As part of my MIT Professional Education applied data science program, I developed a full Python machine-learning project to predict used car prices from structured vehicle data.

The project covers the full modelling workflow: data cleaning, exploratory analysis, feature engineering, model training, performance comparison and interpretation of the main drivers of price.

The objective was not only to build a predictive model, but to translate data science outputs into a practical business use case: estimating vehicle value more consistently and supporting pricing or commercial decision-making.

Tech stack: Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn.