House Price Prediction

A generic machine learning project to predict house prices based on features like bedrooms, bathrooms, size, and zip code

Project Structure

This project has been restructured for better organization:

• app/: Contains the Flask application logic.
  • main.py: The entry point for the web application.
  • templates/: HTML templates for the frontend.
• data/: Stores the datasets used for training and testing.
  • final_dataset.csv: The processed dataset used by the application.
  • Bengaluru_House_Data.csv, ParisHousing.csv, etc.: Raw datasets.
• models/: Contains the serialized machine learning models.
  • RidgeModel.pkl: The trained Ridge Regression model used for predictions.
• notebooks/: Jupyter notebooks for data exploration and model training.
  • House Price Prediction.ipynb: The notebook where the model was created.

Installation

1. Clone the repository.
2. Install the required dependencies:

   pip install flask pandas scikit-learn

Usage

1. Navigate to the app directory (or run from root):

   python app/main.py

2. Open your browser and navigate to http://127.0.0.1:5000.
3. Enter the house details (bedrooms, bathrooms, size, zip code).
4. Click "Predict Price" to see the estimated price.

Development

• To retrain the model, check the notebooks/ directory.
• To modify the web app, edit app/main.py or app/templates/index.html.

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Special Credit 🙌

If he (brother) didn’t tell me, I probably wouldn’t have changed it.

• Name: Aman Ranjan
• GitHub: https://github.com/ranjanport

Grateful for the push and motivation.

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Links 🔗

• GitHub: https://github.com/chandanvyas999
• LinkedIn: https://www.linkedin.com/in/chandan-vyas/