🚀 Predict house prices based on land area using Machine Learning (Linear Regression)
This project is a Machine Learning-based web application that predicts house prices using Linear Regression (Single Variable Model).
It takes land area (in sq.ft) as input and predicts the estimated price using a trained ML model.
- 📊 Simple and clean dataset visualization
- 🤖 Machine Learning model using Linear Regression
- 🎯 Real-time price prediction
- 🌐 Interactive web app built with Streamlit
- 📈 Graphical representation (Area vs Price)
- 📐 Mathematical validation using Y = mX + b
- Python
- Pandas
- Scikit-learn
- Matplotlib
- Streamlit
house-price-prediction-linear-regression/
│
├── app.py
├── dataset.csv
├── requirements.txt
└── README.md
git clone https://github.com/selvan-01/house-price-prediction-linear-regression.git
cd house-price-prediction-linear-regressionpip install -r requirements.txtstreamlit run app.pyThe model follows the Linear Regression formula:
Y = mX + b
Where:
- X → Area (sq.ft)
- Y → Price
- m → Coefficient (slope)
- b → Intercept
- Input: 40000 sq.ft
- Output: Predicted Price (₹)
- Multi-variable prediction (Bedrooms, Location, etc.)
- Location-based pricing
- Advanced UI (Glassmorphism design)
- Deployment (Streamlit Cloud / AWS)
- Model evaluation metrics
- Understanding Linear Regression
- Data preprocessing and visualization
- Building Machine Learning models
- Creating interactive ML web applications
Contributions are welcome. Feel free to fork this repository and improve it.
If you like this project, give it a star on GitHub.
💻 Built with passion for AI & Machine Learning