injurydata
Sudiksha Ale
2024-08-06
#Loading neccessary packages
library(readr)
library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.3.3library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(tidyr)
library(corrplot)## Warning: package 'corrplot' was built under R version 4.3.2## corrplot 0.92 loadedlibrary(DataExplorer)## Warning: package 'DataExplorer' was built under R version 4.3.3library(naniar)## Warning: package 'naniar' was built under R version 4.3.2library(caret)## Warning: package 'caret' was built under R version 4.3.3## Loading required package: latticelibrary(pROC)## Warning: package 'pROC' was built under R version 4.3.3## Type 'citation("pROC")' for a citation.##
## Attaching package: 'pROC'## The following objects are masked from 'package:stats':
##
## cov, smooth, varlibrary(e1071)## Warning: package 'e1071' was built under R version 4.3.3library(skimr)## Warning: package 'skimr' was built under R version 4.3.2##
## Attaching package: 'skimr'## The following object is masked from 'package:naniar':
##
## n_complete#Loading dataset
injury_data <- read.csv("injury_data.csv")
head(injury_data)## Player_Age Player_Weight Player_Height Previous_Injuries Training_Intensity
## 1 24 66.25193 175.7324 1 0.4579290
## 2 37 70.99627 174.5817 0 0.2265216
## 3 32 80.09378 186.3296 0 0.6139703
## 4 28 87.47327 175.5042 1 0.2528581
## 5 25 84.65922 190.1750 0 0.5776318
## 6 38 75.82055 206.6318 1 0.3592087
## Recovery_Time Likelihood_of_Injury
## 1 5 0
## 2 6 1
## 3 2 1
## 4 4 1
## 5 1 1
## 6 4 0#overview of dataset
str(injury_data)## 'data.frame': 1000 obs. of 7 variables:
## $ Player_Age : int 24 37 32 28 25 38 24 36 28 28 ...
## $ Player_Weight : num 66.3 71 80.1 87.5 84.7 ...
## $ Player_Height : num 176 175 186 176 190 ...
## $ Previous_Injuries : int 1 0 0 1 0 1 0 1 1 1 ...
## $ Training_Intensity : num 0.458 0.227 0.614 0.253 0.578 ...
## $ Recovery_Time : int 5 6 2 4 1 4 2 3 1 1 ...
## $ Likelihood_of_Injury: int 0 1 1 1 1 0 0 1 1 0 ...summary(injury_data)## Player_Age Player_Weight Player_Height Previous_Injuries
## Min. :18.00 Min. : 40.19 Min. :145.3 Min. :0.000
## 1st Qu.:22.00 1st Qu.: 67.94 1st Qu.:173.0 1st Qu.:0.000
## Median :28.00 Median : 75.02 Median :180.0 Median :1.000
## Mean :28.23 Mean : 74.79 Mean :179.8 Mean :0.515
## 3rd Qu.:34.00 3rd Qu.: 81.30 3rd Qu.:186.6 3rd Qu.:1.000
## Max. :39.00 Max. :104.65 Max. :207.3 Max. :1.000
## Training_Intensity Recovery_Time Likelihood_of_Injury
## Min. :0.0000307 Min. :1.000 Min. :0.0
## 1st Qu.:0.2410415 1st Qu.:2.000 1st Qu.:0.0
## Median :0.4839116 Median :4.000 Median :0.5
## Mean :0.4905380 Mean :3.466 Mean :0.5
## 3rd Qu.:0.7304045 3rd Qu.:5.000 3rd Qu.:1.0
## Max. :0.9977494 Max. :6.000 Max. :1.0dim(injury_data)## [1] 1000 7#Checking datatypes:
#Printing the datatypes of all columns
str(injury_data)## 'data.frame': 1000 obs. of 7 variables:
