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.
Here we try to analyze which factors were more likely to contribute to the death of the passengers and classify who is more likely to survive depending on the features.
The purpose of this project was to gain introductory exposure to programmatic data analysis concepts, by analysing the factors that determined whether a passenger survived the Titanic disaster or did not. The project makes heavy use of NumPy, Pandas, TFLearn for Neural Network Classifier and Data Visualization Libraries.