Click on the image above to watch a demo on YouTube!
- Developed a machine learning tool using scikit-learn’s ridge regression to provide hyper-local weather predictions, accurately forecasting next-day temperatures within a 2-degree margin.
- Trained the model on historical weather data from local weather station datasets, using predictors like temp_max, temp_min, and the previous day’s precipitation to generate precise predictions.
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Often, local weather stations cannot report accurate forecasts relative to your specific location.
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However, many weather stations have public repositories of historical weather statistics, which may be closer to your specific location.
- Utilizing publicly available weather datasets (ie National Oceanic and Atmospheric Administration) to provide accurate machine learning location-based weather forecasts.
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Ideally, I'd like to be able to predict accurately more than one day in advance while maintaining a 2-degree margin of error.
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Auto update max_temp, min_temp and precip values using APIs