This project implements an anomaly detection model using the Gaussian (normal) distribution to identify anomalies in given data. Anomalies are detected based on their deviation from the normal distribution's bell curve. This approach assumes that normal data points follow a Gaussian distribution, and anomalies lie at the ends of the distribution.
Model: Anomaly detection based on Gaussian distribution. Thresholding: Classification of anomalies using a threshold on the probability density function (PDF) of the normal distribution. Evaluation: Assessment of model performance using precision, recall, and F1-score metrics. Visualization: Graphical representation of the normal distribution and detected anomalies.