With reference to bank call data predict whether the client will subscribe to a term deposit or not.
About Data
link for data - https://github.com/suyashi29/python-su/blob/master/data/BankCalls.xlsx
Input variables
1.age (numeric)
2.job : type of job (categorical: “admin”, “blue-collar”, “entrepreneur”, “housemaid”, “management”, “retired”, “self-employed”, “services”, “student”, “technician”, “unemployed”, “unknown”)
3.marital : marital status (categorical: “divorced”, “married”, “single”, “unknown”)
4.education (categorical: “basic.4y”, “basic.6y”, “basic.9y”, “high.school”, “illiterate”, “professional.course”, “university.degree”, “unknown”)
5.default: has credit in default? (categorical: “no”, “yes”, “unknown”)
6.housing: has housing loan? (categorical: “no”, “yes”, “unknown”)
7.loan: has personal loan? (categorical: “no”, “yes”, “unknown”)
8.contact: contact communication type (categorical: “cellular”, “telephone”)
9.month: last contact month of year (categorical: “jan”, “feb”, “mar”, …, “nov”, “dec”)
10.day_of_week: last contact day of the week (categorical: “mon”, “tue”, “wed”, “thu”, “fri”)
11.duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y=’no’). The duration is not known before a call is performed, also, after the end of the call, y is obviously 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
12.campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)
13.pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted)
14.previous: number of contacts performed before this campaign and for this client (numeric)
15.poutcome: outcome of the previous marketing campaign (categorical: “failure”, “nonexistent”, “success”)
16.emp.var.rate: employment variation rate — (numeric)
17.cons.price.idx: consumer price index — (numeric)
18.cons.conf.idx: consumer confidence index — (numeric)
19.euribor3m: euribor 3 month rate — (numeric)
20.nr.employed: number of employees — (numeric)
Predict variable (desired target):
y — has the client subscribed a term deposit? (binary: “1”, means “Yes”, “0” means “No”)