Titanic_Survival_Analysis
Titanic Case Study:
Analyzing Titanic Survival Variables
A comprehensive analysis of the Titanic disaster, focusing on identifying key factors that influenced passenger survival rates using machine learning and data visualization techniques. Date: 17/Jun/2024
Summary
Introduction:
Title: Analyzing Titanic Survival Variables
Industry_Focus: Maritime Safety and Transportation
Problem Statement: To identify the key factors that influenced passenger survival during the Titanic disaster.
Business Use-Case: Improving safety measures and emergency protocols for maritime travel by understanding the variables that affected survival rates in a historical context. This analysis aims to provide insights that can be applied to modern safety regulations and passenger management.
Goals Metrics:
- Determine the impact of demographic variables (age, gender) on survival rates.
- Analyze the influence of socio-economic status (ticket class) on survival chances.
- Assess the role of embarkation point on passenger survival.
- Identify patterns and correlations between passenger attributes and survival outcomes.
- Develop predictive models to estimate survival probabilities based on the identified variables.
Deliverables:
- A comprehensive report detailing the findings and insights from the data analysis.
- Visualizations (graphs, charts) that illustrate key patterns and relationships in the data.
- A predictive model with performance metrics and validation results.
- Recommendations for improving maritime safety based on the analysis.
- Presentation slides summarizing the study for stakeholders.
Dataset List:
- Titanic Passenger Data from Kaggle (includes demographics, ticket information, survival status).
- Additional historical records (if any) for validation and supplementary analysis.
Websites to scrape data_needed:
- Kaggle Titanic dataset
- Historical archives websites for supplementary passenger records, such as Encyclopedia Titanica
Data_Preparation
1. Cleaning and Processing Data
The main dataset is the train.csv file to be used to build your machine learning models.
Input
import pandas as pd
# Load the CSV file using a raw string
file_path = r"train.csv"
data = pd.read_csv(file_path)
# Display the headers of the table
print(data.columns)
Output
Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp','Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],
dtype='object')
Some column headers are not clear, so data dictionary was searched and these are the following information on the collumn headers.
- “Pclass” = Ticket Class,
- “SibSP” = Siblings and Spouses,
- “Parch” = Parents and Children