CW31825
Ijiola
2025-03-18
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library(readr)
wisc_bc_data <- read_csv("C:/Users/ijiol/OneDrive/Documents/R projects/R for Advanced Topics/Datasets/wisc_bc_data.csv")## Rows: 569 Columns: 32
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): diagnosis
## dbl (31): id, radius_mean, texture_mean, perimeter_mean, area_mean, smoothne...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.#View(wisc_bc_data)
WBC <- wisc_bc_data
dim(WBC)## [1] 569 32head(WBC)## # A tibble: 6 × 32
## id diagnosis radius_mean texture_mean perimeter_mean area_mean
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 842302 M 18.0 10.4 123. 1001
## 2 842517 M 20.6 17.8 133. 1326
## 3 84300903 M 19.7 21.2 130 1203
## 4 84348301 M 11.4 20.4 77.6 386.
## 5 84358402 M 20.3 14.3 135. 1297
## 6 843786 M 12.4 15.7 82.6 477.
## # ℹ 26 more variables: smoothness_mean <dbl>, compactness_mean <dbl>,
## # concavity_mean <dbl>, `concave points_mean` <dbl>, symmetry_mean <dbl>,
## # fractal_dimension_mean <dbl>, radius_se <dbl>, texture_se <dbl>,
## # perimeter_se <dbl>, area_se <dbl>, smoothness_se <dbl>,
## # compactness_se <dbl>, concavity_se <dbl>, `concave points_se` <dbl>,
## # symmetry_se <dbl>, fractal_dimension_se <dbl>, radius_worst <dbl>,
## # texture_worst <dbl>, perimeter_worst <dbl>, area_worst <dbl>, …str(WBC)## spc_tbl_ [569 × 32] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ id : num [1:569] 842302 842517 84300903 84348301 84358402 ...
