from google.colab import files
setdate = files.upload()

import pandas as pd
dataset_med_nod = pd.read_csv('med_nod.csv')

dataset_med_nod.head()

dataset_med_nod.shape

x = dataset_med_nod.iloc[:,0:7].values
y = dataset_med_nod.iloc[:,7].values

x[1,:]

x.shape, y.shape

# split dataset
from sklearn.model_selection import train_test_split
x_train,x_test, y_train,y_test = train_test_split(x,y,test_size=0.3,random_state=0)

x_train.shape, y_train.shape, x_test.shape, y_test.shape


from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD,Adam

model_new = Sequential()
model_new.add(Dense(10,input_shape=(7,),activation='tanh'))
model_new.add(Dense(8,activation='tanh'))
model_new.add(Dense(6,activation='tanh'))
model_new.add(Dense(1,activation='sigmoid'))
model_new.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model_new.summary()

hist = model_new.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=100, batch_size=100)

# Commented out IPython magic to ensure Python compatibility.
import matplotlib.pyplot as plt
# %matplotlib inline
import seaborn as sns
sns.set()
 
acc = hist.history['accuracy']
val = hist.history['val_accuracy']
epochs = range(1, len(acc) + 1)
 
plt.plot(epochs, acc, '-', label='Training accuracy')
plt.plot(epochs, val, ':', label='Validation accuracy')
plt.title('Training and Validation Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend(loc='lower right')
plt.plot()

# Evaluate on test data
test_loss, test_acc = model_new.evaluate(x_test, y_test)

print('Test Accuracy: {}'.format(test_acc))

from sklearn.metrics import confusion_matrix
 
y_predicted = model_new.predict(x_test) > 0.5
mat = confusion_matrix(y_test, y_predicted)
labels = ['class 0', 'class 1']
 
sns.heatmap(mat, square=True, annot=True, fmt='d', cbar=False, cmap='Blues',
            xticklabels=labels, yticklabels=labels)
 
plt.xlabel('Predicted label')
plt.ylabel('Actual label')

from sklearn.metrics import classification_report

print(classification_report(y_test, y_predicted))