Built an end-to-end used car

Built an end-to-end used car price prediction system using regression on a dataset of 50,000 used car listings (manufacturer, model, engine size, fuel type, year, mileage, and price).

I compared three models, linear regression, random forest, and XGBoost, and selected XGBoost as the production model based on its strong performance (MAE ~$195, R² 0.9996). Feature engineering included car age, mileage per year, and a premium-brand flag to boost predictive accuracy. SHAP was used to interpret the model, revealing that car age, engine size, and mileage are the strongest drivers of price.

The final model is deployed as an interactive Streamlit dashboard with three sections: a Price Estimator for instant valuations, a Market Explorer for browsing trends across brands/models, and a Model Insight page showing feature importance and evaluation metrics.

Built end-to-end using Python, pandas/numpy, scikit-learn, XGBoost, SHAP, Streamlit, and Plotly.