🚗 Car Price Prediction using Linear Regression

This project builds a regression model to predict the selling price of used cars based on features like age, current price, kilometers driven, fuel type, seller type, transmission type, and ownership history.

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📦 Dataset

The dataset includes the following columns:

• Year: Year of manufacture
• Present_Price: Price at the time of selling
• Kms_Driven: Distance driven
• Fuel_Type: Petrol / Diesel / CNG
• Seller_Type: Dealer / Individual
• Transmission: Manual / Automatic
• Owner: Number of previous owners
• Selling_Price: Target variable

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📈 Workflow

🧹 1. Data Cleaning & Preprocessing

• Removed irrelevant columns like Car_Name
• Created new feature: Age_Car = 2024 - Year
• Converted categorical features to numerical values
• Replaced string labels with numeric codes

📊 2. Exploratory Data Analysis (EDA)

• Scatter plots of all features vs. Selling_Price
• Histograms for numerical columns
• Bar plots for categorical distributions

🧠 3. Model Training

• Linear Regression using train_test_split with multiple test sizes
• Evaluation metrics used:
  • MAE (Mean Absolute Error)
  • MSE (Mean Squared Error)
  • RMSE (Root Mean Squared Error)
  • R² Score

🔍 4. Feature Engineering

• Applied power transformation on features (powers 1 to 9)
• Found the best transformation (e.g., Owner^9)
• Re-trained model with transformed features

🔁 5. Cross-Validation

• Performed K-Fold (5 splits) cross-validation to ensure stability

🔎 6. Manual Prediction

• Used final model coefficients to predict price of a car with custom input manually

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📊 Results

Metric	Before Tuning	After Tuning
MAE	1.100	0.861
MSE	2.784	1.427
RMSE	1.695	1.194
R² Score	0.822	0.946 ✅

📌 The final model showed improved performance and generalization after removing outliers and applying transformations.

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📂 Project Structure

Regression-Model-Project/ ├── Data/cardata.csv ├── Linear-Regression-Project.ipynb ├── README.md ├── LICENSE ├── .gitignore

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🛠️ Technologies Used

• Python 3.x
• Jupyter Notebook
• pandas, numpy
• matplotlib
• scikit-learn

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📌 Usage Example

To predict a new car's price:

`python

Sample Inputs

age = 10 present_price = 11.23 kms_driven = 42000 fuel_type = 2 # Petrol seller_type = 1 # Dealer transmission = 0 # Manual owner = 1

Model Equation

selling_price = intercept + (c1 * age) + (c2 * present_price) + ...

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📬 Contact

Developed with 💻 and ☕ by Pouya Rahdan

📧 pouya.rahdan5@gmail.com

🔗 GitHub: github.com/pouyarahdan

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📄 License

MIT License. See LICENSE for details.