| Machine Learning
Machine Learning - Iris Dataset
Project Overview
In Python, code at least one machine learning algorithm from scratch to make one or more predictors, usable for a random seed generation from the Iris Dataset. The options for algorithms to incorporate included: general decision trees, ID3 decision trees, neural networks, entropy, information gain and a confusion matrix.
There were examples provided of the different types of machine learning algorithms that could be used for this project, and I integrated all of these options in writing the code for this project.
The full scope of what was to be coded for the project was not defined clearly enough initially, which resulted in mistakenly believing the project was completed upon the fulfilment of the minimum project objectives. To go beyond these minimum requirements, a general decision tree, entropy, and an extension type B neural network was added.
Define & Ideate
The project was vague in its requirements beyond the basic description and targeted IRIS dataset, leaving individual discretion of predictors and algorithms used. To ensure a higher accuracy result that a single predictor could not achieve, the initial plan was to use multiple different methods, specifically, an ID3 decision tree and a confusion matrix.
Test
Testing was conducted frequently throughout the project, to ensure that any errors would be identified early on, in order to manage the large size, scale, and number of elements in the project. The repeated testing and fine-tuning were significant contributing factors for the success of this machine learning code and project as a whole.
While definitely requiring more time and effort during the duration of the coding of this project, it was a wise decision to conduct thorough testing and assessments throughout; as opposed to ensuring only basic functionality and leaving the fine-tuning based on derived results from testing solely to the end, as it optimised the project’s efficiency.
Notable Results
The result was a highly successful predictor, with the ID3 Decision Tree predictor averaging over 80% accuracy and the Extension Type B Neural Network averaging 70% accuracy. Amongst other aspects, it also had an Extension Type B Neural Network Confusion Matrix included in the programmed code.
The high accuracy demonstrates the success of this project, which is further exemplified by it being fundamentally an individual project, and my first time experiencing coding a machine learning program or predictive program. It is therefore a project that I personally take great pride in.
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