[Chicago] Final KNN Program for Informatics class.

[Lewit, Douglas]

Not sure how many people followed that last thread, but here's my final
solution.  The homework states that Java should be used for the assignment,
but the professor announced in class that students can use C++ or Python if
they prefer.  Of those three choices, Python is for sure my favorite!
 (Although I confess that I wouldn't mind spending some time with C++.)

I'm attaching 1) the homework assignment, 2) the text that has to be read
for the assignment, and finally 3) my script for solving the problem.  What
bothers me is that my percentages of successful classification are in the
ballpark of around 66%, sometimes less than that and occasionally more than
that depending on which environment I use to implement the program.  I
thought that in general the KNN algorithm is supposed to yield better
results than that, so maybe my code isn't quite correct.  Oh well.  I know
there are a few redundancies in my code, but after all this work I
absolutely refuse to redo it.  I think it's worth at least a B+.

I BELIEVE (not really sure about this) that Numpy or Scikit or some library
like that has a builtin implementation of the KNN algorithm.  If anyone
knows anything about that, please let me know.  I would like to learn more
about it.

Best,

Douglas Lewit
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186,1.51131,13.69,3.20,1.81,72.81,1.76,5.43,1.19,0.00,7
187,1.51838,14.32,3.26,2.22,71.25,1.46,5.79,1.63,0.00,7
188,1.52315,13.44,3.34,1.23,72.38,0.60,8.83,0.00,0.00,7
189,1.52247,14.86,2.20,2.06,70.26,0.76,9.76,0.00,0.00,7
190,1.52365,15.79,1.83,1.31,70.43,0.31,8.61,1.68,0.00,7
191,1.51613,13.88,1.78,1.79,73.10,0.00,8.67,0.76,0.00,7
192,1.51602,14.85,0.00,2.38,73.28,0.00,8.76,0.64,0.09,7
193,1.51623,14.20,0.00,2.79,73.46,0.04,9.04,0.40,0.09,7
194,1.51719,14.75,0.00,2.00,73.02,0.00,8.53,1.59,0.08,7
195,1.51683,14.56,0.00,1.98,73.29,0.00,8.52,1.57,0.07,7
196,1.51545,14.14,0.00,2.68,73.39,0.08,9.07,0.61,0.05,7
197,1.51556,13.87,0.00,2.54,73.23,0.14,9.41,0.81,0.01,7
198,1.51727,14.70,0.00,2.34,73.28,0.00,8.95,0.66,0.00,7
199,1.51531,14.38,0.00,2.66,73.10,0.04,9.08,0.64,0.00,7
200,1.51609,15.01,0.00,2.51,73.05,0.05,8.83,0.53,0.00,7
201,1.51508,15.15,0.00,2.25,73.50,0.00,8.34,0.63,0.00,7
202,1.51653,11.95,0.00,1.19,75.18,2.70,8.93,0.00,0.00,7
203,1.51514,14.85,0.00,2.42,73.72,0.00,8.39,0.56,0.00,7
204,1.51658,14.80,0.00,1.99,73.11,0.00,8.28,1.71,0.00,7
205,1.51617,14.95,0.00,2.27,73.30,0.00,8.71,0.67,0.00,7
206,1.51732,14.95,0.00,1.80,72.99,0.00,8.61,1.55,0.00,7
207,1.51645,14.94,0.00,1.87,73.11,0.00,8.67,1.38,0.00,7
208,1.51831,14.39,0.00,1.82,72.86,1.41,6.47,2.88,0.00,7
209,1.51640,14.37,0.00,2.74,72.85,0.00,9.45,0.54,0.00,7
210,1.51623,14.14,0.00,2.88,72.61,0.08,9.18,1.06,0.00,7
211,1.51685,14.92,0.00,1.99,73.06,0.00,8.40,1.59,0.00,7
212,1.52065,14.36,0.00,2.02,73.42,0.00,8.44,1.64,0.00,7
213,1.51651,14.38,0.00,1.94,73.61,0.00,8.48,1.57,0.00,7
214,1.51711,14.23,0.00,2.08,73.36,0.00,8.62,1.67,0.00,7
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