Credit Card Fraud Detection using ML: IEEE style paper + Jupyter Notebook

Credit Card Fraud Detection Project

This repository contains the code of our published work in IEEE JBHI. Our main objective was to demonstrate the feasibility of the use of synthetic data to effectively train Machine Learning algorithms, prooving that it benefits classification performance most of the times.

MATLAB code for augmenting small datasets using EigenSample

Location information about commuter activities is vital for planning for travel disruptions and infrastructural development. The Mobility Sensing Project aims to find innovative and novel ways to identify travel patterns from GPS data and other multi-sensory data collected in smartphones. This will be transformative to provide personalised trave…

Data Science Case Study

The computing scripts associated with our paper entitled "Oversampling Highly Imbalanced Indoor Positioning Data using Deep Generative Models".

Use Random Forest to prepare a model on fraud data. Treating those who have taxable income <= 30000 as "Risky" and others are "Good" and A cloth manufacturing company is interested to know about the segment or attributes causes high sale.

Continuing with telemarketing model to predict campaign subscriptions in a portuguese bank institution. For this project I have evaluated the performance of four resampling techniques and selected the best one to implement the logistic model.

A fraud detection project that processes user or credit card data using machine learning and deep learning algorithms.

This repository contains the code for baseline model replication along with all experiments and used datasets as part of the master's thesis on the topic "Detecting Dyslexia Using Deep Learning".

To predict whether the customers will subscribe to the system after 1-month free trial or not.

The aim of this project is to predict fraudulent credit card transactions with the help of different machine learning models.

Detecting Abnormal Markets - Early Warning Systems

Classification of Obesity Status in Indonesia Using XGBoost & ADASYN-N Method

Classify Indonesian Obesity Status using ADASYN-N and Random Forest algorithm

Build, train and compare performances of multiple binary classification machine learning model techniques to detect credit card fraudulent transactions.

Data Science - Random Forest Work

The aim of this project is to predict fraudulent credit card transactions using machine learning models.

Building predictive models to detect and prevent the fraudulent transactions happening on cerdit cards and debit cards. Implementation of 2nd factor authentication for safe and secure transactions.