Rainfall Prediction Classifier with Python Machine Learning Models

In this portfolio entry, I successfully implemented a rainfall prediction classifier using Python and various machine learning algorithms. I worked with a rainfall dataset from the Australian Government’s Bureau of Meteorology, focusing on applying key classification algorithms.

The project’s primary goal was to build a reliable classifier for predicting rainfall the following day. I employed well-known algorithms, including linear regression, logistic regression, support vector machines, decision trees, and k-nearest neighbors. The process algorithm implementation and a meticulous evaluation of model performance.

For the model evaluation, I utilized essential metrics such as Accuracy Score, Jaccard Index, F1-Score, LogLoss, Mean Absolute Error, Mean Squared Error, and R2-Score. The comprehensive report compares metrics, offering insights into the strengths and weaknesses of each algorithm.

Skills & Tools

Predictive Models, Machine Learning, Python, Decision Trees, Linear Regression, Logistic Regression, SVM, KNN