Step-by-Step Walkthrough

1. Data Analysis and Machine Learning Model

The foundation of this project is the data. We began with extensive data analysis to identify trends and correlations, using features like property size, location, and other relevant attributes. After exploring and cleaning the data, we proceeded to build multiple machine learning models:

• Linear Regression: Used as a baseline model.

• Random Forest and XGBoost: More advanced models were applied to capture non-linear relationships and provide higher prediction accuracy.

The final model was chosen based on RMSE (Root Mean Square Error) and cross-validation scores, ensuring a balance between performance and interpretability.

The trained model was saved using joblib, which allowed for easy loading and integration with the Flask backend for predictions.

2. Front-End Interface (HTML, CSS, JS)

The next phase was designing an intuitive web interface where users can interact with the model. The front-end was built using:

• HTML for the page structure,

• CSS for styling to make the interface clean and responsive,

• JavaScript for dynamic interaction and communication with the Flask API.

Live Demo (if possible):

• When the user inputs details like property location, number of bedrooms, and square footage, the web app dynamically sends the data to the backend.

• The interface is highly responsive and allows users to get an instant price prediction with a single click of the "Predict" button.

Here's a quick look at the main form where users input the property features. Once they submit, they receive a price estimate instantly, which updates without reloading the page, thanks to AJAX/Fetch API.

3. Backend with Flask (Python)

The backend of this project was built using Flask, a lightweight Python web framework, to handle the prediction requests:

• The user inputs are collected by the front end and sent as JSON to the Flask API.

• Flask processes the data, applies the trained machine learning model, and returns the predicted price as a JSON response.

• Flask routes are designed to handle these interactions smoothly.

Key Backend Features:

• /predict API Endpoint: This handles the prediction logic by accepting property details and returning the price prediction.

• Real-Time Response: Predictions are computed on the fly and returned in milliseconds, providing a seamless user experience.

4. Cloud Deployment on AWS EC2 with Docker

To ensure that the application is scalable and accessible globally, the entire project was containerized using Docker. This allowed for easy deployment across different environments, ensuring consistency from development to production.

• Dockerization: Both the front-end and the Flask backend were containerized, allowing the entire app to be easily deployed and maintained.

• AWS EC2 Deployment: The application was deployed on AWS EC2 instances to ensure scalability and availability.

• Nginx was used as a reverse proxy to efficiently handle user traffic, ensuring a smooth experience even under load.

Security Measures

Given the sensitivity of real estate data, we also implemented several security features to ensure the safety of user data:

• HTTPS encryption was used to secure data transmission.

• Input validation was done on both the front-end and back-end to prevent malicious attacks like SQL injection or XSS.

• Flask routes were secured and carefully designed to protect user data integrity.

Front-End AJAX Code (To Fetch Prediction)

Technologies and Tools Used

This project brought together a variety of tools and technologies:

• Frontend: HTML, CSS, JavaScript, AJAX/Fetch API for dynamic updates.

• Backend: Python, Flask for API development and model integration.

• Machine Learning: Scikit-learn, XGBoost, Random Forest for predictive modeling.

• Cloud & Deployment: Docker for containerization, AWS EC2 for scalable deployment.

• Version Control: Git for tracking project progress and collaborating with team members.