Browsing by Author "Zahara, Muslima Tuz"

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Ascertaining the Fluctuation of Rice Price in Bangladesh Using Machine Learning Approach
(IEEE, 2020-07) Hasan, Md. Mehedi; Zahara, Muslima Tuz; Sykot, Md. Mahamudunnobi; Nur, Arafat Ullah; Saifuzzaman, Mohd.; Hafiz, Rubaiya
Rice is the most grown crop in Bangladesh. It is consumed as the main food course in Bangladesh. The price of rice makes a difference in whether people will eat or starve. To know what's going to happen in the rice market using pen and paper is a far cry as well as time-consuming. Machine Learning (ML) provides the facilities to predict the price of any products to prevent a future collapse in the market. The goal of this paper is to predict the price of rice using Machine learning approach. Data collected from the Ministry of Agriculture website, Bangladesh was used to predict the price. Several machine learning algorithms were used to make this prediction i.e. Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naïve Bayes, Decision Tree and Random Forest. All these algorithms are analyzed to find out which algorithm provides the best performance. Now, we can predict the price of rice, whether it is reasonable, low, or high based on the results achieved by the mentioned algorithms.
Solving Onion Market Instability by Forecasting Onion Price Using Machine Learning Approach
(Scopus, 2020) Hasan, Md. Mehedi; Zahara, Muslima Tuz; Sykot, Md. Mahamudunnobi; Hafiz, Rubaiya; Mohd. Saifuzzaman
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).