Rumit95 / Customer_Term_Deposit

The classification goal is to predict if the client will subscribe a term deposit (variable y).

Customer_term_deposit

• Job Feature
• Age Feature
• Month Feature
• Education Feature
• Poutcome Feature
• Data Splitting
• Data Balancing
• Model Building
• Hyperparameter tuning the Model
• Data Prediction

Introduction

The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be (or not) subscribed.

Data Set

• This dataset is public available for research. The details are described in [Moro et al., 2011].
• Number of Instances: 45211 for bank-full.csv (4521 for bank.csv)
• Number of Attributes: 16 + output attribute.

Attribute Information

• age (numeric)
• job : type of job (categorical: "admin.","unknown","unemployed","management","housemaid","entrepreneur","student", "blue-collar","self-employed","retired","technician","services")
• marital : marital status (categorical: "married","divorced","single"; note: "divorced" means divorced or widowed)
• education (categorical: "unknown","secondary","primary","tertiary")
• default: has credit in default? (binary: "yes","no")
• balance: average yearly balance, in euros (numeric)
• housing: has housing loan? (binary: "yes","no")
• loan: has personal loan? (binary: "yes","no") # Related with the last contact of the current campaign:
• contact: contact communication type (categorical: "unknown","telephone","cellular")
• day: last contact day of the month (numeric)
• month: last contact month of year (categorical: "jan", "feb", "mar", ..., "nov", "dec")
• duration: last contact duration, in seconds (numeric) # Other attributes:
• campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)
• pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric, -1 means client was not previously contacted)
• previous: number of contacts performed before this campaign and for this client (numeric)
• poutcome: outcome of the previous marketing campaign (categorical: "unknown","other","failure","success")

target column

• y - has the client subscribed a term deposit? (binary: "yes","no")

Project Objective

The classification goal is to predict if the client will subscribe a term deposit (variable y).