At the beginning there are sc emails from managers with a request. Then my preparation for the PACE strategy document. Then questions from the laboratory and my answers to them. Then the lines of code that were necessary to get answers to the questions. And at the end, a summary for the managers and a report.

• Scenario
• Email from Rosie Mae Bradshaw, Data Science Manager
• Email from Orion Rainier, Data Scientist

PACE strategy document {Course 2}

PACE strategy document {Course 2}

Project lab _Questions

How can you best prepare to understand and organize the provided information?

Begin by exploring your dataset and consider reviewing the Data Dictionary.

When reviewing the first few rows of the dataframe, what do you observe about the data? What does each row represent?

• Row Representation: Each row seems to represent a TikTok video, with various attributes related to the claims made in the video. These attributes include identifiers, content details, verification status, author status, and engagement metrics.
• Observations:
  • All videos in the sample are classified as "claim".
  • The verified_status mainly displays "not verified", indicating that the content's accuracy has not been officially confirmed.
  • The author_ban_status varies, with the majority of authors being "active". This status could affect the perception and reach of their content.
  • Engagement metrics like view, like, share, download, and comment counts differ significantly among videos, indicating varying levels of user engagement and content popularity.

When reviewing the data.info() output, what do you notice about the different variables? Are there any null values? Are all of the variables numeric? Does anything else stand out?

• Variable Types: The dataset contains both numeric (e.g., video_id, video_duration_sec, video_view_count) and categorical data (e.g., claim_status, verified_status, author_ban_status).
• Null Values: Typically, this stage is where you'd check for null values. In the data snapshot provided, no nulls are visible in these rows, but you'd need to inspect the entire dataset to be sure.
• Standout Features: Non-numeric data like claim_status, verified_status, and author_ban_status will need to be encoded for use in machine learning models. The varied statuses of verification and author ban could potentially affect the interpretation of engagement metrics.

When reviewing the data.describe() output, what do you notice about the distributions of each variable? Are there any questionable values? Does it seem that there are outlier values?

• Distributions: From the table analysis:
  • Video Duration: Most videos are short, typically under a minute, which is common for TikTok content. This length could limit the amount of information conveyed and thus influence claim verification.
  • Engagement Metrics: There's a wide range in engagement metrics (views, likes, shares, downloads, comments), with some videos showing exceptionally high numbers, suggesting viral content or outliers.
• Outliers: Some videos, like the one in row 7, have significantly fewer likes compared to views. This discrepancy might indicate either a data error or specific content reactions that warrant further investigation.
• Questionable Values: The significant differences in engagement metrics (e.g., a video with many shares but relatively few views) should be examined for data integrity or unique content characteristics.