Performed EDA, Data Pre-processing, Imbalance data and Supervised Machine learning to predict customer transaction is fraud using features such as services that customer has signed up for, customer account information, and demographic information about the customer.
• Handled class imbalance using SMOTE and ADASYN techniques and came up with the best model after analyzing different models.
• Implemented different algorithms like Logistic Regression, Decision Tree, Random Forest, and XGBoost and achieved the recall score of 100% using hyperparameter tuning of Random Forest.
Performed EDA, Data Pre-processing, Imbalance data and Supervised Machine learning to predict customer transaction is fraud using features such as services that customer has signed up for, customer account information, and demographic information about the customer.