predictor variables such as the number of pregnancies the patient has had, the BMI, insulin level, age, etc. A machine learning model can learn from the dataset and predict whether the patient has diabetes based on these predictor variables.

D = data.values
X = D[:, :-1]
y = D[:, -1]

Now, we are splitting the columns of the dataset into features and the target variable. X here contains all the features, and y contains the target variable.

k_fold = KFold(n_splits=10, shuffle=True, random_state=1)

Now, we are initializing the k-fold cross-validation. n_splits here refers to the number of splits. The shuffle argument indicates that we are shuffling the data before splitting. And random_state is used to initialize the pseudo-random number generator that is used for shuffling the data.

classifier = LinearDiscriminantAnalysis()

We are now initializing the classifier using the LinearDiscriminantAnalysis class.

results = cross_val_score(classifier, X, y, cv=k_fold, scoring="accuracy")
mean_score = results.mean()
print("Accuracy: ", mean_score)

Now, we are using the cross_val_score() function to evaluate the performance of the model. We are calculating the accuracy score (What is the accuracy score in machine learning?) for each iteration of the k-fold cross-validation and taking the average of the accuracy scores.

The output of the above program will be:

Accuracy:  0.7759569377990431