Experience a comprehensive exploration of Spotify's musical landscape seamlessly transitioned from Tableau visualizations to SQL analysis. Dive into track inventory, streaming metrics, and sonic trends via interactive dashboards, while leveraging SQL queries for deeper insights into KPIs and cross-platform rankings.

🔎 M.EIC 2021/2022 - 1ˢᵗ year / 1ˢᵗ semester

Created multiple models to analyze a Spotify dataset and predict playlist genre. Dataset download from Kaggle (https://www.kaggle.com/datasets/joebeachcapital/30000-spotify-songs).

Data Science Foundations II | Statistics Fundamentals for Data Science | Sampling for Data Science

Spotify recommendations using Python 🐍

Implementazione di un sistema di raccomandazione di playlist musicali basato su K-Means Clustering, e di algoritmi quali Random Forest, K-Nearest Neighbors, Decision Tree e Regressione Logistica per la predizione della popolaritá di una canzone.

Implements a content-based recommendation system for Spotify using TF-IDF (Term Frequency-Inverse Document Frequency) and Cosine Similarity. The system analyzes song features to recommend similar tracks based on user preferences.

Extract spotify track details from spotify database using this python script