## $ diagnosis : chr [1:569] "M" "M" "M" "M" ...
## $ radius_mean : num [1:569] 18 20.6 19.7 11.4 20.3 ...
## $ texture_mean : num [1:569] 10.4 17.8 21.2 20.4 14.3 ...
## $ perimeter_mean : num [1:569] 122.8 132.9 130 77.6 135.1 ...
## $ area_mean : num [1:569] 1001 1326 1203 386 1297 ...
## $ smoothness_mean : num [1:569] 0.1184 0.0847 0.1096 0.1425 0.1003 ...
## $ compactness_mean : num [1:569] 0.2776 0.0786 0.1599 0.2839 0.1328 ...
## $ concavity_mean : num [1:569] 0.3001 0.0869 0.1974 0.2414 0.198 ...
## $ concave points_mean : num [1:569] 0.1471 0.0702 0.1279 0.1052 0.1043 ...
## $ symmetry_mean : num [1:569] 0.242 0.181 0.207 0.26 0.181 ...
## $ fractal_dimension_mean : num [1:569] 0.0787 0.0567 0.06 0.0974 0.0588 ...
## $ radius_se : num [1:569] 1.095 0.543 0.746 0.496 0.757 ...
## $ texture_se : num [1:569] 0.905 0.734 0.787 1.156 0.781 ...
## $ perimeter_se : num [1:569] 8.59 3.4 4.58 3.44 5.44 ...
## $ area_se : num [1:569] 153.4 74.1 94 27.2 94.4 ...
## $ smoothness_se : num [1:569] 0.0064 0.00522 0.00615 0.00911 0.01149 ...
## $ compactness_se : num [1:569] 0.049 0.0131 0.0401 0.0746 0.0246 ...
## $ concavity_se : num [1:569] 0.0537 0.0186 0.0383 0.0566 0.0569 ...
## $ concave points_se : num [1:569] 0.0159 0.0134 0.0206 0.0187 0.0188 ...
## $ symmetry_se : num [1:569] 0.03 0.0139 0.0225 0.0596 0.0176 ...
## $ fractal_dimension_se : num [1:569] 0.00619 0.00353 0.00457 0.00921 0.00511 ...
## $ radius_worst : num [1:569] 25.4 25 23.6 14.9 22.5 ...
## $ texture_worst : num [1:569] 17.3 23.4 25.5 26.5 16.7 ...
## $ perimeter_worst : num [1:569] 184.6 158.8 152.5 98.9 152.2 ...
## $ area_worst : num [1:569] 2019 1956 1709 568 1575 ...
## $ smoothness_worst : num [1:569] 0.162 0.124 0.144 0.21 0.137 ...
## $ compactness_worst : num [1:569] 0.666 0.187 0.424 0.866 0.205 ...
## $ concavity_worst : num [1:569] 0.712 0.242 0.45 0.687 0.4 ...
## $ concave points_worst : num [1:569] 0.265 0.186 0.243 0.258 0.163 ...
## $ symmetry_worst : num [1:569] 0.46 0.275 0.361 0.664 0.236 ...
## $ fractal_dimension_worst: num [1:569] 0.1189 0.089 0.0876 0.173 0.0768 ...
## - attr(*, "spec")=
## .. cols(
## .. id = col_double(),
## .. diagnosis = col_character(),
## .. radius_mean = col_double(),
## .. texture_mean = col_double(),
## .. perimeter_mean = col_double(),
## .. area_mean = col_double(),
## .. smoothness_mean = col_double(),
## .. compactness_mean = col_double(),
## .. concavity_mean = col_double(),
## .. `concave points_mean` = col_double(),
## .. symmetry_mean = col_double(),
## .. fractal_dimension_mean = col_double(),
## .. radius_se = col_double(),
## .. texture_se = col_double(),
## .. perimeter_se = col_double(),
## .. area_se = col_double(),
## .. smoothness_se = col_double(),
## .. compactness_se = col_double(),
## .. concavity_se = col_double(),
## .. `concave points_se` = col_double(),
## .. symmetry_se = col_double(),
## .. fractal_dimension_se = col_double(),
## .. radius_worst = col_double(),
## .. texture_worst = col_double(),
## .. perimeter_worst = col_double(),
## .. area_worst = col_double(),
## .. smoothness_worst = col_double(),
## .. compactness_worst = col_double(),
## .. concavity_worst = col_double(),
## .. `concave points_worst` = col_double(),
## .. symmetry_worst = col_double(),
## .. fractal_dimension_worst = col_double()
## .. )
## - attr(*, "problems")=<externalptr>WBC1 <- WBC[-1]
dim(WBC1)## [1] 569 31####
table(WBC1$diagnosis)##
## B M
## 357 212#convert to a factor
WBC1$diagnosis <- factor(WBC1$diagnosis, levels = c("B","M"), labels = c("Benign", "Malignant"))
table(WBC1$diagnosis)##
## Benign Malignant
## 357 212round(prop.table(table(WBC1$diagnosis)))##
## Benign Malignant
## 1 0round(prop.table(table(WBC1$diagnosis))* 100, digits =1)##
## Benign Malignant
## 62.7 37.3summary (WBC1[c("radius_mean", "area_mean", "smoothness_mean")])## radius_mean area_mean smoothness_mean
## Min. : 6.981 Min. : 143.5 Min. :0.05263
## 1st Qu.:11.700 1st Qu.: 420.3 1st Qu.:0.08637
## Median :13.370 Median : 551.1 Median :0.09587
## Mean :14.127 Mean : 654.9 Mean :0.09636
## 3rd Qu.:15.780 3rd Qu.: 782.7 3rd Qu.:0.10530
## Max. :28.110 Max. :2501.0 Max. :0.16340# Create normalization function
normalize <- function(x) {
return((x - min(x)) / (max(x) - min(x)))
}
normalize(c(1,2,3,4,5))## [1] 0.00 0.25 0.50 0.75 1.00wbc_N <- as.data.frame(lapply(WBC1[2:31], normalize))
head(wbc_N)## radius_mean texture_mean perimeter_mean area_mean smoothness_mean
