import crime_data
from keras.models import Sequential

# load crime dataset (financial, historical, social data, with labels Criminal/Not Criminal)
dataframe = pandas.read_csv(crime_data.load(), header=None)
X = dataset[:,0:60].astype(float)
Y = dataset[:,60]

# Create Neural Network Model
model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the model
model.fit(X, Y, epochs=150, batch_size=10)

# New potential criminal data
new_person  crime_data.load_new()
# predict criminality (criminal/not criminal)
scores = model.predict(new person)