📈 Regression Algorithms using Scikit-Learn

A practical implementation of the most widely used Regression Algorithms in Machine Learning using Python and Scikit-Learn.

This notebook demonstrates how to build, train, and evaluate multiple regression models on synthetic datasets, making it an excellent learning resource for beginners and students preparing for interviews or placements.

🚀 Algorithms Implemented

✅ Linear Regression

✅ Polynomial Regression

✅ Ridge Regression

✅ Lasso Regression

✅ ElasticNet Regression

✅ Decision Tree Regressor

✅ Random Forest Regressor

✅ Support Vector Regressor (SVR)

✅ K-Nearest Neighbors Regressor (KNN)

✅ XGBoost Regressor

✅ LightGBM Regressor

✅ CatBoost Regressor

📂 Repository Structure

Regression/
│
├── Regression.ipynb
└── README.md

📚 What You'll Learn

• Regression in Machine Learning
• Training regression models
• Model prediction
• Comparing different regression algorithms
• Scikit-Learn workflow
• Ensemble Regression Models
• Boosting Regression Models

🛠️ Technologies Used

• Python
• NumPy
• Scikit-Learn
• XGBoost
• LightGBM
• CatBoost
• Jupyter Notebook

📦 Required Libraries

pip install numpy
pip install scikit-learn
pip install xgboost
pip install lightgbm
pip install catboost

or

pip install numpy scikit-learn xgboost lightgbm catboost

▶️ Run the Notebook

Clone the repository

git clone https://github.com/gnani291/Regression.git

Move into the project

cd Regression

Launch Jupyter Notebook

jupyter notebook

Open

Regression.ipynb

Run all cells.

📈 Regression Models Included

Algorithm	Library
Linear Regression	Scikit-Learn
Polynomial Regression	Scikit-Learn
Ridge Regression	Scikit-Learn
Lasso Regression	Scikit-Learn
ElasticNet Regression	Scikit-Learn
Decision Tree Regressor	Scikit-Learn
Random Forest Regressor	Scikit-Learn
Support Vector Regressor	Scikit-Learn
KNN Regressor	Scikit-Learn
XGBoost Regressor	XGBoost
LightGBM Regressor	LightGBM
CatBoost Regressor	CatBoost

🎯 Ideal For

• Machine Learning Beginners
• Data Science Students
• AI/ML Engineers
• College Projects
• Interview Preparation
• Placement Preparation

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📖 Prerequisites

Basic knowledge of

• Python
• NumPy
• Machine Learning Basics

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⭐ Future Improvements

• Real-world datasets
• Model evaluation metrics
• Hyperparameter tuning
• Cross Validation
• Feature Engineering
• Performance comparison charts

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🤝 Contributing

Contributions are welcome.

1. Fork this repository.
2. Create a feature branch.
3. Commit your changes.
4. Open a Pull Request.

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👨‍💻 Author

Tadiparthi Gnaneswar

B.Tech CSE (AI & ML)

GitHub: https://github.com/gnani291

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⭐ Support

If you found this notebook helpful,

• ⭐ Star this repository
• 🍴 Fork it
• 📢 Share it with others

Happy Learning! 🚀