Overview: Explored the Titanic dataset using Python and Seaborn to uncover insights into passenger demographics and survival outcomes.
Key Findings:
- Positive Correlation: Discovered a positive correlation between passenger class and fare, shedding light on the socio-economic dynamics aboard the Titanic.
- Data Visualization: Leveraged data visualization techniques to examine relationships between variables such as Age, Fare, and Family Size, considering survival as a key factor.
Next Steps:
- These insights pave the way for deeper exploration into the factors influencing survival rates during this historic event.
- Further analysis could uncover additional patterns and contribute to a richer understanding of the Titanic's tragic journey.
Tags:
#DataAnalysis #DataVisualization #TitanicDataset #Insights #Python #Seaborn