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.
PassengerId : Unique ID of the passenger
Survived : Survived (1) or died (0)
Pclass : Passenger's class (1st, 2nd, or 3rd)
Name : Passenger's name
Sex : Passenger's sex
Age : Passenger's age
SibSp : Number of siblings/spouses aboard the Titanic
Parch : Number of parents/children aboard the Titanic
Ticket : Ticket number
Fare : Fare paid for ticket
Cabin : Cabin number
Embarked : Where the passenger got on the ship (C - Cherbourg, S - Southampton, Q = Queenstown)
I implemented Decision Tree from scratch and got a score of 77.990% on submission to Kaggle.