creditcard
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
## 37 24 77.16861 179.3896 0
## 38 38 79.42117 172.6414 0
## 39 26 77.50244 180.8335 1
## 40 24 63.60246 188.8128 1
## 41 35 61.58555 170.2655 0
## 42 21 93.86828 179.9460 0
## 43 31 76.83883 190.3839 0
## 44 35 76.00095 185.0542 1
## 45 26 74.09772 193.0947 0
## 46 38 82.81073 174.3186 0
## 47 19 100.38691 161.6923 1
## 48 37 80.23953 182.0065 1
## 49 32 69.11913 183.6257 0
## 50 24 73.66599 193.6842 1
## 51 29 85.70996 191.8815 0
## 52 25 69.17199 185.3674 1
## 53 32 83.52246 187.6539 0
## 54 20 51.79263 178.0818 0
## 55 31 72.70120 167.8089 0
## 56 34 81.28140 181.1772 0
## 57 21 87.70126 189.8257 1
## 58 35 66.81526 186.5636 1
## 59 25 59.97235 188.3381 1
## 60 21 72.39265 175.7224 1
## 61 19 63.74581 177.3445 0
## 62 23 73.50963 184.8343 0
## 63 39 74.93347 174.5349 1
## 64 27 61.80855 174.8428 1
## 65 21 83.04439 182.9798 1
## 66 39 73.42786 187.6090 1
## 67 35 85.80460 178.0047 1
## 68 29 56.55095 168.3681 0
## 69 19 64.82808 172.6420 0
## 70 27 80.16669 182.0367 0
## 71 21 67.00889 181.9152 1
## 72 31 73.35464 182.4124 1
## 73 33 75.00699 185.2130 1
## 74 32 76.58256 180.0581 1
## 75 25 68.22434 182.6506 0
## 76 31 76.49287 179.5497 0
## 77 25 67.17456 191.4873 1
## 78 38 81.74280 194.3234 0
## 79 33 73.85156 168.1099 0
## 80 30 76.55037 177.4475 1
## 81 35 100.69927 180.3272 1
## 82 32 63.92896 177.3309 1
## 83 38 88.88582 197.9292 1
## 84 30 48.40649 182.2396 1
## 85 26 54.21623 171.9463 1
## 86 32 62.59360 185.9644 0
## 87 30 63.22472 182.7097 0
## 88 18 77.03702 183.0419 1
## 89 24 77.89842 198.0788 0
## 90 26 72.45179 183.5079 1
## 91 18 73.50122 176.9634 1
## 92 29 80.27765 160.4472 1
## 93 25 83.33224 178.3460 1
## 94 28 61.73461 193.0597 0
## 95 36 71.38109 179.0883 1
## 96 34 74.30410 157.5995 0
## 97 25 79.85316 175.0164 1
## 98 20 67.27333 189.9269 1
## 99 20 74.10779 185.4582 1
## 100 18 75.29984 171.6012 0
## 101 22 67.95530 174.3472 1
## 102 27 67.71685 175.8929 1
## 103 24 75.98746 173.3729 1
## 104 26 73.69655 159.7033 0
## 105 24 80.00478 187.6359 0
## 106 26 64.46741 192.5706 0
## 107 25 71.49318 164.8155 1
## 108 29 72.70784 192.4385 0
## 109 19 83.13864 178.9698 1
## 110 18 65.59586 192.2513 0
## 111 33 77.34652 180.3795 0
## 112 22 77.52756 181.3554 1
## 113 20 91.46397 183.7243 0
## 114 29 64.92911 172.0993 1
## 115 25 68.42767 191.9467 1
## 116 39 65.62644 168.7862 0
## 117 20 58.30221 175.9793 1
## 118 18 92.64687 185.2426 1
## 119 20 62.58499 174.9080 0
## 120 22 66.83695 187.7753 0
## 121 32 88.15767 193.7587 1
## 122 31 57.46893 190.1255 0
## 123 20 82.56352 160.6388 1
## 124 18 60.86976 181.2585 1
## 125 22 87.46695 190.2868 0
## 126 31 53.30122 166.8325 0
## 127 24 57.46531 187.2361 0
## 128 26 69.55078 186.6746 0
## 129 32 84.71823 167.8791 1
## 130 32 74.69042 188.3423 1
## 131 27 71.64285 182.7270 0
## 132 30 88.46044 186.5038 0
## 133 36 96.55240 170.3369 1
## 134 24 81.72257 181.5014 1
## 135 34 68.77145 162.9512 1
## 136 37 70.81857 157.2301 1
## 137 21 62.11553 181.4083 0
## 138 22 76.00817 191.0060 1
## 139 24 58.70407 182.5294 1
## 140 30 80.51864 179.3655 0
## 141 32 95.05574 178.1247 1
## 142 28 82.17592 170.5743 1
## 143 21 70.30852 177.7814 0
## 144 30 80.26937 184.2168 0
## 145 24 91.20533 175.6478 0
## 146 36 90.70495 187.5059 1
## 147 39 64.96242 181.9598 1
## 148 19 80.10296 183.1698 0
## 149 27 73.41899 188.8866 0
## 150 30 83.06593 174.7324 1
## 151 38 78.83614 186.5938 0
## 152 23 51.30279 176.1352 0
## 153 29 71.51987 183.3951 0
## 154 29 76.98558 173.5423 1
## 155 37 60.65635 170.8930 1
## 156 28 80.81022 180.9186 1
## 157 24 59.75985 161.1480 1
## 158 18 67.01561 177.0373 1
## 159 18 88.41312 163.9362 1
## 160 37 66.16222 187.7923 0
## 161 30 80.73967 190.5011 1
## 162 26 57.46426 171.3605 1
## 163 20 95.81315 184.2831 0
## 164 24 71.95446 188.9914 1
## 165 23 75.95321 189.0247 1
## 166 25 76.79037 182.1445 0
## 167 26 81.07567 187.3430 0
## 168 22 59.80820 173.5067 0
## 169 18 74.94664 180.8312 1
## 170 36 68.41231 175.3290 0
## 171 27 58.10443 172.6621 1
## 172 29 84.87122 180.3606 1
## 173 32 71.47827 183.4753 1
## 174 39 77.42520 191.6351 1
## 175 26 76.79845 182.6516 0
## 176 37 71.65148 186.4193 0
## 177 34 94.75027 186.5328 1
## 178 34 74.52735 206.8753 0
## 179 37 59.42048 161.8997 1
## 180 29 70.99619 173.1207 1
## 181 24 79.99738 194.7406 1
## 182 19 80.32595 176.4799 0
## 183 20 64.12219 188.8580 1
## 184 34 84.29483 182.3001 1
## 185 22 57.37133 201.0520 0
## 186 34 86.44135 168.8695 0
## 187 34 61.80652 192.2180 0
## 188 34 72.73755 197.4958 0
## 189 19 85.64569 169.8558 1
## 190 19 76.19606 187.8287 0
## 191 39 83.16699 170.3450 1
## 192 22 81.18662 180.3016 0
## 193 18 87.86269 177.4341 0
## 194 18 85.62210 