Titanic Machine Learning from Disaster (Kaggle Competition) by Andrew Conti.
From the post (and from the Kaggle page):
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.”
From Andrew’s post:
Goal for this Notebook:
Show a simple example of an analysis of the Titanic disaster in Python using a full complement of PyData utilities. 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.
This Notebook will show basic examples of:
Data Handling
- Importing Data with Pandas
- Cleaning Data
- Exploring Data through Visualizations with Matplotlib
Data Analysis
- Supervised Machine learning Techniques:
- Logit Regression Model
- Plotting results
- Unsupervised Machine learning Techniques
- Support Vector Machine (SVM) using 3 kernels
- Basic Random Forest
- Plotting results
Valuation of the Analysis
- K-folds cross validation to valuate results locally
- Output the results from the IPython Notebook to Kaggle
Required Libraries:
This is wicked cool!
I first saw this in Kaggle Titanic Contest Tutorial by Danny Bickson.
PS: Don’t miss Andrew Conti’s new homepage.