Table of Content
Team Introduction
Project description
Objective
Business Problem
Process
Insight and Findings
Limitation
Recommendation and Conclusion
Dashboard
TEAM INTRODUCTION
This week after some back and forth regarding the dataset for, our Bootcamp Manage, Damilare Oyetade – AKA Drey – helped us settle for Netflix, yes Netflix this time. At least we can now say ‘Netflix and Chill’ with our full chest LOL, since we’ve prepared special McDonald’s for week 1’s project.
So, we (Team 5) approached the new week with a lot of optimism, mixed with a lot of confidence. First, it was the review of week 1’s project where we scored 80 points out of 100. Corrections were made and our team was commended for the effort. But this week, we are not settling for less; it’s either 100 points or 100 points.
However, what seemed a delicious task to munch like the previous week’s, quickly became something too hot to handle. Before we arrived at using Netflix, we were given a YouTube dataset that required more than we can give. Again, we’re still growing.
Thanks to the guys at Side Hustle we got another Dataset, Netflix, yay!
PROJECT DESCRIPTION
As a group, we were given a hypothetical situation to work with Netflix and derive insights to make informed business decisions for their organization. We were tasked with analyzing various datasets, including customer viewing patterns, ratings, and preferences as well as data on Netflix’s contentment library.
OBJECTIVE
We are to make informed strategies to meet the following objectives
Identify the most popular shows and movies based on viewership data
Analyse viewer behavior and preferences to understand which genres and types of content are most popular
Investigate the relationship between user ratings and viewership data to understand how ratings impact viewership
Evaluate the performance of Netflix’s original content versus licensed content
Identify potential areas for growth and expansion for the company
METHODOLOGY
Based on the objectives, we set out to analyze the dataset given by our Drey. Upon initial review of the data shows we have a problem; the data wasn’t going to help us achieve our set goals. After seeking clarification, we had to continue with the dataset. The data was loaded into Power Query for cleaning and later modelling.
BUSINESS PROBLEM
Due to the limitation of the data presented, we are tasked with the option of creating insights based on the data before us and this means jettisoning our given objectives. We set out to analyze the data for the top 10 content origin, top Directors, ratings with the highest content, and content over time on Netflix.
THE PROCESS
The Netflix data was downloaded from https://www.kaggle.com/datasets/shivamb/netflix-shows, The dataset was cleaned using Power Query and model to establish relationships were also created.
Based on the dataset, we concluded splitting columns to fix issues with columns with multiple values. This is so that we can arrive at valid data point. We also created a Calendar Table for clear understanding of historical data.
We grouped the ratings to further help simple understanding (Adult, Children, Not rated and General).
After cleaning and modelling, it was time for visualization. We used DAX functions to create new measure for things like total contents, total motion pictures, total rating, oldest movie, and so on.
INSIGHTS AND FINDINGS
Our insights from the Netflix dataset
Cards were used to present the summary. By our findings, we were able to consider that there are 14 types of rating, the release year of the oldest movie on Netflix, 4,902 directors worked on 8,807 content, total content is 8,807, countries where the contents originated from are 87, there were 2 types of contents, and a total of 38 total genres.
We also used the multi-row card to analyze highest duration of movies and longest number of seasons for TV shows. Further analyzing the TV show and movie content.
We used a tree map to distinguish between TV shows and movies
This stacked bar chart was used to analyze how content were added to Netflix over years, 2019 has the highest contents.
For the country with the highest content, the United States came first with 2,818 as shown in the stacked bar chart.
The analysis suggests the director of 2008’s Chhota Bheem, Rajiv Chilaka, is the director with the highest content to his name. While America’s film and music video director, Marcus Raboy came second.
All ratings were grouped into Adult, Parental Guidance, Children, General, and Not Rated. Based on this, we discover Netflix has more Adult content than Children.
With the aid of DAX, the table below shows the Year-on-Year variance in percentage.
This bar chart shows us genres with the most content. International movies are the highest of 2800 thousand leaving international TV shows as the fourth
With the aid of Power BI’s Map, we can tell most of the content was from Europe and Asia
LIMITATION
Although we were able to analyse using this dataset, the column for the content views was missing. This affected our ability to create a report based on the objectives presented.
We were saddled with a Netflix dataset that we can review based on stats of content and not popularity. This was a test for reality, but we made do with what was available to answer the business question.
RECCOMENDATION AND CONCLUSION
2019 witnessed high traffic of content on the streaming platform. This was quickly after as the world was struggling with the Pandemic COVID-19 – between 2020 and 2021. As a result, a lot of production where halted. With the rate new online streaming platforms are springing up, it is expedient that Netflix increase produce or acquisition of more contents.
That data showed a huge disparity between contents for adults and children. This is an area of improvement for profit taking. This is important because in the ever-growing children hunger for more content than the adult – is saddled with the responsibility of providing.
It is important for Netflix to make its platform family-friendly and not just adult friendly.
DASHBOARD
