🏠 House Price Prediction App

This is a Streamlit web application that predicts house prices based on user input features such as area, number of bathrooms, stories, and more. The model was trained using a normalized dataset and saved using joblib.

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🚀 Demo

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📦 Features

• Interactive UI to input:
  • Area (in sq. ft)
  • Number of Bathrooms
  • Number of Stories
  • Parking spots
  • Hot Water Heating
  • Air Conditioning
  • Basement
  • Preferred Area
• Normalizes input based on training range
• Loads and uses a pre-trained model (House1_price_prediction.pkl)
• Predicts and displays house price in Pakistani Rupees (₨)

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🧠 Model

• Model used: Xgboost
• Trained on normalized features
• Price output is scaled back to actual price range: 1,750,000 – 13,300,000 RS

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🛠 How to Run Locally

1. Clone the repository

git clone https://github.com/Shoaib1-coder/HousepricePrediction.git
cd HousepricePrediction

2. Create virtual environment (optional but recommended)

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

4. Run the app

streamlit run app.py

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📁 Project Structure

├── app.py                       # Main Streamlit app
├── House1_price_prediction.pkl  # Trained ML model
├── requirements.txt             # Python dependencies
├── runtime.txt                  # Python version for deployment
├── data/                        # Dataset folder (optional)
├── EDA.ipynb                    # Exploratory Data Analysis notebook
└── README.md                    # Project documentation

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🌐 Deployment

This app is compatible with Streamlit Cloud. Make sure the following files are in your root folder:

• app.py
• requirements.txt
• runtime.txt (with python-3.10)
• House1_price_prediction.pkl

Then push to GitHub and deploy from the Streamlit Cloud dashboard.

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✍️ Author

Muhammad Shoaib Sattar
GitHub | Email

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📜 License

This project is licensed under the MIT License.