🏠 House Price Predictor

A machine learning web app that predicts house sale prices using a Linear Regression model trained on the Ames Housing Dataset.

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📊 Model Training & Analysis

View the full training notebook with outputs, charts, and evaluation metrics:

• View on GitHub

🖼️ App Preview

Features

• Predicts sale price from square footage and room details
• Clean dark-themed UI built with Streamlit
• CLI mode via predict.py for quick testing

Project Structure

house-price-prediction/
├── data/               # Raw CSVs (not pushed to git)
├── model/              # Saved model artifacts
│   ├── model.pkl
│   ├── scaler.pkl
│   └── features.pkl
├── app.py              # Streamlit web app
├── predict.py          # CLI predictor
├── main.ipynb          # Training notebook
└── requirements.txt

Run Locally

git clone https://github.com/Darshan-2118/SCT_ML_01.git
cd SCT_ML_01
python -m venv venv
venv\Scripts\activate
pip install -r requirements.txt
streamlit run app.py

Deploy on Streamlit Cloud

1. Push the repo to GitHub (make sure model/*.pkl is not in .gitignore)
2. Go to share.streamlit.io
3. Connect your repo → set app.py as the entry point
4. Click Deploy

Model

• Algorithm: Linear Regression
• Target: log(SalePrice) → exponentiated at prediction time
• Features: GrLivArea, TotalBsmtSF, 1stFlrSF, 2ndFlrSF, BedroomAbvGr, FullBath, HalfBath, BsmtFullBath, TotRmsAbvGrd, TotalSqFt, TotalBaths