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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