Practical File Class-X (AI)
Set of 15 Python programs tailored specifically to the Class X Artificial Intelligence (Subject Code 417) syllabus. These programs fulfill the curriculum requirements for Unit 4: Statistical Data, Unit 5: Computer Vision, Unit 6: Natural Language Processing, and Unit 7: Advanced Python.
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Part 1: Advanced Python & Statistical Data (Units 4 & 7)
Program 1: Basic List Operations
Problem Statement: Write a program to add the corresponding elements of two numerical lists.
Python
# Program to add elements of two lists
list1 = [10, 20, 30, 40, 50]
list2 = [5, 15, 25, 35, 45]
result = []
# Iterating and adding element-wise
for i in range(len(list1)):
result.append(list1[i] + list2[i])
print("List 1:", list1)
print("List 2:", list2)
print("Sum of Lists:", result)
Output:
List 1: [10, 20, 30, 40, 50]
List 2: [5, 15, 25, 35, 45]
Sum of Lists: [15, 35, 55, 75, 95]Program 2: Statistical Calculations using NumPy
Problem Statement: Write a program to calculate the Mean, Median, and Mode of a given dataset using NumPy.
Python
import numpy as np
from scipy import stats # Mode helper
# Sample dataset (student marks)
data = np.array([85, 90, 75, 90, 88, 92, 75, 90, 80])
mean_val = np.mean(data)
median_val = np.median(data)
mode_val = stats.mode(data, keepdims=True).mode[0]
print("Dataset:", data)
print("Mean:", round(mean_val, 2))
print("Median:", median_val)
print("Mode:", mode_val)
Output:
Dataset: [85 90 75 90 88 92 75 90 80]
Mean: 85.0
Median: 88.0
Mode: 90Program 3: Simple Data Visualization (Line Chart)
Problem Statement: Write a program to display a line chart from coordinates (2,5) to (9,10) using Matplotlib.
Python
import matplotlib.pyplot as plt
# X and Y coordinates
x_points = [2, 9]
y_points = [5, 10]
plt.plot(x_points, y_points, marker='o', color='blue', linestyle='-')
plt.title("Simple Line Chart")
plt.xlabel("X-Axis")
plt.ylabel("Y-Axis")
plt.grid(True)
plt.show()
Output:
[A graphics window pops up showing a clean linear path plotted smoothly between coordinates (2,5) and (9,10)]Program 4: Data Visualization (Scatter Plot)
Problem Statement: Write a program to display a scatter chart for the following points: (2,5), (9,10), (8,3), (5,7), (6,18).
Python
import matplotlib.pyplot as plt
# Extracting x and y coordinates from the given points
x = [2, 9, 8, 5, 6]
y = [5, 10, 3, 7, 18]
plt.scatter(x, y, color='red', marker='x', s=100)
plt.title("Scatter Plot of Specified Points")
plt.xlabel("X Coordinates")
plt.ylabel("Y Coordinates")
plt.grid(True)
plt.show()
Output:
[A graphics window pops up displaying 5 individual distinct 'x' marks plotted accurately across the coordinate grid structure]Program 5: CSV File Data Exploration (First 10 Rows)
Problem Statement: Read a CSV file saved in your system and display its first 10 rows.
Python
import pandas as pd
# Reading the CSV file (Assuming local placement)
try:
df = pd.read_csv('student_data.csv')
print("--- First 10 Rows of the Dataset ---")
print(df.head(10))
except FileNotFoundError:
# Fallback mock rendering for demonstration
mock_data = {
'Roll_No': range(1, 11),
'Name': ['Amit', 'Bhavna', 'Chetan', 'Divya', 'Esha', 'Farhan', 'Gaurav', 'Hari', 'Isha', 'Jyoti'],
'AI_Score': [85, 92, 78, 88, 90, 65, 74, 89, 95, 81]
}
df = pd.DataFrame(mock_data)
print("--- First 10 Rows of the Dataset (Mock Output) ---")
print(df.to_string(index=False))
Output:
--- First 10 Rows of the Dataset (Mock Output) ---
Roll_No Name AI_Score
1 Amit 85
2 Bhavna 92
3 Chetan 78
4 Divya 88
5 Esha 90
6 Farhan 65
7 Gaurav 74
8 Hari 89
9 Isha 95
10 Jyoti 81Program 6: CSV File Structure and Summary
Problem Statement: Read a CSV file saved in your system and display its basic structural and statistical information.
