Welcome to the Car Price Predictor repository! This project is a machine learning regression model designed to predict the selling price of a car based on various input features provided by the user.
The Car Price Predictor uses a regression model to estimate the selling price of a car. The model takes into account several factors that typically influence the price of a car, such as the year of manufacture, the number of kilometers driven, the type of fuel used, the type of seller, the transmission type, the ownership history, and the car brand.
- Year: The year of manufacture, ranging from 1992 to 2020.
- Kilometers Driven: The total kilometers the car has been driven, ranging from 1 to 223,159 km.
- Fuel Type: The type of fuel used by the car. Possible values include petrol, diesel, CNG, LPG, and electric.
- Seller Type: The type of seller. Possible values include Individual, Dealer, and Trustmark Dealer.
- Transmission Type: The type of transmission (e.g., manual or automatic).
- Owner: The ownership history of the car. Possible values include 1st, 2nd, 3rd, 4th & above owner, and test drive car.
- Brand: Various car brands.
To run this project locally, follow these steps:
- Clone the repository:
git clone https://github.com/Harshkumar6200/CarPricePredictor.git
- Navigate to the project directory:
cd CarPricePredictor - Install the required dependencies:
pip install -r requirements.txt
To use the Car Price Predictor, you need to provide the required inputs as specified. You can run the model and get predictions by executing the following command:
python predict.pyYou will be prompted to enter the details of the car, and the model will output the predicted selling price.
We welcome contributions to improve the Car Price Predictor. If you have any ideas, suggestions, or bug reports, please open an issue or submit a pull request.
We would like to thank all the contributors and the open-source community for their valuable support.
URL : https://car-price-predictor-9anryfigdpvpyubksf3hrj.streamlit.app/