Car-Price-Predictor

View the live website

Click the link to view the website (https://car-price-predictor-yrn6.onrender.com/)

Project Overview

This project provides a web-based application for predicting car prices using machine learning models. Users can interact with the model and view predictions in real-time through a user-friendly interface built using streamlit framework.

Features

• Custom UI Styling:

  • The interface is enhanced with custom CSS for a modern, polished appearance.
  • Styled buttons and select boxes for an intuitive user experience.

• Title Banner:

  • A visually appealing banner that displays the title "Car Price Prediction Using Machine Learning" with gradient styling.

• Introductory Text:

  • Brief and user-friendly explanation to guide sellers in estimating the value of their car.

• User Input Fields:

  • Ex-showroom price: Input for the car's original price (in Lakhs).
  • Distance driven: Input for kilometers driven by the car.
  • Fuel type: Choose between Petrol, Diesel, or CNG.
  • Seller type: Select whether you're a dealer or an individual.
  • Transmission type: Choose between Manual or Automatic transmission.
  • Previous owners: Select the number of previous owners.
  • Year of purchase: Enter the year of car purchase, and the app calculates the car's age.

• Prediction Button:

  • A "Predict Car Price" button triggers the machine learning model to estimate the car’s price.

• Machine Learning Model:

  • The app uses an XGBoost regression model to predict the car's price based on the provided inputs.

• Result Display:

  • The predicted price is displayed in a user-friendly format. If the prediction is successful, celebratory balloons are shown.

• Error Handling:

  • If something goes wrong with the input or prediction, users are notified with an error message for correction.

Technologies Used

• Python: Core programming language for data analysis and web application development.
• Pandas & NumPy: Data manipulation and numerical computations.
• Scikit-Learn: Machine learning algorithms and tools.
• Matplotlib/Seaborn: Data visualization.
• Flask/Django/Streamlit: Web framework for building the user interface.
• APIs/yfinance: For fetching real-time stock data.

Getting Started

1. Clone the Repository:

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

2. Install Dependencies:

   pip install -r requirements.txt
   

3. Setup and Configuration:

• Configure any necessary API keys or environment variables.
• Adjust any settings for data sources and model parameters.

4. Run the Web Application:

   streamlit run streamlit.py
   

5. Usage:

• Navigate to http://localhost:5000 (or the specified port) to access the web application.
• Input stock ticker symbols and view predictions and visualizations.

Website View

Contribution

Feel free to contribute to this project by submitting issues, feature requests, or pull requests. Please adhere to the project's coding standards and include tests for any new features.

License

This project is licensed under the APACHE 2.0 License. See the LICENSE file for more details