Titanic data

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()

• • Explored the Titanic dataset to understand how sex influenced survival chances.

  • Investigated survival status separated by gender categories.

• Concluded that sex is an important feature affecting survival outcomes.

• Identified gender as a useful input feature for machine learning models predicting Titanic survival.