## $ Player_Age : int 24 37 32 28 25 38 24 36 28 28 ...
## $ Player_Weight : num 66.3 71 80.1 87.5 84.7 ...
## $ Player_Height : num 176 175 186 176 190 ...
## $ Previous_Injuries : int 1 0 0 1 0 1 0 1 1 1 ...
## $ Training_Intensity : num 0.458 0.227 0.614 0.253 0.578 ...
## $ Recovery_Time : int 5 6 2 4 1 4 2 3 1 1 ...
## $ Likelihood_of_Injury: int 0 1 1 1 1 0 0 1 1 0 ...skim_deliveries_data <- skim(injury_data)
print(skim_deliveries_data)## ── Data Summary ────────────────────────
## Values
## Name injury_data
## Number of rows 1000
## Number of columns 7
## _______________________
## Column type frequency:
## numeric 7
## ________________________
## Group variables None
##
## ── Variable type: numeric ──────────────────────────────────────────────────────
## skim_variable n_missing complete_rate mean sd p0 p25
## 1 Player_Age 0 1 28.2 6.54 18 22
## 2 Player_Weight 0 1 74.8 9.89 40.2 67.9
## 3 Player_Height 0 1 180. 9.89 145. 173.
## 4 Previous_Injuries 0 1 0.515 0.500 0 0
## 5 Training_Intensity 0 1 0.491 0.286 0.0000307 0.241
## 6 Recovery_Time 0 1 3.47 1.70 1 2
## 7 Likelihood_of_Injury 0 1 0.5 0.500 0 0
## p50 p75 p100 hist
## 1 28 34 39 ▇▅▅▆▇
## 2 75.0 81.3 105. ▁▃▇▅▁
## 3 180. 187. 207. ▁▃▇▆▁
## 4 1 1 1 ▇▁▁▁▇
## 5 0.484 0.730 0.998 ▇▇▇▇▇
## 6 4 5 6 ▇▃▃▃▃
## 7 0.5 1 1 ▇▁▁▁▇###Data Pre-processing
#Missing values
missing_values <- colSums(is.na(injury_data))
missing_values## Player_Age Player_Weight Player_Height
## 0 0 0
## Previous_Injuries Training_Intensity Recovery_Time
## 0 0 0
## Likelihood_of_Injury
## 0#visualizing missing values
vis_miss(injury_data)
#Removing rows with any missing values
data_clean <- na.omit(injury_data)
data_clean## Player_Age Player_Weight Player_Height Previous_Injuries
## 1 24 66.25193 175.7324 1
## 2 37 70.99627 174.5817 0
## 3 32 80.09378 186.3296 0
## 4 28 87.47327 175.5042 1
## 5 25 84.65922 190.1750 0
## 6 38 75.82055 206.6318 1
## 7 24 70.12605 177.0446 0
## 8 36 79.03821 181.5232 1
## 9 28 64.08610 183.7948 1
## 10 28 66.82999 198.1150 1
## 11 38 90.09771 179.1735 0
## 12 21 79.02034 171.7098 0
## 13 25 71.52473 167.3366 1
## 14 20 83.29139 179.4202 0
## 15 39 87.86975 175.5162 1
## 16 38 70.55362 200.1001 1
## 17 19 70.72592 197.6986 0
## 18 29 69.47354 169.6563 1
## 19 23 84.63850 172.8924 0
## 20 19 84.73557 180.7377 1
## 21 38 88.85903 193.9723 0
## 22 18 85.08603 180.5701 1
## 23 29 58.67782 173.5276 1
## 24 39 77.80964 179.4112 0
## 25 29 64.41615 173.7152 1
## 26 34 69.87086 186.0248 1
## 27 27 76.70594 172.9097 0
## 28 33 83.35565 174.8956 1
## 29 32 66.09157 191.6193 0
## 30 32 51.75280 172.3164 1
## 31 36 76.80531 165.5387 0
## 32 29 101.43572 180.9499 1
## 33 37 72.97154 176.3953 1
## 34 20 75.46915 185.1142 1
## 35 22 92.82164 183.1747 0
## 36 36 80.90275 179.6808 0
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## 80 30 76.55037 177.4475 1
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## 84 30 48.40649 182.2396 1
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## 457 37 80.32860 171.4155 1
## 458 29 62.46192 191.2559 1
## 459 35 88.76803 177.2702 0
## 460 20 82.77016 172.2246 1
## 461 18 79.01626 187.2868 1
## 462 18 68.14390 175.1180 1
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## 465 22 58.49509 178.4041 1
## 466 29 74.49577 189.4497 1
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## 468 18 85.62402 187.5605 1
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## 471 27 63.44432 195.6878 0
## 472 28 78.19223 166.7190 1
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## 477 19 86.07720 169.4461 1
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## 479 35 59.44101 182.0330 1
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## 483 27 72.40750 184.0793 1
## 484 19 87.33687 178.7502 1
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## 490 35 87.22501 165.8350 1
## 491 30 87.14509 192.3512 1
## 492 28 74.10477 176.7991 1
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## 495 21 71.96692 176.2941 0
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## 503 21 73.10006 192.7459 1
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## 511 20 53.99081 171.2152 1
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## 513 34 66.28866 162.5615 0
## 514 34 79.03390 181.1902 1
## 515 37 65.61532 164.7468 1
## 516 33 81.44268 168.6618 0
## 517 39 77.76569 177.8196 0
## 518 30 68.49286 153.6119 1
## 519 36 85.73087 187.7898 0
## 520 34 73.36039 155.9011 0
## 521 21 80.47216 179.2263 0
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## 524 36 95.03407 179.5832 0
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## 531 32 64.23339 177.6230 0
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## 538 38 72.47620 166.4855 1
## 539 21 78.40600 186.7022 1
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## 542 24 78.55551 170.6371 1
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## 545 27 85.46920 172.5687 0
