Big Data Analytics Project

Credit Card Fraud Detection

</center> By:
SOUBHIK SINHA (19BIT0303)
AASHISH BANSAL (19BIT0346)

Component: NAIVE BAYES CLASSIFIER

Connecting to Google Drive¶

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

Mounted at /content/drive

Importing Packages¶

In [2]:
import numpy as np

import pandas as pd

import seaborn as sns

import matplotlib.pyplot as plt

from sklearn.metrics import confusion_matrix,auc,roc_auc_score
from sklearn.metrics import recall_score, precision_score, accuracy_score, f1_score

from sklearn.linear_model import LogisticRegression

from sklearn.model_selection import train_test_split

from sklearn.naive_bayes import GaussianNB
from sklearn.naive_bayes import BernoulliNB

from sklearn.neighbors import KNeighborsClassifier

Loading Dataset¶

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

Exploratory Data Analysis¶

Shape of the Dataset¶

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

In [4]:
print("The shape of the dataset is: ",cred.shape)

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

So, we can see that there are 2,84,807 entries in the dataset provided to us and each entry has 31 columns of data.

Sample of the Dataset¶

In [5]:
cred.head()
Out[5]:
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

Checking the type of data¶

we should see the datatype of the columns because while training, some of the algorithms do not work on the character data and they might need to be converted.

In [6]:
print("The data types present in the dataset are:",list(set(cred.dtypes.tolist())))

The data types present in the dataset are: [dtype('float64'), dtype('int64')]

So, we can see that there are Integer and Floating-point values, and since there is no character data, we do not need to convert the data to any other data type.

Obtaining the Description and other Information of Data¶

In [7]:
cred.describe()
Out[7]:
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
In [8]:
cred.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 284807 entries, 0 to 284806
Data columns (total 31 columns):
 #   Column  Non-Null Count   Dtype  
---  ------  --------------   -----  
 0   Time    284807 non-null  float64
 1   V1      284807 non-null  float64
 2   V2      284807 non-null  float64
 3   V3      284807 non-null  float64
 4   V4      284807 non-null  float64
 5   V5      284807 non-null  float64
 6   V6      284807 non-null  float64
 7   V7      284807 non-null  float64
 8   V8      284807 non-null  float64
 9   V9      284807 non-null  float64
 10  V10     284807 non-null  float64
 11  V11     284807 non-null  float64
 12  V12     284807 non-null  float64
 13  V13     284807 non-null  float64
 14  V14     284807 non-null  float64
 15  V15     284807 non-null  float64
 16  V16     284807 non-null  float64
 17  V17     284807 non-null  float64
 18  V18     284807 non-null  float64
 19  V19     284807 non-null  float64
 20  V20     284807 non-null  float64
 21  V21     284807 non-null  float64
 22  V22     284807 non-null  float64
 23  V23     284807 non-null  float64
 24  V24     284807 non-null  float64
 25  V25     284807 non-null  float64
 26  V26     284807 non-null  float64
 27  V27     284807 non-null  float64
 28  V28     284807 non-null  float64
 29  Amount  284807 non-null  float64
 30  Class   284807 non-null  int64  
dtypes: float64(30), int64(1)
memory usage: 67.4 MB

Checking the number of entries according to the Class¶

In [9]:
print(cred['Class'].value_counts())

0    284315
1       492
Name: Class, dtype: int64

Data Visualization¶

In the visualizations, '0' refers to the valid transactions (these are the transactions which are genuine) whereas '1' refers to the fraud transactions (these transactions are not genuine).

In [10]:
fig, ax = plt.subplots(1, 1)
ax.pie(cred.Class.value_counts(),autopct='%1.5f%%', labels=['Valid','Fraud'], colors=['green','red'])
plt.axis('equal')
plt.ylabel('')
Out[10]:
Text(0, 0.5, '')

Here the percentage of frauds is 0.2% which is very small as compared to the genuine transactions which makes it very difficult to find.

In [11]:
total = len(cred)
ax = sns.countplot(x="Class" , data = cred)
for p in ax.patches:
    ax.annotate('{:.1f}%'.format(100*p.get_height()/total), (p.get_x() + 0.1, p.get_height()+5))

ax.yaxis.set_ticks(np.linspace(0, total, 11))

ax.set_yticklabels(map('{:.5f}%'.format, 100*ax.yaxis.get_majorticklocs()/total))
plt.xticks(range(2), ('Genuine', 'Fraud'))
plt.title("Binary Class Label Distribution")
plt.rcParams['figure.figsize']=(4,3)
plt.show()

Feature Engineering¶

Subplots¶

In [12]:
cred.hist(figsize=(20,20),bins=2)
plt.show()

We know that the Amount and the Time are two of the most important features (we can say this even with by intuition).

Time Feature¶

Lets check whether the feature Time has anything to do wtih the frauds. For this, we can try to find out the time between the transactions.

