Predictive Analysis of Survival Rate on The Titanic
Mentor/s
Prof. Jose Mendoza, Marketing
Participation Type
Poster
Abstract
The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships. One of the reasons that the collision led to such loss of life was that there were not enough lifeboats for the passengers and crew.
There is a common belief that besides luck there were groups that were more likely to survive like women, children and the upper-class. Hence, there is data sets that contain survival outcome of every passenger. To predict survival rate, I would use machine learning and predictive analytics to complete the analysis in order to determine what sort of people had a greater chance of survival.
There are practical implications for my research in the areas of sales forecasting, pricing, shopper marketing, and other Marketing strategies.
College and Major available
Marketing
Location
University Commons
Start Day/Time
4-21-2017 1:00 PM
End Day/Time
4-21-2017 3:00 PM
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.
Predictive Analysis of Survival Rate on The Titanic
University Commons
The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships. One of the reasons that the collision led to such loss of life was that there were not enough lifeboats for the passengers and crew.
There is a common belief that besides luck there were groups that were more likely to survive like women, children and the upper-class. Hence, there is data sets that contain survival outcome of every passenger. To predict survival rate, I would use machine learning and predictive analytics to complete the analysis in order to determine what sort of people had a greater chance of survival.
There are practical implications for my research in the areas of sales forecasting, pricing, shopper marketing, and other Marketing strategies.
