crayglockes/mpesa-transaction-analysis

End-to-end data science project analysing behavioural patterns, user segmentation, and fraud signals in mobile money transactions. # 📱 M-Pesa Transaction Pattern Analysis > End-to-end data science project analysing behavioural patterns, > user segmentation, and fraud signals in mobile money transactions. **📊 Analysis Report →** ## Analysis Pipeline 1. **Data Generation** — 150K realistic synthetic transactions with diurnal activity models, valid Safaricom phone formats, and Safaricom fee schedules 2. **EDA** — Temporal, geographic, and distribution analysis (10 figures) 3. **Feature Engineering** — 30+ behavioural features per user 4. **User Segmentation** — K-Means clustering with PCA visualisation 5. **Anomaly Detection** — Isolation Forest + supervised Random Forest ## Key Results - 4 behaviourally distinct user segments identified - Fraud detection AUC: **0.87+** (5-fold CV) - Top fraud signals: `is_just_below_threshold`, `pct_night_txns`, `amount_cv` - Salary week generates **+18%** transaction volume vs daily average ## Skills Demonstrated `Data Simulation` `EDA` `Feature Engineering` `Clustering` `Anomaly Detection` `Scikit-Learn` `Imbalanced Learning` `Statistical Visualisation`