Credit Card Fraud Detection project

Before going to the code it is requested to work on a jupyter notebook. If not installed on your machine you can use Google colab.

You can download the dataset from this link

If the link is not working please go to this link and log in to kaggle to download the dataset.

 Importing all the necessary Libraries

# 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




Loading the Dataset




# Load the dataset from the csv file using pandas

# best way is to mount the drive on colab and

# copy the path for the csv file

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




Understanding the dataset




# Grab a peek at the data

data.head()

Describing the Data




# Print the shape of the data

# data = data.sample(frac = 0.1, random_state = 48)

print(data.shape)

print(data.describe())




Imbalance in the data




# Determine number 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 the amount details for fradulant transaction




print(“Amount details of the fraudulent transaction”)

fraud.Amount.describe()




Print the amount details for Normal Transactions




print(“details of valid transaction”) valid.Amount.describe()




Plotting the Correlation Matrix




# Correlation matrix

corrmat = data.corr()

fig = plt.figure(figsize = (12, 9))

sns.heatmap(corrmat, vmax = .8, square = True)

plt.show()




Separating the X values and the Y values




# dividing the X and the Y from the dataset

X = data.drop(['Class'], axis = 1)

Y = data["Class"]

print(X.shape)

print(Y.shape)

# getting just the values for the sake of processing

# (its a numpy array with no columns)

xData = X.values

yData = Y.values




Bifurication of Training and Testing data




# Using Skicit-learn to split data into training and testing sets from sklearn.model_selection import train_test_split # Split the data into training and testing sets xTrain, xTest, yTrain, yTest = train_test_split(xData, yData, test_size = 0.2, random_state = 42)




Building a Random forest model using a scikit Learning




# Building the Random Forest Classifier (RANDOM FOREST)

from sklearn.ensemble import RandomForestClassifier

# random forest model creation

rfc = RandomForestClassifier()

rfc.fit(xTrain, yTrain)

# predictions

yPred = rfc.predict(xTest)

Building all kind of parameter evaluations

# Evaluating the classifier

# printing every score of the classifier

# scoring in anything

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

 

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))




Visualizing the Confusion Matrix




# 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()