🚗 Car Price Prediction App

This is a web application built using Streamlit and a Random Forest Regressor model that predicts the selling price of a used car based on various features like company, year, kilometers driven, and fuel type.

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🔍 Features

• Predict the price of a used car using machine learning
• Cleaned and preprocessed dataset from Quikr Cars
• Interactive UI with dropdowns and numeric inputs
• Visualizes feature importance
• Deployable via Streamlit Cloud

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🧠 Model Details

• Algorithm: Random Forest Regressor
• Training Features:
  • company
  • year
  • kms_driven
  • fuel_type
  • age
• Target: Price

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🛠 Setup Instructions

1. Clone the repository

git clone https://github.com/your-username/car-price-predictor.git
cd car-price-predictor
2. Install dependencies
bash
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pip install -r requirements.txt
3. Train the model (optional if model.pkl is provided)
bash
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python train_model.py
4. Run the app
bash
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streamlit run app.py
📁 Project Structure
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car-price-predictor/
│
├── quikr_car.csv           # Raw dataset
├── app.py                  # Streamlit application
├── train_model.py          # Model training script
├── model.pkl               # Trained ML model
├── company_encoder.pkl     # Label encoder for company
├── fuel_type_encoder.pkl   # Label encoder for fuel_type
├── requirements.txt        # Python dependencies
└── README.md               # Project documentation
🔧 Dependencies
nginx
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streamlit
scikit-learn
pandas
numpy
matplotlib
seaborn
joblib
You can install them with:

bash
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pip install -r requirements.txt
🚀 Deployment
You can deploy this app on Streamlit Cloud:

Push the project to a GitHub repository.

Go to streamlit.io/cloud and sign in.

Click "New App" and link your GitHub repo.

Select app.py as the main file and deploy.

📧 Contact
Created by Satyam Jha
Feel free to reach out or contribute to the project!