Beginners guide to concepts and use cases for MI Analysis — Table of Contents · Introduction · Application in Healthcare as an Example ∘ Electronic Health Records ∘ Genetic Variation & Disease Outcomes ∘ Medical Imaging · How MI is calculated? ∘ Context ∘ Formula ∘ General Insight ∘ Calculation ∘ Implementation using Python · How can this be used to determine Variable Importance? · Conclusion · Reference…

Table of Contents · Abstract · Casual Introduction to Data Science & Analytics ∘ Skills of a Data Scientist ∘ Key lessons from the Industry · What are the different analytics levels? · What is Sweetviz? · Analyzing Data with Sweetviz ∘ Step 1: Install Sweetviz and Read Raw Data ∘ Step 2: Create the Report ∘…

Summary of Recommender Systems (Alternate Least Square, LightFM, Matrix Factorization with Neural Networks, and Neural Collaborative Filtering) — 1. Introduction On social media networks, there is a wealth of semi-structured data. This task’s dataset was gathered from Flickr, an online photo-sharing social media network. Flickr allows users to share photos and communicate with one another (friends). The goal is to recommend a list of objects (pictures) to each user accessing…

Introduction to embeddings in natural language processing using Artificial Neural Network and Gensim — 1. Introduction The biggest challenge in NLP is devising a way to represent a word’s meaning. This is a critical component since different individuals communicate the same message differently. E.g., “I liked the food here” and “Quality of food seems good. I loved it!” At the end of the day, both texts…

@Imashk - Definitely. Imagine that your company wants to rollout a marketing campaign to promote a newly launched product. They have selected a sample of n1=100 to run the campaign (Test Group) and n2=200 who won't get any campaign content (Control Group). After a month, you tracked how many customers…

Formula, Definition, Examples, & Python Implementation — 1. Purpose A statistical test called the z-test is run on data that roughly fit the normal distribution, i.e., for data that is not skewed. The z-test can be applied to test differences in proportions between two samples or even one sample for assessing hypotheses. For a population with known variance, it…

Kruskal Wallis Test: Purpose, Scope, Assumptions, Examples, Python Implementation — 1. Purpose Kruskal Wallis is a non-parametric method for evaluating whether samples come from the same distribution. It is used in the comparison of more than two independent or unrelated samples. One-way analysis of variance (ANOVA) is the parametric equivalence of the Kruskal-Wallis test. 1.1 What would be a good Business Use case?

1. Type of Test Chi-square is a non-parametric test, i.e., it does not require normal distribution or variance assumptions about the populations from which the samples are drawn. 2. Purpose The general purpose of the Chi-square test is to compare discrete categorical data (count data). For example, a product may be categorized into two categories, such…

A beginner’s guide to building a binary classification model in R without external packages — The article focuses on developing a logistic regression model from scratch. We will use dummy data to study the performance of a well-known discriminative model, i.e., logistic regression, and reflect on the behavior of learning curves of typical discriminative models as the data size increases. The dataset can be found…