Music Popularity Classification Using MLP Classifier and Django

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Music Popularity Classification Using MLP Classifier and Django

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1. Abstract

With the rapid growth of online music platforms such as Spotify, millions of songs are available to users. Due to this large volume of music, it becomes difficult to predict which songs will become popular. Music popularity depends on various factors such as tempo, energy, loudness, and user preferences.

This project focuses on building a song popularity prediction system using a Machine Learning algorithm called MLP Classifier. The system is trained on song datasets collected using the Spotify API. The model analyzes different song features and predicts whether a song will be popular or not.

Exploratory Data Analysis (EDA) is performed to understand the dataset. The trained model is integrated with a Django web application and deployed on Google Cloud using Google App Engine. This project demonstrates the use of neural networks and web technologies in real-world music analytics.

2. Objectives

The main objectives of this project are:

To understand the working of neural network-based classifiers.
To collect song data using the Spotify API.
To analyze song features using Exploratory Data Analysis.
To preprocess and clean the dataset.
To train an MLP Classifier model.
To evaluate model performance.
To build a song popularity prediction system.
To develop a web application using Django.
To integrate the ML model with the website.
To deploy the system on Google Cloud.

3. Existing System

In the existing system, song popularity is mainly based on manual analysis or simple statistical methods.

The limitations of the existing system are:

No automatic prediction system.
Dependence on user ratings and views.
Time-consuming analysis.
Limited use of machine learning.
No neural network-based prediction.
Less accurate results.
Poor personalization.

These limitations reduce the effectiveness of predicting song popularity.

4. Proposed System

The proposed system uses machine learning and neural networks to predict song popularity automatically.

In this system:

• Song data is collected using Spotify API.

• Dataset is analyzed using EDA.

• Data is cleaned and preprocessed.

• Features are extracted.

• MLP Classifier is trained.

• Model is tested and evaluated.

• Django web interface is developed.

• Application is deployed on Google Cloud.

This system provides accurate and efficient song popularity prediction.

5. Implementation Procedure

The project is implemented using the following steps:

Step 1: Data Collection

Song data is collected using Spotify API and public datasets.

Step 2: Data Loading

Dataset is loaded into Pandas Data Frame.

Step 3: Exploratory Data Analysis (EDA)

Data is analyzed using charts and statistical methods.

Step 4: Data Preprocessing

• Removing missing values

• Normalizing data

• Feature selection

• Removing duplicates

Step 5: Model Training

MLP Classifier is trained using processed data.

Step 6: Model Evaluation

Accuracy, precision, and recall are calculated.

Step 7: Web Development

Django framework is used to build the website.

Step 8: Deployment

Application is deployed on Google Cloud using App Engine.

6. Software Requirements

The software tools required for this project are:

• Python

• Jupyter Notebook

• Pandas, NumPy

• Matplotlib, Seaborn

• Scikit-learn

• Spotify API

• Django

• Google Cloud Platform

• Web Browser

7. Hardware Requirements

The hardware requirements include:

• Processor: Intel i5 or higher

• RAM: 8 GB or higher

• Storage: 256 GB or higher

• System: Laptop/Desktop

• Internet Connection: Stable broadband

Optional:

• Cloud server for hosting

8. Advantages of the Project

Predicts song popularity automatically.
Improves music recommendation systems.
Saves time for music analysts.
Uses neural network-based learning.
Provides accurate predictions.
User-friendly web interface.
Can be extended for playlists.
Scalable for large datasets.
Useful for music companies.
Enhances machine learning skills.

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