🏡 Real Estate Price Prediction Using ML

Overview

This project utilizes Machine Learning models to predict Bengaluru house prices, leveraging data preprocessing and regression techniques for accurate estimations. With Linear Regression, the model achieves 82% accuracy, helping users make informed real estate decisions.

Features

• Price estimation based on key housing attributes.
• Data preprocessing using one-hot encoding for categorical features.
• Flask-based deployment for real-time price predictions.

Technologies Used

• Python & Pandas – Data handling.
• NumPy & Scikit-learn – ML model development.
• Linear Regression & GridSearchCV – Model tuning and optimization.
• Flask – Web-based interface for predictions.

Installation

git clone https://github.com/Lingesh-7/Real-Estate-Prediction-ML
cd Real-Estate-Prediction-ML
pip install -r requirements.txt

Usage

1. Training the Model

python train.py --epochs 10 --batch_size 32

2. Running Price Prediction

python predict.py --features "area=1200, bedrooms=3, location='Indiranagar'"

3. Web-Based Prediction (Flask App)

python app.py

Visit http://localhost:5000 to input property details and get price predictions.

Results

The model predicts house prices in Bengaluru with 82% accuracy, offering valuable insights for buyers and sellers.

Future Improvements

• Improve feature selection for higher accuracy.
• Integrate LLMs for real estate insights.
• Expand to multi-city housing price predictions.