x = df.iloc[:,1:-1].values
y = df.iloc[:,-1].values
from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.25,random_state=10)
from sklearn.metrics import PrecisionRecallDisplay, RocCurveDisplay
from sklearn.metrics import (
ConfusionMatrixDisplay,
confusion_matrix,
accuracy_score,
precision_score,
recall_score,
PrecisionRecallDisplay,
RocCurveDisplay
)
def report(classifier):
y_pred = classifier.predict(x_test) #ye wala
cm = confusion_matrix(y_test, y_pred) #ye wala
display = ConfusionMatrixDisplay(cm, display_labels=classifier.classes_)
display.plot()
print(f”Accuracy: {accuracy_score(y_test, y_pred):.4f}”)
print(f”Precision Score: {precision_score(y_test, y_pred):.4f}”)
print(f”Recall Score: {recall_score(y_test, y_pred):.4f}”)
 from sklearn.neighbors import KNeighborsClassifier
kNN = KNeighborsClassifier(n_neighbors=10)
kNN.fit(x_train,y_train)
kNN_predict=kNN.predict(x_test)
report(KNN)
—-
from sklearn.svm import SVC
svm = SVC(gamma=’auto’,random_state=10)
svm.fit(x_train,y_train)
svm_predict=svm.predict(x_test)