Hey, welcome back.
In this video, we will explore the data sets we have so you can find these three attached in the lecture
resources.
So you have a credit dot csv, file, movies, dot CSV and ratings dot CSV.
Let's open first movies dot csv to see what this is.
So we have multiple columns here.
You can open this with Excel or any other spreadsheet program.
So we have a budget column.
This is obviously the budget of the movie in dollars.
The genre genre is a list of dictionaries.
So because a movie can have multiple genres, it is a list with the ID of the genre and the name, for
example, action.
And then we have another dictionary with the ID of the adventure genre.
So it's a list of genres, basically fantasy science fiction.
So that's for the movie with this home page.
So we also have this home page column as well, the ID of the movie and some keywords.
So the keywords is also a list of dictionaries.
And keywords also have an ID and of course the name.
So culture clash, for example, future.
And so this tag may be repeated in other movies as well.
So basically the future tag will have the same ID in all other movies.
We may need these data when we do.
Content based filtering, for example, when we want to compare the tags of different movies to find
similar movies to recommend to a user who clicked on a particular movie.
So for content based filtering.
We have the language of the movie and then the title of the movie.
So the first one is Avatar.
The overview field will also be very useful when we do content based filtering.
So it gives you the description of the movie, the popularity score.
So how popular this movie is.
Then we have the list of production companies.
Also a list of dictionary.
Production countries as well.
A list of dictionaries.
Release date of the movie, the Revenue.
So how much money it made.
Runtime.
That's the time in minutes.
Spoken languages in the movie inside the movie.
So there may be more than one language.
The status release tagline.
Title again.
It's repeated.
Anyway, that's something you can clean up with pandas.
The average number of votes.
So that's from 0 to 10.
And then the vote count.
So how many votes?
And the average score that was movies dot CSV.
And then we have credit dot CSV here.
We will find some additional data about the movies.
So it's the same database of movies and the two databases share the same ID column.
So that's the ID of the movie Avatar.
And also the title column is the same.
But then we have some additional information here, such as the cast.
So the names of the actors, the crew, which helped to produce the movie.
So these are some additional data and we may need to join to merge these two files into one single dataframe
in Python.
Lastly, we have ratings CFC.
So, so far we looked at data about movies, but here we have data about users.
So we are basically simulating a real world scenario here in real life.
We would have this websites with movies, let's say Netflix.
Working in Netflix as a programmers.
We have databases, we have access to a database.
We know about users.
So, for example, we know that the user with ID one gave a rating of 2.5 to movie with ID 31, and
that's the date of the rating.
So a timestamp basically.
So those are the ratings of user with ID one.
And then we have another user with ID two.
That user gave a rating of four to movie with ID ten, right?
And so we will use this database in conjunction with the other two databases here to do collaborative
filtering.
So to build a recommendation system based on collaborative filtering.
And yeah, these are the three datasets we have.
So getting familiar with the data as we just did, here is the first step with any data science and
machine learning projects.
With that step completed, we're ready to go into Python and load this data there and then we can do
some more exploration.
So so let's do that in the next video.
I'll see you there.