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Competition - Netflix Top 10 Charts

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📖 Background

The Netflix Top 10 charts represent the most popular movies and TV series, with millions of viewers around the globe. Understanding what makes the biggest hits is crucial to making more hits.

💾 The data

There are three datasets taken from Netflix Top 10.

Each dataset is stored as a table in a PostgreSQL database.

  • all_weeks_global: This contains the weekly top 10 list for movies (films) and TV series at a global level.
  • all_weeks_countries: This contains the weekly top 10 list for movies (films) and TV series by country.
  • most_popular: All-time most popular content by number of hours viewed in the first 28 days from launch.

The data source page describes the methodology for data collection in detail. In particular:

  • Content is categorized as Film (English), TV (English), Film (Non-English), and TV (Non-English).
  • Each season of a TV series is considered separately.
    • Popularity is measured as the total number of hours that Netflix members around the world watched each title from Monday to Sunday of the previous week.
  • Weekly reporting is rounded to the nearest 10 000 viewers.

Database integration

To access the data, use the sample integration named "Competition Netflix Top 10".

Unknown integration
DataFrameavailable as
df
variable
SELECT *
	FROM all_weeks_global
    LIMIT 100
This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.
Unknown integration
DataFrameavailable as
df
variable
SELECT *
	FROM all_weeks_countries
    LIMIT 100
This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.
Unknown integration
DataFrameavailable as
df
variable
SELECT *
	FROM most_popular
    LIMIT 100
This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.

💪 Challenge

Explore the dataset to understand the most common attributes of popular Netflix content. Your published notebook should contain a short report on the popular content, including summary statistics, visualizations, statistical models, and text describing any insights you found.

🧑‍⚖️ Judging criteria

The publications will be graded as follows:

  • [20%] Technical approach.
    • Is the approach technically sound?
    • Is the code high quality?
  • [20%] Visualizations
    • Are the visualizations suitable?
    • Can clear insights be gleaned from the visualizations?
  • [30%] Storytelling
    • Does the data underpin the narrative?
    • Does the narrative read coherently?
    • Is the narrative detailed but concise?
  • [30%] Insights and recommendations
    • How clear are the insights and recommendations?
    • Are the insights relevant to the domain?
    • Are limitations of the analysis recognized?

In the event that multiple submissions have an equally high score, the publication with the most upvotes wins.

📘 Rules

To be eligible to win, you must:

  • Submit your response before the deadline. All responses must be submitted in English.

Entrants must be:

  • 18+ years old.
  • Allowed to take part in a skill-based competition from their country. Entrants can not:
  • Be in a country currently sanctioned by the U.S. government.

✅ Checklist before publishing

  • Rename your workspace to make it descriptive of your work. N.B. you should leave the notebook name as notebook.ipynb.
  • Remove redundant cells like the judging criteria, so the workbook is focused on your work.
  • Check that all the cells run without error.

⌛️ Time is ticking. Good luck!

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