Project - Bike Rental Forecasting

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End to End Project - Bikes Assessment - Cleaning the data

Cleaning the data

As 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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