{"id":28391611,"url":"https://github.com/rohithay/ab-testing-gym-membership","last_synced_at":"2026-04-29T13:37:07.369Z","repository":{"id":54479157,"uuid":"522407844","full_name":"rohithay/AB-Testing-Gym-Membership","owner":"rohithay","description":"Evaluate Hypothesis that fitness test intimidates some prospective members of the Gym.","archived":false,"fork":false,"pushed_at":"2022-08-08T07:48:49.000Z","size":555,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-12-27T21:50:23.185Z","etag":null,"topics":["ab-testing","matplotlib","pandas","seaborn","statistical-significance","statistical-tests"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"## AB-Testing-Gym-Membership\n\n#### Evaluate Hypothesis that fitness test intimidates some prospective members of the Gym.\n\n### Description\n\n#### A/B Testing\n\nRandomly assign visitors to the following two groups:\n* **Group A** : Gym visitors who are asked to take a fitness test with a personal trainer.\n* **Group B** : Gym visitors who are asked to proceed directly to fill in the application, skips the fitness test.\n\n#### Hypothesis\n\n* **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.\n* **Alternate Hypothesis (H1)** : There will be **more visitors in Group B** that will purchase membership **than visitors in Group A** that will purchase membership.\n* The significance threshold we will set as the benchmark to either accept or fail to reject the null hypothesis will be: 𝛼 = 0.05\n\n#### Intersting Learnings:  \n\n- [x] How to determine if statistical significance is important or not ?\n- [x] How to build Hypothesis ?\n- [x] How to choose a hypothesis test for a given problem ?\n\n### Install Setup\n\nThis project requires **Python 2.7** and the following Python libraries installed:\n\n- [numpy](http://www.numpy.org/)\n- [pandas](http://pandas.pydata.org)\n- [matplotlib](http://matplotlib.org/)\n- [seaborn](https://seaborn.pydata.org)\n- [scipy.stats](https://docs.scipy.org/doc/scipy/reference/stats.html)\n\nYou will also need to have software installed to run and execute a [Jupyter Notebook](http://ipython.org/notebook.html)\n\nIf 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.\n\n\n### Code\n\nTemplate code is provided in the notebook `ab_test_gym_membership.ipynb` \n[Jupyter Notebook](https://github.com/YRohitha/AB-Testing-Gym-Membership/tree/main/app/ab_test_gym_membership.ipynb) file.\n\n\n### Run\n\nIn a terminal or command window, navigate to the top-level project directory (that contains this README) and run one of the following commands:\n\n```bash\njupyter notebook ab_test_gym_membership.ipynb\n```\nor\n```bash\nipython notebook ab_test_gym_membership.ipynb\n```\nThis will open the Jupyter Notebook software and project file in your web browser.\n\n\n### Data\n\nThere are multiple files in the dataset used in this project will be included as `fitness_tests.csv`, `purchases.csv`, `visits.csv`, `applications.csv` \nThis dataset is sourced from [codeacademy](https://www.codecademy.com/learn) and contains the following attributes:\n\n**Description**\n\nThis data set contains all the records of users for a virtual Gym.\n\n**Features**\n\n`fitness_tests.csv` : Details about the person who took a fitness test\n- `first_name` : First name  \n- `last_name` : Last name \n- `email` : e-mail \n- `gender` : Gender \n- `fitness_test_date` : The date when the person took a fitness test\n\n`purchases.csv` : Details about the Customer who purchased the membership, sent in their 1 month's payment\n- `first_name` \n- `last_name` \n- `email` \n- `gender` \n- `purchase_date` : Invoice date and time. The day when a transaction was generated.\n\n`visits.csv` : Details about users who visited the Gym. Note: Not all visits are generated during the A/B Testing period.\n- `first_name` \n- `last_name` \n- `email` \n- `gender` \n- `visit_date` : The day when the user visited the Gym. \n\n`applications.csv` : Details about users who filled out an application for the gym.\n- `first_name` \n- `last_name`\n- `email`\n- `gender` \n- `application_date` : The day when the user filled out an application for the gym.\n\n### Conclusion\nGym 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).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frohithay%2Fab-testing-gym-membership","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frohithay%2Fab-testing-gym-membership","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frohithay%2Fab-testing-gym-membership/lists"}