Car Price Prediction with Machine Learning

This repository contains the implementation for Task 3 of the CodeAlpha Data Science Internship.

Project Overview

The objective is to build a machine learning regression model to accurately predict used car selling prices based on factors such as vehicle age, showroom price, mileage, fuel type, and brand value.

Methodology

1. Feature Engineering: Calculated vehicle age and performed one-hot encoding on categorical variables (Brand, Fuel Type, Transmission).
2. Model Training: Utilized Random Forest Regressor to capture non-linear relationships.
3. Evaluation Metrics: Evaluated performance using R² Score, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE).

Results

• Algorithm: Random Forest Regressor
• R² Score: ~95%+
• Primary Price Driver: Present showroom price and vehicle age.

How to Run

1. Install dependencies: pip install -r requirements.txt
2. Run the script: python app.py