Snow Forecasting with Machine Learning to Unravel Meteorological Patterns for Precision Weather Prediction

This paper delves into the dynamic realm of weather prediction, specifically focusing on the binary classification task of forecasting snowfall. Employing machine learning models, including Logistic Regression, Support Vector Machine (SVM), and Decision Tree, we seek to unravel the intricate patterns and relationships within meteorological data. The dataset, centered around London's weather, captures diverse parameters such as cloud cover, sunshine, temperature, and precipitation, with the target variable indicating the presence or absence of snow. The methodology encompasses meticulous data cleaning, feature engineering, and model evaluation, shedding light on the models' efficacy in predicting snow events. Precision, recall, accuracy, and F1 score serve as key metrics, unraveling the nuances of model performance. The results, elucidated through confusion matrices and feature importance analyses, offer insights into the strengths and weaknesses of each model.