model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# fit the keras model on the dataset
model.fit(x_train, y_train, epochs=150, batch_size=10)
# evaluate the keras model
_, accuracy = model.evaluate(x_train, y_train)
print('Test Accuracy: %.2f' % (accuracy * 100))
# make probability predictions with the model
predictions = model.predict(x_test)
# round predictions
rounded = [round(x[0]) for x in predictions]
# make class predictions with the model
predictions = model.predict_classes(x_test)
# In[89]:
# Plot accuracy comparisons between scenario 1(assumed features) & scenario 2(features from analysis)
plot_data = pd.read_excel(
r'C:\Users\Life\Desktop\GMU\CS-504\dataset\AccuracyResults.xlsx')
plot_data
# In[98]:
# Plot accuracy comparisons between scenario 1(assumed features) & scenario 2(features from analysis)
# Logistic Regression, SVM, Naive Bayes
plot_data = pd.read_excel(
r'C:\Users\Life\Desktop\GMU\CS-504\dataset\AccuracyResults.xlsx')
model.add(Dense(1, activation='sigmoid'))
# compile keras model, classification model
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# fit keras model
model.fit(np.array(predictor), np.array(y_train), epochs=10, batch_size=10)
model_loss = pd.DataFrame(model.history.history)
model_loss.plot()
# evaluate keras mdoel accuracy
_, accuracy = model.evaluate(pred_dt, y_train)
print('Accuracy: %.2f' % (accuracy * 100))
train_pred = model.predict_classes(predictor)
print(classification_report(y_train, train_pred))
print(confusion_matrix(y_train, train_pred))
#[[286 1]
#Building KNN model -----------------------------------------------
from sklearn.neighbors import KNeighborsClassifier as KNC
knn = KNC(n_neighbors=7)
knn.fit(predictor, y_train.values.ravel())
y_train_pred = knn.predict(predictor)
knn.score(predictor, y_train) # 81.86%
# check prediction accuracy of train data and classification error
print(confusion_matrix(y_train, y_train_pred))
print(classification_report(y_train, y_train_pred)) # accuracy = 82%
