📊 Project Overview
Built a machine learning model to predict car prices using vehicle specifications and features.
🔧 Data & Preprocessing
- 205 cars with 16 features (engine specs, dimensions, fuel type, etc.)
- Encoded categorical variables using Label Encoding
- Scaled features with StandardScaler for better model performance
🤖 Models Compared
| Model | Training Score | Testing Score |
|---|---|---|
| Linear Regression | 84.5% | 79.4% |
| Decision Tree | 91.7% | 85.3% |
| SVR | -10.8% | -9.9% |
| Random Forest | 98.2% | 95.6% |
🏆 Results
- Random Forest performed best with 95.6% accuracy
- Mean Absolute Error: $1,313
- Model can predict prices for new car specifications
💡 Key Insights
- Ensemble methods (Random Forest) handle complex patterns better
- Engine specs and dimensions are major price factors
- Proper data preprocessing is crucial for success
- Some models (like SVR) may not suit all dataset types
🚀 Takeaway
Random Forest proved ideal for this regression problem, balancing accuracy and robustness while handling the complex relationships in car pricing data.
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