Churn in SaaS and readmission in healthcare can both be framed as time-to-event problems, so the same survival methods often apply. Same maths, different vocabulary, different traps.
Every analysis classified under this industry.
Analyses the pipeline built from a real question in this field: each one became a module its owner can re-run.
Estimates price elasticity of demand for avocados using log-log regression of log(total_volume) on log(average_price), controlling for type
Regional avocado price and volume trends across US markets, built as a multi-card deck from a plain-language question.
The same avocado price question answered as an instant snapshot report.
OLS linear regression of weekly avocado average retail price on total sales volume, reporting slope estimate, R-squared, confidence bands, a
Segment e-commerce customers into behavioral groups based on purchase recency, frequency, and monetary value using K-Means clustering for ta
Segment ecommerce customers into behavioral groups using Recency, Frequency, and Monetary (RFM) analysis with K-Means clustering. Identify h
Quantifies how delivery speed affects customer review scores by joining orders and reviews on order_id, computing delivery_days, and running
Predict which customers are at risk of churning using behavioral engagement, transaction history, satisfaction, and demographic features. Id
Identifies which customer attributes drive churn using logistic regression for interpretable odds ratios and random forest for non-linear im
Estimate price elasticity of demand across product categories and regions using multiple linear regression on historical pricing and volume
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