Module 6: Collaborative group project Instructions:
A collaborative group is composed of at most 2 persons. I will allow you to have one person as a group but the requirements remain the same.
Please use one (or two is ok) real world data of interests to you (may refer to the links I posted in the module six materials for selecting data sets). Then use three of the five module methods we have learned from the first five weeks to analyze the dataset(s) you selected:
1. KNN,
2. Naïve Bayes,
3. Decision Tree and Rules,
4. Random Forests and
5. GLM & Logistic Regression.
You may use the dataset(s) you have used in previous weeks but make sure to include all three methods on that one or two specific datasets. You may either explain the pros and cons of the three methods on the dataset you chose and make good points about your findings or you may also look at the results from three perspectives of the three methods and see how using those methods can give you deeper insight to the dataset(s) you chose for this collaborative project. This should be a good way to wrap up what you have learned in these six weeks.
Please include cover page with title, names, institution and date. The main body should include at least Introduction, Method, Analysis and Conclusion (can add more if you want) with references page.
You may work on it as an individual or with at most one other students as a group. Your project should follow APA format and have 1500 -2000 words. You should include photos, graphs, Figures or charts to make your points. The deadline will be the last day of class.
Please don’t hesitate to let me know if there are any questions.
Some examples of real world data sets fyi as follows:
GLM & Logistic Regression Analysis:
Classification:
APA format: https://owl.english.purdue.edu/owl/resource/560/01/
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