Network Activity Anomaly Detection

Developed a classification model for Network Activity Anomaly Detection using neural networks. The process began with preprocessing the categorical columns through one-hot encoding and normalization of training data. Following this, the model was trained using the training data. To ensure robust performance assessment, I employed K-Fold cross-validation. This method validated the model's effectiveness across different subsets of data, enhancing reliability in anomaly detection for network activity.