Sis Maharjan et al, International Journal of Computer Science and Mobile Computing, Vol.9 Issue.4, April- 2020, pg. 11-14 © 2020, IJCSMC All Rights Reserved 11 Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320–088X IMPACT FACTOR: 6.199 IJCSMC, Vol. 9, Issue. 4, April 2020, pg.11 – 14 CROPS RECOMMENDATION BASED ON RAIN DATA USING MACHINE LEARNING Sis Maharjan; Ramsharan Diyali; Asst. Professor Shashikala Computer Science and Engineering Department, Jain University, Ramanagara District, Karnataka 562112, India alexsis1001@gmail.com; babudiyali986@gmail.com; shashi.hk85@gmail.com Abstract— India being an agricultural country, its economy predominantly depends on agriculture yield growth and allied agro-industry products. In India, agriculture is largely influenced by rainwater which is highly unpredictable. India now is rapidly progressing towards technical development. Thus, technology will prove to be beneficial to agriculture which will increase crop productivity resulting in better yields to the farmer. In this paper considering the environmental, physical and economic factors we are using Naïve Bayes to recommend the crop which gives the high yield rate. Main objective of this project is to explore a way by which farmers from beginner to experienced level get maximum profit from agriculture and in the meantime can ease their way of agriculture. Keywords— Machine learning application, Naïve Bayes, Rain Data, Crop recommendation, supervised learning I. INTRODUCTION In this paper we show that we have used Naïve Bayes algorithm source to code a program that will take rain data for a specific region that we provide. Along with the rain data we also provide few suitable crops and the water it requires to grow. Our work here is to verify whether using technology in the most dominant economic field of India i.e. agriculture improves the current status of agriculture. This survey paper will show the way we plan to collect data and process our project. II. LITERATURE SURVEY The paper [1] states the requirements and planning needed for developing a software model for rainfall prediction. The paper [2] makes a study of comparative study of classification algorithms and their performance in predicting the crops for the high efficiency in yield. The paper [3] uses the naïve Bayes algorithm for Crop Prediction on the Region Belts of India. For the testing of software, the data set of Rain is collected from Indian Meteorological Department website. III. METHODOLOGY 3.1 Dataset Collection Dataset for this project is collected from Indian Meteorological Department Website. Collected Dataset is of Karnataka Region which is further divided into 3 sub-regions. They are Coastal Karnataka, North Interior