This project explores a dataset of 114,000 Spotify tracks sourced from Kaggle.
Source: https://www.kaggle.com/datasets/priyamchoksi/spotify-dataset-114k-songs
The goal of this project is to perform an Exploratory Data Analysis (EDA) on the dataset in order to uncover trends and patterns in musical characteristics such as energy, danceability, duration, and popularity. By grouping and analyzing key features by artist, genre, and explicit content, we aim to answer questions like:
- Which genres are most energetic or danceable?
- How do explicit vs non-explicit songs differ across genres?
- What artists have the longest or most popular songs?
- What is the average song length per genre?
Python (pandas, matplotlib, seaborn) for analysis and visualization.