Weather Prediction

Weather forecasting is a critical component of many industries, including agriculture, transportation, energy, and event planning. Accurate forecasts help organizations make informed decisions, reduce costs, and ensure safety.

System Data Flow Diagram (made with eraser.io)

Components

• ML Model for Weather Forecasting

  Random Forest Regression model is used in this problem.

  Output : Temperature

• Scheduler for Regular Updates

  It updates the data and model from local machine to github repo. by taking data from weather api.

  Using Task Scheduler, Daily_Model_Trainer.py and daily_commit.bat files are run to update the model and commit it to repo.

• Supaboard Dashboard

  

ML Model Demo

• Clone repo

git clone https://github.com/CodeRulerNo1/Weather-Prediction

• Install essential libraries

pip install -r requirements.txt

• Run app.py

py -m streamlit run app.py

• Open local website
• Give values to the input parameters
• Press predict button for results

Visualization

• Data Visualiztion

• Model Structure

• Model Performance

• Feature Importance

Acknowledgements

• Dataset Provider
• Weather Api
• StreamLit for Model Demo

Author

• @Ichhabal Singh