Applying SMOTE technique

We shall apply the SMOTE technique only on train data and keep the test data untouched so as to avoid any form of data leakage.

Note:

imblearn.over_sampling.SMOTE : Class to perform over-sampling using SMOTE.
fit_sample : This method of imblearn.over_sampling.SMOTE is used to resample the dataset.

INSTRUCTIONS

From imblearn.over_sampling import SMOTE.

from imblearn.over_sampling import SMOTE

Print the number of class-wise samples before over-sampling using the value_counts method on y_train.
```
print("Before over-sampling:\n", y_train['Class'].<< your code comes here >>)
```
Declare an instance of SMOTE as sm.
```
sm = SMOTE()
```
Use fit_sample method of sm on X_train and y_train['Class'] and store the resampled features and labels in X_train_res and y_train_res respectively.
```
X_train_res, y_train_res = sm.<< your code comes here >>(X_train, y_train['Class'])
```
Print the number of class-wise samples after over-sampling using the value_counts method on y_train.
```
print("After over-sampling:\n", y_train_res.<< your code comes here >>)
```

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