CBSE Class 12 Artificial Intelligence Practical File, Python Programs with Output

The CBSE Class 12 Artificial Intelligence (843) practical covers data handling, model evaluation and the Capstone Project. The programs below were run before publishing, so the confusion matrix, the accuracy and the precision figures you see are the numbers the code actually produced.

import pandas as pd
pd.DataFrame({"hours": [1, 2, 3, 4, 5],
              "score": [35, 48, 55, 68, 79]}).to_csv("study.csv", index=False)
df = pd.read_csv("study.csv")
print(df)
print()
print(df.describe())

   hours  score
0      1     35
1      2     48
2      3     55
3      4     68
4      5     79

          hours      score
count  5.000000   5.000000
mean   3.000000  57.000000
std    1.581139  17.131842
min    1.000000  35.000000
25%    2.000000  48.000000
50%    3.000000  55.000000
75%    4.000000  68.000000
max    5.000000  79.000000

import pandas as pd
import numpy as np
df = pd.DataFrame({"name": ["Aarav", "Diya", "Ishaan"],
                   "marks": [78, np.nan, 66]})
print("Missing values per column:")
print(df.isnull().sum())
df["marks"] = df["marks"].fillna(df["marks"].mean())
print()
print(df)

Missing values per column:
name     0
marks    1
dtype: int64

     name  marks
0   Aarav   78.0
1    Diya   72.0
2  Ishaan   66.0

import pandas as pd
df = pd.DataFrame({"hours": [1, 2, 3, 4, 5, 6, 7, 8],
                   "score": [35, 48, 55, 68, 72, 79, 85, 91]})
train = df.iloc[:6]
test = df.iloc[6:]
print("Training rows:", len(train), " Testing rows:", len(test))
print("Test set:")
print(test)

Training rows: 6  Testing rows: 2
Test set:
   hours  score
6      7     85
7      8     91

actual    = [1, 0, 1, 1, 0, 1, 0, 0]
predicted = [1, 0, 1, 0, 0, 1, 1, 0]
tp = sum(1 for a, p in zip(actual, predicted) if a == 1 and p == 1)
tn = sum(1 for a, p in zip(actual, predicted) if a == 0 and p == 0)
fp = sum(1 for a, p in zip(actual, predicted) if a == 0 and p == 1)
fn = sum(1 for a, p in zip(actual, predicted) if a == 1 and p == 0)
print("True Positive :", tp)
print("True Negative :", tn)
print("False Positive:", fp)
print("False Negative:", fn)
print("Accuracy      :", round((tp + tn) / len(actual) * 100, 2), "%")

True Positive : 3
True Negative : 3
False Positive: 1
False Negative: 1
Accuracy      : 75.0 %

tp, fp, fn = 3, 1, 1
precision = tp / (tp + fp)
recall = tp / (tp + fn)
f1 = 2 * precision * recall / (precision + recall)
print("Precision:", round(precision, 2))
print("Recall   :", round(recall, 2))
print("F1 Score :", round(f1, 2))

Precision: 0.75
Recall   : 0.75
F1 Score : 0.75

text = "an ai model learns from data and a model improves with more data"
bag = {}
for word in text.split():
    bag[word] = bag.get(word, 0) + 1
for word, count in sorted(bag.items(), key=lambda x: (-x[1], x[0]))[:5]:
    print(word, ":", count)

data : 2
model : 2
a : 1
ai : 1
an : 1

import pandas as pd
df = pd.DataFrame({"marks": [35, 48, 55, 68, 79]})
low, high = df["marks"].min(), df["marks"].max()
df["normalised"] = ((df["marks"] - low) / (high - low)).round(3)
print(df)

   marks  normalised
0     35       0.000
1     48       0.295
2     55       0.455
3     68       0.750
4     79       1.000

import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
subjects = ["CS", "IP", "AI"]
students = [42, 31, 27]
plt.bar(subjects, students)
plt.title("Students per subject")
plt.savefig("subjects.png")
print("Chart saved as subjects.png")
print("Bars:", dict(zip(subjects, students)))

Chart saved as subjects.png
Bars: {'CS': 42, 'IP': 31, 'AI': 27}

Related: Class 12 AI syllabus · Class 10 AI syllabus · Try the code in the Python Playground

Every program on this page was executed and its output captured from the run. Curated by Kajal Ma'am (MCA), teaching computer subjects since 2004.