We have to come up with our own cost matrix

1) The data in the Excel file (BankCampaing.xlsx) is the real direct marketing campaign data performed by a bank in Europe. The dataset contains 15 predictor variables (Age, Job, MaritalStatus, Education, Default, Balance, Housing, Loan, Day, Month, Duration, Campaign, PDays, Previous, POutcome). The outcome variable is whether a customer makes a term deposit (Deposit). Each row represents the features of a customer. You want to identify customers who make a term deposit in order to improve the direct marketing campaign. Your job is to develop the direct marketing campaign by using classification techniques (k-NN, Decision Tree, and Logistic Regression, and to select the best model among them to predict which customers will more likely make a term deposit. Develop three classification models by using Rapid Miner software (k-NN, Decision Tree, and Logistic Regression). In your models, use following features in developing your models: Preprocess data Create and apply Cost Matrix (if applicable) Normalize selected variables (if applicable) Change Threshold (if applicable) . O Your project report should include: Confusion Matrix Classification performance measures (Accuracy, Sensitivity, Specificity, Misclassification Cost) Performance comparisons of classification models from different perspectives ROC Curve and Area Under Curve (AUC) Lift Chart or Decile Chart List of 20 customers who will more probably accept direct marketing campaign. . . Explain all of your work. Explain all models you create. Explain all charts you create. Explain all results you report. Explain all discussions you made. 1) The data in the Excel file (BankCampaing.xlsx) is the real direct marketing campaign data performed by a bank in Europe. The dataset contains 15 predictor variables (Age, Job, MaritalStatus, Education, Default, Balance, Housing, Loan, Day, Month, Duration, Campaign, PDays, Previous, POutcome). The outcome variable is whether a customer makes a term deposit (Deposit). Each row represents the features of a customer. You want to identify customers who make a term deposit in order to improve the direct marketing campaign. Your job is to develop the direct marketing campaign by using classification techniques (k-NN, Decision Tree, and Logistic Regression, and to select the best model among them to predict which customers will more likely make a term deposit. Develop three classification models by using Rapid Miner software (k-NN, Decision Tree, and Logistic Regression). In your models, use following features in developing your models: Preprocess data Create and apply Cost Matrix (if applicable) Normalize selected variables (if applicable) Change Threshold (if applicable) . O Your project report should include: Confusion Matrix Classification performance measures (Accuracy, Sensitivity, Specificity, Misclassification Cost) Performance comparisons of classification models from different perspectives ROC Curve and Area Under Curve (AUC) Lift Chart or Decile Chart List of 20 customers who will more probably accept direct marketing campaign. . . Explain all of your work. Explain all models you create. Explain all charts you create. Explain all results you report. Explain all discussions you made