Introduction

The Titanic Passenger List dataset, available on Kaggle, provides detailed information about the passengers aboard the RMS Titanic, which tragically sank on its maiden voyage in 1912. This dataset includes various attributes such as passenger names, ages, genders, ticket classes, and survival status. The purpose of this report is to perform an initial review of the dataset to identify preliminary insights and highlight potential areas for further analysis.

Observations

1. Survival Rate: The dataset contains 891 entries, with 342 passengers surviving and 549 not surviving. This gives an overall survival rate of approximately 38.4%. This initial observation highlights the tragic outcome of the disaster, where less than half of the passengers survived.

2. 

   Gender and Survival: One of the most striking initial observations is the disparity in survival rates between male and female passengers. Gender appears to play a crucial role in survival chances:

   • Females: Out of 314 female passengers, 233 survived, giving a survival rate of 74.2%.

   • Males: Out of 577 male passengers, only 109 survived, resulting in a survival rate of 18.89%.

The stark contrast between male and female survival rates may reflect the "women and children first" protocol followed during the evacuation.

3. 

   Passenger Class and Survival: Another notable pattern I observed is the survival rate across different passenger classes. Analyzing survival rates by class reveals a significant disparity:

   • First Class: Out of 216 passengers, 136 survived, resulting in a survival rate of 62.96%.

   • Second Class: Out of 184 passengers, 87 survived, resulting in a survival rate of 47.28%.

   • Third Class: Out of 491 passengers, only 119 survived, resulting in a survival rate of 24.23%.

The higher survival rate among First Class passengers suggests a possible prioritization during rescue operations, likely due to better access to lifeboats and favorable locations on the ship.

Conclusion

The initial review of the Titanic dataset reveals significant differences in survival rates based on gender and passenger class. Further analysis could explore other factors such as age, fare, and family size.

For more details about the HNG Internship program, please visit HNG Internship https://hng.tech/internship and HNG Hire https://hng.tech/hire.