ajaynair710/car-price-prediction

Car Price Prediction Using Linear Regression

Car Price Prediction

Welcome to the Car Price Prediction project repository! This project focuses on predicting the prices of used cars using machine learning techniques. We use a dataset of car listings, clean and preprocess the data, explore the data to find insights, build and tune a regression model, and finally use it to make predictions.

Table of Contents

• Introduction
• Dataset
• Project Workflow
• Installation
• Usage
• Results
• Contributing
• License

Introduction

In this project, we predict the prices of used cars based on various features such as make, model, year, mileage, and other relevant characteristics. The goal is to build a robust linear regression model that can provide accurate price estimates for car listings.

Dataset

The dataset used in this project contains information about used car listings, including features like make, model, year, mileage, etc. This dataset is available in the repository and is used throughout the project.

Project Workflow

The project is divided into several key steps:

1. Data Cleaning:

   • Handle missing values, incorrect data types, and outliers.
   • Normalize and transform data to ensure consistency.

2. Exploratory Data Analysis (EDA):

   • Visualize the data to understand relationships and patterns.
   • Identify key features and trends that affect car prices.

3. Data Splitting:

   • Split the dataset into training and testing sets to evaluate the model performance.
   • Ensure the data is divided in a way that prevents data leakage.

4. Linear Regression Model Building:

   • Develop a linear regression model to predict car prices.
   • Analyze the model's assumptions and performance.

5. Model Training:

   • Train the regression model using the training dataset.
   • Optimize the model by minimizing the error metrics.

6. Validation:

   • Validate the model using the testing dataset to assess its performance.
   • Use cross-validation techniques to ensure generalizability.

7. Simple Feature Engineering:

   • Create new features or transform existing ones to improve model accuracy.
   • Perform feature scaling, encoding, and selection.

8. Value Regularization:

   • Apply regularization techniques (L1, L2) to prevent overfitting.
   • Adjust model complexity to balance bias and variance.

9. Model Tuning:

   • Fine-tune hyperparameters to enhance model performance.
   • Use grid search or randomized search for optimal parameter selection.

10. Prediction:

    • Use the final trained model to predict car prices on new, unseen data.
    • Evaluate predictions and refine the model as needed.

Installation

To run this project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/ajaynair710/Car-Price-Prediction.git

2. Change into the project directory:

   cd Car-Price-Prediction

3. Create a virtual environment:

   python -m venv venv

4. Activate the virtual environment:
   • On Windows:

     venv\Scripts\activate

   • On macOS/Linux:

     source venv/bin/activate

5. Install the required packages:

   pip install -r requirements.txt

Usage

To use the model for predictions:

1. Ensure the environment is set up as per the installation instructions.
2. Run the Jupyter Notebook or script to train the model and make predictions:

   jupyter notebook Car_Price_Prediction.ipynb

3. Follow the notebook to understand each step and visualize the results.
4. Use the final model to predict car prices by providing the required features.

Results

The results of the project, including model performance metrics, visualizations, and insights, are documented within the Jupyter Notebook. Key performance indicators like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R² score are used to evaluate the model.

Contributing

Contributions are welcome! If you have suggestions for improvements or new features, please open an issue or submit a pull request.

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature/your-feature-name

3. Make your changes.
4. Commit your changes:

   git commit -m 'Add some feature'

5. Push to the branch:

   git push origin feature/your-feature-name

6. Open a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for more details.