## 1 Introduction  
Odors from livestock facilities adversely affect both the quality of life of areas neighboring the facility and the working environment of livestock workers. Odors considerably impair quality of life, and the odor substances can cause severe diseases, such as headaches, vomiting, stress and even cancer [1; 2; 3]. Therefore, it is necessary to improve the environment of livestock facilities by predicting and preventing the occurrence of odors in advance. Currently, Korea's Malodor Prevention Act enforces concentration regulation on complex odors measured using the olfactory measurement method and concentration evaluations of 17 designated odor substance methods, which are instrumental analysis methods. The olfactory measurement method has the advantage of being able to respond to various odor substances and evaluate odors in a situation similar to the sense of damage felt by residents of odor-affected areas. However, it cannot guarantee complete objectivity due to differences in individual physiology and olfactory responses affecting the perception of a smell's concentration [4]. On the other hand, while the odor substance concentration evaluation method presents objective data for a single odor substance, it is insufficient to identify the odor with these substances alone [5]. Therefore, research on odor estimation by measuring the concentration of odor substances is being undertaken in a variety of fields to improve the objectivity of odor evaluation. [PERSON] et al. (2019) [6] conducted a study on estimating odor concentration using a statistical method called partial least squares regression (PLSR) by converting the concentration of odor substances from composting plants into Odor Activity Values (OAV). [PERSON] et al. (2020) [7] reported that the estimation performance was excellent during their study to estimate the concentration of odor substances using on-site odor concentration evaluation and laboratory evaluations of odor samples. Similarly, [PERSON] et al. (2016) [8] used principal component analysis (PCA) to analyze odor and develop a multivariate statistical prediction method. The study utilized a human panelist and chemical measurements of odorants by Proton-Transfer-Reaction Mass Spectrometry (PTR-MS) on-site measurements of odor concentration measured by dilution to the threshold. [PERSON] et al. (2019) [9] applied artificial neural networks (ANN) and decision trees to predict the odor properties of post-fermentation sludge from two biological-mechanical treatment plants located in the north of Poland. In addition, [PERSON] et al. (2020) [10] predicted odor intensity by using odor pollutants and a support vector machine (SVM) to perform visual analysis. [PERSON] et al. (2018) [11] used random forest and PLR to predict and correlate the characteristics of chemical volatile organic compounds (VOCs) and wine. Similarly, [PERSON] et al. (2020) [12] predicted odor using water quality data and ANN. [PERSON] et al. (2021) [13] made the electronic Nose (e-Nose) system to classify the odors in polluted air near a cattle ranch as low, high, or normal. Thus, while many studies based on a large amount of data or complete data are present, studies that utilize a small amount of data or incomplete data are insufficient. This study was conducted based on a small sample of data (\(n=57\)) with a large amount of missing data. The goal was to use various methods to identify the best model for odor prediction in the case of small odor substances with a large amount of missing data. In this analysis process, missing preprocessing was an essential process since missing this step can adversely affect the outcome. Generally, the removal method is suggested for variables with a high missing rate, it leads to data information loss and can reduce predictive performance [14; 15; 16]. Hence, we compared the imputation and removal methods for missing data. Finally, we tried to identify the odor substances that have the most influence on odor prediction.