Spotify Recommendation System App End To End Project

In this project, using the data I collected from Spotify (audio streaming and media services provider), I tried to make a recommendation system that based on audio feature, song genre, artist, etc., the song we choose gives us Suggest songs

This project is a part of my portfolio, showcasing my skills. The main code isn't public, but I'm open to collaboration! Interested? Email me at mr.raz2002@gmail.com

Watch Demo here :

https://meysamraz-song-recommendation-spotify-project.streamlit.app/

Watch Demo here heroku version :

update : heroku may shutdown free hosting. If this link didnt work use link above

https://spotify-song-recommendation.herokuapp.com/

Project Overview :

1 - Setup Spotify Api

• Open spotify developers go to dashboard - create app and get your client id and secret id

2 - Collecting Data

• Collect songs between 2017 and 2022 (1000 per year)

  • id : song id
  • song name : name of the songs
  • artist name : name of artist who sing the song
  • artist genres : genres that artist sings
  • album genres : genres of songs album
  • release_date : release date of song
  • song link : link of song on spotify
  • image : cover image of song
  • song duration : song duration
  • song popularity : song popularity on spotify

• Collect songs that i liked

• Collect audio features of collected songs

  • Danceability: Danceability describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable.
  • Acousticness: A measure from 0.0 to 1.0 of whether the track is acoustic.
  • Energy: Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy.
  • Instrumentalness: Predicts whether a track contains no vocals. The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content.
  • Liveness: Detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live.
  • Loudness: The overall loudness of a track in decibels (dB). Loudness values are averaged across the entire track. Values typical range between -60 and 0 db.
  • Speechiness: Speechiness detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value.
  • Tempo: The overall estimated tempo of a track in beats per minute (BPM). In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration.
  • Valence: A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry).

• Collect audio features of liked songs

• Merge songs and their audio features

• Save data into a csv file

3 - Understand the data

• Shape of the data

• Check column dtypes

• Check is there any null values

• Check the correlation

4 - Cleaning the data

• Drop "album genres" column

• Convert artist genres to str

5 - Analyzing Data

• Number of songs per year

• Songs duration distribution and (min,avg,max)

• Song popularity distribution and (min,avg,mode,max)

• Artist genres wordcolud

• Analyzing audio features

6 - Apply Dmensionality Reduction

• Split audio features from data

• Standard Scaling Audio Features

• Apply PCA

• Put PCA Output Into a Dataframe

• Visualize reduced dimension Data

7 - Apply Clustering

• Select the right value of k for clustring

• Fit reduced dimension data into Kmeans

• Visualize clusterd data (kemans output)

8 - Create Recommendation System

To build the recommendation system, I used audio features, songs, song genres, singers, etc. The more data the features are closer to the songs in question, the more data will be displayed.

9 - User Interface

To create my user interface, I used streamlit framework, a powerful framework that allows me to create the desired user interface completely using Python.

Deploy on server

I used Heroku a cloud platform as a service which provide a free hosting to deploy my app on it. it's and amzaing platform gave me so much flexbilte to deploy your apps

Libraries and FrameWorks used in the project

• streamlit
• pandas
• numpy
• plotly
• matplotlib
• seaborn
• termcolor
• wordcloud
• yellowbrick
• scikit-learn
• beautifulsoup