Spotify Data Analytics

Role:Data Analyst

Year:2025

Overview

Fetches user tracks and listen histories. Applies data cleaning operations, filters top genres, and exports structured aggregates into local databases for analytical processing.

Problem Statement

Users have minimal insight into their streaming patterns beyond Spotify's annual wrapped events, missing long-term genre shifts and streaming frequency trends.

Proposed Solution

Built a Python analytical parser that pulls stream logs via Spotify API, processes the raw data in Pandas, and stores clean aggregates.

System Architecture

OAuth2 authentication queries Spotify APIs. Pandas handles dataframe cleanups and aggregation, writing structural statistics to database tables.

API Client Request

FastAPI routing

SQLAlchemy ORM

MySQL Relational DB

Key Features

Automated API stream ingestion scripts
Pandas dataframe data cleaning pipeline
Data exports to local MySQL tables
Genre listener categorization logic
Historical data comparisons

Challenges Faced

Handling Spotify API rate limits during bulk stream history requests without blocking processing loops.

What I Learned

Gained hands-on experience in API pagination, rate-limit retry mechanisms, and data analysis using Pandas.

Gallery & Mock Previews

Mock System Frame

Active Database log

Technology Stack

Python

Pandas

Spotify API

MySQL

Development StatusCompleted

Source Code

Related Projects

Backend Development

Lead Management System API

Completed

A secure RESTful API for tracking sales leads. Implements JWT token-based authentication and role-based permissions.

Python

FastAPI

SQLAlchemy

MySQL

+1 more

View Case Study

IoT & Automation

Smart Home Automation (MQTT + ESP32)

In Progress

An IoT controller connecting local sensors and relays to an MQTT broker, offering low-latency web triggers.

Python

C++

ESP32

MQTT

+1 more

View Case Study