insurancefrauddetection's Introduction
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ankitnigam1985 skaiphd ssitb ritikakulshrestha suryansh2020 maktwist anupwa saket007saket nilaydesh1993 manishs86 manishbhat5 muluayele999 abdelrhman-gaber aichouramine sankalpadutta summuk satyajitmohapatra-datascientist dotcom3721 nvesram embedevelop fychf abhik-digital n-varshney karthikgotimukula jatin-658 mshans66 srinivasaragavan adarshbaronia teshomebalkew vivekanandhalapeti cprojectai sumitkg86 rajesh-research jkhan-prog hp241t hemachandranmu sfhussain8 venkatesh-akula satyake sjm20066 jannathfatima shaurabhtech dhinagaran-s sushil-ds harishprog datamaniac03 srinivasmadsi satsin727 mmilannaik tadikondavinay divyamsingh18 nimanita humant01 ritikbhambid jeezyb101 usmankhan46 shubhamskg shahaliz sohamdalal17 habit69insurancefrauddetection's Issues
Suggestions For Encoding
One hot encoding makes sense when your variables has classes which has no sense in being compared E.g. Male & Female does not signify anything.
Label encoding assigns a numerical value to the classes in the variable. Education JD < MD.
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