CBSE Class 12 AI Practical File 2025-26 | Python Programs PDF | Code 843

📘 AI Logic School

CBSE Class 12 AI Python Programs
Practical File 2025–26

Complete Python practical file for CBSE Class 12 Artificial Intelligence. Subject Code 843. All programs with code, output and theory explanation.

20+

Programs

843

Subject Code

100%

CBSE

Free

Download

📥 Download Class 12 AI Practical File PDF — Free

Complete file with all programs, code, output, theory and viva questions. Prepared as per CBSE Code 843 official syllabus 2025-26.

⬇️ Download Free PDF 💬 Join WhatsApp Group

Class XII | Subject: Artificial Intelligence | Code: 843 | Session 2025-26
All programs are written in Python 3 using libraries like Pandas, NumPy, Matplotlib, Scikit-learn, and more. Each program includes aim, code, output and explanation. This practical file covers all units of the CBSE Class 12 AI syllabus.

Class 12 AI Subject Code 843 Python Practical File CBSE 2025-26 Pandas Programs Machine Learning Data Science Free PDF Download

📋 What is Included in This Practical File

This Class 12 AI Practical File (Code 843) covers all major topics of the CBSE syllabus — from basic Pandas DataFrames to advanced Machine Learning algorithms. Every program has a clear aim, working Python code, expected output, and a brief theory section to help students understand the concept before the practical exam.

🐍 All Python Programs — Complete List

Program 1–3

Pandas DataFrame Programs

Create DataFrame, access rows/columns, add/delete columns, filter data using conditions.

Pandas

Program 4–5

CSV File Handling

Read CSV files using pandas, display first/last rows, get dataset info and shape.

Pandas

Program 6

Missing Values Handling

Detect null values using isnull(), fill missing data with mean/median using fillna().

Pandas · NumPy

Program 7

Statistical Analysis

Calculate mean, median, mode, standard deviation, variance using NumPy and SciPy.

NumPy · SciPy

Program 8–11

Data Visualisation Charts

Bar chart, Pie chart, Line graph, and Scatter plot using Matplotlib with labels and titles.

Matplotlib

Program 12

Linear Regression

Predict values using Linear Regression model. Train/test split, fit model, evaluate accuracy.

Scikit-learn

Program 13

KNN Classifier

K-Nearest Neighbours classification on dataset. Predict class label with accuracy score.

Scikit-learn

Program 14

Decision Tree

Build Decision Tree classifier, visualise the tree, check accuracy on test data.

Scikit-learn

Program 15

Confusion Matrix

Evaluate model performance using confusion matrix, precision, recall and F1 score.

Scikit-learn

Program 16

Word Cloud

Generate a word cloud from text data to visualise most frequent words using wordcloud library.

WordCloud

Program 17

Data Story — MDMS

Complete data analysis story on Mid-Day Meal Scheme dataset covering all 5 AI Project Cycle steps.

Pandas · Matplotlib

Program 18

K-Means Clustering

Unsupervised learning using K-Means. Group data into clusters and visualise results.

Scikit-learn

Program 19

Gemini AI Chatbot

Build a chatbot using Google Gemini API. Send prompts and receive AI-generated responses in Python.

Google Gemini API

📊 CBSE Class 12 AI Syllabus — Code 843

Unit	Topic	Key Concepts	Marks
Unit 1	Introduction to AI	AI concepts, applications, ethics, bias	10
Unit 2	Data Science Methodology	AI Project Cycle, Pandas, NumPy, Matplotlib	15
Unit 3	Computer Vision	OpenCV, image processing, CNN basics	10
Unit 4	Natural Language Processing	Text processing, NLTK, chatbots	10
Unit 5	Machine Learning	Regression, KNN, Decision Tree, K-Means	15
Total Theory			60 Marks

📝 Practical Exam Marks Distribution

Component	Details	Marks
Practical File	Minimum 15 programs with aim, code and output	10
Practical Exam	Python program on Data Science / ML	15
Viva Voce	Questions on practical programs	5
Project Work	AI project related to SDGs	10
Total Practical		40 Marks

⚙️ Python Libraries Required

Install all libraries before starting: Open Command Prompt and run these commands one by one:

pip install pandas pip install numpy pip install matplotlib pip install scikit-learn pip install wordcloud pip install scipy

❓ Important Viva Questions — Class 12 AI Code 843

Q1. What is a Pandas DataFrame?

