I am preparing my very first Kaggle submission, which will be for the Titanic competition, and was wondering what a respectable or even good score would be? This course is designed to teach the student advanced classification techniques that will enable him to enter Kaggle's Titanic competition and achieve an improved score by using standard machine learning methods. After the student is introduced to the course, he will receive an introduction to Python's machine learning library, sklearn. The student will also be introduced to the website, OpenML, which is a repository of a multitude of datasets. The Titanic dataset in the OpenML website is used in the lessons in this course all the way up to the point that the student is invited to enter the Kaggle Titanic competition and employ all of the advanced classification techniques that he has learned in the course.

This article describes my attempt at the Titanic Machine Learning competition on Kaggle. I have been trying to study Machine Learning but never got as far as being able to solve real-world problems. But after I read two newly released books about practical AI, I was confident enough to enter the Titanic competition. The first part of the article describes preparing the data. The second part shows how I used a Support Vector Machine (SVM). I used the SVM to create a model that predicts the survival of the passengers of the Titanic. The model resulted in a score of 0.779907, which got me in the top 28% of the competition.

The other two authors start their EDAs with more general explorations of the dataset and images themselves.

Dicom is the de-facto file standard in medical imaging.