Data
Kaggle-YouTube-Video-Metadata

Kaggle-YouTube-Video-Metadata

active ARFF CC0: Public Domain Visibility: public Uploaded 23-03-2022 by Elif Ceren Gok
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Introduction The data id collected using YouTube Data Tools from Kaggle YouTube channel. It shows information about all videos from this channel, starting with 2018. Data collection Using YouTube Data Tools one can access the metadata for YouTube channels, videos, comments, upvotes. The data was collected from the Kaggle YouTube video channel. It covers a time period starting from 2018 until today. References YouTube Data Tools, https://tools.digitalmethods.net/ Inspiration Use this amazing dataset to analyze the impact of these videos, by looking to view, like, dislike, favorite, comments. Try to understand from description of the video if some subjects have larger impact. Factor-in the age of each video, with this amazing dataset collecting video metadata starting from 2007.

20 features

positionnumeric316 unique values
0 missing
channel_idstring1 unique values
0 missing
channel_titlestring1 unique values
0 missing
video_idstring316 unique values
0 missing
published_atstring273 unique values
0 missing
video_titlestring313 unique values
0 missing
video_descriptionstring286 unique values
0 missing
video_category_idnumeric1 unique values
0 missing
video_category_labelstring1 unique values
0 missing
durationstring304 unique values
0 missing
duration_secnumeric296 unique values
0 missing
dimensionstring1 unique values
0 missing
definitionstring2 unique values
0 missing
captionnominal2 unique values
0 missing
licensed_contentnumeric0 unique values
316 missing
view_countnumeric309 unique values
0 missing
like_countnumeric138 unique values
0 missing
dislike_countnumeric18 unique values
0 missing
favorite_countnumeric1 unique values
0 missing
comment_countnumeric25 unique values
0 missing

19 properties

316
Number of instances (rows) of the dataset.
20
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
316
Number of missing values in the dataset.
316
Number of instances with at least one value missing.
9
Number of numeric attributes.
1
Number of nominal attributes.
5
Percentage of binary attributes.
100
Percentage of instances having missing values.
Average class difference between consecutive instances.
5
Percentage of missing values.
0.06
Number of attributes divided by the number of instances.
45
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
5
Percentage of nominal attributes.
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.
1
Number of binary attributes.

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