🏠 Match My Mansion 🌟

Overview

"Match My Mansion" is a web application designed to assist users in finding and predicting property prices. It features property listings, price predictions, and personalized recommendations using advanced machine learning techniques. Built with Flask for the back-end, HTML and Tailwind CSS for the front-end, and utilizing a Random Forest model for price prediction, this app also integrates data gathering tools like Selenium and BeautifulSoup.

Features

• Property Listings: Browse and view detailed information about various properties.
• Price Prediction: Predict property prices based on input factors such as location, size, and type.
• Recommendations: Receive tailored property suggestions based on user preferences, including nearby locations, price range, and furnishing type.
• Market Trends: Analyze market trends using statistical visualizations.
• Data Gathering: Collect property data from various sources using web scraping.

Tech Stack

• Back-End: Flask (Python)
• Front-End: HTML, Tailwind CSS
• Machine Learning: Random Forest
• Data Gathering: Selenium, BeautifulSoup
• Database: MySQL

Installation

To set up the project locally, follow these steps:

1. Clone the Repository

   git clone https://github.com/veer-debug/match-my-mansion.git
   cd match-my-mansion

2. Create a Virtual Environment

   python3 -m venv venv
   source venv/bin/activate

3. Install Required Packages

   pip install -r requirements.txt

4. Set Up the Database

   python manage.py migrate

5. Run the Application ```bash

   python app.py
   

Usage

• Property Listings: Navigate to the listings page to view and explore properties.
• Price Prediction: Use the prediction form to estimate property prices based on input details.
• Recommendations: View recommended properties tailored to your preferences.
• Market Trends: Access the statistics page to see visualizations of market trends.

AI Integration

Price Prediction

• Model Used: Random Forest
• Description: The Random Forest model is trained on historical property data to provide accurate price predictions based on features such as location, property size, and amenities. Recommendation System
• Techniques Used: Collaborative filtering and content-based methods.
• Description: Provides personalized property recommendations based on user preferences and historical interactions.

Example of AI in Action

• Data Collection: Gather property data using Selenium and BeautifulSoup.
• Model Training: Train the Random Forest model with historical data to learn price trends.
• Prediction: Users input property features to receive estimated price ranges.
• Recommendations: The system suggests properties based on user preferences and previous interactions.

Contributing

Contributions are welcome! If you have suggestions, improvements, or fixes, please follow these guidelines:

Fork the repository. Create a new branch for your changes. Commit your changes with descriptive messages. Push your changes to your forked repository. Create a pull request from your branch to the main repository. License This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgements

• Flask: A lightweight WSGI web application framework.
• Tailwind CSS: A utility-first CSS framework.
• Random Forest: A machine learning algorithm for predictive modeling.
• Selenium & BeautifulSoup: Tools for web scraping and data collection.