Detection of network traffic anomalies using unsupervised machine learning

Using Unsupervised methods to identify anomalies in user behaviour through IP Profiling

Surface water quality data analysis and prediction of Potomac River, West Virginia, USA. Using time series forecasting, and anomaly detection : ARIMA, SARIMA, Isolation Forest, OCSVM and Gaussian Distribution

One-class classifiers for anomaly detection (outlier detection)

A Variational AutoEncoder implemented with Keras and used to perform Novelty Detection with the EMNIST-Letters Dataset.

Implementation of different algorithms to infer comprehensible explanations from the outcome of an unsupervised outlier detection algorithm

Identifying fake reviews on Sephora using One Class SVM

In this repo, different techniques will be done to analyze Anomaly detection

Implementing anomaly detection models to identify fraudulent transactions in a financial dataset. It explores several methods, including Support Vector Machine (SVM), Isolation Forest, and Gaussian Anomaly Detection (PDF-based).

Kernel Versions of various machine learning algorithms

MINERÍA DE DATOS APLICADA A LA DETECCIÓN DE CRISIS EPILÉPTICAS - GII18.13

Aplicación de técnicas de Machine Learning para identificar posibles fuentes de materia oscura en el catálogo Fermi-LAT

__CourseWork__

This is a project to detect anomalies in pump sensor data using One-Class Support Vector Machines (SVM). The data is preprocessed by dropping columns with missing values and scaled using MinMaxScaler. The one-class SVM classifier is trained and used to predict anomalies in the data, which are then saved in a new file "results.csv".

Statistical Learning Models for Damage Detection in Civil Structures.

Identify fraudulent credit card transactions.

Experimentation with novelty detection

One Class Classifier for detecting positive cases while just trained on negative cases.