In this project, three prospective approaches are demonstrated for pre-processing large data sets in practical time-frames, that can attempt to address the class imbalance by improving the running time of the relevant SMOTE+ENN oversampling techniques, with the aim of improving or enabling classifier performance. The focus of our study was to im…

An ensemble of machine learning models for detecting fraudulent credit card transactions, utilizing advanced techniques for feature selection, data imbalance handling, and hyperparameter tuning.

Over- and under-sampled data using four algorithms and compared two machine learning models that reduce bias to identify the most reliable credit risk prediction model.

A FLASK-based web application that predicts the risk of diabetes based on the answers to a questionnaire. The app currently uses an XGBoost (Extreme Gradient Boosted Model) model trained on a Kaggle Dataset of CDC Data.

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

Machine learning model to predict heart transplant failure and success using XGBoost algorithm and SMOTE/ENN to balance the dataset.

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

Creating various machine learning models to create the most accurate model to predict credit risk

Supervised machine learning model to analyze credit risk

Utilizing data preparation, statistical reasoning, and supervised machine learning to solve a real-world challenge: credit card risk.

Testing various supervised machine learning models to predict a loan applicant's credit risk.

Machine learning models for predicting credit risk in LendingClub dataset.

Analyze of several Machine Learning techniques in order to help Jill decide on a most effective Machine Learning Model to analyze Credit Card Risk applications.

Build and evaluate several machine learning algorithms to predict credit risk.

Supervised Machine Learning project to predict credit risk

There are a number of classification algorithms that can be used to determine loan elgibility. Some algorithms run better than others. We built a loan approver using different Supervised Machine Learning algorithms and compared their accuracies and performances

Using my skills in data preparation, statistical reasoning, and machine learning I employed different techniques to train and evaluate models with unbalanced classes.

Utilized several machine learning models to predict credit risk using Python's imbalanced-learn and scikit-learn libraries