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

Supervised Machine Learning and Credit Risk

The purpose of this analysis was to create a supervised machine learning model that could accurately predict credit risk using python's sklearn library.

Machine-Learning project that uses a variety of credit-related risk factors to predict a potential client's credit risk. Machine Learning models include Logistic Regression, Balanced Random Forest and EasyEnsemble, and a variety of re-sampling techniques are used (Oversampling/SMOTE, Undersampling/Cluster Centroids, and SMOTEENN) to re-sample th…

Perform a Credit Risk Supervised Machin Learning Analysis using scikit-learn and imbalanced-learn libraries.

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

Developed Machine Learning Models to Predict 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.

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…

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.

This repo is about Machine Learning and Classification

Built and evaluated several machine learning algorithms to predict credit risk.

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

Built several supervised machine learning models to predict the credit risk of candidates seeking loans.

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

Using Resampling and Ensemble Learning to look at data and predict default rates on loans.

Perform a Credit Risk Supervised Machin Learning Analysis using scikit-learn and imbalanced-learn libraries.

Machine-learning models to predict credit risk using free data from LendingClub. Imbalanced-learn and Scikit-learn libraries to build and evaluate models by using Resampling and Ensemble Learning

The objective of this analysis was to use machine learning models to accurately predict credit risk.

Build and evaluate several machine learning algorithms to predict credit risk