🚗 Car Price Prediction using Machine Learning

Welcome to the Car Price Prediction project — a machine learning-based web application that predicts the resale value of a used car in India based on key factors like fuel type, car age, kilometers driven, and more. This solution is designed to assist users in making informed decisions when buying or selling used cars.

🔍 Project Objective

To build a reliable machine learning model that can accurately estimate car prices using historical data and make it accessible via an interactive Streamlit web app for real-time predictions.

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✨ Features

• 🔮 Instant Price Prediction based on user inputs.
• 📊 Uses a Random Forest Regressor for high prediction accuracy.
• 📦 Model saved using Pickle for easy deployment.
• 🧠 Encodes categorical features like fuel type, seller type, and transmission.
• 🌐 User-friendly Streamlit interface for quick predictions.
• 📉 Supports inputs such as:
  • Year of Purchase
  • Present Showroom Price
  • Kilometers Driven
  • Number of Owners
  • Fuel Type (Petrol/Diesel/CNG)
  • Seller Type (Dealer/Individual)
  • Transmission Type (Manual/Automatic)

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🛠 Tech Stack

• Python
• Pandas & NumPy for data handling
• Matplotlib & Seaborn for visualization
• Scikit-learn for model building
• Pickle for model serialization
• Streamlit for building the web interface
• VS Code for development and deployment

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🚀 How to Run Locally

1. Clone the repo:

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

2. Install required libraries:

   pip install -r requirements.txt

3. Run the Streamlit app:

   streamlit run app.py

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💡 Use Case

This app can be especially useful for:

• Individuals looking to buy/sell used cars
• Car dealerships for quick price estimation
• Startups in the automobile resale market

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