## 546 24 80.69950 172.0300 1
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## 550 28 72.38600 179.5883 0
## 551 28 76.20500 185.3709 1
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## 554 29 58.56259 173.4998 1
## 555 26 66.14837 173.3787 1
## 556 27 90.52562 174.0219 1
## 557 29 59.47273 183.3881 0
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## 562 30 67.99884 176.3041 1
## 563 25 85.89665 167.0187 0
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## 566 35 68.17008 188.4856 1
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## 568 22 86.26117 177.6323 1
## 569 22 70.45756 177.3702 0
## 570 23 74.80065 161.6429 1
## 571 36 63.43256 183.3007 1
## 572 25 59.67204 186.1993 1
## 573 33 89.45385 169.1092 0
## 574 30 56.75930 178.4418 1
## 575 18 98.28619 180.3786 0
## 576 39 82.25151 185.8457 0
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## 578 34 80.75469 163.7368 0
## 579 24 72.47314 174.7006 1
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## 581 21 73.11103 188.4153 1
## 582 21 80.53073 173.8236 1
## 583 23 74.66313 201.8765 1
## 584 36 65.49652 161.0553 0
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## 587 24 84.70901 177.9925 1
## 588 27 68.82553 167.6786 0
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## 590 24 78.96839 183.7109 1
## 591 20 90.14518 180.4796 1
## 592 30 82.73083 183.3184 1
## 593 30 68.96676 175.7622 1
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## 596 25 73.40776 181.5705 1
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## 598 24 80.51037 180.4138 0
## 599 18 93.46627 185.4871 1
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## 602 34 65.62772 185.7209 1
## 603 18 88.97415 179.1511 1
## 604 23 74.24856 167.5668 1
## 605 38 69.21037 170.3851 0
## 606 23 75.63688 188.3085 1
## 607 29 79.71961 193.9247 0
## 608 30 76.39215 191.7528 1
## 609 30 87.06153 182.3216 0
## 610 32 78.83972 167.9687 1
## 611 38 72.16058 179.9365 0
## 612 33 79.01192 195.6226 0
## 613 39 79.99637 186.1849 0
## 614 38 62.03624 177.9252 0
## 615 28 86.49420 186.2863 0
## 616 22 71.99096 167.3060 0
## 617 21 72.24677 173.0433 1
## 618 20 82.51376 169.1581 1
## 619 36 82.47165 184.6957 0
## 620 37 67.99168 187.1410 0
## 621 35 83.02902 178.4617 0
## 622 32 69.39552 171.2707 0
## 623 26 93.19573 179.8025 1
## 624 34 66.71806 175.1342 0
## 625 31 77.39477 172.0383 1
## 626 32 67.17982 183.3899 1
## 627 18 97.22696 172.4201 0
## 628 38 62.62179 164.3365 0
## 629 39 80.24363 166.5589 1
## 630 39 67.65090 163.9674 1
## 631 20 71.45892 182.4751 0
## 632 33 79.53538 181.5041 0
## 633 28 87.24584 181.0052 0
## 634 29 91.23397 185.8773 1
## 635 27 59.63052 191.9196 1
## 636 33 77.67751 174.0773 0
## 637 25 89.59494 184.0648 1
## 638 23 58.14689 164.2038 1
## 639 29 78.14383 193.7871 0
## 640 38 74.97916 167.5517 0
## 641 25 79.14542 172.8992 1
## 642 21 67.27696 174.0229 1
## 643 25 60.64381 164.7373 0
## 644 35 55.86894 180.3258 0
## 645 22 84.76384 182.1390 0
## 646 26 86.04049 178.1059 0
## 647 21 78.02685 164.5975 0
## 648 39 69.65852 199.8339 1
## 649 34 58.76954 185.4923 1
## 650 26 80.63345 206.3422 1
## 651 18 63.99221 171.9489 1
## 652 38 67.88701 189.5788 0
## 653 37 69.13436 187.7927 0
## 654 30 73.02465 177.0942 0
## 655 33 67.40960 194.1105 1
## 656 30 75.33459 175.9459 1
## 657 31 68.78182 176.3911 1
## 658 20 82.01373 145.2857 0
## 659 23 85.31063 184.0903 1
## 660 35 76.08543 166.7449 0
## 661 36 79.62626 174.4928 0
## 662 22 96.17856 169.7378 0
## 663 32 63.27397 176.3377 0
## 664 19 62.66532 184.4388 0
## 665 27 81.94998 180.1114 0
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## 668 22 92.26834 176.3120 0
## 669 18 75.96434 175.2143 1
## 670 39 68.19113 189.5773 0
## 671 18 65.09308 190.3705 1
## 672 35 81.29414 187.4059 1
## 673 32 65.85041 170.4782 1
## 674 34 75.81277 184.2266 0
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## 676 38 71.93220 190.5253 1
## 677 39 88.14009 174.2713 0
## 678 34 62.85457 163.3339 0
## 679 30 69.66091 186.9024 1
## 680 18 60.92856 154.4163 1
## 681 19 75.72997 172.6849 0
## 682 26 82.80078 169.7680 1
## 683 20 75.75187 169.7007 1
## 684 18 87.27050 172.5097 1
## 685 38 67.50821 192.1737 0
## 686 33 77.25343 188.2056 0
## 687 23 69.72531 177.4827 0
## 688 34 78.61677 191.3362 1
## 689 22 80.03736 169.3770 0
## 690 22 80.82396 168.0172 1
## 691 23 64.29406 188.6360 0
## 692 20 66.10254 185.7964 0
## 693 38 67.95712 175.3974 1
## 694 22 72.61119 168.6454 0
## 695 38 75.87186 194.5973 1
## 696 22 82.08089 162.7990 0
## 697 27 66.51901 174.0157 0
## 698 27 58.98981 182.2858 1
## 699 36 96.55743 198.0216 1
## 700 34 63.68547 183.8310 1
## 701 38 83.36324 184.8030 0
## 702 31 75.31267 174.3062 0
## 703 26 77.51306 185.6848 0
## 704 31 74.98449 181.0531 1
## 705 18 71.15158 164.1154 1
## 706 36 79.63972 187.9293 1
## 707 30 70.61413 