Big Data Analytics Project

Credit Card Fraud Detection

By:
SOUBHIK SINHA    => (19BIT0303)
AASHISH BANSAL => (19BIT0346)

Component: RANDOM FOREST

Connecting to Google Drive¶

In [1]:
from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive

Importing Packages¶

In [2]:
import pandas as ps
import numpy as ny
import matplotlib.pyplot as pypt
from matplotlib import gridspec
import seaborn as sbn

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

from sklearn.metrics import f1_score, matthews_corrcoef
from sklearn.metrics import classification_report, accuracy_score 
from sklearn.metrics import confusion_matrix
from sklearn.metrics import precision_score, recall_score

Loading Dataset¶

In [3]:
cred = ps.read_csv("/content/drive/MyDrive/Project - ITE2013 - Big Data - Credit Card Fraud Detection/dataset/creditcard.csv")

Exploratory Data Analysis¶

In [4]:
cred.head()
Out[4]:
Time V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20 V21 V22 V23 V24 V25 V26 V27 V28 Amount Class
0 0.0 -1.359807 -0.072781 2.536347 1.378155 -0.338321 0.462388 0.239599 0.098698 0.363787 0.090794 -0.551600 -0.617801 -0.991390 -0.311169 1.468177 -0.470401 0.207971 0.025791 0.403993 0.251412 -0.018307 0.277838 -0.110474 0.066928 0.128539 -0.189115 0.133558 -0.021053 149.62 0
1 0.0 1.191857 0.266151 0.166480 0.448154 0.060018 -0.082361 -0.078803 0.085102 -0.255425 -0.166974 1.612727 1.065235 0.489095 -0.143772 0.635558 0.463917 -0.114805 -0.183361 -0.145783 -0.069083 -0.225775 -0.638672 0.101288 -0.339846 0.167170 0.125895 -0.008983 0.014724 2.69 0
2 1.0 -1.358354 -1.340163 1.773209 0.379780 -0.503198 1.800499 0.791461 0.247676 -1.514654 0.207643 0.624501 0.066084 0.717293 -0.165946 2.345865 -2.890083 1.109969 -0.121359 -2.261857 0.524980 0.247998 0.771679 0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752 378.66 0
3 1.0 -0.966272 -0.185226 1.792993 -0.863291 -0.010309 1.247203 0.237609 0.377436 -1.387024 -0.054952 -0.226487 0.178228 0.507757 -0.287924 -0.631418 -1.059647 -0.684093 1.965775 -1.232622 -0.208038 -0.108300 0.005274 -0.190321 -1.175575 0.647376 -0.221929 0.062723 0.061458 123.50 0
4 2.0 -1.158233 0.877737 1.548718 0.403034 -0.407193 0.095921 0.592941 -0.270533 0.817739 0.753074 -0.822843 0.538196 1.345852 -1.119670 0.175121 -0.451449 -0.237033 -0.038195 0.803487 0.408542 -0.009431 0.798278 -0.137458 0.141267 -0.206010 0.502292 0.219422 0.215153 69.99 0

Shape of the Dataset¶

This will tell us the number of rows and columns which are present in the dataset.

In [5]:
print("The shape of the dataset is: {}".format(cred.shape))

The shape of the dataset is: (284807, 31)

