Identify Trending YouTube Content Categories

Project Overview

This project focuses on analyzing YouTube trending videos data to identify patterns, audience behavior, and content performance. Using SQL, Python, and Power BI, I transformed raw data into meaningful insights and interactive dashboards.

Skills & Tools Used
Data Cleaning & Preprocessing (Python)

Cleaned raw datasets by handling missing values and duplicates

Standardized data formats (dates, categories, views)

Used libraries like Pandas and NumPy for efficient data processing

Prepared structured datasets for analysis

Data Analysis (SQL)

Wrote SQL queries to extract key insights:

Top trending categories

Most viewed videos

Engagement patterns (likes, comments)

Performed filtering, grouping, and aggregations

Optimized queries for better performance
Data Visualization (Power BI)

Built interactive dashboards to present insights clearly

Created:

Pie charts (category distribution)

Bar charts (top channels/videos)

KPI cards (views, likes, engagement rate)

Enabled filters for dynamic user interaction
Key Insights

Identified which categories trend the most

Analyzed factors influencing video popularity

Compared engagement across different channels

Provided actionable insights for content strategy

Key Skills Demonstrated

Data Cleaning & Preprocessing

SQL Querying & Data Extraction

Dashboard Design & Visualization

Analytical Thinking & Problem Solving

Business Insight Generation
Outcome

Successfully converted raw YouTube data into clear, interactive dashboards that help understand trends and improve decision-making.