Getting Started¶

Prerequisites¶

Python 3.8 or higher
pip

Installation¶

Clone the source repository:

git clone https://github.com/Gozie02/House-Price-Prediction.git
cd House-Price-Prediction

Install dependencies:

pip install -r requirements.txt

The requirements.txt includes:

Package	Purpose
`streamlit`	Web interface
`scikit-learn`	Model training and inference
`numpy`	Numerical operations
`pickle`	Model serialisation (stdlib)

Running the App¶

The trained models are already serialised as .pkl files in the repository root. You do not need to re-train to run the app.

streamlit run app.py

Streamlit will start a local server and open the app in your browser at http://localhost:8501.

Re-training Models¶

If you want to re-train from scratch, open the notebook:

jupyter notebook house-price-prediction.ipynb

Run all cells in order. The final cells will serialise the trained models back to .pkl files, overwriting the existing ones.

Warning

Re-training will overwrite LRModel.pkl, RidgeModel.pkl, and RandomForestModel.pkl. Back up the existing files first if you want to compare outputs.

Project Structure¶

House-Price-Prediction/
├── house-price-prediction.ipynb  # Training notebook
├── app.py                        # Streamlit application
├── LRModel.pkl                   # Serialised Linear Regression model
├── RidgeModel.pkl                # Serialised Ridge Regression model
├── RandomForestModel.pkl         # Serialised Random Forest model (used in app)
└── requirements.txt