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Intelligence Where You'll Get Your Next Music. Industrial Training Project 2019. under the guidance of Ardent Computech Pvt. Ltd.

Home Page: https://raviprakash11.github.io/MusicRecommendationSystem/

HTML 0.91% CSS 0.34% Jupyter Notebook 98.55% Python 0.20%
musicrecommendationsystem music-recommendation-system recommendationsystem

musicrecommendationsystem's Introduction

MusicRecommendationSystem

Industrial Training Project 2019

About

Intelligence Where You Get Your Next Music.
Rapid development of mobile devices and internet has made possible for us to access different music resources freely. The number of songs available exceeds the listening capacity of single individual. People sometimes feel difficult to choose from millions of songs. Moreover, music service providers need an efficient way to manage songs and help their customers to discover music by giving quality recommendation. Thus, there is a strong need of a good recommendation system. The Music Recommendation System will recommend users the type of music they want to listen. The recommendation will be based on what the user listens as per their previous music. It will be suggesting using various factors which are energy ,tempo ,acousticness, danceability , instrumentalness etc.

Data source: https://www.kaggle.com/tomigelo/spotify-audio-features/

Steps to run codes:

  • Run Anaconda-Navigator

    1. Open Jupyter-notebook.
    2. Open all the .ipynb file in your local machine.
    3. Run all the Cell.
    4. minimize it
  • Run XAMPP

    1. Configure for CGI-BIN python script.
    2. Start Server.
    3. Run index.html file.

INDEX PAGE

ABOUT PAGE

CGI-BIN PAGE

VIEWSOURCE PAGE

TABLEOFCONTENT PAGE

READING RAW

HISTOGRAM

HISTOGRAM

K-Mean

K-Mean

K-Mean

K-Mean

K-Mean

ShowingFeaturesOFSONGS

Popularity

Recommended songs

musicrecommendationsystem's People

Contributors

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