These features can be derived from the raw set of features:
isWorking: Is a workingday and not a holiday, 0: Is not a workingday and is a holiday
monthCount: count of the number of months from the beginning of the dataset
xformHr: transform by shifting the hours by 5 hrs, if the hours are greater than 5, we subtract 5, else we add 19.
dayCnt: count of the days from the beginning of the dataset
xformWorkHr: transforming the hour dataset to make the non-working days to have hours from 25 to 48
cntDeTrended: De-trended count values
Task: Complete the statements for calculating each of the above derived attributes.
Hint
bikesData['isWorking'] = np.where(np.logical_and(bikesData.workingday==1,bikesData.holiday==0),1,0)
bikesData['monthCount'] = mnth_cnt(bikesData)
bikesData['xformHr'] = np.where(bikesData.hr>4,bikesData.hr-5,bikesData.hr+19)
bikesData['dayCount'] = pd.Series(range(bikesData.shape[0]))/24
bikesData['xformWorkHr'] = (1-bikesData.isWorking)*24 + bikesData.xformHr
bikesData['cntDeTrended'] = bikesData.cnt - bike_lm.predict(X)
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