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Weather prediction is gaining popularity very rapidly in the current era of Artificial Intelligence and Technologies. It is essential to predict the temperature of the weather for some time. In this research paper, we tried to find out the pattern of the average temperature of Bangladesh per year as well as the average temperature per season. We used different machine learning algorithms to predict the future temperature of the Bangladesh region. In the experiment, we used machine learning algorithms, such as Linear Regression, Polynomial Regression, Isotonic Regression, and Support Vector Regressor. Isotonic Regression algorithm predicts the training dataset most accurately, but Polynomial Regressor and Support Vector Regressor predicts the future average temperature most accurately.
Ashfaq Ali Shafin. 2019. \u201cMachine Learning Approach to Forecast Average Weather Temperature of Bangladesh\u201d. Global Journal of Computer Science and Technology - D: Neural & AI GJCST-D Volume 19 (GJCST Volume 19 Issue D3): .
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
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Total Score: 101
Country: Bangladesh
Subject: Global Journal of Computer Science and Technology - D: Neural & AI
Authors: Ashfaq Ali Shafin (PhD/Dr. count: 0)
View Count (all-time): 273
Total Views (Real + Logic): 4901
Total Downloads (simulated): 1290
Publish Date: 2019 07, Wed
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Weather prediction is gaining popularity very rapidly in the current era of Artificial Intelligence and Technologies. It is essential to predict the temperature of the weather for some time. In this research paper, we tried to find out the pattern of the average temperature of Bangladesh per year as well as the average temperature per season. We used different machine learning algorithms to predict the future temperature of the Bangladesh region. In the experiment, we used machine learning algorithms, such as Linear Regression, Polynomial Regression, Isotonic Regression, and Support Vector Regressor. Isotonic Regression algorithm predicts the training dataset most accurately, but Polynomial Regressor and Support Vector Regressor predicts the future average temperature most accurately.
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