文件列表:

ModelTraining.ipynb
Recommend.py
dataframebinaryfile.pkl
similaritybinaryfile.pkl

# Spotify Song Recommendation System ## Overview This project aims to build a song recommendation system using natural language processing techniques and cosine similarity on song lyrics. The system is built using Python, pandas, scikit-learn, and NLTK libraries. ## Usage 1. **Data Preparation**:Load the dataset containing song information, including artist, song title, and lyrics. 2. **Data Cleaning**: Clean the text data by converting it to lowercase and removing unwanted characters. 3. **Tokenization and Stemming**: Tokenize the lyrics and apply stemming using NLTK. 4. **TF-IDF Vectorization**: Convert the tokenized lyrics into TF-IDF features. 5. **Cosine Similarity**: Calculate cosine similarity between songs based on their TF-IDF vectors. 6. **Song Recommendation**: Provide recommendations for similar songs based on a given song. ## Serialization The similarity matrix and DataFrame are serialized into binary files for later use. ## Instructions on how to execute the files: 1. Download the dataset from this link [https://www.kaggle.com/datasets/notshrirang/spotify-million-song-dataset/].
2. Run the Commands that are in ModelTraining.ipynb in Anaconda Navigator and it will generate two binary files.
3. Run the MRS.py file using command ‘streamlit run Recommend.py’ in terminal. ### GitHub Repository The Python script used for analysis can be found in this [GitHub repository](https://github.com/VedaSamhitha2810/HarmonyHub/blob/master/ModelTraining.ipynb). Feel free to explore the script and dataset for further insights.

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