Identify fraudulent credit card transactions.

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

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Anomaly Detection

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.

Unsupervised machine learning model to predict fraudulent credit card transactions on a highly imbalanced dataset.

The project explores a range of methods, including both statistical analysis, traditional machine learning and deep learning approaches to anomaly detection a critical aspect of data science and machine learning, with a specific application to the detection of credit card fraud detection and prevention.

Anomaly detection using Local Outlier Factor / Isolation Forest with Python / R

Fraud Detection model based on anonymized credit card transactions based on Isolation Forest Algorithm and Local Outlier Factor

This project aims to detect credit card fraud using Anamoly detection techniques such as Isolation Forest and Local Outlier Factor algorithms.

Fraud Detection model based on anonymized credit card transactions based on Isolation Forest Algorithm and Local Outlier Factor

__CourseWork__

Tugas Besar Mata Kuliah Data Miining

My undergrad research programming sandbox + Capstone project

KI-Analysetool für die Stadtentwicklung in Leipzig, Gewinner des Leipzig Open Data Hackathons 2023 / AI analysis tool for urban development in Leipzig, winner of the Leipzig Open Data Hackathon 2023 (for English, see README-en.md)

Deriving the Local Outlier Factor Score