Description of Dataset¶

In [6]:
cred.describe()
Out[6]:
Time V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20 V21 V22 V23 V24 V25 V26 V27 V28 Amount Class
count 284807.000000 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 284807.000000 284807.000000
mean 94813.859575 1.758743e-12 -8.252298e-13 -9.636929e-13 8.316157e-13 1.591952e-13 4.247354e-13 -3.050180e-13 8.693344e-14 -1.179712e-12 7.094854e-13 1.875015e-12 1.053488e-12 7.137527e-13 -1.491363e-13 -5.225914e-13 -2.280712e-13 -6.428517e-13 4.958987e-13 7.060712e-13 1.766038e-12 -3.406543e-13 -5.713163e-13 -9.725303e-13 1.464139e-12 -6.989087e-13 -5.615260e-13 3.332112e-12 -3.518886e-12 88.349619 0.001727
std 47488.145955 1.958696e+00 1.651309e+00 1.516255e+00 1.415869e+00 1.380247e+00 1.332271e+00 1.237094e+00 1.194353e+00 1.098632e+00 1.088850e+00 1.020713e+00 9.992014e-01 9.952742e-01 9.585956e-01 9.153160e-01 8.762529e-01 8.493371e-01 8.381762e-01 8.140405e-01 7.709250e-01 7.345240e-01 7.257016e-01 6.244603e-01 6.056471e-01 5.212781e-01 4.822270e-01 4.036325e-01 3.300833e-01 250.120109 0.041527
min 0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00 -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01 -2.458826e+01 -4.797473e+00 -1.868371e+01 -5.791881e+00 -1.921433e+01 -4.498945e+00 -1.412985e+01 -2.516280e+01 -9.498746e+00 -7.213527e+00 -5.449772e+01 -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00 -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01 0.000000 0.000000
25% 54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01 -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01 -5.354257e-01 -7.624942e-01 -4.055715e-01 -6.485393e-01 -4.255740e-01 -5.828843e-01 -4.680368e-01 -4.837483e-01 -4.988498e-01 -4.562989e-01 -2.117214e-01 -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01 -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02 5.600000 0.000000
50% 84692.000000 1.810880e-02 6.548556e-02 1.798463e-01 -1.984653e-02 -5.433583e-02 -2.741871e-01 4.010308e-02 2.235804e-02 -5.142873e-02 -9.291738e-02 -3.275735e-02 1.400326e-01 -1.356806e-02 5.060132e-02 4.807155e-02 6.641332e-02 -6.567575e-02 -3.636312e-03 3.734823e-03 -6.248109e-02 -2.945017e-02 6.781943e-03 -1.119293e-02 4.097606e-02 1.659350e-02 -5.213911e-02 1.342146e-03 1.124383e-02 22.000000 0.000000
75% 139320.500000 1.315642e+00 8.037239e-01 1.027196e+00 7.433413e-01 6.119264e-01 3.985649e-01 5.704361e-01 3.273459e-01 5.971390e-01 4.539234e-01 7.395934e-01 6.182380e-01 6.625050e-01 4.931498e-01 6.488208e-01 5.232963e-01 3.996750e-01 5.008067e-01 4.589494e-01 1.330408e-01 1.863772e-01 5.285536e-01 1.476421e-01 4.395266e-01 3.507156e-01 2.409522e-01 9.104512e-02 7.827995e-02 77.165000 0.000000
max 172792.000000 2.454930e+00 2.205773e+01 9.382558e+00 1.687534e+01 3.480167e+01 7.330163e+01 1.205895e+02 2.000721e+01 1.559499e+01 2.374514e+01 1.201891e+01 7.848392e+00 7.126883e+00 1.052677e+01 8.877742e+00 1.731511e+01 9.253526e+00 5.041069e+00 5.591971e+00 3.942090e+01 2.720284e+01 1.050309e+01 2.252841e+01 4.584549e+00 7.519589e+00 3.517346e+00 3.161220e+01 3.384781e+01 25691.160000 1.000000

Number of Frauds¶

In [7]:
valid_transactions = cred[cred['Class'] == 0]
fraud_transactions = cred[cred['Class'] == 1]
percentage = len(fraud_transactions)/float(len(valid_transactions))
print('No. of Valid Transactions : {}'.format(len(cred[cred['Class'] == 0])))
print('No. of Fraudulent Transactions : {}'.format(len(cred[cred['Class'] == 1])))
print("The percentage of Fraud Transactions are: {}".format(percentage*100))

No. of Valid Transactions : 284315
No. of Fraudulent Transactions : 492
The percentage of Fraud Transactions are: 0.17304750013189596

Problem of Unbalanced Dataset¶

Fraud Transaction Amount Details¶

In [8]:
fraud_transactions.Amount.describe()
Out[8]:
count     492.000000
mean      122.211321
std       256.683288
min         0.000000
25%         1.000000
50%         9.250000
75%       105.890000
max      2125.870000
Name: Amount, dtype: float64

Valid Transaction Amount Details¶

In [9]:
valid_transactions.Amount.describe()
Out[9]:
count    284315.000000
mean         88.291022
std         250.105092
min           0.000000
25%           5.650000
50%          22.000000
75%          77.050000
max       25691.160000
Name: Amount, dtype: float64

Have a look over the mean for transaction for the Fraudulet cases - they are much higher than the valid ones.

In [10]:
cormatx = cred.corr()
figure = pypt.figure(figsize = (12,9))
sbn.heatmap(cormatx,vmax = 0.8 , square = True)
pypt.show()

If you see the two consecuive coloured blocks of every feature (from V1,V2,V3....to amount and class), you will see there is not much correlation among the features.

Models¶

Model 1¶

Splitting the Dataset¶

For now, we are not dropping anything.

