Automobile Price Prediction

Overview

A machine learning web application that predicts automobile prices based on vehicle specifications. The system uses a trained Random Forest Regressor model to provide accurate price estimates, helping buyers and sellers make informed decisions in the automotive market.

Key Features

• ML-powered price prediction using Random Forest algorithm
• Interactive web interface for inputting vehicle specifications
• Real-time prediction results with confidence metrics
• Support for multiple vehicle attributes (make, model, year, mileage, fuel type)
• Data preprocessing and feature engineering pipeline
• Model training and evaluation metrics visualization

Technology Stack

• Backend: Python, Flask
• ML Framework: scikit-learn, pandas, numpy
• Frontend: HTML, CSS, Bootstrap
• Model: Random Forest Regressor
• Data Processing: pickle for model serialization

Getting Started

1. Install dependencies: pip install -r requirements.txt
2. Train the model: python train_model.py
3. Run the application: python app.py
4. Access the web interface at http://localhost:5000
5. Enter vehicle specifications and get instant price predictions

Deployment

Suitable for deployment on platforms like Heroku, AWS, or Railway with Python runtime support.