/AB-test-analysis

Examining two marketing strategies implemented in an advertising campaign by utilizing Python and its libraries, particularly Pandas and Plotly.

Primary LanguageJupyter Notebook

AB Test Analysis

This repository contains the code for performing an A/B test analysis. The code is written in Python and is available in the Jupyter Notebook format.

Overview

A/B testing is a common method used in data analysis and experimentation to compare the performance of two different variants, typically referred to as the control group (A) and the treatment group (B). This analysis helps determine if a proposed change or intervention has a statistically significant impact on a particular metric or outcome.

The AB Test Analysis.ipynb notebook provides a step-by-step guide for conducting an A/B test analysis. It covers the following key aspects:

  1. Data Preparation: Loading and examining the dataset, performing any necessary data cleaning or preprocessing steps.
  2. A/B Test Design: Defining the control and treatment groups, selecting an appropriate statistical test, and setting up the hypothesis.
  3. Statistical Analysis: Performing the A/B test using the chosen statistical test, calculating the p-value and effect size, and interpreting the results.
  4. Result Visualization: Visualizing the results using appropriate plots and charts.
  5. Conclusion: Summarizing the findings from the A/B test analysis and drawing conclusions.

How to Use

To run the code in this repository, follow these steps:

  1. Clone or download the repository to your local machine.
  2. Ensure you have Jupyter Notebook installed, along with the necessary dependencies such as Python and relevant libraries (e.g., pandas, numpy, matplotlib, scipy).
  3. Open the AB Test Analysis.ipynb notebook using Jupyter Notebook.
  4. Execute the code cells in the notebook sequentially to perform the A/B test analysis.

Note: It is recommended to have a basic understanding of A/B testing concepts and statistical analysis techniques before using this code.

Data

The CSV files in this repository contains the dataset used for the A/B test analysis. The dataset should be in a suitable format (e.g., CSV, Excel) and should include the necessary columns for the analysis. You may need to update the code in the notebook to load the dataset from the appropriate file location or modify it according to your specific requirements.

License

The code in this repository is available under the MIT License. You are free to use, modify, and distribute the code for your own purposes.

Sample Report

report