Load the dataset and appropriate packages

library(caret)
## Warning: package 'caret' was built under R version 4.1.2
## Loading required package: ggplot2
## Warning: package 'ggplot2' was built under R version 4.1.2
## Loading required package: lattice
library(readxl)
## Warning: package 'readxl' was built under R version 4.1.2
df<-read_excel('labW9.xlsx', 1)

Conduct data exploration and checking and cleaning if necessary

str(df)
## tibble [768 x 9] (S3: tbl_df/tbl/data.frame)
##  $ Pregnancies             : num [1:768] 6 1 8 1 0 5 3 10 2 8 ...
##  $ Glucose                 : num [1:768] 148 85 183 89 137 116 78 115 197 125 ...
##  $ BloodPressure           : num [1:768] 72 66 64 66 40 74 50 0 70 96 ...
##  $ SkinThickness           : num [1:768] 35 29 0 23 35 0 32 0 45 0 ...
##  $ Insulin                 : num [1:768] 0 0 0 94 168 0 88 0 543 0 ...
##  $ BMI                     : num [1:768] 33.6 26.6 23.3 28.1 43.1 25.6 31 35.3 30.5 0 ...
##  $ DiabetesPedigreeFunction: num [1:768] 0.627 0.351 0.672 0.167 2.288 ...
##  $ Age                     : num [1:768] 50 31 32 21 33 30 26 29 53 54 ...
##  $ Outcome                 : num [1:768] 1 0 1 0 1 0 1 0 1 1 ...
summary(df)
##   Pregnancies        Glucose      BloodPressure    SkinThickness  
##  Min.   : 0.000   Min.   :  0.0   Min.   :  0.00   Min.   : 0.00  
##  1st Qu.: 1.000   1st Qu.: 99.0   1st Qu.: 62.00   1st Qu.: 0.00  
##  Median : 3.000   Median :117.0   Median : 72.00   Median :23.00  
##  Mean   : 3.845   Mean   :120.9   Mean   : 69.11   Mean   :20.54  
##  3rd Qu.: 6.000   3rd Qu.:140.2   3rd Qu.: 80.00   3rd Qu.:32.00  
##  Max.   :17.000   Max.   :199.0   Max.   :122.00   Max.   :99.00  
##     Insulin           BMI        DiabetesPedigreeFunction      Age       
##  Min.   :  0.0   Min.   : 0.00   Min.   :0.0780           Min.   :21.00  
##  1st Qu.:  0.0   1st Qu.:27.30   1st Qu.:0.2437           1st Qu.:24.00  
##  Median : 30.5   Median :32.00   Median :0.3725           Median :29.00  
##  Mean   : 79.8   Mean   :31.99   Mean   :0.4719           Mean   :33.24  
##  3rd Qu.:127.2   3rd Qu.:36.60   3rd Qu.:0.6262           3rd Qu.:41.00  
##  Max.   :846.0   Max.   :67.10   Max.   :2.4200           Max.   :81.00  
##     Outcome     
##  Min.   :0.000  
##  1st Qu.:0.000  
##  Median :0.000  
##  Mean   :0.349  
##  3rd Qu.:1.000  
##  Max.   :1.000
colnames(df)
## [1] "Pregnancies"              "Glucose"                 
## [3] "BloodPressure"            "SkinThickness"           
## [5] "Insulin"                  "BMI"                     
## [7] "DiabetesPedigreeFunction" "Age"                     
## [9] "Outcome"

Check for missing values

colSums(is.na(df))
##              Pregnancies                  Glucose            BloodPressure 
##                        0                        0                        0 
##            SkinThickness                  Insulin                      BMI 
##                        0                        0                        0 
## DiabetesPedigreeFunction                      Age                  Outcome 
##                        0                        0                        0

Change outcome to factor

df$Outcome <- as.factor(df$Outcome)

Partition data 70/30 using any method you feel comfortable with

split = 0.7
trainIndex <- createDataPartition(df$Outcome, p = split, list = FALSE)
df_train <- df[trainIndex, ]
df_test <- df[-trainIndex, ]

Check both your training and test subsets

nrow(df_train); nrow(df_test)
## [1] 538
## [1] 230

Check for cross validation if the model allows for it

Initialize cross validation train control

train_control = trainControl(method = "cv", number = 5)

Train your test data using any model you feel is appropriate

Train the model using KNN classifier

set.seed(3333)
model <- train(Outcome~., data = df_train, trControl = train_control, method = "knn")

Plot your model

plot(model)

Predict using your test data onto your model

predictions <- predict(model, newdata = df_test)
predictions
##   [1] 0 0 0 1 0 0 0 0 1 1 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 1 1 0 0 0 1 1 1 1 1 0 0
##  [38] 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 1 0 1 1
##  [75] 0 1 1 0 0 0 0 0 0 0 1 1 0 1 1 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 1 1 0 0 0 1 1
## [112] 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 1 0 0 1 1 0 1 0 1 0 1 0 0
## [149] 0 1 1 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 1 0 0 1 0 0 1
## [186] 0 1 0 0 1 0 0 0 0 0 0 1 0 0 1 1 0 1 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 0 0 0 1
## [223] 0 1 0 1 0 1 0 1
## Levels: 0 1

Evaluate your outcome using any suitable method

confusionMatrix(predictions, df_test$Outcome)
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction   0   1
##          0 124  32
##          1  26  48
##                                           
##                Accuracy : 0.7478          
##                  95% CI : (0.6865, 0.8026)
##     No Information Rate : 0.6522          
##     P-Value [Acc > NIR] : 0.001163        
##                                           
##                   Kappa : 0.4343          
##                                           
##  Mcnemar's Test P-Value : 0.511482        
##                                           
##             Sensitivity : 0.8267          
##             Specificity : 0.6000          
##          Pos Pred Value : 0.7949          
##          Neg Pred Value : 0.6486          
##              Prevalence : 0.6522          
##          Detection Rate : 0.5391          
##    Detection Prevalence : 0.6783          
##       Balanced Accuracy : 0.7133          
##                                           
##        'Positive' Class : 0               
##