owl-mix 0.1.5

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

🦉 OwlMix

OwlMix is a comprehensive Python package for Exploratory Data Analysis (EDA) and data transformation tailored for Marketing Mix Modeling (MMM) workflows. It provides automated report generation, statistical analysis, and data transformation utilities to accelerate MMM projects.

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🚀 Key Features

📊 Data Analysis & Reporting

• Automated EDA Reports: Generate professional HTML and JSON reports with comprehensive statistics and visualizations
• Correlation Analysis: Matrix correlations, lag correlations, and ACF/PACF analysis for time series
• VIF Calculation: Variance Inflation Factor detection for multicollinearity assessment
• Causality Testing: Granger causality tests to identify causal relationships
• Categorical Analysis: Distribution analysis for categorical variables
• KPI vs Features: Analyze relationships between KPIs and marketing features by time period
• Time Series Decomposition: Seasonal decomposition and trend analysis
• Outlier Detection: Visual identification and analysis of outliers

🔧 Data Transformation

• Adstock Effect: Apply advertising carryover effects to media spend data
• Lag Generation: Create lagged features for time series modeling
• Saturation Transformation: Apply saturation curves (Hill, Logistic, Logit) to media variables
• Data Cleanup: Automated data quality checks and handling (missing values, duplicates, etc.)
• Transformation Pipeline: Chainable pipeline for complex data workflows

🎨 Visual & Export Options

• Multiple HTML Templates: Light and dark theme templates for reports
• Interactive Charts: Distribution plots, time series, correlation heatmaps, outlier charts
• JSON Export: Raw report data for programmatic access
• Chart Storage: Automatic chart generation and storage in outputs/charts/

⚙️ Flexible Configuration

• Fine-grained control over analyses to include/exclude
• Customizable precision, date formats, and aggregation frequencies
• Column-specific configurations for targeted analysis
• Template customization support

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📦 Installation

pip install owl-mix

Requirements:

• Python >= 3.12
• pandas >= 1.5
• matplotlib >= 3.7
• seaborn >= 0.12
• statsmodels >= 0.14.6
• scipy >= 1.10
• Jinja2 >= 3.1

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⚡ Quick Start

Basic EDA Report Generation

import pandas as pd
from owlmix.report import OwlMixReport

# Load your data
df = pd.read_csv("your_data.csv")

# Create and generate report
report = OwlMixReport(
    df=df,
    target="sales",              # Target variable for analysis
    date_column="date",          # Date column for time series analysis
    template_name="custom_eda_template.html"  # Optional: use "custom_eda_template_dark.html" for dark theme
)

# Generate HTML and JSON reports
report.run(
    json_file_name="eda_report.json",
    html_file_name="eda_report.html"
)

Output:

• eda_report.json: Structured analysis data in JSON format
• eda_report.html: Interactive HTML report with charts and statistics
• outputs/charts/: Generated visualization files

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🛠️ Advanced Configuration

Customize Analyses

import pandas as pd
from owlmix.report import OwlMixReport

df = pd.read_csv("your_data.csv")

report = OwlMixReport(
    df=df,
    target="sales",
    date_column="date"
)

# Configure time aggregation
report.config.set_time_aggregator_config(
    freq="ME",      # Month-end aggregation
    precision=4     # Decimal precision
)

# Configure categorical columns
report.config.set_categorical_columns_config(
    columns=["product_type", "region", "channel"]
)

# Configure KPI vs Feature analysis
report.config.set_kpi_vs_feature_config(
    columns=["tv_spend", "digital_spend", "radio_spend"],
    date_format="%Y-%m"
)

# Configure VIF (Variance Inflation Factor) analysis
report.config.set_vif_config(
    features=["tv_spend", "digital_spend", "radio_spend"],
    precision=2
)

# Configure ACF/PACF analysis
report.config.set_acf_pacf_config(
    columns=["sales", "digital_spend"],
    n_lags=20
)

# Configure causality testing
report.config.set_causality_test_config(
    max_lag=5,
    error_threshold=0.15
)

report.run(
    json_file_name="report.json",
    html_file_name="report.html"
)