189.7039 0
## 708 30 86.66303 188.0337 0
## 709 21 80.68441 180.3333 1
## 710 18 80.99594 170.4382 0
## 711 34 69.92877 156.9082 1
## 712 25 72.63645 181.5705 0
## 713 19 63.12087 190.8194 0
## 714 25 71.46984 189.9553 1
## 715 24 64.63424 188.1607 0
## 716 19 88.13069 179.2579 0
## 717 39 75.50536 190.8746 1
## 718 39 76.70497 189.2416 1
## 719 20 72.74853 175.7737 0
## 720 35 76.97579 186.7660 1
## 721 29 99.64878 174.6404 0
## 722 18 71.99860 185.4002 1
## 723 39 68.49587 167.0088 0
## 724 29 80.29693 167.7779 0
## 725 22 49.74281 190.5821 1
## 726 34 75.63957 189.3756 0
## 727 33 71.28287 186.3361 0
## 728 32 65.07687 180.8797 1
## 729 32 80.07758 156.0873 1
## 730 22 75.53988 179.2512 0
## 731 38 56.97591 184.5418 1
## 732 31 68.53470 174.6527 1
## 733 19 46.11729 172.8751 0
## 734 28 77.93269 194.4442 0
## 735 36 77.83299 179.3971 1
## 736 24 67.06718 184.5480 1
## 737 23 80.40113 187.7413 1
## 738 19 62.17501 169.9856 0
## 739 23 65.33483 181.9321 0
## 740 35 79.17190 193.1964 0
## 741 19 68.11333 198.1922 0
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## 744 36 70.89069 182.6902 0
## 745 19 71.32776 182.4364 1
## 746 37 67.93243 193.8676 1
## 747 23 78.02450 170.0361 0
## 748 18 68.72072 177.2013 0
## 749 32 75.15687 168.8286 0
## 750 27 67.71268 169.8791 1
## 751 36 59.65701 183.7657 0
## 752 34 84.12435 188.0986 1
## 753 22 79.99527 173.5061 1
## 754 21 97.88682 187.7209 1
## 755 27 64.56027 191.1158 1
## 756 34 72.91040 199.3471 1
## 757 27 73.71493 180.6975 0
## 758 34 84.89369 173.6024 0
## 759 37 69.92892 169.9753 1
## 760 22 63.28958 168.9821 0
## 761 19 87.07809 192.4978 1
## 762 23 71.83350 164.8490 1
## 763 38 83.78644 192.1775 0
## 764 19 82.62198 171.3156 0
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## 766 28 73.25686 178.4844 0
## 767 28 71.08435 172.9635 0
## 768 33 74.84807 189.3198 1
## 769 28 83.63100 198.1334 0
## 770 36 67.03560 173.4257 0
## 771 32 76.08846 175.6776 0
## 772 33 63.81369 178.3668 1
## 773 28 65.05894 185.4106 0
## 774 39 74.69061 178.4652 1
## 775 33 55.39842 193.8726 0
## 776 25 95.60559 191.4662 0
## 777 21 63.06940 168.1454 1
## 778 25 74.59911 185.2097 0
## 779 21 86.68218 195.2034 0
## 780 20 66.37095 175.0522 1
## 781 20 51.97303 177.0574 0
## 782 35 70.55054 174.7196 1
## 783 36 66.25180 174.9292 1
## 784 36 52.23626 163.3155 1
## 785 38 77.84890 166.7169 1
## 786 22 77.07094 180.7747 1
## 787 35 86.42326 195.1360 0
## 788 27 63.79178 173.8664 0
## 789 39 59.00864 173.5724 0
## 790 38 70.93277 197.0152 1
## 791 23 74.11657 168.9174 1
## 792 18 84.21350 163.3876 1
## 793 22 89.10568 171.7148 1
## 794 26 87.14479 173.3722 0
## 795 29 77.76544 162.5255 0
## 796 31 82.99499 178.7196 1
## 797 19 68.59507 189.4845 1
## 798 34 76.68395 167.1405 0
## 799 31 63.08710 180.8758 1
## 800 30 75.62685 183.9594 1
## 801 26 75.88647 179.1662 1
## 802 32 92.22129 181.8569 0
## 803 39 72.71017 181.9733 0
## 804 32 75.90043 171.8727 1
## 805 28 77.36961 194.9858 0
## 806 33 70.45180 192.4776 1
## 807 34 66.97507 191.8991 1
## 808 21 87.98811 190.1368 0
## 809 18 65.19083 164.2502 1
## 810 25 61.72082 189.2606 1
## 811 34 81.80265 186.5560 0
## 812 20 86.57815 164.6348 1
## 813 37 70.48590 177.8509 1
## 814 32 70.63947 182.7679 1
## 815 32 77.82544 196.6432 0
## 816 39 67.92147 187.2472 1
## 817 31 67.18287 171.3543 0
## 818 31 83.79593 196.9539 1
## 819 19 81.07131 180.4378 1
## 820 30 88.11159 177.4416 1
## 821 31 62.47411 176.7288 1
## 822 36 69.97538 163.7644 0
## 823 24 77.81180 178.9023 0
## 824 20 65.74379 184.9749 1
## 825 32 81.41687 176.1462 1
## 826 31 87.24627 184.2045 1
## 827 28 69.16642 171.3561 1
## 828 32 75.38592 178.5907 1
## 829 30 89.55619 183.1654 1
## 830 35 92.99272 181.8966 1
## 831 23 82.45801 163.9453 1
## 832 20 80.93429 175.6099 0
## 833 36 66.46263 196.7746 0
## 834 32 78.59209 175.0755 0
## 835 22 80.56821 180.1581 1
## 836 27 72.89630 179.1324 1
## 837 24 69.05224 195.6824 1
## 838 34 62.18702 192.9037 0
## 839 24 57.62270 188.0753 1
## 840 39 55.52116 170.2676 0
## 841 29 80.79443 183.5918 1
## 842 34 76.47699 185.6794 0
## 843 30 70.16189 173.0140 0
## 844 37 71.68544 171.2948 0
## 845 21 77.10471 197.0501 1
## 846 27 73.35437 176.9920 0
## 847 20 61.59447 196.1498 1
## 848 26 84.13000 189.9709 1
## 849 30 78.04126 184.0788 1
## 850 35 77.65898 184.5253 1
## 851 32 83.37563 165.9958 1
## 852 21 66.86355 182.0677 0
## 853 32 87.13731 194.1702 1
## 854 39 73.59957 186.8134 1
## 855 19 83.88599 181.5434 1
## 856 32 82.34449 197.1194 1
## 857 25 64.61198 178.1496 1
## 858 25 80.02900 173.8044 0
## 859 34 78.26275 181.5383 0
## 860 29 75.30312 187.6647 1
## 861 36 74.05628 162.8221 0
## 862 27 74.14107 182.8487 0
## 863 26 83.12689 188.1035 1
## 864 22 80.77911 177.3036 0
## 865 23 75.30479 191.0696 1
## 866 37 60.08064 186.4119 0
## 867 19 79.94443 201.4138 0
## 868 30 82.23614 172.4940 0
## 869 23 98.00410 