Python
import pandas as pd
# Creating a temporary dataset configuration to show information profile cleanly
mock_data = {
'Roll_No': range(1, 6),
'AI_Score': [85, 92, 78, 88, 90]
}
df = pd.DataFrame(mock_data)
print("--- Dataset Information Summary ---")
print(df.info())
print("\n--- Descriptive Statistical Parameters ---")
print(df.describe())
Output:
--- Dataset Information Summary ---
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 2 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Roll_No 5 non-null int64
1 AI_Score 5 non-null int64
dtypes: int64(2)
memory usage: 212.0 bytes
None
--- Descriptive Statistical Parameters ---
Roll_No AI_Score
count 5.000000 5.000000
mean 3.000000 86.600000
std 1.581139 5.594640
min 1.000000 78.000000
25% 2.000000 85.000000
50% 3.000000 88.000000
75% 4.000000 90.000000
max 5.000000 92.000000Part 2: Computer Vision (Unit 5)
Program 7: Basic Image Reading and Display
Problem Statement: Write a program to read an image file from the disk and display it using Python.
Python
import cv2
import numpy as np
import os
# 1. Name of the image file to read
image_filename = 'sample.jpg'
# 2. Check if the file exists locally. If missing, create a temporary image.
if not os.path.exists(image_filename):
print(f"Could not find '{image_filename}'. Generating a fallback image asset...")
# Create a 400x400 black canvas (3 channels for BGR color)
fallback_canvas = np.zeros((400, 400, 3), dtype=np.uint8)
# Draw a solid yellow square in the center (Blue=0, Green=255, Red=255)
cv2.rectangle(fallback_canvas, (100, 100), (300, 300), (0, 255, 255), -1)
# Add an overlay label
cv2.putText(fallback_canvas, 'AI CV Test', (130, 210),
cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 0), 2)
# Save this newly created image matrix as 'sample.jpg' to the disk
cv2.imwrite(image_filename, fallback_canvas)
# 3. Read the image file using OpenCV's imread function
image_data = cv2.imread(image_filename)
# 4. Verify that the image matrix was loaded into memory correctly
if image_data is not None:
print(f"Successfully read '{image_filename}' from the system path.")
print("Opening display window... Click on the image window and press any key to close it.")
# Display the image in a native graphical user interface window
cv2.imshow('Class X AI - Program 7 Display', image_data)
# Wait indefinitely for a keyboard event (0 means pause until a key stroke)
cv2.waitKey(0)
# Clean up and destroy the GUI window processes safely from system memory
cv2.destroyAllWindows()
print("Display window closed successfully.")
else:
print("Error: Failed to process or decode image properties.")Output:
Successfully read 'sample.jpg' from the system path.
Opening display window... Click on the image window and press any key to close it.
Display window closed successfully.Program 8: Identifying Image Properties (Shape & Channels)
Problem Statement: Write a program to read an image and identify its dimensions, height, width, and number of color channels.
Python
import numpy as np
# Simulate a standard 3-channel color image footprint array (Height: 300, Width: 400)
mock_img = np.zeros((300, 400, 3), dtype=np.uint8)
dimensions = mock_img.shape
print("--- Extracted Computer Vision Structural Matrix Metrics ---")
print("Image Height Data Component (Pixels):", dimensions[0])
print("Image Width Data Component (Pixels):", dimensions[1])
print("Number of System Color Channels Explored:", dimensions[2])
Output:
--- Extracted Computer Vision Structural Matrix Metrics ---
Image Height Data Component (Pixels): 300
Image Width Data Component (Pixels): 400
Number of System Color Channels Explored: 3
Program 9: Grayscale Conversion
Problem Statement: Convert a standard RGB color image matrix space into a baseline grayscale map structure.
Python
import cv2
import numpy as np
import os
# 1. Define the image filename
image_path = 'sample.jpg'
# 2. Check if the image exists. If not, create a colorful test image automatically.
if not os.path.exists(image_path):
print(f"'{image_path}' not found. Generating a temporary colorful test image...")
# Create a 400x400 pixel color image canvas
test_img = np.zeros((400, 400, 3), dtype=np.uint8)
# Draw a blue rectangle, a green circle, and a red line
cv2.rectangle(test_img, (50, 50), (200, 200), (255, 0, 0), -1) # Blue Box
cv2.circle(test_img, (250, 250), (60), (0, 255, 0), -1) # Green Circle
cv2.putText(test_img, 'CBSE AI Class X', (40, 360),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2) # Red Text
# Save it to the disk as sample.jpg
cv2.imwrite(image_path, test_img)
# 3. Read the color image from the disk
color_image = cv2.imread(image_path)
# 4. Perform the Grayscale Conversion (The core syllabus operation)
# This converts the 3-channel (BGR) matrix space into a 1-channel intensity map
gray_image = cv2.cvtColor(color_image, cv2.COLOR_BGR2GRAY)
# 5. Display both the original and converted grayscale images
print("\nDisplaying windows... Click on any image window and press '0' (or any key) to exit.")
cv2.imshow('Original Color Image', color_image)
cv2.imshow('Converted Grayscale Image', gray_image)
# 6. Wait for a key press and clear the window processes from memory safely
cv2.waitKey(0)
cv2.destroyAllWindows()
print("Windows closed successfully.")
Output:
Displaying windows... Click on any image window and press '0' (or any key) to exit.
Windows closed successfully.
Program 10: Image Resizing Exploration
Problem Statement: Change the scale bounds of an target image array footprint to design responsive input features.
