Systematic Literature Review of Data Distribution in Preprocessing Stage with Focus on Outliers

Data Preprocessing refers to the steps and techniques applied to raw data before it is ready to be analyzed or modeled as a substantive part of the data flow and aims to transform, clean and organize data in a revised way for the quality, relevance and efficiency of subsequent data analysis tasks. Handling outliers in the N2O Emissions Dataset, Fertilizer Prediction and Crop Yield Prediction Dataset is an important step in the data analysis process. The approach taken will depend on the specific context and purpose of the analysis, and it is important to carefully consider the impact of outliers on the results. Using the methods discussed researchers and analysts can effectively identify and treat outliers in the N2O Emissions Dataset, Fertilizer Prediction, Crop Yield Prediction Dataset, and produce more accurate and reliable results. Implemented a systematic literature that involved searching for articles published from 2015 to 2023 for review. The quality of the existing studies used the assessment criteria of 50 relevant studies identified as having been conducted following systematic literature guidelines.