The dataset originates from the book "Practical Statistics for Data Scientists" by Peter Bruce, Andrew Bruce, and Peter Gedeck.

Context:

A company selling a high-value service wants to determine which of two web presentations is more effective at selling. Due to the high value and infrequent nature of the sales, as well as the lengthy sales cycle, it would take too long to accumulate enough sales data to identify the superior presentation. Therefore, the company uses a proxy variable to measure effectiveness.

A proxy variable stands in for the true variable of interest, which may be unavailable, too costly, or too time-consuming to measure directly. In this case, the proxy variable is the amount of time users spend on a detailed interior page that describes the service.

Content:

The dataset includes a total of 36 sessions across the two web presentations: 21 sessions for page A and 15 sessions for page B. The goal is to determine if users spend more time on page B compared to page A. If users spend more time on page B, it would suggest that page B is more effective at engaging potential customers, and therefore, does a better selling job.

The time is expressed in hundredths of seconds. For example, a value of 0.1 indicates 10 seconds, and a value of 2.53 indicates 253 seconds.

Dataset

This dataset was created by Slamet Hariyadi

Released under Apache 2.0

Contents

Dataset

This dataset was created by Sonali singh

Contents

It contains the following files:

Analysis of ‘Mobile Games A/B Testing - Cookie Cats’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/mursideyarkin/mobile-games-ab-testing-cookie-cats on 14 February 2022.

--- Dataset description provided by original source is as follows ---

Context

This dataset includes A/B test results of Cookie Cats to examine what happens when the first gate in the game was moved from level 30 to level 40. When a player installed the game, he or she was randomly assigned to either gate_30 or gate_40.

Content

The data we have is from 90,189 players that installed the game while the AB-test was running. The variables are:

userid: A unique number that identifies each player. version: Whether the player was put in the control group (gate_30 - a gate at level 30) or the group with the moved gate (gate_40 - a gate at level 40). sum_gamerounds: the number of game rounds played by the player during the first 14 days after install. retention_1: Did the player come back and play 1 day after installing? retention_7: Did the player come back and play 7 days after installing?

When a player installed the game, he or she was randomly assigned to either.

Acknowledgements

This dataset is taken from DataCamp Cookie Cat is a hugely popular mobile puzzle game developed by Tactile Entertainment

Thanks to them for this dataset! 😻

--- Original source retains full ownership of the source dataset ---

Dataset

This dataset was created by Osuolale Emmanuel

Released under CC BY-SA 3.0

Contents

Analysis of ‘Example Dataset for A/B Test’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ilkeryildiz/example-dataset-for-ab-test on 14 February 2022.

--- Dataset description provided by original source is as follows ---

A company recently introduced a new bidding type, “average bidding”, as an alternative to its exisiting bidding type, called “maximum bidding”. One of our clients, ....com, has decided to test this new feature and wants to conduct an A/B test to understand if average bidding brings more conversions than maximum bidding.

The A/B test has run for 1 month and ....com now expects you to analyze and present the results of this A/B test.

--- Original source retains full ownership of the source dataset ---

Dataset

This dataset was created by Zacks Shen

Contents

It contains the following files:

Dataset

This dataset was created by Çağatay Tüylü

Contents

Context

Online store of sporting goods: clothing, shoes, accessories and sports nutrition.

Content

On the main page of the store they show users banners in order to stimulate their sales. Now one of 5 banners is randomly displayed there. Each banner advertises a specific product or the entire company. Our marketers believe that the experience with banners can vary by segment, and their effectiveness may depend on the characteristics of user behavior.

Acknowledgements

The manager of the company had an offer from partners to sell this place for a banner and advertise another service there (payment is assumed according to the CPC model).

Inspiration

Help the manager make a decision.

This dataset is designed for A/B testing, a method commonly used in statistics and data science to compare two versions of a single variable. The goal is to determine which version performs better. This set serves as a practical case study to showcase A/B testing.

