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Information about a local bank\'s marketing campaign and need to predict whether someone will open a savings account.

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Assignment overview

Information about a local bank's marketing campaign and need to predict whether someone will open a savings account.  

Requirements higher accuracy, AUC score and so on. After model output, we need to extract data from test data set (customer id and outcome). please see sample submission file, the id are fixed and only cbind from predicted outcome . also attached the model_tuningv3.2.R for your reference. any model to use such as random forest , xgboost and glm etc

example

id         outcome

103174 0

134356 0

127090 1

132548 0

137587 1

115477 0

 

Dataset description

Files

train.csv - the training set

test.csv - the test set

sample_submission.csv - a sample submission file in the correct format (Note that the outcome has to be the class probabilities)

Columns

Client information

id - client id (numeric)

age - age of client (numeric)

job - type of job (categorical: "admin.","artisan","entrepreneur", "housemaid", "management", "retired", "self-employed", "services", "student", "technician", "unemployed", "unknown")

civil - marital status of client (categorical: "divorced", "married", "single","unknown"; note: "divorced" means divorced or widowed)

education - education of client (categorical: "4K", "6K", "K9", "K12", "illiterate", "apprenticeship", "university", "unknown")

credit - has credit in default? (categorical: "no","yes","unknown")

hloan - has housing loan? (categorical: "no","yes","unknown")

ploan - has personal loan? (categorical: "no","yes","unknown")

Campaign details

ctype - contact communication type (categorical: "cellular","telephone")

month - last contact month of year (categorical: "jan", "feb", "mar", …, "nov", "dec")

day - last contact day of the week (categorical: "mon","tue","wed","thu","fri")

ccontact - current number of contacts performed during this campaign and for this client (numeric, includes last contact)

lcdays - number of days that passed by since client was last contacted by a previous campaign (numeric; 999 means client was not previously contacted)

pcontact - number of contacts performed before this campaign and for this client (numeric)

presult - outcome previous marketing campaigns (categorical: "failure","nonexistent","success")

Socioeconomic indicators

employment - employment variation rate - quarterly indicator (numeric)

cprice - consumer price index - monthly indicator (numeric)

cconf - consumer confidence index - monthly indicator (numeric)

euri3 - euribor 3 month rate - daily indicator (numeric)

employees - number of employees - quarterly indicator (numeric)

Outcome variable (target)

outcome - has the client opened a saving account? (binary: 1 = "yes", 0 = "no")

 

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Attachments:

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