Used Car Price Prediction: A Complete Machine Learning Regression Project

This is an end-to-end machine learning regression project aimed at predicting used car resale prices. It uses Random Forest and Gradient Boosting algorithms, covering the full workflow of feature engineering, data visualization, and model evaluation. The project is from GitHub author anosh-hash and provides a well-structured practical case for machine learning beginners.

• Original Author/Maintainer: anosh-hash
• Source Platform: GitHub
• Original Link: https://github.com/anosh-hash/Car_price_prediction
• Release Date: June 13, 2026

The project aims to predict resale prices based on features like vehicle brand, current price, and mileage, using a Python tech stack (Pandas, Scikit-learn, Matplotlib, Seaborn) to provide a reproducible end-to-end case for beginners.

The dataset contains 301 car records with 9 core features: Car_Name, Year, Selling_Price (target), Present_Price, Driven_kms, Fuel_Type, Selling_type, Transmission, Owner.

Derived Feature Design:

1. Car_Age (Current Year - Manufacturing Year)
2. Depreciation_Pct (Value loss relative to current price)
3. Kms_Per_Year (Total mileage / Car Age)
4. Brand_Goodwill (Reputation encoded by average brand selling price)

These features reflect an understanding of the used car market: car age affects residual value, depreciation rate reflects value retention ability, etc.

Comparison of three regression models' performance:

Model	MAE	RMSE	R² Score
Linear Regression	1.04	1.65	0.881
Random Forest	0.47	0.84	0.969
Gradient Boosting	0.40	0.69	0.979

Gradient Boosting performed best, explaining 97.9% of price variation, verifying the advantage of ensemble learning in handling non-linear relationships.

Feature Importance Results:

• Present_Price (Current Price): 55% contribution (strongest predictor)
• Brand_Goodwill (Brand Reputation):34% contribution (second)
• Fuel type and number of owners have minor impacts

Implications: Buyers and sellers should focus on current market pricing and brand reputation; factors like fuel type have limited influence.

Technical Highlights:

1. Complete data processing workflow: loading, missing value handling, encoding, train/test split
2. Reproducible environment: clear dependencies (Pandas, NumPy, Scikit-learn, etc.)
3. Rich visualization:9-panel dashboard (price distribution, car age relationships, correlation heatmap, etc.)
4. Model persistence: save models for new data prediction

Application Scenarios:

• Used car trading platforms: provide price references to reduce information asymmetry
• Financial institutions: evaluate vehicle mortgage loan limits
• Insurance companies: calculate total loss compensation amounts

Expansion Directions: introduce maintenance/accident records, try neural networks, build real-time valuation API services

The project demonstrates the complete workflow of using machine learning to solve business problems. It is an ideal entry-level case for learners and provides references for feature design and model comparison for practitioners.

Key Insight: An excellent machine learning solution requires algorithmic knowledge plus business understanding; business features like brand goodwill are key to performance breakthroughs.