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from sklearn.naive_bayes import MultinomialNB

from sklearn.neighbors import KNeighborsClassifier

import matplotlib.pylab as plt

import matplotlib.pylab as plt

import seaborn as sns

import sklearn.metrics as pm

import sklearn.metrics as pm

knn_model = KNeighborsClassifier(n_neighbors=7)
knn_model.fit(X_train, y_train)
y_pred = knn_model.predict(X_test)

import sklearn.metrics as pm
pm.confusion_matrix(y_test, y_pred)

tn, fp, fn, tp = pm.confusion_matrix(y_test, y_pred).ravel()

predictions = knn_model.predict(X_train)

#show the confusion matrix is using heatmap from the seaborn library.

import seaborn as sns
import numpy as np
from sklearn.metrics import confusion_matrix

#Generate the confusion matrix
cf_matrix = pm.confusion_matrix(y_test, y_pred)
ax = sns.heatmap(cf_matrix, annot=True, cmap='Blues', fmt='.2f')
ax.set_title('Confusion Matrix\n\n');
ax.set_xlabel('\nActual Values')
ax.set_ylabel('Predicted Values ');
ax.xaxis.set_ticklabels(['healthy (0)','diabetic (1)'])
ax.yaxis.set_ticklabels(['healthy (0)','diabetic (1)'])

# Display the visualization of the Confusion Matrix.
plt.show()

nb_model = MultinomialNB()
nb_model.fit(X_train, y_train)
y_pred = nb_model.predict(X_test)

import sklearn.metrics as pm
tn, fp, fn, tp = pm.confusion_matrix(y_test, y_pred).ravel()
print('true negative: ', tn)
print('false positive: ', fp)
print('false negative: ', fn)
print('true positive: ', tp)