Regression Deep Learning Model for the Boston Housing Price Using Keras

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: The purpose of this project is to construct a predictive model using various machine learning algorithms and to document the end-to-end steps using a template. The Boston Housing Dataset is a regression situation where we are trying to predict the value of a continuous variable.

INTRODUCTION: The purpose of the analysis is to predict the housing values in the suburbs of Boston by using the home sale transaction history.

ANALYSIS: The baseline performance of the model achieved an average MSE score of 16.91. Using the same training parameters, the model processed the test dataset with an RMSE of 15.93, which was even better than results from the training data.

CONCLUSION: For this dataset, the model built using Keras and TensorFlow achieved a satisfactory result and should be considered for future modeling activities.

Dataset Used: Boston Housing Price Data Set

Dataset ML Model: Regression with numerical attributes

Dataset Reference: https://raw.githubusercontent.com/jbrownlee/Datasets/master/housing.data

One potential source of performance benchmarks: https://machinelearningmastery.com/regression-tutorial-keras-deep-learning-library-python/

The HTML formatted report can be found here on GitHub.