Analyze a given dataset about the voting behavior of number

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Reference no: EM131883573

BI Tools & Techniques Data Mining Assignment

In this assignment you will choose to use a free/open-source data mining tool, KNIME. You are to analyze a given dataset (about the voting behavior of a number of counties in the U.S.) to develop and compare at least three different types of prediction (i.e., classification) methods that predicts weather a county will say "yes" or "no" to legalizing gaming at the ballot). Here are the specifics for this assignment:

Use the following tools - KNIME (download and install on a PC/Laptop).

Download "Voting Behavior" data and the brief data description from the D2L - The data is given in MS Excel format.

Follow the 6 steps in CRISP-DM process model

Understand the domain and the problem you are trying to solve (via literature).
Understand, and preprocess the data (be very critical about the data).
Develop at least three classification models (e.g., Decision Tree, Logit, ANN, etc.).
Compare the accuracy results (use confusion matrixes and comment on the outcome).

Present your results in an organized report

Include a cover page.
Write an "Executive Summary" (1 page long).
Use the 6 steps in CRISP-DM to organize the remainder of the report.
Include a conclusion page, where you need to comment on the tool and techniques you've used. What was good and what was bad, etc.
Make sure to integrate figures (graphs, charts, tables, screen-shots) into the text as you see necessary. Do not use Appendixes.
Try not to exceed 15 pages in total, including the cover (use 12 point Times New Roman fonts, and 1.5 line spacing).

Attachment:- Assignment Files.rar

Reference no: EM131883573

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len1883573

2/28/2018 7:20:09 AM

Deadline: The report is due by Monday 23, 11:59 PM. The report should be uploaded as a single Microsoft Word Document to the Dropbox in D2L by the due date/time. Guidelines/hints: Be critical about the derived variables (e.g., percent whites, blacks, other; percent church members of the population, etc.)… Make sure that they are calculated correctly. Make sure that the data-formats of the variables are consistent with the DM tool you are using (e.g., the nominal and numeric variables should be accurately defined). Pick and choose your independent variables from the list. Do not use them all blindly! Be specific about the actions taken during the data preprocessing; explain your data preprocessing actions in a step-by-step fashion. Summarize the final status of the data. Show the screen shots for the final data set (after the pre-processing and removal of unused variables). Show a screen shot of the complete classification model (Modeling Process in KIME)

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