Spotify Streaming Success Analysis
Data-driven insights into streaming performance patterns and audio features
Data-driven insights into streaming performance patterns and audio features Data-driven insights into streaming performance patterns and audio features
Project Objective & Analytical Scope: This project analyzes a curated dataset of 953 songs spanning nearly a century and 645 unique artists to uncover what drives streaming success on Spotify.
Data Architecture & Methodology: The analytical workflow integrates SQL for extraction and aggregation with Python for processing and visualization.
Feature-Level Performance Drivers: Audio-feature analysis highlights that lower-acousticness tracks tend to generate higher stream counts, indicating listener preference for more produced sound profiles.
Stakeholder Impact & Strategic Value: The insights directly support streaming platforms, music analysts, and data scientists by enabling trend spotting and performance benchmarking.