Machine Learning Regression Approaches Research Articles

Comparison of Air Fare Prediction Using Various Machine Learning Techniques

Due to a rise in air service links globally, air travel is now a popular, significant, and faster form of transportation. Airline rates are thought to be impacted by several factors and are subject to frequent fluctuation, making estimation of these rates a challenging but essential task. The airline uses a pricing structure for the plane ticket. The cost of airline tickets varies based on the time of day, according to the survey. Even the weekends, the tourist season, and the pageant season have an impact on this. An aircraft ticket's price is influenced by several different elements. The buyer only has access to a limited amount of information, which is truly insufficient to estimate flight pricing, but the seller is aware of all the requirements. In this study, we use machine learning regression approaches such as K Nearest Neighbourhood, Decision Tree, and Random Forest to estimate flight fares based on basic data including source, destination, departure time and date, arrival timings, halts, and airline type. The results of the investigation show that the Random Forest Regression Model yields extremely optimal results.

Predicting the financial performance of microfinance institutions with machine learning techniques

Purpose Given the growing emphasis among scholars, practitioners and policymakers on financial sustainability, this study aims to explore the applicability of machine learning techniques in predicting the financial performance of microfinance institutions (MFIs). Design/methodology/approach This study gathered 9,059 firm-year observations spanning from 2003 to 2018 from the World Bank's Mix Market database. To predict the financial performance of MFIs, the authors applied a range of machine learning regression approaches to both training and testing data sets. These included linear regression, partial least squares, linear regression with stepwise selection, elastic net, random forest, quantile random forest, Bayesian ridge regression, K-Nearest Neighbors and support vector regression. All models were implemented using Python. Findings The findings revealed the random forest model as the most suitable choice, outperforming the other models considered. The effectiveness of the random forest model varied depending on specific scenarios, particularly the balance between training and testing data set proportions. More importantly, the results identified operational self-sufficiency as the most critical factor influencing the financial performance of MFIs. Research limitations/implications This study leveraged machine learning on a well-defined data set to identify the factors predicting the financial performance of MFIs. These insights offer valuable guidance for MFIs aiming to predict their long-term financial sustainability. Investors and donors can also use these findings to make informed decisions when selecting their potential recipients. Furthermore, practitioners and policymakers can use these findings to identify potential financial performance vulnerabilities. Originality/value This study stands out by using a global data set to investigate the best model for predicting the financial performance of MFIs, a relatively scarce subject in the existing microfinance literature. Moreover, it uses advanced machine learning techniques to gain a deeper understanding of the factors affecting the financial performance of MFIs.

Predicting popularity trend in social media networks with multi-layer temporal graph neural networks

Predicting what becomes popular on social media is crucial because it helps us understand future topics and public interests based on massive social data. Previous studies mainly focused on picking specific features and checking past statistic numbers, ignoring the hidden impact of messages passing along the complex relationships among different entities. People talk and connect with others on social media; thus, it is essential to consider how information spreads when studying social media networks. This work proposes a multi-layer temporal graph neural network (GNN) framework for predicting what will be popular on social media networks. This framework takes into account the way information spreads among different entities. The proposed method involves multi-layer relations and temporal information within a sequence of social media network snapshots. It learns the temporal representations of target entities in each snapshot and predicts how the popularity of a particular entity will change in future snapshots. The proposed method is evaluated with real-world data across four popularity trend prediction tasks. The experimental results prove that the proposed method performs better than various baselines, including traditional machine learning regression approaches, prior methods for popularity trend prediction, and other GNN models.

Development of a Predictive Model to Correlate the Chemical Structure of Amines with Their Oxidative Degradation Rate in a Post-Combustion Amine-Based CO2 Capture Process Using Multiple Linear Regression and Machine Learning Regression Approaches.

