The challenge is to recognize fraudulent credit card transactions so that the customers of credit card companies are not charged for items that they did not purchase.
Main challenges involved in credit card fraud detection are:
- Enormous Data is processed every day and the model build must be fast enough to respond to the scam in time.
- Imbalanced Data i.e most of the transactions (99.8%) are not fraudulent which makes it really hard for detecting the fraudulent ones
- Data availability as the data is mostly private.
- Misclassified Data can be another major issue, as not every fraudulent transaction is caught and reported.
- Adaptive techniques used against the model by the scammers.
How to tackle these challenges?
- The model used must be simple and fast enough to detect the anomaly and classify it as a fraudulent transaction as quickly as possible.
- Imbalance can be dealt with by properly using some methods which we will talk about in the next paragraph
- For protecting the privacy of the user the dimensionality of the data can be reduced.
- A more trustworthy source must be taken which double-check the data, at least for training the model.
- We can make the model simple and interpretable so that when the scammer adapts to it with just some tweaks we can have a new model up and running to deploy.
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 login to kaggle to download the dataset.
Code : Importing all the necessary Libraries
# import the necessary packagesimport numpy as npimport pandas as pdimport matplotlib.pyplot as pltimport seaborn as snsfrom matplotlib import gridspec |
Code : Loading the Data
# 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 filedata = pd.read_csv("credit.csv") |
Code : Understanding the Data
# Grab a peek at the datadata.head() |
Code : Describing the Data
# Print the shape of the data# data = data.sample(frac = 0.1, random_state = 48)print(data.shape)print(data.describe()) |
Output :
(284807, 31)
Time V1 ... Amount Class
count 284807.000000 2.848070e+05 ... 284807.000000 284807.000000
mean 94813.859575 3.919560e-15 ... 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]
Code : Imbalance in the data
Time to explain the data we are dealing with.
# Determine number of fraud cases in datasetfraud = 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]))) |
Only 0.17% fraudulent transaction out all the transactions. The data is highly Unbalanced. Lets first apply our models without balancing it and if we don’t get a good accuracy then we can find a way to balance this dataset. But first, let’s implement the model without it and will balance the data only if needed.
Code : Print the amount details for Fraudulent Transaction
print(“Amount details of the fraudulent transaction”)fraud.Amount.describe() |
Output :
Amount details of the fraudulent transaction 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
Code : Print the amount details for Normal Transaction
print(“details of valid transaction”)valid.Amount.describe() |
Output :
Amount details of valid transaction 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
As we can clearly notice from this, the average Money transaction for the fraudulent ones is more. This makes this problem crucial to deal with.
Code : Plotting the Correlation Matrix
The correlation matrix graphically gives us an idea of how features correlate with each other and can help us predict what are the features that are most relevant for the prediction.
# Correlation matrixcorrmat = data.corr()fig = plt.figure(figsize = (12, 9))sns.heatmap(corrmat, vmax = .8, square = True)plt.show() |
In the HeatMap we can clearly see that most of the features do not correlate to other features but there are some features that either has a positive or a negative correlation with each other. For example, V2 and V5 are highly negatively correlated with the feature called Amount. We also see some correlation with V20 and Amount. This gives us a deeper understanding of the Data available to us.
Code : Separating the X and the Y values
Dividing the data into inputs parameters and outputs value format
# dividing the X and the Y from the datasetX = 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.valuesyData = Y.values |
Output :
(284807, 30) (284807, )
Training and Testing Data Bifurcation
We will be dividing the dataset into two main groups. One for training the model and the other for Testing our trained model’s performance.
# Using Scikit-learn to split data into training and testing setsfrom sklearn.model_selection import train_test_split# Split the data into training and testing setsxTrain, xTest, yTrain, yTest = train_test_split( xData, yData, test_size = 0.2, random_state = 42) |
Code : Building a Random Forest Model using scikit learn
# Building the Random Forest Classifier (RANDOM FOREST)from sklearn.ensemble import RandomForestClassifier# random forest model creationrfc = RandomForestClassifier()rfc.fit(xTrain, yTrain)# predictionsyPred = rfc.predict(xTest) |
Code : Building all kinds of evaluating parameters
# Evaluating the classifier# printing every score of the classifier# scoring in anythingfrom sklearn.metrics import classification_report, accuracy_score from sklearn.metrics import precision_score, recall_scorefrom sklearn.metrics import f1_score, matthews_corrcoeffrom 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)) |
Output :
The model used is Random Forest classifier The accuracy is 0.9995611109160493 The precision is 0.9866666666666667 The recall is 0.7551020408163265 The F1-Score is 0.8554913294797689 The Matthews correlation coefficient is0.8629589216367891
Code : Visualizing the Confusion Matrix
# printing the confusion matrixLABELS = ['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() |
Output :
Comparison with other algorithms without dealing with the imbalancing of the data.
As you can see with our Random Forest Model we are getting a better result even for the recall which is the most tricky part.
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