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 Iris Flowers dataset is a multi-class classification situation where we are trying to predict one of several (more than two) possible outcomes.

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: This is perhaps the best-known database to be found in the pattern recognition literature. Fisher’s paper is a classic in the field and is referenced frequently to this day. (See Duda & Hart, for example.) The data set contains three classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other two; the latter are NOT linearly separable from each other.

ANALYSIS: After setting up the deep learning model, the model processed the test dataset with an accuracy measurement of 84.00%.

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

Dataset Used: Iris Flowers Dataset

Dataset ML Model: Multi-class classification with numerical attributes

Dataset Reference: https://archive.ics.uci.edu/ml/machine-learning-databases/iris/

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.