155.9725 0
## 195 36 70.46256 184.2423 0
## 196 19 81.32942 181.4334 0
## 197 38 59.73448 194.7908 0
## 198 29 74.42095 186.1114 1
## 199 23 83.18485 202.9088 0
## 200 21 76.33848 181.0704 0
## 201 28 79.46640 178.9447 0
## 202 34 72.86728 178.1564 0
## 203 23 86.29069 175.6041 1
## 204 22 74.19584 157.9225 0
## 205 37 70.34550 177.7981 0
## 206 19 83.86517 159.3215 0
## 207 23 60.94840 184.5203 0
## 208 39 80.01033 167.5994 1
## 209 28 64.57515 171.0567 0
## 210 33 87.77927 173.3059 1
## 211 33 72.38973 177.9506 0
## 212 18 76.34985 180.0687 1
## 213 26 79.12570 190.8448 1
## 214 23 55.70315 174.2247 0
## 215 33 68.49415 184.8015 0
## 216 20 85.43546 207.3087 1
## 217 37 83.67765 183.7735 1
## 218 21 77.83032 178.1951 0
## 219 36 61.50536 172.2085 1
## 220 20 80.85503 178.0567 1
## 221 36 82.13400 171.1378 1
## 222 37 75.47017 182.6866 0
## 223 24 87.23171 181.0354 1
## 224 37 68.16568 158.3778 1
## 225 26 69.90142 179.8144 1
## 226 18 50.15388 190.7615 0
## 227 25 84.19828 176.0089 0
## 228 24 78.13862 173.4710 1
## 229 35 58.18180 166.2230 0
## 230 25 57.41401 161.4201 1
## 231 18 66.27369 188.3135 0
## 232 28 82.93658 178.1852 0
## 233 35 71.99640 187.7202 1
## 234 27 66.95213 195.3132 0
## 235 20 64.93884 189.2125 0
## 236 24 64.49088 174.5763 0
## 237 33 55.66322 181.4487 1
## 238 33 62.92604 173.8523 0
## 239 37 64.29422 172.9171 0
## 240 34 81.82623 183.4282 1
## 241 19 73.34277 171.6771 0
## 242 18 66.74415 157.9085 0
## 243 33 98.09723 171.1185 0
## 244 29 68.36103 176.9312 0
## 245 22 86.67103 195.2140 0
## 246 22 91.21109 175.0906 1
## 247 26 59.30040 185.1192 1
## 248 26 75.73963 178.3169 0
## 249 20 69.82466 192.2641 0
## 250 36 69.01076 191.5710 0
## 251 33 80.55377 191.7618 1
## 252 33 68.77130 162.6602 1
## 253 20 70.04031 182.0672 0
## 254 37 73.31885 187.5894 0
## 255 39 71.26601 183.8154 1
## 256 18 63.26430 186.3660 0
## 257 37 82.95907 180.5835 1
## 258 28 85.48531 172.5522 1
## 259 34 78.43619 163.0290 0
## 260 25 71.23447 173.5443 1
## 261 21 67.94789 206.7811 0
## 262 23 67.96380 175.1435 0
## 263 25 98.57376 160.4061 0
## 264 37 84.37838 187.3265 0
## 265 20 66.59890 188.5546 0
## 266 33 76.64698 192.8732 0
## 267 20 71.37465 184.7098 1
## 268 35 80.83800 188.4560 1
## 269 31 80.43499 198.4022 1
## 270 35 85.83610 172.5453 1
## 271 19 66.49444 170.5615 0
## 272 39 76.04735 169.0541 1
## 273 20 57.51453 177.3423 1
## 274 33 62.42660 194.3342 1
## 275 26 83.84101 185.2807 0
## 276 21 65.49316 184.8869 1
## 277 18 66.59091 161.8069 0
## 278 21 74.91722 173.0681 1
## 279 18 81.94761 161.6434 0
## 280 31 64.39173 191.7125 0
## 281 38 40.19191 195.8565 1
## 282 33 66.29777 187.7957 0
## 283 37 87.05628 170.1321 0
## 284 25 78.43135 194.8202 1
## 285 24 88.23875 177.4675 1
## 286 20 63.68184 167.8554 1
## 287 34 58.60604 170.3560 0
## 288 18 79.62549 175.6984 0
## 289 33 77.40023 180.4958 0
## 290 29 78.05554 178.3866 1
## 291 36 84.32997 165.8733 0
## 292 39 79.21685 192.1332 0
## 293 39 79.05618 178.4939 0
## 294 31 82.15007 172.8625 0
## 295 23 80.02417 191.1030 0
## 296 23 83.57446 168.1659 0
## 297 30 85.08521 163.7111 0
## 298 36 90.31689 192.7249 1
## 299 39 68.50809 198.9139 1
## 300 25 79.80562 175.3790 1
## 301 19 71.29677 179.5403 0
## 302 38 66.66441 168.5334 0
## 303 18 68.10460 180.0549 1
## 304 32 79.94956 175.2429 0
## 305 18 79.18253 178.9787 1
## 306 22 79.00412 184.0926 0
## 307 33 72.74286 169.0185 0
## 308 36 75.03415 176.3819 1
## 309 21 83.17753 161.9622 1
## 310 20 60.10607 176.4501 1
## 311 34 75.24642 179.0657 1
## 312 34 75.89956 182.2898 1
## 313 29 82.53304 182.3958 0
## 314 31 61.49752 189.0392 0
## 315 38 89.95134 164.6033 0
## 316 23 77.13630 165.1372 1
## 317 20 77.66581 185.2896 1
## 318 26 70.98620 159.6104 1
## 319 22 80.38022 181.8330 1
## 320 34 75.47954 168.1971 1
## 321 31 87.08982 195.7281 1
## 322 38 60.58506 182.5045 0
## 323 20 76.75546 177.8084 0
## 324 18 79.08512 192.2772 0
## 325 37 73.13628 169.3660 0
## 326 38 70.95334 171.2659 0
## 327 18 71.37043 167.3855 1
## 328 20 76.74639 171.3496 1
## 329 35 56.90041 180.6112 1
## 330 27 84.65015 184.9395 1
## 331 39 70.19868 189.6142 1
## 332 20 76.27803 161.7528 1
## 333 25 78.67102 178.2814 0
## 334 31 69.74195 168.9184 1
## 335 35 73.68962 177.9471 1
## 336 32 54.64760 203.3416 1
## 337 39 82.32124 182.9186 1
## 338 19 85.80069 188.9946 0
## 339 27 57.99006 199.6069 0
## 340 19 70.93507 191.2736 1
## 341 34 62.70131 175.5209 0
## 342 25 73.22260 167.2417 0
## 343 18 81.48636 179.6986 1
## 344 26 84.87309 174.3229 1
## 345 28 86.21853 164.7548 1
## 346 39 88.10544 182.4092 1
## 347 33 72.77587 182.6589 1
## 348 24 63.53041 197.6912 0
## 349 27 73.85410 176.3824 1
## 350 20 81.33268 182.8012 1
## 351 35 62.72740 173.3451 1
## 352 30 