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 dataset is a multi-class classification situation where we are trying to predict one of several (more than two) possible outcomes.
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: The baseline performance of the model achieved an average accuracy score of 96.67%, which was consistent with other classical machine learning algorithms.
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: Iris Data Set
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/multi-class-classification-tutorial-keras-deep-learning-library/
The HTML formatted report can be found here on GitHub.