scikit-learn compatible tools for building credit risk acceptance models

This project mainly implements the Monotonic Optimal Binning(MOB) algorithm in SAS 9.4. We extend the application of this algorithm which can be applied to numerical and categorical data. In order to avoid the problem of creating too many bins, we optimize the p-value iteratively and provide bins size first binning, monotonicity first binning, a…

A Collection of Python Functions for Vasicek Distribution

A Collection of R Functions for Vasicek Distribution

Credit card credit dataset analyzed using multiple machine learning models to determine which model best fits the data, reduces bias and predicts credit risk. Undersampling and oversampling done using various python libraries (imbalanced-learn and scikit-learn).

In this project, we wanna create Credit Risk Management by using Machine Learning, so we dig into the data. what we do for the next steps are Data Preparation, EDA(Exploratory Data Analysis), Data Visualization, Data Preprocessing (Handling Outliers, Missing Value, Feature Encoding, Standardization, and Normalization), Creating Machine Learning …

This repo has an IBM's Narrative of MLOps. It uses all the services in IBM's Cloud Pak for Data stack to actualise what an MLOps flow looks like.

My practice notebooks from tasks on Kaggle