WT slip tybcs21to30

@Slip-21

Q. 1)Add a JavaScript File in Codeigniter. The Javascript code should check whether a number is

Positive or negative.

Ans:

Html file

<!DOCTYPE html>

<html>

<head>

<title>Number Check</title>

</head>

<body>

<h1>Number Check</h1>

<p>Enter a number to check:</p>

<button onclick=”checkNumber(document.getElementById(‘num’).value)”>Check</button>

</body>

</html>

Create is file check number.js

Function checkNumber(num) {

If (num > 0) {

Alert(“The number is positive.”);

} else if (num < 0) {

Alert(“The number is negative.”);

} else {

Alert(“The number is zero.”);

}

Q. 2)Build a simple linear regression model for User Data.

Ans:

Import pandas as pd

From sklearn.model_selection import train_test_split

From sklearn.linear_model import LinearRegression

From sklearn.metrics import mean_squared_error, r2_score

Import matplotlib.pyplot as plt

# 1. Collect data

Data = pd.read_csv(‘user_data.csv’)

# 2. Preprocess data

Data.dropna(inplace=True)

X = data[‘age’].values.reshape(-1, 1)

Y = data[‘income’].values.reshape(-1, 1)

# 3. Split data

X_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)

# 4. Train the model

Regressor = LinearRegression()

Regressor.fit(x_train, y_train)

# 5. Predict values

Y_pred = regressor.predict(x_test)

# 6. Evaluate model

Mse = mean_squared_error(y_test, y_pred)

R2 = r2_score(y_test, y_pred)

Print(“Mean squared error: “, mse)

Print(“R-squared: “, r2)

# 7. Visualize results

Plt.scatter(x_test, y_test, color=’gray’)

Plt.plot(x_test, y_pred, color=’red’, linewidth=2)

Plt.show()

@Slip-22

Q. 1)Create a table student having attributes(rollno, name, class). Using codeigniter, connect to the

Database and insert 5 recodes in it.

Ans:

<?php

// Establish connection to PostgreSQL database

$conn = pg_connect(“host=localhost dbname=your_database_name user=your_username password=your_password”);

// Check if connection was successful

If (!$conn) {

Echo “Connection failed.”;

Exit;

}

// Create student table

$query = “CREATE TABLE student (

Rollno INTEGER PRIMARY KEY,

Name VARCHAR(50) NOT NULL,

Class VARCHAR(10) NOT NULL

)”;

$result = pg_query($conn, $query);

If (!$result) {

Echo “Error creating table: “ . pg_last_error($conn);

Exit;

} else {

Echo “Table created successfully.<br>”;

}

// Insert 5 records into student table

$insert_query = “INSERT INTO student (rollno, name, class)

VALUES (1, ‘John Doe’, ‘10A’),

(2, ‘Jane Smith’, ‘9B’),

(3, ‘Bob Johnson’, ‘11C’),

(4, ‘Sarah Lee’, ‘12D’),

(5, ‘Tom Brown’, ‘8E’)”;

$insert_result = pg_query($conn, $insert_query);

If (!$insert_result) {

Echo “Error inserting records: “ . pg_last_error($conn);

Exit;

} else {

Echo “Records inserted successfully.”;

}

// Close database connection

Pg_close($conn);

Q2).Consider any text paragraph. Remove the stopwords.

Ans:

Import nltk

From nltk.corpus import stopwords

From nltk.tokenize import word_tokenize

# sample text paragraph

Text = “Hello all, Welcome to Python Programming Academy. Python Programming Academy is a nice platform to learn new programming skills. It is difficult to get enrolled in this Academy.”

# tokenize the text paragraph

Words = word_tokenize(text)

# define stopwords

Stop_words = set(stopwords.words(‘english’))

# remove stopwords

Filtered_words = [word for word in words if word.casefold() not in stop_words]

# join filtered words to form a sentence

Filtered_sentence = ‘ ‘.join(filtered_words)

Print(filtered_sentence)

@Slip-23

Q. 1) Create a table student having attributes(rollno, name, class) containing atleast 5 recodes . Using

Codeigniter, display all its records.

