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A project to understand the results of an A/B test run by an e-commerce website. The company has developed a new web page in order to try and increase the number of users who "convert," meaning the number of users who decide to pay for the company's product.

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Bhardwaj-Saurabh/Analyze_A-B_Test_Results

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Analyze_A-B_Test_Results

A project to understand the results of an A/B test run by an e-commerce website.

Overview

A company has developed a new e-commerce web page in order to try and increase the number of users who "convert," meaning the number of users who decide to pay for the company's product.

The main goal of this project was to understand the results of an A/B test run by the website and provide statistical and practical interpretation on the test results.

Results

There was sufficient evidence found to suggest that the treatment group page converts better than the control group.

  • Statistical Analysis Scope
  • Bootstrapping sampling distributions
  • P-value and type-I error
  • Logistic regression

Tools

Python, Pandas, Numpy, Matplotlib, StatsModels, Scipy Jupyter Notebook Details

Contact

LinkedIn

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A project to understand the results of an A/B test run by an e-commerce website. The company has developed a new web page in order to try and increase the number of users who "convert," meaning the number of users who decide to pay for the company's product.

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