Udacity nanodegree projects: DLND, DRLND, DAND

A/B Testing Result Analysis on Total Conversion

For this project, I will be working to understand the results of an A/B test run by an e-commerce website. The goal is to work through this notebook to help the company understand if they should implement the new page, keep the old page, or perhaps run the experiment longer to make their decision.

This is a Python reproduction of the original case study performed in R language- 'A/B Testing with Machine Learning - A Step-by-Step Tutorial written by Matt Dancho of Business Science'

Using SQL we analyze the AB Test results to determine which test has the best effectiveness based on customer views and order response

Udacity Project 2 - AB Testing Analysis. Utilize practical statistics, regression, and other data analysis tools to help the company determine if they should implement a new webpage.

A/B tests for themes case study

A/B testing Project for Cookie Cats Mobile Game

Frequentist A/B-test RPU Calculator in PyShiny

AB testing fror made up fashion E-Commerce company ChicBeads [Python, Tableau]

Analyzing A/B Test Results of E-Commerce Website

A simple web app that helps identify statistical interactions between A/B tests.

This is a repository with various analytic projects.

Key product analytics for an online casino. [SQL, EDA, A/B testing]

This project evaluates revenue outcomes from three promotional campaigns to identify the most effective approach for launching a new product.

This repository showcases an infrastructure designed for analyzing A/B tests in mobile games. It leverages BigQuery to process Firebase and GA4-based event data and uses Looker Studio for dynamic visualization. The project simplifies A/B test comparisons, enabling stakeholders to view results directly through interactive dashboards.

An A/B test run by an e-commerce website. my goal is to work through this notebook to help the company understand if they should implement the new page, keep the old page, or perhaps run the experiment longer to make their decision.

The data to be used is a part of the 2018 BRFSS Survey Data prepared by CDC. Data cleaning and EDA was performed before modeling. 6 algorithms was applied to build the classification models. Performance were evaluated across metrics of accuracy, precision, recall, F1, and AUC-ROC scores.

This is one of the projects that I worked on during my participation in the Generasi GIGIH 2.0 program by Yayasan Anak Bangsa Bisa and GoTo Group.

Bayesian A/B-test Calculator in PyShiny