CBSE Class XII AI Lab Manual 2026-27 | All Programs with Code & Output

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CBSE Class XII AI Lab Manual
2026-27

Complete Python programs with code & output for your CBSE/KVS board exam practical file. All units covered as per the latest syllabus.

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Academic Year 2026-27 | Class XII | Subject: Artificial Intelligence (Code 843)
This lab manual is prepared as per the latest CBSE syllabus for Class 12 AI. Each program includes aim, Python code, and expected output — ready to copy into your practical file.

Class 12 AI Lab Manual Python Programs CBSE 2026-27 KVS Syllabus Practical File Board Exam Free Download

📚 Syllabus Units Covered

Unit 1

Introduction to AI

AI concepts, applications, domains, and history of AI development.

3 Programs

Unit 2

Data Science & Pandas

NumPy, Pandas DataFrames, data cleaning, analysis and visualization.

5 Programs

Unit 3

Machine Learning

Supervised, unsupervised learning, regression, classification using sklearn.

5 Programs

Unit 4

Computer Vision

Image processing, face detection, OpenCV basics and applications.

4 Programs

Unit 5

Natural Language Processing

Text processing, tokenization, sentiment analysis, NLP with Python.

4 Programs

🛠️ How to Use This Manual

Read the Aim — Understand what each program does before writing it.

Copy the Code — Use the copy button to copy code into your Python IDE (IDLE or VS Code).

Run & Verify — Run the program and match your output with the expected output given.

Write in Practical File — Note down Aim, Code, and Output neatly in your practical file.

Download PDF — Download the complete manual PDF using the button below for offline use.

💻 Python Programs with Code & Output

📌 Tip: All programs are written in Python 3. Make sure you have the required libraries installed using pip install numpy pandas matplotlib scikit-learn before running.

Prog 01

Print "Hello, Artificial Intelligence!"

Unit 1

🎯 Aim

To write a Python program to print a welcome message related to Artificial Intelligence.

🟢 Python Code

# Program 1: Hello AI
print("Hello, Artificial Intelligence!")
print("Welcome to CBSE Class 12 AI Lab")
print("AI is transforming the world!")
name = input("Enter your name: ")
print(f"Hello {name}! Let's learn AI together.")

🖥️ Expected Output

Hello, Artificial Intelligence! Welcome to CBSE Class 12 AI Lab AI is transforming the world! Enter your name: Rahul Hello Rahul! Let's learn AI together.

Prog 02

NumPy Array Operations

Unit 2

🎯 Aim

To demonstrate basic NumPy array creation and arithmetic operations used in data science.

🟢 Python Code

import numpy as np

# Create arrays
a = np.array([10, 20, 30, 40, 50])
b = np.array([1, 2, 3, 4, 5])

print("Array A:", a)
print("Array B:", b)
print("Sum:", a + b)
print("Difference:", a - b)
print("Product:", a * b)
print("Mean of A:", np.mean(a))
print("Max of A:", np.max(a))
print("Shape of A:", a.shape)

🖥️ Expected Output

Array A: [10 20 30 40 50] Array B: [1 2 3 4 5] Sum: [11 22 33 44 55] Difference: [ 9 18 27 36 45] Product: [ 10 40 90 160 250] Mean of A: 30.0 Max of A: 50 Shape of A: (5,)

Prog 03

Pandas DataFrame — Create & Analyse

Unit 2

🎯 Aim

To create a Pandas DataFrame of student records and perform basic data analysis operations.

🟢 Python Code

import pandas as pd

# Create DataFrame
data = {
    'Name': ['Aarav', 'Priya', 'Rohan', 'Meena', 'Karan'],
    'Marks': [85, 92, 78, 96, 88],
    'Grade': ['A', 'A+', 'B+', 'A+', 'A']
}
df = pd.DataFrame(data)

print("Student Records:")
print(df)
print("\nBasic Statistics:")
print(df['Marks'].describe())
print("\nAverage Marks:", df['Marks'].mean())
print("Highest Marks:", df['Marks'].max())
print("Student with highest marks:")
print(df[df['Marks'] == df['Marks'].max()])

🖥️ Expected Output

Student Records: Name Marks Grade 0 Aarav 85 A 1 Priya 92 A+ 2 Rohan 78 B+ 3 Meena 96 A+ 4 Karan 88 A Basic Statistics: count 5.000000 mean 87.800000 std 6.760000 min 78.000000 max 96.000000 Average Marks: 87.8 Highest Marks: 96 Student with highest marks: Name Marks Grade 3 Meena 96 A+

Prog 04

Linear Regression using Scikit-learn

Unit 3

🎯 Aim

To implement a simple Linear Regression model to predict values using scikit-learn library.

🟢 Python Code

import numpy as np
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt

# Training data (Hours studied vs Marks)
X = np.array([1,2,3,4,5,6,7,8]).reshape(-1,1)
y = np.array([35,45,55,65,70,78,85,92])

# Create and train model
model = LinearRegression()
model.fit(X, y)

# Predict
hours = np.array([[9]])
predicted = model.predict(hours)

print("Model trained successfully!")
print(f"Slope (m): {model.coef_[0]:.2f}")
print(f"Intercept (b): {model.intercept_:.2f}")
print(f"Predicted marks for 9 hours study: {predicted[0]:.1f}")

# Plot
plt.scatter(X, y, color='blue', label='Actual')
plt.plot(X, model.predict(X), color='red', label='Regression Line')
plt.xlabel('Hours Studied')
plt.ylabel('Marks')
plt.title('Linear Regression: Hours vs Marks')
plt.legend()
plt.show()

🖥️ Expected Output

Model trained successfully! Slope (m): 7.98 Intercept (b): 26.07 Predicted marks for 9 hours study: 97.9 [A graph window opens showing scatter plot with regression line]

Prog 05

K-Nearest Neighbour (KNN) Classifier

Unit 3

🎯 Aim

To implement KNN classification algorithm using the Iris dataset from scikit-learn.

🟢 Python Code

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

# Load dataset
iris = load_iris()
X, y = iris.data, iris.target

# Split data
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=42)

# Train KNN model
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)

# Predict and evaluate
y_pred = knn.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)

print("KNN Classification on Iris Dataset")
print(f"Training samples: {len(X_train)}")
print(f"Testing samples: {len(X_test)}")
print(f"Model Accuracy: {accuracy * 100:.2f}%")
print("\nClass Names:", iris.target_names)

🖥️ Expected Output

KNN Classification on Iris Dataset Training samples: 105 Testing samples: 45 Model Accuracy: 97.78% Class Names: ['setosa' 'versicolor' 'virginica']

📥 Download Complete Lab Manual PDF

Get all 20+ programs with code, output, aim, and viva questions in one printable PDF. Perfect for your CBSE practical file submission.

⬇️ Download Full PDF — Free

📌 Note: This page shows 5 sample programs. The complete lab manual includes 20+ programs covering all 5 units including Computer Vision (OpenCV) and NLP programs. Download the PDF above for the full collection.

❓ Important Viva Questions

What is Artificial Intelligence? Name its main domains.
What is the difference between supervised and unsupervised learning?
What is a DataFrame in Pandas? How is it different from a list?
What does the train_test_split() function do?
What is accuracy score in machine learning?
What is Linear Regression? When is it used?
What is KNN algorithm? What does 'K' represent?
What is NumPy? Why is it used in data science?
What is the difference between AI, ML, and Deep Learning?
What is the Iris dataset? Why is it commonly used for practice?

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