Abstract
Cow meets a significant number of demands for meats in South Asian countries, and a huge number of cows were sold in Bangladesh on the eve of Eid al-Adha. Cow prices depend on several factors, and determining the price of a cow is a cumbersome task for an inexperienced individual. Nowadays machine learning algorithms are significantly used for accurate price estimation. This study presents an efficient and accurate tool for determining cow prices using several characteristics of cows based on machine learning. Sixteen characteristics of a cow are considered in this study to determine its price. Four different machine learning algorithms were used and evaluated in this study for generating an accurate price prediction model. In this machine learning based systematic analysis, several experimental studies are conducted for evaluating models more precisely. Performance matrices were also used to evaluate machine learning algorithms.
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References
Predicting feeder cattle prices. https://www.alberta.ca/economics-and-marketing-predicting-feeder-cattle-prices.aspx
Cattle traders expect good business this Eid ul Azha. https://www.thedailystar.net/business/economy/news/cattle-traders-readying-eid-market-3048601
Livestock Industry of Bangladesh: Growth, Challenges and Future Opportunities. https://businessinspection.com.bd/livestock-industry-of-bangladesh/
Price Prediction: How Machine Learning Can Help You Grow Your Sales. https://dlabs.ai/blog/price-prediction-how-machine-learning-can-help-you-grow-your-sales/
Rahman, M.A., Kabir, M.A., Haque, M.E., Hossain, B.M.M.: A machine learning-based price prediction for cows. In: 2021 American Journal of Agricultural Science, Engineering, and Technology 5(1), 64–69 (2021)
Lawrence, J., Hoffman, C.: Cattle Price Forecast Errors: Live Cattle Futures and Seasonal Index. Futures, pp. 2000–2008 (1990)
Newton, J., Thraen, C.S., Bozic, M.: Evaluating policy design choices for the margin protection program for dairy producers: an expected indemnity approach. In: 2016 Applied Economic Perspectives and Policy, Agricultural, and Applied Economics Association, 38(4), pp. 712–730 (2016)
Eldridge, R.W.: Kentucky Feeder Cattle Price Analysis: Models for Price Predictions and Grazing Management, p. 173 (2015)
Kinnischtzke, J.N.: Forecasting cattle price trends. In: 1988 North Dakota State University ProQuest Dissertations Publishing, p. 1334941 (1988)
Muwanga, G.S.: Cattle price dispersions, spatial relationships, and price predictions: a case study of selected United States cattle markets. In: 1998 PhD diss, Utah State University (1998)
Marsh, J.M.: A rational distributed lag model of quarterly live cattle prices. In: 1983 American Journal of Agricultural Economics 65(3), 539–47 (1983)
Franzmann, J.R., Walker, R.L.: Trend models of feeder, slaughter, and wholesale beef cattle prices. In: 1972 American J. Agricultural Econ. 54(3), 507–12 (1972)
Coffey, B., Tonsor, G., Schroeder, T.: Impacts of changes in market fundamentals and price momentum on hedging live cattle. In: 2018: J. Agricultural and Res. Econ. 43, 18–33 (2018)
Moser, G., Khatkar, M.S., Hayes, B.J., et al.: Accuracy of direct genomic values in Holstein bulls and cows using subsets of SNP markers. In: 2010 Genet Selection Evol 42, 37 (2010)
Blakely, P.K.: A quarterly feeder cattle price forecasting model with application towards the development of a futures market strategy. In: 1976 PhD diss., Virginia Polytechnic Institute and State University (1976)
Varma, A., Sarma, A., Doshi, S., Nair, R.: House price prediction using machine learning and neural networks. In: 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 1936–1939, Coimbatore, India (2018)
Gumus, M., Kiran, M.S.: Crude oil price forecasting using XGBoost. In: 2017 International Conference on Computer Science and Engineering (UBMK), pp. 1100–1103, Antalya, Turkey (2017)
Machine learning classifiers – the algorithms and how they work. https://monkeylearn.com/blog/what-is-a-classifier/
Understanding Random Forest. https://www.analyticsvidhya.com/blog/2021/06/understandingrandom-forest/
Mean Absolute Error. https://c3.ai/glossary/data-science/mean-absolute-error/
Mean Squared Error: Overview, Examples, Concepts and More. https://www.simplilearn.com/tutorials/statistics-tutorial/mean-squared-error
Root Mean Square Error (RMSE). https://c3.ai/glossary/data-science/root-mean-square-error-rmse/
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Alam, A.K.M.T., Nirob, Z.H., Urme, A.J., Hridoy, R.H., Habib, M., Ahmed, F. (2023). A Systematic Analysis for Machine Learning Based Cow Price Prediction. In: El Ayachi, R., Fakir, M., Baslam, M. (eds) Business Intelligence. CBI 2023. Lecture Notes in Business Information Processing, vol 484 . Springer, Cham. https://doi.org/10.1007/978-3-031-37872-0_2
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