Car Price Prediction with Linear Regression

This project aims to predict car prices using a linear regression model trained on the Car Price Prediction Dataset.

Dataset

• Source: Kaggle – Car Price Prediction Dataset
• Features: "CarName" (dummy), "fueltype", "aspiration", "doornumber", "carbody" (dummy), "drivewheel" (dummy), "enginesize", "horsepower", "citympg", "highwaympg", etc.
• Target variable: price

Installation

Python 3.10+ is recommended to run this project.

# Create virtual environment (optional but recommended)
python -m venv .venv
source .venv/bin/activate      # Windows: .venv\Scripts\activate

# Install required packages
pip install -r requirements.txt

Results

After training the linear regression model, the following results were obtained:

• Intercept: 13223.41
• Coefficients: [330.45, 403.46, 345.89, 2522.73, -18.31, 1726.32, ...]
  (only showing a subset for readability)
• Mean Squared Error (MSE): 9,972,068.10
• R² (Coefficient of Determination): 0.87

Performance Summary

Metric	Value
Intercept	13223.41
MSE	9,972,068.10
R²	0.87

The plot below shows predicted vs. actual car prices:

License

This project is licensed under the MIT License - see the LICENSE file for details.

Usage

Run the project by simply executing:

python main.py