A DataFrame is a 2-dimensional labelled data structure in Pandas — like a table with rows and columns. It is the most commonly used object in data science.

Q2. What is the difference between isnull() and fillna()?

isnull() detects missing values and returns True/False. fillna() replaces missing values with a specified value like mean or median.

Q3. What is Linear Regression?

Linear Regression is a supervised ML algorithm that predicts a continuous output value based on input features by finding the best-fit straight line.

Q4. What is KNN? What does K stand for?

KNN stands for K-Nearest Neighbours. K is the number of nearest data points used to classify a new data point based on majority class.

Q5. What is a Decision Tree?

A Decision Tree is a supervised ML algorithm that splits data into branches based on feature values to make predictions — like a flowchart.

Q6. What is a Confusion Matrix?

A Confusion Matrix shows the performance of a classification model — displaying True Positives, True Negatives, False Positives and False Negatives.

Q7. What is K-Means Clustering?

K-Means is an unsupervised ML algorithm that groups data into K clusters based on similarity. It does not use labelled data.

Q8. What is the AI Project Cycle?

The 5 steps are: Problem Scoping → Data Acquisition → Data Exploration → Modelling → Evaluation.

Q9. What is train_test_split?

It divides the dataset into training data (to build the model) and testing data (to evaluate its accuracy). Typically 80% train, 20% test.

Q10. What is overfitting in Machine Learning?

Overfitting occurs when a model performs very well on training data but poorly on new/test data — it has memorised the training data instead of learning patterns.

📥 Download Complete Class 12 AI Practical File — Free

All 20+ programs with aim, code, output, theory and viva questions. 100% CBSE Code 843 aligned. Session 2025-26.

⬇️ Download Free PDF 💬 Join WhatsApp Group

🔗 More Free Resources — AI Logic School

📗 CLASS 9

Class 9 AI Practical File

15 Python programs for CBSE Class 9 AI Code 417 with output.

📘 CLASS 10

Class 10 AI Practical File

15 Python programs for CBSE Class 10 AI Code 417 with output.

📙 WORKSHEETS

CBSE AI Worksheets

Free printable worksheets for Classes 6 to 12 AI and CT subjects.

Continue learning

📖

Data Science with Python

CBSE · SUB CODE 843 · CLASS 11 · UNIT 3 & 5

Data Science with Python

NumPy · Pandas · Matplotlib · Statistics — with real-world examples

NumPy Pandas Matplotlib Statistics CSV Files

WHAT IS DATA SCIENCE?

Data Science is the process of collecting, cleaning, analysing and visualising data to find useful insights and make decisions. Python is the most popular language for Data Science because of its powerful libraries.

LIBRARY 1

NumPy — Numerical Python

🌡️ DAILY LIFE EXAMPLE — Temperature Tracker

You recorded temperature for 7 days in your city: 38, 40, 37, 42, 39, 41, 36°C. NumPy helps you find average temperature, maximum, minimum in just one line of code — like a super calculator for lists of numbers!

import numpy as np

# 7 days temperature data (in Celsius)
temperature = np.array([38, 40, 37, 42, 39, 41, 36])

print("All temperatures:", temperature)
print("Average temp:   ", np.mean(temperature))      # 39.0
print("Maximum temp:   ", np.max(temperature))       # 42
print("Minimum temp:   ", np.min(temperature))       # 36
print("Std Deviation:  ", np.std(temperature))       # spread of data

# Array operations - add 2 degrees to all values
print("If +2 degrees:  ", temperature + 2)

IMPORTANT NUMPY FUNCTIONS — MUST KNOW

Function	What it does	Example
np.array()	Create an array	np.array([1,2,3])
np.mean()	Calculate average	np.mean([10,20,30]) → 20
np.median()	Find middle value	np.median([1,3,5]) → 3
np.std()	Standard deviation	np.std([2,4,4,4,5,5,7,9]) → 2
np.var()	Variance	np.var(data)
np.arange()	Create number sequence	np.arange(1,10,2) → [1,3,5,7,9]

LIBRARY 2

Pandas — Data Analysis Library

📊 DAILY LIFE EXAMPLE — Student Report Card

Think of Pandas like a super Excel sheet in Python. Your school has data of 500 students — name, marks, attendance, class. Pandas lets you load this data, filter students who scored above 80%, find the class average, and sort by marks — all with just a few lines of code!

