The Second Hand Car Price Estimator is a web application that helps users predict the selling price of second-hand cars based on various features and parameters. Users can input details about the car, such as the car name, brand, model, vehicle age, kilometers driven, seller type, fuel type, transmission type, mileage, engine displacement, maximum power, and number of seats. The application uses a machine learning model to estimate the selling price based on these inputs.
- Easy-to-use interface for entering car details.
- Prediction of selling price using a trained machine learning model.
- Support for various car brands and models.
- Responsive design for use on desktop and mobile devices.
The Second Hand Car Price Estimator web application is powered by a carefully curated dataset of second-hand car listings. The dataset was collected from various sources, including online car marketplaces, classified advertisements, and car dealerships. It contains detailed information about different cars, including their specifications, features, and selling prices.
The dataset includes the following key features:
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Car Name: The make and model of the car (e.g., Maruti Swift, Hyundai i20, Honda City, etc.).
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Brand: The brand or manufacturer of the car (e.g., Maruti, Hyundai, Honda, etc.).
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Model: The specific model or variant of the car (e.g., Swift Dzire, i20 Elite, City VX, etc.).
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Vehicle Age: The age of the car in years (calculated from the manufacturing year to the current year).
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Kilometers Driven: The total distance the car has been driven in kilometers.
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Seller Type: The type of seller (e.g., individual or dealer) offering the car for sale.
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Fuel Type: The type of fuel the car uses (e.g., Petrol, Diesel, CNG, etc.).
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Transmission Type: The type of transmission (e.g., Manual or Automatic).
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Mileage: The fuel efficiency of the car in kilometers per liter (kmpl).
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Engine Displacement: The engine displacement of the car in cubic centimeters (cc).
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Maximum Power: The maximum power output of the car's engine in brake horsepower (bhp).
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Number of Seats: The total number of seats available in the car.
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Selling Price: The selling price of the second-hand car in the local currency.
The dataset was carefully preprocessed to handle missing values, outliers, and data inconsistencies. Feature engineering techniques were applied to extract relevant information and prepare the data for training the machine learning model.
- Clone the repository to your local machine:
git clone https://github.com/yourusername/second-hand-car-price-estimator.git- Navigate to the project directory:
cd second-hand-car-price-estimator
- Install the required dependencies:
pip install -r requirements.txt
- Run the application:
python app.py
- Open your web browser and go to http://localhost:5000/predict to access the application.
- Enter the details of the car in the provided input fields.
- Click on the "Predict Selling Price" button to get the estimated selling price for the car.
- The predicted selling price will be displayed on the screen.
If you would like to contribute to the Second Hand Car Price Estimator, feel free to create a pull request. Your contributions are welcome!
This project is licensed under the Apache-2.0 license. See the [LICENSE] file for details.
The machine learning model used in this project was trained on a dataset from (provide the source of the dataset). Special thanks to (mention any individuals or organizations you want to acknowledge).
Please make sure to fill in the list of supported models under the "Models" section. Additionally, provide the source of the dataset used to train the machine learning model in the "Acknowledgements" section and acknowledge any individuals or organizations you want to credit for their contributions or support.