Calculate the row-wise Euclidean distance matrix of all attributes and plot in Matrix

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

Apply the following exploratory data analysis techniques using Weka or Orange (or other tools of your preference) to the original/cleaned Pima Indian Diabetes Dataset; if not otherwise stated, use the original dataset:

 a.       Compute the mean value and standard deviation for attributes 2, 3, 6, and 7. Remove 0’s that do not make sense prior to computing these statistics. Compute the covariance matrix for four attributes next, compute the correlations for each of the 6 pairs of the 4 attributes. Interpret the statistical findings!

Remove 0’s that do not make sense prior to computing the covariance matrix and correlations.

 b.      Create histograms for attributes 2 and 4. Then create the same histograms for the 2 attributes for the instances of class 1 and for the instances of class 0; interpret the obtained 6 histograms.

 c.       Create box plots for the 6^nd and 8^th attribute; one for the whole dataset and one each for the instances of the two classes. Remove 0’s that do not make sense prior to computing the box plots. Interpret and compare the obtained 6 boxplots!

 d.      Write a conclusion (at most 13 sentences!) summarizing the most important findings of the assignment—what did we learn about the dataset? In particular, discuss the findings obtained related to predicting diabetes.

 e.       Calculate the row-wise Euclidean distance matrix of all attributes and plot in Matrix

 

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