Advanced Song Recommender System

Overview

This project implements an advanced song recommender system using a sophisticated combination of technologies and techniques. The system aims to provide users with personalized music recommendations based on their preferences.

Technologies Used

• HTML & Web Scraping
• BeautifulSoup
• Multi-page scraping
• APIs & Requests
• Spotify API
• Dimensionality Reduction: PCA, ISOMAP, UMAP
• Clustering: K-Means, HDBSCAN
• Storytelling
• Code Workflow

Features

• Collects data from multiple web pages using HTML and web scraping techniques.
• Utilizes BeautifulSoup for efficient parsing of HTML content.
• Integrates APIs and Requests, particularly leveraging the Spotify API for accessing music data.
• Implements dimensionality reduction techniques (PCA, ISOMAP, UMAP) to reduce data complexity while preserving meaningful information.
• Employs clustering algorithms (K-Means, HDBSCAN) to group similar songs effectively.
• Maintains a structured code workflow for clarity, scalability, and maintainability.
• Incorporates storytelling elements into clustering results to provide insightful narratives behind recommended playlists or songs.

Installation

1. Clone the repository: git clone https://github.com/yourusername/advanced-song-recommender.git
2. Run the application: python main.py

Usage

1. Launch the application.
2. Input your preferences or search query.
3. Receive personalized music recommendations.