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Few of the features of bikesData data set may need Scaling. For example, if we see the values of features - temp, hum and windspeed - their values are in different scales (ranges), hence, we need to apply Scaling to these features values for our ML algorithms to work fine on them.
Please follow the below steps:
Please define a python list called
columnsToScale which contains list of features ( 'temp', 'hum', 'windspeed') which needs to be Scaled.
To apply 'Feature Scaling' on these features (columnsToScale), please create an instance of StandardScaler called
scaler and complete and execute the below code:
train_set[columnsToScale] = scaler.<<your code come here>>(train_set[columnsToScale]) test_set[columnsToScale] = scaler.transform(test_set[columnsToScale])
Now check the metrics values (mean, standard deviation, etc.) of the scaled features.
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