188.3071 1
## 870 22 85.75433 169.8268 0
## 871 18 56.00824 163.8463 0
## 872 38 75.78429 159.6447 1
## 873 39 93.11375 185.3832 1
## 874 38 81.23549 181.8425 0
## 875 23 78.28652 175.2095 1
## 876 23 84.00229 162.2650 0
## 877 33 68.77622 173.0961 0
## 878 21 79.78090 180.0409 1
## 879 28 72.49532 187.9317 1
## 880 26 74.80377 179.8346 1
## 881 20 83.28455 182.5206 0
## 882 36 65.73287 176.2387 1
## 883 25 87.87982 197.4922 0
## 884 22 70.77188 169.8175 1
## 885 21 77.60663 173.0076 0
## 886 23 60.99492 175.0764 1
## 887 19 82.19837 185.4270 0
## 888 37 89.46504 169.6114 1
## 889 28 69.85358 181.3572 0
## 890 21 63.89777 181.6491 1
## 891 32 77.77983 173.0564 1
## 892 23 89.54811 183.2998 0
## 893 39 60.50143 169.9219 1
## 894 34 63.93692 168.8293 0
## 895 23 70.45810 180.9961 0
## 896 36 66.79656 163.4024 1
## 897 19 66.82815 158.5520 0
## 898 32 54.05879 185.3283 1
## 899 28 80.89057 190.4897 1
## 900 25 78.38721 175.3320 0
## 901 36 58.01151 173.3193 1
## 902 30 89.57196 190.0554 0
## 903 29 91.81092 168.5446 0
## 904 22 67.49094 166.3663 1
## 905 23 86.66549 187.0764 1
## 906 39 69.27002 180.0904 0
## 907 21 76.81117 190.6488 0
## 908 36 66.64238 158.5973 1
## 909 37 97.14252 165.9933 1
## 910 18 72.03983 180.1433 1
## 911 37 76.26449 184.1767 1
## 912 34 68.12063 188.4700 0
## 913 29 93.61316 181.7482 1
## 914 32 93.86615 181.6466 0
## 915 18 91.30929 176.1060 0
## 916 31 84.56440 195.9090 1
## 917 23 68.75210 168.9698 0
## 918 22 83.27850 183.5902 1
## 919 28 74.92957 170.1219 0
## 920 21 88.12409 185.3478 1
## 921 30 86.15902 169.6298 0
## 922 28 68.50734 187.4382 0
## 923 20 84.47826 181.1210 1
## 924 18 81.82034 197.4464 1
## 925 23 47.26437 177.1898 0
## 926 35 83.00937 184.2497 0
## 927 27 70.68204 172.2034 1
## 928 22 66.13958 177.1211 0
## 929 27 66.56739 181.0575 1
## 930 29 73.64757 164.7278 0
## 931 19 79.80060 165.8238 1
## 932 30 61.34538 185.9986 0
## 933 25 76.89848 178.5391 1
## 934 19 80.76758 174.0707 1
## 935 37 93.61806 172.6301 0
## 936 18 64.23310 173.0151 1
## 937 22 71.50388 181.1255 0
## 938 26 77.61663 184.4504 1
## 939 34 86.47409 159.0789 1
## 940 26 71.95776 194.1283 1
## 941 28 86.98641 193.4170 0
## 942 32 55.62638 156.6935 1
## 943 23 62.46652 190.8593 0
## 944 20 78.48644 190.5522 1
## 945 34 60.89176 180.0045 0
## 946 22 63.25529 167.0283 0
## 947 29 69.72223 190.1523 0
## 948 35 82.71439 177.5877 1
## 949 31 56.04035 185.4888 0
## 950 38 76.70298 202.1920 1
## 951 28 85.32964 182.5184 0
## 952 26 73.05055 170.8312 0
## 953 19 71.31170 176.6427 0
## 954 29 79.01771 172.1055 0
## 955 20 73.07889 184.3349 1
## 956 20 88.90500 171.9393 0
## 957 18 63.88587 175.2715 0
## 958 25 74.48356 167.1557 1
## 959 39 77.05686 178.6791 0
## 960 25 71.21546 181.7675 0
## 961 28 46.28717 153.1521 0
## 962 26 73.39095 182.5957 0
## 963 22 84.00177 179.5430 1
## 964 31 82.78109 172.3501 1
## 965 20 78.54778 200.2863 0
## 966 38 64.02138 189.5848 1
## 967 22 78.95309 169.0360 1
## 968 22 69.26562 188.3824 0
## 969 36 78.53483 192.4450 1
## 970 31 76.21505 181.3202 0
## 971 21 65.23625 183.6144 1
## 972 30 66.07544 183.3835 1
## 973 27 78.55022 159.1418 1
## 974 35 72.78887 178.1313 0
## 975 25 66.59719 177.6109 1
## 976 25 65.77170 194.3016 0
## 977 24 71.96169 183.3373 0
## 978 25 90.68105 177.0239 1
## 979 31 69.73922 179.0988 0
## 980 36 74.85980 190.2339 1
## 981 23 76.01644 178.1542 0
## 982 18 63.68974 173.0180 0
## 983 32 57.37168 177.2430 1
## 984 18 64.73359 189.4070 1
## 985 34 77.47077 184.9616 1
## 986 18 75.87331 180.4427 1
## 987 32 82.02407 181.9154 0
## 988 19 75.96750 174.4026 0
## 989 31 73.90451 178.0605 0
## 990 38 68.95988 173.3670 1
## 991 25 83.84607 177.0522 0
## 992 25 65.80820 193.4618 1
## 993 36 63.89600 169.2213 0
## 994 24 80.67056 173.1128 1
## 995 36 61.66843 167.4570 0
## 996 23 99.14791 165.2909 0
## 997 23 75.79993 178.1323 1
## 998 20 78.47906 173.8239 0
## 999 24 66.91580 197.6169 1
## 1000 36 71.84735 177.1715 0
## Training_Intensity Recovery_Time Likelihood_of_Injury
## 1 4.579290e-01 5 0
## 2 2.265216e-01 6 1
## 3 6.139703e-01 2 1
## 4 2.528581e-01 4 1
## 5 5.776318e-01 1 1
## 6 3.592087e-01 4 0
## 7 8.235522e-01 2 0
## 8 8.206962e-01 3 1
## 9 4.773504e-01 1 1
## 10 3.508191e-01 1 0
## 11 3.625598e-01 3 0
## 12 8.057145e-01 4 0
## 13 3.281795e-01 5 0
## 14 2.090594e-01 4 0
## 15 8.468834e-02 2 1
## 16 4.666931e-01 6 1
## 17 4.821321e-01 5 1
## 18 8.409087e-01 4 1
## 19 2.212387e-01 6 0
## 20 3.809913e-01 6 1
## 21 8.084057e-01 3 0
## 22 8.236236e-01 2 0
## 23 3.846111e-01 2 0
## 24 4.591240e-01 2 0
## 25 3.032859e-01 5 0
## 26 9.325549e-01 1 0
## 27 1.186438e-01 5 0
## 28 9.337494e-01 4 1
## 29 6.838665e-01 6 0
## 30 5.315722e-01 6 1
## 31 3.265906e-02 1 1
## 32 4.755962e-01 4 0
## 33 4.077481e-01 1 0
## 34 1.612188e-01 6 1
## 35 6.564096e-01 5 1
## 36 9.711509e-01 2 1
## 37 5.621888e-01 6 1
## 38 7.147533e-01 3 0
## 39 6.845045e-02 