The sinking of the Titanic is one of the most infamous shipwrecks in history.
On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew.
While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others.
In this repository, I have built a predictive model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, age, gender, socio-economic class, etc).
One dataset is titled train.csv
and the other is titled test.csv
.
Train.csv will contain the details of a subset of the passengers on board (891 to be exact) and importantly, will reveal whether they survived or not, also known as the “ground truth”.
The test.csv
dataset contains similar information but does not disclose the “ground truth” for each passenger. It’s your job to predict these outcomes. Using the model trained on dataset train.csv, I have predicted the survival for each passengerID in the test.csv dataset and then stored the results in submission.csv. The accuracy I was getting for the test.csv file is 0.667. Everypone is requested to contribute in this and improve the accuracy of this model.