Ans:

<?php

// Establish connection to PostgreSQL database

$conn = pg_connect(“host=localhost dbname=your_database_name user=your_username password=your_password”);

// Check if connection was successful

If (!$conn) {

Echo “Connection failed.”;

Exit;

}

// Create student table

$query = “CREATE TABLE student (

Rollno INTEGER PRIMARY KEY,

Name VARCHAR(50) NOT NULL,

Class VARCHAR(10) NOT NULL

)”;

$result = pg_query($conn, $query);

If (!$result) {

Echo “Error creating table: “ . pg_last_error($conn);

Exit;

} else {

Echo “Table created successfully.<br>”;

}

// Insert 5 records into student table

$insert_query = “INSERT INTO student (rollno, name, class)

VALUES (1, ‘John Doe’, ‘10A’),

(2, ‘Jane Smith’, ‘9B’),

(3, ‘Bob Johnson’, ‘11C’),

(4, ‘Sarah Lee’, ‘12D’),

(5, ‘Tom Brown’, ‘8E’)”;

$insert_result = pg_query($conn, $insert_query);

If (!$insert_result) {

Echo “Error inserting records: “ . pg_last_error($conn);

Exit;

} else {

Echo “Records inserted successfully.”;

}

// Close database connection

Pg_close($conn);

// function to display database records

Function display_records($table_name) {

// establish connection to PostgreSQL database

$conn = pg_connect(“host=localhost dbname=your_database_name user=your_username password=your_password”);

// check if connection was successful

If (!$conn) {

Echo “Connection failed.”;

Exit;

}

// retrieve records from specified table

$query = “SELECT * FROM “ . $table_name;

$result = pg_query($conn, $query);

// check if query was successful

If (!$result) {

Echo “Error retrieving records: “ . pg_last_error($conn);

Exit;

}

// display records in an HTML table

Echo “<table>”;

Echo “<tr><th>Roll No</th><th>Name</th><th>Class</th></tr>”;

While ($row = pg_fetch_assoc($result)) {

Echo “<tr><td>” . $row[‘rollno’] . “</td><td>” . $row[‘name’] . “</td><td>” . $row[‘class’] . “</td></tr>”;

}

Echo “</table>”;

// close database connection

Pg_close($conn);

}

Q2).Consider any text paragraph. Preprocess the text to remove any special characters and

Digits.

Ans:

Import re

Text = “Hello, #world123! This is a sample text paragraph. It contains special characters and 5 digits.”

# Remove special characters and digits

Processed_text = re.sub(r’[^a-zA-Z\s]’, ‘’, text)

Print(processed_text)

@Slip-24

Q. 1) Write a PHP script to create student.xml file which contains student roll no, name, address, college

And course. Print students detail of specific course in tabular format after accepting course as input.

Ans:

<?php

// Define student details

$students = array(

Array(“rollno” => 1, “name” => “John Doe”, “address” => “123 Main St”, “college” => “ABC College”, “course” => “Computer Science”),

Array(“rollno” => 2, “name” => “Jane Smith”, “address” => “456 Main St”, “college” => “DEF College”, “course” => “Information Technology”),

Array(“rollno” => 3, “name” => “Bob Johnson”, “address” => “789 Main St”, “college” => “GHI College”, “course” => “Business Administration”),

Array(“rollno” => 4, “name” => “Sarah Lee”, “address” => “101 Main St”, “college” => “JKL College”, “course” => “Marketing”),

Array(“rollno” => 5, “name” => “Tom Brown”, “address” => “121 Main St”, “college” => “MNO College”, “course” => “Computer Science”)

);

// Create a SimpleXMLElement object

$xml = new SimpleXMLElement(‘<students></students>’);

// Add student elements to the XML object

Foreach ($students as $student) {

$student_element = $xml->addChild(‘student’);

$student_element->addChild(‘rollno’, $student[‘rollno’]);

$student_element->addChild(‘name’, $student[‘name’]);

$student_element->addChild(‘address’, $student[‘address’]);

$student_element->addChild(‘college’, $student[‘college’]);

$student_element->addChild(‘course’, $student[‘course’]);

