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Analyzes the results of A/B tests to determine if there is a statistically significant difference between control and treatment groups. It provides a structured approach for performing A/B tests, interpreting results, and making data-informed decisions. A valuable resource for marketers and product managers aiming to optimize user experience.

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A/B Testing Project

Overview

This project analyzes A/B test results to determine if there is a statistically significant difference between two groups (control and treatment).

Project Structure

         ab-testing /
                    │ 
                    ├── data/ # Data files 
                            │ 
                            └── ab_test_results.csv # Example A/B test results 
                    ├── src/ 
                           │ 
                           ├── ab_test.py # A/B testing analysis script 
                           │ 
                           └── utils.py # Utility functions 
                    ├── tests/ # Test scripts 
                             │ 
                             └── test_ab_test.py # Unit tests for A/B testing 
                    ├── requirements.txt # Dependencies 
                    └── README.md # Project documentation

Installation

  1. Clone the repository:

    git clone https://github.com/karimosman89/ab-testing.git
    cd ab-testing
    
  2. Install the required packages:

     pip install -r requirements.txt
    

Usage

  1. Prepare your A/B test results in the /data directory and name it ab_test_results.csv.

  2. Analyze the A/B test results:

         python src/ab_test.py
    

Testing

Run the unit tests to ensure the utility functions work as expected:

 python -m unittest discover -s tests

License

This project is licensed under the MIT License.

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Analyzes the results of A/B tests to determine if there is a statistically significant difference between control and treatment groups. It provides a structured approach for performing A/B tests, interpreting results, and making data-informed decisions. A valuable resource for marketers and product managers aiming to optimize user experience.

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