"The analysis of profitability and the factors that can influence it is of vital importance in business decision making. Thus, the purpose of this study is to examine the relationship between profitability and working capital, leverage, and net trade credit. The study is developed based on a sample of Russian firms, which operate in the agricultural sector, for the period from 2013 to 2017. The result denoted that firms were both, profitable and liquid ones, and bought more than sold on credit. Among other results, the study showed that more profitable firms operated with higher liquidity. Onward, the study suggested that firms should decrease the financial leverage ratio in order to increase profitability"

For learning environments like schools and colleges, predicting the performance of students is one of the most crucial topics since it aids in the creation of practical systems that, among other things, promote academic performance and prevent dropout. The decision-makers and stakeholders in educational institutions always seek tools that help in predicting the number of failed courses for the students. These tools can help in finding and investigating the factors that led to this failure. In this paper, many supervised machine learning algorithms will investigate finding and exploring the optimal algorithm for predicting the number of failed courses of students. An imbalanced dataset will be handled with Synthetic Minority Oversampling TEchinque (SMOTE) to get an equal representation of the final class. Two feature selection approaches will be implemented to find the best approach that produces a highly accurate prediction. Wrapper with Particle Swarm Optimization (SPO) will be applied to find the optimal subset of features, and Info Gain with ranker to get the most correlated individual features to the final class. Many supervised algorithms will be implemented such as (Naïve Bayes, Random Forest, Random Tree, C4.5, LMT, Logistic, and Sequential Minimal Optimization algorithm (SMO)). The findings show that the wrapper filter with SPO-based SMOTE outperforms the Info-Gain filter with SMOTE and improves the performance of the algorithms. Random Forest outperforms the other supervised machine learning algorithms with (85.6%) in TP average rate and Recall, and (96.7%) in ROC curve.Povzetek: Opisana je metoda za napovedovanje uspeha študentov s pomočjo strojnega učenja.