Soybean Oil Price Prediction Using Machine Learning Approach

Abstract

In Bangladesh, market uncertainty is an ongoing issue. The pricing of our ordinary ingredients hence vary so often. It effects the component that we ingest every day considerably. In Bangladesh there are several types of oil. One of them is soybean oil. In Bangladesh, almost every meal includes soybean oil. The prices of the items that are used every day have to be recorded but manually organizing it is a difficult operation. It is quite handy to keep track of the pricing for persons living below the poverty line. Now we have sophisticated devices in this era of artificial intelligence which can find information from the data. Data insight may be used with the use of machine learning algorithms for prediction purposes. Prediction can be a successful means of eliminating market volatility. We strive to identify approaches of machine learning to estimate the future price of soy bean oil in our study. Our analysis is based on raw data from the Ministry of Agriculture of Bangladesh (MOA). Machine Learning has numerous prediction methods. For this, our solution was founded using Gradient Boosting, Decision Tree Regression, Lasso Regression, Linear Regression, MLP Regression, Random Forest algorithms. We compared the accuracy in performance to determine the best accuracy All algorithms perform about symmetrically. Our major objective was to find soybean oil future prices.