2 0
## 40 4.176670e-01 6 0
## 41 1.163585e-01 5 1
## 42 6.127958e-01 1 0
## 43 9.384822e-01 2 1
## 44 6.617777e-01 5 0
## 45 7.680305e-02 4 1
## 46 3.546662e-01 3 0
## 47 5.506634e-01 2 0
## 48 4.032078e-01 1 1
## 49 8.336440e-01 4 1
## 50 8.146710e-01 5 1
## 51 6.118310e-01 3 1
## 52 3.734579e-01 1 1
## 53 2.553364e-01 5 0
## 54 1.058743e-01 5 1
## 55 3.548977e-01 2 0
## 56 4.131725e-01 2 0
## 57 6.758229e-01 1 0
## 58 6.581598e-01 6 0
## 59 7.010814e-02 6 0
## 60 3.952593e-01 5 1
## 61 1.817789e-01 2 1
## 62 1.568127e-01 3 0
## 63 8.273021e-01 3 0
## 64 4.191522e-02 5 1
## 65 4.191829e-01 2 1
## 66 1.682037e-01 1 0
## 67 8.793302e-01 1 1
## 68 5.570405e-01 1 1
## 69 2.313806e-01 3 1
## 70 5.024273e-01 2 0
## 71 7.314864e-01 2 1
## 72 9.581180e-01 3 0
## 73 2.204460e-01 1 0
## 74 8.861818e-01 1 0
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## 77 6.354144e-01 4 1
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## 81 5.312940e-01 2 1
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## 84 7.314965e-01 5 1
## 85 8.244715e-01 6 1
## 86 1.464363e-01 4 1
## 87 8.329297e-01 1 1
## 88 5.402965e-01 1 0
## 89 8.446659e-01 2 0
## 90 4.313017e-01 3 1
## 91 3.790405e-01 2 0
## 92 9.150607e-01 3 0
## 93 2.511693e-01 4 1
## 94 8.442123e-01 1 0
## 95 4.840206e-01 6 0
## 96 5.140954e-01 3 1
## 97 3.084683e-01 4 0
## 98 5.730422e-01 1 1
## 99 3.252112e-01 6 1
## 100 3.926941e-02 3 0
## 101 2.719986e-01 2 0
## 102 6.379043e-03 2 0
## 103 9.783548e-01 2 1
## 104 9.657472e-01 4 1
## 105 3.951553e-01 2 1
## 106 7.283709e-01 5 0
## 107 3.458061e-01 1 0
## 108 6.710932e-01 4 1
## 109 8.054300e-01 6 0
## 110 9.467562e-01 2 0
## 111 4.002123e-01 1 1
## 112 7.832980e-01 3 1
## 113 2.659285e-01 2 0
## 114 9.903323e-01 1 1
## 115 2.576960e-02 5 0
## 116 6.035288e-01 3 1
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## 118 6.880456e-01 6 1
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## 122 6.227534e-01 1 1
## 123 2.226761e-01 1 1
## 124 3.072164e-01 5 0
## 125 5.464585e-01 1 1
## 126 4.171934e-01 2 1
## 127 1.602256e-01 1 1
## 128 1.709364e-01 4 1
## 129 4.181448e-01 6 0
## 130 7.572830e-01 1 0
## 131 8.979646e-01 4 1
## 132 8.412766e-02 1 0
## 133 3.931039e-01 1 0
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## 136 6.617187e-01 6 0
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## 140 2.370775e-02 5 1
## 141 8.349472e-01 2 0
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## 143 1.354595e-01 2 1
## 144 2.314752e-01 5 1
## 145 8.684095e-01 1 0
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## 147 4.196274e-01 2 1
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## 150 5.739499e-01 5 1
## 151 3.928951e-01 2 0
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## 154 1.130254e-02 6 1
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## 205 2.598281e-01 1 1
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## 209 2.299938e-01 6 0
## 210 4.113040e-01 2 1
## 211 2.405316e-01 5 1
## 212 6.723838e-01 4 0
## 213 8.260647e-01 6 0
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## 215 8.243504e-01 1 1
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## 998 2.469612e-01 1 1
## 999 8.596453e-01 2 0
## 1000 1.688100e-01 1 0##Detecting outliers
# Boxplot to detect outliers in Player_Weight
ggplot(injury_data, aes(y = Player_Weight)) +
geom_boxplot() +
labs(title = "Boxplot to Detect Outliers in Player Weight", y = "Player Weight")
# Boxplot to detect outliers in Player_Height
ggplot(injury_data, aes(y = Player_Height)) +
geom_boxplot() +
labs(title = "Boxplot to Detect Outliers in Player Height", y = "Player Height")
#Data standardization
injury_data$scaled_Player_Weight <- scale(injury_data$Player_Weight)
injury_data$scaled_Player_Height <- scale(injury_data$Player_Height)###Exploratory Data Analysis(EDA)
par(mfrow = c(1,2))
hist_colors <- c("#99CCFF", "#99FF98", "#FFCC32", "#99CCDD")
hist(injury_data$Player_Age, main = "Age distribution", xlab = "Age", col = hist_colors[1])
##Univariate Analysis
# Histogram for Player_Age
ggplot(injury_data, aes(x = Player_Age)) +
geom_histogram(binwidth = 1, fill = "blue", color = "black") +
labs(title = "Histogram of Player Age", x = "Player Age", y = "Frequency")
# Histogram for Player_Weight
ggplot(injury_data, aes(x = Player_Weight)) +
geom_histogram(binwidth = 5, fill = "blue", color = "black") +
labs(title = "Histogram of Player Weight", x = "Player Weight", y = "Frequency")
# Histogram for Player_Height
ggplot(injury_data, aes(x = Player_Height)) +
geom_histogram(binwidth = 5, fill = "blue", color = "black") +
labs(title = "Histogram of Player Height", x = "Player Height", y = "Frequency")
# Bar plot for Previous_Injuries
ggplot(injury_data, aes(x = factor(Previous_Injuries))) +
geom_bar(fill = "blue", color = "black") +
labs(title = "Bar Plot of Previous Injuries", x = "Previous Injuries", y = "Count")
