Machine Learning from Disaster
Machine Learning by Disaster is a challenge hosted @ Kaggle where the participants are tasked with predicting survivability of passengers. The challenge is a common entry point, and it is also one of the first challenges I participated in.
This classifier is able to obtain an accuracy of ~.80.
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 shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class.
In this challenge, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy.
Goal & Evaluation
It is your job to predict if a passenger survived the sinking of the Titanic or not. For each PassengerId in the test set, you must predict a 0 or 1 value for the Survived variable.
Your score is the percentage of passengers you correctly predict. This is known simply as "accuracy”.