Actividad 1. Modelo de Regresión - Seguros de Carros
Genaro Rodríguez Alcántara - A00833172
2024-03-04
Análisis de Seguros de Carros
Librerias
# data manipulation & data visualization
library(foreign) # Read Data Stored by 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka', 'dBase'
library(ggplot2) # It is a system for creating graphics
library(dplyr) # A fast, consistent tool for working with data frame like objects
library(mapview) # Quickly and conveniently create interactive visualizations of spatial data with or without background maps
library(naniar) # Provides data structures and functions that facilitate the plotting of missing values and examination of imputations.
library(tmaptools) # A collection of functions to create spatial weights matrix objects from polygon 'contiguities', for summarizing these objects, and for permitting their use in spatial data analysis
library(tmap) # For drawing thematic maps
library(RColorBrewer) # It offers several color palettes
library(dlookr) # A collection of tools that support data diagnosis, exploration, and transformation# predictive modeling
library(regclass) # Contains basic tools for visualizing, interpreting, and building regression models
library(mctest) # Multicollinearity diagnostics
library(lmtest) # Testing linear regression models
library(spdep) # A collection of functions to create spatial weights matrix objects from polygon 'contiguities', for summarizing these objects, and for permitting their use in spatial data analysis
library(sf) # A standardized way to encode spatial vector data
library(spData) # Diverse spatial datasets for demonstrating, benchmarking and teaching spatial data analysis
library(spatialreg) # A collection of all the estimation functions for spatial cross-sectional models
library(caret) # The caret package (short for Classification And Rgression Training) contains functions to streamline the model training process for complex regression and classification problems.
library(e1071) # Functions for latent class analysis, short time Fourier transform, fuzzy clustering, support vector machines, shortest path computation, bagged clustering, naive Bayes classifier, generalized k-nearest neighbor.
library(SparseM) # Provides some basic R functionality for linear algebra with sparse matrices
library(Metrics) # An implementation of evaluation metrics in R that are commonly used in supervised machine learning
library(randomForest) # Classification and regression based on a forest of trees using random inputs
library(jtools) # This is a collection of tools for more efficiently understanding and sharing the results of (primarily) regression analyses
library(xgboost) # The package includes efficient linear model solver and tree learning algorithms
library(DiagrammeR) # Build graph/network structures using functions for stepwise addition and deletion of nodes and edges
library(effects) # Graphical and tabular effect displays, e.g., of interactions, for various statistical models with linear predictors
library(rpart.plot) # Displays a tree diagram that shows the decision rules of the model
library(shinyjs)
library(sp)
#library(geoR)
library(gstat)
library(caret)
library(st)
library(entropy)
library(corpcor)
library(fdrtool)
library(sda)
library(corrplot)
library(lattice)
library(datasets)
library(DataExplorer)
library(car)Dataset
df <- read.csv("/Users/genarorodriguezalcantara/Desktop/Tec/AI - Concentración/Módulo 3 - Modelos de Inteligencia Artificial para datos estructurados/Act 1/automoble_insurance_claims.csv")
df## months_as_customer age policy_number policy_bind_date policy_state
## 1 328 48 521585 10/17/2014 OH
## 2 228 42 342868 6/27/2006 IN
## 3 134 29 687698 9/6/2000 OH
## 4 256 41 227811 5/25/1990 IL
## 5 228 44 367455 6/6/2014 IL
## 6 256 39 104594 10/12/2006 OH
## 7 137 34 413978 6/4/2000 IN
## 8 165 37 429027 2/3/1990 IL
## 9 27 33 485665 2/5/1997 IL
## 10 212 42 636550 7/25/2011 IL
## 11 235 42 543610 5/26/2002 OH
## 12 447 61 214618 5/29/1999 OH
## 13 60 23 842643 11/20/1997 OH
## 14 121 34 626808 10/26/2012 OH
## 15 180 38 644081 12/28/1998 OH
## 16 473 58 892874 10/19/1992 IN
## 17 70 26 558938 6/8/2005 OH
## 18 140 31 275265 11/15/2004 IN
## 19 160 37 921202 12/28/2014 OH
## 20 196 39 143972 8/2/1992 IN
## 21 460 62 183430 6/25/2002 IN
## 22 217 41 431876 11/27/2005 IL
## 23 370 55 285496 5/27/1994 IL
## 24 413 55 115399 2/8/1991 IN
## 25 237 40 736882 2/2/1996 IN
## 26 8 35 699044 12/5/2013 OH
## 27 257 43 863236 9/20/1990 IN
## 28 202 34 608513 7/18/2002 IN
## 29 224 40 914088 2/8/1990 OH
## 30 241 45 596785 3/4/2014 IL
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## 98 29 25 235220 11/1/2014 IL
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## 187 177 34 226330 1/23/2013 IL
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## 191 449 57 293794 4/17/1999 OH
## 192 252 39 868283 2/6/2006 IN
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## 494 231 43 203250 4/22/2010 IN
## 495 338 47 430794 1/25/2008 OH
## 496 261 46 156636 9/10/2000 IN
## 497 321 44 284143 4/23/2008 IL
## 498 0 32 740518 2/18/2011 OH
## 499 405 58 445289 4/24/2012 IL
## 500 304 49 599262 9/25/2001 IN
## 501 1 29 357949 5/24/2006 OH
## 502 26 39 493161 1/30/1992 IN
## 503 202 38 320251 1/24/2009 IL
## 504 289 48 231127 8/29/1995 IL
## 505 61 26 766193 7/31/2011 OH
## 506 334 46 555374 1/5/2013 IL
## 507 12 24 491484 11/18/1994 IL
## 508 86 29 925128 8/30/2014 IL
## 509 83 24 265093 1/1/2006 IN
## 510 126 30 267808 9/10/1998 IL
## 511 209 38 116735 1/28/2010 OH
## 512 283 48 963680 1/4/2003 OH
## 513 194 34 445694 5/24/2004 IL
## 514 184 38 215534 9/12/1994 IL
## 515 479 60 232854 7/7/1997 IL
## 516 284 48 168260 3/1/1991 OH
## 517 65 27 538955 9/29/2001 IN
## 518 222 39 243226 1/10/2012 IL
## 519 196 41 246435 7/5/2001 IL
## 520 253 43 582480 8/7/1991 IL
## 521 280 43 345539 7/24/2012 IN
## 522 5 26 924318 7/27/2014 IL
## 523 220 42 726880 8/8/1994 IN
## 524 85 30 190588 12/9/2001 OH
## 525 266 46 246705 3/14/1990 OH
## 526 41 26 619589 3/28/2006 IL
## 527 316 45 164988 12/23/2013 IL
## 528 285 47 729534 9/30/1991 IN
## 529 379 54 505014 12/27/2001 IL
## 530 15 34 920826 4/7/2005 IN
## 531 354 48 534982 4/8/2003 IL
## 532 342 53 110408 11/14/2005 IN
## 533 169 38 283052 1/7/2005 IL
## 534 339 49 840806 2/14/1994 IN
## 535 259 42 382394 1/23/1996 OH
## 536 65 23 876699 12/12/1999 OH
## 537 254 46 871432 7/15/2004 IL
## 538 440 55 379882 11/7/2012 IL
## 539 478 63 852002 6/29/2009 IL
## 540 230 44 372891 6/26/2000 IN
## 541 138 30 689034 1/9/2002 OH
## 542 239 41 743092 11/11/2013 OH
## 543 93 31 599174 1/14/2008 IL
## 544 37 25 421092 3/4/2003 OH
## 545 254 40 349658 6/7/1994 IN
## 546 131 29 811042 7/4/2013 IN
## 547 230 43 505316 6/30/2002 IN
## 548 313 50 116645 6/30/2004 OH
## 549 210 38 950880 12/19/1998 IN
## 550 101 29 788502 8/31/2014 OH
## 551 153 37 627486 11/10/2005 IN
## 552 337 53 498842 5/4/2000 OH
## 553 360 51 550294 11/26/2001 IL
## 554 428 53 328387 5/6/2014 IL
## 555 204 40 540152 1/27/1991 IL
## 556 364 51 385932 4/28/1992 IL
## 557 185 35 618682 3/4/2000 IN
## 558 63 26 550930 10/12/1995 IL
## 559 210 35 998192 4/25/2014 IL
## 560 194 38 663938 1/26/2011 IN
## 561 294 49 756870 1/26/1996 IN
## 562 272 41 337158 4/8/1991 OH
## 563 27 27 919875 6/29/2002 IN
## 564 251 39 315631 4/9/1999 IN
## 565 180 33 113464 4/19/2009 IN
## 566 392 50 556415 8/22/1991 OH
## 567 143 30 250249 11/28/1991 IN
## 568 371 54 403776 4/27/2012 IN
## 569 292 42 396002 3/4/2007 IN
## 570 165 35 976908 12/31/2012 IL
## 571 158 33 509489 12/21/2013 OH
## 572 241 39 485295 4/28/2005 OH
## 573 103 33 361829 9/17/1994 OH
## 574 402 54 603632 8/16/2003 OH
## 575 102 32 783494 9/2/2014 OH
## 576 182 40 439049 12/12/2011 IN
## 577 282 46 502634 8/17/1991 OH
## 578 222 39 378588 2/29/2004 OH
## 579 415 52 794731 2/22/2015 IN
## 580 51 34 641934 12/25/2013 OH
## 581 255 45 113516 10/13/1990 IL
## 582 143 31 425631 7/5/2014 IL
## 583 130 28 542245 11/25/1991 OH
## 584 242 41 512894 10/2/1990 OH
## 585 96 27 633090 2/17/2009 IL
## 586 180 35 464234 7/17/2005 IL
## 587 150 30 290162 3/12/1994 IN
## 588 463 59 638155 8/3/1994 IL
## 589 472 64 911429 8/25/2012 IN
## 590 75 25 106186 12/2/2011 IL
## 591 193 40 311783 2/25/2005 OH
## 592 43 43 528385 11/7/1997 IL
## 593 253 41 228403 4/20/2004 IN
## 594 152 30 209177 11/17/2009 IN
## 595 160 38 497929 9/19/2009 OH
## 596 56 36 735844 11/8/2009 IN
## 597 286 41 710741 9/12/2001 IL
## 598 3 29 276804 11/27/1992 IL
## 599 286 41 507545 12/7/1998 IL
## 600 239 38 485642 8/25/1990 OH
## 601 64 29 796375 10/22/2011 OH
## 602 98 31 171183 2/1/1990 IN
## 603 16 35 110084 11/27/1990 IL
## 604 70 27 714784 7/16/2004 IN
## 605 75 27 143924 12/10/1993 OH
## 606 246 44 996850 3/8/1995 OH
## 607 110 27 284834 8/3/2009 OH
## 608 236 39 830878 11/3/1996 IN
## 609 267 46 270208 8/9/2004 OH
## 610 463 57 407958 7/20/1991 IL
## 611 303 46 832300 1/14/2005 IN
## 612 137 30 927205 12/16/2011 IL
## 613 56 42 655356 7/7/1996 IL
## 614 75 27 831053 8/5/1992 IN
## 615 131 33 432740 10/9/1990 IL
## 616 153 34 893853 2/27/1994 IL
## 617 255 43 594988 5/6/2007 IN
## 618 103 26 979544 4/21/2014 IL
## 619 97 28 191891 2/11/2010 OH
## 620 214 36 831479 6/4/2000 IL
## 621 438 57 714346 10/5/1991 OH
## 622 87 27 326289 1/3/2004 OH
## 623 27 28 944537 7/23/1992 OH
## 624 206 42 779156 10/10/1993 IL
## 625 127 31 856153 7/9/2002 OH
## 626 422 60 473338 11/14/2010 IN
## 627 303 50 521694 3/3/1997 IL
## 628 228 40 136520 3/1/1997 IN
## 629 239 39 730819 8/18/1990 IN
## 630 330 47 912665 5/28/2014 IL
## 631 128 35 469966 7/22/2004 IN
## 632 147 37 952300 8/2/2009 OH
## 633 287 45 322609 7/5/2007 OH
## 634 142 29 890280 1/24/2010 OH
## 635 162 35 431583 5/15/2000 IL
## 636 140 35 155912 3/21/2008 OH
## 637 106 28 110143 5/7/1990 OH
## 638 292 45 808544 2/5/1991 IL
## 639 34 34 409074 3/19/1992 OH
## 640 290 48 824728 4/24/2013 IL
## 641 182 38 606037 4/10/2009 OH
## 642 362 55 636843 12/1/2008 OH
## 643 143 32 111874 7/5/2000 IL
## 644 183 38 439844 6/11/2014 IL
## 645 254 40 463513 4/23/1995 IL
## 646 249 43 577858 9/16/1990 OH
## 647 169 36 607351 12/11/1998 IN
## 648 235 40 682754 10/9/1995 IL
## 649 112 32 757352 12/21/1999 OH
## 650 16 32 307469 7/28/2002 IL
## 651 128 31 526296 8/3/1993 IL
## 652 103 27 658816 12/16/2007 IN
## 653 356 54 913337 2/10/2008 OH
## 654 109 29 488464 10/1/2006 OH
## 655 2 20 480094 3/9/2003 IN
## 656 198 34 263108 5/29/2003 OH
## 657 107 32 298412 5/6/2002 OH
## 658 252 39 261905 2/28/2004 IL
## 659 303 43 674485 1/14/1999 OH
## 660 101 32 223404 1/23/2002 IL
## 661 446 57 991480 12/9/1992 IN
## 662 330 48 804219 6/24/1998 OH
## 663 211 37 483088 1/6/2011 OH
## 664 172 33 100804 2/24/2012 IL
## 665 316 46 941807 6/25/2011 OH
## 666 435 60 593466 11/21/2006 OH
## 667 344 51 437442 6/27/2008 IL
## 668 204 40 942106 8/30/1993 OH
## 669 278 47 794951 4/21/2008 IN
## 670 434 57 182450 6/23/2000 OH
## 671 209 36 730973 1/11/2010 IN
## 672 250 43 687755 3/28/1990 IL
## 673 61 25 757644 1/29/1998 IN
## 674 80 28 998865 12/5/2014 IL
## 675 25 38 944953 12/7/1995 OH
## 676 4 29 386429 5/27/2002 IL
## 677 32 29 108270 8/9/2002 OH
## 678 125 31 205134 12/2/2012 IN
## 679 276 45 749325 3/22/2000 IL
## 680 148 30 774303 4/13/2002 OH
## 681 222 38 698470 6/17/2008 IN
## 682 32 38 719989 4/7/1994 IL
## 683 78 27 309323 2/29/1992 OH
## 684 238 43 444035 5/11/1996 OH
## 685 313 47 431478 4/3/2013 IN
## 686 334 50 797634 11/12/2009 OH
## 687 190 35 284836 11/5/2008 IN
## 688 194 41 238196 2/15/1993 IL
## 689 290 47 885789 7/21/2008 IN
## 690 26 42 287436 9/11/2010 OH
## 691 254 41 496067 12/22/1995 IL
## 692 199 38 206004 9/26/1991 IL
## 693 137 35 153027 3/11/2010 IN
## 694 134 36 469426 7/15/1990 OH
## 695 73 30 654974 5/10/2009 OH
## 696 289 45 943425 10/28/1999 OH
## 697 176 36 641845 3/30/1995 OH
## 698 145 37 794534 12/14/1991 OH
## 699 164 31 357808 1/31/2011 IN
## 700 186 38 536052 4/21/2006 OH
## 701 85 31 873384 3/10/2004 IL
## 702 162 33 790225 1/5/1991 OH
## 703 396 57 587498 10/15/1996 IL
## 704 270 41 639027 6/21/1994 IL
## 705 168 39 217899 6/13/1994 IL
## 706 274 45 589094 5/27/2003 IN
## 707 263 43 458829 7/6/1996 IN
## 708 152 33 626208 5/8/2005 OH
## 709 46 41 315041 11/2/2010 OH
## 710 276 46 283267 7/29/2012 OH
## 711 234 44 442494 6/6/2002 IN
## 712 64 30 159243 9/19/1991 IL
## 713 456 62 669800 6/24/2009 OH
## 714 58 23 520179 5/29/1992 OH
## 715 475 61 607974 8/12/2004 IL
## 716 96 29 465065 12/24/2006 IN
## 717 99 28 369941 7/24/2007 OH
## 718 38 28 447226 8/17/1994 OH
## 719 259 44 831668 4/10/1996 OH
## 720 241 43 922937 12/11/1992 IN
## 721 437 58 640474 8/1/2010 IN
## 722 130 34 153298 3/23/2009 OH
## 723 269 41 334749 7/29/1996 OH
## 724 103 29 221283 8/23/1994 OH
## 725 284 43 961496 1/5/1992 IL
## 726 189 39 804751 9/11/1997 OH
## 727 267 43 369226 2/10/2002 OH
## 728 39 22 691115 1/28/1993 IN
## 729 140 32 713172 10/23/1996 IL
## 730 243 41 621756 4/21/1997 IN
## 731 116 31 615116 11/9/2008 IN
## 732 219 43 947598 6/20/2002 IN
## 733 96 26 658002 10/21/2005 OH
## 734 149 34 374545 8/28/2005 IN
## 735 246 43 805806 1/16/2013 IN
## 736 293 45 235097 4/28/1992 IL
## 737 339 48 290971 10/10/2005 OH
## 738 160 33 180286 2/8/2009 IL
## 739 224 42 662088 3/6/2005 OH
## 740 194 34 884365 5/17/1994 IN
## 741 385 51 178081 7/20/1990 IN
## 742 100 33 507452 4/17/2005 OH
## 743 371 50 990624 2/10/1994 IN
## 744 175 39 892148 3/29/1995 IN
## 745 373 55 398683 4/30/2007 IN
## 746 258 41 605100 2/15/2001 IL
## 747 255 39 143109 7/9/2001 OH
## 748 37 31 230223 9/6/2008 IL
## 749 322 44 769602 12/19/2004 IL
## 750 204 38 420815 11/15/2000 IL
## 751 76 31 973546 3/14/2007 OH
## 752 193 40 608039 12/28/2004 IL
## 753 405 55 250162 7/5/1999 IL
## 754 435 58 786432 11/15/1997 IN
## 755 54 35 445195 9/27/2010 IN
## 756 144 35 938634 8/30/1993 IL
## 757 92 32 482495 1/29/1998 IL
## 758 173 36 796005 8/18/2007 OH
## 759 436 60 910604 4/14/1992 IN
## 760 155 35 327488 8/9/1993 OH
## 761 78 31 715202 4/2/1991 OH
## 762 440 57 648852 3/15/2007 IL
## 763 264 43 516959 5/1/2010 IL
## 764 66 30 984456 6/24/2003 IN
## 765 366 50 801331 7/8/1990 IN
## 766 188 37 786103 9/24/1994 OH
## 767 224 39 684193 6/20/2012 IL
## 768 253 46 247505 4/19/2006 IL
## 769 446 61 259792 4/7/1999 IL
## 770 169 37 185124 12/7/2001 IL
## 771 255 46 760700 11/25/2006 IL
## 772 209 39 362407 12/6/1996 IN
## 773 210 37 389525 7/10/2012 OH
## 774 174 33 179538 4/7/2014 IN
## 775 70 28 265437 10/11/2003 IL
## 776 89 32 266247 1/17/2015 IN
## 777 458 61 921851 12/7/1992 IN
## 778 239 40 488724 11/29/2004 IN
## 779 161 38 192524 1/2/2004 IL
## 780 446 61 338070 1/25/2006 IN
## 781 476 61 865607 4/18/1993 IN
## 782 70 29 963285 12/9/2006 IN
## 783 233 41 728491 8/30/1997 OH
## 784 122 33 553436 6/3/1991 IL
## 785 335 48 440616 9/6/1995 IL
## 786 257 40 463237 2/9/2000 IN
## 787 85 27 753452 7/23/1996 IL
## 788 133 30 920554 9/21/2005 IN
## 789 119 34 594783 12/30/2011 IL
## 790 169 34 725330 7/21/1996 IN
## 791 225 39 607259 4/8/1996 OH
## 792 84 32 979336 3/4/2001 IL
## 793 169 39 865201 10/19/2001 OH
## 794 124 32 140977 8/18/2006 IN
## 795 320 48 787351 4/28/2013 IL
## 796 297 47 272330 11/29/2009 IN
## 797 421 56 728025 2/15/1990 IN
## 798 136 33 804608 4/12/2002 OH
## 799 46 24 718829 2/21/1999 OH
## 800 34 24 482404 6/18/2011 IN
## 801 95 30 331170 3/26/1995 IL
## 802 140 36 753056 5/3/1991 IN
## 803 200 34 910365 12/19/2001 IN
## 804 123 29 379268 8/5/2012 IN
## 805 267 46 362843 8/9/2004 OH
## 806 290 42 135400 1/20/2014 IN
## 807 45 37 798579 12/19/2011 IN
## 808 186 38 250833 7/28/2008 IN
## 809 135 34 824116 5/5/1998 IL
## 810 110 33 322613 4/16/1995 IN
## 811 259 43 871305 2/14/1992 IL
## 812 114 30 488037 7/11/2007 OH
## 813 404 56 485813 4/7/2010 IN
## 814 282 48 886473 3/10/1991 OH
## 815 57 25 907113 1/20/1996 IL
## 816 215 38 833321 3/1/2010 IN
## 817 140 30 521592 6/15/2014 IL
## 818 250 42 254837 11/25/2004 IN
## 819 286 41 634499 8/26/2000 IL
## 820 356 47 574707 8/23/2005 IN
## 821 65 29 476839 8/9/1990 IL
## 822 187 34 149601 3/28/2003 IN
## 823 386 53 630683 10/23/2007 OH
## 824 197 41 500639 6/27/1996 OH
## 825 166 37 352120 12/11/1994 IN
## 826 293 49 569245 12/5/1995 IL
## 827 179 32 907012 12/15/1996 OH
## 828 76 24 700074 6/6/2011 OH
## 829 105 28 866805 12/13/1995 OH
## 830 97 26 951863 10/28/1997 OH
## 831 148 36 211578 1/4/1996 IL
## 832 77 26 357394 5/9/2008 IL
## 833 295 46 863749 12/5/2009 IN
## 834 126 28 596914 1/5/1992 IN
## 835 132 32 684653 11/15/1997 OH
## 836 370 55 528259 12/22/2012 IN
## 837 257 43 797636 5/19/1992 IN
## 838 9 24 326180 5/25/2002 IL
## 839 185 34 620075 4/21/2010 OH
## 840 234 43 965187 3/26/1990 OH
## 841 253 44 516182 5/12/2007 OH
## 842 233 39 728839 1/2/2001 OH
## 843 274 44 771509 8/10/2006 IN
## 844 297 48 264221 7/28/2014 IL
## 845 273 47 602704 9/27/2011 OH
## 846 147 37 672416 4/20/2013 IN
## 847 285 42 545506 3/20/1991 IN
## 848 289 43 777533 12/21/2002 OH
## 849 427 60 953334 12/3/2005 IN
## 850 380 53 369781 5/25/2011 IL
## 851 13 21 990998 10/18/2006 IN
## 852 282 43 982678 7/19/2006 OH
## 853 312 47 646069 6/8/2002 OH
## 854 266 46 331683 2/12/2009 OH
## 855 30 36 364055 5/14/2001 IN
## 856 198 36 521854 2/16/2001 IN
## 857 290 45 737252 11/18/1993 OH
## 858 260 46 344480 2/18/1990 OH
## 859 233 43 898519 5/21/2000 OH
## 860 130 30 957816 8/26/2012 IL
## 861 230 42 175960 11/16/2004 IN
## 862 212 40 489618 1/23/2003 IL
## 863 299 44 717044 11/7/2008 OH
## 864 91 26 101421 10/19/1999 IL
## 865 398 53 793948 12/20/1990 IL
## 866 218 43 737483 2/14/1996 IL
## 867 152 33 695117 6/10/2001 IN
## 868 212 39 167466 3/17/2010 OH
## 869 242 44 664732 7/30/2003 IL
## 870 80 27 143038 9/17/2014 OH
## 871 260 43 979963 6/3/2009 IN
## 872 133 34 467841 10/11/1994 IN
## 873 290 45 219028 7/18/1991 OH
## 874 322 49 130156 9/24/2001 IL
## 875 228 39 762951 9/19/2012 IN
## 876 195 37 376879 7/11/1991 IL
## 877 247 39 599031 10/29/1991 IN
## 878 405 57 676255 12/28/1999 IN
## 879 144 37 985446 10/11/2012 OH
## 880 338 47 884180 8/19/1995 IL
## 881 121 34 571462 2/11/1991 IN
## 882 398 55 815883 7/2/1991 OH
## 883 9 30 258265 4/10/1994 IL
## 884 115 31 569714 12/4/2005 OH
## 885 280 48 180008 7/16/2014 IL
## 886 254 45 633375 9/17/2003 IL
## 887 141 30 556538 7/15/2000 IL
## 888 441 55 669501 7/29/2009 IN
## 889 381 55 963761 4/13/1991 OH
## 890 191 38 753005 11/20/2005 IL
## 891 145 34 454758 5/20/1990 IN
## 892 479 60 698589 11/28/2002 IL
## 893 215 35 330119 6/15/2004 IL
## 894 41 33 164464 9/26/2010 OH
## 895 45 31 927354 9/15/1990 IN
## 896 156 38 231508 9/16/2009 IL
## 897 246 45 272910 8/12/1999 IN
## 898 178 39 305758 3/8/2009 IL
## 899 237 43 950542 4/27/2009 OH
## 900 127 34 291544 8/2/2006 OH
## 901 1 33 388616 12/6/1995 OH
## 902 5 21 577992 11/13/2002 IN
## 903 64 28 342830 11/9/1991 IL
## 904 142 30 491170 1/14/1998 IN
## 905 97 27 175553 4/25/2002 OH
## 906 121 31 439341 7/20/1991 IN
## 907 225 43 221186 8/13/2004 OH
## 908 425 53 868031 6/24/1990 OH
## 909 285 44 844117 8/21/1991 OH
## 910 192 38 744557 2/25/2011 IN
## 911 285 48 159536 2/4/2013 IL
## 912 98 26 727109 2/20/2001 IN
## 913 175 36 155604 3/3/1992 OH
## 914 259 45 608443 12/21/2006 IL
## 915 140 36 117862 7/14/2000 OH
## 916 231 37 991553 12/12/1991 OH
## 917 186 38 727443 7/1/2013 OH
## 918 229 41 378587 12/16/1998 OH
## 919 180 36 420948 1/3/2015 IL
## 920 188 33 457188 4/1/1994 IL
## 921 214 40 118236 8/15/2000 OH
## 922 178 38 987524 11/13/2014 IL
## 923 55 35 490596 2/4/2011 IL
## 924 90 31 524215 6/24/1990 OH
## 925 135 30 913464 1/21/2009 IN
## 926 277 46 398484 11/7/1992 IL
## 927 211 38 752504 5/15/1997 IN
## 928 156 32 449263 3/20/1992 IL
## 929 84 30 844007 7/17/1995 IN
## 930 136 32 686522 12/27/2000 IN
## 931 310 48 670142 8/6/1999 IN
## 932 123 34 607687 3/3/2007 OH
## 933 243 44 967713 12/25/1997 IL
## 934 36 37 291902 11/6/2013 IL
## 935 146 31 149839 9/21/1990 OH
## 936 154 34 840225 10/5/1999 OH
## 937 204 40 643226 4/7/1992 OH
## 938 458 59 535879 3/5/2009 IN
## 939 147 31 746630 2/10/1997 IN
## 940 279 45 598308 1/28/1992 IN
## 941 308 47 720356 9/16/2013 OH
## 942 284 48 724752 5/16/2008 IL
## 943 108 31 148498 1/4/2002 IN
## 944 421 57 110122 4/2/2002 IN
## 945 266 42 281388 7/16/1998 IL
## 946 412 56 728600 8/15/2002 IL
## 947 31 32 231548 9/7/1999 IL
## 948 465 63 531160 1/12/2012 IL
## 949 126 31 889003 8/18/1996 OH
## 950 407 55 193213 3/11/1996 OH
## 951 101 27 557218 11/23/1997 IL
## 952 187 37 125591 8/8/2013 IN
## 953 252 46 227244 11/30/1996 IN
## 954 229 43 791425 6/18/1997 IN
## 955 246 39 354455 4/19/2007 IN
## 956 190 38 601042 9/19/2007 OH
## 957 95 32 433663 12/21/1996 IN
## 958 205 42 471938 2/3/2008 IL
## 959 41 25 564654 7/16/2003 OH
## 960 137 35 645723 5/5/1991 OH
## 961 194 34 573572 6/16/1991 IL
## 962 128 35 437960 4/3/2001 IN
## 963 150 37 649800 3/16/2014 OH
## 964 104 30 544225 8/3/2010 OH
## 965 163 37 390256 11/25/2009 IN
## 966 80 26 488597 5/8/2001 IL
## 967 65 29 133889 6/14/2004 OH
## 968 179 32 931901 8/7/1994 OH
## 969 372 50 769475 8/26/2004 OH
## 970 398 55 844062 5/25/1990 OH
## 971 213 35 844129 9/20/1990 OH
## 972 79 25 732169 11/5/2000 OH
## 973 232 44 221854 10/3/1994 OH
## 974 230 37 776950 4/11/2005 IL
## 975 234 41 291006 5/16/1990 IN
## 976 240 40 845751 9/11/2004 IN
## 977 143 33 889764 11/30/1993 OH
## 978 266 42 929306 3/6/2003 IN
## 979 89 32 515457 12/18/1996 IN
## 980 229 37 556270 2/21/1995 IN
## 981 245 40 908935 12/11/2009 IL
## 982 50 44 525862 10/18/2000 OH
## 983 230 43 490514 2/9/2007 IN
## 984 17 39 774895 10/28/2006 IL
## 985 163 36 974522 1/27/2000 IN
## 986 29 32 669809 4/5/2002 OH
## 987 232 42 182953 4/30/2013 IN
## 988 235 39 836349 5/1/2013 IL
## 989 295 46 591269 1/9/1999 IN
## 990 22 21 550127 7/4/2007 IN
## 991 286 43 663190 2/5/1994 IL
## 992 257 44 109392 7/12/2006 OH
## 993 94 26 215278 10/24/2007 IN
## 994 124 28 674570 12/8/2001 OH
## 995 141 30 681486 3/24/2007 IN
## 996 3 38 941851 7/16/1991 OH
## 997 285 41 186934 1/5/2014 IL
## 998 130 34 918516 2/17/2003 OH
## 999 458 62 533940 11/18/2011 IL
## 1000 456 60 556080 11/11/1996 OH
## policy_csl policy_deductable policy_annual_premium umbrella_limit
## 1 250/500 1000 1406.91 0
## 2 250/500 2000 1197.22 5000000
## 3 100/300 2000 1413.14 5000000
## 4 250/500 2000 1415.74 6000000
## 5 500/1000 1000 1583.91 6000000
## 6 250/500 1000 1351.10 0
## 7 250/500 1000 1333.35 0
## 8 100/300 1000 1137.03 0
## 9 100/300 500 1442.99 0
## 10 100/300 500 1315.68 0
## 11 100/300 500 1253.12 4000000
## 12 100/300 2000 1137.16 0
## 13 500/1000 500 1215.36 3000000
## 14 100/300 1000 936.61 0
## 15 250/500 2000 1301.13 0
## 16 100/300 2000 1131.40 0
## 17 500/1000 1000 1199.44 5000000
## 18 500/1000 500 708.64 6000000
## 19 500/1000 500 1374.22 0
## 20 500/1000 2000 1475.73 0
## 21 250/500 1000 1187.96 4000000
## 22 500/1000 2000 875.15 0
## 23 100/300 2000 972.18 0
## 24 100/300 2000 1268.79 0
## 25 100/300 1000 883.31 0
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## incident_date incident_type collision_type incident_severity
## 1 1/25/2015 Single Vehicle Collision Side Collision Major Damage
## 2 1/21/2015 Vehicle Theft ? Minor Damage
## 3 2/22/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 4 1/10/2015 Single Vehicle Collision Front Collision Major Damage
## 5 2/17/2015 Vehicle Theft ? Minor Damage
## 6 1/2/2015 Multi-vehicle Collision Rear Collision Major Damage
## 7 1/13/2015 Multi-vehicle Collision Front Collision Minor Damage
## 8 2/27/2015 Multi-vehicle Collision Front Collision Total Loss
## 9 1/30/2015 Single Vehicle Collision Front Collision Total Loss
## 10 1/5/2015 Single Vehicle Collision Rear Collision Total Loss
## 11 1/6/2015 Single Vehicle Collision Front Collision Total Loss
## 12 2/15/2015 Multi-vehicle Collision Front Collision Major Damage
## 13 1/22/2015 Single Vehicle Collision Rear Collision Total Loss
## 14 1/8/2015 Parked Car ? Minor Damage
## 15 1/15/2015 Single Vehicle Collision Rear Collision Total Loss
## 16 1/29/2015 Multi-vehicle Collision Side Collision Major Damage
## 17 2/22/2015 Multi-vehicle Collision Rear Collision Major Damage
## 18 1/6/2015 Single Vehicle Collision Side Collision Total Loss
## 19 1/19/2015 Single Vehicle Collision Side Collision Total Loss
## 20 2/22/2015 Multi-vehicle Collision Side Collision Major Damage
## 21 1/1/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 22 2/10/2015 Multi-vehicle Collision Side Collision Total Loss
## 23 1/11/2015 Multi-vehicle Collision Rear Collision Major Damage
## 24 1/19/2015 Single Vehicle Collision Front Collision Total Loss
## 25 2/24/2015 Single Vehicle Collision Rear Collision Minor Damage
## 26 1/9/2015 Multi-vehicle Collision Rear Collision Major Damage
## 27 1/28/2015 Parked Car ? Minor Damage
## 28 1/7/2015 Vehicle Theft ? Minor Damage
## 29 1/8/2015 Single Vehicle Collision Side Collision Minor Damage
## 30 2/15/2015 Single Vehicle Collision Rear Collision Minor Damage
## 31 1/18/2015 Multi-vehicle Collision Side Collision Major Damage
## 32 2/28/2015 Multi-vehicle Collision Side Collision Major Damage
## 33 2/24/2015 Multi-vehicle Collision Front Collision Total Loss
## 34 1/9/2015 Multi-vehicle Collision Front Collision Major Damage
## 35 2/12/2015 Single Vehicle Collision Side Collision Total Loss
## 36 1/24/2015 Single Vehicle Collision Front Collision Major Damage
## 37 1/9/2015 Single Vehicle Collision Rear Collision Total Loss
## 38 1/18/2015 Parked Car ? Minor Damage
## 39 1/21/2015 Multi-vehicle Collision Rear Collision Major Damage
## 40 1/8/2015 Single Vehicle Collision Front Collision Major Damage
## 41 1/3/2015 Single Vehicle Collision Rear Collision Minor Damage
## 42 1/1/2015 Single Vehicle Collision Side Collision Major Damage
## 43 1/16/2015 Multi-vehicle Collision Side Collision Minor Damage
## 44 2/10/2015 Single Vehicle Collision Rear Collision Total Loss
## 45 2/14/2015 Single Vehicle Collision Front Collision Minor Damage
## 46 2/21/2015 Multi-vehicle Collision Rear Collision Total Loss
## 47 2/18/2015 Multi-vehicle Collision Rear Collision Total Loss
## 48 1/10/2015 Multi-vehicle Collision Front Collision Major Damage
## 49 2/26/2015 Vehicle Theft ? Trivial Damage
## 50 1/1/2015 Single Vehicle Collision Rear Collision Total Loss
## 51 1/3/2015 Multi-vehicle Collision Front Collision Minor Damage
## 52 1/17/2015 Vehicle Theft ? Trivial Damage
## 53 2/22/2015 Vehicle Theft ? Minor Damage
## 54 1/27/2015 Multi-vehicle Collision Side Collision Major Damage
## 55 2/27/2015 Parked Car ? Minor Damage
## 56 1/6/2015 Single Vehicle Collision Rear Collision Minor Damage
## 57 2/28/2015 Multi-vehicle Collision Front Collision Major Damage
## 58 2/22/2015 Parked Car ? Minor Damage
## 59 1/7/2015 Single Vehicle Collision Front Collision Total Loss
## 60 1/6/2015 Multi-vehicle Collision Side Collision Minor Damage
## 61 1/10/2015 Multi-vehicle Collision Rear Collision Major Damage
## 62 2/11/2015 Multi-vehicle Collision Side Collision Total Loss
## 63 1/12/2015 Single Vehicle Collision Side Collision Minor Damage
## 64 2/6/2015 Multi-vehicle Collision Front Collision Major Damage
## 65 1/20/2015 Multi-vehicle Collision Rear Collision Total Loss
## 66 2/22/2015 Multi-vehicle Collision Front Collision Minor Damage
## 67 1/30/2015 Single Vehicle Collision Side Collision Minor Damage
## 68 2/2/2015 Multi-vehicle Collision Side Collision Minor Damage
## 69 1/10/2015 Single Vehicle Collision Front Collision Major Damage
## 70 2/27/2015 Parked Car ? Minor Damage
## 71 2/20/2015 Multi-vehicle Collision Side Collision Major Damage
## 72 2/8/2015 Single Vehicle Collision Front Collision Total Loss
## 73 2/11/2015 Single Vehicle Collision Rear Collision Minor Damage
## 74 2/23/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 75 3/1/2015 Multi-vehicle Collision Side Collision Minor Damage
## 76 1/15/2015 Multi-vehicle Collision Front Collision Total Loss
## 77 1/14/2015 Multi-vehicle Collision Front Collision Minor Damage
## 78 2/17/2015 Multi-vehicle Collision Front Collision Total Loss
## 79 1/24/2015 Vehicle Theft ? Minor Damage
## 80 1/21/2015 Single Vehicle Collision Rear Collision Major Damage
## 81 2/19/2015 Multi-vehicle Collision Side Collision Minor Damage
## 82 1/3/2015 Vehicle Theft ? Trivial Damage
## 83 1/29/2015 Vehicle Theft ? Minor Damage
## 84 1/19/2015 Vehicle Theft ? Trivial Damage
## 85 1/19/2015 Multi-vehicle Collision Side Collision Major Damage
## 86 2/2/2015 Multi-vehicle Collision Front Collision Major Damage
## 87 1/30/2015 Multi-vehicle Collision Front Collision Minor Damage
## 88 1/8/2015 Single Vehicle Collision Side Collision Total Loss
## 89 1/30/2015 Parked Car ? Trivial Damage
## 90 1/7/2015 Multi-vehicle Collision Side Collision Major Damage
## 91 2/24/2015 Multi-vehicle Collision Front Collision Total Loss
## 92 2/2/2015 Single Vehicle Collision Side Collision Major Damage
## 93 2/28/2015 Parked Car ? Trivial Damage
## 94 2/9/2015 Multi-vehicle Collision Front Collision Major Damage
## 95 1/19/2015 Multi-vehicle Collision Rear Collision Total Loss
## 96 2/21/2015 Vehicle Theft ? Minor Damage
## 97 1/14/2015 Single Vehicle Collision Side Collision Major Damage
## 98 1/22/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 99 2/22/2015 Parked Car ? Trivial Damage
## 100 1/26/2015 Vehicle Theft ? Trivial Damage
## 101 1/24/2015 Single Vehicle Collision Rear Collision Minor Damage
## 102 2/19/2015 Single Vehicle Collision Side Collision Major Damage
## 103 1/29/2015 Multi-vehicle Collision Front Collision Minor Damage
## 104 2/15/2015 Parked Car ? Minor Damage
## 105 2/12/2015 Multi-vehicle Collision Side Collision Total Loss
## 106 1/1/2015 Vehicle Theft ? Minor Damage
## 107 1/13/2015 Multi-vehicle Collision Rear Collision Major Damage
## 108 3/1/2015 Multi-vehicle Collision Front Collision Major Damage
## 109 2/2/2015 Single Vehicle Collision Front Collision Major Damage
## 110 1/27/2015 Multi-vehicle Collision Rear Collision Major Damage
## 111 1/12/2015 Single Vehicle Collision Rear Collision Minor Damage
## 112 1/6/2015 Single Vehicle Collision Front Collision Major Damage
## 113 1/30/2015 Multi-vehicle Collision Rear Collision Total Loss
## 114 3/1/2015 Multi-vehicle Collision Side Collision Major Damage
## 115 2/24/2015 Vehicle Theft ? Minor Damage
## 116 1/24/2015 Multi-vehicle Collision Side Collision Total Loss
## 117 2/26/2015 Single Vehicle Collision Rear Collision Minor Damage
## 118 1/19/2015 Multi-vehicle Collision Side Collision Major Damage
## 119 1/17/2015 Single Vehicle Collision Rear Collision Minor Damage
## 120 2/20/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 121 2/22/2015 Single Vehicle Collision Side Collision Minor Damage
## 122 1/23/2015 Single Vehicle Collision Front Collision Major Damage
## 123 1/31/2015 Single Vehicle Collision Front Collision Major Damage
## 124 1/5/2015 Multi-vehicle Collision Front Collision Total Loss
## 125 2/3/2015 Single Vehicle Collision Rear Collision Total Loss
## 126 2/17/2015 Multi-vehicle Collision Rear Collision Total Loss
## 127 1/9/2015 Single Vehicle Collision Side Collision Total Loss
## 128 2/8/2015 Parked Car ? Minor Damage
## 129 2/6/2015 Single Vehicle Collision Front Collision Major Damage
## 130 1/14/2015 Multi-vehicle Collision Front Collision Major Damage
## 131 1/22/2015 Multi-vehicle Collision Front Collision Minor Damage
## 132 2/23/2015 Multi-vehicle Collision Front Collision Major Damage
## 133 2/22/2015 Multi-vehicle Collision Front Collision Major Damage
## 134 1/24/2015 Single Vehicle Collision Side Collision Total Loss
## 135 2/9/2015 Single Vehicle Collision Side Collision Minor Damage
## 136 1/19/2015 Single Vehicle Collision Rear Collision Major Damage
## 137 1/1/2015 Vehicle Theft ? Minor Damage
## 138 1/7/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 139 2/1/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 140 2/21/2015 Multi-vehicle Collision Front Collision Total Loss
## 141 1/21/2015 Single Vehicle Collision Rear Collision Total Loss
## 142 1/19/2015 Vehicle Theft ? Trivial Damage
## 143 2/27/2015 Parked Car ? Minor Damage
## 144 2/14/2015 Single Vehicle Collision Rear Collision Major Damage
## 145 2/1/2015 Multi-vehicle Collision Rear Collision Total Loss
## 146 2/2/2015 Single Vehicle Collision Front Collision Major Damage
## 147 2/1/2015 Multi-vehicle Collision Side Collision Major Damage
## 148 1/19/2015 Single Vehicle Collision Front Collision Minor Damage
## 149 2/18/2015 Single Vehicle Collision Rear Collision Major Damage
## 150 2/8/2015 Single Vehicle Collision Side Collision Major Damage
## 151 1/9/2015 Single Vehicle Collision Side Collision Total Loss
## 152 2/27/2015 Multi-vehicle Collision Rear Collision Major Damage
## 153 1/8/2015 Multi-vehicle Collision Rear Collision Total Loss
## 154 1/8/2015 Multi-vehicle Collision Rear Collision Total Loss
## 155 2/24/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 156 2/12/2015 Single Vehicle Collision Front Collision Major Damage
## 157 2/18/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 158 2/6/2015 Parked Car ? Minor Damage
## 159 2/14/2015 Multi-vehicle Collision Front Collision Total Loss
## 160 2/5/2015 Vehicle Theft ? Minor Damage
## 161 1/25/2015 Parked Car ? Minor Damage
## 162 1/16/2015 Single Vehicle Collision Front Collision Total Loss
## 163 1/28/2015 Multi-vehicle Collision Side Collision Total Loss
## 164 1/21/2015 Single Vehicle Collision Front Collision Major Damage
## 165 1/7/2015 Multi-vehicle Collision Rear Collision Total Loss
## 166 2/23/2015 Multi-vehicle Collision Side Collision Minor Damage
## 167 2/14/2015 Single Vehicle Collision Rear Collision Major Damage
## 168 2/27/2015 Multi-vehicle Collision Front Collision Minor Damage
## 169 2/21/2015 Parked Car ? Trivial Damage
## 170 1/5/2015 Parked Car ? Minor Damage
## 171 1/30/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 172 2/26/2015 Single Vehicle Collision Rear Collision Total Loss
## 173 1/10/2015 Multi-vehicle Collision Rear Collision Total Loss
## 174 2/23/2015 Multi-vehicle Collision Front Collision Major Damage
## 175 2/5/2015 Vehicle Theft ? Trivial Damage
## 176 1/21/2015 Single Vehicle Collision Rear Collision Minor Damage
## 177 1/7/2015 Single Vehicle Collision Front Collision Minor Damage
## 178 1/11/2015 Single Vehicle Collision Rear Collision Minor Damage
## 179 2/6/2015 Single Vehicle Collision Front Collision Total Loss
## 180 1/24/2015 Vehicle Theft ? Trivial Damage
## 181 2/18/2015 Multi-vehicle Collision Side Collision Major Damage
## 182 1/12/2015 Single Vehicle Collision Side Collision Minor Damage
## 183 1/17/2015 Single Vehicle Collision Side Collision Total Loss
## 184 1/12/2015 Single Vehicle Collision Rear Collision Minor Damage
## 185 1/6/2015 Single Vehicle Collision Front Collision Major Damage
## 186 2/25/2015 Single Vehicle Collision Front Collision Total Loss
## 187 2/16/2015 Multi-vehicle Collision Front Collision Total Loss
## 188 2/25/2015 Vehicle Theft ? Minor Damage
## 189 2/21/2015 Multi-vehicle Collision Rear Collision Major Damage
## 190 2/16/2015 Single Vehicle Collision Rear Collision Total Loss
## 191 2/9/2015 Parked Car ? Minor Damage
## 192 2/13/2015 Single Vehicle Collision Side Collision Minor Damage
## 193 1/11/2015 Single Vehicle Collision Front Collision Total Loss
## 194 1/2/2015 Vehicle Theft ? Minor Damage
## 195 2/12/2015 Multi-vehicle Collision Rear Collision Total Loss
## 196 2/5/2015 Multi-vehicle Collision Side Collision Total Loss
## 197 1/24/2015 Vehicle Theft ? Trivial Damage
## 198 1/12/2015 Parked Car ? Trivial Damage
## 199 1/16/2015 Single Vehicle Collision Front Collision Major Damage
## 200 1/13/2015 Vehicle Theft ? Minor Damage
## 201 2/2/2015 Vehicle Theft ? Trivial Damage
## 202 2/28/2015 Single Vehicle Collision Rear Collision Minor Damage
## 203 1/18/2015 Parked Car ? Trivial Damage
## 204 2/15/2015 Multi-vehicle Collision Rear Collision Major Damage
## 205 1/31/2015 Multi-vehicle Collision Rear Collision Total Loss
## 206 2/10/2015 Single Vehicle Collision Rear Collision Minor Damage
## 207 2/26/2015 Single Vehicle Collision Rear Collision Total Loss
## 208 2/25/2015 Multi-vehicle Collision Side Collision Major Damage
## 209 1/8/2015 Multi-vehicle Collision Side Collision Total Loss
## 210 1/14/2015 Parked Car ? Trivial Damage
## 211 2/3/2015 Vehicle Theft ? Minor Damage
## 212 1/5/2015 Vehicle Theft ? Trivial Damage
## 213 1/24/2015 Multi-vehicle Collision Side Collision Minor Damage
## 214 1/26/2015 Multi-vehicle Collision Side Collision Major Damage
## 215 1/21/2015 Single Vehicle Collision Side Collision Total Loss
## 216 2/6/2015 Multi-vehicle Collision Side Collision Minor Damage
## 217 1/7/2015 Single Vehicle Collision Front Collision Minor Damage
## 218 2/10/2015 Vehicle Theft ? Trivial Damage
## 219 2/23/2015 Single Vehicle Collision Rear Collision Total Loss
## 220 1/9/2015 Multi-vehicle Collision Side Collision Minor Damage
## 221 2/22/2015 Single Vehicle Collision Front Collision Major Damage
## 222 2/21/2015 Single Vehicle Collision Front Collision Minor Damage
## 223 2/4/2015 Single Vehicle Collision Side Collision Total Loss
## 224 1/7/2015 Multi-vehicle Collision Side Collision Total Loss
## 225 1/17/2015 Multi-vehicle Collision Side Collision Major Damage
## 226 1/28/2015 Single Vehicle Collision Front Collision Total Loss
## 227 2/22/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 228 2/16/2015 Single Vehicle Collision Front Collision Major Damage
## 229 2/17/2015 Multi-vehicle Collision Front Collision Total Loss
## 230 1/9/2015 Single Vehicle Collision Rear Collision Minor Damage
## 231 2/9/2015 Single Vehicle Collision Side Collision Total Loss
## 232 2/16/2015 Single Vehicle Collision Side Collision Minor Damage
## 233 2/18/2015 Multi-vehicle Collision Side Collision Total Loss
## 234 1/11/2015 Multi-vehicle Collision Rear Collision Major Damage
## 235 2/4/2015 Multi-vehicle Collision Front Collision Major Damage
## 236 1/22/2015 Multi-vehicle Collision Side Collision Minor Damage
## 237 1/10/2015 Single Vehicle Collision Front Collision Total Loss
## 238 1/10/2015 Multi-vehicle Collision Rear Collision Total Loss
## 239 2/16/2015 Single Vehicle Collision Side Collision Major Damage
## 240 2/8/2015 Single Vehicle Collision Front Collision Minor Damage
## 241 1/26/2015 Single Vehicle Collision Front Collision Major Damage
## 242 2/1/2015 Single Vehicle Collision Front Collision Major Damage
## 243 2/2/2015 Parked Car ? Trivial Damage
## 244 2/6/2015 Multi-vehicle Collision Rear Collision Total Loss
## 245 1/1/2015 Vehicle Theft ? Trivial Damage
## 246 1/19/2015 Single Vehicle Collision Rear Collision Major Damage
## 247 1/17/2015 Single Vehicle Collision Side Collision Minor Damage
## 248 2/1/2015 Multi-vehicle Collision Rear Collision Total Loss
## 249 1/2/2015 Parked Car ? Minor Damage
## 250 2/20/2015 Single Vehicle Collision Rear Collision Major Damage
## 251 2/5/2015 Multi-vehicle Collision Rear Collision Total Loss
## 252 2/27/2015 Multi-vehicle Collision Side Collision Major Damage
## 253 1/27/2015 Vehicle Theft ? Trivial Damage
## 254 1/26/2015 Single Vehicle Collision Side Collision Major Damage
## 255 1/13/2015 Multi-vehicle Collision Rear Collision Major Damage
## 256 1/14/2015 Multi-vehicle Collision Side Collision Major Damage
## 257 2/22/2015 Multi-vehicle Collision Side Collision Total Loss
## 258 1/17/2015 Single Vehicle Collision Side Collision Major Damage
## 259 2/2/2015 Vehicle Theft ? Trivial Damage
## 260 1/7/2015 Multi-vehicle Collision Rear Collision Major Damage
## 261 2/2/2015 Parked Car ? Trivial Damage
## 262 2/7/2015 Single Vehicle Collision Front Collision Major Damage
## 263 1/18/2015 Multi-vehicle Collision Rear Collision Major Damage
## 264 1/10/2015 Multi-vehicle Collision Side Collision Minor Damage
## 265 1/30/2015 Single Vehicle Collision Side Collision Minor Damage
## 266 1/25/2015 Vehicle Theft ? Trivial Damage
## 267 1/29/2015 Multi-vehicle Collision Side Collision Major Damage
## 268 2/24/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 269 1/4/2015 Multi-vehicle Collision Rear Collision Major Damage
## 270 2/25/2015 Multi-vehicle Collision Front Collision Total Loss
## 271 1/27/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 272 2/19/2015 Parked Car ? Minor Damage
## 273 1/31/2015 Multi-vehicle Collision Front Collision Major Damage
## 274 1/18/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 275 1/10/2015 Multi-vehicle Collision Front Collision Minor Damage
## 276 1/28/2015 Multi-vehicle Collision Front Collision Major Damage
## 277 2/16/2015 Multi-vehicle Collision Side Collision Total Loss
## 278 2/8/2015 Single Vehicle Collision Rear Collision Minor Damage
## 279 1/20/2015 Multi-vehicle Collision Side Collision Major Damage
## 280 2/6/2015 Multi-vehicle Collision Side Collision Minor Damage
## 281 1/24/2015 Multi-vehicle Collision Side Collision Minor Damage
## 282 1/14/2015 Vehicle Theft ? Trivial Damage
## 283 2/18/2015 Vehicle Theft ? Minor Damage
## 284 1/10/2015 Single Vehicle Collision Rear Collision Major Damage
## 285 2/13/2015 Multi-vehicle Collision Side Collision Major Damage
## 286 2/17/2015 Single Vehicle Collision Front Collision Total Loss
## 287 1/7/2015 Multi-vehicle Collision Rear Collision Total Loss
## 288 1/29/2015 Vehicle Theft ? Trivial Damage
## 289 2/13/2015 Single Vehicle Collision Rear Collision Major Damage
## 290 2/1/2015 Parked Car ? Minor Damage
## 291 1/29/2015 Single Vehicle Collision Side Collision Major Damage
## 292 2/6/2015 Multi-vehicle Collision Front Collision Total Loss
## 293 1/28/2015 Multi-vehicle Collision Rear Collision Major Damage
## 294 1/21/2015 Single Vehicle Collision Side Collision Minor Damage
## 295 1/16/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 296 2/17/2015 Single Vehicle Collision Side Collision Total Loss
## 297 2/1/2015 Multi-vehicle Collision Front Collision Minor Damage
## 298 2/25/2015 Parked Car ? Trivial Damage
## 299 2/26/2015 Parked Car ? Minor Damage
## 300 2/6/2015 Multi-vehicle Collision Rear Collision Major Damage
## 301 2/3/2015 Vehicle Theft ? Trivial Damage
## 302 1/27/2015 Multi-vehicle Collision Side Collision Total Loss
## 303 1/21/2015 Multi-vehicle Collision Rear Collision Major Damage
## 304 1/24/2015 Parked Car ? Trivial Damage
## 305 1/17/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 306 2/23/2015 Single Vehicle Collision Front Collision Major Damage
## 307 1/9/2015 Single Vehicle Collision Side Collision Major Damage
## 308 2/15/2015 Multi-vehicle Collision Rear Collision Total Loss
## 309 1/16/2015 Multi-vehicle Collision Rear Collision Total Loss
## 310 1/14/2015 Multi-vehicle Collision Side Collision Total Loss
## 311 1/1/2015 Multi-vehicle Collision Rear Collision Major Damage
## 312 2/16/2015 Multi-vehicle Collision Front Collision Major Damage
## 313 1/4/2015 Single Vehicle Collision Side Collision Total Loss
## 314 1/12/2015 Multi-vehicle Collision Side Collision Total Loss
## 315 1/8/2015 Multi-vehicle Collision Side Collision Minor Damage
## 316 1/22/2015 Single Vehicle Collision Rear Collision Minor Damage
## 317 1/13/2015 Multi-vehicle Collision Side Collision Total Loss
## 318 1/18/2015 Multi-vehicle Collision Side Collision Total Loss
## 319 1/19/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 320 1/31/2015 Single Vehicle Collision Side Collision Major Damage
## 321 2/6/2015 Single Vehicle Collision Front Collision Total Loss
## 322 2/4/2015 Multi-vehicle Collision Rear Collision Major Damage
## 323 1/2/2015 Single Vehicle Collision Front Collision Total Loss
## 324 2/26/2015 Multi-vehicle Collision Rear Collision Total Loss
## 325 2/8/2015 Multi-vehicle Collision Rear Collision Major Damage
## 326 2/7/2015 Multi-vehicle Collision Side Collision Total Loss
## 327 2/17/2015 Multi-vehicle Collision Front Collision Total Loss
## 328 1/12/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 329 2/1/2015 Multi-vehicle Collision Side Collision Major Damage
## 330 2/3/2015 Single Vehicle Collision Front Collision Major Damage
## 331 2/21/2015 Single Vehicle Collision Side Collision Major Damage
## 332 1/9/2015 Multi-vehicle Collision Front Collision Major Damage
## 333 2/16/2015 Parked Car ? Trivial Damage
## 334 2/4/2015 Parked Car ? Trivial Damage
## 335 2/24/2015 Single Vehicle Collision Side Collision Total Loss
## 336 2/1/2015 Single Vehicle Collision Rear Collision Minor Damage
## 337 2/5/2015 Multi-vehicle Collision Side Collision Minor Damage
## 338 1/7/2015 Single Vehicle Collision Rear Collision Total Loss
## 