There are 1 repository under categorical-data topic.
Arrays for working with categorical data (both nominal and ordinal)
Multivariate and Multichannel Discrete Hidden Markov Models for Categorical Sequences
Discover relevant information about categorical data with entity embeddings using Neural Networks (powered by Keras)
Naive Bayes with support for categorical and continuous data
Bayesian entropy estimation in Python - via the Nemenman-Schafee-Bialek algorithm
Outlier detection for categorical data
Solution to Kaggle's Mercari Price Suggestion Competition
IDAO 2022: Machine Learning Bootcamp
Machine Learning/Pattern Recognition Models to analyze and predict if a client will subscribe for a term deposit given his/her marketing campaign related data
Quickly make tables of descriptive statistics (i.e., counts, percentages, confidence intervals) for categorical variables. This package is designed to work in a tidyverse pipeline, and consideration has been given to get results from R to Microsoft Word ® with minimal pain.
A one-dimensional proportional chart web component for visualizing categorical data
Bayesian bi-clustering of categorical data
Bayesian network analysis in R
Surrogate residuals for cumulative link and general regression models in R
Repository for my 2018 summer internship at GDP Labs, Indonesia about Generative Adversarial Network
Predictive Analysis Course's notes for Computer Science B.S. at Ca' Foscari University of Venice
Toolbox for categorical time-series analysis.
Categorial and numerical (ordinal and nonordinal) Data Clustering Algorithm
The source code of POP to detect outliers in high-dimensional categorical data published in CIKM17.
IST470 Categorical data analysis term paper.
Method to study the cyclic and spectral properties of categorical time-series.
Predicting the incidents raised by the customer
Categorical Functional Data Analysis
Power and Sample Size Calculation for the Cochran-Mantel-Haenszel Chi-Squared Test
🔥[IEEE TPAMI 2023] Official repository TPAMI 2023 paper "Exploiting Field Dependencies for Learning on Categorical Data"
Data science & ML
EDA (Exploratory Data Analysis) -1: Loading the Datasets, Data type conversions,Removing duplicate entries, Dropping the column, Renaming the column, Outlier Detection, Missing Values and Imputation (Numerical and Categorical), Scatter plot and Correlation analysis, Transformations, Automatic EDA Methods (Pandas Profiling and Sweetviz).