Credit card fraud detection using machine learning techniques

An analysis on credit risk

Credit Card Fraud Detection using Python and Machine Learning.

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

Supervised Machine Learning and Credit Risk

Code to detect credit card fraud detecton

I will use various techniques to train and evaluate models with imbalanced classes.

Using machine learning to train and evaluate models with unbalanced classes to determine the best models to predict credit risk.

Supervised Machine Learning and Credit Risk

Data preparation, statistical reasoning and machine learning are used to solve an unbalanced classification problem. Different techniques are employed to train and evaluate models with unbalanced classes.

This Github repository contains cross selling of health insurance customers on vehicle insurance product. We have to predict whether a customer would be interested in Vehicle Insurance or not by building a ML model. Exploring Insights/Inferences by performing EDA on the given project data. Finding the high accuracy

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.

Supervised machine learning to train and evaluate models based on loan risk.

Analysis of different machine learning models' performance on predicting credit default

Module 12 - Using the imblearn , I'll use a logistic regression model to compare 2 versions of a dataset. First, I’ll use the original data. Next, I’ll resample the data by using RandomOverSampler. In both cases, I’ll get the count of the target classes, train a logistic regression classifier, calculate the balanced accuracy score, generate a con

The Repository is created to cover undersampling and oversampling methods to deal imbalance problem.

The purpose of this study is to recommend whether PureLending should use machine learning to predict credit risk. Several machine learning models are built employing different techniques, then they are compared and analyzed to provide the recommendation.

Supervised Machine Learning

Uses several machine learning models to identify loan applicants likely to default on payments