The very famous Titanic: Machine Learning from Disaster Challenge by Kaggle! Predict survival on the Titanic using Excel, Python, R & Random Forests
If you're new to data science and machine learning, or looking for a simple intro to the Kaggle competitions platform, this is the best place to start. Continue reading below the competition description to discover a number of tutorials, benchmark models, and more.
Competition Description
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.
Code Sharing With Kaggle Kernels
You can write, run, and view best practice code and visualizations of the Titanic dataset on Kaggle Kernels. You can also use Kernels to submit directly to the competition. Here are a few helpful kernels from Kaggle data scientists and community members to get started with:
Exploring Survival on the Titanic: Data exploration + visualizations and a random forest benchmark model. Great example of how to present your data science work using R Markdown. Random Forest Benchmark (R): Basic random forest model for you to build on and submit. A Journey Through Titanic: Nicely scripted data exploration and benchmark model in Python. Decision Tree Visualization and Submission: Benchmark model in R with a nice example of a decision tree visualization.