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Using the Machine Learning model to detect credit card fraud

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When we move towards the digital world, we shouldn’t forget that cybersecurity has been playing a major role in our life. Talks about digital security have been stiff. The main challenge we would face is abnormality.

During an online transaction, most of the product-lovers prefer credit cards. The credit limit available in credit cards would allow us to purchase even when our bank balance is insufficient. But this is great news for cyber attackers eyeing your money.

For tackling this problem, we should depend upon a system to make hardpressed transactions effortless.

This is where we need a system to track the transaction patterns. With AI, we can abort any abnormal transaction, precisely for credit card fraud detection AI.

As of now, we will come across a number of machine learning algorithms to classify unusual transactions where Artificial Intelligence detect fraud. We only need past data and the right algorithm to fit the data in the right form in case of credit card fraud detection ai.

How do we make this happen? Let’s look into the process of credit card fraud detection AI:

Import the needed libraries

The best step to detect credit card fraud detection with AI is to import the libraries. The best practice would be to import the necessary libraries in a single section for the purpose of quick modification. To use the credit card data, we can use the PCA’s transformed version or RFECV, RFE, VIF and SelectKBest to get the best model features.

Import Dataset

Machine learning helps with fraud detection. It’s quite simple to import the dataset when you use the pandas module in python. You can run the run command for importing your data. 

data=pd.read_csv("creditcard.csv")

Process and Understand the Data

The thing you need to notice about the data is its imbalance towards a single feature. This is quite valid for these kinds of data. Since many banks have adopted proper security mechanisms, hackers would find it hard to make drastic moves when we have fraud detection solution with AI.

Even now, when we find some system vulnerability, such activities can rise in number.

We cannot point out feature selection as the use case. You can apply feature selection mechanisms to know if the results have been optimised with credit card fraud detection using deep learning.

The best practice would be to scale the variable. Instead, we can leverage a standard scaler such as this:

sc = StandardScaler()

amt = data['Amt'].values

data['Amt'] = sc.fit_transform(amt.reshape(-1, 1))

This variable is also the time where we can involve external deciding factors. We can use our modelling process to stop it.

data.drop(['Time'], axis=1, inplace=True)

This way, we can also check for any duplication in the transactions and check how many transactions we have. Then, we need to remove the duplicates.

The below line of code can help you get rid of the duplicates:

data.drop_duplicates(inplace=True)

Train & Test Split

Before you split the train & test, we have got to define independent and dependent variables.

Let’s keep the dependent variable name as X and the independent variable as Y.

X = data.drop('Class', axis = 1).values

y = data['Class'].values

Now, it’s time for us to test and train data.

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 1)

Now, we have got two different data set- The train data can be used to train our model whereas the unseen data can be used to test our model.

Model Building

In credit card fraud detection machine learning, we can define models easily using a single code line.

This single line of code can fit the data model easily. Tuning these models through the selection of different optimised parameters is also possible. When the accuracy is much more even when the parameter tuning is done properly, we needn’t make it complex.

We can use plenty of models such as Decision Tree, K-Nearest neighbours, logistic regression, support vector machine models, random forest and XGBoost model.

Then all you need to do is look for the F1-score and confusion matrix. This will help you out to build the model. In comparison to different models, XGBoost model has come out as the winner. It provides you with many accurate results when compared to other models.

To put it in a nutshell

Training the machine learning model properly can result in accurate results while detecting credit card fraud. You needn’t get surprised by how accurate it would be. That’s what machine learning is for. We can hope that this practice can help a number of users to get rid of credit card fraud. In the future, we can expect that AI be used to detect fraud.

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