91.60137 188.7317 0
## 353 24 83.97889 178.7538 1
## 354 21 72.93433 168.2741 1
## 355 30 67.89128 179.8653 1
## 356 37 68.30879 183.9052 0
## 357 18 75.74206 182.5527 1
## 358 25 72.70597 190.4590 1
## 359 31 84.50503 165.5194 1
## 360 33 71.57024 171.5726 0
## 361 31 80.05502 186.3979 1
## 362 29 74.71075 172.7473 1
## 363 36 77.69669 182.4047 1
## 364 32 72.69944 155.8025 0
## 365 19 82.07716 207.1595 1
## 366 19 82.22540 177.3028 1
## 367 39 79.59929 191.9396 1
## 368 36 81.07514 176.6310 0
## 369 39 65.55631 184.1470 0
## 370 39 78.23246 194.9192 1
## 371 34 87.49202 191.6581 0
## 372 37 79.91405 177.9555 0
## 373 27 64.69282 178.6475 0
## 374 23 74.50233 190.0357 1
## 375 32 78.74978 203.3324 1
## 376 39 67.42600 181.9228 0
## 377 28 61.41729 199.5706 0
## 378 22 71.83902 177.5859 0
## 379 18 67.59168 189.5196 1
## 380 25 74.85711 191.0116 1
## 381 38 59.08595 175.9004 0
## 382 29 80.64701 172.5292 0
## 383 29 68.65395 191.4539 0
## 384 22 84.95752 174.6844 0
## 385 24 72.23101 171.0565 1
## 386 21 74.74611 173.1604 0
## 387 23 73.26965 174.6439 1
## 388 30 68.11840 188.5662 0
## 389 37 99.72279 185.1504 0
## 390 32 70.04134 187.1566 0
## 391 20 85.23522 184.2138 0
## 392 25 64.06371 174.0616 0
## 393 37 78.72131 186.9710 1
## 394 33 79.78872 191.8245 0
## 395 30 74.75823 185.8450 0
## 396 35 69.28995 184.4966 0
## 397 27 72.36678 172.3295 1
## 398 36 70.96710 182.1182 1
## 399 34 99.02026 171.7976 1
## 400 36 74.77397 179.7597 1
## 401 22 80.24889 163.5973 0
## 402 38 92.81625 163.6744 0
## 403 26 66.23681 178.0895 0
## 404 29 75.37660 174.5156 1
## 405 38 72.30814 181.0679 0
## 406 18 74.13862 178.6523 0
## 407 18 73.20988 180.3019 1
## 408 32 67.68123 186.0861 1
## 409 19 76.95410 175.3673 1
## 410 39 64.37367 177.8056 0
## 411 33 84.88883 194.5502 0
## 412 25 81.23310 180.0308 1
## 413 30 70.06537 178.2079 0
## 414 38 70.55017 180.8251 1
## 415 18 77.75511 178.3864 1
## 416 33 67.01455 172.9043 0
## 417 24 93.11980 170.0197 1
## 418 22 78.57926 172.3657 1
## 419 39 78.93263 188.7292 1
## 420 20 81.81445 173.7009 0
## 421 29 69.20537 178.4508 1
## 422 33 85.89010 187.0074 1
## 423 36 76.14533 177.2161 1
## 424 22 84.50145 186.3336 1
## 425 39 73.02047 164.5921 1
## 426 31 88.17278 178.6748 1
## 427 22 79.01344 180.7890 1
## 428 32 96.82507 180.2314 0
## 429 34 71.30478 163.4708 0
## 430 31 77.04549 192.0311 1
## 431 37 64.68424 171.4533 0
## 432 22 69.70702 183.3504 0
## 433 29 81.18687 180.0796 1
## 434 33 86.29324 190.0371 0
## 435 33 104.65010 201.8749 1
## 436 38 74.03371 167.0964 1
## 437 24 65.66787 173.5109 1
## 438 21 82.53326 181.8909 1
## 439 18 63.72173 168.9465 1
## 440 22 80.64277 187.8218 0
## 441 27 69.97744 188.4337 1
## 442 39 77.11595 185.5945 0
## 443 22 85.91046 182.3658 1
## 444 21 73.89267 193.6971 1
## 445 19 84.32879 168.4437 1
## 446 37 83.73159 180.6527 0
## 447 27 56.35668 172.6120 1
## 448 36 65.98399 188.8992 1
## 449 18 59.38193 161.6666 1
## 450 22 95.01737 179.7192 1
## 451 30 73.94608 183.3524 0
## 452 21 92.80829 170.5251 1
## 453 33 58.67048 173.7381 0
## 454 33 69.27232 180.1538 1
## 455 19 83.00498 189.5510 0
## 456 34 76.79275 169.6713 0
## 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
## 463 36 78.23800 190.2742 0
## 464 28 72.08510 173.3628 1
## 465 22 58.49509 178.4041 1
## 466 29 74.49577 189.4497 1
## 467 20 73.43270 177.7374 0
## 468 18 85.62402 187.5605 1
## 469 18 52.65574 179.3445 0
## 470 25 77.35200 199.8169 1
## 471 27 63.44432 195.6878 0
## 472 28 78.19223 166.7190 1
## 473 29 78.74280 185.2224 1
## 474 30 74.13155 171.8360 0
## 475 29 83.07293 181.9151 0
## 476 31 62.54234 180.6837 0
## 477 19 86.07720 169.4461 1
## 478 36 67.91921 190.5848 1
## 479 35 59.44101 182.0330 1
## 480 20 72.27604 177.1532 1
## 481 34 54.04842 175.6189 0
## 482 25 77.54174 181.3978 1
## 483 27 72.40750 184.0793 1
## 484 19 87.33687 178.7502 1
## 485 36 81.48924 151.6748 1
## 486 26 61.08004 180.1689 1
## 487 24 66.61613 165.5852 1
## 488 21 80.20664 177.9055 1
## 489 38 83.47079 165.9544 0
## 490 35 87.22501 165.8350 1
## 491 30 87.14509 192.3512 1
## 492 28 74.10477 176.7991 1
## 493 21 78.62656 176.0334 1
## 494 38 87.98739 184.8252 0
## 495 21 71.96692 176.2941 0
## 496 27 78.79726 201.0381 0
## 497 22 71.73119 177.2698 0
## 498 26 69.49052 183.6513 0
## 499 20 75.48385 189.7094 0
## 500 34 68.12457 196.0225 1
## 501 20 73.69084 172.5799 0
## 502 33 64.65045 175.8204 0
## 503 21 73.10006 192.7459 1
## 504 35 79.48390 188.1006 0
## 505 34 69.11236 190.7518 1
## 506 24 75.09292 166.3130 1
## 507 22 76.39343 195.4800 0
## 508 29 79.52535 172.3859 1
## 509 34 73.66420 181.9229 0
## 510 30 85.85866 178.5178 0
## 511 20 53.99081 171.2152 1
## 512 26 104.37753 165.8746 1
## 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
