https://github.com/aniketwdubey/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.
https://github.com/aniketwdubey/rainfall-prediction-end-to-end-ml-project

css data-science data-visualization flask html machine-learning python rainfall-prediction

Last synced: 13 days ago
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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.

• Host: GitHub
• URL: https://github.com/aniketwdubey/rainfall-prediction-end-to-end-ml-project
• Owner: aniketwdubey
• Created: 2022-10-14T09:34:09.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2022-11-01T13:50:38.000Z (about 2 years ago)
• Last Synced: 2024-04-16T02:02:33.869Z (8 months ago)
• Topics: css, data-science, data-visualization, flask, html, machine-learning, python, rainfall-prediction
• Language: Jupyter Notebook
• Homepage:
• Size: 55.7 MB
• Stars: 15
• Watchers: 1
• Forks: 5
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 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.

![alt text](static/workFlow.png)

## 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.