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Detecting Fraudulent Contracts

You work in the contracts department of a software company and are asked to detect fraudulent (or wrong) contracts based on their contract value. Given the PDF versions of the contracts, you need to extract their contract value (and, optionally, any other fields you find useful) and detect outliers among them. You can either use simpler outlier detection techniques, such as those based on statistics or visualization, or more advanced ones based on machine learning.

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Contractsread pdflocationamountcleanAgreement No.dateamountremove rowsamountproductproductcontracts>20product splitamountremove rowsgroup productsproductcontractsproductbefore afterList Files/Folders Tika ParserURL Input Path to String String Manipulation String Manipulation String Manipulation String Manipulation Numeric Outliers String to Number String Manipulation GroupBy Row Filter Reference RowSplitter Numeric Outliers Concatenate GroupBy Joiner Column Renamer Contractsread pdflocationamountcleanAgreement No.dateamountremove rowsamountproductproductcontracts>20product splitamountremove rowsgroup productsproductcontractsproductbefore afterList Files/Folders Tika ParserURL Input Path to String String Manipulation String Manipulation String Manipulation String Manipulation Numeric Outliers String to Number String Manipulation GroupBy Row Filter Reference RowSplitter Numeric Outliers Concatenate GroupBy Joiner Column Renamer

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