ModelValidation.py

import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import to_categorical

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

source = pd.read_csv("Datasets/titanic_all_numeric.csv", delimiter=",")
# Source file times one convert True/False into 1/0
source = source * 1
# Convert DataFrame into the numpy matrix
source = source.as_matrix()
# Select whole of the columns except for the target value
predictors = np.delete(source, 0, 1)
# Number of the features or attributes
n_cols = predictors.shape[1]
# Select the first column which contained target values
target = source[:, 0]
# You can also obtain survived (target variable) by using source.survived
# Convert the target to categorical target
target = to_categorical(target)

# Define a model
model = Sequential()
model.add(Dense(100, activation='relu', input_shape=(n_cols,)))
model.add(Dense(100, activation='relu'))
model.add(Dense(2, activation='softmax'))

# Compile the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
# Fit the model
model.fit(predictors, target, epochs=20, validation_split=0.3)