Anomaly Detection in Credit Card Payments
-
Updated
Feb 1, 2022 - Jupyter Notebook
Anomaly Detection in Credit Card Payments
Machine learning project conducted together with Volvo Cars
My solution for the Spark Cognition Challenge. Questions and Assignment for Data Scientists.
Anomaly/outlier detection using Isolation forest
This Repository provides the basic code snippets for all the most widely used ML Algorithms like Supervised, Unsupervised, and Recommender system algorithms.
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"
To detect Credit Card Fraud by using SVR, Isolation Forest and Local Outlier Factor.
Generating feature importances for outliers identified through Isolation Forests
calibration of photographic film
Cyclone Preheater Abnormality Study
Identifying outliers in well logs with machine learning.
Defenzio is a cutting-edge prototype designed to detect zero-day attacks using Deep-learning.Mitigate potential security threats in real-time.
This is one of the kaggle challenges to identify fraudulent credit card transactions.
Data analysis and outliers detection of air quality data.
Breaking down the isolation forest algorithm in R and then using it to clean online survey data. The clean dataset was further analysed to derive interesting conclusions.
Add a description, image, and links to the isolation-forest topic page so that developers can more easily learn about it.
To associate your repository with the isolation-forest topic, visit your repo's landing page and select "manage topics."