This repository is dedicated to the implementation and analysis of two machine learning techniques: Decision Trees and K-Nearest Neighbors (KNN). These classic algorithms are applied to the well-known Iris dataset from the UCI Machine Learning Repository. The Iris dataset was introduced by the renowned statistician and biologist Ronald Fisher in 1936 and further enriched by Edgar Anderson to quantify morphological variation of Iris flower species. It contains 50 samples from each of three species of Iris (Iris setosa, Iris virginica, and Iris versicolor) with four features measured: the lengths and the widths of the sepals and petals in centimeters.
The aim of this project is to utilize Decision Trees and KNN in classifying the different species of the Iris flowers based on their morphological features. This task involves comparing the performance of both algorithms and understanding their strengths and weaknesses in the context of the Iris dataset.
- decision_tree: A folder containing the implementation of the Decision Tree classifier, analysis, and visualization of the tree.
- KNN: A folder dedicated to the implementation of the KNN algorithm, including the process of finding the optimal 'k' value and analyzing the results.
- relatorio: A folder containing explanation of some techniques, data visualizations and the conclusion. (In portuguese only)
This project is open-sourced under the MIT license. See the LICENSE file for more details.
This project is part of an assignment for the Artificial Intelligence course.