Importance of supervised learning in prediction analysis

Abstract

Counterfeit medicines are fake medicines which are either contaminated or contain the wrong or no active ingredient. Up to 30% of medicines in developing countries are counterfeit. Using Supervised Machine learning techniques we build a predictive model for predicting sales figures given other information related to counterfeit medicine selling operations. Thus, by predicting the values we can identify these illegal operations and counter them. In this paper we have also mentioned the importance of Data mining and Machine Learning algorithms with some comparison analysis.

The rating model of Ukraine's regions according to the level of economic development

“…Assessment of economic development of a separate region of the country may be based on the identification of homogeneous aggregates of regions described by the set of economic indicators. In the case of a large number of indicators, it is expedient to use the methods of machine learning and data analysis [15]. In particular, cluster analysis is an effective method that allows to group regions into homogeneous sets using a wide range of indicators.…”

Capítulo 3. Inteligencia Artificial en la comunicación científica

“…El aprendizaje automático (Machine Learning) se describe como el procedimiento que emplea algoritmos para utilizar información, aprender de ella y anticipar patrones futuros en el tema correspondiente. En esencia, es la creación de sistemas que adquieren conocimiento a partir de datos o experiencias previas (Flarence et al, 2018), lo que significa que el desarrollo del aprendizaje automático, en un sistema, implica la construcción de una entidad capaz de aprender a partir de la experiencia.…”

Evaluating the effectiveness of supervised learning models for antibiotic pollution detection from biochip data

“…Both are relied on the information provided by the pre-determinate classification. This type of learning from labelled data using classification and regression model, which is applied in applications where historical data predicts likely feature events, such as classification flower dataset, 17 image and object recognition, 19 predictive analytic in counterfeit medicine 20 and spam detection. 21…”