Credit-Card-Fraud-Detection

In this kernel, we are going to predict whether a credit card transaction is fraud or not using Machine Learning.

The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.

Due to confidentiality issues, the input variables are transformed into numerical using PCA transformations.

Data Sources : Kaggle Dataset

More information : here

Outline:

1. Set up

2. Explore and Prepare Data

3. Splitting Data

4. Outliers

5. Undersampling

6. train models

7. ROC Curves

8. Fine Tuning XGBoost

9. Evaluate on test set

10. Summary

References :

hands-on machine learning with scikit-learn and tensorflow 2nd edition

Kaggle Notebook

My contact info: karansharma4336@gmail.com