339 2/6/2015 Single Vehicle Collision Front Collision Minor Damage
## 340 1/21/2015 Multi-vehicle Collision Front Collision Total Loss
## 341 3/1/2015 Multi-vehicle Collision Side Collision Total Loss
## 342 2/24/2015 Single Vehicle Collision Rear Collision Minor Damage
## 343 2/2/2015 Single Vehicle Collision Rear Collision Major Damage
## 344 1/31/2015 Vehicle Theft ? Minor Damage
## 345 1/9/2015 Single Vehicle Collision Rear Collision Total Loss
## 346 2/23/2015 Single Vehicle Collision Side Collision Minor Damage
## 347 1/28/2015 Single Vehicle Collision Rear Collision Minor Damage
## 348 1/14/2015 Single Vehicle Collision Front Collision Total Loss
## 349 2/3/2015 Multi-vehicle Collision Side Collision Total Loss
## 350 2/17/2015 Single Vehicle Collision Front Collision Major Damage
## 351 1/30/2015 Single Vehicle Collision Side Collision Minor Damage
## 352 2/3/2015 Multi-vehicle Collision Rear Collision Major Damage
## 353 2/3/2015 Single Vehicle Collision Rear Collision Minor Damage
## 354 1/31/2015 Single Vehicle Collision Front Collision Minor Damage
## 355 2/6/2015 Multi-vehicle Collision Front Collision Minor Damage
## 356 2/6/2015 Single Vehicle Collision Rear Collision Total Loss
## 357 1/29/2015 Multi-vehicle Collision Side Collision Major Damage
## 358 1/7/2015 Multi-vehicle Collision Side Collision Minor Damage
## 359 1/20/2015 Single Vehicle Collision Side Collision Major Damage
## 360 2/28/2015 Single Vehicle Collision Side Collision Total Loss
## 361 2/4/2015 Multi-vehicle Collision Rear Collision Major Damage
## 362 1/12/2015 Multi-vehicle Collision Rear Collision Major Damage
## 363 2/28/2015 Parked Car ? Trivial Damage
## 364 1/8/2015 Multi-vehicle Collision Side Collision Major Damage
## 365 1/25/2015 Parked Car ? Minor Damage
## 366 2/26/2015 Parked Car ? Minor Damage
## 367 2/3/2015 Vehicle Theft ? Trivial Damage
## 368 1/31/2015 Single Vehicle Collision Front Collision Minor Damage
## 369 2/1/2015 Single Vehicle Collision Side Collision Minor Damage
## 370 2/21/2015 Multi-vehicle Collision Rear Collision Major Damage
## 371 1/30/2015 Multi-vehicle Collision Front Collision Major Damage
## 372 1/31/2015 Multi-vehicle Collision Side Collision Total Loss
## 373 1/13/2015 Single Vehicle Collision Front Collision Total Loss
## 374 2/16/2015 Parked Car ? Minor Damage
## 375 2/4/2015 Multi-vehicle Collision Side Collision Minor Damage
## 376 2/7/2015 Multi-vehicle Collision Front Collision Total Loss
## 377 1/16/2015 Single Vehicle Collision Rear Collision Major Damage
## 378 2/28/2015 Single Vehicle Collision Side Collision Total Loss
## 379 2/8/2015 Multi-vehicle Collision Side Collision Minor Damage
## 380 1/2/2015 Multi-vehicle Collision Front Collision Major Damage
## 381 1/13/2015 Multi-vehicle Collision Side Collision Total Loss
## 382 1/7/2015 Single Vehicle Collision Front Collision Minor Damage
## 383 1/5/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 384 2/28/2015 Single Vehicle Collision Rear Collision Minor Damage
## 385 1/4/2015 Single Vehicle Collision Front Collision Minor Damage
## 386 2/2/2015 Multi-vehicle Collision Front Collision Minor Damage
## 387 2/23/2015 Single Vehicle Collision Side Collision Major Damage
## 388 2/10/2015 Single Vehicle Collision Front Collision Total Loss
## 389 2/16/2015 Multi-vehicle Collision Side Collision Total Loss
## 390 1/4/2015 Single Vehicle Collision Front Collision Major Damage
## 391 1/30/2015 Single Vehicle Collision Rear Collision Major Damage
## 392 1/25/2015 Multi-vehicle Collision Side Collision Total Loss
## 393 1/22/2015 Multi-vehicle Collision Rear Collision Total Loss
## 394 1/1/2015 Multi-vehicle Collision Front Collision Total Loss
## 395 2/14/2015 Multi-vehicle Collision Rear Collision Major Damage
## 396 1/15/2015 Vehicle Theft ? Minor Damage
## 397 1/6/2015 Single Vehicle Collision Side Collision Major Damage
## 398 2/8/2015 Single Vehicle Collision Front Collision Minor Damage
## 399 2/17/2015 Vehicle Theft ? Trivial Damage
## 400 1/21/2015 Single Vehicle Collision Rear Collision Minor Damage
## 401 2/9/2015 Multi-vehicle Collision Front Collision Total Loss
## 402 2/12/2015 Single Vehicle Collision Front Collision Total Loss
## 403 2/24/2015 Multi-vehicle Collision Rear Collision Major Damage
## 404 2/2/2015 Single Vehicle Collision Side Collision Minor Damage
## 405 2/14/2015 Single Vehicle Collision Front Collision Minor Damage
## 406 2/15/2015 Multi-vehicle Collision Side Collision Total Loss
## 407 2/28/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 408 1/18/2015 Multi-vehicle Collision Side Collision Total Loss
## 409 1/6/2015 Single Vehicle Collision Rear Collision Minor Damage
## 410 1/19/2015 Vehicle Theft ? Trivial Damage
## 411 1/17/2015 Parked Car ? Minor Damage
## 412 2/12/2015 Parked Car ? Trivial Damage
## 413 2/11/2015 Multi-vehicle Collision Front Collision Minor Damage
## 414 2/21/2015 Multi-vehicle Collision Front Collision Major Damage
## 415 1/2/2015 Vehicle Theft ? Minor Damage
## 416 1/11/2015 Single Vehicle Collision Front Collision Total Loss
## 417 2/23/2015 Single Vehicle Collision Rear Collision Total Loss
## 418 1/28/2015 Single Vehicle Collision Rear Collision Total Loss
## 419 2/1/2015 Vehicle Theft ? Trivial Damage
## 420 2/24/2015 Single Vehicle Collision Side Collision Minor Damage
## 421 1/20/2015 Single Vehicle Collision Rear Collision Major Damage
## 422 1/14/2015 Single Vehicle Collision Front Collision Total Loss
## 423 2/5/2015 Single Vehicle Collision Front Collision Major Damage
## 424 1/11/2015 Single Vehicle Collision Side Collision Minor Damage
## 425 1/2/2015 Multi-vehicle Collision Front Collision Major Damage
## 426 2/12/2015 Multi-vehicle Collision Side Collision Major Damage
## 427 1/3/2015 Single Vehicle Collision Front Collision Total Loss
## 428 2/25/2015 Single Vehicle Collision Side Collision Minor Damage
## 429 1/19/2015 Multi-vehicle Collision Rear Collision Major Damage
## 430 1/3/2015 Single Vehicle Collision Front Collision Total Loss
## 431 2/12/2015 Multi-vehicle Collision Front Collision Total Loss
## 432 2/24/2015 Multi-vehicle Collision Side Collision Total Loss
## 433 2/25/2015 Single Vehicle Collision Front Collision Major Damage
## 434 2/17/2015 Single Vehicle Collision Front Collision Minor Damage
## 435 2/9/2015 Single Vehicle Collision Rear Collision Major Damage
## 436 2/15/2015 Single Vehicle Collision Side Collision Total Loss
## 437 2/25/2015 Single Vehicle Collision Side Collision Major Damage
## 438 2/5/2015 Parked Car ? Trivial Damage
## 439 1/12/2015 Vehicle Theft ? Trivial Damage
## 440 1/1/2015 Parked Car ? Trivial Damage
## 441 2/19/2015 Single Vehicle Collision Rear Collision Total Loss
## 442 1/14/2015 Multi-vehicle Collision Front Collision Total Loss
## 443 1/30/2015 Single Vehicle Collision Side Collision Major Damage
## 444 1/20/2015 Parked Car ? Minor Damage
## 445 2/18/2015 Parked Car ? Minor Damage
## 446 1/7/2015 Single Vehicle Collision Rear Collision Total Loss
## 447 1/16/2015 Single Vehicle Collision Rear Collision Total Loss
## 448 1/15/2015 Single Vehicle Collision Front Collision Minor Damage
## 449 2/1/2015 Single Vehicle Collision Front Collision Total Loss
## 450 1/6/2015 Multi-vehicle Collision Rear Collision Major Damage
## 451 2/2/2015 Single Vehicle Collision Rear Collision Minor Damage
## 452 2/25/2015 Single Vehicle Collision Side Collision Total Loss
## 453 2/10/2015 Parked Car ? Trivial Damage
## 454 2/22/2015 Multi-vehicle Collision Rear Collision Total Loss
## 455 1/31/2015 Single Vehicle Collision Side Collision Minor Damage
## 456 2/12/2015 Multi-vehicle Collision Front Collision Minor Damage
## 457 1/6/2015 Vehicle Theft ? Minor Damage
## 458 1/1/2015 Single Vehicle Collision Side Collision Major Damage
## 459 1/7/2015 Single Vehicle Collision Side Collision Total Loss
## 460 2/21/2015 Single Vehicle Collision Side Collision Minor Damage
## 461 2/14/2015 Single Vehicle Collision Rear Collision Minor Damage
## 462 2/16/2015 Single Vehicle Collision Side Collision Minor Damage
## 463 2/4/2015 Multi-vehicle Collision Front Collision Major Damage
## 464 1/22/2015 Multi-vehicle Collision Side Collision Total Loss
## 465 2/15/2015 Multi-vehicle Collision Front Collision Total Loss
## 466 1/7/2015 Single Vehicle Collision Rear Collision Major Damage
## 467 2/23/2015 Multi-vehicle Collision Side Collision Major Damage
## 468 1/6/2015 Multi-vehicle Collision Side Collision Major Damage
## 469 2/7/2015 Multi-vehicle Collision Front Collision Minor Damage
## 470 2/14/2015 Multi-vehicle Collision Side Collision Minor Damage
## 471 2/15/2015 Single Vehicle Collision Front Collision Major Damage
## 472 2/14/2015 Multi-vehicle Collision Front Collision Minor Damage
## 473 2/5/2015 Multi-vehicle Collision Side Collision Total Loss
## 474 1/4/2015 Vehicle Theft ? Trivial Damage
## 475 2/20/2015 Parked Car ? Minor Damage
## 476 2/5/2015 Multi-vehicle Collision Side Collision Total Loss
## 477 1/15/2015 Single Vehicle Collision Rear Collision Major Damage
## 478 1/5/2015 Single Vehicle Collision Side Collision Total Loss
## 479 1/1/2015 Vehicle Theft ? Minor Damage
## 480 1/26/2015 Single Vehicle Collision Front Collision Major Damage
## 481 2/13/2015 Multi-vehicle Collision Rear Collision Major Damage
## 482 1/14/2015 Single Vehicle Collision Rear Collision Minor Damage
## 483 1/31/2015 Multi-vehicle Collision Front Collision Major Damage
## 484 1/22/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 485 1/30/2015 Single Vehicle Collision Rear Collision Minor Damage
## 486 2/17/2015 Single Vehicle Collision Front Collision Total Loss
## 487 2/20/2015 Single Vehicle Collision Rear Collision Total Loss
## 488 2/4/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 489 2/23/2015 Single Vehicle Collision Side Collision Total Loss
## 490 2/8/2015 Multi-vehicle Collision Front Collision Total Loss
## 491 2/21/2015 Vehicle Theft ? Minor Damage
## 492 1/13/2015 Multi-vehicle Collision Front Collision Minor Damage
## 493 1/17/2015 Multi-vehicle Collision Side Collision Major Damage
## 494 2/18/2015 Single Vehicle Collision Rear Collision Minor Damage
## 495 1/14/2015 Multi-vehicle Collision Rear Collision Major Damage
## 496 1/27/2015 Single Vehicle Collision Rear Collision Minor Damage
## 497 2/12/2015 Vehicle Theft ? Trivial Damage
## 498 1/17/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 499 1/13/2015 Single Vehicle Collision Front Collision Major Damage
## 500 1/21/2015 Single Vehicle Collision Side Collision Minor Damage
## 501 1/1/2015 Single Vehicle Collision Side Collision Minor Damage
## 502 2/18/2015 Single Vehicle Collision Side Collision Major Damage
## 503 2/4/2015 Single Vehicle Collision Side Collision Major Damage
## 504 1/9/2015 Single Vehicle Collision Front Collision Total Loss
## 505 2/14/2015 Multi-vehicle Collision Rear Collision Major Damage
## 506 1/3/2015 Single Vehicle Collision Rear Collision Minor Damage
## 507 2/4/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 508 1/30/2015 Single Vehicle Collision Front Collision Minor Damage
## 509 2/20/2015 Multi-vehicle Collision Front Collision Total Loss
## 510 1/7/2015 Multi-vehicle Collision Rear Collision Total Loss
## 511 1/31/2015 Single Vehicle Collision Rear Collision Total Loss
## 512 2/17/2015 Parked Car ? Trivial Damage
## 513 1/24/2015 Single Vehicle Collision Rear Collision Total Loss
## 514 2/2/2015 Multi-vehicle Collision Side Collision Minor Damage
## 515 1/9/2015 Single Vehicle Collision Rear Collision Total Loss
## 516 2/28/2015 Single Vehicle Collision Side Collision Total Loss
## 517 1/17/2015 Single Vehicle Collision Front Collision Total Loss
## 518 2/9/2015 Multi-vehicle Collision Front Collision Major Damage
## 519 1/14/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 520 2/2/2015 Vehicle Theft ? Trivial Damage
## 521 2/17/2015 Single Vehicle Collision Front Collision Total Loss
## 522 1/25/2015 Single Vehicle Collision Rear Collision Total Loss
## 523 2/13/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 524 2/20/2015 Multi-vehicle Collision Rear Collision Total Loss
## 525 1/8/2015 Parked Car ? Minor Damage
## 526 2/28/2015 Vehicle Theft ? Trivial Damage
## 527 2/12/2015 Multi-vehicle Collision Side Collision Major Damage
## 528 1/6/2015 Vehicle Theft ? Trivial Damage
## 529 2/15/2015 Single Vehicle Collision Front Collision Total Loss
## 530 1/20/2015 Single Vehicle Collision Front Collision Major Damage
## 531 1/3/2015 Single Vehicle Collision Front Collision Minor Damage
## 532 1/26/2015 Single Vehicle Collision Front Collision Major Damage
## 533 1/25/2015 Multi-vehicle Collision Front Collision Total Loss
## 534 1/18/2015 Vehicle Theft ? Minor Damage
## 535 1/12/2015 Multi-vehicle Collision Rear Collision Total Loss
## 536 1/15/2015 Single Vehicle Collision Side Collision Major Damage
## 537 1/10/2015 Multi-vehicle Collision Front Collision Major Damage
## 538 1/23/2015 Multi-vehicle Collision Side Collision Minor Damage
## 539 2/21/2015 Parked Car ? Minor Damage
## 540 2/17/2015 Single Vehicle Collision Front Collision Total Loss
## 541 1/6/2015 Single Vehicle Collision Front Collision Major Damage
## 542 2/18/2015 Parked Car ? Trivial Damage
## 543 2/17/2015 Multi-vehicle Collision Rear Collision Major Damage
## 544 1/24/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 545 1/21/2015 Single Vehicle Collision Front Collision Minor Damage
## 546 2/3/2015 Single Vehicle Collision Front Collision Major Damage
## 547 1/7/2015 Single Vehicle Collision Side Collision Total Loss
## 548 2/2/2015 Multi-vehicle Collision Side Collision Major Damage
## 549 1/13/2015 Vehicle Theft ? Minor Damage
## 550 2/28/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 551 1/16/2015 Multi-vehicle Collision Side Collision Total Loss
## 552 2/1/2015 Single Vehicle Collision Rear Collision Minor Damage
## 553 1/30/2015 Vehicle Theft ? Minor Damage
## 554 2/16/2015 Parked Car ? Trivial Damage
## 555 1/7/2015 Multi-vehicle Collision Side Collision Minor Damage
## 556 1/10/2015 Single Vehicle Collision Rear Collision Major Damage
## 557 1/31/2015 Vehicle Theft ? Trivial Damage
## 558 2/13/2015 Vehicle Theft ? Minor Damage
## 559 2/22/2015 Multi-vehicle Collision Side Collision Total Loss
## 560 1/8/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 561 2/4/2015 Multi-vehicle Collision Side Collision Total Loss
## 562 2/4/2015 Single Vehicle Collision Front Collision Minor Damage
## 563 2/26/2015 Single Vehicle Collision Front Collision Total Loss
## 564 1/8/2015 Single Vehicle Collision Rear Collision Minor Damage
## 565 2/15/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 566 1/14/2015 Single Vehicle Collision Side Collision Minor Damage
## 567 1/12/2015 Single Vehicle Collision Rear Collision Minor Damage
## 568 1/18/2015 Multi-vehicle Collision Front Collision Major Damage
## 569 1/15/2015 Multi-vehicle Collision Rear Collision Total Loss
## 570 2/24/2015 Single Vehicle Collision Front Collision Total Loss
## 571 2/7/2015 Multi-vehicle Collision Rear Collision Total Loss
## 572 2/22/2015 Multi-vehicle Collision Side Collision Minor Damage
## 573 1/19/2015 Multi-vehicle Collision Side Collision Major Damage
## 574 2/28/2015 Single Vehicle Collision Rear Collision Major Damage
## 575 2/4/2015 Single Vehicle Collision Side Collision Total Loss
## 576 2/17/2015 Single Vehicle Collision Front Collision Total Loss
## 577 2/17/2015 Single Vehicle Collision Front Collision Minor Damage
## 578 1/17/2015 Multi-vehicle Collision Rear Collision Major Damage
## 579 2/2/2015 Multi-vehicle Collision Rear Collision Total Loss
## 580 2/12/2015 Single Vehicle Collision Side Collision Major Damage
## 581 1/4/2015 Single Vehicle Collision Front Collision Total Loss
## 582 2/21/2015 Multi-vehicle Collision Side Collision Major Damage
## 583 1/23/2015 Multi-vehicle Collision Front Collision Minor Damage
## 584 2/12/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 585 1/23/2015 Parked Car ? Trivial Damage
## 586 2/10/2015 Vehicle Theft ? Minor Damage
## 587 1/10/2015 Vehicle Theft ? Trivial Damage
## 588 2/12/2015 Multi-vehicle Collision Front Collision Major Damage
## 589 2/21/2015 Single Vehicle Collision Front Collision Major Damage
## 590 1/18/2015 Multi-vehicle Collision Side Collision Total Loss
## 591 2/28/2015 Multi-vehicle Collision Side Collision Total Loss
## 592 1/19/2015 Single Vehicle Collision Rear Collision Major Damage
## 593 1/11/2015 Single Vehicle Collision Rear Collision Total Loss
## 594 2/11/2015 Single Vehicle Collision Side Collision Major Damage
## 595 1/9/2015 Multi-vehicle Collision Side Collision Major Damage
## 596 1/20/2015 Single Vehicle Collision Rear Collision Major Damage
## 597 2/26/2015 Parked Car ? Trivial Damage
## 598 2/2/2015 Parked Car ? Trivial Damage
## 599 1/4/2015 Multi-vehicle Collision Front Collision Major Damage
## 600 2/19/2015 Multi-vehicle Collision Front Collision Major Damage
## 601 1/12/2015 Single Vehicle Collision Side Collision Minor Damage
## 602 1/24/2015 Single Vehicle Collision Front Collision Minor Damage
## 603 2/17/2015 Single Vehicle Collision Rear Collision Total Loss
## 604 1/13/2015 Vehicle Theft ? Trivial Damage
## 605 2/28/2015 Multi-vehicle Collision Side Collision Minor Damage
## 606 1/3/2015 Single Vehicle Collision Rear Collision Minor Damage
## 607 2/5/2015 Single Vehicle Collision Side Collision Total Loss
## 608 1/1/2015 Parked Car ? Minor Damage
## 609 1/6/2015 Multi-vehicle Collision Front Collision Total Loss
## 610 2/3/2015 Single Vehicle Collision Front Collision Minor Damage
## 611 2/12/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 612 2/16/2015 Multi-vehicle Collision Front Collision Minor Damage
## 613 2/25/2015 Single Vehicle Collision Rear Collision Total Loss
## 614 1/21/2015 Multi-vehicle Collision Front Collision Minor Damage
## 615 1/28/2015 Parked Car ? Minor Damage
## 616 2/27/2015 Multi-vehicle Collision Side Collision Total Loss
## 617 2/10/2015 Multi-vehicle Collision Side Collision Major Damage
## 618 1/18/2015 Multi-vehicle Collision Front Collision Minor Damage
## 619 1/13/2015 Single Vehicle Collision Rear Collision Total Loss
## 620 1/31/2015 Multi-vehicle Collision Front Collision Minor Damage
## 621 1/15/2015 Single Vehicle Collision Side Collision Minor Damage
## 622 2/13/2015 Single Vehicle Collision Side Collision Minor Damage
## 623 2/13/2015 Parked Car ? Trivial Damage
## 624 1/3/2015 Single Vehicle Collision Rear Collision Major Damage
## 625 1/13/2015 Parked Car ? Minor Damage
## 626 2/14/2015 Single Vehicle Collision Front Collision Major Damage
## 627 1/20/2015 Multi-vehicle Collision Side Collision Major Damage
## 628 1/18/2015 Parked Car ? Trivial Damage
## 629 1/3/2015 Single Vehicle Collision Front Collision Major Damage
## 630 3/1/2015 Multi-vehicle Collision Side Collision Total Loss
## 631 1/1/2015 Multi-vehicle Collision Front Collision Minor Damage
## 632 1/29/2015 Multi-vehicle Collision Rear Collision Total Loss
## 633 1/10/2015 Single Vehicle Collision Front Collision Minor Damage
## 634 1/29/2015 Single Vehicle Collision Rear Collision Major Damage
## 635 2/20/2015 Single Vehicle Collision Side Collision Major Damage
## 636 1/21/2015 Parked Car ? Trivial Damage
## 637 1/18/2015 Vehicle Theft ? Minor Damage
## 638 1/9/2015 Vehicle Theft ? Trivial Damage
## 639 1/13/2015 Multi-vehicle Collision Side Collision Major Damage
## 640 2/13/2015 Multi-vehicle Collision Side Collision Minor Damage
## 641 1/8/2015 Single Vehicle Collision Side Collision Total Loss
## 642 2/17/2015 Multi-vehicle Collision Rear Collision Total Loss
## 643 1/15/2015 Single Vehicle Collision Rear Collision Major Damage
## 644 1/24/2015 Single Vehicle Collision Side Collision Major Damage
## 645 2/28/2015 Single Vehicle Collision Front Collision Minor Damage
## 646 2/16/2015 Multi-vehicle Collision Rear Collision Total Loss
## 647 2/9/2015 Multi-vehicle Collision Front Collision Total Loss
## 648 1/24/2015 Single Vehicle Collision Side Collision Minor Damage
## 649 2/11/2015 Parked Car ? Trivial Damage
## 650 1/27/2015 Multi-vehicle Collision Side Collision Total Loss
## 651 1/19/2015 Multi-vehicle Collision Side Collision Major Damage
## 652 2/20/2015 Multi-vehicle Collision Front Collision Minor Damage
## 653 1/10/2015 Multi-vehicle Collision Front Collision Major Damage
## 654 2/21/2015 Multi-vehicle Collision Side Collision Minor Damage
## 655 2/1/2015 Single Vehicle Collision Side Collision Minor Damage
## 656 2/25/2015 Single Vehicle Collision Side Collision Major Damage
## 657 2/24/2015 Vehicle Theft ? Trivial Damage
## 658 2/21/2015 Multi-vehicle Collision Front Collision Minor Damage
## 659 1/8/2015 Single Vehicle Collision Rear Collision Minor Damage
## 660 1/10/2015 Single Vehicle Collision Front Collision Minor Damage
## 661 2/14/2015 Multi-vehicle Collision Front Collision Total Loss
## 662 2/26/2015 Single Vehicle Collision Side Collision Major Damage
## 663 1/30/2015 Multi-vehicle Collision Front Collision Total Loss
## 664 1/7/2015 Multi-vehicle Collision Side Collision Total Loss
## 665 2/27/2015 Single Vehicle Collision Side Collision Minor Damage
## 666 2/13/2015 Single Vehicle Collision Front Collision Total Loss
## 667 2/2/2015 Multi-vehicle Collision Side Collision Major Damage
## 668 2/27/2015 Single Vehicle Collision Rear Collision Total Loss
## 669 2/2/2015 Single Vehicle Collision Rear Collision Major Damage
## 670 1/17/2015 Multi-vehicle Collision Rear Collision Major Damage
## 671 1/12/2015 Multi-vehicle Collision Rear Collision Total Loss
## 672 1/20/2015 Single Vehicle Collision Front Collision Minor Damage
## 673 2/5/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 674 1/20/2015 Single Vehicle Collision Rear Collision Major Damage
## 675 2/24/2015 Vehicle Theft ? Trivial Damage
## 676 2/21/2015 Vehicle Theft ? Trivial Damage
## 677 2/11/2015 Multi-vehicle Collision Front Collision Minor Damage
## 678 1/24/2015 Parked Car ? Trivial Damage
## 679 1/6/2015 Multi-vehicle Collision Front Collision Minor Damage
## 680 1/15/2015 Multi-vehicle Collision Side Collision Minor Damage
## 681 2/22/2015 Single Vehicle Collision Front Collision Total Loss
## 682 1/21/2015 Parked Car ? Trivial Damage
## 683 2/3/2015 Parked Car ? Trivial Damage
## 684 2/16/2015 Multi-vehicle Collision Rear Collision Total Loss
## 685 2/7/2015 Single Vehicle Collision Front Collision Major Damage
## 686 1/14/2015 Single Vehicle Collision Rear Collision Major Damage
## 687 2/2/2015 Parked Car ? Trivial Damage
## 688 2/6/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 689 1/15/2015 Multi-vehicle Collision Side Collision Minor Damage
## 690 2/24/2015 Single Vehicle Collision Side Collision Major Damage
## 691 1/7/2015 Single Vehicle Collision Front Collision Minor Damage
## 692 2/4/2015 Single Vehicle Collision Front Collision Major Damage
## 693 2/4/2015 Parked Car ? Minor Damage
## 694 1/25/2015 Multi-vehicle Collision Side Collision Minor Damage
## 695 2/25/2015 Multi-vehicle Collision Rear Collision Total Loss
## 696 2/27/2015 Vehicle Theft ? Trivial Damage
## 697 2/11/2015 Single Vehicle Collision Rear Collision Total Loss
## 698 1/4/2015 Single Vehicle Collision Rear Collision Major Damage
## 699 1/26/2015 Vehicle Theft ? Trivial Damage
## 700 3/1/2015 Multi-vehicle Collision Front Collision Total Loss
## 701 2/6/2015 Multi-vehicle Collision Front Collision Major Damage
## 702 2/9/2015 Multi-vehicle Collision Rear Collision Major Damage
## 703 2/5/2015 Parked Car ? Minor Damage
## 704 1/3/2015 Multi-vehicle Collision Rear Collision Total Loss
## 705 2/24/2015 Multi-vehicle Collision Rear Collision Major Damage
## 706 1/14/2015 Single Vehicle Collision Side Collision Minor Damage
## 707 1/8/2015 Single Vehicle Collision Rear Collision Minor Damage
## 708 2/14/2015 Single Vehicle Collision Rear Collision Minor Damage
## 709 1/23/2015 Multi-vehicle Collision Rear Collision Total Loss
## 710 1/5/2015 Single Vehicle Collision Front Collision Minor Damage
## 711 2/19/2015 Single Vehicle Collision Side Collision Major Damage
## 712 2/7/2015 Parked Car ? Minor Damage
## 713 2/7/2015 Multi-vehicle Collision Side Collision Major Damage
## 714 1/20/2015 Single Vehicle Collision Rear Collision Major Damage
## 715 2/18/2015 Single Vehicle Collision Front Collision Major Damage
## 716 1/11/2015 Multi-vehicle Collision Rear Collision Major Damage
## 717 1/22/2015 Single Vehicle Collision Front Collision Total Loss
## 718 2/23/2015 Multi-vehicle Collision Side Collision Minor Damage
## 719 1/15/2015 Single Vehicle Collision Front Collision Minor Damage
## 720 2/20/2015 Vehicle Theft ? Trivial Damage
## 721 1/19/2015 Single Vehicle Collision Front Collision Total Loss
## 722 1/10/2015 Parked Car ? Trivial Damage
## 723 1/16/2015 Single Vehicle Collision Side Collision Major Damage
## 724 2/12/2015 Parked Car ? Minor Damage
## 725 1/23/2015 Multi-vehicle Collision Side Collision Total Loss
## 726 2/13/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 727 1/27/2015 Parked Car ? Minor Damage
## 728 2/14/2015 Single Vehicle Collision Rear Collision Major Damage
## 729 2/1/2015 Single Vehicle Collision Side Collision Major Damage
## 730 2/23/2015 Single Vehicle Collision Front Collision Major Damage
## 731 1/20/2015 Single Vehicle Collision Side Collision Major Damage
## 732 1/8/2015 Single Vehicle Collision Side Collision Major Damage
## 733 2/23/2015 Single Vehicle Collision Front Collision Total Loss
## 734 2/4/2015 Single Vehicle Collision Rear Collision Total Loss
## 735 2/9/2015 Single Vehicle Collision Side Collision Minor Damage
## 736 2/18/2015 Multi-vehicle Collision Front Collision Total Loss
## 737 2/10/2015 Multi-vehicle Collision Rear Collision Total Loss
## 738 1/20/2015 Multi-vehicle Collision Front Collision Total Loss
## 739 1/27/2015 Single Vehicle Collision Front Collision Minor Damage
## 740 1/21/2015 Vehicle Theft ? Minor Damage
## 741 2/18/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 742 2/26/2015 Single Vehicle Collision Rear Collision Minor Damage
## 743 1/29/2015 Multi-vehicle Collision Side Collision Major Damage
## 744 2/8/2015 Multi-vehicle Collision Front Collision Major Damage
## 745 1/19/2015 Multi-vehicle Collision Rear Collision Major Damage
## 746 2/8/2015 Multi-vehicle Collision Rear Collision Major Damage
## 747 1/7/2015 Multi-vehicle Collision Side Collision Total Loss
## 748 2/13/2015 Multi-vehicle Collision Side Collision Total Loss
## 749 2/15/2015 Multi-vehicle Collision Side Collision Major Damage
## 750 2/14/2015 Single Vehicle Collision Side Collision Total Loss
## 751 1/31/2015 Vehicle Theft ? Minor Damage
## 752 1/28/2015 Single Vehicle Collision Side Collision Minor Damage
## 753 3/1/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 754 1/10/2015 Multi-vehicle Collision Rear Collision Total Loss
## 755 2/25/2015 Multi-vehicle Collision Side Collision Minor Damage
## 756 1/30/2015 Multi-vehicle Collision Side Collision Major Damage
## 757 2/6/2015 Single Vehicle Collision Rear Collision Total Loss
## 758 2/8/2015 Single Vehicle Collision Rear Collision Total Loss
## 759 1/13/2015 Single Vehicle Collision Front Collision Minor Damage
## 760 1/18/2015 Single Vehicle Collision Front Collision Minor Damage
## 761 3/1/2015 Multi-vehicle Collision Side Collision Minor Damage
## 762 2/2/2015 Multi-vehicle Collision Rear Collision Major Damage
## 763 1/20/2015 Multi-vehicle Collision Rear Collision Major Damage
## 764 1/24/2015 Multi-vehicle Collision Front Collision Major Damage
## 765 1/4/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 766 1/17/2015 Single Vehicle Collision Rear Collision Total Loss
## 767 2/4/2015 Multi-vehicle Collision Rear Collision Total Loss
## 768 2/14/2015 Multi-vehicle Collision Front Collision Major Damage
## 769 1/7/2015 Single Vehicle Collision Rear Collision Minor Damage
## 770 2/2/2015 Multi-vehicle Collision Side Collision Total Loss
## 771 1/12/2015 Multi-vehicle Collision Front Collision Minor Damage
## 772 1/1/2015 Single Vehicle Collision Side Collision Minor Damage
## 773 2/22/2015 Single Vehicle Collision Front Collision Total Loss
## 774 1/16/2015 Multi-vehicle Collision Rear Collision Total Loss
## 775 1/28/2015 Multi-vehicle Collision Side Collision Minor Damage
## 776 1/23/2015 Parked Car ? Trivial Damage
## 777 2/25/2015 Single Vehicle Collision Front Collision Minor Damage
## 778 2/11/2015 Multi-vehicle Collision Rear Collision Total Loss
## 779 1/3/2015 Multi-vehicle Collision Front Collision Total Loss
## 780 1/30/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 781 1/18/2015 Single Vehicle Collision Front Collision Total Loss
## 782 1/4/2015 Single Vehicle Collision Front Collision Minor Damage
## 783 1/20/2015 Vehicle Theft ? Minor Damage
## 784 2/12/2015 Parked Car ? Trivial Damage
## 785 2/19/2015 Multi-vehicle Collision Front Collision Total Loss
## 786 2/21/2015 Multi-vehicle Collision Front Collision Major Damage
## 787 2/2/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 788 2/1/2015 Multi-vehicle Collision Front Collision Minor Damage
## 789 1/8/2015 Single Vehicle Collision Front Collision Major Damage
## 790 1/16/2015 Single Vehicle Collision Rear Collision Minor Damage
## 791 2/20/2015 Parked Car ? Trivial Damage
## 792 1/30/2015 Single Vehicle Collision Rear Collision Total Loss
## 793 1/19/2015 Multi-vehicle Collision Rear Collision Total Loss
## 794 2/25/2015 Single Vehicle Collision Side Collision Total Loss
## 795 1/24/2015 Multi-vehicle Collision Front Collision Minor Damage
## 796 1/16/2015 Multi-vehicle Collision Side Collision Total Loss
## 797 1/12/2015 Single Vehicle Collision Rear Collision Major Damage
## 798 1/4/2015 Single Vehicle Collision Side Collision Minor Damage
## 799 2/2/2015 Single Vehicle Collision Side Collision Major Damage
## 800 2/1/2015 Parked Car ? Trivial Damage
## 801 2/14/2015 Single Vehicle Collision Rear Collision Minor Damage
## 802 2/8/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 803 1/22/2015 Single Vehicle Collision Front Collision Total Loss
## 804 1/14/2015 Single Vehicle Collision Rear Collision Minor Damage
## 805 2/3/2015 Parked Car ? Trivial Damage
## 806 1/20/2015 Single Vehicle Collision Rear Collision Minor Damage
## 807 1/1/2015 Single Vehicle Collision Side Collision Major Damage
## 808 2/2/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 809 1/23/2015 Single Vehicle Collision Side Collision Total Loss
## 810 1/26/2015 Single Vehicle Collision Rear Collision Major Damage
## 811 1/2/2015 Multi-vehicle Collision Side Collision Total Loss
## 812 2/25/2015 Vehicle Theft ? Minor Damage
## 813 1/15/2015 Single Vehicle Collision Front Collision Minor Damage
## 814 2/9/2015 Vehicle Theft ? Minor Damage
## 815 1/16/2015 Multi-vehicle Collision Front Collision Total Loss
## 816 2/1/2015 Single Vehicle Collision Rear Collision Total Loss
## 817 2/10/2015 Multi-vehicle Collision Side Collision Minor Damage
## 818 1/3/2015 Single Vehicle Collision Rear Collision Major Damage
## 819 1/1/2015 Vehicle Theft ? Trivial Damage
## 820 2/23/2015 Single Vehicle Collision Side Collision Minor Damage
## 821 1/28/2015 Vehicle Theft ? Trivial Damage
## 822 2/22/2015 Multi-vehicle Collision Side Collision Minor Damage
## 823 1/3/2015 Single Vehicle Collision Front Collision Minor Damage
## 824 1/10/2015 Single Vehicle Collision Front Collision Major Damage
## 825 1/25/2015 Single Vehicle Collision Rear Collision Total Loss
## 826 1/10/2015 Single Vehicle Collision Front Collision Total Loss
## 827 1/28/2015 Single Vehicle Collision Front Collision Minor Damage
## 828 2/18/2015 Multi-vehicle Collision Side Collision Total Loss
## 829 1/24/2015 Multi-vehicle Collision Rear Collision Major Damage
## 830 1/20/2015 Multi-vehicle Collision Side Collision Total Loss
## 831 1/10/2015 Single Vehicle Collision Side Collision Total Loss
## 832 1/7/2015 Multi-vehicle Collision Rear Collision Major Damage
## 833 1/16/2015 Vehicle Theft ? Minor Damage
## 834 1/1/2015 Multi-vehicle Collision Rear Collision Total Loss
## 835 1/7/2015 Vehicle Theft ? Minor Damage
## 836 2/17/2015 Vehicle Theft ? Minor Damage
## 837 2/26/2015 Single Vehicle Collision Rear Collision Total Loss
## 838 1/31/2015 Vehicle Theft ? Trivial Damage
## 839 2/27/2015 Multi-vehicle Collision Side Collision Major Damage
## 840 3/1/2015 Single Vehicle Collision Rear Collision Major Damage
## 841 2/13/2015 Parked Car ? Trivial Damage
## 842 1/8/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 843 2/5/2015 Vehicle Theft ? Minor Damage
## 844 2/20/2015 Multi-vehicle Collision Front Collision Major Damage
## 845 1/24/2015 Multi-vehicle Collision Side Collision Minor Damage
## 846 1/31/2015 Multi-vehicle Collision Side Collision Major Damage
## 847 1/23/2015 Single Vehicle Collision Side Collision Total Loss
## 848 1/11/2015 Multi-vehicle Collision Side Collision Minor Damage
## 849 2/20/2015 Single Vehicle Collision Side Collision Major Damage
## 850 2/24/2015 Parked Car ? Trivial Damage
## 851 1/19/2015 Multi-vehicle Collision Side Collision Total Loss
## 852 1/8/2015 Single Vehicle Collision Side Collision Major Damage
## 853 3/1/2015 Multi-vehicle Collision Side Collision Total Loss
## 854 1/21/2015 Single Vehicle Collision Front Collision Minor Damage
## 855 2/28/2015 Multi-vehicle Collision Side Collision Total Loss
## 856 1/26/2015 Multi-vehicle Collision Rear Collision Total Loss
## 857 1/31/2015 Multi-vehicle Collision Side Collision Minor Damage
## 858 2/20/2015 Single Vehicle Collision Front Collision Minor Damage
## 859 2/17/2015 Multi-vehicle Collision Side Collision Minor Damage
## 860 2/2/2015 Multi-vehicle Collision Side Collision Total Loss
## 861 2/6/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 862 2/11/2015 Multi-vehicle Collision Side Collision Minor Damage
## 863 1/25/2015 Single Vehicle Collision Rear Collision Minor Damage
## 864 2/23/2015 Multi-vehicle Collision Front Collision Minor Damage
## 865 1/29/2015 Single Vehicle Collision Front Collision Total Loss
## 866 1/2/2015 Single Vehicle Collision Rear Collision Major Damage
## 867 2/6/2015 Single Vehicle Collision Front Collision Minor Damage
## 868 2/14/2015 Multi-vehicle Collision Rear Collision Total Loss
## 869 2/4/2015 Multi-vehicle Collision Front Collision Minor Damage
## 870 2/12/2015 Multi-vehicle Collision Rear Collision Major Damage
## 871 2/12/2015 Single Vehicle Collision Front Collision Total Loss
## 872 1/28/2015 Parked Car ? Minor Damage
## 873 1/10/2015 Multi-vehicle Collision Side Collision Major Damage
## 874 1/19/2015 Single Vehicle Collision Rear Collision Major Damage
## 875 1/19/2015 Single Vehicle Collision Side Collision Minor Damage
## 876 1/28/2015 Single Vehicle Collision Side Collision Major Damage
## 877 2/16/2015 Vehicle Theft ? Trivial Damage
## 878 1/8/2015 Multi-vehicle Collision Front Collision Total Loss
## 879 1/24/2015 Multi-vehicle Collision Rear Collision Major Damage
## 880 2/18/2015 Single Vehicle Collision Front Collision Total Loss
## 881 1/21/2015 Single Vehicle Collision Front Collision Major Damage
## 882 1/8/2015 Single Vehicle Collision Rear Collision Major Damage
## 883 2/2/2015 Single Vehicle Collision Rear Collision Total Loss
## 884 3/1/2015 Multi-vehicle Collision Side Collision Major Damage
## 885 2/4/2015 Single Vehicle Collision Front Collision Minor Damage
## 886 2/27/2015 Multi-vehicle Collision Rear Collision Total Loss
## 887 2/9/2015 Single Vehicle Collision Front Collision Total Loss
## 888 2/19/2015 Parked Car ? Minor Damage
## 889 1/12/2015 Single Vehicle Collision Rear Collision Major Damage
## 890 2/7/2015 Multi-vehicle Collision Side Collision Major Damage
## 891 1/13/2015 Single Vehicle Collision Side Collision Total Loss
## 892 1/18/2015 Multi-vehicle Collision Rear Collision Total Loss
## 893 1/15/2015 Vehicle Theft ? Trivial Damage
## 894 2/23/2015 Vehicle Theft ? Minor Damage
## 895 2/17/2015 Parked Car ? Minor Damage
## 896 2/17/2015 Multi-vehicle Collision Front Collision Major Damage
## 897 1/18/2015 Parked Car ? Trivial Damage
## 898 1/17/2015 Multi-vehicle Collision Front Collision Minor Damage
## 899 2/25/2015 Single Vehicle Collision Rear Collision Major Damage
## 900 1/6/2015 Vehicle Theft ? Minor Damage
## 901 2/26/2015 Single Vehicle Collision Side Collision Total Loss
## 902 2/11/2015 Single Vehicle Collision Side Collision Total Loss
## 903 2/13/2015 Multi-vehicle Collision Side Collision Minor Damage
## 904 1/10/2015 Single Vehicle Collision Side Collision Minor Damage
## 905 1/24/2015 Multi-vehicle Collision Side Collision Total Loss
## 906 2/15/2015 Multi-vehicle Collision Side Collision Major Damage
## 907 1/9/2015 Multi-vehicle Collision Rear Collision Major Damage
## 908 1/31/2015 Multi-vehicle Collision Front Collision Minor Damage
## 909 2/4/2015 Vehicle Theft ? Minor Damage
## 910 1/30/2015 Multi-vehicle Collision Rear Collision Total Loss
## 911 2/27/2015 Single Vehicle Collision Side Collision Minor Damage
## 912 2/5/2015 Multi-vehicle Collision Front Collision Total Loss
## 913 1/30/2015 Multi-vehicle Collision Rear Collision Major Damage
## 914 1/3/2015 Single Vehicle Collision Side Collision Minor Damage
## 915 3/1/2015 Multi-vehicle Collision Front Collision Major Damage
## 916 2/13/2015 Single Vehicle Collision Rear Collision Major Damage
## 917 2/25/2015 Vehicle Theft ? Trivial Damage
## 918 1/23/2015 Single Vehicle Collision Side Collision Major Damage
## 919 2/19/2015 Multi-vehicle Collision Front Collision Minor Damage
## 920 2/8/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 921 2/4/2015 Single Vehicle Collision Rear Collision Total Loss
## 922 2/22/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 923 1/14/2015 Vehicle Theft ? Minor Damage
## 924 1/6/2015 Single Vehicle Collision Rear Collision Total Loss
## 925 1/19/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 926 1/24/2015 Vehicle Theft ? Minor Damage
## 927 1/27/2015 Single Vehicle Collision Front Collision Total Loss
## 928 1/13/2015 Single Vehicle Collision Front Collision Major Damage
## 929 1/15/2015 Vehicle Theft ? Trivial Damage
## 930 2/4/2015 Single Vehicle Collision Side Collision Major Damage
## 931 1/4/2015 Multi-vehicle Collision Rear Collision Major Damage
## 932 1/14/2015 Multi-vehicle Collision Side Collision Total Loss
## 933 1/27/2015 Multi-vehicle Collision Side Collision Minor Damage
## 934 2/12/2015 Multi-vehicle Collision Side Collision Minor Damage
## 935 2/3/2015 Multi-vehicle Collision Rear Collision Major Damage
## 936 1/26/2015 Multi-vehicle Collision Side Collision Total Loss
## 937 1/9/2015 Single Vehicle Collision Front Collision Minor Damage
## 938 2/17/2015 Single Vehicle Collision Front Collision Total Loss
## 939 1/16/2015 Single Vehicle Collision Front Collision Major Damage
## 940 1/12/2015 Multi-vehicle Collision Rear Collision Major Damage
## 941 1/3/2015 Parked Car ? Minor Damage
## 942 1/22/2015 Multi-vehicle Collision Side Collision Major Damage
## 943 1/13/2015 Parked Car ? Minor Damage
## 944 1/31/2015 Multi-vehicle Collision Front Collision Minor Damage
## 945 2/4/2015 Single Vehicle Collision Side Collision Total Loss
## 946 1/20/2015 Multi-vehicle Collision Front Collision Total Loss
## 947 2/7/2015 Single Vehicle Collision Side Collision Major Damage
## 948 2/5/2015 Single Vehicle Collision Side Collision Minor Damage
## 949 1/2/2015 Single Vehicle Collision Front Collision Total Loss
## 950 2/8/2015 Multi-vehicle Collision Side Collision Total Loss
## 951 1/13/2015 Parked Car ? Minor Damage
## 952 1/16/2015 Single Vehicle Collision Side Collision Total Loss
## 953 1/30/2015 Multi-vehicle Collision Front Collision Total Loss
## 954 2/26/2015 Vehicle Theft ? Minor Damage
## 955 1/27/2015 Single Vehicle Collision Rear Collision Minor Damage
## 956 1/28/2015 Single Vehicle Collision Side Collision Total Loss
## 957 2/23/2015 Multi-vehicle Collision Side Collision Total Loss
## 958 1/18/2015 Single Vehicle Collision Rear Collision Minor Damage
## 959 2/15/2015 Multi-vehicle Collision Front Collision Major Damage
## 960 2/4/2015 Vehicle Theft ? Minor Damage
## 961 1/12/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 962 1/2/2015 Vehicle Theft ? Trivial Damage
## 963 1/27/2015 Single Vehicle Collision Rear Collision Total Loss
## 964 2/9/2015 Vehicle Theft ? Minor Damage
## 965 2/6/2015 Vehicle Theft ? Minor Damage
## 966 1/3/2015 Single Vehicle Collision Front Collision Minor Damage
## 967 1/12/2015 Multi-vehicle Collision Rear Collision Minor Damage
## 968 1/22/2015 Single Vehicle Collision Rear Collision Major Damage
## 969 1/3/2015 Multi-vehicle Collision Side Collision Major Damage
## 970 2/23/2015 Vehicle Theft ? Trivial Damage
## 971 1/22/2015 Single Vehicle Collision Side Collision Total Loss
## 972 2/13/2015 Single Vehicle Collision Front Collision Total Loss
## 973 2/8/2015 Single Vehicle Collision Rear Collision Major Damage
## 974 1/9/2015 Single Vehicle Collision Rear Collision Major Damage
## 975 2/8/2015 Multi-vehicle Collision Side Collision Major Damage
## 976 1/10/2015 Multi-vehicle Collision Rear Collision Total Loss
## 977 1/26/2015 Multi-vehicle Collision Side Collision Major Damage
## 978 2/21/2015 Multi-vehicle Collision Side Collision Major Damage
## 979 1/23/2015 Single Vehicle Collision Front Collision Total Loss
## 980 2/5/2015 Single Vehicle Collision Rear Collision Total Loss
## 981 2/15/2015 Single Vehicle Collision Rear Collision Minor Damage
## 982 1/8/2015 Multi-vehicle Collision Front Collision Total Loss
## 983 1/1/2015 Multi-vehicle Collision Front Collision Minor Damage
## 984 2/26/2015 Parked Car ? Trivial Damage
## 985 2/28/2015 Single Vehicle Collision Side Collision Total Loss
## 986 1/13/2015 Single Vehicle Collision Side Collision Major Damage
## 987 2/19/2015 Single Vehicle Collision Rear Collision Minor Damage
## 988 1/13/2015 Single Vehicle Collision Side Collision Major Damage
## 989 2/17/2015 Single Vehicle Collision Rear Collision Minor Damage
## 990 2/15/2015 Multi-vehicle Collision Rear Collision Total Loss
## 991 1/31/2015 Single Vehicle Collision Rear Collision Minor Damage
## 992 2/6/2015 Single Vehicle Collision Rear Collision Total Loss
## 993 1/23/2015 Multi-vehicle Collision Front Collision Major Damage
## 994 2/17/2015 Multi-vehicle Collision Side Collision Total Loss
## 995 1/22/2015 Parked Car ? Minor Damage
## 996 2/22/2015 Single Vehicle Collision Front Collision Minor Damage
## 997 1/24/2015 Single Vehicle Collision Rear Collision Major Damage
## 998 1/23/2015 Multi-vehicle Collision Side Collision Minor Damage
## 999 2/26/2015 Single Vehicle Collision Rear Collision Major Damage
## 1000 2/26/2015 Parked Car ? Minor Damage
## authorities_contacted incident_state incident_city incident_location
## 1 Police SC Columbus 9935 4th Drive
## 2 Police VA Riverwood 6608 MLK Hwy
## 3 Police NY Columbus 7121 Francis Lane
## 4 Police OH Arlington 6956 Maple Drive
## 5 None NY Arlington 3041 3rd Ave
## 6 Fire SC Arlington 8973 Washington St
## 7 Police NY Springfield 5846 Weaver Drive
## 8 Police VA Columbus 3525 3rd Hwy
## 9 Police WV Arlington 4872 Rock Ridge
## 10 Other NC Hillsdale 3066 Francis Ave
## 11 Police NY Northbend 1558 1st Ridge
## 12 Fire SC Springfield 5971 5th Hwy
## 13 Ambulance SC Northbend 6655 5th Drive
## 14 None SC Springfield 6582 Elm Lane
## 15 Police SC Springfield 6851 3rd Drive
## 16 Other WV Hillsdale 9573 Weaver Ave
## 17 Other NY Riverwood 5074 3rd St
## 18 Police WV Northbend 4546 Tree St
## 19 Other NY Northbrook 3842 Solo Ridge
## 20 Police VA Columbus 8101 3rd Ridge
## 21 Police NY Columbus 5380 Pine St
## 22 Police SC Arlington 8957 Weaver Drive
## 23 Ambulance SC Hillsdale 2526 Embaracadero Ave
## 24 Ambulance WV Northbend 5667 4th Drive
## 25 Other VA Riverwood 2502 Apache Hwy
## 26 Other OH Arlington 3418 Texas Lane
## 27 Police PA Arlington 2533 Elm St
## 28 None VA Northbrook 3790 Andromedia Hwy
## 29 Other SC Northbend 3220 Rock Drive
## 30 Police SC Northbrook 2100 Francis Drive
## 31 Ambulance SC Columbus 4687 5th Drive
## 32 Police WV Riverwood 9038 2nd Lane
## 33 Police NY Arlington 6092 5th Ave
## 34 Ambulance NY Hillsdale 8353 Britain Ridge
## 35 Fire WV Hillsdale 3540 Maple St
## 36 Other WV Springfield 3104 Sky Drive
## 37 Police NY Northbrook 4981 Weaver St
## 38 None WV Arlington 6676 Tree Lane
## 39 Police NY Hillsdale 3930 Embaracadero St
## 40 Ambulance NC Columbus 3422 Flute St
## 41 Ambulance WV Columbus 