A simple Machine Learning web application built using Streamlit that predicts house prices based on user input features. This project demonstrates an end-to-end ML workflow — from model training to deployment on Streamlit Cloud.
👉 https://houseprice-me.streamlit.app
- Interactive and user-friendly UI
- Predicts house prices in real-time
- Trained ML regression model
- Deployed on Streamlit Cloud
- Lightweight and fast
- Algorithm used: Regression (Linear / ML-based)
- Libraries: scikit-learn, pandas, numpy
- Model serialization: joblib / pickle
The model is trained on housing data and then loaded into the Streamlit app for prediction.
- Python 3.10
- Streamlit (Web App Framework)
- Pandas & NumPy (Data handling)
- Scikit-learn (Machine Learning)
- Altair (Data visualization)
houseprice/
│
├── app.py # Main Streamlit app
├── requirements.txt # Project dependencies
├── pipe.pkl # Trained ML model
└── README.md # Project documentation
- Clone the repository
git clone https://github.com/Sayedcodes/houseprice.git
cd houseprice
pip install -r requirements.txt
streamlit run app.py
- Create virtual environment (optional but recommended)
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate- Install dependencies
pip install -r requirements.txt- Run the app
streamlit run app.pySteps followed:
- Push project to GitHub
- Add
requirements.txtwith version pinning - Add
runtime.txtto lock Python version - Deploy using Streamlit Cloud
- Add more features & better dataset
- Improve UI/UX
- Add model evaluation metrics
- Add multiple ML models
Sayed Mohammad Hamza
- GitHub: https://github.com/Sayedcodes
If you like this project, give it a ⭐ on GitHub — it really helps!
Happy Coding 🚀