Analyze dataset related to direct marketing campaign of financial company to create a ML model to predict whether s to predict if the client will subscribe (yes/no) to a term deposit.This will help us to look for strategies in order to improve future marketing campaigns for the bank.
1 - age (numeric)
2 - job : type of job (categorical:'admin.','blue-collar','entrepreneur','housemaid','management','retired','selfemployed','services','student','technician','unemployed','unknown')
3 - marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed)
4 - education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unk nown')
5 - default: has credit in default? (categorical: 'no','yes','unknown')
6 - balance:
7 - housing: has housing loan? (categorical: 'no','yes','unknown')
8 - loan: has personal loan? (categorical: 'no','yes','unknown')
9 - contact: contact communication type (categorical: 'cellular','telephone')
10 - month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec')
11 - day: last contact day of the week (categorical: 'mon','tue','wed','thu','fri')
12 - duration: last contact duration, in seconds (numeric).
13 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)
14 - 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)
15 - previous: number of contacts performed before this campaign and for this client (numeric)
16 - poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')
Output Feature:
deposit: The classification goal is to predict if the client will subscribe (yes/no) to a term deposit