# Histogram for Training_Intensity
ggplot(injury_data, aes(x = Training_Intensity)) +
geom_histogram(binwidth = 0.05, fill = "blue", color = "black") +
labs(title = "Histogram of Training Intensity", x = "Training Intensity", y = "Frequency")
# Histogram for Recovery_Time
ggplot(injury_data, aes(x = Recovery_Time)) +
geom_histogram(binwidth = 1, fill = "blue", color = "black") +
labs(title = "Histogram of Recovery Time", x = "Recovery Time", y = "Frequency")
# Bar plot for Likelihood_of_Injury
ggplot(injury_data, aes(x = factor(Likelihood_of_Injury))) +
geom_bar(fill = "blue", color = "black") +
labs(title = "Bar Plot of Likelihood of Injury", x = "Likelihood of Injury", y = "Count")
##Bi-variate Analysis:
# Scatter plot for Player_Weight vs Player_Height
ggplot(injury_data, aes(x = Player_Weight, y = Player_Height)) +
geom_point() +
geom_smooth(method = "lm") +
labs(title = "Scatter Plot of Player Weight vs Height", x = "Player Weight", y = "Player Height")## `geom_smooth()` using formula = 'y ~ x'
# Box plot for Player_Weight by Likelihood_of_Injury
ggplot(injury_data, aes(x = factor(Likelihood_of_Injury), y = Player_Weight)) +
geom_boxplot() +
labs(title = "Box Plot of Player Weight by Likelihood of Injury", x = "Likelihood of Injury", y = "Player Weight")
# Correlation matrix for numeric variables
cor_matrix <- cor(injury_data %>% select(Player_Age, Player_Weight, Player_Height, Previous_Injuries, Training_Intensity, Recovery_Time), use = "complete.obs")
corrplot(cor_matrix, method = "circle")
##Multi-variate Analysis
# Pair plot for numeric variables
pairs(injury_data %>% select(Player_Age, Player_Weight, Player_Height, Training_Intensity, Recovery_Time))
# Plotting interactions between multiple variables
ggplot(injury_data, aes(x = Player_Weight, y = Player_Height, color = factor(Likelihood_of_Injury))) +
geom_point() +
facet_wrap(~ factor(Previous_Injuries)) +
labs(title = "Scatter Plot of Weight vs Height Faceted by Previous Injuries", x = "Player Weight", y = "Player Height")
simple Linear Regression
# Ensuring that data is already cleaned and preprocessed.
# Splitting the dataset
set.seed(123)
train_indices <- sample(seq_len(nrow(injury_data)), size = 0.7 * nrow(injury_data))
train_data <- injury_data[train_indices, ]
test_data <- injury_data[-train_indices, ]# Fitting the simple linear regression model
model <- lm(Player_Weight ~ Player_Height, data = train_data)
# Summarizing the model
summary(model)##
## Call:
## lm(formula = Player_Weight ~ Player_Height, data = train_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -34.603 -6.919 0.226 6.568 29.998
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 72.07797 6.91578 10.422 <2e-16 ***
## Player_Height 0.01387 0.03836 0.362 0.718
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.09 on 698 degrees of freedom
## Multiple R-squared: 0.0001874, Adjusted R-squared: -0.001245
## F-statistic: 0.1308 on 1 and 698 DF, p-value: 0.7177#Evaluating the model
# Prediction on the test data
test_data$predicted_weight <- predict(model, newdata = test_data)
# Calculating performance metrics
mse <- mean((test_data$Player_Weight - test_data$predicted_weight)^2)
rmse <- sqrt(mse)
mae <- mean(abs(test_data$Player_Weight - test_data$predicted_weight))
# Printing performance metrics
cat("Mean Squared Error (MSE):", mse, "\n")## Mean Squared Error (MSE): 88.9962cat("Root Mean Squared Error (RMSE):", rmse, "\n")## Root Mean Squared Error (RMSE): 9.43378cat("Mean Absolute Error (MAE):", mae, "\n")## Mean Absolute Error (MAE): 7.639875##Results
# Plot the regression line with the data points
ggplot(train_data, aes(x = Player_Height, y = Player_Weight)) +
geom_point(color = 'blue') +
geom_smooth(method = "lm", color = "red", se = FALSE) +
labs(title = "Simple Linear Regression: Player Weight vs. Player Height",
x = "Player Height",
y = "Player Weight")## `geom_smooth()` using formula = 'y ~ x'
##Multiple Linear Regression
# Splitting the dataset
set.seed(123) # For reproducibility
train_indices <- sample(seq_len(nrow(injury_data)), size = 0.7 * nrow(injury_data))
train_data <- injury_data[train_indices, ]
test_data <- injury_data[-train_indices, ]# Fitting the multiple linear regression model
model <- lm(Player_Weight ~ Player_Age + Player_Height + Previous_Injuries + Training_Intensity + Recovery_Time, data = train_data)
# Summarizing the model
summary(model)##
## Call:
## lm(formula = Player_Weight ~ Player_Age + Player_Height + Previous_Injuries +
## Training_Intensity + Recovery_Time, data = train_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.607 -6.956 0.127 6.598 29.179
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 75.07605 7.09878 10.576 <2e-16 ***
