About Project

Project Overview

This project aims to predict house prices based on various factors such as location, area, number of bedrooms, and more. It is built using Python, Flask, HTML, CSS, and Bootstrap. The machine learning model ensures accuracy and helps buyers and sellers make better decisions.

Objectives

• Provide accurate house price predictions.
• Help buyers and sellers in decision making.
• Implement machine learning concepts in a real-world project.

Key Features

• User-friendly interface.
• Accurate ML-based predictions.
• Scalable for future enhancements.

Future Scope

In the major project phase, this system can be enhanced by adding more datasets, integrating live housing data APIs, improving the model with deep learning, and deploying on cloud platforms for real-time usage.

Technology Stack

• Frontend: HTML, CSS, Bootstrap
• Backend: Flask, Python
• Machine Learning: Scikit-learn, Pandas, NumPy
• Database: MySQL / SQLite (if required)