In this study, the degradation behavior of 30 different amines was investigated, which were categorized into four distinct groups: alkanolamines, sterically hindered alkanolamines, multialkylamines, and cyclic amines. These experiments were conducted over a period of 14 days at a temperature of 60 °C, with a feed gas comprising 99.9% O2 flowing at a rate of 200 mL/min. The primary objective was to establish a correlation between the chemical structures of these amines and their susceptibilities to degradation. To assess this, the concentration of the amines at various time points was measured to determine their degradation rates. Results showed that secondary amines exhibited degradation rates higher than those of primary and tertiary amines. Amines with cyclic structures demonstrated lower oxidative degradation rates. Longer alkyl chain lengths decreased degradation rates in all amine types because of their electronic and steric hindrance properties. A higher number of hydroxyl groups increased the degradation rate by destabilizing the free radical. An increase in hydroxyl groups in nonsterically hindered amines increased the degradation rate by decreasing free radical stability. In contrast, for sterically hindered amines, an increase in hydroxyl groups decreased the degradation rate because the steric hindrance effect is now more dominant than the electron-withdrawing effect. An increase in the number of amino groups led to higher degradation rates due to the presence of more reactive sites for free radical formation. Amines with tert-alkyl groups exhibited higher degradation rates than those with straight chains. Moreover, branched alkyl groups located between amino and hydroxyl groups significantly increased the degradation rates. Two degradation models, a semiempirical statistical model and a CatBoost machine learning regression model, were developed to predict amine degradation rates based on their chemical structure and relevant properties. To train these models, a data set of 27 different amines was used, while another set of 3 amines was reserved for testing the model's predictive performance. The average absolute deviations (AAD) achieved were, respectively, 22.2 and 0.3%.

Using a Machine Learning Regression Approach to Predict the Aroma Partitioning in Dairy Matrices

Aroma partitioning in food is a challenging area of research due to the contribution of several physical and chemical factors that affect the binding and release of aroma in food matrices. The partition coefficient measured by the Kmg value refers to the partition coefficient that describes how aroma compounds distribute themselves between matrices and a gas phase, such as between different components of a food matrix and air. This study introduces a regression approach to predict the Kmg value of aroma compounds of a wide range of physicochemical properties in dairy matrices representing products of different compositions and/or processing. The approach consists of data cleaning, grouping based on the temperature of Kmg analysis, pre-processing (log transformation and normalization), and, finally, the development and evaluation of prediction models with regression methods. We compared regression analysis with linear regression (LR) to five machine-learning-based regression algorithms: Random Forest Regressor (RFR), Gradient Boosting Regression (GBR), Extreme Gradient Boosting (XGBoost, XGB), Support Vector Regression (SVR), and Artificial Neural Network Regression (NNR). Explainable AI (XAI) was used to calculate feature importance and therefore identify the features that mainly contribute to the prediction. The top three features that were identified are log P, specific gravity, and molecular weight. For the prediction of the Kmg in dairy matrices, R2 scores of up to 0.99 were reached. For 37.0 °C, which resembles the temperature of the mouth, RFR delivered the best results, and, at lower temperatures of 7.0 °C, typical for a household fridge, XGB performed best. The results from the models work as a proof of concept and show the applicability of a data-driven approach with machine learning to predict the Kmg value of aroma compounds in different dairy matrices.

Global zenith wet delay modeling with surface meteorological data and machine learning

The tropospheric delay is a major error source for space geodetic techniques, and the performance of its modeling is significantly limited due to the high spatiotemporal variability of the moisture in the lower atmosphere. In this study, global modeling of the tropospheric zenith wet delay (ZWD) was realized based on surface meteorological data obtained from radiosondes and Global Positioning System (GPS) radio occultation (RO) measurements through the random forest (RF) and backpropagation neural network (BPNN) regression analysis. The modeling performance was further validated based on two kinds of global atmospheric profiles for the year 2020. Our results show that the ZWD modeling accuracy gained by two machine learning regression approaches is significantly improved by taking into account surface meteorological parameters, especially the surface water vapor pressure when compared to the Global Pressure and Temperature 3 (GPT3) model. When surface meteorological data are available, the RF-B model yields ZWD estimations with an overall agreement of 3.1 cm in comparison with the sounding profiles and 2.4 cm in contrast to the GPS RO atmospheric profiles. The RF-B is superior to other models based on surface meteorological parameters for ZWD calculation, e.g., the accuracy improves by 21.8–23.8% against the approach by Saastamoinen and 7–12.2% against the formula by Askne and Nordius.

Prioritizing determinants of cognitive function in healthy middle-aged and older adults: insights from a machine learning regression approach in the Canadian longitudinal study on aging.

