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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.
Fast Estimation of Linear Models with IV and High Dimensional Categorical Variables
A library for the hyperparameter optimization of deep neural networks
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.
Random Graphs, Random Matrices, FK Dependent Categorical, Galton-Watson
This Repo Contains Machine Learning Projects covering Supervised and Unsupervised ML algorithms. Contains solutions of various hackathon solutions (kaggle, AV , ineuron)
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
Predict future housing sale price using advanced regression technique (Random Forest)
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.
Creation of a binary classifier used to predict the success rate of applicants when funded by a specific company.
Encode Categorical Features based on Target/Class
How to deal with Missing Values, Categorical Variables, Pipelines, Cross-Validation, XGBoost, Data Leakage
Types of Variables in Research: Numeric/Quantitative vs Categorical
This is a Kaggle task inspired notebook: exploring correlation + bonus trying ppscore package
Implementation of Naive Bayes algorithm for categorical data
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 repo consists of the various practices and concepts that we come across in the domain of DS and ML
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.
A list of python notebooks for Machine learning basics- regression and classification.
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
The project involves the study of performance analysis of the missForest imputation method for imputing continuous and categorical variables simultaneously.
Set of functions based on ggplot2::ggplot() for optimising the visualization process of categorical variables.
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.
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.