Music Recommendation App
AI-Powered Personalized Music Recommendation System
Project Description
🎵 AI-Powered Personalized Music Recommendation System
Music holds a significant place in human life. Its many benefits, such as strengthening memory, balancing mood, and providing motivation, are supported by scientific studies. In line with this, I developed a system that recommends music based on people's moods and music preferences. This system aims to provide suggestions by analyzing the genres that best suit the user's musical taste.
🎯 Project Objective
The main goal of my project was to develop an AI algorithm that suggests music that users may like, based on their listening habits and preferences. Today, there are thousands of different music genres and platforms, making it difficult to find the right music. With this application, the goal is to help users quickly access music that suits their taste.
🧠 Technologies and Approach
Backend: Python
Modeling: A recommendation engine was built using the KNN (K-Nearest Neighbors) algorithm with the Scikit-learn library.
Data Analysis: Music data obtained from Spotify playlists was processed with the help of the Pandas library.
Data Source: Music data was obtained by analyzing users' playlists via the Spotify API.
📊 Model Training and Recommendation Mechanism
The project consists of two main phases:
Feature Extraction and Model Training: Music data retrieved from Spotify was used to create a dataset, identifying characteristics like tempo, genre, energy, and danceability. The model was trained using these features.
Recommendation System: The trained model provides recommendations based on the playlist ID or song link provided by the user. The recommendations are made by selecting the most suitable songs from the general music pool in the system.
🧩 Use Cases
Recommendation via Playlist ID: The user inputs a playlist ID into the system. The system creates a user profile based on the characteristics of the songs in the playlist and recommends new songs with similar features.
Recommendation via Single Song: The user inputs a song link, and the system lists songs similar in genre and technical features to the entered song.
📱 Multi-Platform Support
This system is not limited to desktop environments. The Python-based API is configured to handle requests from various applications over a local network. Thus, the recommendation system can be integrated into mobile, web, or desktop applications.
🌍 Expanding and Updating Music Pool
The system currently recommends music primarily based on genre. However, in the near future, the goal is to provide richer and more diverse suggestions by considering attributes such as tempo, instrumentation, and vocal structure of songs.
🚀 Results and Future Plans
With this project, users will not only find music they love but also experience an open approach to discovering new genres. The expansion of the application's user base will also enhance the accuracy and success of the system. This AI-powered recommendation system could eventually evolve into a discovery platform that could shape the future of the music industry.