Spotify-Hit-Prediction

This project predicts the likelihood of a song becoming a hit based on its Spotify audio features. Additionally, it evaluates trends in song characteristics over time and provides an interactive Streamlit app for user-friendly predictions.

Features

• Data Exploration:

  • Analyze audio features like danceability, energy, tempo, and their relationship with Billboard chart positions.
  • Identify correlations between Spotify audio features and a song’s success.

• Classification Task:

  • Predict whether a song ranks in the Top 10 on the Billboard charts (binary classification).

• Regression Task:

  • Predict chart_position (lower is better) or weeks_on_chart to estimate song longevity.

• Evaluation Metrics:

  • Regression: MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error).
  • Classification: Accuracy, precision, recall, F1-score, and confusion matrix.

• Interactive App:

  • A Streamlit app allows users to predict song success interactively by providing audio feature inputs.

Project Structure

• Data Preprocessing:

  • Merge Spotify and Billboard datasets.
  • Create derived features (e.g., top_10 for binary classification).
  • Handle class imbalance using techniques like SMOTE or class weights.

• Modeling:

  • Train models like Random Forest Regressor, XGBoost Classifier, and Logistic Regression.
  • Perform hyperparameter tuning using GridSearchCV for optimal performance.

• Visualization:

  • Feature importance analysis.
  • Correlation heatmaps.
  • Trends in features over time (e.g., danceability vs. chart date).

Requirements

• Python 3.8+
• Libraries:
  • pandas, numpy: Data manipulation.
  • matplotlib, seaborn: Visualization.
  • scikit-learn: Machine learning models and evaluation metrics.
  • xgboost: Gradient Boosting Classifier.
  • streamlit: Interactive app development.
  • imblearn: Oversampling techniques for handling class imbalance.

How to Run

1. Data Preparation

• Ensure the following datasets are available:
  • spotify_songs.csv: Contains Spotify audio features.
  • billboard_data.csv: Contains Billboard chart rankings.

2. Install Dependencies

Install the required Python libraries:

pip install pandas numpy matplotlib seaborn scikit-learn xgboost imbalanced-learn streamlit

3. Run the Notebook

• Open the Jupyter Notebook: hit_prediction.ipynb.
• Run all cells sequentially to:
  • Prepare data.
  • Train models.
  • Evaluate performance.
  • Visualize trends and predictions.

4. Launch Streamlit App

Run the following command to start the Streamlit app:

streamlit run hit_prediction_app.py

Interact with the app to predict song success by adjusting sliders for audio features like tempo, energy, and loudness.

Key Results

• Baseline Model:

  • Compared the Random Forest model to a baseline (mean prediction) for benchmarking.

• Classification Performance:

  • Improved prediction of top_10 hits using oversampling and hyperparameter tuning.

• Feature Importance:

  • Identified key features influencing song success, such as danceability and energy.

Future Improvements

1. Advanced Feature Engineering:

   • Add interaction terms like tempo_to_energy_ratio.
   • Normalize features for better model performance.

2. Explainable AI:

   • Use SHAP to explain individual predictions.

3. Additional Models:

   • Test other classifiers like Support Vector Machines (SVM) or Gradient Boosting.

4. Enhanced Visualizations:

   • Include rolling averages for trends analysis.
   • Plot precision-recall curves for classification tasks.

Acknowledgments

• Spotify Web API for providing song audio features.
• Billboard Charts for chart performance data.

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Feel free to extend or adapt this project for other predictive tasks related to music analytics!