This project utilizes machine learning techniques to predict car prices based on various features such as mileage, year of manufacture, and model. The goal is to build a regression model that accurately estimates the price of a car given its attributes.
The primary algorithm used in this project is linear regression. Linear regression is a simple yet powerful technique that models the relationship between a dependent variable (car price) and one or more independent variables (features).
- Jupyter Lab: Interactive development environment for Python that allows easy experimentation and visualization.
- Flask: Python web framework used for building the web application.
- HTML/CSS with Bootstrap: Frontend framework used for designing and styling the web interface to display the prediction results.