The Project Conclusion

We also made some modifications to the model by having the data undersampled and oversampled (with imbalance learn).

The results are shown here:

-> Random Undersampling

Classifiers	F1 Score	Accuracy Score	Precision Score	Recall Score	ROC AUC Score
Random Guessing	0.501245	0.501367	0.501347	0.501149	0.501358
Logistic Regression	0.944553	0.945775	0.966213	0.923844	0.945772
Neural Network	0.957754	0.957991	0.963063	0.952695	0.958023
Random Forest	0.995382	0.995362	0.99106	0.999742	0.995364
Gaussian Naive Bayes	0.87051	0.877277	0.920874	0.825597	0.877288

-> Oversampling with Imbalance Learn

Classifiers	F1 Score	Accuracy Score	Precision Score	Recall Score	ROC AUC Score
Random Guessing	0.498242	0.500363	0.497933	0.498552	0.500346
Logistic Regression	0.907205	0.910514	0.93717	0.879102	0.910364
Neural Network	0.928982	0.929131	0.926331	0.932271	0.929096
Random Forest	0.994949	0.994948	0.990014	0.999934	0.994972
Gaussian Naive Bayes	0.800169	0.820146	0.893598	0.72521	0.819687

Thank you for reading our journey from start to end of this project.