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.
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ML Model for Weather Forecasting
Random Forest Regression model is used in this problem.
Output : Temperature
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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.
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Supaboard Dashboard
- 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
- Data Visualiztion
- Model Structure
- Model Performance
- Feature Importance
