🎧 Spotify Insight: Sentiment, Clustering & Popularity Prediction

This project analyzes Spotify song data by engineering features from song titles and audio attributes to:

• Perform sentiment analysis on song titles.
• Cluster songs by emotional tone using K-Means.
• Visualize trends with Seaborn and WordCloud.
• Build and evaluate machine learning models (Linear Regression, KNN, Random Forest) to predict song popularity based on textual and audio characteristics.

📂 Project Structure

The repository includes:

• Spotify Song Analysis.ipynb: Jupyter Notebook containing the complete analysis, from data preprocessing to model evaluation.
• README.md: This file, providing an overview and instructions.

📊 Features

• Sentiment Analysis: Analyzes the sentiment of song titles using natural language processing techniques.
• Clustering: Groups songs based on emotional tone using K-Means clustering.
• Visualization: Generates insightful visualizations with Seaborn and WordCloud to explore trends.
• Popularity Prediction: Builds and evaluates machine learning models to predict song popularity based on engineered features. Go Packages

🚀 Getting Started

Prerequisites Ensure you have the following installed:

• Python 3.6 or higher
• Jupyter Notebook
• Required Python libraries:
  • pandas
  • numpy
  • matplotlib
  • seaborn
  • wordcloud
  • scikit-learn
  • nltk

Installation

1. Clone the repository:

git clone https://github.com/crbridget/spotify-insight.git
cd spotify-insight

2. Install the required libraries:

pip install -r requirements.txt

Note: If requirements.txt is not provided, install the libraries individually using pip install library_name.

3. Launch Jupyter Notebook:

jupyter notebook

4. Open Spotify Song Analysis.ipynb and run the cells sequentially to execute the analysis.

📈 Results

The analysis provides:

• Sentiment scores for song titles.
• Clusters of songs grouped by emotional tone.
• Visualizations highlighting trends in the data.
• Performance metrics of machine learning models predicting song popularity.

🤝 Contributing

Contributions are welcome! Please fork the repository and submit a pull request for any enhancements or bug fixes.

📄 License

This project is open-source and available under the MIT License.