In [13]:
print("TIME FEATURE")
cred["HOURS"] = cred["Time"]/3600 # convert to hours
cred["HOURS"].tail()

TIME FEATURE
Out[13]:
284802    47.996111
284803    47.996389
284804    47.996667
284805    47.996667
284806    47.997778
Name: HOURS, dtype: float64
In [14]:
fig, (ax1, ax2) = plt.subplots(2, 1, sharex = True, figsize=(6,10))
ax1.hist(cred.HOURS[cred.Class==0],bins=48,color='blue',alpha=0.7)
ax1.set_title('VALID')

ax2.hist(cred.HOURS[cred.Class==1],bins=48,color='orange',alpha=0.7)
ax2.set_title('FRAUD')

plt.xlabel('TIME IN HOURS')
plt.ylabel('NUMBER OF TRANSACTTIONS')
Out[14]:
Text(0, 0.5, 'NUMBER OF TRANSACTTIONS')

So, we have obtained the time between the transactions and we can use that to find a new relation, so we can drop the old column data.

In [15]:
cred = cred.drop(['Time'],axis = 1)
In [16]:
cred
Out[16]:
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 HOURS
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 0.000000
1 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 0.000000
2 -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 0.000278
3 -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 0.000278
4 -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 0.000556
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
284802 -11.881118 10.071785 -9.834783 -2.066656 -5.364473 -2.606837 -4.918215 7.305334 1.914428 4.356170 -1.593105 2.711941 -0.689256 4.626942 -0.924459 1.107641 1.991691 0.510632 -0.682920 1.475829 0.213454 0.111864 1.014480 -0.509348 1.436807 0.250034 0.943651 0.823731 0.77 0 47.996111
284803 -0.732789 -0.055080 2.035030 -0.738589 0.868229 1.058415 0.024330 0.294869 0.584800 -0.975926 -0.150189 0.915802 1.214756 -0.675143 1.164931 -0.711757 -0.025693 -1.221179 -1.545556 0.059616 0.214205 0.924384 0.012463 -1.016226 -0.606624 -0.395255 0.068472 -0.053527 24.79 0 47.996389
284804 1.919565 -0.301254 -3.249640 -0.557828 2.630515 3.031260 -0.296827 0.708417 0.432454 -0.484782 0.411614 0.063119 -0.183699 -0.510602 1.329284 0.140716 0.313502 0.395652 -0.577252 0.001396 0.232045 0.578229 -0.037501 0.640134 0.265745 -0.087371 0.004455 -0.026561 67.88 0 47.996667
284805 -0.240440 0.530483 0.702510 0.689799 -0.377961 0.623708 -0.686180 0.679145 0.392087 -0.399126 -1.933849 -0.962886 -1.042082 0.449624 1.962563 -0.608577 0.509928 1.113981 2.897849 0.127434 0.265245 0.800049 -0.163298 0.123205 -0.569159 0.546668 0.108821 0.104533 10.00 0 47.996667
284806 -0.533413 -0.189733 0.703337 -0.506271 -0.012546 -0.649617 1.577006 -0.414650 0.486180 -0.915427 -1.040458 -0.031513 -0.188093 -0.084316 0.041333 -0.302620 -0.660377 0.167430 -0.256117 0.382948 0.261057 0.643078 0.376777 0.008797 -0.473649 -0.818267 -0.002415 0.013649 217.00 0 47.997778

284807 rows × 31 columns

Amount Feature¶

To make a fraud, the amount will genuinely be considered by the fraduants because if the transactions are very huge, then they can be flagged easily but if the amount is kept relative to genuine transactions, then it will be difficult to detect.

In [17]:
fig, (ax3,ax4) = plt.subplots(2,1, figsize = (6,10), sharex = True)
ax3.hist(cred.Amount[cred.Class==0],bins=50,color='red',alpha=0.6)
ax3.set_yscale('log')
ax3.set_title('Valid')
ax3.set_ylabel('No. of transactions')

ax4.hist(cred.Amount[cred.Class==1],bins=50,color='orange',alpha=0.6)
ax4.set_yscale('log')
ax4.set_title('Fraud')
ax4.set_xlabel('Amount')
ax4.set_ylabel('No. of transactions')
Out[17]:
Text(0, 0.5, 'No. of transactions')

Scaling Amount and Time¶

In [18]:
from sklearn.preprocessing import StandardScaler
cred['SCALED_AMT'] = StandardScaler().fit_transform(cred['Amount'].values.reshape(-1,1))
cred['SCALED_TIME'] = StandardScaler().fit_transform(cred['HOURS'].values.reshape(-1,1))

Now, we can drop these columns.

In [19]:
cred = cred.drop(['Amount'],axis=1)
In [20]:
cred = cred.drop(['HOURS'],axis=1)
In [21]:
cred.head()
Out[21]:
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 Class SCALED_AMT SCALED_TIME
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 0 0.244964 -1.996583
1 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 0 -0.342475 -1.996583
2 -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 0 1.160686 -1.996562
3 -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 0 0.140534 -1.996562
4 -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 0 -0.073403 -1.996541

Other Columns¶

Let's check the correlations among the features. Those are - V1,V2,.....,V28 (total 28 columns).

