Skip to content

Latest commit

 

History

History
36 lines (26 loc) · 2.55 KB

README.md

File metadata and controls

36 lines (26 loc) · 2.55 KB
Raw

The Car Price Prediction Web App is a Flask-based web application that utilizes machine learning to predict the approximate price of used cars based on various features. The app is designed to provide users with a quick and reliable estimation of a car's value, making it a valuable tool for both car buyers and sellers.

Features:

  • Input Form: Users can enter relevant details about the car they are interested in, such as the car's make, model, year of manufacture, fuel type, and other specifications.
  • Machine Learning Model: The web app employs a trained machine learning model, built using regression techniques, to analyze the input data and predict the car's price based on historical data.
  • Instant Prediction: Upon submitting the car details, the app provides an instant prediction of the car's estimated price, displayed on the screen.
  • User-Friendly Interface: The user interface is intuitive and easy to navigate, allowing users to input information seamlessly and receive price predictions quickly.

Technologies Used:

  • Flask: Used to create the web application and handle HTTP requests.
  • Machine Learning (Python): Used to train the regression model for car price prediction.
  • HTML/CSS/JavaScript: Used for designing and styling the frontend of the web app.
  • Pandas & NumPy: Utilized for data manipulation and analysis.
  • Scikit-learn: Employed for implementing machine learning algorithms.
  • Pickle: Used to save and load the trained machine learning model.

Setup and Usage:

  1. Clone the repository to your local machine.
  2. Install the required dependencies using pip install -r requirements.txt.
  3. Run the Flask app using python app.py.
  4. Access the web app via your browser at http://localhost:5000.

Data Source: https://quikr.com

The car price prediction model is trained using a dataset obtained from reputable sources that include a wide variety of car specifications and their corresponding prices. The dataset is cleaned and preprocessed to ensure accurate predictions.

Contributing:

Contributions to this project are welcome! If you find any bugs or have suggestions for improvements, please feel free to open an issue or submit a pull request.

License:

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

Note:

This web app is intended for educational and informational purposes only. The price predictions provided are estimates and may not represent the actual market value of the cars. Users are encouraged to conduct thorough research and consult with experts before making any car-related decisions.