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Variable Name Term deposit Age Job Marital Education Default: has credit in default? Housing Loan Contact Month Day_of_week Duration Campaign Pdays Previous Ethnicity Poutcome Emp.var.rate Cons.price.idx Cons.conf.idx Euribor (3m) Nr.employed: Description of the Dataset Description Has the client subscribed a term deposit? 1 if yes, 0 if no. Age of Customer in Years Job Status Marital Status Level of education Default Status Has availed Housing Loan? Has availed Personal Loan? Contact communication type Last contact month of year Last contact day of the week Last contact duration, in seconds # of contacts performed during this campaign and for this client (includes last contact) # of days that passed by after the client was last contacted from a previous campaign (999 means client was not previously contacted) # of contacts performed before this campaign and for this client Caucasian is the reference ethnic category; Ethnicity African: Is the customer of African ethnicity? 1=Yes, 0=No; Outcome of the previous marketing campaign Employment variation rate: quarterly indicator Consumer price index: monthly indicator Consumer confidence index: monthly indicator Euribor 3 month rate # of employees: quarterly indicator Category Binary ('1', '0') Numeric Categorical Categorical Categorical Categorical Categorical Categorical Categorical Categorical Categorical Numeric Numeric Numeric Numeric Numeric Categorical Numeric Numeric Numeric Numeric Numeric Duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then Term Deposit='0'). Yet, the duration is not known before a call is performed. Also, after the end of the call Term Deposit is clearly known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.

Variable Name Term deposit Age Job Marital Education Default: has credit in default? Housing Loan Contact Month Day_of_week Duration Campaign Pdays Previous Ethnicity Poutcome Emp.var.rate Cons.price.idx Cons.conf.idx Euribor (3m) Nr.employed: Description of the Dataset Description Has the client subscribed a term deposit? 1 if yes, 0 if no. Age of Customer in Years Job Status Marital Status Level of education Default Status Has availed Housing Loan? Has availed Personal Loan? Contact communication type Last contact month of year Last contact day of the week Last contact duration, in seconds # of contacts performed during this campaign and for this client (includes last contact) # of days that passed by after the client was last contacted from a previous campaign (999 means client was not previously contacted) # of contacts performed before this campaign and for this client Caucasian is the reference ethnic category; Ethnicity African: Is the customer of African ethnicity? 1=Yes, 0=No; Outcome of the previous marketing campaign Employment variation rate: quarterly indicator Consumer price index: monthly indicator Consumer confidence index: monthly indicator Euribor 3 month rate # of employees: quarterly indicator Category Binary ('1', '0') Numeric Categorical Categorical Categorical Categorical Categorical Categorical Categorical Categorical Categorical Numeric Numeric Numeric Numeric Numeric Categorical Numeric Numeric Numeric Numeric Numeric Duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then Term Deposit='0'). Yet, the duration is not known before a call is performed. Also, after the end of the call Term Deposit is clearly known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Variable Name Term deposit Age Job Marital Education Default: has credit in default? Housing Loan Contact Month Day_of_week Duration Campaign Pdays Previous Ethnicity Poutcome Emp.var.rate Cons.price.idx Cons.conf.idx Euribor (3m) Nr.employed: Description of the Dataset Description Has the client subscribed a term deposit? 1 if yes, 0 if no. Age of Customer in Years Job Status Marital Status Level of education Default Status Has availed Housing Loan? Has availed Personal Loan? Contact communication type Last contact month of year Last contact day of the week Last contact duration, in seconds # of contacts performed during this campaign and for this client (includes last contact) # of days that passed by after the client was last contacted from a previous campaign (999 means client was not previously contacted) # of contacts performed before this campaign and for this client Caucasian is the reference ethnic category; Ethnicity African: Is the customer of African ethnicity? 1=Yes, 0=No; Outcome of the previous marketing campaign Employment variation rate: quarterly indicator Consumer price index: monthly indicator Consumer confidence index: monthly indicator Euribor 3 month rate # of employees: quarterly indicator Category Binary ('1', '0') Numeric Categorical Categorical Categorical Categorical Categorical Categorical Categorical Categorical Categorical Numeric Numeric Numeric Numeric Numeric Categorical Numeric Numeric Numeric Numeric Numeric Duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then Term Deposit='0'). Yet, the duration is not known before a call is performed. Also, after the end of the call Term Deposit is clearly known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.
