Machine Learning Logistic Regression Model w/ Python 

This analysis predicts whether it will rain tomorrow in Australia using a logistic regression model. The dataset contains a decade of historical weather data from various Australian stations, with the target variable "RainTomorrow" indicating rain occurrence.

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The workflow includes data preprocessing, exploratory data analysis, feature selection, model training, and evaluation. Key steps involve handling missing values, visualising distributions, and analysing feature correlations.

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After feature selection, a logistic regression model is trained and evaluated. Metrics including accuracy, precision, recall, and F1-score gauge the model's performance. The confusion matrix visualises predictions. Finally, we explore given new datapoints, will it rain the following day.

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Overall, this project provides insights into rain prediction using logistic regression. Enhancements could involve further feature exploration and advanced techniques for more accurate predictions.