Data Transformation Pipeline

from owlmix.transform import MMMTransformPipeline

# Create transformation pipeline
pipeline = MMMTransformPipeline(df, date_column="date")

# Apply transformations
pipeline.adstock(
    columns=["tv_spend", "digital_spend"],
    decay_rate=0.5,
    window=4
)

pipeline.create_lags(
    columns=["sales"],
    lags=[1, 2, 4, 13]
)

pipeline.saturation(
    columns=["tv_spend", "digital_spend"],
    method="hill",
    k_values=[100, 200]
)

pipeline.cleanup(
    handle_missing="mean",
    remove_duplicates=True
)

# Get transformed data
transformed_df = pipeline.get_data()

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📊 Report Sections

The generated HTML report includes comprehensive sections:

Section	Description
Dataset Overview	Basic information, data types, missing values, memory usage
Summary Statistics	Descriptive statistics (mean, std, min, max, quantiles)
Data Quality	Missing value patterns, duplicate analysis
Distributions	Histograms and density plots for all numeric variables
Outlier Analysis	Box plots and outlier identification
Correlation Matrix	Pairwise correlations with heatmap visualization
Lag Correlations	Time-lagged correlation analysis for time series
VIF Analysis	Multicollinearity detection using Variance Inflation Factor
ACF/PACF	Autocorrelation and partial autocorrelation for seasonality detection
Causality Tests	Granger causality tests for causal relationships
Time Comparisons	Period-over-period comparisons (YoY, MoM)
KPI vs Features	Relationship between target and marketing features over time
Categorical Distributions	Distribution analysis for categorical variables

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🔧 Core Modules

owlmix.eda

Exploratory Data Analysis module with:

• EDAAnalyzer: Core statistical analysis engine
• SummaryBuilder: Comprehensive summary generation
• OwlMixEDA: Main EDA orchestrator

Features:

• Correlation analysis (matrix, lag, causality)
• VIF calculation for multicollinearity
• ACF/PACF analysis for seasonality
• Categorical and distribution analysis
• Outlier detection and visualization

owlmix.transform

Data transformation module for MMM preprocessing:

• adstock(): Apply advertising carryover effects
• create_lags(): Generate lagged features
• saturation(): Apply saturation curves (Hill, Logistic, Logit)
• cleanup_data(): Data quality utilities
• MMMTransformPipeline: Chainable pipeline for complex workflows

owlmix.report

Report generation module:

• OwlMixReport: Main report generator
• HTML template rendering with customizable themes
• JSON data export
• Chart generation and storage

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📈 Example Use Cases

Marketing Mix Modeling Workflow

import pandas as pd
from owlmix.report import OwlMixReport
from owlmix.transform import MMMTransformPipeline

# Load raw data
df = pd.read_csv("mmm_data.csv")

# Step 1: Transform data
pipeline = MMMTransformPipeline(df, date_column="date")
pipeline.adstock(columns=["tv", "digital", "radio"], decay_rate=0.5)
pipeline.create_lags(columns=["sales"], lags=[1, 4, 13])
df_transformed = pipeline.get_data()

# Step 2: Analyze with EDA
report = OwlMixReport(
    df=df_transformed,
    target="sales",
    date_column="date"
)
report.config.set_vif_config(
    features=["tv", "digital", "radio"],
    precision=3
)
report.run(
    json_file_name="mmm_eda.json",
    html_file_name="mmm_eda.html"
)

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📚 Documentation

• EDA Documentation
• Transform Documentation
• Saturation Curves

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💡 Contributing

Contributions are welcome! Please feel free to submit pull requests or open issues on GitHub.

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📄 License

MIT License - see LICENSE file for details

Author: Sarbadal Pal ([e-mail])

Repository: github.com/sarbadal/owl-mix

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📚 Documentation

Detailed documentation is available in the docs/ folder:

• docs/eda.md → EDA module details
• docs/transform.md → Data transformation features
• docs/saturation.md → Saturation modeling

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🧪 Examples

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

• eda_basic.py - Basic EDA report generation
• eda_full_workflow.py - Complete workflow example
• mmm_workflow_example.py - Marketing Mix Modeling example

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🧠 Use Case: Marketing Mix Modeling

OwlMix is designed for MMM workflows where you need to:

1. Explore relationships between marketing spend and sales
2. Identify multicollinearity issues with VIF
3. Analyze time-based patterns and correlations
4. Generate professional reports for stakeholders

Perfect for preprocessing data before building MMM models!

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!