This project, part of the Udacity Nanodegree program, focuses on analyzing the results of an A/B test conducted by an e-commerce website. The goal is to help the company decide whether to implement the new webpage, keep the old one, or extend the experiment duration for a more informed decision.
A/B tests are a common practice in data analysis and data science. This project analyzes the results of an A/B test, providing insights into the performance of a new webpage compared to the old one. The ultimate goal is to assist the company in making decisions about whether to implement the new webpage, retain the old one, or continue the experiment for further evaluation.
- Python libraries:
- NumPy
- Pandas
- Matplotlib
- Statsmodels
- Seaborn
- Jupyter Notebooks
Results The analysis provides insights into the performance of the new and old web pages based on statistical testing and visualizations. The results guide the decision-making process for the company, helping them choose between implementing the new webpage, keeping the old one, or extending the experiment.
Acknowledgments This project was completed as part of the Udacity Nanodegree program. Special thanks to Udacity for providing the dataset and guidance throughout the project.