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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, and chi merge binning methods for users to discretize data more conveniently.
A Collection of Python 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).
A Collection of R Functions for Vasicek Distribution
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 Model with few algorithms, Evaluation Model, looking for Feature Importance, gain insight, and create a recommendation for Credit Risk Management
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.
Submitted Solution to Kaggle's Home Credit Competition
My practice notebooks from tasks on Kaggle