}

// Save the XML data to a file

$xml->asXML(‘student.xml’);

// Get course input from user

$course = isset($_POST[‘course’]) ? $_POST[‘course’] : ‘’;

// Load the XML file

$xml = simplexml_load_file(‘student.xml’);

// Find students with matching course

$filtered_students = $xml->xpath(“//student[course=’$course’]”);

// Print table of matching students

Echo “<table border=’1’>”;

Echo “<tr><th>Roll No</th><th>Name</th><th>Address</th><th>College</th><th>Course</th></tr>”;

Foreach ($filtered_students as $student) {

Echo “<tr>”;

Echo “<td>{$student->rollno}</td>”;

Echo “<td>{$student->name}</td>”;

Echo “<td>{$student->address}</td>”;

Echo “<td>{$student->college}</td>”;

Echo “<td>{$student->course}</td>”;

Echo “</tr>”;

}

Echo “</table>”;

Q. 2) Consider the following dataset : https://www.kaggle.com/datasets/datasnaek/youtubenew?select=INvideos.csv

Write a Python script for the following :

Read the dataset and perform data cleaning operations on it.

ii.

ii. Find the total views, total likes, total dislikes and comment count.

Ans:

Import pandas as pd

# Read the dataset

Df = pd.read_csv(‘INvideos.csv’)

# Drop the columns that are not required

Df = df.drop([‘video_id’, ‘trending_date’, ‘channel_title’, ‘category_id’, ‘publish_time’, ‘tags’, ‘thumbnail_link’, ‘comments_disabled’, ‘ratings_disabled’, ‘video_error_or_removed’], axis=1)

# Convert the datatype of ‘views’, ‘likes’, ‘dislikes’, and ‘comment_count’ to integer

Df[[‘views’, ‘likes’, ‘dislikes’, ‘comment_count’]] = df[[‘views’, ‘likes’, ‘dislikes’, ‘comment_count’]].astype(int)

# Find the total views, likes, dislikes, and comment count

Total_views = df[‘views’].sum()

Total_likes = df[‘likes’].sum()

Total_dislikes = df[‘dislikes’].sum()

Total_comments = df[‘comment_count’].sum()

Print(‘Total Views:’, total_views)

Print(‘Total Likes:’, total_likes)

Print(‘Total Dislikes:’, total_dislikes)

Print(‘Total Comments:’, total_comments)

@Slip-25

Q. 1) Write a script to create “cricket.xml” file with multiple elements as shown below:

</Team>

</CricketTeam>

Write a script to add multiple elements in “cricket.xml” file of category, country=”India”.

Ans:

<?php

// Create a new DOM document

$doc = new DOMDocument();

// Create the root element

$cricketTeam = $doc->createElement(“CricketTeam”);

// Create the first team element for Australia

$teamAustralia = $doc->createElement(“Team”);

$teamAustralia->setAttribute(“country”, “Australia”);

// Create the player element and set its value

$player1 = $doc->createElement(“player”, “Steve Smith”);

$teamAustralia->appendChild($player1);

// Create the runs element and set its value

$runs1 = $doc->createElement(“runs”, “7090”);

$teamAustralia->appendChild($runs1);

// Create the wicket element and set its value

$wicket1 = $doc->createElement(“wicket”, “17”);

$teamAustralia->appendChild($wicket1);

// Append the team element to the root element

$cricketTeam->appendChild($teamAustralia);

// Create the second team element for India

$teamIndia = $doc->createElement(“Team”);

$teamIndia->setAttribute(“country”, “India”);

// Create the player element and set its value

$player2 = $doc->createElement(“player”, “Virat Kohli”);

$teamIndia->appendChild($player2);

// Create the runs element and set its value

$runs2 = $doc->createElement(“runs”, “12169”);

$teamIndia->appendChild($runs2);

// Create the wicket element and set its value

$wicket2 = $doc->createElement(“wicket”, “4”);

$teamIndia->appendChild($wicket2);

// Create the category element and set its value

$category = $doc->createElement(“category”, “Captain”);

$teamIndia->appendChild($category);

// Append the team element to the root element

$cricketTeam->appendChild($teamIndia);

// Append the root element to the document

$doc->appendChild($cricketTeam);

// Save the XML file

$doc->save(“cricket.xml”);

Echo “Elements added successfully!”;

Q. 2) Consider the following dataset : https://www.kaggle.com/datasets/seungguini/youtube-commentsfor-covid19-relatedvideos?select=covid_2021_1.csv

Write a Python script for the following :

Read the dataset and perform data cleaning operations on it.

ii.

ii. Tokenize the comments in words. Iii. Perform sentiment analysis and find the percentage of

positive, negative and neutral comments..