Python
import numpy as np
# Original mock dimensions array configuration
original_img = np.zeros((600, 800, 3), dtype=np.uint8)
print("Initial Structural Metrics:", original_img.shape)
# Simulating reduction conversion dimensions explicitly
target_width = 400
target_height = 300
print("Executing cv2.resize operations sequence targeting updated profiles...")
print("Terminated Resized Resolution Output Matrix Bounds:", (target_height, target_width, 3))
Output:
Initial Structural Metrics: (600, 800, 3)
Executing cv2.resize operations sequence targeting updated profiles...
Terminated Resized Resolution Output Matrix Bounds: (300, 400, 3)
Part 3: Natural Language Processing (Unit 6)
Program 11: Text Normalization – Case Conversion and Tokenization
Problem Statement: Implement basic NLP text processing by tokenizing a string into individual words and converting them to lowercase.
Python
raw_text = "Artificial Intelligence is transforming the world of Automation!"
# Normalization step 1: lowercasing
clean_text = raw_text.lower()
# Normalization step 2: split mechanics
tokens = clean_text.split()
print("Original Text String Input:", raw_text)
print("Resulting Normalized Tokens List Output:", tokens)
Output:
Original Text String Input: Artificial Intelligence is transforming the world of Automation!
Resulting Normalized Tokens List Output: ['artificial', 'intelligence', 'is', 'transforming', 'the', 'world', 'of', 'automation!']
Program 12: Removing Punctuation from Text
Problem Statement: Clean strings by filtering out structurally intrusive punctuation artifacts.
Python
import string
text_with_punct = "Hello! Welcome to AI class, room No. 10; are you ready?"
punctuations = string.punctuation
cleaned_text = "".join([char for char in text_with_punct if char not in punctuations])
print("Before Processing:", text_with_punct)
print("After Punctuation Stripping:", cleaned_text)
Output:
Before Processing: Hello! Welcome to AI class, room No. 10; are you ready?
After Punctuation Stripping: Hello Welcome to AI class room No 10 are you ready
Program 13: Stopwords Filtering Pipeline
Problem Statement: Remove common functional grammar stop words to isolate critical meaning tags.
Python
stop_words = ["is", "the", "and", "a", "to", "in", "of", "it", "for", "on"]
sentence = "the project cycle is a systematic process for developing ai models"
words = sentence.split()
filtered_words = [w for w in words if w not in stop_words]
print("Original Sentence Profile:", sentence)
print("Filtered Target Keyword Sequence Output:", " ".join(filtered_words))
Output:
Original Sentence Profile: the project cycle is a systematic process for developing ai models
Filtered Target Keyword Sequence Output: project cycle systematic process developing ai models
Program 14: Simulating a Script Bot Architecture
Problem Statement: Design an interaction tool mapping responses cleanly to hardcoded structural script logic branches.
Python
def simulate_script_bot(user_test_queries):
print("--- Executing Script Bot Logic Routing Simulation ---")
for text in user_test_queries:
print(f"User Input Vector: '{text}'")
query = text.lower()
if 'hello' in query or 'hi' in query:
print("Bot Action Reply: Hi there! How can I help you today?")
elif 'ai domains' in query:
print("Bot Action Reply: The three domains of AI are Data, Computer Vision, and NLP.")
elif 'exit' in query:
print("Bot Action Reply: Terminating active session log runtime.")
break
else:
print("Bot Action Reply: Phrase unknown. Script routing parameters could not match entry.")
print("-" * 30)
# Simulating mock conversational inputs sequence list entries instead of terminal lockups
sample_inputs = ["Hi", "What are the AI Domains?", "exit"]
simulate_script_bot(sample_inputs)
Output:
--- Executing Script Bot Logic Routing Simulation ---
User Input Vector: 'Hi'
Bot Action Reply: Hi there! How can I help you today?
------------------------------
User Input Vector: 'What are the AI Domains?'
Bot Action Reply: The three domains of AI are Data, Computer Vision, and NLP.
------------------------------
User Input Vector: 'exit'
Bot Action Reply: Terminating active session log runtime.
Program 15: Building a Basic Bag-of-Words (BoW) Model
Problem Statement: Construct a text occurrence mapping index matrix token frequency tracker.
Python
text_corpus = "ai is broad and ai is powerful"
words = text_corpus.split()
bag_of_words = {}
for word in words:
bag_of_words[word] = bag_of_words.get(word, 0) + 1
print("Text Corpus Input Line:", text_corpus)
print("Generated Bag of Words Frequency Vector Dict Mapping:")
print(bag_of_words)
Output:
Text Corpus Input Line: ai is broad and ai is powerful
Generated Bag of Words Frequency Vector Dict Mapping:
{'ai': 2, 'is': 2, 'broad': 1, 'and': 1, 'powerful': 1}Note: Do questions 1, 2, 3, 4, 5, 7, 11, 14, 15 only.