Q. 1) Write a PHP script for the following: Design a form to accept a number from the user.
Perform the operations and show the results.

Fibonacci Series.
To find sum of the digits of that number.
(Use the concept of self processing page.) [Marks 15]

<!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>Number Operations</title> </head> <body> <h2>Number Operations</h2> <form action="process.php" method="post"> <label for="number">Enter a number:</label> <input type="text" id="number" name="number" required><br><br> <input type="submit" value="Submit"> </form> </body> </html> <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>Number Operations Result</title> </head> <body> <?php if ($_SERVER["REQUEST_METHOD"] == "POST") { // Retrieve the number from the form $number = $_POST['number']; // Perform Fibonacci series calculation function fibonacci($n) { $fib = []; $fib[0] = 0; $fib[1] = 1; for ($i = 2; $i <= $n; $i++) { $fib[$i] = $fib[$i-1] + $fib[$i-2]; } return $fib; } $fibonacci_series = implode(", ", fibonacci($number)); // Calculate the sum of digits $sum_of_digits = array_sum(str_split($number)); // Display results echo "<h2>Results</h2>"; echo "<p>Fibonacci Series up to $number: $fibonacci_series</p>"; echo "<p>Sum of Digits of $number: $sum_of_digits</p>"; } ?> </body> </html>

Q. 2 ) Build a logistic regression model for Student Score Dataset.
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix

df = pd.read_csv('student_scores.csv')

print(df.head())
)
X = df[['Feature1', 'Feature2', ...]] # Features
y = df['Label'] # Target variable

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Create a logistic regression model

model = LogisticRegression()

model.fit(X_train, y_train)

Make predictions on the testing set

y_pred = model.predict(X_test)

print('Accuracy:', accuracy_score(y_test, y_pred))

print('Classification Report:\n', classification_report(y_test, y_pred))
print('Confusion Matrix:\n', confusion_matrix(y_test, y_pred))

new_data = [[value1, value2, ...]]
predicted_label = model.predict(new_data)
print('Predicted Label:', predicted_label)

Ds29

Create a logistic regression model

Make predictions on the testing set