Netflix shows dataset

As Netflix ramps up its original content spending many people have commented about the quality of the shows it is putting out. Ever since Netflix launched its first original series with House of Cards inthe streaming service has put its original content front and center.

Executives cherry pick impressive stats for their originals without giving access to comprehensive viewing dataand their quarterly reports regularly feature charts showing the Instagram followers of young actors launched to fame through Netflix originals.

10 Happy Netflix Shows To Watch Right Now

I will explore the following three questions:. The Dataset I am using for this analysis contains information about Netflix Original Content series premiers from February to May The full dataset can be found on data. How many Netflix Originals Premier each month?

How has that trend changed over time? This trend is even more apparent when looking at a month by month count of new show premiers. It is clear that Netflix is accelerating the pace at which they produce new content! With this acceleration in new original content being produced in a relatively short time period how has the quality of Netflix shows fared? The first look at ratings over time shows that the Average show rating has dropped from around 90 with the first show to around This would certainly seem to show that Originals from Netflix are getting worse but does this tell the whole story?

There may only be a certain amount of new content that they can consistently vet. That means that if they focused more on the front end of their content funnel they could save money by producing more highly rated shows instead of their current mode of approving many shows and cancelling the ones that are lowly rated. It is clear so far that Netflix is successful in creating some but not all of its original content.

So what are characteristics of shows that are highly rated? Based just on IMDB rating there aren't any noticeable trends in content lengths or number of episodes.

netflix shows dataset

Based on show genre there are some obvious trends. Using the data available I will make a simple Logistic Regression model to predict the status of a show. Since the mean rating of renewed vs ended shows seems to be a major difference a very simple model which would be intuitive would be to predict a higher IMDB rating as renewed and a lower rating as ended.

My model will take into account more features than just rating and hopefully will be able to provide some insights into why shows are renewed or ended by Netflix management.

For how small the dataset is that I am working with and how simple the model is these accuracy scores are pretty good!

Now we will look at the coefficients of the logistic regression to see which features are most important for determining if a show will be renewed or ended?You seem to have CSS turned off. Please don't fill out this field.

This is my Master Degree project, I am trying to improve the movie prediction by using machine learning techniques, for the Netflix data set. This project is done under guidance of Dr. Richard Maclin, at University of Minnesota Duluth. Study of Netflix Dataset Web Site.

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Dataset parsing and shaping

The practices of social distancing and quarantining are making us spend more time at home which we would have otherwise spent either commuting to work on weekdays or going to malls on weekends. As someone who enjoys watching movies, I decided to spend my time during the lockdown watching 1—2 movies every evening on Netflix.

As I sip my coffee and scroll through the titles on Netflix, I realized I spend an awful lot of time deciding which movie I would like to watch. I usually search 15—20 titles by genres and read their description before zoning in on a movie.

While I kept searching for movies, still unsure which movie to pick, I wondered if the whole content team of Netflix addresses the same question every day: Which content would I pick for my subscribers?

For a streaming platform such as Netflix, content would be one of the most important strategic levers to increase its paid subscriber base. See Fig1 below. If all the paid subscribers of Netflix formed a country, say Republic of Netflix, it would be the 9th most populous country in the world, just losing the 8th spot to Bangladesh by a million. Furthermore, interestingly Q4 has seen a surge in trial users to 9.

Did most of those trial users become paid users creating a peak for paid users in Q1?

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While the trial users have been decreasing every quarter inthe paid net additional users are increasing from Q2 to Q4. Are users directly creating paid accounts without a trial period!? Without user-level data, it is difficult to answer these questions.

The revenue and marketing spends are mentioned in the historical segments section of the Q4 financial statements. The calculation for content spend is a little bit calculated due to different types of content that Netflix streams. The following table gives you a snap-shot of the financials in terms of paid user additions, Revenues, Content spends and Marketing spend for each of the last 12 quarters. In Q1, the content spending decreased for the first time and the paid new additional users was at a all-time high in last 12 quarters.

This explains why content spend per user was at a all time-low in last 12 quarters. In the next quarter, Q2, the number of paid net user additions fell drastically from 9. Looks like the content spend in the last quarter might have an influence on the net paid user additions in the next quarter, Q2.

I computed the above metrics just to check if there are any major shifts in content spending and we figured that Q2 saw such a shift. I am pretty sure that most of the stock analysts who track Netflix are busy right now with content analysis from its financial statements to prepare for the upcoming earnings announcement on 21st April, As the content choices it made not only delighted its 89 million users as of Q4, but also resulted in an increase of 78 million paid users in the next three years including me!

So what content has Netflix been adding to please its users? M ore I grappled with the above question, the more I wanted to figure an approach to answer that question. As chance would have it, I stumbled upon an excellent article in the Netflix Tech Blog section which elaborates how Netflix relies on predictive data-modeling for assessing its content consumption across languages.

I think it is a great blog for anyone who is passionate about both movies and analytics. Given such an emphasis on data-driven decisions to figure out both the content and its creation life-cycle, I surmise those decisions should surface as patterns in the titles that it adds to its platform.

I decided to put this additional time during the COVID lockdown period to learn a bit of NLP natural-language-processing and check if I can apply any basic NLP techniques to figure out the choices that Netflix made with regard to its content. Since Netflix relies on consumer level data to arrive at those choices, we can maybe uncover a few customer preferences along the way.