## 522 29 65.82840 180.1502 0
## 523 26 74.69112 185.3806 0
## 524 36 95.03407 179.5832 0
## 525 29 84.62966 200.1791 0
## 526 26 78.07621 182.1632 1
## 527 24 70.51079 190.5361 0
## 528 31 75.67499 172.7654 1
## 529 37 76.97566 181.2076 1
## 530 36 79.49228 195.4757 1
## 531 32 64.23339 177.6230 0
## 532 33 81.68408 181.9223 0
## 533 38 64.10407 189.8085 1
## 534 22 91.31126 172.5777 0
## 535 20 67.28372 158.3844 1
## 536 29 77.96628 182.5557 0
## 537 37 85.05143 189.8867 0
## 538 38 72.47620 166.4855 1
## 539 21 78.40600 186.7022 1
## 540 38 89.35182 186.7697 1
## 541 33 80.64403 157.2650 0
## 542 24 78.55551 170.6371 1
## 543 30 58.15215 176.0417 0
## 544 38 65.66278 178.2038 0
## 545 27 85.46920 172.5687 0
## 546 24 80.69950 172.0300 1
## 547 31 82.21466 196.7148 1
## 548 22 81.35397 178.9244 1
## 549 20 87.89757 173.0496 0
## 550 28 72.38600 179.5883 0
## 551 28 76.20500 185.3709 1
## 552 35 64.71326 180.5163 0
## 553 32 62.21977 177.9427 0
## 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
## 558 34 71.96464 170.0582 0
## 559 23 62.06855 183.9327 0
## 560 24 68.99026 194.1299 0
## 561 31 76.69694 183.2571 0
## 562 30 67.99884 176.3041 1
## 563 25 85.89665 167.0187 0
## 564 27 90.33978 195.7811 0
## 565 26 52.50904 168.2998 0
## 566 35 68.17008 188.4856 1
## 567 19 75.59142 186.3852 1
## 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
## 577 37 81.79973 180.0380 1
## 578 34 80.75469 163.7368 0
## 579 24 72.47314 174.7006 1
## 580 30 78.92085 182.2463 0
## 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
## 585 29 66.29427 174.3630 1
## 586 39 65.15728 179.1697 0
## 587 24 84.70901 177.9925 1
## 588 27 68.82553 167.6786 0
## 589 36 55.87897 184.4786 0
## 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
## 594 35 59.13188 163.8264 1
## 595 37 73.65732 172.8336 0
## 596 25 73.40776 181.5705 1
## 597 26 65.30800 181.6274 1
## 598 24 80.51037 180.4138 0
## 599 18 93.46627 185.4871 1
## 600 20 77.22283 174.1885 1
## 601 30 76.60476 180.2668 0
## 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
## 666 35 94.46543 187.0964 1
## 667 30 72.39501 192.6563 0
## 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
## 675 28 90.83465 181.0947 0
## 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
## 742 35 74.37343 180.3994 0
## 743 32 69.36351 177.6206 0
## 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
## 765 30 74.70110 173.1479 0
## 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
## 75 9.343041e-01 5 0
## 76 9.164205e-01 6 0
## 77 6.354144e-01 4 1
## 78 6.298207e-01 6 0
## 79 4.032106e-01 2 0
## 80 7.523979e-01 2 0
## 81 5.312940e-01 2 1
## 82 6.774381e-01 6 1
## 83 4.284579e-01 4 1
## 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
## 117 6.595060e-01 5 1
## 118 6.880456e-01 6 1
## 119 1.203876e-01 1 0
## 120 9.385298e-01 5 0
## 121 1.811833e-01 6 1
## 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
## 134 1.002456e-01 5 0
## 135 1.659248e-02 2 1
## 136 6.617187e-01 6 0
## 137 6.024494e-01 2 0
## 138 1.630191e-01 3 0
## 139 2.338128e-01 5 1
## 140 2.370775e-02 5 1
## 141 8.349472e-01 2 0
## 142 9.746906e-01 5 0
## 143 1.354595e-01 2 1
## 144 2.314752e-01 5 1
## 145 8.684095e-01 1 0
## 146 9.264638e-01 2 0
## 147 4.196274e-01 2 1
## 148 5.062247e-02 4 1
## 149 3.939183e-02 5 0
## 150 5.739499e-01 5 1
## 151 3.928951e-01 2 0
## 152 2.865243e-02 1 0
## 153 5.832524e-01 1 0
## 154 1.130254e-02 6 1
## 155 7.873720e-01 1 0
## 156 3.064328e-01 5 0
## 157 4.025800e-02 4 1
## 158 5.883522e-01 2 1
## 159 3.976844e-01 3 0
## 160 9.738004e-01 4 0
## 161 5.441987e-01 3 1
## 162 2.752392e-01 4 1
## 163 7.094389e-01 1 1
## 164 2.714920e-01 5 0
## 165 9.042433e-01 3 0
## 166 3.747220e-01 4 0
## 167 5.498686e-01 5 1
## 168 5.057310e-02 3 0
## 169 4.261212e-01 2 1
## 170 8.323635e-01 4 0
## 171 8.056488e-01 2 1
## 172 2.243186e-01 1 0
## 173 2.260791e-01 2 0
## 174 8.173053e-01 2 1
## 175 9.303984e-01 6 1
## 176 9.536092e-02 4 0
## 177 4.500586e-01 1 1
## 178 3.374543e-01 2 1
## 179 8.706885e-01 5 0
## 180 8.352491e-02 3 1
## 181 2.111259e-01 2 0
## 182 7.524683e-01 1 0
## 183 5.129385e-02 2 1
## 184 4.925354e-01 3 1
## 185 4.421070e-01 1 0
## 186 3.344012e-01 4 0
## 187 3.945723e-01 1 0
## 188 5.299406e-01 4 0
## 189 1.613674e-01 1 0
## 190 5.719959e-01 3 0
## 191 8.054323e-01 3 1
## 192 7.601609e-01 1 1
## 193 1.538999e-01 2 1
## 194 1.492495e-01 3 0
## 195 2.681744e-01 4 1
## 196 3.610747e-01 4 0
## 197 4.084556e-01 5 0
## 198 6.796972e-01 6 1
## 199 5.668043e-02 6 0
## 200 3.467270e-02 6 0
## 201 3.919106e-01 4 0
## 202 6.971637e-01 5 0
## 203 1.934353e-01 6 0
## 204 6.415045e-01 6 0
## 205 2.598281e-01 1 1
## 206 8.860861e-01 1 0
## 207 8.956899e-01 1 0
## 208 2.972872e-01 2 0
## 