Predicting-the-Success-of-Bank-Telemarketing-using-various-machine-learning-techniques

Classification goal:

Data from the direct marketing campaigns (phone calls) of a Portuguese banking institution were collected and the classification goal is to predict if the client will subscribe to a term deposit.

Source of the dataset:

The dataset was taken from the UCI repository.

Process description:

This repository contains a python class to predict the success of Bank Telemarketing using the following Machine Learning techniques with the incorporation of dimensionality reduction techniques (the user gets to choose it):

1. LogisticRegression
2. KNeighborsClassifier
3. SVC
4. MLPClassifier
5. DecisionTreeClassifier
6. GaussianNB
7. Perceptron
8. RandomForestClassifier
9. BaggingClassifier
10. AdaBoostClassifier
11. GradientBoostingClassifier
12. ExtraTreesClassifier
13. RidgeClassifier
14. Lasso
15. BernoulliNB
16. RandomForestRegressor

Result metrics:

1. classification_report
2. confusion_matrix
3. accuracy_score
4. roc_auc_score