A Data-Driven Exploration of Titanic Survival Factors
This project analyzes the Titanic disaster to uncover key survival factors using Python, turning raw data into meaningful insights and visualizations.
Overview
This post presents my data analysis of the Titanic dataset using Python. The goal was to uncover patterns behind passenger survival, explore feature importance, and visualize trends using various libraries like pandas and plotly. ITs ofk done
Steps Performed
- Loaded and cleaned the Titanic dataset
- Explored numerical and categorical variables
- Visualized survival trends such as gender, Passenger class, family size, etc
- Presented survival probability per group
- Summarized key findings
Summary of Insights
Age and Survival: Younger passengers, particularly children, had a higher survival rate, highlighting a potential priority given during evacuation.
Gender and Survival: Female passengers were significantly more likely to survive than males, reflecting the historical ‘women and children first’ rescue policy.
Passenger Class: First-class passengers had notably higher survival rates compared to second and third-class travelers, suggesting that socio-economic status played a major role in access to lifeboats and safety.
Embarkation Port: Passengers who boarded at Cherbourg showed slightly better survival outcomes. This may reflect a higher concentration of first-class passengers from that port.
Family Size: Passengers with small family groups (1–4 members) had better survival chances. Those alone or in large families faced more difficulty during the chaos, likely due to separation or logistical challenges.
Visual preview
Survival Rate by Passenger Class and Gender
This chart offers a glimpse into one survival scenario. View the full report for a deeper analysis.
This interactive Plotly chart illustrates the survival rates across different passenger classes and genders. The visualization clearly shows that first-class passengers had a significantly higher chance of survival compared to those in second or third class, highlighting the influence of social and economic status during the disaster.
Conclusion
This Titanic analysis project helped me strengthen my EDA skills and practice real-world data storytelling. It demonstrates not just technical skill but also how to communicate findings clearly to non-technical audiences.
For a detailed report with code, graphs, and step-by-step explanation view the interactive HTML version here.
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