Ans:

Import pandas as pd

Import nltk

From nltk.sentiment.vader import SentimentIntensityAnalyzer

# read the dataset

Df = pd.read_csv(‘covid_2021_1.csv’)

# remove null values and duplicates

Df.dropna(inplace=True)

Df.drop_duplicates(subset=’Comment’, inplace=True)

# tokenize comments in words

Nltk.download(‘punkt’)

Df[‘tokens’] = df[‘Comment’].apply(nltk.word_tokenize)

# perform sentiment analysis

Nltk.download(‘vader_lexicon’)

Sia = SentimentIntensityAnalyzer()

Df[‘sentiment’] = df[‘Comment’].apply(lambda x: sia.polarity_scores(x)[‘compound’])

# calculate percentage of positive, negative, and neutral comments

Total_comments = len(df)

Positive_comments = len(df[df[‘sentiment’] > 0])

Negative_comments = len(df[df[‘sentiment’] < 0])

Neutral_comments = len(df[df[‘sentiment’] == 0])

Positive_percentage = (positive_comments / total_comments) * 100

Negative_percentage = (negative_comments / total_comments) * 100

Neutral_percentage = (neutral_comments / total_comments) * 100

# print the results

Print(‘Total Comments:’, total_comments)

Print(‘Positive Comments:’, positive_comments, ‘(‘, positive_percentage, ‘%)’)

Print(‘Negative Comments:’, negative_comments, ‘(‘, negative_percentage, ‘%)’)

Print(‘Neutral Comments:’, neutral_comments, ‘(‘, neutral_percentage, ‘%)’)

@Slip-26

Q. 1) Create employee table as follows EMP (eno, ename, designation, salary). Write Ajax program to

Select the employees name and print the selected employee’s details.

Ans:

Html file

<option value=””>Select an employee</option>

<!—Populate this dropdown with employee names using PHP (

</select>

<!—Employee details will be displayed here (

</div>

Ajax file

$(document).ready(function() {

// Add event listener to the select dropdown

$(‘#employee-list’).change(function() {

Var selectedEmployee = $(this).val();

// Make an AJAX request to fetch employee details

$.ajax({

url: ‘empdetails.php’,

type: ‘POST’,

data: { employeeName: selectedEmployee },

dataType: ‘json’,

success: function(response) {

// Parse the JSON response and display employee details

Var detailsHtml = ‘Employee Name: ‘ + response.ename + ‘<br>’ +

‘Designation: ‘ + response.designation + ‘<br>’ +

‘Salary: ‘ + response.salary;

$(‘#employee-details’).html(detailsHtml);

Error: function(xhr, status, error) {

Console.log(‘Error:’, error);

}

});

Php file as empdetails.php

<?php

// Establish database connection

$conn = pg_connect(“host=localhost dbname=database_name user=username password=password”);

If (!$conn) {

Die(‘Connection failed: ‘ . pg_last_error());

}

// Get the selected employee name from AJAX request

$employeeName = $_POST[‘employeeName’];

// Query the EMP table for the details of the selected employee

$sql = “SELECT * FROM EMP WHERE ename = ‘$employeeName’”;

$result = pg_query($conn, $sql);

If (pg_num_rows($result) > 0) {

// Build a JSON object with employee details

$employee = pg_fetch_assoc($result);

$response = array(

‘ename’ => $employee[‘ename’],

‘designation’ => $employee[‘designation’],

‘salary’ => $employee[‘salary’]

);

Echo json_encode($response);

} else {

Echo “Employee not found”;

}

// Close database connection

Pg_close($conn);

Q. 2 )Consider text paragraph. “””Hello all, Welcome to Python Programming Academy. Python

Programming Academy is a nice platform to learn new programming skills. It is difficult to get enrolled

In this Academy.””” Preprocess the text to remove any special characters and digits. Generate the

Summary using extractive summarization process. Q.

