Student Exercise | EDA | Lending Club Case Study

(Python, R, C/C++) Isolation Forest and variations such as SCiForest and EIF, with some additions (outlier detection + similarity + NA imputation)

Implementation of TKDE paper "Deep Isolation Forest for Anomaly Detection"

offical implementation of TKDE paper "Deep isolation forest for anomaly detection"

A machine learning-based anomaly detection system designed to identify anomalous transactions in consumer banking and financial activities.

A Spark/Scala implementation of the isolation forest unsupervised outlier detection algorithm.

Credit Card Fraud Detection: An ML project on credit card fraud detection using various ML techniques to classify transactions as fraudulent or legitimate. This project involves data analysis, preparation, and use of models like Logistic regression, KNN, Decision Trees, Random Forest, XGBoost, and SVM, along with various oversampling technique.

calibration of photographic film

Forex analysis and anomaly detection for data obtained using the REST API of the European Central Bank.

Defenzio is a cutting-edge prototype designed to detect zero-day attacks using Deep-learning.Mitigate potential security threats in real-time.

Identifying outliers in well logs with machine learning.

Cyclone Preheater Abnormality Study

Métodos para extração de outliers

Exploratory Data Analysis - Telecom Customer Churn

Anomaly Detection in Optical Networks

Time series anomaly detection, time series classification & dynamic time warping, performed on a dataset of Canadian weather measurements.

An approach for anomaly detection in Industrial Control Systems (ICS), using Water Treatment Dataset (SWaT). The implementation incorporates cutting-edge machine learning techniques, including Isolation Forest and Autoencoder models, augmented by Dynamic Time Warping (DTW) algorithm.

Comparison of various anomaly detection algorithms using scikit-learn and visualization through Plotly Dash

Anomaly Detection strategies were used for detection of credit card fraud.