Regression Modelling for Vehicle Pricing
Used Cars Price Prediction
A machine learning project that analyses second-hand vehicle attributes and predicts listing prices, built around a clean dataset and a reproducible evaluation method.
Challenge / Objective
Objective: predict used-car prices from listing attributes, with a documented process that can be repeated rather than a one-off result.
Approach
Start with data quality and feature understanding, then model. Keep evaluation methodology explicit so the numbers can be interpreted rather than quoted.
Solution
A cleaned dataset, analysed and prepared features, a regression model, and diagnostic plots supporting the evaluation.
- Data preprocessing
- Feature analysis
- Regression model development
- Documented evaluation methodology
Architecture
Listing data → preprocessing → feature analysis → model training → price prediction, with evaluation on a held-out split at the end of the chain.
How it flows
- Listing data
- Preprocessing
- Feature analysis
- Model training
- Price prediction
Implementation
Pandas for cleaning and feature preparation, scikit-learn for regression modelling, Matplotlib for diagnostics. Splits are fixed so runs are comparable.
Technology
AI / ML
- Machine Learning
- Scikit-learn
Development
- Python
Data
- Pandas
- Matplotlib
Current state
Current state: the modelling pipeline runs end to end with documented evaluation methodology. No accuracy percentage or business saving is published, because neither has been verified for publication.
Key takeaways
Most of the useful gain came from cleaning and feature preparation. Documenting the evaluation method mattered more than reporting a single headline score.
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Project
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