,0,1 +6894,ENFJ,25.6,0.19205377490964615,0.04,0.08,0.3,0.04,0.0,1.02,0.0,2.94,0,0,0,0 +6895,INFJ,21.06,0.17849482328959246,0.68,0.02,0.62,0.24,0.0,0.74,0.0,2.06,1,0,0,0 +6896,ENTJ,17.32,0.18218604398034896,0.24,0.34,0.0,0.02,0.0,0.32,0.0,1.1,0,0,1,0 +6897,ENTP,33.62,0.1855855382658941,0.04,0.08,0.0,0.0,0.04,0.88,0.0,4.36,0,0,1,1 +6898,ISTP,17.04,0.18722046251792895,0.1,0.32,0.14,0.0,0.0,0.68,0.0,1.78,1,1,1,1 +6899,ENFP,32.92,0.1843034390109263,0.0,0.12,0.3,0.0,0.0,0.8,0.0,3.64,0,0,0,1 +6900,ENFJ,32.8,0.18053145897535033,0.02,0.2,0.14,0.0,0.02,0.88,0.0,4.04,0,0,0,0 +6901,INFP,25.8,0.1919852931875582,0.02,0.2,0.04,0.0,0.0,0.68,0.0,3.46,1,0,0,1 +6902,INTP,31.28,0.17960722331083398,0.0,0.0,0.02,0.0,0.0,0.72,0.0,4.06,1,0,1,1 +6903,INFJ,30.58,0.17992382413449196,0.0,0.14,0.2,0.0,0.02,0.84,0.0,3.26,1,0,0,0 +6904,INFP,18.98,0.18720425011717223,0.0,0.22,0.7,0.0,0.0,0.3,0.0,1.9,1,0,0,1 +6905,INFP,31.56,0.18346403004093398,0.0,0.12,0.34,0.0,0.02,0.98,0.0,3.56,1,0,0,1 +6906,INFJ,32.86,0.1850148842727368,0.08,0.26,0.22,0.0,0.04,0.86,0.0,4.12,1,0,0,0 +6907,INFP,31.84,0.18329183488748282,0.08,0.3,0.24,0.02,0.0,1.0,0.0,4.54,1,0,0,1 +6908,INTJ,30.7,0.18080760963814344,0.0,0.08,0.02,0.0,0.02,0.76,0.0,3.28,1,0,1,0 +6909,ENFP,5.82,0.19231791949740665,0.04,0.04,0.04,0.0,0.0,0.16,0.0,0.56,0,0,0,1 +6910,INFJ,16.6,0.19129963343142595,0.0,0.16,0.34,0.0,0.0,0.28,0.0,1.8,1,0,0,0 +6911,INTP,25.92,0.18082596674550813,0.04,0.22,0.04,0.02,0.0,0.56,0.0,2.56,1,0,1,1 +6912,ENFP,32.38,0.18717755857455448,0.06,0.44,0.5,0.0,0.0,1.18,0.0,3.56,0,0,0,1 +6913,ISTJ,33.2,0.18699181764711217,0.02,0.18,0.86,0.02,0.0,0.86,0.0,4.1,1,1,1,0 +6914,INFP,16.92,0.18613364864279305,0.06,0.44,0.18,0.0,0.0,0.26,0.0,2.04,1,0,0,1 +6915,ENFP,22.8,0.19361336754903635,0.04,0.28,0.08,0.0,0.02,0.9,0.0,2.34,0,0,0,1 +6916,INTP,25.96,0.179607752501638,0.02,0.2,0.14,0.0,0.04,0.64,0.0,1.94,1,0,1,1 +6917,INTJ,28.84,0.1939347858308397,0.0,0.22,0.04,0.0,0.04,1.3,0.0,3.24,1,0,1,0 +6918,INTJ,15.52,0.18137447734583687,0.08,0.12,0.0,0.06,0.0,0.22,0.0,2.0,1,0,1,0 +6919,INFP,34.48,0.18363694813223122,0.0,0.04,0.04,0.0,0.0,0.92,0.0,3.9,1,0,0,1 +6920,INTP,25.62,0.17475179161175705,0.3,0.34,0.0,0.04,0.0,0.84,0.0,1.96,1,0,1,1 +6921,INTJ,19.52,0.1918811824956853,0.02,0.26,0.0,0.0,0.0,2.0,0.0,1.82,1,0,1,0 +6922,ENFP,27.24,0.18294288913788667,0.0,0.3,0.42,0.0,0.02,0.68,0.0,3.16,0,0,0,1 +6923,INTP,31.68,0.18255854909975977,0.02,0.18,0.02,0.0,0.04,0.88,0.02,3.52,1,0,1,1 +6924,INFP,23.64,0.189739651540445,0.0,0.08,0.4,0.0,0.0,0.66,0.0,1.64,1,0,0,1 +6925,INFP,28.18,0.17705422464167672,0.14,0.12,0.02,0.04,0.04,0.62,0.0,3.88,1,0,0,1 +6926,INFP,25.54,0.19354624000000006,0.1,0.18,0.2,0.04,0.04,0.46,0.0,3.08,1,0,0,1 +6927,ESFP,29.96,0.18628766779907815,0.0,0.32,0.04,0.0,0.0,1.04,0.0,3.42,0,1,0,1 +6928,INFP,26.12,0.17600871660757222,0.06,0.12,0.02,0.0,0.02,0.58,0.0,3.08,1,0,0,1 +6929,ENFP,18.28,0.18119350568724923,0.0,0.3,0.18,0.0,0.0,0.44,0.0,2.24,0,0,0,1 +6930,ISTJ,30.46,0.18899270404424462,0.0,0.52,0.48,0.0,0.02,0.98,0.0,3.4,1,1,1,0 +6931,INTP,30.12,0.1910287930924325,0.0,0.28,0.04,0.0,0.02,0.8,0.0,3.22,1,0,1,1 +6932,INFP,13.66,0.19467372460158897,0.02,0.24,0.0,0.0,0.0,0.3,0.0,1.4,1,0,0,1 +6933,ISFJ,34.16,0.18342971993301765,0.02,0.1,0.12,0.0,0.0,1.1,0.02,3.72,1,1,0,0 +6934,INTP,17.02,0.19010775783858325,0.06,0.16,0.16,0.06,0.0,0.34,0.0,2.12,1,0,1,1 +6935,INTP,23.68,0.17917706504103473,0.08,0.38,0.08,0.06,0.02,0.62,0.0,1.8,1,0,1,1 +6936,ISTP,30.28,0.18110043944008536,0.0,0.16,0.04,0.0,0.0,0.72,0.0,2.96,1,1,1,1 +6937,INTJ,24.28,0.17613182597810179,0.0,0.18,0.0,0.0,0.0,0.5,0.0,2.2,1,0,1,0 +6938,INTJ,32.36,0.17285219652170625,0.0,0.02,0.0,0.0,0.02,0.84,0.0,3.58,1,0,1,0 +6939,INTJ,29.56,0.1844573831044973,0.0,0.18,0.08,0.0,0.0,0.7,0.0,4.04,1,0,1,0 +6940,INFP,29.92,0.1848745853237824,0.0,0.06,0.24,0.0,0.0,1.02,0.0,4.2,1,0,0,1 +6941,INTJ,22.2,0.18423537366535217,0.14,0.14,0.1,0.06,0.0,0.64,0.0,2.76,1,0,1,0 +6942,ENTP,24.3,0.1752183408415416,0.1,0.14,0.0,0.04,0.0,0.6,0.0,1.7,0,0,1,1 +6943,INTP,27.02,0.1921496382607055,0.04,0.18,0.02,0.02,0.0,0.62,0.0,3.26,1,0,1,1 +6944,INFJ,31.22,0.18353014409757082,0.06,0.32,0.18,0.0,0.02,0.9,0.0,3.9,1,0,0,0 +6945,ENTP,13.52,0.18627760795327433,0.0,0.16,0.02,0.0,0.0,0.18,0.0,1.58,0,0,1,1 +6946,ENTJ,21.46,0.19189663160571616,0.02,0.32,0.7,0.0,0.0,0.58,0.0,2.7,0,0,1,0 +6947,INTP,23.4,0.17988703200005826,0.18,0.32,0.04,0.1,0.0,0.52,0.0,2.6,1,0,1,1 +6948,INFJ,31.28,0.17734964201114833,0.04,0.12,0.0,0.0,0.0,0.76,0.0,3.2,1,0,0,0 +6949,INTP,12.18,0.1834427744446945,0.4,0.6,0.06,0.0,0.0,0.22,0.0,1.04,1,0,1,1 +6950,ENFP,31.22,0.18569585266718397,0.04,0.2,0.36,0.04,0.0,0.88,0.0,3.46,0,0,0,1 +6951,ENTJ,27.5,0.17829353199856968,0.0,0.06,0.08,0.0,0.0,0.64,0.0,2.76,0,0,1,0 +6952,ENTP,14.8,0.17851503676000427,0.1,0.22,0.1,0.04,0.0,0.34,0.0,1.98,0,0,1,1 +6953,ISTJ,9.74,0.18154889934481863,0.14,0.14,0.02,0.02,0.02,0.26,0.0,0.7,1,1,1,0 +6954,INFJ,31.9,0.1821366221766518,0.0,0.06,0.16,0.0,0.02,1.12,0.0,3.64,1,0,0,0 +6955,ENTJ,22.16,0.18360965978200539,0.28,0.38,0.06,0.06,0.0,0.76,0.0,3.1,0,0,1,0 +6956,INTJ,24.48,0.18982421238038955,0.0,0.26,0.72,0.0,0.0,0.32,0.0,2.78,1,0,1,0 +6957,INTJ,23.16,0.18757559721110567,0.0,0.16,0.08,0.0,0.0,0.38,0.0,2.68,1,0,1,0 +6958,INTJ,16.08,0.18189390076154474,0.04,0.36,0.16,0.0,0.0,0.22,0.0,1.58,1,0,1,0 +6959,ENFP,28.36,0.1844203740809466,0.0,0.12,0.44,0.0,0.04,0.7,0.0,3.32,0,0,0,1 +6960,ISFP,22.8,0.19145616207336488,0.08,0.2,0.36,0.0,0.0,2.18,0.0,2.9,1,1,0,1 +6961,INFJ,29.0,0.18212032271815407,0.0,0.06,0.2,0.0,0.02,0.9,0.0,3.32,1,0,0,0 +6962,INFP,29.16,0.1815713872642147,0.04,0.08,0.0,0.0,0.02,0.82,0.0,3.12,1,0,0,1 +6963,INFP,28.32,0.18317171484428224,0.08,0.1,0.54,0.04,0.0,0.56,0.0,3.48,1,0,0,1 +6964,INTP,7.86,0.18162331371661383,0.98,1.0,0.16,0.0,0.02,0.18,0.0,1.0,1,0,1,1 +6965,ENTP,18.72,0.18850767862141637,0.06,0.2,0.0,0.02,0.02,0.28,0.0,2.26,0,0,1,1 +6966,ISTP,28.7,0.18878917242175108,0.0,0.1,0.08,0.0,0.0,0.94,0.0,4.14,1,1,1,1 +6967,ISTJ,24.5,0.1942399343897112,0.02,0.32,0.16,0.0,0.0,0.62,0.0,3.12,1,1,1,0 +6968,INFP,22.7,0.18259516409883406,0.0,0.18,0.32,0.0,0.0,0.44,0.0,2.42,1,0,0,1 +6969,INTP,32.36,0.1823638644589545,0.0,0.18,0.12,0.0,0.0,0.74,0.0,4.22,1,0,1,1 +6970,ENFP,24.84,0.1864620580766012,0.0,0.12,0.22,0.0,0.0,0.58,0.0,3.04,0,0,0,1 +6971,INFP,25.68,0.1841660036345788,0.18,0.2,0.4,0.0,0.06,0.92,0.12,2.86,1,0,0,1 +6972,INTP,28.44,0.19337617546712405,0.06,0.36,0.38,0.04,0.0,0.82,0.0,3.08,1,0,1,1 +6973,INFP,26.36,0.18321364151206357,0.06,0.1,0.4,0.02,0.0,0.72,0.0,3.68,1,0,0,1 +6974,ISTP,21.52,0.1766575052408054,0.14,0.14,0.24,0.04,0.0,0.34,0.06,1.96,1,1,1,1 +6975,INTJ,23.02,0.17315095083520077,0.12,0.18,0.0,0.06,0.0,0.44,0.0,2.06,1,0,1,0 +6976,INFP,24.18,0.18095527140495865,0.0,0.14,0.04,0.0,0.0,0.3,0.0,3.26,1,0,0,1 +6977,INTJ,32.1,0.1834170730884213,0.0,0.04,0.16,0.0,0.0,0.96,0.0,4.28,1,0,1,0 +6978,ENTJ,28.34,0.18714739139059713,0.0,0.18,0.12,0.0,0.02,1.04,0.0,2.84,0,0,1,0 +6979,INFJ,18.44,0.17901220199331863,0.0,0.1,0.08,0.0,0.0,0.4,0.0,2.4,1,0,0,0 +6980,INTP,23.58,0.18337022563205893,0.0,0.08,0.04,0.0,0.0,0.64,0.0,3.04,1,0,1,1 +6981,INTP,14.36,0.1909324035621115,0.08,0.16,0.02,0.0,0.0,0.32,0.0,2.02,1,0,1,1 +6982,INTP,21.36,0.1816721874000592,0.02,0.22,0.0,0.0,0.0,0.42,0.0,2.02,1,0,1,1 +6983,INFJ,29.76,0.17757715917494749,0.0,0.0,0.12,0.0,0.08,0.62,0.0,3.58,1,0,0,0 +6984,INTP,27.1,0.19111280600028394,0.0,0.24,0.12,0.0,0.02,0.78,0.0,3.36,1,0,1,1 +6985,INFJ,26.86,0.18606798106428935,0.02,0.24,0.16,0.0,0.0,0.68,0.0,3.44,1,0,0,0 +6986,ENFJ,23.3,0.18156299526866776,0.02,0.28,0.0,0.0,0.0,0.38,0.0,3.92,0,0,0,0 +6987,INFP,28.24,0.17733792788071442,0.06,0.14,0.32,0.06,0.0,0.92,0.0,3.42,1,0,0,1 +6988,INFP,31.16,0.17861165851973795,0.12,0.18,0.12,0.0,0.08,0.86,0.0,3.16,1,0,0,1 +6989,ENFP,25.7,0.17964776992165749,0.08,0.32,0.24,0.04,0.0,0.62,0.0,3.16,0,0,0,1 +6990,INFJ,22.42,0.18244883180047916,0.12,0.12,0.0,0.04,0.0,0.44,0.0,1.98,1,0,0,0 +6991,ENFP,31.34,0.18086221379145417,0.0,0.28,0.24,0.0,0.0,0.9,0.0,3.5,0,0,0,1 +6992,INFP,31.54,0.18661857110763236,0.0,0.16,0.16,0.0,0.0,1.0,0.04,2.74,1,0,0,1 +6993,INTP,21.32,0.18652345228542444,0.0,0.48,0.42,0.0,0.0,0.88,0.0,1.66,1,0,1,1 +6994,INFP,25.1,0.18545138562493296,0.0,0.02,0.42,0.0,0.0,1.42,0.0,1.48,1,0,0,1 +6995,INTP,9.08,0.20044522675641202,0.0,0.06,0.6,0.0,0.0,0.0,0.0,1.46,1,0,1,1 +6996,ISFP,29.42,0.18485865927046982,0.0,0.14,0.0,0.0,0.04,0.78,0.0,4.24,1,1,0,1 +6997,INFP,12.68,0.1873738011426756,0.08,0.2,0.08,0.0,0.02,0.38,0.0,1.44,1,0,0,1 +6998,ISFP,25.34,0.18779304241514505,0.02,0.06,0.62,0.02,0.0,0.62,0.0,3.44,1,1,0,1 +6999,ISTJ,22.32,0.18965997415121205,0.06,0.12,0.58,0.06,0.02,0.42,0.04,2.18,1,1,1,0 +7000,INFJ,32.98,0.184281636024131,0.0,0.06,0.24,0.0,0.08,1.08,0.0,4.34,1,0,0,0 +7001,ISTP,30.72,0.18601412611591467,0.12,0.3,0.2,0.0,0.02,0.84,0.0,3.64,1,1,1,1 +7002,INFP,27.08,0.18474442212323255,0.04,0.08,0.16,0.0,0.0,0.58,0.0,3.38,1,0,0,1 +7003,INFJ,24.7,0.18489666342131306,0.0,0.3,0.14,0.0,0.0,0.56,0.0,3.84,1,0,0,0 +7004,INFJ,20.06,0.18090921091511594,0.08,0.18,0.06,0.0,0.04,0.54,0.0,1.96,1,0,0,0 +7005,INTJ,28.84,0.1874852044742188,0.02,0.14,0.06,0.02,0.0,1.02,0.0,3.3,1,0,1,0 +7006,INFJ,20.8,0.1978337544917807,0.0,0.18,0.16,0.0,0.0,0.86,0.0,2.76,1,0,0,0 +7007,INTP,28.38,0.18988073224852065,0.0,0.22,0.22,0.0,0.0,0.78,0.0,3.72,1,0,1,1 +7008,INFP,27.98,0.18454173689639058,0.16,0.32,0.04,0.02,0.0,0.84,0.02,2.9,1,0,0,1 +7009,INFP,29.62,0.18408102930293607,0.0,0.08,0.24,0.0,0.0,0.74,0.0,4.04,1,0,0,1 +7010,INFP,29.78,0.19808181814644404,0.0,0.46,0.28,0.0,0.0,0.96,0.0,3.74,1,0,0,1 +7011,INFP,23.06,0.18646867920138516,0.06,0.28,0.36,0.0,0.0,0.92,0.0,1.7,1,0,0,1 +7012,INFJ,32.6,0.1820980524042959,0.02,0.06,0.06,0.0,0.0,0.88,0.0,3.54,1,0,0,0 +7013,ISFP,28.56,0.18233670412331676,0.02,0.12,0.06,0.0,0.0,0.94,0.0,2.74,1,1,0,1 +7014,INFP,33.02,0.18371215704866803,0.04,0.18,0.0,0.0,0.0,0.88,0.02,3.9,1,0,0,1 +7015,INFP,20.86,0.1848705638007906,0.54,0.58,0.04,0.06,0.08,0.5,0.0,2.54,1,0,0,1 +7016,INFP,22.64,0.18149646330209573,0.16,0.18,0.12,0.0,0.0,0.56,0.0,2.2,1,0,0,1 +7017,INTP,23.2,0.18143773452039635,0.14,0.26,0.04,0.08,0.0,0.58,0.0,2.38,1,0,1,1 +7018,INFP,30.5,0.1846734114442332,0.02,0.12,0.52,0.02,0.0,0.7,0.0,3.84,1,0,0,1 +7019,INTP,23.12,0.18679972535189285,0.02,0.14,0.04,0.0,0.0,0.84,0.0,1.14,1,0,1,1 +7020,INTP,25.3,0.18347277964995426,0.02,0.1,0.06,0.02,0.04,0.46,0.0,2.72,1,0,1,1 +7021,INTJ,27.6,0.1876883947685119,0.0,0.12,0.28,0.0,0.0,0.64,0.0,2.84,1,0,1,0 +7022,ENFP,24.72,0.18253641024367684,0.06,0.18,0.16,0.02,0.0,0.48,0.02,2.54,0,0,0,1 +7023,ISFJ,18.12,0.1857375308986896,0.06,0.2,0.0,0.02,0.0,0.54,0.0,1.64,1,1,0,0 +7024,INFP,32.56,0.1825214906524809,0.1,0.26,0.06,0.0,0.0,0.94,0.0,3.0,1,0,0,1 +7025,ISFP,19.6,0.18885914749594127,0.0,0.14,0.16,0.0,0.0,0.94,0.0,0.76,1,1,0,1 +7026,INTJ,18.82,0.1901012564689811,0.04,0.12,0.06,0.02,0.0,0.38,0.0,2.66,1,0,1,0 +7027,INTJ,29.16,0.17877716282545245,0.06,0.32,0.0,0.0,0.0,0.96,0.0,2.74,1,0,1,0 +7028,ENFP,29.96,0.18389731092761294,0.08,0.22,0.44,0.0,0.04,0.7,0.0,2.86,0,0,0,1 +7029,ENTP,23.1,0.1876613712301028,0.08,0.24,0.0,0.06,0.0,0.44,0.0,2.12,0,0,1,1 +7030,INFP,20.6,0.18030756259823844,0.16,0.1,0.16,0.1,0.02,0.34,0.0,2.4,1,0,0,1 +7031,ENTJ,25.8,0.18439130287191347,0.04,0.36,0.08,0.02,0.02,0.88,0.0,2.34,0,0,1,0 +7032,INFP,33.08,0.18393024903963306,0.0,0.1,0.06,0.0,0.0,0.9,0.0,3.92,1,0,0,1 +7033,INFJ,14.98,0.1890834449358552,0.04,0.22,0.06,0.0,0.02,0.42,0.0,1.34,1,0,0,0 +7034,ENTP,25.92,0.18467942991413097,0.06,0.04,0.08,0.0,0.08,0.62,0.0,2.58,0,0,1,1 +7035,INTP,22.68,0.18161644821374723,0.14,0.3,0.06,0.0,0.0,0.5,0.0,2.7,1,0,1,1 +7036,ENFP,23.1,0.18628400560265873,0.1,0.32,0.54,0.02,0.06,0.68,0.0,2.58,0,0,0,1 +7037,INFJ,26.38,0.18877036001271824,0.0,0.16,0.2,0.0,0.02,0.66,0.0,2.86,1,0,0,0 +7038,INTJ,13.2,0.18346722214437086,0.0,0.08,0.24,0.0,0.0,0.34,0.0,1.16,1,0,1,0 +7039,INTP,28.38,0.18674319559206348,0.02,0.24,0.12,0.0,0.0,0.8,0.0,3.1,1,0,1,1 +7040,INFP,27.34,0.1875,0.06,0.2,0.12,0.0,0.02,0.92,0.0,3.28,1,0,0,1 +7041,ISFP,10.76,0.19810406710796305,0.0,0.02,0.0,0.0,0.0,0.08,0.0,1.86,1,1,0,1 +7042,INFP,21.12,0.17596024691986972,0.06,0.26,0.0,0.02,0.0,0.38,0.0,1.3,1,0,0,1 +7043,INTJ,9.14,0.18138784579909983,0.0,0.02,0.0,0.0,0.02,0.12,0.0,1.12,1,0,1,0 +7044,ESFP,29.24,0.19003583579554578,0.1,0.22,0.26,0.02,0.0,0.8,0.0,3.42,0,1,0,1 +7045,INTP,27.0,0.1807581740312437,0.04,0.2,0.06,0.0,0.08,0.54,0.0,2.62,1,0,1,1 +7046,INTJ,26.58,0.1870234356303958,0.06,0.1,0.02,0.0,0.0,0.8,0.0,3.72,1,0,1,0 +7047,INFP,34.84,0.18483573632549594,0.0,0.14,0.38,0.0,0.0,1.02,0.0,1.98,1,0,0,1 +7048,ISFP,30.54,0.18854075740242135,0.08,0.14,0.58,0.06,0.02,0.8,0.0,4.52,1,1,0,1 +7049,ISTP,8.34,0.1850282528001418,0.14,0.06,0.28,0.1,0.0,0.04,0.0,0.72,1,1,1,1 +7050,ENTP,26.94,0.17224482248520712,0.0,0.06,0.0,0.0,0.0,0.64,0.0,2.62,0,0,1,1 +7051,INTP,26.24,0.1760095778242375,0.12,0.18,0.04,0.06,0.04,0.64,0.0,3.34,1,0,1,1 +7052,ISTJ,6.36,0.17942033813736824,0.72,0.72,0.02,0.0,0.02,0.08,0.0,0.74,1,1,1,0 +7053,ENTP,9.3,0.1985428857554983,0.02,0.08,0.36,0.0,0.0,0.66,0.0,0.86,0,0,1,1 +7054,ENTP,29.34,0.1894171525100231,0.0,0.32,0.14,0.0,0.04,1.16,0.0,3.16,0,0,1,1 +7055,INFP,31.62,0.18772872807900287,0.0,0.32,0.32,0.0,0.02,0.8,0.0,3.38,1,0,0,1 +7056,INFJ,26.1,0.1918331656087014,0.02,0.04,0.16,0.0,0.02,1.12,0.0,4.02,1,0,0,0 +7057,ISTJ,28.54,0.1817195379307983,0.0,0.1,0.1,0.0,0.0,0.66,0.0,3.34,1,1,1,0 +7058,INTP,29.04,0.17925444347384062,0.0,0.1,0.06,0.0,0.0,0.7,0.0,4.12,1,0,1,1 +7059,ISFP,23.7,0.1864650840437586,0.18,0.42,0.06,0.04,0.02,0.46,0.0,3.04,1,1,0,1 +7060,ENFJ,27.02,0.17945306301755198,0.06,0.12,0.38,0.0,0.0,0.76,0.0,3.24,0,0,0,0 +7061,ENFP,24.28,0.18429764045950317,0.1,0.2,0.2,0.04,0.0,0.6,0.0,2.48,0,0,0,1 +7062,INFP,17.62,0.1927148724875608,0.26,0.3,0.72,0.02,0.02,0.54,0.0,1.46,1,0,0,1 +7063,INFJ,28.92,0.19741712731150887,0.0,0.52,0.34,0.0,0.0,1.18,0.02,2.8,1,0,0,0 +7064,INFJ,31.22,0.18663972106028706,0.0,0.14,0.14,0.0,0.0,0.84,0.0,4.08,1,0,0,0 +7065,INFJ,16.14,0.18031505715303855,0.0,0.16,0.02,0.0,0.0,0.22,0.0,1.54,1,0,0,0 +7066,INTP,27.54,0.18102710524671484,0.0,0.22,0.1,0.0,0.0,0.52,0.0,3.3,1,0,1,1 +7067,INTJ,30.66,0.18296962732315045,0.0,0.12,0.14,0.0,0.04,0.78,0.0,3.24,1,0,1,0 +7068,INFP,32.04,0.18133329653884034,0.04,0.18,0.06,0.0,0.06,0.82,0.0,3.7,1,0,0,1 +7069,ENTP,26.88,0.18180031220655757,0.0,0.28,0.0,0.0,0.02,0.72,0.0,2.44,0,0,1,1 +7070,ENFP,29.34,0.1904737413357441,0.0,0.16,0.86,0.0,0.0,0.72,0.0,3.3,0,0,0,1 +7071,INFJ,29.7,0.18616479982640347,0.0,0.38,0.26,0.0,0.0,0.7,0.0,3.66,1,0,0,0 +7072,ENTP,22.76,0.18278060697147216,0.18,0.12,0.14,0.1,0.0,0.48,0.02,2.4,0,0,1,1 +7073,INFP,17.02,0.19256202916533388,0.0,0.22,0.4,0.0,0.02,0.78,0.0,1.38,1,0,0,1 +7074,INTP,30.42,0.18682129701357894,0.08,0.22,0.04,0.0,0.02,0.84,0.0,3.16,1,0,1,1 +7075,INFJ,33.66,0.1814392795034048,0.0,0.08,0.08,0.0,0.0,0.9,0.0,4.46,1,0,0,0 +7076,INTJ,24.18,0.1855177438383295,0.0,0.14,0.2,0.0,0.0,0.4,0.0,3.32,1,0,1,0 +7077,ENTJ,16.24,0.17239395686069536,0.4,0.02,0.0,0.16,0.0,0.4,0.0,1.58,0,0,1,0 +7078,INTP,10.64,0.17618784078794753,0.5,0.18,0.02,0.22,0.0,0.18,0.02,0.84,1,0,1,1 +7079,ENTP,30.38,0.1859335719957177,0.0,0.42,0.1,0.0,0.0,0.74,0.02,3.32,0,0,1,1 +7080,ISTP,20.96,0.1856005887077881,0.2,0.28,0.12,0.04,0.0,0.54,0.02,2.06,1,1,1,1 +7081,INFP,28.6,0.19089470518587534,0.02,0.1,0.08,0.0,0.02,0.62,0.0,2.28,1,0,0,1 +7082,ISTJ,34.96,0.1826825093729042,0.0,0.06,0.06,0.0,0.0,0.92,0.0,3.76,1,1,1,0 +7083,INFP,27.86,0.18019138601870696,0.0,0.18,0.1,0.0,0.02,0.74,0.0,2.7,1,0,0,1 +7084,ISTP,35.0,0.18256772343219327,0.0,0.32,0.14,0.0,0.0,1.0,0.0,3.78,1,1,1,1 +7085,INFP,20.1,0.1867553757470122,0.36,0.5,0.94,0.0,0.02,0.42,0.02,2.12,1,0,0,1 +7086,INTJ,28.88,0.18175736858940367,0.32,0.12,0.22,0.08,0.0,0.86,0.02,2.64,1,0,1,0 +7087,INFJ,29.8,0.1787209591271686,0.02,0.26,0.06,0.0,0.02,0.82,0.0,3.08,1,0,0,0 +7088,ENTJ,26.24,0.1797571398702675,0.0,0.08,0.0,0.0,0.06,0.62,0.0,3.52,0,0,1,0 +7089,ENTJ,21.76,0.18942280233903522,0.0,0.2,0.0,0.0,0.0,0.52,0.0,3.36,0,0,1,0 +7090,INFP,26.12,0.1835759137816079,0.26,0.36,0.04,0.0,0.02,0.76,0.0,2.5,1,0,0,1 +7091,INTP,17.3,0.1818049577919545,0.12,0.08,0.04,0.02,0.02,0.28,0.0,2.32,1,0,1,1 +7092,ISTJ,22.6,0.18326014161648282,0.08,0.34,0.02,0.0,0.0,0.48,0.0,2.4,1,1,1,0 +7093,ISTJ,21.78,0.18602574570226904,0.0,0.2,0.06,0.0,0.0,0.26,0.0,2.1,1,1,1,0 +7094,INFP,22.6,0.18456612259839256,0.2,0.38,0.12,0.0,0.0,0.7,0.0,2.6,1,0,0,1 +7095,INFJ,28.98,0.18345003139892524,0.0,0.08,0.06,0.0,0.0,0.68,0.0,3.5,1,0,0,0 +7096,INTJ,22.92,0.17660192291770013,0.16,0.3,0.0,0.0,0.0,0.44,0.0,1.94,1,0,1,0 +7097,INTP,22.88,0.18163048933903617,0.16,0.22,0.06,0.06,0.02,0.58,0.0,2.36,1,0,1,1 +7098,ENFP,31.58,0.19198815532836075,0.0,0.22,0.52,0.0,0.02,1.22,0.0,3.54,0,0,0,1 +7099,INFJ,22.1,0.18306522034747835,0.0,0.0,0.22,0.0,0.06,0.4,0.0,1.56,1,0,0,0 +7100,INTP,29.32,0.17923115036882398,0.12,0.08,0.02,0.04,0.0,0.72,0.0,2.8,1,0,1,1 +7101,INTJ,30.62,0.17557968889294034,0.1,0.28,0.0,0.02,0.0,0.88,0.0,2.44,1,0,1,0 +7102,ISFP,24.12,0.18904142048633873,0.0,0.06,0.0,0.0,0.06,1.52,0.0,2.34,1,1,0,1 +7103,INFJ,26.14,0.18097693588331026,0.32,0.42,0.02,0.0,0.06,0.98,0.0,3.06,1,0,0,0 +7104,ENFP,24.82,0.18818718554328756,0.0,0.22,0.44,0.0,0.02,0.8,0.0,2.88,0,0,0,1 +7105,INTP,28.68,0.1879572597742602,0.04,0.18,0.04,0.0,0.0,1.04,0.0,3.84,1,0,1,1 +7106,INFJ,27.22,0.18437384858588135,0.06,0.06,0.02,0.02,0.06,0.62,0.0,3.56,1,0,0,0 +7107,INFP,26.86,0.19106178346279887,0.16,0.28,0.24,0.0,0.04,0.76,0.0,3.8,1,0,0,1 +7108,ENTP,12.98,0.18818443716993224,0.02,0.12,0.06,0.02,0.0,0.18,0.0,1.54,0,0,1,1 +7109,ENTP,29.52,0.18311947660036199,0.06,0.18,0.02,0.0,0.0,0.82,0.0,2.88,0,0,1,1 +7110,INFP,29.84,0.1857924692440975,0.04,0.24,0.4,0.0,0.06,0.72,0.0,3.96,1,0,0,1 +7111,INTP,16.3,0.18515142273626245,0.0,0.2,0.32,0.0,0.0,0.22,0.0,1.76,1,0,1,1 +7112,INFP,32.48,0.18609785677444954,0.02,0.1,0.34,0.0,0.06,0.94,0.0,4.22,1,0,0,1 +7113,INTP,27.92,0.18453673566512352,0.0,0.2,0.06,0.0,0.0,0.7,0.0,3.88,1,0,1,1 +7114,INTP,28.44,0.17827527208715813,0.08,0.26,0.02,0.0,0.1,0.7,0.0,2.88,1,0,1,1 +7115,INFP,31.64,0.1874174645187291,0.1,0.1,0.24,0.06,0.02,1.06,0.0,4.28,1,0,0,1 +7116,INTP,33.66,0.18140589569160995,0.0,0.12,0.12,0.0,0.0,1.02,0.0,4.3,1,0,1,1 +7117,INFP,26.38,0.19384304919013817,0.04,0.06,0.06,0.02,0.02,0.9,0.0,2.78,1,0,0,1 +7118,ENTJ,25.66,0.18463814801204906,0.0,0.02,0.02,0.0,0.0,0.64,0.0,2.78,0,0,1,0 +7119,INFJ,28.4,0.18183851664572448,0.06,0.16,0.08,0.0,0.02,0.52,0.0,3.82,1,0,0,0 +7120,INFJ,25.18,0.1875708415924949,0.08,0.26,0.18,0.08,0.0,0.52,0.0,3.02,1,0,0,0 +7121,INFP,28.94,0.18550475206059763,0.1,0.04,0.16,0.06,0.0,1.1,0.0,3.3,1,0,0,1 +7122,INFJ,29.4,0.18506265296600113,0.02,0.18,0.04,0.0,0.0,0.82,0.0,2.98,1,0,0,0 +7123,INFP,27.38,0.18130528095733608,0.0,0.04,0.08,0.0,0.0,0.62,0.0,2.98,1,0,0,1 +7124,ENTP,23.12,0.18986155742264194,0.04,0.32,0.28,0.02,0.0,0.82,0.0,2.5,0,0,1,1 +7125,INFP,20.94,0.1794010740197336,0.06,0.14,0.0,0.0,0.02,0.42,0.0,2.14,1,0,0,1 +7126,ISFJ,30.14,0.18867549902962283,0.0,0.16,0.16,0.0,0.0,0.82,0.0,4.12,1,1,0,0 +7127,ENTJ,28.44,0.18247802625233642,0.0,0.28,0.08,0.0,0.04,1.48,0.0,3.12,0,0,1,0 +7128,INFJ,25.72,0.1864346062114488,0.02,0.2,0.38,0.0,0.08,0.66,0.0,2.46,1,0,0,0 +7129,ISFP,27.7,0.18390627493361392,0.06,0.08,0.0,0.0,0.0,1.06,0.0,3.46,1,1,0,1 +7130,INFJ,34.48,0.18657925053381516,0.0,0.2,0.28,0.0,0.02,1.12,0.02,4.54,1,0,0,0 +7131,ENFP,19.24,0.18175577578588561,0.0,0.24,0.06,0.0,0.0,0.48,0.02,2.12,0,0,0,1 +7132,ISTP,30.58,0.18167528808455446,0.02,0.4,0.08,0.02,0.0,1.18,0.02,2.76,1,1,1,1 +7133,INFP,31.42,0.1877771000800038,0.0,0.28,0.3,0.0,0.02,0.92,0.0,3.76,1,0,0,1 +7134,INFJ,27.36,0.17991194128707624,0.02,0.08,0.12,0.0,0.0,0.78,0.0,2.18,1,0,0,0 +7135,ENTP,21.98,0.18622980655612917,0.02,0.36,0.2,0.02,0.0,0.48,0.0,2.24,0,0,1,1 +7136,INTP,19.58,0.1835230076934243,0.02,0.28,0.0,0.0,0.02,0.28,0.0,2.14,1,0,1,1 +7137,ISTJ,16.24,0.19094821087093902,0.34,0.46,0.12,0.0,0.0,0.22,0.0,2.46,1,1,1,0 +7138,INFP,26.44,0.1845724495378428,0.06,0.2,0.12,0.0,0.0,0.86,0.0,3.4,1,0,0,1 +7139,ENTP,28.64,0.18019360323500025,0.04,0.2,0.0,0.0,0.0,0.7,0.0,3.34,0,0,1,1 +7140,INFJ,30.6,0.1804969197537354,0.04,0.1,0.2,0.0,0.0,0.82,0.02,2.72,1,0,0,0 +7141,INFP,24.32,0.18170881295731067,0.2,0.02,0.3,0.02,0.0,0.88,0.0,2.76,1,0,0,1 +7142,INFP,28.62,0.18468348939511367,0.16,0.38,0.5,0.04,0.06,1.18,0.0,3.38,1,0,0,1 +7143,INTP,18.46,0.18246583965162058,0.3,0.4,0.06,0.02,0.02,0.36,0.0,2.22,1,0,1,1 +7144,INFJ,32.74,0.1779766802794618,0.02,0.44,0.02,0.0,0.0,0.86,0.0,2.96,1,0,0,0 +7145,ENTP,31.2,0.1798906823277586,0.0,0.08,0.16,0.0,0.0,0.84,0.0,3.86,0,0,1,1 +7146,INFJ,25.98,0.18759653750651045,0.1,0.16,0.08,0.0,0.0,0.64,0.0,3.42,1,0,0,0 +7147,ISFP,25.74,0.18534776038643894,0.04,0.14,0.1,0.0,0.0,0.62,0.0,3.68,1,1,0,1 +7148,INFP,24.18,0.189250142830507,0.02,0.16,0.46,0.0,0.02,0.58,0.0,2.84,1,0,0,1 +7149,INFP,25.8,0.18902832645068432,0.06,0.18,0.26,0.04,0.0,0.82,0.0,3.8,1,0,0,1 +7150,INFP,30.24,0.17975840194211146,0.16,0.24,0.06,0.14,0.0,0.92,0.02,2.92,1,0,0,1 +7151,ISFP,20.34,0.19127411464215063,0.1,0.1,0.12,0.08,0.0,0.58,0.0,2.96,1,1,0,1 +7152,INTJ,26.72,0.17808330213330967,0.1,0.04,0.08,0.08,0.02,0.66,0.02,2.8,1,0,1,0 +7153,INTP,23.68,0.18802749799323737,0.14,0.38,0.02,0.0,0.08,0.56,0.0,2.68,1,0,1,1 +7154,ENFP,28.24,0.18523655430181515,0.0,0.14,0.22,0.0,0.0,1.04,0.0,2.54,0,0,0,1 +7155,ENFP,18.74,0.18450238984855846,0.02,0.06,1.3,0.0,0.0,0.48,0.0,2.24,0,0,0,1 +7156,INTJ,21.08,0.18504443670608184,0.02,0.12,0.12,0.02,0.02,0.4,0.0,2.62,1,0,1,0 +7157,INTJ,28.0,0.18323885114429445,0.0,0.2,0.06,0.0,0.0,0.8,0.0,3.26,1,0,1,0 +7158,INFJ,23.26,0.18427912346206682,0.0,0.12,0.0,0.0,0.0,0.56,0.02,2.04,1,0,0,0 +7159,INTJ,19.7,0.18339136739304301,0.1,0.18,0.0,0.0,0.0,0.3,0.0,1.38,1,0,1,0 +7160,INTP,27.1,0.17893124778379635,0.02,0.18,0.16,0.0,0.0,0.68,0.0,3.3,1,0,1,1 +7161,ISFP,9.26,0.1902433677712712,0.0,0.0,0.02,0.0,0.0,0.54,0.0,0.94,1,1,0,1 +7162,ISFP,32.32,0.18722010484437682,0.0,0.26,0.08,0.0,0.0,0.98,0.0,2.62,1,1,0,1 +7163,ESTJ,20.6,0.19806562080614556,0.02,0.24,0.96,0.0,0.0,1.14,0.0,1.32,0,1,1,0 +7164,ISFJ,21.86,0.18855938754568632,0.04,0.1,0.68,0.0,0.02,0.32,0.0,3.18,1,1,0,0 +7165,INTP,32.3,0.17811047330501412,0.06,0.24,0.0,0.0,0.06,0.98,0.0,3.68,1,0,1,1 +7166,INFJ,23.24,0.16291014132298465,0.06,0.02,0.02,0.0,0.04,0.72,0.0,2.1,1,0,0,0 +7167,ENFP,27.42,0.18202114352549284,0.02,0.1,0.0,0.0,0.0,0.72,0.0,2.32,0,0,0,1 +7168,ENFJ,14.66,0.17206415045050402,0.48,0.18,0.02,0.26,0.0,0.3,0.02,1.28,0,0,0,0 +7169,INTJ,15.62,0.18059153615692514,0.08,0.04,0.08,0.04,0.02,0.32,0.0,1.66,1,0,1,0 +7170,INTP,19.98,0.1766770978047878,0.2,0.12,0.04,0.1,0.0,0.52,0.0,1.78,1,0,1,1 +7171,INTJ,29.18,0.18177674265993507,0.0,0.14,0.04,0.0,0.0,0.82,0.0,2.56,1,0,1,0 +7172,INTJ,27.2,0.1793610301654119,0.02,0.2,0.06,0.0,0.0,0.78,0.0,2.84,1,0,1,0 +7173,INTP,10.04,0.1869095450826626,0.02,0.18,0.0,0.02,0.0,0.06,0.0,0.86,1,0,1,1 +7174,ISTP,22.22,0.1856943452060295,0.1,0.16,0.04,0.02,0.0,0.54,0.0,1.66,1,1,1,1 +7175,INFJ,29.02,0.18294839660174056,0.0,0.1,0.08,0.0,0.04,0.6,0.0,3.68,1,0,0,0 +7176,ISTP,30.92,0.18407921241213981,0.0,0.08,0.08,0.0,0.0,0.74,0.0,3.9,1,1,1,1 +7177,ISTP,14.68,0.18385685379884945,0.38,0.38,0.06,0.0,0.04,0.34,0.0,1.44,1,1,1,1 +7178,INFJ,22.14,0.18465333110571822,0.08,0.28,0.04,0.0,0.0,0.62,0.0,2.26,1,0,0,0 +7179,INTJ,30.96,0.1845728559744552,0.02,0.46,0.04,0.0,0.0,0.8,0.0,4.28,1,0,1,0 +7180,ENTJ,30.1,0.18317432935131026,0.0,0.14,0.88,0.0,0.0,0.88,0.0,3.08,0,0,1,0 +7181,INFJ,29.72,0.179963239048683,0.0,0.18,0.04,0.0,0.06,0.88,0.0,2.98,1,0,0,0 +7182,ENFP,33.26,0.18121354508536766,0.0,0.14,0.18,0.0,0.02,1.02,0.0,3.82,0,0,0,1 +7183,ENTP,25.46,0.18301731662256113,0.0,0.22,0.62,0.0,0.0,0.7,0.02,3.18,0,0,1,1 +7184,ENTP,30.0,0.1828759501153521,0.02,0.28,0.0,0.0,0.0,0.84,0.0,2.34,0,0,1,1 +7185,ESFP,6.92,0.19315416535222882,0.0,0.1,0.16,0.0,0.0,0.08,0.0,0.9,0,1,0,1 +7186,ENTP,25.26,0.18213406574047036,0.04,0.04,0.0,0.0,0.0,0.56,0.0,2.88,0,0,1,1 +7187,INTP,30.64,0.1894108578401835,0.02,0.22,0.16,0.02,0.08,1.14,0.02,3.58,1,0,1,1 +7188,INTJ,26.8,0.1794609405416561,0.12,0.3,0.0,0.0,0.02,0.88,0.0,2.68,1,0,1,0 +7189,INTP,24.6,0.18525848853966487,0.02,0.16,0.32,0.0,0.06,1.02,0.0,2.36,1,0,1,1 +7190,ENFP,31.4,0.17881441981534632,0.04,0.14,0.56,0.0,0.0,0.84,0.0,3.24,0,0,0,1 +7191,INFJ,30.4,0.17776040121791298,0.08,0.16,0.0,0.0,0.02,0.8,0.0,3.26,1,0,0,0 +7192,INTP,33.76,0.17791059548071883,0.0,0.14,0.0,0.0,0.0,0.94,0.0,3.98,1,0,1,1 +7193,ISTP,30.94,0.18144832415865253,0.1,0.2,0.06,0.04,0.0,0.76,0.0,2.24,1,1,1,1 +7194,INTJ,12.92,0.19002121751513,0.0,0.08,0.0,0.0,0.0,0.2,0.0,1.14,1,0,1,0 +7195,ENFP,23.82,0.19261109292276649,0.1,0.14,0.6,0.06,0.02,0.94,0.0,2.74,0,0,0,1 +7196,ENTJ,24.2,0.18715670632788545,0.1,0.38,0.16,0.06,0.0,0.88,0.0,2.54,0,0,1,0 +7197,INFJ,31.6,0.19087015685887887,0.04,0.1,0.18,0.0,0.02,0.98,0.0,3.6,1,0,0,0 +7198,ISFP,28.64,0.1792135382183852,0.04,0.16,0.02,0.0,0.06,0.7,0.0,3.62,1,1,0,1 +7199,INFJ,30.16,0.17423573459391814,0.02,0.06,0.04,0.0,0.0,0.8,0.0,3.36,1,0,0,0 +7200,INTP,20.26,0.1926496715457159,0.02,0.32,0.06,0.0,0.0,0.94,0.0,1.62,1,0,1,1 +7201,INTP,22.62,0.1861923086139071,0.04,0.36,0.32,0.0,0.0,0.48,0.0,2.6,1,0,1,1 +7202,ENTP,22.82,0.18089146314570684,0.0,0.28,0.18,0.0,0.0,0.42,0.0,2.36,0,0,1,1 +7203,ENTP,13.9,0.17712760352118948,0.02,0.1,0.26,0.02,0.0,0.12,0.0,0.86,0,0,1,1 +7204,INTP,25.56,0.18441910919843144,0.02,0.14,0.18,0.02,0.0,0.7,0.0,2.4,1,0,1,1 +7205,INFJ,17.54,0.19241688665595963,0.32,0.86,0.92,0.06,0.04,0.7,0.0,1.82,1,0,0,0 +7206,INFJ,19.62,0.18638168139127018,0.12,0.34,0.06,0.0,0.0,0.58,0.0,2.02,1,0,0,0 +7207,ENFJ,20.38,0.18799418640633725,0.0,0.2,0.5,0.0,0.0,0.72,0.0,2.28,0,0,0,0 +7208,ENFP,32.08,0.19068443980349045,0.0,0.2,0.28,0.0,0.0,1.3,0.0,4.36,0,0,0,1 +7209,INTJ,19.18,0.1770656601604435,0.0,0.22,0.0,0.0,0.0,0.52,0.0,0.72,1,0,1,0 +7210,INTP,34.72,0.17956769859584074,0.0,0.22,0.04,0.0,0.02,1.0,0.0,3.2,1,0,1,1 +7211,ISTJ,33.98,0.1790670240390975,0.02,0.08,0.06,0.02,0.0,0.94,0.0,4.56,1,1,1,0 +7212,INTP,26.42,0.17719504675288758,0.0,0.08,0.08,0.0,0.0,0.56,0.0,3.32,1,0,1,1 +7213,INFP,14.32,0.20069684217528722,0.0,0.18,0.12,0.0,0.02,0.2,0.0,1.52,1,0,0,1 +7214,INTP,27.58,0.1802095943723824,0.06,0.14,0.02,0.0,0.1,0.66,0.02,3.54,1,0,1,1 +7215,INFP,21.68,0.18391995906248335,0.04,0.06,0.3,0.02,0.02,0.3,0.0,2.64,1,0,0,1 +7216,INFP,21.28,0.18480702331934634,0.02,0.2,0.78,0.0,0.0,0.4,0.0,2.24,1,0,0,1 +7217,INFJ,28.52,0.18319279650302195,0.02,0.08,0.04,0.02,0.0,0.54,0.0,3.28,1,0,0,0 +7218,INTJ,12.82,0.18499682033570125,0.32,0.28,0.02,0.04,0.0,0.34,0.0,1.54,1,0,1,0 +7219,ISFJ,27.68,0.18760855403943685,0.02,0.22,0.52,0.02,0.0,0.76,0.0,3.66,1,1,0,0 +7220,INTJ,30.02,0.18099576743149925,0.1,0.2,0.12,0.0,0.0,0.76,0.0,2.94,1,0,1,0 +7221,ENTP,22.04,0.18957828529669118,0.0,0.38,0.12,0.0,0.0,0.44,0.0,1.86,0,0,1,1 +7222,INTP,20.08,0.19141644615992442,0.02,0.16,0.22,0.02,0.0,0.26,0.0,2.2,1,0,1,1 +7223,INFJ,27.58,0.19212856415436663,0.0,0.5,0.0,0.0,0.0,0.52,0.0,4.04,1,0,0,0 +7224,INFJ,31.64,0.18135735926694155,0.02,0.12,0.02,0.02,0.04,0.98,0.0,4.54,1,0,0,0 +7225,INTP,32.6,0.1890744243309886,0.0,0.06,0.12,0.0,0.0,0.98,0.02,3.54,1,0,1,1 +7226,ISFP,23.64,0.19751199869885952,0.22,0.3,0.08,0.04,0.02,0.62,0.0,3.34,1,1,0,1 +7227,INTJ,25.52,0.18261089289114374,0.04,0.2,0.0,0.0,0.0,0.74,0.0,2.74,1,0,1,0 +7228,ENFJ,30.46,0.18751467222249776,0.0,0.5,0.3,0.0,0.02,1.06,0.0,3.68,0,0,0,0 +7229,INFJ,11.36,0.1932422541728448,0.0,0.18,0.1,0.0,0.02,0.16,0.0,1.66,1,0,0,0 +7230,INFJ,30.06,0.1812500197315662,0.0,0.14,0.16,0.0,0.02,1.08,0.0,3.18,1,0,0,0 +7231,INFP,24.14,0.18828235359849674,0.08,0.22,0.12,0.02,0.04,0.82,0.0,3.16,1,0,0,1 +7232,INFJ,19.32,0.1810948546075913,0.0,0.2,0.3,0.0,0.04,0.58,0.0,2.4,1,0,0,0 +7233,INFP,28.78,0.17767112152297587,0.08,0.28,0.0,0.0,0.04,0.7,0.0,2.56,1,0,0,1 +7234,ENFP,26.82,0.18634151515895356,0.06,0.46,0.2,0.02,0.0,0.68,0.0,2.92,0,0,0,1 +7235,INFP,28.72,0.18494440010951393,0.14,0.18,0.24,0.06,0.06,1.06,0.0,4.08,1,0,0,1 +7236,INTJ,27.48,0.1875161092853252,0.0,0.14,0.02,0.0,0.0,0.84,0.0,3.28,1,0,1,0 +7237,ESTP,14.72,0.20384252678333523,0.02,0.12,0.24,0.0,0.0,1.4,0.0,0.76,0,1,1,1 +7238,ISTP,20.7,0.1823171060776813,0.06,0.28,0.0,0.0,0.0,0.32,0.0,2.0,1,1,1,1 +7239,ESTP,19.76,0.19198005338159435,0.0,0.28,0.14,0.0,0.0,0.28,0.0,2.26,0,1,1,1 +7240,INFP,28.14,0.1815258085354804,0.1,0.1,0.0,0.0,0.0,0.68,0.0,3.58,1,0,0,1 +7241,ENTP,33.04,0.17975001590731374,0.0,0.08,0.02,0.0,0.16,1.12,0.02,3.28,0,0,1,1 +7242,INFP,31.66,0.18456567209535102,0.02,0.3,0.08,0.0,0.0,0.92,0.0,3.24,1,0,0,1 +7243,INFJ,30.24,0.18315636769617358,0.0,0.26,0.06,0.0,0.0,0.92,0.0,2.9,1,0,0,0 +7244,INTJ,28.7,0.179716746165922,0.0,0.08,0.0,0.0,0.04,0.6,0.0,3.32,1,0,1,0 +7245,ENFP,26.48,0.181491426542561,0.06,0.18,0.12,0.0,0.0,0.58,0.0,2.58,0,0,0,1 +7246,INFJ,29.22,0.1911172506547975,0.0,0.18,0.32,0.0,0.02,0.66,0.0,3.24,1,0,0,0 +7247,INFJ,28.14,0.18921442167437416,0.0,0.16,0.18,0.0,0.0,1.72,0.0,3.72,1,0,0,0 +7248,ENFP,30.8,0.18891625791884384,0.02,0.2,0.74,0.0,0.0,0.72,0.0,3.24,0,0,0,1 +7249,INFP,20.34,0.19019613551887113,0.04,0.16,0.44,0.0,0.0,0.6,0.0,2.06,1,0,0,1 +7250,INFP,17.48,0.18212114158163267,0.18,0.26,0.06,0.0,0.08,0.24,0.0,2.48,1,0,0,1 +7251,ENFP,19.0,0.19220519400302338,0.0,0.22,0.14,0.0,0.0,0.28,0.02,2.24,0,0,0,1 +7252,ESTJ,17.14,0.17656867825255104,0.58,0.34,0.04,0.26,0.0,0.4,0.04,1.82,0,1,1,0 +7253,INFJ,28.18,0.18136912833317673,0.14,0.18,0.0,0.0,0.0,0.76,0.0,1.54,1,0,0,0 +7254,ISTJ,27.14,0.17782407674263698,0.06,0.18,0.08,0.0,0.02,0.6,0.0,3.6,1,1,1,0 +7255,INTP,25.32,0.17500158281956965,0.06,0.18,0.06,0.04,0.0,0.54,0.0,2.42,1,0,1,1 +7256,ISFP,31.04,0.19116956815088626,0.0,0.18,0.9,0.0,0.0,1.74,0.0,4.46,1,1,0,1 +7257,ENFP,25.98,0.19724250144473546,0.0,0.04,0.34,0.0,0.04,0.64,0.0,2.96,0,0,0,1 +7258,INFP,26.14,0.18727563014242918,0.08,0.16,0.28,0.0,0.0,0.68,0.02,3.26,1,0,0,1 +7259,INTJ,20.08,0.19825030598833976,0.0,0.06,0.3,0.0,0.0,0.5,0.0,2.9,1,0,1,0 +7260,INFJ,26.02,0.18366525935247724,0.04,0.14,0.04,0.0,0.02,0.52,0.0,3.22,1,0,0,0 +7261,INFJ,22.0,0.18838311554565992,0.22,0.18,0.16,0.04,0.0,0.66,0.0,3.06,1,0,0,0 +7262,INFP,29.2,0.17871504584135448,0.14,0.32,0.14,0.04,0.02,0.78,0.0,3.18,1,0,0,1 +7263,INTJ,29.36,0.18354960586627514,0.08,0.1,0.22,0.0,0.02,1.02,0.0,3.04,1,0,1,0 +7264,INFJ,27.82,0.18710370785766126,0.02,0.16,0.04,0.02,0.0,0.72,0.0,3.74,1,0,0,0 +7265,ENTP,30.06,0.1945096883066231,0.0,0.56,0.3,0.0,0.0,0.68,0.0,2.98,0,0,1,1 +7266,INTP,18.74,0.1840055104875595,0.12,0.32,0.0,0.0,0.0,0.46,0.0,1.98,1,0,1,1 +7267,INFJ,12.3,0.18265007407541173,0.6,0.64,0.04,0.0,0.04,0.62,0.0,1.38,1,0,0,0 +7268,ISFP,23.52,0.1903831764629009,0.0,0.16,0.06,0.0,0.0,0.42,0.0,2.34,1,1,0,1 +7269,ISFJ,18.12,0.18994932787265156,0.0,0.08,0.6,0.0,0.0,0.3,0.0,2.02,1,1,0,0 +7270,INTJ,9.84,0.17677965869086457,0.36,0.38,0.0,0.0,0.02,0.14,0.0,1.12,1,0,1,0 +7271,INTJ,30.16,0.17765491767458763,0.04,0.44,0.02,0.0,0.02,0.78,0.0,3.12,1,0,1,0 +7272,INTP,29.5,0.1833158502017433,0.02,0.1,0.02,0.0,0.0,0.66,0.04,4.26,1,0,1,1 +7273,INTJ,26.98,0.18326059965389496,0.02,0.12,0.12,0.02,0.0,0.64,0.0,2.72,1,0,1,0 +7274,ENTP,29.3,0.18263654868960055,0.0,0.16,0.18,0.0,0.0,0.74,0.0,3.36,0,0,1,1 +7275,ENTJ,28.32,0.17995666493834228,0.1,0.14,0.12,0.0,0.02,0.74,0.0,3.02,0,0,1,0 +7276,ISTP,25.96,0.18729983999999997,0.02,0.14,0.2,0.0,0.04,0.52,0.0,3.02,1,1,1,1 +7277,ESTP,24.18,0.1823589449948729,0.08,0.24,0.06,0.0,0.0,0.48,0.0,2.36,0,1,1,1 +7278,INFJ,24.9,0.1806597453623274,0.18,0.42,0.16,0.0,0.0,0.62,0.0,2.04,1,0,0,0 +7279,ENTJ,21.14,0.18060241244242528,0.08,0.28,0.04,0.0,0.0,0.4,0.0,2.0,0,0,1,0 +7280,INFJ,35.2,0.18768061710034545,0.0,0.22,0.38,0.0,0.0,1.04,0.0,4.5,1,0,0,0 +7281,INTP,30.18,0.17488665165622216,0.0,0.08,0.0,0.0,0.0,0.68,0.0,2.98,1,0,1,1 +7282,INTJ,13.48,0.18716916687690427,0.16,0.26,0.08,0.0,0.0,0.22,0.0,1.06,1,0,1,0 +7283,ISFJ,23.64,0.21145389824450875,0.04,0.38,0.02,0.0,0.0,1.22,0.0,2.52,1,1,0,0 +7284,ENTP,23.92,0.17646789316885278,0.02,0.1,0.02,0.0,0.06,0.5,0.0,1.44,0,0,1,1 +7285,INFP,17.36,0.18806234433008412,0.0,0.34,0.08,0.0,0.0,0.34,0.0,1.1,1,0,0,1 +7286,INFP,30.76,0.18490750986916593,0.0,0.18,0.22,0.0,0.06,1.2,0.08,2.52,1,0,0,1 +7287,ENFP,26.8,0.19297955268301267,0.0,0.2,0.82,0.0,0.0,0.56,0.02,2.86,0,0,0,1 +7288,INTP,34.98,0.18000675419214254,0.0,0.02,0.0,0.0,0.02,0.94,0.0,4.36,1,0,1,1 +7289,ENFP,31.6,0.18540521187775846,0.18,0.18,0.44,0.1,0.0,0.72,0.0,3.72,0,0,0,1 +7290,INTJ,30.88,0.19180754356075172,0.04,0.4,0.1,0.0,0.02,1.44,0.0,3.82,1,0,1,0 +7291,INFP,30.22,0.18397545142540087,0.08,0.1,0.06,0.04,0.04,0.92,0.0,4.36,1,0,0,1 +7292,INFP,11.62,0.1856806796566158,0.28,0.22,0.02,0.02,0.0,0.14,0.0,1.4,1,0,0,1 +7293,INFJ,26.58,0.18279686802710052,0.26,0.3,0.14,0.04,0.0,0.82,0.0,2.5,1,0,0,0 +7294,ESTP,25.08,0.18740192233756245,0.1,0.22,0.6,0.08,0.0,0.62,0.0,2.98,0,1,1,1 +7295,INFP,28.08,0.18653008238231936,0.08,0.18,0.12,0.0,0.0,0.92,0.0,3.46,1,0,0,1 +7296,INTJ,18.04,0.17939038182363604,0.04,0.1,0.0,0.0,0.0,0.42,0.0,1.44,1,0,1,0 +7297,INFJ,31.44,0.1684742686159055,0.0,0.1,0.04,0.0,0.0,1.04,0.0,2.14,1,0,0,0 +7298,INTP,23.42,0.17635228568852415,0.06,0.18,0.0,0.02,0.06,0.54,0.0,1.76,1,0,1,1 +7299,INTP,7.72,0.18677922529611485,0.0,0.12,0.0,0.0,0.0,0.18,0.0,0.88,1,0,1,1 +7300,ENFJ,24.92,0.19223021379877275,0.02,0.26,0.5,0.0,0.0,0.64,0.02,3.12,0,0,0,0 +7301,ISFJ,28.62,0.18369220437274553,0.0,0.06,0.46,0.0,0.0,0.64,0.0,3.44,1,1,0,0 +7302,ISTP,31.94,0.18810945516082217,0.0,0.18,0.02,0.0,0.0,0.9,0.0,3.7,1,1,1,1 +7303,INFJ,20.34,0.19396129319256597,0.26,0.22,0.1,0.12,0.02,0.48,0.0,2.6,1,0,0,0 +7304,ISFP,31.38,0.18283066432624226,0.0,0.2,0.04,0.0,0.02,1.0,0.0,4.16,1,1,0,1 +7305,INTJ,24.96,0.1882094153672326,0.0,0.56,0.04,0.0,0.04,0.7,0.0,2.24,1,0,1,0 +7306,ENFP,8.44,0.1929717086210737,0.1,0.16,1.0,0.02,0.0,0.38,0.0,0.76,0,0,0,1 +7307,ISTJ,24.38,0.18269099469291264,0.0,0.3,0.18,0.0,0.04,0.6,0.0,1.76,1,1,1,0 +7308,ENTP,30.78,0.18920219751968656,0.02,0.1,0.34,0.0,0.02,1.64,0.0,3.0,0,0,1,1 +7309,INTP,22.0,0.18254070504837078,0.02,0.34,0.0,0.0,0.0,0.52,0.0,2.24,1,0,1,1 +7310,ESFJ,34.2,0.18265537865032433,0.0,0.1,0.18,0.0,0.0,0.8,0.0,4.36,0,1,0,0 +7311,INTP,23.98,0.18206727495861308,0.06,0.14,0.04,0.02,0.0,0.42,0.0,0.92,1,0,1,1 +7312,ENTJ,21.36,0.18541755047375716,0.2,0.24,0.1,0.04,0.0,0.62,0.0,2.52,0,0,1,0 +7313,ISFP,29.36,0.18229726909911442,0.0,0.12,0.06,0.0,0.0,0.74,0.0,3.36,1,1,0,1 +7314,INFJ,24.12,0.18161534321611794,0.12,0.24,0.08,0.0,0.06,0.64,0.0,3.1,1,0,0,0 +7315,ENFJ,32.16,0.18927837737492031,0.08,0.46,0.3,0.0,0.02,0.88,0.0,3.28,0,0,0,0 +7316,ENFP,28.94,0.19329519261702321,0.02,0.3,0.5,0.0,0.0,1.06,0.04,2.68,0,0,0,1 +7317,INTP,29.32,0.1747032959412527,0.0,0.14,0.0,0.0,0.02,0.76,0.0,2.92,1,0,1,1 +7318,ISTJ,26.26,0.18565751292797833,0.14,0.3,0.02,0.02,0.02,0.66,0.0,2.8,1,1,1,0 +7319,ISFJ,32.32,0.1856063755593176,0.04,0.3,0.04,0.0,0.0,0.82,0.0,4.4,1,1,0,0 +7320,INFP,24.22,0.1762076305397748,0.2,0.1,0.04,0.14,0.0,0.58,0.0,2.54,1,0,0,1 +7321,INFP,26.62,0.18502746895513453,0.1,0.2,0.02,0.04,0.0,0.78,0.0,2.2,1,0,0,1 +7322,INFJ,32.5,0.18445149688684515,0.0,0.16,0.06,0.0,0.02,1.02,0.0,3.64,1,0,0,0 +7323,INTP,32.14,0.18592363224041553,0.04,0.26,0.18,0.04,0.02,1.5,0.0,3.9,1,0,1,1 +7324,INFJ,16.82,0.18789480511859055,0.34,0.34,0.06,0.0,0.0,0.68,0.02,1.86,1,0,0,0 +7325,INFJ,29.98,0.1817785692238759,0.0,0.16,0.58,0.0,0.0,0.92,0.0,3.8,1,0,0,0 +7326,INFJ,15.38,0.18230708656147424,1.46,1.46,0.28,0.0,0.0,0.4,0.0,1.92,1,0,0,0 +7327,ENFP,23.56,0.1830496287930432,0.0,0.22,0.48,0.0,0.02,0.7,0.0,2.04,0,0,0,1 +7328,INFJ,33.44,0.18414347654625804,0.0,0.12,0.26,0.0,0.0,0.98,0.0,3.86,1,0,0,0 +7329,ESFP,32.54,0.19130402150483422,0.0,0.2,0.56,0.0,0.04,1.18,0.0,4.62,0,1,0,1 +7330,INTP,31.38,0.18344200579402922,0.02,0.12,0.28,0.0,0.1,0.9,0.0,4.48,1,0,1,1 +7331,INTP,27.56,0.1794760865840956,0.04,0.12,0.04,0.0,0.0,0.66,0.02,2.44,1,0,1,1 +7332,INFJ,27.48,0.19012064807351475,0.0,0.26,0.28,0.0,0.0,1.24,0.0,3.3,1,0,0,0 +7333,INFJ,30.14,0.18843719385222074,0.0,0.24,0.06,0.0,0.0,0.7,0.0,3.76,1,0,0,0 +7334,INTP,30.38,0.184456549019542,0.02,0.1,0.04,0.0,0.0,0.8,0.0,3.6,1,0,1,1 +7335,INTJ,29.8,0.17843965008275006,0.0,0.1,0.12,0.0,0.04,0.78,0.0,3.82,1,0,1,0 +7336,INFJ,9.98,0.1874470002143801,0.72,0.74,0.06,0.02,0.0,0.36,0.0,1.14,1,0,0,0 +7337,ENFP,24.8,0.17678392576920743,0.26,0.36,0.2,0.0,0.0,0.66,0.0,1.76,0,0,0,1 +7338,ENFP,22.64,0.1911493560930516,0.12,0.12,0.88,0.04,0.0,0.68,0.0,3.7,0,0,0,1 +7339,INFP,29.04,0.1911891565800322,0.04,0.14,0.0,0.02,0.0,0.8,0.0,3.2,1,0,0,1 +7340,ISFJ,16.24,0.19630313501294047,0.2,0.22,0.52,0.08,0.0,0.24,0.0,2.0,1,1,0,0 +7341,INFJ,13.92,0.1869776074318043,0.56,0.62,0.22,0.06,0.0,0.42,0.0,1.8,1,0,0,0 +7342,ENTJ,32.22,0.18146792268453532,0.0,0.2,0.1,0.0,0.0,0.74,0.0,4.78,0,0,1,0 +7343,INFP,34.62,0.1815644417413661,0.0,0.16,0.02,0.0,0.04,1.04,0.0,4.14,1,0,0,1 +7344,INTP,15.82,0.1858430710088232,0.02,0.34,0.0,0.0,0.02,0.24,0.0,1.46,1,0,1,1 +7345,INTP,25.0,0.18677496808481023,0.02,0.28,0.18,0.0,0.0,0.82,0.0,2.54,1,0,1,1 +7346,ISFP,24.46,0.18406026698880035,0.0,0.16,0.44,0.0,0.0,0.58,0.0,1.78,1,1,0,1 +7347,ISTP,11.1,0.18737624990654184,0.48,0.44,0.46,0.3,0.02,0.1,0.0,2.0,1,1,1,1 +7348,INFP,25.6,0.18007292401431416,0.0,0.08,0.12,0.0,0.02,0.58,0.02,2.5,1,0,0,1 +7349,INTP,29.54,0.1760826201605107,0.0,0.24,0.04,0.0,0.0,0.78,0.0,3.0,1,0,1,1 +7350,ENTP,17.84,0.18569469562121751,0.0,0.26,0.02,0.0,0.0,0.68,0.0,1.84,0,0,1,1 +7351,ISFJ,35.48,0.185216551874522,0.0,0.12,0.08,0.0,0.02,0.92,0.0,3.94,1,1,0,0 +7352,INTP,23.14,0.18516997236165164,0.08,0.26,0.0,0.02,0.0,0.52,0.0,2.36,1,0,1,1 +7353,INFP,29.18,0.1826027510083335,0.06,0.1,0.18,0.02,0.08,0.72,0.0,3.04,1,0,0,1 +7354,ENFJ,14.84,0.18853218704707525,0.0,0.14,0.02,0.0,0.0,0.3,0.0,1.76,0,0,0,0 +7355,INTP,35.08,0.19287744635996668,0.0,0.12,0.28,0.0,0.0,0.84,0.0,4.66,1,0,1,1 +7356,ENTP,22.44,0.18574329880044457,0.08,0.32,0.12,0.0,0.0,0.54,0.0,2.14,0,0,1,1 +7357,INFP,24.4,0.18113689946217093,0.02,0.18,0.34,0.0,0.0,0.64,0.0,2.66,1,0,0,1 +7358,ENFP,32.54,0.18783288888888888,0.0,0.24,0.5,0.0,0.0,1.16,0.0,3.38,0,0,0,1 +7359,INTP,23.5,0.17851905139270977,0.1,0.22,0.02,0.02,0.04,0.66,0.0,2.78,1,0,1,1 +7360,INFP,29.58,0.17758489238317235,0.04,0.18,0.04,0.0,0.06,0.76,0.0,2.8,1,0,0,1 +7361,INTP,23.98,0.1776945488412514,0.1,0.22,0.06,0.08,0.04,0.52,0.0,2.3,1,0,1,1 +7362,INFP,17.98,0.18314618262691226,0.0,0.2,0.46,0.0,0.0,0.34,0.0,2.66,1,0,0,1 +7363,INTJ,5.32,0.1836068350333095,0.02,0.0,0.0,0.0,0.02,0.08,0.0,0.68,1,0,1,0 +7364,ENFP,19.92,0.1904748015933567,0.3,0.4,0.24,0.0,0.02,0.42,0.0,2.12,0,0,0,1 +7365,INFJ,28.24,0.1907457954700226,0.14,0.24,0.62,0.0,0.02,1.3,0.0,2.36,1,0,0,0 +7366,ESFP,25.96,0.18305461904012219,0.02,0.2,0.08,0.0,0.04,0.64,0.0,2.34,0,1,0,1 +7367,INTP,18.9,0.18343133023484368,0.04,0.24,0.14,0.0,0.02,0.18,0.0,2.24,1,0,1,1 +7368,ENFJ,16.2,0.20002373471757393,0.02,0.26,0.54,0.0,0.0,0.64,0.0,1.48,0,0,0,0 +7369,INFP,27.74,0.18994355461917092,0.0,0.16,0.12,0.0,0.02,0.96,0.0,2.86,1,0,0,1 +7370,INFP,32.14,0.18271632168784538,0.0,0.14,0.14,0.0,0.02,0.82,0.0,4.26,1,0,0,1 +7371,INFJ,32.24,0.18596734038000076,0.0,0.1,0.12,0.0,0.02,0.94,0.0,3.98,1,0,0,0 +7372,ENTJ,11.6,0.17324125916628255,0.6,0.08,0.04,0.32,0.0,0.38,0.0,1.66,0,0,1,0 +7373,INTP,28.14,0.17811667067982562,0.0,0.26,0.0,0.0,0.0,0.66,0.0,2.86,1,0,1,1 +7374,INFP,30.32,0.17869254967009404,0.0,0.22,0.02,0.0,0.14,0.6,0.0,2.22,1,0,0,1 +7375,ISTP,23.04,0.18309603455707352,0.3,0.46,0.06,0.0,0.0,0.56,0.0,2.7,1,1,1,1 +7376,ESFP,13.22,0.19119406633142955,0.0,0.24,0.26,0.0,0.0,0.66,0.0,1.78,0,1,0,1 +7377,INTP,30.24,0.1845016039531662,0.02,0.18,0.08,0.0,0.02,1.06,0.0,2.86,1,0,1,1 +7378,INFP,25.04,0.19197516558119163,0.02,0.32,0.2,0.0,0.0,0.86,0.0,2.86,1,0,0,1 +7379,ENFJ,32.48,0.18926871393410505,0.08,0.18,0.2,0.04,0.0,0.9,0.0,4.74,0,0,0,0 +7380,ENFP,25.9,0.1790334570208404,0.26,0.4,0.0,0.0,0.0,0.7,0.0,2.84,0,0,0,1 +7381,ENFJ,30.44,0.19940269582410422,0.0,0.34,0.72,0.0,0.02,0.9,0.0,1.6,0,0,0,0 +7382,INFP,28.62,0.19061378501315138,0.04,0.18,0.02,0.0,0.0,0.72,0.0,2.94,1,0,0,1 +7383,INFP,18.46,0.18209191608601605,0.16,0.1,0.4,0.02,0.0,0.34,0.0,1.16,1,0,0,1 +7384,INFJ,22.74,0.19537338522331085,0.02,0.26,0.28,0.0,0.0,1.26,0.0,2.34,1,0,0,0 +7385,INFJ,31.7,0.1879907287451404,0.04,0.2,0.3,0.02,0.0,0.9,0.0,4.32,1,0,0,0 +7386,INFJ,25.1,0.17987287066182053,0.04,0.4,0.3,0.0,0.0,0.56,0.0,2.14,1,0,0,0 +7387,ENFP,18.82,0.18515429657621843,0.02,0.2,0.38,0.0,0.0,0.16,0.0,2.36,0,0,0,1 +7388,INFP,29.56,0.18053189166301736,0.02,0.12,0.06,0.02,0.04,0.72,0.0,3.78,1,0,0,1 +7389,INFP,20.98,0.18860313377022514,0.02,0.28,0.44,0.0,0.0,0.4,0.0,2.94,1,0,0,1 +7390,INFP,6.56,0.19029211869115667,0.18,0.14,0.02,0.02,0.0,0.02,0.0,0.44,1,0,0,1 +7391,ENFP,30.04,0.1771018122719899,0.02,0.16,0.22,0.0,0.1,0.84,0.0,2.68,0,0,0,1 +7392,ENTJ,27.58,0.17965917714016533,0.1,0.2,0.06,0.0,0.0,0.72,0.0,2.74,0,0,1,0 +7393,ISTJ,21.94,0.1932607000377438,0.0,0.16,0.82,0.0,0.0,0.64,0.0,2.12,1,1,1,0 +7394,INFJ,32.3,0.1894914858208961,0.04,0.34,0.1,0.0,0.0,1.12,0.0,3.58,1,0,0,0 +7395,ISFP,20.9,0.19236703795362675,0.02,0.3,0.56,0.0,0.0,0.82,0.0,2.58,1,1,0,1 +7396,INTP,28.22,0.18733019458270891,0.04,0.3,0.08,0.0,0.0,0.72,0.0,3.24,1,0,1,1 +7397,INTJ,25.6,0.17774211025975936,0.0,0.12,0.0,0.0,0.0,0.9,0.02,2.56,1,0,1,0 +7398,ISFJ,23.98,0.187400411647712,0.3,0.2,0.0,0.16,0.02,0.48,0.0,2.22,1,1,0,0 +7399,INFJ,30.3,0.18911859613685142,0.0,0.14,0.36,0.0,0.0,1.14,0.0,3.46,1,0,0,0 +7400,ENFP,33.4,0.18982525206864162,0.0,0.1,0.2,0.0,0.0,1.02,0.0,3.58,0,0,0,1 +7401,INFP,24.48,0.18786592937331772,0.04,0.2,0.16,0.0,0.02,0.88,0.0,3.04,1,0,0,1 +7402,INFJ,28.1,0.182459474602406,0.02,0.16,0.16,0.0,0.0,0.6,0.0,1.84,1,0,0,0 +7403,ESFJ,22.5,0.18777966256221962,0.02,0.08,0.08,0.0,0.0,0.68,0.0,2.12,0,1,0,0 +7404,ISFJ,24.98,0.18835263844429567,0.04,0.12,0.1,0.04,0.02,0.44,0.0,3.04,1,1,0,0 +7405,INTJ,21.26,0.19421318198149815,0.0,0.3,0.48,0.0,0.0,0.86,0.0,2.28,1,0,1,0 +7406,INTP,30.1,0.18015000904061804,0.0,0.08,0.04,0.0,0.0,0.76,0.0,3.48,1,0,1,1 +7407,INFP,26.18,0.1884692280161548,0.08,0.34,0.06,0.0,0.04,0.62,0.0,2.78,1,0,0,1 +7408,INFP,25.54,0.19120429601223976,0.06,0.32,0.2,0.0,0.02,0.48,0.0,4.16,1,0,0,1 +7409,ENTP,20.04,0.19023581582544524,0.0,0.02,0.14,0.0,0.0,0.32,0.02,2.26,0,0,1,1 +7410,ENFP,27.34,0.18839703501354352,0.0,0.22,0.08,0.0,0.0,0.48,0.0,2.6,0,0,0,1 +7411,INTJ,28.62,0.18529424165951203,0.0,0.14,0.12,0.0,0.0,0.9,0.0,3.78,1,0,1,0 +7412,ISTP,16.44,0.1720513860295201,0.02,0.14,0.0,0.02,0.0,0.28,0.0,1.54,1,1,1,1 +7413,ENTP,30.04,0.17688888095491992,0.0,0.16,0.1,0.0,0.0,0.7,0.0,3.06,0,0,1,1 +7414,INFJ,19.1,0.18173014447046942,0.2,0.06,0.06,0.06,0.04,0.44,0.0,2.16,1,0,0,0 +7415,ISFJ,27.08,0.18135313159481756,0.04,0.34,0.2,0.04,0.0,0.76,0.02,2.56,1,1,0,0 +7416,ENTP,19.32,0.17310955838197542,0.38,0.0,0.14,0.36,0.0,0.44,0.0,1.32,0,0,1,1 +7417,ISFP,30.7,0.1841070787758547,0.04,0.14,0.0,0.0,0.02,0.6,0.0,4.14,1,1,0,1 +7418,INFP,26.38,0.1823314922770074,0.02,0.04,0.04,0.0,0.0,0.86,0.0,2.28,1,0,0,1 +7419,INTP,28.9,0.1868626924172755,0.08,0.1,0.12,0.0,0.0,0.84,0.04,3.28,1,0,1,1 +7420,ISTP,13.06,0.1890508457817282,0.04,0.18,0.02,0.0,0.0,0.3,0.0,1.26,1,1,1,1 +7421,INTP,5.84,0.1840122885279017,0.0,0.04,0.04,0.0,0.0,0.14,0.0,0.9,1,0,1,1 +7422,INTJ,29.74,0.18224199020332105,0.0,0.16,0.16,0.0,0.0,0.84,0.0,3.28,1,0,1,0 +7423,ENFP,19.68,0.18570318066952057,0.16,0.14,0.3,0.08,0.02,0.56,0.0,2.12,0,0,0,1 +7424,INFP,22.94,0.19310484822765736,0.0,0.14,0.1,0.0,0.0,0.52,0.0,3.52,1,0,0,1 +7425,INFJ,33.36,0.18115161793782186,0.02,0.2,0.14,0.0,0.0,0.86,0.0,2.34,1,0,0,0 +7426,INFJ,23.36,0.18238520544951634,0.12,0.18,0.16,0.0,0.0,0.64,0.0,2.36,1,0,0,0 +7427,ENTJ,28.74,0.18768459256473097,0.02,0.24,0.1,0.0,0.0,0.74,0.0,2.12,0,0,1,0 +7428,INTP,27.44,0.18016817000296043,0.0,0.06,0.0,0.0,0.0,0.62,0.0,3.56,1,0,1,1 +7429,INFP,24.86,0.18053576403991412,0.0,0.04,0.04,0.0,0.04,0.66,0.0,2.18,1,0,0,1 +7430,INTP,26.82,0.1851167554056514,0.0,0.1,0.12,0.0,0.0,0.78,0.0,2.74,1,0,1,1 +7431,INFP,16.98,0.18164588961168943,0.28,0.22,0.04,0.06,0.04,0.26,0.04,2.14,1,0,0,1 +7432,INFP,32.3,0.18654499929148263,0.02,0.1,0.22,0.0,0.04,0.76,0.0,5.06,1,0,0,1 +7433,INFJ,26.32,0.1777880694165993,0.0,0.1,0.0,0.0,0.0,0.3,0.0,2.32,1,0,0,0 +7434,INFJ,25.48,0.1915870932555645,0.0,0.08,0.26,0.0,0.0,0.66,0.0,3.26,1,0,0,0 +7435,ENTJ,24.84,0.1879952373612796,0.06,0.38,0.12,0.06,0.0,0.56,0.0,2.96,0,0,1,0 +7436,ISFP,23.74,0.19123365446863275,0.0,0.08,0.48,0.0,0.0,0.64,0.0,3.1,1,1,0,1 +7437,INFP,32.5,0.18011190845517525,0.02,0.08,0.02,0.0,0.0,0.92,0.0,3.82,1,0,0,1 +7438,INTP,19.84,0.18319454211538352,0.4,0.42,0.32,0.1,0.0,0.72,0.0,2.82,1,0,1,1 +7439,INFP,12.36,0.18963378799175482,0.08,0.24,0.16,0.0,0.0,0.36,0.0,1.44,1,0,0,1 +7440,ENTP,26.38,0.1832014749469066,0.0,0.06,0.04,0.0,0.0,0.58,0.0,2.12,0,0,1,1 +7441,INTJ,20.54,0.18891036381589207,0.06,0.34,0.2,0.04,0.0,0.58,0.02,1.7,1,0,1,0 +7442,INTJ,25.66,0.18640746312235823,0.08,0.08,0.0,0.06,0.02,0.82,0.0,3.08,1,0,1,0 +7443,ENTP,29.26,0.18533289848597467,0.06,0.4,0.06,0.0,0.04,0.54,0.0,2.18,0,0,1,1 +7444,INFP,28.9,0.18584071084097242,0.08,0.18,0.18,0.0,0.0,0.86,0.0,3.8,1,0,0,1 +7445,ENTP,19.0,0.1881610212662595,0.04,0.08,0.02,0.0,0.12,0.42,0.0,2.7,0,0,1,1 +7446,INFP,18.34,0.19100569346115842,0.02,0.14,0.26,0.0,0.02,0.68,0.0,1.82,1,0,0,1 +7447,INFP,28.44,0.19228053224849811,0.08,0.1,0.44,0.0,0.0,0.76,0.0,3.82,1,0,0,1 +7448,ISFJ,23.2,0.1859208434566419,0.44,0.52,0.02,0.0,0.02,0.6,0.0,2.4,1,1,0,0 +7449,INFP,25.34,0.19475885554253286,0.02,0.24,0.2,0.0,0.08,1.08,0.0,3.48,1,0,0,1 +7450,INFJ,31.32,0.18564419929781645,0.02,0.16,0.06,0.0,0.0,1.12,0.0,4.4,1,0,0,0 +7451,INTP,32.7,0.1860266272829146,0.0,0.16,0.02,0.0,0.02,1.24,0.0,3.42,1,0,1,1 +7452,ENTP,16.56,0.1830091306241086,0.02,0.04,0.06,0.02,0.02,0.22,0.0,1.58,0,0,1,1 +7453,INFP,26.08,0.1918744981510687,0.0,0.08,0.68,0.0,0.02,0.9,0.0,2.92,1,0,0,1 +7454,INFP,28.28,0.18107560073485282,0.02,0.24,0.1,0.0,0.04,0.94,0.0,2.96,1,0,0,1 +7455,INFP,31.36,0.18566678322606467,0.04,0.16,0.1,0.0,0.06,0.74,0.0,4.46,1,0,0,1 +7456,INTP,29.02,0.19382352743081918,0.02,0.52,0.26,0.0,0.04,1.14,0.0,3.02,1,0,1,1 +7457,ISTP,30.14,0.18288553108087227,0.02,0.22,0.02,0.0,0.0,0.76,0.02,3.14,1,1,1,1 +7458,INTP,30.68,0.18560727054027867,0.08,0.3,0.0,0.04,0.02,0.76,0.0,3.88,1,0,1,1 +7459,INTJ,19.94,0.1773368302400793,0.1,0.08,0.12,0.08,0.02,0.34,0.0,2.36,1,0,1,0 +7460,INTP,33.04,0.1836531083921535,0.0,0.2,0.22,0.0,0.0,1.1,0.0,4.9,1,0,1,1 +7461,INFJ,27.96,0.18746967859353258,0.16,0.14,0.02,0.0,0.0,1.04,0.0,3.08,1,0,0,0 +7462,ISTP,13.9,0.18727895684800658,0.24,0.26,0.0,0.02,0.04,0.16,0.0,1.78,1,1,1,1 +7463,INTJ,24.44,0.18705824522386222,0.24,0.38,0.06,0.08,0.0,0.6,0.0,2.3,1,0,1,0 +7464,INFJ,27.24,0.17798349337686029,0.1,0.14,0.1,0.0,0.0,0.72,0.0,2.66,1,0,0,0 +7465,INFP,27.48,0.1866673163762819,0.0,0.18,0.58,0.0,0.02,0.64,0.0,3.36,1,0,0,1 +7466,ENFP,16.76,0.1804111905423251,0.48,0.34,0.02,0.06,0.0,0.58,0.0,1.78,0,0,0,1 +7467,INFJ,29.82,0.1849973355643598,0.02,0.1,0.24,0.0,0.04,0.72,0.0,3.54,1,0,0,0 +7468,INTP,21.06,0.1820580194014989,0.0,0.04,0.0,0.0,0.0,0.58,0.0,1.74,1,0,1,1 +7469,INTP,5.48,0.1885013189198514,0.68,0.72,0.0,0.0,0.04,0.12,0.0,1.04,1,0,1,1 +7470,INFJ,13.04,0.19419274347048418,0.28,0.26,0.72,0.06,0.0,0.2,0.02,1.58,1,0,0,0 +7471,INTJ,25.08,0.1817650704475681,0.06,0.2,0.1,0.02,0.0,0.6,0.0,2.72,1,0,1,0 +7472,ENFP,31.4,0.1886017735295375,0.02,0.18,0.18,0.0,0.0,1.28,0.0,3.04,0,0,0,1 +7473,INTJ,28.96,0.17945982458825913,0.2,0.18,0.12,0.04,0.02,0.82,0.0,2.64,1,0,1,0 +7474,INFJ,18.16,0.19100461922160486,0.02,0.06,0.08,0.0,0.08,0.86,0.0,2.48,1,0,0,0 +7475,INTJ,25.96,0.18886394959758648,0.04,0.32,0.4,0.02,0.0,0.74,0.02,2.46,1,0,1,0 +7476,ENTP,17.08,0.18674191243602284,0.06,0.06,0.22,0.06,0.0,0.56,0.0,2.0,0,0,1,1 +7477,INFP,33.66,0.17943119526090776,0.02,0.16,0.0,0.0,0.0,0.88,0.0,4.3,1,0,0,1 +7478,INFJ,24.5,0.19220665977990287,0.0,0.14,1.02,0.0,0.0,0.82,0.0,3.66,1,0,0,0 +7479,INTP,19.86,0.19134310429510065,0.48,0.4,0.08,0.16,0.02,0.68,0.0,2.64,1,0,1,1 +7480,ESTJ,24.18,0.18457761125190808,0.0,0.28,0.0,0.0,0.0,0.68,0.0,2.46,0,1,1,0 +7481,ISTP,22.64,0.17875577164556203,0.0,0.2,0.0,0.0,0.02,0.44,0.0,2.38,1,1,1,1 +7482,ENTP,26.42,0.18415384060405118,0.0,0.56,0.06,0.0,0.0,0.48,0.0,2.62,0,0,1,1 +7483,INTJ,23.08,0.17949699289013257,0.04,0.12,0.0,0.04,0.04,0.42,0.02,2.48,1,0,1,0 +7484,INTJ,24.44,0.1875,0.02,0.14,0.14,0.0,0.0,0.7,0.0,2.24,1,0,1,0 +7485,INFP,28.86,0.18377128755987807,0.06,0.1,0.04,0.0,0.02,0.72,0.0,3.68,1,0,0,1 +7486,INFP,32.5,0.18034544977735542,0.02,0.32,0.04,0.0,0.0,1.12,0.0,4.36,1,0,0,1 +7487,INTP,21.64,0.17845757628776424,0.08,0.14,0.22,0.0,0.02,0.62,0.0,2.58,1,0,1,1 +7488,INFP,23.3,0.18121751512993942,0.0,0.06,0.08,0.0,0.0,0.4,0.0,3.24,1,0,0,1 +7489,INFJ,15.16,0.17793345395054577,0.1,0.06,0.0,0.08,0.0,0.2,0.0,2.08,1,0,0,0 +7490,ENTP,26.78,0.18459606243496354,0.0,0.38,0.1,0.0,0.02,0.8,0.0,2.8,0,0,1,1 +7491,ENTJ,23.64,0.17519574790536346,0.0,0.12,0.04,0.0,0.02,0.66,0.0,2.0,0,0,1,0 +7492,INTJ,27.5,0.1728469575133462,0.02,0.44,0.02,0.0,0.02,0.6,0.04,2.74,1,0,1,0 +7493,ISFJ,26.16,0.18615569119612327,0.02,0.18,0.8,0.0,0.02,0.64,0.02,3.56,1,1,0,0 +7494,INFP,27.64,0.1879622195406834,0.02,0.16,0.36,0.0,0.02,0.66,0.0,3.08,1,0,0,1 +7495,INFP,31.06,0.18259819136550512,0.04,0.22,0.54,0.0,0.0,1.02,0.0,3.64,1,0,0,1 +7496,ENFP,23.7,0.19414781233681852,0.02,0.56,0.26,0.0,0.0,0.46,0.0,2.98,0,0,0,1 +7497,INFP,24.58,0.17699205390092548,0.0,0.0,0.08,0.0,0.06,0.5,0.0,1.56,1,0,0,1 +7498,ENFP,27.24,0.18496873287189522,0.02,0.1,0.04,0.0,0.0,0.72,0.0,2.94,0,0,0,1 +7499,INFP,21.56,0.18508435992114064,0.02,0.1,0.4,0.02,0.04,0.46,0.0,2.66,1,0,0,1 +7500,INTJ,30.92,0.19290368695515847,0.02,0.22,0.18,0.02,0.0,1.1,0.0,3.02,1,0,1,0 +7501,INTJ,26.8,0.18513886017127112,0.0,0.18,0.16,0.0,0.0,0.8,0.0,2.26,1,0,1,0 +7502,INFP,19.74,0.18454353430082093,0.04,0.38,0.18,0.0,0.0,0.3,0.0,1.48,1,0,0,1 +7503,ENFP,7.48,0.1852877808556479,0.0,0.14,0.04,0.0,0.0,0.12,0.0,1.06,0,0,0,1 +7504,INFJ,23.5,0.18001194141610705,0.08,0.18,0.0,0.0,0.02,0.36,0.0,2.7,1,0,0,0 +7505,ISTP,20.98,0.18905398779564733,0.1,0.32,0.02,0.02,0.0,0.38,0.0,2.44,1,1,1,1 +7506,INFP,25.92,0.17910592653584853,0.26,0.16,0.08,0.12,0.04,0.76,0.0,3.24,1,0,0,1 +7507,INFJ,31.52,0.18775640998612808,0.0,0.5,0.8,0.0,0.0,1.0,0.0,3.18,1,0,0,0 +7508,ISTP,20.42,0.18687965640623466,0.0,0.16,0.06,0.0,0.0,0.42,0.0,2.18,1,1,1,1 +7509,INTP,23.54,0.17944113215558602,0.12,0.22,0.0,0.08,0.0,0.56,0.0,2.54,1,0,1,1 +7510,INFJ,30.28,0.18364219271766966,0.02,0.16,0.34,0.04,0.02,0.86,0.0,3.8,1,0,0,0 +7511,INTJ,24.96,0.18434781924915652,0.14,0.42,0.14,0.0,0.0,0.58,0.0,2.66,1,0,1,0 +7512,INFP,28.34,0.18071093823640308,0.06,0.2,0.14,0.0,0.0,0.74,0.0,3.3,1,0,0,1 +7513,INTJ,13.66,0.17385501352402374,0.4,0.28,0.16,0.12,0.0,0.54,0.0,1.56,1,0,1,0 +7514,INFJ,33.22,0.18626864193612383,0.0,0.06,0.06,0.0,0.02,0.78,0.0,4.34,1,0,0,0 +7515,INTP,21.7,0.18169654002789218,0.0,0.08,0.0,0.0,0.0,0.4,0.02,1.88,1,0,1,1 +7516,ISFJ,29.94,0.18813995126637745,0.02,0.12,0.14,0.02,0.02,1.18,0.0,3.24,1,1,0,0 +7517,ENTP,24.0,0.18606193895596815,0.0,0.22,0.22,0.0,0.0,0.48,0.0,2.66,0,0,1,1 +7518,INFP,17.88,0.17705461478709134,0.14,0.14,0.26,0.02,0.06,0.4,0.0,1.24,1,0,0,1 +7519,INTP,26.84,0.18143022550374052,0.1,0.34,0.24,0.04,0.02,0.8,0.0,2.8,1,0,1,1 +7520,INTP,25.06,0.1810865303389525,0.0,0.18,0.04,0.0,0.0,0.42,0.0,2.54,1,0,1,1 +7521,INTJ,29.8,0.18101991049270943,0.0,0.08,0.14,0.0,0.0,0.88,0.0,3.68,1,0,1,0 +7522,ENTP,27.96,0.18306842491336495,0.0,0.08,0.0,0.0,0.02,0.46,0.0,4.44,0,0,1,1 +7523,INFJ,30.92,0.1824210652054085,0.04,0.28,0.06,0.0,0.08,0.82,0.02,2.8,1,0,0,0 +7524,INTJ,29.0,0.18351522315708307,0.0,0.26,0.14,0.0,0.0,0.68,0.0,3.84,1,0,1,0 +7525,INTP,26.88,0.18355855477677738,0.04,0.24,0.06,0.02,0.0,0.6,0.0,2.94,1,0,1,1 +7526,INTP,30.92,0.18360062712107064,0.02,0.38,0.28,0.02,0.04,0.92,0.0,3.58,1,0,1,1 +7527,ENFP,15.24,0.1958008004890889,0.0,0.16,0.2,0.0,0.0,0.14,0.0,1.7,0,0,0,1 +7528,ENTP,21.6,0.1877746088779079,0.02,0.2,0.2,0.02,0.02,0.4,0.0,2.74,0,0,1,1 +7529,ENFP,24.16,0.18517428841172248,0.04,0.3,0.1,0.0,0.0,0.46,0.0,2.68,0,0,0,1 +7530,ESFP,14.86,0.18297743240111897,0.32,0.22,0.04,0.06,0.0,0.3,0.0,2.12,0,1,0,1 +7531,ENFP,27.92,0.18195812961581093,0.06,0.26,0.3,0.06,0.02,0.64,0.0,2.9,0,0,0,1 +7532,INTP,19.44,0.1775279687339862,0.04,0.2,0.02,0.0,0.02,0.54,0.02,1.9,1,0,1,1 +7533,INTP,27.02,0.18250427544901773,0.0,0.04,0.0,0.0,0.04,0.58,0.06,3.26,1,0,1,1 +7534,INTJ,22.76,0.187828159420731,0.12,0.24,0.04,0.0,0.0,0.32,0.0,2.4,1,0,1,0 +7535,INFJ,20.1,0.18506698425350196,0.0,0.08,0.28,0.0,0.0,0.44,0.0,1.66,1,0,0,0 +7536,ISTP,10.9,0.18224632002685073,0.02,0.12,0.04,0.0,0.04,0.26,0.0,1.28,1,1,1,1 +7537,ISFP,28.66,0.18438710604043201,0.04,0.06,0.12,0.0,0.12,0.56,0.0,3.74,1,1,0,1 +7538,INTP,26.72,0.1725496389465286,0.06,0.22,0.12,0.0,0.04,0.6,0.0,1.92,1,0,1,1 +7539,ENTP,20.3,0.19432176943608792,0.08,0.36,0.08,0.02,0.0,0.34,0.0,2.52,0,0,1,1 +7540,INTJ,28.88,0.18523932311254118,0.0,0.38,0.06,0.0,0.0,0.64,0.0,3.4,1,0,1,0 +7541,INFJ,30.42,0.18465271050729104,0.0,0.1,0.1,0.0,0.0,0.78,0.0,3.02,1,0,0,0 +7542,ENTJ,18.22,0.19059318245472298,0.0,0.34,0.52,0.0,0.0,0.3,0.0,1.52,0,0,1,0 +7543,ISTP,22.12,0.18610009073433292,0.06,0.2,0.06,0.0,0.0,0.46,0.0,2.96,1,1,1,1 +7544,INFP,24.06,0.18397760767397484,0.22,0.24,0.02,0.02,0.06,0.68,0.0,3.48,1,0,0,1 +7545,ENFJ,29.06,0.18187653342706323,0.04,0.38,0.06,0.0,0.0,0.88,0.0,1.68,0,0,0,0 +7546,ISTJ,24.84,0.1850016769557613,0.2,0.24,0.56,0.0,0.0,0.7,0.02,2.68,1,1,1,0 +7547,INTP,30.12,0.18184720213455807,0.0,0.1,0.2,0.0,0.0,0.8,0.0,4.1,1,0,1,1 +7548,INFP,31.24,0.18607292790927918,0.04,0.14,0.14,0.0,0.14,0.8,0.0,4.6,1,0,0,1 +7549,ISFP,24.46,0.18623975046526073,0.18,0.26,0.12,0.1,0.0,0.76,0.0,2.7,1,1,0,1 +7550,ISTJ,25.66,0.1810330530198988,0.02,0.24,0.14,0.0,0.04,0.5,0.0,2.4,1,1,1,0 +7551,INFJ,29.34,0.18666408169243992,0.14,0.38,0.18,0.0,0.0,1.02,0.0,2.42,1,0,0,0 +7552,INTJ,17.24,0.1894047807517108,0.06,0.1,0.02,0.04,0.02,0.5,0.0,1.74,1,0,1,0 +7553,ESTP,24.84,0.18795321284063585,0.02,0.22,0.02,0.0,0.0,0.48,0.0,2.76,0,1,1,1 +7554,ISFP,17.34,0.18726240279926018,0.02,0.2,0.26,0.02,0.0,0.26,0.0,2.44,1,1,0,1 +7555,ENTP,24.82,0.1875848090003426,0.08,0.28,0.24,0.02,0.02,0.44,0.0,1.8,0,0,1,1 +7556,INTP,30.8,0.18882539945793259,0.04,0.24,0.08,0.0,0.0,1.1,0.02,3.34,1,0,1,1 +7557,INTJ,29.92,0.18479951000884456,0.0,0.22,0.08,0.0,0.0,0.82,0.0,3.0,1,0,1,0 +7558,ENFJ,22.78,0.1911374601648596,0.3,0.4,0.0,0.0,0.0,0.7,0.0,2.62,0,0,0,0 +7559,ISFJ,34.7,0.18920863997390955,0.02,0.06,0.06,0.0,0.0,1.12,0.02,5.0,1,1,0,0 +7560,INFP,32.02,0.17917817267813727,0.08,0.22,0.04,0.0,0.0,0.84,0.0,3.68,1,0,0,1 +7561,INFJ,29.26,0.18821838483834266,0.0,0.2,0.06,0.0,0.0,0.72,0.0,2.84,1,0,0,0 +7562,INFJ,25.2,0.18140248062847827,0.0,0.28,0.08,0.0,0.02,0.6,0.0,2.54,1,0,0,0 +7563,INFJ,30.36,0.1851782152132184,0.04,0.24,0.12,0.0,0.0,0.72,0.0,4.5,1,0,0,0 +7564,INFP,25.62,0.18407778777160885,0.02,0.08,0.24,0.02,0.0,0.7,0.0,2.66,1,0,0,1 +7565,INTP,28.44,0.1985699891308912,0.04,0.08,0.28,0.04,0.0,1.98,0.0,3.52,1,0,1,1 +7566,ISFP,15.96,0.18757466400528258,0.18,0.44,0.44,0.02,0.06,0.22,0.0,2.16,1,1,0,1 +7567,ENFP,25.7,0.1876342641932411,0.04,0.18,0.2,0.04,0.0,0.68,0.0,2.64,0,0,0,1 +7568,INFJ,24.84,0.17683891798075058,0.12,0.14,0.02,0.0,0.0,0.58,0.0,3.04,1,0,0,0 +7569,INFP,14.64,0.18591971412871583,0.06,0.18,1.08,0.0,0.0,0.16,0.0,1.64,1,0,0,1 +7570,ENTJ,31.52,0.17925556474480153,0.02,0.36,0.06,0.0,0.0,0.94,0.0,3.46,0,0,1,0 +7571,INFP,29.2,0.18361799307958476,0.04,0.1,0.32,0.0,0.0,0.9,0.0,3.14,1,0,0,1 +7572,INFP,31.14,0.1784293803009147,0.06,0.06,0.02,0.0,0.0,0.86,0.0,3.2,1,0,0,1 +7573,INFP,22.64,0.19326865149029282,0.02,0.26,0.08,0.0,0.0,0.64,0.0,3.26,1,0,0,1 +7574,ISFJ,9.16,0.17778086208816937,0.8,0.78,0.04,0.0,0.0,0.44,0.0,1.02,1,1,0,0 +7575,INTJ,25.0,0.18381363453503977,0.14,0.5,0.08,0.0,0.0,0.96,0.02,2.78,1,0,1,0 +7576,ISTP,21.3,0.1831518294803978,0.18,0.24,0.0,0.04,0.0,0.92,0.0,1.7,1,1,1,1 +7577,ENFJ,29.34,0.1872171780926103,0.0,0.26,0.02,0.0,0.0,0.9,0.02,3.36,0,0,0,0 +7578,INTJ,14.34,0.18173492839028205,0.0,0.06,0.0,0.0,0.0,0.3,0.0,1.86,1,0,1,0 +7579,ENTJ,25.82,0.17585742470991317,0.0,0.18,0.04,0.0,0.0,0.68,0.0,1.86,0,0,1,0 +7580,INTP,22.58,0.18086171468611878,0.06,0.2,0.06,0.0,0.0,0.6,0.0,2.08,1,0,1,1 +7581,ISFJ,22.32,0.18210558442045044,0.0,0.18,0.08,0.0,0.0,0.36,0.0,2.18,1,1,0,0 +7582,INFP,35.28,0.18544908750232988,0.04,0.2,0.36,0.0,0.0,0.9,0.0,3.02,1,0,0,1 +7583,INFP,27.94,0.1859134810486162,0.1,0.38,0.0,0.0,0.04,0.78,0.0,3.26,1,0,0,1 +7584,INTP,26.82,0.18112823028771066,0.02,0.1,0.08,0.0,0.04,0.72,0.0,2.78,1,0,1,1 +7585,INFP,22.86,0.185533246145235,0.4,0.44,0.06,0.06,0.0,0.52,0.0,1.8,1,0,0,1 +7586,INFJ,20.76,0.19039773502363722,0.14,0.08,0.24,0.08,0.02,0.4,0.0,2.26,1,0,0,0 +7587,INTP,26.42,0.1783512143711008,0.04,0.12,0.0,0.02,0.0,0.58,0.0,3.44,1,0,1,1 +7588,INTJ,32.16,0.18435814471961037,0.04,0.2,0.24,0.0,0.0,0.8,0.0,3.74,1,0,1,0 +7589,ENFP,30.16,0.18216277747264825,0.0,0.26,0.18,0.0,0.02,0.58,0.0,3.62,0,0,0,1 +7590,INTJ,24.08,0.18647250067090745,0.16,0.2,0.0,0.02,0.02,0.64,0.0,2.84,1,0,1,0 +7591,INFP,34.42,0.1862130324725679,0.02,0.12,0.04,0.0,0.0,0.92,0.0,4.48,1,0,0,1 +7592,INTJ,26.44,0.18201195302896092,0.0,0.36,0.1,0.0,0.02,0.62,0.0,2.9,1,0,1,0 +7593,INFJ,24.78,0.1862838021763935,0.02,0.16,0.0,0.02,0.0,0.44,0.0,3.3,1,0,0,0 +7594,INFJ,23.56,0.18925888068678176,0.04,0.34,0.36,0.0,0.04,0.6,0.0,2.54,1,0,0,0 +7595,ISTP,23.88,0.1806500434976551,0.4,0.24,0.0,0.06,0.0,0.6,0.0,2.36,1,1,1,1 +7596,INFP,22.72,0.18063688089678537,0.06,0.14,0.02,0.04,0.0,0.36,0.0,3.28,1,0,0,1 +7597,INFP,10.9,0.19333393625022924,0.04,0.04,0.2,0.0,0.06,0.1,0.0,1.36,1,0,0,1 +7598,INTJ,22.66,0.17932778979881644,0.1,0.2,0.08,0.06,0.0,0.58,0.0,2.16,1,0,1,0 +7599,INTP,23.28,0.17892678954703584,0.1,0.08,0.0,0.02,0.04,0.78,0.02,2.3,1,0,1,1 +7600,INFJ,33.14,0.18714784986785155,0.0,0.04,0.18,0.0,0.04,0.84,0.0,3.94,1,0,0,0 +7601,INFJ,25.4,0.17925340093511877,0.0,0.1,0.22,0.0,0.0,0.62,0.0,2.5,1,0,0,0 +7602,INTJ,20.14,0.17776142493262737,0.26,0.36,0.04,0.06,0.0,0.48,0.0,1.84,1,0,1,0 +7603,INTP,27.42,0.1765325781261932,0.02,0.1,0.04,0.0,0.0,0.64,0.0,1.82,1,0,1,1 +7604,ENTP,29.74,0.17582329073205816,0.0,0.14,0.26,0.0,0.0,0.74,0.0,3.22,0,0,1,1 +7605,ENTP,27.74,0.18710365372943266,0.04,0.46,0.1,0.02,0.0,0.62,0.0,2.78,0,0,1,1 +7606,ENFJ,30.54,0.18942790410480825,0.0,0.32,0.2,0.0,0.0,1.16,0.0,2.2,0,0,0,0 +7607,INFP,23.46,0.1852059272420809,0.02,0.2,0.1,0.02,0.0,0.62,0.0,2.48,1,0,0,1 +7608,INTP,15.38,0.1935060912554852,0.04,0.3,0.04,0.0,0.02,0.32,0.0,2.06,1,0,1,1 +7609,INTJ,27.44,0.1911326502169002,0.0,0.64,0.08,0.0,0.0,0.52,0.0,1.14,1,0,1,0 +7610,INTP,24.36,0.18289541888505959,0.16,0.24,0.04,0.08,0.02,0.66,0.0,1.98,1,0,1,1 +7611,INFJ,23.0,0.18446257875704133,0.3,0.06,0.1,0.24,0.02,0.64,0.02,2.26,1,0,0,0 +7612,ISFJ,32.14,0.18249551474404724,0.0,0.36,0.08,0.0,0.04,0.76,0.0,3.2,1,1,0,0 +7613,INFJ,29.62,0.19005614995960668,0.0,0.22,0.3,0.0,0.0,0.78,0.0,3.58,1,0,0,0 +7614,INFP,24.78,0.18565140791744528,0.0,0.08,0.06,0.0,0.02,0.8,0.0,1.5,1,0,0,1 +7615,INFJ,28.66,0.18479133296905087,0.16,0.36,0.12,0.02,0.0,0.68,0.0,4.08,1,0,0,0 +7616,INFP,29.08,0.18265452383571537,0.02,0.12,0.04,0.0,0.0,0.8,0.02,3.66,1,0,0,1 +7617,INTP,18.72,0.18192367400704376,0.06,0.2,0.06,0.02,0.0,0.3,0.0,1.68,1,0,1,1 +7618,INFP,7.52,0.18941260945446528,0.56,0.56,0.0,0.0,0.0,0.14,0.0,0.9,1,0,0,1 +7619,ISTJ,24.06,0.18807359079265254,0.06,0.3,0.34,0.02,0.0,0.58,0.0,2.98,1,1,1,0 +7620,ENTP,23.92,0.18394758418005028,0.0,0.12,0.1,0.0,0.0,0.44,0.0,2.36,0,0,1,1 +7621,INTP,30.12,0.1839275794178272,0.02,0.32,0.16,0.02,0.04,0.86,0.0,3.26,1,0,1,1 +7622,INFJ,32.6,0.18114982832931556,0.0,0.14,0.12,0.0,0.0,1.1,0.0,2.54,1,0,0,0 +7623,INFJ,25.0,0.1858046495464488,0.16,0.14,0.0,0.06,0.0,0.52,0.0,2.52,1,0,0,0 +7624,ISTJ,7.98,0.18953975314913657,0.4,0.46,0.02,0.0,0.02,0.26,0.0,0.6,1,1,1,0 +7625,ENTP,17.94,0.19745479668335472,0.1,0.18,0.46,0.02,0.0,0.52,0.0,2.14,0,0,1,1 +7626,ENFP,14.36,0.1918827998597063,0.34,0.46,0.24,0.0,0.04,0.54,0.02,1.58,0,0,0,1 +7627,ENFP,26.54,0.18642010637067866,0.04,0.1,0.52,0.0,0.0,0.72,0.0,1.38,0,0,0,1 +7628,INFJ,30.28,0.18283726716976204,0.0,0.16,0.06,0.0,0.0,0.78,0.0,1.34,1,0,0,0 +7629,INFP,26.68,0.18791881125474674,0.06,0.26,0.12,0.0,0.0,0.7,0.0,3.44,1,0,0,1 +7630,INFP,28.02,0.1905510000405696,0.02,0.2,0.24,0.0,0.08,0.72,0.0,3.44,1,0,0,1 +7631,INTJ,18.38,0.1842333271111431,0.02,0.16,0.04,0.0,0.0,0.46,0.0,2.0,1,0,1,0 +7632,ENFP,14.06,0.19413223140495867,0.0,0.12,0.06,0.0,0.0,0.44,0.02,1.66,0,0,0,1 +7633,ISTP,22.1,0.1775948870447853,0.0,0.24,0.02,0.0,0.0,0.34,0.0,1.9,1,1,1,1 +7634,ENTP,25.28,0.18340919210659465,0.08,0.16,0.08,0.0,0.0,0.7,0.02,3.12,0,0,1,1 +7635,INFP,26.14,0.18822299152896801,0.06,0.34,1.5,0.0,0.06,1.04,0.0,3.52,1,0,0,1 +7636,INFP,24.9,0.17871417442834645,0.16,0.42,0.0,0.0,0.0,0.56,0.0,2.58,1,0,0,1 +7637,INTJ,25.28,0.18833270091648746,0.0,0.22,0.38,0.0,0.02,0.62,0.0,3.4,1,0,1,0 +7638,ENTP,21.78,0.18633558198773562,0.0,0.16,0.0,0.0,0.0,0.62,0.0,2.24,0,0,1,1 +7639,ENFP,10.36,0.2033756469135286,0.02,0.18,0.24,0.0,0.0,0.5,0.0,1.28,0,0,0,1 +7640,ESTJ,11.06,0.1826219859403859,0.6,0.42,0.0,0.08,0.0,0.18,0.02,1.46,0,1,1,0 +7641,INTP,21.4,0.1895177333289961,0.06,0.2,0.1,0.04,0.0,0.44,0.0,2.1,1,0,1,1 +7642,ISFJ,26.32,0.1893458367912017,0.0,0.14,0.16,0.0,0.0,0.76,0.0,3.4,1,1,0,0 +7643,INFP,31.92,0.1883831948041612,0.08,0.28,0.88,0.0,0.16,0.96,0.0,4.0,1,0,0,1 +7644,ISTP,25.72,0.1868052313920147,0.0,0.04,0.1,0.0,0.0,1.2,0.0,3.32,1,1,1,1 +7645,INFP,30.76,0.1781530062028765,0.0,0.32,0.0,0.0,0.0,0.86,0.0,2.98,1,0,0,1 +7646,INTP,18.3,0.18475036657823357,0.2,0.2,0.12,0.08,0.0,0.34,0.0,2.66,1,0,1,1 +7647,INTP,24.52,0.18243180054054559,0.02,0.26,0.04,0.0,0.0,0.52,0.0,2.58,1,0,1,1 +7648,ENFP,28.18,0.18575249305675337,0.02,0.12,0.02,0.0,0.0,0.62,0.0,3.78,0,0,0,1 +7649,INFP,29.42,0.1849615718295542,0.06,0.3,0.3,0.06,0.0,0.62,0.0,2.1,1,0,0,1 +7650,INTP,23.94,0.18019003326140848,0.0,0.14,0.0,0.0,0.0,0.44,0.02,2.42,1,0,1,1 +7651,ENTP,28.1,0.17505275354363473,0.0,0.28,0.02,0.0,0.0,0.72,0.0,2.5,0,0,1,1 +7652,INTP,16.52,0.1850735692277092,0.0,0.14,0.06,0.0,0.0,0.26,0.0,2.34,1,0,1,1 +7653,ENFP,30.86,0.17867286479108685,0.0,0.26,0.0,0.0,0.02,0.72,0.0,3.0,0,0,0,1 +7654,INTP,22.8,0.18805101046464068,0.0,0.32,0.02,0.0,0.0,1.66,0.0,1.36,1,0,1,1 +7655,ISTP,25.08,0.1891618438995499,0.04,0.2,0.3,0.0,0.0,0.58,0.04,2.8,1,1,1,1 +7656,ISTP,20.6,0.18527270025719503,0.22,0.46,0.04,0.08,0.04,0.42,0.0,2.08,1,1,1,1 +7657,ENTP,30.64,0.19368025364142222,0.08,0.34,1.18,0.0,0.0,1.02,0.0,3.5,0,0,1,1 +7658,INFP,31.84,0.18785561805504827,0.02,0.08,0.0,0.0,0.06,1.08,0.0,3.78,1,0,0,1 +7659,ENTP,31.04,0.18606109769996912,0.14,0.16,0.1,0.02,0.02,0.98,0.0,3.78,0,0,1,1 +7660,ENFP,29.46,0.18492664978324835,0.08,0.16,0.24,0.02,0.02,0.6,0.0,3.32,0,0,0,1 +7661,ENTP,19.48,0.18952639376149463,0.0,0.5,0.0,0.0,0.0,0.22,0.0,1.78,0,0,1,1 +7662,INFJ,30.6,0.18346346925351145,0.0,0.34,0.28,0.0,0.0,0.84,0.0,2.78,1,0,0,0 +7663,INFJ,25.92,0.18906036968733664,0.1,0.16,0.22,0.06,0.0,0.68,0.0,3.44,1,0,0,0 +7664,INFJ,31.1,0.18468624975089515,0.06,0.12,0.14,0.0,0.0,0.8,0.0,3.3,1,0,0,0 +7665,INTP,25.76,0.17877279278006095,0.1,0.26,0.08,0.0,0.0,0.68,0.0,3.72,1,0,1,1 +7666,ENTJ,19.78,0.1862371857062617,0.06,0.38,0.42,0.0,0.0,0.5,0.0,1.88,0,0,1,0 +7667,INTJ,26.0,0.18121154935321937,0.12,0.48,0.0,0.0,0.0,0.64,0.0,1.8,1,0,1,0 +7668,INTP,30.64,0.18240922772411308,0.0,0.02,0.02,0.0,0.0,1.04,0.0,3.84,1,0,1,1 +7669,INFP,14.22,0.1785750227452116,0.02,0.16,0.06,0.0,0.0,0.32,0.0,1.46,1,0,0,1 +7670,INFP,30.5,0.19285827427488694,0.0,0.36,0.04,0.0,0.0,0.88,0.0,3.84,1,0,0,1 +7671,INTP,28.5,0.1810913656388143,0.0,0.14,0.02,0.0,0.0,0.76,0.0,3.6,1,0,1,1 +7672,ENTP,24.32,0.18448253008565246,0.02,0.54,0.04,0.0,0.0,0.96,0.0,2.2,0,0,1,1 +7673,INTJ,28.14,0.19138206316149856,0.0,0.4,0.04,0.0,0.02,0.6,0.0,3.44,1,0,1,0 +7674,INFJ,28.74,0.1848397445532451,0.04,0.2,0.12,0.0,0.0,0.7,0.0,3.1,1,0,0,0 +7675,ENTP,24.08,0.18951586361688455,0.08,0.24,0.14,0.02,0.0,1.02,0.0,1.88,0,0,1,1 +7676,ISFP,26.46,0.18320050780896613,0.0,0.06,0.0,0.0,0.0,0.82,0.0,1.74,1,1,0,1 +7677,INTJ,25.0,0.18075883930812564,0.08,0.34,0.04,0.04,0.02,0.48,0.0,2.22,1,0,1,0 +7678,INFP,30.76,0.182321954538833,0.02,0.32,0.3,0.0,0.0,0.84,0.0,1.34,1,0,0,1 +7679,ENFP,30.02,0.18246134300954567,0.0,0.22,0.06,0.0,0.0,0.7,0.0,3.1,0,0,0,1 +7680,INFJ,24.28,0.1873376272604392,0.02,0.14,0.04,0.0,0.0,0.46,0.0,3.5,1,0,0,0 +7681,INFP,27.72,0.17688270769963715,0.12,0.1,0.02,0.0,0.02,0.64,0.0,3.5,1,0,0,1 +7682,INFJ,27.88,0.18449023776384138,0.12,0.2,0.0,0.0,0.06,0.7,0.0,3.62,1,0,0,0 +7683,INTP,25.44,0.18251232647151197,0.04,0.12,0.06,0.04,0.02,0.56,0.0,3.06,1,0,1,1 +7684,ISTJ,29.68,0.18602657318115234,0.02,0.28,0.04,0.02,0.0,0.54,0.0,3.2,1,1,1,0 +7685,INTP,32.98,0.18023499001797152,0.0,0.14,0.12,0.0,0.0,0.94,0.0,3.92,1,0,1,1 +7686,INTJ,32.12,0.17882767855291345,0.0,0.14,0.0,0.0,0.0,0.86,0.0,3.18,1,0,1,0 +7687,ENTJ,22.18,0.18927428363400015,0.02,0.22,0.14,0.02,0.02,0.58,0.0,1.76,0,0,1,0 +7688,INFJ,25.4,0.18058581248127728,0.0,0.16,0.18,0.0,0.06,0.62,0.0,3.34,1,0,0,0 +7689,INTJ,28.94,0.19142509756866144,0.0,0.32,0.24,0.0,0.0,1.06,0.0,3.2,1,0,1,0 +7690,INFP,34.5,0.18677686551973582,0.0,0.06,0.0,0.0,0.02,0.96,0.02,4.46,1,0,0,1 +7691,ESTP,17.22,0.18069827047471332,0.06,0.16,0.02,0.0,0.02,0.24,0.0,1.76,0,1,1,1 +7692,INTP,22.9,0.18495232975857373,0.02,0.14,0.08,0.0,0.08,0.92,0.0,2.36,1,0,1,1 +7693,ENFP,18.1,0.19296483819786958,0.0,0.34,0.84,0.0,0.0,0.36,0.0,1.26,0,0,0,1 +7694,INFJ,29.94,0.18405861631700107,0.02,0.28,0.1,0.0,0.02,0.94,0.0,3.02,1,0,0,0 +7695,INFJ,15.56,0.191017187522637,0.0,0.26,0.36,0.0,0.0,0.38,0.0,2.16,1,0,0,0 +7696,ISTJ,27.1,0.18151427429574196,0.0,0.26,0.78,0.0,0.0,0.56,0.0,3.08,1,1,1,0 +7697,INTP,15.72,0.18015385723955346,0.0,0.28,0.0,0.0,0.0,0.14,0.0,1.0,1,0,1,1 +7698,INTP,23.48,0.1918634195323446,0.1,0.12,0.0,0.0,0.04,1.22,0.0,2.98,1,0,1,1 +7699,ENTP,22.22,0.18122396137991123,0.0,0.18,0.06,0.0,0.0,0.42,0.0,1.6,0,0,1,1 +7700,ISFJ,26.3,0.1843927107615382,0.06,0.38,0.02,0.0,0.0,0.58,0.0,2.74,1,1,0,0 +7701,ENTJ,28.38,0.1863883765504215,0.02,0.18,0.08,0.0,0.0,0.92,0.0,4.08,0,0,1,0 +7702,INFP,25.8,0.17859952556165126,0.06,0.06,0.2,0.0,0.02,0.52,0.0,1.42,1,0,0,1 +7703,INTP,25.98,0.18379606987946945,0.02,0.04,0.0,0.0,0.02,0.54,0.0,3.3,1,0,1,1 +7704,INTP,16.34,0.17531152347517456,0.2,0.18,0.12,0.06,0.0,0.3,0.0,1.72,1,0,1,1 +7705,INFJ,18.98,0.18130761208387097,0.28,0.34,0.0,0.0,0.0,0.6,0.0,2.3,1,0,0,0 +7706,ENFP,23.34,0.18016203955712082,0.0,0.06,0.46,0.0,0.0,0.42,0.0,2.48,0,0,0,1 +7707,ISFP,11.66,0.18544172497216296,0.0,0.0,0.26,0.0,0.0,0.32,0.0,1.7,1,1,0,1 +7708,INTJ,22.98,0.1895490403660952,0.0,0.2,0.12,0.0,0.0,0.66,0.0,2.82,1,0,1,0 +7709,INFP,13.34,0.19125083447794294,0.0,0.1,0.14,0.0,0.0,0.2,0.0,1.62,1,0,0,1 +7710,ENTP,30.98,0.1829435080346597,0.02,0.06,0.12,0.02,0.1,0.58,0.0,3.72,0,0,1,1 +7711,ESTP,16.32,0.19134032291205733,0.28,0.22,0.06,0.16,0.0,0.44,0.0,2.48,0,1,1,1 +7712,ENFP,32.2,0.1862485870730025,0.0,0.18,0.16,0.0,0.0,0.88,0.0,3.8,0,0,0,1 +7713,INTP,22.98,0.1810106553436949,0.04,0.18,0.04,0.0,0.02,0.6,0.0,2.24,1,0,1,1 +7714,INFJ,31.44,0.18248410150411953,0.0,0.18,0.04,0.0,0.0,0.96,0.0,3.28,1,0,0,0 +7715,ISFP,21.28,0.1871185054234413,0.12,0.3,0.14,0.0,0.0,0.36,0.0,2.44,1,1,0,1 +7716,INFP,26.06,0.17914022784974354,0.0,0.18,0.2,0.0,0.0,0.84,0.0,3.38,1,0,0,1 +7717,INTP,27.86,0.17788559309154284,0.14,0.08,0.0,0.06,0.08,0.68,0.0,3.7,1,0,1,1 +7718,INFP,21.5,0.19835981339518186,0.04,0.24,0.22,0.0,0.02,0.84,0.0,2.06,1,0,0,1 +7719,INFP,21.62,0.18452679920985926,0.16,0.24,0.16,0.04,0.0,0.56,0.0,2.46,1,0,0,1 +7720,ENFP,30.46,0.18943808551123192,0.04,0.26,0.2,0.04,0.0,1.04,0.0,3.54,0,0,0,1 +7721,INFP,18.94,0.1905050194807737,0.02,0.08,0.08,0.0,0.0,0.44,0.02,2.62,1,0,0,1 +7722,INFJ,26.46,0.18466890940683695,0.0,0.18,0.02,0.0,0.0,0.54,0.0,3.0,1,0,0,0 +7723,ENTP,29.8,0.18404401844105414,0.06,0.24,0.1,0.0,0.02,0.74,0.0,3.58,0,0,1,1 +7724,INTJ,26.38,0.1792410614809148,0.08,0.36,0.08,0.0,0.02,0.64,0.0,2.12,1,0,1,0 +7725,INFP,28.14,0.18033007163542145,0.06,0.08,0.06,0.0,0.1,0.74,0.0,2.44,1,0,0,1 +7726,ENFP,16.24,0.18021784089192097,0.26,0.3,0.32,0.0,0.04,0.28,0.0,1.76,0,0,0,1 +7727,INTP,30.18,0.18640175511448231,0.02,0.08,0.26,0.0,0.06,0.8,0.0,3.7,1,0,1,1 +7728,INFP,10.02,0.18871793310414786,0.52,0.36,0.12,0.14,0.02,0.16,0.0,1.0,1,0,0,1 +7729,ENFJ,25.88,0.18814907285961485,0.02,0.08,0.38,0.0,0.06,0.4,0.0,3.1,0,0,0,0 +7730,INFJ,35.14,0.18033757373801695,0.0,0.02,0.02,0.0,0.02,0.98,0.0,3.8,1,0,0,0 +7731,INTP,31.0,0.1906818490733768,0.0,0.26,0.4,0.0,0.0,0.86,0.0,3.98,1,0,1,1 +7732,ENTJ,35.8,0.18233024717878685,0.0,0.22,0.18,0.0,0.02,0.94,0.02,4.4,0,0,1,0 +7733,ISFP,24.88,0.18706479485235666,0.04,0.1,0.0,0.0,0.04,0.74,0.0,3.82,1,1,0,1 +7734,INTP,23.42,0.1785604495977657,0.08,0.14,0.04,0.0,0.04,0.42,0.0,2.42,1,0,1,1 +7735,ESTP,27.28,0.18106135314146787,0.0,0.12,0.0,0.0,0.0,0.6,0.02,2.92,0,1,1,1 +7736,INFP,23.48,0.18470612705268016,0.1,0.06,0.06,0.04,0.1,0.48,0.0,3.14,1,0,0,1 +7737,INTJ,15.3,0.1844903035298939,0.04,0.12,0.18,0.02,0.0,0.36,0.0,1.82,1,0,1,0 +7738,ESTJ,14.32,0.18214521492493516,0.0,0.12,0.48,0.0,0.0,0.38,0.0,1.4,0,1,1,0 +7739,ENTP,28.28,0.18541522980383285,0.02,0.2,0.04,0.0,0.0,0.8,0.0,3.2,0,0,1,1 +7740,INTJ,16.34,0.193249735332286,0.0,0.2,0.02,0.0,0.0,0.5,0.0,1.88,1,0,1,0 +7741,INFJ,27.46,0.18683721769560369,0.02,0.2,0.26,0.0,0.0,0.52,0.0,3.3,1,0,0,0 +7742,INTP,34.76,0.18665510759399132,0.0,0.08,0.36,0.0,0.0,0.94,0.02,4.08,1,0,1,1 +7743,INFP,26.36,0.18489223031473792,0.08,0.1,0.04,0.0,0.06,0.74,0.0,3.46,1,0,0,1 +7744,ISTP,21.1,0.18191423024495942,0.2,0.36,0.3,0.06,0.0,0.44,0.06,2.24,1,1,1,1 +7745,INFJ,32.8,0.18167401057522833,0.04,0.14,0.04,0.0,0.0,0.88,0.0,3.5,1,0,0,0 +7746,INTP,28.62,0.18355035324150987,0.06,0.18,0.04,0.0,0.04,1.08,0.0,2.84,1,0,1,1 +7747,ENFP,14.56,0.18455502271290894,0.58,0.84,0.3,0.02,0.04,0.32,0.0,1.44,0,0,0,1 +7748,INTJ,15.2,0.1796268024463853,0.34,0.06,0.04,0.26,0.0,0.36,0.0,2.7,1,0,1,0 +7749,INTP,25.5,0.1768736465557414,0.06,0.1,0.08,0.0,0.0,0.64,0.0,2.6,1,0,1,1 +7750,INFJ,21.2,0.18293621442688504,0.02,0.1,0.12,0.02,0.0,0.54,0.0,3.0,1,0,0,0 +7751,INFJ,32.3,0.18936744387772014,0.02,0.18,0.24,0.0,0.02,0.98,0.0,3.88,1,0,0,0 +7752,INFP,20.62,0.1829829888358033,0.1,0.28,0.0,0.0,0.0,0.5,0.0,2.18,1,0,0,1 +7753,ENFP,28.38,0.19119605365064987,0.0,0.1,0.26,0.0,0.0,0.7,0.0,2.5,0,0,0,1 +7754,INTJ,25.62,0.1869309196768298,0.04,0.2,0.18,0.04,0.0,0.64,0.0,3.08,1,0,1,0 +7755,INTP,22.82,0.18997426887273636,0.0,0.16,0.26,0.0,0.02,0.82,0.0,3.2,1,0,1,1 +7756,INTP,28.48,0.18265072176020397,0.0,0.16,0.14,0.0,0.02,0.8,0.0,2.74,1,0,1,1 +7757,INFP,25.54,0.18687890665481108,0.1,0.08,0.04,0.04,0.02,0.72,0.0,2.72,1,0,0,1 +7758,INFP,23.36,0.18558331145845508,0.02,0.14,0.16,0.02,0.0,0.54,0.0,3.34,1,0,0,1 +7759,INFP,9.26,0.17911169647744396,0.02,0.02,0.0,0.0,0.04,0.18,0.0,1.3,1,0,0,1 +7760,INTP,21.42,0.1822874531943417,0.18,0.32,0.02,0.0,0.04,0.46,0.0,2.38,1,0,1,1 +7761,INTP,23.52,0.1835466529375846,0.02,0.14,0.02,0.0,0.0,0.64,0.0,1.94,1,0,1,1 +7762,INFJ,29.8,0.19085716714296666,0.0,0.34,0.28,0.0,0.02,0.9,0.0,3.34,1,0,0,0 +7763,INTP,15.72,0.1832539366138791,0.0,0.08,0.1,0.0,0.0,0.36,0.0,1.84,1,0,1,1 +7764,INTJ,26.7,0.1883108022266035,0.08,0.2,0.04,0.06,0.0,0.78,0.02,2.88,1,0,1,0 +7765,INFJ,30.64,0.17664055647913451,0.08,0.1,0.04,0.08,0.0,0.88,0.0,3.14,1,0,0,0 +7766,ENTP,24.0,0.18467411450855,0.02,0.54,0.12,0.0,0.0,0.78,0.0,2.5,0,0,1,1 +7767,ISTJ,23.12,0.1875365764439864,0.04,0.22,0.06,0.0,0.0,0.62,0.06,2.76,1,1,1,0 +7768,ENTP,20.32,0.18930264007975214,0.0,0.24,0.44,0.0,0.0,0.72,0.0,2.44,0,0,1,1 +7769,ENFJ,21.8,0.19820621996832116,0.0,0.34,1.3,0.0,0.0,0.96,0.0,2.36,0,0,0,0 +7770,ENFJ,26.12,0.1893052000211337,0.04,0.26,0.4,0.0,0.0,0.5,0.0,3.16,0,0,0,0 +7771,INTJ,28.98,0.18794510244160378,0.02,0.36,0.26,0.0,0.0,0.78,0.0,3.24,1,0,1,0 +7772,ISTJ,29.4,0.18066961251830155,0.0,0.16,0.18,0.0,0.04,0.68,0.0,3.12,1,1,1,0 +7773,ESTP,27.88,0.1902872619710263,0.0,0.18,0.04,0.0,0.02,0.92,0.02,2.7,0,1,1,1 +7774,INFP,22.9,0.18353680919657867,0.18,0.22,0.04,0.0,0.06,0.62,0.02,1.72,1,0,0,1 +7775,INFJ,26.96,0.1817767115374349,0.02,0.08,0.02,0.0,0.0,0.88,0.0,2.88,1,0,0,0 +7776,INTJ,18.34,0.16945923213534733,0.26,0.26,0.04,0.0,0.02,0.26,0.0,1.44,1,0,1,0 +7777,ENTP,28.12,0.18501701872809068,0.02,0.22,0.08,0.0,0.1,0.56,0.02,2.92,0,0,1,1 +7778,ISTJ,25.2,0.18331421001692869,0.18,0.12,0.2,0.1,0.02,0.54,0.0,2.96,1,1,1,0 +7779,ISTJ,29.48,0.18595713107298575,0.06,0.22,0.08,0.0,0.06,0.66,0.0,3.68,1,1,1,0 +7780,INFP,23.98,0.18437616114546782,0.0,0.36,0.12,0.0,0.0,0.6,0.0,1.86,1,0,0,1 +7781,INFP,25.26,0.18683048147691578,0.14,0.14,0.1,0.0,0.0,0.9,0.0,3.3,1,0,0,1 +7782,ISTP,28.8,0.18031518730853346,0.02,0.1,0.04,0.0,0.0,0.7,0.0,3.22,1,1,1,1 +7783,INTJ,30.74,0.18002332697552917,0.04,0.2,0.04,0.0,0.02,0.86,0.0,3.56,1,0,1,0 +7784,INTJ,23.32,0.18471503252347163,0.0,0.04,0.0,0.0,0.0,0.6,0.0,2.56,1,0,1,0 +7785,ENFJ,29.08,0.1849529520218588,0.06,0.28,0.58,0.02,0.04,0.76,0.0,3.06,0,0,0,0 +7786,INFP,32.46,0.1851275687114595,0.0,0.22,0.2,0.0,0.02,0.92,0.0,3.42,1,0,0,1 +7787,INFP,26.94,0.18856545897742583,0.04,0.3,0.12,0.02,0.0,0.54,0.0,3.44,1,0,0,1 +7788,INTJ,15.36,0.18547913258888948,0.14,0.14,0.02,0.0,0.02,0.26,0.02,2.18,1,0,1,0 +7789,ENFP,22.54,0.18337990284645256,0.06,0.7,0.24,0.0,0.0,0.48,0.0,2.64,0,0,0,1 +7790,ENFP,24.84,0.2001186229093722,0.02,0.26,1.32,0.0,0.0,0.84,0.0,2.12,0,0,0,1 +7791,INTP,23.2,0.18525167136584886,0.0,0.24,0.04,0.0,0.06,0.42,0.0,2.64,1,0,1,1 +7792,INFJ,21.08,0.19556062983156233,0.02,0.2,0.12,0.0,0.08,0.44,0.02,2.12,1,0,0,0 +7793,INFJ,32.36,0.18827381211353494,0.08,0.04,0.2,0.0,0.0,0.8,0.0,1.94,1,0,0,0 +7794,INFJ,16.34,0.18678452176492447,0.04,0.22,0.0,0.0,0.0,0.9,0.0,0.96,1,0,0,0 +7795,ENFP,29.48,0.1829800860395829,0.02,0.1,0.26,0.02,0.04,0.84,0.0,2.96,0,0,0,1 +7796,ENTP,23.1,0.18506025893221226,0.06,0.12,0.08,0.0,0.0,0.4,0.0,3.24,0,0,1,1 +7797,INTJ,10.86,0.16776444903781318,0.64,0.46,0.0,0.28,0.0,0.24,0.02,0.78,1,0,1,0 +7798,INFJ,32.54,0.18792767933564775,0.0,0.14,0.34,0.0,0.02,1.02,0.0,2.74,1,0,0,0 +7799,INTJ,28.72,0.18019007112648983,0.0,0.28,0.2,0.0,0.04,0.7,0.0,2.46,1,0,1,0 +7800,ISFP,1.16,0.18875624936230997,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.08,1,1,0,1 +7801,INTP,15.28,0.1852315809223125,0.02,0.24,0.1,0.0,0.0,0.12,0.02,1.52,1,0,1,1 +7802,INTJ,20.74,0.18157471668113884,0.16,0.38,0.08,0.02,0.0,0.4,0.0,1.88,1,0,1,0 +7803,INTP,15.88,0.17628786279596848,0.0,0.16,0.04,0.0,0.0,0.32,0.0,1.32,1,0,1,1 +7804,ESFP,17.42,0.18197734461874118,0.02,0.14,0.0,0.0,0.02,0.4,0.0,1.18,0,1,0,1 +7805,INFP,18.98,0.1875454297640817,0.02,0.34,0.08,0.0,0.02,0.5,0.02,2.34,1,0,0,1 +7806,INTP,16.4,0.1965586177808423,0.0,0.18,0.16,0.0,0.02,0.42,0.0,1.92,1,0,1,1 +7807,INFJ,24.3,0.17915063588796554,0.02,0.04,0.0,0.0,0.0,0.48,0.02,2.92,1,0,0,0 +7808,ISTP,25.54,0.1896868979929539,0.0,0.4,0.08,0.0,0.02,0.54,0.0,2.26,1,1,1,1 +7809,INTJ,20.44,0.1895993706372233,0.2,0.4,0.04,0.04,0.0,0.44,0.0,3.04,1,0,1,0 +7810,ENFJ,28.86,0.18561904790595782,0.08,0.2,0.12,0.0,0.02,0.76,0.0,3.32,0,0,0,0 +7811,INFJ,29.42,0.18833336955033314,0.22,0.24,0.08,0.1,0.0,0.96,0.0,3.14,1,0,0,0 +7812,INTP,21.16,0.17752112320874552,0.14,0.28,0.0,0.0,0.02,0.42,0.0,2.1,1,0,1,1 +7813,INTJ,30.3,0.1825164899845544,0.0,0.14,0.12,0.0,0.0,0.74,0.0,3.82,1,0,1,0 +7814,INFP,33.16,0.18435351886255708,0.0,0.08,0.26,0.0,0.0,0.96,0.0,1.58,1,0,0,1 +7815,ENFP,25.82,0.18833134466435414,0.0,0.32,0.36,0.0,0.0,1.16,0.0,2.62,0,0,0,1 +7816,INTJ,23.52,0.18214981674393557,0.12,0.12,0.0,0.06,0.02,0.44,0.0,3.14,1,0,1,0 +7817,ENTP,19.48,0.18849738208906583,0.04,0.24,0.16,0.0,0.0,0.46,0.0,2.72,0,0,1,1 +7818,INFJ,31.68,0.18178132252899235,0.0,0.28,0.1,0.0,0.02,0.74,0.0,3.56,1,0,0,0 +7819,INFJ,27.04,0.18743578217736642,0.0,0.2,0.22,0.0,0.0,0.6,0.0,3.54,1,0,0,0 +7820,ENFP,29.46,0.18554628798881106,0.0,0.18,0.14,0.0,0.0,0.8,0.0,3.92,0,0,0,1 +7821,INFP,27.88,0.1903546338987523,0.02,0.18,0.46,0.0,0.0,0.8,0.0,3.72,1,0,0,1 +7822,INTJ,21.28,0.19553603617077212,0.0,0.42,0.18,0.0,0.0,0.48,0.0,2.68,1,0,1,0 +7823,INFP,29.44,0.18651117303217718,0.02,0.18,0.16,0.0,0.0,0.88,0.0,3.12,1,0,0,1 +7824,ENTJ,20.06,0.17726192778437083,0.04,0.1,0.04,0.0,0.0,0.32,0.02,1.56,0,0,1,0 +7825,INFJ,26.9,0.18274965346151673,0.08,0.12,0.0,0.0,0.0,0.62,0.0,3.42,1,0,0,0 +7826,INFP,24.78,0.18512842342311928,0.0,0.2,0.34,0.0,0.0,0.62,0.0,1.94,1,0,0,1 +7827,INTJ,27.18,0.179247253785222,0.02,0.0,0.06,0.0,0.0,0.62,0.0,2.38,1,0,1,0 +7828,INTJ,27.58,0.1862846268803118,0.0,0.08,0.08,0.0,0.02,0.78,0.0,3.42,1,0,1,0 +7829,INFP,23.1,0.1855854301974059,0.16,0.22,0.02,0.04,0.0,0.7,0.0,2.86,1,0,0,1 +7830,ISTJ,19.42,0.18003892314795633,0.08,0.24,0.02,0.0,0.02,0.5,0.0,1.94,1,1,1,0 +7831,INFP,18.0,0.1880787005883904,0.0,0.18,0.0,0.0,0.0,0.52,0.0,1.96,1,0,0,1 +7832,INFP,19.44,0.1913980654092504,0.0,0.3,0.36,0.0,0.0,0.42,0.0,2.08,1,0,0,1 +7833,ISTP,24.92,0.18718050095771074,0.04,0.18,0.04,0.02,0.06,0.64,0.0,2.64,1,1,1,1 +7834,INTJ,14.14,0.19004773970995642,0.22,0.2,0.26,0.02,0.04,0.28,0.0,2.02,1,0,1,0 +7835,INFP,20.94,0.19058348229735966,0.06,0.14,0.22,0.02,0.0,0.46,0.06,2.92,1,0,0,1 +7836,ENTP,19.76,0.1839446821684976,0.18,0.12,0.02,0.08,0.02,0.44,0.0,1.82,0,0,1,1 +7837,INFP,19.02,0.18363427466435586,0.06,0.12,0.24,0.06,0.0,0.5,0.0,1.52,1,0,0,1 +7838,INFJ,24.88,0.18826696619281877,0.3,0.3,0.12,0.26,0.0,0.66,0.0,2.36,1,0,0,0 +7839,ISTP,26.64,0.17864071100297477,0.04,0.18,0.04,0.0,0.02,0.64,0.02,3.48,1,1,1,1 +7840,INTJ,15.26,0.18653143930812707,0.0,0.14,0.0,0.0,0.0,0.12,0.0,1.54,1,0,1,0 +7841,INTJ,19.48,0.18001076069619062,0.0,0.42,0.14,0.0,0.0,0.46,0.0,1.78,1,0,1,0 +7842,INTJ,31.62,0.1819804069793802,0.06,0.22,0.04,0.0,0.0,0.88,0.0,3.06,1,0,1,0 +7843,ENTJ,31.94,0.18154250328391822,0.02,0.16,0.1,0.0,0.0,0.78,0.0,3.58,0,0,1,0 +7844,INTP,23.06,0.18169333220360662,0.08,0.06,0.04,0.08,0.02,0.56,0.0,3.2,1,0,1,1 +7845,ENFP,22.84,0.18228722413304396,0.14,0.22,0.42,0.0,0.0,0.52,0.0,2.16,0,0,0,1 +7846,INFP,27.84,0.18713062969840125,0.0,0.24,0.3,0.0,0.0,0.58,0.0,2.52,1,0,0,1 +7847,INFJ,29.66,0.18570898399495955,0.06,0.1,0.24,0.06,0.04,0.7,0.0,3.6,1,0,0,0 +7848,ENFP,32.0,0.18998483895421045,0.0,0.14,0.06,0.0,0.0,0.88,0.0,4.32,0,0,0,1 +7849,INFJ,22.18,0.18181349071809272,0.12,0.34,0.02,0.02,0.0,0.56,0.0,2.26,1,0,0,0 +7850,INTJ,21.72,0.18670079971262843,0.04,0.12,0.0,0.04,0.0,0.58,0.0,2.76,1,0,1,0 +7851,ISTJ,26.34,0.17971560756223723,0.0,0.14,0.02,0.0,0.02,0.52,0.0,3.14,1,1,1,0 +7852,ENFP,25.94,0.19079221989383097,0.04,0.16,0.96,0.0,0.04,0.72,0.0,3.14,0,0,0,1 +7853,ENTJ,21.62,0.1757042224069374,0.26,0.28,0.02,0.06,0.0,0.5,0.0,1.78,0,0,1,0 +7854,INFJ,31.64,0.18246387644028442,0.08,0.2,0.2,0.0,0.0,0.92,0.0,3.84,1,0,0,0 +7855,ENTP,11.46,0.18010594023349136,0.18,0.16,0.06,0.08,0.0,0.1,0.0,1.3,0,0,1,1 +7856,INFJ,26.04,0.1798460469260667,0.0,0.18,0.18,0.0,0.0,0.62,0.0,3.26,1,0,0,0 +7857,ENFJ,31.2,0.1886681311630646,0.0,0.16,0.4,0.0,0.0,0.82,0.0,4.18,0,0,0,0 +7858,INFJ,18.52,0.1831042291655291,0.02,0.08,0.02,0.0,0.04,0.36,0.0,2.24,1,0,0,0 +7859,INTP,25.86,0.18412636528325776,0.0,0.1,0.1,0.0,0.02,0.66,0.0,2.52,1,0,1,1 +7860,INTP,25.28,0.18055191186545827,0.0,0.04,0.18,0.0,0.0,0.54,0.0,3.24,1,0,1,1 +7861,ISFP,10.58,0.19515142281494868,0.26,0.22,0.96,0.12,0.04,0.1,0.0,1.16,1,1,0,1 +7862,ISFJ,29.66,0.1850623921524648,0.0,0.1,0.18,0.0,0.0,0.78,0.0,3.84,1,1,0,0 +7863,INFJ,30.66,0.18902244032669424,0.0,0.04,0.24,0.0,0.0,0.76,0.0,2.38,1,0,0,0 +7864,INFJ,33.2,0.18171303506229947,0.0,0.08,0.18,0.0,0.02,0.96,0.0,3.2,1,0,0,0 +7865,INFP,4.22,0.19683230750120748,0.14,0.14,0.0,0.0,0.02,0.06,0.0,0.54,1,0,0,1 +7866,ENTP,29.68,0.1736233669397462,0.0,0.12,0.12,0.0,0.0,0.82,0.0,3.1,0,0,1,1 +7867,INTP,14.98,0.18312273640051188,0.02,0.34,0.22,0.0,0.02,0.36,0.0,1.28,1,0,1,1 +7868,INFJ,28.16,0.18881469443008436,0.0,0.38,0.06,0.0,0.0,0.72,0.0,2.56,1,0,0,0 +7869,INTP,24.9,0.17932697900597344,0.14,0.2,0.18,0.08,0.08,0.8,0.0,2.36,1,0,1,1 +7870,INFJ,25.88,0.18532360818496973,0.02,0.2,0.0,0.0,0.06,0.44,0.0,2.72,1,0,0,0 +7871,INFP,22.6,0.18553993591543327,0.22,0.5,0.16,0.0,0.0,0.62,0.0,1.78,1,0,0,1 +7872,INTJ,18.5,0.1857161495304551,0.0,0.12,0.08,0.0,0.0,0.5,0.0,2.44,1,0,1,0 +7873,INTP,25.3,0.1803247099840378,0.0,0.08,0.02,0.0,0.0,0.44,0.0,2.24,1,0,1,1 +7874,INFP,28.4,0.1850343522516627,0.0,0.04,0.36,0.0,0.0,0.72,0.0,2.82,1,0,0,1 +7875,INFP,28.22,0.18539266571236085,0.0,0.12,0.02,0.0,0.02,0.74,0.0,4.46,1,0,0,1 +7876,INFJ,11.16,0.18755931197768014,0.38,0.48,0.22,0.0,0.02,0.54,0.0,1.2,1,0,0,0 +7877,INTP,22.66,0.18686459035905573,0.18,0.28,0.16,0.06,0.0,0.48,0.0,2.5,1,0,1,1 +7878,INFP,28.54,0.17926917483780522,0.22,0.14,0.04,0.04,0.02,0.74,0.0,3.08,1,0,0,1 +7879,INFP,29.98,0.18874829397937648,0.04,0.26,1.24,0.02,0.0,0.9,0.0,1.54,1,0,0,1 +7880,INTP,32.06,0.18910498058200756,0.0,0.02,0.12,0.0,0.0,0.86,0.0,3.98,1,0,1,1 +7881,INFJ,29.92,0.1892950797243381,0.0,0.22,0.06,0.0,0.02,1.04,0.0,3.58,1,0,0,0 +7882,INTJ,22.48,0.17689986282578873,0.0,0.04,0.0,0.0,0.0,0.52,0.0,2.34,1,0,1,0 +7883,ISTJ,26.8,0.19260683198268588,0.0,0.28,0.1,0.0,0.0,0.68,0.0,2.8,1,1,1,0 +7884,ENTJ,32.48,0.18631664374106166,0.0,0.22,0.06,0.0,0.0,1.18,0.02,4.1,0,0,1,0 +7885,ENFP,27.0,0.1957441388680144,0.0,0.04,1.94,0.0,0.0,1.06,0.0,3.0,0,0,0,1 +7886,INTP,31.96,0.18090038290657678,0.02,0.12,0.0,0.0,0.0,0.7,0.0,3.16,1,0,1,1 +7887,INTJ,29.12,0.1820090348206202,0.0,0.0,0.1,0.0,0.0,0.5,0.0,3.08,1,0,1,0 +7888,INFJ,30.3,0.18782638872529958,0.04,0.08,0.22,0.0,0.0,1.48,0.0,3.26,1,0,0,0 +7889,ENTP,25.28,0.18233761679879704,0.04,0.28,0.04,0.0,0.06,0.44,0.0,2.72,0,0,1,1 +7890,INTP,15.26,0.18647281009299618,0.04,0.22,0.04,0.02,0.0,0.3,0.0,1.74,1,0,1,1 +7891,INFJ,34.7,0.1864715241974666,0.0,0.06,0.1,0.0,0.0,1.14,0.0,4.3,1,0,0,0 +7892,ISFP,21.96,0.18527497600002432,0.08,0.28,0.06,0.04,0.04,0.58,0.0,1.6,1,1,0,1 +7893,INTP,17.06,0.18703903044901954,0.02,0.12,0.24,0.0,0.0,0.46,0.0,1.94,1,0,1,1 +7894,ENFP,30.52,0.18515741888687334,0.0,0.24,0.92,0.0,0.0,0.76,0.0,4.02,0,0,0,1 +7895,ENTJ,16.84,0.1891024973985432,0.06,0.22,0.62,0.02,0.0,0.22,0.0,1.24,0,0,1,0 +7896,ENTP,26.84,0.1791553514542663,0.0,0.18,0.04,0.0,0.02,0.6,0.0,3.12,0,0,1,1 +7897,INFJ,16.04,0.18215130032847388,0.1,0.18,0.06,0.0,0.0,0.3,0.0,1.68,1,0,0,0 +7898,INFJ,31.22,0.18391669712284656,0.0,0.08,0.3,0.0,0.0,0.76,0.02,4.02,1,0,0,0 +7899,INFP,22.24,0.18138504721201956,0.22,0.26,0.08,0.0,0.0,0.42,0.0,3.02,1,0,0,1 +7900,INFP,23.04,0.18275798835312307,0.0,0.16,0.1,0.0,0.0,0.56,0.0,2.84,1,0,0,1 +7901,INFJ,24.74,0.18572040961861902,0.0,0.04,0.1,0.0,0.0,0.72,0.0,3.14,1,0,0,0 +7902,ESTP,20.66,0.19151248842447136,0.0,0.12,0.0,0.0,0.0,0.46,0.0,0.88,0,1,1,1 +7903,INFJ,28.74,0.18984649171560722,0.06,0.34,0.46,0.0,0.04,0.78,0.0,2.84,1,0,0,0 +7904,ISTP,25.24,0.18728735484595818,0.0,0.34,0.24,0.0,0.02,0.46,0.0,0.98,1,1,1,1 +7905,INFJ,31.08,0.18027490868315374,0.0,0.22,0.0,0.0,0.0,0.8,0.0,4.2,1,0,0,0 +7906,INFP,27.5,0.18159473002546594,0.0,0.06,0.28,0.0,0.02,0.66,0.0,2.96,1,0,0,1 +7907,INTP,20.64,0.1952314821969889,0.02,0.44,0.0,0.0,0.0,0.38,0.0,1.94,1,0,1,1 +7908,INFJ,15.18,0.18035618440292003,0.04,0.14,0.02,0.0,0.0,0.38,0.0,2.04,1,0,0,0 +7909,ENTP,23.38,0.18654270291055447,0.04,0.08,0.02,0.0,0.0,0.7,0.0,1.78,0,0,1,1 +7910,INFJ,30.5,0.18881941965377638,0.04,0.16,0.24,0.0,0.02,0.86,0.0,2.82,1,0,0,0 +7911,INTP,23.34,0.18057096151248567,0.12,0.2,0.06,0.02,0.0,0.6,0.02,2.66,1,0,1,1 +7912,INTJ,28.66,0.18631375075585208,0.1,0.32,0.06,0.02,0.0,1.0,0.02,3.24,1,0,1,0 +7913,ENFP,30.42,0.1885164228694665,0.0,0.22,0.34,0.0,0.02,0.82,0.0,3.64,0,0,0,1 +7914,INTJ,28.02,0.18674163387882228,0.04,0.54,0.0,0.0,0.0,0.8,0.0,3.02,1,0,1,0 +7915,INTP,26.1,0.19180381893838233,0.14,0.32,0.3,0.0,0.02,0.94,0.0,3.34,1,0,1,1 +7916,INTP,24.62,0.1927712112829701,0.04,0.22,0.24,0.0,0.0,0.6,0.0,3.52,1,0,1,1 +7917,INTP,19.18,0.17716351557093424,0.16,0.48,0.0,0.0,0.0,0.56,0.0,1.96,1,0,1,1 +7918,ENTP,25.68,0.1838136491647699,0.08,0.18,0.08,0.02,0.0,0.88,0.0,2.48,0,0,1,1 +7919,ENFP,19.2,0.18506809383569447,0.0,0.2,0.12,0.0,0.02,0.3,0.0,1.8,0,0,0,1 +7920,INFP,17.66,0.18806048501874287,0.1,0.14,0.04,0.04,0.0,0.3,0.0,2.46,1,0,0,1 +7921,INFJ,15.48,0.1888660789759691,0.0,0.0,0.02,0.0,0.0,0.4,0.0,1.7,1,0,0,0 +7922,INTP,26.64,0.18523082164288132,0.06,0.22,0.04,0.0,0.0,0.7,0.0,2.98,1,0,1,1 +7923,INFJ,31.16,0.18296233730555225,0.0,0.08,0.14,0.0,0.0,0.76,0.0,3.72,1,0,0,0 +7924,ENFP,29.32,0.17811992110977865,0.14,0.1,0.0,0.0,0.0,0.86,0.04,1.94,0,0,0,1 +7925,ENTJ,27.06,0.1864154851189394,0.08,0.14,0.24,0.06,0.0,0.68,0.0,2.22,0,0,1,0 +7926,INTJ,22.42,0.18212791397262149,0.0,0.08,0.02,0.0,0.0,0.42,0.0,2.58,1,0,1,0 +7927,INFJ,16.96,0.18755498130369536,0.0,0.08,0.02,0.0,0.0,0.24,0.0,2.36,1,0,0,0 +7928,ENTJ,29.94,0.18844776663359925,0.0,0.18,0.16,0.0,0.0,0.72,0.0,3.14,0,0,1,0 +7929,ENTJ,26.24,0.18892775642562715,0.06,0.28,0.04,0.0,0.0,0.6,0.0,2.78,0,0,1,0 +7930,INFJ,19.46,0.1918852749411506,0.06,0.2,0.46,0.06,0.0,0.6,0.0,2.04,1,0,0,0 +7931,ENFP,28.9,0.18382521718593806,0.0,0.06,0.48,0.0,0.0,0.86,0.0,3.18,0,0,0,1 +7932,INTP,32.2,0.19037844059363337,0.0,0.04,0.04,0.0,0.08,0.72,0.0,5.14,1,0,1,1 +7933,INFJ,25.86,0.17784252271912626,0.14,0.38,0.12,0.04,0.02,0.82,0.0,1.38,1,0,0,0 +7934,INTJ,28.62,0.17707146694922213,0.0,0.06,0.0,0.0,0.0,0.66,0.0,2.96,1,0,1,0 +7935,INFP,16.46,0.20116237398752984,0.28,0.32,0.0,0.02,0.0,0.42,0.0,2.12,1,0,0,1 +7936,ENTJ,27.14,0.18525269031639927,0.0,0.12,0.14,0.0,0.0,0.58,0.0,2.98,0,0,1,0 +7937,ISFP,27.9,0.18472523795148127,0.08,0.32,0.4,0.0,0.04,0.62,0.0,3.54,1,1,0,1 +7938,ISTJ,17.52,0.1866235011549516,0.0,0.24,0.0,0.0,0.0,0.4,0.02,1.92,1,1,1,0 +7939,ISTP,28.74,0.18317535732321472,0.02,0.16,0.46,0.0,0.0,0.66,0.08,2.18,1,1,1,1 +7940,INFJ,22.64,0.18982166235920991,0.04,0.14,0.04,0.0,0.02,0.62,0.0,2.74,1,0,0,0 +7941,INTP,22.76,0.19383503587830464,0.0,0.1,0.1,0.0,0.0,0.94,0.0,3.44,1,0,1,1 +7942,INFP,24.06,0.18225885427305213,0.2,0.2,0.02,0.0,0.0,1.02,0.0,2.7,1,0,0,1 +7943,ESTP,30.44,0.19794869354453895,0.0,0.28,0.16,0.0,0.0,1.1,0.0,3.32,0,1,1,1 +7944,INTJ,18.82,0.18598023358985963,0.14,0.26,0.04,0.0,0.02,0.2,0.0,2.16,1,0,1,0 +7945,ISTP,16.54,0.18454615590918177,0.08,0.16,0.02,0.04,0.04,0.18,0.0,2.54,1,1,1,1 +7946,INFJ,16.52,0.18582350800861713,0.1,0.14,0.02,0.0,0.0,0.34,0.0,1.6,1,0,0,0 +7947,INFJ,26.22,0.18424443509021834,0.04,0.12,0.46,0.0,0.0,0.66,0.02,2.66,1,0,0,0 +7948,INFJ,27.68,0.18326117926065805,0.0,0.12,0.04,0.0,0.0,0.62,0.0,3.0,1,0,0,0 +7949,INFP,24.52,0.17699980192798037,0.0,0.62,0.04,0.0,0.08,0.62,0.0,2.84,1,0,0,1 +7950,INFP,25.76,0.188464452539375,0.0,0.3,0.4,0.0,0.0,0.9,0.0,3.02,1,0,0,1 +7951,INTP,22.78,0.18574987456709408,0.04,0.24,0.02,0.04,0.0,0.7,0.0,1.86,1,0,1,1 +7952,INFJ,28.36,0.18425788691561837,0.0,0.1,0.1,0.0,0.02,0.68,0.0,4.22,1,0,0,0 +7953,INTP,26.94,0.17600360472659562,0.02,0.08,0.0,0.0,0.0,0.66,0.0,2.78,1,0,1,1 +7954,INFP,26.78,0.18312665704134853,0.12,0.08,0.0,0.02,0.0,0.68,0.0,3.1,1,0,0,1 +7955,INTJ,27.56,0.1889615778575681,0.12,0.36,0.04,0.0,0.04,0.78,0.0,3.82,1,0,1,0 +7956,ISFJ,17.3,0.1825746769295954,0.02,0.24,0.06,0.02,0.0,0.32,0.0,1.72,1,1,0,0 +7957,INTP,22.42,0.17609101545778835,0.1,0.14,0.0,0.04,0.0,0.56,0.0,1.52,1,0,1,1 +7958,INFJ,27.92,0.18222176181233932,0.04,0.16,0.18,0.02,0.0,0.72,0.0,3.02,1,0,0,0 +7959,ENTJ,25.2,0.18266796979422348,0.12,0.2,0.06,0.08,0.0,0.66,0.0,2.32,0,0,1,0 +7960,INFP,22.16,0.1798136891905006,0.04,0.14,0.14,0.0,0.0,0.66,0.0,3.0,1,0,0,1 +7961,INTP,23.52,0.18358388147601623,0.0,0.02,0.02,0.0,0.0,0.58,0.0,2.4,1,0,1,1 +7962,INFP,29.02,0.1883951901577039,0.06,0.26,0.12,0.0,0.02,0.78,0.0,3.9,1,0,0,1 +7963,ISTJ,23.52,0.1865432873814295,0.02,0.1,0.14,0.02,0.0,0.66,0.0,2.46,1,1,1,0 +7964,INFP,25.56,0.1801831909226616,0.02,0.12,0.0,0.0,0.02,0.56,0.0,3.06,1,0,0,1 +7965,INTJ,17.94,0.17855367243941161,0.04,0.22,0.02,0.0,0.0,0.36,0.0,1.28,1,0,1,0 +7966,ISTJ,16.76,0.1888141565866878,0.0,0.02,0.1,0.0,0.0,0.58,0.0,2.26,1,1,1,0 +7967,ENTP,25.1,0.19353560565308822,0.0,0.34,0.26,0.0,0.0,1.9,0.0,2.6,0,0,1,1 +7968,INFJ,24.64,0.1835621478147107,0.06,0.14,0.08,0.0,0.04,0.48,0.0,2.48,1,0,0,0 +7969,ESTJ,31.6,0.18805629451848216,0.0,0.26,0.12,0.0,0.0,0.92,0.0,2.78,0,1,1,0 +7970,ENTJ,26.7,0.18209510613431917,0.0,1.34,0.0,0.0,0.0,0.68,0.0,3.04,0,0,1,0 +7971,ISTJ,21.6,0.17802871889210742,0.16,0.32,0.08,0.0,0.04,0.62,0.0,2.14,1,1,1,0 +7972,INTP,20.52,0.1859590431376869,0.04,0.16,0.52,0.0,0.02,0.4,0.0,2.3,1,0,1,1 +7973,ISTP,24.02,0.17993961388221202,0.08,0.34,0.02,0.0,0.0,0.52,0.0,2.4,1,1,1,1 +7974,INFP,29.04,0.17742233073547886,0.0,0.04,0.34,0.0,0.04,0.58,0.0,2.68,1,0,0,1 +7975,ENFP,34.56,0.1821185699130741,0.0,0.18,0.24,0.0,0.0,1.02,0.0,3.42,0,0,0,1 +7976,ENFJ,31.94,0.18669405843268524,0.0,0.16,0.02,0.0,0.04,1.16,0.0,2.82,0,0,0,0 +7977,INTP,31.34,0.1840059488894788,0.0,0.12,0.02,0.0,0.02,0.68,0.0,3.22,1,0,1,1 +7978,INFJ,30.86,0.1926895751735377,0.0,0.16,0.08,0.0,0.0,1.32,0.0,3.44,1,0,0,0 +7979,INTP,22.38,0.18717052334129572,0.16,0.14,0.1,0.02,0.0,0.52,0.0,3.14,1,0,1,1 +7980,ISFJ,33.0,0.17482324554156936,0.0,0.08,0.08,0.0,0.0,1.02,0.0,2.94,1,1,0,0 +7981,INFJ,30.52,0.18681967381994258,0.0,0.22,1.28,0.0,0.02,1.06,0.0,3.5,1,0,0,0 +7982,INFP,26.8,0.19049521971629607,0.02,0.12,0.42,0.0,0.04,0.78,0.0,3.5,1,0,0,1 +7983,ENFJ,31.16,0.18368873098734634,0.0,0.02,0.22,0.0,0.0,0.72,0.0,3.0,0,0,0,0 +7984,ENTJ,11.74,0.18007456437444327,0.0,0.16,0.18,0.0,0.0,0.26,0.0,1.12,0,0,1,0 +7985,INTJ,27.94,0.18352883767430184,0.04,0.14,0.1,0.02,0.0,0.88,0.0,3.62,1,0,1,0 +7986,ISFJ,14.52,0.19088780594307148,0.62,0.12,0.06,0.0,0.02,0.44,0.02,1.6,1,1,0,0 +7987,INTJ,25.52,0.18199283134743,0.12,0.42,0.02,0.06,0.0,0.7,0.0,2.58,1,0,1,0 +7988,INFP,14.32,0.17921764698162673,0.0,0.06,0.02,0.0,0.02,0.38,0.0,1.76,1,0,0,1 +7989,INFJ,21.96,0.17698302867958468,0.06,0.14,0.0,0.02,0.0,0.52,0.0,2.38,1,0,0,0 +7990,ESTJ,21.32,0.17724578489636703,0.12,0.2,0.14,0.02,0.0,0.44,0.0,1.48,0,1,1,0 +7991,INFP,30.18,0.1812117981533245,0.1,0.12,0.0,0.02,0.1,0.8,0.0,3.36,1,0,0,1 +7992,INTP,27.44,0.18369972022529812,0.02,0.16,0.12,0.0,0.0,0.58,0.0,2.6,1,0,1,1 +7993,INFJ,27.1,0.1781846886936456,0.0,0.06,0.0,0.0,0.02,0.72,0.0,2.76,1,0,0,0 +7994,INTP,14.1,0.17971783483715298,0.04,0.12,0.02,0.0,0.0,0.34,0.0,1.0,1,0,1,1 +7995,ISTP,27.44,0.17635371514017653,0.0,0.26,0.06,0.0,0.0,0.64,0.0,2.92,1,1,1,1 +7996,INFP,24.28,0.18858223062381851,0.0,0.12,0.86,0.0,0.0,0.64,0.0,3.42,1,0,0,1 +7997,ENFP,25.6,0.18343695897703186,0.0,0.18,0.42,0.0,0.0,0.56,0.0,2.32,0,0,0,1 +7998,INTJ,25.24,0.18413524164742806,0.02,0.22,0.4,0.0,0.0,0.62,0.0,2.4,1,0,1,0 +7999,INTJ,20.06,0.19464372404664676,0.0,0.2,0.08,0.0,0.02,0.48,0.04,2.48,1,0,1,0 +8000,ENFP,27.64,0.19741256500537013,0.0,0.38,1.08,0.0,0.02,0.96,0.02,3.4,0,0,0,1 +8001,INFP,20.86,0.18248119916186334,0.02,0.18,0.02,0.0,0.0,0.6,0.0,2.06,1,0,0,1 +8002,INFJ,26.3,0.18535712272327676,0.18,0.08,0.04,0.12,0.02,0.62,0.0,2.92,1,0,0,0 +8003,ESTJ,35.42,0.1832942256782327,0.0,0.18,0.14,0.0,0.0,1.04,0.0,1.68,0,1,1,0 +8004,INTP,26.68,0.17958533006217395,0.06,0.16,0.02,0.04,0.0,0.76,0.0,3.28,1,0,1,1 +8005,INFP,36.98,0.18655096262462473,0.0,0.0,0.08,0.0,0.0,1.06,0.0,4.98,1,0,0,1 +8006,ENFP,33.94,0.1819877667613892,0.06,0.1,0.16,0.0,0.04,0.92,0.0,2.82,0,0,0,1 +8007,ENTP,26.44,0.18342179740762557,0.1,0.24,0.02,0.02,0.0,0.7,0.0,2.8,0,0,1,1 +8008,INFJ,26.86,0.18667543986530813,0.12,0.16,0.14,0.02,0.02,0.7,0.0,3.12,1,0,0,0 +8009,ENTP,27.98,0.1937031544854736,0.0,0.48,0.22,0.0,0.0,0.9,0.0,3.32,0,0,1,1 +8010,INFP,27.92,0.19552366385784364,0.0,0.28,0.06,0.0,0.0,0.86,0.0,2.54,1,0,0,1 +8011,INTP,27.7,0.17652199449078929,0.06,0.16,0.02,0.0,0.0,0.66,0.02,2.04,1,0,1,1 +8012,ISFJ,24.26,0.1785712063380851,0.02,0.08,0.08,0.0,0.0,0.46,0.0,2.36,1,1,0,0 +8013,ENFP,23.48,0.18874685146450085,0.0,0.16,0.48,0.0,0.0,0.52,0.0,3.52,0,0,0,1 +8014,ENTP,22.04,0.19653279186434996,0.08,0.16,0.28,0.04,0.0,1.02,0.0,2.4,0,0,1,1 +8015,INFJ,16.84,0.1861222091831209,0.02,0.06,0.14,0.0,0.0,0.46,0.0,2.1,1,0,0,0 +8016,ENFP,27.78,0.18789700083775496,0.02,0.12,0.46,0.0,0.0,0.96,0.0,2.74,0,0,0,1 +8017,INFJ,23.84,0.18746400288051573,0.02,0.36,0.08,0.0,0.0,0.58,0.0,2.58,1,0,0,0 +8018,INFP,19.12,0.18200656456828768,0.04,0.1,0.12,0.0,0.04,0.3,0.0,2.54,1,0,0,1 +8019,INFP,24.04,0.17960755817826973,0.16,0.18,0.04,0.0,0.0,0.76,0.0,2.34,1,0,0,1 +8020,ENTP,24.26,0.18439652924453764,0.08,0.18,0.08,0.0,0.0,0.6,0.0,2.8,0,0,1,1 +8021,INTP,23.72,0.181429970133579,0.12,0.16,0.04,0.06,0.08,0.56,0.0,2.52,1,0,1,1 +8022,INTJ,20.96,0.18373773851108313,0.04,0.1,0.2,0.0,0.0,0.44,0.0,2.06,1,0,1,0 +8023,INTJ,20.44,0.19339432908427548,0.0,0.2,0.22,0.0,0.0,0.66,0.0,2.56,1,0,1,0 +8024,ENFP,27.96,0.17650594153835925,0.32,0.12,0.0,0.1,0.0,0.82,0.0,3.2,0,0,0,1 +8025,INTJ,22.94,0.19181317999845424,0.08,0.3,0.38,0.02,0.02,0.7,0.0,2.5,1,0,1,0 +8026,INFP,24.8,0.18417406387103363,0.14,0.22,0.12,0.14,0.06,0.62,0.0,2.62,1,0,0,1 +8027,INFP,11.72,0.1816574833339168,0.16,0.24,0.0,0.0,0.0,0.2,0.0,1.18,1,0,0,1 +8028,INTP,26.32,0.1861977777242631,0.0,0.18,0.04,0.0,0.0,1.02,0.0,3.16,1,0,1,1 +8029,INTP,27.52,0.1794006421786059,0.1,0.16,0.0,0.04,0.0,0.76,0.0,2.96,1,0,1,1 +8030,INFP,31.82,0.18150579909816925,0.02,0.12,0.14,0.0,0.0,0.84,0.0,3.02,1,0,0,1 +8031,ENFP,25.08,0.19269178555726563,0.06,0.16,0.56,0.0,0.0,0.68,0.0,3.18,0,0,0,1 +8032,INFP,28.98,0.1789038651666573,0.0,0.14,0.32,0.0,0.0,0.7,0.0,4.06,1,0,0,1 +8033,INFJ,28.3,0.19140988793461158,0.0,0.12,0.0,0.0,0.0,0.86,0.0,3.38,1,0,0,0 +8034,INFP,32.3,0.19578589547397118,0.02,0.2,0.22,0.0,0.0,0.88,0.0,4.1,1,0,0,1 +8035,INTJ,31.1,0.19399508752076128,0.0,0.3,0.34,0.0,0.0,1.18,0.0,4.0,1,0,1,0 +8036,INTP,19.14,0.17768447705399706,0.02,0.3,0.12,0.0,0.0,0.34,0.0,2.04,1,0,1,1 +8037,ENFP,23.58,0.1861141146196407,0.12,0.24,0.28,0.04,0.04,0.54,0.0,2.68,0,0,0,1 +8038,INFJ,17.26,0.1912083575264915,0.04,0.26,0.24,0.0,0.04,0.28,0.0,1.66,1,0,0,0 +8039,INTP,24.94,0.18763528134562585,0.02,0.3,0.0,0.0,0.02,0.6,0.0,2.82,1,0,1,1 +8040,ISTP,20.84,0.19260151777576615,0.0,0.14,0.24,0.0,0.0,0.24,0.0,2.72,1,1,1,1 +8041,INFP,26.94,0.18544050629078404,0.02,0.16,0.04,0.0,0.0,0.68,0.0,2.54,1,0,0,1 +8042,INFP,21.08,0.18709828705424808,0.2,0.54,0.26,0.0,0.0,0.46,0.0,2.24,1,0,0,1 +8043,ISFJ,22.4,0.1876857078159598,0.2,0.36,0.64,0.0,0.0,0.4,0.0,2.78,1,1,0,0 +8044,ENFP,31.48,0.1901060095181914,0.0,0.3,1.94,0.0,0.02,1.1,0.0,4.34,0,0,0,1 +8045,INTJ,23.36,0.186222605902738,0.0,0.22,0.02,0.0,0.02,0.52,0.0,1.86,1,0,1,0 +8046,INFJ,20.26,0.18746128233018342,0.34,0.48,0.32,0.02,0.02,0.56,0.04,2.18,1,0,0,0 +8047,INTJ,17.42,0.18001013904542862,0.0,0.12,0.08,0.0,0.0,0.2,0.02,1.54,1,0,1,0 +8048,INFJ,26.3,0.18346152405448934,0.1,0.2,0.24,0.0,0.0,1.3,0.04,2.52,1,0,0,0 +8049,INFP,19.86,0.17965354293101085,0.06,0.22,0.04,0.0,0.04,0.44,0.0,2.44,1,0,0,1 +8050,INTP,26.94,0.18114246647428622,0.02,0.1,0.34,0.0,0.0,0.56,0.0,3.42,1,0,1,1 +8051,INTJ,30.82,0.17695556861121023,0.04,0.2,0.16,0.02,0.04,0.84,0.0,2.6,1,0,1,0 +8052,ENTP,24.08,0.18178082219737973,0.26,0.36,0.3,0.0,0.0,0.56,0.0,2.08,0,0,1,1 +8053,INFJ,27.48,0.18382928939633753,0.0,0.34,0.02,0.0,0.0,0.84,0.0,2.42,1,0,0,0 +8054,ENFP,23.2,0.184646397685038,0.02,0.26,0.14,0.0,0.0,0.6,0.0,3.22,0,0,0,1 +8055,ISFP,27.1,0.19218186971920165,0.0,0.38,0.24,0.0,0.0,0.76,0.0,2.68,1,1,0,1 +8056,ISFP,21.76,0.18545296832918098,0.0,0.3,0.06,0.0,0.0,0.22,0.0,1.54,1,1,0,1 +8057,INTJ,23.26,0.18626691797599446,0.0,0.28,0.46,0.0,0.0,0.8,0.0,2.28,1,0,1,0 +8058,INTP,31.04,0.1825173648622864,0.0,0.28,0.04,0.0,0.02,0.92,0.0,2.92,1,0,1,1 +8059,INFP,32.9,0.18771413260494565,0.0,0.02,0.24,0.0,0.02,1.22,0.0,4.22,1,0,0,1 +8060,ENTJ,22.7,0.18352972954247251,0.02,0.28,0.02,0.02,0.0,0.5,0.0,1.82,0,0,1,0 +8061,INFJ,18.58,0.18207527924569256,0.08,0.14,0.0,0.02,0.0,0.58,0.0,2.22,1,0,0,0 +8062,INTJ,16.16,0.177611386168548,0.1,0.12,0.0,0.0,0.08,0.4,0.0,1.58,1,0,1,0 +8063,ISTJ,21.08,0.1863742865636148,0.02,0.26,0.06,0.0,0.0,0.3,0.0,2.68,1,1,1,0 +8064,INFP,25.42,0.18585766367769083,0.14,0.26,0.3,0.04,0.06,0.82,0.0,2.84,1,0,0,1 +8065,ENFP,28.6,0.1821642000806668,0.02,0.24,0.0,0.0,0.06,0.92,0.0,3.02,0,0,0,1 +8066,INFJ,29.7,0.18354473626598194,0.08,0.2,0.74,0.02,0.0,0.76,0.0,3.74,1,0,0,0 +8067,ENFP,16.5,0.18633625012331712,0.0,0.08,0.08,0.0,0.0,0.28,0.0,2.48,0,0,0,1 +8068,ENFP,31.18,0.18334204018587036,0.0,0.1,0.28,0.0,0.04,0.76,0.0,3.94,0,0,0,1 +8069,INFP,27.66,0.18444752711512338,0.02,0.12,0.48,0.0,0.0,0.88,0.0,3.38,1,0,0,1 +8070,INTP,25.54,0.18396388394888727,0.0,0.36,0.22,0.0,0.0,0.62,0.0,2.96,1,0,1,1 +8071,ENTJ,15.16,0.18406138598859115,0.36,0.56,0.42,0.04,0.04,0.42,0.0,1.76,0,0,1,0 +8072,ENTP,24.02,0.19076370759676153,0.0,0.36,0.0,0.0,0.0,0.54,0.02,2.36,0,0,1,1 +8073,ENFP,22.1,0.18957828529669118,0.04,0.12,0.08,0.0,0.08,0.44,0.0,2.88,0,0,0,1 +8074,ISFP,13.86,0.18553026770451247,0.12,0.16,0.12,0.0,0.06,0.28,0.0,1.78,1,1,0,1 +8075,INFP,6.28,0.17722043703121598,0.0,0.06,0.0,0.0,0.0,0.06,0.0,0.52,1,0,0,1 +8076,ENTJ,19.5,0.1838037344044733,0.08,0.22,0.28,0.0,0.02,0.4,0.0,2.3,0,0,1,0 +8077,INFP,21.66,0.18610403600991005,0.22,0.54,0.1,0.0,0.02,0.44,0.0,2.02,1,0,0,1 +8078,INTJ,26.44,0.18213975,0.0,0.18,0.22,0.0,0.0,0.64,0.0,3.02,1,0,1,0 +8079,INFJ,24.76,0.18163849327982193,0.02,0.28,0.02,0.0,0.0,0.5,0.0,3.14,1,0,0,0 +8080,INFJ,28.58,0.17884190646019765,0.02,0.06,0.0,0.0,0.02,0.58,0.0,3.1,1,0,0,0 +8081,INTJ,28.22,0.18198845316751727,0.0,0.2,0.14,0.0,0.0,0.42,0.0,2.76,1,0,1,0 +8082,ENFP,24.36,0.19111797570956932,0.1,0.1,0.6,0.1,0.02,0.66,0.0,0.84,0,0,0,1 +8083,INTJ,21.36,0.17502646860928528,0.1,0.06,0.02,0.06,0.0,0.46,0.0,2.18,1,0,1,0 +8084,INFP,18.22,0.18569495172760392,0.04,0.1,0.04,0.0,0.06,0.38,0.0,2.52,1,0,0,1 +8085,INFP,26.78,0.18080194019745993,0.0,0.36,0.0,0.0,0.02,0.54,0.0,2.66,1,0,0,1 +8086,INFJ,32.68,0.18222798989140088,0.0,0.18,0.26,0.0,0.0,1.0,0.0,3.86,1,0,0,0 +8087,INFP,26.44,0.17889970879242392,0.2,0.22,0.02,0.0,0.02,0.66,0.0,2.92,1,0,0,1 +8088,ISFP,29.98,0.18576355568662348,0.0,0.2,0.08,0.0,0.04,0.94,0.0,4.54,1,1,0,1 +8089,INTP,26.04,0.18255695123003485,0.0,0.24,0.0,0.0,0.0,0.52,0.0,3.3,1,0,1,1 +8090,INFP,27.16,0.1803914265357307,0.02,0.1,0.0,0.0,0.0,0.6,0.0,4.0,1,0,0,1 +8091,INTJ,30.34,0.18552092534221,0.0,0.0,0.04,0.0,0.0,0.96,0.0,3.96,1,0,1,0 +8092,ISTJ,26.64,0.19432962491719274,0.24,0.62,0.12,0.0,0.04,0.84,0.0,3.46,1,1,1,0 +8093,INFJ,31.44,0.17263128534091254,0.0,0.14,0.06,0.0,0.0,0.88,0.0,2.78,1,0,0,0 +8094,ENTP,12.74,0.17695990033859663,0.28,0.36,0.2,0.08,0.02,0.14,0.0,1.54,0,0,1,1 +8095,ENFP,23.06,0.1845393228263666,0.12,0.52,0.16,0.04,0.0,0.54,0.0,1.78,0,0,0,1 +8096,ENTJ,22.7,0.1824960397676664,0.0,0.1,0.12,0.0,0.0,0.54,0.0,2.06,0,0,1,0 +8097,INFP,25.92,0.18303936279372374,0.2,0.2,0.14,0.08,0.02,0.7,0.0,1.26,1,0,0,1 +8098,INTP,31.74,0.17870816958321367,0.0,0.28,0.02,0.0,0.0,0.8,0.0,2.88,1,0,1,1 +8099,INFP,33.72,0.18530937220513707,0.0,0.2,0.16,0.0,0.0,0.76,0.0,3.8,1,0,0,1 +8100,INFJ,30.34,0.18193036849791783,0.02,0.12,0.0,0.0,0.0,0.78,0.02,3.74,1,0,0,0 +8101,INFJ,31.82,0.19375127261336053,0.0,0.06,0.16,0.0,0.02,0.88,0.0,4.48,1,0,0,0 +8102,INFP,25.84,0.18021216847468619,0.04,0.12,0.0,0.0,0.0,0.62,0.0,3.42,1,0,0,1 +8103,INFP,33.52,0.176026402461172,0.0,0.06,0.06,0.0,0.04,0.8,0.0,3.9,1,0,0,1 +8104,INTP,6.94,0.1848209937488191,0.44,0.44,0.0,0.0,0.0,0.08,0.0,0.5,1,0,1,1 +8105,ENFP,22.74,0.18226564349363644,0.02,0.24,0.2,0.02,0.0,1.02,0.0,2.34,0,0,0,1 +8106,INTP,20.38,0.18863921663187722,0.0,0.3,0.04,0.0,0.0,0.68,0.0,2.04,1,0,1,1 +8107,INFP,35.2,0.1841395418674706,0.06,0.1,0.0,0.0,0.02,0.94,0.0,4.5,1,0,0,1 +8108,INFP,26.68,0.18827944201653674,0.0,0.24,0.12,0.0,0.0,0.92,0.0,3.62,1,0,0,1 +8109,INFP,28.22,0.18548288255656914,0.04,0.12,0.28,0.02,0.04,0.88,0.0,2.74,1,0,0,1 +8110,INFJ,13.96,0.1952412361855974,0.02,0.24,0.38,0.0,0.0,0.14,0.0,1.66,1,0,0,0 +8111,INTJ,23.58,0.1810581580535304,0.0,0.28,0.06,0.0,0.0,0.4,0.0,2.74,1,0,1,0 +8112,INFJ,32.04,0.18214961684374328,0.04,0.04,0.04,0.0,0.0,0.9,0.0,2.9,1,0,0,0 +8113,ISFP,17.58,0.18724285153667825,0.0,0.06,0.18,0.0,0.02,0.44,0.0,1.04,1,1,0,1 +8114,INFJ,23.1,0.17573422674862035,0.22,0.3,0.24,0.04,0.0,0.66,0.02,1.58,1,0,0,0 +8115,INFP,28.32,0.1800559600393433,0.02,0.1,0.08,0.0,0.0,0.68,0.0,3.64,1,0,0,1 +8116,INTP,22.2,0.18019682839739554,0.1,0.24,0.06,0.0,0.0,0.54,0.02,2.3,1,0,1,1 +8117,INTP,13.54,0.18160488653135104,0.0,0.24,0.26,0.0,0.0,0.46,0.0,1.34,1,0,1,1 +8118,INFP,27.04,0.17806278991195526,0.22,0.44,0.04,0.0,0.04,0.84,0.0,2.48,1,0,0,1 +8119,INTJ,29.12,0.18487247735135162,0.0,0.2,0.12,0.0,0.0,0.9,0.0,2.38,1,0,1,0 +8120,INFJ,31.28,0.18406547481828253,0.02,0.2,0.16,0.0,0.02,1.06,0.08,2.78,1,0,0,0 +8121,INTP,29.88,0.17775067620333804,0.06,0.9,0.02,0.02,0.02,0.86,0.0,2.18,1,0,1,1 +8122,ENFJ,19.62,0.19005034899982487,0.14,0.26,0.22,0.02,0.0,0.5,0.0,2.72,0,0,0,0 +8123,INTJ,24.88,0.18119362291927452,0.0,0.22,0.04,0.0,0.02,0.64,0.0,2.26,1,0,1,0 +8124,INFJ,28.5,0.19275720844103233,0.0,0.18,0.3,0.0,0.0,0.78,0.0,3.16,1,0,0,0 +8125,INTJ,22.5,0.18164998091508494,0.28,0.38,0.02,0.08,0.0,0.78,0.0,2.54,1,0,1,0 +8126,INTP,12.64,0.18210524055817223,0.4,0.38,0.22,0.02,0.04,0.34,0.04,1.3,1,0,1,1 +8127,INFJ,30.72,0.1751557603669857,0.02,0.28,0.16,0.0,0.0,0.86,0.0,4.44,1,0,0,0 +8128,INTJ,15.36,0.18946665955093248,0.02,0.38,0.02,0.0,0.0,0.28,0.0,1.94,1,0,1,0 +8129,INFP,29.72,0.18592885294041023,0.04,0.1,0.26,0.04,0.06,0.78,0.0,3.56,1,0,0,1 +8130,ESFJ,32.38,0.18295745578241482,0.0,0.22,0.08,0.0,0.0,1.18,0.0,3.42,0,1,0,0 +8131,INTJ,22.3,0.18579094269166285,0.0,0.22,0.08,0.0,0.0,0.34,0.0,2.64,1,0,1,0 +8132,ENFJ,25.4,0.18713227648142028,0.06,0.14,0.34,0.0,0.04,0.56,0.04,2.94,0,0,0,0 +8133,ENFP,11.7,0.21044740452667757,0.02,0.04,0.26,0.0,0.02,0.74,0.0,1.4,0,0,0,1 +8134,INFJ,26.36,0.18305037247431405,0.02,0.12,0.16,0.0,0.0,0.98,0.0,3.1,1,0,0,0 +8135,ENTP,21.84,0.18778355350963102,0.12,0.44,0.1,0.02,0.0,0.52,0.0,2.2,0,0,1,1 +8136,INTJ,17.36,0.19229812278390024,0.06,0.46,0.22,0.02,0.0,0.24,0.0,1.82,1,0,1,0 +8137,INTP,13.1,0.1815592258186402,0.22,0.22,0.08,0.04,0.0,0.22,0.0,1.16,1,0,1,1 +8138,INFJ,25.42,0.1741085456805403,0.14,0.1,0.04,0.08,0.0,0.54,0.0,2.26,1,0,0,0 +8139,ISTP,30.5,0.19100761267758568,0.0,0.08,0.16,0.0,0.02,0.84,0.02,4.08,1,1,1,1 +8140,INTJ,26.64,0.18292944397855537,0.0,0.18,0.22,0.0,0.04,0.48,0.0,3.24,1,0,1,0 +8141,INFP,33.06,0.18660319778442847,0.0,0.22,0.1,0.0,0.0,1.02,0.0,4.0,1,0,0,1 +8142,INFP,21.16,0.1954475966124773,0.0,0.28,0.38,0.0,0.0,1.0,0.0,2.5,1,0,0,1 +8143,INTP,25.72,0.18972516251065932,0.18,0.32,0.18,0.06,0.06,1.16,0.0,2.68,1,0,1,1 +8144,INFJ,26.82,0.17942023552734687,0.3,0.26,0.18,0.12,0.04,0.74,0.02,3.04,1,0,0,0 +8145,INTP,27.86,0.1903260684817964,0.0,0.14,0.48,0.0,0.0,1.3,0.06,3.26,1,0,1,1 +8146,INFJ,30.16,0.18624672188555522,0.0,0.3,0.36,0.0,0.0,1.06,0.0,3.14,1,0,0,0 +8147,ISFJ,17.12,0.1936377915755221,0.18,0.4,0.0,0.0,0.06,0.38,0.0,2.42,1,1,0,0 +8148,INFJ,22.02,0.18499730129452863,0.06,0.24,0.16,0.04,0.0,0.66,0.0,2.68,1,0,0,0 +8149,INTJ,17.72,0.1787215194590103,0.5,0.4,0.04,0.12,0.02,0.46,0.0,1.88,1,0,1,0 +8150,INTP,18.62,0.17905920027341005,0.1,0.36,0.08,0.0,0.0,0.22,0.0,1.34,1,0,1,1 +8151,INTJ,5.5,0.19021328302713753,0.02,0.0,0.0,0.0,0.0,0.3,0.0,0.7,1,0,1,0 +8152,INFP,24.6,0.18377673156568652,0.04,0.14,0.34,0.02,0.0,0.54,0.0,2.36,1,0,0,1 +8153,INTP,30.56,0.18363657594679278,0.16,0.74,0.08,0.04,0.0,1.14,0.0,2.96,1,0,1,1 +8154,INTP,25.16,0.1867293009701786,0.1,0.28,0.04,0.02,0.0,0.52,0.0,2.34,1,0,1,1 +8155,INFJ,28.28,0.18645758481259525,0.0,0.04,0.06,0.0,0.02,0.66,0.0,3.88,1,0,0,0 +8156,INFJ,30.38,0.18126253438243398,0.02,0.06,0.3,0.0,0.0,0.78,0.0,3.54,1,0,0,0 +8157,INFJ,23.48,0.18290841589901652,0.0,0.16,0.36,0.0,0.0,0.44,0.0,2.78,1,0,0,0 +8158,INTP,30.38,0.18141710696991492,0.02,0.16,0.0,0.0,0.0,0.98,0.0,3.04,1,0,1,1 +8159,INTP,28.9,0.18132112771840564,0.06,0.24,0.1,0.0,0.1,0.78,0.04,3.08,1,0,1,1 +8160,INFJ,31.08,0.1841007933282344,0.1,0.36,0.2,0.02,0.0,0.78,0.0,3.22,1,0,0,0 +8161,INFJ,26.52,0.17978085889717074,0.0,0.02,0.0,0.0,0.0,0.7,0.04,2.36,1,0,0,0 +8162,INTP,13.78,0.1881685221092678,0.08,0.16,0.34,0.02,0.0,0.1,0.0,2.0,1,0,1,1 +8163,ISFP,31.7,0.17938047427134324,0.04,0.18,0.02,0.02,0.0,0.84,0.0,3.3,1,1,0,1 +8164,INTP,27.92,0.17734530688054598,0.08,0.16,0.02,0.0,0.0,0.74,0.0,2.5,1,0,1,1 +8165,INFP,19.46,0.19089913418876056,0.06,0.32,0.42,0.02,0.02,0.8,0.0,2.12,1,0,0,1 +8166,ENFP,9.44,0.19984152414201256,0.34,0.48,1.82,0.08,0.02,0.12,0.0,1.14,0,0,0,1 +8167,INFP,29.58,0.1893051688764173,0.08,0.38,0.54,0.0,0.0,1.0,0.0,3.66,1,0,0,1 +8168,INFJ,22.98,0.18422605985577162,0.02,0.14,0.2,0.0,0.0,0.68,0.0,2.14,1,0,0,0 +8169,INFJ,34.9,0.19051985958168002,0.02,0.08,0.0,0.0,0.02,1.92,0.0,2.58,1,0,0,0 +8170,ENTJ,20.26,0.18473392239412523,0.18,0.5,0.0,0.0,0.0,0.58,0.0,2.08,0,0,1,0 +8171,INFJ,27.6,0.18684997837072362,0.0,0.14,0.08,0.0,0.0,0.72,0.0,3.74,1,0,0,0 +8172,INTP,19.64,0.18890739963786696,0.04,0.14,0.14,0.04,0.0,0.36,0.0,2.26,1,0,1,1 +8173,ENFP,26.18,0.18866897887482545,0.02,0.1,0.46,0.0,0.0,0.64,0.0,3.18,0,0,0,1 +8174,ENFP,26.1,0.19401280809626273,0.02,0.14,0.12,0.0,0.06,0.66,0.0,2.78,0,0,0,1 +8175,INTJ,30.04,0.18346050312094522,0.02,0.4,0.02,0.02,0.0,0.98,0.0,2.86,1,0,1,0 +8176,INTJ,19.36,0.1947485552034436,0.0,0.32,0.3,0.0,0.02,0.7,0.0,2.3,1,0,1,0 +8177,INTJ,19.12,0.18330636634362235,0.12,0.14,0.12,0.04,0.0,0.5,0.0,2.74,1,0,1,0 +8178,INFP,25.26,0.1834650559863263,0.28,0.4,0.08,0.02,0.02,0.62,0.0,3.04,1,0,0,1 +8179,INFP,29.02,0.17912820815454913,0.0,0.06,0.22,0.0,0.0,0.56,0.0,3.02,1,0,0,1 +8180,INFJ,20.2,0.1849229487713453,0.02,0.16,0.14,0.0,0.0,0.5,0.02,2.12,1,0,0,0 +8181,INTP,18.16,0.17964448386645726,0.02,0.06,0.06,0.0,0.0,0.42,0.0,1.24,1,0,1,1 +8182,ISTJ,34.12,0.18406026698880035,0.0,0.12,0.0,0.0,0.0,1.08,0.0,4.34,1,1,1,0 +8183,INFJ,27.44,0.18510921134975686,0.06,0.08,0.0,0.0,0.04,0.7,0.0,3.56,1,0,0,0 +8184,ENFP,28.34,0.19174125341999626,0.0,0.6,0.32,0.0,0.02,0.74,0.0,3.7,0,0,0,1 +8185,INFJ,31.54,0.18773311920891125,0.08,0.16,0.02,0.0,0.0,0.82,0.0,3.44,1,0,0,0 +8186,INFP,7.56,0.18843759325862808,0.2,0.22,0.16,0.1,0.0,0.12,0.0,0.42,1,0,0,1 +8187,ENFP,28.9,0.18248210256706196,0.0,0.04,0.22,0.0,0.0,0.78,0.0,3.24,0,0,0,1 +8188,ISFP,24.16,0.18006688761921563,0.06,0.2,0.02,0.04,0.0,0.52,0.0,3.24,1,1,0,1 +8189,ENFJ,21.78,0.18763826336921371,0.02,0.48,0.2,0.0,0.02,0.7,0.0,2.2,0,0,0,0 +8190,INFP,35.2,0.17885634499955722,0.0,0.06,0.12,0.0,0.02,1.0,0.0,4.26,1,0,0,1 +8191,ENTP,27.42,0.17531371682441055,0.06,0.22,0.0,0.0,0.02,0.9,0.0,1.98,0,0,1,1 +8192,ISTP,23.1,0.1823065108048667,0.34,0.34,0.0,0.12,0.02,0.6,0.0,2.28,1,1,1,1 +8193,INFJ,29.46,0.18243326771603172,0.0,0.14,0.06,0.0,0.0,0.74,0.0,3.52,1,0,0,0 +8194,INFJ,30.66,0.18436677552225142,0.06,0.08,0.02,0.0,0.0,0.74,0.0,4.1,1,0,0,0 +8195,INFJ,14.88,0.1868540094590286,0.12,0.18,0.06,0.02,0.02,0.2,0.0,2.1,1,0,0,0 +8196,INFJ,23.4,0.18868160738186462,0.0,0.1,0.12,0.0,0.02,0.42,0.0,2.56,1,0,0,0 +8197,INFP,9.3,0.18771655719398636,0.54,0.42,0.0,0.1,0.0,0.18,0.02,1.7,1,0,0,1 +8198,INFJ,23.62,0.19050206391141103,0.3,0.54,0.06,0.0,0.0,0.6,0.02,2.86,1,0,0,0 +8199,INTP,24.2,0.17953482709118498,0.0,0.96,0.08,0.0,0.02,0.38,0.0,2.32,1,0,1,1 +8200,ENTP,28.62,0.18915630549940565,0.06,0.24,0.18,0.04,0.04,0.86,0.0,2.56,0,0,1,1 +8201,INTJ,28.74,0.182149919288658,0.0,0.16,0.0,0.0,0.02,0.72,0.0,3.04,1,0,1,0 +8202,INTJ,23.78,0.18059576350459575,0.04,0.08,0.0,0.0,0.0,0.54,0.0,2.18,1,0,1,0 +8203,ENFP,28.44,0.1889840269451724,0.02,0.38,0.98,0.0,0.0,0.7,0.0,3.82,0,0,0,1 +8204,INTP,18.76,0.1856040216498321,0.12,0.36,0.12,0.02,0.0,0.28,0.0,2.44,1,0,1,1 +8205,INTJ,18.02,0.18722375724261606,0.02,0.14,0.2,0.0,0.02,0.46,0.0,2.2,1,0,1,0 +8206,INFP,26.7,0.18643198387503152,0.04,0.12,0.02,0.0,0.04,0.76,0.0,2.94,1,0,0,1 +8207,INTP,28.0,0.17348140078040275,0.0,0.12,0.0,0.0,0.0,0.58,0.0,2.98,1,0,1,1 +8208,INFJ,17.94,0.19070569801595444,0.2,0.24,0.34,0.0,0.0,0.42,0.0,1.82,1,0,0,0 +8209,INFP,16.58,0.18591092002631746,0.12,0.2,0.1,0.0,0.0,0.36,0.0,2.18,1,0,0,1 +8210,ENTP,18.64,0.188635429208916,0.02,0.4,0.06,0.0,0.0,0.4,0.0,2.14,0,0,1,1 +8211,INTJ,31.68,0.17809046296163902,0.02,0.12,0.08,0.02,0.0,0.74,0.0,3.52,1,0,1,0 +8212,ENTP,27.16,0.1839967475918087,0.0,0.36,0.08,0.0,0.0,0.64,0.0,2.78,0,0,1,1 +8213,INFP,21.7,0.18040571609263176,0.4,0.36,0.16,0.1,0.02,0.62,0.0,1.78,1,0,0,1 +8214,INFP,30.02,0.1824418399529217,0.16,0.26,0.08,0.0,0.0,0.8,0.0,4.06,1,0,0,1 +8215,INTP,20.42,0.19349879116402485,0.12,0.16,0.0,0.02,0.0,1.46,0.0,1.9,1,0,1,1 +8216,ENTP,28.32,0.1843142301157636,0.0,0.14,0.14,0.0,0.0,0.78,0.0,3.32,0,0,1,1 +8217,ENTP,32.1,0.18661109832605643,0.0,0.22,0.08,0.0,0.0,0.98,0.0,3.08,0,0,1,1 +8218,INFP,22.24,0.1875,0.02,0.16,0.12,0.0,0.06,0.54,0.0,2.26,1,0,0,1 +8219,INTJ,22.04,0.18550759208702264,0.16,0.2,0.0,0.16,0.0,0.52,0.0,2.14,1,0,1,0 +8220,INFJ,23.5,0.18916447223442262,0.0,1.08,0.0,0.0,0.0,0.48,0.0,2.26,1,0,0,0 +8221,INFP,27.4,0.19009981681163324,0.0,0.2,0.28,0.0,0.0,0.72,0.0,3.04,1,0,0,1 +8222,INFJ,25.84,0.18430135953640714,0.18,0.3,0.02,0.0,0.04,0.7,0.0,2.58,1,0,0,0 +8223,ISTP,19.02,0.1864225284198007,0.02,0.24,0.04,0.0,0.0,0.26,0.0,2.48,1,1,1,1 +8224,INFJ,13.14,0.18805983793633121,0.0,0.42,0.16,0.0,0.02,0.12,0.0,1.54,1,0,0,0 +8225,INFJ,17.64,0.18468605290776285,0.44,0.24,0.1,0.2,0.0,0.46,0.0,2.7,1,0,0,0 +8226,INTP,30.64,0.18752891176091774,0.04,0.04,0.04,0.04,0.0,0.78,0.0,4.08,1,0,1,1 +8227,INTP,17.54,0.18519893621995054,0.12,0.12,0.04,0.04,0.0,0.28,0.0,2.34,1,0,1,1 +8228,ISTP,23.18,0.18594254565084703,0.0,0.18,0.04,0.0,0.0,0.46,0.0,3.38,1,1,1,1 +8229,ISTJ,33.02,0.18529856157017796,0.0,0.22,0.04,0.0,0.0,0.86,0.0,3.76,1,1,1,0 +8230,ENFP,29.18,0.18384176116411444,0.0,0.22,0.08,0.0,0.0,0.66,0.0,3.04,0,0,0,1 +8231,INTJ,26.7,0.18410429263728034,0.0,0.12,0.06,0.0,0.06,0.6,0.0,3.42,1,0,1,0 +8232,INFJ,32.42,0.1847455440570819,0.0,0.16,0.14,0.0,0.0,0.84,0.0,4.4,1,0,0,0 +8233,ENFP,32.8,0.18177938541100835,0.0,0.14,0.08,0.0,0.0,1.08,0.0,3.5,0,0,0,1 +8234,ENTP,9.26,0.1907714443545175,0.0,0.14,0.0,0.0,0.0,0.36,0.0,0.48,0,0,1,1 +8235,INFP,24.18,0.1835186781434763,0.14,0.16,0.44,0.02,0.02,0.88,0.0,2.44,1,0,0,1 +8236,INTJ,28.72,0.18795284236822257,0.02,0.32,0.08,0.0,0.0,0.84,0.0,3.68,1,0,1,0 +8237,INFJ,23.52,0.187735592396401,0.06,0.26,0.28,0.04,0.04,0.6,0.0,2.82,1,0,0,0 +8238,INTJ,17.46,0.18091488894608548,0.18,0.1,0.0,0.0,0.0,0.18,0.0,1.3,1,0,1,0 +8239,INTJ,29.12,0.18318725559312687,0.0,0.08,0.5,0.0,0.0,0.86,0.0,3.28,1,0,1,0 +8240,ENFP,25.88,0.18449870726802642,0.04,0.04,0.1,0.02,0.0,0.54,0.0,1.42,0,0,0,1 +8241,INFJ,25.94,0.19358556004756247,0.02,0.36,0.26,0.0,0.0,0.84,0.0,3.18,1,0,0,0 +8242,INTJ,22.84,0.18290980101650528,0.14,0.2,0.16,0.06,0.04,0.8,0.02,3.04,1,0,1,0 +8243,ENFP,26.1,0.18812520729016377,0.02,0.44,0.42,0.02,0.0,0.62,0.0,2.4,0,0,0,1 +8244,INTP,23.68,0.18470962524716486,0.02,0.32,0.14,0.0,0.0,0.38,0.0,1.78,1,0,1,1 +8245,INFP,32.32,0.18604877178426021,0.0,0.12,0.14,0.0,0.0,0.76,0.0,3.22,1,0,0,1 +8246,INFP,25.16,0.18986352212401097,0.16,0.36,0.24,0.0,0.02,0.7,0.0,2.56,1,0,0,1 +8247,ENFJ,19.98,0.17966974967520272,0.04,0.32,0.64,0.0,0.0,0.42,0.0,1.4,0,0,0,0 +8248,INFP,33.98,0.1875930009968456,0.0,0.2,0.52,0.0,0.0,0.96,0.0,4.2,1,0,0,1 +8249,INFP,26.88,0.18226732339141355,0.1,0.22,0.22,0.0,0.0,0.8,0.02,2.52,1,0,0,1 +8250,INTP,25.22,0.18282641273004524,0.0,0.08,0.04,0.0,0.06,0.86,0.0,4.2,1,0,1,1 +8251,INTP,25.62,0.18859407076440046,0.0,0.18,0.0,0.0,0.0,1.38,0.0,2.74,1,0,1,1 +8252,ENTJ,33.42,0.18736384929423128,0.0,0.04,0.64,0.0,0.0,0.88,0.0,3.78,0,0,1,0 +8253,INTP,29.16,0.18471212058757888,0.0,0.26,0.02,0.0,0.0,0.94,0.0,2.96,1,0,1,1 +8254,ISFJ,21.2,0.19212432450003938,0.02,0.32,0.58,0.0,0.0,0.6,0.0,2.28,1,1,0,0 +8255,INTJ,17.08,0.1822932912557528,0.0,0.0,0.0,0.0,0.0,0.32,0.0,1.92,1,0,1,0 +8256,ENFJ,24.48,0.19262336793426263,0.02,0.5,0.04,0.0,0.0,0.56,0.0,2.58,0,0,0,0 +8257,INFP,26.56,0.18500550455439688,0.02,0.12,0.16,0.0,0.04,0.46,0.0,3.08,1,0,0,1 +8258,INFJ,26.3,0.18712610552450595,0.0,0.26,0.1,0.0,0.02,0.58,0.02,2.84,1,0,0,0 +8259,INTP,30.48,0.1812122502115071,0.06,0.16,0.0,0.02,0.0,0.76,0.0,3.34,1,0,1,1 +8260,INTP,13.9,0.1777476083817211,0.54,0.5,0.0,0.0,0.0,0.3,0.0,1.42,1,0,1,1 +8261,INFP,23.48,0.18771570303155044,0.0,0.38,0.4,0.0,0.0,0.72,0.0,2.38,1,0,0,1 +8262,INTP,23.5,0.18265954311703483,0.28,0.38,0.06,0.0,0.1,0.58,0.0,3.12,1,0,1,1 +8263,INFJ,22.06,0.181432260293772,0.1,0.16,0.38,0.0,0.12,0.42,0.0,2.34,1,0,0,0 +8264,INTJ,24.94,0.1806262019886074,0.02,0.16,0.08,0.0,0.0,0.6,0.0,3.08,1,0,1,0 +8265,INFJ,29.74,0.17512059576288402,0.0,0.56,0.14,0.0,0.0,0.64,0.0,3.14,1,0,0,0 +8266,INFJ,29.1,0.17663758678823874,0.0,0.02,0.02,0.0,0.0,0.74,0.0,2.98,1,0,0,0 +8267,ENTP,18.82,0.18279289263616122,0.1,0.42,0.08,0.0,0.0,0.44,0.0,1.74,0,0,1,1 +8268,INTP,26.7,0.18992445751908915,0.04,0.18,0.3,0.0,0.0,0.72,0.0,3.7,1,0,1,1 +8269,ENFP,28.04,0.18310866776109047,0.02,0.28,0.18,0.0,0.0,0.68,0.0,3.26,0,0,0,1 +8270,ENTJ,23.36,0.18111728824458334,0.2,0.38,0.12,0.0,0.0,0.68,0.0,3.1,0,0,1,0 +8271,ENFP,28.96,0.18262213340224392,0.06,0.24,0.26,0.06,0.0,0.9,0.0,3.58,0,0,0,1 +8272,INFP,32.36,0.18539479442921375,0.0,0.04,0.14,0.0,0.0,1.1,0.0,4.38,1,0,0,1 +8273,ISFP,24.86,0.18335667953457943,0.0,0.26,0.12,0.0,0.0,0.6,0.0,2.18,1,1,0,1 +8274,ENFJ,29.88,0.18584503490409227,0.0,0.04,0.18,0.0,0.1,0.94,0.02,4.2,0,0,0,0 +8275,INTJ,33.0,0.18091862965402913,0.02,0.16,0.12,0.0,0.0,0.96,0.0,3.86,1,0,1,0 +8276,ENFP,21.84,0.18728627512290488,0.04,0.3,0.14,0.0,0.0,0.5,0.0,2.26,0,0,0,1 +8277,INFP,22.78,0.1878259446856956,0.0,0.24,0.42,0.0,0.02,0.66,0.0,2.34,1,0,0,1 +8278,INFJ,29.9,0.18625908772851948,0.08,0.26,0.24,0.02,0.0,0.8,0.0,3.66,1,0,0,0 +8279,INFP,30.84,0.18981890614260263,0.0,0.24,0.16,0.0,0.0,1.04,0.0,3.98,1,0,0,1 +8280,INTJ,13.22,0.1797197808159722,0.74,0.78,0.02,0.02,0.02,0.2,0.02,1.22,1,0,1,0 +8281,ISTJ,28.88,0.1848368078439966,0.04,0.22,0.2,0.0,0.02,0.68,0.0,2.42,1,1,1,0 +8282,ENFP,24.84,0.18541120934013305,0.02,0.18,0.12,0.0,0.02,0.44,0.0,1.94,0,0,0,1 +8283,INFJ,29.26,0.1902780836821787,0.02,0.32,0.06,0.0,0.0,1.24,0.02,3.22,1,0,0,0 +8284,ENTP,17.24,0.20561377085936292,0.02,0.36,0.44,0.02,0.0,0.34,0.0,1.6,0,0,1,1 +8285,INTP,27.78,0.185642467589178,0.06,0.28,0.04,0.0,0.0,0.58,0.0,3.0,1,0,1,1 +8286,INTJ,29.0,0.18682025561877544,0.0,0.26,0.2,0.0,0.02,0.94,0.0,3.04,1,0,1,0 +8287,ESTP,17.96,0.1914436677987894,0.04,0.34,0.06,0.0,0.0,0.28,0.0,0.5,0,1,1,1 +8288,ENTP,20.48,0.18546317379031504,0.06,0.14,0.06,0.0,0.0,0.44,0.0,1.98,0,0,1,1 +8289,ISTJ,33.32,0.19127418586262693,0.0,0.12,0.58,0.0,0.0,1.12,0.06,4.24,1,1,1,0 +8290,INFJ,22.2,0.18935525708539874,0.0,0.02,0.3,0.0,0.02,0.5,0.0,3.22,1,0,0,0 +8291,INTP,29.02,0.17961138620616365,0.02,0.26,0.0,0.0,0.0,0.76,0.0,2.54,1,0,1,1 +8292,INTP,14.34,0.1907964138688474,0.02,0.16,0.62,0.0,0.02,0.44,0.0,1.34,1,0,1,1 +8293,INTP,31.68,0.18011455098279375,0.06,0.28,0.02,0.02,0.0,1.66,0.0,3.66,1,0,1,1 +8294,ENTP,19.52,0.18336522773574485,0.02,0.12,0.04,0.0,0.0,0.68,0.0,1.7,0,0,1,1 +8295,ISTP,6.26,0.17942150201832902,0.04,0.04,0.0,0.0,0.0,0.08,0.0,0.54,1,1,1,1 +8296,INFJ,28.12,0.18131787866453666,0.02,0.12,0.14,0.0,0.0,0.7,0.0,3.16,1,0,0,0 +8297,INTP,28.26,0.18494606239545122,0.0,0.14,0.04,0.0,0.0,0.72,0.0,3.14,1,0,1,1 +8298,ISTP,33.92,0.18628578120150766,0.0,0.2,0.02,0.0,0.0,0.88,0.0,3.74,1,1,1,1 +8299,ISTP,25.06,0.18593023074198348,0.0,0.2,0.12,0.0,0.0,1.3,0.0,2.06,1,1,1,1 +8300,ISTP,25.24,0.18061655886229958,0.02,0.1,0.04,0.0,0.0,0.68,0.0,1.86,1,1,1,1 +8301,ISTP,20.88,0.19608854535778056,0.06,0.18,0.04,0.04,0.0,1.36,0.0,2.12,1,1,1,1 +8302,INFJ,25.3,0.18258270840596888,0.16,0.42,0.02,0.02,0.0,0.46,0.0,3.44,1,0,0,0 +8303,INTJ,28.32,0.17773800350508198,0.0,0.26,0.02,0.0,0.0,0.78,0.0,2.2,1,0,1,0 +8304,ENTJ,29.1,0.1800209242463706,0.0,0.0,0.08,0.0,0.0,1.0,0.0,2.72,0,0,1,0 +8305,INTP,28.12,0.17847929958287506,0.0,0.2,0.06,0.0,0.0,0.74,0.0,3.06,1,0,1,1 +8306,INTP,28.78,0.18619486197269505,0.02,0.44,0.4,0.0,0.0,0.9,0.0,2.76,1,0,1,1 +8307,ENTP,21.96,0.18039085306675873,0.04,0.12,0.04,0.0,0.0,0.46,0.0,1.98,0,0,1,1 +8308,ISTP,18.26,0.1819884858529582,0.14,0.46,0.16,0.02,0.0,0.32,0.0,1.98,1,1,1,1 +8309,ENTP,18.84,0.19590780795654078,0.0,0.1,0.18,0.0,0.0,0.4,0.0,2.42,0,0,1,1 +8310,INFJ,22.46,0.18460217512919916,0.0,0.04,0.1,0.0,0.0,0.54,0.02,2.14,1,0,0,0 +8311,INFP,13.1,0.19638071752506858,0.0,0.26,0.12,0.0,0.0,1.02,0.0,1.18,1,0,0,1 +8312,INFJ,17.34,0.1995417243310475,0.0,0.44,1.4,0.0,0.0,0.62,0.0,2.22,1,0,0,0 +8313,ENFP,25.4,0.1813184496947817,0.04,0.38,0.02,0.02,0.0,0.64,0.0,2.2,0,0,0,1 +8314,ENFP,35.08,0.18806595716038182,0.0,0.24,0.28,0.0,0.02,1.4,0.0,4.38,0,0,0,1 +8315,INFP,26.2,0.1840759678597516,0.12,0.14,0.26,0.02,0.04,0.8,0.0,3.24,1,0,0,1 +8316,ENTP,23.24,0.18729420802666716,0.0,0.08,0.04,0.0,0.0,0.7,0.0,2.3,0,0,1,1 +8317,INFJ,29.86,0.18565016004404641,0.06,0.28,0.36,0.0,0.0,0.82,0.0,3.1,1,0,0,0 +8318,INFP,18.68,0.18107741860229706,0.02,0.16,0.16,0.0,0.0,0.26,0.0,2.5,1,0,0,1 +8319,ENFP,27.56,0.18596844559669207,0.0,0.14,0.32,0.0,0.02,0.98,0.0,2.76,0,0,0,1 +8320,ENTP,30.24,0.18547245949342506,0.0,0.22,0.04,0.0,0.0,0.88,0.06,4.32,0,0,1,1 +8321,INFJ,31.6,0.18653074997447627,0.04,0.3,0.24,0.0,0.0,0.86,0.0,3.62,1,0,0,0 +8322,INFP,28.96,0.1846808567130059,0.16,0.22,0.36,0.0,0.02,0.86,0.0,2.62,1,0,0,1 +8323,INFJ,24.18,0.18558103539256882,0.04,0.16,0.0,0.0,0.0,0.62,0.0,2.84,1,0,0,0 +8324,ISTP,20.32,0.18588915597823,0.0,0.24,0.0,0.0,0.0,0.38,0.02,1.72,1,1,1,1 +8325,INTP,25.34,0.17542172542227805,0.0,0.1,0.0,0.0,0.02,0.48,0.0,2.86,1,0,1,1 +8326,ISTP,26.24,0.18718690313305986,0.0,0.16,0.16,0.0,0.04,0.72,0.0,2.6,1,1,1,1 +8327,INFP,18.76,0.1826928297693517,0.38,0.38,0.0,0.0,0.02,0.42,0.0,1.68,1,0,0,1 +8328,INFJ,28.32,0.1776305396478872,0.0,0.04,0.0,0.0,0.12,0.88,0.0,2.76,1,0,0,0 +8329,INFJ,18.38,0.1812121717440148,0.16,0.22,0.08,0.1,0.02,0.38,0.0,1.82,1,0,0,0 +8330,INFP,25.92,0.18765162741231373,0.04,0.16,0.4,0.0,0.0,0.6,0.0,2.26,1,0,0,1 +8331,INFP,30.32,0.18793744397529422,0.0,0.12,0.48,0.0,0.02,1.14,0.0,1.68,1,0,0,1 +8332,INFP,32.24,0.19295910794106988,0.0,0.18,0.22,0.0,0.04,1.36,0.0,3.62,1,0,0,1 +8333,INFP,28.86,0.1842080317646196,0.02,0.3,0.1,0.0,0.0,0.92,0.0,2.52,1,0,0,1 +8334,INFP,30.54,0.18600526321530103,0.0,0.1,0.44,0.0,0.0,1.06,0.0,3.62,1,0,0,1 +8335,INFP,16.36,0.18811928378669027,0.46,0.52,0.08,0.0,0.06,0.3,0.0,1.84,1,0,0,1 +8336,INFP,27.42,0.19003796711858456,0.02,0.08,0.14,0.0,0.06,0.64,0.0,2.82,1,0,0,1 +8337,ESTP,19.88,0.19152782753528616,0.04,0.24,0.18,0.0,0.0,0.26,0.0,1.88,0,1,1,1 +8338,INFJ,25.3,0.1863108088962286,0.04,0.3,0.28,0.04,0.04,1.14,0.0,2.8,1,0,0,0 +8339,INFP,30.5,0.18843696419410563,0.06,0.2,0.3,0.0,0.0,0.92,0.0,3.8,1,0,0,1 +8340,ENTP,15.06,0.18653607682002316,0.04,0.16,0.12,0.02,0.0,0.2,0.0,1.74,0,0,1,1 +8341,ENFP,24.84,0.1739544392707062,0.12,0.16,0.2,0.02,0.02,0.66,0.02,1.7,0,0,0,1 +8342,INFP,26.94,0.1796778460020285,0.04,0.06,0.0,0.0,0.0,0.4,0.0,3.22,1,0,0,1 +8343,ISTJ,22.84,0.18171610467832963,0.02,0.34,0.02,0.0,0.0,0.42,0.0,2.06,1,1,1,0 +8344,ENTP,18.86,0.18639743257229277,0.0,0.16,0.06,0.0,0.0,0.36,0.0,1.46,0,0,1,1 +8345,INFP,27.98,0.18889660396414593,0.0,0.14,0.22,0.0,0.02,0.74,0.0,3.12,1,0,0,1 +8346,INFP,27.04,0.1865704022163808,0.0,0.3,0.18,0.0,0.0,0.76,0.0,2.58,1,0,0,1 +8347,INFP,22.12,0.1886791357850244,0.48,0.16,0.36,0.38,0.02,0.7,0.0,2.72,1,0,0,1 +8348,INTJ,23.16,0.17482243731961492,0.18,0.28,0.0,0.02,0.02,0.64,0.0,2.22,1,0,1,0 +8349,INTJ,33.32,0.20050471288776964,0.04,0.14,0.22,0.0,0.02,1.8,0.0,3.44,1,0,1,0 +8350,INTP,16.28,0.18539753902508416,0.02,0.12,0.0,0.0,0.0,0.24,0.0,0.74,1,0,1,1 +8351,ISFP,29.12,0.18208391486704933,0.02,0.22,0.08,0.0,0.0,0.72,0.0,4.16,1,1,0,1 +8352,INTP,20.9,0.1901531496902733,0.12,0.16,0.24,0.04,0.02,0.68,0.0,2.36,1,0,1,1 +8353,INTP,24.2,0.18640862016265392,0.16,0.48,0.06,0.0,0.0,0.54,0.0,2.46,1,0,1,1 +8354,INTP,28.24,0.174590880835278,0.0,0.28,0.0,0.0,0.0,0.62,0.0,2.46,1,0,1,1 +8355,INFJ,30.86,0.1806007737132815,0.08,0.12,0.08,0.02,0.0,0.86,0.0,3.88,1,0,0,0 +8356,INFP,22.52,0.18372495731570443,0.14,0.2,0.06,0.02,0.02,0.54,0.0,2.64,1,0,0,1 +8357,INTJ,20.32,0.1880290550843736,0.06,0.34,0.12,0.02,0.0,0.48,0.0,2.42,1,0,1,0 +8358,ESTJ,27.3,0.18709291451300128,0.0,0.02,0.1,0.0,0.02,0.68,0.0,3.4,0,1,1,0 +8359,INFJ,28.7,0.18394792211983224,0.0,0.32,0.16,0.0,0.0,1.04,0.0,2.54,1,0,0,0 +8360,ENTP,23.18,0.17855008681139012,0.02,0.04,0.08,0.0,0.0,0.42,0.0,1.96,0,0,1,1 +8361,INFJ,31.46,0.17995175347402984,0.04,0.18,0.2,0.0,0.0,0.8,0.0,3.2,1,0,0,0 +8362,INFP,16.7,0.18280587860356243,0.22,0.1,0.08,0.14,0.0,0.36,0.0,1.34,1,0,0,1 +8363,INTJ,27.38,0.18371422791123748,0.0,0.28,0.16,0.0,0.0,0.52,0.0,3.38,1,0,1,0 +8364,INFJ,16.9,0.1788192915247039,0.34,0.12,0.0,0.04,0.0,0.34,0.0,1.86,1,0,0,0 +8365,INFP,25.78,0.19003549447190304,0.02,0.14,0.24,0.0,0.02,0.72,0.0,2.64,1,0,0,1 +8366,INFP,22.44,0.19530380584303242,0.02,0.06,1.1,0.0,0.04,0.54,0.0,2.72,1,0,0,1 +8367,ENFP,34.58,0.17930650955814076,0.0,0.04,0.04,0.0,0.0,0.96,0.0,3.78,0,0,0,1 +8368,INFJ,20.94,0.19098712396988887,0.04,0.3,0.1,0.04,0.02,0.56,0.04,2.62,1,0,0,0 +8369,INTP,25.78,0.1829486979222154,0.0,0.12,0.12,0.0,0.0,0.72,0.0,2.86,1,0,1,1 +8370,ISFP,29.1,0.18542589793260908,0.0,0.06,0.4,0.0,0.0,0.66,0.0,3.52,1,1,0,1 +8371,INFP,24.96,0.18532444707914245,0.0,0.18,0.14,0.0,0.02,0.62,0.02,3.2,1,0,0,1 +8372,ISTJ,24.94,0.1890184413580247,0.02,0.3,0.12,0.0,0.02,0.56,0.0,3.16,1,1,1,0 +8373,ISTP,21.42,0.1879464590324967,0.4,0.5,0.06,0.02,0.04,0.54,0.0,3.02,1,1,1,1 +8374,ENFP,26.52,0.18023585431841832,0.04,0.36,0.0,0.0,0.0,0.62,0.0,3.2,0,0,0,1 +8375,INFP,14.58,0.17529859969289088,0.32,0.18,0.14,0.16,0.0,0.36,0.04,1.52,1,0,0,1 +8376,INTP,23.82,0.18342053015053486,0.04,0.28,0.0,0.0,0.02,0.44,0.0,2.4,1,0,1,1 +8377,ISFP,21.12,0.19107260874536863,0.16,0.18,0.16,0.16,0.02,0.52,0.02,2.82,1,1,0,1 +8378,ISFP,34.74,0.1847932108520548,0.0,0.3,0.02,0.0,0.0,0.92,0.0,4.38,1,1,0,1 +8379,INTP,31.18,0.18334008487572984,0.02,0.08,0.0,0.0,0.08,0.72,0.0,4.44,1,0,1,1 +8380,ENTP,15.36,0.18516323794557185,0.04,0.28,0.08,0.0,0.0,0.36,0.0,1.1,0,0,1,1 +8381,ENFJ,26.5,0.1839827181528788,0.06,0.08,0.44,0.0,0.0,0.6,0.0,2.86,0,0,0,0 +8382,INFJ,22.42,0.1965037715766579,0.0,0.06,0.36,0.0,0.04,1.62,0.02,2.38,1,0,0,0 +8383,ENTP,22.24,0.18969924022268128,0.02,0.24,1.52,0.0,0.0,0.58,0.0,1.68,0,0,1,1 +8384,ISFP,28.5,0.17764916470618416,0.0,0.14,0.12,0.0,0.02,0.92,0.0,2.72,1,1,0,1 +8385,INFP,29.48,0.1793842500155259,0.04,0.14,0.28,0.0,0.0,1.26,0.0,2.54,1,0,0,1 +8386,INFP,32.18,0.18566155776177323,0.02,0.32,0.04,0.0,0.0,1.0,0.0,3.68,1,0,0,1 +8387,INFP,26.76,0.18576845081036764,0.04,0.06,0.04,0.0,0.0,0.56,0.04,3.06,1,0,0,1 +8388,ENTJ,24.28,0.1847325535505644,0.0,0.14,0.04,0.0,0.0,0.76,0.0,1.84,0,0,1,0 +8389,INFP,26.78,0.1938995897635404,0.04,0.46,1.18,0.0,0.0,1.68,0.0,3.08,1,0,0,1 +8390,INTP,25.62,0.17877934247719748,0.06,0.06,0.02,0.0,0.0,0.54,0.0,2.34,1,0,1,1 +8391,INTP,24.98,0.17987259020406599,0.12,0.28,0.16,0.0,0.0,0.6,0.0,2.86,1,0,1,1 +8392,ENFP,27.44,0.18281695545097523,0.1,0.14,0.06,0.06,0.0,0.64,0.0,3.04,0,0,0,1 +8393,INFP,33.7,0.17953690709158343,0.0,0.06,0.02,0.0,0.0,0.9,0.0,4.28,1,0,0,1 +8394,INFP,18.06,0.19037532367448975,0.06,0.18,0.16,0.0,0.0,0.48,0.0,2.32,1,0,0,1 +8395,ISFP,27.94,0.18686228870507762,0.02,0.28,0.12,0.0,0.0,0.98,0.0,3.6,1,1,0,1 +8396,INFP,15.26,0.19176141174097172,0.3,0.44,0.4,0.0,0.0,0.48,0.0,1.44,1,0,0,1 +8397,INFP,29.62,0.18457544967125958,0.12,0.16,0.16,0.0,0.02,0.78,0.0,4.04,1,0,0,1 +8398,ENTP,22.04,0.18660802295161022,0.04,0.3,0.48,0.0,0.0,0.36,0.0,2.96,0,0,1,1 +8399,INFJ,32.82,0.18643607172400084,0.0,0.14,0.26,0.0,0.0,0.86,0.0,3.92,1,0,0,0 +8400,INTJ,26.72,0.1939921269699029,0.0,0.14,0.08,0.0,0.0,1.96,0.0,2.76,1,0,1,0 +8401,INTJ,23.64,0.1803209442654209,0.0,0.04,0.16,0.0,0.04,0.62,0.0,2.26,1,0,1,0 +8402,ENFJ,30.12,0.1896767738523178,0.0,0.2,0.04,0.0,0.0,1.12,0.0,3.62,0,0,0,0 +8403,INFJ,25.52,0.18641963970956357,0.02,0.34,0.06,0.0,0.0,0.82,0.0,3.32,1,0,0,0 +8404,INTJ,15.2,0.19087880586295744,0.06,0.28,0.14,0.0,0.02,0.22,0.0,1.8,1,0,1,0 +8405,ENTP,11.48,0.18533134448617955,0.32,0.42,0.02,0.02,0.02,0.16,0.0,1.56,0,0,1,1 +8406,ESFJ,12.52,0.18855305613531795,0.0,0.16,0.0,0.0,0.0,0.22,0.0,1.56,0,1,0,0 +8407,INTP,22.28,0.18236399772507736,0.0,0.38,0.12,0.0,0.0,0.36,0.0,2.8,1,0,1,1 +8408,INFP,29.3,0.18781140588610587,0.0,0.18,0.36,0.0,0.0,0.8,0.0,3.48,1,0,0,1 +8409,INFJ,18.42,0.1883238650320328,0.02,0.24,0.08,0.0,0.0,0.48,0.0,2.02,1,0,0,0 +8410,ENTP,16.62,0.1867655631429875,0.18,0.38,0.36,0.12,0.0,0.38,0.0,1.22,0,0,1,1 +8411,INTJ,16.54,0.18044038836901566,0.0,0.12,0.04,0.0,0.06,0.46,0.0,2.16,1,0,1,0 +8412,INTP,22.2,0.17706555000309082,0.06,0.14,0.06,0.0,0.08,0.42,0.0,2.06,1,0,1,1 +8413,INFP,28.96,0.19062661637337763,0.0,0.16,0.24,0.0,0.02,0.7,0.0,3.96,1,0,0,1 +8414,INFP,27.64,0.19587303233690342,0.02,0.08,0.12,0.0,0.0,2.04,0.02,2.82,1,0,0,1 +8415,ENTP,24.8,0.1844204071791062,0.24,0.44,0.06,0.0,0.02,0.92,0.02,2.64,0,0,1,1 +8416,ISTP,26.16,0.18337633818109556,0.02,0.28,0.08,0.0,0.04,0.84,0.0,2.7,1,1,1,1 +8417,ENTJ,24.48,0.18112204482112537,0.12,0.32,0.0,0.0,0.0,0.62,0.0,2.18,0,0,1,0 +8418,ENTP,12.44,0.191664905672068,0.02,0.26,0.76,0.0,0.0,0.08,0.0,0.8,0,0,1,1 +8419,ISTP,15.24,0.18389082995379763,0.02,0.06,0.58,0.02,0.0,0.18,0.0,2.2,1,1,1,1 +8420,INFP,23.36,0.1843556185991926,0.22,0.34,0.04,0.02,0.0,0.64,0.04,2.6,1,0,0,1 +8421,INFP,28.92,0.21064756837908732,0.06,0.56,0.02,0.0,0.0,1.6,0.04,2.06,1,0,0,1 +8422,INTJ,24.26,0.1773537517320025,0.08,0.08,0.16,0.02,0.02,0.64,0.0,2.18,1,0,1,0 +8423,INTJ,29.74,0.1813611670923608,0.06,0.36,0.18,0.02,0.0,0.94,0.0,2.84,1,0,1,0 +8424,INTP,23.08,0.1852458624482993,0.04,0.04,0.1,0.02,0.02,0.92,0.0,2.56,1,0,1,1 +8425,ENFJ,25.9,0.18211707415394682,0.1,0.2,0.18,0.02,0.0,0.5,0.0,2.1,0,0,0,0 +8426,ESFP,24.46,0.18066216943922708,0.04,0.1,0.12,0.0,0.0,0.48,0.0,3.04,0,1,0,1 +8427,ENTJ,25.52,0.17623343999999996,0.0,0.12,0.06,0.0,0.0,0.54,0.0,2.56,0,0,1,0 +8428,INFP,22.24,0.1809466655625501,0.02,0.1,0.04,0.02,0.0,0.52,0.0,1.0,1,0,0,1 +8429,ENFP,27.38,0.18954423503564888,0.0,0.28,0.74,0.0,0.0,0.76,0.0,2.6,0,0,0,1 +8430,ISFJ,17.9,0.182090976086331,0.06,0.24,0.32,0.0,0.02,0.26,0.0,1.84,1,1,0,0 +8431,ENTP,30.04,0.18331922657589864,0.0,0.06,0.02,0.0,0.0,0.78,0.0,3.16,0,0,1,1 +8432,ENFP,25.3,0.18693725663714023,0.02,0.18,0.22,0.0,0.0,0.68,0.0,3.06,0,0,0,1 +8433,ENFP,23.1,0.20099918001462694,0.02,0.18,0.44,0.0,0.02,1.46,0.0,2.54,0,0,0,1 +8434,ENTP,26.08,0.18611698514270977,0.02,0.08,0.06,0.0,0.0,0.7,0.04,2.6,0,0,1,1 +8435,INTP,24.32,0.17743256507092267,0.16,0.3,0.0,0.0,0.0,0.52,0.0,1.52,1,0,1,1 +8436,INFP,27.94,0.1821095686131223,0.02,0.28,0.06,0.0,0.0,0.64,0.0,2.58,1,0,0,1 +8437,INFJ,27.28,0.18218841165076202,0.0,0.12,0.12,0.0,0.0,0.8,0.0,2.62,1,0,0,0 +8438,INFP,29.76,0.18900623827534518,0.02,0.26,0.16,0.0,0.0,0.76,0.0,3.96,1,0,0,1 +8439,ISTP,27.72,0.18211240194133269,0.36,0.34,0.06,0.0,0.02,0.84,0.02,2.48,1,1,1,1 +8440,ISFP,12.1,0.18316933527636314,0.68,0.7,0.04,0.06,0.0,0.28,0.0,1.5,1,1,0,1 +8441,INFP,34.42,0.18585852969513422,0.0,0.1,0.06,0.0,0.02,1.12,0.0,4.3,1,0,0,1 +8442,ENTP,28.24,0.1902353683696648,0.0,0.16,0.06,0.0,0.0,0.64,0.0,3.38,0,0,1,1 +8443,INFJ,31.52,0.18066423183549296,0.02,0.42,0.1,0.0,0.0,0.82,0.0,2.9,1,0,0,0 +8444,INFJ,27.9,0.18662348873876095,0.0,0.02,0.14,0.0,0.0,0.58,0.0,3.22,1,0,0,0 +8445,ENFP,23.38,0.19100238659360927,0.08,0.48,0.8,0.0,0.0,1.06,0.0,2.4,0,0,0,1 +8446,ESFJ,30.68,0.1857912707536559,0.12,0.18,0.06,0.0,0.0,0.74,0.0,4.88,0,1,0,0 +8447,INTJ,31.4,0.18130672111580848,0.0,0.1,0.06,0.0,0.0,0.8,0.0,4.24,1,0,1,0 +8448,INTP,24.62,0.1860685385476051,0.0,0.32,0.0,0.0,0.0,0.64,0.0,1.98,1,0,1,1 +8449,ENTJ,19.72,0.18943241960853552,0.12,0.14,0.0,0.0,0.0,0.64,0.0,2.04,0,0,1,0 +8450,INFP,20.9,0.18494860501049226,0.0,0.26,0.14,0.0,0.0,0.54,0.0,2.06,1,0,0,1 +8451,INFJ,17.44,0.18344566663429973,0.0,0.86,0.02,0.0,0.0,0.22,0.0,1.56,1,0,0,0 +8452,INFJ,28.2,0.18473079437775608,0.04,0.06,0.5,0.0,0.18,0.88,0.0,3.2,1,0,0,0 +8453,INFP,32.76,0.18012301834058625,0.0,0.36,1.04,0.0,0.02,0.92,0.0,3.58,1,0,0,1 +8454,ENFP,19.3,0.1851192595229619,0.0,0.3,0.38,0.0,0.0,0.42,0.0,1.88,0,0,0,1 +8455,ISTJ,25.9,0.1877118780664733,0.0,0.24,0.06,0.0,0.0,0.88,0.0,2.9,1,1,1,0 +8456,INFJ,22.8,0.1851576906944855,0.24,0.32,0.0,0.02,0.0,0.48,0.0,2.1,1,0,0,0 +8457,INFJ,13.6,0.18432485355479455,0.22,0.4,0.16,0.0,0.0,0.26,0.0,1.36,1,0,0,0 +8458,ISFJ,15.94,0.18266570146175973,0.02,0.14,0.2,0.0,0.0,0.32,0.0,2.0,1,1,0,0 +8459,INTJ,9.0,0.18625333078423328,0.24,0.2,0.08,0.04,0.0,0.12,0.0,0.96,1,0,1,0 +8460,INFP,28.14,0.18530467192461378,0.02,0.14,0.5,0.0,0.02,0.58,0.0,4.18,1,0,0,1 +8461,INFP,20.66,0.18963479890747678,0.24,0.28,0.08,0.04,0.0,0.64,0.0,2.66,1,0,0,1 +8462,INFP,19.42,0.17648767662489792,0.04,0.28,0.02,0.0,0.0,0.34,0.0,1.82,1,0,0,1 +8463,ISFP,27.04,0.18344423926597905,0.2,0.08,0.2,0.2,0.0,0.68,0.0,3.38,1,1,0,1 +8464,INTJ,21.84,0.18537943838889095,0.04,0.2,0.16,0.0,0.02,0.54,0.0,2.48,1,0,1,0 +8465,INTP,31.64,0.1760043982471061,0.0,0.02,0.02,0.0,0.02,0.7,0.0,2.42,1,0,1,1 +8466,INFP,23.94,0.18785370137178248,0.2,0.32,0.18,0.04,0.02,0.5,0.0,2.66,1,0,0,1 +8467,INTP,28.4,0.18577916105530703,0.04,0.06,0.04,0.02,0.0,0.8,0.0,2.9,1,0,1,1 +8468,INTJ,22.16,0.1848121321865098,0.04,0.14,0.04,0.0,0.04,0.54,0.0,2.48,1,0,1,0 +8469,INFP,31.5,0.18167903221678433,0.02,0.12,0.2,0.0,0.0,0.9,0.02,3.1,1,0,0,1 +8470,ESTP,23.74,0.19174298383780386,0.08,0.46,0.3,0.04,0.0,0.72,0.0,2.14,0,1,1,1 +8471,INFP,32.02,0.191967031566963,0.02,0.32,0.62,0.02,0.06,1.06,0.02,3.64,1,0,0,1 +8472,INTJ,18.18,0.17444927632985635,0.0,0.0,0.0,0.0,0.0,0.3,0.0,0.78,1,0,1,0 +8473,INTJ,29.28,0.18559353152251648,0.02,0.12,0.3,0.0,0.04,0.84,0.0,4.16,1,0,1,0 +8474,INTP,26.14,0.17566680226256082,0.0,0.18,0.06,0.0,0.0,0.72,0.0,2.6,1,0,1,1 +8475,INTP,24.3,0.17969789050705026,0.06,0.34,0.06,0.0,0.0,0.5,0.0,2.3,1,0,1,1 +8476,INTP,25.36,0.18142509628054557,0.16,0.24,0.1,0.04,0.14,0.6,0.0,3.06,1,0,1,1 +8477,INFJ,8.44,0.17647232240249214,0.72,0.38,0.28,0.34,0.0,0.1,0.0,0.84,1,0,0,0 +8478,INFP,23.24,0.19430956918230788,0.12,0.1,0.28,0.0,0.0,0.48,0.0,3.0,1,0,0,1 +8479,ISFP,19.02,0.19053504053296821,0.04,0.1,0.22,0.0,0.04,0.58,0.0,2.12,1,1,0,1 +8480,INFP,26.3,0.20113472253086226,0.0,0.36,0.04,0.0,0.0,0.88,0.0,2.64,1,0,0,1 +8481,ENTP,24.12,0.1928881736885605,0.14,0.26,0.06,0.06,0.02,0.68,0.0,2.24,0,0,1,1 +8482,INFJ,27.94,0.1845223562723321,0.02,0.52,0.64,0.02,0.0,0.6,0.0,2.74,1,0,0,0 +8483,ENTP,17.1,0.18862273164113288,0.12,0.3,0.0,0.06,0.0,0.34,0.0,1.68,0,0,1,1 +8484,INFJ,21.14,0.18333978389558972,0.06,0.2,0.3,0.04,0.02,0.44,0.0,1.58,1,0,0,0 +8485,INFP,18.12,0.18135835993121482,0.02,0.18,0.02,0.0,0.04,0.36,0.0,1.46,1,0,0,1 +8486,ISTJ,20.98,0.18423542330883702,0.0,0.26,0.18,0.0,0.0,0.34,0.0,1.92,1,1,1,0 +8487,INTP,24.78,0.174224503426871,0.06,0.1,0.04,0.02,0.0,0.46,0.0,1.82,1,0,1,1 +8488,INTP,8.8,0.184068942239216,0.6,0.44,0.04,0.1,0.04,0.12,0.0,0.94,1,0,1,1 +8489,ENFP,33.66,0.1836069961245846,0.0,0.12,0.04,0.0,0.02,1.2,0.0,4.06,0,0,0,1 +8490,INTP,18.56,0.1869983881231879,0.06,0.32,0.24,0.04,0.0,0.34,0.0,2.04,1,0,1,1 +8491,INTP,15.02,0.18424115086924286,0.28,0.46,0.06,0.02,0.02,0.26,0.0,1.34,1,0,1,1 +8492,ENTP,31.24,0.1787560403353091,0.02,0.18,1.18,0.0,0.0,0.8,0.0,1.84,0,0,1,1 +8493,ENFP,18.3,0.17687279621964305,0.42,0.18,0.12,0.18,0.0,0.46,0.0,2.18,0,0,0,1 +8494,ENFP,26.44,0.1852470385188885,0.02,0.34,0.5,0.0,0.0,1.0,0.0,2.06,0,0,0,1 +8495,INFP,23.32,0.17768125808253962,0.0,0.18,0.08,0.0,0.0,0.48,0.0,1.8,1,0,0,1 +8496,ISTP,20.06,0.17617062700515276,0.08,0.16,0.04,0.04,0.0,0.42,0.0,2.48,1,1,1,1 +8497,INFP,33.4,0.19192570006740634,0.1,0.3,0.32,0.0,0.02,1.24,0.0,4.38,1,0,0,1 +8498,INFP,31.12,0.17842325694212327,0.02,0.14,0.06,0.0,0.0,0.78,0.0,2.5,1,0,0,1 +8499,INTP,6.26,0.18019756711457585,0.02,0.08,0.04,0.0,0.0,0.1,0.0,0.68,1,0,1,1 +8500,INTP,25.32,0.1848014536159838,0.16,0.2,0.1,0.0,0.0,0.86,0.0,3.22,1,0,1,1 +8501,INTJ,27.56,0.1855215590182253,0.04,0.28,0.08,0.0,0.02,0.72,0.0,2.74,1,0,1,0 +8502,ENFP,31.64,0.18891238378099176,0.0,0.18,0.48,0.0,0.0,1.1,0.0,3.94,0,0,0,1 +8503,INFJ,27.56,0.18205297663325734,0.02,0.1,0.22,0.02,0.0,0.88,0.0,3.44,1,0,0,0 +8504,INTJ,23.1,0.19618962458751987,0.08,0.24,0.48,0.02,0.04,1.1,0.0,2.34,1,0,1,0 +8505,INFP,32.7,0.18487950443103893,0.0,0.08,0.22,0.0,0.0,0.88,0.0,4.42,1,0,0,1 +8506,INTP,29.3,0.1836948017521602,0.04,0.26,0.1,0.02,0.0,0.9,0.0,3.02,1,0,1,1 +8507,INTJ,29.2,0.18352329814673113,0.02,0.1,0.04,0.02,0.0,0.74,0.0,3.3,1,0,1,0 +8508,INFJ,26.98,0.18519642107946044,0.04,0.08,0.22,0.0,0.0,0.62,0.0,3.74,1,0,0,0 +8509,INTJ,16.0,0.18433657799533876,0.04,0.16,0.0,0.0,0.0,0.3,0.0,1.84,1,0,1,0 +8510,INTP,22.86,0.19210349419150194,0.02,0.2,0.32,0.02,0.04,0.48,0.0,3.2,1,0,1,1 +8511,INTP,30.1,0.1867094459486284,0.0,0.1,0.02,0.0,0.0,0.68,0.0,3.36,1,0,1,1 +8512,ENTP,30.9,0.18578129548409633,0.06,0.14,0.12,0.04,0.02,0.8,0.04,2.06,0,0,1,1 +8513,ENFP,18.5,0.1902297763934362,0.26,0.26,0.56,0.0,0.0,0.46,0.06,3.12,0,0,0,1 +8514,INTJ,27.62,0.17539397597202622,0.0,0.28,0.06,0.0,0.04,0.7,0.0,2.16,1,0,1,0 +8515,INFJ,28.88,0.19287191785064672,0.0,0.38,0.58,0.0,0.0,0.98,0.0,3.1,1,0,0,0 +8516,INTJ,24.42,0.17434243927417434,0.0,0.46,0.06,0.0,0.0,0.74,0.0,2.08,1,0,1,0 +8517,ENTJ,9.3,0.17547315241547104,0.06,0.12,0.0,0.0,0.0,0.28,0.0,0.82,0,0,1,0 +8518,INTJ,18.06,0.18550086119985076,0.04,0.0,0.0,0.04,0.0,0.46,0.0,2.04,1,0,1,0 +8519,ISTP,14.02,0.18487405542128246,0.1,0.34,0.02,0.0,0.0,0.2,0.0,1.22,1,1,1,1 +8520,INFP,25.58,0.19305042517194448,0.02,0.48,0.14,0.0,0.04,0.6,0.0,3.82,1,0,0,1 +8521,ISFJ,19.68,0.18863673738684172,0.04,0.14,0.06,0.0,0.0,0.36,0.0,2.84,1,1,0,0 +8522,ISFP,28.68,0.18369190982525085,0.06,0.16,0.0,0.0,0.02,0.74,0.0,3.46,1,1,0,1 +8523,ISTJ,24.32,0.1836440452327263,0.04,0.18,0.1,0.02,0.0,0.78,0.0,2.54,1,1,1,0 +8524,INTJ,26.96,0.18644829858622183,0.1,0.24,0.2,0.06,0.0,0.92,0.0,4.0,1,0,1,0 +8525,INFP,26.32,0.1847202982821106,0.0,0.28,0.06,0.0,0.04,0.66,0.0,2.66,1,0,0,1 +8526,INTP,26.72,0.19134669979694943,0.0,0.2,0.02,0.0,0.0,1.06,0.0,3.3,1,0,1,1 +8527,INFJ,22.9,0.18857411483292347,0.0,0.12,0.04,0.0,0.0,0.4,0.0,2.72,1,0,0,0 +8528,INFP,10.02,0.18757523321617403,0.14,0.16,0.02,0.0,0.0,0.24,0.0,1.1,1,0,0,1 +8529,INTJ,28.54,0.18030194250795234,0.06,0.14,0.14,0.04,0.02,0.58,0.0,2.96,1,0,1,0 +8530,INTJ,16.22,0.1818802301594109,0.06,0.22,0.0,0.04,0.0,0.36,0.0,1.46,1,0,1,0 +8531,INFP,33.52,0.18682577954862692,0.02,0.12,0.38,0.0,0.0,1.0,0.0,4.62,1,0,0,1 +8532,INFJ,23.1,0.1920843622344629,0.0,0.26,0.26,0.0,0.0,0.48,0.0,2.9,1,0,0,0 +8533,ISTP,27.04,0.18316016892842651,0.0,0.16,0.06,0.0,0.02,0.54,0.0,2.88,1,1,1,1 +8534,ENFP,23.06,0.1877710905502064,0.06,0.48,0.28,0.0,0.0,0.48,0.02,2.74,0,0,0,1 +8535,INFJ,33.6,0.18521388443975642,0.02,0.2,0.12,0.0,0.0,1.12,0.0,4.16,1,0,0,0 +8536,ENFP,33.1,0.18035582260925323,0.02,0.26,0.0,0.0,0.04,0.96,0.0,2.18,0,0,0,1 +8537,ENTP,28.06,0.18295193537690524,0.02,0.14,0.36,0.0,0.0,0.66,0.0,3.24,0,0,1,1 +8538,INTP,11.28,0.18546521464023746,0.4,0.52,0.02,0.04,0.04,0.08,0.0,0.94,1,0,1,1 +8539,INFP,11.6,0.18194357031329061,0.0,0.0,0.16,0.0,0.0,0.22,0.0,1.32,1,0,0,1 +8540,INFP,29.92,0.18659648099692303,0.38,0.14,0.14,0.16,0.0,1.06,0.02,4.3,1,0,0,1 +8541,ISTP,15.9,0.19234746277307302,0.02,0.18,0.16,0.0,0.0,0.5,0.0,2.08,1,1,1,1 +8542,INFJ,19.02,0.19087190480733482,0.0,0.06,0.1,0.0,0.02,0.84,0.0,1.86,1,0,0,0 +8543,INTJ,31.4,0.18678587703100358,0.02,0.24,0.02,0.0,0.08,0.86,0.0,3.22,1,0,1,0 +8544,ENFJ,23.16,0.191453348440992,0.0,0.14,0.26,0.0,0.0,0.42,0.0,2.74,0,0,0,0 +8545,INTP,23.68,0.1835203346720163,0.0,0.44,0.24,0.0,0.02,0.52,0.0,2.8,1,0,1,1 +8546,INFP,24.36,0.18061291736480367,0.0,0.32,0.12,0.0,0.0,0.54,0.0,2.62,1,0,0,1 +8547,ENFJ,27.0,0.18361490004164938,0.04,0.34,0.18,0.0,0.02,0.68,0.0,2.94,0,0,0,0 +8548,INTP,22.88,0.1764313657798593,0.02,0.22,0.04,0.0,0.02,0.48,0.0,2.16,1,0,1,1 +8549,INTP,33.52,0.18395868138808985,0.02,0.24,0.28,0.0,0.0,0.98,0.0,3.24,1,0,1,1 +8550,INTP,20.62,0.1804016206983343,0.0,0.08,0.08,0.0,0.0,0.46,0.0,2.22,1,0,1,1 +8551,INFJ,29.76,0.18507539349679472,0.0,0.28,0.32,0.0,0.0,1.02,0.0,2.84,1,0,0,0 +8552,INTJ,27.54,0.1791629520894814,0.0,0.14,0.02,0.0,0.04,0.74,0.0,1.8,1,0,1,0 +8553,INFP,24.12,0.19500472585513834,0.0,0.32,0.82,0.0,0.02,0.86,0.0,2.9,1,0,0,1 +8554,INFP,26.28,0.1884586001032114,0.04,0.1,0.22,0.0,0.1,0.62,0.0,3.28,1,0,0,1 +8555,ISTP,21.02,0.19902255805334312,0.0,0.2,0.22,0.0,0.0,0.24,0.02,0.78,1,1,1,1 +8556,ENFP,25.44,0.19099453850324988,0.06,0.34,1.2,0.0,0.02,0.7,0.0,2.92,0,0,0,1 +8557,INTP,19.02,0.18298191981832476,0.0,0.08,0.0,0.0,0.0,0.3,0.0,2.2,1,0,1,1 +8558,INTP,18.64,0.1814571044276444,0.06,0.32,0.36,0.0,0.02,0.34,0.0,2.06,1,0,1,1 +8559,INTP,29.1,0.1933965993576065,0.1,0.14,0.02,0.04,0.0,2.7,0.0,2.16,1,0,1,1 +8560,ISTJ,25.22,0.18526860109843257,0.1,0.2,0.24,0.06,0.0,0.64,0.02,2.12,1,1,1,0 +8561,INTJ,25.38,0.18031209171914683,0.06,0.24,0.16,0.0,0.02,0.72,0.0,2.78,1,0,1,0 +8562,INTJ,24.38,0.18568669623224326,0.08,0.08,0.04,0.0,0.0,0.56,0.0,3.3,1,0,1,0 +8563,INFP,29.46,0.1878359798435891,0.02,0.2,0.16,0.0,0.0,0.72,0.0,4.14,1,0,0,1 +8564,INFP,25.54,0.19030491924256818,0.1,0.22,0.32,0.02,0.0,0.78,0.0,2.2,1,0,0,1 +8565,INFJ,25.94,0.18197336297117359,0.08,0.22,0.12,0.0,0.0,0.64,0.0,2.94,1,0,0,0 +8566,INTP,18.26,0.18234472205011665,0.0,0.22,0.0,0.0,0.0,0.26,0.0,1.74,1,0,1,1 +8567,INFP,11.44,0.1866479708122551,0.34,0.36,0.3,0.04,0.06,0.18,0.0,0.88,1,0,0,1 +8568,INFJ,30.34,0.1824851221517928,0.0,0.08,0.16,0.0,0.0,0.74,0.0,4.26,1,0,0,0 +8569,ISFJ,16.88,0.20088054096404212,0.0,0.34,0.5,0.0,0.04,0.52,0.0,2.1,1,1,0,0 +8570,INFP,22.24,0.18849906169029115,0.02,0.1,0.56,0.0,0.0,0.48,0.0,3.12,1,0,0,1 +8571,INTP,30.68,0.18079433026184333,0.02,0.04,0.0,0.02,0.0,0.98,0.02,2.88,1,0,1,1 +8572,INTJ,27.86,0.19125558692075958,0.08,0.4,0.06,0.0,0.0,0.74,0.0,3.12,1,0,1,0 +8573,ENTP,21.68,0.1840863557316505,0.08,0.56,0.02,0.0,0.0,0.32,0.02,1.98,0,0,1,1 +8574,INTP,14.8,0.18175285570346578,0.22,0.32,0.02,0.0,0.0,0.28,0.0,1.56,1,0,1,1 +8575,ENTP,30.9,0.18595418350167553,0.0,0.24,0.02,0.0,0.0,0.98,0.0,2.36,0,0,1,1 +8576,INTJ,28.44,0.18841090627479726,0.0,0.56,0.3,0.0,0.0,0.82,0.0,3.96,1,0,1,0 +8577,INTJ,16.58,0.1941306479299725,0.0,0.24,0.96,0.0,0.0,1.26,0.0,1.9,1,0,1,0 +8578,INFJ,22.18,0.18721554311188096,0.04,0.3,0.36,0.0,0.0,0.58,0.0,2.32,1,0,0,0 +8579,INTP,23.16,0.1823940288619755,0.06,0.12,0.0,0.04,0.08,0.44,0.0,3.06,1,0,1,1 +8580,INTP,15.12,0.17795414055164555,0.26,0.18,0.06,0.24,0.02,0.36,0.02,1.44,1,0,1,1 +8581,ENTP,27.42,0.1869176927888991,0.06,0.4,0.02,0.02,0.06,0.66,0.0,3.32,0,0,1,1 +8582,ISFP,29.36,0.19042029781809566,0.0,0.2,0.48,0.0,0.0,0.68,0.02,3.26,1,1,0,1 +8583,ISTP,19.28,0.19061551512866431,0.02,0.2,0.16,0.0,0.0,0.36,0.0,1.94,1,1,1,1 +8584,INFP,29.76,0.18143854788884273,0.0,0.3,0.1,0.0,0.0,1.28,0.0,3.14,1,0,0,1 +8585,ISTJ,29.32,0.18954904036609524,0.0,0.2,0.06,0.0,0.02,1.16,0.02,3.9,1,1,1,0 +8586,ENFP,28.02,0.18066781979549296,0.02,0.26,0.16,0.02,0.0,0.74,0.0,2.94,0,0,0,1 +8587,ENFP,26.38,0.18261374486762413,0.02,0.36,0.3,0.02,0.0,0.76,0.0,1.96,0,0,0,1 +8588,ISFP,19.9,0.19089154387429363,0.12,0.08,0.44,0.02,0.02,0.72,0.0,3.3,1,1,0,1 +8589,INTP,30.84,0.18042587368216184,0.0,0.16,0.24,0.0,0.0,0.94,0.0,3.42,1,0,1,1 +8590,ENFP,22.2,0.18494108170850715,0.0,0.34,0.06,0.0,0.0,0.4,0.0,1.92,0,0,0,1 +8591,INFJ,19.06,0.18761368801648617,0.0,0.14,0.02,0.0,0.04,0.38,0.0,2.1,1,0,0,0 +8592,INFP,32.8,0.1789874726243468,0.0,0.04,0.12,0.0,0.0,0.9,0.0,3.68,1,0,0,1 +8593,ENTP,30.14,0.17670809620761158,0.1,0.22,0.08,0.0,0.0,0.74,0.0,2.54,0,0,1,1 +8594,INTP,25.68,0.1805540148277852,0.2,0.36,0.26,0.0,0.0,0.72,0.02,2.46,1,0,1,1 +8595,ENTP,31.66,0.18205257335919384,0.0,0.2,0.06,0.0,0.0,0.74,0.0,3.62,0,0,1,1 +8596,ENFP,25.94,0.1850798446234262,0.04,0.3,0.22,0.0,0.0,0.62,0.0,2.82,0,0,0,1 +8597,ISTJ,31.14,0.18303451760894401,0.0,0.14,0.44,0.0,0.02,0.76,0.0,3.76,1,1,1,0 +8598,INTP,23.66,0.17985369081462604,0.0,0.22,0.3,0.0,0.0,0.6,0.0,2.32,1,0,1,1 +8599,ENTP,26.56,0.18249824489280733,0.02,0.22,0.0,0.0,0.02,0.56,0.0,1.9,0,0,1,1 +8600,INFJ,16.26,0.19273320655437676,0.18,0.28,0.56,0.02,0.02,0.44,0.0,2.14,1,0,0,0 +8601,INFP,29.9,0.18862252671911858,0.04,0.24,0.24,0.0,0.0,1.54,0.0,2.46,1,0,0,1 +8602,ENFP,17.34,0.18244447168816474,0.06,0.1,0.16,0.0,0.0,0.34,0.0,2.08,0,0,0,1 +8603,INFP,24.78,0.18717093925928377,0.14,0.22,0.02,0.0,0.02,0.6,0.02,3.22,1,0,0,1 +8604,INFP,22.5,0.1883616710950308,0.1,0.26,0.42,0.0,0.0,0.54,0.0,2.86,1,0,0,1 +8605,INTJ,21.28,0.18600745068896954,0.0,0.08,0.18,0.0,0.04,0.36,0.02,2.2,1,0,1,0 +8606,ENFP,29.34,0.18548817146541505,0.04,0.16,0.22,0.0,0.0,0.86,0.0,2.6,0,0,0,1 +8607,ENTJ,30.34,0.17683418435904327,0.0,0.18,0.02,0.0,0.0,0.76,0.0,2.74,0,0,1,0 +8608,ENFP,33.1,0.1872930350773123,0.0,0.22,0.16,0.0,0.0,1.0,0.0,4.4,0,0,0,1 +8609,INFP,34.02,0.1892598322807087,0.0,0.14,0.72,0.0,0.0,0.88,0.02,4.52,1,0,0,1 +8610,ISFP,28.58,0.1790079902768785,0.06,0.12,0.24,0.0,0.0,0.78,0.0,2.78,1,1,0,1 +8611,INTP,23.9,0.1861815778691061,0.12,0.52,0.08,0.04,0.0,0.56,0.0,2.64,1,0,1,1 +8612,ENTJ,6.5,0.1904188471485073,0.0,0.08,0.16,0.0,0.0,0.16,0.0,0.94,0,0,1,0 +8613,INTP,16.26,0.18733902131585528,0.28,0.3,0.04,0.02,0.02,0.34,0.0,2.08,1,0,1,1 +8614,INTJ,13.64,0.19373311066325838,0.06,0.22,0.08,0.02,0.0,0.12,0.0,1.78,1,0,1,0 +8615,INFP,33.26,0.18130083102493072,0.02,0.12,0.06,0.0,0.0,1.0,0.0,3.26,1,0,0,1 +8616,ISFP,24.14,0.18733874442305706,0.12,0.3,0.08,0.06,0.04,0.46,0.1,3.18,1,1,0,1 +8617,ENFP,31.4,0.19264129153440585,0.0,0.1,0.28,0.0,0.0,0.82,0.02,3.18,0,0,0,1 +8618,ENTJ,27.72,0.1762509031450067,0.16,0.28,0.0,0.02,0.0,0.74,0.0,1.86,0,0,1,0 +8619,INTP,28.64,0.18246882287616825,0.0,0.14,0.24,0.0,0.0,0.72,0.12,3.04,1,0,1,1 +8620,ENFP,23.38,0.18117570806589803,0.02,0.12,0.36,0.0,0.0,0.44,0.0,2.56,0,0,0,1 +8621,INTP,23.4,0.18529966028822445,0.02,0.28,0.16,0.0,0.1,0.62,0.0,3.16,1,0,1,1 +8622,ISFJ,27.74,0.18186102119360337,0.02,0.26,0.02,0.0,0.02,0.56,0.0,2.8,1,1,0,0 +8623,INFP,10.42,0.18568369501489954,0.72,0.38,0.04,0.08,0.0,0.18,0.0,1.72,1,0,0,1 +8624,INFJ,8.18,0.1814329245220073,0.38,0.14,0.1,0.06,0.02,0.06,0.0,1.0,1,0,0,0 +8625,INTP,26.72,0.18897792115614367,0.0,0.18,0.08,0.0,0.02,1.36,0.02,3.42,1,0,1,1 +8626,INTJ,16.24,0.17378672036558387,0.32,0.18,0.14,0.18,0.0,0.36,0.0,1.56,1,0,1,0 +8627,ENFP,26.22,0.19164031771750623,0.12,0.24,0.38,0.0,0.0,0.76,0.0,3.62,0,0,0,1 +8628,ISTP,30.8,0.1820667533270757,0.02,0.14,0.1,0.02,0.02,0.92,0.0,4.22,1,1,1,1 +8629,INTP,14.4,0.19351408731685438,0.08,0.28,0.02,0.0,0.0,0.5,0.0,1.78,1,0,1,1 +8630,ISFP,17.86,0.17666910892604118,0.12,0.12,0.16,0.04,0.02,0.32,0.0,1.16,1,1,0,1 +8631,INFP,26.5,0.1864264997819953,0.08,0.18,0.3,0.0,0.04,0.56,0.0,3.08,1,0,0,1 +8632,ISTJ,19.98,0.19258924589927884,0.12,0.26,0.16,0.08,0.0,0.48,0.02,1.86,1,1,1,0 +8633,ENTP,31.88,0.182730461571733,0.0,0.22,0.14,0.0,0.0,0.76,0.0,3.8,0,0,1,1 +8634,INFP,24.66,0.18260595121793205,0.04,0.28,0.06,0.02,0.02,0.68,0.0,1.96,1,0,0,1 +8635,INFJ,25.58,0.19222579191663425,0.0,0.18,0.08,0.0,0.0,0.56,0.0,2.7,1,0,0,0 +8636,INFP,31.28,0.18367728844478787,0.02,0.02,0.1,0.0,0.06,0.7,0.0,3.52,1,0,0,1 +8637,INTJ,32.22,0.18198286048009277,0.02,0.2,0.26,0.0,0.0,0.9,0.0,3.76,1,0,1,0 +8638,INFP,18.28,0.1824661638511809,0.0,0.38,0.22,0.0,0.0,0.3,0.0,1.46,1,0,0,1 +8639,ENTP,27.82,0.19446765681494393,0.04,0.52,0.36,0.02,0.0,1.36,0.0,3.26,0,0,1,1 +8640,INTP,24.48,0.1836360882156383,0.0,0.1,0.04,0.0,0.02,0.74,0.0,2.64,1,0,1,1 +8641,INFP,27.14,0.1832478821464601,0.02,0.1,0.16,0.0,0.02,0.56,0.0,3.3,1,0,0,1 +8642,INFJ,26.02,0.1811412469829075,0.08,0.1,0.1,0.0,0.02,0.58,0.0,2.26,1,0,0,0 +8643,ENTP,23.02,0.1807069509387542,0.0,0.4,0.24,0.0,0.0,0.32,0.0,2.78,0,0,1,1 +8644,INFJ,29.8,0.17959735245241162,0.02,0.08,0.12,0.0,0.0,0.68,0.02,3.1,1,0,0,0 +8645,INFJ,29.8,0.18418355945490292,0.2,0.3,0.42,0.02,0.0,0.8,0.0,3.22,1,0,0,0 +8646,INFP,27.16,0.1850431497960104,0.02,0.08,0.3,0.0,0.08,0.62,0.0,1.86,1,0,0,1 +8647,INTP,27.66,0.18725960019622231,0.0,0.16,0.04,0.0,0.0,0.58,0.02,3.4,1,0,1,1 +8648,INFP,25.06,0.1841573429881064,0.18,0.24,0.26,0.12,0.04,0.7,0.1,2.9,1,0,0,1 +8649,INFP,5.32,0.18788687619846342,0.74,0.66,0.02,0.0,0.0,0.2,0.0,0.5,1,0,0,1 +8650,INFJ,22.64,0.18464236271220386,0.1,0.24,0.12,0.06,0.02,0.42,0.0,2.38,1,0,0,0 +8651,ISTP,26.36,0.18470222204030526,0.02,0.3,0.0,0.0,0.0,0.72,0.0,2.62,1,1,1,1 +8652,ISFJ,28.6,0.17545516662159855,0.04,0.12,0.26,0.0,0.0,0.7,0.0,2.54,1,1,0,0 +8653,INFP,32.72,0.18537392023930618,0.06,0.18,0.06,0.0,0.0,0.8,0.0,3.9,1,0,0,1 +8654,ISTJ,31.28,0.1867194228336967,0.0,0.24,0.28,0.0,0.0,0.7,0.0,3.78,1,1,1,0 +8655,INFJ,26.16,0.1842586830917353,0.14,0.44,0.12,0.0,0.06,0.76,0.0,2.86,1,0,0,0 +8656,INFJ,30.52,0.1818836355970476,0.02,0.36,0.24,0.0,0.02,0.86,0.0,2.84,1,0,0,0 +8657,INTJ,27.58,0.17884007398652574,0.0,0.06,0.04,0.0,0.0,0.58,0.0,1.4,1,0,1,0 +8658,ESFJ,35.18,0.17566165552535348,0.0,0.16,0.06,0.0,0.0,1.0,0.0,3.3,0,1,0,0 +8659,ENFP,26.24,0.18887549650981078,0.02,0.2,0.04,0.0,0.04,0.58,0.0,3.12,0,0,0,1 +8660,INFP,21.74,0.17792113500915882,0.08,0.22,0.0,0.0,0.0,0.74,0.0,1.78,1,0,0,1 +8661,ENTP,31.52,0.19419433983404946,0.0,0.22,0.06,0.0,0.0,1.4,0.0,3.96,0,0,1,1 +8662,INTJ,26.54,0.18370862663923432,0.04,0.12,0.04,0.0,0.0,0.54,0.0,3.5,1,0,1,0 +8663,INTP,15.36,0.17813657294424518,0.04,0.12,0.0,0.0,0.0,0.16,0.02,1.34,1,0,1,1 +8664,INTP,22.68,0.1851030618857531,0.06,0.08,0.0,0.0,0.0,0.74,0.0,3.42,1,0,1,1 +8665,ENTP,27.4,0.18490749547484506,0.02,0.16,0.18,0.0,0.0,0.86,0.0,2.38,0,0,1,1 +8666,INTJ,22.82,0.18923119302944216,0.08,0.28,0.22,0.0,0.08,0.36,0.0,2.68,1,0,1,0 +8667,ENTP,28.66,0.1810649821263962,0.02,0.32,0.28,0.0,0.0,0.72,0.0,2.18,0,0,1,1 +8668,INTJ,22.42,0.1779959049195191,0.12,0.28,0.1,0.0,0.02,0.38,0.0,2.12,1,0,1,0 +8669,INFJ,23.8,0.18421415366904026,0.3,0.28,0.22,0.18,0.02,0.8,0.02,2.7,1,0,0,0 +8670,ISFP,15.92,0.1805570749738721,0.14,0.18,0.12,0.02,0.0,0.14,0.0,1.78,1,1,0,1 +8671,ENFP,26.18,0.19151461900306085,0.04,0.2,0.66,0.0,0.0,0.82,0.02,3.28,0,0,0,1 +8672,INTP,18.96,0.18654349215679777,0.04,0.18,0.02,0.0,0.0,0.38,0.0,1.48,1,0,1,1 +8673,INFP,34.1,0.18756591783541904,0.0,0.18,0.06,0.0,0.0,0.94,0.0,4.18,1,0,0,1 +8674,INFP,27.22,0.18793735567467182,0.06,0.12,0.1,0.0,0.0,0.48,0.0,3.54,1,0,0,1 diff --git a/your-project/data/reallife_data_insta.csv b/your-project/data/reallife_data_insta.csv new file mode 100644 index 00000000..02cdc734 --- /dev/null +++ b/your-project/data/reallife_data_insta.csv @@ -0,0 +1,8 @@ +,name,type,text_ready +0,Darya,ESFP,quarantin expect swipe realiti swipe see photo yesterday age affect hello santa wish christma mind back girl hat seri casual day uni swipe idiot thank steve nice iphon front camera miss thi edit still believ sunscreen prevent huge sunburn rather part peopl ask got free time except small town girl live capitalist world clueless might seriou might hungri want blue want yellow wall fell nearli year ago constantli fall face im pretti ok exam unpopular opinion univers fun exhibit christma around corner wish extent deadlin occas even get valley la go sightse diamond girl best friend especi trade em plane ticket uber ride threat warn talk begin semest tomorrow come fli let fli let fli away saltier pacif hangri b studi nyfalosangel williamroyal98 thi whole gotta studi sun shine get nerv exampl inner rage know might spontan book flight ask ou would want check flight back aswel vitamin finish first semerst hsg still afford porsch studi also enjoy holiday kid still short littl human week uni icloud googl drive ask pay storag uni start tomorrow alreadi see either lost caus find classroom b look like annabel caus sleep weak oh boy thi last week summer think weather decid give earli tast fall alreadi water fall darya