Feeding new data to a model

[RawlinsCross]

Good evening, kindof a beginner question here on how to properly feed new data (i.e. not seen during the model development) to a binary classification model. I have run my training and test data on several models and have chosen a particular model (Logistic Regression) to proceed with. I kindof took my new data set, divided it into two separate datasets (one dataset with 0s and the other with 1s) and fed each to the model - figuring this is dead wrong?

Any help on the matter would be greatly appreciate. Below is my code attempt:

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# load new data eval_file = 'StickyNickel_NewData.csv' X_new, y_new = load_dataset(eval_file) # separate the data between 0 and 1 classes # first non-sticky nickel (class 0) row_ix = where(y_new == 0)[0] X_new_0 = X_new[row_ix] # second sticky nickel (class 1) row_ix = where(y_new == 1)[0] X_new_1 = X_new[row_ix]   results = list() # predict non-sticky cases yhat = model.predict_proba(X_new_0) mean_0 = yhat.mean(0)[0] print(mean_0) results.append(yhat[:, 0])   # predict stick_cases yhat = model.predict_proba(X_new_1) mean_1 = yhat.mean(0)[0] print(mean_1) results.append(yhat[:, 0])   pyplot.boxplot(results, labels=['0', '1']) pyplot.show()