4862 Lincoln Hwy
## 42 Police WV Northbrook 5719 2nd Lane
## 43 Other SC Springfield 3221 Solo Ridge
## 44 Other NC Arlington 6660 MLK Drive
## 45 Other SC Springfield 1699 Oak Drive
## 46 Other NY Arlington 4234 Cherokee Lane
## 47 Fire NC Northbend 7476 4th St
## 48 Fire WV Arlington 8907 Tree Ave
## 49 Police NY Arlington 6619 Flute Ave
## 50 Other WV Springfield 6011 Britain St
## 51 Fire NY Riverwood 5104 Francis Drive
## 52 None NY Arlington 2280 4th Ave
## 53 None WV Northbend 2644 Elm Drive
## 54 Police NC Columbus 7466 MLK Ridge
## 55 Police VA Northbend 5821 2nd St
## 56 Fire NY Arlington 6723 Best Drive
## 57 Fire SC Columbus 4866 4th Hwy
## 58 None VA Riverwood 5418 Britain Ave
## 59 Ambulance WV Riverwood 4296 Pine Hwy
## 60 Police VA Hillsdale 2299 1st St
## 61 Police NY Springfield 6618 Cherokee Drive
## 62 Other OH Springfield 7459 Flute St
## 63 Other WV Hillsdale 3567 4th Drive
## 64 Fire WV Northbend 2457 Washington Ave
## 65 Fire VA Riverwood 1269 Flute Drive
## 66 Ambulance NY Arlington 1218 Sky Hwy
## 67 Fire SC Springfield 9169 Pine Ridge
## 68 Fire WV Hillsdale 8538 Texas Lane
## 69 Ambulance WV Northbrook 5783 Oak Ave
## 70 None NC Riverwood 7721 Washington Ridge
## 71 Other SC Hillsdale 8006 Maple Hwy
## 72 Other WV Northbrook 6751 Pine Ridge
## 73 Ambulance NC Arlington 2324 Texas Ridge
## 74 Fire SC Riverwood 7923 Elm Ave
## 75 Ambulance VA Springfield 4755 Best Lane
## 76 Ambulance SC Riverwood 5053 Tree Drive
## 77 Fire NY Springfield 2078 3rd Ave
## 78 Police WV Northbrook 2804 Best St
## 79 None SC Springfield 7877 Sky Lane
## 80 Fire SC Northbrook 6530 Weaver Ave
## 81 Ambulance NC Arlington 3087 Oak Hwy
## 82 None NC Northbrook 7098 Lincoln Hwy
## 83 Police NY Northbrook 5124 Maple St
## 84 None VA Hillsdale 2333 Maple Lane
## 85 Ambulance NY Hillsdale 1012 5th Lane
## 86 Fire NY Northbrook 7477 MLK Drive
## 87 Other SC Columbus 9489 3rd St
## 88 Fire VA Springfield 2087 Apache Ave
## 89 None WV Arlington 5540 Sky St
## 90 Other SC Columbus 7238 2nd St
## 91 Fire SC Arlington 8442 Britain Hwy
## 92 Police NY Columbus 1331 Britain Hwy
## 93 None WV Springfield 5260 Francis Drive
## 94 Ambulance NC Riverwood 1135 Solo Lane
## 95 Ambulance SC Northbend 9737 Solo Hwy
## 96 None OH Northbrook 3289 Britain Drive
## 97 Fire VA Springfield 6550 Andromedia St
## 98 Other SC Hillsdale 1679 2nd Hwy
## 99 None WV Columbus 3998 Flute St
## 100 None NC Northbrook 2430 MLK Ave
## 101 Police SC Northbend 7717 Britain Hwy
## 102 Police WV Northbend 7773 Tree Hwy
## 103 Fire NC Northbrook 2199 Texas Drive
## 104 Police WV Northbend 1028 Sky Lane
## 105 Police SC Northbend 4154 Lincoln Hwy
## 106 None WV Columbus 8085 Andromedia St
## 107 Police VA Northbend 4793 4th Ridge
## 108 Ambulance WV Columbus 7428 Sky Hwy
## 109 Fire NY Northbrook 2306 5th Lane
## 110 Fire NY Springfield 3052 Weaver Ridge
## 111 Police SC Riverwood 5211 Weaver Drive
## 112 Fire NC Springfield 7253 MLK St
## 113 Other WV Columbus 1454 5th Ridge
## 114 Other VA Hillsdale 5622 Best Ridge
## 115 None VA Northbrook 4574 Britain Hwy
## 116 Other NY Riverwood 4539 Texas St
## 117 Police SC Hillsdale 8118 Elm Ridge
## 118 Police NY Northbend 3814 Britain Drive
## 119 Fire NY Hillsdale 4614 MLK Ave
## 120 Police NY Arlington 1628 Best Drive
## 121 Fire SC Hillsdale 8381 Solo Hwy
## 122 Ambulance NY Northbrook 2100 MLK St
## 123 Ambulance VA Columbus 5071 Flute Ridge
## 124 Other NC Arlington 7551 Britain Lane
## 125 Ambulance WV Northbend 2275 Best Lane
## 126 Ambulance NY Hillsdale 1598 3rd Drive
## 127 Other WV Columbus 7740 MLK St
## 128 Police PA Northbrook 1240 Tree Lane
## 129 Fire NY Northbend 8983 Francis Ridge
## 130 Fire SC Northbend 7756 Solo Drive
## 131 Other NY Arlington 9034 Weaver Ridge
## 132 Other WV Columbus 1126 Texas Hwy
## 133 Ambulance NC Arlington 2808 Elm St
## 134 Police WV Northbend 5061 Francis Ave
## 135 Ambulance WV Northbend 4965 MLK Drive
## 136 Police NY Springfield 8668 Flute St
## 137 Police VA Riverwood 2577 Washington Drive
## 138 Fire SC Springfield 7709 Rock Lane
## 139 Fire NC Springfield 9358 Texas Ridge
## 140 Other VA Springfield 8080 Oak Lane
## 141 Police NY Northbend 6408 Weaver Ridge
## 142 None SC Northbrook 5532 Weaver Ridge
## 143 None SC Northbend 9101 2nd Hwy
## 144 Ambulance SC Springfield 8576 Andromedia St
## 145 Police WV Springfield 6315 2nd Lane
## 146 Police NC Northbrook 1536 Flute Drive
## 147 Fire NC Northbend 4672 MLK St
## 148 Fire WV Northbrook 2204 Washington Lane
## 149 Other SC Springfield 9484 Pine Drive
## 150 Other SC Springfield 5431 3rd Ridge
## 151 Other NY Columbus 7121 Britain Drive
## 152 Fire NC Hillsdale 8586 1st Ridge
## 153 Police NY Riverwood 7582 Pine Drive
## 154 Fire WV Columbus 1388 Embaracadero Hwy
## 155 Ambulance NC Northbrook 5621 4th Ave
## 156 Police NY Riverwood 8150 Washington Ridge
## 157 Police NY Columbus 4268 2nd Ave
## 158 None WV Northbend 6375 2nd Lane
## 159 Ambulance WV Springfield 3770 Flute Drive
## 160 Police NY Columbus 1562 Britain St
## 161 Police VA Springfield 1681 Cherokee Hwy
## 162 Ambulance SC Northbrook 7523 Oak Lane
## 163 Ambulance NY Hillsdale 1815 Cherokee Drive
## 164 Fire SC Arlington 9316 Pine Ave
## 165 Fire PA Columbus 2733 Texas Drive
## 166 Ambulance WV Columbus 7684 Francis Ridge
## 167 Fire WV Riverwood 8991 Embaracadero Ave
## 168 Ambulance NY Northbend 4905 Francis Ave
## 169 Police SC Hillsdale 7783 Lincoln Hwy
## 170 None NY Hillsdale 8749 Tree St
## 171 Police NY Arlington 4985 Sky Lane
## 172 Ambulance PA Riverwood 7534 MLK Hwy
## 173 Other SC Arlington 8689 Maple Hwy
## 174 Fire PA Hillsdale 9153 3rd Hwy
## 175 None WV Riverwood 5904 1st Drive
## 176 Other PA Hillsdale 4519 Embaracadero St
## 177 Ambulance OH Hillsdale 9706 MLK Lane
## 178 Fire SC Columbus 6012 Texas Hwy
## 179 Other WV Northbend 4098 Weaver Ridge
## 180 None NY Hillsdale 6193 1st Hwy
## 181 Fire VA Riverwood 4053 Sky Lane
## 182 Police SC Springfield 8964 Francis St
## 183 Police VA Springfield 9748 Sky Drive
## 184 Police SC Northbend 2293 Washington Ave
## 185 Ambulance NY Northbend 3656 Solo Ave
## 186 Fire NY Northbend 8579 Apache Drive
## 187 Ambulance NC Riverwood 2003 Maple Hwy
## 188 Police NY Northbrook 5445 Tree Hwy
## 189 Other WV Hillsdale 9730 2nd Hwy
## 190 Ambulance VA Columbus 7819 Oak St
## 191 None SC Riverwood 1845 Best St
## 192 Police SC Springfield 2500 Tree St
## 193 Police SC Arlington 6955 Pine Drive
## 194 Police VA Hillsdale 6165 Rock Ridge
## 195 Other VA Northbrook 3653 Elm Drive
## 196 Fire NC Columbus 5812 3rd Hwy
## 197 Police WV Springfield 4939 Best St
## 198 None WV Arlington 4964 Elm Lane
## 199 Ambulance NY Riverwood 9588 Solo St
## 200 Police SC Arlington 8718 Apache Lane
## 201 Police NY Hillsdale 3590 Best Hwy
## 202 Fire WV Hillsdale 6149 Best Ridge
## 203 Police NY Springfield 4116 Embaracadero Lane
## 204 Fire SC Riverwood 3486 Flute Ave
## 205 Ambulance WV Northbend 5994 5th Ave
## 206 Police SC Springfield 9138 3rd St
## 207 Fire SC Columbus 3743 Andromedia Ridge
## 208 Police VA Springfield 7644 Tree Ridge
## 209 Police SC Riverwood 3167 2nd St
## 210 Police WV Columbus 3327 Lincoln Drive
## 211 Police NC Hillsdale 8621 Best Ridge
## 212 None OH Columbus 3878 Tree Lane
## 213 Ambulance NY Riverwood 9760 Solo Lane
## 214 Other SC Springfield 9138 1st St
## 215 Other NC Columbus 3414 Elm Ave
## 216 Ambulance NC Springfield 3172 Tree Ridge
## 217 Fire NY Columbus 6104 Oak Ave
## 218 None NC Springfield 9742 5th Ridge
## 219 Other WV Hillsdale 8782 3rd St
## 220 Fire SC Northbrook 9798 Sky Ridge
## 221 Other NY Hillsdale 5483 Francis Drive
## 222 Ambulance NY Springfield 2005 Texas Hwy
## 223 Fire NY Hillsdale 6634 Texas Ridge
## 224 Fire NY Columbus 8655 Cherokee Lane
## 225 Fire VA Arlington 4955 Lincoln Ridge
## 226 Ambulance NY Springfield 7705 Best Ridge
## 227 Other NY Riverwood 5838 Pine Lane
## 228 Other NY Arlington 7331 Sky Hwy
## 229 Fire WV Hillsdale 5640 Embaracadero Lane
## 230 Other SC Columbus 9610 Cherokee St
## 231 Other NY Columbus 3550 Washington Ave
## 232 Police SC Arlington 5277 Texas Lane
## 233 Other WV Springfield 3654 Cherokee Ave
## 234 Police OH Hillsdale 7380 5th Hwy
## 235 Other PA Riverwood 2539 Embaracadero Ridge
## 236 Fire NY Riverwood 4693 Lincoln Hwy
## 237 Fire NY Arlington 2376 Sky Ridge
## 238 Fire SC Columbus 1273 Rock Lane
## 239 Other NY Springfield 8281 Lincoln Lane
## 240 Fire WV Northbrook 6429 4th Hwy
## 241 Fire SC Riverwood 2201 4th Lane
## 242 Other VA Columbus 5506 Best St
## 243 None SC Hillsdale 8404 Embaracadero St
## 244 Ambulance NY Riverwood 2117 Lincoln Hwy
## 245 None PA Springfield 6359 MLK Ridge
## 246 Fire NY Arlington 9751 Sky Ridge
## 247 Fire NY Northbend 9020 Elm Ave
## 248 Ambulance SC Columbus 1830 Sky St
## 249 None SC Hillsdale 6067 Weaver Ridge
## 250 Fire NC Arlington 1840 Embaracadero Ave
## 251 Fire SC Springfield 4058 Tree Drive
## 252 Police SC Northbend 4983 MLK Ridge
## 253 Police WV Springfield 9744 Texas Drive
## 254 Fire OH Riverwood 8821 Elm St
## 255 Fire SC Northbrook 2886 Tree Ridge
## 256 Ambulance NY Hillsdale 5236 Weaver Drive
## 257 Other SC Arlington 5862 Apache Ridge
## 258 Police SC Columbus 7859 4th Ridge
## 259 Police NY Springfield 6259 Weaver St
## 260 Fire SC Northbend 9980 Lincoln Ave
## 261 None SC Riverwood 7828 Cherokee Ave
## 262 Police NC Arlington 5812 Oak St
## 263 Ambulance NY Arlington 2318 Washington Hwy
## 264 Police SC Hillsdale 8809 Flute St
## 265 Police SC Northbend 3184 Oak Ave
## 266 Police NC Hillsdale 6493 Lincoln Lane
## 267 Fire PA Columbus 7162 Maple Ave
## 268 Fire SC Springfield 5455 Tree Ridge
## 269 Ambulance PA Springfield 5778 Pine Ridge
## 270 Fire SC Hillsdale 3797 Solo Lane
## 271 Police VA Arlington 9373 Pine Hwy
## 272 None NY Hillsdale 1365 Francis Ave
## 273 Police WV Arlington 9239 Washington Ridge
## 274 Ambulance NY Arlington 3416 Washington Drive
## 275 Fire NY Columbus 1923 2nd Hwy
## 276 Ambulance SC Riverwood 6451 1st Hwy
## 277 Ambulance WV Columbus 1267 Francis Hwy
## 278 Other PA Northbrook 4158 Washington Lane
## 279 Police WV Northbend 3796 Cherokee Drive
## 280 Police SC Northbend 7434 Oak Hwy
## 281 Fire PA Columbus 5178 Weaver Hwy
## 282 Police NY Arlington 8477 Francis Hwy
## 283 Police SC Northbrook 7693 Britain Lane
## 284 Police WV Riverwood 3658 Rock Drive
## 285 Police WV Riverwood 2617 Andromedia Drive
## 286 Other VA Northbrook 9279 Oak Hwy
## 287 Ambulance SC Hillsdale 3439 Andromedia Hwy
## 288 None SC Riverwood 5901 Elm Drive
## 289 Ambulance SC Northbrook 3982 Washington Hwy
## 290 Police VA Northbrook 3376 5th Drive
## 291 Ambulance NC Arlington 3936 Tree Drive
## 292 Ambulance OH Springfield 6605 Tree Ave
## 293 Fire NC Northbend 3102 Apache St
## 294 Fire NY Columbus 7756 Pine Hwy
## 295 Police NC Columbus 7142 5th Lane
## 296 Fire SC Northbrook 2914 Oak Drive
## 297 Police PA Hillsdale 6522 Apache Drive
## 298 Police SC Northbend 5279 Pine Ridge
## 299 None WV Northbend 8078 Britain Hwy
## 300 Other PA Northbend 1133 Apache St
## 301 Police SC Riverwood 2873 Flute Ave
## 302 Police NY Riverwood 8509 Apache St
## 303 Police NC Arlington 8245 4th Hwy
## 304 Police NY Columbus 3094 Best Lane
## 305 Other NY Springfield 8188 Tree Ave
## 306 Other NY Arlington 5224 5th Lane
## 307 Fire SC Riverwood 2230 1st St
## 308 Fire SC Arlington 6719 Flute St
## 309 Other NY Northbrook 8064 4th Ave
## 310 Fire NY Northbend 2469 Francis Lane
## 311 Other SC Springfield 4671 5th Ridge
## 312 Ambulance SC Northbrook 6985 Maple Lane
## 313 Other SC Hillsdale 7791 Britain Ridge
## 314 Police WV Hillsdale 6355 4th Hwy
## 315 Other SC Northbend 3495 Britain Drive
## 316 Police WV Northbend 2980 Sky Ridge
## 317 Other WV Riverwood 5914 Oak Ave
## 318 Ambulance VA Northbend 3835 5th Ave
## 319 Police NY Northbend 5925 Tree Hwy
## 320 Other NY Springfield 6250 1st Ridge
## 321 Fire NY Columbus 1346 5th Lane
## 322 Other SC Hillsdale 1128 Maple Lane
## 323 Police WV Riverwood 6309 Cherokee Ave
## 324 Police PA Northbend 4618 Flute Ave
## 325 Other VA Springfield 6191 Oak Lane
## 326 Police NY Northbend 1316 Britain Ridge
## 327 Fire NY Hillsdale 5924 Maple Drive
## 328 Police NY Arlington 8917 Tree Ridge
## 329 Other NY Columbus 3966 Francis Ridge
## 330 Other WV Springfield 1507 Solo Ave
## 331 Fire NY Springfield 4272 Oak Ridge
## 332 Ambulance NY Riverwood 4434 Lincoln Ave
## 333 None SC Northbend 7529 Solo Ridge
## 334 None VA Arlington 8096 Apache Hwy
## 335 Fire NC Arlington 9417 Tree Hwy
## 336 Police WV Hillsdale 3809 Texas Lane
## 337 Fire SC Columbus 1540 Apache Lane
## 338 Police NY Springfield 2337 Lincoln Hwy
## 339 Other NY Hillsdale 6770 1st St
## 340 Fire NY Columbus 4119 Texas St
## 341 Police VA Arlington 4347 2nd Ridge
## 342 Other SC Columbus 1091 1st Drive
## 343 Police NY Northbend 8203 Lincoln Ave
## 344 None WV Northbend 9154 MLK Hwy
## 345 Police VA Columbus 5780 4th Ave
## 346 Police VA Hillsdale 6945 Texas Hwy
## 347 Ambulance WV Springfield 5639 1st Ridge
## 348 Other VA Arlington 3834 Pine St
## 349 Other SC Arlington 1358 Maple St
## 350 Fire SC Riverwood 7460 Apache Lane
## 351 Other VA Columbus 5771 Sky Ave
## 352 Ambulance NC Northbend 2865 Maple Lane
## 353 Fire VA Riverwood 8940 Elm Ave
## 354 Police SC Hillsdale 1215 Pine Hwy
## 355 Ambulance NY Springfield 6874 Maple Ridge
## 356 Fire WV Hillsdale 8834 Elm Drive
## 357 Other WV Columbus 8542 Lincoln Ridge
## 358 Police NY Springfield 9397 Francis St
## 359 Ambulance SC Springfield 4907 Andromedia Drive
## 360 Other SC Columbus 4429 Washington St
## 361 Ambulance OH Columbus 2651 MLK Lane
## 362 Ambulance SC Northbend 2942 1st Lane
## 363 None VA Arlington 6317 Best St
## 364 Other WV Northbend 1555 Washington Lane
## 365 Police SC Hillsdale 1919 4th Lane
## 366 None NC Arlington 5480 3rd Ridge
## 367 None SC Northbrook 8864 Tree Ridge
## 368 Ambulance VA Northbend 2777 Solo Drive
## 369 Police NC Northbend 9929 Rock Drive
## 370 Other WV Northbend 4143 Maple Ridge
## 371 Other NC Columbus 7121 Rock St
## 372 Police VA Riverwood 9067 Texas Ave
## 373 Other SC Hillsdale 9245 Weaver Ridge
## 374 None WV Arlington 4585 Francis Ave
## 375 Police SC Northbend 6738 Francis Hwy
## 376 Other NY Springfield 7576 Pine Ridge
## 377 Ambulance WV Arlington 9105 Tree Lane
## 378 Fire VA Northbrook 2299 Britain Drive
## 379 Ambulance NY Springfield 1914 Francis St
## 380 Other VA Columbus 6658 Weaver St
## 381 Police SC Northbend 1985 5th Ave
## 382 Police SC Springfield 1707 Sky Ave
## 383 Other NY Springfield 6456 Andromedia Drive
## 384 Ambulance NC Arlington 5649 Texas Ave
## 385 Police NY Riverwood 1220 MLK Ave
## 386 Fire NY Northbend 1589 Pine St
## 387 Ambulance WV Northbrook 8906 Elm Lane
## 388 Other NY Hillsdale 2654 Elm Drive
## 389 Other NY Columbus 6681 Texas Ridge
## 390 Ambulance WV Northbrook 7782 Rock St
## 391 Ambulance WV Arlington 9286 Oak Ave
## 392 Fire NY Arlington 8758 5th St
## 393 Fire NY Columbus 7281 Maple Hwy
## 394 Other WV Northbend 7571 Elm Ridge
## 395 Other OH Arlington 6738 Washington Hwy
## 396 None SC Northbend 4188 Britain Ave
## 397 Other NY Riverwood 6934 Lincoln Ave
## 398 Fire VA Hillsdale 6390 Apache St
## 399 Police WV Columbus 7615 Weaver Drive
## 400 Ambulance NY Columbus 6409 Cherokee Drive
## 401 Police WV Springfield 1123 5th Lane
## 402 Fire NY Columbus 5168 5th Ave
## 403 Ambulance NY Columbus 3697 Apache Drive
## 404 Ambulance NC Northbend 1910 Sky Ave
## 405 Other SC Riverwood 8954 Apache Lane
## 406 Fire WV Hillsdale 3110 Lincoln Lane
## 407 Police WV Arlington 6035 Rock Ave
## 408 Police VA Hillsdale 2220 1st Lane
## 409 Fire OH Riverwood 4972 Francis Lane
## 410 None NC Springfield 6957 Weaver Drive
## 411 None WV Northbrook 1512 Rock Lane
## 412 Police SC Arlington 3693 Pine Ave
## 413 Fire NY Riverwood 9879 Apache Drive
## 414 Fire PA Northbend 2494 Andromedia Drive
## 415 None NC Riverwood 4615 Embaracadero Ave
## 416 Other WV Northbrook 1929 Britain Drive
## 417 Police NY Riverwood 5051 Elm St
## 418 Ambulance NY Hillsdale 9910 Maple Ave
## 419 Police NC Riverwood 5602 Britain St
## 420 Other NY Springfield 6889 Cherokee St
## 421 Ambulance SC Northbend 3926 Rock Lane
## 422 Ambulance SC Northbend 6717 Best Drive
## 423 Other WV Columbus 6117 4th Ave
## 424 Fire SC Northbend 2668 Cherokee St
## 425 Ambulance NY Arlington 6838 Flute Lane
## 426 Police WV Northbrook 6583 MLK Ridge
## 427 Ambulance WV Arlington 6492 4th Lane
## 428 Police NC Hillsdale 7299 Apache St
## 429 Ambulance NC Springfield 2756 Britain Hwy
## 430 Fire NY Columbus 9360 3rd Drive
## 431 Ambulance OH Arlington 1655 Francis Hwy
## 432 Other VA Columbus 9720 Lincoln Hwy
## 433 Police NC Northbend 7066 Texas Ave
## 434 Other NY Columbus 9728 Britain Hwy
## 435 Fire NC Riverwood 4486 Cherokee Ridge
## 436 Police PA Northbend 8021 Flute Ave
## 437 Fire PA Riverwood 2774 Apache Drive
## 438 Police NC Hillsdale 2787 MLK St
## 439 Police WV Northbrook 9847 Elm St
## 440 None VA Hillsdale 4629 Elm Ridge
## 441 Fire NY Columbus 5585 Washington Drive
## 442 Other WV Hillsdale 3925 Sky St
## 443 Fire SC Hillsdale 3903 Oak Ave
## 444 None VA Columbus 3805 Lincoln Hwy
## 445 None NC Arlington 4055 2nd Drive
## 446 Police SC Northbend 3707 Oak Ridge
## 447 Fire NY Springfield 7327 Lincoln Drive
## 448 Fire NC Riverwood 9369 Flute Hwy
## 449 Ambulance SC Arlington 4239 Weaver Ave
## 450 Ambulance WV Northbend 6044 Weaver Drive
## 451 Ambulance NY Northbrook 8879 1st Drive
## 452 Fire SC Northbrook 9488 Best Drive
## 453 None NY Hillsdale 7500 Texas Ridge
## 454 Other NY Arlington 2048 3rd Ridge
## 455 Other NY Northbend 3419 Apache St
## 456 Police WV Springfield 9875 MLK Ave
## 457 Police NY Columbus 3553 Texas Ave
## 458 Ambulance NC Riverwood 4335 1st St
## 459 Other NC Hillsdale 9070 Tree Ave
## 460 Ambulance NY Hillsdale 3900 Texas St
## 461 Other SC Riverwood 9657 5th Ave
## 462 Ambulance WV Northbend 5765 Washington St
## 463 Police SC Arlington 5997 Embaracadero Drive
## 464 Police NY Springfield 1738 Solo Lane
## 465 Other SC Columbus 2903 Weaver Drive
## 466 Police VA Springfield 8926 Texas Ridge
## 467 Fire SC Northbend 4231 3rd Ave
## 468 Ambulance VA Springfield 8049 4th St
## 469 Fire WV Northbrook 6501 5th Drive
## 470 Fire NC Hillsdale 7909 Andromedia Hwy
## 471 Other NY Riverwood 5865 Sky Lane
## 472 Ambulance WV Springfield 1957 Washington Ave
## 473 Ambulance WV Riverwood 7649 Texas St
## 474 None SC Columbus 1992 Britain Drive
## 475 Police PA Springfield 9685 Sky Ridge
## 476 Police NC Hillsdale 3457 Texas Lane
## 477 Other SC Columbus 7693 Cherokee Lane
## 478 Police VA Northbend 3167 4th Ridge
## 479 Police NC Riverwood 1617 Rock Drive
## 480 Fire WV Arlington 7877 3rd Ridge
## 481 Fire SC Springfield 9325 Lincoln Drive
## 482 Fire PA Northbrook 5855 Apache St
## 483 Other WV Riverwood 1328 Texas Lane
## 484 Other NY Springfield 4567 Pine Ave
## 485 Ambulance NC Springfield 7575 Pine St
## 486 Other NY Springfield 2850 Washington St
## 487 Fire VA Riverwood 9169 Cherokee Hwy
## 488 Police NY Springfield 6443 Washington Ridge
## 489 Other SC Northbend 6751 5th Hwy
## 490 Ambulance NY Columbus 2289 Weaver Ridge
## 491 None WV Arlington 8306 1st Drive
## 492 Ambulance VA Northbend 2603 Andromedia Hwy
## 493 Other NY Northbrook 6479 Francis Ave
## 494 Police NY Northbend 6428 Andromedia Lane
## 495 Other OH Northbrook 9081 Cherokee Hwy
## 496 Other SC Springfield 1532 Washington St
## 497 Police NY Northbend 4625 MLK Drive
## 498 Ambulance NY Springfield 1529 Elm Ridge
## 499 Police NY Arlington 2086 Francis Drive
## 500 Other NC Northbend 9066 Best Ridge
## 501 Police SC Northbrook 7178 Best Drive
## 502 Police WV Columbus 9821 Francis Ave
## 503 Other WV Hillsdale 7061 Cherokee Drive
## 504 Other SC Springfield 1325 1st Lane
## 505 Fire VA Hillsdale 3769 Sky St
## 506 Police WV Hillsdale 8489 Pine Hwy
## 507 Police NY Riverwood 6329 Apache Ave
## 508 Police NY Northbrook 9293 Pine Lane
## 509 Fire NY Arlington 9224 Sky Drive
## 510 Police NY Northbend 8862 Maple Ridge
## 511 Police WV Arlington 3492 Flute Lane
## 512 Police NY Hillsdale 6484 Tree Drive
## 513 Ambulance WV Northbend 4554 Sky Ave
## 514 Ambulance PA Northbrook 5201 Texas Hwy
## 515 Ambulance NY Arlington 3982 Weaver Lane
## 516 Other NY Northbrook 3660 Andromedia Hwy
## 517 Ambulance WV Arlington 7135 Flute Lane
## 518 Fire SC Springfield 4414 Solo Drive
## 519 Ambulance SC Hillsdale 2920 5th Ave
## 520 Police NY Northbrook 2986 MLK Drive
## 521 Fire NY Riverwood 1580 Maple Lane
## 522 Ambulance WV Springfield 3706 Texas Hwy
## 523 Fire SC Northbend 9109 Britain Drive
## 524 Fire SC Northbend 2290 4th Ave
## 525 Police NC Northbrook 4232 Britain Ridge
## 526 Police SC Riverwood 6677 Andromedia Drive
## 527 Ambulance VA Springfield 5868 Sky Hwy
## 528 Police SC Columbus 3053 Lincoln Drive
## 529 Ambulance WV Riverwood 7041 Tree Ridge
## 530 Police WV Northbrook 7223 Embaracadero St
## 531 Police SC Columbus 8081 Flute Ridge
## 532 Ambulance SC Springfield 8618 Texas Lane
## 533 Other VA Riverwood 3508 Washington St
## 534 None SC Columbus 2193 4th Ridge
## 535 Fire WV Northbend 8897 Sky St
## 536 Other NY Arlington 9611 Pine Ridge
## 537 Ambulance WV Arlington 7825 1st Ridge
## 538 Police NY Riverwood 3039 Oak Hwy
## 539 None VA Northbend 8204 Pine Lane
## 540 Fire SC Riverwood 9787 Andromedia Ave
## 541 Other VA Hillsdale 9633 Rock Hwy
## 542 Police NC Arlington 6303 1st Drive
## 543 Police NC Arlington 2014 Rock Ave
## 544 Ambulance SC Northbrook 8983 Tree St
## 545 Fire VA Arlington 6260 5th Lane
## 546 Fire SC Hillsdale 2725 Britain Ridge
## 547 Ambulance VA Hillsdale 3089 Oak Ridge
## 548 Other OH Northbrook 6206 3rd Ridge
## 549 Police VA Springfield 7240 5th Ridge
## 550 Ambulance SC Arlington 8100 3rd Ave
## 551 Ambulance NC Arlington 3282 4th Lane
## 552 Fire SC Arlington 3227 Maple Ave
## 553 Police SC Hillsdale 4264 Lincoln Ridge
## 554 Police NC Springfield 2215 Best Ave
## 555 Other SC Northbend 5363 Weaver Lane
## 556 Other NY Northbrook 2397 Cherokee Ave
## 557 Police WV Arlington 9794 Embaracadero St
## 558 Police NC Northbrook 1810 Elm Hwy
## 559 Fire WV Riverwood 9603 Texas Lane
## 560 Ambulance WV Arlington 5650 Sky Drive
## 561 Police WV Columbus 9633 MLK Lane
## 562 Fire NY Arlington 4981 Flute Hwy
## 563 Fire NY Northbrook 9078 Francis Ridge
## 564 Police NY Hillsdale 1381 Francis Ave
## 565 Ambulance VA Columbus 6435 Texas Ave
## 566 Other WV Springfield 1248 MLK Ridge
## 567 Fire SC Riverwood 3323 1st Lane
## 568 Ambulance SC Columbus 6971 Best Ridge
## 569 Other WV Riverwood 7488 Lincoln Lane
## 570 Other SC Springfield 9007 Francis Hwy
## 571 Ambulance WV Springfield 1491 Francis Ridge
## 572 Police SC Northbrook 3659 Oak Lane
## 573 Ambulance NY Northbend 4176 Britain Hwy
## 574 Other NY Springfield 5189 Francis Drive
## 575 Other OH Northbend 6515 Oak Lane
## 576 Other SC Northbend 7168 Andromedia Ridge
## 577 Police NY Springfield 7954 Tree Ridge
## 578 Other SC Northbrook 1956 Apache St
## 579 Police WV Arlington 9918 Andromedia Drive
## 580 Fire NY Springfield 5499 Flute Ridge
## 581 Other NC Riverwood 3311 2nd Drive
## 582 Other NC Arlington 7609 Rock St
## 583 Ambulance SC Hillsdale 4652 Flute Drive
## 584 Fire NY Northbrook 6853 Sky Hwy
## 585 Police WV Arlington 7780 Flute Lane
## 586 None OH Springfield 1687 3rd Lane
## 587 Police WV Springfield 6378 Britain Ave
## 588 Other SC Columbus 1306 Andromedia St
## 589 Fire NY Northbrook 3664 Francis Ridge
## 590 Other WV Springfield 5985 Lincoln Lane
## 591 Ambulance PA Hillsdale 3706 4th Hwy
## 592 Ambulance WV Arlington 6603 Francis Hwy
## 593 Police SC Northbrook 7069 4th Hwy
## 594 Fire VA Northbrook 5093 Flute Lane
## 595 Fire SC Hillsdale 5894 Flute Drive
## 596 Police NC Springfield 8459 Apache Ave
## 597 Police WV Columbus 7447 Lincoln Ridge
## 598 Police PA Springfield 1821 Andromedia Ridge
## 599 Ambulance NY Columbus 6859 Flute Ridge
## 600 Police WV Northbrook 4175 Elm Ridge
## 601 Ambulance VA Hillsdale 5007 Oak St
## 602 Other NC Riverwood 5790 Flute Ridge
## 603 Other SC Hillsdale 8704 Britain Lane
## 604 Police WV Columbus 7816 MLK Lane
## 605 Police SC Hillsdale 3618 Maple Lane
## 606 Other NY Arlington 7705 Lincoln Drive
## 607 Ambulance SC Springfield 8602 Washington Ridge
## 608 Police NY Springfield 2832 Andromedia Lane
## 609 Police VA Riverwood 9760 4th Hwy
## 610 Police NY Springfield 2509 Rock Drive
## 611 Fire NY Northbrook 2063 Weaver St
## 612 Other WV Columbus 3818 Texas Ridge
## 613 Other NY Columbus 3929 Elm Ave
## 614 Police SC Northbrook 9911 Britain Lane
## 615 Police NY Northbend 3246 Britain Ridge
## 616 Other WV Springfield 2696 Cherokee Ridge
## 617 Fire SC Columbus 5249 4th Ave
## 618 Police NY Arlington 4721 Cherokee Hwy
## 619 Ambulance SC Springfield 8212 Flute Ridge
## 620 Ambulance NY Springfield 3592 MLK Ridge
## 621 Police SC Hillsdale 6494 4th Ave
## 622 Police NY Riverwood 6608 Apache Lane
## 623 Police WV Riverwood 1553 Lincoln St
## 624 Fire VA Hillsdale 7628 4th Lane
## 625 None SC Riverwood 3028 5th St
## 626 Ambulance NC Northbrook 8949 Rock Hwy
## 627 Fire SC Springfield 9751 Tree St
## 628 Police WV Northbend 4702 Texas Drive
## 629 Fire SC Riverwood 2757 4th Hwy
## 630 Other NY Northbrook 6678 Weaver Drive
## 631 Fire NY Columbus 8667 Weaver Lane
## 632 Other SC Springfield 4931 Maple Drive
## 633 Fire VA Springfield 3808 5th Ave
## 634 Police NY Columbus 1725 Solo Lane
## 635 Police WV Arlington 8097 Maple Lane
## 636 None SC Columbus 3320 5th Hwy
## 637 Police WV Hillsdale 9573 2nd Ave
## 638 Police WV Northbend 8336 1st Ridge
## 639 Ambulance NC Columbus 3998 4th Hwy
## 640 Fire VA Northbrook 3966 Oak Hwy
## 641 Police NY Riverwood 7601 Andromedia Lane
## 642 Police NY Hillsdale 5160 2nd Hwy
## 643 Ambulance SC Springfield 3288 Tree Lane
## 644 Police NC Hillsdale 5874 1st Hwy
## 645 Other NY Riverwood 6467 Best Ave
## 646 Fire SC Arlington 6309 5th Ave
## 647 Ambulance NY Springfield 8212 Rock Ave
## 648 Ambulance NY Riverwood 4107 MLK Ridge
## 649 None NY Arlington 4558 3rd Hwy
## 650 Ambulance WV Columbus 1762 Maple Hwy
## 651 Ambulance NY Springfield 5532 Francis Lane
## 652 Police NY Springfield 6158 Sky Ridge
## 653 Other SC Northbend 9214 Elm Ridge
## 654 Police NY Riverwood 1833 Solo Ave
## 655 Other WV Columbus 1953 Sky Lane
## 656 Other WV Hillsdale 6834 1st Drive
## 657 Police SC Arlington 9562 4th Ridge
## 658 Other NC Riverwood 4835 Britain Ridge
## 659 Fire NC Springfield 8548 Cherokee Ridge
## 660 Ambulance SC Columbus 2352 MLK Drive
## 661 Police SC Northbrook 9734 2nd Ridge
## 662 Ambulance VA Springfield 3122 Apache Drive
## 663 Police WV Northbend 9816 Britain St
## 664 Ambulance NY Northbend 8214 Flute St
## 665 Ambulance NY Arlington 6259 Lincoln Hwy
## 666 Ambulance WV Arlington 4492 Andromedia Ave
## 667 Fire NY Northbend 6179 3rd Ridge
## 668 Ambulance SC Arlington 3799 Embaracadero Drive
## 669 Ambulance VA Hillsdale 5071 1st Lane
## 670 Other SC Columbus 6574 4th Drive
## 671 Ambulance PA Hillsdale 2711 Britain Ave
## 672 Fire SC Hillsdale 4214 MLK Ridge
## 673 Ambulance WV Northbend 7976 Britain Drive
## 674 Police SC Northbend 4995 Weaver Ridge
## 675 None VA Northbend 1515 Embaracadero St
## 676 Police SC Hillsdale 2968 Andromedia Ave
## 677 Ambulance NY Arlington 9236 2nd Hwy
## 678 None NY Arlington 9639 Britain Ridge
## 679 Fire SC Columbus 9422 Washington Ridge
## 680 Police NC Riverwood 1213 4th Lane
## 681 Police NY Hillsdale 3872 5th Drive
## 682 Police NC Springfield 9397 5th Hwy
## 683 Police SC Northbend 8876 1st St
## 684 Ambulance NC Hillsdale 3397 5th Ave
## 685 Ambulance SC Hillsdale 3263 Pine Ridge
## 686 Fire NY Columbus 8639 5th Hwy
## 687 None NY Riverwood 5743 4th Ridge
## 688 Police WV Springfield 3555 Francis Ridge
## 689 Ambulance WV Northbend 4939 Oak Lane
## 690 Police SC Riverwood 3100 Best St
## 691 Police NY Riverwood 3029 5th Ave
## 692 Police WV Columbus 8941 Solo Ridge
## 693 Police WV Northbrook 4447 Francis Hwy
## 694 Ambulance WV Northbrook 7701 Tree St
## 695 Other WV Springfield 4653 Pine St
## 696 Police WV Riverwood 8742 4th St
## 697 Police WV Hillsdale 7316 Texas Ave
## 698 Other VA Arlington 2950 MLK Ave
## 699 Police SC Springfield 8233 Tree Drive
## 700 Police VA Arlington 4642 Rock Ridge
## 701 Other WV Arlington 7733 Britain Lane
## 702 Fire NY Hillsdale 2753 Cherokee Ave
## 703 None NY Springfield 3995 Lincoln Hwy
## 704 Ambulance SC Columbus 4095 MLK St
## 705 Fire NY Northbend 5782 Rock Drive
## 706 Ambulance NY Columbus 2900 Sky Drive
## 707 Fire SC Riverwood 1515 Pine Lane
## 708 Ambulance NY Arlington 4876 Washington Drive
## 709 Ambulance SC Hillsdale 5779 2nd Lane
## 710 Ambulance SC Columbus 6706 Francis Drive
## 711 Ambulance NC Springfield 6384 5th Ridge
## 712 None SC Riverwood 3006 Lincoln Ridge
## 713 Other PA Hillsdale 5352 Lincoln Drive
## 714 Fire NC Riverwood 6110 Rock Ridge
## 715 Fire SC Columbus 7797 Tree Ridge
## 716 Ambulance NC Springfield 4910 1st Lane
## 717 Other NY Columbus 8766 Lincoln Lane
## 718 Ambulance WV Northbrook 6399 Oak Drive
## 719 Other NY Northbend 3127 Flute St
## 720 Police PA Northbend 8920 Best Ave
## 721 Ambulance SC Northbrook 7314 Tree Drive
## 722 None NY Riverwood 8872 Oak Ridge
## 723 Ambulance WV Riverwood 5022 1st St
## 724 Police OH Columbus 3423 Francis Ave
## 725 Fire WV Columbus 9529 4th Drive
## 726 Police VA Northbrook 1818 Tree St
## 727 None NC Northbend 4431 Rock St
## 728 Police SC Northbend 4782 Sky Lane
## 729 Fire VA Northbrook 7112 Weaver Ave
## 730 Fire WV Hillsdale 4020 Best Drive
## 731 Police SC Riverwood 2037 5th Drive
## 732 Ambulance VA Northbrook 4699 Texas Ridge
## 733 Ambulance SC Riverwood 1832 Elm Hwy
## 734 Fire WV Columbus 5226 Maple St
## 735 Fire NY Hillsdale 3771 4th St
## 736 Fire WV Northbend 8701 5th Lane
## 737 Fire SC Hillsdale 7574 4th St
## 738 Ambulance NC Riverwood 1989 Solo Lane
## 739 Fire WV Springfield 6331 MLK Ave
## 740 None WV Riverwood 8453 Elm St
## 741 Other WV Riverwood 1422 Flute Ave
## 742 Other NC Columbus 5058 4th Lane
## 743 Other WV Springfield 3098 Oak Lane
## 744 Other SC Arlington 9103 MLK Lane
## 745 Other SC Arlington 8624 Francis Ave
## 746 Fire NC Riverwood 2905 Embaracadero Drive
## 747 Fire WV Springfield 3443 Maple Ridge
## 748 Fire WV Northbend 1618 Maple Hwy
## 749 Fire NY Northbrook 3751 Tree Hwy
## 750 Ambulance SC Northbrook 6848 Elm Hwy
## 751 Police WV Northbrook 4237 4th St
## 752 Fire NY Columbus 6581 Rock Ridge
## 753 Fire NC Northbend 7236 Apache Lane
## 754 Other NY Columbus 3846 4th Hwy
## 755 Fire VA Springfield 5028 Maple Ridge
## 756 Police WV Springfield 7426 Rock Drive
## 757 Ambulance WV Columbus 5771 Best St
## 758 Fire SC Springfield 9818 Cherokee Ave
## 759 Ambulance VA Arlington 7819 2nd Ave
## 760 Ambulance NY Northbrook 1331 Elm Ridge
## 761 Other NY Hillsdale 9240 Britain Ave
## 762 Police SC Arlington 6668 Andromedia Ridge
## 763 Other NY Columbus 5276 2nd Lane
## 764 Fire SC Arlington 2889 Weaver St
## 765 Police NY Arlington 1879 4th Lane
## 766 Ambulance SC Columbus 5499 Elm Hwy
## 767 Fire VA Riverwood 8822 Sky St
## 768 Ambulance NY Columbus 4254 Best Ridge
## 769 Other VA Springfield 5812 Weaver Ave
## 770 Police SC Northbrook 7155 Apache Drive
## 771 Fire WV Northbrook 1376 Pine St
## 772 Fire VA Northbrook 3340 3rd Hwy
## 773 Ambulance NY Northbend 3097 4th Drive
## 774 Ambulance WV Northbrook 1916 Elm St
## 775 Police WV Arlington 8917 Cherokee Lane
## 776 Police WV Northbrook 8492 Weaver Hwy
## 777 Fire WV Columbus 3753 Francis Lane
## 778 Police NC Springfield 4545 4th Ridge
## 779 Police WV Springfield 2272 Embaracadero Drive
## 780 Police NC Hillsdale 5341 5th Ave
## 781 Fire WV Hillsdale 7745 Washington Ridge
## 782 Ambulance NC Riverwood 1275 4th Ridge
## 783 None NY Springfield 4857 Weaver St
## 784 None NY Hillsdale 8211 Sky Hwy
## 785 Police WV Columbus 8617 Best Ave
## 786 Ambulance NY Columbus 9856 Apache St
## 787 Fire NY Northbend 1951 Best Ave
## 788 Fire SC Riverwood 1824 5th Lane
## 789 Fire OH Springfield 7393 Washington St
## 790 Fire VA Arlington 1386 Britain St
## 791 None SC Northbrook 7928 Maple Ridge
## 792 Other NY Northbend 1546 Cherokee Ave
## 793 Police VA Riverwood 2003 2nd Hwy
## 794 Ambulance NY Columbus 9418 5th Hwy
## 795 Fire WV Northbrook 8770 1st Lane
## 796 Fire WV Columbus 1087 Flute Drive
## 797 Ambulance NY Hillsdale 2217 Tree Lane
## 798 Fire NC Northbrook 6741 Oak Ridge
## 799 Fire NY Northbrook 2123 MLK Ridge
## 800 None VA Hillsdale 4390 4th Drive
## 801 Police SC Arlington 1437 3rd Lane
## 802 Other NY Riverwood 1186 Rock St
## 803 Police NY Arlington 4394 Oak St
## 804 Ambulance NC Arlington 8368 Cherokee Ave
## 805 None WV Arlington 4905 Best Lane
## 806 Ambulance WV Hillsdale 3618 Sky Ave
## 807 Fire SC Arlington 5459 MLK Ave
## 808 Ambulance NY Springfield 1371 Texas Lane
## 809 Fire NC Northbend 2654 Embaracadero St
## 810 Ambulance VA Northbend 2123 Texas Ave
## 811 Other NY Northbend 4538 Flute Hwy
## 812 None WV Arlington 4434 Weaver St
## 813 Ambulance VA Hillsdale 2798 1st Ave
## 814 None WV Springfield 2809 Francis Lane
## 815 Ambulance VA Riverwood 7281 Oak St
## 816 Police SC Arlington 9878 Washington Ave
## 817 Other SC Columbus 2537 5th Ave
## 818 Other SC Columbus 8493 Apache Drive
## 819 Police WV Northbend 2878 Britain Hwy
## 820 Other SC Columbus 2862 Tree Ridge
## 821 None VA Hillsdale 4453 Best Ave
## 822 Fire NY Arlington 5191 4th St
## 823 Police WV Northbend 1364 Best St
## 824 Police NC Northbrook 8946 2nd Drive
## 825 Ambulance PA Riverwood 3726 MLK Hwy
## 826 Police SC Riverwood 2820 Britain St
## 827 Fire NY Arlington 2646 MLK Drive
## 828 Ambulance WV Riverwood 6256 Elm St
## 829 Fire SC Riverwood 9724 Maple St
## 830 Other NC Riverwood 7397 4th Drive
## 831 Police PA Northbrook 3488 Flute Lane
## 832 Police NY Hillsdale 9082 3rd Lane
## 833 None VA Arlington 1941 5th Ridge
## 834 Police WV Northbend 5333 MLK Lane
## 835 Police SC Riverwood 4577 Sky Hwy
## 836 None NC Northbrook 4814 Lincoln Lane
## 837 Police VA Northbend 2381 1st Hwy
## 838 None NC Arlington 6939 3rd Hwy
## 839 Fire SC Arlington 5269 Flute Hwy
## 840 Police SC Northbrook 7197 2nd Drive
## 841 None SC Arlington 1741 Best Ridge
## 842 Fire SC Northbrook 9148 4th Hwy
## 843 Police WV Springfield 4279 Solo Drive
## 844 Other NY Springfield 9177 Texas Ave
## 845 Police VA Northbrook 5969 Francis St
## 846 Ambulance SC Columbus 9942 Tree Ave
## 847 Fire VA Hillsdale 5474 Weaver Hwy
## 848 Fire NY Riverwood 1102 Apache Hwy
## 849 Ambulance NY Springfield 9214 Texas Drive
## 850 Police NC Northbend 8991 Texas Hwy
## 851 Police NY Hillsdale 9580 MLK Ave
## 852 Ambulance SC Northbend 5868 Best Drive
## 853 Fire NY Northbend 5318 5th Ave
## 854 Fire WV Hillsdale 7502 Rock Lane
## 855 Police WV Northbrook 4627 Elm Ridge
## 856 Fire SC Springfield 5584 Britain Lane
## 857 Fire WV Northbend 7002 Oak Hwy
## 858 Fire NY Columbus 4780 Best Drive
## 859 Other SC Northbrook 8995 1st Ave
## 860 Other SC Columbus 5586 2nd St
## 861 Other NY Northbend 1589 Best Ave
## 862 Other NY Northbrook 1880 Weaver Drive
## 863 Ambulance WV Springfield 7295 Tree Hwy
## 864 Other SC Arlington 8832 Pine Drive
## 865 Fire WV Hillsdale 1620 Oak Ave
## 866 Fire SC Hillsdale 3847 Elm Hwy
## 867 Fire NY Hillsdale 3177 MLK Ridge
## 868 Ambulance NY Riverwood 3929 Oak Drive
## 869 Other SC Springfield 1469 Lincoln Drive
## 870 Police OH Arlington 9719 4th Lane
## 871 Fire NY Riverwood 3196 Cherokee St
## 872 None WV Northbend 8492 Andromedia Ridge
## 873 Ambulance NY Hillsdale 1353 Washington St
## 874 Ambulance WV Hillsdale 6731 Andromedia Hwy
## 875 Other NC Riverwood 5769 Texas Lane
## 876 Ambulance SC Arlington 2849 Pine Drive
## 877 Police WV Springfield 2577 Texas Ridge
## 878 Other NY Northbend 3841 Washington Lane
## 879 Fire SC Columbus 8125 Texas Ridge
## 880 Other NC Arlington 4826 5th St
## 881 Fire VA Northbrook 1578 5th Lane
## 882 Fire VA Hillsdale 6440 Rock Lane
## 883 Police NY Northbrook 5806 Embaracadero St
## 884 Ambulance WV Riverwood 1472 4th Drive
## 885 Fire WV Hillsdale 5839 Weaver Lane
## 886 Police NC Columbus 7630 Rock Drive
## 887 Fire SC Riverwood 7144 Andromedia St
## 888 None VA Arlington 9988 Rock Ridge
## 889 Fire SC Northbrook 7544 Washington Ave
## 890 Ambulance VA Hillsdale 7201 Washington Ave
## 891 Ambulance NC Northbend 8805 Cherokee Drive
## 892 Other SC Arlington 3275 Pine St
## 893 None WV Columbus 7785 Lincoln Lane
## 894 None NC Arlington 4994 Lincoln Drive
## 895 None NY Springfield 1298 Maple Hwy
## 896 Fire WV Northbend 2644 MLK Drive
## 897 None SC Riverwood 5630 1st Drive
## 898 Police NY Springfield 6137 MLK St
## 899 Police WV Northbrook 5383 Maple Drive
## 900 None NC Arlington 4460 4th Lane
## 901 Other NY Columbus 8524 Pine Lane
## 902 Police NY Northbrook 8456 1st Ave
## 903 Fire SC Riverwood 3639 Flute Hwy
## 904 Other WV Riverwood 7900 Sky Hwy
## 905 Other VA Riverwood 7835 Cherokee Hwy
## 906 Fire VA Northbend 1030 Pine Lane
## 907 Police SC Columbus 9278 Francis Ridge
## 908 Fire WV Northbend 6604 Apache Drive
## 909 Police WV Northbrook 2311 4th St
## 910 Other SC Northbend 9523 Solo Hwy
## 911 Police PA Springfield 3171 Andromedia Lane
## 912 Police WV Northbend 2492 Lincoln Lane
## 913 Police NC Northbend 4477 5th Ave
## 914 Fire WV Springfield 6724 Andromedia St
## 915 Fire NY Riverwood 7495 Washington Ave
## 916 Fire SC Hillsdale 4291 Sky Hwy
## 917 None NY Hillsdale 5650 Rock Ave
## 918 Other NY Columbus 6888 Elm Ridge
## 919 Ambulance NY Northbrook 2352 Sky Drive
## 920 Police NY Hillsdale 5280 Pine Ave
## 921 Other WV Northbrook 6638 Tree Drive
## 922 Other NY Northbrook 5678 Lincoln Drive
## 923 Police WV Columbus 4496 Pine Lane
## 924 Other SC Hillsdale 8845 5th Ave
## 925 Ambulance WV Riverwood 9317 Apache Ave
## 926 Police VA Arlington 8638 3rd Ave
## 927 Fire NY Columbus 3061 Francis Hwy
## 928 Other NY Northbrook 1173 Andromedia Ave
## 929 Police VA Springfield 6068 2nd St
## 930 Police SC Arlington 7937 Weaver Ridge
## 931 Police SC Springfield 2823 Weaver Lane
## 932 Fire PA Columbus 1809 Sky St
## 933 Other NC Springfield 9352 Washington Ave
## 934 Ambulance NY Columbus 2697 Oak Drive
## 935 Ambulance VA Riverwood 1110 4th Drive
## 936 Other WV Hillsdale 7535 5th Lane
## 937 Police NY Northbrook 9043 Maple Hwy
## 938 Police VA Hillsdale 3777 Maple Ave
## 939 Other NY Northbrook 5608 Solo St
## 940 Other SC Arlington 6981 Weaver St
## 941 None NY Springfield 4369 Maple Lane
## 942 Fire NY Columbus 6931 Elm St
## 943 None SC Arlington 7583 Washington Ave
## 944 Police NC Arlington 7552 3rd St
## 945 Police SC Northbrook 1654 Pine St
## 946 Police SC Springfield 6058 Andromedia Hwy
## 947 Fire WV Hillsdale 6536 MLK Hwy
## 948 Other WV Columbus 8198 Embaracadero Lane
## 949 Police WV Hillsdale 3447 Solo Ave
## 950 Police WV Arlington 1806 Weaver Ridge
## 951 None SC Arlington 7930 Texas Ave
## 952 Ambulance NC Riverwood 7082 Oak Ridge
## 953 Other VA Northbend 6357 Texas Lane
## 954 None WV Hillsdale 9322 Rock Hwy
## 955 Ambulance WV Northbrook 6684 Solo Lane
## 956 Other NY Riverwood 4885 Oak Lane
## 957 Police SC Springfield 7846 Andromedia Drive
## 958 Police WV Columbus 3915 Embaracadero St
## 959 Fire NC Hillsdale 4242 Rock Lane
## 960 Police VA Hillsdale 7405 Oak St
## 961 Ambulance NY Northbend 9633 4th St
## 962 Police VA Columbus 3492 Britain St
## 963 Police VA Arlington 7973 4th St
## 964 Police WV Riverwood 3952 Andromedia Lane
## 965 Police WV Springfield 6702 Andromedia St
## 966 Other SC Arlington 5455 Oak Hwy
## 967 Other NC Columbus 2253 Maple Ave
## 968 Police NC Arlington 7897 Lincoln St
## 969 Police NY Northbend 8811 Maple Hwy
## 970 Police SC Northbend 8167 Apache Ave
## 971 Fire SC Northbrook 5475 Rock Lane
## 972 Fire VA Northbend 8215 Flute Drive
## 973 Fire NY Northbend 1320 Flute Lane
## 974 Ambulance SC Columbus 1229 5th Ave
## 975 Police SC Riverwood 3884 Pine Lane
## 976 Fire VA Northbend 7108 Tree St
## 977 Fire WV Arlington 8014 Embaracadero Drive
## 978 Ambulance WV Springfield 4937 Flute Drive
## 979 Other NY Columbus 2889 Francis St
## 980 Other NY Columbus 7504 Flute Drive
## 981 Ambulance OH Northbend 7570 Cherokee Drive
## 982 Police NY Northbend 4710 Lincoln Hwy
## 983 Police NY Arlington 7511 1st Ave
## 984 Police SC Arlington 7042 Maple Ridge
## 985 Ambulance WV Springfield 4475 Lincoln Ridge
## 986 Other WV Columbus 9439 MLK St
## 987 Other WV Riverwood 8269 Sky Hwy
## 988 Other NC Hillsdale 5663 Oak Lane
## 989 Fire NY Columbus 4633 5th Lane
## 990 Police SC Arlington 9682 Cherokee Ridge
## 991 Fire NY Northbrook 4755 1st St
## 992 Other WV Riverwood 5312 Francis Ridge
## 993 Fire OH Springfield 1705 Weaver St
## 994 Other OH Hillsdale 1643 Washington Hwy
## 995 None SC Northbend 6516 Solo Drive
## 996 Fire NC Northbrook 6045 Andromedia St
## 997 Fire SC Northbend 3092 Texas Drive
## 998 Police NC Arlington 7629 5th St
## 999 Other NY Arlington 6128 Elm Lane
## 1000 Police WV Columbus 1416 Cherokee Ridge
## incident_hour_of_the_day number_of_vehicles_involved property_damage
## 1 5 1 YES
## 2 8 1 ?
## 3 7 3 NO
## 4 5 1 ?
## 5 20 1 NO
## 6 19 3 NO
## 7 0 3 ?
## 8 23 3 ?
## 9 21 1 NO
## 10 14 1 NO
## 11 22 1 YES
## 12 21 3 YES
## 13 9 1 YES
## 14 5 1 NO
## 15 12 1 NO
## 16 12 4 YES
## 17 0 3 ?
## 18 9 1 NO
## 19 19 1 YES
## 20 8 3 ?
## 21 20 3 NO
## 22 15 3 ?
## 23 20 3 NO
## 24 15 1 ?
## 25 6 1 NO
## 26 16 3 NO
## 27 4 1 YES
## 28 5 1 YES
## 29 21 1 NO
## 30 5 1 NO
## 31 22 4 NO
## 32 10 3 NO
## 33 16 3 YES
## 34 1 3 NO
## 35 17 1 YES
## 36 15 1 YES
## 37 3 1 ?
## 38 16 1 NO
## 39 4 3 ?
## 40 4 1 ?
## 41 19 1 NO
## 42 1 1 ?
## 43 17 3 YES
## 44 23 1 NO
## 45 14 1 YES
## 46 17 3 NO
## 47 11 3 YES
## 48 19 3 NO
## 49 5 1 ?
## 50 19 1 NO
## 51 19 3 ?
## 52 4 1 ?
## 53 8 1 ?
## 54 7 3 YES
## 55 5 1 NO
## 56 3 1 YES
## 57 12 3 ?
## 58 19 1 NO
## 59 22 1 YES
## 60 12 3 NO
## 61 15 3 YES
## 62 23 3 NO
## 63 12 1 NO
## 64 20 3 YES
## 65 16 3 NO
## 66 6 3 YES
## 67 12 1 YES
## 68 17 3 NO
## 69 8 1 NO
## 70 13 1 NO
## 71 12 2 ?
## 72 7 1 NO
## 73 10 1 NO
## 74 7 3 NO
## 75 18 3 YES
## 76 22 3 NO
## 77 18 3 NO
## 78 22 3 NO
## 79 15 1 YES
## 80 16 1 ?
## 81 6 3 NO
## 82 10 1 ?
## 83 3 1 YES
## 84 13 1 NO
## 85 16 2 ?
## 86 13 3 YES
## 87 9 3 NO
## 88 2 1 ?
## 89 9 1 NO
## 90 12 3 YES
## 91 12 2 YES
## 92 14 1 NO
## 93 9 1 NO
## 94 3 3 NO
## 95 21 3 NO
## 96 5 1 NO
## 97 11 1 YES
## 98 4 4 YES
## 99 6 1 ?
## 100 10 1 NO
## 101 23 1 YES
## 102 13 1 YES
## 103 16 3 ?
## 104 3 1 NO
## 105 15 3 NO
## 106 4 1 YES
## 107 6 3 ?
## 108 22 2 NO
## 109 1 1 ?
## 110 16 3 NO
## 111 18 1 ?
## 112 0 1 YES
## 113 12 3 ?
## 114 13 2 ?
## 115 9 1 YES
## 116 14 3 NO
## 117 16 1 NO
## 118 20 3 ?
## 119 4 1 NO
## 120 7 3 ?
## 121 22 1 NO
## 122 11 1 NO
## 123 7 1 ?
## 124 0 4 YES
## 125 1 1 YES
## 126 12 3 NO
## 127 8 1 NO
## 128 9 1 ?
## 129 22 1 YES
## 130 0 3 ?
## 131 0 3 YES
## 132 3 3 ?
## 133 21 3 ?
## 134 0 1 ?
## 135 16 1 ?
## 136 14 1 NO
## 137 9 1 ?
## 138 9 3 NO
## 139 21 3 ?
## 140 19 3 NO
## 141 2 1 YES
## 142 9 1 ?
## 143 5 1 ?
## 144 14 1 ?
## 145 20 3 ?
## 146 18 1 YES
## 147 13 3 ?
## 148 21 1 ?
## 149 14 1 NO
## 150 1 1 ?
## 151 17 1 ?
## 152 4 3 ?
## 153 23 3 YES
## 154 13 3 NO
## 155 20 3 NO
## 156 16 1 YES
## 157 10 3 ?
## 158 8 1 ?
## 159 17 3 ?
## 160 9 1 ?
## 161 0 1 YES
## 162 12 1 ?
## 163 20 2 NO
## 164 3 1 YES
## 165 18 3 NO
## 166 4 3 NO
## 167 18 1 ?
## 168 18 3 NO
## 169 7 1 ?
## 170 18 1 NO
## 171 23 3 YES
## 172 1 1 ?
## 173 15 3 YES
## 174 2 3 ?
## 175 3 1 NO
## 176 13 1 ?
## 177 1 1 NO
## 178 16 1 YES
## 179 0 1 YES
## 180 1 1 ?
## 181 17 3 ?
## 182 13 1 YES
## 183 12 1 YES
## 184 11 1 ?
## 185 18 1 ?
## 186 17 1 YES
## 187 22 3 ?
## 188 9 1 YES
## 189 11 3 ?
## 190 11 1 NO
## 191 8 1 YES
## 192 18 1 ?
## 193 13 1 NO
## 194 8 1 YES
## 195 9 3 ?
## 196 17 2 YES
## 197 3 1 ?
## 198 6 1 YES
## 199 17 1 YES
## 200 7 1 ?
## 201 9 1 NO
## 202 3 1 ?
## 203 6 1 NO
## 204 7 3 YES
## 205 21 3 NO
## 206 14 1 NO
## 207 0 1 ?
## 208 14 3 ?
## 209 13 4 NO
## 210 8 1 NO
## 211 7 1 NO
## 212 9 1 ?
## 213 12 3 YES
## 214 6 3 ?
## 215 11 1 YES
## 216 5 3 ?
## 217 14 1 NO
## 218 17 1 ?
## 219 0 1 ?
## 220 21 3 NO
## 221 18 1 YES
## 222 17 1 NO
## 223 19 1 YES
## 224 17 3 ?
## 225 23 3 ?
## 226 4 1 YES
## 227 2 2 YES
## 228 2 1 ?
## 229 10 3 YES
## 230 2 1 ?
## 231 18 1 NO
## 232 18 1 NO
## 233 7 4 ?
## 234 19 3 ?
## 235 10 3 ?
## 236 16 3 NO
## 237 17 1 YES
## 238 2 3 NO
## 239 19 1 ?
## 240 0 1 YES
## 241 16 1 ?
## 242 20 1 YES
## 243 10 1 ?
