Provide the console output of the routines that you have implemented in the text input box in the format as specified in the problems (whenever required).
Clearly put the problem numbers in appropriate headers and sub-headers on the notebook.
Do not display information that is not being sought.
Images or data files, if any, should be kept in folders such as “./images/im_name.jpg” or “./data/data_file.cvs.”
You should build upon the code already provided in the updated notebook (the link to Google Drive is on the Module now) in this module wherever appropriate .
If you have any questions etc., bring it on the discussion boardfor this Module.
Please note that peer-review is only learning from other’s work. You can comment and critique. Grading will be performed by the grader.
Question 1
(An open ended asignment) Visit the University of California open source data repository. Pick up an appropriate dataset of your choice preferably one with default-task ‘classification’. Follow the examples provided during the class to get some insight from
the data that you have selected. In the process of doing so utilize any tools and techniques at your disposal including (and not limited to). Observation of covariance and correlation between different features (columns, explanatory variables).
Distribution of different features and observations.
Utilize PCA to describe low-dimension representations.
Use plots such as Scree-plot, Bi-plot etc.
Discuss and clustering behaviours around the classes exposed by the first few PCs.
Anything else that you find interesting.
In [ ]:
# Your code starts here
Question 2
Use the Python code for image compression to compress the national flags of different countries of your choice. The smallest
rank r gives the numerical rank of the flag. Pick up any 5 national flags and arrange them in the order of decreasing numerical
rank*.
*: the numerical rank could be given by the number of singular values larger than
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