This project was done as part of Udacity's Data Analyst Nanodegree - Term 1.

The goal of this project is to support decision making for an e-commerce company by analyzing the results of an A/B test. The company needs to decide whether they should implement the new version of their web page or keep the old page, or perhaps run the experiment longer to make their decision.

• Assessed the data, and made decisions about dealing with duplicates, and mismatched values.
• Performed probability computations, hypothesis testing and logistic regression on the data using Pandas, Numpy, and the statsmodels module in Python.
• Made recommendations backed by statistical inferences for deciding the web page version and documented limitations of the analysis