Titanic ML competition

The competition is simple: use machine learning to create a model that predicts which passengers survived the Titanic shipwreck.

Overview

(from https://www.kaggle.com/c/titanic/data)

The data has been split into two groups:

• training set (train.csv)
• test set (test.csv)

The training set should be used to build your machine learning models. For the training set, we provide the outcome (also known as the “ground truth”) for each passenger. Your model will be based on “features” like passengers’ gender and class. You can also use feature engineering to create new features.

The test set should be used to see how well your model performs on unseen data. For the test set, we do not provide the ground truth for each passenger. It is your job to predict these outcomes. For each passenger in the test set, use the model you trained to predict whether or not they survived the sinking of the Titanic.

We also include gender_submission.csv, a set of predictions that assume all and only female passengers survive, as an example of what a submission file should look like.

Data dictionary

(from https://www.kaggle.com/c/titanic/data)

Variable	Definition	Key
survival	Survival	0 = No, 1 = Yes
pclass	Ticket class	1 = 1st, 2 = 2nd, 3 = 3rd
name	Passenger's name	Last name - Title - Name(s) - (Nick name)
sex	Sex
Age	Age in years
sibsp	# of siblings / spouses aboard the Titanic
parch	# of parents / children aboard the Titanic
ticket	Ticket number
fare	Passenger fare
cabin	Cabin number
embarked	Port of Embarkation	C = Cherbourg, Q = Queenstown, S = Southampton

Variable Notes

(from https://www.kaggle.com/c/titanic/data)

pclass: A proxy for socio-economic status (SES)

• 1st = Upper
• 2nd = Middle
• 3rd = Lower

age: Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5

sibsp: The dataset defines family relations in this way...

• Sibling = brother, sister, stepbrother, stepsister
• Spouse = husband, wife (mistresses and fiancés were ignored)

parch: The dataset defines family relations in this way...

• Parent = mother, father
• Child = daughter, son, stepdaughter, stepson
• Some children travelled only with a nanny, therefore parch=0 for them.

Getting into the challenge!

From a typical Scikit-Learn workflow, we're going to follow these steps:

0. Standard library imports
1. Get the data ready
2. Pick a model/estimator
3. Fit the model to the data and make a prediction
4. Evaluate the model
5. Improve through experimentation
6. Submit predictions

Check the notebook to see the code in details.