Nobody wrote these in advance. Someone described a question in plain language, and our build pipeline designed an analysis for it — chose the method, wrote the R, validated the output, and rendered the report you can open below. Each one became a module its owner can re-run on new data whenever they like.
These are commissioned. You bring a question we have never seen and get an analysis built for it, which is yours to keep and re-run. Library case studies are the opposite: prebuilt and instant — already written, always available, ready to run. Most people start with the library and commission a build when they need something specific.
Estimates price elasticity of demand for avocados using log-log regression of log(total_volume) on log(average_price), controlling for type (conventio
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, and residua
Segment e-commerce customers into behavioral groups based on purchase recency, frequency, and monetary value using K-Means clustering for targeted mar
Segment ecommerce customers into behavioral groups using Recency, Frequency, and Monetary (RFM) analysis with K-Means clustering. Identify high-value,
Quantifies how delivery speed affects customer review scores by joining orders and reviews on order_id, computing delivery_days, and running OLS regre
Predict which customers are at risk of churning using behavioral engagement, transaction history, satisfaction, and demographic features. Identifies h
Identifies which customer attributes drive churn using logistic regression for interpretable odds ratios and random forest for non-linear importance c
Estimate price elasticity of demand across product categories and regions using multiple linear regression on historical pricing and volume data. Iden
Detects fraudulent credit card transactions using isolation forest (unsupervised) and logistic regression (supervised) scoring methods, enabling side-
Binary classification model to identify fraudulent credit card transactions from 28 PCA-transformed features using SMOTE oversampling and multiple alg
Statistical analysis of fraudulent vs legitimate credit card transactions using exploratory visualizations, distribution comparisons, temporal analysi
Forecast FMCG revenue 3-6 months ahead using seasonal decomposition and ARIMA modeling on historical monthly sales data. Decomposes trend, seasonal, a
Which passenger characteristics predicted survival — the classic teaching dataset, analysed end to end as a commissioned deck.
The Titanic survival question delivered as an instant snapshot.
Which physicochemical properties drive expert wine quality scores, as a commissioned multi-card deck.
The wine quality question delivered as an instant snapshot.
Exploratory analysis of IBM HR employee engagement data exploring satisfaction dimensions, department differences, tenure patterns, and attrition prof
Comprehensive analysis of global tech industry layoffs (2020-2024) with temporal trends, sector breakdowns, geographic patterns, and AI-focused compan
Identifies which employee attributes predict attrition using logistic regression (interpretable odds ratios with 95% CI) and XGBoost with SHAP values
Identifies anomalous telemetry segments using supervised machine learning classification. Analyzes signal statistical features, peak characteristics,
Time series analysis and forecasting of demand across multiple SKUs, warehouses, regions, and suppliers. Uses ARIMA modeling with seasonal decompositi
Predict which orders will experience late delivery using shipping mode, scheduled duration, market region, and product attributes. Binary classificati
Comprehensive exploratory data analysis of laptop specifications, pricing, and market segments with automated profiling of distributions, correlations
Comprehensive exploratory analysis of customer personality profiles, demographic segments, spending patterns across product categories, purchase chann
Binary classification model to predict customer churn using demographic, service usage, and billing features. Identifies high-risk customers and quant
Analyze customer lifetime value (LTV) distribution, identify high-value vs at-risk customers, and understand what subscription patterns and service ad
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