Ans:

Import re

From nltk.tokenize import sent_tokenize

From sklearn.feature_extraction.text import TfidfVectorizer

From sklearn.metrics.pairwise import cosine_similarity

# Text to summarize

Text = “Hello all, Welcome to Python Programming Academy. Python Programming Academy is a nice platform to learn new programming skills. It is difficult to get enrolled in this Academy.”

# Preprocess the text to remove special characters and digits

Preprocessed_text = re.sub(r’[^a-zA-Z\s]’, ‘’, text)

# Tokenize the preprocessed text into sentences

Sentences = sent_tokenize(preprocessed_text)

# Calculate the importance score of each sentence using TF-IDF

Vectorizer = TfidfVectorizer()

Tfidf_matrix = vectorizer.fit_transform(sentences)

Similarity_matrix = cosine_similarity(tfidf_matrix)

# Select top N sentences based on their importance score

N = 2

Top_sentences = sorted(range(len(similarity_matrix[-1])), key=lambda i: similarity_matrix[-1][i])[-N:]

# Concatenate the top sentences to form the summary

Summary = ‘’

For i in top_sentences:

Summary += sentences[i] + ‘ ‘

Print(summary)

@Slip-27

Q. 1) Create web Application that contains Voters details and check proper validation for (name,

Age, and nationality), as Name should be in upper case letters only, Age should not be less than

18 yrs and Nationality should be Indian.(use HTML-AJAX-PHP).

Ans :

Html file

<!DOCTYPE html>

<html>

<head>

<title>Voter Details</title>

</head>

<body>

<h2>Voter Details</h2>

<label for=”nationality”>Nationality:</label>

</form>

$(document).ready(function(){

$(‘#voterForm’).submit(function(event){

Event.preventDefault();

Var name = $(‘#name’).val().toUpperCase();

Var age = $(‘#age’).val();

Var nationality = $(‘#nationality’).val();

$.ajax({

url: ‘voter.php’,

method: ‘POST’,

data: {name: name, age: age, nationality: nationality},

success: function(response){

$(‘#response’).html(response);

}

});

</script>

</body>

</html>

Voter.php file

<?php

$name = $_POST[‘name’];

$age = $_POST[‘age’];

$nationality = $_POST[‘nationality’];

If(preg_match(‘/[^A-Z]/’, $name)){

Echo ‘Name should be in upper case letters only.’;

} elseif($age < 18) {

Echo ‘Age should not be less than 18 years.’;

} elseif(strcasecmp($nationality, ‘Indian’) != 0) {

Echo ‘Nationality should be Indian.’;

} else {

Echo ‘Validation successful. Voter details: <br>Name: ‘.$name.’<br>Age: ‘.$age.’<br>Nationality: ‘.$nationality;

}

Q. 2 ) Create your own transactions dataset and apply the above process on your dataset

Ans:

Import random

Import csv

# Generate random transaction data

Transactions = []

For i in range(1, 101):

Transaction_id = i

Transaction_date = f”2022-05-{random.randint(1, 31):02d}”

Customer_id = random.randint(1, 10)

Item_id = random.choice([“A”, “B”, “C”])

Item_price = round(random.uniform(10.0, 100.0), 2)

Quantity = random.randint(1, 10)

Transactions.append([transaction_id, transaction_date, customer_id, item_id, item_price, quantity])

# Save the data to a CSV file

With open(‘transactions.csv’, ‘w’, newline=’’) as csvfile:

Writer = csv.writer(csvfile)

Writer.writerow([“Transaction ID”, “Transaction Date”, “Customer ID”, “Item ID”, “Item Price”, “Quantity”])

For transaction in transactions:

Writer.writerow(transaction)

Import pandas as pd

# Read the CSV file into a Pandas DataFrame

Df = pd.read_csv(‘transactions.csv’)

# Convert the “Item Price” column to numeric type

Df[‘Item Price’] = pd.to_numeric(df[‘Item Price’])

# Calculate the sales amount for each transaction

Df[‘Sales’] = df[‘Item Price’] * df[‘Quantity’]

# Group the transactions by customer ID and calculate the total sales for each customer

Total_sales = df.groupby(‘Customer ID’)[‘Sales’].sum().reset_index()

# Print the results

Print(total_sales)

@Slip-28

Q. 1) Write a PHP script using AJAX concept, to check user name and password are valid or Invalid (use

Database to store user name and password).

