NAC Breda Player Analytics

Data Analysis & Storytelling module at BUas, Year 1 Block B. Real dataset provided by NAC Breda football club.

End-to-end data analysis and ML pipeline for NAC Breda's player recruitment. Cleaned and explored a dataset of 16,535 footballers across 140 features, segmented players by position, and built a Logistic Regression classifier to identify high-potential forward prospects.

Key Highlights

01.Cleaned a 16,535-record, 140-feature player dataset with mean imputation and visualised before/after distributions.
02.Segmented the dataset into four position groups to enable position-specific EDA and targeted modelling.
03.Built a Logistic Regression binary classifier; evaluated with accuracy, precision, recall, F1-score, and k-fold cross-validation.
04.Delivered an ethical analysis covering transparency and accountability standards for responsible use of player data.