A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python

A mixed attributes predictive algorithm implemented in Python.

MSC Project - Artifical Categorical Datasets

Rust crate for clustering categorical data

Interactive ML Toolset

Apply ensemble technique of model stacking to predict patient's readmission

* Basis EDA * Handling Null/Missing Values * Handling Outliers * Handling Skewness * Handling Categorical Features * Data Normalization and Scaling * Feature Engineering

Data Analysis with Python project from freeCodeCamp (3 of 5)

Built and optimized a predictive regression model of housing prices with historical CA housing data.

glmdisc Python package: discretization, factor level grouping, interaction discovery for logistic regression

A set of tools for machine learning (for the current day, there are active learning utilities and implementations of some stacking-based techniques).

Project of a coursework - Categorical Data Analysis (M.Stat Semester 2) under the supervision of Prof. Arindam Chatterjee.,ISID

Scikit-Learn compatible transformer that turns categorical variables into dense entity embeddings.

Given a person's data, the task is to predict that in which category the person's weight should fit in. This is a Multiclassification project.

Encode Categorical Features (unmaintained)

A Python framework for deploying recommendation models for form fields.

Exploratory Data Analysis standard templated in markdown and txt format

Category transformation