Understanding the problem, approach, and real-world impact of data-driven vehicle pricing
Used car dealerships and private sellers face a persistent challenge: how to price a used vehicle fairly, quickly, and competitively. Overpricing leads to slow inventory turnover and lost customers. Underpricing leaves money on the table.
Traditional pricing methods rely on human experience, which introduces inconsistency and bias. Market conditions change rapidly, and keeping up with pricing trends across hundreds of vehicle configurations is impossible without data-driven tools.
"Given a set of vehicle attributes, predict the resale price in GBP of a used Ford car."
Build a regression model that achieves R-squared of 0.80 or higher on a held-out test set, while providing interpretable feature importance.
Sourced the Ford Used Car Dataset from Kaggle, containing 17,966 real market listings from the United Kingdom with 9 features per vehicle.
Conducted comprehensive EDA with 22 visualizations covering distributions, correlations, outliers, and category-level price analysis.
Compared One-Hot vs Label Encoding. Applied StandardScaler to numerical features. One-Hot outperformed by 11% R².
Chose Linear Regression for interpretability and speed. Trained in under 100ms with clear coefficient-based feature importance.
5-Fold cross-validation confirms stability (std < 0.01). Learning curve analysis shows no overfitting. Residual analysis validates assumptions.
Model saved with joblib for reproducibility. Complete pipeline documented. Ready for integration into real-world applications.
Loaded dataset, checked data quality, identified 0 missing values and 154 duplicates
Created 22 visualizations, analyzed correlations, detected outliers, studied distributions
Compared encoding strategies, scaled features, prepared train/test split
Trained Linear Regression, achieved R² of 0.840 on test set
5-Fold CV, residual analysis, feature importance, learning curves
Saved model, documented pipeline, created this interactive website