Giter Site home page Giter Site logo

victorljay / song-recommender Goto Github PK

View Code? Open in Web Editor NEW
0.0 1.0 0.0 1.47 MB

Song recommender built end-to-end combining BeautifulSoap4 and Spotify API

Jupyter Notebook 99.64% Python 0.36%
spotify-api python3 clustering beautifulsoup4 webscraping song-recommender

song-recommender's Introduction

Ironhack Logo

Song Recommender P-9000

Víctor López

DAFT October 2020, Barcelona & 11/11/2020

Content

Project Description

Song Recommender P-9000 suggest a song based on the input of the user. There are two scenarios:

  • If the hot is trendy (Billboard HOT 100), it will return another song from that list.
  • If not, it will search the song features of the input and suggest another, from a Spotify list, that belongs to the same cluster.

The goal of the project is discover new songs that match with your preferences.

Dataset

The dataset that we used was the original contained on the folder, the accidents-2017.csv. From that data, we isolated part of it in different CSV files mentioned above.

Workflow

  • Web scrapping the Billboard HOT 100 and creation of a Dataframe with those songs.
  • Obtaining, in this case, 10K Songs from a Spotify playlist using the Spotify API.
  • Create a dataframe of those new songs and cluster it in 7 different groups based on their song features.
  • Project runs. The user inputs a song and the prototype will return another one. If the input is found on the first Dataframe, it will return another song from that one. If not, the prototype will detect the song features of the input, match their features with the correct cluster and return another song from the cluster it belongs.

For better clarification, see the image below:

workflow image

Organization

The organization of the folder is the following one:

  1. Initial folder where you can find the README, .gitignorefile and three folders (data, images and your-code). The data and images folders contain just one file, being one CSV file and the image of the workflow, respectively.
  2. On the your-code folder, there you can find the final code with all the project and the steps. Also, you can find another folder named old-files, where there are other jupyter notebooks used for the creation of that project.

Links

Include links to your repository, slides and kanban board. Feel free to include any other links associated with your project.

Repository

song-recommender's People

Contributors

victorljay avatar

Watchers

 avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.