Supervised learning

Question-What factors influenced whether passengers survived or died on the Titanic?

The first factor we will be seeing is sex.

This graph shows the number of deaths and survivals based on gender.

import matplotlib.pyplot as plt

import seaborn as sns

# Group data by Sex and Survived and count the occurrences

sex_survival_counts = train_data.groupby(['Sex', 'Survived']).size().reset_index(name='Count')

# Map the Survived values to labels

sex_survival_counts['Survived'] = sex_survival_counts['Survived'].map({0: 'Deaths', 1: 'Survived'})

# Create a grouped bar plot

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

sns.barplot(data=sex_survival_counts, x='Sex', y='Count', hue='Survived', palette='viridis')

plt.title('Survival and Death Counts by Sex')

plt.xlabel('Sex')

plt.ylabel('Number of Passengers')

plt.legend(title='Survival Status')

plt.show()

The third factor we will be seeing is the embarkment point, which refers to the port from which passengers boarded the Titanic

import matplotlib.pyplot as plt

import seaborn as sns

# Create a count plot of the Embarked column

plt.figure(figsize=(7, 5))

sns.countplot(data=train_data, x='Embarked', palette='viridis')

plt.title('Passenger Count by Embarkation Port')

plt.xlabel('Embarkation Port')

plt.ylabel('Number of Passengers')

plt.show()

The third factor we will be seeing is class, which refers to the passenger’s travel class — First, Second, or Third.

import matplotlib.pyplot as plt

import seaborn as sns

# Create a count plot of the Pclass column

plt.figure(figsize=(7, 5))

sns.countplot(data=train_data, x='Pclass', palette='viridis')

plt.title('Passenger Count by Passenger Class')

plt.xlabel('Passenger Class')

plt.ylabel('Number of Passengers')

# Optional: Add labels for better readability

# plt.xticks([0, 1, 2], ['First Class', 'Second Class', 'Third Class'])

plt.show()