AB Testing Mobile Game

In this project i will walk through an ab testing for an popular mobile game app i will show step-by-step of an ab testing using python

This project is based on a mini project from Datacamp.

Cookie Cats is a hugely popular mobile puzzle game developed by Tactile Entertainment. It's a classic "connect three"-style puzzle game where the player must connect tiles of the same color to clear the board and win the level. It also features singing cats. Check out this short demo:

As players progress through the levels of the game, they will occasionally encounter gates that force them to wait a non-trivial amount of time or make an in-app purchase to progress. In addition to driving in-app purchases, these gates serve the important purpose of giving players an enforced break from playing the game, hopefully resulting in that the player's enjoyment of the game being increased and prolonged.

But where should the gates be placed? Initially the first gate was placed at level 30. In this project, we're going to analyze an AB-test where we moved the first gate in Cookie Cats from level 30 to level 40. In particular, we will look at the impact on player retention.

The data 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 test group (gate_40 - a gate at level 40).
• sum_gamerounds - the number of game rounds played by the player during the first week after installation
• 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 gate_30 or gate_40.

AB Testing Process

1. Understanding business problem & data
2. Detect and resolve problems in the data (Missing Value, Outliers, Unexpected Value)
3. Look summary stats and plots
4. Apply hypothesis testing and check assumptions
   • Check Normality & Homogeneity
   • Apply tests (Shapiro, Levene Test, T-Test, Welch Test, Mann Whitney U Test)
5. Evaluate the results
6. Make inferences
7. Recommend business decision to your customer/director/ceo etc.

At the End of the Notebook i will report the result

Libraries and Packages :

• matplotlib
• seaborn
• pandas
• numpy
• scipy