Building a YouTube Analytics Dashboard Using Python, YouTube Data API, and Power BI
Introduction
As a B.Tech student interested in Data Analytics and Artificial Intelligence, I wanted to build a project that would help me understand the complete data analytics workflow. Instead of working with a ready-made dataset, I decided to collect real-world data from YouTube using the YouTube Data API and build an interactive dashboard in Power BI.
This project helped me learn API integration, data preprocessing, data analysis, and dashboard development.
Project Objective
The goal of this project was to:
Collect YouTube video performance data
Clean and process the data using Python
Analyze key metrics such as views, likes, comments, and engagement
Build an interactive Power BI dashboard to visualize insights
Technologies Used
Python
Pandas
YouTube Data API v3
Google Cloud Console
Power BI
GitHub
Project Workflow
The project followed the workflow below:
YouTube Data API → Python → Pandas → CSV Dataset → Power BI Dashboard → GitHub
Step 1: Setting Up the YouTube Data API
I started by creating a project in Google Cloud Console and enabling the YouTube Data API v3.
After generating an API key, I used Python to connect to the API and fetch video statistics such as:
Views
Likes
Comments
Channel Name
Video Title
Upload Date
This allowed me to work with real YouTube data instead of a static dataset.
Step 2: Data Collection and Processing
Using Python and Pandas, I collected the data and stored it in CSV format.
Some preprocessing tasks included:
Removing unnecessary columns
Handling missing values
Formatting dates
Creating calculated metrics for analysis
The cleaned dataset was then exported for visualization.
Step 3: Exploratory Data Analysis
Before creating the dashboard, I explored the dataset to understand trends and patterns.
I analyzed:
Most viewed videos
Most popular channels
Engagement metrics
Content performance
This helped me identify the key metrics to include in the dashboard.
Step 4: Building the Power BI Dashboard
After importing the cleaned dataset into Power BI, I created an interactive dashboard with:
KPI Cards
Total Views
Total Likes
Total Comments
Total Videos
Visualizations
Top Videos by Views
Top Channels by Views
Top Videos by Engagement Rate
Interactive Features
Channel Filter
Upload Year Filter
Upload Month Filter
These features allow users to explore the data dynamically.
Dashboard Preview
Key Insights
Some interesting insights from the analysis were:
Certain educational channels generated significantly higher view counts.
Videos with high views generally received higher engagement.
Engagement rates varied across different types of content.
Dashboard filters made it easy to compare performance between channels and time periods.
What I Learned
Through this project, I gained practical experience in:
API Integration
Data Collection
Data Cleaning
Pandas for Data Analysis
Power BI Dashboard Development
Git and GitHub
More importantly, I learned how to transform raw data into meaningful insights.
Conclusion
This project gave me hands-on experience with the complete data analytics pipeline, from collecting data through an API to building an interactive dashboard.
It strengthened my understanding of Python, Pandas, Power BI, and data visualization concepts. In the future, I would like to extend this project by adding machine learning features such as view prediction and engagement forecasting.
Thank you for reading!
GitHub Repository:
https://github.com/devisri424/YouTube-Analytics-Dashboard-using-Python-YouTube-Data-API-and-Power-BI
