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🧠 Machine Learning Model Comparison

📋 Overview

This project demonstrates the implementation and comparison of multiple Machine Learning models using Python.
It covers both classification and regression algorithms, includes cross-validation, and evaluates performance using metrics like accuracy, R² score, and mean squared error.

🚀 Features

  • Compare all major classification models:
    • Logistic Regression
    • K-Nearest Neighbors (KNN)
    • Support Vector Machine (SVM)
    • Decision Tree
    • Random Forest
    • Naive Bayes
    • Gradient Boosting
    • AdaBoost
    • XGBoost
    • LightGBM
    • CatBoost
    • Neural Network (MLP)
  • Cross-validation for robust performance measurement
  • Extendable to regression models
  • Simple and beginner-friendly codebase

📂 Project Structure

📁 machine-learning-model-comparison/ ├── data/ # Dataset files (CSV or others) ├── models/ # Saved trained models (optional) ├── notebooks/ # Jupyter notebooks for exploration ├── scripts/ │ ├── classification_models.py # Compare all classifiers │ ├── regression_models.py # Compare regression models │ └── cross_validation.py # K-fold validation comparison ├── requirements.txt # Python dependencies └── README.md # Project documentation

🧰 Tech Stack

Python 🐍 scikit-learn XGBoost LightGBM CatBoost Pandas, NumPy, Matplotlib

🧑‍💻 Author

Abir Majumdar 📧 abirmajumdar112@gmail.com 🔗 https://github.com/STYLO009