Deepak Kumar is taking an Advanced Python course taught by Ma'am Nusrat. As part of a class project, he is working on detecting online payment fraud using machine learning. He loads a dataset containing historical fraudulent transaction information to train a model to classify payments as fraudulent or non-fraudulent. After preprocessing the data, Deepak splits it into training and test sets. He trains a model and checks the accuracy on the test set, achieving close to 100% accuracy. He concludes machine learning can effectively detect online payment fraud.

1. NAME: DEEPAK KUMAR
COURSE: ADVANCED PYTHON
TEACHER: MA’AM NUSRAT

2. TITLE:
ONLINE PAYMENTS FRAUD
DETECTION

3. • Introduction
• 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. For this task, I took
dataset online, which contains historical
information about fraudulent
transactions which can be used to
detect fraud in online payments.

4. Here, Import the libraries and load the dataset in the program.

5. Check the NaN value in columns. If available fill that by mean.

6. Here, I find which type of transaction is used.

7. That pi chart shows the transaction accordint to the percentage.

8. Here, I assign the number to each transaction type.

9. Using the sklearn model split the data into dependent and
independent variables. And train the model

10. Using the sklearn model split the data into dependent and
independent variables. And train the model.
Finally check the accuracy using the test data it’s almost
100% which is good.

11. Result

12. Conclusion
So this is the method to detect online payments fraud with
machine learning using Python. Detecting online payment
fraud is one of the applications of data science in the
finance department.
This will be very helpful for the finance department they can
easily analyze and predict the according the their data

13. Thank you