CIS8008 Assignment 3: Predictive Modeling of Australian Weather Data

This report presents an analysis of Australian weather data using RapidMiner, focusing on predicting rainfall. The assignment begins with exploratory data analysis (EDA) to identify key variables and missing data patterns within the weatherAUS.csv dataset. Decision tree models are then constructed to predict rainfall based on selected weather variables, including humidity3pm, sunshine, and rainfall. The report details the model building process, the final decision tree diagram, and associated rules for prediction. Furthermore, logistic regression is employed to assess the probability of rainfall, considering factors like rainfall, wind gust speed, and humidity. The study compares the performance of decision tree and logistic regression models through cross-validation, revealing the accuracy of each method. The report also delves into the application of big data analytics within the water utility industry, exploring data sources, ETL processes, and the role of OLAP and data mining in improving operational efficiency and decision-making. The report provides detailed insights into the model-building process, the final decision tree diagram, associated rules for prediction, and the comparative analysis of the predictive models.