USING KNIME    What data to include and how to classify it...

USING KNIME 

What data to include and how to classify it (Integer, string) 

Data Set Information:

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 assess if the product (bank term deposit) would be ('yes') or not ('no') subscribed.

Attribute Information:

Input variables:
# bank client data:
1 - age 
2 - job : type of job 
3 - marital : marital status 
4 - education 
5 - default: has history of loan default? 
6 - balance: checking account balance
7 - housing: has housing loan? 
8 - loan: has personal loan? 
# related with the last contact of the current campaign:
9 - contact: contact communication type 
10 - day: last contact day of the month
11 - month: last contact month of year 
12 - duration: last contact duration, in seconds. Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, 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.
# other attributes:
13 - campaign: number of contacts performed during this campaign and for this client (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)

Output variable (desired target):
17 - y - has the client subscribed to a term deposit?

You are employed as a business analyst by a multinational banking enterprise. The bank's Executive Vice President of Marketing and Chief Marketing Officer wants you to do an analysis of the Portuguese bank data to see if the subscription of a new savings product (term deposit) can be predicted.

Before any advanced analysis can be done however, you must first examine the raw data in detail, thoroughly describe it, find raw data problems and fix them. Only then will it be possible to do any meaningful analysis. As with any analytic project, the first order of business is to complete an EDA and then report those findings to the executive team. The stakes are high. The projected deposits for the new product may exceed $25 billion within one year.

In this assignment you will become familiar with the native KNIME functionality to accomplish the EDA tasks and the construction of two types of predictive models. The calculations in this assignment are to be done only with native KNIME nodes.

Using only native KNIME nodes to do the calculations, answer the following questions:

Q1: How many observations (rows) and how many variables (columns) are there in the raw data?