Update ANN.md

ANN/ANN.md

@@ -4,6 +4,7 @@
 ![ANN](ANN-Day7.jpg)
 
 # Importing the libraries
+```python
 import numpy as np
 import matplotlib.pyplot as plt
 import pandas as pd
@@ -16,8 +17,9 @@ import keras
 from keras.models import Sequential
 from keras.layers import Dense
 from sklearn.metrics import confusion_matrix
-
+````
 # Importing the dataset
+```python
 dataset = pd.read_csv('data.csv')
 del dataset['Unnamed: 32']
 X = dataset.iloc[:, 3:13].values
@@ -26,51 +28,64 @@ y = dataset.iloc[:, 13].values
 print(dataset.head())
 
 print(dataset.tail())
-
+````
+```python
 labelencoder_X_1 = LabelEncoder()
 X[:, 1] = labelencoder_X_1.fit_transform(X[:, 1])
 labelencoder_X_2 = LabelEncoder()
 X[:, 2] = labelencoder_X_2.fit_transform(X[:, 2])
 onehotencoder = OneHotEncoder(categorical_features = [1])
 X = onehotencoder.fit_transform(X).toarray()
 X = X[:, 1:]
-
+````
 # Splitting the dataset into the Training set and Test set
+```python
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
-
+````
 # Feature Scaling
+```python
 sc = StandardScaler()
 X_train = sc.fit_transform(X_train)
 X_test = sc.transform(X_test)
-
+````
 # Initialising the ANN
+```python
 classifier = Sequential()
-
+````
 # Adding the input layer and the first hidden layer
+```python
 classifier.add(Dense(6,kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
-
+````
 # Adding the second hidden layer
+```python
 classifier.add(Dense(6, kernel_initializer = 'uniform', activation = 'relu'))
-
+````
 # Adding the output layer
+```python
 classifier.add(Dense(1, kernel_initializer = 'uniform', activation = 'sigmoid'))
-
+````
 # Compiling the ANN
+```python
 classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
-
+```
 # Fitting the ANN to the Training set
+```python
 classifier.fit(X_train, y_train, batch_size = 10, epochs = 100)
-
-# Part 3 - Making the predictions and evaluating the model
+````
+# Making the predictions and evaluating the model
 
 # Predicting the Test set results
+```python
 y_pred = classifier.predict(X_test)
 y_pred = (y_pred > 0.5)
-
+````
 # Making the Confusion Matrix
+```python
 cm = confusion_matrix(y_test, y_pred)
 print("Our accuracy is {}%".format(((cm[0][0] + cm[1][1])/57)*100))
-
+````
 # Plot of Confusion Matrix
+```python
 sns.heatmap(cm,annot=True)
 plt.savefig('h.png')
+````