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(a) What are the values that the temperature and windy attributes have? (4 marks)

16 / 01 / 2019 Statistics

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Task 1: Data Preprocessing (10 marks)
Load a dataset by clicking the Open file button in the top left corner of the panel. Inside the data folder, which is supplied when Weka is installed, you will find a file named weather.nominal.arff.  
As shown in the Weka interface, the weather data has 14 instances, and 5 attributes called outlook, temperature, humidity, windy, and play. Click on the name of an attribute in the left subpanel to see information about the selected attribute on the right, such as its values and other details. This information is also shown in the form of a histogram. All attributes in this dataset are “nominal”—that is, they have a predefined finite set of values. The last attribute, play, is the “class” attribute; its value can be yes or no. Answer the followings:
(a) What are the values that the temperature and windy attributes have? (4 marks)
(b) What is the class value of instance number 6 in the weather data? (2 marks)
(c) Load the weather.numeric.arff dataset and open it in the editor by clicking the Edit button from the row of buttons at the top of the Preprocess panel in Weka Interface and answer the following question. How many numeric and how many nominal attributes does this dataset have? (4 marks)
 
Task 2: Visualization and Analysis (10 marks)
Load iris.arff dataset in Weka and answer following questions.
(a) How many instances and how many attributes does this dataset have? (2 marks)
(b) What is the range of possible values for each of the 4 attributes that can be observed in the dataset? (4 marks)
(c) Present a scatter plot visualization of this dataset and find which two classes have more overlapping tendency and which one is likely to be a separate class as observed in the attribute-pair based plotting. Alternatively, you may use the 3D visualization feature provided in Weka to find which two classes have more overlapping tendency and which one is likely to be a separate class using different combination of any three featuring attributes out of four attributes in the dataset. (4 marks)


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