Project 22 : Credit Card Fraud Detection Using Machine Learning

Welcome to Project 22 of our Machine Learning series! In this project, we delve into the critical domain of credit card fraud detection using advanced machine learning techniques.
Credit card fraud is a significant concern for financial institutions and consumers alike. With the rise of online transactions, detecting fraudulent activities has become increasingly challenging. In this tutorial, we'll guide you through the process of building a robust fraud detection system using Python and popular machine learning libraries such as scikit-learn.
In this video, you'll learn:
The importance of credit card fraud detection and its impact on financial security.
Preprocessing techniques for handling imbalanced datasets commonly encountered in fraud detection tasks.
Implementing various machine learning algorithms such as Logistic Regression, Random Forest, and Gradient Boosting for fraud detection.
Evaluating the performance of the models using metrics like precision, recall, and F1-score.
Fine-tuning the models for improved performance and reliability.
Whether you're a beginner looking to understand the fundamentals of machine learning or an experienced practitioner aiming to enhance your skills in fraud detection, this project is designed to provide valuable insights and hands-on experience.
🔗 Useful Links & Resources:
Dataset Source: www.kaggle.com/datasets/mlg-u...
Code Repository: github.com/Chando0185/credit_...
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[@BiswajitSibun-n4b]

Best Explanation sir

[@RAZZKIRAN]

thank u, great, useful

[@arunchaudhary1722]

The video which deserves 221K views, ends up getting 221 views. It cannot be more saddening for a Machine learning enthusiast like me.
But please carry on, hopefully some day you will become a viral youtuber (which you truly deserve).
My best regards.

[@knowledgedoctor3849]

I Believe My Ganesha, All Will Be OK🌻
Thanks For Your Support, Keep Praying🌻 Har Har Mahadev 🔱

[@arunchaudhary1722]

@@knowledgedoctor3849 Har Har Mahadev 🔱

[@aniketkumre4231]

You are life saver 😊
Actually I mentioned this project in my resume and I want to clear my doubts after watching your video. I cleared all my doubts

[@knowledgedoctor3849]

I'm not bro, Mahadeva is the life saver🌻
Keep praying & support ❣️

[@aniketkumre4231]

@@knowledgedoctor3849 yes brother.
Har Har Mahadev😍

[@dedisupardi2815]

Cool 🎉

[@nisargpanchal4884]

Just one doubt, you are giving all metric scores on train/test split after doing under or oversampling but in actual operation our model is given imbalenced data in real time, when i tried checking the classification report on original train/test split I was getting a bad score

[@meet3047jhgg]

here he had done oversampling before the train test split hence the data leakage had happened due to which he had got the high accuracy ,but the real flow is to oversample only on training data and then training the model and testing which will so the result that the model is good for cases of not a fraud but will behave incorrect at actual fraud cases

[@younesgasmi8518]

​@@meet3047jhggif we apply undersampling before splitting the dataset..is this correct and don't give the data leakage issue?

[@maximalopgaming2942]

Sir please make one new video on real time face recognition attendance system on your multiple of deep learning series please sir 🙏

[@tuankhainguyen5283]

Which methodology was used in this project sir ?

[@ritikchaurasia708]

Can you make project on the nlp

[@knowledgedoctor3849]

Yeah🌻