Data Mining 2

1- Stock quotes analysis. Go to [login to view URL], enter a symbol in step 1 (e.g., “HD”) and click “Historical Quotes” in step 2. On the next page, you can select different time frame (e.g., “3 Years”), view the data, and download the data as a .csv file using the link at the bottom of the page. Please note that if you download the .csv file when the stocks are still trading, the first line has a different format. You can either remove that first line or download the .csv files when the stock market is closed. Using the python code template provided, write a python program that does the following analysis: (a) Given a single filename, attribute type, and normalization method, compute the corresponding normalized attributed values. Using range [0, 1.0] for min-max normalization. Output two values per line: the original attributed value and the normalized attribute value, separated by the < tab > key. E.g., python [login to view URL] -f1 [login to view URL] -a1 volume -n min_max (b) Given two filenames, their corresponding attribute types, compute the correlation coefficient between the two attributes types in the two files. Output a single value, which is the correlation coefficient. E.g., python [login to view URL] -f1 [login to view URL] -a1 high -f2 [login to view URL] -a2 low E.g., python [login to view URL] -f1 [login to view URL] -a1 open -f2 [login to view URL] -a2 open Name your python code as LastName_FirstName_HW2.py. Please attach the .py file here. 2- Using the same website [login to view URL], obtain the 3-year data for “HD” and plot the following: (a) A single plot showing the temporal change of the “High” and ”Low” attributes. (b) A boxplot for the “Open” and “Close/Last” attributes. (c) The 10-bin equal-width histogram for the “Volume” attribute. (d) Any plot that interests you (using this HD dataset). Put all your plots in a single pdf file named in the format [login to view URL] and attach it here.