# netflix data scientist Poll of the Day

So, I am working on developing an AI that can analyze data from the netflix streaming service. So far, I am pretty confident that the thing will pick out the kinds of things I want to track, and then I can develop my own AI to do the same.

The problem is that while the internet is great for providing access to a vast amount of information, it doesn’t provide much of the data that you would like to collect. One of the first things Netflix has done is provide aggregated, visualized data about Netflix viewing habits. The data shows that people tend to watch Netflix as a snack first, and then binge it when they are feeling tired. The problem is that these kinds of visualizations can be pretty cool, but not always useful.

The thing about visualizations is that the information they provide is pretty easy to understand. One of the problems is that the data isnt always accurate because it doesnt always account for when a user actually watches a movie. And so if you want to see how well your audience is doing, youll have to check the actual data. But the problem with that is that the data isnt always accurate.

The problem is that Netflix data scientists are usually very lazy. They’re not always very good at presenting their work for the public, so the data doesn’t always match the information you’re seeing in the visualization. This can be a very bad thing, because if the visualization is wrong, someone could easily miss a critical detail.

For example, Netflix is giving us some information about how the popularity of movies is changing over time. But to get this information, these data scientists have to go through a lot of work. Theyre using a formula to analyze the data and make calculations. While this is good for generating ideas for how to improve the way the data is presented, the fact that this formula could potentially be flawed is just as important.

The formula for which this data scientist uses is called a “regression”. It is a tool that is part of the math used to create graphs and statistical models. One important example of a regression is when we want to predict the popularity of an event based on other data. For example, we might want to know how many users watched a movie in a certain month. We can use this information to create a prediction model.

A regression model is made up of data that we collect and then use to predict the value of an event. For example, we might have a list of movies rented in a certain month and a list of movies watched in that month. We can use these two lists to make a prediction model.

The most common way to do regression is to use a linear regression model. A linear regression model simply multiplies a number by a coefficient and returns a new number. In the example above, we’ll be multiplying a number by 0.3. A regression model will give us a new number that is the predicted value of the event.

The problem with regression is that it can be very hard to predict values for the event that you’ve just observed. In this example, after we’ve watched a movie, the value for the event is “I guess I should have rented more movies.” That’s a hard prediction to make. There are two different ways to make the prediction we just made. One is to make a number that’s the probability of the event happening.

Another way to make the prediction we just made is to make a new number that is the predicted value of the event. 