Youtube Dataset Analysis

Use Cases

Possible uses for this dataset could include:

• Sentiment analysis in a variety of forms
• Categorising YouTube videos based on their comments and statistics.
• Training ML algorithms like RNNs to generate their own YouTube comments.
• Analysing what factors affect how popular a YouTube video will be.
• Statistical analysis over time.

Dataset Description

This dataset includes several months (and counting) of data on daily trending YouTube videos.

Data is included for the US, GB, DE, CA, and FR regions (USA, Great Britain, Germany, Canada, and France, respectively), with up to 200 listed trending videos per day.

EDIT: Now includes data from RU, MX, KR, JP and IN regions (Russia, Mexico, South Korea, Japan and India respectively) over the same time period.

Each region’s data is in a separate file. Data includes the video title, channel title, publish time, tags, views, likes and dislikes, description, and comment count.

The data also includes a category_id field, which varies between regions. To retrieve the categories for a specific video, find it in the associated JSON. One such file is included for each of the five regions in the dataset.

Citation

Mitchell J. (November 30, 2018). Trending YouTube Video Statistics, Version 1. Retrieved September 6, 2025 from https://www.kaggle.com/datasets/datasnaek/youtube-new/version/1.

Data Preparation

• Merge input/youtube-new/**videos.csv into a single dataframe (videos_df).
• Merge input/youtube-new/**_category_id.json into a single dataframe (categories_df).

Data Preprocessing & Cleaning

• Extract the country_cd from the filename.
• Convert the category_id column into int.
• Convert the trending_date into datetime
• Drop the unnecessary columns (filename, video_id, kind, etag, snippet_channelId)
• Merge the categories_df into the videos_df on category_id and country_cd.
• Rename columns snippet_title to category_title, snippet_assignable to category_assignable.
• Write the cleaned dataframe to working/videos_cleaned.csv

Notebooks

• Data Ingestion