This Machine Learning project demonstrates the implementation of Multiple Linear Regression using Python and scikit-learn to build a predictive model for numerical forecasting. It covers:
✅ Data Preprocessing (Handling missing values, encoding categorical data) ✅ Feature Selection (Identifying the most relevant predictors) ✅ Mathematical Understanding of Linear Regression ✅ Model Training using scikit-learn ✅ Performance Evaluation with R² Score & Mean Squared Error (MSE)
Technologies Used • Python (NumPy, Pandas, Matplotlib, Seaborn) • Machine Learning with scikit-learn • Data Visualization & Insights
Key Highlights
🔹 Step-by-step breakdown of Linear Regression and its real-world applications 🔹 Model trained & evaluated using scikit-learn for accurate predictions 🔹 Visualizations & analysis to understand feature impact