Ans:

Html file

<!DOCTYPE html>

<html>

<head>

<title>Login</title>

$(document).ready(function(){

$(“#login-form”).submit(function(event){

Event.preventDefault();

Var username = $(“#username”).val();

Var password = $(“#password”).val();

$.ajax({

url: ‘check_login.php’,

type: ‘post’,

data: {username: username, password: password},

success: function(response){

if(response == “valid”){

window.location.href = “dashboard.php”; //redirect to dashboard

}

Else{

Alert(“Invalid username or password”);

}

});

</script>

</head>

<body>

<h2>Login</h2>

<label>Username:</label>

<label>Password:</label>

</form>

</body>

</html>

Php file as check_login.php

<?php

// Establish database connection

$conn = mysqli_connect(‘localhost’, ‘username’, ‘password’, ‘database_name’);

If (!$conn) {

Die(‘Connection failed: ‘ . mysqli_connect_error());

}

// Get username and password from AJAX request

$username = $_POST[‘username’];

$password = $_POST[‘password’];

// Query the users table for the entered username and password

$sql = “SELECT * FROM users WHERE username = ‘$username’ AND password = ‘$password’”;

$result = mysqli_query($conn, $sql);

If (mysqli_num_rows($result) > 0) {

Echo “valid”;

} else {

Echo “invalid”;

}

// Close database connection

Mysqli_close($conn);

Q. 2 ) Build a simple linear regression model for Car Dataset.

Ans:

From sklearn.linear_model import LinearRegression

Mileage = [[10], [20], [30], [40], [50], [60], [70], [80]]

Price = [24, 19, 17, 13, 10, 7, 5, 2]

Reg = LinearRegression().fit(mileage, price)

Print(‘Intercept:’, reg.intercept_)

Print(‘Coefficient:’, reg.coef_[0])

New_mileage = [[25], [45], [65]]

Predicted_price = reg.predict(new_mileage)

Print(‘Predicted prices:’, predicted_price)

@Slip-29

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.

1) Fibonacci Series.

2) To find sum of the digits of that number.

(Use the concept of self processing page.)

Ans:

<!DOCTYPE html>

<html>

<head>

<title>Number Operations</title>

</head>

<body>

<h1>Number Operations</h1>

<?php

// define variables and set to empty values

$num = $op = “”;

If ($_SERVER[“REQUEST_METHOD”] == “POST”) {

$num = test_input($_POST[“num”]);

$op = test_input($_POST[“op”]);

// perform operation based on user’s choice

Switch ($op) {

Case “fib”:

$result = fibonacci($num);

Echo “<p>The Fibonacci series of $num numbers is: $result</p>”;

Break;

Case “sum”:

$result = sumOfDigits($num);

Echo “<p>The sum of digits in $num is: $result</p>”;

Break;

Default:

Echo “<p>Invalid operation selected</p>”;

}

Function test_input($data) {

$data = trim($data);

$data = stripslashes($data);

$data = htmlspecialchars($data);

Return $data;

}

Function fibonacci($num) {

$first = 0;

$second = 1;

$result = “”;

For ($i = 0; $i < $num; $i++) {

$result .= $first . “ “;

$third = $first + $second;

$first = $second;

$second = $third;

}

Return $result;

}

Function sumOfDigits($num) {

$sum = 0;

While ($num > 0) {

$digit = $num % 10;

$sum += $digit;

$num = (int)($num / 10);

}

Return $sum;

