Load Titanic data from sklearn library, plot the following with proper legend and axis labels: a. Plot bar chart to show the frequency of survivors and non-survivors for male and female passengers separately b. Draw a scatter plot for any two selected features c. Compare density distribution for features age and passenger fare d. Use a pair plot to show pairwise bivariate distribution

 Load Titanic data from sklearn library, plot the following with proper legend and axis labels:

a. Plot bar chart to show the frequency of survivors and non-survivors for male and

female passengers separately

b. Draw a scatter plot for any two selected features

c. Compare density distribution for features age and passenger fare

d. Use a pair plot to show pairwise bivariate distribution


CODE





import seaborn as sns

import matplotlib.pyplot as plt


# Load the Titanic dataset from seaborn

titanic = sns.load_dataset('titanic')


# Plot bar chart to show the frequency of survivors and non-survivors for male and female passengers separately

plt.figure(figsize=(8, 6))

sns.countplot(x='sex', hue='survived', data=titanic)

plt.xlabel('Sex')

plt.ylabel('Frequency')

plt.title('Survivors vs Non-Survivors by Gender')

plt.legend(title='Survived', labels=['No', 'Yes'])

plt.show()


# Draw a scatter plot for any two selected features

plt.figure(figsize=(8, 6))

sns.scatterplot(x='age', y='fare', data=titanic)

plt.xlabel('Age')

plt.ylabel('Fare')

plt.title('Scatter Plot: Age vs Fare')

plt.show()


# Compare density distribution for features age and passenger fare

plt.figure(figsize=(8, 6))

sns.kdeplot(data=titanic, x='age', label='Age')

sns.kdeplot(data=titanic, x='fare', label='Fare')

plt.xlabel('Value')

plt.ylabel('Density')

plt.title('Density Distribution: Age vs Fare')

plt.legend()

plt.show()


# Use a pair plot to show pairwise bivariate distribution

sns.pairplot(titanic)

plt.show()


Comments

Post a Comment

Popular posts from this blog

Using Titanic dataset, do the following: a. Find total number of passengers with age less than 30 b. Find total fare paid by passengers of first class c. Compare number of survivors of each passenger class

  Using Titanic dataset, do the following: a. Find total number of passengers with age less than 30 b. Find total fare paid by passengers of first class c. Compare number of survivors of each passenger class CODE import seaborn as sns # Load the Titanic dataset from seaborn titanic = sns.load_dataset('titanic') # Find the total number of passengers with age less than 30 passengers_age_less_than_30 = titanic[titanic['age'] < 30] total_passengers_age_less_than_30 = len(passengers_age_less_than_30) print(f"Total number of passengers with age less than 30: {total_passengers_age_less_than_30}") # Find the total fare paid by passengers of first class total_fare_first_class = titanic[titanic['class'] == 'First']['fare'].sum() print(f"Total fare paid by passengers of first class: {total_fare_first_class}") # Compare the number of survivors of each passenger class survivors_by_class = titanic.groupby('class')['survived...
Read more

Differentiate between Unimodal, Bimodal and Multimodal with graphic representation.

Image
Differentiate between Unimodal, Bimodal and Multimodal with graphic representation.  Unimodal, bimodal, and multimodal are terms used to describe the shape and characteristics of a distribution. They indicate the number of peaks or modes present in the data. Here's a differentiation between unimodal, bimodal, and multimodal distributions along with graphic representations: Unimodal Distribution: A unimodal distribution has a single peak or mode, indicating that there is one dominant value or range of values in the dataset. It is the most common type of distribution. Graphic Representation of Unimodal Distribution: In a histogram or density plot, a unimodal distribution appears as a single prominent peak. Example of Unimodal Distribution: In a dataset representing the heights of individuals, a unimodal distribution would occur if most individuals have a similar height, resulting in a single peak at the most common height. Bimodal Distribution: A bimodal distribution has two distinct...
Read more

Import iris data using sklearn library . Compute mean, mode, median, standard deviation, confidence interval and standard error for each feature ii. Compute correlation coefficients between each pair of features and plot heatmap iii. Find covariance between length of sepal and petal iv. Build contingency table for class feature

Import iris data using sklearn library or (Download IRIS data from: (https://archive.ics.uci.edu/ml/datasets/iris or import it from sklearn.datasets) i. Compute mean, mode, median, standard deviation, confidence interval and standard error for each feature ii. Compute correlation coefficients between each pair of features and plot heatmap iii. Find covariance between length of sepal and petal iv. Build contingency table for class feature CODE  import numpy as np import pandas as pd from sklearn.datasets import load_iris from scipy import stats import seaborn as sns import matplotlib.pyplot as plt # Load the Iris dataset iris = load_iris() data = iris.data feature_names = iris.feature_names target = iris.target target_names = iris.target_names # Convert data to a pandas DataFrame for easier analysis df = pd.DataFrame(data, columns=feature_names) df['class'] = target_names[target] # Compute mean, mode, median, standard deviation, confidence interval, and standard error for each f...
Read more