House Rent Prediction System Using Machine Learning – Real-Time Rental Estimator
House Rent Prediction System
Predict house rental prices in seconds using a smart, data-driven web application powered by machine learning. This project is designed to analyze key property features—like location, furnishing status, and square footage—and deliver accurate rent predictions based on historical trends from Indian cities.
Ideal for educational use, product demos, or property listing prototypes.
Project Summary
This system utilizes a machine learning regression model trained on real-world rental data. Through an intuitive Flask-built web interface, users enter property details and instantly receive a trustworthy rent estimate.
Whether you’re exploring data science or working on real estate technology, this project offers a seamless blend of backend intelligence and frontend simplicity.
🛠 Project Specifications
Project Name | House Rent Prediction |
---|---|
Language/s Used | Python |
Type | Web Application |
Developer | UPDATEGADH |
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✅ Available Features
- ML model trained on authentic Indian house rent datasets
- Flask-based responsive web interface
- Input support for city, furnishing status, square footage, number of bedrooms, etc.
- Real-time prediction results based on entered data
- Preprocessing and model training scripts included
- Dataset and model pipeline managed locally—no database setup needed
- Modular code structure with reusable components
- Clean folder hierarchy and professional documentation
Included in the Package
The project is delivered as a complete working ZIP archive containing:
app.py
– Flask app main filemodel_trainer.py
– Script to process data and train the modelData/rent.csv
– Dataset used for trainingtemplates/
– HTML templates for UIstatic/
– Static assets (e.g., preview image, CSS)src/
– Core Python modules: ingestion, transformation, utilsrequirements.txt
– All dependencies listed for setup
Ready to Explore?
This project offers a complete implementation of machine learning-powered rent prediction with a production-style architecture. No additional tools or databases required—just Python, a dataset, and a clear workflow.
Perfect for:
- Academic submissions
- Real estate prototype systems
- Data science portfolio projects
- ML & Flask practice deployments
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