Automobile Price Prediction
A feature-engineering-heavy pricing model for a 205-vehicle automobile dataset, paired with a PCA/t-SNE dimensionality reduction deep-dive — including an honest flag on a headline accuracy figure that needs re-validation before it's quoted publicly.
The Challenge
A 15-feature linear regression model explains 73% of automobile price variance on a properly validated train/test split. A companion PCA/t-SNE analysis claims 99.93%, but that figure needs re-validation before it's trusted or quoted publicly.
Predicting vehicle price from technical specification is a classic feature-engineering problem: a 205-row automobile dataset with 26 raw features spanning categorical, numerical, and free-text fields, many of them noisy (brand name typos), correlated, or high-cardinality. The goal was a defensible, interpretable pricing model, plus a deeper look at whether dimensionality reduction could extract the true underlying structure driving price.
Approach
Results
The primary model, linear regression on 15 features selected via mutual information and RFE, explained 73% of price variance on a proper train/test split, with engine size, horsepower, and curb weight standing out as the strongest price predictors, alongside a clear negative relationship between fuel efficiency and price.
The PCA deep-dive found that just three principal components, driven primarily by symboling (insurance risk rating), wheelbase, and car width, captured over 37% of total price variance, and t-SNE visualisation showed distinct clustering of vehicles into lower, medium, and high-cost bands.
Flagged for validation before this goes any further: the companion PCA/t-SNE write-up also reports a Random Forest reaching R² = 0.9993 on this same 205-row dataset. That figure is high enough, and the accompanying methodology notes thin enough on an explicit held-out test split at that stage, to warrant re-running the evaluation with a clean, PCA-fitted-on-training-only pipeline before quoting it publicly. Until re-validated, the defensible headline result is the 73% R² from the properly split linear regression model, not the 99.93% figure.
Business Impact
The validated model gives a defensible, feature-level basis for pricing strategy: engine performance and vehicle dimensions justify premium positioning, while fuel efficiency and price pull in different directions, useful for segmenting eco-focused models from performance models rather than trying to win on both axes at once. The PCA/t-SNE work is a promising direction for a lighter-weight pricing model and clearer market segmentation, but the very high accuracy figure needs to clear proper validation before it informs any real pricing decision.