Automated Modular Data Analysis and Visualization System with Predictive Analytics Using Machine Learning for Agriculture field
Economy of an India is majorly depending on growth of agricultural yields, and its allied agro industry products. Prediction of agricultural yield growth is a most difficult for the agriculture departments across iglobe. 1The agricultural yields growth is depending on several factors. In this paper historical data is analyzed and a predictive model was designed. 1Several Regression models such as linear model, multiple linear model and nonlinear models were tested for an effective prediction, or for forecasting the agricultural yield for a variety of crops. Along with this the crop trade for local farmers is a very complicated and tedious task and can get easily mislead by the system we are proposing helps them to analyze the crop availability and also according to market prices can be able to predict various characteristics of the trade. The proposed method is capable of producing the visual representation after data analysis and provides the prediction results in a visual format. And also the unstructured data analysis is implemented in the system. In the proposed method, the pre-processed input data will be sent to perform a descriptive analysis and a predictive analysis. In the descriptive analysis, the data is analyzed and the summary of the analysis is given as the output.In Predictive analysis, there are steps to be considered for the analysis. At the end summary of predicted results are given as output and summary of both descriptive analysis and predictive analysis is given as final report in visual format.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Copyright & License
All Research Plus Journals (RPJ) publish open access articles under the terms of the Creative Commons Attribution (CC BY-SA 4.0) https://creativecommons.org/licenses/by-sa/4.0/ License which permits use, distribution and reproduction in any medium, provided the original work is properly cited & ShareAlike terms followed.
Copyright on any research article in a journal published by a RPJ is retained by the author(s). Authors grant RPJ a license to publish the article and identify itself as the original publisher. Upon author(s) by giving permission to RPJ either via RPJ journal portal or other channel to publish their research work in RPJ agrees to all the terms and conditions of https://creativecommons.org/licenses/by-sa/4.0/ License and terms & condition set by RPJ.
3rd party copyright
It is the responsibility of author(s) to secure all necessary copyright permissions for the use of 3rd-party materials in their manuscript.
Research Plus Journals Open Access articles posted to repositories or websites are without warranty from RPJ of any kind, either express or implied, including, but not limited to, warranties of merchantability, fitness for a particular purpose, or non-infringement. To the fullest extent permitted by law RPJ disclaims all liability for any loss or damage arising out of, or in connection, with the use of or inability to use the content.