Transcribed Image Text:Variable Name Term deposit Age Job Marital Education Default: has credit in default? Housing Loan Contact Month Day_of_week Duration Campaign Pdays Previous Ethnicity Poutcome Emp.var.rate Cons.price.idx Cons.conf.idx Euribor (3m) Nr.employed: Description of the Dataset Description Has the client subscribed a term deposit? 1 if yes, 0 if no. Age of Customer in Years Job Status Marital Status Level of education Default Status Has availed Housing Loan? Has availed Personal Loan? Contact communication type Last contact month of year Last contact day of the week Last contact duration, in seconds # of contacts performed during this campaign and for this client (includes last contact) # of days that passed by after the client was last contacted from a previous campaign (999 means client was not previously contacted) # of contacts performed before this campaign and for this client Caucasian is the reference ethnic category; Ethnicity African: Is the customer of African ethnicity? 1=Yes, 0=No; Outcome of the previous marketing campaign Employment variation rate: quarterly indicator Consumer price index: monthly indicator Consumer confidence index: monthly indicator Euribor 3 month rate # of employees: quarterly indicator Category Binary ('1', '0') Numeric Categorical Categorical Categorical Categorical Categorical Categorical Categorical Categorical Categorical Numeric Numeric Numeric Numeric Numeric Categorical Numeric Numeric Numeric Numeric Numeric Duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then Term Deposit='0'). Yet, the duration is not known before a call is performed. Also, after the end of the call Term Deposit is clearly known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.
(e) Partition the dataset randomly into training (70%) and test (30%) samples. Fit your models on the training dataset and report the performance of the model in the test set. (f) Conduct a cross validation test of your model out-of-sample prediction performance. (g) Plot the ROC for your model on a graph and compute the respective AUCs, Area Under the Curve performance metrics. (h) Explain the information relayed in your answers to questions (e), (f) and (g). (i) Comment on whether there is an important ethical aspect, which should be considered, in relation to the deployment of your preferred model. (j) Report any other performance evaluation and discussion that you view as useful to the bank, in its aim to determine factors pertaining to the loan approval/rejection rate. Notes: Consider utilising additional references outside of the course material. Ensure that you can justify all decisions made in the assignment, using quantitative rationale where possible.
Transcribed Image Text:(e) Partition the dataset randomly into training (70%) and test (30%) samples. Fit your models on the training dataset and report the performance of the model in the test set. (f) Conduct a cross validation test of your model out-of-sample prediction performance. (g) Plot the ROC for your model on a graph and compute the respective AUCs, Area Under the Curve performance metrics. (h) Explain the information relayed in your answers to questions (e), (f) and (g). (i) Comment on whether there is an important ethical aspect, which should be considered, in relation to the deployment of your preferred model. (j) Report any other performance evaluation and discussion that you view as useful to the bank, in its aim to determine factors pertaining to the loan approval/rejection rate. Notes: Consider utilising additional references outside of the course material. Ensure that you can justify all decisions made in the assignment, using quantitative rationale where possible.
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(e) Partition the dataset randomly into training (70%) and test (30%) samples. Fit your models on the training dataset and report the performance of the model in the test set. (f) Conduct a cross validation test of your model out-of-sample prediction performance. (g) Plot the ROC for your model on a graph and compute the respective AUCs, Area Under the Curve performance metrics. (h) Explain the information relayed in your answers to questions (e), (f) and (g). (i) Comment on whether there is an important ethical aspect, which should be considered, in relation to the deployment of your preferred model. (j) Report any other performance evaluation and discussion that you view as useful to the bank, in its aim to determine factors pertaining to the loan approval/rejection rate. Notes: Consider utilising additional references outside of the course material. Ensure that you can justify all decisions made in the assignment, using quantitative rationale where possible.
