Abstract:
Market uncertainty is a continuing problem in Bangladesh. As a result, the prices of our
common materials fluctuate a lot. It has a significant impact on the components we use
every day. In Bangladesh, potato is the third most commonly cultivated crop. In
Bangladesh, it is served as the main meal. Bangladesh is a developing country. Potatoes
are the third most popular vegetable in Bangladesh, after rice and wheat, with low-income
individuals eating more potatoes than other vegetables. The price of potato affects whether
people would eat or go hungry. In this era of artificial intelligence, we now have advance
software that can extract information from data. Machine Learning is currently quite
popular for predicting this sort of unpredictable fluctuation. We created our dataset using
information obtained from Bangladesh's Ministry of Agriculture. We used six typical
regression techniques to estimate the price of potato. We used Random Forest Regressor
(RFR), Decision Tree, Gradient Boosting, Lasso regressor, Linear Regression and Neural
Network Regressor models to predict the daily potato price. All of the models we've created
yield results that are very satisfactory. Among all the models we created, the Random
Forest Regressor (RFR) produced the best results in all stages