## 244 21 3 ?
## 245 3 1 YES
## 246 7 1 YES
## 247 19 1 YES
## 248 15 3 ?
## 249 7 1 ?
## 250 19 1 ?
## 251 13 2 YES
## 252 2 3 NO
## 253 5 1 YES
## 254 21 1 ?
## 255 21 3 NO
## 256 7 3 NO
## 257 16 3 YES
## 258 13 1 YES
## 259 2 1 YES
## 260 19 3 ?
## 261 17 1 ?
## 262 3 1 ?
## 263 17 3 NO
## 264 5 3 ?
## 265 9 1 NO
## 266 9 1 NO
## 267 9 3 ?
## 268 22 3 NO
## 269 15 3 YES
## 270 14 3 YES
## 271 6 3 ?
## 272 6 1 NO
## 273 22 4 YES
## 274 14 3 ?
## 275 16 3 NO
## 276 10 3 ?
## 277 9 3 YES
## 278 4 1 NO
## 279 6 3 ?
## 280 15 3 YES
## 281 12 3 NO
## 282 3 1 NO
## 283 14 1 YES
## 284 10 1 ?
## 285 17 3 ?
## 286 8 1 YES
## 287 14 3 YES
## 288 6 1 ?
## 289 6 1 YES
## 290 8 1 NO
## 291 13 1 YES
## 292 0 3 ?
## 293 14 3 ?
## 294 13 1 YES
## 295 16 4 ?
## 296 13 1 ?
## 297 15 3 YES
## 298 3 1 ?
## 299 7 1 YES
## 300 16 2 NO
## 301 15 1 NO
## 302 21 3 YES
## 303 23 3 YES
## 304 5 1 ?
## 305 15 3 YES
## 306 14 1 ?
## 307 1 1 ?
## 308 14 3 NO
## 309 17 3 ?
## 310 20 3 NO
## 311 10 3 NO
## 312 3 3 ?
## 313 0 1 ?
## 314 10 3 ?
## 315 13 3 YES
## 316 14 1 NO
## 317 22 3 NO
## 318 8 3 YES
## 319 1 3 ?
## 320 19 1 YES
## 321 17 1 YES
## 322 13 3 YES
## 323 4 1 ?
## 324 14 3 NO
## 325 4 2 YES
## 326 23 3 YES
## 327 21 3 YES
## 328 23 3 YES
## 329 6 3 ?
## 330 21 1 NO
## 331 23 1 ?
## 332 3 3 ?
## 333 8 1 NO
## 334 4 1 ?
## 335 22 1 ?
## 336 16 1 NO
## 337 14 3 NO
## 338 13 1 YES
## 339 20 1 ?
## 340 0 3 ?
## 341 23 3 YES
## 342 13 1 YES
## 343 8 1 ?
## 344 3 1 ?
## 345 16 1 ?
## 346 19 1 YES
## 347 0 1 YES
## 348 12 1 ?
## 349 21 3 YES
## 350 0 1 ?
## 351 22 1 YES
## 352 20 3 ?
## 353 0 1 NO
## 354 20 1 ?
## 355 1 3 YES
## 356 11 1 NO
## 357 14 3 YES
## 358 20 3 YES
## 359 22 1 ?
## 360 12 1 NO
## 361 3 3 YES
## 362 15 3 YES
## 363 8 1 YES
## 364 13 3 NO
## 365 8 1 NO
## 366 7 1 ?
## 367 9 1 ?
## 368 15 1 NO
## 369 5 1 ?
## 370 15 4 YES
## 371 22 3 YES
## 372 16 2 ?
## 373 7 1 NO
## 374 2 1 YES
## 375 17 4 ?
## 376 12 3 ?
## 377 9 1 ?
## 378 16 1 ?
## 379 19 3 ?
## 380 14 3 ?
## 381 18 3 ?
## 382 23 1 YES
## 383 15 3 ?
## 384 18 1 YES
## 385 16 1 NO
## 386 12 3 NO
## 387 16 1 NO
## 388 21 1 ?
## 389 15 3 YES
## 390 21 1 YES
## 391 1 1 YES
## 392 17 3 ?
## 393 5 3 NO
## 394 15 3 ?
## 395 2 4 NO
## 396 3 1 YES
## 397 19 1 NO
## 398 17 1 YES
## 399 7 1 ?
## 400 21 1 NO
## 401 10 2 YES
## 402 11 1 YES
## 403 23 3 YES
## 404 14 1 ?
## 405 10 1 NO
## 406 6 3 ?
## 407 10 3 ?
## 408 5 3 ?
## 409 17 1 ?
## 410 3 1 NO
## 411 9 1 ?
## 412 6 1 YES
## 413 22 3 NO
## 414 10 3 ?
## 415 4 1 YES
## 416 23 1 NO
## 417 19 1 ?
## 418 22 1 YES
## 419 6 1 NO
## 420 6 1 NO
## 421 18 1 NO
## 422 22 1 ?
## 423 21 1 ?
## 424 12 1 ?
## 425 6 3 ?
## 426 0 4 ?
## 427 11 1 NO
## 428 19 1 ?
## 429 22 3 ?
## 430 2 1 YES
## 431 16 4 YES
## 432 23 3 ?
## 433 21 1 YES
## 434 10 1 ?
## 435 12 1 ?
## 436 6 1 NO
## 437 6 1 ?
## 438 7 1 ?
## 439 3 1 NO
## 440 10 1 YES
## 441 14 1 NO
## 442 17 3 NO
## 443 16 1 YES
## 444 3 1 NO
## 445 7 1 NO
## 446 13 1 YES
## 447 20 1 YES
## 448 23 1 YES
## 449 11 1 NO
## 450 0 4 ?
## 451 2 1 YES
## 452 3 1 NO
## 453 3 1 YES
## 454 15 3 ?
## 455 23 1 ?
## 456 9 3 YES
## 457 20 1 ?
## 458 5 1 ?
## 459 15 1 YES
## 460 13 1 YES
## 461 16 1 NO
## 462 0 1 YES
## 463 10 3 NO
## 464 14 3 NO
## 465 1 3 YES
## 466 16 1 ?
## 467 2 3 NO
## 468 23 3 ?
## 469 19 3 ?
## 470 23 3 NO
## 471 10 1 ?
## 472 12 3 YES
## 473 15 3 NO
## 474 3 1 NO
## 475 19 1 ?
## 476 16 3 NO
## 477 16 1 NO
## 478 10 1 ?
## 479 6 1 NO
## 480 18 1 NO
## 481 20 3 NO
## 482 14 1 NO
## 483 8 3 NO
## 484 17 2 YES
## 485 9 1 NO
## 486 0 1 ?
## 487 2 1 NO
## 488 23 3 ?
## 489 8 1 NO
## 490 6 3 NO
## 491 3 1 YES
## 492 14 3 YES
## 493 16 3 NO
## 494 12 1 ?
## 495 1 3 NO
## 496 19 1 ?
## 497 7 1 ?
## 498 6 4 ?
## 499 11 1 ?
## 500 2 1 YES
## 501 15 1 ?
## 502 0 1 NO
## 503 12 1 YES
## 504 1 1 ?
## 505 16 2 YES
## 506 2 1 NO
## 507 13 3 NO
## 508 0 1 NO
## 509 0 3 ?
## 510 16 3 YES
## 511 8 1 NO
## 512 9 1 ?
## 513 11 1 ?
## 514 6 3 ?
## 515 18 1 NO
## 516 11 1 ?
## 517 17 1 ?
## 518 21 3 ?
## 519 0 3 NO
## 520 9 1 NO
## 521 1 1 NO
## 522 22 1 YES
## 523 20 4 NO
## 524 9 3 YES
## 525 5 1 NO
## 526 12 1 YES
## 527 6 3 NO
## 528 8 1 NO
## 529 14 1 ?
## 530 10 1 YES
## 531 12 1 ?
## 532 12 1 ?
## 533 12 3 NO
## 534 13 1 NO
## 535 17 3 NO
## 536 14 1 NO
## 537 3 3 ?
## 538 18 3 YES
## 539 5 1 YES
## 540 19 1 NO
## 541 0 1 ?
## 542 22 1 ?
## 543 21 3 YES
## 544 4 3 YES
## 545 10 1 YES
## 546 5 1 ?
## 547 13 1 ?
## 548 18 3 YES
## 549 6 1 ?
## 550 0 3 ?
## 551 5 3 ?
## 552 8 1 YES
## 553 5 1 YES
## 554 9 1 YES
## 555 10 3 YES
## 556 16 1 YES
## 557 8 1 ?
## 558 5 1 NO
## 559 11 3 NO
## 560 15 3 ?
## 561 23 3 YES
## 562 23 1 NO
## 563 23 1 ?
## 564 10 1 YES
## 565 12 4 ?
## 566 4 1 NO
## 567 16 1 NO
## 568 18 3 ?
## 569 15 3 YES
## 570 8 1 NO
## 571 4 3 NO
## 572 20 3 NO
## 573 1 3 NO
## 574 19 1 NO
## 575 11 1 NO
## 576 13 1 YES
## 577 2 1 ?
## 578 9 3 YES
## 579 15 2 YES
## 580 23 1 ?
## 581 16 1 NO
## 582 21 4 YES
## 583 21 2 YES
## 584 3 3 NO
## 585 4 1 ?
## 586 17 1 ?
## 587 7 1 YES
## 588 14 2 ?
## 589 13 1 NO
## 590 23 2 ?
## 591 23 3 ?
## 592 16 1 ?
## 593 17 1 NO
## 594 9 1 YES
## 595 13 3 NO
## 596 13 1 YES
## 597 3 1 ?
## 598 3 1 ?
## 599 16 3 ?
## 600 12 3 NO
## 601 4 1 ?
## 602 3 1 ?
## 603 0 1 NO
## 604 19 1 NO
## 605 15 2 ?
## 606 6 1 NO
## 607 3 1 ?
## 608 17 1 YES
## 609 4 4 NO
## 610 14 1 NO
## 611 3 3 NO
## 612 4 3 ?
## 613 13 1 ?
## 614 23 3 NO
## 615 3 1 ?
## 616 0 3 ?
## 617 14 2 ?
## 618 14 2 NO
## 619 22 1 ?
## 620 17 3 NO
## 621 14 1 ?
## 622 2 1 ?
## 623 4 1 ?
## 624 2 1 ?
## 625 10 1 YES
## 626 11 1 YES
## 627 1 4 YES
## 628 20 1 ?
## 629 10 1 NO
## 630 20 2 ?
## 631 10 3 ?
## 632 2 2 NO
## 633 19 1 NO
## 634 10 1 YES
## 635 14 1 YES
## 636 5 1 ?
## 637 8 1 YES
## 638 4 1 NO
## 639 4 3 ?
## 640 20 2 ?
## 641 18 1 ?
## 642 0 3 NO
## 643 1 1 ?
## 644 17 1 NO
## 645 23 1 ?
## 646 4 3 YES
## 647 0 3 NO
## 648 11 1 NO
## 649 4 1 ?
## 650 0 3 ?
## 651 2 3 NO
## 652 11 3 YES
## 653 23 3 NO
## 654 17 3 NO
## 655 22 1 NO
## 656 18 1 YES
## 657 8 1 ?
## 658 15 3 ?
## 659 20 1 ?
## 660 4 1 ?
## 661 10 3 NO
## 662 10 1 YES
## 663 22 3 YES
## 664 15 3 NO
## 665 13 1 ?
## 666 23 1 NO
## 667 18 3 ?
## 668 7 1 YES
## 669 21 1 NO
## 670 15 3 NO
## 671 17 3 ?
## 672 2 1 NO
## 673 1 3 YES
## 674 3 1 ?
## 675 0 1 NO
## 676 4 1 NO
## 677 11 3 YES
## 678 4 1 YES
## 679 11 3 ?
## 680 4 3 NO
## 681 20 1 ?
## 682 22 1 YES
## 683 4 1 NO
## 684 21 4 NO
## 685 20 1 YES
## 686 7 1 NO
## 687 4 1 NO
## 688 17 3 ?
## 689 20 3 YES
## 690 10 1 NO
## 691 8 1 YES
## 692 6 1 ?
## 693 4 1 YES
## 694 17 3 NO
## 695 15 3 ?
## 696 9 1 NO
## 697 17 1 ?
## 698 13 1 ?
## 699 5 1 YES
## 700 23 3 YES
## 701 1 2 NO
## 702 17 4 NO
## 703 3 1 YES
## 704 17 3 ?
## 705 23 3 YES
## 706 13 1 YES
## 707 17 1 YES
## 708 2 1 YES
## 709 23 3 NO
## 710 17 1 NO
## 711 3 1 NO
## 712 16 1 NO
## 713 13 3 ?
## 714 8 1 NO
## 715 23 1 YES
## 716 15 3 YES
## 717 3 1 NO
## 718 4 3 NO
## 719 8 1 YES
## 720 21 1 NO
## 721 23 1 YES
## 722 8 1 ?
## 723 21 1 YES
## 724 5 1 NO
## 725 12 3 NO
## 726 7 3 ?
## 727 0 1 NO
## 728 14 1 YES
## 729 13 1 ?
## 730 1 1 ?
## 731 23 1 NO
## 732 1 1 ?
## 733 9 1 YES
## 734 3 1 ?
## 735 0 1 NO
## 736 13 3 NO
## 737 18 3 NO
## 738 17 3 ?
## 739 11 1 ?
## 740 0 1 YES
## 741 14 3 ?
## 742 4 1 NO
## 743 2 3 NO
## 744 9 3 YES
## 745 21 4 ?
## 746 0 3 NO
## 747 17 3 YES
## 748 21 3 NO
## 749 20 3 YES
## 750 5 1 NO
## 751 7 1 NO
## 752 6 1 NO
## 753 2 4 YES
## 754 19 3 ?
## 755 21 3 YES
## 756 3 3 ?
## 757 22 1 ?
## 758 22 1 YES
## 759 16 1 NO
## 760 0 1 ?
## 761 1 3 ?
## 762 19 3 YES
## 763 0 3 ?
## 764 2 3 ?
## 765 5 3 YES
## 766 6 1 ?
## 767 21 3 YES
## 768 11 3 YES
## 769 3 1 YES
## 770 4 3 ?
## 771 2 3 NO
## 772 22 1 ?
## 773 8 1 ?
## 774 14 3 YES
## 775 14 4 NO
## 776 5 1 YES
## 777 18 1 NO
## 778 20 3 ?
## 779 0 3 YES
## 780 1 3 ?
## 781 10 1 ?
## 782 12 1 NO
## 783 6 1 NO
## 784 1 1 NO
## 785 21 3 NO
## 786 3 3 YES
## 787 14 4 YES
## 788 19 3 NO
## 789 7 1 YES
## 790 0 1 ?
## 791 6 1 YES
## 792 0 1 YES
## 793 9 3 YES
## 794 23 1 YES
## 795 13 3 NO
## 796 0 3 ?
## 797 7 1 ?
## 798 23 1 YES
## 799 7 1 NO
## 800 20 1 YES
## 801 22 1 YES
## 802 10 3 ?
## 803 10 1 ?
## 804 17 1 YES
## 805 3 1 YES
## 806 10 1 NO
## 807 1 1 YES
## 808 1 3 NO
## 809 7 1 ?
## 810 19 1 ?
## 811 3 3 NO
## 812 3 1 NO
## 813 23 1 NO
## 814 7 1 ?
## 815 0 3 NO
## 816 10 1 ?
## 817 4 4 ?
## 818 16 1 ?
## 819 3 1 YES
## 820 5 1 YES
## 821 14 1 ?
## 822 7 3 ?
## 823 16 1 ?
## 824 6 1 NO
## 825 10 1 YES
## 826 19 1 ?
## 827 10 1 ?
## 828 6 3 NO
## 829 12 3 NO
## 830 10 3 YES
## 831 0 1 NO
## 832 12 3 YES
## 833 10 1 YES
## 834 3 4 NO
## 835 21 1 YES
## 836 6 1 ?
## 837 0 1 NO
## 838 6 1 NO
## 839 20 3 YES
## 840 4 1 NO
## 841 9 1 NO
## 842 20 3 ?
## 843 7 1 NO
## 844 18 3 ?
## 845 0 3 NO
## 846 11 3 ?
## 847 13 1 NO
## 848 19 3 YES
## 849 23 1 YES
## 850 23 1 NO
## 851 19 3 YES
## 852 19 1 NO
## 853 17 3 NO
## 854 18 1 NO
## 855 17 3 NO
## 856 11 3 ?
## 857 22 3 ?
## 858 7 1 NO
## 859 17 3 ?
## 860 16 3 NO
## 861 13 3 NO
## 862 17 3 YES
## 863 3 1 YES
## 864 15 3 ?
## 865 16 1 YES
## 866 18 1 ?
## 867 18 1 NO
## 868 22 2 NO
## 869 15 3 NO
## 870 16 3 YES
## 871 18 1 ?
## 872 8 1 NO
## 873 23 3 YES
## 874 18 1 ?
## 875 10 1 YES
## 876 12 1 NO
## 877 5 1 YES
## 878 21 3 ?
## 879 17 3 YES
## 880 4 1 YES
## 881 11 1 NO
## 882 18 1 ?
## 883 12 1 ?
## 884 18 3 YES
## 885 16 1 NO
## 886 19 3 YES
## 887 13 1 NO
## 888 4 1 NO
## 889 8 1 YES
## 890 19 3 NO
## 891 18 1 YES
## 892 9 2 ?
## 893 6 1 ?
## 894 8 1 YES
## 895 6 1 ?
## 896 23 3 ?
## 897 13 1 NO
## 898 3 3 NO
## 899 23 1 NO
## 900 8 1 YES
## 901 23 1 YES
## 902 23 1 YES
## 903 9 3 NO
## 904 22 1 YES
## 905 22 3 YES
## 906 15 3 NO
## 907 16 3 NO
## 908 17 3 ?
## 909 3 1 YES
## 910 10 3 ?
## 911 9 1 NO
## 912 13 2 ?
## 913 15 3 YES
## 914 23 1 ?
## 915 2 4 YES
## 916 14 1 YES
## 917 7 1 ?
## 918 23 1 NO
## 919 7 3 YES
## 920 8 3 YES
## 921 17 1 NO
## 922 10 3 NO
## 923 9 1 NO
## 924 2 1 YES
## 925 18 3 NO
## 926 4 1 ?
## 927 12 1 ?
## 928 15 1 YES
## 929 9 1 YES
## 930 6 1 YES
## 931 11 4 YES
## 932 13 3 NO
## 933 4 3 ?
## 934 20 3 YES
## 935 0 3 NO
## 936 18 4 ?
## 937 6 1 ?
## 938 23 1 ?
## 939 4 1 ?
## 940 21 3 ?
## 941 7 1 ?
## 942 19 3 ?
## 943 5 1 NO
## 944 22 3 YES
## 945 12 1 YES
## 946 19 3 NO
## 947 10 1 ?
## 948 7 1 NO
## 949 17 1 NO
## 950 0 3 ?
## 951 9 1 NO
## 952 21 1 ?
## 953 22 3 NO
## 954 3 1 NO
## 955 16 1 YES
## 956 14 1 YES
## 957 21 3 YES
## 958 19 1 NO
## 959 13 3 NO
## 960 21 1 NO
## 961 11 3 NO
## 962 16 1 ?
## 963 9 1 NO
## 964 8 1 NO
## 965 7 1 ?
## 966 12 1 ?
## 967 21 3 YES
## 968 4 1 YES
## 969 18 3 NO
## 970 7 1 ?
## 971 13 1 YES
## 972 0 1 NO
## 973 22 1 ?
## 974 15 1 YES
## 975 3 3 NO
## 976 18 2 YES
## 977 17 3 ?
## 978 18 3 ?
## 979 11 1 ?
## 980 17 1 NO
## 981 12 1 YES
## 982 15 3 ?
## 983 0 3 ?
## 984 9 1 ?
## 985 1 1 YES
## 986 22 1 ?
## 987 11 1 YES
## 988 10 1 ?
## 989 5 1 YES
## 990 3 3 YES
## 991 18 1 ?
## 992 21 1 NO
## 993 6 3 YES
## 994 20 3 ?
## 995 6 1 ?
## 996 20 1 YES
## 997 23 1 YES
## 998 4 3 ?
## 999 2 1 ?
## 1000 6 1 ?
## bodily_injuries witnesses police_report_available total_claim_amount
## 1 1 2 YES 71610
## 2 0 0 ? 5070
## 3 2 3 NO 34650
## 4 1 2 NO 63400
## 5 0 1 NO 6500
## 6 0 2 NO 64100
## 7 0 0 ? 78650
## 8 2 2 YES 51590
## 9 1 1 YES 27700
## 10 2 1 ? 42300
## 11 2 2 ? 87010
## 12 1 2 YES 114920
## 13 1 0 NO 56520
## 14 1 1 NO 7280
## 15 0 2 YES 46200
## 16 0 0 NO 63120
## 17 1 2 YES 52110
## 18 0 2 YES 77880
## 19 1 0 NO 72930
## 20 2 0 NO 60400
## 21 1 0 ? 47160
## 22 1 2 ? 37840
## 23 0 0 YES 71520
## 24 2 2 ? 98160
## 25 1 3 NO 77880
## 26 1 3 YES 71500
## 27 1 3 YES 9020
## 28 2 1 ? 5720
## 29 1 0 YES 69840
## 30 2 2 NO 91650
## 31 0 0 ? 75600
## 32 2 2 ? 67140
## 33 2 3 NO 29790
## 34 1 2 ? 77110
## 35 0 1 YES 64800
## 36 2 0 YES 53100
## 37 1 1 YES 60200
## 38 1 1 YES 5330
## 39 2 0 ? 62300
## 40 0 3 NO 60170
## 41 2 2 ? 40000
## 42 1 1 ? 97080
## 43 1 0 NO 51660
## 44 2 2 NO 51120
## 45 0 2 ? 56400
## 46 2 3 ? 55120
## 47 1 1 ? 77110
## 48 2 1 NO 62800
## 49 2 0 YES 7290
## 50 0 0 ? 76600
## 51 0 3 YES 81800
## 52 1 2 ? 7260
## 53 2 1 YES 4300
## 54 1 0 ? 70510
## 55 0 1 YES 2640
## 56 1 2 NO 78900
## 57 2 3 ? 56430
## 58 1 3 NO 2400
## 59 0 1 NO 65790
## 60 1 1 NO 62920
## 61 2 1 ? 69480
## 62 0 3 NO 44280
## 63 0 3 YES 56300
## 64 2 2 YES 68520
## 65 0 0 NO 59130
## 66 2 2 NO 82320
## 67 0 1 NO 89700
## 68 1 1 NO 33930
## 69 0 3 NO 68530
## 70 0 1 ? 4300
## 71 2 3 ? 68310
## 72 0 2 NO 61290
## 73 1 2 NO 30100
## 74 2 0 YES 57120
## 75 1 0 YES 42930
## 76 2 0 NO 51210
## 77 1 1 YES 89400
## 78 0 2 ? 59730
## 79 2 2 YES 8060
## 80 1 2 ? 72200
## 81 1 2 NO 50800
## 82 2 1 ? 6600
## 83 2 2 NO 7500
## 84 0 3 YES 6490
## 85 2 3 NO 60940
## 86 0 1 ? 58300
## 87 2 1 ? 68400
## 88 2 1 YES 64240
## 89 0 1 YES 4700
## 90 2 0 ? 45120
## 91 1 1 ? 66950
## 92 0 3 ? 98340
## 93 0 1 NO 5900
## 94 1 1 ? 70680
## 95 1 0 NO 93720
## 96 2 0 YES 6930
## 97 0 3 NO 72930
## 98 1 2 YES 64890
## 99 1 2 YES 5400
## 100 0 0 ? 5600
## 101 0 0 ? 79300
## 102 1 0 YES 52800
## 103 2 3 NO 28800
## 104 1 1 NO 2970
## 105 0 0 ? 93480
## 106 1 3 YES 4320
## 107 0 2 YES 79800
## 108 1 0 ? 74200
## 109 0 0 ? 70590
## 110 0 3 YES 60940
## 111 1 2 YES 74700
## 112 0 0 ? 70000
## 113 2 0 NO 81070
## 114 1 1 ? 57720
## 115 1 1 YES 7080
## 116 0 1 ? 47700
## 117 0 3 NO 51260
## 118 2 2 NO 70400
## 119 1 3 YES 90000
## 120 0 1 ? 72820
## 121 2 0 ? 69300
## 122 2 1 NO 76560
## 123 0 1 YES 55440
## 124 1 0 NO 77130
## 125 1 1 ? 42000
## 126 2 3 YES 36300
## 127 0 0 ? 40320
## 128 2 0 ? 3960
## 129 0 3 YES 63840
## 130 1 2 ? 44730
## 131 1 2 NO 84720
## 132 2 2 NO 61500
## 133 0 1 ? 51000
## 134 2 1 NO 46800
## 135 1 1 ? 78120
## 136 1 2 ? 69200
## 137 0 0 NO 3690
## 138 0 3 ? 65500
## 139 1 2 YES 76120
## 140 0 2 YES 73560
## 141 0 1 ? 52030
## 142 2 3 ? 5170
## 143 2 1 YES 8190
## 144 2 3 ? 70800
## 145 0 3 YES 45630
## 146 2 1 ? 99320
## 147 2 0 NO 64000
## 148 1 1 ? 47300
## 149 2 3 ? 71680
## 150 1 0 YES 112320
## 151 1 0 ? 82720
## 152 2 1 ? 48060
## 153 1 3 YES 63570
## 154 2 3 ? 63240
## 155 1 3 ? 54240
## 156 2 3 NO 37280
## 157 2 1 YES 72100
## 158 2 3 NO 6500
## 159 2 0 ? 78240
## 160 2 0 ? 6200
## 161 2 3 ? 6160
## 162 2 0 ? 76050
## 163 2 1 YES 86060
## 164 2 0 NO 107900
## 165 2 0 YES 99990
## 166 2 2 YES 61380
## 167 2 1 YES 71280
## 168 0 0 NO 64000
## 169 0 1 NO 5940
## 170 1 0 NO 6700
## 171 0 0 ? 51740
## 172 0 1 YES 53600
## 173 1 0 ? 44910
## 174 0 2 YES 48100
## 175 0 1 NO 6100
## 176 1 0 YES 79600
## 177 1 1 YES 77040
## 178 0 1 ? 62590
## 179 0 1 NO 85150
## 180 2 1 NO 4950
## 181 2 0 YES 51100
## 182 2 3 ? 100800
## 183 2 3 NO 90970
## 184 1 1 YES 81840
## 185 0 2 NO 54900
## 186 2 3 NO 88660
## 187 1 1 NO 18000
## 188 1 3 YES 5500
## 189 1 0 ? 73920
## 190 0 2 YES 101860
## 191 0 1 YES 5390
## 192 1 2 ? 50490
## 193 0 3 NO 55500
## 194 1 1 YES 7040
## 195 0 1 YES 40160
## 196 2 3 NO 55680
## 197 1 1 NO 5300
## 198 0 3 ? 5200
## 199 2 1 NO 59400
## 200 1 1 ? 2520
## 201 2 1 YES 5760
## 202 2 0 YES 76700
## 203 0 2 NO 5920
## 204 0 3 ? 64350
## 205 1 2 ? 19080
## 206 2 1 NO 54400
## 207 1 0 NO 59800
## 208 0 3 YES 72000
## 209 2 3 NO 65070
## 210 0 1 NO 8800
## 211 2 0 NO 6120
## 212 1 2 ? 7080
## 213 2 3 YES 34320
## 214 1 0 ? 53460
## 215 1 3 ? 81360
## 216 1 2 YES 81070
## 217 2 2 NO 63120
## 218 2 1 NO 7200
## 219 2 3 ? 70290
## 220 1 0 NO 60190
## 221 1 2 YES 61380
## 222 2 2 NO 28100
## 223 0 0 NO 49060
## 224 1 1 NO 57060
## 225 0 3 YES 77880
## 226 0 1 YES 73500
## 227 2 0 ? 88920
## 228 0 2 NO 47630
## 229 1 3 NO 59040
## 230 0 3 NO 79530
## 231 0 2 YES 53680
## 232 2 3 YES 33550
## 233 2 1 YES 69100
## 234 2 0 NO 79750
## 235 0 2 YES 53600
## 236 1 0 ? 76560
## 237 1 1 NO 41130
## 238 2 3 NO 78650
## 239 1 1 ? 71060
## 240 1 1 ? 38830
## 241 0 0 YES 53500
## 242 2 0 NO 73700
## 243 2 1 YES 6300
## 244 2 2 NO 65400
## 245 1 2 NO 3200
## 246 0 3 YES 75400
## 247 0 2 YES 58140
## 248 0 1 ? 98670
## 249 0 3 NO 5900
## 250 1 3 YES 64100
## 251 0 1 ? 55440
## 252 1 2 NO 80850
## 253 1 1 NO 7480
## 254 2 3 NO 53640
## 255 2 2 NO 63250
## 256 0 1 ? 59040
## 257 1 0 YES 50500
## 258 0 1 NO 57690
## 259 1 3 YES 5940
## 260 0 2 YES 47790
## 261 0 1 NO 3850
## 262 2 0 ? 59000
## 263 0 1 ? 70600
## 264 1 1 ? 61490
## 265 1 0 YES 57640
## 266 1 0 NO 6890
## 267 2 2 ? 53280
## 268 0 0 ? 78300
## 269 0 3 NO 41490
## 270 2 3 YES 68970
## 271 2 0 YES 85300
## 272 0 0 NO 3080
## 273 2 0 ? 71760
## 274 1 0 NO 59700
## 275 0 2 ? 64920
## 276 0 1 NO 37530
## 277 1 2 NO 64080
## 278 1 0 ? 60390
## 279 0 1 YES 64350
## 280 2 1 YES 70900
## 281 1 3 ? 46560
## 282 2 1 ? 4730
## 283 0 0 YES 6820
## 284 0 2 ? 59900
## 285 1 3 YES 79560
## 286 0 1 ? 70290
## 287 2 0 NO 63910
## 288 1 0 NO 6400
## 289 1 2 YES 66780
## 290 0 2 ? 8760
## 291 0 1 ? 94160
## 292 0 2 NO 51570
## 293 1 0 NO 52700
## 294 2 2 ? 101010
## 295 1 2 YES 53400
## 296 1 2 NO 72120
## 297 2 2 YES 77100
## 298 2 3 ? 3300
## 299 1 0 ? 5940
## 300 1 0 ? 63720
## 301 1 3 NO 7680
## 302 1 2 ? 93730
## 303 0 2 ? 87300
## 304 0 2 NO 5670
## 305 0 0 NO 65800
## 306 2 1 YES 36720
## 307 1 1 YES 52800
## 308 1 2 YES 59100
## 309 1 1 YES 77440
## 310 2 2 NO 45700
## 311 2 2 YES 80740
## 312 0 3 ? 31350
## 313 0 0 ? 35000
## 314 2 2 NO 68000
## 315 2 0 ? 84500
## 316 2 1 ? 75500
## 317 1 3 ? 90600
## 318 1 1 ? 64320
## 319 1 0 ? 31700
## 320 0 1 YES 74280
## 321 0 2 YES 80520
## 322 0 3 YES 63600
## 323 1 2 YES 32800
## 324 0 2 NO 44190
## 325 2 2 NO 50400
## 326 1 1 YES 88400
## 327 0 2 ? 66550
## 328 0 0 ? 65780
## 329 0 2 YES 51810
## 330 1 0 NO 55660
## 331 2 3 ? 44640
## 332 1 3 NO 77660
## 333 2 2 ? 5640
## 334 2 2 ? 3190
## 335 0 0 YES 53440
## 336 0 1 YES 65250
## 337 2 1 NO 44280
## 338 2 0 NO 70290
## 339 1 1 YES 87100
## 340 1 3 ? 50380
## 341 2 2 NO 64800
## 342 1 3 NO 70400
## 343 2 2 YES 57860
## 344 0 3 NO 6240
## 345 2 3 NO 66600
## 346 0 2 YES 70920
## 347 2 0 YES 39480
## 348 2 2 YES 63240
## 349 1 0 ? 67650
## 350 1 3 NO 74200
## 351 1 3 NO 64900
## 352 1 0 NO 35900
## 353 1 3 ? 52200
## 354 0 2 NO 78000
## 355 0 0 ? 67200
## 356 0 0 ? 63250
## 357 1 1 YES 68760
## 358 0 2 ? 65040
## 359 1 3 ? 82800
## 360 1 0 YES 61700
## 361 2 1 YES 78100
## 362 2 1 ? 65520
## 363 0 2 NO 4500
## 364 0 2 ? 42700
## 365 2 2 YES 5580
## 366 0 0 NO 3600
## 367 2 0 ? 2800
## 368 1 0 YES 54000
## 369 0 2 ? 48950
## 370 0 1 ? 77800
## 371 2 1 NO 52560
## 372 1 2 YES 44110
## 373 1 3 ? 74360
## 374 1 2 ? 6120
## 375 0 0 YES 62280
## 376 1 0 YES 26730
## 377 1 0 NO 66200
## 378 1 2 ? 45500
## 379 2 0 ? 53040
## 380 1 2 NO 50800
## 381 1 3 ? 44200
## 382 1 3 ? 62920
## 383 0 2 YES 49950
## 384 0 1 YES 56430
## 385 2 2 YES 100210
## 386 1 0 YES 49140
## 387 0 1 ? 66840
## 388 1 2 ? 62460
## 389 0 1 ? 62810
## 390 1 2 ? 54160
## 391 0 2 NO 48400
## 392 0 0 NO 51480
## 393 2 1 NO 51700
## 394 1 2 ? 65520
## 395 2 2 NO 47700
## 396 2 0 NO 5220
## 397 1 0 ? 73320
## 398 0 2 YES 74900
## 399 0 1 YES 3190
## 400 1 0 YES 76920
## 401 1 3 NO 77990
## 402 1 0 ? 59670
## 403 2 0 NO 44880
## 404 0 2 NO 82830
## 405 1 3 NO 84480
## 406 2 3 NO 79800
## 407 0 3 YES 53020
## 408 0 0 NO 24200
## 409 1 3 YES 43230
## 410 2 3 NO 3190
## 411 0 3 NO 5850
## 412 2 0 YES 6820
## 413 2 2 NO 69480
## 414 1 2 YES 94560
## 415 1 2 ? 7800
## 416 1 3 NO 61270
## 417 0 2 YES 71440
## 418 1 2 YES 55600
## 419 1 3 NO 5000
## 420 2 0 ? 95810
## 421 2 2 ? 69300
## 422 1 0 NO 81120
## 423 0 0 ? 91260
## 424 0 0 ? 60600
## 425 0 3 YES 64800
## 426 0 0 ? 66880
## 427 0 2 ? 58200
## 428 1 0 NO 60570
## 429 0 0 NO 69680
## 430 2 3 NO 55700
## 431 1 2 YES 62370
## 432 0 0 YES 54340
## 433 1 0 NO 55170
## 434 2 3 YES 58500
## 435 0 3 ? 59940
## 436 1 0 NO 73400
## 437 1 2 ? 41850
## 438 2 2 NO 6400
## 439 1 1 NO 3190
## 440 2 1 YES 5900
## 441 0 2 NO 57330
## 442 0 3 NO 81960
## 443 0 0 ? 70400
## 444 2 1 ? 3770
## 445 0 0 ? 7400
## 446 1 1 ? 54810
## 447 2 3 YES 49400
## 448 2 2 NO 68750
## 449 1 0 ? 61500
## 450 0 3 NO 76890
## 451 0 1 ? 56070
## 452 2 0 YES 56000
## 453 1 0 NO 4290
## 454 2 1 NO 60750
## 455 2 3 NO 48730
## 456 2 0 NO 95150
## 457 0 1 YES 7480
## 458 2 1 YES 79800
## 459 0 2 ? 103560
## 460 2 3 ? 79500
## 461 2 2 NO 76230
## 462 0 2 NO 59520
## 463 2 1 ? 47760
## 464 2 0 NO 84590
## 465 2 2 ? 61650
## 466 2 1 ? 81400
## 467 0 1 YES 58410
## 468 0 0 ? 38610
## 469 2 2 NO 57600
## 470 2 2 NO 53190
## 471 2 3 NO 58300
## 472 1 2 NO 64620
## 473 2 0 YES 90480
## 474 1 0 NO 7080
## 475 2 2 ? 6490
## 476 1 1 NO 55900
## 477 0 3 YES 63800
## 478 0 1 YES 58160
## 479 1 2 NO 6300
## 480 2 1 NO 104610
## 481 0 2 NO 69850
## 482 0 0 ? 62900
## 483 0 3 ? 59670
## 484 2 0 ? 81500
## 485 2 0 ? 50000
## 486 2 1 ? 48290
## 487 0 0 ? 59070
## 488 0 0 NO 63300
## 489 0 3 YES 65780
## 490 0 2 ? 75400
## 491 1 1 NO 2250
## 492 1 3 ? 54120
## 493 0 2 NO 69480
## 494 1 2 NO 66950
## 495 2 3 ? 64100
## 496 0 3 ? 80280
## 497 1 2 NO 4680
## 498 1 1 NO 39720
## 499 0 0 ? 63580
## 500 1 1 YES 73370
## 501 2 3 YES 86790
## 502 0 2 ? 49800
## 503 1 1 NO 77440
## 504 1 0 ? 42900
## 505 1 0 NO 53820
## 506 2 1 NO 57330
## 507 2 1 ? 53370
## 508 2 2 YES 62920
## 509 0 1 NO 61600
## 510 2 0 NO 74160
## 511 2 0 ? 80100
## 512 2 3 NO 6560
## 513 2 1 YES 58800
## 514 0 2 NO 53730
## 515 0 0 NO 60600
## 516 0 3 YES 35750
## 517 1 2 YES 42840
## 518 1 0 NO 87960
## 519 1 0 NO 47800
## 520 0 1 ? 3840
## 521 0 2 ? 77000
## 522 1 3 ? 88110
## 523 0 2 YES 47740
## 524 2 1 YES 58960
## 525 1 2 ? 2160
## 526 1 1 ? 6890
## 527 2 0 YES 78870
## 528 1 1 NO 2700
## 529 0 1 NO 75960
## 530 1 3 ? 75570
## 531 2 3 YES 90240
## 532 0 0 NO 80960
## 533 1 3 YES 79080
## 534 0 3 NO 6820
## 535 1 2 YES 62590
## 536 1 0 YES 52400
## 537 1 3 YES 63580
## 538 2 1 NO 61400
## 539 1 3 NO 4700
## 540 0 2 YES 74140
## 541 2 2 NO 83160
## 542 0 2 YES 10790
## 543 1 3 NO 48070
## 544 0 0 YES 51030
## 545 0 1 ? 43280
## 546 1 3 NO 76400
## 547 0 2 ? 75460
## 548 2 1 YES 69000
## 549 1 2 NO 8640
## 550 2 1 ? 67210
## 551 0 3 NO 42500
## 552 0 1 NO 86400
## 553 2 2 ? 4620
## 554 2 2 NO 6930
## 555 1 2 YES 41700
## 556 2 1 NO 77330
## 557 2 3 YES 4950
## 558 0 2 YES 5160
## 559 2 1 ? 24570
## 560 1 0 ? 53680
## 561 1 1 YES 42900
## 562 0 0 NO 84100
## 563 1 3 ? 61560
## 564 0 0 ? 44240
## 565 2 3 YES 57700
## 566 2 2 YES 108030
## 567 2 0 YES 54300
## 568 1 2 ? 32280
## 569 1 1 NO 84600
## 570 1 3 YES 69700
## 571 0 1 NO 36400
## 572 0 2 YES 37520
## 573 2 3 ? 79090
## 574 0 2 YES 67770
## 575 1 0 NO 47400
## 576 0 2 ? 71100
## 577 2 2 NO 69400
## 578 2 1 ? 55000
## 579 1 3 YES 51090
## 580 2 3 NO 64200
## 581 2 0 YES 67320
## 582 2 0 ? 76120
## 583 2 1 NO 85020
## 584 0 1 ? 68090
## 585 1 2 NO 6030
## 586 1 3 NO 5100
## 587 1 3 YES 4590
## 588 1 2 NO 72400
## 589 2 3 NO 70900
## 590 2 3 YES 65100
## 591 2 1 YES 64260
## 592 2 1 ? 79970
## 593 2 0 ? 56610
## 594 1 1 ? 84590
## 595 2 1 YES 66780
## 596 1 2 YES 58500
## 597 2 0 NO 5000
## 598 2 1 ? 5000
## 599 1 3 YES 54450
## 600 1 3 ? 61920
## 601 1 2 NO 43700
## 602 2 0 ? 64080
## 603 2 1 ? 55000
## 604 2 3 ? 4400
## 605 0 1 YES 71640
## 606 1 0 NO 61740
## 607 0 3 ? 57500
## 608 0 0 ? 8700
## 609 2 1 ? 77100
## 610 2 0 YES 59400
## 611 1 0 NO 54890
## 612 2 1 ? 74030
## 613 1 2 YES 61490
## 614 0 2 YES 79560
## 615 0 1 NO 4900
## 616 1 0 NO 77770
## 617 2 2 ? 74700
## 618 2 2 ? 40600
## 619 1 0 ? 45270
## 620 0 3 ? 47080
## 621 0 0 NO 40700
## 622 2 1 YES 34650
## 623 2 0 ? 3200
## 624 1 1 NO 78980
## 625 1 0 NO 6160
## 626 1 1 NO 85250
## 627 1 2 NO 72840
## 628 0 2 NO 6050
## 629 2 3 YES 87890
## 630 0 2 YES 60500
## 631 2 1 NO 88220
## 632 1 2 NO 53680
## 633 0 2 NO 53800
## 634 1 2 NO 54360
## 635 0 3 YES 54340
## 636 0 2 YES 2860
## 637 2 1 YES 5490
## 638 0 2 ? 7370
## 639 1 0 NO 50800
## 640 1 1 ? 41520
## 641 2 3 YES 89650
## 642 0 3 NO 39690
## 643 2 0 NO 62260
## 644 0 1 YES 51920
## 645 2 2 ? 53460
## 646 2 1 YES 57100
## 647 2 3 ? 77440
## 648 1 2 NO 68300
## 649 0 2 ? 5060
## 650 0 2 ? 59400
## 651 0 2 ? 69930
## 652 1 1 NO 77700
## 653 2 1 ? 68750
## 654 1 3 YES 91080
## 655 1 3 YES 48360
## 656 1 1 YES 95000
## 657 1 3 NO 3900
## 658 0 3 NO 59400
## 659 2 0 ? 60210
## 660 0 3 YES 43600
## 661 0 0 NO 62800
## 662 1 3 ? 59500
## 663 1 0 ? 53460
## 664 0 1 NO 41690
## 665 0 1 YES 63100
## 666 2 1 ? 62880
## 667 2 2 YES 75400
## 668 1 1 NO 46200
## 669 2 2 YES 58500
## 670 1 3 NO 66240
## 671 1 3 ? 65440
## 672 0 3 ? 64200
## 673 1 0 NO 32320
## 674 0 1 ? 33480
## 675 1 1 YES 4320
## 676 0 2 ? 4200
## 677 2 0 NO 57970
## 678 1 2 ? 4320
## 679 0 2 ? 69300
## 680 2 2 NO 32480
## 681 2 2 YES 60480
## 682 0 0 NO 2640
## 683 0 3 NO 6050
## 684 0 0 NO 42700
## 685 1 3 NO 40260
## 686 2 0 ? 50000
## 687 0 1 NO 3840
## 688 0 2 ? 95900
## 689 1 3 YES 56160
## 690 2 3 ? 63030
## 691 2 2 NO 63470
## 692 0 2 NO 44440
## 693 1 1 NO 6600
## 694 2 0 YES 77200
## 695 0 0 YES 57000
## 696 2 1 NO 2700
## 697 2 1 YES 47300
## 698 2 3 ? 55110
## 699 1 0 NO 4320
## 700 2 2 ? 68760
## 701 2 1 ? 74400
## 702 2 1 NO 35300
## 703 1 3 ? 2640
## 704 1 1 NO 60190
## 705 1 3 NO 41580
## 706 0 0 NO 58500
## 707 2 3 YES 79320
## 708 0 2 ? 82610
## 709 1 3 ? 78600
## 710 1 2 ? 51390
## 711 1 0 NO 70200
## 712 2 1 NO 4900
## 713 1 3 NO 66480
## 714 2 1 NO 50380
## 715 0 2 ? 64350
## 716 2 3 YES 55400
## 717 2 2 YES 49900
## 718 0 3 YES 74880
## 719 0 0 YES 105820
## 720 1 0 YES 7150
## 721 0 0 NO 55800
## 722 1 3 YES 5830
## 723 2 1 NO 85900
## 724 2 3 NO 7110
## 725 0 0 YES 36960
## 726 2 0 YES 64400
## 727 1 3 YES 1920
## 728 0 1 YES 86130
## 729 2 3 ? 82170
## 730 1 0 YES 50300
## 731 0 0 NO 44200
## 732 2 2 YES 66660
## 733 2 3 NO 78320
## 734 0 1 NO 105040
## 735 2 1 ? 50700
## 736 1 1 NO 51210
## 737 2 1 ? 51840
## 738 2 3 YES 52800
## 739 0 0 YES 55200
## 740 0 3 NO 9100
## 741 1 3 ? 67600
## 742 1 2 NO 40800
## 743 2 1 YES 84500
## 744 2 2 YES 71610
## 745 0 2 NO 60600
## 746 2 3 NO 81240
## 747 0 3 NO 29300
## 748 1 1 YES 76450
## 749 0 3 ? 49400
## 750 0 1 ? 90530
## 751 2 1 NO 8030
## 752 0 0 YES 63900
## 753 0 2 NO 38640
## 754 1 1 YES 41490
## 755 2 0 ? 79090
## 756 0 0 ? 87900
## 757 2 3 YES 53400
## 758 2 3 NO 52030
## 759 0 2 NO 82060
## 760 0 3 ? 48360
## 761 2 1 ? 52290
## 762 0 3 ? 68200
## 763 2 1 NO 60750
## 764 0 2 YES 65560
## 765 1 3 NO 70290
## 766 2 0 YES 45000
## 767 2 1 ? 61800
## 768 0 0 NO 64570
## 769 2 0 YES 70500
## 770 2 1 YES 57900
## 771 1 0 NO 57860
## 772 1 1 NO 37800
## 773 1 3 YES 63300
## 774 0 1 YES 44200
## 775 1 0 YES 31680
## 776 1 2 ? 100
## 777 2 1 NO 56340
## 778 0 0 YES 69740
## 779 2 2 YES 60480
## 780 2 1 NO 80880
## 781 1 2 YES 49390
## 782 0 3 ? 69360
## 783 0 1 ? 3740
## 784 0 1 NO 5060
## 785 0 0 YES 35860
## 786 2 1 ? 50050
## 787 0 0 NO 59070
## 788 1 2 ? 28440
## 789 2 1 YES 45540
## 790 0 0 YES 38700
## 791 2 1 YES 5830
## 792 0 2 ? 57240
## 793 0 3 NO 46200
## 794 0 1 NO 57700
## 795 2 0 NO 56160
## 796 0 3 YES 44400
## 797 2 3 ? 92730
## 798 0 1 YES 30700
## 799 2 0 ? 56600
## 800 0 1 ? 3960
## 801 0 3 ? 34800
## 802 2 0 YES 79500
## 803 2 2 NO 56000
## 804 1 0 YES 73260
## 805 2 3 YES 4950
## 806 0 1 NO 48000
## 807 0 1 YES 52200
## 808 2 3 ? 73800
## 809 1 2 NO 78200
## 810 2 3 NO 55200
## 811 0 2 YES 57060
## 812 0 3 YES 4680
## 813 2 0 YES 53100
## 814 1 2 NO 3520
## 815 0 1 YES 72900
## 816 0 1 NO 70700
## 817 0 0 ? 60170
## 818 1 1 ? 74800
## 819 2 0 ? 4100
## 820 0 3 ? 61490
## 821 0 0 ? 7200
## 822 0 0 ? 45100
## 823 2 3 YES 66660
## 824 2 2 YES 76400
## 825 1 1 NO 58300
## 826 1 1 ? 57310
## 827 0 1 ? 53100
## 828 1 1 ? 74700
## 829 2 2 NO 60500
## 830 2 3 NO 84920
## 831 2 0 NO 61050
## 832 2 0 ? 69080
## 833 1 3 YES 4560
## 834 0 1 ? 67800
## 835 1 1 YES 5600
## 836 0 2 ? 9000
## 837 0 1 YES 85320
## 838 0 3 YES 5940
## 839 1 1 ? 51370
## 840 2 2 YES 51600
## 841 0 3 ? 5400
## 842 1 0 YES 48870
## 843 2 1 ? 5590
## 844 0 2 ? 54960
## 845 1 3 ? 39800
## 846 0 1 ? 56160
## 847 0 3 ? 52700
## 848 1 3 ? 68580
## 849 1 2 YES 90860
## 850 0 3 NO 5700
## 851 2 0 YES 94930
## 852 1 2 NO 46800
## 853 2 3 NO 56320
## 854 1 3 YES 83490
## 855 2 2 ? 57900
## 856 1 3 YES 49410
## 857 0 1 NO 66200
## 858 0 1 NO 64080
## 859 2 3 YES 42500
## 860 2 3 ? 48950
## 861 1 2 YES 58850
## 862 0 2 YES 82400
## 863 2 0 ? 54240
## 864 0 2 ? 74200
## 865 2 1 ? 47430
## 866 1 3 YES 68200
## 867 1 0 NO 63900
## 868 0 3 YES 59300
## 869 1 2 YES 66900
## 870 1 3 ? 40810
## 871 1 0 ? 75400
## 872 2 0 YES 4200
## 873 0 0 YES 52650
## 874 0 2 YES 42240
## 875 1 0 YES 59490
## 876 0 2 NO 44200
## 877 1 2 ? 7700
## 878 2 3 ? 61440
## 879 1 3 NO 54400
## 880 2 1 ? 58560
## 881 1 1 NO 67300
## 882 1 3 YES 36740
## 883 0 0 NO 85690
## 884 0 3 NO 34160
## 885 2 2 ? 61320
## 886 0 0 ? 79680
## 887 1 0 YES 61740
## 888 0 0 NO 6400
## 889 1 2 YES 60600
## 890 2 0 NO 56320
## 891 2 0 NO 52250
## 892 0 3 ? 53900
## 893 2 1 NO 2640
## 894 0 0 ? 8970
## 895 1 3 ? 6000
## 896 0 3 ? 55200
## 897 0 3 YES 7260
## 898 2 0 ? 64680
## 899 0 1 NO 59200
## 900 1 3 ? 4700
## 901 0 3 NO 69400
## 902 0 0 NO 40500
## 903 2 1 NO 60000
## 904 2 3 NO 67320
## 905 2 1 YES 75690
## 906 1 2 ? 64300
## 907 2 0 YES 64400
## 908 1 2 ? 97080
## 909 1 0 ? 5500
## 910 0 2 NO 30700
## 911 1 2 YES 33480
## 912 0 1 YES 65430
## 913 2 1 YES 42680
## 914 1 1 NO 87780
## 915 0 2 YES 72800
## 916 2 0 ? 71190
## 917 1 1 YES 3600
## 918 1 3 NO 62640
## 919 2 1 ? 69630
## 920 1 0 ? 76010
## 921 1 0 YES 44220
## 922 0 3 NO 57200
## 923 0 3 NO 3080
## 924 1 0 YES 75790
## 925 0 1 NO 32670
## 926 2 3 NO 3870
## 927 0 3 YES 91520
## 928 1 3 YES 74690
## 929 1 3 ? 4620
## 930 0 0 NO 55000
## 931 0 2 NO 59400
## 932 1 1 ? 55260
## 933 2 1 YES 51400
## 934 1 3 ? 48780
## 935 1 3 ? 52380
## 936 2 3 YES 74360
## 937 1 0 ? 53400
## 938 2 2 YES 71800
## 939 0 0 ? 68240
## 940 1 0 ? 61050
## 941 1 1 YES 5590
## 942 0 0 ? 46860
## 943 1 3 NO 4290
## 944 0 0 NO 78500
## 945 1 3 YES 70830
## 946 0 2 NO 68040
## 947 2 0 ? 63600
## 948 0 3 ? 43560
## 949 1 1 NO 60840
## 950 2 3 YES 68160
## 951 1 0 NO 5170
## 952 0 3 ? 57700
## 953 0 2 NO 89520
## 954 1 0 YES 4620
## 955 0 3 ? 45180
## 956 0 0 NO 45100
## 957 0 3 YES 83160
## 958 2 1 NO 86130
## 959 1 3 NO 48000
## 960 0 0 YES 3300
## 961 1 2 YES 57200
## 962 2 0 ? 7590
## 963 0 2 ? 80080
## 964 0 0 YES 4800
## 965 2 1 YES 3900
## 966 0 0 NO 90400
## 967 0 0 ? 62900
## 968 1 2 NO 54200
## 969 2 2 YES 51800
## 970 2 3 ? 6600
## 971 2 0 YES 74140
## 972 2 1 ? 67800
## 973 1 1 YES 55400
## 974 2 3 ? 49100
## 975 2 1 ? 98280
## 976 0 2 ? 66550
## 977 2 2 ? 70400
## 978 1 1 NO 53280
## 979 2 3 YES 84590
## 980 0 2 YES 54560
## 981 0 2 YES 82170
## 982 1 2 NO 61100
## 983 0 3 YES 51900
## 984 2 1 ? 3440
## 985 2 1 NO 51390
## 986 0 2 ? 76900
## 987 2 3 ? 77000
## 988 0 3 ? 60320
## 989 1 1 NO 60700
## 990 1 2 ? 53280
## 991 2 2 YES 34290
## 992 0 1 NO 46980
## 993 1 2 YES 36700
## 994 0 1 ? 60200
## 995 1 2 YES 6480
## 996 0 1 ? 87200
## 997 2 3 ? 108480
## 998 2 3 YES 67500
## 999 0 1 YES 46980
## 1000 0 3 ? 5060
## injury_claim property_claim vehicle_claim auto_make auto_model
## 1 6510 13020 52080 Saab 92x
## 2 780 780 3510 Mercedes E400
## 3 7700 3850 23100 Dodge RAM
## 4 6340 6340 50720 Chevrolet Tahoe
## 5 1300 650 4550 Accura RSX
## 6 6410 6410 51280 Saab 95
## 7 21450 7150 50050 Nissan Pathfinder
## 8 9380 9380 32830 Audi A5
## 9 2770 2770 22160 Toyota Camry
## 10 4700 4700 32900 Saab 92x
## 11 7910 15820 63280 Ford F150
## 12 17680 17680 79560 Audi A3
## 13 4710 9420 42390 Saab 95
## 14 1120 1120 5040 Toyota Highlander
## 15 4200 8400 33600 Dodge Neon
## 16 10520 10520 42080 Accura MDX
## 17 5790 5790 40530 Nissan Maxima
## 18 14160 7080 56640 Suburu Legacy
## 19 6630 13260 53040 Accura TL
## 20 6040 6040 48320 Nissan Pathfinder
## 21 0 5240 41920 Suburu Impreza
## 22 0 4730 33110 Accura RSX
## 23 17880 5960 47680 Suburu Forrestor
## 24 8180 16360 73620 Dodge RAM
## 25 7080 14160 56640 Ford Escape
## 26 16500 11000 44000 Ford Escape
## 27 1640 820 6560 Toyota Camry
## 28 1040 520 4160 Suburu Forrestor
## 29 7760 15520 46560 Dodge Neon
## 30 14100 14100 63450 Accura TL
## 31 12600 12600 50400 Toyota Corolla
## 32 7460 7460 52220 Ford F150
## 33 3310 3310 23170 BMW 3 Series
## 34 14020 14020 49070 Suburu Impreza
## 35 10800 5400 48600 Audi A3
## 36 10620 5310 37170 Mercedes C300
## 37 6020 6020 48160 Suburu Forrestor
## 38 1230 820 3280 Suburu Legacy
## 39 12460 6230 43610 Jeep Wrangler
## 40 10940 10940 38290 Nissan Pathfinder
## 41 8000 4000 28000 BMW M5
## 42 16180 16180 64720 BMW X5
## 43 5740 5740 40180 Dodge RAM
## 44 5680 5680 39760 Mercedes E400
## 45 11280 11280 33840 Toyota Highlander
## 46 6890 0 48230 Accura MDX
## 47 0 14020 63090 Honda Civic
## 48 6280 6280 50240 Audi A3
## 49 810 810 5670 Volkswagen Passat
## 50 15320 7660 53620 Mercedes C300
## 51 16360 8180 57260 Nissan Pathfinder
## 52 1320 660 5280 BMW M5
## 53 430 430 3440 Toyota Corolla
## 54 12820 12820 44870 Suburu Forrestor
## 55 480 480 1680 Ford F150
## 56 15780 7890 55230 Chevrolet Silverado
## 57 0 6270 50160 Honda CRV
## 58 300 300 1800 Chevrolet Silverado
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## 683 550 1100 4400 Volkswagen Passat
## 684 4270 4270 34160 Saab 92x
## 685 3660 7320 29280 Accura MDX
## 686 5000 10000 35000 Chevrolet Silverado
## 687 640 320 2880 Saab 92x
## 688 13700 20550 61650 Saab 95
## 689 6240 12480 37440 Audi A5
## 690 5730 11460 45840 Saab 92x
## 691 5770 11540 46160 BMW X6
## 692 8080 4040 32320 BMW X6
## 693 1200 1200 4200 Jeep Grand Cherokee
## 694 9650 9650 57900 Ford Escape
## 695 0 11400 45600 Audi A3
## 696 300 300 2100 Honda Accord
## 697 4300 8600 34400 Volkswagen Jetta
## 698 5010 10020 40080 Nissan Maxima
## 699 480 480 3360 Mercedes E400
## 700 11460 5730 51570 Saab 95
## 701 14880 7440 52080 BMW M5
## 702 3530 3530 28240 Mercedes E400
## 703 480 480 1680 Volkswagen Passat
## 704 4630 9260 46300 Mercedes ML350
## 705 3780 7560 30240 Chevrolet Malibu
## 706 11700 0 46800 Accura MDX
## 707 13220 6610 59490 Nissan Ultima
## 708 7510 7510 67590 Ford Escape
## 709 13100 19650 45850 Dodge RAM
## 710 5710 5710 39970 Volkswagen Jetta
## 711 7020 7020 56160 Ford F150
## 712 490 1470 2940 Jeep Wrangler
## 713 5540 11080 49860 Saab 92x
## 714 4580 9160 36640 Chevrolet Tahoe
## 715 9900 9900 44550 Mercedes E400
## 716 5540 11080 38780 Chevrolet Silverado
## 717 4990 9980 34930 Dodge Neon
## 718 12480 12480 49920 Accura TL
## 719 16280 16280 73260 Audi A3
## 720 1300 650 5200 Volkswagen Jetta
## 721 11160 11160 33480 Dodge RAM
## 722 1060 1060 3710 Dodge RAM
## 723 17180 17180 51540 Suburu Forrestor
## 724 790 1580 4740 Accura MDX
## 725 6720 3360 26880 Chevrolet Tahoe
## 726 6440 6440 51520 Dodge Neon
## 727 480 0 1440 Chevrolet Tahoe
## 728 15660 7830 62640 Suburu Legacy
## 729 14940 7470 59760 Chevrolet Silverado
## 730 10060 5030 35210 Suburu Legacy
## 731 4420 8840 30940 Suburu Forrestor
## 732 6060 6060 54540 Toyota Highlander
## 733 7120 14240 56960 Saab 92x
## 734 16160 16160 72720 Dodge RAM
## 735 5070 5070 40560 Accura RSX
## 736 11380 5690 34140 Jeep Wrangler
## 737 8640 8640 34560 Volkswagen Jetta
## 738 5280 5280 42240 Nissan Pathfinder
## 739 9200 13800 32200 Honda Civic
## 740 1400 1400 6300 Chevrolet Malibu
## 741 13520 6760 47320 Suburu Legacy
## 742 6800 6800 27200 BMW X5
## 743 13000 13000 58500 Volkswagen Passat
## 744 13020 6510 52080 Toyota Highlander
## 745 6060 12120 42420 Dodge RAM
## 746 6770 20310 54160 Mercedes C300
## 747 2930 5860 20510 Audi A3
## 748 6950 13900 55600 Dodge RAM
## 749 9880 4940 34580 Jeep Wrangler
## 750 16460 16460 57610 Ford F150
## 751 1460 730 5840 Mercedes E400
## 752 6390 6390 51120 Accura TL
## 753 4830 4830 28980 Chevrolet Tahoe
## 754 9220 4610 27660 Mercedes E400
## 755 14380 7190 57520 Nissan Maxima
## 756 17580 8790 61530 Dodge Neon
## 757 5340 5340 42720 Jeep Wrangler
## 758 9460 9460 33110 Accura MDX
## 759 14920 7460 59680 Saab 93
## 760 8060 8060 32240 Nissan Maxima
## 761 5810 11620 34860 Nissan Maxima
## 762 12400 12400 43400 Jeep Wrangler
## 763 13500 6750 40500 Jeep Wrangler
## 764 11920 11920 41720 Volkswagen Passat
## 765 12780 12780 44730 Mercedes C300
## 766 5000 5000 35000 Suburu Forrestor
## 767 12360 6180 43260 Suburu Impreza
## 768 5870 11740 46960 Jeep Wrangler
## 769 7050 14100 49350 Suburu Forrestor
## 770 17370 5790 34740 Audi A5
## 771 5260 10520 42080 Volkswagen Jetta
## 772 8400 4200 25200 Chevrolet Silverado
## 773 6330 6330 50640 Toyota Highlander
## 774 8840 4420 30940 Saab 93
## 775 3520 3520 24640 Toyota Camry
## 776 10 20 70 Audi A3
## 777 6260 6260 43820 Volkswagen Passat
## 778 6340 6340 57060 Dodge Neon
## 779 5040 15120 40320 Volkswagen Jetta
## 780 6740 13480 60660 Nissan Ultima
## 781 8980 4490 35920 Suburu Legacy
## 782 11560 11560 46240 Dodge RAM
## 783 680 680 2380 Chevrolet Malibu
## 784 460 920 3680 Nissan Ultima
## 785 3260 6520 26080 BMW X5
## 786 7700 3850 38500 Ford Fusion
## 787 10740 5370 42960 Toyota Camry
## 788 3160 3160 22120 Dodge Neon
## 789 8280 8280 28980 Honda Civic
## 790 7740 3870 27090 Volkswagen Passat
## 791 1060 530 4240 Nissan Pathfinder
## 792 4770 9540 42930 BMW X6
## 793 4200 8400 33600 Suburu Legacy
## 794 5770 5770 46160 Toyota Camry
## 795 4680 9360 42120 Saab 95
## 796 5550 5550 33300 Jeep Grand Cherokee
## 797 16860 8430 67440 Mercedes E400
## 798 3070 6140 21490 Toyota Corolla
## 799 11320 5660 39620 Toyota Camry
## 800 660 660 2640 Audi A3
## 801 3480 6960 24360 Accura MDX
## 802 7950 7950 63600 Chevrolet Tahoe
## 803 5600 5600 44800 Chevrolet Malibu
## 804 16280 0 56980 Volkswagen Jetta
## 805 900 450 3600 Toyota Camry
## 806 4800 9600 33600 Saab 95
## 807 10440 5220 36540 Nissan Pathfinder
## 808 12300 12300 49200 Audi A3
## 809 15640 7820 54740 Audi A3
## 810 13800 9200 32200 Saab 93
## 811 6340 6340 44380 Ford Fusion
## 812 520 520 3640 Chevrolet Malibu
## 813 5310 5310 42480 Accura MDX
## 814 640 320 2560 Accura MDX
## 815 14580 14580 43740 Nissan Maxima
## 816 7070 14140 49490 Volkswagen Passat
## 817 5470 10940 43760 Nissan Pathfinder
## 818 13600 6800 54400 Ford Fusion
## 819 820 410 2870 Chevrolet Malibu
## 820 5590 11180 44720 Dodge RAM
## 821 720 1440 5040 Audi A5
## 822 8200 4100 32800 Nissan Pathfinder
## 823 12120 6060 48480 Honda Civic
## 824 15280 7640 53480 Volkswagen Jetta
## 825 10600 10600 37100 Ford F150
## 826 5210 10420 41680 Volkswagen Passat
## 827 5900 5900 41300 Suburu Impreza
## 828 14940 7470 52290 Suburu Forrestor
## 829 12100 6050 42350 Audi A5
## 830 7720 15440 61760 Jeep Wrangler
## 831 5550 11100 44400 Dodge Neon
## 832 12560 6280 50240 Audi A5
## 833 760 380 3420 Nissan Pathfinder
## 834 11300 11300 45200 Ford F150
## 835 1120 560 3920 Suburu Legacy
## 836 900 1800 6300 Mercedes ML350
## 837 21330 7110 56880 Nissan Pathfinder
## 838 540 1080 4320 Audi A5
## 839 9340 4670 37360 BMW M5
## 840 10320 5160 36120 Dodge Neon
## 841 600 600 4200 Chevrolet Malibu
## 842 5430 5430 38010 Saab 95
## 843 860 860 3870 BMW X5
## 844 6870 0 48090 Toyota Corolla
## 845 7960 3980 27860 Jeep Wrangler
## 846 6240 6240 43680 Ford Fusion
## 847 5270 10540 36890 Toyota Corolla
## 848 7620 7620 53340 Jeep Wrangler
## 849 12980 19470 58410 Volkswagen Jetta
## 850 570 570 4560 Saab 93
## 851 8630 8630 77670 Accura RSX
## 852 4680 9360 32760 Audi A5
## 853 7040 7040 42240 Mercedes ML350
## 854 7590 15180 60720 Nissan Pathfinder
## 855 5790 5790 46320 Saab 95
## 856 5490 5490 38430 Audi A3
## 857 6620 6620 52960 Suburu Impreza
## 858 10680 10680 42720 Toyota Camry
## 859 8500 4250 29750 Nissan Pathfinder
## 860 8900 4450 35600 Suburu Legacy
## 861 10700 10700 37450 Accura MDX
## 862 8240 8240 65920 Nissan Pathfinder
## 863 6780 6780 40680 Suburu Impreza
## 864 7420 7420 59360 Jeep Wrangler
## 865 5270 5270 36890 Toyota Camry
## 866 13640 6820 47740 Dodge RAM
## 867 7100 7100 49700 Accura TL
## 868 11860 5930 41510 Dodge RAM
## 869 6690 13380 46830 Suburu Forrestor
## 870 3710 7420 29680 Ford F150
## 871 15080 7540 52780 Honda CRV
## 872 420 840 2940 Jeep Wrangler
## 873 5850 5850 40950 Ford F150
## 874 7680 7680 26880 Chevrolet Malibu
## 875 6610 6610 46270 Mercedes ML350
## 876 4420 4420 35360 Jeep Wrangler
## 877 770 1540 5390 Saab 93
## 878 10240 10240 40960 Saab 93
## 879 5440 10880 38080 Chevrolet Silverado
## 880 9760 9760 39040 Mercedes E400
## 881 6730 6730 53840 Dodge RAM
## 882 3340 6680 26720 BMW X5
## 883 15580 15580 54530 BMW 3 Series
## 884 0 4270 29890 Audi A5
## 885 10220 10220 40880 BMW M5
## 886 13280 13280 53120 BMW 3 Series
## 887 6860 6860 48020 Audi A3
## 888 640 640 5120 Honda Civic
## 889 12120 6060 42420 Accura TL
## 890 10240 5120 40960 Volkswagen Jetta
## 891 9500 4750 38000 Suburu Legacy
## 892 5390 10780 37730 Saab 95
## 893 220 440 1980 Jeep Wrangler
## 894 1380 1380 6210 Dodge Neon
## 895 1000 1000 4000 Suburu Impreza
## 896 11040 5520 38640 Saab 92x
## 897 660 1320 5280 Saab 92x
## 898 11760 11760 41160 Jeep Wrangler
## 899 0 11840 47360 Chevrolet Malibu
## 900 470 940 3290 Saab 92x
## 901 6940 6940 55520 Mercedes C300
## 902 4050 4050 32400 Nissan Pathfinder
## 903 5000 10000 45000 Honda Accord
## 904 11220 11220 44880 Volkswagen Jetta
## 905 8410 8410 58870 Saab 95
## 906 6430 6430 51440 Chevrolet Silverado
## 907 6440 6440 51520 BMW X5
## 908 16180 16180 64720 Saab 92x
## 909 500 500 4500 Honda Civic
## 910 3070 6140 21490 Jeep Wrangler
## 911 3720 3720 26040 Nissan Pathfinder
## 912 14540 7270 43620 Jeep Wrangler
## 913 3880 7760 31040 Accura RSX
## 914 7980 7980 71820 Honda CRV
## 915 14560 14560 43680 Honda Accord
## 916 0 7910 63280 Mercedes C300
## 917 400 400 2800 Honda Civic
## 918 10440 10440 41760 Mercedes C300
## 919 12660 6330 50640 Toyota Corolla
## 920 13820 6910 55280 Dodge RAM
## 921 8040 4020 32160 Accura MDX
## 922 5200 10400 41600 BMW M5
## 923 560 560 1960 Nissan Ultima
## 924 13780 6890 55120 Accura RSX
## 925 5940 2970 23760 Honda Accord
## 926 430 860 2580 Jeep Wrangler
## 927 8320 16640 66560 BMW X6
## 928 6790 13580 54320 Dodge RAM
## 929 420 840 3360 Audi A5
## 930 10000 10000 35000 Toyota Camry
## 931 13200 6600 39600 Saab 93
## 932 6140 0 49120 Nissan Ultima
## 933 5140 10280 35980 Honda Civic
## 934 5420 10840 32520 Dodge Neon
## 935 5820 5820 40740 Toyota Highlander
## 936 13520 13520 47320 Toyota Highlander
## 937 5340 5340 42720 Honda CRV
## 938 14360 14360 43080 Jeep Grand Cherokee
## 939 8530 0 59710 Toyota Corolla
## 940 11100 11100 38850 Dodge RAM
## 941 860 860 3870 Suburu Impreza
## 942 8520 8520 29820 Volkswagen Passat
## 943 780 780 2730 Volkswagen Passat
## 944 15700 7850 54950 Audi A3
## 945 7870 7870 55090 Jeep Grand Cherokee
## 946 15120 7560 45360 Suburu Forrestor
## 947 5300 10600 47700 Audi A5
## 948 4840 4840 33880 Suburu Legacy
## 949 13520 6760 40560 Suburu Forrestor
## 950 11360 11360 45440 Ford Escape
## 951 940 470 3760 Toyota Camry
## 952 5770 5770 46160 Nissan Maxima
## 953 14920 14920 59680 Audi A3
## 954 420 840 3360 Volkswagen Jetta
## 955 5020 5020 35140 Honda CRV
## 956 9020 4510 31570 Nissan Maxima
## 957 15120 15120 52920 Toyota Camry
## 958 15660 15660 54810 Chevrolet Silverado
## 959 9600 4800 33600 Dodge RAM
## 960 600 600 2100 Saab 92x
## 961 11440 5720 40040 Toyota Camry
## 962 1380 690 5520 Accura MDX
## 963 12320 12320 55440 Chevrolet Silverado
## 964 960 480 3360 BMW 3 Series
## 965 390 780 2730 Volkswagen Jetta
## 966 9040 9040 72320 BMW 3 Series
## 967 6290 12580 44030 Jeep Grand Cherokee
## 968 5420 10840 37940 Nissan Ultima
## 969 5180 10360 36260 Accura MDX
## 970 600 1200 4800 Accura MDX
## 971 13480 6740 53920 Ford Escape
## 972 13560 6780 47460 Mercedes C300
## 973 5540 11080 38780 Jeep Grand Cherokee
## 974 9820 4910 34370 Suburu Impreza
## 975 15120 7560 75600 Chevrolet Tahoe
## 976 6050 12100 48400 Ford Escape
## 977 14080 7040 49280 Accura RSX
## 978 4440 8880 39960 Suburu Impreza
## 979 15380 15380 53830 Dodge Neon
## 980 9920 9920 34720 Saab 95
## 981 7470 7470 67230 Suburu Forrestor
## 982 6110 12220 42770 Dodge Neon
## 983 5190 10380 36330 BMW M5
## 984 430 430 2580 Suburu Legacy
## 985 5710 11420 34260 Toyota Corolla
## 986 7690 7690 61520 Jeep Wrangler
## 987 15400 7700 53900 Toyota Highlander
## 988 9280 9280 41760 Chevrolet Tahoe
## 989 12140 6070 42490 Honda Civic
## 990 5920 0 47360 Chevrolet Malibu
## 991 3810 3810 26670 Jeep Grand Cherokee
## 992 0 5220 41760 Accura TL
## 993 3670 7340 25690 Nissan Pathfinder
## 994 6020 6020 48160 Volkswagen Passat
## 995 540 1080 4860 Honda Civic
## 996 17440 8720 61040 Honda Accord
## 997 18080 18080 72320 Volkswagen Passat
## 998 7500 7500 52500 Suburu Impreza
## 999 5220 5220 36540 Audi A5
## 1000 460 920 3680 Mercedes E400
## auto_year fraud_reported X_c39
## 1 2004 Y NA
## 2 2007 Y NA
## 3 2007 N NA
## 4 2014 Y NA
## 5 