Spotify Analysis with Python

Uncover trends in music and streaming behavior with data. Spotify has become the leading global music streaming platform, hosting millions of tracks across every imaginable genre. In this project, you’ll analyze a Spotify dataset containing information such as track names, artists, genres, release years, popularity scores, and audio features (e.g., danceability, energy, tempo). Using Pandas for data manipulation and Seaborn for visualization, you’ll explore the patterns that shape modern music trends. You’ll start by cleaning and preparing the dataset—checking for missing values, standardizing categorical variables like genres, and formatting numerical columns such as duration or tempo. With Pandas, you’ll group tracks by year, artist, or genre to calculate averages and identify trends over time. Next, you’ll apply Seaborn visualizations to reveal insights. Line plots will show how music characteristics (like tempo or energy) have evolved, bar plots will compare the popularity of genres, and scatter plots will highlight relationships between popularity and audio features. You’ll investigate questions like: Which artists or genres dominate Spotify’s popularity charts? How have musical attributes like tempo, energy, or danceability changed over decades? What features seem to correlate most with popularity? By the end of this project, you will be able to: Clean and prepare a music dataset using Pandas. Summarize and compare songs by genre, year, and artist. Create Seaborn visualizations to highlight streaming and audio trends. Interpret results to understand how data reflects cultural shifts in music. This project is both technical and cultural, showing how data analysis can uncover the evolution of music preferences worldwide. By exploring Spotify data, you’ll gain practical analytical skills while connecting with the sound of global audiences.