🚗 Car Price Prediction System

🌟 Features

🚀 Accurate Predictions

📊 30+ Brands

💻 Interactive UI

📱 Responsive

⚡ Real-time

🔍 Detailed Analysis

🛠️ Tech Stack

Backend

Frontend

ML Models

🚀 Quick Start

Clone repository

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

Create virtual environment

python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

Install dependencies

pip install -r requirements.txt

Run application

python app.py

Access at: http://localhost:5000

📊 Model Performance

Metric	Score
R² Score	0.92
Mean Error	₹12,500
Training Time	45 seconds
Best Model	Random Forest (n_estimators=200)

🌐 API Endpoints

Python:

import requests

data = {
    "company": "Hyundai",
    "name": "Grand i10",
    "year": 2018,
    "kms_driven": 35000,
    "fuel_type": "Petrol"
}

response = requests.post("http://localhost:5000/predict", data=data)
print(response.json())```

Javascript:

fetch('/predict', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/x-www-form-urlencoded',
  },
  body: new URLSearchParams({
    company: 'Hyundai',
    name: 'Grand i10',
    year: 2018,
    kms_driven: 35000,
    fuel_type: 'Petrol'
  })
})
.then(response => response.json())
.then(data => console.log(data));

Response

Successful Response:

{
  "success": true,
  "predicted_price": "₹425,000",
  "confidence": 0.92,
  "model": "Random Forest"
}

Error Response:

{
  "success": false,
  "error": "Brand must be one of: Maruti, Hyundai, Honda, ..."
}

Error Handling

Common error responses include:

• 400 Bad Request - Missing required parameters
• 422 Unprocessable Entity - Invalid input values
• 500 Internal Server Error - Server-side issues

Always check the success flag in the response before processing results.

🚙 Supported Brands

And more ...

🤝 How to Contribute

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request