K-nearest neighbour algorithm is giving notfitted error - machine-learning

Question

K-Nearest neighbours
I am trying to perform knn algorithm on heart disease prediction database. When I try to pickel it and create model.pkl it is giving me the notfitted error. When I am running the code it is giving me the accurate prediciton but when pickel it shows the error. How should I fit this data. I am new to machine learning so please help.
from sklearn.neighbors import KNeighborsClassifier
dataset = pd.get_dummies(df, columns = ['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'ca', 'thal'])
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
standardScaler = StandardScaler()
columns_to_scale = ['age', 'trestbps', 'chol', 'thalach', 'oldpeak']
dataset[columns_to_scale] = standardScaler.fit_transform(dataset[columns_to_scale])
y = dataset['target']
X = dataset.drop(['target'], axis = 1)
from sklearn.model_selection import cross_val_score
knn_scores = []
for k in range(1,21):
knn_classifier = KNeighborsClassifier(n_neighbors = k)
score=cross_val_score(knn_classifier,X,y,cv=10)
knn_scores.append(score.mean())
plt.plot([k for k in range(1, 21)], knn_scores, color = 'red')
for i in range(1,21):
plt.text(i, knn_scores[i-1], (i, knn_scores[i-1]))
plt.xticks([i for i in range(1, 21)])
plt.xlabel('Number of Neighbors (K)')
plt.ylabel('Scores')
plt.title('K Neighbors Classifier scores for different K values')
Text(0.5, 1.0, 'K Neighbors Classifier scores for different K values')
knn_classifier
knn_classifier = KNeighborsClassifier(n_neighbors = 12)
score=cross_val_score(knn_classifier,X,y,cv=10)
score.mean()
0.8448387096774195
import pickle
pickle.dump(knn_classifier, open('model.pkl', 'wb'))
Heart_disease_detector_model = pickle.load(open('model.pkl', 'rb'))
y_pred = Heart_disease_detector_model.predict(X_test)
print('Accuracy of K – Nearest Neighbor model = ',accuracy_score(y_test, y_pred))
---------------------------------------------------------------------------
> NotFittedError Traceback (most recent call last)
> <ipython-input-79-c37bd716088c> in <module>
> 2 pickle.dump(knn_classifier, open('model.pkl', 'wb'))
> 3 Heart_disease_detector_model = pickle.load(open('model.pkl', 'rb'))
> ----> 4 y_pred = Heart_disease_detector_model.predict(X_test)
> 5 print('Accuracy of K – Nearest Neighbor model = ',accuracy_score(y_test, y_pred))
>
> c:\users\jahnavi padala\miniconda3\lib\site-packages\sklearn\neighbors\_classification.py
> in predict(self, X)
> 195 X = check_array(X, accept_sparse='csr')
> 196
> --> 197 neigh_dist, neigh_ind = self.kneighbors(X)
> 198 classes_ = self.classes_
> 199 _y = self._y
>
> c:\users\jahnavi padala\miniconda3\lib\site-packages\sklearn\neighbors\_base.py in
> kneighbors(self, X, n_neighbors, return_distance)
> 647 [2]]...)
> 648 """
> --> 649 check_is_fitted(self)
> 650
> 651 if n_neighbors is None:
>
> c:\users\jahnavi padala\miniconda3\lib\site-packages\sklearn\utils\validation.py in
> inner_f(*args, **kwargs)
> 61 extra_args = len(args) - len(all_args)
> 62 if extra_args <= 0:
> ---> 63 return f(*args, **kwargs)
> 64
> 65 # extra_args > 0
>
> c:\users\jahnavi padala\miniconda3\lib\site-packages\sklearn\utils\validation.py in
> check_is_fitted(estimator, attributes, msg, all_or_any)
> 1096
> 1097 if not attrs:
> -> 1098 raise NotFittedError(msg % {'name': type(estimator).__name__})
> 1099
> 1100
>
> NotFittedError: This KNeighborsClassifier instance is not fitted yet. Call 'fit' with appropriate arguments before using this
> estimator.

Answer 1

The error is telling you that the classifier is not yet fitted, which is exactly what it sounds like--you need to fit the model before using it. Do something like this before getting the accuracy score:
knn_classifier.fit(X, y)
So you will end up with this:
knn_classifier
knn_classifier = KNeighborsClassifier(n_neighbors = 12)
knn_classifier.fit(X, y)

Answer 2

You can't create a pickle without fitting the model. Before line pickle.dump(knn_classifier, open('model.pkl', 'wb')) write knn_classifier.fit(*your_X, your_Y*)