Weather Prediction using Machine Learning

Built a Gradient Boosting Classifier to forecast rainfall patterns, achieving 86% test accuracy across complex

meteorological variables.

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Performed EDA and feature engineering using Pandas and NumPy to clean weather data, identify influential variables,

and improve model reliability.

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Analyzed weather patterns and outliers through correlation and distribution visualizations, using Seaborn to

communicate regional climate insights.