A fraud detection (exploratory) project using machine learning algorithms over 11 million real transactions

About Six different techniques are employed to train and evaluate models with unbalanced classes. Algorithms are used to predict credit risk. Performance of these different models is compared and recommendations are suggested based on results. Topics

Supervised Machin Learning Analysis using scikit-learn and imbalanced-learn libraries.

Using supervised machine learning to predict credit risk. Trying oversampling, under sampling, combination sampling and ensemble learning to find the model with the best fit

Using Supervised Machine Learning algorithms to identify credit risks

This project applies supervised machine learning models to predict credit risk, and compare algorithm effectiveness in an unbalanced classification problem

Analysis of a dataset using different techniques to train and evaluate models with unbalanced classes, aimed at reducing bias and predicting accurate credit risk.

Apply machine learning to solve the challenge of credit risk

Assess credit risk of applicants using supervised machine learning. Several different machine learning techniques such as SMOTE, SMOTEENN, RANDOM FOREST, EASY ENSEMBLE were applied, the models were assessed using accuracy score, precision and accuracy to choose the best technique that applies to this type of 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 scikit-learn machine learning models using several sampling techniques.