Fluent Essays

You need to do:1. I have completed the excel form named Assignment_1, please correct the errors/deficiencies, refer to Bank Data notes and metadata. 2. Only do part A of following question.Question: Based on the CRISP DM model and the data set provided to you do the following:A) Explore the data - clearly describe the data and identify problems/issues, if any.B) Prepare the data for analysisC) Prepare models (charts, graphs, etc) using Excel for the Bank to better understand the information contained in the data. Create a PivotTable, a PivotTable Chart, a PivoteTable Slicer and PivotTable Timeline to filter the data. Finally, advise the Bank about which customers would positively respond to a telemarketing campaign. You are required to justify your recommendation properly . (10 marks for the Excel models and 5 Marks for the recommendation)The ultimate objective of this part of the exercise is to make sure Bank management and employees, who know nothing about how to model and the techniques of modeling, could understand what is contained in the data and your models. To do so, write down a report detailing what you did in steps a, b, and c above in an easy to understand form for the client company. The report should be a maximum of 5 pages in length, single line spaced, 1-inch pagination on all sides, 12-point normal font. cxwwfi_bank_data_notes_and_metadata.pdf bank_data.xlsx suvvd5_group_assignment__1.pdf assignment_1.xlsx Unformatted Attachment Preview Data Set Information and Citation: Bank Marketing (with social/economic context). Created by: Sérgio Moro (ISCTE-IUL), Paulo Cortez (Univ. Minho) and Paulo Rita (ISCTE-IUL) @ 2014 Citation: The details of this data set are described in S. Moro, P. Cortez and P. Rita. A DataDriven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, http://dx.doi.org/10.1016/j.dss.2014.03.001 A) Data Set Information: File name: bank-data.xlsx; 41188 rows (sampled customers) and 21 columns (features) ordered by date (from May 2008 to November 2010). The goal is to predict if the client will subscribe a bank term deposit (variable y). The data was collected from a Portuguese retail bank in an attempt to sell long-term deposits to customers through telemarketing phone calls. The goal was to identify those customers who would buy the product by analyzing and modeling the data. Once potential customers are identified, human agents execute phone calls to a list of clients to sell the deposit (outbound) or, if meanwhile the client calls the contact-center for any other reason, he/she is asked to subscribe the deposit (inbound). Thus, the result is a binary unsuccessful or successful contact. The binary classification goal is to predict if the client will subscribe a bank term deposit (variable y). B) Attribute information: Input variables: # bank client data: 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; note: divorced means divorced or widowed) 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) # related with the last contact of the current campaign: 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). 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. 1 # other attributes: 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) # social and economic context attributes 16. emp.var.rate: employment variation rate - quarterly indicator (numeric) 17. cons.price.idx: consumer price index - monthly indicator (numeric) 18. cons.conf.idx: consumer confidence index - monthly indicator (numeric) 19. euribor3m: euribor 3-month rate - daily indicator (numeric) 20. nr.employed: number of employees - quarterly indicator (numeric) Output variable (desired target): 21. y - has the client subscribed a term deposit? (binary: yes, no) C) Missing Attribute Values: There are several missing values in some categorical attributes, all coded with the unknown label. These missing values can be treated as a possible class label or using deletion or imputation techniques. 2 age 56 57 37 40 56 45 59 41 24 25 41 25 29 57 35 54 35 46 50 39 30 55 55 41 37 35 59 39 54 55 46 59 49 54 54 55 34 52 41 56 58 32 38 57 44 42 job housemaid services services admin. services services admin. blue-collar technician services blue-collar services blue-collar housemaid blue-collar retired blue-collar blue-collar blue-collar management unemployed blue-collar retired technician admin. technician technician self-employed technician unknown admin. technician blue-collar management blue-collar unknown services technician admin. technician management entrepreneur admin. admin. admin. technician marital married married married married married married married married single single married single single divorced married married married married married single married married single single married married married married single married married married married married divorced 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