/Rainfall-Prediction-end-to-end-ML-project

The main motive of the project is to predict the amount of rainfall in Vidarbha region or state well in advance. We predict average rainfall using past data.

Primary LanguageJupyter Notebook

Rainfall-Prediction-end-to-end-ML-project

The main motive of the project is to predict the amount of rainfall in Vidarbha region or state well in advance. We predict average rainfall using past data.

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Tech Stack

  • Front-End: HTML, CSS
  • Back-End: Flask
  • IDE: Jupyter notebook, vscode

How to Run the Project

  1. Clone the repository

  2. Set up a virtual environment (optional but recommended):

    python -m venv env
source env/bin/activate  # On Windows, use `env\Scripts\activate`

  3. Install required dependencies:

    pip install -r requirements.txt

  4. Train the model and create pickle file:

    python app.py

    This will train the model using the provided dataset and save it as a pickle file.

  5. Run the Flask app:

    python main.py

    The Flask app will start running, typically on http://127.0.0.1:5000/.

CONCLUSION

XGBoost and Random Forest performed better compared to other models. However, if speed is an important thing to consider, we can stick with Random Forest instead of XGBoost.

Improvements that can be done:

Here we can collect more data and use neurals networks more computational power could be really useful for us.