Explanation

Credit Card Fraud Classification using Python

Kaggle dataset loaded in Python using pandas. Null values handled and categorical data encoded. Features like Amount, Category, State, Merchant, Location used for model training.
Target:is_fraud.
Algorithms used: Logistic Regression, KNN, Naive Bayes, Decision Tree, Random Forest, SVC.
Models stored as pickle files and predictions made based on user inputs.

December end shows spike in total transaction volume ~4 lakh+.

Result

Random Forest achieved 90%+ accuracy. Categories like shopping network payment (shopping_net),unclassified sites( misc_net) and grocery point of sale (grocery_pos or supermarket) had some frauds. Overall, most categories were clean.

Conclusion

The project successfully implemented a credit card fraud detection system using various machine learning algorithms. The Random Forest model with an accuracy of over 90%. The analysis of transaction categories provided fraud patterns and more robust fraud detection systems.