Context
IMDB is a popular website for rating films and series I always go there if I want to watch something new and many many users trust it's rankings
The data is about most popular 7k Films and series on IMDB with rates, The Data is Ideal for Exploratory Data Analysis and You can also use Regression to predict Rate
Content
These are our columns:
Name: Name of the film/series
Data: Creation date
Rate: IMDB's Rate
Votes: Number of voters
Genre: Genres , Actions , Drama, Romance, etc...
Duration: Duration of the episode , film
Type: whether it's film or series
Certificate:
TV-Y: Designed to be appropriate for all children
TV-Y7: Suitable for ages 7 and up
G: Suitable for General Audiences
TV-G: Suitable for General Audiences
PG: Parental Guidance suggested
TV-PG: Parental Guidance suggested
PG-13: Parents strongly cautioned. May be Inappropriate for ages 12 and under.
TV-14: Parents strongly cautioned. May not be suitable for ages 14 and under.
R: Restricted. May be inappropriate for ages 17 and under.
TV-MA: For Mature Audiences. May not be suitable for ages 17 and under.
NC-17: Inappropriate for ages 17 and under
Episodes: Number of Episodes only for series
Nudity, violence.. :How much does it have of these
Acknowledgements
I got these data using Web Scraping you can see the code to get the data here
Inspiration
- The Data is ideal is ideal for EDA you can See how various features affects the rate like
- How date affects the rate ?
- Is there any genre appears more on specific date ?
- How genre affects rate ?
- Does series get higher rates ?
- Does Nudity, Fighting , Violence affects rate ?
- You can Also predict the Rate with Regression