E-news Express Project - Problem StatementSubmission type:File UploadDue Date:Jun 12, 2:29 AMTotal Score:60Available from:May 27, 10:30 AM Description Background: An online news portal aims to expand...

Description

Background:

An online news portal aims to expand its business by acquiring new subscribers. Every visitor to the website takes certain actions based on their interest. The company plans to analyze these interests and wants to determine whether a new feature will be effective or not. Companies often analyze users' responses to two variants of a product to decide which of the two variants is more effective. This experimental technique is known as a/b testing that is used to determine whether a new feature attracts users based on a chosen metric.

Suppose you are hired as a Data Scientist in E-news Express. The design team of the company has created a new landing page. You have been assigned the task to decide whether the new landing page is more effective to gather new subscribers. Suppose you randomly selected 100 users and divided them equally into two groups. The old landing page is served to the first group (control group) and the new landing page is served to the second group (treatment group). Various data about the customers in both groups are collected in 'abtest.csv'. Perform the statistical analysis to answer the following questions using the collected data.

Objective:

Statistical analysis of business data. Explore the dataset and extract insights from the data. The idea is for you to get comfortable with doing statistical analysis in Python.

You are expected to perform the statistical analysis to answer the following questions:

1. Explore the dataset and extract insights using Exploratory Data Analysis.


2. Dotheusersspendmoretimeonthenewlandingpagethanthe old landingpage?


3. Is the conversion rate (the proportion of users who visit the landing page and get converted) for the new page greater than the conversion rate for the old page?


4. Does the converted status depend on the preferred language?[Hint: Create a contingency table using the pandas.crosstab() function]


5. Isthemeantimespentonthenewpage sameforthedifferentlanguageusers?

*Consider a significance level of 0.05 for all tests.

Data Dictionary:

1. user_id-ThisrepresentstheuserIDofthe person visitingthewebsite.


2. group-Thisrepresentswhether the user belongs to the first group (control) or the second group (treatment).


3. landing_page-Thisrepresentswhetherthelandingpageisneworold.


4. time_spent_on_the_page-Thisrepresentsthetime (inminutes)spentbythe user on the landing page.


5. converted-Thisrepresentswhetherthe user gets converted to a subscriber of the news portal ornot.


6. language_preferred-Thisrepresentsthelanguagechosenbythe user toviewthelandingpage.

Best Practices for Notebook :

• The notebook should be well-documented, with inline comments explaining the functionality of code and markdown cells containing comments on the observations and insights.


• The notebook should be run from start to finish in a sequential manner before submission.


• It is preferable to remove all warnings and errors before submission.


• The notebook should be submitted as an HTML file (.html) and NOT as a notebook file (.ipynb)

Best Practices for Presentation :

• The presentation should be made keeping in mind that the audience will be a business leader like CMO, COO, CFO, or CEO.


• The key points in the presentation should be the following
  
  
  • business overview of the problem and solution approach
  
  
  • key findings and insights which are important to make business decisions
  
  
  • key conclusions made on the basis of the statistical analysis
  
  
  
  


• Focus on explaining the takeaways in an easy-to-understand manner.


• Copying and pasting from the notebook is not a good idea, and it is better to avoid showing codes unless they are the focal point of your presentation.


• The presentation should be submitted as a PDFfile (.pdf) and NOT as a .pptx file.

Submission Guidelines:

Please note the following:

1. There are two parts to the submission:
   
   
   1. A well commented Jupyter notebook [format - .html]
   
   
   2. A presentation as you would present to the top management/business leaders [format - .pdf ](you have to export/save the .pptx file as .pdf)
   
   
   
   


2. Any assignment found copied/ plagiarized with other groups will not be graded and awarded zero marks


3. Please ensure timely submission as a post-deadline assignment will not be accepted


4. Submission will not be evaluated if,
   
   
   1. it is submitted post-deadline, or,
   
   
   2. more than 2 files are submitted
   
   
   
   

Happy Learning!!