In this exercise, you will build a linear predictive model to predict crime rate based on several factors. The data is in the “crime-rate” file. You will build the model by writing your own script for gradient search. Experiment with 2-3 learning rates to see the effect of learning rate on the search. in python and using CrimeRate.xlsx file

Here is what the CrimeRate files looks like

123 4 5 6 7 8 9 10 == 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 X1 A 478 494 643 341 773 603 484 546 424 548 506 819 541 491 514 371 457 437 570 432 619 357 623 547 792 799 X2 B 184 213 347 565 327 260 325 102 38 226 137 369 109 809 29 245 118 148 387 98 608 218 254 697 827 693 X3 C X4 40 32 57 31 67 25 34 33 36 31 35 30 44 32 30 16 29 36 30 23 33 35 38 44 28 35 D 12 74 72 70 71 72 68 68 62 69 66 60 81 66 67 65 64 64 62 59 56 46 54 54 45 57 57 X5 E 77 11 11 18 11 9 8 23 12 13 7 9 13 4 9 11 12 10 12 7 5 5 15 15 22 14 20 26 12 9 X6 F 31 43 16 25 29 32 24 28 25 58 21 77 37 37 35 42 21 81 31 50 24 27 22 18 23 60 X7 G 20 18 16 19 24 15 14 11 12 15 9 36 12 16 11 14 10 27 16 15 8 13 11 8 11 18 H J K L M N The data (X1, X2, X3, X4, X5, X6, X7) are for each city. X1 = total overall reported crime rate per 1 million residents X2 = reported violent crime rate per 100,000 residents X3 = annual police funding in $/resident X4 =% of people 25 years+ with 4 yrs. of high school X5 =% of 16 to 19 year-olds not in highschool and not highschool graduates. X6 =% of 18 to 24 year-olds in college X7 =% of people 25 years+ with at least 4 years of college The goal is to build a linear predictive model to predict reported crime rate (X1)