This project demonstrates the creation of a music recommender system using Python. It includes text data preprocessing, implementation of recommendation logic, and analysis of a song dataset.
The goal of this project is to develop a system that recommends music based on specific criteria or user preferences. The project is divided into the following main sections:
- Data Cleaning and Preprocessing: Handling missing data and preparing text for analysis.
- Recommender Function: Implementing the logic to generate music recommendations.
The dataset used in this project is spotify_millsongdata.csv, which contains information such as:
- Song lyrics
- Metadata (e.g., artist, title)
- Data Loading:
- The dataset is loaded using
pandas. - A sample of 500 rows is used for processing.
- The dataset is loaded using
- Text Preprocessing:
- Tokenization, lemmatization, and other NLP techniques are applied using the
nltklibrary. - Stopwords are removed, and song lyrics are cleaned.
- Tokenization, lemmatization, and other NLP techniques are applied using the
- Recommendation Logic:
- A custom function is implemented to recommend songs based on textual similarity or metadata.
The project requires the following Python libraries:
pandasfor data manipulationnltkfor natural language processingrefor regular expression-based text cleaning
- Clone this repository to your local machine.
- Install the required dependencies:
pip install pandas nltk
- Open the Jupyter Notebook to explore the workflow and test the recommender system.
- The system suggests relevant songs based on similarity in lyrics and metadata.
- The project highlights the use of text preprocessing in building a basic recommender system.
- Incorporate a more advanced model, such as content-based or collaborative filtering.
- Include additional features like genre or user ratings for more accurate recommendations.
This project was created by Marcellin, passionate about data science and machine learning.