The preservation of healthy cognitive function is a crucial step toward reducing the growing burden of cognitive decline and impairment. Our study aims to identify the characteristics of an individual that play the greatest roles in determining healthy cognitive function in mid to late life. Data on the characteristics of an individual that influence their health, also known as determinants of health, were extracted from the baseline cohort of the Canadian Longitudinal Study of Aging (2015). Cognitive function was a normalized latent construct score summarizing eight cognitive tests administered as a neuropsychological battery by CLSA staff. A higher cognitive function score indicated better functioning. A penalized regression model was used to select and order determinants based on their strength of association with cognitive function. Forty determinants (40) were entered into the model including demographic and socioeconomic factors, lifestyle and health behaviors, clinical measures, chronic diseases, mental health status, social support and the living environment. The study sample consisted mainly of White, married, men and women aged 45-64 years residing in urban Canada. Mean overall cognitive function score for the study sample was 99.5, with scores ranging from 36.6 to 169.2 (lowest to highest cognitive function). Thirty-five (35) determinants were retained in the final model as significantly associated with healthy cognitive functioning. The determinants demonstrating the strongest associations with healthy cognitive function, were race, immigrant status, nutritional risk, community belongingness, and satisfaction with life. The determinants demonstrating the weakest associations with healthy cognitive function, were physical activity, greenness and neighborhood deprivation. Greater prioritization and integration of demographic and socioeconomic factors and lifestyle and health behaviors, such greater access to healthy foods and enhancing aid programs for low-income and immigrant families, into future health interventions and policies can produce the greatest gains in preserving healthy cognitive function in mid to late life.

Predictive modelling of sweep's specific draft using machine learning regression approaches

AbstractModelling and optimizing soil‐tool interaction parameters for tillage operations is crucial for developing efficient and precise tools. This study focused on a specific draft of sweep tools in the soil bin filled with vertisol, considering factors such as tool geometry, cone index, working depth and operational speed. Data analysis showed that the range of specific draft values, from 9.51 to 38.95 kN/m2. Machine learning models, including artificial neural network (ANN), support vector machine (SVM), bagged trees (BT) and boosted trees (BoT), were developed using experimental data to predict the specific draft of sweep tools with hyperparameter configuration. The developed machine learning models have also been compared with the predictive multiple linear regression (MLR) model, and it was found that the predictive performance of the machine learning models was better than the MLR model during training and testing. The fine‐tuned ANN model achieved impressive statistical performance with the lowest mean absolute error (MAE) of 0.489 kN/m2, root mean square error (RMSE) of 0.619 kN/m2, standard error of prediction (SEP) of 3.462% and highest coefficient of determination (R2) of .99 during testing. R2 values for BT, BoT, SVM and MLR models were .97, .96, .94 and .83, respectively, for specific draft predictions. The findings from this study have practical implications for optimizing sweep tool design and improving tillage operation. Manufacturers and farmers can benefit from predictive modelling using machine learning to design and select appropriate tillage tools for specific soil conditions. This approach can lead to improved soil health, increased yields and reduced costs.

A machine learning regression approach for predicting uplift capacity of buried pipelines in anisotropic clays

The uplift capacity of pipeline systems in geotechnical engineering is influenced by internal loading and external factors, making it a significant consideration in pipeline design problems. Previous research has conducted experimental tests and numerical solutions to investigate the relationship between force and displacement or the resistance of pipelines in numerous soil media. This paper proposes a machine-learning regression technique to predict the uplift capacity of buried pipelines in anisotropic clays with parametric analysis. Specifically, the Multivariate Adaptive Regression Spline (MARS) is employed to establish the relationship between input parameters, namely the depth ratio (H/D), anisotropic strength ratio (re), load inclination (β), overburden factor (γH/Suc), adhesion factor (α), and the output uplift resistance (N) obtained from the finite element limit analysis (FELA), utilizing the AUS material model integrated with the OptumG2 commercial program. Furthermore, the sensitivity analysis outcome shows the embedded depth ratio is the most critical parameter, followed by the anisotropic strength ratio, overburden factor, load inclination, and adhesion factor. Additionally, the shear velocity field contours show that when the depth ratio and the load inclination increase, the dissipation of shear changes. Design data visualizations, tables, graph contours, and empirical equations are created and can be utilized to aid in the development of practical designs.

