This report presents a classification and clustering analysis using NBA and FIFA datasets. Part A compares classification tree and binary logistic regression models for predicting whether an NBA player is in ESPN’s Top 50. Part B applies hierarchical clustering and K-means clustering to profile FIFA players based on selected performance attributes.

This report presents an exploratory data analysis of team-level performance in the FIFA Women’s World Cup. Using statistical visualisation, correlation analysis, hypothesis testing, principal component analysis and regression modelling, the study examines the performance factors associated with tournament progression. The findings highlight the importance of attacking efficiency as a key determinant of success at the highest level of women’s international football.