import pandas as pd

# Create a student DataFrame (like a table)
data = {
    'Name':    ['Aarav','Priya','Rahul','Sneha','Arjun'],
    'Marks':   [85, 92, 78, 95, 88],
    'Subject': ['AI','AI','AI','AI','AI'],
    'Class':   [11, 11, 11, 11, 11]
}
df = pd.DataFrame(data)

print(df)                           # Show full table
print("\nAverage marks:", df['Marks'].mean())
print("Top scorer:", df['Name'][df['Marks'].idxmax()])

# Filter students with marks above 85
toppers = df[df['Marks'] > 85]
print("\nToppers:\n", toppers)

READING CSV FILES WITH PANDAS

CSV (Comma Separated Values) is a simple file format for storing data — like a spreadsheet saved as plain text. Example: student_data.csv contains hundreds of rows of student information.

import pandas as pd

# Load data from CSV file
df = pd.read_csv('student_data.csv')

print(df.head())         # First 5 rows
print(df.tail())         # Last 5 rows
print(df.shape)          # (rows, columns)
print(df.describe())     # Statistics summary
print(df.isnull().sum()) # Check missing values

# Save to new CSV
df.to_csv('cleaned_data.csv', index=False)

MUST-KNOW PANDAS FUNCTIONS

Function	Purpose
df.head(n)	Show first n rows (default 5)
df.describe()	Show count, mean, std, min, max of all columns
df.shape	Returns (number of rows, number of columns)
df.isnull()	Find missing/empty values in data
df.dropna()	Remove rows with missing values
df.fillna(value)	Fill missing values with a specific value

LIBRARY 3

Matplotlib — Data Visualization

📈 DAILY LIFE EXAMPLE — Class Performance Graph

Your teacher wants to show how the class performed in 5 subjects. Instead of showing a boring table of numbers, Matplotlib creates a bar graph or pie chart — making it easy to see which subject students did best in!

5 TYPES OF GRAPHS — CBSE SYLLABUS

1. LINE GRAPH

import matplotlib.pyplot as plt

months = ['Jan','Feb','Mar','Apr']
sales  = [200, 350, 300, 450]

plt.plot(months, sales, marker='o')
plt.title('Monthly Sales')
plt.xlabel('Month')
plt.ylabel('Sales')
plt.show()

2. BAR GRAPH

import matplotlib.pyplot as plt

subjects = ['Maths','AI','English',
            'Science','Hindi']
marks    = [85, 92, 78, 88, 76]

plt.bar(subjects, marks, color='teal')
plt.title('Subject-wise Marks')
plt.ylabel('Marks')
plt.show()

3. PIE CHART

import matplotlib.pyplot as plt

labels  = ['Pass','Fail','Absent']
sizes   = [75, 15, 10]
colors  = ['green','red','gray']

plt.pie(sizes, labels=labels,
        colors=colors, autopct='%1.1f%%')
plt.title('Exam Results')
plt.show()

4. HISTOGRAM

import matplotlib.pyplot as plt

marks = [45,55,60,65,70,70,75,
         80,80,80,85,90,90,95]

plt.hist(marks, bins=5,
         color='steelblue')
plt.title('Marks Distribution')
plt.xlabel('Marks Range')
plt.ylabel('Number of Students')
plt.show()

5. SCATTER PLOT

import matplotlib.pyplot as plt

study_hours = [2,3,4,5,6,7,8]
exam_marks  = [50,55,65,70,78,85,92]

plt.scatter(study_hours, exam_marks,
            color='purple')
plt.title('Study Hours vs Marks')
plt.xlabel('Hours Studied')
plt.ylabel('Marks Scored')
plt.show()

UNIT 5

Statistics for Data Science

🎯 DAILY LIFE EXAMPLE — Cricket Match Scores

Virat Kohli scored these runs in 7 matches: 45, 82, 67, 23, 95, 56, 38. Let's calculate the key statistics to understand his performance!

import numpy as np
from scipy import stats

# Virat's runs in 7 matches
runs = np.array([45, 82, 67, 23, 95, 56, 38])

mean     = np.mean(runs)      # Average: 58.0
median   = np.median(runs)    # Middle value: 56.0
mode_val = stats.mode(runs)   # Most frequent (no repeat here)
std_dev  = np.std(runs)       # How spread out: ~23.6
variance = np.var(runs)       # Variance: ~557

print(f"Mean (Average):      {mean:.1f} runs")
print(f"Median (Middle):     {median:.1f} runs")
print(f"Standard Deviation:  {std_dev:.1f} runs")
print(f"Variance:            {variance:.1f}")

# Interpretation:
# High std deviation = inconsistent player
# Low std deviation  = consistent player

➕

MEAN

Sum of all values ÷ count. Like sharing pizza equally among friends.

