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.
Additional Notes: This is a replication, with some small modifications, of Dr. Jason Brownlee’s blog post, PyTorch Tutorial: How to Develop Deep Learning Models with Python. I plan to leverage Dr. Brownlee’s tutorial code and build a PyTorch-based notebook template for future uses.
INTRODUCTION: The purpose of the analysis is to predict the housing values (thousands of dollar) in the suburbs of Boston by using the home sale transaction history.
ANALYSIS: After setting up the deep learning model, the model processed the test dataset with a root mean squared error (RMSE) of 8.906.
CONCLUSION: For this dataset, the model built using PyTorch achieved a satisfactory result and should be considered for future modeling activities.
Dataset Used: Boston Housing Dataset
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/pytorch-tutorial-develop-deep-learning-models/
The HTML formatted report can be found here on GitHub.