Predict if a student will Pass or Fail based on study hours and sleep hours using Decision Tree algorithm.

# Import libraries
from sklearn.tree import DecisionTreeClassifier, export_text

# Sample data
# Features: [study_hours, sleep_hours]
X = [
    [5, 7],  # studied 5 hrs, slept 7 hrs
    [3, 6],
    [8, 5],
    [1, 4],
    [4, 8]
]

# Labels: Pass = 1, Fail = 0
y = [1, 0, 1, 0, 1]

# Create model
model = DecisionTreeClassifier()
model.fit(X, y)

# Show the tree in text form
tree_rules = export_text(model, feature_names=["study_hours", "sleep_hours"])
print(tree_rules)

# Predict for a new student
new_student = [[2, 7]]  # studies 2 hrs, sleeps 7 hrs
prediction = model.predict(new_student)

print("Prediction:", "Pass" if prediction[0] == 1 else "Fail")

Output:--

|--- study_hours <= 3.50
|   |--- class: 0
|--- study_hours >  3.50
|   |--- class: 1

Prediction: Fail ❌