Comparison of a novel machine learning approach with dynamical downscaling for Australian precipitation

Dynamical downscaling (DD), and machine learning (ML) based techniques have been widely applied to downscale global climate models and reanalyses to a finer spatiotemporal scale, but the relative performance of these two methods remains unclear. We implement an ML regression approach using a multi-layer perceptron (MLP) with a novel loss function to downscale coarse-resolution precipitation from the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia from grids of 12–48 km to 5 km, using the Australia Gridded Climate Data observations as the target. A separate MLP is developed for each coarse grid to predict the fine grid values within it, by combining coarse-scale time-varying meteorological variables with fine-scale static surface properties as predictors. The resulting predictions (on out-of-sample test periods) are more accurate than DD in capturing the rainfall climatology, as well as the frequency distribution and spatiotemporal variability of daily precipitation, reducing biases in daily extremes by 15%–85% with 12 km prediction fields. When prediction fields are coarsened, the skill of the MLP decreases—at 24 km relative bias increases by ∼10%, and at 48 km it increases by another ∼4%—but skill remains comparable to or, for some metrics, much better than DD. These results show that ML-based downscaling benefits from higher-resolution driving data but can still improve on DD (and at far less computational cost) when downscaling from a global climate model grid of ∼50 km.

Convenient prediction of TcPO2 level using machine learning techniques with the PPG signal’s characteristic features

BackgroundTranscutaneous oxygen tension (TcPO2) is a noninvasive method for assessing the oxygen supply of the skin in clinics. However, it is difficult to deploy broadly in primary care settings due to its high cost and time-consuming. This study proposed a fast and inexpensive technique for predicting TcPO2 using the characteristic features of the photoplethysmography (PPG) signals and various machine learning algorithms. MethodsThe toe PPG signals of 149 subjects were collected, and 12 characteristic features were extracted according to contour analysis, followed by TcPO2 measurements. TcPO2 measurements were performed on the dorsum of the foot by PeriFlux 6000 TcPO2 system. In addition to PPG features, physiological parameters were also taken into account. A variety of machine learning regression approaches, including extreme gradient boost (XGBoost), random forest regression (RFR), support vector machine regression (SVR) and Ridge regression (Ridge), were used to predict TcPO2 levels. To evaluate the performance of machine learning algorithms, different performance indicators such as mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and correlation coefficient were employed. Correlation analysis and Bland-Altman plot analysis were performed to visualize the data. ResultsXGBoost outperformed the other three regression models in predicting TcPO2 (R = 0.835, MAE = 5.23 mmHg, RMSE = 6.87 mmHg, MAPE = 9.99%). Reflection index, diabetes, duration of diabetes, heart rate, crest time, the time between the systolic and diastolic peaks (ΔT) were the top six contributions to TcPO2 prediction in the XGBoost model. The PPG characteristic features play a dominant role in TcPO2 estimation. Diabetes and duration of diabetes were also significant factors in TcPO2 prediction. ConclusionsWe compared four machine learning methods for TcPO2 prediction. The XGBoost model had the best predictive performance of all the algorithms investigated and could be used to forecast TcPO2 values. The TcPO2 prediction model based on PPG signal reduces the cost and shortens the measurement time of TcPO2, providing significant benefits for primary care.

A machine learning regression approach for pre-renovation construction waste auditing

The activity from which construction waste arises includes new construction, renovation, and demolition. Renovation waste was traditionally considered as trivial but in recent years, it has gradually come into the focus of construction waste management (CWM). Waste auditing prior to renovation (termed ‘pre-renovation auditing’ or PRA) provides a departure point for good CWM. The core of PRA is accurately predicting renovation waste generation. Benefiting from a valuable dataset containing 351 building renovation projects in Hong Kong, this research aims to develop a robust renovation waste estimation approach. By using machine learning regression, a model containing several easy-to-access features was developed. By simply inputting the feature data (including renovation work type and cost; building type, year, height, and floor area; as well as floor height and the number of renovated floors) into the model, the renovation waste generated from the project can be reliably estimated. Validation experiments indicate that the method has a root mean squared error (RMSE) of 141.52, mean absolute error (MAE) of 79.69, and R-square of 0.83. Comparative experiments show that our method performs better than prevailing ones as reported in the literature. This study thus contributes a novel waste prediction model for pre-renovation waste auditing. With proper modifications, the model can be applied to other regions with different building and renovation features. Future research is recommended to further explore advanced waste estimation methods by integrating new technologies for finer CWM.

