main.py

# import the necessary packages 
import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt 
import seaborn as sns 
from matplotlib import gridspec 
from sklearn.model_selection import train_test_split 
from sklearn.ensemble import RandomForestClassifier 
from sklearn.metrics import classification_report, accuracy_score 
from sklearn.metrics import precision_score, recall_score 
from sklearn.metrics import f1_score, matthews_corrcoef 
from sklearn.metrics import confusion_matrix 

data = pd.read_csv("creditcard.csv") 

data.head()

print(data.shape) 
print(data.describe()) 

# shows the no. of fraud cases in dataset 
fraud = data[data['Class'] == 1] 
valid = data[data['Class'] == 0] 
outlierFraction = len(fraud)/float(len(valid)) 
print(outlierFraction) 
print('Fraud Cases: {}'.format(len(data[data['Class'] == 1]))) 
print('Valid Transactions: {}'.format(len(data[data['Class'] == 0]))) 

print("Amount details of the fraudulent transaction") 
fraud.Amount.describe() 

print("details of valid transaction") 
valid.Amount.describe() 

# Correlation matrix 
corrmat = data.corr() 
fig = plt.figure(figsize = (12, 9)) 
sns.heatmap(corrmat, vmax = .8, square = True) 
plt.show() 


# divides the X,Y from the dataset 
X = data.drop(['Class'], axis = 1) 
Y = data["Class"] 
print(X.shape) 
print(Y.shape) 

# get just the values for processing 
# (its a numpy array with no columns) 
xData = X.values 
yData = Y.values 

# Split the data into training and testing sets (by using scikit-learn)
xTrain, xTest, yTrain, yTest = train_test_split( 
		xData, yData, test_size = 0.2, random_state = 42) 
print("Number of fraud cases in the test set: {}".format(np.sum(yTest == 1)))

# Building the Random Forest Classifier (RANDOM FOREST) 
# random forest model creation 
rfc = RandomForestClassifier() 
rfc.fit(xTrain, yTrain) 
# predictions 
yPred = rfc.predict(xTest) 



# Evaluating the classifier 
# printing every score of the classifier 
# scoring in anything 


# n_outliers = len(fraud) 
# n_errors = (yPred != yTest).sum() 
# print("The model used is Random Forest classifier") 

# acc = accuracy_score(yTest, yPred) 
# print("The accuracy is {}".format(acc)) 

# prec = precision_score(yTest, yPred) 
# print("The precision is {}".format(prec)) 

# rec = recall_score(yTest, yPred) 
# print("The recall is {}".format(rec)) 

# f1 = f1_score(yTest, yPred) 
# print("The F1-Score is {}".format(f1)) 

# MCC = matthews_corrcoef(yTest, yPred) 
# print("The Matthews correlation coefficient is{}".format(MCC)) 


# # printing the confusion matrix 
# LABELS = ['Normal', 'Fraud'] 
# conf_matrix = confusion_matrix(yTest, yPred) 
# plt.figure(figsize =(12, 12)) 
# sns.heatmap(conf_matrix, xticklabels = LABELS, 
# 			yticklabels = LABELS, annot = True, fmt ="d"); 
# plt.title("Confusion matrix") 
# plt.ylabel('True class') 
# plt.xlabel('Predicted class') 
# plt.show() 

n_outliers = len(fraud) 
n_errors = (yPred != yTest).sum() 
print("Number of outliers (fraud): {}".format(n_outliers))
print("Number of errors: {}".format(n_errors))

print("The model used is Random Forest classifier") 

acc = accuracy_score(yTest, yPred) 
print("The accuracy is {}".format(acc)) 

prec = precision_score(yTest, yPred) 
print("The precision is {}".format(prec)) 

rec = recall_score(yTest, yPred) 
print("The recall is {}".format(rec)) 

f1 = f1_score(yTest, yPred) 
print("The F1-Score is {}".format(f1)) 

MCC = matthews_corrcoef(yTest, yPred) 
print("The Matthews correlation coefficient is {}".format(MCC)) 

# printing the confusion matrix 
LABELS = ['Normal', 'Fraud'] 
conf_matrix = confusion_matrix(yTest, yPred) 
plt.figure(figsize=(12, 12)) 
sns.heatmap(conf_matrix, xticklabels=LABELS, yticklabels=LABELS, annot=True, fmt="d"); 
plt.title("Confusion matrix") 
plt.ylabel('True class') 
plt.xlabel('Predicted class') 
plt.show()