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A Python data pipeline analyzing how BMW, Mercedes-Benz, and Audi depreciate compared to Ford, using 87,000 real Craigslist listings. Caught and fixed a major data quality issue (55% of raw data was duplicate listings), quantified value retention by brand, and trained a Random Forest model predicting price within $4,200 average error. Audi retains value best of the three luxury brands at every time horizon.
The network for creativity
Join 1.25M professional creatives like you
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Creatives on Contra have earned over $150M and we are just getting started
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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.
Hype, hype and hype... It's the first page of the notebook that I write in tells me.
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