Instacart Customer Segmentation
Using Python and Tableau to understand customer spending behavior and drive revenue in multiple ways.
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A descriptive analysis of animal bones from a Historic Archaeological site in Louisville, Kentucky.
This analysis was conducted on an archaeological faunal assemblage recovered from a historic site in Louisville, Kentucky. Reflecting the cultural diversity of its inhabitants, including African-American, Western European American, and Eastern European Immigrants, the assemblage offers insights into past meat consumption practices. Composed predominantly of waste food remains, with a substantial number of mammal, bird, and fish specimens, the assemblage provides a rich source for historical interpretation.
The first task was to use python to create the appropriate grouping classes for the different populations. The groups are
Then, getting appropriate proportions in a dynamic way required Fixed Level-Of-Detail (LOD) Expressions in Tableau's convenient table calculations.
Below is the two page dashboard, showing the required analysis. Food Utility Index (FUI) scores are High, Medium, Low (H/M/L) and indicate value of the animal and bone portion. Cut Portions are shown in the second dashboard, and provide higher grain detail into the differences found in each site.
Lessons Learned:
Using Python and Tableau to understand customer spending behavior and drive revenue in multiple ways.
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Modeling customer behavior with Python's XGBoost library and SHAP values for feature importance.
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