A/B testing (also known as split testing) is a powerful statistical method for comparing two or more versions of a product or service to determine which performs better. By randomly assigning users to different groups, we can control for confounding variables and isolate the treatment effect.

In marketing, A/B testing is widely used to refine and improve key elements such as:

• Increasing email open and click-through rates
• Optimizing social media ad performance
• Enhancing product recommendations on websites
• And many other use cases

This project showcases A/B testing analysis on two distinct datasets from different domains: a fast food marketing campaign and a mobile game player experiment. Both experiments aim to test whether specific interventions (marketing strategies or game design changes) significantly affect user behavior.

This dataset comes from a simulated fast food marketing experiment that tests the effectiveness of three different promotional plans.

This dataset is derived from the mobile game Cookie Cats, where developers tested a change in game design: the placement of a gate that blocks player progress until a task is completed.

To avoid installing the Python interpreter globally on your computer, you can use a virtual environment. Follow these steps to create and use a virtual environment:

If you don't have virtualenv installed, you can install it using pip:

pip install virtualenv

Navigate to your project directory and create a virtual environment:

virtualenv venv

• On Linux or macOS:

  source venv/bin/activate

• On Windows:

  .\venv\Scripts\activate

With the virtual environment activated, install the packages listed in requirements.txt:

pip install -r requirements.txt

This way, you can use a local environment without installing the Python interpreter globally on your computer.

• To install Jupyter Notebook, run the following command:

  pip install notebook

To use Jupyter Notebook, follow these steps:

• Open Jupyter Notebook by running:

  jupyter notebook

1. Install Streamlit:

  pip install streamlit

2. Run the Streamlit app:

  streamlit run dashboard.py