<jupyter_start><jupyter_text>Applying LogisticRegression Model<jupyter_code>#Applying LogisticRegression Model 

LogisticRegressionModel = LogisticRegression(penalty='l2',solver='sag',C=1.0,random_state=33)
LogisticRegressionModel.fit(X_train, y_train)
#Calculating Details
print('LogisticRegressionModel Train Score is : ' , LogisticRegressionModel.score(X_train, y_train))
print('LogisticRegressionModel Test Score is : ' , LogisticRegressionModel.score(X_test, y_test))
print('LogisticRegressionModel Classes are : ' , LogisticRegressionModel.classes_)
print('LogisticRegressionModel No. of iteratios is : ' , LogisticRegressionModel.n_iter_)
#Calculating Prediction
y_pred = LogisticRegressionModel.predict(X_test)
y_pred_prob = LogisticRegressionModel.predict_proba(X_test)
print('Predicted Value for LogisticRegressionModel is : ' , y_pred[:10])
print('Prediction Probabilities Value for LogisticRegressionModel is : ' , y_pred_prob[:10])
#Calculating Confusion Matrix
CM = confusion_matrix(y_test, y_pred)
print('Confusion Matrix is : \n', CM)
# drawing confusion matrix
sns.heatmap(CM, center = True)
plt.show()<jupyter_output><empty_output><jupyter_text>Applying SVC Model<jupyter_code>#Applying SVC Model 

'''
sklearn.svm.SVC(C=1.0, kernel=’rbf’, degree=3, gamma=’auto_deprecated’, coef0=0.0, shrinking=True,
                probability=False, tol=0.001, cache_size=200, class_weight=None,verbose=False,
                max_iter=-1, decision_function_shape='ovr’, random_state=None)
'''
SVCModel = SVC(kernel= 'rbf',# it can be also linear,poly,sigmoid,precomputed
               max_iter=100,C=1.0,gamma='auto')
SVCModel.fit(X_train, y_train)
#Calculating Details
print('SVCModel Train Score is : ' , SVCModel.score(X_train, y_train))
print('SVCModel Test Score is : ' , SVCModel.score(X_test, y_test))
print('----------------------------------------------------')
#Calculating Prediction
y_pred = SVCModel.predict(X_test)
print('Predicted Value for SVCModel is : ' , y_pred[:10])
#Calculating Confusion Matrix
CM = confusion_matrix(y_test, y_pred)
print('Confusion Matrix is : \n', CM)
# drawing confusion matrix
sns.heatmap(CM, center = True)
plt.show()<jupyter_output><empty_output>
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