Big Data Analytics Project

Credit Card Fraud Detection

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

Component: DECISION TREE

Importing Libraries¶

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

Mounted at /content/drive
In [ ]:
import pandas as ps

from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report

Importing Dataset¶

In [ ]:
cred = ps.read_csv("/content/drive/MyDrive/Project - ITE2013 - Big Data - Credit Card Fraud Detection/dataset/creditcard.csv")
In [ ]:
cred
Out[ ]:
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
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
284802 172786.0 -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
284803 172787.0 -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
284804 172788.0 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
284805 172788.0 -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
284806 172792.0 -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

284807 rows × 31 columns

Shape of Dataset¶

In [ ]:
print(f"Dataset Shape : \n {cred.shape}")

Dataset Shape : 
 (284807, 31)

Exploratory Data Analysis¶

In [ ]:
print(cred.describe())

                Time            V1  ...         Amount          Class
count  284807.000000  2.848070e+05  ...  284807.000000  284807.000000
mean    94813.859575  1.758743e-12  ...      88.349619       0.001727
std     47488.145955  1.958696e+00  ...     250.120109       0.041527
min         0.000000 -5.640751e+01  ...       0.000000       0.000000
25%     54201.500000 -9.203734e-01  ...       5.600000       0.000000
50%     84692.000000  1.810880e-02  ...      22.000000       0.000000
75%    139320.500000  1.315642e+00  ...      77.165000       0.000000
max    172792.000000  2.454930e+00  ...   25691.160000       1.000000

[8 rows x 31 columns]
In [ ]:
cred.head()
Out[ ]:
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
In [ ]:
print(f"Columns / Feature / Variable names : \n {cred.columns}")

Columns / Feature / Variable names : 
 Index(['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'],
      dtype='object')
In [ ]:
print(f"Unique values of target variable / Attribute : \n {cred['Class'].unique()}")

Unique values of target variable / Attribute : 
 [0 1]
In [ ]:
# 0 means valid , 1 means fraudulent
In [ ]:
print(f"Number of samples under each target value (0 and 1) : \n {cred['Class'].value_counts()}")

Number of samples under each target value (0 and 1) : 
 0    284315
1       492
Name: Class, dtype: int64

Feature Engineering¶

Removing unneeded Features¶

In [ ]:
cred = cred.drop(['Time'], axis=1)
print(f"Features left after removal of 'Time' feature : \n{cred.columns}")

Features left after removal of 'Time' feature : 
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', 'Amount', 'Class'],
      dtype='object')

Checking for NULL or NaN values¶

In [ ]:
print(f"Dataset information : \n {cred.info()}")

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 284807 entries, 0 to 284806
Data columns (total 30 columns):
 #   Column  Non-Null Count   Dtype  
---  ------  --------------   -----  
 0   V1      284807 non-null  float64
 1   V2      284807 non-null  float64
 2   V3      284807 non-null  float64
 3   V4      284807 non-null  float64
 4   V5      284807 non-null  float64
 5   V6      284807 non-null  float64
 6   V7      284807 non-null  float64
 7   V8      284807 non-null  float64
 8   V9      284807 non-null  float64
 9   V10     284807 non-null  float64
 10  V11     284807 non-null  float64
 11  V12     284807 non-null  float64
 12  V13     284807 non-null  float64
 13  V14     284807 non-null  float64
 14  V15     284807 non-null  float64
 15  V16     284807 non-null  float64
 16  V17     284807 non-null  float64
 17  V18     284807 non-null  float64
 18  V19     284807 non-null  float64
 19  V20     284807 non-null  float64
 20  V21     284807 non-null  float64
 21  V22     284807 non-null  float64
 22  V23     284807 non-null  float64
 23  V24     284807 non-null  float64
 24  V25     284807 non-null  float64
 25  V26     284807 non-null  float64
 26  V27     284807 non-null  float64
 27  V28     284807 non-null  float64
 28  Amount  284807 non-null  float64
 29  Class   284807 non-null  int64  
dtypes: float64(29), int64(1)
memory usage: 65.2 MB
Dataset information : 
 None

Data Transformation¶

In [ ]:
print(f"Some Amount Column / Feature values : \n {cred['Amount'][0:4]}")

Some Amount Column / Feature values : 
 0    149.62
1      2.69
2    378.66
3    123.50
Name: Amount, dtype: float64

Data Preprocessing¶

In [ ]:
cred['norm_amount'] = StandardScaler().fit_transform(cred['Amount'].values.reshape(-1,1))
cred = cred.drop(['Amount'], axis=1)
print(f"Some Amount Couln values - After application of StandardScalar : \n {cred['norm_amount'][0:4]}")

Some Amount Couln values - After application of StandardScalar : 
 0    0.244964
1   -0.342475
2    1.160686
3    0.140534
Name: norm_amount, dtype: float64

Creation of features and target¶

Here, the "Class" column is dropped because it is the column which contains the result and we need to predict it, so we cannot use it as a feature.

In [ ]:
X = cred.drop(['Class'], axis=1)
Y = cred[['Class']]
In [ ]:
X
Out[ ]:
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 norm_amount
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 0.244964
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 -0.342475
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 1.160686
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 0.140534
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 -0.073403
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
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 -0.350151
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 -0.254117
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 -0.081839
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 -0.313249
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 0.514355

284807 rows × 29 columns

In [ ]:
Y
Out[ ]:
Class
0 0
1 0
2 0
3 0
4 0
... ...
284802 0
284803 0
284804 0
284805 0
284806 0

284807 rows × 1 columns

Splitting the dataset for training and testing¶

In [ ]:
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=0)
print(X_train.shape)
print(X_test.shape)
print(Y_train.shape)
print(Y_test.shape)

(199364, 29)
(85443, 29)
(199364, 1)
(85443, 1)

Creating model for Decision Tree Algorithm¶

In [ ]:
def decision_tree_classification(X_train, Y_train, X_test, Y_test):
    dec_tree_classf = DecisionTreeClassifier()
    print("START MODEL TRAINING ...")
    dec_tree_classf.fit(X_train, Y_train.values.ravel())
    print("COMPLETTION OF MODEL TRAINING")
    accuracy = dec_tree_classf.score(X_test, Y_test)
    print(f'Accuracy : {accuracy}')
    Y_predict = dec_tree_classf.predict(X_test)
    
    #CONFUSION MATRIX
    print(f"Confusion Matrix : \n {confusion_matrix(Y_test, Y_predict)}")
    
    # Classification report for F1_score
    print(f"Classification Report :- \n {classification_report(Y_test, Y_predict)}")

# DECISION TREE CLASSIFICATION MODEL CALLED
decision_tree_classification(X_train, Y_train, X_test, Y_test)

START MODEL TRAINING ...
COMPLETTION OF MODEL TRAINING
Accuracy : 0.9992509626300574
Confusion Matrix : 
 [[85266    30]
 [   34   113]]
Classification Report :- 
               precision    recall  f1-score   support

           0       1.00      1.00      1.00     85296
           1       0.79      0.77      0.78       147

    accuracy                           1.00     85443
   macro avg       0.89      0.88      0.89     85443
weighted avg       1.00      1.00      1.00     85443

Summary of Results¶

the scores are converted into percentages with approximate values ---

Accuracy = 99.92%
Precision = 78%
Recall = 76%
F1_Score = 77%