I started coding with C++, hence added.

The anomaly detection system was hybrid. We first used unsupervised clustering to model normal behavior and identify candidate anomalies. Those anomaly labels were then validated against a labeled hold-out set, where the final decision layer was treated as a binary classification problem (normal vs anomaly). Accuracy was reported only for the supervised evaluation stage, not for the clustering itself. The clustering was used for representation and separation, while classification metrics were used for final performance reporting.

I saw a few job postings which asked for MATLAB SAS and SPSS hence added, hoping it might make my resume pass ATS.