CBSE Class 12 AI Practical File 2025-26 | Python Programs PDF | Code 843
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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.
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📥 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 GroupClass 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.
📋 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.
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