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This project seeks to analyze the Titanic dataset and determine scenarios in which passengers, particularly men in third class who embarked from Queenstown, could have potentially survived. The goal is to use historical context and data analysis to imagine alternative outcomes had these passengers had access to modern knowledge or guidance during the disaster.
I want to determine scenarios where the least likely to survive could have survived if a certain sequence of events were to happen. This is a challenging task but something to keep me busy as a side project for fun.
The dataset used in this project is the Titanic dataset, commonly used in data science for machine learning and data analysis examples. It contains passenger information like age, sex, class, fare, port of embarkation, and survival status.
I used the Titanic data like many of other Data Scientists when I was a young Data Scientist using R. I remember it being challenging. But it is fun going through it again with python.
Similarly, like re-reading "The Odyssey" as an adult and gaining a new appreciation vs. high school self.
The project includes data exploration and visualization, data cleaning, feature engineering, and statistical analysis. The analysis is conducted in Python, using libraries like pandas for data manipulation, matplotlib and seaborn for data visualization, and scikit-learn for machine learning.
The initial analysis shows that men in third class who embarked from Queenstown had a significantly lower survival rate compared to other groups. Using this insight, the project then focuses on exploring various "what-if" scenarios. Could different actions or decisions have led to a better outcome for these passengers? How could modern knowledge or guidance have changed their fate?
While the results are speculative and meant to be a thought experiment rather than a definitive conclusion, they provide a unique perspective on the tragic event and offer a novel way to engage with historical data.
The next steps in this project involve delving deeper into historical accounts of the Titanic sinking and identifying specific survival strategies that could have been employed. These strategies will be used to further refine the "what-if" scenarios and provide more detailed insights.
In addition, machine learning models could be trained to predict survival based on passenger characteristics, which could help identify key factors that influenced survival and provide more context for the "what-if" scenarios.
Contributions to this project are welcome! If you have a "what-if" scenario to suggest, or if you have expertise in the history of the Titanic sinking, your insights would be greatly appreciated.
Please feel free to open an issue or submit a pull request.