In this project, we build and train a model to predict if a customer will defer on a particular loan on an imbalanced dataset. We'll build a layered ANN for this and try to make our model better using Hyperparamater Optimization, before exploring Oversampling to make it more accurate.

Using Python to build and evaluate several machine learning models to predict credit risk.

Sprint 6, Task 1

Improving Machine Learning Performance by: feature engineering, oversampling and hyperparameter tuning

Credit risk is an inherently unbalanced classification problem, as good loans easily outnumber risky loans. Therefore, you’ll need to employ different techniques to train and evaluate models with unbalanced classes. Using the credit card credit dataset from LendingClub, a peer-to-peer lending services company,

osman: OverSampling by Deep Generative Models A pip package which oversamples class imbalance binary data by Deep Generative Models.

🔍 Evaluating Sampling Techniques for Healthcare Insurance Fraud Detection in Imbalanced Dataset.

Mostly in Banking domains or credit card use cases, the data for predicting a transaction as fraudulent is extremely low due to less evidence for fraud cases resulting in an Imbalanced Dataset for ML use cases. This notebook deals with 3 techniques of handling such cases.

Oversample 44.1kHz WAV file to 352.8kHz

Different Techniques to Handle Imbalanced Data Set

Supervised machine learning model to classify loan applicants into high and low risk categories

A Python Package to Create Synthetic Tabular Data

Class imbalance correction algorithm for multiple-instance data

This repository represents several projects completed in IE HST's MS in Business Analytics and Big Data's Financial Analytics course.

The purpose of this work is to predict which customers are about to leave the bank. To do this, the main classification algorithms will be used to predict whether a customer from 1999 will still be a customer in 2000 or not.

Experiments with imbalanced data using undersampling and oversampling techniques.

This is an end-to-end machine learning model in which I implement random-forest and decision tree classifiers to predict heart disease. I utilized cross-validation, and oversampling to deal with an imbalanced dataset.

Build and evaluate several machine learning algorithms to predict credit risk

Supervised Learning..Build/Evaluate Machine algorithms to predict credit risk