Welcome to the Spotify Music Recommendation System! This project demonstrates a music recommendation system built using a K-Nearest Neighbors (KNN) algorithm. The system is deployed using Flask for the backend API and Streamlit for the user interface. The model leverages a dataset of 30,000 songs with various features to recommend songs based on user input.
The main goal of this project is to recommend songs based on an input song name. The project includes the following key components:
Data Preprocessing: Handling missing values, removing duplicates, and converting song names into numerical representations.
- Visualizing the distribution of various features to gain insights.
- Understanding the correlations between different audio features.
Model Training: Training a KNN model on the processed data to find similar songs.
API Development: Creating a Flask API to handle recommendation requests.
User Interface: Building a Streamlit app to provide a user-friendly interface for inputting song names and displaying recommendations.
Steps to test the app, run the following commands:
- Clone the Repository, cd into the folder.
- Install Dependencies
pip install -r requirements.txt
- Start the Flask Server using the app.py file, and run the code in terminal.
python app.py
- Start the Streamlit App using streamlit app.py file
In a new terminal, run:
streamlit run streamlit_app.py
- Test the Recommendation System
- Select the song and click recommend
