Data
Top-10000-Movies-Based-On-Ratings

Top-10000-Movies-Based-On-Ratings

active ARFF CC0: Public Domain Visibility: public Uploaded 24-03-2022 by Elif Ceren Gok
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Context People love movies because: It takes you on a journey. Its an escape from reality. Being a vivid movie watcher I always get amazed how sites like Netflix and Hotstar always exactly suggest the next movie I planned to watch on the back of mind. I researched a lot and decide to come up with something similar to that, so I decided to start with extracting a huge dataset of movies people love to watch and apply analysis on it. Content The dataset contains the following information: Popularity: How popular the movie is. Vote Count: Number of people voted. Title: Name of the movie. Vote Average: Average number of people voted to watch this movie. Overview: Brief overview of what movie is (storyline). Release Date: Date when the movie was released. Inspiration I would love to get the following answer: Relationship between popularity and average vote count? Which machine algorithm would be effective to find relationship between movies?

6 features

Popularitynumeric7004 unique values
0 missing
Vote_Countnumeric2768 unique values
0 missing
Titilestring9672 unique values
0 missing
Vote_Averagenumeric70 unique values
0 missing
Overviewstring9969 unique values
25 missing
Release_Datestring6139 unique values
19 missing

19 properties

10000
Number of instances (rows) of the dataset.
6
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
44
Number of missing values in the dataset.
44
Number of instances with at least one value missing.
3
Number of numeric attributes.
0
Number of nominal attributes.
0
Percentage of binary attributes.
0.44
Percentage of instances having missing values.
0.07
Percentage of missing values.
Average class difference between consecutive instances.
50
Percentage of numeric attributes.
0
Number of attributes divided by the number of instances.
0
Percentage of nominal attributes.
Percentage of instances belonging to the most frequent class.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.

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