Machine Learning projects

This project focuses on preparing and analyzing a database to train models that are capable of classifying whether a fungus is edible or poisonous.

In this project, two different models were used to see their advantages over each other. The two models are used to group data without annotations and are in the area of unsupervised learning since they do not require true labels.

Principal component analysis (PCA) is a technique to reduce the dimensionality of a database and to reduce the computational cost without affecting the performance of any artificial intelligence model. For this project, two databases were applied to PCA and two machine learning models, decision tree and k-nearest neighbors were trained.

In this project, a classification of type of cancer and type of star was made through the implementation of the k-nearest neighbors (KNN) model and an evaluation technique called K-Fold.

In this project, databases were searched which contain an imbalance of classes in their decision attribute, in order to apply the synthetic data creation method and thus have a suitable database for training a Machine Learning model.

ID3 stands for Iterative Dichotomiser 3 and is so named because the algorithm iteratively (repeatedly) dichotomizes (divides) features into two or more groups at each step. Generally,
ID3 is only used for classification problems with nominal features only. In this project, some databases will be used from which the gain and entropy will be calculated to build the id3 decision tree, which will tell us what action should be taken based on certain conditions.