| Autor: |
Arun Prakash Agarwal, Santosh Kr. Gupta |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS). |
| DOI: |
10.1109/icccis51004.2021.9397078 |
| Popis: |
Sugar is an important commodity that is consumed all around the world. India is the largest producer country of sugar that produced 33 million metric tons of sugar in 2018/2019. Sugar production depends upon many parameters such as Area under sugar cane, production of sugar cane, Yield of cane per hectare, No. of Factories in operation, Total cane crushed, etc. The present paper" Predicting Total Sugar Production Using Multivariable Linear Regression" emphasis the forecast of the production of sugar. Forecasting enables to control of the business by anticipating risks and opportunities. Accurate forecasting is essential for the business Time-series data of sugar production from 1931 to 2018 is used to make the model using multivariate linear regression for predicting total sugar production. The Source of data is Co-operative Sugar Vol.51, No.6 February 2020. For analysis 80% data used for training the model and 20% data used for testing the model. Heatmap is used for finding the correlation among the different parameters. Multivariate linear regression model best fit for predicting the total production of sugar. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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