A machine learning project that demonstrates genre prediction for songs based on their lyrical content. The model analyzes text patterns in lyrics to classify songs into various genres such as Pop, Rock, Hip-Hop, and more.
🛠️ Technologies Used
- Python
- Natural Language Processing (NLP)
- Machine Learning algorithms
- Text preprocessing and feature extraction
- Data analysis libraries (pandas, numpy)
- Scikit-learn for model training
🧠 How It Works
- Dataset contains lyrics from various music genres
- Text preprocessing cleans and normalizes lyrical content
- Feature extraction converts lyrics into numerical representations
- Machine learning models are trained on the processed data
- Model predicts genre based on new lyrical input
🚀 Features
- Multi-Genre Classification: Supports various music genres
- Text Analysis: Advanced NLP techniques for lyric processing
- Model Training: Supervised learning approach for classification
- Performance Evaluation: Metrics to assess prediction accuracy
- Jupyter Notebook: Interactive development and visualization
📊 Implementation
The project uses natural language processing techniques to extract meaningful features from song lyrics, then applies classification algorithms to predict the most likely genre. The notebook includes data exploration, preprocessing steps, model training, and evaluation metrics.
Explore the source code on GitHub: AgnivaMaiti/Genre-Classification