Step 2: Create a Python MLP model with the same data

Next, we use the training data set to create an MLP model using Sci-kit learn, a popular package for data science in Python.

MLP models are deep learning models that work well with exceptionally large data sets; however, these models require extensive computational resources and do not handle missing values. Black-box models like MLP models also lack simple interpretation. In contrast, Random Forest models in MSS can ingest missing values and are traditional machine learning models which provide a fundamental algorithmic structure for simpler interpretation.

Prepare the data

In this section we remove the rows of data that contain missing values. Then we subset into test and training data sets.
from sklearn.neural_network import MLPRegressor
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split

from sklearn.metrics import mean_absolute_error
import pandas

import numpy

#Read from the prepared Ames data.
AmesData = pandas.read_csv("AmesHousingTestData.csv")
#List out variables and pull the subset of data from the csv.
headers = list(["Sale Price","Lot Area","Total Basement SF","1st Floor SF","2nd Floor SF","Garage Area SF","Total Rooms","Year Built","Year Remod/Add","Zoning","Type","Heating Quality","Kitchen Quality","Garage Quality","Exterior Quality","Sample_Id","prediction-score"])
AmesData = AmesData[AmesData.columns.intersection(headers)]

#Remove all rows containing any missing numeric data.
test = AmesData.stack()

#subset into test and train
testData = AmesData.loc[AmesData['Sample_Id'] == "Test"]
trainData = AmesData.loc[AmesData['Sample_Id'] == "Training"]
xTest = testData.drop(columns=['Sale Price','Sample_Id'])
xTrain = trainData.drop(columns=['Sale Price','Sample_Id'])
yTest = testData['Sale Price']
yTrain = trainData['Sale Price']

#Size of training data
trainSize = xTrain.shape[1]
testSize = xTest.shape[1]

Create the MLP model and make predictions

In this section we create the model, use the model to make predictions, and summarize the model performance.
#create MLP model
MLPReg = MLPRegressor(random_state=1,max_iter=200,learning_rate_init=0.2,beta_1=0.8).fit(xTrain,yTrain)
from sklearn.metrics import mean_absolute_error

#generate predictions

#obtain summary statistics about current performance
#MAD Calculation, also known as Mean Absolute Error
MADValue_Python = mean_absolute_error(yTest, pyPred)