This project focuses on analyzing Spotify track data using data analytics techniques and Python libraries such as Matplotlib, Seaborn, Pandas, and NumPy. The goal is to derive meaningful insights and answer various questions about the tracks based on the available dataset.

Code and data for an analytics project in Python to identify the most brutal Cannibal Corpse song. Using Spotify Audio Features and NLP on lyrics.

My Spotify API + MySQL mini data engineering / analytics project

G03: Final Machine Learning Project against the Spotify dataset that consists of 5K+ records for the USC Viterbi Data Analytics Boot Camp

This project leverages the Spotify API and AWS Lambda to automate data extraction, followed by seamless transformations using triggers. Organized storage on Amazon S3 and analytics table creation with AWS Glue and Athena provide a comprehensive data pipeline

As part of the course "Cognitive Challenge", of IE's Master in Big Data and Business Analytics, this group project integrates IBM's Watson Assistant with Spotify in order to create an interactive chatbot which can define Spotify playlists based on user-specified moods and users.

Analyze the Spotify dataset to uncover insights on song popularity, artist trends, and track characteristics. This project involves data cleaning, loading the prepared data into an SQL database, and building a Power BI dashboard to visualize music data insights.

Fun Spotify data analytic project

The Spotify Data Analysis Project demonstrates the power of data analysis in fields like business, research, and meteorology. Using Python, I analyzed music datasets to extract insights, visualizing data and discovering patterns. This project enhanced my skills and understanding of how data connects to music, preparing me for future endeavors.

This is a Data Analytics Project . Which I have made using Python language. I have created this project in IDE in jupyter notebook. In this project, I have used some Python packages like Pandas, Numpy, Matplotlib, Seaborn. Like Spotify is a music platform. The data from Spotify contains features of Artist, Tracks, Popularity Songs, Music which we have done in this project.

Spotify End-to-End ETL Pipeline — Data Analytics Project (Public)

Mini Project: Spotify Recommendation. Created for Week 13 of Ironhack's Data Analytics Bootcamp.

Spotify Data Analysis project using Python. Performed EDA, visualization, and genre insights using Spotify datasets. Created meaningful graphs and insights for Data Analytics portfolio.

Python project to pull data from Spotify, YouTube, and other music streaming platforms for analytics purposes

This project aims to constantly fetch data from spotify API and provide analytics on various parameters.

This project demonstrates an end 2 end real-time data engineering pipeline for Spotify music analytics using the Modern Data Stack.

Implement Complete Data Pipeline Project using Spotify Integrating with Spotify API. The project will involve writing a transformation function, storing files properly on AWS S3, and building analytics tables using Glue and Athena.

A data analytics project consisted of my own personal Spotify premium data. This analysis uses libraries such as Pandas and Numpys in the Python environment, to properly look at and access the data downloaded directly from Spotify

A data analytics project on global Spotify tracks using PostgreSQL to uncover popularity trends, genre insights, artist performance, and audio feature correlations.

This project demonstrates an **end-to-end data engineering pipeline** built on **Microsoft Azure** using Spotify data. The goal is to ingest raw data from the Spotify API, transform it using Spark, and create analytics-ready datasets following data engineering best practices.

A comprehensive data analytics project leveraging Power BI and a Spotify dataset to visualize and analyze music streaming patterns, artist performance, and audio characteristics.

This project features a meticulously designed Spotify dashboard, offering deep insights into listening patterns, user engagement, and genre preferences through advanced visualizations and data-driven analytics for nuanced musical understanding.

A complete data engineering project that extracts data from the Spotify Web API using Python, automates ingestion with AWS Lambda and CloudWatch, transforms and stores it in Amazon S3, and enables analytics via AWS Glue, Athena, and the Glue Data Catalog.

The project will establish an automated data pipeline on AWS to extract, transform, and analyze Spotify data. It will harness AWS Lambda for scheduled data fetching from the Spotify API to S3, with transformation processes for data cleaning and formatting. AWS Glue and Athena will create analytics tables for efficient querying, aiming to offer a sc

This project automates data extraction, transformation, and analysis for Spotify playlists, using AWS Lambda, S3, Snowflake, and Snowsight dashboards. It enables real-time insights into song popularity, artist trends, and playlist analytics.

This Power BI dashboard project dives deep into Spotify's music data to uncover trends, patterns, and insights across genres, artists, and listener behavior. Whether you're a data enthusiast, music lover, or aspiring analyst, this project showcases how data visualization can bring streaming analytics to life.

This project demonstrates an end-to-end ETL (Extract, Transform, Load) pipeline designed for Spotify Data analytics. It integrates raw transactional and financial data from multiple sources, processes it using modern data engineering tools, and delivers clean, structured datasets for Business Intelligence (BI) reporting and dashboards

A comprehensive Power BI dashboard for analyzing music data from the Spotify API. This project demonstrates a full-stack data analytics workflow, from Python-based data extraction to a fully interactive and real-time Power BI visualization. It provides deep insights into artist performance, track popularity, and release trends.

A modular Python project for analyzing Spotify tracks and audio features. Includes structured EDA, genre and temporal insights, automated visualizations, and a baseline ML model for popularity prediction. Built as a clean, reproducible, portfolio-ready data analytics pipeline.

My fifth semester Big Data Analytics course project, Song Recommendation System designed to offer highly personalized music suggestions to users based on their preferences. Leveraging the extensive Spotify Million Playlist Dataset, the system employs content-based filtering, Big Data techniques, and innovative features like emotion-based filters.