2009 N NA
## 6 2003 Y NA
## 7 2012 N NA
## 8 2015 N NA
## 9 2012 N NA
## 10 1996 N NA
## 11 2002 N NA
## 12 2006 N NA
## 13 2000 N NA
## 14 2010 N NA
## 15 2003 Y NA
## 16 1999 Y NA
## 17 2012 N NA
## 18 2015 N NA
## 19 2015 N NA
## 20 2014 N NA
## 21 2011 N NA
## 22 1996 N NA
## 23 2000 Y NA
## 24 2011 Y NA
## 25 2005 N NA
## 26 2006 Y NA
## 27 2005 N NA
## 28 2003 Y NA
## 29 2009 N NA
## 30 2011 N NA
## 31 2005 N NA
## 32 2006 Y NA
## 33 2008 N NA
## 34 2015 N NA
## 35 1999 N NA
## 36 1995 Y NA
## 37 2004 Y NA
## 38 2001 N NA
## 39 2007 N NA
## 40 2011 Y NA
## 41 2010 N NA
## 42 2001 Y NA
## 43 2010 N NA
## 44 2005 N NA
## 45 2014 N NA
## 46 2002 N NA
## 47 2014 N NA
## 48 2003 Y NA
## 49 1995 N NA
## 50 2000 N NA
## 51 1998 N NA
## 52 2008 N NA
## 53 2000 N NA
## 54 1999 N NA
## 55 2009 N NA
## 56 1995 N NA
## 57 2014 N NA
## 58 2014 N NA
## 59 2007 N NA
## 60 2000 N NA
## 61 2014 Y NA
## 62 1997 N NA
## 63 2011 N NA
## 64 2003 Y NA
## 65 2006 Y NA
## 66 1995 Y NA
## 67 2009 Y NA
## 68 1998 N NA
## 69 1997 N NA
## 70 2004 N NA
## 71 2003 Y NA
## 72 1995 Y NA
## 73 1999 N NA
## 74 2002 N NA
## 75 2005 N NA
## 76 2010 N NA
## 77 1998 N NA
## 78 2005 N NA
## 79 2004 N NA
## 80 2013 Y NA
## 81 2005 N NA
## 82 2005 N NA
## 83 2002 N NA
## 84 2002 N NA
## 85 2013 Y NA
## 86 2007 N NA
## 87 2007 N NA
## 88 2015 N NA
## 89 2002 N NA
## 90 2011 Y NA
## 91 1995 N NA
## 92 2004 Y NA
## 93 2007 N NA
## 94 2009 N NA
## 95 2005 N NA
## 96 2001 N NA
## 97 2006 Y NA
## 98 2013 Y NA
## 99 1999 N NA
## 100 2007 N NA
## 101 2007 N NA
## 102 2004 N NA
## 103 2013 N NA
## 104 2008 N NA
## 105 2014 N NA
## 106 2002 N NA
## 107 2000 Y NA
## 108 1997 N NA
## 109 2000 Y NA
## 110 2006 Y NA
## 111 2010 N NA
## 112 2015 Y NA
## 113 2001 N NA
## 114 2007 N NA
## 115 2001 N NA
## 116 1997 Y NA
## 117 2002 N NA
## 118 2011 Y NA
## 119 2015 N NA
## 120 2010 N NA
## 121 2000 N NA
## 122 2008 Y NA
## 123 1999 Y NA
## 124 1995 N NA
## 125 2011 N NA
## 126 2013 N NA
## 127 2001 N NA
## 128 1998 N NA
## 129 2006 Y NA
## 130 2000 Y NA
## 131 1999 N NA
## 132 2012 N NA
## 133 2013 N NA
## 134 2002 N NA
## 135 2006 N NA
## 136 1996 Y NA
## 137 2015 N NA
## 138 2010 N NA
## 139 1999 N NA
## 140 1995 N NA
## 141 2008 N NA
## 142 2001 N NA
## 143 2013 N NA
## 144 2012 Y NA
## 145 2011 N NA
## 146 2002 Y NA
## 147 1996 Y NA
## 148 2014 N NA
## 149 2007 Y NA
## 150 2011 Y NA
## 151 2014 N NA
## 152 2009 N NA
## 153 2006 Y NA
## 154 1997 N NA
## 155 2013 Y NA
## 156 1996 Y NA
## 157 2006 N NA
## 158 2013 N NA
## 159 2013 N NA
## 160 1999 N NA
## 161 2014 N NA
## 162 1997 N NA
## 163 2002 N NA
## 164 1997 Y NA
## 165 2011 N NA
## 166 2012 N NA
## 167 2012 Y NA
## 168 1999 N NA
## 169 2015 N NA
## 170 2011 N NA
## 171 2006 N NA
## 172 2007 Y NA
## 173 2001 N NA
## 174 2014 N NA
## 175 2002 N NA
## 176 2011 N NA
## 177 1998 N NA
## 178 2003 N NA
## 179 1998 N NA
## 180 2010 N NA
## 181 2006 N NA
## 182 1997 N NA
## 183 2009 N NA
## 184 2011 Y NA
## 185 2013 N NA
## 186 2007 Y NA
## 187 2009 N NA
## 188 2010 N NA
## 189 2003 Y NA
## 190 2007 N NA
## 191 2008 N NA
## 192 2001 N NA
## 193 2012 N NA
## 194 2015 N NA
## 195 2003 N NA
## 196 2006 N NA
## 197 2001 Y NA
## 198 2006 N NA
## 199 2014 N NA
## 200 1997 N NA
## 201 2011 N NA
## 202 2006 N NA
## 203 2015 N NA
## 204 2009 N NA
## 205 1995 N NA
## 206 2011 N NA
## 207 1999 Y NA
## 208 2005 N NA
## 209 2003 N NA
## 210 2002 N NA
## 211 2015 N NA
## 212 1999 N NA
## 213 2009 N NA
## 214 2004 Y NA
## 215 2009 Y NA
## 216 2006 Y NA
## 217 2008 N NA
## 218 2004 N NA
## 219 1998 Y NA
## 220 1999 N NA
## 221 2009 Y NA
## 222 2012 N NA
## 223 1995 N NA
## 224 1995 N NA
## 225 2008 N NA
## 226 1999 N NA
## 227 2010 N NA
## 228 2005 Y NA
## 229 2015 N NA
## 230 2000 N NA
## 231 2005 N NA
## 232 2005 N NA
## 233 2006 N NA
## 234 1999 N NA
## 235 2015 Y NA
## 236 2009 N NA
## 237 1999 N NA
## 238 1996 Y NA
## 239 2010 N NA
## 240 1999 N NA
## 241 2015 N NA
## 242 2001 Y NA
## 243 2001 N NA
## 244 2012 N NA
## 245 2014 N NA
## 246 2012 Y NA
## 247 2008 N NA
## 248 2010 Y NA
## 249 2009 N NA
## 250 2001 N NA
## 251 2009 Y NA
## 252 2011 Y NA
## 253 1998 N NA
## 254 2004 Y NA
## 255 2002 Y NA
## 256 1995 N NA
## 257 2004 N NA
## 258 2010 Y NA
## 259 2014 N NA
## 260 1995 Y NA
## 261 2014 N NA
## 262 2013 Y NA
## 263 2013 Y NA
## 264 2001 N NA
## 265 2010 N NA
## 266 1999 N NA
## 267 2006 Y NA
## 268 2009 N NA
## 269 2001 N NA
## 270 2007 N NA
## 271 2003 N NA
## 272 2012 N NA
## 273 2010 Y NA
## 274 2002 N NA
## 275 1997 N NA
## 276 2008 N NA
## 277 2012 N NA
## 278 2004 Y NA
## 279 2011 Y NA
## 280 1997 N NA
## 281 2012 N NA
## 282 2013 Y NA
## 283 2003 N NA
## 284 1997 Y NA
## 285 2009 N NA
## 286 2008 N NA
## 287 1999 N NA
## 288 1997 N NA
## 289 2013 Y NA
## 290 2013 N NA
## 291 1996 N NA
## 292 2010 N NA
## 293 2014 Y NA
## 294 1998 N NA
## 295 2010 Y NA
## 296 2009 N NA
## 297 2010 N NA
## 298 2003 N NA
## 299 2003 N NA
## 300 2006 N NA
## 301 2008 N NA
## 302 2001 N NA
## 303 2013 N NA
## 304 1997 N NA
## 305 2001 N NA
## 306 2009 Y NA
## 307 1996 Y NA
## 308 1998 Y NA
## 309 1999 N NA
## 310 2008 N NA
## 311 2014 Y NA
## 312 2001 Y NA
## 313 2012 N NA
## 314 2014 N NA
## 315 2009 N NA
## 316 1998 N NA
## 317 2009 N NA
## 318 1998 N NA
## 319 2006 N NA
## 320 2006 Y NA
## 321 2005 N NA
## 322 2010 N NA
## 323 2012 N NA
## 324 2015 N NA
## 325 2000 Y NA
## 326 2010 N NA
## 327 2003 N NA
## 328 2014 N NA
## 329 2002 Y NA
## 330 2009 Y NA
## 331 2005 N NA
## 332 2004 Y NA
## 333 2005 N NA
## 334 1995 N NA
## 335 2004 N NA
## 336 2002 N NA
## 337 2008 N NA
## 338 1999 N NA
## 339 2000 N NA
## 340 1996 N NA
## 341 2014 Y NA
## 342 2000 N NA
## 343 2008 Y NA
## 344 2011 N NA
## 345 2012 Y NA
## 346 2002 N NA
## 347 2002 N NA
## 348 2005 N NA
## 349 2009 N NA
## 350 2002 Y NA
## 351 2011 N NA
## 352 2007 Y NA
## 353 2011 N NA
## 354 2000 N NA
## 355 1995 N NA
## 356 1997 N NA
## 357 1995 N NA
## 358 2010 N NA
## 359 2004 Y NA
## 360 2005 N NA
## 361 1997 Y NA
## 362 2011 Y NA
## 363 1999 N NA
## 364 2011 Y NA
## 365 2005 Y NA
## 366 2013 Y NA
## 367 2015 N NA
## 368 1996 N NA
## 369 2011 Y NA
## 370 2002 N NA
## 371 1995 N NA
## 372 2015 N NA
## 373 2009 N NA
## 374 2002 Y NA
## 375 2012 N NA
## 376 2006 N NA
## 377 2013 N NA
## 378 2009 N NA
## 379 2006 N NA
## 380 2013 Y NA
## 381 2014 N NA
## 382 2012 N NA
## 383 2004 Y NA
## 384 1995 Y NA
## 385 2014 N NA
## 386 2007 N NA
## 387 2014 N NA
## 388 2003 N NA
## 389 1999 N NA
## 390 2009 N NA
## 391 2005 N NA
## 392 2013 N NA
## 393 2003 N NA
## 394 2009 N NA
## 395 2011 Y NA
## 396 2015 N NA
## 397 2012 N NA
## 398 2003 N NA
## 399 2015 N NA
## 400 2002 N NA
## 401 2012 N NA
## 402 2008 N NA
## 403 2004 Y NA
## 404 2004 N NA
## 405 2012 Y NA
## 406 1999 N NA
## 407 1999 N NA
## 408 2008 N NA
## 409 2001 N NA
## 410 1995 N NA
## 411 2006 N NA
## 412 2005 N NA
## 413 2008 N NA
## 414 1995 N NA
## 415 1997 N NA
## 416 1999 N NA
## 417 2005 N NA
## 418 2009 N NA
## 419 2005 N NA
## 420 2007 N NA
## 421 1996 N NA
## 422 1995 N NA
## 423 2012 N NA
## 424 2004 N NA
## 425 2000 Y NA
## 426 1996 Y NA
## 427 2013 N NA
## 428 2011 N NA
## 429 2008 Y NA
## 430 2014 N NA
## 431 2001 N NA
## 432 2005 N NA
## 433 2005 Y NA
## 434 1999 N NA
## 435 1995 N NA
## 436 1996 N NA
## 437 1997 N NA
## 438 2005 Y NA
## 439 2004 N NA
## 440 2010 N NA
## 441 2006 N NA
## 442 2008 N NA
## 443 2001 Y NA
## 444 2002 N NA
## 445 2014 N NA
## 446 1999 Y NA
## 447 2005 N NA
## 448 2009 N NA
## 449 2013 N NA
## 450 2007 N NA
## 451 2012 N NA
## 452 2003 N NA
## 453 1997 N NA
## 454 2003 N NA
## 455 2011 N NA
## 456 2007 N NA
## 457 2003 N NA
## 458 2011 Y NA
## 459 1997 N NA
## 460 1995 N NA
## 461 1997 Y NA
## 462 2001 N NA
## 463 2013 Y NA
## 464 2013 N NA
## 465 2006 N NA
## 466 1998 N NA
## 467 2007 N NA
## 468 2007 N NA
## 469 2015 N NA
## 470 1996 N NA
## 471 2014 Y NA
## 472 2003 N NA
## 473 2000 N NA
## 474 2001 N NA
## 475 2011 Y NA
## 476 2015 N NA
## 477 1998 Y NA
## 478 1996 Y NA
## 479 2001 Y NA
## 480 1999 Y NA
## 481 1999 N NA
## 482 2006 N NA
## 483 2004 Y NA
## 484 2013 N NA
## 485 2009 N NA
## 486 1995 N NA
## 487 2003 N NA
## 488 2012 N NA
## 489 2013 N NA
## 490 2005 Y NA
## 491 2005 N NA
## 492 2007 N NA
## 493 2007 N NA
## 494 2015 N NA
## 495 2014 Y NA
## 496 2013 N NA
## 497 1999 N NA
## 498 2002 N NA
## 499 1997 Y NA
## 500 2013 N NA
## 501 2003 N NA
## 502 2015 N NA
## 503 2005 N NA
## 504 1999 N NA
## 505 2015 Y NA
## 506 1998 N NA
## 507 2011 N NA
## 508 2005 N NA
## 509 2003 N NA
## 510 2002 N NA
## 511 2015 N NA
## 512 2003 N NA
## 513 1997 N NA
## 514 2013 Y NA
## 515 2001 N NA
## 516 2001 N NA
## 517 2004 N NA
## 518 1999 Y NA
## 519 2009 N NA
## 520 2014 N NA
## 521 2004 N NA
## 522 2003 N NA
## 523 2005 N NA
## 524 2004 N NA
## 525 2004 N NA
## 526 1997 N NA
## 527 2013 N NA
## 528 2013 N NA
## 529 2010 N NA
## 530 2010 Y NA
## 531 1995 N NA
## 532 2000 N NA
## 533 2012 N NA
## 534 2009 N NA
## 535 2006 N NA
## 536 2005 Y NA
## 537 2004 N NA
## 538 1995 N NA
## 539 2007 Y NA
## 540 2015 N NA
## 541 2011 N NA
## 542 2013 N NA
## 543 2014 N NA
## 544 1996 N NA
## 545 1996 Y NA
## 546 2003 N NA
## 547 2002 N NA
## 548 1995 Y NA
## 549 2008 N NA
## 550 1996 N NA
## 551 1999 N NA
## 552 2001 N NA
## 553 2009 Y NA
## 554 2013 N NA
## 555 2013 N NA
## 556 2014 Y NA
## 557 2011 N NA
## 558 2004 N NA
## 559 2006 Y NA
## 560 2011 N NA
## 561 2010 N NA
## 562 2009 Y NA
## 563 2008 N NA
## 564 1997 N NA
## 565 2002 N NA
## 566 2002 N NA
## 567 2010 N NA
## 568 2010 Y NA
## 569 2007 N NA
## 570 2008 N NA
## 571 1998 N NA
## 572 2010 N NA
## 573 2014 N NA
## 574 2013 Y NA
## 575 2004 Y NA
## 576 2008 N NA
## 577 2012 N NA
## 578 1996 Y NA
## 579 2003 N NA
## 580 2007 Y NA
## 581 2006 N NA
## 582 1999 N NA
## 583 2010 N NA
## 584 2009 N NA
## 585 2007 N NA
## 586 2000 N NA
## 587 2013 N NA
## 588 1999 Y NA
## 589 2002 N NA
## 590 2011 N NA
## 591 1999 N NA
## 592 1996 Y NA
## 593 1995 N NA
## 594 2000 Y NA
## 595 1996 N NA
## 596 2001 N NA
## 597 2003 N NA
## 598 2008 Y NA
## 599 2007 N NA
## 600 2003 N NA
## 601 2007 Y NA
## 602 1997 N NA
## 603 2008 Y NA
## 604 2000 N NA
## 605 2008 N NA
## 606 1997 N NA
## 607 2010 N NA
## 608 2003 N NA
## 609 1996 N NA
## 610 1995 N NA
## 611 2006 N NA
## 612 2005 N NA
## 613 1998 N NA
## 614 2003 N NA
## 615 2010 N NA
## 616 2000 N NA
## 617 2007 Y NA
## 618 2010 N NA
## 619 2006 N NA
## 620 2006 N NA
## 621 2000 Y NA
## 622 1996 N NA
## 623 2012 N NA
## 624 1997 Y NA
## 625 1996 N NA
## 626 1999 N NA
## 627 2010 N NA
## 628 2010 N NA
## 629 2014 Y NA
## 630 1999 Y NA
## 631 2008 N NA
## 632 2004 N NA
## 633 2006 N NA
## 634 1995 Y NA
## 635 2001 N NA
## 636 1997 Y NA
## 637 1999 N NA
## 638 1997 N NA
## 639 1997 Y NA
## 640 2014 N NA
## 641 2005 N NA
## 642 1998 N NA
## 643 1995 N NA
## 644 2001 Y NA
## 645 2009 N NA
## 646 2014 N NA
## 647 2004 N NA
## 648 2003 N NA
## 649 2012 N NA
## 650 1995 Y NA
## 651 2013 Y NA
## 652 2008 N NA
## 653 2007 Y NA
## 654 1995 N NA
## 655 2000 N NA
## 656 2001 N NA
## 657 2010 N NA
## 658 2010 Y NA
## 659 2013 N NA
## 660 2010 N NA
## 661 2012 N NA
## 662 2006 N NA
## 663 2009 N NA
## 664 1999 N NA
## 665 2000 N NA
## 666 2007 Y NA
## 667 2006 Y NA
## 668 1997 N NA
## 669 2010 N NA
## 670 2003 N NA
## 671 2014 N NA
## 672 2002 N NA
## 673 2000 N NA
## 674 2011 N NA
## 675 1995 N NA
## 676 2008 N NA
## 677 2006 N NA
## 678 2003 N NA
## 679 2010 N NA
## 680 1997 N NA
## 681 2001 N NA
## 682 2015 N NA
## 683 2009 N NA
## 684 1995 Y NA
## 685 1996 Y NA
## 686 2008 N NA
## 687 1998 N NA
## 688 1999 N NA
## 689 2002 N NA
## 690 1996 N NA
## 691 1999 N NA
## 692 2007 Y NA
## 693 2005 N NA
## 694 1996 N NA
## 695 2013 N NA
## 696 2006 N NA
## 697 2006 N NA
## 698 2002 N NA
## 699 2002 N NA
## 700 1998 Y NA
## 701 2003 Y NA
## 702 1996 Y NA
## 703 2000 N NA
## 704 2014 Y NA
## 705 2015 Y NA
## 706 1995 Y NA
## 707 1997 N NA
## 708 2002 Y NA
## 709 2004 Y NA
## 710 2007 Y NA
## 711 2012 Y NA
## 712 2015 N NA
## 713 2012 Y NA
## 714 2007 Y NA
## 715 1998 N NA
## 716 2004 Y NA
## 717 1998 N NA
## 718 2000 N NA
## 719 2002 N NA
## 720 2003 N NA
## 721 2004 N NA
## 722 2015 N NA
## 723 2005 Y NA
## 724 2005 N NA
## 725 2007 N NA
## 726 1997 N NA
## 727 2011 N NA
## 728 2009 Y NA
## 729 1995 Y NA
## 730 1999 Y NA
## 731 1997 N NA
## 732 2006 N NA
## 733 2007 N NA
## 734 1999 N NA
## 735 2006 N NA
## 736 2015 N NA
## 737 2001 N NA
## 738 2006 N NA
## 739 1998 N NA
## 740 2003 N NA
## 741 2007 N NA
## 742 2004 N NA
## 743 2011 Y NA
## 744 2012 Y NA
## 745 2007 Y NA
## 746 2008 Y NA
## 747 2010 N NA
## 748 1995 N NA
## 749 2010 N NA
## 750 2003 N NA
## 751 1995 N NA
## 752 2001 N NA
## 753 1997 N NA
## 754 2004 N NA
## 755 2012 N NA
## 756 1995 N NA
## 757 1996 N NA
## 758 1995 N NA
## 759 2005 N NA
## 760 1997 N NA
## 761 1997 N NA
## 762 2007 Y NA
## 763 2015 Y NA
## 764 2015 Y NA
## 765 2012 N NA
## 766 2003 N NA
## 767 2007 N NA
## 768 2001 N NA
## 769 2007 N NA
## 770 2005 N NA
## 771 2011 N NA
## 772 1995 N NA
## 773 2000 N NA
## 774 1995 N NA
## 775 2006 N NA
## 776 2002 N NA
## 777 2003 N NA
## 778 2003 N NA
## 779 2003 N NA
## 780 2005 N NA
## 781 2009 N NA
## 782 2009 N NA
## 783 2011 N NA
## 784 2003 N NA
## 785 2005 Y NA
## 786 2008 Y NA
## 787 2012 N NA
## 788 2007 N NA
## 789 1998 Y NA
## 790 2012 N NA
## 791 2011 N NA
## 792 1995 Y NA
## 793 2015 N NA
## 794 2003 N NA
## 795 1995 N NA
## 796 1999 N NA
## 797 2004 Y NA
## 798 2015 N NA
## 799 1999 N NA
## 800 1998 N NA
## 801 1999 N NA
## 802 2000 N NA
## 803 2009 N NA
## 804 2014 Y NA
## 805 1995 N NA
## 806 2013 N NA
## 807 2005 N NA
## 808 1995 N NA
## 809 2009 N NA
## 810 2015 Y NA
## 811 2012 N NA
## 812 2013 N NA
## 813 2005 Y NA
## 814 2013 N NA
## 815 2010 N NA
## 816 2008 N NA
## 817 2006 N NA
## 818 2009 Y NA
## 819 2009 N NA
## 820 2009 N NA
## 821 1999 N NA
## 822 2011 N NA
## 823 2006 N NA
## 824 1997 Y NA
## 825 2001 N NA
## 826 2002 N NA
## 827 2006 N NA
## 828 1997 N NA
## 829 1995 N NA
## 830 2006 Y NA
## 831 2009 N NA
## 832 2009 Y NA
## 833 2007 N NA
## 834 2011 N NA
## 835 2005 N NA
## 836 2015 N NA
## 837 2006 N NA
## 838 2001 Y NA
## 839 2000 Y NA
## 840 2011 N NA
## 841 1998 N NA
## 842 1999 N NA
## 843 2000 N NA
## 844 2002 Y NA
## 845 2014 N NA
## 846 2015 Y NA
## 847 2005 N NA
## 848 2010 N NA
## 849 2004 Y NA
## 850 2001 N NA
## 851 2014 N NA
## 852 2007 Y NA
## 853 2000 N NA
## 854 1996 N NA
## 855 2008 N NA
## 856 2015 N NA
## 857 2012 N NA
## 858 2005 N NA
## 859 2000 N NA
## 860 2005 N NA
## 861 1999 N NA
## 862 2006 N NA
## 863 2009 N NA
## 864 1996 N NA
## 865 2000 N NA
## 866 2003 Y NA
## 867 2014 N NA
## 868 2008 N NA
## 869 1999 Y NA
## 870 2000 Y NA
## 871 2011 N NA
## 872 2013 N NA
## 873 2001 Y NA
## 874 2007 N NA
## 875 1995 N NA
## 876 2002 Y NA
## 877 2000 N NA
## 878 2008 N NA
## 879 2015 Y NA
## 880 2002 N NA
## 881 2000 Y NA
## 882 1998 Y NA
## 883 2011 N NA
## 884 2005 Y NA
## 885 1998 N NA
## 886 2004 N NA
## 887 2002 N NA
## 888 2002 N NA
## 889 2011 N NA
## 890 2007 N NA
## 891 2012 N NA
## 892 2006 N NA
## 893 2001 N NA
## 894 2011 N NA
## 895 2000 N NA
## 896 1998 Y NA
## 897 2008 N NA
## 898 2010 N NA
## 899 1998 N NA
## 900 1998 N NA
## 901 2000 N NA
## 902 1998 N NA
## 903 1997 N NA
## 904 1996 N NA
## 905 2014 N NA
## 906 2002 Y NA
## 907 2011 N NA
## 908 2005 N NA
## 909 2010 N NA
## 910 2010 N NA
## 911 2012 N NA
## 912 2001 N NA
## 913 2006 Y NA
## 914 2011 N NA
## 915 1998 N NA
## 916 1997 Y NA
## 917 1999 N NA
## 918 2009 N NA
## 919 1998 N NA
## 920 1995 Y NA
## 921 2000 N NA
## 922 2011 N NA
## 923 1998 N NA
## 924 2007 N NA
## 925 2003 N NA
## 926 2010 N NA
## 927 2005 Y NA
## 928 2008 Y NA
## 929 1998 N NA
## 930 2008 Y NA
## 931 1996 Y NA
## 932 2000 N NA
## 933 1996 N NA
## 934 2008 N NA
## 935 2010 N NA
## 936 2005 Y NA
## 937 2003 N NA
## 938 1995 N NA
## 939 2013 Y NA
## 940 2011 Y NA
## 941 2002 N NA
## 942 1998 N NA
## 943 1998 N NA
## 944 2015 N NA
## 945 2005 N NA
## 946 1997 N NA
## 947 1997 Y NA
## 948 2015 N NA
## 949 2011 N NA
## 950 2010 N NA
## 951 2001 N NA
## 952 2000 N NA
## 953 2014 N NA
## 954 2012 N NA
## 955 2004 N NA
## 956 2013 N NA
## 957 2003 N NA
## 958 1995 N NA
## 959 1995 N NA
## 960 2008 N NA
## 961 2005 N NA
## 962 2012 N NA
## 963 2013 N NA
## 964 2006 N NA
## 965 2008 Y NA
## 966 1995 N NA
## 967 1998 N NA
## 968 2014 Y NA
## 969 2003 N NA
## 970 2012 N NA
## 971 2007 N NA
## 972 1995 N NA
## 973 2002 Y NA
## 974 1996 Y NA
## 975 2007 Y NA
## 976 2008 N NA
## 977 2002 N NA
## 978 2015 Y NA
## 979 1999 N NA
## 980 2004 N NA
## 981 1999 N NA
## 982 2008 N NA
## 983 2011 Y NA
## 984 2002 N NA
## 985 2013 N NA
## 986 1995 N NA
## 987 2015 Y NA
## 988 2012 Y NA
## 989 1997 N NA
## 990 2015 N NA
## 991 2013 N NA
## 992 2002 N NA
## 993 2010 N NA
## 994 2012 N NA
## 995 1996 N NA
## 996 2006 N NA
## 997 2015 N NA
## 998 1996 N NA
## 999 1998 N NA
## 1000 2007 N NAAnálisis
1. Preguntas Clave
Brevemente responder con tus propias palabras 2 de las siguientes 3 preguntas:
i) ¿Qué es Supervised Machine Learning y cuáles son algunas de sus aplicaciones en Inteligencia de Negocios?
- El aprendizaje automático supervisado es un enfoque donde los modelos se entrenan utilizando datos etiquetados para hacer predicciones o tomar decisiones. Algunas aplicaciones en inteligencia de negocios incluyen la predicción de ventas, análisis de riesgos crediticios y recomendación de productos entre otros.
ii) ¿Cuáles son los principales algoritmos de Supervised Machine Learning? Brevemente describir con tus propias palarbas 5 – 7 de los principales algoritmos de Supervised Machine Learning. Algunos de los principales algoritmos de Supervised Machine Learning incluyen:
- Regresión lineal: Se utiliza para modelar la relación entre variables continuas.
- Regresión logística: Es útil para la clasificación binaria basada en variables independientes.
- Árboles de decisión: Son modelos que dividen los datos en subconjuntos más pequeños basados en características.
- Bosques aleatorios: Consisten en múltiples árboles de decisión para mejorar la precisión y evitar el sobreajuste.
- Máquinas de vectores de soporte (SVM): Se utilizan para la clasificación y la regresión, encontrando el hiperplano que mejor separa los datos.
iii) ¿Qué es la R2 Ajustada? ¿Qué es la métrica RMSE? ¿Cuál es la diferencia entre la R2 Ajustada y la métrica RMSE? - La R2 Ajustada es una métrica que ajusta el coeficiente de determinación R2 para tomar en cuenta el número de predictores o predicciones en el modelo y la cantidad de datos. El RMSE (Root Mean Squared Error) es una medida de la diferencia entre los valores predichoss por un modelo y los valores observados. La diferencia clave entre la R2 Ajustada y el RMSE es que la primera evalúa la proporción de varianza explicada por el modelo mientras que el segundo mide la precisión de las predicciones en términos de unidades de la variable de respuesta.
2. Análisis Exploratorio de los Datos (EDA)
Incluir los siguientes elementos:
- Identificación de NA’s
- Reemplazo de NA’s
- Medidas descriptivas
- Medidas de dispersión
- Identificación de patrones y/o tendencias en los datos mediante el uso de gráficos incluyendo bar plots, line plots, pie plots, histogramas, matriz de correlación, box plot, scatter plot, qq- plot, etc Mostrar al menos 4 – 6 gráficos.
str(df)## 'data.frame': 1000 obs. of 40 variables:
## $ months_as_customer : int 328 228 134 256 228 256 137 165 27 212 ...
## $ age : int 48 42 29 41 44 39 34 37 33 42 ...
## $ policy_number : int 521585 342868 687698 227811 367455 104594 413978 429027 485665 636550 ...
## $ policy_bind_date : chr "10/17/2014" "6/27/2006" "9/6/2000" "5/25/1990" ...
## $ policy_state : chr "OH" "IN" "OH" "IL" ...
## $ policy_csl : chr "250/500" "250/500" "100/300" "250/500" ...
## $ policy_deductable : int 1000 2000 2000 2000 1000 1000 1000 1000 500 500 ...
## $ policy_annual_premium : num 1407 1197 1413 1416 1584 ...
## $ umbrella_limit : int 0 5000000 5000000 6000000 6000000 0 0 0 0 0 ...
## $ insured_zip : int 466132 468176 430632 608117 610706 478456 441716 603195 601734 600983 ...
## $ insured_sex : chr "MALE" "MALE" "FEMALE" "FEMALE" ...
## $ insured_education_level : chr "MD" "MD" "PhD" "PhD" ...
## $ insured_occupation : chr "craft-repair" "machine-op-inspct" "sales" "armed-forces" ...
## $ insured_hobbies : chr "sleeping" "reading" "board-games" "board-games" ...
## $ insured_relationship : chr "husband" "other-relative" "own-child" "unmarried" ...
## $ capital.gains : int 53300 0 35100 48900 66000 0 0 0 0 0 ...
## $ capital.loss : int 0 0 0 -62400 -46000 0 -77000 0 0 -39300 ...
## $ incident_date : chr "1/25/2015" "1/21/2015" "2/22/2015" "1/10/2015" ...
## $ incident_type : chr "Single Vehicle Collision" "Vehicle Theft" "Multi-vehicle Collision" "Single Vehicle Collision" ...
## $ collision_type : chr "Side Collision" "?" "Rear Collision" "Front Collision" ...
## $ incident_severity : chr "Major Damage" "Minor Damage" "Minor Damage" "Major Damage" ...
## $ authorities_contacted : chr "Police" "Police" "Police" "Police" ...
## $ incident_state : chr "SC" "VA" "NY" "OH" ...
## $ incident_city : chr "Columbus" "Riverwood" "Columbus" "Arlington" ...
## $ incident_location : chr "9935 4th Drive" "6608 MLK Hwy" "7121 Francis Lane" "6956 Maple Drive" ...
## $ incident_hour_of_the_day : int 5 8 7 5 20 19 0 23 21 14 ...
## $ number_of_vehicles_involved: int 1 1 3 1 1 3 3 3 1 1 ...
## $ property_damage : chr "YES" "?" "NO" "?" ...
## $ bodily_injuries : int 1 0 2 1 0 0 0 2 1 2 ...
## $ witnesses : int 2 0 3 2 1 2 0 2 1 1 ...
## $ police_report_available : chr "YES" "?" "NO" "NO" ...
## $ total_claim_amount : int 71610 5070 34650 63400 6500 64100 78650 51590 27700 42300 ...
## $ injury_claim : int 6510 780 7700 6340 1300 6410 21450 9380 2770 4700 ...
## $ property_claim : int 13020 780 3850 6340 650 6410 7150 9380 2770 4700 ...
## $ vehicle_claim : int 52080 3510 23100 50720 4550 51280 50050 32830 22160 32900 ...
## $ auto_make : chr "Saab" "Mercedes" "Dodge" "Chevrolet" ...
## $ auto_model : chr "92x" "E400" "RAM" "Tahoe" ...
## $ auto_year : int 2004 2007 2007 2014 2009 2003 2012 2015 2012 1996 ...
## $ fraud_reported : chr "Y" "Y" "N" "Y" ...
## $ X_c39 : logi NA NA NA NA NA NA ...Limpieza de Datos
# Identificar las columnas que son de tipo caracter
columnas_caracter <- sapply(df, is.character)
# Convertir las columnas de tipo caracter a factores
df[columnas_caracter] <- lapply(df[columnas_caracter], factor)
# Convertir la columna policy_bind_date a tipo de dato fecha
df$policy_bind_date <- as.Date(df$policy_bind_date, format = "%m/%d/%Y")
str(df)## 'data.frame': 1000 obs. of 40 variables:
## $ months_as_customer : int 328 228 134 256 228 256 137 165 27 212 ...
## $ age : int 48 42 29 41 44 39 34 37 33 42 ...
## $ policy_number : int 521585 342868 687698 227811 367455 104594 413978 429027 485665 636550 ...
## $ policy_bind_date : Date, format: "2014-10-17" "2006-06-27" ...
## $ policy_state : Factor w/ 3 levels "IL","IN","OH": 3 2 3 1 1 3 2 1 1 1 ...
## $ policy_csl : Factor w/ 3 levels "100/300","250/500",..: 2 2 1 2 3 2 2 1 1 1 ...
## $ policy_deductable : int 1000 2000 2000 2000 1000 1000 1000 1000 500 500 ...
## $ policy_annual_premium : num 1407 1197 1413 1416 1584 ...
## $ umbrella_limit : int 0 5000000 5000000 6000000 6000000 0 0 0 0 0 ...
## $ insured_zip : int 466132 468176 430632 608117 610706 478456 441716 603195 601734 600983 ...
## $ insured_sex : Factor w/ 2 levels "FEMALE","MALE": 2 2 1 1 2 1 2 2 1 2 ...
## $ insured_education_level : Factor w/ 7 levels "Associate","College",..: 6 6 7 7 1 7 7 1 7 7 ...
## $ insured_occupation : Factor w/ 14 levels "adm-clerical",..: 3 7 12 2 12 13 10 13 8 9 ...
## $ insured_hobbies : Factor w/ 20 levels "base-jumping",..: 18 16 3 3 3 4 3 1 10 5 ...
## $ insured_relationship : Factor w/ 6 levels "husband","not-in-family",..: 1 3 4 5 5 5 1 5 4 6 ...
## $ capital.gains : int 53300 0 35100 48900 66000 0 0 0 0 0 ...
## $ capital.loss : int 0 0 0 -62400 -46000 0 -77000 0 0 -39300 ...
## $ incident_date : Factor w/ 60 levels "1/1/2015","1/10/2015",..: 18 14 46 2 40 12 5 51 24 27 ...
## $ incident_type : Factor w/ 4 levels "Multi-vehicle Collision",..: 3 4 1 3 4 1 1 1 3 3 ...
## $ collision_type : Factor w/ 4 levels "?","Front Collision",..: 4 1 3 2 1 3 2 2 2 3 ...
## $ incident_severity : Factor w/ 4 levels "Major Damage",..: 1 2 2 1 2 1 2 3 3 3 ...
## $ authorities_contacted : Factor w/ 5 levels "Ambulance","Fire",..: 5 5 5 5 3 2 5 5 5 4 ...
## $ incident_state : Factor w/ 7 levels "NC","NY","OH",..: 5 6 2 3 2 5 2 6 7 1 ...
## $ incident_city : Factor w/ 7 levels "Arlington","Columbus",..: 2 6 2 1 1 1 7 2 1 3 ...
## $ incident_location : Factor w/ 1000 levels "1012 5th Lane",..: 997 629 686 670 221 892 540 277 430 225 ...
## $ incident_hour_of_the_day : int 5 8 7 5 20 19 0 23 21 14 ...
## $ number_of_vehicles_involved: int 1 1 3 1 1 3 3 3 1 1 ...
## $ property_damage : Factor w/ 3 levels "?","NO","YES": 3 1 2 1 2 2 1 1 2 2 ...
## $ bodily_injuries : int 1 0 2 1 0 0 0 2 1 2 ...
## $ witnesses : int 2 0 3 2 1 2 0 2 1 1 ...
## $ police_report_available : Factor w/ 3 levels "?","NO","YES": 3 1 2 2 2 2 1 3 3 1 ...
## $ total_claim_amount : int 71610 5070 34650 63400 6500 64100 78650 51590 27700 42300 ...
## $ injury_claim : int 6510 780 7700 6340 1300 6410 21450 9380 2770 4700 ...
## $ property_claim : int 13020 780 3850 6340 650 6410 7150 9380 2770 4700 ...
## $ vehicle_claim : int 52080 3510 23100 50720 4550 51280 50050 32830 22160 32900 ...
## $ auto_make : Factor w/ 14 levels "Accura","Audi",..: 11 9 5 4 1 11 10 2 13 11 ...
## $ auto_model : Factor w/ 39 levels "3 Series","92x",..: 2 13 31 34 32 4 30 6 9 2 ...
## $ auto_year : int 2004 2007 2007 2014 2009 2003 2012 2015 2012 1996 ...
## $ fraud_reported : Factor w/ 2 levels "N","Y": 2 2 1 2 1 2 1 1 1 1 ...
## $ X_c39 : logi NA NA NA NA NA NA ...# Identify missing values
df_na <- sum(is.na(df))
df_na## [1] 1000df$X_c39 <- NULL
df_na <- sum(is.na(df))
df_na## [1] 0EDA
# Identify the name of the variables
colnames(df)## [1] "months_as_customer" "age"
## [3] "policy_number" "policy_bind_date"
## [5] "policy_state" "policy_csl"
## [7] "policy_deductable" "policy_annual_premium"
## [9] "umbrella_limit" "insured_zip"
## [11] "insured_sex" "insured_education_level"
## [13] "insured_occupation" "insured_hobbies"
## [15] "insured_relationship" "capital.gains"
## [17] "capital.loss" "incident_date"
## [19] "incident_type" "collision_type"
## [21] "incident_severity" "authorities_contacted"
## [23] "incident_state" "incident_city"
## [25] "incident_location" "incident_hour_of_the_day"
## [27] "number_of_vehicles_involved" "property_damage"
## [29] "bodily_injuries" "witnesses"
## [31] "police_report_available" "total_claim_amount"
## [33] "injury_claim" "property_claim"
## [35] "vehicle_claim" "auto_make"
## [37] "auto_model" "auto_year"
## [39] "fraud_reported"df_ds <- summary(df)
df_ds## months_as_customer age policy_number policy_bind_date
## Min. : 0.0 Min. :19.00 Min. :100804 Min. :1990-01-08
## 1st Qu.:115.8 1st Qu.:32.00 1st Qu.:335980 1st Qu.:1995-09-19
## Median :199.5 Median :38.00 Median :533135 Median :2002-04-01
## Mean :204.0 Mean :38.95 Mean :546239 Mean :2002-02-08
## 3rd Qu.:276.2 3rd Qu.:44.00 3rd Qu.:759100 3rd Qu.:2008-04-21
## Max. :479.0 Max. :64.00 Max. :999435 Max. :2015-02-22
##
## policy_state policy_csl policy_deductable policy_annual_premium
## IL:338 100/300 :349 Min. : 500 Min. : 433.3
## IN:310 250/500 :351 1st Qu.: 500 1st Qu.:1089.6
## OH:352 500/1000:300 Median :1000 Median :1257.2
## Mean :1136 Mean :1256.4
## 3rd Qu.:2000 3rd Qu.:1415.7
## Max. :2000 Max. :2047.6
##
## umbrella_limit insured_zip insured_sex insured_education_level
## Min. :-1000000 Min. :430104 FEMALE:537 Associate :145
## 1st Qu.: 0 1st Qu.:448404 MALE :463 College :122
## Median : 0 Median :466446 High School:160
## Mean : 1101000 Mean :501214 JD :161
## 3rd Qu.: 0 3rd Qu.:603251 Masters :143
## Max. :10000000 Max. :620962 MD :144
## PhD :125
## insured_occupation insured_hobbies insured_relationship
## machine-op-inspct: 93 reading : 64 husband :170
## prof-specialty : 85 exercise : 57 not-in-family :174
## tech-support : 78 paintball : 57 other-relative:177
## exec-managerial : 76 bungie-jumping: 56 own-child :183
## sales : 76 camping : 55 unmarried :141
## craft-repair : 74 golf : 55 wife :155
## (Other) :518 (Other) :656
## capital.gains capital.loss incident_date
## Min. : 0 Min. :-111100 2/2/2015 : 28
## 1st Qu.: 0 1st Qu.: -51500 2/17/2015: 26
## Median : 0 Median : -23250 1/7/2015 : 25
## Mean : 25126 Mean : -26794 1/10/2015: 24
## 3rd Qu.: 51025 3rd Qu.: 0 1/24/2015: 24
## Max. :100500 Max. : 0 2/4/2015 : 24
## (Other) :849
## incident_type collision_type incident_severity
## Multi-vehicle Collision :419 ? :178 Major Damage :276
## Parked Car : 84 Front Collision:254 Minor Damage :354
## Single Vehicle Collision:403 Rear Collision :292 Total Loss :280
## Vehicle Theft : 94 Side Collision :276 Trivial Damage: 90
##
##
##
## authorities_contacted incident_state incident_city
## Ambulance:196 NC:110 Arlington :152
## Fire :223 NY:262 Columbus :149
## None : 91 OH: 23 Hillsdale :141
## Other :198 PA: 30 Northbend :145
## Police :292 SC:248 Northbrook :122
## VA:110 Riverwood :134
## WV:217 Springfield:157
## incident_location incident_hour_of_the_day number_of_vehicles_involved
## 1012 5th Lane : 1 Min. : 0.00 Min. :1.000
## 1028 Sky Lane : 1 1st Qu.: 6.00 1st Qu.:1.000
## 1030 Pine Lane : 1 Median :12.00 Median :1.000
## 1087 Flute Drive: 1 Mean :11.64 Mean :1.839
## 1091 1st Drive : 1 3rd Qu.:17.00 3rd Qu.:3.000
## 1102 Apache Hwy : 1 Max. :23.00 Max. :4.000
## (Other) :994
## property_damage bodily_injuries witnesses police_report_available
## ? :360 Min. :0.000 Min. :0.000 ? :343
## NO :338 1st Qu.:0.000 1st Qu.:1.000 NO :343
## YES:302 Median :1.000 Median :1.000 YES:314
## Mean :0.992 Mean :1.487
## 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :2.000 Max. :3.000
##
## total_claim_amount injury_claim property_claim vehicle_claim
## Min. : 100 Min. : 0 Min. : 0 Min. : 70
## 1st Qu.: 41812 1st Qu.: 4295 1st Qu.: 4445 1st Qu.:30292
## Median : 58055 Median : 6775 Median : 6750 Median :42100
## Mean : 52762 Mean : 7433 Mean : 7400 Mean :37929
## 3rd Qu.: 70592 3rd Qu.:11305 3rd Qu.:10885 3rd Qu.:50822
## Max. :114920 Max. :21450 Max. :23670 Max. :79560
##
## auto_make auto_model auto_year fraud_reported
## Dodge : 80 RAM : 43 Min. :1995 N:753
## Saab : 80 Wrangler: 42 1st Qu.:2000 Y:247
## Suburu : 80 A3 : 37 Median :2005
## Nissan : 78 Neon : 37 Mean :2005
## Chevrolet: 76 MDX : 36 3rd Qu.:2010
## BMW : 72 Jetta : 35 Max. :2015
## (Other) :534 (Other) :770df_descr <- describe(df)
df_descr## # A tibble: 18 × 26
## described_variables n na mean sd se_mean IQR skewness
## <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 months_as_customer 1000 0 2.04e+2 1.15e+2 3.64e+0 1.61e2 0.362
## 2 age 1000 0 3.89e+1 9.14e+0 2.89e-1 1.2 e1 0.479
## 3 policy_number 1000 0 5.46e+5 2.57e+5 8.13e+3 4.23e5 0.0390
## 4 policy_deductable 1000 0 1.14e+3 6.12e+2 1.93e+1 1.5 e3 0.478
## 5 policy_annual_premium 1000 0 1.26e+3 2.44e+2 7.72e+0 3.26e2 0.00440
## 6 umbrella_limit 1000 0 1.10e+6 2.30e+6 7.27e+4 0 1.81
## 7 insured_zip 1000 0 5.01e+5 7.17e+4 2.27e+3 1.55e5 0.817
## 8 capital.gains 1000 0 2.51e+4 2.79e+4 8.81e+2 5.10e4 0.479
## 9 capital.loss 1000 0 -2.68e+4 2.81e+4 8.89e+2 5.15e4 -0.391
## 10 incident_hour_of_the_day 1000 0 1.16e+1 6.95e+0 2.20e-1 1.1 e1 -0.0356
## 11 number_of_vehicles_invo… 1000 0 1.84e+0 1.02e+0 3.22e-2 2 e0 0.503
## 12 bodily_injuries 1000 0 9.92e-1 8.20e-1 2.59e-2 2 e0 0.0148
## 13 witnesses 1000 0 1.49e+0 1.11e+0 3.51e-2 1 e0 0.0196
## 14 total_claim_amount 1000 0 5.28e+4 2.64e+4 8.35e+2 2.88e4 -0.595
## 15 injury_claim 1000 0 7.43e+3 4.88e+3 1.54e+2 7.01e3 0.265
## 16 property_claim 1000 0 7.40e+3 4.82e+3 1.53e+2 6.44e3 0.378
## 17 vehicle_claim 1000 0 3.79e+4 1.89e+4 5.97e+2 2.05e4 -0.621
## 18 auto_year 1000 0 2.01e+3 6.02e+0 1.90e-1 1 e1 -0.0483
## # ℹ 18 more variables: kurtosis <dbl>, p00 <dbl>, p01 <dbl>, p05 <dbl>,
## # p10 <dbl>, p20 <dbl>, p25 <dbl>, p30 <dbl>, p40 <dbl>, p50 <dbl>,
## # p60 <dbl>, p70 <dbl>, p75 <dbl>, p80 <dbl>, p90 <dbl>, p95 <dbl>,
## # p99 <dbl>, p100 <dbl>df_var <- var(df)## Warning in var(df): NAs introduced by coerciondf_var## months_as_customer age policy_number
## months_as_customer 1.325104e+04 9.702018e+02 1.703130e+06
## age 9.702018e+02 8.354484e+01 1.395997e+05
## policy_number 1.703130e+06 1.395997e+05 6.608139e+10
## policy_bind_date NA NA NA
## policy_state NA NA NA
## policy_csl NA NA NA
## policy_deductable 1.888144e+03 1.632352e+02 -1.059731e+06
## policy_annual_premium 1.410315e+02 3.214568e+01 1.416366e+06
## umbrella_limit 4.098745e+06 3.806326e+05 5.296036e+09
## insured_zip 1.476998e+05 1.678029e+04 1.305565e+08
## insured_sex NA NA NA
## insured_education_level NA NA NA
## insured_occupation NA NA NA
## insured_hobbies NA NA NA
## insured_relationship NA NA NA
## capital.gains 2.053193e+04 -1.802445e+03 7.023384e+07
## capital.loss 6.537987e+04 1.892620e+03 -4.095652e+07
## incident_date NA NA NA
## incident_type NA NA NA
## collision_type NA NA NA
## incident_severity NA NA NA
## authorities_contacted NA NA NA
## incident_state NA NA NA
## incident_city NA NA NA
## incident_location NA NA NA
## incident_hour_of_the_day 5.652515e+01 5.538026e+00 2.013220e+02
## number_of_vehicles_involved 1.728322e+00 2.058338e-01 3.518009e+03
## property_damage NA NA NA
## bodily_injuries -9.593273e-01 -1.175335e-01 -9.610359e+02
## witnesses 7.468871e+00 5.318559e-01 -3.617162e+03
## police_report_available NA NA NA
## total_claim_amount 1.887564e+05 1.685908e+04 -1.222275e+08
## injury_claim 3.670575e+04 3.369297e+03 -1.099333e+07
## property_claim 1.940521e+04 2.685543e+03 -1.324355e+07
## vehicle_claim 1.326454e+05 1.080424e+04 -9.799063e+07
## auto_make NA NA NA
## auto_model NA NA NA
## auto_year -2.024645e-01 7.443043e-02 -2.829887e+02
## fraud_reported NA NA NA
## policy_bind_date policy_state policy_csl
## months_as_customer NA NA NA
## age NA NA NA
## policy_number NA NA NA
## policy_bind_date NA NA NA
## policy_state NA NA NA
## policy_csl NA NA NA
## policy_deductable NA NA NA
## policy_annual_premium NA NA NA
## umbrella_limit NA NA NA
## insured_zip NA NA NA
## insured_sex NA NA NA
## insured_education_level NA NA NA
## insured_occupation NA NA NA
## insured_hobbies NA NA NA
## insured_relationship NA NA NA
## capital.gains NA NA NA
## capital.loss NA NA NA
## incident_date NA NA NA
## incident_type NA NA NA
## collision_type NA NA NA
## incident_severity NA NA NA
## authorities_contacted NA NA NA
## incident_state NA NA NA
## incident_city NA NA NA
## incident_location NA NA NA
## incident_hour_of_the_day NA NA NA
## number_of_vehicles_involved NA NA NA
## property_damage NA NA NA
## bodily_injuries NA NA NA
## witnesses NA NA NA
## police_report_available NA NA NA
## total_claim_amount NA NA NA
## injury_claim NA NA NA
## property_claim NA NA NA
## vehicle_claim NA NA NA
## auto_make NA NA NA
## auto_model NA NA NA
## auto_year NA NA NA
## fraud_reported NA NA NA
## policy_deductable policy_annual_premium
## months_as_customer 1.888144e+03 1.410315e+02
## age 1.632352e+02 3.214568e+01
## policy_number -1.059731e+06 1.416366e+06
## policy_bind_date NA NA
## policy_state NA NA
## policy_csl NA NA
## policy_deductable 3.743784e+05 -4.848613e+02
## policy_annual_premium -4.848613e+02 5.961772e+04
## umbrella_limit 1.527928e+07 -3.504155e+06
## insured_zip 1.993895e+05 5.664307e+05
## insured_sex NA NA
## insured_education_level NA NA
## insured_occupation NA NA
## insured_hobbies NA NA
## insured_relationship NA NA
## capital.gains 6.005009e+05 -9.349637e+04
## capital.loss -4.048617e+05 1.615803e+05
## incident_date NA NA
## incident_type NA NA
## collision_type NA NA
## incident_severity NA NA
## authorities_contacted NA NA
## incident_state NA NA
## incident_city NA NA
## incident_location NA NA
## incident_hour_of_the_day 2.591752e+02 -2.678940e+00
## number_of_vehicles_involved 3.192793e+01 -1.144158e+01
## property_damage NA NA
## bodily_injuries -1.142342e+01 5.362622e+00
## witnesses 4.531331e+01 6.326876e-01
## police_report_available NA NA
## total_claim_amount 3.689501e+05 5.862176e+04
## injury_claim 1.167917e+05 -2.101409e+04
## property_claim 1.912697e+05 -1.372875e+04
## vehicle_claim 6.088869e+04 9.336460e+04
## auto_make NA NA
## auto_model NA NA
## auto_year 9.608809e+01 -7.230676e+01
## fraud_reported NA NA
## umbrella_limit insured_zip insured_sex
## months_as_customer 4.098745e+06 1.476998e+05 NA
## age 3.806326e+05 1.678029e+04 NA
## policy_number 5.296036e+09 1.305565e+08 NA
## policy_bind_date NA NA NA
## policy_state NA NA NA
## policy_csl NA NA NA
## policy_deductable 1.527928e+07 1.993895e+05 NA
## policy_annual_premium -3.504155e+06 5.664307e+05 NA
## umbrella_limit 5.278077e+12 3.240420e+09 NA
## insured_zip 3.240420e+09 5.141121e+09 NA
## insured_sex NA NA NA
## insured_education_level NA NA NA
## insured_occupation NA NA NA
## insured_hobbies NA NA NA
## insured_relationship NA NA NA
## capital.gains -3.026763e+09 1.259612e+07 NA
## capital.loss -1.553189e+09 9.948976e+07 NA
## incident_date NA NA NA
## incident_type NA NA NA
## collision_type NA NA NA
## incident_severity NA NA NA
## authorities_contacted NA NA NA
## incident_state NA NA NA
## incident_city NA NA NA
## incident_location NA NA NA
## incident_hour_of_the_day -3.714154e+05 4.124026e+03 NA
## number_of_vehicles_involved -4.978879e+04 2.005228e+03 NA
## property_damage NA NA NA
## bodily_injuries 4.285085e+04 1.687420e+03 NA
## witnesses -1.720420e+04 1.578176e+03 NA
## police_report_available NA NA NA
## total_claim_amount -2.447073e+09 -6.412337e+07 NA
## injury_claim -5.092246e+08 -6.122698e+06 NA
## property_claim -2.637003e+08 -2.366451e+06 NA
## vehicle_claim -1.674148e+09 -5.563422e+07 NA
## auto_make NA NA NA
## auto_model NA NA NA
## auto_year 1.367337e+05 -1.412044e+04 NA
## fraud_reported NA NA NA
## insured_education_level insured_occupation
## months_as_customer NA NA
## age NA NA
## policy_number NA NA
## policy_bind_date NA NA
## policy_state NA NA
## policy_csl NA NA
## policy_deductable NA NA
## policy_annual_premium NA NA
## umbrella_limit NA NA
## insured_zip NA NA
## insured_sex NA NA
## insured_education_level NA NA
## insured_occupation NA NA
## insured_hobbies NA NA
## insured_relationship NA NA
## capital.gains NA NA
## capital.loss NA NA
## incident_date NA NA
## incident_type NA NA
## collision_type NA NA
## incident_severity NA NA
## authorities_contacted NA NA
## incident_state NA NA
## incident_city NA NA
## incident_location NA NA
## incident_hour_of_the_day NA NA
## number_of_vehicles_involved NA NA
## property_damage NA NA
## bodily_injuries NA NA
## witnesses NA NA
## police_report_available NA NA
## total_claim_amount NA NA
## injury_claim NA NA
## property_claim NA NA
## vehicle_claim NA NA
## auto_make NA NA
## auto_model NA NA
## auto_year NA NA
## fraud_reported NA NA
## insured_hobbies insured_relationship capital.gains
## months_as_customer NA NA 2.053193e+04
## age NA NA -1.802445e+03
## policy_number NA NA 7.023384e+07
## policy_bind_date NA NA NA
## policy_state NA NA NA
## policy_csl NA NA NA
## policy_deductable NA NA 6.005009e+05
## policy_annual_premium NA NA -9.349637e+04
## umbrella_limit NA NA -3.026763e+09
## insured_zip NA NA 1.259612e+07
## insured_sex NA NA NA
## insured_education_level NA NA NA
## insured_occupation NA NA NA
## insured_hobbies NA NA NA
## insured_relationship NA NA NA
## capital.gains NA NA 7.768588e+08
## capital.loss NA NA -3.674132e+07
## incident_date NA NA NA
## incident_type NA NA NA
## collision_type NA NA NA
## incident_severity NA NA NA
## authorities_contacted NA NA NA
## incident_state NA NA NA
## incident_city NA NA NA
## incident_location NA NA NA
## incident_hour_of_the_day NA NA -3.178587e+03
## number_of_vehicles_involved NA NA 1.750553e+03
## property_damage NA NA NA
## bodily_injuries NA NA 1.276185e+03
## witnesses NA NA -5.467575e+02
## police_report_available NA NA NA
## total_claim_amount NA NA 1.175952e+07
## injury_claim NA NA 3.528093e+06
## property_claim NA NA -1.047625e+05
## vehicle_claim NA NA 8.336193e+06
## auto_make NA NA NA
## auto_model NA NA NA
## auto_year NA NA 5.264676e+03
## fraud_reported NA NA NA
## capital.loss incident_date incident_type
## months_as_customer 6.537987e+04 NA NA
## age 1.892620e+03 NA NA
## policy_number -4.095652e+07 NA NA
## policy_bind_date NA NA NA
## policy_state NA NA NA
## policy_csl NA NA NA
## policy_deductable -4.048617e+05 NA NA
## policy_annual_premium 1.615803e+05 NA NA
## umbrella_limit -1.553189e+09 NA NA
## insured_zip 9.948976e+07 NA NA
## insured_sex NA NA NA
## insured_education_level NA NA NA
## insured_occupation NA NA NA
## insured_hobbies NA NA NA
## insured_relationship NA NA NA
## capital.gains -3.674132e+07 NA NA
## capital.loss 7.898403e+08 NA NA
## incident_date NA NA NA
## incident_type NA NA NA
## collision_type NA NA NA
## incident_severity NA NA NA
## authorities_contacted NA NA NA
## incident_state NA NA NA
## incident_city NA NA NA
## incident_location NA NA NA
## incident_hour_of_the_day -4.894552e+03 NA NA
## number_of_vehicles_involved -4.265122e+02 NA NA
## property_damage NA NA NA
## bodily_injuries -5.628124e+02 NA NA
## witnesses -1.290859e+03 NA NA
## police_report_available NA NA NA
## total_claim_amount -2.675643e+07 NA NA
## injury_claim -6.318256e+06 NA NA
## property_claim -3.100158e+06 NA NA
## vehicle_claim -1.733802e+07 NA NA
## auto_make NA NA NA
## auto_model NA NA NA
## auto_year -9.571921e+03 NA NA
## fraud_reported NA NA NA
## collision_type incident_severity
## months_as_customer NA NA
## age NA NA
## policy_number NA NA
## policy_bind_date NA NA
## policy_state NA NA
## policy_csl NA NA
## policy_deductable NA NA
## policy_annual_premium NA NA
## umbrella_limit NA NA
## insured_zip NA NA
## insured_sex NA NA
## insured_education_level NA NA
## insured_occupation NA NA
## insured_hobbies NA NA
## insured_relationship NA NA
## capital.gains NA NA
## capital.loss NA NA
## incident_date NA NA
## incident_type NA NA
## collision_type NA NA
## incident_severity NA NA
## authorities_contacted NA NA
## incident_state NA NA
## incident_city NA NA
## incident_location NA NA
## incident_hour_of_the_day NA NA
## number_of_vehicles_involved NA NA
## property_damage NA NA
## bodily_injuries NA NA
## witnesses NA NA
## police_report_available NA NA
## total_claim_amount NA NA
## injury_claim NA NA
## property_claim NA NA
## vehicle_claim NA NA
## auto_make NA NA
## auto_model NA NA
## auto_year NA NA
## fraud_reported NA NA
## authorities_contacted incident_state incident_city
## months_as_customer NA NA NA
## age NA NA NA
## policy_number NA NA NA
## policy_bind_date NA NA NA
## policy_state NA NA NA
## policy_csl NA NA NA
## policy_deductable NA NA NA
## policy_annual_premium NA NA NA
## umbrella_limit NA NA NA
## insured_zip NA NA NA
## insured_sex NA NA NA
## insured_education_level NA NA NA
## insured_occupation NA NA NA
## insured_hobbies NA NA NA
## insured_relationship NA NA NA
## capital.gains NA NA NA
## capital.loss NA NA NA
## incident_date NA NA NA
## incident_type NA NA NA
## collision_type NA NA NA
## incident_severity NA NA NA
## authorities_contacted NA NA NA
## incident_state NA NA NA
## incident_city NA NA NA
## incident_location NA NA NA
## incident_hour_of_the_day NA NA NA
## number_of_vehicles_involved NA NA NA
## property_damage NA NA NA
## bodily_injuries NA NA NA
## witnesses NA NA NA
## police_report_available NA NA NA
## total_claim_amount NA NA NA
## injury_claim NA NA NA
## property_claim NA NA NA
## vehicle_claim NA NA NA
## auto_make NA NA NA
## auto_model NA NA NA
## auto_year NA NA NA
## fraud_reported NA NA NA
## incident_location incident_hour_of_the_day
## months_as_customer NA 5.652515e+01
## age NA 5.538026e+00
## policy_number NA 2.013220e+02
## policy_bind_date NA NA
## policy_state NA NA
## policy_csl NA NA
## policy_deductable NA 2.591752e+02
## policy_annual_premium NA -2.678940e+00
## umbrella_limit NA -3.714154e+05
## insured_zip NA 4.124026e+03
## insured_sex NA NA
## insured_education_level NA NA
## insured_occupation NA NA
## insured_hobbies NA NA
## insured_relationship NA NA
## capital.gains NA -3.178587e+03
## capital.loss NA -4.894552e+03
## incident_date NA NA
## incident_type NA NA
## collision_type NA NA
## incident_severity NA NA
## authorities_contacted NA NA
## incident_state