To explore this, I looked at two data sources to triangulate its content strategy —. In this section, Netflix communicates about its content choices, reasons and how they fared in that quarter. The above data source is top-down approach in terms of what Netflix communicates about its content strategy. Using the titles on Netflix and their description, the actual content strategy could be deciphered. Data source: I used the dataset from kaggle.

The above dataset does not classify the content into three types of content that Netflix classifies. In this article, we will not classify the content choices by self-produced titles, branded and licensed titles and only licensed titles. I found the content on this super helpful to learn text mining and to plot the below charts.In the following 4 chapters, you will quickly find the 26 most important statistics relating to "Binge watching in the U.

The most important key figures provide you with a compact summary of the topic of "Binge watching in the U. Feel free to contact us anytime using our contact form or visit our FAQ page.

Content strategy of Netflix in the recent years

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Binge watching in the U. Overview Key figures Statistics. Published by Amy WatsonApr 23, Since the advent of on-demand viewing and online streaming in the late s, binge-watching has become a global phenomenon. Furthermore, because some companies, such as popular video-streaming service Netflixbegan releasing episodes of its series in blocks, binge-watching is becoming the norm rather than the exception.

In fact, according to a survey, some 90 percent of Millennials and 88 percent of those in the Gen Z category engaged in binge-watching TV series.

There is little consensus regarding how many hours of watching a TV show actually amounts to binging, but a recent Netflix survey concludes that most Americans define it as watching between two and six episodes in one sitting.

Other behaviors are more extreme, involving entire seasons or even whole series over a few days. Some of the reasons given for binge-watching include liking to see the whole story at once and not liking the suspense of waiting a week to find out what happens.

This text provides general information. Statista assumes no liability for the information given being complete or correct. Due to varying update cycles, statistics can display more up-to-date data than referenced in the text.From the README : The movie rating files contain over million ratings from thousand randomly-chosen, anonymous Netflix customers over 17 thousand movie titles.

netflix shows dataset

The data were collected between October, and December, and reflect the distribution of all ratings received during this period. The ratings are on a scale from 1 to 5 integral stars.

netflix shows dataset

To protect customer privacy, each customer id has been replaced with a randomly-assigned id. The date of each rating and the title and year of release for each movie id are also provided.

We include in the repository a tiny subset of the original dataset for development purposes.

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Source Contents Index datasets Contents Dataset parsing and shaping Types Datasets. Description Netflix prize dataset From the README : The movie rating files contain over million ratings from thousand randomly-chosen, anonymous Netflix customers over 17 thousand movie titles.

Produced by Haddock version 2. Instance details Defined in Numeric. Eq UserId Source. Show UserId Source. Eq MovieId Source. Show MovieId Source. Eq Train Source. Show Train Source. Fields testRating :: RatingDate. Eq Test Source. Show Test Source. Eq Movie Source. Show Movie Source. Eq RatingDate Source. Show RatingDate Source.In a data-driven environment like Netflix, data visualization plays a key role.

It must. In The Visual OrganizationI offer the following definition of data visualization. Dataviz signifies the practice of representing data through visual and often interactive means.

An individual dataviz represents information after it been abstracted in some schematic form. Finally, contemporary data visualization technologies are capable of incorporating what we now call Big Data.

According to its corporate blog, Netflix considers data visualization to be of paramount importance. And, like other Visual Organizations covered in this section, Netflix uses data-visualization tools on a continuous basis, not occasionally. That is, Netflix employees routinely look to existing dataviz tools to tweak algorithms, garner new insights, and solve pressing business issues.

Jeff Magnusson serves as the manager of data platform architecture at the company. Magnusson presented with Charles Smith, a colleague and a software engineer. These canons explain why Netflix is the quintessential Visual Organization.

At the heart of its business lie some of the most sophisticated Big Data tools on the planet, including no shortage of dataviz applications. At a high level, these tools serve the interests of two critical constituencies: customers and technical professionals.

At first glance, they are eerily similar. They both display older white men with blood on their hands—Kevin Spacey and Patrick Stewart, respectively—against primarily black backgrounds.

Figure 3. Given the cost of producing high-quality original content, why would Netflix create the cover for a new series in a vacuum? With subscribers bombarded by nearly unlimited options, why leave such a potentially critical aspect completely to chance?

After all, Netflix possesses the data to make the most informed business decision possible. Still, you can bet that its head honchos carefully reviewed subscriber data when selecting the covers to these series.This is a cached version of the website. Click here to view the live site. Netflix Prize Data Set Netflix. The data consists of about million movie ratings, and the goal is to predict missing entries in the movie-user rating matrix.

There are overcustomers in the dataset, each identified by a unique integer id. The title and release year for each movie is also provided.

There are over 17, movies in the dataset, each identified by a unique integer id. The dataset contains over million ratings. The ratings were collected between October and December and reflect the distribution of all ratings received during this period. Each rating has a customer id, a movie id, the date of the rating, and the value of the rating. As part of the original Netflix Prize a set of ratings was identified whose rating values were not provided in the original dataset.

The object of the Prize was to accurately predict the ratings from this 'qualifying' set. CustomerID: Arbitrarily assigned unique integer in the range [ Rating: Number of 'stars' assigned to a movie by a customer; an integer from 1 to 5. Title: English language title of the movie on the Netflix website.

YearOfRelease: Year a movie was released in the range [ May correspond to the release of corresponding DVD, not necessarily its theaterical release.

netflix shows dataset

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