Dataset: Youtube Subscribers Data 2024

About this Dataset

Youtube Subscribers Data 2024

In this dataset, we aim to extract, clean, and analyze subscriber statistics from YouTube channels using Python's pandas library. The primary objective is to create a comprehensive dataset that reflects the latest trends in YouTube subscriber counts, enabling further analysis and insights into the platform's most popular content creators.

Objectives:

Data Extraction: Utilize the pd.read_html function to scrape subscriber data from a reliable online source, specifically focusing on the Wikipedia page listing the most-subscribed YouTube channels.
Data Cleaning: Perform necessary data cleaning operations to ensure the dataset is accurate and usable. This includes handling null values, converting data types, and removing any irrelevant columns.
Data Export: Save the cleaned dataset as a CSV file for easy access and sharing. The dataset will be named in a search-friendly manner to enhance discoverability.
Kaggle Publication: Create a Kaggle dataset to share our findings with the data science community, ensuring that all necessary metadata is provided for usability.
Visualization: Optionally, create visual representations of the data to highlight trends and comparisons among channels.

Methodology:
Data Scraping: We will start by scraping the relevant data from the chosen Wikipedia page using pd.read_html, which allows us to directly convert HTML tables into pandas DataFrames.
Data Cleaning: The dataset will undergo a thorough cleaning process, including:
Removing rows with null values in critical columns (e.g., subscriber counts).
Converting subscriber counts from object types to float for numerical analysis.
Dropping unnecessary columns that do not contribute to our analysis.
Exporting Data: The final cleaned DataFrame will be exported to a CSV file format for easy manipulation and sharing.
Kaggle Dataset Creation: We will upload the CSV file to Kaggle, ensuring it includes a detailed description and tags for better visibility.
Visualization (Optional): Depending on time and resources, we may create visualizations using libraries like Matplotlib or Seaborn to illustrate key insights from the dataset.
Expected Outcomes:
By the end of this project, we will have a well-organized dataset that provides valuable insights into YouTube channel popularity based on subscriber counts. This dataset can serve as a foundation for further analysis or as a resource for researchers interested in social media trends. Feel free to modify any part of this description to better align with your project's specifics or personal style!

Tables

Youtube Subscribers Data

@kaggle.rashminslnk_youtube_subscribers_data_2024.youtube_subscribers_data

6.19 KB
50 rows
6 columns


CREATE TABLE youtube_subscribers_data (
  "name" VARCHAR,
  "brand_channel" VARCHAR,
  "subscribers_millions" DOUBLE,
  "primary_language" VARCHAR,
  "category" VARCHAR,
  "country" VARCHAR
);