## Player_Age -0.06314 0.05807 -1.087 0.2773
## Player_Height 0.01183 0.03841 0.308 0.7583
## Previous_Injuries 0.19388 0.76360 0.254 0.7996
## Training_Intensity 1.42859 1.36833 1.044 0.2968
## Recovery_Time -0.47921 0.22412 -2.138 0.0329 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.07 on 694 degrees of freedom
## Multiple R-squared: 0.01003, Adjusted R-squared: 0.002895
## F-statistic: 1.406 on 5 and 694 DF, p-value: 0.22# Prediction on the test data
test_data$predicted_weight <- predict(model, newdata = test_data)
# Calculating performance metrics
mse <- mean((test_data$Player_Weight - test_data$predicted_weight)^2)
rmse <- sqrt(mse)
mae <- mean(abs(test_data$Player_Weight - test_data$predicted_weight))
# Printing performance metrics
cat("Mean Squared Error (MSE):", mse, "\n")## Mean Squared Error (MSE): 88.90948cat("Root Mean Squared Error (RMSE):", rmse, "\n")## Root Mean Squared Error (RMSE): 9.429182cat("Mean Absolute Error (MAE):", mae, "\n")## Mean Absolute Error (MAE): 7.639736# Scatter plot of actual vs predicted values
ggplot(test_data, aes(x = Player_Weight, y = predicted_weight)) +
geom_point(color = 'blue') +
geom_abline(intercept = 0, slope = 1, color = 'red') +
labs(title = "Actual vs Predicted Player Weight",
x = "Actual Player Weight",
y = "Predicted Player Weight")
##Logistic Regression Model
# Ensuring that data is already cleaned and preprocessed
# Splitting the dataset
set.seed(123)
train_indices <- sample(seq_len(nrow(injury_data)), size = 0.7 * nrow(injury_data))
train_data <- injury_data[train_indices, ]
test_data <- injury_data[-train_indices, ]# Fitting the logistic regression model
model <- glm(Likelihood_of_Injury ~ Player_Age + Player_Weight + Player_Height + Previous_Injuries + Training_Intensity + Recovery_Time,
data = train_data,
family = binomial)
# Summarizing the model
summary(model)##
## Call:
## glm(formula = Likelihood_of_Injury ~ Player_Age + Player_Weight +
## Player_Height + Previous_Injuries + Training_Intensity +
## Recovery_Time, family = binomial, data = train_data)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.2163268 1.5275882 -0.796 0.4259
## Player_Age 0.0008996 0.0116048 0.078 0.9382
## Player_Weight -0.0015617 0.0075823 -0.206 0.8368
## Player_Height 0.0061296 0.0076692 0.799 0.4241
## Previous_Injuries 0.1518969 0.1524798 0.996 0.3192
## Training_Intensity 0.5605853 0.2742300 2.044 0.0409 *
## Recovery_Time -0.0445360 0.0449203 -0.991 0.3215
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 970.38 on 699 degrees of freedom
## Residual deviance: 963.26 on 693 degrees of freedom
## AIC: 977.26
##
## Number of Fisher Scoring iterations: 4# Calculate RMSE
rmse <- sqrt(mean((test_data$predicted_prob_cv - as.numeric(as.character(test_data$Likelihood_of_Injury)))^2))
print(paste("Root Mean Squared Error(RMSE):", round(rmse, 2)))## [1] "Root Mean Squared Error(RMSE): NaN"#Model evaluation
test_data$predicted_prob <- predict(model, newdata = test_data, type = "response")
# Converting probabilities to binary predictions using 0.5 as threshold
test_data$predicted_class <- ifelse(test_data$predicted_prob > 0.5, 1, 0)
# Confusion matrix to evaluate the model
conf_matrix <- confusionMatrix(factor(test_data$predicted_class), factor(test_data$Likelihood_of_Injury))
print(conf_matrix)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 72 61
## 1 76 91
##
## Accuracy : 0.5433
## 95% CI : (0.4851, 0.6007)
## No Information Rate : 0.5067
## P-Value [Acc > NIR] : 0.1126
##
## Kappa : 0.0853
##
## Mcnemar's Test P-Value : 0.2317
##
## Sensitivity : 0.4865
## Specificity : 0.5987
## Pos Pred Value : 0.5414
## Neg Pred Value : 0.5449
## Prevalence : 0.4933
## Detection Rate : 0.2400
## Detection Prevalence : 0.4433
## Balanced Accuracy : 0.5426
##
## 'Positive' Class : 0
## # Calculating performance metrics
accuracy <- conf_matrix$overall['Accuracy']
precision <- conf_matrix$byClass['Pos Pred Value']
recall <- conf_matrix$byClass['Sensitivity']
f1_score <- conf_matrix$byClass['F1']
cat("Accuracy:", accuracy, "\n")## Accuracy: 0.5433333cat("Precision:", precision, "\n")## Precision: 0.5413534cat("Recall:", recall, "\n")## Recall: 0.4864865cat("F1 Score:", f1_score, "\n")## F1 Score: 0.5124555# Calculating and plotting ROC curve
roc_curve <- roc(test_data$Likelihood_of_Injury, test_data$predicted_prob)## Setting levels: control = 0, case = 1## Setting direction: controls < casesauc_value <- auc(roc_curve)
print(auc_value)## Area under the curve: 0.5515plot(roc_curve, main = "ROC Curve", col = "blue", lwd = 2)
abline(a = 0, b = 1, lty = 2, col = "red")
##Conclusion
The evaluation metrics show how well the logistic regression model performs in predicting the likelihood of injury among players. The confusion matrix, accuracy, precision, recall, F1-score, and AUC provide a comprehensive understanding of the model’s strengths and areas for improvement. Adjusting the model, adding more features, or trying different algorithms might further enhance predictive performance.
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