NS-SVM: Bolstering Chicken Egg Harvesting Prediction with Normalization and Standardization

Abstract

Breeding chickens and chicken eggs are poignant, and recent studies have applied computer science to optimize this field, including chicken egg harvesting prediction. However, existing research does not emphasize the importance of data transformation to obtain optimum chicken egg harvesting prediction. This paper proposes the normalization and standardization-bolstered support vector machine (NS-SVM) method, namely normalization, and standardization, to improve the prediction of chicken egg harvest using SVM. First, we obtain the chicken egg dataset from Africa using Kaggle. The problem and solution become urgent, whereas chicken egg production can ease businesspeople to invest in chicken eggs. We adopt the normalization and standardization method from previous research. However, the notation is to differentiate the method from legacy SVM. The dataset has up to 13 features. Then we apply standard pre- processing such as label encoding and random oversampling. We also review the dataset feature using the Pearson correlation coefficient (PCC). We use two SVM kernels: radial basis function (RBF) and the 2nd-degree polynomial. Then we again apply the same model but by applying normalization and standardization. We use cross- validation with 𝑲 = 𝟏𝟎 to measure the Accuracy of the compared models. The results show that normalization and standardization positively affect the prediction model of the two SVM kernels. The model with the highest performance is NS-SVM with a 2nd-degree kernel, namely 𝑨𝒄𝒄𝒖𝒓𝒂𝒄𝒚 = 𝟎. 𝟗𝟗𝟔. At the same time, the model with the lowest performance is SVM with RBF, namely𝑨𝒄𝒄𝒖𝒓𝒂𝒄𝒚 = 𝟎. 𝟗𝟖𝟔. In addition, the results of ROC AUC analysis show that the performance of our model on the imbalanced dataset with a moderate degree is 𝑨𝑼𝑪 = 𝟎.𝟗𝟐𝟕 to 𝟎.𝟗𝟗𝟑.