🎵 Hit Predictor AI: Sonic Analytics

Hit Predictor AI is a data-driven application designed to assist music producers and artists in evaluating the potential popularity of their tracks. By analyzing audio features and artist metrics, the app predicts a "Popularity Score" and provides actionable feedback ("Doctor's Orders") to help optimize tracks for commercial success.

🔗 Check it out HERE

📊 Market Analyzer

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👩‍⚕️ Song Doctor

🚀 Features

• Hit Prediction Engine: Uses a Machine Learning model (Random Forest) to predict a song's popularity score (0-100) based on audio features and artist fame.
• Audio Analysis: Automatically extracts key sonic features from audio files using librosa:
  • BPM (Tempo)
  • Energy (RMS)
  • Brightness (Spectral Centroid)
  • Rhythm Strength
• "Doctor's Orders" ⚠️: Provides specific, data-backed advice if your track deviates from the sonic norms of successful songs in your genre (e.g., "Your track is too dark," or "Rhythm is too aggressive").
• Visualizations: Interactive charts to compare your track against genre averages.

📂 Project Structure

├── app.py                 # Main Streamlit application
├── analysis.ipynb         # Data analysis, feature extraction, and model training
├── models/
│   └── popularity_prediction_model.pkl  # Trained ML Model
├── data/
│   ├── apple_music_dataset.csv          # Raw data
│   └── final_data.csv                   # Processed data with audio features
├── ANALYTIC_REPORT.md
└── README.md              # Project documentation

🛠️ Tech Stack

• Frontend: Streamlit
• Data Processing: Pandas, NumPy
• Audio Analysis: `Librosa
• Machine Learning: Scikit-learn, Joblib
• Visualization: Plotly, Seaborn, `Matplotlib
• APIs: Spotify Web API (for feature enrichment)

📊 Analytic Report

For a deep dive into the statistical analysis, hypothesis testing (e.g., "The Fame Effect"), and model performance underlying this project, please refer to the Analytic Report.

⚙️ Installation & Usage

1. Clone the repository

git clone [https://github.com/lucytheboss/Sonic-Analytics-Hit-Predictor.git](https://github.com/lucytheboss/Sonic-Analytics-Hit-Predictor.git)
cd Sonic-Analytics-Hit-Predictor

2. Install dependencies

pip install -r requirements.txt
Note: You will need ffmpeg installed on your system for librosa to process audio files.

3. Run the App

streamlit run app.py

🧠 How It Works

• Upload Audio: User uploads an audio file (.mp3, .wav).
• Select Genre: User selects the primary genre of the track.
• Analyze: The app extracts audio features in real-time.
• Predict: The pre-trained model calculates a popularity score.
• Optimize: The app compares the track's features to the "Gold Standard" for that genre and suggests edits.