Credit Card Fraud Detection
Yash Bhatt
# Problem Statement
- Customers are using online and offline banking very frequently these days. The number of fraudulent transactions has also increased drastically due to which credit card companies are facing a lot of challenges.
- To detect fraudulent activities and to get profit by not losing the customers' information is the main goal for the Bank. Also to retain the more profitable customers by detecting and stopping the fraudulent activities.
- So, We need to classify the fraudulent credit card transactions to avoid losses.
# Dataset Used
# Data Imbalance Techniques
- SMOTE
- ADASYN
# Hyperparameter Tuning
- Random Search CV
# Model Evaluation
- ROC / AUC curve
- Precision
- Recall
# ML Models
- Random Forest Classifier
- XGBOOST
- Decision Tree Classifier
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Posted May 18, 2023
Detect credit card fraud using ML models (Random Forest, XGBoost, Decision Tree) with data imbalance techniques, hyperparameter tuning, and evaluation metrics.
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