NA NA
## incident_city NA NA
## incident_location NA NA
## incident_hour_of_the_day NA 4.832159e+01
## number_of_vehicles_involved NA 8.555395e-01
## property_damage NA NA
## bodily_injuries NA -1.970450e-01
## witnesses NA 5.042242e-02
## police_report_available NA NA
## total_claim_amount NA 3.995424e+04
## injury_claim NA 5.624392e+03
## property_claim NA 6.021358e+03
## vehicle_claim NA 2.830849e+04
## auto_make NA NA
## auto_model NA NA
## auto_year NA 8.935616e-01
## fraud_reported NA NA
## number_of_vehicles_involved property_damage
## months_as_customer 1.728322e+00 NA
## age 2.058338e-01 NA
## policy_number 3.518009e+03 NA
## policy_bind_date NA NA
## policy_state NA NA
## policy_csl NA NA
## policy_deductable 3.192793e+01 NA
## policy_annual_premium -1.144158e+01 NA
## umbrella_limit -4.978879e+04 NA
## insured_zip 2.005228e+03 NA
## insured_sex NA NA
## insured_education_level NA NA
## insured_occupation NA NA
## insured_hobbies NA NA
## insured_relationship NA NA
## capital.gains 1.750553e+03 NA
## capital.loss -4.265122e+02 NA
## incident_date NA NA
## incident_type NA NA
## collision_type NA NA
## incident_severity NA NA
## authorities_contacted NA NA
## incident_state NA NA
## incident_city NA NA
## incident_location NA NA
## incident_hour_of_the_day 8.555395e-01 NA
## number_of_vehicles_involved 1.038117e+00 NA
## property_damage NA NA
## bodily_injuries 1.172372e-02 NA
## witnesses -1.660961e-02 NA
## police_report_available NA NA
## total_claim_amount 7.378070e+03 NA
## injury_claim 1.117208e+03 NA
## property_claim 1.076978e+03 NA
## vehicle_claim 5.183885e+03 NA
## auto_make NA NA
## auto_model NA NA
## auto_year 2.117948e-01 NA
## fraud_reported NA NA
## bodily_injuries witnesses
## months_as_customer -9.593273e-01 7.468871e+00
## age -1.175335e-01 5.318559e-01
## policy_number -9.610359e+02 -3.617162e+03
## policy_bind_date NA NA
## policy_state NA NA
## policy_csl NA NA
## policy_deductable -1.142342e+01 4.531331e+01
## policy_annual_premium 5.362622e+00 6.326876e-01
## umbrella_limit 4.285085e+04 -1.720420e+04
## insured_zip 1.687420e+03 1.578176e+03
## insured_sex NA NA
## insured_education_level NA NA
## insured_occupation NA NA
## insured_hobbies NA NA
## insured_relationship NA NA
## capital.gains 1.276185e+03 -5.467575e+02
## capital.loss -5.628124e+02 -1.290859e+03
## incident_date NA NA
## incident_type NA NA
## collision_type NA NA
## incident_severity NA NA
## authorities_contacted NA NA
## incident_state NA NA
## incident_city NA NA
## incident_location NA NA
## incident_hour_of_the_day -1.970450e-01 5.042242e-02
## number_of_vehicles_involved 1.172372e-02 -1.660961e-02
## property_damage NA NA
## bodily_injuries 6.726086e-01 -5.109109e-03
## witnesses -5.109109e-03 1.235066e+00
## police_report_available NA NA
## total_claim_amount 1.019685e+03 -3.261009e+02
## injury_claim 1.894168e+02 -1.347603e+02
## property_claim 1.572838e+02 2.822517e+02
## vehicle_claim 6.729846e+02 -4.735922e+02
## auto_make NA NA
## auto_model NA NA
## auto_year -1.012773e-01 3.061451e-01
## fraud_reported NA NA
## police_report_available total_claim_amount
## months_as_customer NA 1.887564e+05
## age NA 1.685908e+04
## policy_number NA -1.222275e+08
## policy_bind_date NA NA
## policy_state NA NA
## policy_csl NA NA
## policy_deductable NA 3.689501e+05
## policy_annual_premium NA 5.862176e+04
## umbrella_limit NA -2.447073e+09
## insured_zip NA -6.412337e+07
## insured_sex NA NA
## insured_education_level NA NA
## insured_occupation NA NA
## insured_hobbies NA NA
## insured_relationship NA NA
## capital.gains NA 1.175952e+07
## capital.loss NA -2.675643e+07
## incident_date NA NA
## incident_type NA NA
## collision_type NA NA
## incident_severity NA NA
## authorities_contacted NA NA
## incident_state NA NA
## incident_city NA NA
## incident_location NA NA
## incident_hour_of_the_day NA 3.995424e+04
## number_of_vehicles_involved NA 7.378070e+03
## property_damage NA NA
## bodily_injuries NA 1.019685e+03
## witnesses NA -3.261009e+02
## police_report_available NA NA
## total_claim_amount NA 6.970410e+08
## injury_claim NA 1.037393e+08
## property_claim NA 1.032654e+08
## vehicle_claim NA 4.900363e+08
## auto_make NA NA
## auto_model NA NA
## auto_year NA -5.683013e+03
## fraud_reported NA NA
## injury_claim property_claim vehicle_claim
## months_as_customer 3.670575e+04 1.940521e+04 1.326454e+05
## age 3.369297e+03 2.685543e+03 1.080424e+04
## policy_number -1.099333e+07 -1.324355e+07 -9.799063e+07
## policy_bind_date NA NA NA
## policy_state NA NA NA
## policy_csl NA NA NA
## policy_deductable 1.167917e+05 1.912697e+05 6.088869e+04
## policy_annual_premium -2.101409e+04 -1.372875e+04 9.336460e+04
## umbrella_limit -5.092246e+08 -2.637003e+08 -1.674148e+09
## insured_zip -6.122698e+06 -2.366451e+06 -5.563422e+07
## insured_sex NA NA NA
## insured_education_level NA NA NA
## insured_occupation NA NA NA
## insured_hobbies NA NA NA
## insured_relationship NA NA NA
## capital.gains 3.528093e+06 -1.047625e+05 8.336193e+06
## capital.loss -6.318256e+06 -3.100158e+06 -1.733802e+07
## incident_date NA NA NA
## incident_type NA NA NA
## collision_type NA NA NA
## incident_severity NA NA NA
## authorities_contacted NA NA NA
## incident_state NA NA NA
## incident_city NA NA NA
## incident_location NA NA NA
## incident_hour_of_the_day 5.624392e+03 6.021358e+03 2.830849e+04
## number_of_vehicles_involved 1.117208e+03 1.076978e+03 5.183885e+03
## property_damage NA NA NA
## bodily_injuries 1.894168e+02 1.572838e+02 6.729846e+02
## witnesses -1.347603e+02 2.822517e+02 -4.735922e+02
## police_report_available NA NA NA
## total_claim_amount 1.037393e+08 1.032654e+08 4.900363e+08
## injury_claim 2.382369e+07 1.327862e+07 6.663697e+07
## property_claim 1.327862e+07 2.327798e+07 6.670878e+07
## vehicle_claim 6.663697e+07 6.670878e+07 3.566905e+08
## auto_make NA NA NA
## auto_model NA NA NA
## auto_year -4.028051e+02 -4.210868e+02 -4.859121e+03
## fraud_reported NA NA NA
## auto_make auto_model auto_year fraud_reported
## months_as_customer NA NA -2.024645e-01 NA
## age NA NA 7.443043e-02 NA
## policy_number NA NA -2.829887e+02 NA
## policy_bind_date NA NA NA NA
## policy_state NA NA NA NA
## policy_csl NA NA NA NA
## policy_deductable NA NA 9.608809e+01 NA
## policy_annual_premium NA NA -7.230676e+01 NA
## umbrella_limit NA NA 1.367337e+05 NA
## insured_zip NA NA -1.412044e+04 NA
## insured_sex NA NA NA NA
## insured_education_level NA NA NA NA
## insured_occupation NA NA NA NA
## insured_hobbies NA NA NA NA
## insured_relationship NA NA NA NA
## capital.gains NA NA 5.264676e+03 NA
## capital.loss NA NA -9.571921e+03 NA
## incident_date NA NA NA NA
## incident_type NA NA NA NA
## collision_type NA NA NA NA
## incident_severity NA NA NA NA
## authorities_contacted NA NA NA NA
## incident_state NA NA NA NA
## incident_city NA NA NA NA
## incident_location NA NA NA NA
## incident_hour_of_the_day NA NA 8.935616e-01 NA
## number_of_vehicles_involved NA NA 2.117948e-01 NA
## property_damage NA NA NA NA
## bodily_injuries NA NA -1.012773e-01 NA
## witnesses NA NA 3.061451e-01 NA
## police_report_available NA NA NA NA
## total_claim_amount NA NA -5.683013e+03 NA
## injury_claim NA NA -4.028051e+02 NA
## property_claim NA NA -4.210868e+02 NA
## vehicle_claim NA NA -4.859121e+03 NA
## auto_make NA NA NA NA
## auto_model NA NA NA NA
## auto_year NA NA 3.619058e+01 NA
## fraud_reported NA NA NA NAData Visualization
Visualización de la base de datos General
plot_histogram(df)
plot_boxplot(df, by = "total_claim_amount")
plot_bar(df)
Visualización de la Variable Dependiente
print("Variable Dependiente: Total Claim Amount")## [1] "Variable Dependiente: Total Claim Amount"summary(df$total_claim_amount)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 100 41812 58055 52762 70592 114920hist(df$total_claim_amount, main = "Histograma de Total Claim Amount")
plot(df$incident_type, df$total_claim_amount, main = "Gráfico de Dispersión: Incident Type vs Total Claim Amount")
Cambios realizados en las bases de datos
Eliminación de variables insignificantes
# df original
df_og <- df
# eliminando variables insignifcantes
df$incident_location <- NULL
df$policy_bind_date <- NULL
df$incident_date <- NULL
df$insured_zip <- NULL
df$auto_model <- NULL
df$insured_hobbies <- NULL
df$policy_number <- NULL
df$policy_deductable <- NULL
df$authorities_contacted <- NULL
df$collision_type <- NULL
df$vehicle_claim <-NULL
df$injury_claim <- NULL
df$property_claim <- NULL
df$number_of_vehicles_involved <- NULLConversión de df -> df2 como numérica
## copia de la base de datos para hacerla numerica y factor
df2 <- df
# Identificar las columnas que son de tipo caracter
columnas_int <- sapply(df2, is.integer)
# Convertir las columnas de tipo caracter a factores
df2[columnas_int] <- lapply(df2[columnas_int], as.numeric)
str(df2)## 'data.frame': 1000 obs. of 25 variables:
## $ months_as_customer : num 328 228 134 256 228 256 137 165 27 212 ...
## $ age : num 48 42 29 41 44 39 34 37 33 42 ...
## $ policy_state : Factor w/ 3 levels "IL","IN","OH": 3 2 3 1 1 3 2 1 1 1 ...
## $ policy_csl : Factor w/ 3 levels "100/300","250/500",..: 2 2 1 2 3 2 2 1 1 1 ...
## $ policy_annual_premium : num 1407 1197 1413 1416 1584 ...
## $ umbrella_limit : num 0e+00 5e+06 5e+06 6e+06 6e+06 0e+00 0e+00 0e+00 0e+00 0e+00 ...
## $ insured_sex : Factor w/ 2 levels "FEMALE","MALE": 2 2 1 1 2 1 2 2 1 2 ...
## $ insured_education_level : Factor w/ 7 levels "Associate","College",..: 6 6 7 7 1 7 7 1 7 7 ...
## $ insured_occupation : Factor w/ 14 levels "adm-clerical",..: 3 7 12 2 12 13 10 13 8 9 ...
## $ insured_relationship : Factor w/ 6 levels "husband","not-in-family",..: 1 3 4 5 5 5 1 5 4 6 ...
## $ capital.gains : num 53300 0 35100 48900 66000 0 0 0 0 0 ...
## $ capital.loss : num 0 0 0 -62400 -46000 0 -77000 0 0 -39300 ...
## $ incident_type : Factor w/ 4 levels "Multi-vehicle Collision",..: 3 4 1 3 4 1 1 1 3 3 ...
## $ incident_severity : Factor w/ 4 levels "Major Damage",..: 1 2 2 1 2 1 2 3 3 3 ...
## $ incident_state : Factor w/ 7 levels "NC","NY","OH",..: 5 6 2 3 2 5 2 6 7 1 ...
## $ incident_city : Factor w/ 7 levels "Arlington","Columbus",..: 2 6 2 1 1 1 7 2 1 3 ...
## $ incident_hour_of_the_day: num 5 8 7 5 20 19 0 23 21 14 ...
## $ property_damage : Factor w/ 3 levels "?","NO","YES": 3 1 2 1 2 2 1 1 2 2 ...
## $ bodily_injuries : num 1 0 2 1 0 0 0 2 1 2 ...
## $ witnesses : num 2 0 3 2 1 2 0 2 1 1 ...
## $ police_report_available : Factor w/ 3 levels "?","NO","YES": 3 1 2 2 2 2 1 3 3 1 ...
## $ total_claim_amount : num 71610 5070 34650 63400 6500 ...
## $ auto_make : Factor w/ 14 levels "Accura","Audi",..: 11 9 5 4 1 11 10 2 13 11 ...
## $ auto_year : num 2004 2007 2007 2014 2009 ...
## $ fraud_reported : Factor w/ 2 levels "N","Y": 2 2 1 2 1 2 1 1 1 1 ...# base de datos numerica
df2_numeric <- df2
# Identificar las columnas que son de tipo caracter
columnas_caracter <- sapply(df2_numeric, is.character)
# Convertir las columnas de tipo caracter a factores
df2_numeric[columnas_caracter] <- lapply(df2_numeric[columnas_caracter], factor)
# Obtener índices de las variables categóricas
factor_cols <- sapply(df2_numeric, is.factor)
# Convertir las variables categóricas de factor a numeric
df2_numeric[factor_cols] <- lapply(df2_numeric[factor_cols], as.numeric)
# Identificar las columnas que son de tipo caracter
columnas_int2 <- sapply(df2_numeric, is.integer)
# Convertir las columnas de tipo caracter a factores
df2_numeric[columnas_int2] <- lapply(df2_numeric[columnas_int2], as.numeric)
str(df2_numeric)## 'data.frame': 1000 obs. of 25 variables:
## $ months_as_customer : num 328 228 134 256 228 256 137 165 27 212 ...
## $ age : num 48 42 29 41 44 39 34 37 33 42 ...
## $ policy_state : num 3 2 3 1 1 3 2 1 1 1 ...
## $ policy_csl : num 2 2 1 2 3 2 2 1 1 1 ...
## $ policy_annual_premium : num 1407 1197 1413 1416 1584 ...
## $ umbrella_limit : num 0e+00 5e+06 5e+06 6e+06 6e+06 0e+00 0e+00 0e+00 0e+00 0e+00 ...
## $ insured_sex : num 2 2 1 1 2 1 2 2 1 2 ...
## $ insured_education_level : num 6 6 7 7 1 7 7 1 7 7 ...
## $ insured_occupation : num 3 7 12 2 12 13 10 13 8 9 ...
## $ insured_relationship : num 1 3 4 5 5 5 1 5 4 6 ...
## $ capital.gains : num 53300 0 35100 48900 66000 0 0 0 0 0 ...
## $ capital.loss : num 0 0 0 -62400 -46000 0 -77000 0 0 -39300 ...
## $ incident_type : num 3 4 1 3 4 1 1 1 3 3 ...
## $ incident_severity : num 1 2 2 1 2 1 2 3 3 3 ...
## $ incident_state : num 5 6 2 3 2 5 2 6 7 1 ...
## $ incident_city : num 2 6 2 1 1 1 7 2 1 3 ...
## $ incident_hour_of_the_day: num 5 8 7 5 20 19 0 23 21 14 ...
## $ property_damage : num 3 1 2 1 2 2 1 1 2 2 ...
## $ bodily_injuries : num 1 0 2 1 0 0 0 2 1 2 ...
## $ witnesses : num 2 0 3 2 1 2 0 2 1 1 ...
## $ police_report_available : num 3 1 2 2 2 2 1 3 3 1 ...
## $ total_claim_amount : num 71610 5070 34650 63400 6500 ...
## $ auto_make : num 11 9 5 4 1 11 10 2 13 11 ...
## $ auto_year : num 2004 2007 2007 2014 2009 ...
## $ fraud_reported : num 2 2 1 2 1 2 1 1 1 1 ...Creación de dataframes con variables significantes
# dataframe significante 1
df_sig <- df2 %>% select(total_claim_amount, incident_type, insured_occupation, insured_education_level, incident_severity, property_damage, incident_city, incident_hour_of_the_day, fraud_reported)
# dataframe significante 2
df_sig_2 <- df2 %>% select(total_claim_amount, incident_type, insured_occupation, insured_education_level, incident_severity, property_damage, incident_city)
# dataframe significante 3
df_sig_3 <- df2 %>% select(total_claim_amount, incident_type, insured_occupation, insured_education_level, incident_severity, property_damage, incident_city, age, incident_hour_of_the_day)
str(df_sig_2)## 'data.frame': 1000 obs. of 7 variables:
## $ total_claim_amount : num 71610 5070 34650 63400 6500 ...
## $ incident_type : Factor w/ 4 levels "Multi-vehicle Collision",..: 3 4 1 3 4 1 1 1 3 3 ...
## $ insured_occupation : Factor w/ 14 levels "adm-clerical",..: 3 7 12 2 12 13 10 13 8 9 ...
## $ insured_education_level: Factor w/ 7 levels "Associate","College",..: 6 6 7 7 1 7 7 1 7 7 ...
## $ incident_severity : Factor w/ 4 levels "Major Damage",..: 1 2 2 1 2 1 2 3 3 3 ...
## $ property_damage : Factor w/ 3 levels "?","NO","YES": 3 1 2 1 2 2 1 1 2 2 ...
## $ incident_city : Factor w/ 7 levels "Arlington","Columbus",..: 2 6 2 1 1 1 7 2 1 3 ...# dataframe significante 2 numerico
df_sig_2_numeric <- df_sig_2
# Obtener índices de las variables categóricas
factor_cols <- sapply(df_sig_2_numeric, is.factor)
# Convertir las variables categóricas de factor a numeric
df_sig_2_numeric[factor_cols] <- lapply(df_sig_2_numeric[factor_cols], as.numeric)
str(df_sig_2_numeric)## 'data.frame': 1000 obs. of 7 variables:
## $ total_claim_amount : num 71610 5070 34650 63400 6500 ...
## $ incident_type : num 3 4 1 3 4 1 1 1 3 3 ...
## $ insured_occupation : num 3 7 12 2 12 13 10 13 8 9 ...
## $ insured_education_level: num 6 6 7 7 1 7 7 1 7 7 ...
## $ incident_severity : num 1 2 2 1 2 1 2 3 3 3 ...
## $ property_damage : num 3 1 2 1 2 2 1 1 2 2 ...
## $ incident_city : num 2 6 2 1 1 1 7 2 1 3 ...# dataframe significante 3 numerico
df_sig_3_numeric <- df_sig_3
# Obtener índices de las variables categóricas
factor_cols <- sapply(df_sig_3_numeric, is.factor)
# Convertir las variables categóricas de factor a numeric
df_sig_3_numeric[factor_cols] <- lapply(df_sig_3_numeric[factor_cols], as.numeric)
str(df_sig_3_numeric)## 'data.frame': 1000 obs. of 9 variables:
## $ total_claim_amount : num 71610 5070 34650 63400 6500 ...
## $ incident_type : num 3 4 1 3 4 1 1 1 3 3 ...
## $ insured_occupation : num 3 7 12 2 12 13 10 13 8 9 ...
## $ insured_education_level : num 6 6 7 7 1 7 7 1 7 7 ...
## $ incident_severity : num 1 2 2 1 2 1 2 3 3 3 ...
## $ property_damage : num 3 1 2 1 2 2 1 1 2 2 ...
## $ incident_city : num 2 6 2 1 1 1 7 2 1 3 ...
## $ age : num 48 42 29 41 44 39 34 37 33 42 ...
## $ incident_hour_of_the_day: num 5 8 7 5 20 19 0 23 21 14 ...Correlaciones entre variables encontradas
Gráfico de correlación entre todas las variables (25 variables)
corr_matrix <- cor(df2_numeric)
corrplot(corr_matrix, method = "circle", type = "upper",
tl.col = "black", tl.srt = 45, tl.cex = 0.8,
title = "Gráfico de Correlación entre Variables")
Gráfico barras de correlación entre todas las variables (+25 variables)
corr_matrix <- cor(df2_numeric)
correlations_tca <- corr_matrix["total_claim_amount", ]
correlations_tca## months_as_customer age policy_state
## 0.062107997 0.069862626 -0.006002148
## policy_csl policy_annual_premium umbrella_limit
## -0.055758174 0.009093729 -0.040344089
## insured_sex insured_education_level insured_occupation
## -0.023726894 0.076560823 0.003692476
## insured_relationship capital.gains capital.loss
## 0.002228922 0.015980468 -0.036060304
## incident_type incident_severity incident_state
## -0.276686293 -0.365295498 -0.043880710
## incident_city incident_hour_of_the_day property_damage
## 0.041231035 0.217702387 0.045498734
## bodily_injuries witnesses police_report_available
## 0.047092935 -0.011114188 -0.006060850
## total_claim_amount auto_make auto_year
## 1.000000000 -0.057507784 -0.035780937
## fraud_reported
## 0.163651489correlations_tca_df <- as.data.frame(correlations_tca)
correlations_tca_df$Variable <- rownames(correlations_tca_df)
rownames(correlations_tca_df) <- NULL
names(correlations_tca_df)[1] <- "Correlation"
correlations_tca_df$Color <- ifelse(correlations_tca_df$Correlation > 0, "blue", "red")
ggplot(data = correlations_tca_df, aes(x = Variable, y = Correlation, fill = Color)) +
geom_bar(stat = "identity") +
geom_text(aes(label = round(Correlation, 2)), vjust = -0.5, size = 3.5) + # Agregar valores encima de las barras
labs(title = "Correlación de Total Claim Amount con las Variables",
x = "Variables",
y = "Correlación con TCA") +
scale_fill_manual(values = c("blue", "red")) + # Asignar colores manualmente
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Gráfico barras de correlación entre variables significantes (7 variables)
# Gráfico de correlación entre las variables significativas numericas
corr_matrix2 <- cor(df_sig_2_numeric)
correlations_tca2 <- corr_matrix2["total_claim_amount", ]
correlations_tca2## total_claim_amount incident_type insured_occupation
## 1.000000000 -0.276686293 0.003692476
## insured_education_level incident_severity property_damage
## 0.076560823 -0.365295498 0.045498734
## incident_city
## 0.041231035correlations_tca_df2 <- as.data.frame(correlations_tca2)
correlations_tca_df2$Variable <- rownames(correlations_tca_df2)
rownames(correlations_tca_df2) <- NULL
names(correlations_tca_df2)[1] <- "Correlation"
correlations_tca_df2$Color <- ifelse(correlations_tca_df2$Correlation > 0, "blue", "red")
ggplot(data = correlations_tca_df2, aes(x = Variable, y = Correlation, fill = Color)) +
geom_bar(stat = "identity") +
geom_text(aes(label = round(Correlation, 2)), vjust = -0.5, size = 3.5) + # Agregar valores encima de las barras
labs(title = "Correlación de Total Claim Amount con las Variables significativas",
x = "Variables",
y = "Correlación con TCA") +
scale_fill_manual(values = c("blue", "red")) + # Asignar colores manualmente
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Gráfico barras de correlación entre variables significantes (9 variables)
# Gráfico de correlación entre las variables significativas
corr_matrix3 <- cor(df_sig_3_numeric)
correlations_tca3 <- corr_matrix3["total_claim_amount", ]
correlations_tca3## total_claim_amount incident_type insured_occupation
## 1.000000000 -0.276686293 0.003692476
## insured_education_level incident_severity property_damage
## 0.076560823 -0.365295498 0.045498734
## incident_city age incident_hour_of_the_day
## 0.041231035 0.069862626 0.217702387correlations_tca_df3 <- as.data.frame(correlations_tca3)
correlations_tca_df3$Variable <- rownames(correlations_tca_df3)
rownames(correlations_tca_df3) <- NULL
names(correlations_tca_df3)[1] <- "Correlation"
correlations_tca_df3$Color <- ifelse(correlations_tca_df3$Correlation > 0, "blue", "red")
ggplot(data = correlations_tca_df3, aes(x = Variable, y = Correlation, fill = Color)) +
geom_bar(stat = "identity") +
geom_text(aes(label = round(Correlation, 2)), vjust = -0.5, size = 3.5) + # Agregar valores encima de las barras
labs(title = "Correlación de Total Claim Amount con las Variables más significativas",
x = "Variables",
y = "Correlación con TCA") +
scale_fill_manual(values = c("blue", "red")) + # Asignar colores manualmente
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Training and Test sets
# the training set is used to build the model and the test set to evaluate its predictive accuracy.
set.seed(123) # What is set.seed()? We want to make sure that we get the same results for randomization each time you run the script.
partition <- createDataPartition(y = df$total_claim_amount, p=0.7, list=F)
train = df[partition, ]
test = df[-partition, ]
train_numeric = df2_numeric[partition, ]
test_numeric = df2_numeric[-partition, ]3. Descripción del EDA
EDA
A partir de los resultados de EDA describir la especificación del modelo de regresión lineal a estimar. Brevemente, describir cómo es el posible impacto de cada una de las variables explicativas sobre la principal variable de estudio.
Con el EDA, pudimos descubrir qué variable era la dependiente en nuestro análisis, había dos opciones que parecían viables, “total_claim_amount” y “policy_annual_premium”. Ambas mostraban una buena variabilidad, lo cual nos ayudó a observar diferentes patrones y relaciones con los datos. Al final del día, la variable dependiente definitiva resultó ser “total_claim_amount”.
De igual manera, gracias a este EDA, pudimos familiarizarnos con la base de datos, revisando las correlaciones, la variabilidad y la distribución de los datos. Se hicieron visualizaciones generales para identificar y mostrar de manera más sencilla los resultados. Además, pudimos observar el comportamiento de todas las variables explicativas con la variable dependiente en un mismo gráfico.
Hallazgos:
Se identificaron 2 o 3 variables con suficiente variabilidad para ser consideradas como variables dependientes, pero solo 2 de ellas estaban relacionadas con los gastos del seguro.
La mayoría de los incidentes fueron choques, especialmente multi-colisiones.
Solo tres variables mostraron una correlación mayor a 0.1. Estas son:
- incident hour of the day: 0.22
- incident severity: -0.37
- incident type: -0.28
- La mayoría de los incidentes ocurrieron entre las 12 a.m., 4 a.m., 6 p.m. y 11 p.m. Se observó un patrón de choques, principalmente entre las 5 a.m. y las 9 a.m., seguido de un aumento en los choques a partir de las 11 a.m. hasta las 6 p.m., donde comenzaron a disminuir hasta las 11 p.m.
Cambios en la Base de Datos
Se realizaron diversos ajustes en la base de datos para prepararla adecuadamente para análisis y modelado. Esto incluyó la eliminación de columnas con valores NA para evitar errores en los cálculos subsiguientes, así como la conversión de variables de tipo character a factor y de integer a numeric. Posteriormente, se duplicó la base de datos limpia para convertirla completamente en datos numéricos, lo que facilitaría su uso en modelos futuros. Utilizando un modelo de regresión y las correlaciones identificadas durante el EDA, se realizaron tres ajustes adicionales a la base de datos. En el primer ajuste, se eliminaron variables no significativas y se realizó un modelo de OLS con las restantes, evaluando su comportamiento y correlación con la variable dependiente mediante gráficos. En el segundo ajuste, se eliminaron más variables poco significativas, conservando solo aquellas que podrían tener un impacto percibido. Por último, en el tercer ajuste se buscaron mejoras en el modelo OLS y se abordaron problemas como la heterocedasticidad y la multicolinealidad. Finalmente, todas las bases de datos resultantes se duplicaron para asegurar que estuvieran disponibles en formato numérico para futuros análisis.
4. Estimación de modelos de Supervised Machine Learning (SML):
OLS Regresión
OLS con base principal
# OLS con base principal
ols_model <- lm(total_claim_amount ~., data = df)
summary(ols_model)##
## Call:
## lm(formula = total_claim_amount ~ ., data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -39193 -8330 -265 7259 47104
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.634e+05 1.596e+05 1.024 0.3060
## months_as_customer -8.262e+00 1.059e+01 -0.780 0.4356
## age 1.888e+02 1.332e+02 1.417 0.1568
## policy_stateIN 1.463e+03 1.185e+03 1.235 0.2173
## policy_stateOH -2.420e+01 1.139e+03 -0.021 0.9831
## policy_csl250/500 -5.353e+02 1.135e+03 -0.472 0.6373
## policy_csl500/1000 7.629e+01 1.185e+03 0.064 0.9487
## policy_annual_premium -1.657e+00 1.943e+00 -0.853 0.3938
## umbrella_limit 4.998e-06 2.075e-04 0.024 0.9808
## insured_sexMALE -6.488e+02 9.436e+02 -0.688 0.4919
## insured_education_levelCollege -3.297e+03 1.877e+03 -1.756 0.0793
## insured_education_levelHigh School 2.268e+03 1.739e+03 1.304 0.1925
## insured_education_levelJD -7.920e+02 1.739e+03 -0.455 0.6490
## insured_education_levelMasters 2.166e+03 1.773e+03 1.222 0.2222
## insured_education_levelMD 1.487e+03 1.774e+03 0.838 0.4022
## insured_education_levelPhD 1.642e+03 1.855e+03 0.885 0.3765
## insured_occupationarmed-forces 1.582e+03 2.590e+03 0.611 0.5414
## insured_occupationcraft-repair 1.528e+03 2.560e+03 0.597 0.5506
## insured_occupationexec-managerial 2.342e+03 2.553e+03 0.917 0.3591
## insured_occupationfarming-fishing -3.811e+03 2.783e+03 -1.369 0.1712
## insured_occupationhandlers-cleaners 4.180e+03 2.760e+03 1.515 0.1302
## insured_occupationmachine-op-inspct -3.404e+02 2.419e+03 -0.141 0.8881
## insured_occupationother-service 1.364e+03 2.574e+03 0.530 0.5963
## insured_occupationpriv-house-serv 1.944e+03 2.591e+03 0.751 0.4531
## insured_occupationprof-specialty 2.302e+03 2.436e+03 0.945 0.3451
## insured_occupationprotective-serv 1.454e+03 2.627e+03 0.554 0.5800
## insured_occupationsales -8.387e+02 2.510e+03 -0.334 0.7383
## insured_occupationtech-support 1.486e+03 2.486e+03 0.598 0.5501
## insured_occupationtransport-moving 2.320e+03 2.552e+03 0.909 0.3636
## insured_relationshipnot-in-family 1.156e+01 1.635e+03 0.007 0.9944
## insured_relationshipother-relative 9.239e+02 1.616e+03 0.572 0.5677
## insured_relationshipown-child -6.776e+02 1.601e+03 -0.423 0.6721
## insured_relationshipunmarried 3.065e+01 1.711e+03 0.018 0.9857
## insured_relationshipwife 1.268e+03 1.672e+03 0.758 0.4485
## capital.gains -9.130e-03 1.707e-02 -0.535 0.5928
## capital.loss -1.823e-02 1.706e-02 -1.069 0.2855
## incident_typeParked Car -5.545e+04 2.284e+03 -24.276 <2e-16
## incident_typeSingle Vehicle Collision 2.610e+03 1.051e+03 2.484 0.0132
## incident_typeVehicle Theft -5.588e+04 2.233e+03 -25.024 <2e-16
## incident_severityMinor Damage -8.378e+02 1.439e+03 -0.582 0.5607
## incident_severityTotal Loss -1.804e+03 1.395e+03 -1.293 0.1962
## incident_severityTrivial Damage -4.592e+02 2.672e+03 -0.172 0.8636
## incident_stateNY 2.462e+03 1.700e+03 1.449 0.1478
## incident_stateOH -1.974e+03 3.441e+03 -0.574 0.5663
## incident_statePA 7.911e+02 3.069e+03 0.258 0.7966
## incident_stateSC 3.175e+03 1.728e+03 1.837 0.0665
## incident_stateVA 2.149e+03 2.033e+03 1.057 0.2908
## incident_stateWV 2.313e+03 1.781e+03 1.299 0.1943
## incident_cityColumbus 9.305e+01 1.732e+03 0.054 0.9572
## incident_cityHillsdale 1.037e+03 1.775e+03 0.584 0.5591
## incident_cityNorthbend 2.174e+03 1.730e+03 1.257 0.2093
## incident_cityNorthbrook 1.987e+02 1.816e+03 0.109 0.9129
## incident_cityRiverwood 1.994e+03 1.786e+03 1.117 0.2645
## incident_citySpringfield 3.421e+03 1.692e+03 2.022 0.0434
## incident_hour_of_the_day 3.067e+01 7.050e+01 0.435 0.6636
## property_damageNO 6.241e+02 1.142e+03 0.547 0.5848
## property_damageYES 2.821e+03 1.165e+03 2.422 0.0156
## bodily_injuries 1.298e+03 5.751e+02 2.257 0.0243
## witnesses -1.200e+02 4.256e+02 -0.282 0.7781
## police_report_availableNO -2.072e+03 1.140e+03 -1.818 0.0693
## police_report_availableYES -1.575e+02 1.174e+03 -0.134 0.8933
## auto_makeAudi 1.730e+03 2.576e+03 0.672 0.5019
## auto_makeBMW 2.874e+03 2.535e+03 1.134 0.2572
## auto_makeChevrolet 2.455e+03 2.473e+03 0.993 0.3211
## auto_makeDodge 2.629e+03 2.447e+03 1.074 0.2831
## auto_makeFord 3.712e+03 2.513e+03 1.477 0.1400
## auto_makeHonda 9.228e+01 2.694e+03 0.034 0.9727
## auto_makeJeep 8.587e+02 2.574e+03 0.334 0.7387
## auto_makeMercedes 2.440e+03 2.596e+03 0.940 0.3476
## auto_makeNissan -9.484e+02 2.481e+03 -0.382 0.7024
## auto_makeSaab -7.521e+02 2.472e+03 -0.304 0.7610
## auto_makeSuburu -9.354e+02 2.473e+03 -0.378 0.7053
## auto_makeToyota -1.122e+03 2.538e+03 -0.442 0.6586
## auto_makeVolkswagen -1.282e+03 2.552e+03 -0.502 0.6155
## auto_year -5.635e+01 7.957e+01 -0.708 0.4791
## fraud_reportedY 7.153e+02 1.278e+03 0.560 0.5758
##
## (Intercept)
## months_as_customer
## age
## policy_stateIN
## policy_stateOH
## policy_csl250/500
## policy_csl500/1000
## policy_annual_premium
## umbrella_limit
## insured_sexMALE
## insured_education_levelCollege .
## insured_education_levelHigh School
## insured_education_levelJD
## insured_education_levelMasters
## insured_education_levelMD
## insured_education_levelPhD
## insured_occupationarmed-forces
## insured_occupationcraft-repair
## insured_occupationexec-managerial
## insured_occupationfarming-fishing
## insured_occupationhandlers-cleaners
## insured_occupationmachine-op-inspct
## insured_occupationother-service
## insured_occupationpriv-house-serv
## insured_occupationprof-specialty
## insured_occupationprotective-serv
## insured_occupationsales
## insured_occupationtech-support
## insured_occupationtransport-moving
## insured_relationshipnot-in-family
## insured_relationshipother-relative
## insured_relationshipown-child
## insured_relationshipunmarried
## insured_relationshipwife
## capital.gains
## capital.loss
## incident_typeParked Car ***
## incident_typeSingle Vehicle Collision *
## incident_typeVehicle Theft ***
## incident_severityMinor Damage
## incident_severityTotal Loss
## incident_severityTrivial Damage
## incident_stateNY
## incident_stateOH
## incident_statePA
## incident_stateSC .
## incident_stateVA
## incident_stateWV
## incident_cityColumbus
## incident_cityHillsdale
## incident_cityNorthbend
## incident_cityNorthbrook
## incident_cityRiverwood
## incident_citySpringfield *
## incident_hour_of_the_day
## property_damageNO
## property_damageYES *
## bodily_injuries *
## witnesses
## police_report_availableNO .
## police_report_availableYES
## auto_makeAudi
## auto_makeBMW
## auto_makeChevrolet
## auto_makeDodge
## auto_makeFord
## auto_makeHonda
## auto_makeJeep
## auto_makeMercedes
## auto_makeNissan
## auto_makeSaab
## auto_makeSuburu
## auto_makeToyota
## auto_makeVolkswagen
## auto_year
## fraud_reportedY
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14470 on 924 degrees of freedom
## Multiple R-squared: 0.7222, Adjusted R-squared: 0.6997
## F-statistic: 32.03 on 75 and 924 DF, p-value: < 2.2e-16Log OLS con base principal
# Log OLS con base principal
log_ols_model <- lm(log(total_claim_amount) ~., data = df)
summary(log_ols_model)##
## Call:
## lm(formula = log(total_claim_amount) ~ ., data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6778 -0.1607 0.0259 0.2044 0.8614
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.283e+01 3.411e+00 3.761 0.00018
## months_as_customer -7.082e-05 2.264e-04 -0.313 0.75451
## age 2.779e-03 2.847e-03 0.976 0.32931
## policy_stateIN 3.490e-02 2.533e-02 1.378 0.16863
## policy_stateOH -2.191e-03 2.435e-02 -0.090 0.92831
## policy_csl250/500 -1.882e-03 2.426e-02 -0.078 0.93818
## policy_csl500/1000 4.694e-03 2.532e-02 0.185 0.85297
## policy_annual_premium -3.469e-05 4.152e-05 -0.836 0.40360
## umbrella_limit 3.238e-09 4.436e-09 0.730 0.46551
## insured_sexMALE 1.106e-02 2.017e-02 0.548 0.58358
## insured_education_levelCollege -7.365e-02 4.012e-02 -1.836 0.06668
## insured_education_levelHigh School 3.130e-02 3.718e-02 0.842 0.40011
## insured_education_levelJD -2.854e-03 3.718e-02 -0.077 0.93882
## insured_education_levelMasters 1.351e-02 3.790e-02 0.357 0.72149
## insured_education_levelMD -1.488e-02 3.792e-02 -0.393 0.69477
## insured_education_levelPhD 2.490e-02 3.965e-02 0.628 0.53022
## insured_occupationarmed-forces 7.385e-02 5.535e-02 1.334 0.18245
## insured_occupationcraft-repair 7.423e-02 5.471e-02 1.357 0.17518
## insured_occupationexec-managerial 8.876e-02 5.457e-02 1.627 0.10417
## insured_occupationfarming-fishing 1.343e-02 5.949e-02 0.226 0.82150
## insured_occupationhandlers-cleaners 9.516e-02 5.899e-02 1.613 0.10705
## insured_occupationmachine-op-inspct 3.914e-02 5.169e-02 0.757 0.44911
## insured_occupationother-service 8.161e-02 5.503e-02 1.483 0.13839
## insured_occupationpriv-house-serv 9.825e-02 5.537e-02 1.774 0.07632
## insured_occupationprof-specialty 7.702e-02 5.208e-02 1.479 0.13947
## insured_occupationprotective-serv 7.427e-02 5.614e-02 1.323 0.18619
## insured_occupationsales 2.466e-02 5.364e-02 0.460 0.64587
## insured_occupationtech-support -7.908e-03 5.313e-02 -0.149 0.88172
## insured_occupationtransport-moving 1.033e-01 5.454e-02 1.895 0.05845
## insured_relationshipnot-in-family -6.159e-03 3.494e-02 -0.176 0.86011
## insured_relationshipother-relative 1.852e-02 3.455e-02 0.536 0.59205
## insured_relationshipown-child -1.087e-02 3.421e-02 -0.318 0.75079
## insured_relationshipunmarried -1.027e-02 3.658e-02 -0.281 0.77891
## insured_relationshipwife 9.811e-03 3.574e-02 0.275 0.78376
## capital.gains -4.669e-07 3.648e-07 -1.280 0.20089
## capital.loss -1.236e-07 3.645e-07 -0.339 0.73472
## incident_typeParked Car -2.455e+00 4.883e-02 -50.273 < 2e-16
## incident_typeSingle Vehicle Collision 4.320e-02 2.246e-02 1.924 0.05468
## incident_typeVehicle Theft -2.386e+00 4.773e-02 -49.997 < 2e-16
## incident_severityMinor Damage -2.717e-02 3.077e-02 -0.883 0.37734
## incident_severityTotal Loss -3.658e-02 2.982e-02 -1.227 0.22025
## incident_severityTrivial Damage -7.576e-02 5.710e-02 -1.327 0.18492
## incident_stateNY 4.032e-02 3.633e-02 1.110 0.26733
## incident_stateOH -6.550e-03 7.354e-02 -0.089 0.92905
## incident_statePA 2.861e-02 6.559e-02 0.436 0.66283
## incident_stateSC 6.115e-02 3.693e-02 1.656 0.09810
## incident_stateVA 3.478e-02 4.346e-02 0.800 0.42374
## incident_stateWV 7.310e-03 3.807e-02 0.192 0.84774
## incident_cityColumbus -9.938e-04 3.702e-02 -0.027 0.97859
## incident_cityHillsdale 1.938e-02 3.794e-02 0.511 0.60963
## incident_cityNorthbend 2.851e-02 3.698e-02 0.771 0.44101
## incident_cityNorthbrook -1.958e-02 3.881e-02 -0.505 0.61400
## incident_cityRiverwood 2.003e-02 3.816e-02 0.525 0.59982
## incident_citySpringfield 4.937e-02 3.616e-02 1.365 0.17250
## incident_hour_of_the_day 1.406e-03 1.507e-03 0.933 0.35121
## property_damageNO 6.122e-03 2.440e-02 0.251 0.80198
## property_damageYES 3.838e-02 2.490e-02 1.542 0.12352
## bodily_injuries 2.004e-02 1.229e-02 1.630 0.10344
## witnesses -2.803e-03 9.096e-03 -0.308 0.75802
## police_report_availableNO -2.239e-02 2.436e-02 -0.919 0.35823
## police_report_availableYES 1.182e-02 2.509e-02 0.471 0.63773
## auto_makeAudi -7.845e-02 5.505e-02 -1.425 0.15450
## auto_makeBMW 3.162e-02 5.418e-02 0.584 0.55967
## auto_makeChevrolet -2.042e-02 5.287e-02 -0.386 0.69936
## auto_makeDodge 3.174e-02 5.231e-02 0.607 0.54417
## auto_makeFord 3.815e-03 5.372e-02 0.071 0.94340
## auto_makeHonda -3.189e-02 5.758e-02 -0.554 0.57979
## auto_makeJeep -3.171e-02 5.501e-02 -0.577 0.56440
## auto_makeMercedes 2.921e-02 5.549e-02 0.526 0.59871
## auto_makeNissan -1.137e-02 5.304e-02 -0.214 0.83034
## auto_makeSaab -3.476e-02 5.284e-02 -0.658 0.51083
## auto_makeSuburu -3.871e-02 5.286e-02 -0.732 0.46417
## auto_makeToyota -4.772e-02 5.424e-02 -0.880 0.37924
## auto_makeVolkswagen -5.664e-02 5.455e-02 -1.038 0.29938
## auto_year -1.001e-03 1.701e-03 -0.589 0.55616
## fraud_reportedY 8.491e-03 2.732e-02 0.311 0.75601
##
## (Intercept) ***
## months_as_customer
## age
## policy_stateIN
## policy_stateOH
## policy_csl250/500
## policy_csl500/1000
## policy_annual_premium
## umbrella_limit
## insured_sexMALE
## insured_education_levelCollege .
## insured_education_levelHigh School
## insured_education_levelJD
## insured_education_levelMasters
## insured_education_levelMD
## insured_education_levelPhD
## insured_occupationarmed-forces
## insured_occupationcraft-repair
## insured_occupationexec-managerial
## insured_occupationfarming-fishing
## insured_occupationhandlers-cleaners
## insured_occupationmachine-op-inspct
## insured_occupationother-service
## insured_occupationpriv-house-serv .
## insured_occupationprof-specialty
## insured_occupationprotective-serv
## insured_occupationsales
## insured_occupationtech-support
## insured_occupationtransport-moving .
## insured_relationshipnot-in-family
## insured_relationshipother-relative
## insured_relationshipown-child
## insured_relationshipunmarried
## insured_relationshipwife
## capital.gains
## capital.loss
## incident_typeParked Car ***
## incident_typeSingle Vehicle Collision .
## incident_typeVehicle Theft ***
## incident_severityMinor Damage
## incident_severityTotal Loss
## incident_severityTrivial Damage
## incident_stateNY
## incident_stateOH
## incident_statePA
## incident_stateSC .
## incident_stateVA
## incident_stateWV
## incident_cityColumbus
## incident_cityHillsdale
## incident_cityNorthbend
## incident_cityNorthbrook
## incident_cityRiverwood
## incident_citySpringfield
## incident_hour_of_the_day
## property_damageNO
## property_damageYES
## bodily_injuries
## witnesses
## police_report_availableNO
## police_report_availableYES
## auto_makeAudi
## auto_makeBMW
## auto_makeChevrolet
## auto_makeDodge
## auto_makeFord
## auto_makeHonda
## auto_makeJeep
## auto_makeMercedes
## auto_makeNissan
## auto_makeSaab
## auto_makeSuburu
## auto_makeToyota
## auto_makeVolkswagen
## auto_year
## fraud_reportedY
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3092 on 924 degrees of freedom
## Multiple R-squared: 0.9117, Adjusted R-squared: 0.9045
## F-statistic: 127.2 on 75 and 924 DF, p-value: < 2.2e-16AIC OLS base principal
AIC(ols_model)## [1] 22072.33AIC(log_ols_model) ## [1] 565.616RMSE’s base principal
RMSE_ols_model <- sqrt(mean(ols_model$residuals^2))
RMSE_ols_model## [1] 13908.13RMSE_log_ols_model <- sqrt(mean(log_ols_model$residuals^2))
RMSE_log_ols_model## [1] 0.2972652OLS con base ajustada 1
ols_model_df_sig <- lm(total_claim_amount ~., data = df_sig)
summary(ols_model_df_sig)##
## Call:
## lm(formula = total_claim_amount ~ ., data = df_sig)
##
## Residuals:
## Min 1Q Median 3Q Max
## -41470 -8718 -439 7693 46243
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 57916.65 2779.61 20.836 < 2e-16 ***
## incident_typeParked Car -55791.79 2225.91 -25.065 < 2e-16 ***
## incident_typeSingle Vehicle Collision 2835.55 1025.80 2.764 0.00581 **
## incident_typeVehicle Theft -55987.07 2168.36 -25.820 < 2e-16 ***
## insured_occupationarmed-forces 866.48 2527.00 0.343 0.73176
## insured_occupationcraft-repair 1145.14 2495.97 0.459 0.64648
## insured_occupationexec-managerial 2051.07 2487.04 0.825 0.40974
## insured_occupationfarming-fishing -4331.04 2723.36 -1.590 0.11209
## insured_occupationhandlers-cleaners 3663.19 2697.60 1.358 0.17480
## insured_occupationmachine-op-inspct -749.17 2360.69 -0.317 0.75105
## insured_occupationother-service 827.46 2506.95 0.330 0.74142
## insured_occupationpriv-house-serv 1386.16 2499.79 0.555 0.57936
## insured_occupationprof-specialty 1770.95 2397.51 0.739 0.46029
## insured_occupationprotective-serv 778.20 2574.28 0.302 0.76249
## insured_occupationsales -1141.03 2452.66 -0.465 0.64188
## insured_occupationtech-support 945.65 2443.17 0.387 0.69880
## insured_occupationtransport-moving 1732.45 2513.35 0.689 0.49080
## insured_education_levelCollege -2534.71 1816.36 -1.395 0.16319
## insured_education_levelHigh School 1904.28 1695.11 1.123 0.26155
## insured_education_levelJD -542.18 1695.41 -0.320 0.74919
## insured_education_levelMasters 2111.80 1737.67 1.215 0.22455
## insured_education_levelMD 1612.09 1732.79 0.930 0.35242
## insured_education_levelPhD 1180.27 1804.92 0.654 0.51332
## incident_severityMinor Damage -833.18 1404.44 -0.593 0.55316
## incident_severityTotal Loss -1644.82 1376.64 -1.195 0.23246
## incident_severityTrivial Damage -483.53 2611.60 -0.185 0.85315
## property_damageNO 503.70 1117.03 0.451 0.65214
## property_damageYES 2671.52 1140.76 2.342 0.01939 *
## incident_cityColumbus 891.84 1695.04 0.526 0.59891
## incident_cityHillsdale 1472.32 1720.93 0.856 0.39247
## incident_cityNorthbend 2437.06 1695.75 1.437 0.15100
## incident_cityNorthbrook 566.41 1787.57 0.317 0.75142
## incident_cityRiverwood 2152.00 1741.14 1.236 0.21677
## incident_citySpringfield 3710.59 1662.46 2.232 0.02584 *
## incident_hour_of_the_day 29.20 69.08 0.423 0.67257
## fraud_reportedY 981.96 1250.82 0.785 0.43262
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14450 on 964 degrees of freedom
## Multiple R-squared: 0.7108, Adjusted R-squared: 0.7004
## F-statistic: 67.71 on 35 and 964 DF, p-value: < 2.2e-16OLS con base ajustada 2
ols_model_df_sig_2 <- lm(total_claim_amount ~., data = df_sig_2)
summary(ols_model_df_sig_2)##
## Call:
## lm(formula = total_claim_amount ~ ., data = df_sig_2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -41389 -8868 -385 7578 46233
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 58835.8 2548.4 23.088 < 2e-16 ***
## incident_typeParked Car -55892.9 2201.8 -25.386 < 2e-16 ***
## incident_typeSingle Vehicle Collision 2852.2 1023.3 2.787 0.00542 **
## incident_typeVehicle Theft -56092.3 2142.7 -26.178 < 2e-16 ***
## insured_occupationarmed-forces 912.6 2524.0 0.362 0.71775
## insured_occupationcraft-repair 1220.2 2492.5 0.490 0.62458
## insured_occupationexec-managerial 2198.5 2479.6 0.887 0.37550
## insured_occupationfarming-fishing -4238.4 2717.5 -1.560 0.11917
## insured_occupationhandlers-cleaners 3646.1 2691.7 1.355 0.17588
## insured_occupationmachine-op-inspct -725.6 2358.7 -0.308 0.75842
## insured_occupationother-service 801.3 2504.1 0.320 0.74904
## insured_occupationpriv-house-serv 1421.4 2495.3 0.570 0.56907
## insured_occupationprof-specialty 1746.7 2395.7 0.729 0.46612
## insured_occupationprotective-serv 807.1 2571.9 0.314 0.75374
## insured_occupationsales -1076.8 2449.9 -0.440 0.66037
## insured_occupationtech-support 1007.0 2440.5 0.413 0.67997
## insured_occupationtransport-moving 1779.8 2511.2 0.709 0.47864
## insured_education_levelCollege -2497.6 1814.1 -1.377 0.16891
## insured_education_levelHigh School 1895.4 1691.2 1.121 0.26267
## insured_education_levelJD -494.6 1691.2 -0.292 0.77000
## insured_education_levelMasters 2082.7 1735.1 1.200 0.23029
## insured_education_levelMD 1597.7 1726.5 0.925 0.35498
## insured_education_levelPhD 1164.1 1798.4 0.647 0.51760
## incident_severityMinor Damage -1319.9 1260.0 -1.048 0.29510
## incident_severityTotal Loss -2087.9 1241.3 -1.682 0.09289 .
## incident_severityTrivial Damage -1027.4 2516.9 -0.408 0.68322
## property_damageNO 484.2 1114.7 0.434 0.66413
## property_damageYES 2691.4 1137.2 2.367 0.01815 *
## incident_cityColumbus 877.9 1692.8 0.519 0.60417
## incident_cityHillsdale 1473.5 1718.1 0.858 0.39132
## incident_cityNorthbend 2419.2 1693.0 1.429 0.15335
## incident_cityNorthbrook 503.8 1784.6 0.282 0.77779
## incident_cityRiverwood 2128.7 1737.6 1.225 0.22084
## incident_citySpringfield 3673.4 1660.2 2.213 0.02716 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14440 on 966 degrees of freedom
## Multiple R-squared: 0.7106, Adjusted R-squared: 0.7007
## F-statistic: 71.88 on 33 and 966 DF, p-value: < 2.2e-16Log OLS con base ajustada 2
log_ols_model_df_sig_2 <- lm(log(total_claim_amount) ~., data = df_sig_2)
summary(ols_model_df_sig_2)##
## Call:
## lm(formula = total_claim_amount ~ ., data = df_sig_2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -41389 -8868 -385 7578 46233
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 58835.8 2548.4 23.088 < 2e-16 ***
