Abstract
Economic development is aided by farming. Farming is primarily defined by the utilization of family labours, which is limited in terms of land, water, and capital resources. Farmers must choose which agricultural goods to be produced based on various parameters which needs technical methods to make the best decision for cultivation. Machine learning algorithms shall be the best choice to solve above problem and predict the prices of agricultural goods based on the weather conditions. This paper suggests different regression models have been investigated to predict vegetable prices. Based on this predictions, vegetable prices are forecasted to aids farmers to plan their next crop and to avoid hyperinflation. In this work, weather data are collected using web scraping whereas the weather data is collected from the weather channel website for appropriate period. The dependency of weather data and the vegetable price is derived by graphically plotting both date for every five days. The data set is created by combining both the weather and vegetable data. The machine learning algorithms such as decision tree, random forest, linear regression are be applied on the weather-price dataset for testing and training. Here 80% of the data is utilized for training whereas 20% data is utilized for testing. The accuracy prediction of different models are calculated and compared. Decision Tree Regressor shows highest prediction accuracy of 92.17%
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Sridevi, S., Hemalatha, K., Gowthami, S. (2023). Collating Weather Data and Grocery Cost Using Machine Learning Techniques. In: Shaw, R.N., Paprzycki, M., Ghosh, A. (eds) Advanced Communication and Intelligent Systems. ICACIS 2023. Communications in Computer and Information Science, vol 1921. Springer, Cham. https://doi.org/10.1007/978-3-031-45124-9_3
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DOI: https://doi.org/10.1007/978-3-031-45124-9_3
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