Car Price Predictor

Overview

The Car Price Predictor is a machine learning-based web application designed to predict the price of a car based on various input parameters. It utilizes historical car price data and applies regression models to provide accurate price estimations.

Features

• Accurate Predictions: Uses machine learning models to estimate car prices.
• User-Friendly Interface: Simple and intuitive UI for inputting car details.
• Data-Driven Analysis: Utilizes historical data for improved accuracy.
• Multiple Model Support: Implements different regression techniques.
• Real-time Estimations: Provides instant price predictions based on user input.

Technology Stack

• Frontend: Streamlit (or any web framework)
• Backend: Flask / FastAPI
• Database: PostgreSQL / SQLite
• Machine Learning Model: Scikit-learn, Pandas, NumPy
• Deployment: AWS / Heroku / Render

Installation

Prerequisites

• Python 3.x
• Virtual Environment (Optional but recommended)

Steps

1. Clone the repository:

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

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install dependencies:

   pip install -r requirements.txt

4. Train the model (if not already trained):

   python train_model.py

5. Run the application:

   python app.py

6. Open http://localhost:5000 in a web browser.

Usage

1. Enter car details such as brand, model, year, mileage, fuel type, etc.
2. Click on the predict button to get the estimated price.
3. View the predicted price along with confidence intervals.

Future Enhancements

• Deep Learning Integration
• More Features for Better Accuracy
• Mobile App Support

License

This project is licensed under the MIT License.