## incident_typeParked Car -55892.9 2201.8 -25.386 < 2e-16 ***
## incident_typeSingle Vehicle Collision 2852.2 1023.3 2.787 0.00542 **
## incident_typeVehicle Theft -56092.3 2142.7 -26.178 < 2e-16 ***
## insured_occupationarmed-forces 912.6 2524.0 0.362 0.71775
## insured_occupationcraft-repair 1220.2 2492.5 0.490 0.62458
## insured_occupationexec-managerial 2198.5 2479.6 0.887 0.37550
## insured_occupationfarming-fishing -4238.4 2717.5 -1.560 0.11917
## insured_occupationhandlers-cleaners 3646.1 2691.7 1.355 0.17588
## insured_occupationmachine-op-inspct -725.6 2358.7 -0.308 0.75842
## insured_occupationother-service 801.3 2504.1 0.320 0.74904
## insured_occupationpriv-house-serv 1421.4 2495.3 0.570 0.56907
## insured_occupationprof-specialty 1746.7 2395.7 0.729 0.46612
## insured_occupationprotective-serv 807.1 2571.9 0.314 0.75374
## insured_occupationsales -1076.8 2449.9 -0.440 0.66037
## insured_occupationtech-support 1007.0 2440.5 0.413 0.67997
## insured_occupationtransport-moving 1779.8 2511.2 0.709 0.47864
## insured_education_levelCollege -2497.6 1814.1 -1.377 0.16891
## insured_education_levelHigh School 1895.4 1691.2 1.121 0.26267
## insured_education_levelJD -494.6 1691.2 -0.292 0.77000
## insured_education_levelMasters 2082.7 1735.1 1.200 0.23029
## insured_education_levelMD 1597.7 1726.5 0.925 0.35498
## insured_education_levelPhD 1164.1 1798.4 0.647 0.51760
## incident_severityMinor Damage -1319.9 1260.0 -1.048 0.29510
## incident_severityTotal Loss -2087.9 1241.3 -1.682 0.09289 .
## incident_severityTrivial Damage -1027.4 2516.9 -0.408 0.68322
## property_damageNO 484.2 1114.7 0.434 0.66413
## property_damageYES 2691.4 1137.2 2.367 0.01815 *
## incident_cityColumbus 877.9 1692.8 0.519 0.60417
## incident_cityHillsdale 1473.5 1718.1 0.858 0.39132
## incident_cityNorthbend 2419.2 1693.0 1.429 0.15335
## incident_cityNorthbrook 503.8 1784.6 0.282 0.77779
## incident_cityRiverwood 2128.7 1737.6 1.225 0.22084
## incident_citySpringfield 3673.4 1660.2 2.213 0.02716 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14440 on 966 degrees of freedom
## Multiple R-squared: 0.7106, Adjusted R-squared: 0.7007
## F-statistic: 71.88 on 33 and 966 DF, p-value: < 2.2e-16AIC OLS base ajustada 2
AIC(ols_model_df_sig_2) ## [1] 22029.23AIC(log_ols_model_df_sig_2) ## [1] 515.2428RMSE’s OLS base ajustada 2
RMSE_ols_model_df_sig_2 <- sqrt(mean(ols_model_df_sig_2$residuals^2))
RMSE_ols_model_df_sig_2## [1] 14195.44RMSE_log_ols_model_df_sig_2 <- sqrt(mean(log_ols_model_df_sig_2$residuals^2))
RMSE_log_ols_model_df_sig_2## [1] 0.3023055OLS con base ajustada 3
ols_model_df_sig_3 <- lm(total_claim_amount ~., data = df_sig_3)
summary(ols_model_df_sig_3)##
## Call:
## lm(formula = total_claim_amount ~ ., data = df_sig_3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -41309 -8685 -459 7380 46216
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 55336.33 3312.81 16.704 < 2e-16 ***
## incident_typeParked Car -55772.28 2223.38 -25.084 < 2e-16 ***
## incident_typeSingle Vehicle Collision 2876.00 1023.84 2.809 0.00507 **
## incident_typeVehicle Theft -55917.60 2166.09 -25.815 < 2e-16 ***
## insured_occupationarmed-forces 1016.53 2525.70 0.402 0.68742
## insured_occupationcraft-repair 1222.72 2492.83 0.490 0.62390
## insured_occupationexec-managerial 2241.59 2481.31 0.903 0.36655
## insured_occupationfarming-fishing -4188.89 2720.93 -1.540 0.12401
## insured_occupationhandlers-cleaners 3631.39 2693.56 1.348 0.17792
## insured_occupationmachine-op-inspct -678.36 2358.57 -0.288 0.77370
## insured_occupationother-service 817.48 2503.74 0.327 0.74411
## insured_occupationpriv-house-serv 1442.52 2497.49 0.578 0.56368
## insured_occupationprof-specialty 1779.14 2394.84 0.743 0.45772
## insured_occupationprotective-serv 851.42 2571.94 0.331 0.74068
## insured_occupationsales -1075.76 2449.51 -0.439 0.66063
## insured_occupationtech-support 1079.58 2440.74 0.442 0.65836
## insured_occupationtransport-moving 1713.59 2510.44 0.683 0.49503
## insured_education_levelCollege -2376.25 1814.85 -1.309 0.19073
## insured_education_levelHigh School 2075.61 1695.31 1.224 0.22113
## insured_education_levelJD -311.52 1694.28 -0.184 0.85416
## insured_education_levelMasters 2199.36 1736.71 1.266 0.20568
## insured_education_levelMD 1596.91 1730.67 0.923 0.35639
## insured_education_levelPhD 1281.17 1802.91 0.711 0.47750
## incident_severityMinor Damage -1337.51 1259.53 -1.062 0.28854
## incident_severityTotal Loss -2033.86 1242.79 -1.637 0.10206
## incident_severityTrivial Damage -926.38 2516.63 -0.368 0.71288
## property_damageNO 462.30 1114.95 0.415 0.67850
## property_damageYES 2666.98 1139.49 2.341 0.01946 *
## incident_cityColumbus 750.47 1694.11 0.443 0.65788
## incident_cityHillsdale 1315.39 1720.69 0.764 0.44478
## incident_cityNorthbend 2307.57 1694.14 1.362 0.17349
## incident_cityNorthbrook 498.56 1783.92 0.279 0.77994
## incident_cityRiverwood 2044.55 1738.52 1.176 0.23987
## incident_citySpringfield 3593.59 1660.25 2.164 0.03067 *
## age 82.15 50.66 1.622 0.10521
## incident_hour_of_the_day 18.21 69.21 0.263 0.79249
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14440 on 964 degrees of freedom
## Multiple R-squared: 0.7115, Adjusted R-squared: 0.701
## F-statistic: 67.91 on 35 and 964 DF, p-value: < 2.2e-16log_ols_model_df_sig_3_2 <- lm(log(total_claim_amount) ~ incident_type + insured_occupation + log(insured_education_level) + incident_severity + log(property_damage) + log(incident_city) + age + incident_hour_of_the_day, data = df_sig_3_numeric)
summary(log_ols_model_df_sig_3_2)##
## Call:
## lm(formula = log(total_claim_amount) ~ incident_type + insured_occupation +
## log(insured_education_level) + incident_severity + log(property_damage) +
## log(incident_city) + age + incident_hour_of_the_day, data = df_sig_3_numeric)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2388 -0.3865 0.1808 0.5714 1.6898
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.278528 0.177817 63.428 < 2e-16 ***
## incident_type -0.235892 0.025387 -9.292 < 2e-16 ***
## insured_occupation 0.002060 0.006690 0.308 0.75823
## log(insured_education_level) 0.125780 0.043044 2.922 0.00356 **
## incident_severity -0.375571 0.029041 -12.932 < 2e-16 ***
## log(property_damage) 0.019271 0.059279 0.325 0.74518
## log(incident_city) 0.040423 0.041561 0.973 0.33098
## age 0.001927 0.002955 0.652 0.51442
## incident_hour_of_the_day 0.027891 0.003938 7.083 2.67e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8491 on 991 degrees of freedom
## Multiple R-squared: 0.286, Adjusted R-squared: 0.2802
## F-statistic: 49.62 on 8 and 991 DF, p-value: < 2.2e-16#str(df_sig_3)Log OLS con base ajustada 3
log_ols_model_df_sig_3 <- lm(log(total_claim_amount) ~., data = df_sig_3)
summary(log_ols_model_df_sig_3)##
## Call:
## lm(formula = log(total_claim_amount) ~ ., data = df_sig_3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7561 -0.1617 0.0304 0.2041 0.8732
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.8521344 0.0705248 153.877 <2e-16
## incident_typeParked Car -2.4536276 0.0473324 -51.838 <2e-16
## incident_typeSingle Vehicle Collision 0.0494580 0.0217960 2.269 0.0235
## incident_typeVehicle Theft -2.3843704 0.0461130 -51.707 <2e-16
## insured_occupationarmed-forces 0.0585178 0.0537685 1.088 0.2767
## insured_occupationcraft-repair 0.0623392 0.0530686 1.175 0.2404
## insured_occupationexec-managerial 0.0841949 0.0528235 1.594 0.1113
## insured_occupationfarming-fishing -0.0011762 0.0579246 -0.020 0.9838
## insured_occupationhandlers-cleaners 0.0892386 0.0573419 1.556 0.1200
## insured_occupationmachine-op-inspct 0.0302661 0.0502105 0.603 0.5468
## insured_occupationother-service 0.0694957 0.0533010 1.304 0.1926
## insured_occupationpriv-house-serv 0.0871831 0.0531680 1.640 0.1014
## insured_occupationprof-specialty 0.0671224 0.0509825 1.317 0.1883
## insured_occupationprotective-serv 0.0577667 0.0547529 1.055 0.2917
## insured_occupationsales 0.0235741 0.0521464 0.452 0.6513
## insured_occupationtech-support -0.0156746 0.0519598 -0.302 0.7630
## insured_occupationtransport-moving 0.0937129 0.0534437 1.753 0.0798
## insured_education_levelCollege -0.0529995 0.0386356 -1.372 0.1705
## insured_education_levelHigh School 0.0304977 0.0360906 0.845 0.3983
## insured_education_levelJD 0.0100943 0.0360687 0.280 0.7796
## insured_education_levelMasters 0.0235745 0.0369720 0.638 0.5239
## insured_education_levelMD -0.0096042 0.0368434 -0.261 0.7944
## insured_education_levelPhD 0.0161010 0.0383812 0.420 0.6749
## incident_severityMinor Damage -0.0311071 0.0268136 -1.160 0.2463
## incident_severityTotal Loss -0.0422740 0.0264572 -1.598 0.1104
## incident_severityTrivial Damage -0.0902544 0.0535754 -1.685 0.0924
## property_damageNO 0.0005451 0.0237357 0.023 0.9817
## property_damageYES 0.0351187 0.0242580 1.448 0.1480
## incident_cityColumbus 0.0073501 0.0360652 0.204 0.8386
## incident_cityHillsdale 0.0203812 0.0366309 0.556 0.5781
## incident_cityNorthbend 0.0254538 0.0360657 0.706 0.4805
## incident_cityNorthbrook -0.0193846 0.0379771 -0.510 0.6099
## incident_cityRiverwood 0.0177731 0.0370105 0.480 0.6312
## incident_citySpringfield 0.0522992 0.0353443 1.480 0.1393
## age 0.0017972 0.0010785 1.666 0.0960
## incident_hour_of_the_day 0.0010141 0.0014734 0.688 0.4915
##
## (Intercept) ***
## incident_typeParked Car ***
## incident_typeSingle Vehicle Collision *
## incident_typeVehicle Theft ***
## insured_occupationarmed-forces
## insured_occupationcraft-repair
## insured_occupationexec-managerial
## insured_occupationfarming-fishing
## insured_occupationhandlers-cleaners
## insured_occupationmachine-op-inspct
## insured_occupationother-service
## insured_occupationpriv-house-serv
## insured_occupationprof-specialty
## insured_occupationprotective-serv
## insured_occupationsales
## insured_occupationtech-support
## insured_occupationtransport-moving .
## insured_education_levelCollege
## insured_education_levelHigh School
## insured_education_levelJD
## insured_education_levelMasters
## insured_education_levelMD
## insured_education_levelPhD
## incident_severityMinor Damage
## incident_severityTotal Loss
## incident_severityTrivial Damage .
## property_damageNO
## property_damageYES
## incident_cityColumbus
## incident_cityHillsdale
## incident_cityNorthbend
## incident_cityNorthbrook
## incident_cityRiverwood
## incident_citySpringfield
## age .
## incident_hour_of_the_day
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3073 on 964 degrees of freedom
## Multiple R-squared: 0.909, Adjusted R-squared: 0.9057
## F-statistic: 275.1 on 35 and 964 DF, p-value: < 2.2e-16AIC OLS base ajustada 3
AIC(ols_model_df_sig_3) ## [1] 22030.34AIC(log_ols_model_df_sig_3) ## [1] 515.6572RMSE OLS base ajustada 3
RMSE_ols_model_df_sig_3 <- sqrt(mean(ols_model_df_sig_3$residuals^2))
RMSE_ols_model_df_sig_3## [1] 14174.95RMSE_log_ols_model_df_sig_3 <- sqrt(mean(log_ols_model_df_sig_3$residuals^2))
RMSE_log_ols_model_df_sig_3## [1] 0.301764XGBoost Regresión
# DATOS DE ENTRENAMIENTO DATA FRAME significante 2
set.seed(123) # What is set.seed()? We want to make sure that we get the same results for randomization each time you run the script.
cv_data <- createDataPartition(y = df_sig_2$total_claim_amount, p=0.7, list=F)
cv_train = df_sig_2[cv_data, ]
cv_test = df_sig_2[-cv_data, ]
# define explanatory variables (X's) and dependent variable (Y) in training set
train_x = data.matrix(cv_train[, -1])
train_y = cv_train[,1]
# define explanatory variables (X's) and dependent variable (Y) in testing set
test_x = data.matrix(cv_test[, -1])
test_y = cv_test[, 1]# DATOS DE ENTRENAMIENTO DATA FRAME significante 3
set.seed(123) # What is set.seed()? We want to make sure that we get the same results for randomization each time you run the script.
cv_data2 <- createDataPartition(y = df_sig_3$total_claim_amount, p=0.7, list=F)
cv_train2 = df_sig_3[cv_data2, ]
cv_test2 = df_sig_3[-cv_data2, ]
# define explanatory variables (X's) and dependent variable (Y) in training set
train_x2 = data.matrix(cv_train2[, -1])
train_y2 = cv_train2[,1]
# define explanatory variables (X's) and dependent variable (Y) in testing set
test_x2 = data.matrix(cv_test2[, -1])
test_y2 = cv_test2[, 1]XGBoost df ajustada 2
# XGBoost de significante 2
# define final training and testing sets
xgb_train = xgb.DMatrix(data = train_x, label = train_y)
xgb_test = xgb.DMatrix(data = test_x, label = test_y)
# Lets fit XGBoost regression model and display RMSE for both training and testing data at each round
watchlist = list(train=xgb_train, test=xgb_test)
model_xgb = xgb.train(data=xgb_train, max.depth=3, watchlist=watchlist, nrounds=70) # the more the number of rounds selected, the longer the time to display the results. ## [1] train-rmse:43244.021224 test-rmse:43534.645022
## [2] train-rmse:32925.811469 test-rmse:33244.256749
## [3] train-rmse:26371.535333 test-rmse:26674.064907
## [4] train-rmse:22426.536974 test-rmse:22834.853185
## [5] train-rmse:19138.317338 test-rmse:19619.239048
## [6] train-rmse:17277.309291 test-rmse:17823.524131
## [7] train-rmse:16366.560471 test-rmse:17278.849746
## [8] train-rmse:15689.362969 test-rmse:16750.901684
## [9] train-rmse:15423.861799 test-rmse:16448.592369
## [10] train-rmse:14641.716346 test-rmse:15831.366965
## [11] train-rmse:14229.956142 test-rmse:15565.048772
## [12] train-rmse:14140.726420 test-rmse:15576.754285
## [13] train-rmse:14040.824155 test-rmse:15579.574820
## [14] train-rmse:13823.490506 test-rmse:15447.584681
## [15] train-rmse:13795.871016 test-rmse:15440.364537
## [16] train-rmse:13744.744332 test-rmse:15440.163324
## [17] train-rmse:13680.358193 test-rmse:15509.571225
## [18] train-rmse:13605.424282 test-rmse:15532.008911
## [19] train-rmse:13506.650984 test-rmse:15490.959037
## [20] train-rmse:13465.377167 test-rmse:15540.493907
## [21] train-rmse:13455.110966 test-rmse:15519.063626
## [22] train-rmse:13369.750591 test-rmse:15586.135804
## [23] train-rmse:13315.765680 test-rmse:15580.212022
## [24] train-rmse:13264.028187 test-rmse:15634.308820
## [25] train-rmse:13179.905715 test-rmse:15601.492993
## [26] train-rmse:13135.446303 test-rmse:15597.188425
## [27] train-rmse:13102.654202 test-rmse:15579.155779
## [28] train-rmse:13030.516079 test-rmse:15635.175614
## [29] train-rmse:12985.483762 test-rmse:15659.466083
## [30] train-rmse:12948.159727 test-rmse:15679.452445
## [31] train-rmse:12889.233174 test-rmse:15710.866464
## [32] train-rmse:12865.565378 test-rmse:15731.668411
## [33] train-rmse:12810.784942 test-rmse:15797.645055
## [34] train-rmse:12764.879181 test-rmse:15825.903313
## [35] train-rmse:12736.585882 test-rmse:15833.034686
## [36] train-rmse:12679.307528 test-rmse:15786.012215
## [37] train-rmse:12638.151313 test-rmse:15779.930630
## [38] train-rmse:12604.365116 test-rmse:15806.418343
## [39] train-rmse:12563.343063 test-rmse:15826.789921
## [40] train-rmse:12501.909623 test-rmse:15848.216573
## [41] train-rmse:12472.443780 test-rmse:15898.185683
## [42] train-rmse:12434.864348 test-rmse:15906.875677
## [43] train-rmse:12402.280440 test-rmse:15873.651350
## [44] train-rmse:12381.050777 test-rmse:15883.049803
## [45] train-rmse:12343.828793 test-rmse:15879.177724
## [46] train-rmse:12328.842224 test-rmse:15864.918923
## [47] train-rmse:12289.276970 test-rmse:15889.970394
## [48] train-rmse:12259.418263 test-rmse:15916.837856
## [49] train-rmse:12216.142808 test-rmse:15951.995406
## [50] train-rmse:12187.751512 test-rmse:16004.322929
## [51] train-rmse:12161.894887 test-rmse:16022.290251
## [52] train-rmse:12133.481525 test-rmse:16016.224153
## [53] train-rmse:12087.793197 test-rmse:16077.452030
## [54] train-rmse:12063.674102 test-rmse:16103.160021
## [55] train-rmse:12045.376078 test-rmse:16106.022499
## [56] train-rmse:12021.034569 test-rmse:16105.053018
## [57] train-rmse:12001.989908 test-rmse:16126.373716
## [58] train-rmse:11973.394069 test-rmse:16125.377143
## [59] train-rmse:11952.809008 test-rmse:16142.856128
## [60] train-rmse:11937.977292 test-rmse:16142.284822
## [61] train-rmse:11911.063998 test-rmse:16170.608473
## [62] train-rmse:11894.555965 test-rmse:16185.807164
## [63] train-rmse:11873.680597 test-rmse:16232.502887
## [64] train-rmse:11849.542525 test-rmse:16222.537981
## [65] train-rmse:11830.154347 test-rmse:16223.292451
## [66] train-rmse:11814.868284 test-rmse:16217.758837
## [67] train-rmse:11789.451116 test-rmse:16244.675306
## [68] train-rmse:11756.889829 test-rmse:16244.533388
## [69] train-rmse:11722.694429 test-rmse:16272.364596
## [70] train-rmse:11709.608054 test-rmse:16288.255066# Looks like the lowest RMSE for both training and test dataset is achieved at 59 round.
# Lets estimate our final regression model
reg_xgb = xgboost(data = xgb_train, max.depth = 3, nrounds = 59, verbose = 0) # setting verbose = 0 avoids to display the training and testing error for each round.
prediction_xgb_test<-predict(reg_xgb, xgb_test)
RMSE_XGB <- rmse((prediction_xgb_test), cv_test$total_claim_amount)
RMSE_XGB## [1] 16142.86# Lets do some diagnostic check of regression residuals
xgb_reg_residuals<-cv_test$total_claim_amount - prediction_xgb_test
plot(xgb_reg_residuals, xlab= "Dependent Variable", ylab = "Residuals", main = 'XGBoost Regression Residuals')
abline(0,0)
# Plot first 3 trees of model
xgb.plot.tree(model=reg_xgb, trees=0:2)importance_matrix <- xgb.importance(model = reg_xgb)
xgb.plot.importance(importance_matrix, xlab = "Explanatory Variables X's Importance")
XGBoost df ajustada 3
# XGBoost de significante 3
# define final training and testing sets
xgb_train2 = xgb.DMatrix(data = train_x2, label = train_y2)
xgb_test2 = xgb.DMatrix(data = test_x2, label = test_y2)
# Lets fit XGBoost regression model and display RMSE for both training and testing data at each round
watchlist2 = list(train=xgb_train2, test=xgb_test)
model_xgb2 = xgb.train(data=xgb_train2, max.depth=3, watchlist=watchlist, nrounds=70) # the more the number of rounds selected, the longer the time to display the results. ## [1] train-rmse:44507.872273 test-rmse:44832.333897
## [2] train-rmse:34099.015289 test-rmse:34378.880427
## [3] train-rmse:27440.443434 test-rmse:27788.180102
## [4] train-rmse:23335.447691 test-rmse:23669.797394
## [5] train-rmse:19446.074906 test-rmse:20004.160946
## [6] train-rmse:17197.847439 test-rmse:17831.584829
## [7] train-rmse:16056.723514 test-rmse:16675.967922
## [8] train-rmse:15814.135869 test-rmse:16437.029407
## [9] train-rmse:15421.488240 test-rmse:16177.763023
## [10] train-rmse:15007.946331 test-rmse:15844.500348
## [11] train-rmse:14912.347992 test-rmse:15710.505899
## [12] train-rmse:14884.890005 test-rmse:15724.203063
## [13] train-rmse:14631.172382 test-rmse:15505.846463
## [14] train-rmse:14602.366325 test-rmse:15477.412392
## [15] train-rmse:14938.625201 test-rmse:15824.499431
## [16] train-rmse:14911.979425 test-rmse:15797.476070
## [17] train-rmse:15325.984239 test-rmse:16169.338378
## [18] train-rmse:15335.449074 test-rmse:16247.586409
## [19] train-rmse:15222.192874 test-rmse:16176.374086
## [20] train-rmse:15306.691803 test-rmse:16304.318212
## [21] train-rmse:15505.168587 test-rmse:16530.827888
## [22] train-rmse:15497.468868 test-rmse:16551.612802
## [23] train-rmse:15479.320609 test-rmse:16558.957011
## [24] train-rmse:15667.089190 test-rmse:16658.180854
## [25] train-rmse:15624.564296 test-rmse:16725.801519
## [26] train-rmse:15511.990541 test-rmse:16617.054201
## [27] train-rmse:15340.880365 test-rmse:16482.271077
## [28] train-rmse:15583.586494 test-rmse:16685.396286
## [29] train-rmse:15599.225308 test-rmse:16700.328710
## [30] train-rmse:14916.668513 test-rmse:16046.339220
## [31] train-rmse:14923.843115 test-rmse:16053.264775
## [32] train-rmse:15444.273764 test-rmse:16492.979071
## [33] train-rmse:15419.798180 test-rmse:16486.038300
## [34] train-rmse:15397.743117 test-rmse:16464.918440
## [35] train-rmse:15376.314767 test-rmse:16430.736656
## [36] train-rmse:15392.221439 test-rmse:16442.779417
## [37] train-rmse:15382.821062 test-rmse:16454.004759
## [38] train-rmse:15885.339230 test-rmse:16932.447261
## [39] train-rmse:15891.746708 test-rmse:16938.546551
## [40] train-rmse:16374.278055 test-rmse:17398.036517
## [41] train-rmse:16357.623095 test-rmse:17355.407772
## [42] train-rmse:15930.846860 test-rmse:16971.224598
## [43] train-rmse:15865.688369 test-rmse:16969.297461
## [44] train-rmse:15788.331304 test-rmse:16904.063789
## [45] train-rmse:15830.048562 test-rmse:16894.249857
## [46] train-rmse:15815.861786 test-rmse:16913.459807
## [47] train-rmse:15800.846160 test-rmse:16899.097487
## [48] train-rmse:15807.679418 test-rmse:16905.633512
## [49] train-rmse:15848.298694 test-rmse:16925.090811
## [50] train-rmse:15814.711740 test-rmse:16905.429126
## [51] train-rmse:15839.356269 test-rmse:16938.004138
## [52] train-rmse:15832.434972 test-rmse:16918.789215
## [53] train-rmse:15778.327611 test-rmse:16867.375623
## [54] train-rmse:16042.145924 test-rmse:17126.996644
## [55] train-rmse:16049.691162 test-rmse:17129.612799
## [56] train-rmse:16250.017557 test-rmse:17244.117412
## [57] train-rmse:16227.059307 test-rmse:17247.634968
## [58] train-rmse:16238.348060 test-rmse:17272.503220
## [59] train-rmse:16236.375667 test-rmse:17306.296252
## [60] train-rmse:16337.858520 test-rmse:17405.523394
## [61] train-rmse:16716.868638 test-rmse:17779.867500
## [62] train-rmse:16925.878631 test-rmse:17994.450580
## [63] train-rmse:16909.005127 test-rmse:17977.994974
## [64] train-rmse:16649.942291 test-rmse:17753.486174
## [65] train-rmse:16717.685409 test-rmse:17883.730745
## [66] train-rmse:16802.482806 test-rmse:17968.133158
## [67] train-rmse:16743.982224 test-rmse:17901.999610
## [68] train-rmse:16954.190661 test-rmse:18057.153738
## [69] train-rmse:16966.757069 test-rmse:18069.580116
## [70] train-rmse:16964.517110 test-rmse:18089.171042# Looks like the lowest RMSE for both training and test dataset is achieved at 59 round.
# Lets estimate our final regression model
reg_xgb2 = xgboost(data = xgb_train2, max.depth = 3, nrounds = 59, verbose = 0) # setting verbose = 0 avoids to display the training and testing error for each round.
prediction_xgb_test2<-predict(reg_xgb2, xgb_test2)
RMSE_XGB2 <- rmse(prediction_xgb_test2, cv_test2$total_claim_amount)
RMSE_XGB2## [1] 15623.94# Lets do some diagnostic check of regression residuals
xgb_reg_residuals2<-cv_test2$total_claim_amount - prediction_xgb_test2
plot(xgb_reg_residuals2, xlab= "Dependent Variable", ylab = "Residuals", main = 'XGBoost Regression Residuals')
abline(0,0)
# Plot first 3 trees of model
xgb.plot.tree(model=reg_xgb2, trees=0:2)importance_matrix2 <- xgb.importance(model = reg_xgb2)
xgb.plot.importance(importance_matrix2, xlab = "Explanatory Variables X's Importance")
Decision Trees
Decision Trees df ajustada 2
decision_tree_model <- rpart(total_claim_amount ~ incident_type + insured_occupation + insured_education_level + incident_severity + property_damage + incident_city, data = train)
# summary(decision_tree_regression)
plot(decision_tree_model, compress = TRUE)
text(decision_tree_model, use.n = TRUE)
rpart.plot(decision_tree_model)
RMSE of Decision Tree df ajustada 2
decision_tree_prediction <- predict(decision_tree_model,test)
RMSE_decision_tree <- rmse(decision_tree_prediction, test$total_claim_amount)
RMSE_decision_tree## [1] 14962.24Decision Trees df ajustada 3
decision_tree_model2 <- rpart(total_claim_amount ~ incident_type + insured_occupation + insured_education_level + incident_severity + property_damage + incident_city + age + incident_hour_of_the_day, data = train)
# summary(decision_tree_regression)
plot(decision_tree_model2, compress = TRUE)
text(decision_tree_model2, use.n = TRUE)
rpart.plot(decision_tree_model2)
RMSE of Decision Tree df ajustada 3
decision_tree_prediction2 <- predict(decision_tree_model2,test)
RMSE_decision_tree2 <- rmse(decision_tree_prediction2, test$total_claim_amount)
RMSE_decision_tree2## [1] 14962.24Random Forest
Random Forest df ajustada 2
rf_model <- randomForest(total_claim_amount ~ ., data= cv_train, proximity=TRUE)
# random_forest<-randomForest(MEDV~.,data=train_alt,importance=TRUE, proximity=TRUE)
print(rf_model) ### the train data set model accuracy is around 85%.##
## Call:
## randomForest(formula = total_claim_amount ~ ., data = cv_train, proximity = TRUE)
## Type of random forest: regression
## Number of trees: 500
## No. of variables tried at each split: 2
##
## Mean of squared residuals: 220513490
## % Var explained: 68.45Prediction of Random Forest df ajustada 2
# Prediction & Confusion Matrix – test data
rf_prediction <- predict(rf_model,cv_test)
rf_prediction## 1 3 4 5 8 13 17 19
## 62944.784 60486.656 58424.611 8865.605 63750.153 63531.364 55608.553 61792.249
## 21 25 34 38 39 49 50 55
## 61256.203 64464.557 58642.224 8565.191 63072.302 6550.590 69930.066 9940.357
## 59 60 61 64 65 69 70 73
## 64135.680 58510.044 60310.106 64207.270 68178.338 67094.407 9018.008 54485.008
## 74 76 77 80 81 97 98 99
## 63015.060 66138.064 63991.298 61537.096 60817.506 57633.218 60531.693 5764.899
## 100 101 103 106 108 109 112 120
## 5879.677 60836.863 54757.062 11273.980 61017.911 64398.835 71224.818 50898.898
## 126 131 134 136 140 142 149 152
## 61876.804 56606.045 65567.177 67856.804 60905.168 6317.098 66080.196 59363.776
## 154 159 162 164 165 168 171 177
## 60264.066 62913.858 57068.920 60876.161 62163.240 57418.661 59469.881 58932.311
## 178 179 181 183 184 187 202 204
## 65434.798 59906.200 65679.135 66492.006 58441.966 66397.618 63923.311 59764.573
## 205 208 210 213 214 218 224 233
## 61251.148 64031.408 6647.240 66872.971 64252.386 6487.412 61123.071 62913.858
## 235 237 239 251 252 265 266 270
## 58981.640 57406.955 59805.544 61901.545 60094.703 60167.780 6021.680 57732.445
## 279 280 281 287 289 290 291 292
## 59925.690 56898.322 59765.767 64108.196 62269.640 9550.806 56399.055 53763.872
## 293 296 299 307 313 315 320 325
## 61228.592 66725.640 9582.291 60109.146 65384.633 49782.883 57633.218 60094.674
## 326 327 328 331 337 338 339 340
## 66678.842 58783.052 63946.828 63686.846 58673.975 63230.971 58253.158 63755.320
## 343 345 348 351 355 358 360 365
## 59397.857 65202.014 56206.136 60528.788 69012.072 54838.545 58325.589 8242.744
## 368 372 376 377 378 385 387 390
## 53456.527 60350.994 63325.228 60458.427 61427.169 61373.186 64191.909 60604.578
## 392 394 396 400 402 404 406 410
## 59305.747 62870.191 9354.243 58509.240 63147.051 54879.816 57943.576 6393.409
## 411 414 421 423 425 427 428 430
## 7421.220 64519.208 58433.937 60567.702 56608.781 56332.401 63389.245 64660.123
## 431 440 441 446 448 456 457 459
## 59953.720 7547.500 56895.647 63746.363 60229.015 53710.488 9057.528 61039.574
## 462 467 476 482 483 484 488 493
## 63098.682 62885.464 60103.715 59348.141 66301.224 58923.169 61357.248 62342.654
## 505 508 511 512 513 516 519 522
## 65418.856 61869.704 61060.592 6782.125 65860.559 64175.981 62256.582 57677.899
## 523 528 529 530 531 535 538 539
## 57602.239 6225.389 57812.277 64469.903 54852.862 58725.228 59749.025 8245.456
## 540 544 545 547 550 552 553 554
## 62060.842 64947.101 61148.194 66989.194 58546.660 60127.667 12841.743 5223.193
## 559 560 562 574 576 582 594 597
## 66051.439 62647.270 57641.224 59625.219 65915.022 54301.900 62918.994 5106.108
## 604 607 611 613 615 616 619 624
## 4804.513 60541.443 59853.896 61964.188 9606.768 63933.258 55764.895 59123.576
## 633 637 643 644 652 658 664 669
## 60161.514 9717.329 72222.772 62536.377 62919.803 59828.634 68019.223 56959.652
## 670 672 674 684 685 687 688 693
## 71195.730 56333.534 58648.556 63383.191 63123.782 5640.346 56344.168 9650.794
## 694 699 702 707 710 712 718 719
## 64709.162 5539.471 63696.339 66619.311 61065.696 7926.754 58907.678 63000.193
## 721 722 725 735 736 737 739 740
## 63591.023 5759.672 60371.357 59998.342 62712.926 55609.478 64625.519 9033.266
## 743 744 748 749 751 754 757 760
## 63263.215 61350.772 63223.533 61399.627 7860.109 64415.722 63851.706 60115.395
## 761 762 763 766 769 774 775 776
## 61901.607 55995.327 59598.066 65996.660 74222.040 57547.896 61647.529 5971.533
## 779 782 784 787 794 796 797 799
## 56766.233 60515.087 5769.812 58496.232 58207.405 61314.192 57633.687 63398.454
## 801 804 805 806 811 814 816 817
## 56904.309 59200.953 6440.848 56307.882 61263.007 10904.611 65474.439 55076.862
## 821 822 829 831 837 838 840 841
## 6141.644 58096.451 63908.934 65617.245 60399.277 6613.187 67904.529 7260.798
## 845 870 872 877 879 881 882 888
## 57261.277 59084.501 8930.445 5822.266 61707.910 64347.657 58504.506 7561.026
## 890 892 896 900 906 908 910 917
## 60216.933 61051.025 61665.733 10666.959 55079.563 58209.555 60217.049 6232.442
## 919 926 927 932 937 940 944 953
## 58858.312 9026.443 62579.410 62494.273 61845.665 60622.526 59469.881 58960.429
## 954 956 960 963 968 974 976 977
## 8658.852 63088.994 8658.852 53679.688 57818.979 63533.939 61818.257 57914.007
## 981 985 989 998
## 66053.950 63042.945 62665.777 60844.171RMSE of Random Forest df ajustada 2
# confusionMatrix(rf_prediction_train_data, train$MEDV) # a confusion matrix is essentially a table that categorizes predictions against actual values.
RMSE_rf <- rmse(rf_prediction, cv_test$total_claim_amount)
RMSE_rf## [1] 15697.65Random Forest df ajustada 3
rf_model2 <- randomForest(total_claim_amount ~ ., data= cv_train2, proximity=TRUE)
# random_forest<-randomForest(MEDV~.,data=train_alt,importance=TRUE, proximity=TRUE)
print(rf_model2) ### the train data set model accuracy is around 85%.##
## Call:
## randomForest(formula = total_claim_amount ~ ., data = cv_train2, proximity = TRUE)
## Type of random forest: regression
## Number of trees: 500
## No. of variables tried at each split: 2
##
## Mean of squared residuals: 213588214
## % Var explained: 69.44Prediction of Random Forest df ajustada 3
# Prediction & Confusion Matrix – test data
rf_prediction2 <- predict(rf_model2,cv_test2)
rf_prediction2## 1 3 4 5 8 13 17 19
## 64164.925 60052.851 60482.504 16645.063 63175.431 60650.564 57309.162 61835.549
## 21 25 34 38 39 49 50 55
## 62698.548 61297.695 56685.544 17002.522 63114.078 6767.500 64295.437 11789.428
## 59 60 61 64 65 69 70 73
## 60522.207 59490.563 57794.149 63643.525 65150.920 63702.295 16587.961 54797.577
## 74 76 77 80 81 97 98 99
## 59228.524 64277.284 62128.936 63384.817 59875.853 59479.769 53262.411 7058.098
## 100 101 103 106 108 109 112 120
## 7747.822 61800.859 57081.142 13031.811 67341.606 63530.664 72931.203 51799.601
## 126 131 134 136 140 142 149 152
## 63668.510 52700.294 60254.369 63194.766 58413.713 6994.299 61895.753 58695.171
## 154 159 162 164 165 168 171 177
## 60456.898 61787.808 60935.388 58486.208 60887.843 57840.705 59800.806 59250.130
## 178 179 181 183 184 187 202 204
## 66834.186 55194.501 66010.508 66679.514 61044.013 65199.148 68170.376 62110.109
## 205 208 210 213 214 218 224 233
## 62881.792 64309.254 7348.990 66163.460 62392.879 7680.845 57275.699 59212.976
## 235 237 239 251 252 265 266 270
## 60113.187 56707.031 58419.182 60414.345 58092.974 55610.034 8297.777 58920.700
## 279 280 281 287 289 290 291 292
## 59003.592 60535.159 61576.627 62580.243 59114.273 9953.145 57157.528 59778.242
## 293 296 299 307 313 315 320 325
## 60478.189 67121.230 9358.093 60122.020 61753.223 56838.234 58702.035 53441.285
## 326 327 328 331 337 338 339 340
## 65229.535 60603.020 61130.265 62520.478 59014.574 61538.908 60437.628 62855.781
## 343 345 348 351 355 358 360 365
## 56191.274 64476.983 58704.046 62337.380 59329.230 55565.514 58586.856 12410.253
## 368 372 376 377 378 385 387 390
## 57542.532 59727.188 64049.206 61208.306 62671.116 63471.994 62774.576 59308.764
## 392 394 396 400 402 404 406 410
## 60270.486 69190.053 10300.975 59276.698 65059.664 59221.267 56074.715 9487.653
## 411 414 421 423 425 427 428 430
## 10042.236 60575.875 59088.959 65557.960 57765.177 56250.514 63998.942 63550.209
## 431 440 441 446 448 456 457 459
## 59842.125 9511.687 60602.058 62307.894 62395.193 54918.949 17641.211 63504.592
## 462 467 476 482 483 484 488 493
## 57540.067 62798.317 62258.974 58820.601 62220.310 56502.871 65251.806 59686.174
## 505 508 511 512 513 516 519 522
## 66484.329 58026.979 58844.726 7703.765 65167.019 63164.688 61605.586 59963.242
## 523 528 529 530 531 535 538 539
## 58385.815 6829.475 56695.918 66212.128 61851.495 57413.716 61809.472 14775.696
## 540 544 545 547 550 552 553 554
## 63210.924 54634.333 58087.592 63650.740 56558.943 55206.210 14819.606 7042.934
## 559 560 562 574 576 582 594 597
## 63139.353 58420.578 58210.587 58189.584 66046.459 55361.899 62332.429 7595.022
## 604 607 611 613 615 616 619 624
## 7364.412 58577.857 59914.234 62876.324 10111.299 62278.725 59150.365 57230.973
## 633 637 643 644 652 658 664 669
## 61534.622 8766.382 68793.479 63820.415 62771.361 61318.245 63253.160 57993.850
## 670 672 674 684 685 687 688 693
## 66171.807 56566.508 56339.127 66431.749 67509.112 5962.073 57896.141 11449.352
## 694 699 702 707 710 712 718 719
## 56724.946 5976.794 59851.938 68659.730 62238.686 15101.041 53459.824 60294.908
## 721 722 725 735 736 737 739 740
## 67615.389 5571.697 63761.583 57073.236 63805.956 64404.927 65021.363 12147.201
## 743 744 748 749 751 754 757 760
## 63242.986 60884.932 62642.598 60469.490 8403.466 63087.948 65372.478 58123.566
## 761 762 763 766 769 774 775 776
## 56146.522 59563.360 69029.835 60759.080 65607.125 57699.138 59598.282 6364.039
## 779 782 784 787 794 796 797 799
## 59291.260 60966.060 8044.096 58582.238 59797.225 61268.994 54475.828 59591.413
## 801 804 805 806 811 814 816 817
## 55697.386 59409.961 8460.814 58788.096 61128.512 11816.356 62163.443 51778.904
## 821 822 829 831 837 838 840 841
## 8327.296 57327.916 62222.980 61361.852 64500.141 7706.587 63793.969 8642.395
## 845 870 872 877 879 881 882 888
## 58689.684 58537.858 10202.138 5749.636 61840.247 64939.776 58405.028 11728.769
## 890 892 896 900 906 908 910 917
## 61476.621 63991.537 65330.986 11153.297 55120.826 63423.215 59843.828 6775.959
## 919 926 927 932 937 940 944 953
## 56664.995 10329.668 63561.594 61905.434 58523.380 62065.897 60212.893 61817.843
## 954 956 960 963 968 974 976 977
## 10288.143 64591.964 16335.857 59721.646 58591.599 63267.168 60550.892 57467.229
## 981 985 989 998
## 66205.914 62184.443 58922.251 54036.115RMSE of Random Forest df ajustada 3
# confusionMatrix(rf_prediction_train_data, train$MEDV) # a confusion matrix is essentially a table that categorizes predictions against actual values.
RMSE_rf2 <- rmse(rf_prediction2, cv_test2$total_claim_amount)
RMSE_rf2## [1] 15672.665. Pruebas de Diagnóstico
Ver los Resultados Obtenidos de la Estmación de Modelos de Regresión
Multicolinealidad
# dataframe original con 25 variables
# OLS
vif(ols_model)## GVIF Df GVIF^(1/(2*Df))
## months_as_customer 7.095942 1 2.663821
## age 7.072698 1 2.659454
## policy_state 1.154230 2 1.036509
## policy_csl 1.163546 2 1.038594
## policy_annual_premium 1.073619 1 1.036156
## umbrella_limit 1.084744 1 1.041511
## insured_sex 1.057547 1 1.028371
## insured_education_level 1.644689 6 1.042334
## insured_occupation 2.194290 13 1.030687
## insured_relationship 1.456512 5 1.038320
## capital.gains 1.079980 1 1.039221
## capital.loss 1.096340 1 1.047063
## incident_type 2.820275 3 1.188635
## incident_severity 3.568533 3 1.236180
## incident_state 1.544100 6 1.036867
## incident_city 1.485733 6 1.033543
## incident_hour_of_the_day 1.146167 1 1.070592
## property_damage 1.161971 2 1.038242
## bodily_injuries 1.061729 1 1.030402
## witnesses 1.067444 1 1.033172
## police_report_available 1.157251 2 1.037187
## auto_make 2.219722 13 1.031144
## auto_year 1.093489 1 1.045700
## fraud_reported 1.451294 1 1.204697# Log OLS
vif(log_ols_model)## GVIF Df GVIF^(1/(2*Df))
## months_as_customer 7.095942 1 2.663821
## age 7.072698 1 2.659454
## policy_state 1.154230 2 1.036509
## policy_csl 1.163546 2 1.038594
## policy_annual_premium 1.073619 1 1.036156
## umbrella_limit 1.084744 1 1.041511
## insured_sex 1.057547 1 1.028371
## insured_education_level 1.644689 6 1.042334
## insured_occupation 2.194290 13 1.030687
## insured_relationship 1.456512 5 1.038320
## capital.gains 1.079980 1 1.039221
## capital.loss 1.096340 1 1.047063
## incident_type 2.820275 3 1.188635
## incident_severity 3.568533 3 1.236180
## incident_state 1.544100 6 1.036867
## incident_city 1.485733 6 1.033543
## incident_hour_of_the_day 1.146167 1 1.070592
## property_damage 1.161971 2 1.038242
## bodily_injuries 1.061729 1 1.030402
## witnesses 1.067444 1 1.033172
## police_report_available 1.157251 2 1.037187
## auto_make 2.219722 13 1.031144
## auto_year 1.093489 1 1.045700
## fraud_reported 1.451294 1 1.204697# dataframe significante con 7 variables
# OLS
vif(ols_model_df_sig_2)## GVIF Df GVIF^(1/(2*Df))
## incident_type 2.394520 3 1.156653
## insured_occupation 1.283897 13 1.009658
## insured_education_level 1.196933 6 1.015093
## incident_severity 2.401742 3 1.157234
## property_damage 1.065991 2 1.016105
## incident_city 1.168093 6 1.013032# Log OLS
vif(log_ols_model_df_sig_2)## GVIF Df GVIF^(1/(2*Df))
## incident_type 2.394520 3 1.156653
## insured_occupation 1.283897 13 1.009658
## insured_education_level 1.196933 6 1.015093
## incident_severity 2.401742 3 1.157234
## property_damage 1.065991 2 1.016105
## incident_city 1.168093 6 1.013032# dataframe significante con 9 variables
# OLS
vif(ols_model_df_sig_3)## GVIF Df GVIF^(1/(2*Df))
## incident_type 2.464727 3 1.162237
## insured_occupation 1.298641 13 1.010101
## insured_education_level 1.219301 6 1.016660
## incident_severity 2.416283 3 1.158398
## property_damage 1.071134 2 1.017328
## incident_city 1.176336 6 1.013626
## age 1.027678 1 1.013745
## incident_hour_of_the_day 1.109347 1 1.053255# Log OLS
vif(log_ols_model_df_sig_3)## GVIF Df GVIF^(1/(2*Df))
## incident_type 2.464727 3 1.162237
## insured_occupation 1.298641 13 1.010101
## insured_education_level 1.219301 6 1.016660
## incident_severity 2.416283 3 1.158398
## property_damage 1.071134 2 1.017328
## incident_city 1.176336 6 1.013626
## age 1.027678 1 1.013745
## incident_hour_of_the_day 1.109347 1 1.053255Heterocedasticidad
# dataframe original con 25 variables
# OLS
bptest(ols_model)##
## studentized Breusch-Pagan test
##
## data: ols_model
## BP = 137.48, df = 75, p-value = 1.47e-05# Log OLS
bptest(log_ols_model)##
## studentized Breusch-Pagan test
##
## data: log_ols_model
## BP = 83.469, df = 75, p-value = 0.2354# dataframe significante con 7 variables
# OLS
bptest(ols_model_df_sig_2)##
## studentized Breusch-Pagan test
##
## data: ols_model_df_sig_2
## BP = 109.47, df = 33, p-value = 3.962e-10# Log OLS
bptest(log_ols_model_df_sig_2)##
## studentized Breusch-Pagan test
##
## data: log_ols_model_df_sig_2
## BP = 48.103, df = 33, p-value = 0.04339# dataframe significante con 9 variables
# OLS
bptest(ols_model_df_sig_3)##
## studentized Breusch-Pagan test
##
## data: ols_model_df_sig_3
## BP = 110.3, df = 35, p-value = 1.004e-09# Log OLS
bptest(log_ols_model_df_sig_3)##
## studentized Breusch-Pagan test
##
## data: log_ols_model_df_sig_3
## BP = 48.039, df = 35, p-value = 0.06997Normalidad de los Residuales
# dataframe original con 25 variables
shapiro.test(residuals(ols_model)) # Test de Shapiro-Wilk##
## Shapiro-Wilk normality test
##
## data: residuals(ols_model)
## W = 0.99059, p-value = 5.162e-06qqnorm(residuals(ols_model)) # Q-Q plot
qqline(residuals(ols_model))
shapiro.test(residuals(log_ols_model)) # Test de Shapiro-Wilk##
## Shapiro-Wilk normality test
##
## data: residuals(log_ols_model)
## W = 0.89972, p-value < 2.2e-16qqnorm(residuals(log_ols_model)) # Q-Q plot
qqline(residuals(log_ols_model))
# dataframe significante con 7 variables
shapiro.test(residuals(ols_model_df_sig_2)) # Test de Shapiro-Wilk##
## Shapiro-Wilk normality test
##
## data: residuals(ols_model_df_sig_2)
## W = 0.99038, p-value = 3.988e-06qqnorm(residuals(ols_model_df_sig_2)) # Q-Q plot
qqline(residuals(ols_model_df_sig_2))
shapiro.test(residuals(log_ols_model_df_sig_2)) # Test de Shapiro-Wilk##
## Shapiro-Wilk normality test
##
## data: residuals(log_ols_model_df_sig_2)
## W = 0.89534, p-value < 2.2e-16qqnorm(residuals(log_ols_model_df_sig_2)) # Q-Q plot
qqline(residuals(log_ols_model_df_sig_2))
# dataframe significante con 9 variables
shapiro.test(residuals(ols_model_df_sig_3)) # Test de Shapiro-Wilk##
## Shapiro-Wilk normality test
##
## data: residuals(ols_model_df_sig_3)
## W = 0.99016, p-value = 3.097e-06qqnorm(residuals(ols_model_df_sig_3)) # Q-Q plot
qqline(residuals(ols_model_df_sig_3))
shapiro.test(residuals(log_ols_model_df_sig_3)) # Test de Shapiro-Wilk##
## Shapiro-Wilk normality test
##
## data: residuals(log_ols_model_df_sig_3)
## W = 0.89527, p-value < 2.2e-16qqnorm(residuals(log_ols_model_df_sig_3)) # Q-Q plot
qqline(residuals(log_ols_model_df_sig_3))
Nota: En caso de que las pruebas de diagnóstico identifiquen cualquiera de los anteriores a) – e) plantear una solución para mejorar la estimación de la especificiación del modelo.
Se realizaron modificaciones en la base de datos con el objetivo de mejorar su calidad para su aplicación en modelos analíticos. Se eliminaron variables consideradas insignificantes para reducir la multicolinealidad, aunque esta no estaba muy presente, se identificaron dos variables con un factor de inflación de la varianza (VIF) mayor a 5 pero menor a 10. Además, se aplicó la función logarítmica (log()) a ciertas variables para mitigar la heterocedasticidad, logrando éxito en este aspecto. Estos cambios en la base de datos reflejaron hasta cuatro ajustes distintos, con la intención de prepararla para su implementación en modelos analíticos y evaluar la mejor opción disponible.
6. Evaluación y Selección de Modelo de Regresión
- Mediante el cálculo de la métrica RMSE para cada uno de los modelos estimados en 4) seleccionar el modelo que muestra los mejores resultados estimados.
- Presentar los valores de la métrica RMSE de cada uno de los modelos estimados en 4) en un gráfico de barras.
RMSE de los Modelos
Elaborar dataframe de los RMSE’s
rmse_values <- data.frame(
Model = c("OLS df2", "OLS df3", "Log-OLS df2", "Log-OLS df3", "XGBoost df2", "XGBoost df3", "Decision Tree df2", "Decision Tree df3", "Random Forest df2", "Random Forest df3"),
RMSE = c(RMSE_ols_model_df_sig_2, RMSE_ols_model_df_sig_3, RMSE_log_ols_model_df_sig_2, RMSE_log_ols_model_df_sig_3, RMSE_XGB, RMSE_XGB2, RMSE_decision_tree, RMSE_decision_tree2, RMSE_rf, RMSE_rf2)
)
rmse_values## Model RMSE
## 1 OLS df2 1.419544e+04
## 2 OLS df3 1.417495e+04
## 3 Log-OLS df2 3.023055e-01
## 4 Log-OLS df3 3.017640e-01
## 5 XGBoost df2 1.614286e+04
## 6 XGBoost df3 1.562394e+04
## 7 Decision Tree df2 1.496224e+04
## 8 Decision Tree df3 1.496224e+04
## 9 Random Forest df2 1.569765e+04
## 10 Random Forest df3 1.567266e+04Gráficos de los RMSE’s
# Crear el gráfico de barras con valores encima de las barras
ggplot(rmse_values, aes(x = Model, y = RMSE)) +
geom_bar(stat = "identity", fill = "skyblue") +
geom_text(aes(label = round(RMSE, 3)), vjust = -0.5, size = 3.5) + # Agregar valores encima de las barras
labs(title = "RMSE por Modelo de Regresión",
x = "Modelo de Regresión",
y = "RMSE") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Conclusión
Desarrollar una breve descripción de los 6 – 10 principales hallazgos de:
a. EDA
b. Modelo seleccionado:
- ¿Cuáles son las variables que contribuyen a explicar los cambios de la principal variable de estudio?
incident type
incident severity
incident city “Springfield”
property damage “Yes”
- ¿Cómo es el impacto de dichas variables explicativas sobre la variable dependiente?
incident type: negativo
incident severity: negativo
incident city “Springfield”: positivo
property damage “Yes”: positivo
- ¿Los resultados estimados del modelo seleccionado son similares a los otros modelos estimados? ¿Cuáles son las diferencias?
Al concluir el análisis, se llegó a la conclusión de que el modelo óptimo es el de Regresión Logarítmica de Mínimos Cuadrados Ordinarios (Log OLS) aplicado al segundo o tercer data frame ajustado, ya que ambos revelaron las mismas variables importantes. Aunque ninguno de los dos modelos mostró multicolinealidad, el segundo data frame ajustado presentó heterocedasticidad. A pesar de que ambos modelos no evidenciaron una autocorrelación serial pronunciada, sus valores p (p-values) excedieron 0.05, indicando una presencia moderada de autocorrelación, aunque el valor de Durbin-Watson (D-W) se aproximó a 2.
Por último, tanto el modelo Log OLS del segundo como del tercer data frame ajustado demostraron tener el error cuadrático medio (RMSE) más bajo, con una medida de 0.302 en ambos casos.
---
title: "Actividad 1. Modelo de Regresión - Seguros de Carros"
author: "Genaro Rodríguez Alcántara - A00833172"
date: "2024-03-04"
output: 
  html_document: 
    toc: TRUE
    toc_float: TRUE
    code_download: TRUE
---

