Car Price Prediction Using Machine Learning

This project utilizes a Random Forest model to forecast car prices based on various input features, with predictions delivered through an interactive Streamlit web application. Users can easily enter car details to get instant price estimates.

Key Components:

Data Preprocessing:

The project makes use of a ColumnTransformer and Pipeline to streamline feature encoding and scaling, ensuring data is appropriately prepared for modeling.

Random Forest Model:

A Random Forest Regressor is trained to offer precise and dependable car price predictions.

Streamlit Web App:

The user-friendly interface allows users to input car details and receive real-time price predictions effortlessly.

Model Storage with Pickle:

The trained model and preprocessing pipeline are serialized using pickle, enabling fast and convenient model deployment and retrieval.

This approach ensures a seamless, efficient system for predicting car prices while providing an interactive experience for end users.