Anomaly Detection in Credit Card Payments

Machine learning project conducted together with Volvo Cars

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

My solution for the Spark Cognition Challenge. Questions and Assignment for Data Scientists.

Anomaly/outlier detection using Isolation forest

An exploration of data analysis techniques and standard ML algorithms on QSAR oral toxicity dataset. - 2021 - Yıldız Technical University

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

outlier detection & k-means clustering

Using Isolation Forest

Anomaly detection with SKAB corpus

[Competition] KAMP : 2022년 제2회 K-인공지능 제조데이터 분석 경진대회 : 시계열 분석 기반 용해물 품질 예측 AI 모델 개발

This repository contains code for building a random forest model to predict the prognosis of breast cancer patients.

This poc allow you to train & check a pseudo Isolation forest algorithm to detect malware & other vulnerability.

Condition monitoring of electrical assets are vital to prevent a developing fault from going unnoticed. This project aims to build anomaly detection models to identify significant changes in asset conditions and allow the operators to conduct timely maintenance.

Ce projet est réalisé dans le cardre de mon projet académique de fin de cursus ingénieur informatique en data science. L'objectif est de réalisé la détection d'anomalie qui sont de types varié en utilisant les machine learning et le deep learning

INF367 Selected Topics in Artificial Intelligence: Project 1 - Isolation Forest anomaly detection

To detect Credit Card Fraud by using SVR, Isolation Forest and Local Outlier Factor.

A Loyalty Program for E-commerce websites built upon Machine Learning techniques.