'''
Fitting the model
You're at the most fun part. You'll now fit the model. Recall that the data to be used as predictive features is loaded in a NumPy matrix called predictors and the data to be predicted is stored in a NumPy matrix called target. Your model is pre-written and it has been compiled with the code from the previous exercise.

Instructions
100 XP
Fit the model. Remember that the first argument is the predictive features (predictors), and the data to be predicted (target) is the second argument.
'''
SOLUTION
# Import necessary modules
import keras
from keras.layers import Dense
from keras.models import Sequential

# Specify the model
n_cols = predictors.shape[1]
model = Sequential()
model.add(Dense(50, activation='relu', input_shape = (n_cols,)))
model.add(Dense(32, activation='relu'))
model.add(Dense(1))

# Compile the model
model.compile(optimizer='adam', loss='mean_squared_error')

# Fit the model
model.fit(predictors, target)