![](/Users/genarorodriguezalcantara/Desktop/Tec/AI - Concentración/Módulo 2 - Machine Learning/BD/giphy-1.gif)

# Análisis de Seguros de Carros

## Librerias
```{r message=FALSE, warning=FALSE}
# data manipulation & data visualization
library(foreign)        # Read Data Stored by 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka', 'dBase'
library(ggplot2)        # It is a system for creating graphics
library(dplyr)          # A fast, consistent tool for working with data frame like objects
library(mapview)        # Quickly and conveniently create interactive visualizations of spatial data with or without background maps
library(naniar)         # Provides data structures and functions that facilitate the plotting of missing values and examination of imputations.
library(tmaptools)       # A collection of functions to create spatial weights matrix objects from polygon 'contiguities', for summarizing these objects, and for permitting their use in spatial data analysis
library(tmap)           # For drawing thematic maps
library(RColorBrewer)   # It offers several color palettes 
library(dlookr)         # A collection of tools that support data diagnosis, exploration, and transformation
```

```{r message=FALSE, warning=FALSE}
# predictive modeling
library(regclass)       # Contains basic tools for visualizing, interpreting, and building regression models
library(mctest)         # Multicollinearity diagnostics
library(lmtest)         # Testing linear regression models
library(spdep)          # A collection of functions to create spatial weights matrix objects from polygon 'contiguities', for summarizing these objects, and for permitting their use in spatial data analysis
library(sf)             # A standardized way to encode spatial vector data
library(spData)         # Diverse spatial datasets for demonstrating, benchmarking and teaching spatial data analysis
library(spatialreg)     # A collection of all the estimation functions for spatial cross-sectional models
library(caret)          # The caret package (short for Classification And Rgression Training) contains functions to streamline the model training process for complex regression and classification problems.
library(e1071)          # Functions for latent class analysis, short time Fourier transform, fuzzy clustering, support vector machines, shortest path computation, bagged clustering, naive Bayes classifier, generalized k-nearest neighbor.
library(SparseM)        # Provides some basic R functionality for linear algebra with sparse matrices
library(Metrics)        # An implementation of evaluation metrics in R that are commonly used in supervised machine learning
library(randomForest)   # Classification and regression based on a forest of trees using random inputs 
library(jtools)         # This is a collection of tools for more efficiently understanding and sharing the results of (primarily) regression analyses 
library(xgboost)        # The package includes efficient linear model solver and tree learning algorithms
library(DiagrammeR)     # Build graph/network structures using functions for stepwise addition and deletion of nodes and edges 
library(effects)        # Graphical and tabular effect displays, e.g., of interactions, for various statistical models with linear predictors
library(rpart.plot)     # Displays a tree diagram that shows the decision rules of the model 
library(shinyjs)
library(sp)
#library(geoR)
library(gstat)
library(caret)
library(st)
library(entropy)
library(corpcor)
library(fdrtool)
library(sda)
library(corrplot)
library(lattice)
library(datasets)
library(DataExplorer)
library(car)
```

## Dataset
```{r}
df <- read.csv("/Users/genarorodriguezalcantara/Desktop/Tec/AI - Concentración/Módulo 3 - Modelos de Inteligencia Artificial para datos estructurados/Act 1/automoble_insurance_claims.csv")
df
```

## 

---

# Análisis

## 1. Preguntas Clave
Brevemente responder con tus propias palabras 2 de las siguientes 3 preguntas:

**i) ¿Qué es Supervised Machine Learning y cuáles son algunas de sus aplicaciones en Inteligencia de Negocios?**

- El aprendizaje automático supervisado es un enfoque donde los modelos se entrenan utilizando datos etiquetados para hacer predicciones o tomar decisiones. Algunas aplicaciones en inteligencia de negocios incluyen la predicción de ventas, análisis de riesgos crediticios y recomendación de productos entre otros.


**ii) ¿Cuáles son los principales algoritmos de Supervised Machine Learning? Brevemente describir con tus propias palarbas 5 – 7 de los principales algoritmos de Supervised Machine Learning.**
Algunos de los principales algoritmos de Supervised Machine Learning incluyen:

- Regresión lineal: Se utiliza para modelar la relación entre variables continuas.
- Regresión logística: Es útil para la clasificación binaria basada en variables independientes.
- Árboles de decisión: Son modelos que dividen los datos en subconjuntos más pequeños basados en características.
- Bosques aleatorios: Consisten en múltiples árboles de decisión para mejorar la precisión y evitar el sobreajuste.
- Máquinas de vectores de soporte (SVM): Se utilizan para la clasificación y la regresión, encontrando el hiperplano que mejor separa los datos.

**iii) ¿Qué es la R2 Ajustada? ¿Qué es la métrica RMSE? ¿Cuál es la diferencia entre la R2 Ajustada y la métrica RMSE?**
- La R2 Ajustada es una métrica que ajusta el coeficiente de determinación R2 para tomar en cuenta el número de predictores o predicciones en el modelo y la cantidad de datos. El RMSE (Root Mean Squared Error) es una medida de la diferencia entre los valores predichoss por un modelo y los valores observados. La diferencia clave entre la R2 Ajustada y el RMSE es que la primera evalúa la proporción de varianza explicada por el modelo mientras que el segundo mide la precisión de las predicciones en términos de unidades de la variable de respuesta.

## 2. Análisis Exploratorio de los Datos (EDA)

Incluir los siguientes elementos:

- a. Identificación de NA’s

- b. Reemplazo de NA’s

- c. Medidas descriptivas

- d. Medidas de dispersión

- e. Identificación de patrones y/o tendencias en los datos mediante el uso de gráficos incluyendo bar plots, line plots, pie plots, histogramas, matriz de correlación, box plot, scatter plot, qq- plot, etc Mostrar al menos 4 – 6 gráficos.  

```{r}
str(df)
```

### Limpieza de Datos
```{r}
# Identificar las columnas que son de tipo caracter
columnas_caracter <- sapply(df, is.character)

# Convertir las columnas de tipo caracter a factores
df[columnas_caracter] <- lapply(df[columnas_caracter], factor)
# Convertir la columna policy_bind_date a tipo de dato fecha
df$policy_bind_date <- as.Date(df$policy_bind_date, format = "%m/%d/%Y")

str(df)
```

```{r}
# Identify missing values
df_na <-  sum(is.na(df))
df_na
```

```{r}
df$X_c39 <- NULL
df_na <-  sum(is.na(df))
df_na
```

### EDA
```{r}
# Identify the name of the variables
colnames(df)
```

```{r}
df_ds <- summary(df)
df_ds
```

```{r}
df_descr <- describe(df)
df_descr
```

```{r}
df_var <- var(df)
df_var
```

### Data Visualization

#### Visualización de la base de datos General
```{r message=FALSE, warning=FALSE}
plot_histogram(df)
plot_boxplot(df, by = "total_claim_amount")
plot_bar(df)
```

#### Visualización de la Variable Dependiente
```{r}
print("Variable Dependiente: Total Claim Amount")
summary(df$total_claim_amount)
```

```{r}
hist(df$total_claim_amount, main = "Histograma de Total Claim Amount")
plot(df$incident_type, df$total_claim_amount, main = "Gráfico de Dispersión: Incident Type vs Total Claim Amount")
```

### Cambios realizados en las bases de datos

#### Eliminación de variables insignificantes
```{r}
# df original
df_og <- df

# eliminando variables insignifcantes
df$incident_location <- NULL
df$policy_bind_date <- NULL
df$incident_date <- NULL
df$insured_zip <- NULL
df$auto_model <- NULL
df$insured_hobbies <- NULL
df$policy_number <- NULL
df$policy_deductable <- NULL
df$authorities_contacted <- NULL
df$collision_type <- NULL


df$vehicle_claim <-NULL
df$injury_claim <- NULL
df$property_claim <- NULL
df$number_of_vehicles_involved <- NULL
```

#### Conversión de df -> df2 como numérica
```{r}
## copia de la base de datos para hacerla numerica y factor
df2 <- df

# Identificar las columnas que son de tipo caracter
columnas_int <- sapply(df2, is.integer)

# Convertir las columnas de tipo caracter a factores
df2[columnas_int] <- lapply(df2[columnas_int], as.numeric)

str(df2)

# base de datos numerica
df2_numeric <- df2

# Identificar las columnas que son de tipo caracter
columnas_caracter <- sapply(df2_numeric, is.character)

# Convertir las columnas de tipo caracter a factores
df2_numeric[columnas_caracter] <- lapply(df2_numeric[columnas_caracter], factor)

# Obtener índices de las variables categóricas
factor_cols <- sapply(df2_numeric, is.factor)

# Convertir las variables categóricas de factor a numeric
df2_numeric[factor_cols] <- lapply(df2_numeric[factor_cols], as.numeric)

# Identificar las columnas que son de tipo caracter
columnas_int2 <- sapply(df2_numeric, is.integer)

# Convertir las columnas de tipo caracter a factores
df2_numeric[columnas_int2] <- lapply(df2_numeric[columnas_int2], as.numeric)

str(df2_numeric)
```

#### Creación de dataframes con variables significantes
```{r}
# dataframe significante 1
df_sig <- df2 %>% select(total_claim_amount, incident_type, insured_occupation, insured_education_level, incident_severity, property_damage, incident_city, incident_hour_of_the_day, fraud_reported)

# dataframe significante 2
df_sig_2 <- df2 %>% select(total_claim_amount, incident_type, insured_occupation, insured_education_level, incident_severity, property_damage, incident_city)

# dataframe significante 3
df_sig_3 <- df2 %>% select(total_claim_amount, incident_type, insured_occupation, insured_education_level, incident_severity, property_damage, incident_city, age, incident_hour_of_the_day)

str(df_sig_2)

# dataframe significante 2 numerico
df_sig_2_numeric <- df_sig_2

# Obtener índices de las variables categóricas
factor_cols <- sapply(df_sig_2_numeric, is.factor)

# Convertir las variables categóricas de factor a numeric
df_sig_2_numeric[factor_cols] <- lapply(df_sig_2_numeric[factor_cols], as.numeric)

str(df_sig_2_numeric)

# dataframe significante 3 numerico
df_sig_3_numeric <- df_sig_3

# Obtener índices de las variables categóricas
factor_cols <- sapply(df_sig_3_numeric, is.factor)

# Convertir las variables categóricas de factor a numeric
df_sig_3_numeric[factor_cols] <- lapply(df_sig_3_numeric[factor_cols], as.numeric)

str(df_sig_3_numeric)
```

#### Correlaciones entre variables encontradas

##### Gráfico de correlación entre todas las variables (25 variables)
```{r message=FALSE, warning=FALSE}
corr_matrix <- cor(df2_numeric)
corrplot(corr_matrix, method = "circle", type = "upper", 
         tl.col = "black", tl.srt = 45, tl.cex = 0.8, 
         title = "Gráfico de Correlación entre Variables")
```

##### Gráfico barras de correlación entre todas las variables (+25 variables)
```{r}
corr_matrix <- cor(df2_numeric)
correlations_tca <- corr_matrix["total_claim_amount", ]
correlations_tca

correlations_tca_df <- as.data.frame(correlations_tca)

correlations_tca_df$Variable <- rownames(correlations_tca_df)
rownames(correlations_tca_df) <- NULL

names(correlations_tca_df)[1] <- "Correlation"

correlations_tca_df$Color <- ifelse(correlations_tca_df$Correlation > 0, "blue", "red")

ggplot(data = correlations_tca_df, aes(x = Variable, y = Correlation, fill = Color)) +
  geom_bar(stat = "identity") +
  geom_text(aes(label = round(Correlation, 2)), vjust = -0.5, size = 3.5) +  # Agregar valores encima de las barras
  labs(title = "Correlación de Total Claim Amount con las Variables",
       x = "Variables",
       y = "Correlación con TCA") +
  scale_fill_manual(values = c("blue", "red")) +  # Asignar colores manualmente
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
```

##### Gráfico barras de correlación entre variables significantes (7 variables)
```{r}
# Gráfico de correlación entre las variables significativas numericas
corr_matrix2 <- cor(df_sig_2_numeric)
correlations_tca2 <- corr_matrix2["total_claim_amount", ]
correlations_tca2

correlations_tca_df2 <- as.data.frame(correlations_tca2)

correlations_tca_df2$Variable <- rownames(correlations_tca_df2)
rownames(correlations_tca_df2) <- NULL

names(correlations_tca_df2)[1] <- "Correlation"

correlations_tca_df2$Color <- ifelse(correlations_tca_df2$Correlation > 0, "blue", "red")

ggplot(data = correlations_tca_df2, aes(x = Variable, y = Correlation, fill = Color)) +
  geom_bar(stat = "identity") +
  geom_text(aes(label = round(Correlation, 2)), vjust = -0.5, size = 3.5) +  # Agregar valores encima de las barras
  labs(title = "Correlación de Total Claim Amount con las Variables significativas",
       x = "Variables",
       y = "Correlación con TCA") +
  scale_fill_manual(values = c("blue", "red")) +  # Asignar colores manualmente
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
```

##### Gráfico barras de correlación entre variables significantes (9 variables)
```{r}
# Gráfico de correlación entre las variables significativas
corr_matrix3 <- cor(df_sig_3_numeric)
correlations_tca3 <- corr_matrix3["total_claim_amount", ]
correlations_tca3

correlations_tca_df3 <- as.data.frame(correlations_tca3)

correlations_tca_df3$Variable <- rownames(correlations_tca_df3)
rownames(correlations_tca_df3) <- NULL

names(correlations_tca_df3)[1] <- "Correlation"

correlations_tca_df3$Color <- ifelse(correlations_tca_df3$Correlation > 0, "blue", "red")

ggplot(data = correlations_tca_df3, aes(x = Variable, y = Correlation, fill = Color)) +
  geom_bar(stat = "identity") +
  geom_text(aes(label = round(Correlation, 2)), vjust = -0.5, size = 3.5) +  # Agregar valores encima de las barras
  labs(title = "Correlación de Total Claim Amount con las Variables más significativas",
       x = "Variables",
       y = "Correlación con TCA") +
  scale_fill_manual(values = c("blue", "red")) +  # Asignar colores manualmente
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
```

### Training and Test sets 
```{r}
# the training set is used to build the model and the test set to evaluate its predictive accuracy.
set.seed(123) # What is set.seed()? We want to make sure that we get the same results for randomization each time you run the script.   
partition <- createDataPartition(y = df$total_claim_amount, p=0.7, list=F)
train = df[partition, ]
test  = df[-partition, ]

train_numeric = df2_numeric[partition, ]
test_numeric  = df2_numeric[-partition, ]
```

## 3. Descripción del EDA

### EDA
A partir de los resultados de EDA describir la especificación del modelo de regresión lineal a estimar. Brevemente, describir cómo es el posible impacto de cada una de las variables explicativas sobre la principal variable de estudio.  
  
Con el EDA, pudimos descubrir qué variable era la dependiente en nuestro análisis, había dos opciones que parecían viables, "total_claim_amount" y "policy_annual_premium". Ambas mostraban una buena variabilidad, lo cual nos ayudó a observar diferentes patrones y relaciones con los datos. Al final del día, la variable dependiente definitiva resultó ser "total_claim_amount".

De igual manera, gracias a este EDA, pudimos familiarizarnos con la base de datos, revisando las correlaciones, la variabilidad y la distribución de los datos. Se hicieron visualizaciones generales para identificar y mostrar de manera más sencilla los resultados. Además, pudimos observar el comportamiento de todas las variables explicativas con la variable dependiente en un mismo gráfico.

Hallazgos:

1. Se identificaron 2 o 3 variables con suficiente variabilidad para ser consideradas como variables dependientes, pero solo 2 de ellas estaban relacionadas con los gastos del seguro.

2. La mayoría de los incidentes fueron choques, especialmente multi-colisiones.

3. Solo tres variables mostraron una correlación mayor a 0.1. Estas son:
- 1. incident hour of the day: 0.22
- 2. incident severity: -0.37
- 3. incident type: -0.28

4. La mayoría de los incidentes ocurrieron entre las 12 a.m., 4 a.m., 6 p.m. y 11 p.m. Se observó un patrón de choques, principalmente entre las 5 a.m. y las 9 a.m., seguido de un aumento en los choques a partir de las 11 a.m. hasta las 6 p.m., donde comenzaron a disminuir hasta las 11 p.m.

## Cambios en la Base de Datos

Se realizaron diversos ajustes en la base de datos para prepararla adecuadamente para análisis y modelado. Esto incluyó la eliminación de columnas con valores NA para evitar errores en los cálculos subsiguientes, así como la conversión de variables de tipo character a factor y de integer a numeric. Posteriormente, se duplicó la base de datos limpia para convertirla completamente en datos numéricos, lo que facilitaría su uso en modelos futuros. Utilizando un modelo de regresión y las correlaciones identificadas durante el EDA, se realizaron tres ajustes adicionales a la base de datos. En el primer ajuste, se eliminaron variables no significativas y se realizó un modelo de OLS con las restantes, evaluando su comportamiento y correlación con la variable dependiente mediante gráficos. En el segundo ajuste, se eliminaron más variables poco significativas, conservando solo aquellas que podrían tener un impacto percibido. Por último, en el tercer ajuste se buscaron mejoras en el modelo OLS y se abordaron problemas como la heterocedasticidad y la multicolinealidad. Finalmente, todas las bases de datos resultantes se duplicaron para asegurar que estuvieran disponibles en formato numérico para futuros análisis.

## 4. Estimación de modelos de Supervised Machine Learning (SML):

### OLS Regresión
#### OLS con base principal
```{r}
# OLS con base principal
ols_model <- lm(total_claim_amount ~., data = df)
summary(ols_model)
```
#### Log OLS con base principal
```{r}
# Log OLS con base principal
log_ols_model <- lm(log(total_claim_amount) ~., data = df)
summary(log_ols_model)
```
#### AIC OLS base principal
```{r}
AIC(ols_model)
AIC(log_ols_model) 
```

#### RMSE's base principal
```{r}
RMSE_ols_model     <- sqrt(mean(ols_model$residuals^2))
RMSE_ols_model
RMSE_log_ols_model <- sqrt(mean(log_ols_model$residuals^2))
RMSE_log_ols_model
```

#### OLS con base ajustada 1
```{r}
ols_model_df_sig <- lm(total_claim_amount ~., data = df_sig)
summary(ols_model_df_sig)
```

#### OLS con base ajustada 2
```{r}
ols_model_df_sig_2 <- lm(total_claim_amount ~., data = df_sig_2)
summary(ols_model_df_sig_2)
```

#### Log OLS con base ajustada 2
```{r}
log_ols_model_df_sig_2 <- lm(log(total_claim_amount) ~., data = df_sig_2)
summary(ols_model_df_sig_2)
```

#### AIC OLS base ajustada 2
```{r}
AIC(ols_model_df_sig_2)     
AIC(log_ols_model_df_sig_2)  
```

#### RMSE's OLS base ajustada 2
```{r}
RMSE_ols_model_df_sig_2     <- sqrt(mean(ols_model_df_sig_2$residuals^2))
RMSE_ols_model_df_sig_2

RMSE_log_ols_model_df_sig_2 <- sqrt(mean(log_ols_model_df_sig_2$residuals^2))
RMSE_log_ols_model_df_sig_2
```

#### OLS con base ajustada 3
```{r}
ols_model_df_sig_3 <- lm(total_claim_amount ~., data = df_sig_3)
summary(ols_model_df_sig_3)
```

```{r}
log_ols_model_df_sig_3_2 <- lm(log(total_claim_amount) ~ incident_type + insured_occupation + log(insured_education_level) + incident_severity + log(property_damage) + log(incident_city) + age + incident_hour_of_the_day, data = df_sig_3_numeric)
summary(log_ols_model_df_sig_3_2)
#str(df_sig_3)
```


#### Log OLS con base ajustada 3
```{r}
log_ols_model_df_sig_3 <- lm(log(total_claim_amount) ~., data = df_sig_3)
summary(log_ols_model_df_sig_3)
```
#### AIC OLS base ajustada 3
```{r}
AIC(ols_model_df_sig_3)     
AIC(log_ols_model_df_sig_3)  
```

#### RMSE OLS base ajustada 3
```{r}
RMSE_ols_model_df_sig_3     <- sqrt(mean(ols_model_df_sig_3$residuals^2))
RMSE_ols_model_df_sig_3

RMSE_log_ols_model_df_sig_3 <- sqrt(mean(log_ols_model_df_sig_3$residuals^2))
RMSE_log_ols_model_df_sig_3
```

### XGBoost Regresión
```{r}
# DATOS DE ENTRENAMIENTO DATA FRAME significante 2
set.seed(123) # What is set.seed()? We want to make sure that we get the same results for randomization each time you run the script.   
cv_data   <- createDataPartition(y = df_sig_2$total_claim_amount, p=0.7, list=F)
cv_train = df_sig_2[cv_data, ]
cv_test = df_sig_2[-cv_data, ]

# define explanatory variables (X's) and dependent variable (Y) in training set
train_x = data.matrix(cv_train[, -1])
train_y = cv_train[,1]

# define explanatory variables (X's) and dependent variable (Y) in testing set
test_x = data.matrix(cv_test[, -1])
test_y = cv_test[, 1]
```

```{r}
# DATOS DE ENTRENAMIENTO DATA FRAME significante 3
set.seed(123) # What is set.seed()? We want to make sure that we get the same results for randomization each time you run the script.   
cv_data2   <- createDataPartition(y = df_sig_3$total_claim_amount, p=0.7, list=F)
cv_train2 = df_sig_3[cv_data2, ]
cv_test2 = df_sig_3[-cv_data2, ]

# define explanatory variables (X's) and dependent variable (Y) in training set
train_x2 = data.matrix(cv_train2[, -1])
train_y2 = cv_train2[,1]

# define explanatory variables (X's) and dependent variable (Y) in testing set
test_x2 = data.matrix(cv_test2[, -1])
test_y2 = cv_test2[, 1]
```

#### XGBoost df ajustada 2
```{r}
# XGBoost de significante 2
# define final training and testing sets
xgb_train = xgb.DMatrix(data = train_x, label = train_y)
xgb_test  = xgb.DMatrix(data = test_x, label = test_y)

# Lets fit XGBoost regression model and display RMSE for both training and testing data at each round
watchlist = list(train=xgb_train, test=xgb_test)
model_xgb = xgb.train(data=xgb_train, max.depth=3, watchlist=watchlist, nrounds=70) # the more the number of rounds selected, the longer the time to display the results. 
```

```{r}
# Looks like the lowest RMSE for both training and test dataset is achieved at 59 round. 
# Lets estimate our final regression model
reg_xgb = xgboost(data = xgb_train, max.depth = 3, nrounds = 59, verbose = 0) # setting verbose = 0 avoids to display the training and testing error for each round. 
prediction_xgb_test<-predict(reg_xgb, xgb_test)
RMSE_XGB <- rmse((prediction_xgb_test), cv_test$total_claim_amount)
RMSE_XGB
```

```{r}
# Lets do some diagnostic check of regression residuals 
xgb_reg_residuals<-cv_test$total_claim_amount - prediction_xgb_test
plot(xgb_reg_residuals, xlab= "Dependent Variable", ylab = "Residuals", main = 'XGBoost Regression Residuals')
abline(0,0)

# Plot first 3 trees of model
xgb.plot.tree(model=reg_xgb, trees=0:2)
importance_matrix <- xgb.importance(model = reg_xgb)
xgb.plot.importance(importance_matrix, xlab = "Explanatory Variables X's Importance")
```

#### XGBoost df ajustada 3
```{r}
# XGBoost de significante 3
# define final training and testing sets
xgb_train2 = xgb.DMatrix(data = train_x2, label = train_y2)
xgb_test2  = xgb.DMatrix(data = test_x2, label = test_y2)

# Lets fit XGBoost regression model and display RMSE for both training and testing data at each round
watchlist2 = list(train=xgb_train2, test=xgb_test)
model_xgb2 = xgb.train(data=xgb_train2, max.depth=3, watchlist=watchlist, nrounds=70) # the more the number of rounds selected, the longer the time to display the results. 
```

```{r}
# Looks like the lowest RMSE for both training and test dataset is achieved at 59 round. 
# Lets estimate our final regression model
reg_xgb2 = xgboost(data = xgb_train2, max.depth = 3, nrounds = 59, verbose = 0) # setting verbose = 0 avoids to display the training and testing error for each round. 
prediction_xgb_test2<-predict(reg_xgb2, xgb_test2)
RMSE_XGB2 <- rmse(prediction_xgb_test2, cv_test2$total_claim_amount)
RMSE_XGB2
```

```{r}
# Lets do some diagnostic check of regression residuals 
xgb_reg_residuals2<-cv_test2$total_claim_amount - prediction_xgb_test2
plot(xgb_reg_residuals2, xlab= "Dependent Variable", ylab = "Residuals", main = 'XGBoost Regression Residuals')
abline(0,0)

# Plot first 3 trees of model
xgb.plot.tree(model=reg_xgb2, trees=0:2)
importance_matrix2 <- xgb.importance(model = reg_xgb2)
xgb.plot.importance(importance_matrix2, xlab = "Explanatory Variables X's Importance")
```

### Decision Trees

#### Decision Trees df ajustada 2
```{r}
decision_tree_model <- rpart(total_claim_amount ~ incident_type + insured_occupation + insured_education_level + incident_severity + property_damage + incident_city, data = train)

# summary(decision_tree_regression)
plot(decision_tree_model, compress = TRUE)
text(decision_tree_model, use.n = TRUE)
rpart.plot(decision_tree_model)
```

#### RMSE of Decision Tree df ajustada 2
```{r}
decision_tree_prediction <- predict(decision_tree_model,test)
RMSE_decision_tree <- rmse(decision_tree_prediction, test$total_claim_amount)
RMSE_decision_tree
```

#### Decision Trees df ajustada 3
```{r}
decision_tree_model2 <- rpart(total_claim_amount ~ incident_type + insured_occupation + insured_education_level + incident_severity + property_damage + incident_city + age + incident_hour_of_the_day, data = train)

# summary(decision_tree_regression)
plot(decision_tree_model2, compress = TRUE)
text(decision_tree_model2, use.n = TRUE)
rpart.plot(decision_tree_model2)
```

#### RMSE of Decision Tree df ajustada 3
```{r}
decision_tree_prediction2 <- predict(decision_tree_model2,test)
RMSE_decision_tree2 <- rmse(decision_tree_prediction2, test$total_claim_amount)
RMSE_decision_tree2
```

### Random Forest
#### Random Forest df ajustada 2
```{r}
rf_model <- randomForest(total_claim_amount ~ ., data= cv_train, proximity=TRUE)
# random_forest<-randomForest(MEDV~.,data=train_alt,importance=TRUE, proximity=TRUE) 
print(rf_model) ### the train data set model accuracy is around 85%.
```

#### Prediction of Random Forest df ajustada 2
```{r}
# Prediction & Confusion Matrix – test data
rf_prediction <- predict(rf_model,cv_test)
rf_prediction
```

#### RMSE of Random Forest df ajustada 2
```{r}
# confusionMatrix(rf_prediction_train_data, train$MEDV) # a confusion matrix is essentially a table that categorizes predictions against actual values.
RMSE_rf <- rmse(rf_prediction, cv_test$total_claim_amount)
RMSE_rf
```

#### Random Forest df ajustada 3
```{r}
rf_model2 <- randomForest(total_claim_amount ~ ., data= cv_train2, proximity=TRUE)
# random_forest<-randomForest(MEDV~.,data=train_alt,importance=TRUE, proximity=TRUE) 
print(rf_model2) ### the train data set model accuracy is around 85%.
```

#### Prediction of Random Forest df ajustada 3
```{r}
# Prediction & Confusion Matrix – test data
rf_prediction2 <- predict(rf_model2,cv_test2)
rf_prediction2
```

#### RMSE of Random Forest df ajustada 3
```{r}
# confusionMatrix(rf_prediction_train_data, train$MEDV) # a confusion matrix is essentially a table that categorizes predictions against actual values.
RMSE_rf2 <- rmse(rf_prediction2, cv_test2$total_claim_amount)
RMSE_rf2
```

## 5. Pruebas de Diagnóstico
Ver los Resultados Obtenidos de la Estmación de Modelos de Regresión

### Multicolinealidad
```{r}
# dataframe original con 25 variables

# OLS
vif(ols_model)
# Log OLS
vif(log_ols_model)
```

```{r}
# dataframe significante con 7 variables

# OLS
vif(ols_model_df_sig_2)

# Log OLS
vif(log_ols_model_df_sig_2)
```

```{r}
# dataframe significante con 9 variables

# OLS
vif(ols_model_df_sig_3)

# Log OLS
vif(log_ols_model_df_sig_3)
```

### Heterocedasticidad
```{r}
# dataframe original con 25 variables

# OLS
bptest(ols_model)

# Log OLS
bptest(log_ols_model)
```

```{r}
# dataframe significante con 7 variables

# OLS
bptest(ols_model_df_sig_2)
# Log OLS
bptest(log_ols_model_df_sig_2)
```

```{r}
# dataframe significante con 9 variables

# OLS
bptest(ols_model_df_sig_3)
# Log OLS
bptest(log_ols_model_df_sig_3)
```

### Normalidad de los Residuales
```{r}
# dataframe original con 25 variables
shapiro.test(residuals(ols_model))  # Test de Shapiro-Wilk
qqnorm(residuals(ols_model))  # Q-Q plot
qqline(residuals(ols_model))

shapiro.test(residuals(log_ols_model))  # Test de Shapiro-Wilk
qqnorm(residuals(log_ols_model))  # Q-Q plot
qqline(residuals(log_ols_model))
```

```{r}
# dataframe significante con 7 variables
shapiro.test(residuals(ols_model_df_sig_2))  # Test de Shapiro-Wilk
qqnorm(residuals(ols_model_df_sig_2))  # Q-Q plot
qqline(residuals(ols_model_df_sig_2))

shapiro.test(residuals(log_ols_model_df_sig_2))  # Test de Shapiro-Wilk
qqnorm(residuals(log_ols_model_df_sig_2))  # Q-Q plot
qqline(residuals(log_ols_model_df_sig_2))
```

```{r}
# dataframe significante con 9 variables
shapiro.test(residuals(ols_model_df_sig_3))  # Test de Shapiro-Wilk
qqnorm(residuals(ols_model_df_sig_3))  # Q-Q plot
qqline(residuals(ols_model_df_sig_3))

shapiro.test(residuals(log_ols_model_df_sig_3))  # Test de Shapiro-Wilk
qqnorm(residuals(log_ols_model_df_sig_3))  # Q-Q plot
qqline(residuals(log_ols_model_df_sig_3))
```

**Nota: En caso de que las pruebas de diagnóstico identifiquen cualquiera de los anteriores a) – e) plantear una solución para mejorar la estimación de la especificiación del modelo.**

Se realizaron modificaciones en la base de datos con el objetivo de mejorar su calidad para su aplicación en modelos analíticos. Se eliminaron variables consideradas insignificantes para reducir la multicolinealidad, aunque esta no estaba muy presente, se identificaron dos variables con un factor de inflación de la varianza (VIF) mayor a 5 pero menor a 10. Además, se aplicó la función logarítmica (log()) a ciertas variables para mitigar la heterocedasticidad, logrando éxito en este aspecto. Estos cambios en la base de datos reflejaron hasta cuatro ajustes distintos, con la intención de prepararla para su implementación en modelos analíticos y evaluar la mejor opción disponible.

## 6. Evaluación y Selección de Modelo de Regresión

* a. Mediante el cálculo de la métrica RMSE para cada uno de los modelos estimados en 4) seleccionar el modelo que muestra los mejores resultados estimados.  

* b. Presentar los valores de la métrica RMSE de cada uno de los modelos estimados en 4) en un gráfico de barras.  

### RMSE de los Modelos
#### Elaborar *dataframe* de los RMSE's
```{r}
rmse_values <- data.frame(
  Model = c("OLS df2", "OLS df3", "Log-OLS df2", "Log-OLS df3", "XGBoost df2", "XGBoost df3", "Decision Tree df2", "Decision Tree df3", "Random Forest df2", "Random Forest df3"),
  RMSE = c(RMSE_ols_model_df_sig_2, RMSE_ols_model_df_sig_3, RMSE_log_ols_model_df_sig_2, RMSE_log_ols_model_df_sig_3, RMSE_XGB, RMSE_XGB2, RMSE_decision_tree, RMSE_decision_tree2, RMSE_rf, RMSE_rf2)
)

rmse_values
```

#### Gráficos de los RMSE's
```{r}
# Crear el gráfico de barras con valores encima de las barras
ggplot(rmse_values, aes(x = Model, y = RMSE)) +
  geom_bar(stat = "identity", fill = "skyblue") +
  geom_text(aes(label = round(RMSE, 3)), vjust = -0.5, size = 3.5) +  # Agregar valores encima de las barras
  labs(title = "RMSE por Modelo de Regresión",
       x = "Modelo de Regresión",
       y = "RMSE") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
```

# Conclusión

### Desarrollar una breve descripción de los 6 – 10 principales hallazgos de:

#### a. EDA 

#### b. Modelo seleccionado:  

i) ¿Cuáles son las variables que contribuyen a explicar los cambios de la principal variable de estudio?  

- incident type

- incident severity

- incident city "Springfield"

- property damage "Yes"

ii) ¿Cómo es el impacto de dichas variables explicativas sobre la variable dependiente?

- incident type: negativo 

- incident severity: negativo

- incident city "Springfield": positivo

- property damage "Yes": positivo

iii) ¿Los resultados estimados del modelo seleccionado son similares a los otros modelos estimados? ¿Cuáles son las diferencias?  

Al concluir el análisis, se llegó a la conclusión de que el modelo óptimo es el de Regresión Logarítmica de Mínimos Cuadrados Ordinarios (Log OLS) aplicado al segundo o tercer data frame ajustado, ya que ambos revelaron las mismas variables importantes. Aunque ninguno de los dos modelos mostró multicolinealidad, el segundo data frame ajustado presentó heterocedasticidad. A pesar de que ambos modelos no evidenciaron una autocorrelación serial pronunciada, sus valores p (p-values) excedieron 0.05, indicando una presencia moderada de autocorrelación, aunque el valor de Durbin-Watson (D-W) se aproximó a 2. 

Por último, tanto el modelo Log OLS del segundo como del tercer data frame ajustado demostraron tener el error cuadrático medio (RMSE) más bajo, con una medida de 0.302 en ambos casos.
