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IS 685 Course Project this project, your group will use Weka to analyze the classification task which determines if the ent will subscribe for a

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IS 685 Course Project this project, your group will use Weka to analyze the classification task which determines if the ent will subscribe for a term deposit (variable target) based on the result of a bank marketing mpaign. The data is stored in a (CSV) file, where each line represents a person. For each line, there e 21 columns: the first 20 columns represent a customer's characteristics, and the last column is a bel of their decision (either yes or no). Here are the explanations of those attributes: put 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'inarkiedidisingle' 'unknown'; note: 'divorced' means divorced or widowed) 4 - education (categorical: 'basic. 4y', 'basic. 6y', 'basic.9Y', 'high. wchool', 'iliterate', 'profession al. course', 'university. degree', 'unknown') 5 - default: has credit in default? (categorical: 'no'i'i'yes' 'unknoin') 6 - housing: has housing loan? (categorical: 'ng'ini'yes' 'n'unknown') 7 - loan: has personal loan? (categorical: 'no'in'yes'in'unknown') related with the last contact of the current campaign: 8 - contact: contact communication type (categorical: 'cellular' 'telephore') 9 - month: last contact nonth of year (categorical: 'Jan', ' feb ' s 'mar', ..., 'ngy', ' geg) 10 - day of week: 1 ast contact day of the week (categorical: 'mon', 'tue', 'wed', 'thy', '\{xs,') 11 - duration: last contact duracion, in seconds (numeric) \# 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 pertormed before this campaign and for this elient (numeric) 15 - poutcome: outcome of the previous marketing campaign (categorical: 'failure' ' nonexistentidin succeas') * social and economic context attributes 16 - emp.yaz. Eate: employment variation rate - quarterly indicator (numeric) 17 - gons,price,idx: consumer price index - monthly indicator (numeric) 18 - cogs.conf, 2 dx: consumer confidence index - monthly indicator (numeric) 19 - euribor 3m : euribos 3 month rate - daily indicator (numerie) 20 - ns,employed: number of employees - quarterly indicator (numeric) Output variable (desired target): 21 - Y - hay the client subscribed a term deposit? (binary: 'yeg' 'ieng') 1. Develop classification models using Weka and evaluate your models using 10 -fold cross validation. 2. Try to improve your decision tree algorithm. Some examples of strategies are: - Different splitting strategies - Different stopping criteria - Pruning tree after splitting - Using different attributes (you don't need to use all the attributes in the dataset) The goal here is for you to try different ways to improve the classifier. You should discuss what you have tried and why you think it performs better in the description tat file. For example, - Initial (in step 1):0.85 - After applying strategy 1:0.89 - After applying strategy 2:0.91 eliverables Word document file should include: a. How you implemented the initial tree (Section A ) and why you chose your approaches

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