Created a Neural Network with Keras for the Titanic Survival Prediction Dataset.
The dataset was given in the Kaggle competition:
where, given some passenger data the algorithm will predict who will die and who will survive by using some ML algorithms.
In this case, a dense (fully-connected) Neural Network with two hidden layers was used. Survival is treated as a binary variable: 0 if dead and 1 if survived.
Input:
Train.csv
PassengerId - a number giving the unique ID of the passenger Survived - survival (0 = No, 1 = Yes) Pclass - passenger class (1 to 3) Name - passenger full name Sex - male or female Age - age of the passenger SibSp - number of siblings or spouses on board Parch - number of parents or children on board Ticket - ticket number Fare - ticket fare Cabin - (if applicable) cabin in the ship Embarked - port of embarkation (C = Cherbourg; Q = Queenstown; S = Southampton)
Test.csv
Same as Train.csv, but with Survival column omitted
The features that were used to do the training were Pclass, Sex, Age, SibSp, and Parch
To run the program type: python titanic_train.py followed by python predict_survival.py which will read in the model file titanic.model produced by the training script to make predictions on the Test.csv file returning an array of 0s and 1s corresponding to the predicted death or survival of the passenger under consideration.