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
## 214 6.730921e-01 4 0
## 215 8.243504e-01 1 1
## 216 3.969922e-01 6 1
## 217 1.563170e-01 3 1
## 218 7.379509e-01 2 1
## 219 3.604745e-01 2 0
## 220 6.712708e-01 6 1
## 221 2.706439e-01 1 1
## 222 8.122963e-02 2 0
## 223 9.925818e-01 1 0
## 224 1.562015e-01 5 1
## 225 9.884210e-01 4 1
## 226 9.772799e-01 3 1
## 227 7.938181e-01 4 1
## 228 6.594230e-01 4 0
## 229 5.778071e-01 5 0
## 230 8.661015e-01 2 1
## 231 2.894395e-01 3 1
## 232 4.676812e-01 2 1
## 233 6.193900e-01 6 1
## 234 4.111905e-01 4 1
## 235 4.274865e-01 6 0
## 236 3.302847e-01 1 0
## 237 5.642318e-01 4 0
## 238 8.505745e-01 2 1
## 239 2.015284e-01 4 1
## 240 9.344330e-01 1 0
## 241 6.890877e-01 5 0
## 242 8.232732e-01 6 1
## 243 5.561907e-01 6 0
## 244 7.795167e-01 2 1
## 245 1.620040e-02 4 1
## 246 8.183804e-01 2 0
## 247 4.013886e-02 5 0
## 248 8.899134e-01 4 0
## 249 9.919627e-01 4 1
## 250 2.940675e-01 2 1
## 251 2.103186e-01 2 0
## 252 7.653634e-01 1 1
## 253 2.530263e-01 5 1
## 254 8.655624e-01 1 1
## 255 1.028426e-01 6 0
## 256 1.259552e-01 4 1
## 257 9.791516e-01 2 0
## 258 6.738389e-01 4 0
## 259 8.468872e-01 4 1
## 260 3.243294e-01 3 0
## 261 6.764837e-01 6 1
## 262 5.942418e-01 3 0
## 263 6.031501e-01 4 0
## 264 6.825325e-01 2 1
## 265 5.753592e-01 4 0
## 266 4.290624e-01 6 0
## 267 2.759227e-01 3 0
## 268 7.685813e-01 2 1
## 269 2.262724e-01 4 1
## 270 6.923543e-01 5 0
## 271 2.333328e-01 6 0
## 272 6.253302e-01 6 0
## 273 7.470783e-01 5 1
## 274 2.187139e-01 4 1
## 275 5.994330e-02 6 0
## 276 1.309737e-01 2 1
## 277 6.059970e-01 4 1
## 278 8.494464e-01 4 1
## 279 4.499682e-02 3 1
## 280 7.341513e-01 6 0
## 281 3.413186e-01 6 1
## 282 4.785284e-01 5 1
## 283 9.288595e-01 3 0
## 284 3.319715e-01 3 0
## 285 4.653369e-01 6 1
## 286 1.368241e-02 4 1
## 287 8.159872e-02 3 0
## 288 2.585916e-01 5 0
## 289 2.785224e-02 3 0
## 290 6.313753e-01 5 1
## 291 4.263984e-01 3 1
## 292 5.481452e-01 5 0
## 293 1.746472e-01 3 1
## 294 2.959320e-01 3 0
## 295 6.638245e-01 2 1
## 296 9.653027e-01 5 0
## 297 5.041677e-02 5 0
## 298 8.903843e-01 5 1
## 299 5.768812e-01 1 1
## 300 5.639196e-01 3 1
## 301 5.002785e-01 5 1
## 302 6.939409e-02 6 1
## 303 8.987685e-02 1 1
## 304 6.009120e-01 2 0
## 305 3.409586e-01 5 1
## 306 9.172681e-01 5 0
## 307 4.065905e-01 1 0
## 308 1.432815e-01 3 1
## 309 7.147188e-01 6 0
## 310 2.933534e-01 2 0
## 311 5.254703e-01 5 1
## 312 6.978328e-01 3 1
## 313 9.002641e-01 6 1
## 314 7.921912e-01 6 0
## 315 6.763611e-01 2 1
## 316 6.796332e-01 4 1
## 317 9.459246e-01 6 1
## 318 2.958241e-01 4 1
## 319 1.112094e-03 6 0
## 320 2.719546e-01 2 1
## 321 2.178914e-01 1 0
## 322 6.618346e-01 2 1
## 323 6.337116e-01 2 0
## 324 5.934614e-01 1 0
## 325 1.624344e-02 1 0
## 326 7.287781e-01 3 1
## 327 3.235304e-01 6 0
## 328 6.650560e-01 3 1
## 329 5.565784e-01 1 1
## 330 3.427309e-01 4 1
## 331 1.345763e-01 4 1
## 332 9.441890e-02 2 1
## 333 8.315183e-01 5 0
## 334 9.183975e-01 2 1
## 335 6.497880e-01 2 0
## 336 1.034341e-01 5 0
## 337 4.020272e-01 6 0
## 338 7.289392e-01 1 0
## 339 7.798209e-01 4 0
## 340 1.182186e-01 2 0
## 341 3.071885e-05 5 0
## 342 7.121370e-01 3 1
## 343 3.565962e-01 2 0
## 344 2.544813e-01 4 1
## 345 1.289747e-02 1 0
## 346 5.402994e-01 2 0
## 347 8.511507e-01 2 0
## 348 9.576316e-01 6 0
## 349 5.657450e-01 5 1
## 350 5.144613e-01 3 1
## 351 8.505639e-02 3 1
## 352 5.488728e-01 1 0
## 353 3.799791e-01 4 0
## 354 6.071353e-01 1 0
## 355 3.887263e-01 2 1
## 356 2.402991e-01 2 0
## 357 9.496528e-02 3 1
## 358 3.149184e-01 4 1
## 359 9.664483e-02 5 0
## 360 1.768970e-01 4 0
## 361 9.873362e-01 2 1
## 362 4.441036e-01 5 1
## 363 5.318718e-01 4 1
## 364 8.736185e-01 4 0
## 365 9.955689e-01 5 0
## 366 5.827144e-01 2 0
## 367 8.124738e-01 4 0
## 368 3.269410e-01 5 0
## 369 3.057440e-01 6 0
## 370 4.027037e-01 6 1
## 371 6.725073e-01 2 1
## 372 6.821917e-01 5 1
## 373 3.152394e-01 6 0
## 374 1.332099e-01 5 1
## 375 6.323963e-01 4 0
## 376 1.283259e-01 2 0
## 377 5.784925e-01 6 1
## 378 6.934221e-01 4 0
## 379 7.006115e-01 1 1
## 380 7.534721e-01 1 1
## 381 8.728594e-01 2 0
## 382 4.999590e-01 6 1
## 383 7.302492e-01 6 1
## 384 6.191727e-01 6 1
## 385 1.866399e-01 5 1
## 386 2.568964e-02 4 0
## 387 2.845414e-01 6 1
## 388 4.430337e-01 4 1
## 389 6.171059e-01 2 1
## 390 8.499593e-01 6 0
## 391 1.962457e-01 2 1
## 392 1.255498e-01 6 0
## 393 9.634799e-01 1 1
## 394 1.080446e-01 3 1
## 395 4.784911e-01 2 1
## 396 5.855637e-01 6 0
## 397 5.413744e-01 3 0
## 398 8.603216e-02 2 0
## 399 5.702691e-02 5 0
## 400 1.054834e-01 6 0
## 401 5.855877e-01 5 0
## 402 5.443449e-01 1 1
## 403 2.337408e-01 6 0
## 404 6.377741e-01 1 1
## 405 8.200675e-01 3 0
## 406 4.157896e-02 2 0
## 407 4.981299e-01 4 0
## 408 6.893104e-01 1 