https://github.com/gugs881/Spotify-Music-Recommendation-System-with-Clustering-and-NLP

🎧 Spotify Music Recommendation System with Clustering and NLP
This notebook implements a Spotify music recommendation system using both clustering algorithms and Natural Language Processing (NLP). The goal is to recommend similar songs based on audio features or the combination of track and artist names.

🎼 Dataset
Source: spotify-2023.csv
Includes features like:
track_name, artist(s)name, streams
Audio features: bpm, danceability%, valence_%, energy_%, acousticness_%, instrumentalness_%, liveness_%, speechiness_%
🔍 Main Components

1. 🔧 Preprocessing
   Missing values replaced with 0.
   Audio features scaled using StandardScaler and MinMaxScaler.
2. 🎯 Clustering-Based Recommendation
   Algorithm: KMeans (5 clusters)
   Songs are clustered by audio similarity.
   A cosine similarity matrix is calculated using normalized audio features.
   A function recomendar_musicas suggests the most similar songs based on a given track.
3. 🧠 NLP-Based Recommendation
   Combined track_name and artist(s)_name into a single string.
   Applied TF-IDF vectorization with English stopwords.
   Built a cosine similarity matrix from the TF-IDF vectors.
   A second recommendation function recomendar_musicas_nlp finds similar songs based on name and artist semantics.
   ✅ Results
   Both recommendation systems return a list of 5 similar songs given an input track. They use:

🎵 Clustering: based on musical attributes (numerical similarity)
🧾 NLP: based on names and artist relationships (textual similarity)
📚 Libraries Used
pandas, matplotlib, seaborn
sklearn: KMeans, TF-IDF, cosine similarity, scalers
scipy (indirectly via cosine_similarity)
💡 Possible Improvements
Deploy with Streamlit for user interaction.
Combine both systems into a hybrid model.
Apply collaborative filtering based on user data (if available).