categorical-variables
There are 41 repositories under categorical-variables topic.
AutoViML/featurewiz
Use advanced feature engineering strategies and select best features from your data set with a single line of code. Created by Ram Seshadri. Collaborators welcome.
WinVector/vtreat
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.
FixedEffects/FixedEffectModels.jl
Fast Estimation of Linear Models with IV and High Dimensional Categorical Variables
bbopt/HyperNOMAD
A library for the hyperparameter optimization of deep neural networks
nphdang/Bandit-BO
Bayesian Optimization for Categorical and Continuous Inputs
imbi-heidelberg/DescrTab2
This package provides functions to create descriptive statistics tables for continuous and categorical variables.
chen0040/java-statistical-inference
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
MavericksDS/pycorr
A simple library to calculate correlation between variables. Currently provides correlation between nominal variables.
JSzitas/categoryEncodings
Multiple methods to (quickly) encode factor variables, using data.table
abhmalik/categorical-feature-importances-without-one-hot-encoding-dummies
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.
Cobord/Various-Probability
Random Graphs, Random Matrices, FK Dependent Categorical, Galton-Watson
vigneshSs-07/Complete-AtoZ-MLProjects
This Repo Contains Machine Learning Projects covering Supervised and Unsupervised ML algorithms. Contains solutions of various hackathon solutions (kaggle, AV , ineuron)
Ab2207/Customer-Churn
A Machine Learning project to predict Customer Churn including all stages of a project life cycle from data procurement to deployment.
CleverInsight/sparx
Data Munging, Data Wrangling and Data Preparation Simplified
vaitybharati/P21.-Hypothesis-Testing-Chi2-Test-Athletes-and-Smokers-
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.
Aryanakh7/Neural_Network_Charity_Analysis
Creation of a binary classifier used to predict the success rate of applicants when funded by a specific company.
bgltn/anova_analysis
ANOVA_diamonds_analysis
gshashank84/ResponseEncoding
Encode Categorical Features based on Target/Class
GurnaikLall/Kaggle-Intermediate-Machine-Learning
How to deal with Missing Values, Categorical Variables, Pipelines, Cross-Validation, XGBoost, Data Leakage
jose-jpm-magalhaes/Types_of_Variables_in_Research
Types of Variables in Research: Numeric/Quantitative vs Categorical
msoczi/categorical_naive_bayes
Implementation of Naive Bayes algorithm for categorical data
RezaKhosravi72/ANN-ChurnModelling
This code demonstrates the basic end-to-end workflow of developing, training, and evaluating a deep artificial neural network classifier on a real-world classification problem involving preprocessing of categorical variables.
SherylPhilip/Course-4---Machine-Learning---Intro-and-Intermediate
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.
styles3544/Machine-Learning-Tutorials
This repo consists of the various practices and concepts that we come across in the domain of DS and ML
tyrus-yuen/Workshop-on-Data-Analysis-and-Statistical-Computing-Project-2
CUHK Course code: STAT 3011 | This course is designed to strengthen students' ability in statistical computing as well as in processing and analysing data. Students are required to participate in several term projects with emphasis on techniques of data management and analysis.
zjg540066169/AuxSurvey
Source Code for Paper "Improving survey inference using administrative records without releasing individual-level continuous data"
aayush301/machine-learning-basics
A list of python notebooks for Machine learning basics- regression and classification.
ashishyadav24092000/MultipleLinearRegression_CategoricalVariables
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.
JM53-SPS/JM-BUS336PROJECTS
Repository of my projects for the BUS336 Course
kavilivishnu/Deep_Learning_MNIST_and_Fashion_MNSIT
A Deep Learning Project on "IMAGE DETECTION" using MNIST and FASHION MNIST datasets. We will be using many combinations of activation fucntions, loss and other normalization techniques to show how the accuracy improves if certain parameters are added to the netwrok and many such implementations.
matt-wilder/Bhapkar-test-in-Python
A function to run Bhapkar's test from Bhapkar (1968) 'On the analysis of contingency tables with a quantitative response' Biometrics, 24 (2): 329-38.
nirmal2i43a5/Categorical-Variables-and-Encoding
This repository explores various techniques for handling categorical variables in data preprocessing, focusing on methods such as one-hot encoding, label encoding, and their applications in machine learning models.
Pevicsanch/label_encoding
Dealing with categorial data: CATCODE simple fuction to label encoding with Excel
spacebakery/NBA-Trends-Project
Data Science Foundations I | Exploratory Data Analysis in Python | Summarizing Relationship Between Two Features