fall casual sit floor caus stand overr promis count light saw squirrel lost count set real hous guess quiet set baywatch hire perfum actual bugspray want u buy nasti smelli stuff good laugh start wonder spotlight give tan caus sun come yet joke asid tho beyond grate share stage mani talent peopl love feel limitless second person stra look quickli turn away tri look casual possibl moment probabl thought plan convinc mom paint one wall pink even though know happen happi internationalwomensday ladi tell stud nice back bring year probabl face facebook victori dissapoint new friendship new experi wa lucki enough travel sever time thank peopl made year wa enough kind word laughter happi tear mess u rg club summer like pardon look back week full new friend experi emot put smile face teacher work inspir everi day peopl made summer best one yet love alway special place heart obgottheheat happi nation danc day everybodi need attitud life punintend obgottheheat +0,Theresa,ENFP,light time simplicit chapter waiter pour wine without ask year friday love herzallerliebst wie sommerferien f r erwachsen irgendwi sommer und sonn und zitronenei und stella und ach man wa soll ich sagen da best leben stellamarieluis da ist einfach gl ck wenn man euch hat diesem bild ist viel richtig ernst gucken geht also doch shishacloth supportyourlocalbrand shishacloth casual durch die gegend sen halt lieb f r frau aber f r dy meisten happyweltfrauentag mein entscheidungsfreud captur stellamarieluis kein termin und leicht im dispo futur zukunftsprognos gl cklich marilyn monro moment beim br tchen holen reflect kein sorgen kein wellen kein condition liebenswichtig wie immer gute gespr che und bl de ideen klassisch arbeiten halt mag ich lieb ich viel zu sch n kalt bier di guess love sunris moonlight rainstorm everyth ha soul semi zufrieden mit dem fr hst ck ich hab da mit dem ernst gucken nicht mitbekommen verliebtverliebt troubl never look goddamn fine und wenn da abendlicht genau dieser farb ist say someth dutch superlekk pas auf dein f e auf herz euch behalt ich find ich gut wenn lisbz mein w sche aufh ngt find fire umziehen mit lieblingspizza und lieblingswg lieb ich ist da kunst oder kann da weg kennt ihr dy menschen die stehen bleiben um den himmel zu fotografieren one power happi weltfrauentag gr nde warum ich kein blogger bin sommer +0,Nico,ENFP,post thi random pictur fill void go within lot past year sort thing happi say refocus energ work great thing impress serfau good start swipe right best drink game dog berlin boat still flex lad weekend madrid alway good idea hang hangov histor site drip hard stand close miss bro push best everi day never wa better duo golden boy rest easi get settl berlin three tough fun year whu last weekend final graduat degre best busi school germani importantli new friend life anoth place call home graduat whu boy class much fun let get moolah pic blurri day wa sharp probabl spectacular view ever wa drive along highway la sf act loyal thi pic lil b bit next minut probabl spectacular view ever wa drive along highway la sf hey googl hire want bike back mountainview googl siliconvalley throwback amaz road trip thi fall view def worth hike hike natur travel roadtrip yosemit keep eye star keep foot ground clearli see enjoy back socal place beauti pacif beer beach chill california home uglyfeet goldclapmus good day dude never hold back weekendmood thank kaleandm thi awesom parti crewlov reiseminist coco tb one best game ever still hold club record recept per game v cottbu w31 recept yard td thank zbreez trust pas throwback best winter ever kid group gave u name lil crackhead becaus got crack time fuckhashtag thi one big time cost u good men hospitalreunion repost luebeckcougar much way improv though person getbettereveryday gamestat cougar cougarn cougarsprid luebeck luebeckcougar footbal americanfootbal gfl amgid afvd gridiron awaygam ritterhud badger day credit gridiron design new year eve alp crew illuminati tb good old time sunday meant gameday monday newspap sit librari studi right wish could go back time believ becom realiti creat world sundaymood mom one tri get surf game think much better form beach water though haha germansaintmermaid lennartmhr tabl mountain lion head lennartmhr hate season thi shoutout overdu best music speaker realcoleworld tb bro enzeocha wa make swedish defens look like th grader hoffentlich liegt da der famili crossingfing toodamnhot touchdown crazi game work dani piedrabuena tri look smart suit haha back receiv thi work colleagu think famou lol thank much colin tb toughest job ever good time offic break beach better barcelona barca beach playa sun smile spain sleepyfacethank love birthday night new car wish m4 test drive still goosebump bmw m4 madrid race mess nfl runningback perfect day el retiro morn routin scooter ride madrid work watch scooter sun suit +0,Katie,INFJ,sonndorf vi autumn fall color igersaustria outdoor loveaustria beauti natur naturelov naturephotographi memori sun afternoon garden sonndorf v autumn fall color igersaustria outdoor loveaustria beauti natur naturelov naturephotographi loveaustria window sun afternoon sonndorf iv autumn fall color igersaustria outdoor loveaustria beauti natur naturelov naturephotographi loveaustria window sun afternoon sonndorf iii autumn fall color igersaustria outdoor loveaustria beauti natur naturelov naturephotographi loveaustria window sun afternoon sonndorf ii autumn fall color igersaustria outdoor loveaustria beauti natur naturelov naturephotographi loveaustria window sun afternoon sonndorf autumn fall color igersaustria outdoor loveaustria beauti natur naturelov naturephotographi loveaustria window sun afternoon beauti fall lech arlberg mystic fog lech arlberg fog igersaustria outdoor loveaustria beauti natur naturelov naturephotographi mountain fall autumn color arlberg naxo greec vom wind verweht schoten fieren raumerwind mannle mannluv bervorn schotenlo reffen fock gro bugmann heckmann sibl goal love team ferienhort ferienhortamwolfgangse campelici love team wish anoth awesom breakfast like today motto diver themottoisdivers mottoamfluss love breakfast accept liveyourlif beauti book architectur stift admont austria igersaustria beauti monasteri stiftadmont librari book architectur aesthet symmetri color rose blue hiddengem excit spring igersaustria outdoor beauti natur naturelov naturephotographi outdoor color spring exploreaustria austria hallstatt nofilterneed excit spring igersaustria outdoor beauti natur naturelov naturephotographi outdoor color spring exploreaustria austria hallstatt nofilterneed think found willi wonka chocol mountain beauti switzerland mountain color blue white snow beauti natur naturelov naturephotographi outdoor moretocom igersaustria nofilt travellov switzerland jungfraujoch topofeurop chocolatemountain charlieandthechocolatefactori willywonka switzerland suiss schweiz beauti natur lake mountain color blue autumn fall favouriteseason igersswitzerland igersaustria outdoor walk summer atterse austria igersaustria outdoor naturephotographi macrophotographi naturelov plant green sun detail leaf beauti garden memori missingsumm anticip autumn kinkaku ji kyoto japan japan kyoto asia travel templ tradit throwback memori beauti garden goldenhour igersaustria outdoor travellov travelwithfriend pond natur naturelov naturephotographi reflect japan focu kyoto japan igersaustria japan kyoto asia travel faraway autumn fall templ tradit awesom architectur wood ancient travelwithfriend island travellov throwback memori spring love spring flower pink beauti natur love instagood instaspr garden tree enjoy love warm weather nofilterneed top world mountain arlberg austria ski snowboard enjoy life holiday fun famili friend fog cloud beauti blue white naturephotographi outdoor autumn iii fall autumn leav falltim season season instafal instagood instaautumn photooftheday sunset color orang red autumnweath fallweath natur nofilt brotherandsist famili fun autumn ii autumn fall enjoy light sunset beauti famili brother photographi model natur instagood instafal autumn fall autumn leav falltim season season instafal instagood instaautumn photooftheday color orang blue autumnweath fallweath natur sunset famili brother beauti sunset acropoli spring beauti natur butterfli red blue black white rose pink color spring concret anim love cute fli away springtim instagood instaspr instalove161w need sunshin right travel travel vacat visit travel instatravel instago instagood trip holiday photooftheday fun travel tourism tourist instapassport instatravel miss beach warm weather japan fukuoka templ bigciti skyscrap red tradit travel week japanrailpass jrpass friend park beauti sightse love asia fukuoka japan japan fukuoka templ bigciti skyscrap red tradit travel week japanrailpass jrpass friend park beauti sightse love asia final put pic japan phone thi wa taken nara fabiakailphotographi asia asian tokyo japan nara natur deer cute cheeki anim wood templ famou throwback travel summer earli morn train ride japan landscap wood river mirror cloud water green blue nofilt earli morn train beauti natur love backpack okinawan beauti okinawa beach sand sea blue sky summer hot sesokojima beauti photographi natur favourit nofiltr outdoor ferienhort green fog mountain morn happi friend love holiday miss littl tiger natur sky sun summer garden beauti pretti sunset cat cat catsagram catstagram instagood kitten kitti kitten pet love home alreadi look forward spend summer holiday spring home natur sky sun spring beauti pretti sunset landscap amaz view trip tree mountain landscap lover landscapelov landscap lover igersaustria austria canon alreadi miss thi see next year favourit place arlberg austria need thi summer austria brother fun cloudi day mountain one day +0,Dylan,ENTJ,happi golden birthday kayla year earth photo say one kind feel lucki call sister tremend proud accomplish thi past year unceas desir travel explor inspir wait see thi come year bring know finish colleg strong jump career passion everyth happi kayla day love blood thicker water thi mud hand alabama rocki start weekend alabam danger propel chop head memori day mark first winter summer chang cabin onli one great pic u happi nation sibl day squirt love proud onli one great pic u happi nation sibl day squirt love proud siblingsforthewin finelookingduo mynumerouno lilsismayhem lookitthempearlywhit miss littl beast sleep tight audi tt quattro purrofakitti growlofatig mapetiteblanchevoitur offici view next year switzerland valai balconyview lesrochesstud hospit naturalswissbeauti lifeinthemountain lastyear offici view next year switzerland valai balconyview lesrochesstud hospit naturalswissbeauti lifeinthemountain lastyear great fortun today spot incred rare bentley continent gtz zagato world lausann switzerland bentley continent zagato rare of9 dreamcar themcurv iwanttoseetheinsurancebil supercarsofswitzerland carlov best respit final deep swiss alp suffic switzerland finalsweek audi tt quattro swissalp freedom squintyey welcom crib castlehop neverstopexplor switzerland sion notreallymycrib schoolstartstomorrow lovinlif naturalbeauti matterhorn glori matterhorn zermatt swissalp neverstopexplor lovinlif myhandwasfreez bienvenu suiss switzerland mountaindriv somuchspac sierr lesrochesswitzerland lovinlif great day water come close summer day count wind chevytruck hd mastercraft lakeday minnesota summerbummin mastercraft2015 stormi day lake stormyday ironrang northernmn nimbusamongu summertim land free home brave murica freedom summer homesweethom sightforsoreey freedomwithachanceofliberti letskickit bit late wa great bro miss alreadi bryce gordon next time keep real p shanghai xintiandi brotim whenimetyouinthesumm keepitr pul hotel jingan templ area shanghai pul hotelschoolday urbanresort lhw shouldhavebroughtmyswimsuit fun sun boracay thedistrict letsgodiv architectureonpoint paradis beauti boracay sunset boracay philippin missionwork agif nobeachtimejustpreachtim great valentin day everyon lovelivalov thi wait get back valentinesday chocol loveisintheair wouldyoubemyvalentin girlslovechocol tbt cute deviou p deviou youngm grinchonesi santahat christmasch kaylathatfac attemptedsmil saocut ihategrowingup takemeback new addit famili readi log dive time guam g shock divewatch adventuretim speed bless chevrolet camaro ifyourenotfirstyourelast guam lastday iwannagofast transform niceparkingjob time go wreck dive guam dive wreckdiv wwii deepblu dontworrygotmytetanusshot smile two padi certifi advanc open water diver success guamdayf growinggil gtd firstachievementof2015 crazyey success first day dive beauti island guam gtd icanbreatheunderwat ftclub buoyancyisanissu guam padicertif scuba getoffthegrass tbt new year first semest colleg ha real guy bratansandbratishka russianmob thenest firstmomentsof2015 livinglarg americanamongstrussian big car big citi cadillac escalad shanghai jinqiao bigblackrid blueski weekendendeavour anoth night hanoi hanoi bike peopl cityviewcaf yourebeingwatch eyesintheski drive youngandfre guess work isnt bad p shanghai sunni internship hyattonthebund livingthedream showmethemoney start internship internship firstdayofwork livingthelif gettingthingsdon pathtosuccess idkwhatimdo noobth wishmeluck florida summertim florida boatsnho bromanc johnnygmcdonald raini dayz risk back old day risk theguy boardgam sweden strategi hipster final get see thi kid powerrang superfli southafrican hashtag hmu oneandonli steal latino diy main bitchz sosexi immaluckyguy sunnyday shanghaiskylin tbt lastyr swagdawg flathatsnsunglass selfi swaggi befor justin bieber wa even born happi golden birthday kayla year earth photo say one kind feel lucki call sister tremend proud accomplish thi past year unceas desir travel explor inspir wait see thi come year bring know finish colleg strong jump career passion everyth happi kayla day love lol bluefrog openwid whatisthischees schooluniformreppin great day water come close summer day count wind chevytruck hd mastercraft lakeday minnesota summerbummin mastercraft2015 +0,Gustav,ISFJ,optiim gntm watch best teammaureen lovemaureen marrym throwbackto2013 gntm nureinekann men night goodphoto rockclimb heclimb climber climberlif boulder boulder mountain mountain mountainlov slowandsteadywinstherac hashtag like4lik follow4follow instalif igerslinz instaboi thank mom f r year child support final lockdown even though realli product least gi tract ha litter hepoop pooper poop humanne humanwast humansaregross gross toiletpap corona lockdown climber heclimb boulder boulder rockclimb climberlif believ thi well true either way believ mani peopl live live unawar thi fact fact spreadtheknowledg educ educateyourself climber climb rockclimb heclimb boulder boulderersofinstagram water bamboo gotta get proudein man oh yeah proudein get nearli forgot take foodporn pictur henc half finish serv proudein nocarb highprotein limitierendeamino urewa lowcarb protein food foodporn egg protein nutrit heclimb climber rockclimb rockclimb boulder boulder would thought think thi great point eat thi delici food mani u crave dure time like chocol scienc research fact truth food nobodytellsyouthat heclimb rockclimb rockclimb mountain boulder boulder climber climberlif better start work beach bodi shape time thi also involv right diet btw thi avocado ate ha calori fat yeah read right fat dkm14 ha hi entir bodi kudo come think realli understand peopl still think thi healthi food given stat oh yeah manag soil twice dure process devour thi delici yet sin treat helsi avocado fact truth nofakenew food foodporn beachbodi nocarb lowprotein dietfood diet veggi heclimb climber rockclimb boulder workout de joy part today thank wikipedia carri probabl almost everi student last year school even univers wikipedia educ knowledg enzyklopedia thank climber heclimb rockclimb rockclimb wikipediaisavalidsourc wikipediaisnotacrediblesourc seriou offseason snack schwedenbombensemmerl yummi niemetz snack climb heclimb mountain gottagetdemcaloriesin le tape last time also le skin heclimb climb boulder mountain mountain outdoor train nopainnogain nohandsphotographi one salzkammergut classic mahdlgupf via ferrata wa nice take thi quit long thu exhaust challeng struggl paid view top wa breathtak viaferrata mountain heclimb mountain beauti use enough tape niemanddaheim heclimb climb sportsclimb hand train nopainnogain hard rout sent gloxwaldbeauti climb even beauti facial expressionskeep roll niemanddaheim bauki flo sonnleitnerjulia heclimb climber rockclimb mountain mountain granitecrew th via ferrata done geilerhengst wilderritt heclimb climber mountain mountain misternemo fun dress roman petrinum +0,Jonas,ESFJ,hang tree rome vivi duenwald shoutout best famili ever far proudmemb lovey yep weather wa nice imshreddingonsunshin janabrahm find kiss real fake top santiago trek manquehu start summit week alreadi wa amaz dure last two year start met mani inspir interest peopl gain much experi could never get studi support two year ago respons speaker content last year lead thi year project bring student founder investor st gallen wa best experi life far thank whole start team made thi event possibl look forward new episod life even though still enough start startsummit aufstieg erst liga come wasf reinteam ersteliga grizzli stgallen guck mal da ist wasser im stein washabichdaeigentlichf reinenhutauf hutcrew nachuntenguck lachdochm n suchen wa blast start summit welcom founder student investor corpor thank lot partner made thi event possibl particip made thi event great success contribut thi lit show parti rest team made good time prepar thi time mobil littl wa awesom part thi team hoibes maschinebr tscher visit old love boston boston goodtobeback chtegernmodel newburystreet spontan climb cristo sunset wa sure one best moment magellan posttrip brasil cristo rio brasil sunset posttrip magellan light hous colonia de suiza magellan lighthous uruguay exchang view wa amaz time peterescardo day explor switzerland show cultur fun meet awesom peopl uruguay look forward second part uruguay exchang switzerland uruguay day zurich nosleep minut milano tuttoben ac hsg ball annamaria2662 warnic staytilltheend hsg prom bali incred horizon infin pool gettingsometanon bali seminyak cruis nutshel around mallorca banana crew yachtlif step journey washington c scaryd washington dc mamori nation mall lincoln whitehous capit usa travel eastcoast nice day philadelphia scaryd philadelphia libertybel rocki skylin philli usa travel fun step memori holiday cape cod king friend matzee49 cesar77mus shion wtnb tiziana cristina lenajmay scaryd niqqi18 lilita2824 cape cod capecod massachusett memori holiday nice wheather beach kingsboston sky basebal lesson pro jdagreda matzee49 basebal lesson bestteacherev jesu kingscolleg boston sunset sunris acadia nation park main sunset sunris acadia nation park main usa beauti sun sky horizon barharbor boston celtic cleveland cavali playoff game basketbal nba boston celtic v cleveland cavali playoff third game lost tdgarden letsgocelt king colleg boston king colleg boston friend fuck freez raini boston marathon bostonmarathon boston marathon raini shitwheath cold lastmil king friend stanway stadium boston red sox washington nation king colleg stanway boston redsox v washington nation basebal stadium sunglassesmusthav season vergingwieimflug freestyl ski funpark kicker valdifassa itali war ne geil ausbildung zeit alpbach skilehr obertauern traumjob bluecrew lo hombr aleman en costa rica christmastre great time costa rica nicaragua alemurillov parti palmen weiber und n bier janabrahm sister mallorca mall chill pool mar sol beer sun espa nai holiday deutschland ghana good luck today v castelao fortaleza wm wc deutschland germani alemanha ghana gana todomundo good luck jogapramim heissaufheuteabend footbal stadium viertelfinal v franc ich bin da wovor dein eltern dich immer gewarnt haben mottowoch tag sido kiffer harzer penner fun renderlund sidosmask die moral von der geschicht bremen ist geil hamburg nicht derbysieg einszunul werder heftigst choreo ultra bremen stadion hundertst derbi sitdownhsv stimmung fan happi nice day schadehamburg skifoan saalbach hinterglemm snow mountain oleoleol saalbacherschneekatzen fun fehmarn isschongeil sea occean sky cloud sail follow instinct vacat chil sandiego beach sun blue sky follow sea wave vacat good time sandiego missionbeach beach hot sun sunset follow sea pacif cali california red sky cloud cloudporn boy basebal game losangel dodger v cincinnati red stadium sky cloud la california cali fun dodgerstadium highway california highway sun vacat happi trolflex parti friend old gold fun fact club night music sunglass l4l saalbach ski schnee snow fun mountain