README.md

Online-Payments-Fraud-Detection

To identify online payment fraud with machine learning, we need to train a machine learning model for classifying fraudulent and non-fraudulent payments. For this, we need a dataset containing information about online payment fraud, so that we can understand what type of transactions lead to fraud.

Download dataset 🔗

I will start this task by importing the necessary Python libraries and the dataset

import pandas as pd
import numpy as np
data = pd.read_csv("credit card.csv")
print(data.head())

• Output

   step      type    amount     nameOrig  oldbalanceOrg  newbalanceOrig  \
0     1   PAYMENT   9839.64  C1231006815       170136.0       160296.36   
1     1   PAYMENT   1864.28  C1666544295        21249.0        19384.72   
2     1  TRANSFER    181.00  C1305486145          181.0            0.00   
3     1  CASH_OUT    181.00   C840083671          181.0            0.00   
4     1   PAYMENT  11668.14  C2048537720        41554.0        29885.86   

      nameDest  oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  
0  M1979787155             0.0             0.0        0               0  
1  M2044282225             0.0             0.0        0               0  
2   C553264065             0.0             0.0        1               0  
3    C38997010         21182.0             0.0        1               0  
4  M1230701703             0.0             0.0        0               0  

dataset has any null values or not:

print(data.isnull().sum())

• Output

step              0
type              0
amount            0
nameOrig          0
oldbalanceOrg     0
newbalanceOrig    0
nameDest          0
oldbalanceDest    0
newbalanceDest    0
isFraud           0
isFlaggedFraud    0
dtype: int64

ook at the type of transaction mentioned in the dataset:

# Exploring transaction type
print(data.type.value_counts())

• Output

CASH_OUT    2237500
PAYMENT     2151495
CASH_IN     1399284
TRANSFER     532909
DEBIT         41432
Name: type, dtype: int64

type = data["type"].value_counts()
transactions = type.index
quantity = type.values

import plotly.express as px
figure = px.pie(data, 
             values=quantity, 
             names=transactions,hole = 0.5, 
             title="Distribution of Transaction Type")
figure.show()

correlation between the features of the data with the isFraud column:

# Checking correlation
correlation = data.corr()
print(correlation["isFraud"].sort_values(ascending=False))

• Output

isFraud           1.000000
amount            0.076688
isFlaggedFraud    0.044109
step              0.031578
oldbalanceOrg     0.010154
newbalanceDest    0.000535
oldbalanceDest   -0.005885
newbalanceOrig   -0.008148
Name: isFraud, dtype: float64

transform the values of the isFraud column into No Fraud and Fraud labels to have a better understanding of the output:

data["type"] = data["type"].map({"CASH_OUT": 1, "PAYMENT": 2, 
                                 "CASH_IN": 3, "TRANSFER": 4,
                                 "DEBIT": 5})
data["isFraud"] = data["isFraud"].map({0: "No Fraud", 1: "Fraud"})
print(data.head())

• Output

   step  type    amount     nameOrig  oldbalanceOrg  newbalanceOrig  \
0     1     2   9839.64  C1231006815       170136.0       160296.36   
1     1     2   1864.28  C1666544295        21249.0        19384.72   
2     1     4    181.00  C1305486145          181.0            0.00   
3     1     1    181.00   C840083671          181.0            0.00   
4     1     2  11668.14  C2048537720        41554.0        29885.86   

      nameDest  oldbalanceDest  newbalanceDest   isFraud  isFlaggedFraud  
0  M1979787155             0.0             0.0  No Fraud               0  
1  M2044282225             0.0             0.0  No Fraud               0  
2   C553264065             0.0             0.0     Fraud               0  
3    C38997010         21182.0             0.0     Fraud               0  
4  M1230701703             0.0             0.0  No Fraud               0 

train a classification model to classify fraud and non-fraud transactions.

# splitting the data
from sklearn.model_selection import train_test_split
x = np.array(data[["type", "amount", "oldbalanceOrg", "newbalanceOrig"]])
y = np.array(data[["isFraud"]])

train the online payments fraud detection model:

# training a machine learning model
from sklearn.tree import DecisionTreeClassifier
xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.10, random_state=42)
model = DecisionTreeClassifier()
model.fit(xtrain, ytrain)
print(model.score(xtest, ytest))

• Output

0.9997391011878755

classify whether a transaction is a fraud or not by feeding about a transaction into the model:

# prediction
#features = [type, amount, oldbalanceOrg, newbalanceOrig]
features = np.array([[4, 9000.60, 9000.60, 0.0]])
print(model.predict(features))

• Output

['Fraud']