In [22]:
import seaborn as sbn
import matplotlib.gridspec as gridspec
gs = gridspec.GridSpec(28, 1)
plt.figure(figsize=(6,28*4))
for i, col in enumerate(cred[cred.iloc[:,0:28].columns]):
    ax5 = plt.subplot(gs[i])
    sbn.distplot(cred[col][cred.Class == 1], bins=50, color='orange')
    sbn.distplot(cred[col][cred.Class == 0], bins=50, color='blue')
    ax5.set_xlabel('')
    ax5.set_title('FEATURE / VARIABLE : ' + str(col))
plt.show()

/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

A Correlation matrix is a table showing correlation coefficients between sets of variables.

In [23]:
correlation = cred.corr()
In [24]:
plt.figure(figsize=(14,10))
sns.heatmap(correlation, cmap="inferno",linecolor="white",linewidths=0.5)
Out[24]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f74efe7ef50>

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 - Gaussian Naive Bayes¶

Splitting the Dataset¶

For now, we are not dropping anything.

In [25]:
drop_list = []
cred = cred.drop(drop_list,axis=1)
print("The columns of the Dataset for are: {}".format(cred.columns))

The columns of the Dataset for are: Index(['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', 'Class', 'SCALED_AMT',
       'SCALED_TIME'],
      dtype='object')
In [26]:
Y = cred['Class'].values
X = cred.drop(['Class'],axis=1).values
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42, stratify=Y)
In [27]:
print("Training Data Size : ", len(Y_train), "\nTesting Data Size : ", len(Y_test))
print("Fraud cases in Testing Data obtained : ", sum(Y_test))

Training Data Size :  227845 
Testing Data Size :  56962
Fraud cases in Testing Data obtained :  98

Creating the Model¶

In [28]:
model_GNB = GaussianNB()

Fitting the Model¶

In [29]:
model_GNB.fit(X_train, Y_train)
Out[29]:
GaussianNB()

Predicting using the Model on Training Data¶

In [30]:
train_predict = model_GNB.predict(X_train)

Predicting using the Model on Testing Data¶

In [31]:
Y_predict = model_GNB.predict(X_test)

Obtaining the Probabilities¶

In [32]:
Y_predict_prob = model_GNB.predict_proba(X_test)

Recall¶

In [33]:
print("recall : ", recall_score(Y_test,Y_predict))

recall :  0.8469387755102041

Precision¶

In [34]:
print("precision : ", precision_score(Y_test,Y_predict))

precision :  0.058781869688385266

F1-Score¶

In [35]:
print("F1_score : ", f1_score(Y_test,Y_predict))

F1_score :  0.10993377483443707

Accuracy¶

In [36]:
print("accuracy : ", accuracy_score(Y_test,Y_predict))

accuracy :  0.9764053228468101

Model 2 - Columns Dropped¶

Splitting the Dataset¶

Now, we are dropping the columns based on the subplots which we analysed above.

In [37]:
drop_list = ['V1', 'V2', 'V5', 'V6', 'V7', 'V8', 'V9', 'V13', 'V15', 'V17', 'V18', 'V19', 'V20', 'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28']
cred = cred.drop(drop_list,axis=1)
print("The columns of the Dataset for are: {}".format(cred.columns))

The columns of the Dataset for are: Index(['V3', 'V4', 'V10', 'V11', 'V12', 'V14', 'V16', 'Class', 'SCALED_AMT',
       'SCALED_TIME'],
      dtype='object')
In [38]:
Y = cred['Class'].values
X = cred.drop(['Class'],axis=1).values
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42, stratify=Y)
In [39]:
print("Training Data Size : ", len(Y_train), "\nTesting Data Size : ", len(Y_test))
print("Fraud cases in Testing Data obtained : ", sum(Y_test))

Training Data Size :  227845 
Testing Data Size :  56962
Fraud cases in Testing Data obtained :  98

Creating the Model¶

In [40]:
model_modified = GaussianNB()

Fitting the Model¶

In [41]:
model_modified_fit = model_modified.fit(X_train, Y_train)

Predicting Using the Model¶

In [42]:
model_modified_fit_predict = model_modified_fit.predict(X_test)

Predicting Possibilities¶

In [43]:
model_modified_predict_prob = model_modified_fit.predict_proba(X_test)

Accuracy¶

In [44]:
accuracy = accuracy_score(Y_test, model_modified_fit_predict)
print("Accuracy: ",accuracy)

Accuracy:  0.9918191074751589

F1-Score¶

In [45]:
f1score = f1_score(Y_test, model_modified_fit_predict)
print("F1-Score: ", f1score)

F1-Score:  0.2695924764890282

Precision¶

In [46]:
precision = precision_score(Y_test, model_modified_fit_predict)
print("Precision: ", precision)

Precision:  0.15925925925925927

Recall¶

In [47]:
recall = recall_score(Y_test, model_modified_fit_predict)
print("Recall Score: ", recall)

Recall Score:  0.8775510204081632

Misclassified Samples¶

In [48]:
misclassified_samples = X_test[Y_test != model_modified_fit_predict]
In [49]:
misclassified_sample_count = misclassified_samples.shape[0]
print("No. of Misclassified Samples:", misclassified_sample_count)

No. of Misclassified Samples: 466