AB-Testing-Gym-Membership

Evaluate Hypothesis that fitness test intimidates some prospective members of the Gym.

Description

A/B Testing

Randomly assign visitors to the following two groups:

• Group A : Gym visitors who are asked to take a fitness test with a personal trainer.
• Group B : Gym visitors who are asked to proceed directly to fill in the application, skips the fitness test.

Hypothesis

• Null Hypothesis (H0) : There will no difference between the visitors in Group A that purchase membership and the visitors in Group B that purchase membership.
• Alternate Hypothesis (H1) : There will be more visitors in Group B that will purchase membership than visitors in Group A that will purchase membership.
• The significance threshold we will set as the benchmark to either accept or fail to reject the null hypothesis will be: 𝛼 = 0.05

Intersting Learnings:

• How to determine if statistical significance is important or not ?
• How to build Hypothesis ?
• How to choose a hypothesis test for a given problem ?

Install Setup

This project requires Python 2.7 and the following Python libraries installed:

• numpy
• pandas
• matplotlib
• seaborn
• scipy.stats

You will also need to have software installed to run and execute a Jupyter Notebook

If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included. Make sure that you select the Python 2.7 installer and not the Python 3.x installer.

Code

Template code is provided in the notebook ab_test_gym_membership.ipynb Jupyter Notebook file.

Run

In a terminal or command window, navigate to the top-level project directory (that contains this README) and run one of the following commands:

jupyter notebook ab_test_gym_membership.ipynb

or

ipython notebook ab_test_gym_membership.ipynb

This will open the Jupyter Notebook software and project file in your web browser.

Data

There are multiple files in the dataset used in this project will be included as fitness_tests.csv, purchases.csv, visits.csv, applications.csv This dataset is sourced from codeacademy and contains the following attributes:

Description

This data set contains all the records of users for a virtual Gym.

Features

fitness_tests.csv : Details about the person who took a fitness test

• first_name : First name
• last_name : Last name
• email : e-mail
• gender : Gender
• fitness_test_date : The date when the person took a fitness test

purchases.csv : Details about the Customer who purchased the membership, sent in their 1 month's payment

• first_name
• last_name
• email
• gender
• purchase_date : Invoice date and time. The day when a transaction was generated.

visits.csv : Details about users who visited the Gym. Note: Not all visits are generated during the A/B Testing period.

• first_name
• last_name
• email
• gender
• visit_date : The day when the user visited the Gym.

applications.csv : Details about users who filled out an application for the gym.

• first_name
• last_name
• email
• gender
• application_date : The day when the user filled out an application for the gym.

Conclusion

Gym visitors that completed a fitness test (Group A), were more likely to make a purchase than those visitors that did not complete a fitness test (Group B).