Added threshold segmentation analysis modules docs #111
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WalkthroughThis change updates the analysis documentation to include detailed explanations and examples for HML and Threshold segmentation. The document now features updated image references, clearer segmentation descriptions, and Python examples using the Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant HML as HMLSegmentation
participant DF as DataFrame
User->>HML: Call HMLSegmentation(df, zero_value_customers)
HML->>DF: Analyze spending distribution
DF-->>HML: Return segmentation metrics
HML->>User: Return segmented result (Heavy, Medium, Light)
sequenceDiagram
participant User
participant Threshold as ThresholdSegmentation
participant DF as DataFrame
User->>Threshold: Call ThresholdSegmentation(df, thresholds, segments, zero_value_customers)
Threshold->>DF: Compute segmentation based on thresholds
DF-->>Threshold: Return calculated segments
Threshold->>User: Return segmented segments
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🧹 Nitpick comments (2)
docs/analysis_modules.md (2)
464-477: Enhance Clarity in HML Segmentation Description.
The updated HML segmentation section is comprehensive and outlines the criteria and handling of zero spend very clearly. As a minor suggestion, consider explicitly mentioning whether the "Zero" segment is an available option (in addition to including with light customers or excluding them) and ensure consistent language when describing percentile splits and handling of zero spend.
519-536: Threshold Segmentation Description is Detailed and Informative.
The updated description clearly explains the flexibility of Threshold Segmentation versus the fixed HML splits. It effectively enumerates the benefits of custom thresholds and the additional handling options for zero values. As a suggestion, consider briefly clarifying that the thresholds are interpreted as percentiles (if that is the case) to remove any ambiguity for the reader.
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🔇 Additional comments (2)
docs/analysis_modules.md (2)
484-513: HML Segmentation Example is Clear and Functional.
The Python code example effectively demonstrates the creation of a representative DataFrame using a Pareto distribution, the application ofHMLSegmentationwith thezero_value_customers="include_with_light"parameter, and the subsequent visualization of segment spend using a bar plot. The in-line comments aid in understanding the steps, and the sample data is realistic for retail analytics purposes.
541-580: Threshold Segmentation Example is Comprehensive and Consistent.
The example code mirrors the clarity of the HML example. It shows how to generate sample transaction data, define custom thresholds and segment names, and applyThresholdSegmentationwith thezero_value_customers="separate_segment"setting. The subsequent visualization using a bar plot reinforces the explanation well.
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PR Type
Documentation
Description
Added detailed explanation for HML segmentation.
Introduced Threshold Segmentation with flexible thresholds.
Included Python code examples for both segmentation methods.
Updated images for HML and Threshold Segmentation sections.
Changes walkthrough 📝
analysis_modules.md
Add HML and Threshold Segmentation documentationdocs/analysis_modules.md
Summary by CodeRabbit
Documentation
New Features