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The Titanic Survival Prediction project uses machine learning to forecast survival based on age, gender, class, and family size. It provides insights into social dynamics and highlights the power of data-driven decision-making.

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Titanic_survival_prediction_project

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  • The Sinking of the Titanic and Building a Predictive Model to Understand Survival Factors
    • The RMS Titanic, a British passenger liner operated by the White Star Line, sank in the North Atlantic Ocean on 15 April 1912 after striking an iceberg during its maiden voyage from Southampton, UK to New York City, United States. With an estimated 2,224 passengers and crew aboard, the disaster claimed over 1,500 lives, making it the deadliest sinking of a single ship up to that time. The Titanic was equipped with 16 lifeboat davits, each capable of lowering three lifeboats, for a total of 48 boats. However, the ship carried only 20 lifeboats, and four of them were collapsible and difficult to launch while the ship was sinking. When the ship sank, many of the lifeboats that had been lowered were only about half full.

    • The disaster was a turning point in maritime history and continues to capture public attention, providing inspiration for many artistic works. The sinking of the Titanic highlighted regulatory and procedural failures, which led to worldwide shock and outrage at the huge loss of life.

    • Using passenger data such as name, age, gender, socio-economic class, etc., we can build a predictive model to understand the factors that increased a person's chance of survival. While there was an element of luck involved in surviving, some groups of people were more likely to survive than others.

    • The disaster was met with worldwide shock and outrage, both at the huge loss of life and at the regulatory and procedural failures that had led to it. (from https://en.wikipedia.org/wiki/Titanic)

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The Titanic Survival Prediction project uses machine learning to forecast survival based on age, gender, class, and family size. It provides insights into social dynamics and highlights the power of data-driven decision-making.

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