🎵 Music Streaming Behavior: Springfield vs. Shelbyville Analysis

🎯 Project Overview

This project performs a comparative analysis of listening habits between two cities using streaming platform data. The primary focus was to validate behavioral hypotheses through advanced Data Wrangling and descriptive statistical analysis, transforming raw data into actionable insights for content strategy.

🧪 Business Hypothesis

The study tested the following premise: "User activity varies significantly depending on the day of the week and the specific geographical location."

🛠️ Tech Stack & Methodology

• Libraries: Pandas (Dataframe manipulation), Matplotlib (Data Visualization).
• Data Wrangling Pipeline:
  • Schema Standardization: Refactored headers to consistent snake_case format.
  • Data Integrity: Implemented imputation for missing values (NaN -> unknown) in critical dimensions (artist, genre, track).
  • Deduplication: Removal of explicit duplicates and unification of implicit variants (e.g., merging 'hip', 'hop', and 'hip-hop' into a single genre).

📊 Exploratory Data Analysis (EDA)

The analysis was structured around three main pillars:

1. Temporal Activity by City

Custom functions were developed to filter playback volumes by day and location, revealing distinct consumption patterns.

2. Audience Segmentation

Identified the most popular genres in each region to understand the cultural identity of Springfield vs. Shelbyville users.

📈 Key Insights

• Volume Dominance: Springfield exhibits a more active user base with clear activity peaks on Mondays and Fridays.
• Mid-week "Valley" Pattern: Both cities show a significant decrease in engagement during mid-week (Wednesday).
• Hypothesis Validation: The study confirmed that location and temporal factors are key predictors of user engagement.

💡 Data Impact

This analytical pipeline enables streaming platforms to optimize recommendation algorithms and schedule content releases during high-traffic days to maximize reach and user retention.