1
## 409 2.523119e-01 4 0
## 410 3.077709e-01 4 0
## 411 6.136379e-01 2 1
## 412 8.979353e-01 3 1
## 413 8.099244e-01 2 1
## 414 5.831302e-01 6 1
## 415 7.302217e-01 1 0
## 416 3.648225e-01 4 1
## 417 6.409655e-01 2 0
## 418 4.661816e-01 2 0
## 419 1.899361e-01 2 1
## 420 7.017383e-01 1 0
## 421 5.564646e-01 3 1
## 422 3.586216e-01 2 0
## 423 9.113392e-01 1 1
## 424 2.093412e-02 4 1
## 425 3.157732e-01 6 1
## 426 5.688140e-02 5 1
## 427 7.665273e-01 1 1
## 428 7.021075e-01 5 1
## 429 3.313522e-01 2 0
## 430 6.756629e-01 2 0
## 431 3.959372e-01 1 0
## 432 7.562538e-01 5 1
## 433 4.541837e-01 2 0
## 434 4.122797e-01 4 1
## 435 9.347299e-01 3 0
## 436 2.508791e-01 3 0
## 437 1.204473e-01 2 1
## 438 5.845431e-01 5 0
## 439 9.693218e-01 6 0
## 440 3.778392e-01 2 0
## 441 6.203676e-02 6 1
## 442 3.393053e-01 6 0
## 443 5.059317e-01 5 1
## 444 1.619230e-01 6 0
## 445 6.581022e-01 4 0
## 446 9.977494e-01 5 0
## 447 4.517395e-01 2 0
## 448 3.547286e-01 4 1
## 449 4.007042e-01 1 0
## 450 1.151462e-01 3 1
## 451 8.832736e-01 2 1
## 452 4.148257e-01 2 0
## 453 3.872744e-01 2 0
## 454 6.596903e-01 4 1
## 455 4.420334e-01 1 1
## 456 6.482335e-01 5 0
## 457 6.144629e-02 1 0
## 458 8.137233e-01 4 0
## 459 9.405040e-01 2 0
## 460 6.487654e-01 3 0
## 461 9.545659e-01 2 1
## 462 1.514323e-01 5 0
## 463 4.774162e-01 6 0
## 464 6.018067e-01 3 1
## 465 5.817366e-01 2 0
## 466 4.014618e-01 3 0
## 467 3.380844e-01 6 1
## 468 1.268240e-01 1 0
## 469 3.917425e-01 4 1
## 470 1.626608e-01 2 0
## 471 7.343657e-01 5 0
## 472 2.088309e-01 1 0
## 473 5.856531e-02 2 1
## 474 5.523661e-01 1 1
## 475 5.952297e-01 5 1
## 476 8.719131e-01 2 1
## 477 5.769816e-01 6 0
## 478 3.454809e-01 6 1
## 479 8.030119e-01 5 1
## 480 5.402789e-01 3 1
## 481 7.928028e-02 5 0
## 482 5.658506e-01 3 0
## 483 9.080185e-01 2 1
## 484 3.963126e-01 3 1
## 485 2.963258e-01 2 0
## 486 1.434695e-01 2 1
## 487 1.514455e-01 4 1
## 488 4.327715e-01 2 1
## 489 5.956798e-01 3 1
## 490 8.061541e-02 4 0
## 491 9.395686e-01 4 0
## 492 7.551336e-01 2 0
## 493 5.866239e-01 5 1
## 494 8.284387e-01 5 1
## 495 8.018676e-02 6 0
## 496 4.768389e-01 4 0
## 497 6.300931e-01 2 0
## 498 8.286529e-01 5 1
## 499 7.834696e-01 2 0
## 500 2.770692e-01 2 1
## 501 9.408802e-01 4 1
## 502 1.243458e-01 6 0
## 503 8.751336e-01 4 0
## 504 9.709402e-01 1 1
## 505 1.771535e-01 5 0
## 506 7.220661e-01 5 0
## 507 4.028552e-02 5 1
## 508 4.054228e-01 3 1
## 509 5.162833e-01 2 1
## 510 5.806452e-01 4 1
## 511 9.359372e-01 5 1
## 512 6.722507e-01 1 0
## 513 4.812780e-01 5 1
## 514 8.096750e-01 6 1
## 515 9.505328e-01 2 1
## 516 2.206615e-02 2 0
## 517 9.825213e-01 4 1
## 518 8.613214e-02 3 1
## 519 8.143868e-01 4 1
## 520 2.803348e-01 5 0
## 521 1.321797e-01 6 1
## 522 4.398556e-01 1 1
## 523 6.439566e-01 4 1
## 524 3.809518e-01 3 0
## 525 5.456541e-02 4 0
## 526 5.994611e-01 2 0
## 527 9.024033e-01 5 0
## 528 3.832154e-01 5 1
## 529 2.163328e-01 5 1
## 530 4.425773e-01 5 0
## 531 4.836271e-02 5 0
## 532 8.204876e-01 5 1
## 533 8.263408e-01 1 1
## 534 5.880005e-01 3 0
## 535 3.531029e-01 5 0
## 536 7.997834e-01 3 0
## 537 5.546870e-01 6 0
## 538 8.259323e-01 5 1
## 539 6.314588e-01 4 0
## 540 7.844055e-01 5 0
## 541 5.985630e-01 2 1
## 542 4.141411e-01 1 1
## 543 9.579459e-01 4 1
## 544 5.411138e-01 4 1
## 545 6.050895e-01 2 1
## 546 2.204876e-01 5 0
## 547 6.256508e-01 1 0
## 548 5.716076e-01 4 0
## 549 1.848384e-01 1 1
## 550 5.958710e-02 4 0
## 551 6.043593e-01 5 1
## 552 7.636923e-01 6 0
## 553 5.234014e-01 4 0
## 554 2.267726e-01 6 1
## 555 6.665220e-01 4 1
## 556 8.010573e-02 3 0
## 557 4.418337e-01 5 0
## 558 1.633668e-01 2 0
## 559 1.844591e-01 6 0
## 560 2.022939e-01 2 0
## 561 3.867071e-01 5 1
## 562 5.115021e-02 2 1
## 563 3.977934e-01 5 1
## 564 5.117074e-01 2 1
## 565 4.832943e-01 6 0
## 566 3.827841e-01 3 0
## 567 8.391466e-01 2 1
## 568 1.453267e-01 6 0
## 569 5.056967e-01 4 1
## 570 6.204142e-02 5 0
## 571 7.115568e-02 1 0
## 572 5.738658e-01 1 0
## 573 5.660184e-01 5 1
## 574 8.780335e-01 3 1
## 575 5.578737e-01 2 1
## 576 9.597910e-01 3 1
## 577 4.869485e-02 1 0
## 578 9.800768e-02 6 0
## 579 4.406586e-02 5 0
## 580 1.852769e-01 5 0
## 581 5.412701e-01 3 1
## 582 6.455722e-01 6 0
## 583 4.558516e-02 3 0
## 584 9.459709e-01 2 0
## 585 8.421654e-01 4 1
## 586 2.966125e-01 6 1
## 587 7.718753e-02 4 1
## 588 1.698871e-01 2 0
## 589 1.269444e-01 4 1
## 590 1.232838e-01 1 0
## 591 5.185617e-01 4 0
## 592 2.458474e-01 2 0
## 593 3.581397e-01 4 1
## 594 9.895053e-01 5 1
## 595 6.843142e-01 3 0
## 596 9.489807e-01 1 0
## 597 1.425566e-01 5 1
## 598 3.821395e-01 5 1
## 599 5.547318e-01 2 1
## 600 7.675806e-02 3 0
## 601 4.187444e-03 3 0
## 602 6.704343e-01 6 0
## 603 6.418195e-01 1 1
## 604 4.107888e-01 2 1
## 605 4.896441e-01 1 1
## 