Start with the decision you need to improve. Not an algorithm. These guides connect business questions to machine learning for rows and columns, real datasets, benchmark results, and a Google Sheets workflow you can try.
No machine learning background required. Each guide explains which problems fit structured data, what inputs matter, how to read a benchmark, and where privacy or decision risk needs extra care.
Credit risk · customer acquisition · valuation · forecasting
Machine learning in finance improves recurring decisions. Which applications need review? Which customers may respond to an offer? What value or amount should a team expect? The answers often sit in customer, account, transaction, and asset tables.
Explore Finance & Banking use casesRisk prediction · diagnosis support · patient outcomes · resource planning
Healthcare data often comes in rows and columns: laboratory values, observations, diagnoses, utilization history, appointments, and operational measures. Machine learning can prioritize review, estimate risk, and help plan resources. The intended decision and the limits of the data must stay explicit.
Explore Healthcare & Life Sciences use casesDemand prediction · employee analytics · access decisions · quality control
Operational teams already manage requests, jobs, assets, measurements, schedules, costs, and outcomes. That data can support a model when a prediction changes a real decision. Good starting points include staffing, maintenance, routing, and review.
Explore Operations & Workforce Analytics use casesCustomer behaviour · retention · filtering · inventory planning
Commercial machine learning should answer a concrete operating question. Who may leave? Who is likely to attend? Which message needs review? What quantity should a team plan for? Customer, subscription, booking, product, and interaction systems store these signals in structured tables.
Explore Commerce & Industry use casesVehicle classification · valuation · maintenance · fleet decisions
Automotive and mobility decisions use specifications, operating cost, usage, condition, sensor summaries, and service history. Tabular models turn those attributes into estimates for appraisal, fleet selection, maintenance, and quality review.
Explore Automotive & Mobility use casesPolicy analysis · risk triage · fairness · resource allocation
Public-service datasets often cover eligibility, income, safety, demand, or risk. They explain the mechanics of classification. They also show why prediction and decision-making aren't the same thing. Historical outcomes reflect institutions, access, measurement choices, and past policy. They don't reflect individual behavior alone.
Explore Public Services & Risk use casesCustomer records, transactions, bookings, measurements, asset registers, and case histories are tabular data. A classification model predicts a category such as churn or approval. A regression model predicts a number such as price, demand, or duration.
The benchmark pages show how models performed on held-out data. Each result describes one dataset and one evaluation. It doesn't promise the same result on your data. The domain guides explain target definitions, useful inputs, evaluation choices, and operational consequences.
Across these guides, 21 real datasets provide concrete examples rather than invented case studies.