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Since most of our data has already been scaled, we should scale the columns that are not yet scaled (Amount and Time).
We shall use StandardScaler to scale the "Amount" column and the "Time" column.
From sklearn.preprocessing
import StandardScaler
from sklearn.preprocessing import StandardScaler
Get StandardScaler()
instances.
scaler_amount = StandardScaler()
scaler_time = StandardScaler()
Use fit_transform
of scaler_amount
on the X_train['Amount']
and save the transformed values in X_train['normAmount']
.
X_train['normAmount'] = scaler_amount .<< your code comes here >>(X_train['Amount'].values.reshape(-1, 1))
Use transform
of scaler_amount
on the X_test['Amount']
and save the transformed values in X_test['normAmount']
.
X_test['normAmount'] = scaler_amount .<< your code comes here >>(X_test['Amount'].values.reshape(-1, 1))
Use fit_transform
of scaler_time
on the X_train['Time']
and save the transformed values in X_train['normTime']
.
X_train['normTime'] = scaler_time .<< your code comes here >>(X_train['Time'].values.reshape(-1, 1))
Use transform
of scaler_time
on the X_test['Time']
and save the transformed values in X_test['normTime']
.
X_test['normTime'] = scaler_time .<< your code comes here >>(X_test['Time'].values.reshape(-1, 1))
Drop Time
and Amount
columns from X_train
and X_test
.
X_train = X_train.drop(['Time', 'Amount'], axis=1)
X_test = X_test.drop(['Time', 'Amount'], axis=1)
Display the top 5 rows of X_train
.
X_train.head()
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