/DataAnalytics-AB_Testing

Statistical Analysis: Project with A/B testing and Machine Learning methodologies

Primary LanguageJupyter Notebook

Statistical Analysis: A/B testing and Machine Learning

Hi all, this is a Data Analytics Project ! - Project with A/B testing and Machine Learning methodology.

Project Objective

  • The purpose of this project is to learn how to apply A/B testing and Machine Learning methodology in order to understand which version is more popular for users and also uncover the relations between each variable.

Methods Used

  • Inferential Statistics
  • Data Visualization
  • Calculated Probability
  • A/B Testing
  • Machine Learning

Model Used

  • Statistical Analysis:

    • A/B Testing

  • Machine Learning:

    • Logistic Regression

Technologies and Packages Used

  • Python, Jupyter Notebook
  • Numpy, Matplotlib.pyplot
  • Pandas, Statsmodels.api

Project Description

  • Motivation:

    • A/B tests are very commonly performed by data analysts and data scientists in the industry. It is also important to get some practice working with the difficulties of these. For this project, I analyze and understand the results of an A/B test run by an e-commerce website. My goal is to help the company understand if they should implement the new page, keep the old page, or perhaps run the experiment longer to make their decision.

  • Data and Scope:

    • The size of dataset is around 300,000 variables with 5 independent factors used in A/B testing in the beginning. Then, I add more factors to utilize with Logistic Regression methodology to get a deeper understanding of this data. Since this dataset is clean and well-organized, there is no need to implement extra data cleansing. Instead, calculated probability is the first and the most crucial section to get a persuasive and robust model in this project.

  • Methodology Approach:

    • Calculated Probability:

      1. Calculate the proportion of users converted.
      2. Group the dataset with treatment into with/without new_version.
      3. Check if there is a duplicated and then drop it.
      4. Compute the probabilities for each assigned group.

    • A/B Testing:

      1. Calculate required probabilities for A/B testing.
      2. Visualize the results with histogram plot for easy understanding.
      3. Compute the p-value and interpret the results into business words.
      4. Apply A/B testing with statistics package to know the testing results.

    • Modeling Approach:

      1. Utilize Logistic Regression methodology for a categorical based dataset.
      2. Summarize the Logistic Regression model.
      3. Merge with countries data and then summarize the model again.
      4. Test with more factors and interpret the results into business words.

Conclusion:

  • From the results of Calculated Probability, there are more than 50% of users willing to land new_pages which makes the model more convincing and meaningful. Although only around 11.96% of users are willing to convert to the new_pages version, the result is still acceptable in the business world. Furthermore, the probability of an individual received the new page in either "control" or "treatment" groups are around 12% which means not only the size of both groups are similar but the conversion rates are close. Therefore, I can distinguish the design of this experimental model is strong and persuasive.

  • From the results of A/B Testing, p-value is around 9.6% which is higher than Type I error(5%); therefore, I should fail to reject the null (H0). In addition, I can tell the actual p-value of control group is higher than the treatment group during the calculation which means no overfitting. On the other hand, since old verison has higher p-value than the new one, I should keep the old version and do not change to the new version. Meanwhile, since the probabilities of using new_pages and old_pages are the same, there is no bias here..

  • From the results of Modeling Approach, Logisitc Regression is an excellent choice in this case because the response variables are Categorical Variables. Moreover, the result will be stronger and more convincing with new variables added such as 'timestamp'. More specifically, I can classify this factor into 'morning', 'afternoon' and 'evening'. Also I can classify them as 'weekday' and 'weekend' for better performance. On the other hand, it will make regression model more complex and I need to check it carefully to see if variables are dependable with each other. If yes, I need to add higher order term to get the better prediction results. Otherwise, the results should be trustworthy.