Undrained basal stability of braced circular excavations in anisotropic and non-homogeneous clays

This paper introduces the plastic stability solutions of braced circular excavations in anisotropic and non–homogeneous clays. Using the framework of Finite Element Limit Analysis (FELA) under axisymmetric conditions, the upper bound (UB) and lower bound (LB) solutions of the stability of excavations can be obtained. The clay is set to be anisotropic, where the Anisotropic Undrained Shear (AUS) model is used as a failure criterion of the surrounding soil. The results of this study are the proposed stability number which is the normalized parameter of the maximum unit weight and the anisotropic undrained shear strength of clay. Four dimensionless parameters are considered in the study: the anisotropic strength ratio, the depth–radius ratio, the depth–embedment ratio, and the strength gradient ratio. The impact of all considered dimensionless parameters on the results of the FELA solutions is examined. A machine learning regression approach, Multivariate Adaptive Regression Splines (MARS), is employed to develop an empirical design equation to predict the stability number of braced circular excavations in anisotropic and non–homogeneous clays. The proposed MARS equation can be a useful and reliable equation to estimate the basal stability of this excavation problem in practice.

THE APPLICATION OF TIME-SERIES FORECASTING MODELS IN GROCERY RETAIL INDUSTRY

The grocery retail landscape in Ukraine has witnessed profound transformations, driven by disruptions like the COVID-19 pandemic and full-scale invasion, leading to unstable consumer behavior and market dynamics. In response, forecasting models must evolve to consider stochastic exogenous factors, such as blackout periods and air alarms. This study explores advanced time series forecasting models and proposes a comprehensive framework for optimal model selection. The study introduces the Neural Prophet, a model that combines interpretability and predictive power by incorporating components like non-periodic trends, periodic seasonality, holiday effects, and regressors. The research methodology involves a comparative analysis of classical time series forecasting methods, machine learning regression approaches, and neural networks. Noteworthy models include LightGBM, RNN, TCN, and Neural Hierarchical Interpolation for Time Series (N-HiTS). Optuna hyperparameter optimization and k-fold cross-validation enhance model accuracy. The study applies the proposed framework to forecast order quantities in the e-commerce segment of the Ukrainian grocery retail company. The system accommodates diverse factors like weather, holidays, and promotions, providing robust decision support. Anomalies are detected using the IQR method, and missing values are filled using Exponentially Weighted Moving Average. Results show the Neural Prophet consistently outperforming other models in 65% of cases, emphasizing its superiority. However, a complete transition to neural models results in reduced accuracy, highlighting the need for a nuanced approach based on data characteristics. The study presents a sophisticated framework for forecasting accuracy, supporting effective operational decision-making. Future research should explore ensemble methods while maintaining computational efficiency, aligning with the ongoing pursuit of optimized forecasting accuracy for informed decision-making in grocery retail.

Assimilation of Deep Learning and Machine Learning Schemes into a Remote Sensing-Incorporated Crop Model to Simulate Barley and Wheat Productivities

Deep learning (DL) and machine learning (ML) procedures are prevailing data-driven schemes capable of advancing crop-modelling practices that assimilate these techniques into a mathematical crop model. A DL or ML modelling scheme can effectively represent complicated algorithms. This study reports on an advanced fusion methodology for evaluating the leaf area index (LAI) of barley and wheat that employs remotely sensed information based on deep neural network (DNN) and ML regression approaches. We investigated the most appropriate ML regressors for exploring LAI estimations of barley and wheat through the relationships between the LAI values and four vegetation indices. After analysing ten ML regression models, we concluded that the gradient boost (GB) regressor most effectively estimated the LAI for both barley and wheat. Furthermore, the GB regressor outperformed the DNN regressor, with model efficiencies of 0.89 for barley and 0.45 for wheat. Additionally, we verified that it would be possible to simulate LAI using proximal and remote sensing data based on assimilating the DNN and ML regressors into a process-based mathematical crop model. In summary, we have demonstrated that if DNN and ML schemes are integrated into a crop model, they can facilitate crop growth and boost productivity monitoring.

