Regression Projects Repository 📚 About

This repository contains a variety of regression problem solutions ranging from basic to advanced techniques. Here, I experiment with popular machine learning algorithms to build predictive models on real-world datasets. 🚀 Contents

Linear Regression — Simple and multiple linear regression models

Polynomial Regression — For modeling nonlinear relationships

Decision Tree & Random Forest Regression — Tree-based methods

Support Vector Regression (SVR) — Kernel-based regression

XGBoost & LightGBM — Gradient boosting algorithms for enhanced performance

Neural Networks for Regression — Deep learning-based regression models

Feature Engineering & Data Preprocessing — Preparing data to boost model accuracy

Model Evaluation Metrics — RMSE, MAE, R², and other performance metrics

🛠️ Technologies Used

Python

scikit-learn

XGBoost, LightGBM

TensorFlow / PyTorch

Pandas, NumPy, Matplotlib, Seaborn

📈 Why Regression?

Regression is one of the most fundamental machine learning tasks for predicting continuous outcomes. It’s widely used across finance, healthcare, economics, engineering, and many other fields.