YouTube Songs Analysis with Power BI

Description

YouTube Songs Analysis with Power BI is a project focused on analyzing YouTube song data using Power BI. It provides insights into song performance, popularity, and user engagement by utilizing comprehensive visualizations and reports.

Instructions

This project instruction is provided by Mentroness Community where I am taking a one-month data analyst internship. I derived this Power BI Dashboard independently.

Installation

1. Clone the repository to your local machine.
2. Download the Power BI Desktop application if you haven't already.
3. Open the .pbix file in Power BI Desktop to view the reports and dashboards.

Usage

1. Explore the interactive Power BI dashboard to analyze YouTube song data.
2. Refer to the project documentation for detailed instructions on using specific features.
3. Use the provided dataset for further analysis or extension of the project.

Dataset Details

• video_id: Unique identifier for each YouTube video.
• channelTitle: Title of the YouTube channel publishing the song.
• title: Title of the YouTube song video.
• description: Description provided for the YouTube song video.
• tags: Tags associated with the YouTube song video.
• publishedAt: Date and time when the YouTube song video was published.
• viewCount: Number of views received by the YouTube song video.
• likeCount: Number of likes received by the YouTube song video.
• favoriteCount: Number of times the YouTube song video has been marked as a favorite.
• commentCount: Number of comments posted on the YouTube song video.
• duration: Duration of the YouTube song video.
• definition: Video definition or quality (e.g., HD, SD).
• caption: Availability of captions for the YouTube song video.

Project Objectives

1. Data Cleaning and Preparation:
   • Clean and preprocess the dataset, handling missing values or outliers.
   • Convert relevant columns to appropriate data types.
2. Exploratory Data Analysis (EDA):
   • Explore patterns and distributions in view counts, like counts, and comments.
   • Identify trends in the popularity and engagement of YouTube song videos.
3. Content and Channel Analysis:
   • Analyze the distribution of videos across different channels.
   • Identify popular tags and their correlation with view counts.
4. Temporal Trends:
   • Explore how YouTube song video metrics vary over time.
   • Identify peak publishing times and their impact on engagement.
5. User Engagement Insights:
   • Investigate relationships between likes, comments, and views.
   • Identify factors influencing user engagement with YouTube song videos.

Contributing

If you'd like to contribute to this project, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and commit them with descriptive messages.
4. Push your changes to your fork.
5. Submit a pull request detailing your changes and any relevant information.

License

This project is licensed under the MIT License.

Credits

Developed by Yoon Thiri Ko.