Data Analysis and Machine Learning Projects

This repository contains two comprehensive data analysis projects focusing on behavioral detection and mobile device analysis. Each project employs various statistical and machine learning techniques to derive meaningful insights from the data.

Project 1: Behavior Detection Analysis

Overview

This project focuses on analyzing behavioral patterns through statistical methods and hypothesis testing. The analysis is divided into three main components:

1. Behavior Detection

   • Implementation of behavioral pattern recognition
   • Analysis of behavioral indicators
   • Pattern identification and classification

2. Descriptive Statistics

   • Statistical analysis of behavioral data
   • Key metrics calculation
   • Distribution analysis and visualization

3. Hypothesis Testing

   • Statistical hypothesis formulation and testing
   • Significance level analysis
   • Data-driven conclusion validation

Technical Implementation

• Implemented using Python in Jupyter Notebooks
• Utilizes statistical analysis libraries
• Incorporates data visualization techniques

Project 2: Mobile Data Analysis

Overview

This project focuses on analyzing mobile device data using various unsupervised learning techniques and data extraction methods. The project consists of four main components:

1. Data Extraction

   • Extraction of mobile device specifications from JSON files
   • Data cleaning and preprocessing
   • Structured data transformation to CSV format

2. Unsupervised Learning Analysis

   • Implementation of multiple clustering techniques:
     • K-means clustering
     • DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
   • Pattern recognition in mobile device specifications
   • Market segment identification

Technical Stack

• Python 3.x
• Key Libraries:
  • pandas
  • numpy
  • scikit-learn
  • json
  • matplotlib/seaborn (for visualization)

Project Structure

├── Part1/
│   ├── Behavior_Detection.ipynb
│   ├── Descriptive_Statistics.ipynb
│   └── hypothesis_test.ipynb
├── Part2/
│   ├── extract_mobile_data.ipynb
│   ├── Unsupervised_dbscan.ipynb
│   ├── Unsupervised_Kmean.ipynb
│   └── Unsuperviseddd.ipynb
└── README.md

Contributing

While this is a personal project, suggestions and improvements are welcome.

License

This project is licensed under the MIT License - see the LICENSE file for details.