owl-mix 0.1.1

Exploratory data analysis and transformation toolkit for Marketing Mix Modeling (MMM)

🦉 Owl Mix

Owl Mix is a Python package designed for data transformation and exploratory data analysis (EDA), specifically tailored for Marketing Mix Modeling (MMM) workflows.

It helps data scientists and analysts:

• Transform raw marketing data into model-ready features
• Perform structured exploratory analysis
• Understand relationships between media variables and target outcomes

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🚀 Key Features

🔄 Transformations (MMM-ready)

• Lag creation
• Adstock (carryover effects)
• Saturation (diminishing returns)
• Pipeline-based transformation workflow

📊 Exploratory Data Analysis

• Dataset summary (missing values, types, stats)
• Correlation analysis
• Lag correlation with target variable
• Exportable text reports

🧱 Modular Design

• Clean and extensible architecture
• Easily add custom transformations and EDA components

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📦 Installation

pip install owl-mix

Then import in Python:

import owlmix

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⚡ Quick Example

from owlmix.transform.pipeline import TransformPipeline
from owlmix.eda import EDAAnalyzer
from owlmix.utils.cleanup import final_cleanup
 
# Step 1: Transform data
pipeline = TransformPipeline()
 
pipeline.add_lag("tv_spend", lag=1)
pipeline.add_adstock("tv_spend", decay=0.5)
pipeline.add_saturation("tv_spend", method="hill", k=100, s=2)
 
df_transformed = pipeline.run(df)
 
# Step 2: Cleanup
df_clean = final_cleanup(df_transformed)
 
# Step 3: EDA
eda = EDAAnalyzer(df_clean, target="sales")
 
print(eda.basic_stats())
print(eda.correlation())
 
report = eda.summary()
 
with open("eda_report.txt", "w") as f:
    f.write(report)

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📁 Project Structure

owlmix/
│
├── eda/
│   ├── analyzer.py
│   ├── stats.py
│   ├── correlation.py
│   ├── summary.py
│
├── transform/
│   ├── pipeline.py
│   ├── lag.py
│   ├── adstock.py
│   ├── saturation.py
│
├── utils/
│   ├── cleanup.py
│
examples/
docs/

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📚 Documentation

Detailed documentation is available in the docs/ folder:

• docs/eda.md → EDA module
• docs/transform.md → Transformation pipeline
• docs/saturation.md → Saturation methods

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🧪 Examples

Ready-to-run examples are available in the examples/ folder:

• eda_basic.py
• eda_full_workflow.py
• transform_pipeline_example.py
• mmm_workflow_example.py ⭐

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🧠 Use Case: Marketing Mix Modeling (MMM)

Owl Mix is particularly useful for:

• Preprocessing marketing data
• Feature engineering for MMM
• Understanding lagged media effects
• Generating EDA reports before modeling

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🔧 Roadmap

Planned enhancements:

• Visualization support (plots, heatmaps)
• HTML report generation
• Automated MMM diagnostics
• CLI support

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🤝 Contributing

Contributions are welcome!

Feel free to:

• Open issues
• Suggest features
• Submit pull requests

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📄 License

This project is licensed under the MIT License.

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⭐ Support

If you find this project useful, consider giving it a star ⭐ on GitHub!