Fraudulent transactions using Machine Learning: Developed a model for predicting fraudulent transactions , which will predict fraud.Although by checking the count of Fraud and Legal transaction from the "is_Fraud" column, I got to know that the data is imbalanced .As count of Fraud Transaction is too less as compared to legal (no fraud ) transaction . So it is Imbalanced data ,as the No_Fraud Class has a very high number of observations and the Is_Fraud Class has a very low number of observations .Hence for an Imbalanced Class dataset F1 score is the most appropriate metric. Then the model which gives the best F1 score gives a more accurate result.
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View Code? Open in Web Editor NEWfraudulent transactions using Machine Learning: Developed a model for predicting fraudulent transactions , which will predict fraud.Although by checking the count of Fraud and Legal transaction from the "is_Fraud" column, I got to know that the data is imbalanced .As count of Fraud Transaction is too less as compared to legal (no fraud ) transaction . So it is Imbalanced data ,as the No_Fraud Class has a very high number of observations and the Is_Fraud Class has a very low number of observations .Hence for an Imbalanced Class dataset F1 score is the most appropriate metric. Then the model which gives the best F1 score gives a more accurate result.