🎯

MEDIAN

Middle value when sorted. Used for salaries to avoid effect of very high earners.

🔁

MODE

Most frequent value. Like the most popular shoe size in a shop.

📏

STD DEV

How spread out data is from the mean. Low = consistent, High = spread out.

EXAM TIPS — DATA SCIENCE

NumPy = for numerical operations on arrays and matrices
Pandas = for tabular data (rows and columns) — like Excel in Python
Matplotlib = for creating graphs and charts
CSV = Comma Separated Values — most common data file format
df.head() shows first 5 rows | df.describe() shows statistics
Always check for missing values with df.isnull().sum() before analysis

AI Logic School · Data Science with Python · CBSE Class 11 · Sub Code 843 · Units 3 & 5

Continue learning

📖

💻 Senior Secondary Tech Lab

Advanced Python, Data Science, and AI for Classes 11 & 12

📊 Data Science with Python

Master Pandas and NumPy. Learn to analyze datasets, handle CSV files, and visualize trends using PyPlot.

import pandas as pd
df = pd.read_csv('data.csv')

Explore Tutorials

🧠 Machine Learning Basics

Understand Regression, Classification, and Neural Networks. We break down complex math into simple logic.

Read Theory

📂 Practical File Solutions

The complete 2025-26 Python Practical List. Solved programs with output screenshots for your lab internal exams.

Download List

🚀 Career Spotlight: AI & Data Science

Why learn this? The skills you build in Class 12 are the foundation for careers in:

Data Analysis
Software Engineering
AI Research
Cyber Forensics

Continue learning

📖

AI Logic School

CBSE Class 12 AI Practical File 2025-26 | Python Programs PDF | Code 843

CBSE Class 12 AI Python Programs
Practical File 2025–26

📥 Download Class 12 AI Practical File PDF — Free

📋 What is Included in This Practical File

🐍 All Python Programs — Complete List

📊 CBSE Class 12 AI Syllabus — Code 843

📝 Practical Exam Marks Distribution

⚙️ Python Libraries Required

❓ Important Viva Questions — Class 12 AI Code 843

📥 Download Complete Class 12 AI Practical File — Free

🔗 More Free Resources — AI Logic School

Data Science with Python

Data Science with Python

NumPy — Numerical Python

Pandas — Data Analysis Library

Matplotlib — Data Visualization

Statistics for Data Science

Senior Secondary: Python & Data Science Hub

💻 Senior Secondary Tech Lab

📊 Data Science with Python

🧠 Machine Learning Basics

📂 Practical File Solutions

🚀 Career Spotlight: AI & Data Science

CBSE Class 9 AI Practical File 2026-27 | 15 Python Programs with Output | Code 417

CBSE Class 12 AI Practical File 2025-26 | Python Programs PDF | Code 843

CBSE AI Project — Image Recognition with Python 2025-26 | Class 10 & 12 | OpenCV MobileNet | Code 417 & 843

The increasing reliance on AI systems raises concerns about the privacy of personal data.

Logic & CT: Master Computational Thinking

Today's AI & CT Challenge: Can you solve this in 40 seconds?

RAG AI Kya Hai? Retrieval-Augmented Generation Explained in Hindi | CBSE AI Students 2025-26

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

Class 10 | Chapter 1 AI Reflection, Project Cycle & Ethics

CBSE Class 12 AI Python ProgramsPractical File 2025–26

📥 Download Class 12 AI Practical File PDF — Free

📋 What is Included in This Practical File

🐍 All Python Programs — Complete List

📊 CBSE Class 12 AI Syllabus — Code 843

📝 Practical Exam Marks Distribution

⚙️ Python Libraries Required

❓ Important Viva Questions — Class 12 AI Code 843

📥 Download Complete Class 12 AI Practical File — Free

🔗 More Free Resources — AI Logic School

Data Science with Python

NumPy — Numerical Python

Pandas — Data Analysis Library

Matplotlib — Data Visualization

Statistics for Data Science

💻 Senior Secondary Tech Lab

📊 Data Science with Python

🧠 Machine Learning Basics

📂 Practical File Solutions

🚀 Career Spotlight: AI & Data Science

CBSE Class 12 AI Python Programs
Practical File 2025–26