Marketing-campaigns-Data-Analysis

Marketing campaigns of a Portuguese banking institution Data

The goal of this dataset is to train a model so that it predicts whether their customers will likely to subscribe bank term deposit. However, in this exercise/project. we are simply going to observe and analyse the distrubtion of the data,search for outlier and missing values and asses the relationship between features.

Introduction of data:

I will be using data of bank here. 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.

• Input variables: bank client data:

1. • age (numeric)
2. • job : type of job (categorical:"admin.","unknown","unemployed","management","housemaid","entrepreneur","student", "blue-collar","self-employed","retired","technician","services")
3. • marital : marital status (categorical: "married","divorced","single"; note: "divorced" means divorced or widowed)
4. • education (categorical: "unknown","secondary","primary","tertiary")
5. • default: has credit in default? (binary: "yes","no")
6. • balance: average yearly balance, in euros (numeric)
7. • housing: has housing loan? (binary: "yes","no")
8. • loan: has personal loan? (binary: "yes","no") related with the last contact of the current campaign:
9. • contact: contact communication type (categorical: "unknown","telephone","cellular")
10. • day: last contact day of the month (numeric)
11. • month: last contact month of year (categorical: "jan", "feb", "mar", ..., "nov", "dec")
12. • duration: last contact duration, in seconds (numeric) other attributes:
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, -1 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: "unknown","other","failure","success")

Output variable (desired target):

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