Lab

CNN-based image classifier trained to recognize sports categories from photos.

SVM vs Random Forest comparison on 918 examples. SVM achieved F1-Score of 0.897 on test set.

Fraud detection using clustering and anomaly detection on imbalanced transaction data.

Customer segmentation by satisfaction level using clustering, evaluated against target labels.

Regression on Ames Housing dataset with Random Forest feature selection and Gradient Boosting.

Multi-class classification to predict hominid species from morphological features.