Abstract:
Price is the key factor in financial activities. Unexpected fluctuation in price is the sign of market instability. Nowadays Machine learning provides enormous techniques to forecast price of products to cope up with market instability. In this paper, we look into the application of machine learning approach to forecast the price of onion. The forecast is based on the data collected from Ministry of Agriculture, Bangladesh. For making prediction we used machine learning algorithms e.g. K- Nearest Neighbor (KNN), Naïve Bayes, Decision Tree, Neural Network (NN), Support Vector Machine (SVM). Then we assessed and compared our techniques to find which technique provides the best performance in term of accuracy. We find all of our techniques provide analogous performance. By above mentioned techniques we seek to classify whether the price of onion would be preferable (low), economical (mid), expensive (high).
