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

This repository contains code to predict the success of a telemarketing campaign in a Portuguese bank using 15 Machine Learning techniques and analyze the results using 4 metrics: classification_report, confusion_matrix, accuracy_score, roc_auc_score.

Primary LanguagePython

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