https://github.com/rohithay/ab-testing-gym-membership
Evaluate Hypothesis that fitness test intimidates some prospective members of the Gym.
https://github.com/rohithay/ab-testing-gym-membership
ab-testing matplotlib pandas seaborn statistical-significance statistical-tests
Last synced: about 2 months ago
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Evaluate Hypothesis that fitness test intimidates some prospective members of the Gym.
- Host: GitHub
- URL: https://github.com/rohithay/ab-testing-gym-membership
- Owner: rohithay
- Created: 2022-08-08T04:46:30.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2022-08-08T07:48:49.000Z (almost 4 years ago)
- Last Synced: 2025-12-27T21:50:23.185Z (6 months ago)
- Topics: ab-testing, matplotlib, pandas, seaborn, statistical-significance, statistical-tests
- Language: Jupyter Notebook
- Homepage:
- Size: 542 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
## 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:
- [x] How to determine if statistical significance is important or not ?
- [x] How to build Hypothesis ?
- [x] 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](http://www.numpy.org/)
- [pandas](http://pandas.pydata.org)
- [matplotlib](http://matplotlib.org/)
- [seaborn](https://seaborn.pydata.org)
- [scipy.stats](https://docs.scipy.org/doc/scipy/reference/stats.html)
You will also need to have software installed to run and execute a [Jupyter Notebook](http://ipython.org/notebook.html)
If you do not have Python installed yet, it is highly recommended that you install the [Anaconda](http://continuum.io/downloads) 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](https://github.com/YRohitha/AB-Testing-Gym-Membership/tree/main/app/ab_test_gym_membership.ipynb) 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:
```bash
jupyter notebook ab_test_gym_membership.ipynb
```
or
```bash
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](https://www.codecademy.com/learn) 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).