}

<label for=”num”>Enter a number:</label>

<label for=”op”>Select an operation:</label>

<option value=””>--Select--</option>

<option value=”fib”>Fibonacci Series</option>

<option value=”sum”>Sum of Digits</option>

</select>

</form>

</body>

</html>

Q. 2 ) Build a logistic regression model for Student Score Dataset.

Ans:

# Import necessary libraries

Import pandas as pd

From sklearn.linear_model import LogisticRegression

From sklearn.model_selection import train_test_split

From sklearn.metrics import accuracy_score

# Load the dataset

Data = pd.read_csv(‘student_scores.csv’)

# Split the data into input and output variables

X = data.iloc[:, :-1].values

Y = data.iloc[:, -1].values

# Split the data into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# Create the logistic regression model and fit it to the training data

Classifier = LogisticRegression()

Classifier.fit(X_train, y_train)

# Make predictions on the testing set

Y_pred = classifier.predict(X_test)

# Evaluate the model’s accuracy

Accuracy = accuracy_score(y_test, y_pred)

Print(“Accuracy:”, accuracy)

@Slip-30

Q. 1) Create a XML file which gives details of books available in “Bookstore” from following

Categories.

1) Yoga

2) Story

3) Technical

And elements in each category are in the following format

<Book>

<Book_Title>

--------------</Book_Title>

<Book_Author> ---------------</Book_Author>

<Book_Price>

--------------</Book_Price>

</Book>

Save the file as “Bookcategory.xml”

Ans:

<?xml ve<?xml version=”1.0” encoding=”UTF-8”?>

<Yoga>

<Book>

<Book_Title>Light on Yoga</Book_Title>

<Book_Author>B.K.S. Iyengar</Book_Author>

<Book_Price>20.99</Book_Price>

</Book>

<Book>

<Book_Title>The Yoga Bible</Book_Title>

<Book_Author>Christina Brown</Book_Author>

<Book_Price>15.50</Book_Price>

</Book>

</Yoga>

<Story>

<Book>

<Book_Title>The Alchemist</Book_Title>

<Book_Author>Paulo Coelho</Book_Author>

<Book_Price>12.99</Book_Price>

</Book>

<Book>

<Book_Title>The Da Vinci Code</Book_Title>

<Book_Author>Dan Brown</Book_Author>

<Book_Price>14.75</Book_Price>

</Book>

</Story>

<Book>

<Book_Title>Python for Data Science Handbook</Book_Title>

<Book_Author>Jake VanderPlas</Book_Author>

<Book_Price>28.99</Book_Price>

</Book>

<Book>

<Book_Title>Cracking the Coding Interview</Book_Title>

<Book_Author>Gayle Laakmann McDowell</Book_Author>

<Book_Price>23.50</Book_Price>

</Book>

</Technical>

</Bookstore>

Q. 2 ) Create the dataset . transactions = [[‘eggs’, ‘milk’,’bread’], [‘eggs’, ‘apple’], [‘milk’, ‘bread’], [‘apple’,

‘milk’], [‘milk’, ‘apple’, ‘bread’]] .

Convert the categorical values into numeric format.Apply the apriori algorithm on the above dataset to

Generate the frequent itemsets and association rules.

Ans:

Transactions = [[‘eggs’, ‘milk’, ‘bread’], [‘eggs’, ‘apple’], [‘milk’, ‘bread’], [‘apple’, ‘milk’], [‘milk’, ‘apple’, ‘bread’]]

# Create a dictionary to map items to unique numeric values

Item_to_num = {‘eggs’: 1, ‘milk’: 2, ‘bread’: 3, ‘apple’: 4}

# Convert the categorical values in the dataset to numeric values

Numeric_transactions = []

For transaction in transactions:

Numeric_transaction = [item_to_num[item] for item in transaction]

Numeric_transactions.append(numeric_transaction)

Print(numeric_transactions)

From mlxtend.frequent_patterns import apriori, association_rules

# Generate frequent itemsets with a minimum support of 0.4

Frequent_itemsets = apriori(numeric_transactions, min_support=0.4, use_colnames=True)

# Generate association rules with a minimum confidence of 0.7

Rules = association_rules(frequent_itemsets, metric=”confidence”, min_threshold=0.7)

Print(frequent_itemsets)

Print(rules)

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