https://github.com/computingvictor/topyoutubersscraper

Web scraping top 1000 YouTube channels' metrics.
https://github.com/computingvictor/topyoutubersscraper

Last synced: 1 day ago
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Web scraping top 1000 YouTube channels' metrics.

• Host: GitHub
• URL: https://github.com/computingvictor/topyoutubersscraper
• Owner: ComputingVictor
• License: mit
• Created: 2023-09-17T17:34:11.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2023-09-17T19:45:46.000Z (about 1 year ago)
• Last Synced: 2023-09-17T20:38:27.996Z (about 1 year ago)
• Language: Jupyter Notebook
• Size: 1000 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# YouTube Top Streamers Analysis

## Overview

This project is focused on scraping data from the [HypeAuditor website](https://hypeauditor.com/es/top-youtube/) to analyze the top YouTube streamers. It gathers information about streamers' rankings, usernames, categories, subscribers, countries, visit counts, likes, comments, and channel links.

## Table of Contents

- [Getting Started](#getting-started)

- [Prerequisites](#prerequisites)

- [Usage](#usage)

- [Data](#data)

- [Data Cleaning](#data-cleaning)

- [Contributing](#contributing)

- [License](#license)

## Getting Started

To get started with this project, you'll need to clone the repository to your local machine. Follow the instructions below:

```bash

git clone https://github.com/your-username/youtube-top-streamers-analysis.git

cd youtube-top-streamers-analysis

```

## Prerequisites

Make sure you have the following prerequisites installed on your system:

- Python 3

- pip (Python package manager)

You can install the required Python packages using the following command:

```bash

pip install -r requirements.txt

```

## Data

The data collected includes the following columns:

- Rank

- Username

- Categories

- Subscribers

- Country

- Visits

- Likes

- Comments

- Links

## Data Cleaning

Data cleaning is an essential step to ensure accurate analysis. We handle missing values and normalize categories for better analysis.

## Contributing

Contributions to this project are welcome. You can contribute by:

- Reporting issues

- Providing bug fixes

- Adding new features

- Improving documentation

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.