Used classification and clustering to analyze survival determinants Situation: Analyzed Titanic passenger data to identify survival factors and support risk assessment strategies for modern shipping. Task: Determine key attributes influencing survival outcomes and model risk based on class, gender, and age. Action: Cleaned and prepared nominal dataset; applied J48 decision tree (C4.5) and KMeans clustering in WEKA to uncover patterns; tuned decision tree parameters for mo

Applied CRISP-DM and WEKA for market basket analysis Situation: A bookstore wanted to boost sales and improve customer satisfaction through better recommendations and store layout. The business needed insights into which book categories are often purchased together, to enable product bundling and more effective cross-selling. Task: As a business analyst student, my objective was to discover associations between book purchases using data mining techniques. The main question w

Situation: Volvo, aiming to improve its car sales strategy and market positioning, needed to understand how different customer segments behave, especially in relation to financing options and car model preferences. The task was to analyze the Volvo dataset using data mining techniques to support targeted marketing and decision-making. Task: As a business analyst student, my goal was to segment customers effectively, determine which financing options influenced car purchases,