606 4.154430e-01 6 0
## 607 2.400504e-02 4 0
## 608 3.353716e-01 5 1
## 609 1.770823e-01 1 0
## 610 9.842419e-02 5 1
## 611 9.567204e-01 2 0
## 612 5.993597e-01 4 1
## 613 7.286563e-01 2 0
## 614 3.153637e-01 4 0
## 615 3.931471e-01 4 1
## 616 2.369240e-01 5 1
## 617 9.696567e-02 2 1
## 618 1.790316e-01 2 1
## 619 7.975140e-01 1 1
## 620 6.775458e-01 2 0
## 621 5.469289e-01 1 1
## 622 4.750899e-01 3 1
## 623 9.224343e-01 3 1
## 624 7.273495e-02 3 0
## 625 2.806008e-01 6 1
## 626 3.499379e-01 2 1
## 627 7.824258e-01 6 1
## 628 9.925146e-01 2 0
## 629 2.406705e-01 1 0
## 630 8.743073e-01 6 1
## 631 8.306228e-01 6 0
## 632 2.246574e-01 6 1
## 633 3.993326e-01 4 0
## 634 4.096729e-01 1 1
## 635 9.782207e-01 3 0
## 636 1.808919e-01 1 1
## 637 7.991580e-01 4 1
## 638 3.341927e-01 2 0
## 639 7.308703e-01 1 1
## 640 4.200110e-01 6 1
## 641 5.783598e-01 3 1
## 642 8.326118e-01 3 1
## 643 8.041656e-01 2 0
## 644 8.664232e-01 1 1
## 645 5.961733e-02 4 0
## 646 6.920506e-01 2 0
## 647 1.399874e-01 1 0
## 648 4.158186e-01 5 0
## 649 5.485884e-01 3 1
## 650 4.027395e-01 4 1
## 651 5.196117e-01 6 0
## 652 9.965263e-01 4 1
## 653 1.352173e-01 6 0
## 654 6.754560e-01 4 1
## 655 3.960010e-01 3 1
## 656 1.330098e-01 6 1
## 657 1.587368e-01 1 0
## 658 9.492731e-01 6 0
## 659 8.796015e-01 2 1
## 660 9.070420e-01 1 0
## 661 9.915328e-01 2 0
## 662 2.083154e-01 4 1
## 663 3.554393e-01 1 0
## 664 6.692190e-01 4 1
## 665 4.838025e-01 5 1
## 666 4.183895e-01 4 1
## 667 3.578112e-01 4 0
## 668 5.937806e-01 2 1
## 669 5.755934e-01 6 1
## 670 1.614578e-01 5 0
## 671 4.717398e-01 4 0
## 672 5.534718e-01 4 0
## 673 5.702869e-01 5 0
## 674 2.101072e-01 5 1
## 675 7.422714e-01 1 0
## 676 2.519909e-02 2 0
## 677 3.548319e-01 5 0
## 678 7.802126e-01 5 0
## 679 5.638929e-01 3 1
## 680 2.611624e-01 6 0
## 681 6.949531e-01 5 1
## 682 5.673909e-01 2 0
## 683 7.962436e-01 6 1
## 684 7.350252e-01 6 1
## 685 6.101079e-01 4 0
## 686 4.881777e-01 4 1
## 687 1.331821e-01 4 0
## 688 2.613330e-01 4 1
## 689 4.190995e-01 3 1
## 690 5.992993e-01 5 1
## 691 5.137079e-01 4 0
## 692 2.881845e-01 1 1
## 693 6.464358e-03 4 0
## 694 4.962392e-01 5 1
## 695 2.856260e-01 4 1
## 696 7.346083e-01 3 1
## 697 2.394627e-02 2 1
## 698 5.848278e-01 2 1
## 699 9.414742e-01 5 1
## 700 1.744093e-01 2 0
## 701 4.716495e-01 2 0
## 702 9.142199e-02 1 0
## 703 6.256819e-01 5 1
## 704 5.505317e-01 5 0
## 705 4.073655e-01 2 1
## 706 5.213770e-01 2 0
## 707 8.965747e-01 2 0
## 708 1.962943e-01 6 1
## 709 2.256451e-02 3 1
## 710 8.616649e-01 4 0
## 711 5.771171e-01 3 0
## 712 8.906585e-01 5 0
## 713 5.969452e-01 1 0
## 714 8.101611e-01 4 1
## 715 4.296734e-01 1 0
## 716 7.498599e-01 6 1
## 717 9.130415e-01 5 1
## 718 5.721230e-01 6 0
## 719 1.807852e-01 5 1
## 720 2.687976e-01 1 0
## 721 1.986446e-01 1 1
## 722 2.468570e-01 1 1
## 723 3.064154e-01 5 0
## 724 5.551338e-01 2 1
## 725 5.879916e-01 1 1
## 726 4.264997e-01 6 0
## 727 6.154208e-01 4 1
## 728 8.157493e-02 5 1
## 729 8.778824e-02 6 0
## 730 1.718295e-01 3 0
## 731 5.180775e-01 3 1
## 732 2.139947e-01 4 1
## 733 2.832844e-01 2 1
## 734 4.003128e-01 1 0
## 735 8.118661e-01 5 1
## 736 1.386214e-02 6 1
## 737 6.493788e-01 1 1
## 738 6.688765e-01 6 1
## 739 7.986558e-01 5 1
## 740 9.327529e-01 3 1
## 741 2.013785e-02 1 0
## 742 1.537837e-01 4 0
## 743 8.861713e-01 2 1
## 744 4.585186e-01 4 1
## 745 5.645906e-01 2 0
## 746 6.630272e-01 6 0
## 747 6.790666e-01 1 0
## 748 9.317404e-01 1 1
## 749 9.969494e-01 2 1
## 750 7.201575e-01 3 1
## 751 2.872212e-01 5 1
## 752 8.797524e-01 4 0
## 753 4.922091e-02 6 0
## 754 2.310809e-01 4 0
## 755 8.973857e-01 4 0
## 756 2.561915e-01 6 1
## 757 2.206511e-01 6 1
## 758 1.381253e-01 1 0
## 759 8.591118e-01 4 0
## 760 5.000583e-01 5 0
## 761 6.748932e-01 5 0
## 762 2.390664e-01 2 0
## 763 7.580404e-01 6 1
## 764 7.600055e-01 1 1
## 765 3.125980e-01 5 0
## 766 4.106826e-01 3 0
## 767 2.096903e-01 1 0
## 768 9.765610e-01 1 1
## 769 6.456026e-01 5 1
## 770 9.345439e-01 3 1
## 771 3.033855e-01 2 0
## 772 7.818909e-01 5 1
## 773 7.658586e-01 3 0
## 774 6.916557e-01 1 1
## 775 9.656390e-01 3 1
## 776 3.935553e-01 1 1
## 777 1.307173e-01 5 0
## 778 6.717089e-01 6 0
## 779 7.293098e-01 5 1
## 780 5.750606e-01 4 0
## 781 2.068179e-01 4 0
## 782 5.044559e-01 4 0
## 783 3.121897e-01 5 0
## 784 4.274665e-01 2 0
## 785 6.452186e-01 5 0
## 786 7.452147e-01 6 0
## 787 2.322247e-01 3 1
## 788 4.053254e-01 5 0
## 789 2.691616e-01 5 0
## 790 1.986316e-01 1 1
## 791 7.833872e-01 1 1
## 792 9.092527e-01 1 1
## 793 3.702322e-01 5 1
## 794 7.512789e-01 2 0
## 795 8.969953e-01 3 1
## 796 8.461242e-01 3 1
## 797 1.828134e-01 6 0
## 798 9.562919e-01 2 0
## 799 3.766764e-01 2 0
## 800 3.392407e-01 3 1
## 801 6.243133e-02 4 0
## 802 4.861737e-01 1 1
## 803 