This project encompasses a detailed analysis of two distinct datasets: Aumake International Limited's privacy and security policies and a diabetes dataset. The Aumake analysis examines the company's privacy and security statements, referencing the nine core principles of Australian data governance, including the "No harm rule," honesty, transparency, fairness, choice, accuracy, safety, accountability, stewardship, and enforcement. The diabetes dataset analysis utilizes RapidMiner Studio to perform Exploratory Data Analysis (EDA), including the creation of summary statistics and correlation analysis to identify key variables for predicting diabetes. The project explores data cleaning processes, scatter plots, and correlation tables to determine the most significant factors, such as age, BMI, insulin, and glucose levels. The analysis aims to understand the relationships between variables and their predictive power, providing insights into data-driven decision-making and the application of data mining techniques.
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