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

Kernel Versions of various machine learning algorithms

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

__CourseWork__

Statistical Learning Models for Damage Detection in Civil Structures.

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".

Identify fraudulent credit card transactions.

Experimentation with novelty detection

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

At Infosys Springboard, I worked on a project focused on unsupervised anomaly detection in healthcare providers. I implemented three machine learning algorithms—Isolation Forest, Elliptic Envelope, and One-Class SVM—as well as a deep learning approach using autoencoders. Additionally, I conducted individual SHAP analysis