Predict the Inflation of Food Prices based on the Linear Regression Models

Abstract

Using the large "Global Food Price Inflation" dataset from Kaggle, this paper provides a thorough analysis of food price inflation in Cameroon. In the framework of Cameroon's dynamic economy, the research focuses on comprehending the complex financial indicators and their relationship to trends in food prices between 2015 and 2017. The study addresses the crucial role that agriculture plays in Cameroon's economy by using both polynomial and linear regression models to analyze and forecast food inflation rates. Because of its simple methodology, the linear regression model works better for making short-term predictions because it can capture the main inflation rate trend without getting bogged down in the details of capturing small variations in the data. Conversely, polynomial regression tends to overfit in this situation even though it is skilled at simulating non-linear relationships. The study's conclusions emphasize how crucial it is to select the right model for economic forecasting and stress the value of clarity and simplicity in identifying key economic trends, particularly in short-term scenarios. This research adds significant knowledge to the field of economic analysis and forecasting methodology; it is especially pertinent to policymakers, economists, and researchers who specialize in developing economies.