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…

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

We'll use Python to build and evaluate several machine learning models to predict credit risk. Being able to predict credit risk with machine learning algorithms can help banks and financial institutions predict anomalies, reduce risk cases, monitor portfolios, and provide recommendations on what to do in cases of fraud.

Developed Machine Learning Models to Predict Credit Risk

Using machine learning to determine which model is best at predicting credit risk amongst random oversampling, SMOTE, ClusterCentroids, SMOTEENN, Balanced Random Forest, or Easy Ensemble Classifier (AdaBoost).

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

Supervised Machine Learning and Credit Risk

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

An analysis on credit risk

Using my skills in data preparation, statistical reasoning, and machine learning I employed different techniques 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…

Using Scikit-learn and Imbalanced-learn to build and evaluate ML models that predict credit risk

Employ different techniques to train and evaluate models with unbalanced classes. Evaluate the performance of these models and make recommendations on their suitability to predict 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.

A Comparative Study in Customer Churn Prediction through Multilayer Perceptrons and Support Vector Machines

This project aims to predict credit risk using various ensemble machine learning techniques. I have also tried to handle imbalance by using various sampling methods.

‘Buy Now, Pay Later’ (BNPL) is a type of short-term financing used by start-ups like Slice, ZestMoney, Simpl, LazyPay, and Uni, are lowering the bars while approving applications. Building models to detect such customers beforehand.

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