Artificial Intelligence and Machine learning using Classification Method for Building models

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) techniques, particularly classification methods, have gained significant traction across various domains for building predictive models. Classification algorithms are essential components of AI and ML systems that enable the categorization of data into predefined classes or labels based on their features. This paper provides an overview of AI and ML methodologies, focusing specifically on the utilization of classification methods for constructing robust and accurate predictive models.The primary objective of this paper is to elucidate the principles and applications of classification techniques in AI and ML. It explores popular classification algorithms such as Decision Trees, Support Vector Machines (SVM), Logistic Regression, Random Forest, and Neural Networks, detailing their underlying mechanisms, advantages, and limitations. Furthermore, the paper discusses the preprocessing steps, feature engineering techniques, and model evaluation methods associated with classification-based model development.Through real-world case studies and examples, this paper demonstrates the versatility and effectiveness of classification algorithms in solving diverse problems, including image recognition, text classification, sentiment analysis, medical diagnosis, fraud detection, and customer churn prediction. It highlights the importance of data quality, model interpretability, and domain knowledge in the successful implementation of classification-based AI and ML solutions.