House Price Prediction using Python Machine Learning (ML)

“House Price Prediction System” is a web-based application which uses machine learning to predict house prices based on historical data and key property features with a Django-based web platform. This system predicts house prices by analyzing factors such as location, area, number of bedrooms and bathrooms, and property type. By learning patterns from past data, it provides reliable estimates and helps users understand which factors influence property prices the most. The project involves collecting and preparing data, selecting important features, building predictive models using algorithms like Linear Regression, Decision Trees, and Random Forests, and evaluating their performance. Users can input property details through a simple interface and get an estimated price instantly.

The platform is designed with two modules – User and Admin – to provide seamless interaction for buyers and efficient management tools for administrators.

Key Features of the System

User Module

The User Module is designed for buyers or general users who want to estimate house prices. Key functionalities include:

1. Input Property Details – Users can enter information about the property such as location, area, number of bedrooms, bathrooms, and garage.
2. Price Prediction – The system processes the input data and predicts the estimated price of the property using machine learning models.
3. View Prediction History – Users can check previous predictions they have made for reference.
4. User Profile Management – Users can manage their personal details, login credentials, and preferences.

The User Module focuses on providing a simple, interactive interface so users can quickly get house price estimates without technical knowledge.

Admin Module

1. Dashboard – Administrators can view important system statistics such as total users and prediction outcomes.
2. User Management – Admin can view, manage, and control all registered users.
3. Prediction History – Admin can track user details and their predictions.
4. Reports & Analytics – Generates summarized reports of predictions which is predicted by users.
5. Role-Based Access – Only authorized administrators can access this panel.
6. Admin Profile Management – Users can manage their personal details, login credentials, and preferences.

 Technologies Used:

• Programming Language: Python

• Machine Learning Libraries: Scikit-learn, Pandas, NumPy, djangorestframework

• Data Visualisation: Matplotlib, Seaborn

• Database: SQLite/ CSV dataset

• Frontend: Django

• IDE: VS Code / PyCharm