Python Programs for CBSE Class 10 AI Projects

Published on August 13, 2025 by @mritxperts
CBSE Class 10 AI Python Projects

Introduction

Artificial Intelligence is an exciting field and CBSE Class 10 AI curriculum encourages students to build simple, meaningful projects using the AI Project Cycle. These projects help students understand problem solving, data collection, data processing, model building, and result evaluation.

Below are six CBSE Class 10 AI project ideas in Python with complete code. All projects are simple enough for beginners and can be run in Google Colab, Jupyter Notebook, or any Python IDE.

1. Sentiment Analysis of Student Reviews

Objective

Classify short student reviews as positive, negative, or neutral using Natural Language Processing.

Dataset

Create a CSV file with 50–100 one-line reviews from classmates about school events, facilities, or movies.

Steps

  1. Collect and label the data.
  2. Clean the text.
  3. Train a sentiment analysis model.
  4. Test it with new reviews.

Python Code

from textblob import TextBlob

# Sample reviews
reviews = [
    "The sports day was amazing and full of fun",
    "I did not enjoy the event at all",
    "It was okay but could have been better"
]

for review in reviews:
    blob = TextBlob(review)
    sentiment = blob.sentiment.polarity
    if sentiment > 0:
        mood = "Positive"
    elif sentiment < 0:
        mood = "Negative"
    else:
        mood = "Neutral"
    print(f"Review: {review} - Sentiment: {mood}")

Expected Output

Each review will be labeled as Positive, Negative, or Neutral.

Viva Questions

  • What is sentiment polarity?
  • Why do we clean text before processing?

2. Handwritten Digit Recognition

Objective

Recognize handwritten digits (0–9) using a simple machine learning model.

Dataset

Use the built-in digits dataset from scikit-learn.

Steps

  1. Load the dataset.
  2. Train a logistic regression classifier.
  3. Test on unseen data.

Python Code

from sklearn import datasets, metrics
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

# Load dataset
digits = datasets.load_digits()

# Split into train and test
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.2)

# Train model
model = LogisticRegression(max_iter=10000)
model.fit(X_train, y_train)

# Predict
y_pred = model.predict(X_test)

# Accuracy
print("Accuracy:", metrics.accuracy_score(y_test, y_pred))

Expected Output

An accuracy score between 0.90 and 0.95 depending on system and random split.

Viva Questions

  • What is a pixel?
  • How does the model learn from image data?

3. School FAQ Chatbot

Objective

Answer common school questions using a rule-based chatbot.

Dataset

Create a Python dictionary with common questions and answers.

Steps

  1. Map keywords to responses.
  2. Take input from the user.
  3. Display matching responses.

Python Code

responses = {
    "hello": "Hello, how can I help you?",
    "admission": "Admissions are open till 30th September.",
    "fees": "The annual school fee is fifty thousand rupees.",
    "timing": "School timing is 8 AM to 2 PM.",
    "goodbye": "Goodbye and have a nice day."
}

while True:
    user_input = input("You: ").lower()
    if user_input in responses:
        print("Bot:", responses[user_input])
        if user_input == "goodbye":
            break
    else:
        print("Bot: Sorry, I do not have an answer for that.")

Expected Output

The chatbot answers correctly for known questions and politely for unknown ones.

Viva Questions

  • How is a rule-based chatbot different from a machine learning chatbot?
  • What happens when no matching response is found?

4. Color Detection in Images

Objective

Detect the RGB values of a specific pixel in an image.

Dataset

Any school or personal image saved as image.jpg.

Steps

  1. Load the image.
  2. Pick coordinates.
  3. Read RGB values.

Python Code

import cv2

# Load image
image = cv2.imread('image.jpg')

# Coordinates
x, y = 50, 100
(b, g, r) = image[y, x]
print(f"Pixel at ({x},{y}) - Red: {r}, Green: {g}, Blue: {b}")

# Show image
cv2.imshow("Image", image)
cv2.waitKey(0)
cv2.destroyAllWindows()

Expected Output

RGB values for the selected pixel.

Viva Questions

  • What is RGB?
  • How does light affect color detection?

5. Marks Prediction from Study Hours

Objective

Predict student marks based on daily study hours.

Dataset

Hours studied and marks obtained for 30–50 students.

Steps

  1. Create dataset.
  2. Train a regression model.
  3. Predict marks for given hours.

Python Code

from sklearn.linear_model import LinearRegression
import numpy as np

# Dataset
X = np.array([1, 2, 3, 4, 5]).reshape(-1, 1)
y = np.array([35, 50, 65, 75, 85])

# Train model
model = LinearRegression()
model.fit(X, y)

# Predict for 6 hours of study
predicted = model.predict([[6]])
print(f"Predicted Marks: {predicted[0]:.2f}")

Expected Output

Predicted marks for 6 study hours, around 90 depending on dataset.

Viva Questions

  • What is regression?
  • Why is it important to split data into train and test sets?

6. News Topic Classification

Objective

Classify short news headlines into categories like sports, politics, technology, and entertainment.

Dataset

Prepare a CSV file with at least 100 headlines.

Steps

  1. Clean text.
  2. Convert to features.
  3. Train a classifier.
  4. Test accuracy.

Python Code

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB
from sklearn import metrics

# Sample data
headlines = [
    "India wins cricket match",
    "New smartphone launched in market",
    "Government passes new education bill",
    "Actor announces new movie release"
]
labels = ["sports", "technology", "politics", "entertainment"]

# Vectorize text
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(headlines)

# Train-test split
X_train, X_test, y_train, y_test = train_test_split(X, labels, test_size=0.25)

# Train model
model = MultinomialNB()
model.fit(X_train, y_train)

# Predict
y_pred = model.predict(X_test)
print("Accuracy:", metrics.accuracy_score(y_test, y_pred))

Expected Output

Accuracy score based on given dataset.

Viva Questions

  • What is Bag of Words?
  • How do you handle unknown words in text classification?

Conclusion

These Python programs cover various AI topics in the CBSE Class 10 syllabus such as NLP, computer vision, regression, and classification. Each can be expanded into a complete project report by adding objective, problem statement, dataset description, methodology, results, conclusion, and ethical considerations.