1.090136e-01 1 0
## 804 2.485398e-01 3 0
## 805 3.169426e-01 2 1
## 806 8.088263e-01 6 0
## 807 8.754887e-01 4 0
## 808 3.024663e-01 3 0
## 809 8.586981e-01 1 0
## 810 8.572309e-01 5 0
## 811 7.646510e-01 2 1
## 812 1.466951e-01 5 1
## 813 6.903228e-01 6 0
## 814 9.450241e-01 3 1
## 815 6.825405e-01 1 1
## 816 3.316168e-01 4 1
## 817 5.005793e-01 5 0
## 818 6.457250e-01 4 0
## 819 5.105704e-01 6 0
## 820 1.199529e-01 1 1
## 821 3.120640e-01 5 1
## 822 8.023960e-01 6 0
## 823 8.614180e-01 4 0
## 824 1.373000e-01 4 1
## 825 9.523868e-01 5 1
## 826 3.288226e-01 1 0
## 827 6.623137e-01 4 1
## 828 7.518251e-01 3 1
## 829 8.119462e-01 6 1
## 830 9.457497e-01 5 1
## 831 3.129195e-01 2 1
## 832 8.585065e-01 2 1
## 833 1.316869e-01 1 1
## 834 7.050409e-01 3 0
## 835 7.419777e-01 4 0
## 836 6.777232e-01 2 1
## 837 2.523147e-01 1 1
## 838 8.730431e-01 6 1
## 839 1.688585e-01 3 0
## 840 5.363479e-02 3 0
## 841 7.174650e-01 1 1
## 842 4.754757e-01 1 0
## 843 8.490980e-01 4 1
## 844 3.833971e-01 1 1
## 845 1.682786e-01 4 1
## 846 8.364422e-01 4 1
## 847 5.489096e-01 5 1
## 848 1.891958e-01 3 1
## 849 7.203840e-01 6 1
## 850 5.111136e-01 3 0
## 851 6.043130e-01 3 1
## 852 4.608983e-01 5 0
## 853 8.287090e-01 4 1
## 854 8.295627e-01 4 1
## 855 7.093888e-01 1 0
## 856 1.054014e-01 5 0
## 857 8.388539e-01 1 1
## 858 6.710130e-01 5 0
## 859 6.970242e-01 1 1
## 860 3.629828e-01 2 1
## 861 8.792796e-01 3 0
## 862 7.253716e-01 3 0
## 863 1.125905e-01 6 0
## 864 4.077780e-01 4 0
## 865 8.863804e-01 6 1
## 866 4.719670e-02 2 0
## 867 9.614166e-01 6 0
## 868 6.791626e-01 3 0
## 869 6.313760e-01 4 0
## 870 2.705593e-01 2 0
## 871 2.487918e-01 3 1
## 872 9.420582e-01 1 1
## 873 2.982725e-01 5 0
## 874 7.755053e-01 2 0
## 875 3.931218e-01 4 1
## 876 1.392541e-01 6 0
## 877 1.071530e-02 6 1
## 878 5.442573e-01 2 1
## 879 9.757788e-01 4 0
## 880 3.691362e-01 2 1
## 881 3.571923e-01 4 0
## 882 3.440916e-01 6 0
## 883 5.205438e-01 3 1
## 884 4.406649e-01 1 1
## 885 8.799146e-01 3 0
## 886 5.055208e-01 1 1
## 887 5.077578e-01 6 0
## 888 1.383716e-01 3 1
## 889 9.827395e-01 3 1
## 890 2.499729e-01 2 0
## 891 7.336407e-02 5 0
## 892 6.631462e-01 6 0
## 893 5.953664e-01 4 1
## 894 4.151457e-01 6 0
## 895 4.221922e-01 4 0
## 896 7.850639e-01 4 1
## 897 1.957126e-01 1 1
## 898 8.202533e-01 4 0
## 899 6.487471e-01 4 0
## 900 4.189698e-01 2 0
## 901 4.580286e-01 3 0
## 902 2.566472e-01 6 1
## 903 7.521143e-01 6 1
## 904 5.128747e-01 4 0
## 905 3.208545e-01 5 0
## 906 5.960501e-01 6 0
## 907 9.957345e-01 4 1
## 908 4.352623e-01 1 0
## 909 9.206914e-01 1 0
## 910 3.450168e-01 1 1
## 911 5.473271e-02 4 1
## 912 2.042421e-01 2 0
## 913 4.186951e-01 5 0
## 914 2.196349e-01 4 1
## 915 2.270382e-04 2 0
## 916 9.150015e-01 2 0
## 917 8.438794e-01 6 1
## 918 7.422922e-01 4 1
## 919 1.678146e-01 4 1
## 920 1.203636e-01 2 0
## 921 6.760475e-02 2 1
## 922 7.240292e-01 2 1
## 923 9.288080e-01 6 0
## 924 4.305358e-01 4 0
## 925 1.191407e-01 2 1
## 926 5.123928e-01 1 1
## 927 3.488319e-01 4 0
## 928 3.748249e-01 6 0
## 929 6.213306e-01 1 1
## 930 7.187181e-01 3 1
## 931 1.106036e-01 4 0
## 932 3.559023e-02 3 1
## 933 9.943303e-01 2 0
## 934 2.327283e-01 6 1
## 935 5.383052e-02 2 0
## 936 1.898753e-01 6 0
## 937 3.895785e-02 5 1
## 938 3.918109e-01 2 1
## 939 8.024018e-01 3 0
## 940 3.412444e-01 6 1
## 941 4.441834e-01 2 1
## 942 6.755868e-01 4 1
## 943 5.088941e-01 6 0
## 944 8.614457e-01 6 1
## 945 8.660357e-01 2 1
## 946 9.910695e-03 5 0
## 947 7.780463e-01 2 1
## 948 4.607336e-01 3 1
## 949 9.888405e-01 3 0
## 950 4.832321e-01 6 0
## 951 9.868959e-01 6 1
## 952 7.545694e-01 2 1
## 953 2.486049e-01 3 0
## 954 5.383950e-01 3 0
## 955 9.921356e-01 5 1
## 956 8.575306e-01 4 1
## 957 9.736689e-02 5 1
## 958 7.063720e-01 1 0
## 959 3.455796e-01 5 1
## 960 6.982262e-03 2 0
## 961 4.034810e-01 4 0
## 962 2.347544e-01 2 0
## 963 8.188693e-01 1 1
## 964 3.373306e-01 4 1
## 965 8.275237e-01 6 1
## 966 5.979388e-04 3 0
## 967 3.078192e-01 5 1
## 968 2.411652e-01 2 0
## 969 2.335674e-01 1 0
## 970 6.818662e-02 3 0
## 971 1.871243e-01 5 0
## 972 5.555496e-01 3 0
## 973 2.913611e-01 5 0
## 974 4.173948e-01 6 1
## 975 3.768669e-01 4 0
## 976 8.759049e-01 3 1
## 977 9.052990e-01 3 0
## 978 4.929570e-01 3 1
## 979 3.301560e-01 4 1
## 980 1.068007e-01 1 1
## 981 9.534029e-01 5 1
## 982 6.022694e-02 6 0
## 983 2.976503e-01 3 0
## 984 2.980662e-01 4 0
## 985 3.204013e-01 1 0
## 986 1.642868e-01 5 1
## 987 7.831614e-01 4 1
## 988 1.224979e-01 6 1
## 989 6.349067e-01 6 0
## 990 1.238460e-01 5 1
## 991 7.053564e-02 3 0
## 992 9.094295e-01 6 1
## 993 1.901288e-01 1 1
## 994 8.212502e-01 4 1
## 995 5.446309e-01 1 1
## 996 7.785652e-02 3 1
## 997 9.554482e-01 1 0
## 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.
`