it is my first time doing something with financial data. I have a dataset with account numbers and some other information about each client (some clients span more than one row since we have info for each month in a different row). I managed to clean and create some models, here are the confusion matrices, the classification reports and AUC:
Logistic regression
[[185847 62897]
[ 1 1061]]
precision recall f1-score support
not buy 1.00 0.75 0.86 248744
buy 0.02 1.00 0.03 1062
accuracy 0.75 249806
macro avg 0.51 0.87 0.44 249806
weighted avg 1.00 0.75 0.85 249806
AUC train = 0.9168592981611143
AUC test = 0.9150300677458543
Random Forest Classifier:
[[245503 3241]
[ 960 102]]
precision recall f1-score support
not buy 1.00 0.99 0.99 248744
buy 0.03 0.10 0.05 1062
accuracy 0.98 249806
macro avg 0.51 0.54 0.52 249806
weighted avg 0.99 0.98 0.99 249806
AUC train = 0.9996866568080237
AUC test = 0.9139101966925902
Gradient Boosting Classifier:
[[184940 63804]
[ 3 1059]]
precision recall f1-score support
not buy 1.00 0.74 0.85 248744
buy 0.02 1.00 0.03 1062
accuracy 0.74 249806
macro avg 0.51 0.87 0.44 249806
weighted avg 1.00 0.74 0.85 249806
AUC train = 0.8800353734759541
AUC test = 0.8657829269466372
Voting Classifier (from all the three above):
[[211316 37428]
[ 213 849]]
precision recall f1-score support
not buy 1.00 0.85 0.92 248744
buy 0.02 0.80 0.04 1062
accuracy 0.85 249806
macro avg 0.51 0.82 0.48 249806
weighted avg 0.99 0.85 0.91 249806
AUC train = 0.9987531510931085
AUC test = 0.9160262741936392
Since I do not have any experience I am not sure which model is producing better results. Can you help me understand which one and why? Thank you!