All credits goes to: Tatev Karen Aslanyan and Lunar Tech.

More from Tatev and Lunar Tech: https://lunartech.ai/

https://github.com/TatevKaren/CaseStudies/tree/main/AB%20Testing

COMPLETE GUIDE TO A/B TESTING: https://news.lunartech.ai/simple-and-complet-guide-to-a-b-testing-c34154d0ce5a

Vanguard is an investment management company, providing a variety of financial services. The question to address related to the user experience of its online platform, and whether a newly designed user interface will enhance completion rates for client transactions. The dataset uses the following key performance indicators (KPIs): a) user engagement scores b) task completion rates and c) user survey feedback.

Nike, Adidas and Converse Shoes Dataset for Classification

This dataset was obtained from Kaggle: https://www.kaggle.com/datasets/die9origephit/nike-adidas-and-converse-imaged/

Dataset Collection Methodology:

"The dataset was obtained downloading images from Google images. The images with a .webp format were transformed into .jpg images. The obtained images were randomly shuffled and resized so that all the images had a resolution of 240x240 pixels. Then, they were split into train and test datasets and saved."

Versions:

• v1: original_raw-images: the original images without Preprocessing or Augmentation applied, other than Auto-Orient to remove EXIF data. These images are in the original train/test split from Kaggle: 237 images in each train set and 38 images in each test set
• v2: original_trainTestSplit-augmented3x: the original train/test split, augmented with 3x image generation. This version was not trained with Roboflow Train.
• v3: original_trainTestSplit-augmented5x: the original train/test split, augmented with 5x image generation. This version was not trained with Roboflow Train.
• v4: rawImages_70-20-10split: the original images without Preprocessing or Augmentation applied, other than Auto-Orient to remove EXIF data. Dataset splies were modified to a 70% train, 20% valid, 10% test train/valid/test split
  • NOTE: 70%/20%/10% split: 576 images in train set, 166 images in valid set, 83 images in test set
• v5: 70-20-10split-augmented3x: modified to a 70% train, 20% valid, 10% test train/valid/test split, augmented with 3x image generation. This version was trained with Roboflow Train.
• v6: 70-20-10split-augmented5x: modified to a 70% train, 20% valid, 10% test train/valid/test split, augmented with 5x image generation. This version was trained with Roboflow Train.

Dataset

This dataset was created by Ahmed Hany

Contents

Dataset

This dataset was created by Zeynep Erturan

Released under Apache 2.0

Contents

🌟 Enjoying the Dataset? 🌟

If this dataset helped you uncover new insights or make your day a little brighter. Thanks a ton for checking it out! Let’s keep those insights rolling! 🔥📈

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F23961675%2Ff3761bd2d7ee460ad464de8f25634f63%2Fsteve-johnson-z6LlNgsDeug-unsplash.jpg?generation=1740481184467263&alt=media" alt="">

Dataset Description:

This dataset contains website conversion data for Bluetooth speaker sales. The dataset tracks user sessions on different landing page variants, with the primary goal of analyzing conversion rates, user behavior, and other factors influencing sales. It includes detailed user engagement metrics such as time spent, pages visited, device type, sign-in methods, and geographical information.

Use Case:

This dataset can be used for various analytical tasks including:

A/B testing and multivariate analysis to compare landing page designs.
User segmentation by demographics (age, gender, location, etc.).
Conversion rate optimization (CRO) analysis.
Predictive modeling for conversion likelihood based on session characteristics.
Revenue and payment analysis.

Dataset

This dataset was created by Tetiana Klimonova

Contents

It contains the following files:

Context

Famous dataset to estimate the impact of a training program (National Supported Work Demonstration) on the income of beneficiaries in 1978. Very useful to perform AB test example

Dataset

This dataset was created by Zahra Zolghadr

Contents

Dataset

This dataset was created by Mohamed-El haddad

Contents

Dataset

This dataset was created by Alaa Dewan

Contents