This repository implements a Collaborative Filtering algorithm.

This project focused on measuring the magnitude of differences between groups in experimental or observational data. While statistical significance tells whether a difference exists, effect size shows how big or meaningful that difference is.

Using pearsonr and chi2_contingency from scipy stats to analyze data from the NBA (National Basketball Association) and explore possible associations.

This repo implements scalable, reusable Python scripts to compute key effect size metrics—including Pearson’s r, Eta-squared, Partial Eta-squared, and Cohen’s d—to help quantify relationships and differences in data for statistical analysis.