Classifying Marine Species Data with Root Cause and Propose a Solution

Abstract

We have successfully categorised the vast majority of the data on marine species based on specific features in this research. The next step is to identify endangered or threatened species that meet the criteria. Classify the vast majority of the data and provide explanations for and remedies to the classification problems presented here. One is a decision tree algorithm, and the other is a logistic regression approach, both of which are used in our work. Classifying large amounts of data according to need and constraints is a common application of the decision tree technique. In a prospective, methodologically sound approach, the logistic regression algorithm is applied for predetermined root causes and treatment options. This application proposes a strategy for efficiently exploring and analysing large amounts of data in light of specific situations and needs. In order to comprehend classification and segregation and reach a numerical output or regression, we employ decision trees. This technique is a form of supervised learning employed in the process of problem classification. In this method, we identify the most important characteristics and conditions and use them to divide the data into two or more groups. Automated procedures rely on a collection of algorithms and tools to perform the heavy lifting of data-driven decision making and branching. In order to meet our requirements, the initially unsorted data must be analysed in numerous steps based on various properties and separated in order to reduce the amount of unpredictability, or entropy. It helps in the creation of efficient machine learning models that can make reliable predictions quickly. Discrete values (often binary values like 0/1) are estimated from a set of independent factors using logistic regression. It helps in estimating the possibility of an event by adjusting the logic function to the data. In this context, these algorithms perform admirably. Since there are only two possible results, we call logistic regression a binary classifier.