CIS111-6: Intelligent Systems and Data Mining | My Assignment Tutor

Submission DeadlineMarks and FeedbackBefore 10am on: Tue 4/05/2021 Tue 11/05/202120 working days after deadline (L4, 5 and 7)15 working days after deadline (L6)10 working days after deadline (block delivery) Unit title & codeCIS111-6: Intelligent Systems and Data MiningAssignment number and titleAssignment 1: Data Mining Solutions for Direct Marketing CampaignAssessment typeWRWeighting of assessment50%Unit learning outcomesAnalyse a Data Mining technique capable of supporting practitioners to make reliable decisions which require predictive modelling, for example, in a Business scenarioDemonstrate results of using an efficient technique which is capable of finding a solution to a given predictive problem represented by a data setEvaluate the accuracy of the technique in terms of differences between the predicted values and the given data What am I required to do in this assignment?Task Students will develop a DM solution for saving the cost of a direct marketing campaign by reducing false positive (wasted call) and false negative (missed customer) decisions. Working on this assignment, students can consider the following scenario. A Bank has decided to save the cost of a direct marketing campaign based on phone calls offering a product to a client. A cost efficient solution is expected to support the campaign with predictions for a given client profile whether the client buys the product or not. Examples of cost-efficient DM solutions for direct marketing are provided on the UCI Machine Learning repository describing a Bank Marketing problem. How students will work Each student is expected to run individual experiments to find an efficient solution and  describe experimental results in an individual report. Students could work on the assignment task as: (i) a groupmanager, (ii) a groupmember, or (iii) an individual. If students will work in a group, the group manager arranges the comparison and ranking of designed solutions. Method and Technology To design a solution, students will use Data Mining techniques such as Decision Trees.  Students are recommended to use R scripting: (i) a Cloud CoCalc or (ii) a development suite RStudio free for students. Other scripting languages such as Python could be also used. Project Code and Data     The assignment project code is available as an R Script. The Bank Marketing data set is available as a csv file. Other data sets (Kaggle or UCI) could also be used. Report submission and report template Each solution will be evaluated in terms of the costs of false decisions made on the validation data. Reports will be submitted via BREO.  Reports can be prepared with a template. BREO similarity in reports must be < 20% (scripting is not counted).Is there a size limit?2000 words on averageWhat do I need to do to pass? (Threshold Expectations from UIF)Follow a CoCalc tutorial to create an individual account (or install RStudio) (10%)Create an R project containing the given project script and data set (10%)Apply a Decision Tree technique to solve the Bank Marketing task (5%)Work on scripting problems is evaluated and students are expected to demonstrate the knowledge on how to find a solution by using related manuals and google search (10%)Analyse problems of designing a solution which will provide a high prediction accuracy (7%)In total 42% to passHow do I produce high quality work that merits a good grade?Identify a set of parameters required to be adjusted within DM techniques in order to optimise a solution in terms of prediction accuracy (10%)Explain how the parameters of a DM technique influence the prediction accuracy (10%)Run experiments in order to verify the solution designed on the given data set (10%)Analyse and compare the results of the experiments in a group and with results known from the literature (13%)In total 85%How does assignment relate to what we are doing in scheduled sessions?Data Mining techniques and use cases developed in R will be considered during  lectures and tutorials.

QUALITY: 100% ORIGINAL PAPER – NO PLAGIARISM – CUSTOM PAPER

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