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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.
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.
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'
A/B tests for themes case study
Using SQL we analyze the AB Test results to determine which test has the best effectiveness based on customer views and order response
A/B testing Project for Cookie Cats Mobile Game
Replication of a RCT (A/B Test) originally done by Dr. Andrew Friedson.
Bayesian A/B-test Calculator in PyShiny
Frequentist A/B-test RPU Calculator in PyShiny
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.
Projects completed for Data Analyst Course by Yandex.Practicum. Main working tools: Python and Pandas.
This is a repository with various analytic projects.
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.
Analyzing A/B Test Results of E-Commerce Website
AB testing fror made up fashion E-Commerce company ChicBeads [Python, Tableau]
A simple web app that helps identify statistical interactions between A/B tests.
Key product analytics for an online casino. [SQL, EDA, A/B testing]
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.
Some of my solutions to projects on Codecademy.
AB Testing analysis from Udacity, including determining the experiment size and analyzing the results.