Untitled

from sklearn.model_selection import train_test_split 
from sklearn.neighbors import KNeighborsClassifier 
from sklearn.metrics import accuracy_score ------------------ DATASET ------------------ 
# Dummy student data: [Exam Score 1, Exam Score 2] 
X = np.array([ 
[85, 75], [90, 95], [60, 50], [45, 30], 
[30, 40], [95, 95], [70, 60], [50, 55], 
[40, 45], [60, 70] 
]) 
# Output labels: 1 = Pass, 0 = Fail 
y = np.array([1, 1, 0, 0, 0, 1, 1, 0, 0, 1]) ------------------ SPLIT DATA ------------------ 
X_train, X_test, y_train, y_test = train_test_split( 
X, y, test_size=0.2, random_state=42) ------------------ MODEL ------------------ 
# Create KNN model with k=3 
knn = KNeighborsClassifier(n_neighbors=3) 
# Train the model 
knn.fit(X_train, y_train) ------------------ TESTING ------------------ 
# Predict test data 
y_pred = knn.predict(X_test) 
# Calculate accuracy 
accuracy = accuracy_score(y_test, y_pred) 
print("Accuracy on test set: {:.2f}".format(accuracy)) ------------------ USER INPUT ------------------ 
# Take input from user 
exam_score1 = float(input("Enter Exam Score 1: ")) 
exam_score2 = float(input("Enter Exam Score 2: ")) 
# Convert input into array 
user_input = np.array([[exam_score1, exam_score2]]) 
# Predict result 
predicted_outcome = knn.predict(user_input) 
# Display result 
if predicted_outcome[0] == 1: 
print("Based on the exam scores provided, the student is predicted to pass.") 
else: 
print("Based on the exam scores provided, the student is predicted to fail.")