In [11]:
Y = cred["Class"]
X = cred.drop(['Class'],axis = 1)
In [12]:
x_Train, x_Test, y_Train, y_Test = train_test_split(X, Y, test_size = 0.2, random_state = 42)

Creating the Models¶

In [13]:
model_default = RandomForestClassifier()

Fitting on the Model¶

In [14]:
model_default.fit(x_Train, y_Train)
Out[14]:
RandomForestClassifier()

Predicting Results¶

In [15]:
y_Predict = model_default.predict(x_Test)

Precision¶

In [16]:
precision = precision_score(y_Test, y_Predict)
print("Precision: {}".format(precision))

Precision: 0.9746835443037974

Accuracy¶

In [17]:
accuracy = accuracy_score(y_Test, y_Predict)
print("Accuracy: {}".format(accuracy))

Accuracy: 0.9995962220427653

F1-Score¶

In [18]:
f1_score = f1_score(y_Test, y_Predict)
print("F1-Score: {}".format(f1_score))

F1-Score: 0.8700564971751412

Recall¶

In [19]:
recall = recall_score(y_Test, y_Predict)
print("Recall: {}".format(recall))

Recall: 0.7857142857142857

Confusion Matrix¶

In [20]:
LABELS = ['Valid Transactions', 'Fraudulent Transactions']
confusion_matrix = confusion_matrix(y_Test, y_Predict)
pypt.figure(figsize =(4, 4))
sbn.heatmap(confusion_matrix, xticklabels = LABELS, yticklabels = LABELS, annot = True, fmt ="d");
pypt.title("Confusion Matrix")
pypt.ylabel('KNOWN TRUE CLASS')
pypt.xlabel('Predicted Class')
pypt.show()

Model 2 - Columns Dropped¶

Splitting the Dataset¶

For now, we are dropping the columns based on correlation.

In [21]:
Y = cred["Class"]
X = cred.drop(['V1', 'V2', 'V5', 'V6', 'V7', 'V8', 'V9', 'V13', 'V15', 'V17', 'V18', 'V19', 'V20', 'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28'],axis = 1)
In [22]:
x_Train, x_Test, y_Train, y_Test = train_test_split(X, Y, test_size = 0.2, random_state = 42)

Creating the Models¶

In [23]:
model_default = RandomForestClassifier()

Fitting on the Model¶

In [24]:
model_default.fit(x_Train, y_Train)
Out[24]:
RandomForestClassifier()

Predicting Results¶

In [25]:
y_Predict = model_default.predict(x_Test)

Precision¶

In [26]:
precision = precision_score(y_Test, y_Predict)
print("Precision: {}".format(precision))

Precision: 1.0

Accuracy¶

In [27]:
accuracy = accuracy_score(y_Test, y_Predict)
print("Accuracy: {}".format(accuracy))

Accuracy: 1.0

Recall¶

In [28]:
recall = recall_score(y_Test, y_Predict)
print("Recall: {}".format(recall))

Recall: 1.0

User Input Testing¶

In [29]:
time = input("Enter the Time: ")
amount = input("Enter the Amount: ")
v3 = input("Enter V3: ")
v4 = input("Enter V4: ")
v10 = input("Enter V10: ")
v11 = input("Enter V11: ")
v12 = input("Enter V12: ")
v14 = input("Enter V14: ")
v16 = input("Enter V16: ")
c = input("Enter the expected class: ")

Enter the Time: 1.0
Enter the Amount: 378.0
Enter V3: 0.265789
Enter V4: 0.248654
Enter V10: 0.483531
Enter V11: 0.765954
Enter V12: 0.798568
Enter V14: 0.916425
Enter V16: 0.463523
Enter the expected class: 1
In [30]:
df = ps.DataFrame(columns=["Time","Amount","V3", "V4", "V10", "V11", "V12", "V14", "V16", "Class"])
df1 = ps.DataFrame(data=[[time,amount,v3,v4,v10,v11,v12,v14,v16,c]],columns=["Time","Amount","V3", "V4", "V10", "V11", "V12", "V14", "V16", "Class"])
df = ps.concat([df,df1],axis=0)
checking = model_default.predict(df)
if(checking[0]==0):
    print("This is a Genuine Transaction.")
else:
    print("This is a Fraud Transaction.")

This is a Fraud Transaction.

/usr/local/lib/python3.7/dist-packages/sklearn/base.py:488: FutureWarning: The feature names should match those that were passed during fit. Starting version 1.2, an error will be raised.
Feature names must be in the same order as they were in fit.

  warnings.warn(message, FutureWarning)