Machine Learning Practice Projects

A collection of 11 mini weekly projects + ML pipeline projects covering core Machine Learning concepts — from statistics and EDA to supervised learning, unsupervised learning, and production-ready pipelines — built using Python and Scikit-learn.

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📦 Installation

Prerequisites

Python 3.8+ required.

Install all libraries at once

pip install -r requirements.txt

Or install individually

pip install pandas numpy matplotlib seaborn scikit-learn scipy xgboost imbalanced-learn plotly ydata-profiling kagglehub fastapi uvicorn joblib

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📁 Project Structure

machine-learning-practice/
│
├── Mini_weekly_project-1/          # Statistics Fundamentals
│   ├── statistics_fundamentals.ipynb
│   └── student_scores.csv
│
├── Mini_weekly_project-2/          # Inferential Statistics & Probability
│   ├── inferential_statistics_probability.ipynb
│   └── Weather Data in India from 1901 to 2017.csv
│
├── Mini_weekly_project-3/          # Linear Algebra & Calculus
│   ├── linear_algebra_calculus_Intuitive.ipynb
│   └── Salary_Data.csv
│
├── Mini_weekly_project-4/          # EDA + ML Models
│   ├── titanic/
│   │   ├── titanic_dataset.ipynb
│   │   ├── model_pickel
│   │   ├── gender_submission.csv
│   │   └── Testing_fastapi_rf/
│   │       ├── main.py
│   │       └── model_pickel_titanic_rf
│   ├── black friday/
│   │   └── black_friday_dataset.ipynb
│   └── coaster/
│       ├── introduction_to_EDA_coaster.ipynb
│       ├── coaster_db.csv
│       └── output.png
│
├── Mini_weekly_project-5/          # Regression Practice
│   ├── project_week_5.ipynb
│   ├── practise.ipynb
│   ├── homeprices.csv
│   └── 500hits.csv
│
├── Mini_weekly_project-6/          # Calorie Prediction
│   └── project-6.ipynb
│
├── Mini_weekly_project-7/          # Spotify Analysis
│   ├── week_7_project_spotify.ipynb
│   ├── week-7_practise.ipynb
│   └── iris_gridsearch.pkl
│
├── Mini_weekly_project-8/          # Diabetes Classification (Naive Bayes)
│   └── diabetes_naive_bayes.ipynb
│
├── Mini_weekly_project-9/          # Disease Risk Prediction (KNN)
│   └── knn_disease_risk.ipynb
│
├── Mini_weekly_project-10/         # SVC + SVR
│   ├── SVC/
│   │   ├── banking_svc.ipynb
│   │   └── bank-additional-names.txt
│   └── SVR/
│       └── price_svr.ipynb
│
├── Mini_weekly_project-11/         # Clustering (K-Means & DBSCAN)
│   ├── K_means_clusterning_heir/
│   │   └── online_retail.ipynb
│   └── Unsupervised(DBSCAN)/
│       ├── country_data_dbscan.ipynb
│       ├── Country-data.csv
│       ├── data-dictionary.csv
│       └── Country_data_report.html
│
├── ml_pipeline_learning/           # ML Pipelines
│   ├── vehicle/
│   │   ├── vehicle_emissions.csv
│   │   ├── vehicle_emmi.ipynb
│   │   └── vehicle_emmission_pipeline.joblib
│   ├── bike_rental.ipynb
│   ├── bike_pipeline_final.pkl
│   ├── hour.csv
│   └── test.csv
│
├── datasets.md                     # Download links for large datasets
├── requirements.txt
└── README.md

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📚 Projects Overview

📊 Mini Weekly Projects

Project	Topic	Algorithms / Concepts
Project-1	Statistics Fundamentals	Mean, Median, Std Dev, Distributions
Project-2	Inferential Statistics & Probability	Hypothesis Testing, Normal, Binomial, Poisson
Project-3	Linear Algebra & Calculus	Matrices, Gradients, Salary Regression
Project-4	EDA + ML Models	Random Forest, FastAPI, EDA, Decision Tree
Project-5	Regression Practice	Linear Regression, Model Evaluation
Project-6	Calorie Prediction	Regression, Feature Engineering, Scaling
Project-7	Spotify Analysis	GridSearchCV, PCA, Cross Validation
Project-8	Diabetes Classification	Naive Bayes, SMOTE, ROC-AUC
Project-9	Disease Risk Prediction	KNN, StandardScaler, Classification Report
Project-10	SVC + SVR	Support Vector Classifier, Support Vector Regressor
Project-11	Clustering	K-Means, Hierarchical, DBSCAN, Silhouette Score

🔧 ML Pipeline Projects

Project	Topic	Algorithms / Concepts
Bike Rental	Hourly bike rental count prediction	Random Forest Regressor, Pipeline, ColumnTransformer, StandardScaler, OneHotEncoder, SimpleImputer
Vehicle Emissions	Vehicle emissions prediction	Random Forest, XGBoost, Pipeline, OrdinalEncoder, Custom Transformers, RandomizedSearchCV, KFold, joblib

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📥 Datasets

Large datasets are not included in this repo due to size. See datasets.md for all download links.

Small datasets included directly in the repo:

• student_scores.csv
• Weather Data in India from 1901 to 2017.csv
• Salary_Data.csv
• coaster_db.csv
• homeprices.csv
• 500hits.csv
• gender_submission.csv
• Country-data.csv
• data-dictionary.csv
• bank-additional-names.txt
• hour.csv — bike rental hourly data
• test.csv — bike rental test data
• vehicle_emissions.csv

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🛠️ Technologies Used

Tool	Purpose
Python 3.x	Core language
Pandas	Data manipulation
NumPy	Numerical computing
Matplotlib / Seaborn	Visualization
Scikit-learn	ML models, pipelines, preprocessing, evaluation
SciPy	Statistics, clustering
XGBoost	Gradient boosting
Imbalanced-learn	SMOTE for class imbalance
Plotly	Interactive charts
YData Profiling	Auto EDA reports
FastAPI + Uvicorn	REST API for Titanic model
Pickle / Joblib	Model saving and loading

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👤 Author

Vatsal

Weekly ML Projects Machine Learning | Scikit-learn | Pipelines | Python