![]() The missing values will be converted to zero. ![]() Afterwords we will convert the feature into a numeric variable. Therefore we’re going to extract these and create a new feature, that contains a persons deck. A cabin number looks like ‘C123’ and the letter refers to the deck. First I thought, we have to delete the ‘Cabin’ variable but then I found something interesting. train_df = train_df.drop(, axis=1) Missing Data:Ĭabin: As a reminder, we have to deal with Cabin (687), Embarked (2) and Age (177). I will not drop it from the test set, since it is required there for the submission. Data Preprocessingįirst, I will drop ‘PassengerId’ from the train set, because it does not contribute to a persons survival probability. Here we can see that you had a high probabilty of survival with 1 to 3 realitves, but a lower one if you had less than 1 or more than 3 (except for some cases with 6 relatives). Thomas Andrews, her architect, died in the disaster. The Titanic was built by the Harland and Wolff shipyard in Belfast. The RMS Titanic was the largest ship afloat at the time it entered service and was the second of three Olympic-class ocean liners operated by the White Star Line. There were an estimated 2,224 passengers and crew aboard the ship, and more than 1,500 died, making it one of the deadliest commercial peacetime maritime disasters in modern history. The RMS Titanic was a British passenger liner that sank in the North Atlantic Ocean in the early morning hours of 15 April 1912, after it collided with an iceberg during its maiden voyage from Southampton to New York City. In this challenge, we are asked to predict whether a passenger on the titanic would have been survived or not. I initially wrote this post on, as part of the “Titanic: Machine Learning from Disaster” Competition. It provides information on the fate of passengers on the Titanic, summarized according to economic status (class), sex, age and survival. In this blog-post, I will go through the whole process of creating a machine learning model on the famous Titanic dataset, which is used by many people all over the world.
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