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Car Price Prediction Using Machine Learning 🚗📊

This project applies machine learning techniques to predict the selling price of used cars based on various factors like the car’s age, present price, kilometers driven, fuel type, and transmission type. The dataset is processed, analyzed, and modeled using Linear Regression and Lasso Regression, achieving high accuracy.

🔹 Key Features

• Data Preprocessing: Handling missing values, encoding categorical features, and feature selection.
• Exploratory Data Analysis (EDA): Understanding the dataset with visualizations and statistics.
• Machine Learning Models:
  • Linear Regression: Establishes a relationship between car attributes and price.
  • Lasso Regression: Applies regularization to reduce overfitting.
• Model Evaluation: Uses R-squared error and visualization to assess accuracy.
• Data Visualization: Scatter plots to compare actual vs. predicted prices.

📂 Project Structure

• car data.csv: Dataset containing car details and selling prices.
• car_price_prediction.ipynb: Jupyter Notebook with data preprocessing, model training, and evaluation.
• README.md: Project overview and instructions.

📊 Results

• Linear Regression R² Score: ~0.88 (Training), ~0.83 (Testing)
• Lasso Regression R² Score: ~0.84 (Training), ~0.83 (Testing)

🚀 Getting Started

1. Clone the repository:

   git clone https://github.com/your-username/car-price-prediction.git
   cd car-price-prediction

2. Install dependencies:

   pip install -r requirements.txt

3. Run the Jupyter Notebook to train and evaluate the models.

📌 Future Enhancements

• Implement additional regression models (e.g., Ridge, Decision Tree).
• Optimize hyperparameters using GridSearchCV.
• Deploy as a web app using Flask or Streamlit.

📢 Contributions are welcome! Feel free to raise issues or suggest improvements.

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