Outlier detection analysis of consumers with explanations.

Outlier detection analysis project where I utilize machine learning techniques to identify anomalous consumer behavior based on their spending patterns. The chosen approach is based on an unsupervised K-Nearest Neighbors (KNN) model with a soft-min distance metric, ensuring accurate detection of outliers. Additionally, our model offers robust explainability through feature-based explanations, providing insights into the factors driving these anomalies.

Business Case: Enhancing Customer Insights and Product Strategy

Imagine having the ability to pinpoint which products are most likely to be purchased by anomalous customers. This knowledge can revolutionize your business strategy, enabling you to:

1. Optimize Product Offerings:

Question: Do you want to determine which products are most likely to be bought by anomalous customers?

Benefit: By identifying products favored by outliers, you can tailor your inventory to meet the unique demands of this segment, potentially uncovering new revenue streams.

2. Improve Customer Segmentation:

Question: Are you looking to refine your customer segmentation strategies to better understand diverse spending behaviors?

Benefit: This model helps in distinguishing between typical and atypical spending patterns, allowing for more precise customer segmentation and targeted marketing campaigns.

3. Enhance Fraud Detection:

Question: How can you strengthen your fraud detection mechanisms to safeguard against suspicious transactions?

Benefit: Detecting anomalies in spending behavior is crucial for identifying fraudulent activities. Our explainable model provides clear insights into why certain transactions are flagged, aiding in quicker and more accurate fraud detection.

4. Boost Customer Loyalty:

Question: Would you like to enhance customer loyalty by understanding and addressing the unique needs of outlier customers?

Benefit: By recognizing and catering to the preferences of anomalous customers, you can create personalized experiences that foster loyalty and long-term engagement.

5. Optimize Pricing Strategies:

Question: How can you adjust your pricing strategies to capture the maximum value from diverse customer segments?

Benefit: Understanding the spending behavior of outliers can inform dynamic pricing strategies, ensuring competitive pricing that appeals to all customer segments, including those with atypical purchasing patterns.

Model Capabilities and Benefits

Unsupervised Learning: Our KNN-based model operates without the need for labeled data, making it highly adaptable to various datasets and business contexts.

Explainability: Equipped with feature-based explanations, our model provides clear insights into the factors contributing to anomalies, enhancing transparency and trust in the results.

Reproducibility: The entire analysis is designed to be reproducible, ensuring consistency and reliability in findings across different datasets and time periods.

Project Organization

├── LICENSE            <- Open-source license if one is chosen
├── Makefile           <- Makefile with convenience commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── deploy-model-streaming     <- deployment scripts
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── pyproject.toml     <- Project configuration file with package metadata for 
│                         anomalous_customers and configuration for tools like black
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.cfg          <- Configuration file for flake8
│
└── anomalous_customers   <- Source code for use in this project.
    │
    ├── __init__.py             <- Makes anomalous_customers a Python module
    │
    ├── config.py               <- Store useful variables and configuration
    │
    ├── dataset.py              <- Scripts to download or generate data
    │
    ├── features.py             <- Code to create features for modeling
    │
    └── plots.py                <- Code to create visualizations

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Deployment

To use this model, visit the Streamlit app:

https://why-anomalous.streamlit.app/

This link will take you to an interactive interface where you can explore the anomaly detection analysis and get detailed insights into consumer spending behaviors.

Further improvements

• extend the logarithm transform function to include a more generalized approach that can handle different types of transformations based on skewness.