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    I am building a decision-support tool that acts like an investment banker for hospital deals. The workflow starts with raw historical statements—revenue, profit, cash flow, debt and related line items—pulled in from spreadsheets or a database. I need this data to be cleaned and validated automatically so that outliers, missing values and inconsistent formats are handled without manual intervention. Once the dataset is in shape, the system must calculate core ratios—operating and EBITDA margins, liquidity and leverage indicators, year-over-year growth rates, cash conversion, risk flags—and feed them into a machine-learning pipeline. I have no fixed allegiance to any one algorithm, but I would like to begin with a solid linear regression benchmark and keep the door...

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