Detect Credit Card Fraud in Just Few Minutes
Last Updated on January 25, 2024 by Editorial Team
Author(s): Sara M.
Originally published on Towards AI.
In this article I want to show you how a complex analysis in a critical sector like banking could be done in a short time and without programming expertise.
So, for the demo, I picked the Pranjal Saxena article that proposes a solution using Machine Learning & Python to do the analysis.
The point is to try to do the analysis with only ChatGPT4.
The dataset is the same as used in the article.
Letβs begin.
ChatGPT for Data Analysis β A Beginnerβs Guide
An complete tutorial on using ChatGPT for data analysis.
towardsdatascience.com
βData Processing & Understandingβ Step
Without importing any library and without writing any line of code, we will just ask ChatGPT to give us the information
And his answer after analyzing the dataset we give him is:
The result are the same as Pranjal Saxena result.
Isnβt it amazing ?
We got the result with 0 code, no environment needed, not a single libraryβ¦
Next step, is Data Cleaning. We will ask him to remove duplicates if found.
The same result was found again.
What is interesting is that you can see the code used !
βTrain & Test Split & Model Buildingβ Step
I have asked him without going into details to check the accuracy of βdecision tree modelβ :
Here again, we got the same result (99,92% ) as Pranjal Saxena result.
and about the βF1-scoreβ check:
We can go further and check the other models but itβs not the purpose.
ChatGPT doesnβt need you to tell him how to Analyze
Until now, the analysis done was guided by us.
What if we tell him to do his own analysis ?! (I upload a test file with 100 000 credit cards)
And the surprise that he proposes a detailed approach to analyze the Data which I found very complete.
Here is his answer:
Some results from the βData Inspectionβ Step:
Some results from the βExploratory Data Analysisβ Step:
Conclusion
This demonstration underlines the transformative potential of AI in Data Analysis. ChatGPT-4βs ability to replicate expert-level analysis without requiring coding skills or specific environment setups signifies a shift in how data-driven decisions can be approached. The next article will be about automating the AI analysis process with a data pipeline using AWS services.
If you have any questions, feedback, or would like to share your experiences, please feel free to reach out in the comments section.
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Published via Towards AI