Solving Spotify Multiclass Genre Classification Problem
Introduction
The music industry has become more popular, and how people listen to music is changing like wildfire. The development of music streaming services has increased the demand for automatic music categorization and recommendation systems.
Spotify, one of the world’s leading music streaming sites, has millions of subscribers and a massive song catalog. Yet, for customers to have a personalized music experience, Spotify must recommend tracks that fit their preferences. Spotify uses machine learning algorithms to guide and categorizes music based on the Genre.
This project will focus on the Spotify Multiclass Genre Classification problem, where we download the Dataset from Kaggle.
Goal: This project aims to develop a model that classifies the Genre that can accurately predict the Genre of a music track on spotify.
Learning Objectives

• To investigate the link between music genres on Spotify and their acoustic characteristics.
  
• To create a classification model based on auditory characteristics to predict the genre of a given song.
  
• To investigate the distribution of various spotify music genres in the dataset.
  
• To clean and preprocess data in order to prepare it for modeling.
  
• To assess the categorization model’s performance and improve its accuracy.
  

Table of Contents

• Prerequisites
  
• Project Pipeline
  
• Project
  
• Conclusion
  

Prerequisites
Before we begin implementation, we must install and import some of the libraries. The libraries listed below are required:

• Pandas: A library for data manipulation and analysis.
• NumPy: A scientific computing package used for matrix computations.
• Matplotlib: A plotting library for the Python programming language.
• Seaborn: A data visualization library based on matplotlib.
• Sklearn: A machine learning library for building models for classification
• TensorFlow: A popular open-source library for building and training deep learning models.