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 contest, 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.
This Kaggle Getting Started Competition provides an ideal starting place for people who may not have a lot of experience in data science and machine learning.
Show a simple example of an analysis of the Titanic disaster in Python. This is aimed for those looking to get into the field or those who are already in the field and looking to see an example of an analysis done with Python.
- Importing Data with Pandas
- Cleaning Data
- Exploring Data through Visualizations with Matplotlib and Seaborn
- Supervised Machine learning Techniques:
- K Nearest Neighbour
- Gaussian Naive Bayes
- Support Vector Machine (SVM)
- Decision Tree Classifier
- Plotting results.