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
-
Updated
May 25, 2017 - Java
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
Data Munging, Data Wrangling and Data Preparation Simplified
Random Graphs, Random Matrices, FK Dependent Categorical, Galton-Watson
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.
Machine learning tests
This is a Kaggle task inspired notebook: exploring correlation + bonus trying ppscore package
Bayesian Optimization for Categorical and Continuous Inputs
Predict future housing sale price using advanced regression technique (Random Forest)
The project involves the study of performance analysis of the missForest imputation method for imputing continuous and categorical variables simultaneously.
This Repo Contains Machine Learning Projects covering Supervised and Unsupervised ML algorithms. Contains solutions of various hackathon solutions (kaggle, AV , ineuron)
A set of gretl transformers for encoding categorical variables into numeric with different techniques
A simple library to calculate correlation between variables. Currently provides correlation between nominal variables.
Creation of a binary classifier used to predict the success rate of applicants when funded by a specific company.
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. De…
Encode Categorical Features based on Target/Class
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.
A Machine Learning project to predict Customer Churn including all stages of a project life cycle from data procurement to deployment.
Multiple methods to (quickly) encode factor variables, using data.table
Set of functions based on ggplot2::ggplot() for optimising the visualization process of categorical variables.
Add a description, image, and links to the categorical-variables topic page so that developers can more easily learn about it.
To associate your repository with the categorical-variables topic, visit your repo's landing page and select "manage topics."