In this step, we will train a KNN Classifier on the noisy images so that it can predict the non-noisy image from the same.
Import the KNeighborsClassifier from scikit-learn:
from sklearn.neighbors import << your code goes here >>
Train the KNeighborsClassifier:
knn_clf = KNeighborsClassifier()
<< your code goes here >>.fit(X_train_mod, y_train_mod)
As mentioned earlier, here we are training the model on the noisy image and their corresponding original images as labels so that it can predict the original image from it's noisy version.
Note - Having trouble with the assessment engine? Follow the steps listed here
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