Transcribed Image Text:(e) Partition the dataset randomly into training (70%) and test (30%) samples. Fit your models on the training dataset and report the performance of the model in the test set. (f) Conduct a cross validation test of your model out-of-sample prediction performance. (g) Plot the ROC for your model on a graph and compute the respective AUCs, Area Under the Curve performance metrics. (h) Explain the information relayed in your answers to questions (e), (f) and (g). (i) Comment on whether there is an important ethical aspect, which should be considered, in relation to the deployment of your preferred model. (j) Report any other performance evaluation and discussion that you view as useful to the bank, in its aim to determine factors pertaining to the loan approval/rejection rate. Notes: Consider utilising additional references outside of the course material. Ensure that you can justify all decisions made in the assignment, using quantitative rationale where possible.
Variable Name Term deposit Age Job Marital Education Default: has credit in default? Housing Loan Contact Month Day_of_week Duration Campaign Pdays Previous Ethnicity Poutcome Emp.var.rate Cons.price.idx Cons.conf.idx Euribor (3m) Nr.employed: Description of the Dataset Description Has the client subscribed a term deposit? 1 if yes, 0 if no. Age of Customer in Years Job Status Marital Status Level of education Default Status Has availed Housing Loan? Has availed Personal Loan? Contact communication type Last contact month of year Last contact day of the week Last contact duration, in seconds # of contacts performed during this campaign and for this client (includes last contact) # of days that passed by after the client was last contacted from a previous campaign (999 means client was not previously contacted) # of contacts performed before this campaign and for this client Caucasian is the reference ethnic category; Ethnicity African: Is the customer of African ethnicity? 1=Yes, 0=No; Outcome of the previous marketing campaign Employment variation rate: quarterly indicator Consumer price index: monthly indicator Consumer confidence index: monthly indicator Euribor 3 month rate # of employees: quarterly indicator Category Binary ('1', '0') Numeric Categorical Categorical Categorical Categorical Categorical Categorical Categorical Categorical Categorical Numeric Numeric Numeric Numeric Numeric Categorical Numeric Numeric Numeric Numeric Numeric Duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then Term Deposit='0'). Yet, the duration is not known before a call is performed. Also, after the end of the call Term Deposit is clearly known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.
Transcribed Image Text:Variable Name Term deposit Age Job Marital Education Default: has credit in default? Housing Loan Contact Month Day_of_week Duration Campaign Pdays Previous Ethnicity Poutcome Emp.var.rate Cons.price.idx Cons.conf.idx Euribor (3m) Nr.employed: Description of the Dataset Description Has the client subscribed a term deposit? 1 if yes, 0 if no. Age of Customer in Years Job Status Marital Status Level of education Default Status Has availed Housing Loan? Has availed Personal Loan? Contact communication type Last contact month of year Last contact day of the week Last contact duration, in seconds # of contacts performed during this campaign and for this client (includes last contact) # of days that passed by after the client was last contacted from a previous campaign (999 means client was not previously contacted) # of contacts performed before this campaign and for this client Caucasian is the reference ethnic category; Ethnicity African: Is the customer of African ethnicity? 1=Yes, 0=No; Outcome of the previous marketing campaign Employment variation rate: quarterly indicator Consumer price index: monthly indicator Consumer confidence index: monthly indicator Euribor 3 month rate # of employees: quarterly indicator Category Binary ('1', '0') Numeric Categorical Categorical Categorical Categorical Categorical Categorical Categorical Categorical Categorical Numeric Numeric Numeric Numeric Numeric Categorical Numeric Numeric Numeric Numeric Numeric Duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then Term Deposit='0'). Yet, the duration is not known before a call is performed. Also, after the end of the call Term Deposit is clearly known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.
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