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Apply NowAs we observe, some of the attributes are not required as per the requirement of the project: ['instant','casual','registered','atemp','dteday']. These can be dropped.
Task 1: Define a variable columnsToDrop to store the columns to drop: ['instant','casual','registered','atemp','dteday']
Task 2: Please drop these columns using drop() function: instant, casual, registered, atemp, dteday and store the resulting dataframe in bikesData
Hint
columnsToDrop = ['instant','casual','registered','atemp','dteday']
bikesData = bikesData.drop(columnsToDrop,1)
Some of the numerical columns will have to be scaled: ['temp','hum','windspeed']
Task 3: Define a variable columnsToScale to store the columns to scale: ['temp','hum','windspeed']
Task 4: Scale these columns using StandardScaler() function.
Hint
bikesData[columnsToScale] = scaler.fit_transform(bikesData[columnsToScale])
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