A concise end-to-end machine learning workflow exploring four regression algorithms — Decision Tree, Random Forest, KNN, and Support Vector Regression (SVR). The project includes EDA, preprocessing, model training, and performance comparison.
Full EDA-to-Modeling workflow
Clean data preprocessing & feature scaling
Comparison of 4 regression algorithms
Evaluation with MAE, MSE, and R²
Clear visualizations & insights
🌳 Decision Tree Regressor
🌲 Random Forest Regressor
📍 KNN Regressor
⚙️ Support Vector Regression (SVR)
Random Forest delivered the most stable performance.
SVR performed well with proper scaling.
KNN was sensitive to feature distribution.
Decision Trees provided high interpretability.