australian_only_ann.py

# -*- coding: utf-8 -*-
"""
Created on Tue Jul 21 12:44:34 2020

@author: AjaySai
"""


# Artificial Neural Network

# Importing the libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
tf.__version__

# Part 1 - Data Preprocessing

# Importing the dataset
dataset = pd.read_csv('Credit_Card_Applications.csv')
X = dataset.iloc[:, :-1].values 
y = dataset.iloc[:, -1].values
print(X)
print(y)


# Splitting the dataset into the 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 = 0)

# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

# Part 2 - Building the ANN

# Initializing the ANN
ann = tf.keras.models.Sequential()

# Adding the input layer and the first hidden layer
ann.add(tf.keras.layers.Dense(units=6, activation='relu'))

# Adding the second hidden layer
ann.add(tf.keras.layers.Dense(units=6, activation='relu'))

# Adding the output layer
ann.add(tf.keras.layers.Dense(units=1, activation='sigmoid'))

# Part 3 - Training the ANN

# Compiling the ANN
ann.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])

# Training the ANN on the Training set
ann.fit(X_train, y_train, batch_size = 32, epochs = 100)

# Part 4 - Making the predictions and evaluating the model



# Predicting the Test set results
y_pred = ann.predict(X_test)
y_pred = (y_pred > 0.5)
print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))

from sklearn.metrics import confusion_matrix, accuracy_score,precision_score
from sklearn.metrics import roc_curve, roc_auc_score
cm = confusion_matrix(y_test, y_pred)
print(cm)
acs=accuracy_score(y_test, y_pred)
prs=precision_score(y_test, y_pred)
sensitivity1 = cm[0,0]/(cm[0,0]+cm[0,1])
specificity1 = cm[1,1]/(cm[1,0]+cm[1,1])
print("acuuracy =",acs)
print('Specificity : ', specificity1)
print('Sensitivity : ', sensitivity1 )
print("precision :",prs)

false_positive_rate1, true_positive_rate1, threshold1 = roc_curve(y_test, y_pred)
print('roc_auc_score for ANN: ', roc_auc_score(y_test, y_pred))
plt.title('Receiver Operating Characteristic - SOM+ANN')
plt.plot(false_positive_rate1, true_positive_rate1)
plt.plot([0, 1], ls="--")
plt.plot([0, 0], [1, 0] , c=".7"), plt.plot([1, 1] , c=".7")
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.show()