categorical-variables

vtreat is a data frame processor/conditioner that prepares real-world data for predictive modeling in a statistically sound manner. Distributed under choice of GPL-2 or GPL-3 license.

Use advanced feature engineering strategies and select best features from your data set with a single line of code.

A library for the hyperparameter optimization of deep neural networks

Bayesian Optimization for Categorical and Continuous Inputs

This package provides functions to create descriptive statistics tables for continuous and categorical variables.

Opinionated statistical inference engine with fluent api to make it easier for conducting statistical inference with little or no knowledge of statistical inference principles involved

A simple library to calculate correlation between variables. Currently provides correlation between nominal variables.

Multiple methods to (quickly) encode factor variables, using data.table

Feature Importance of categorical variables by converting them into dummy variables (One-hot-encoding) can skewed or hard to interpret results. Here I present a method to get around this problem using H2O.

A Machine Learning project to predict Customer Churn including all stages of a project life cycle from data procurement to deployment.

Data Munging, Data Wrangling and Data Preparation Simplified

Hypothesis-Testing-Chi2-Test-Athletes-and-Smokers. Assume Null Hypothesis as Ho: Independence of categorical variables (Athlete and Smoking not related). Thus Alternate Hypothesis as Ha: Dependence of categorical variables (Athlete and Smoking is somewhat/significantly related). As (p_value = 0.00038) < (α = 0.05); Reject Null Hypothesis i.e. Dependence among categorical variables Thus Athlete and Smoking is somewhat/significantly related.

Random Graphs, Random Matrices, FK Dependent Categorical, Galton-Watson

This repository contains one of the pre-requisite notebooks for my internship as a Data Analyst at Technocolabs. It includes some of the micro-courses from kaggle.

This is a Kaggle task inspired notebook: exploring correlation + bonus trying ppscore package

Creation of a binary classifier used to predict the success rate of applicants when funded by a specific company.

Implementation of Naive Bayes algorithm for categorical data

This Contains Machine Learning Projects covering Supervised and Unsupervised ML algorithms. Contains solutions of various hackathon solutions (kaggle, AV , ineuron)

This repo consists of the various practices and concepts that we come across in the domain of DS and ML

Machine learning tests

Encode Categorical Features based on Target/Class

Set of functions based on ggplot2::ggplot() for optimising the visualization process of categorical variables.

The project involves the study of performance analysis of the missForest imputation method for imputing continuous and categorical variables simultaneously.

Predict future housing sale price using advanced regression technique (Random Forest)

This python code shows howw regression is handled in case of categorical variables using duumies. It calculates the multiple regression code and shows the regression table. It also performs the residual analysis.

A set of gretl transformers for encoding categorical variables into numeric with different techniques