Titanic Dataset Analysis

About

Welcome to my data analysis project that delves into the tragic yet captivating history of the Titanic. The sinking of the Titanic in 1912 remains one of the most infamous maritime disasters in history, claiming the lives of many passengers. In this project, I aim to explore the factors that played a crucial role in determining who survived and who did not.

Objective:
The primary goal of this project was to investigate and reveal insights about the survival rates on the Titanic. By analyzing the dataset, I sought to uncover patterns related to gender, passenger class, and age that influenced passengers' fates.

Here are the Highlights:

Passenger Count:
The Titanic dataset records a total of 891 passengers who were aboard the ill-fated ship.
This is essential in order to understand the scale of the tragedy.

Gender Distribution:

Exploring gender distribution among the passengers revealed intriguing disparities. I discovered that there were more males than females on board.

Survival vs. Death:

The survival rate is a touching aspect of the Titanic story. I explored the dataset to find out that a significant portion of passengers did not survive, while some were fortunate to escape the disaster.

Class Distribution:

Passengers on the Titanic were divided into different classes: First, Second, and Third class. This distribution played a crucial role in the survival of passengers, and I uncovered some thought-provoking insights about it.

Age Distribution:

The age distribution among passengers varies widely, and I categorized passengers into age groups. Analyzing the survival rates within these age categories revealed which groups had a higher chance of survival.

Key Insights:

• Women had a significantly higher survival rate compared to men.
• First-class passengers were more likely to survive than those in second or third class.
• Children and young adults had a notably higher survival rate.

Conclusion:
This analysis provides a profound look into the human aspect of the Titanic tragedy. It illustrates how demographics such as gender, age, and class influenced the chances of survival. The project not only enabled me to gain proficiency in data analysis but also allowed me to connect with the historical narrative in a unique way.

Click Here for the dataset, working python script and report.