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# Predicting the Test set results
y_pred = classifier.predict(X_test)
y_pred = (y_pred > 0.5)

# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred) 

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# Fitting the ANN to the Training set
classifier.fit(X_train, y_train, batch_size = 10, epochs = 100)

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# Adding the input layer and the first hidden layer
classifier.add(Dense(units = 16, kernel_initializer = 'uniform', activation = 'relu', input_dim = 30))

# Adding the second hidden layer
classifier.add(Dense(units = 16, kernel_initializer = 'uniform', activation = 'relu'))

# Adding the output layer
classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))

# Compiling the ANN

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# Initialising the ANN
classifier = Sequential()

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# Importing the Keras libraries and packages
import keras
from keras.models import Sequential
from keras.layers import Dense

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#Splitting into Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)

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#Encoding Categorical Data
from sklearn.preprocessing import LabelEncoder
labelencoder = LabelEncoder()

y = labelencoder.fit_transform(y)

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#Seperating dependent and independent variables. 

X = dataset.iloc[:, 2:32].values  #Note: Exclude Last column with all NaN values.
y = dataset.iloc[:, 1].values

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import numpy as np
import pandas as pd

#Importing dataset
dataset = pd.read_csv('breast_cancer.csv')

#Check the first 5 rows of the dataset. 
	dataset.head(5)

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#Uploading the Dataset 
from google.colab import files
uploaded = files.upload()

#Save uploaded file on the Virtual Machine's 
#Thanks to user3800642 from StackOverflow 

with open("breast_cancer.csv", 'w') as f:
    f.write(uploaded[uploaded.keys()[0]])