Machine learning regression approach for analysis of bearing capacity of conical foundations in heterogenous and anisotropic clays

Machine learning regression approach for analysis of bearing capacity of conical foundations in heterogenous and anisotropic clays

A machine learning regression approach for predicting the bearing capacity of a strip footing on rock mass under inclined and eccentric load

In this study, the Multivariate Adaptive Regression Splines (MARS) model is employed to create a data-driven prediction for the bearing capacity of a strip footing on rock mass subjected to an inclined and eccentric load. The strengths of rock masses are based on the Hoek-Brown failure criterion. To develop the set of training data in MARS, the lower and upper bound finite element limit analysis (FELA) is carried out to obtain the numerical results of the bearing capacity of a strip footing with the width of B. There are six considered dimensionless variables, including the geological strength index (GSI), the rock constant/yield parameter (mi), the dimensionless strength (γB/σci), the adhesion factor (α), load inclined angle from the vertical axis (β), and the eccentricity of load (e/B). A total of 5,120 FELA solutions of the bearing capacity factor (P/σciB) are obtained and used as a training data set. The influences of all dimensionless variables on the bearing capacity factors and the failure mechanisms are investigated and discussed in detail. The sensitivity analysis of these dimensionless variables is also examined.

Correlates of quality of life, happiness and life satisfaction among European adults older than 50 years: A machine‐learning approach

Background and objectivesPrevious research has documented the role of different categories of psychosocial factors (i.e., sociodemographic factors, personality, subjective life circumstances, activity, physical health, and childhood circumstances) in predicting subjective well-being and quality of life among older adults. No previous study has simultaneously modeled a large number of these psychosocial factors using a well-powered sample and machine learning algorithms to predict quality of life, happiness, and life satisfaction among older adults. The aim of this paper was to investigate the correlates of quality of life, happiness, and life satisfaction among European adults older than 50 years using machine learning techniques. Research design and methodsData drawn from the Survey of Health, Ageing and Retirement in Europe (SHARE) Wave 7 were used. Participants were 62,500 persons aged 50 years and over living in 26 Continental EU Member States, Switzerland, and Israel. Multiple machine learning regression approaches were used. ResultsThe algorithms captured 53%, 33%, and 18% of the variance of quality of life, life satisfaction, and happiness, respectively. The most important categories of correlates of quality of life and life satisfaction were physical health and subjective life circumstances. Sociodemographic factors (mostly country of residence) and psychological variables were the most important categories of correlates of happiness. Discussion and implicationsThis study highlights subjective poverty, self-perceived health, country of residence, subjective survival probability, and personality factors (especially neuroticism) as important correlates of quality of life, happiness, and life satisfaction. These findings provide evidence-based recommendations for practice and/or policy implications.

Automation and the changing nature of work.

This study identifies the job attributes, and in particular skills and abilities, which predict the likelihood a job is recently automatable drawing on the Josten and Lordan (2020) classification of automatability, EU labour force survey data and a machine learning regression approach. We find that skills and abilities which relate to non-linear abstract thinking are those that are the safest from automation. We also find that jobs that require ‘people’ engagement interacted with ‘brains’ are also less likely to be automated. The skills that are required for these jobs include soft skills. Finally, we find that jobs that require physically making objects or physicality more generally are most likely to be automated unless they involve interaction with ‘brains’ and/or ‘people’.

An application of machine learning regression to feature selection: a study of logistics performance and economic attribute

This study demonstrates how to profit from up-to-date dynamic economic big data, which contributes to selecting economic attributes that indicate logistics performance as reflected by the Logistics Performance Index (LPI). The analytical technique employs a high degree of productivity in machine learning (ML) for prediction or regression using adequate economic features. The goal of this research is to determine the ideal collection of economic attributes that best characterize a particular anticipated variable for predicting a country’s logistics performance. In addition, several potential ML regression algorithms may be used to optimize prediction accuracy. The feature selection of filter techniques of correlation and principal component analysis (PCA), as well as the embedded technique of LASSO and Elastic-net regression, is utilized. Then, based on the selected features, the ML regression approaches artificial neural network (ANN), multi-layer perceptron (MLP), support vector regression (SVR), random forest regression (RFR), and Ridge regression are used to train and validate the data set. The findings demonstrate that the PCA and Elastic-net feature sets give the closest to adequate performance based on the error measurement criteria. A feature union and intersection procedure of an acceptable feature set are used to make a more precise decision. Finally, the union of feature sets yields the best results. The findings suggest that ML algorithms are capable of assisting in the selection of a proper set of economic factors that indicate a country's logistics performance. Furthermore, the ANN was shown to be the best effective prediction model in this investigation.