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Festival Data
Music Festival Analysis
This project showcases my journey in web scraping and analyzing data from music festivals. I've scraped data from various festival websites, cleaned it, and performed analysis to identify trends such as popular artists, locations, and festival dates.
Project Overview
This was a solo project focused on scraping data from music festivals to gather lineup details, locations, and dates. The objective was to build a clean dataset and analyze event patterns across regions and time. This project demonstrates the complete data science pipeline from collection to visualization and insights.
Analysis Process
Web Scraping
Built Python scraping tools to extract data from multiple festival sites with automated data collection.
Data Cleaning
Structured and cleaned the data using Pandas and NumPy for consistent analysis-ready datasets.
EDA & Visualization
Performed EDA to visualize trends like artist recurrence and festival density across regions and time.
Data Science Stack
Analysis Results
Peak Festival Year
This visualization highlights the year with the most number of bands performing across all festivals, showcasing the total count of bands for that peak year and identifying industry trends.
Most Active Band
Visualization showing the band with the most performances across all festivals, along with the festivals they performed at and the years, revealing touring patterns.
Skills Mastered
Data Wrangling
Developed practical skills in real-world data wrangling and cleaning for analysis-ready datasets
Automation
Built automated scraping tools and data collection pipelines for efficient processing
Pattern Detection
Learned pattern detection and data lifecycle from raw collection to actionable insights