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.

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.

Fast Estimation of Linear Models with IV and High Dimensional Categorical Variables

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.

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

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

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. De…

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

Dealing with categorial data: CATCODE simple fuction to label encoding with Excel

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.

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.