## 1 0.5210374 0.0226581 0.5459885 0.3637328 0.5937528
## 2 0.6431445 0.2725736 0.6157833 0.5015907 0.2898799
## 3 0.6014956 0.3902604 0.5957432 0.4494168 0.5143089
## 4 0.2100904 0.3608387 0.2335015 0.1029056 0.8113208
## 5 0.6298926 0.1565776 0.6309861 0.4892895 0.4303512
## 6 0.2588386 0.2025702 0.2679842 0.1415058 0.6786133
## compactness_mean concavity_mean concave.points_mean symmetry_mean
## 1 0.7920373 0.7031396 0.7311133 0.6863636
## 2 0.1817680 0.2036082 0.3487575 0.3797980
## 3 0.4310165 0.4625117 0.6356859 0.5095960
## 4 0.8113613 0.5656045 0.5228628 0.7762626
## 5 0.3478928 0.4639175 0.5183897 0.3782828
## 6 0.4619962 0.3697282 0.4020378 0.5186869
## fractal_dimension_mean radius_se texture_se perimeter_se area_se
## 1 0.6055181 0.35614702 0.12046941 0.36903360 0.27381126
## 2 0.1413227 0.15643672 0.08258929 0.12444047 0.12565979
## 3 0.2112468 0.22962158 0.09430251 0.18037035 0.16292179
## 4 1.0000000 0.13909107 0.17587518 0.12665504 0.03815479
## 5 0.1868155 0.23382220 0.09306489 0.22056260 0.16368757
## 6 0.5511794 0.08075321 0.11713225 0.06879329 0.03808008
## smoothness_se compactness_se concavity_se concave.points_se symmetry_se
## 1 0.1592956 0.35139844 0.13568182 0.3006251 0.31164518
## 2 0.1193867 0.08132304 0.04696970 0.2538360 0.08453875
## 3 0.1508312 0.28395470 0.09676768 0.3898466 0.20569032
## 4 0.2514532 0.54321507 0.14295455 0.3536655 0.72814769
## 5 0.3323588 0.16791841 0.14363636 0.3570752 0.13617943
## 6 0.1970629 0.23431069 0.09272727 0.2153817 0.19372995
## fractal_dimension_se radius_worst texture_worst perimeter_worst area_worst
## 1 0.1830424 0.6207755 0.1415245 0.6683102 0.45069799
## 2 0.0911101 0.6069015 0.3035714 0.5398177 0.43521431
## 3 0.1270055 0.5563856 0.3600746 0.5084417 0.37450845
## 4 0.2872048 0.2483102 0.3859275 0.2413467 0.09400806
## 5 0.1457996 0.5197439 0.1239339 0.5069476 0.34157491
## 6 0.1446596 0.2682319 0.3126333 0.2639076 0.13674794
## smoothness_worst compactness_worst concavity_worst concave.points_worst
## 1 0.6011358 0.6192916 0.5686102 0.9120275
## 2 0.3475533 0.1545634 0.1929712 0.6391753
## 3 0.4835898 0.3853751 0.3597444 0.8350515
## 4 0.9154725 0.8140117 0.5486422 0.8848797
## 5 0.4373638 0.1724151 0.3194888 0.5584192
## 6 0.7127386 0.4827837 0.4277157 0.5982818
## symmetry_worst fractal_dimension_worst
## 1 0.5984624 0.4188640
## 2 0.2335896 0.2228781
## 3 0.4037059 0.2134330
## 4 1.0000000 0.7737111
## 5 0.1575005 0.1425948
## 6 0.4770353 0.4549390#splitting the data into test and train dataset.
wbcd_train <-wbc_N[1:469, ]
wbcd_test <- wbc_N[470:569, ]
wbcd_train_labels <- WBC1[1:469, 1]
wbcd_train_labels## # A tibble: 469 × 1
## diagnosis
## <fct>
## 1 Malignant
## 2 Malignant
## 3 Malignant
## 4 Malignant
## 5 Malignant
## 6 Malignant
## 7 Malignant
## 8 Malignant
## 9 Malignant
## 10 Malignant
## # ℹ 459 more rowswbcd_test_labels <- WBC1[470:569, 1]
#training the model
library(class)## Warning: package 'class' was built under R version 4.4.3wbcd_test_pred <- knn(train = wbcd_train, test = wbcd_test, cl= wbcd_train_labels$diagnosis, k=21)
wbcd_test_pred## [1] Benign Benign Benign Benign Benign Benign Benign
## [8] Benign Benign Benign Malignant Benign Benign Benign
## [15] Benign Benign Benign Benign Malignant Benign Benign
## [22] Benign Benign Malignant Benign Benign Benign Benign
## [29] Benign Malignant Malignant Benign Malignant Benign Malignant
## [36] Benign Benign Benign Benign Benign Malignant Benign
## [43] Benign Malignant Benign Benign Benign Malignant Malignant
## [50] Benign Benign Benign Malignant Benign Benign Benign
## [57] Benign Benign Benign Benign Benign Benign Benign
## [64] Benign Malignant Benign Malignant Malignant Benign Benign
## [71] Benign Benign Benign Benign Benign Benign Benign
## [78] Benign Benign Benign Benign Benign Benign Benign
## [85] Benign Benign Benign Benign Benign Benign Benign
## [92] Benign Benign Malignant Malignant Malignant Malignant Malignant
## [99] Malignant Benign
## Levels: Benign Malignant#step 4 Evaluate model performance
library(gmodels)## Warning: package 'gmodels' was built under R version 4.4.3CrossTable(x = wbcd_test_labels$diagnosis, y = wbcd_test_pred, prop.chisq = FALSE)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 100
##
##
## | wbcd_test_pred
## wbcd_test_labels$diagnosis | Benign | Malignant | Row Total |
## ---------------------------|-----------|-----------|-----------|
## Benign | 77 | 0 | 77 |
## | 1.000 | 0.000 | 0.770 |
## | 0.975 | 0.000 | |
## | 0.770 | 0.000 | |
## ---------------------------|-----------|-----------|-----------|
## Malignant | 2 | 21 | 23 |
## | 0.087 | 0.913 | 0.230 |
## | 0.025 | 1.000 | |
## | 0.020 | 0.210 | |
## ---------------------------|-----------|-----------|-----------|
## Column Total | 79 | 21 | 100 |
## | 0.790 | 0.210 | |
## ---------------------------|-----------|-----------|-----------|
##
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