I use neural networks to predict which passengers will survive, with 66 % accuracy.
Question
Predict who will survive.
Data
It is described on https://www.kaggle.com/c/titanic/data.
Method
Neutral networks
Code
## Import necessary modules import keras from keras.layers import Dense from keras.models import Sequential from keras.utils import to_categorical ## Import data df = pd.read_csv('titatic.csv') df.head() ## y (survived) and x (all other variables) # y: df['survived'] is binary (n x 1) so create a (n x 2) matrix y = to_categorical(df['survived']) # X: drop y column from df and save as a matrix X = df.drop(['survived'], axis=1).as_matrix() ## Specify model = Sequential() n_cols = df.shape[1] - 1 #use this to chose number of nodes shape = (n_cols,) model.add(Dense(32, activation='relu', input_shape=shape) model.add(Dense(2, activation='softmax')) ## Compile model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy']) ## Fit model.fit(X, y) ## Output: Accuracy is 66%
For an explanation of accuracy, read my post on evaluation of binary classificers.