Machine Learning Algorithms for Regression Analysis and Predictions of Numerical Data
Diyana Kinaneva, Georgi Hristov, Petko Kyuchukov, Georgi Georgiev, Plamen Zahariev, Rosen Daskalov
Abstract
Machine learning has become extremely popular in recent years due to its ability to train models to deal with complex task. Machine learning (ML) algorithms are one of the fundamentals behind Artificial Intelligence (AI), which is now widely spread among different areas of our lives. The success of the machine-learning algorithm very depends on the training datasets. In order to achieve good accuracy ML algorithms must be trained with well-prepared input datasets. Data preparation is a set of procedures that helps make the dataset more suitable for machine learning. The goal of the paper is to summarize different techniques for data preparation and to make analysis which of them directly affect the accuracy of the final model. Different ML algorithms are considers and tested for training a model to predict numerical variables which is not based on neural networks.