Rent Prediction Project

The Rent Prediction Project aims to forecast rental prices for properties based on various features, helping tenants find affordable housing and assisting investors in making informed decisions. This project utilizes machine learning techniques to analyze data collected from rental listings and predict future rental prices.

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🚀 Demo

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🧐 Features

• Rental Price Prediction: Estimates rental prices based on features like location, property size, amenities, and furnishing status.
• Rental Trend Insights: Provides insights into rental trends across major cities, allowing users to observe market patterns.
• Interactive Web Interface: User-friendly interface built with Flask, deployed on AWS EC2, enabling real-time rental predictions.
• Data Collection & Analysis: Data scraped from Makan.com, with extensive preprocessing and feature engineering for model accuracy.

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💻 Built with

• Programming Language: Python
• Libraries & Tools: Pandas, NumPy, Scikit-learn, Selenium, Flask
• Machine Learning Models: Decision Tree Regressor, XGBoost, CatBoost
• Deployment: AWS EC2 for server hosting
• Data Collection: Selenium for web scraping

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📂 Project Structure

Rent_Prediction_Project/
├── data/                    # Raw and cleaned datasets
├── notebooks/               # Jupyter notebooks for data exploration and modeling
├── app/                     # Flask application files for web interface
├── models/                  # Trained model files and scripts for model training
├── README.md                # Project documentation
└── requirements.txt         # Dependencies for project setup