地球资源数据云——数据资源详情

2024 年 YouTube 订阅者数据

发布时间：2026-03-17 14:30:25资源ID：2032013088692539393资源类型：免费

该数据集《Youtube Subscribers Data 2024》主要用于监督学习任务，数据形态以表格为主，应用场景偏向文本内容分析。 题目说明："Harnessing Pandas for Data Extraction and Analysis" 任务类型：表格监督学习。 建议流程：先做缺失值/异常值处理与特征编码，再比较逻辑回归、随机森林、XGBoost。 评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。 可用文件：youtube_subscribers_data.csv。 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.

2024 年 YouTube 订阅者数据

摘要概览

该数据集《Youtube Subscribers Data 2024》主要用于监督学习任务，数据形态以表格为主，应用场景偏向文本内容分析。 题目说明："Harnessing Pandas for Data Extraction and Analysis"

任务类型：表格监督学习。

建议流程：先做缺失值/异常值处理与特征编码，再比较逻辑回归、随机森林、XGBoost。

评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。

可用文件：youtube_subscribers_data.csv。

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.