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.
- Part1: Exploratory Data Analysis(EDA):
- Analysis of the features.
- Finding any felations or trends considering multiple features.
- Part2: Feature Engineering and Data Cleaning:
- Adding any few features.
- Removing redundant features.
- Converting features into suitable form for modeling.
- Part3: Predictive Modeling:
- Running Basic Algorithms.
- Cross Validation.
- Ensembling.
- Important Features Extraction.