Car Price Prediction: A Complete ML Project

📊 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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