International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-5, January 2020 2790 Retrieval Number: D8650118419/2020©BEIESP DOI:10.35940/ijrte.D8650.018520 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Paddy Crop Production Analysis Based on SVM and KNN Classifier Pankaj Bhambri, Inderjit Singh Dhanoa, Vijay Kumar Sinha, Jasmine Kaur Abstract: In earlier times, the people used to fulfill their own requirements by cultivating the crops in their own land regions. In the economy of a nation, an important role is played by the farming sector. A variety of fungal and bacterial infections infect various crops. Reducing the use of insect killers is a prominent demand of sustainable development. The minimum use of pesticides saves environment and increases the quality of crops. To improve the accuracy of paddy production prediction the KNN is implemented for the paddy production prediction in data mining. The SVM classifieris also implemented which is compared with the KNNclassifier. The presented and earlier classifier will be applied in python and it is expected that accuracy will be improved and execution time will be reduced. It is analyzed that KNN performs well as compared to SVM classifier for the paddy production prediction as per the obtained analytic results. Keywords: Paddy Crop Production, SVM Classifier, KNN classifier. I. INTRODUCTION Data Mining is easy and effective to process the sorted information. Most of the large organizations use data mining such that the issue of including huge amount of data in warehouses can be resolved by applying simple methods. There are several data mining tools applied in the software for generating and automated analysis process. Furthermore, it is important to use historical data such that new information can be extracted. Thus, the use of software is reduced through the automated analysis process. The time and cost can be reduced using this software. Since the application fields can easily use this technology for analysis, all the complex issues are resolved by it [1]. Thus, the major objective of applying this technique is sorting the data available in unorganized manner. The scientific data, military intelligence, business transactions and satellite pictures are collectively used for generating such large rich source of data. From such collective raw data, extracting only the required data is necessary. For data mining, knowledge discovery is prominent. Theiterative process is applied here for extracting important information. The predictive analytics decides the future of data mining. The terms data mining and data extraction seems like similar. However, there is an important difference between these two terms. In data extraction, the data is obtained from one data source and loaded into a targeted database. Revised Manuscript Received on January 15, 2020. Dr. Pankaj Bhambri, Dept. of Info. Tech., Guru Nanak Dev Engineering College, Ludhiana, Punjab, India. E-mail: pkbhambri@gndec.ac.in , Dr. Inderjit Singh Dhanoa, Dept. of CSE, Guru Nanak Dev Engineering College, Ludhiana, Punjab, India. E-mail: cecm.cse.vk@gmail.com , Dr. Vijay Kumar Sinha, Dept. of CSE, Chandigarh Engineering College, Mohali, Punjab, India. E-mail: inderjit26@gndec.ac.in , Er. Jasmine Kaur, Dept. of CSE, IKGPTU, Jalandhar, Punjab, India. E-mail:jasminekaur@gmail.com Therefore, the data can be extracted from a source or legacy system for its storage into a standard database or data storehouse. However, in data mining, the unclear or hidden predictive information is extracted from big databases or data storehouses. Further, data mining as knowledge discovery is used to search patterns in data warehouses [2]. Here, computational methods from statistics and pattern identification are utilized by data mining. Therefore, the nature of data mining is defined by the searching of patterns in data. Different data mining tools and techniques are used to build a predictive analytical model. Large databases are accessed to extract data in primary step. Here, the crop yield needs to be forecasted and the ideal condition needs to be identified such that high yield of paddy production can be performed [3] K-Nearest neighbor is a lazy learner technique. This algorithm depends on learning by analogy. It is a supervised classification method. This classifier is used extensively for classification purpose. This classifier waits till the last minute prior to build some model on a specified tuple as compared to earlier classifiers. The training tuples are characterized in N-dimensional space in this classifier. This classification model looks for the k training tuples nearest to the indefinite sample in case of an indefinite tuple. Then, this classifier puts the sample in the closest class. This algorithm can be implemented easily. This algorithm performs quickly in case of small data sets. However, this algorithm performs slowly on huge amount of data and big size data. This approach is responsive to the value of k [4]. The performance of the classification model also gets affected by this. Support vector machines classification model gives good performance on unknown data. The maximum margin classification model is the simplest example of this algorithm. This classification model provides solution of the most fundamental classification issue. This issue is known as binary classification with linear separable training data. This classification model finds hyperplane with the maximal margin. In support vector machine, some slack variables are established to manage the nonlinear separable cases. Some training errors could be handled using this phenomenon. This reduces the effect of noise in training data. Through the selection of uppermost probability, classification is executed. This classifier involves a penalty metric that permit a definite amount of misclassification. This is mainly imperative for non separable training sets [5]. II. RELATED WORK UraiwanInyaem, et al., (2018) stated that various investigations had been performed in the domain of data mining.