Hslip5
Q1]
<!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/css/bootstrap.min.css"> <title>Your Name - Personal Website</title> </head> <body> <!-- Navbar --> <nav class="navbar navbar-expand-lg navbar-light bg-light"> <a class="navbar-brand" href="#">Your Name</a> <button class="navbar-toggler" type="button" data-toggle="collapse" data-target="#navbarNav" aria-controls="navbarNav" aria-expanded="false" aria-label="Toggle navigation"> <span class="navbar-toggler-icon"></span> </button> <div class="collapse navbar-collapse" id="navbarNav"> <ul class="navbar-nav ml-auto"> <li class="nav-item active"> <a class="nav-link" href="#about">About</a> </li> <li class="nav-item"> <a class="nav-link" href="#education">Education</a> </li> <li class="nav-item"> <a class="nav-link" href="#experience">Experience</a> </li> </ul> </div> </nav> <!-- About Section --> <section id="about" class="container mt-5"> <div class="row"> <div class="col-md-6"> <h2>About Me</h2> <p>Your personal information goes here.</p> </div> </div> </section> <!-- Education Section --> <section id="education" class="container mt-5"> <div class="row"> <div class="col-md-6"> <h2>Education</h2> <p>Your educational information goes here.</p> </div> </div> </section> <!-- Experience Section --> <section id="experience" class="container mt-5"> <div class="row"> <div class="col-md-6"> <h2>Experience</h2> <p>Your job profile information goes here.</p> </div> </div> </section> <!-- Bootstrap JS and Popper.js --> <script src="https://code.jquery.com/jquery-3.2.1.slim.min.js"></script> <script src="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.12.9/umd/popper.min.js"></script> <script src="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/js/bootstrap.min.js"></script> </body> </html>Q2]
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
Q.2 A) Generate a random array of 50 integers
np.random.seed(42) # Setting seed for reproducibility
random_data = np.random.randint(0, 100, 50)
Display using different plots
fig, axes = plt.subplots(2, 2, figsize=(12, 8))
Line chart
axes[0, 0].plot(random_data, color='blue', marker='o')
axes[0, 0].set_title('Line Chart')
axes[0, 0].set_xlabel('Index')
axes[0, 0].set_ylabel('Value')
Scatter plot
axes[0, 1].scatter(range(len(random_data)), random_data, color='green', marker='o')
axes[0, 1].set_title('Scatter Plot')
axes[0, 1].set_xlabel('Index')
axes[0, 1].set_ylabel('Value')
Histogram
sns.histplot(random_data, bins=10, kde=True, color='orange', ax=axes[1, 0])
axes[1, 0].set_title('Histogram')
axes[1, 0].set_xlabel('Value')
axes[1, 0].set_ylabel('Frequency')
Box plot
sns.boxplot(x=random_data, color='red', ax=axes[1, 1])
axes[1, 1].set_title('Box Plot')
axes[1, 1].set_xlabel('Value')
plt.tight_layout()
plt.show()
Q.2 B) Load data from User_Data.csv and print information
user_data = pd.read_csv('User_Data.csv')
Print shape, number of rows and columns
print("Shape of the data:", user_data.shape)
Print data types
print("\nData types:")
print(user_data.dtypes)
Print feature names
print("\nFeature names:")
print(user_data.columns)
Print data description
print("\nData description:")
print(user_data.describe())