Machine Learning Playground

This repository is a collection of ML Projects, built primarily using in Jupyter notebooks*. The project is collabaration between Sasi Pawan and I.

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Project 1: Regression

This project implements and compares multiple regression techniques using a unified workflow.

Models implemented

1. Linear Regression
2. Ridge Regression
3. Lasso Regression
4. Elastic Net
5. Stochastic Gradient Descent Regressor (SGDRegressor)
6. Polynomial Regression

Tech stack

scikit-learn, pandas, numpy, matplotlib

Data preprocessing

• Categorical features converted using one-hot encoding
• Feature scaling performed using StandardScaler

Evaluation metrics

• R² Score
• Adjusted R²
• Mean Squared Error (MSE)
• Root Mean Squared Error (RMSE)
• Mean Absolute Error (MAE)

Hyperparameter tuning

• GridSearchCV used to tune and compare all applicable models

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