final team project1

Age could be translated into values from 1 to 10. Weight is almost always missing. Payer code and medical specialty is usually missing.

Too many missing weight values for it to be a useful feature and removing ids that could lead to bias. Payer code and medical specialty are missing over 50% of the time so those were also removed.

Changes ? values in race to Unknown to make results more readable.

Changes ? values in race to Unknown to make results more readable.

partitions data into training and test

Normalizes large columns like num lab procedures and num medications to prevent bias in logistic regression as large values can lead to bias.

Normalizes large columns like num lab procedures and num medications to prevent bias in logistic regression as large values can lead to bias.

Too many missing weight values for it to be a useful feature and removing ids that could lead to bias. Payer code and medical specialty are missing over 50% of the time so those were also removed.

Changes diag 1 back to String

Removes .0 values that were a holdover from changing a float to a string, but keeps decimals.