House Price Predictor

A machine learning project that predicts house prices based on historical data.
The project includes:

• A Jupyter Notebook for data analysis, feature engineering, and model training.
• A Flask web application for serving the trained model with a simple web interface.

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Features

• Data preprocessing and exploratory analysis.
• Model training using regression algorithms.
• Web interface to input features and get price predictions.
• Pre-trained model included (model.pkl) for quick demo.

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Project Structure

House-price-predictor/ │ ├── app/ │ ├── app.py # Flask server script │ ├── model.pkl # Trained ML model │ ├── train.csv # Training dataset │ ├── test.csv # Test dataset │ └── templates/ │ └── index.html # Web UI │ ├── House prices prediction.ipynb # Data analysis & training notebook (PDF in repo)

Installation

1. Clone the repository

git clone https://github.com/<your-username>/house-price-predictor.git
cd house-price-predictor/app
python app.py