• Akash Kumar Bhagat

India's economy is mostly based on agricultural yield growth and related agro-industry goods, since it is an agricultural nation. Agriculture is an important and necessary sector, but making even a modest profit in this field is difficult, if not impossible in certain instances. This is due to the huge impact of environmental variables on agriculture, such as water, weather, and so on. Rainwater, which is extremely unpredictable in India, has a significant impact on agriculture. India is now making tremendous progress in terms of technological development. As a consequence, technology will benefit agriculture by increasing crop production, resulting in higher yields for farmers. The loss can certainly be reduced if we concentrate more on crop selection. Crop selection is influenced by a variety of variables, including physical ones such as soil and season, economic considerations such as market price, human characteristics such as experience, crop knowledge, crop profiles, and the availability of resources like as machinery and manpower. By selecting the appropriate crop type with a high yield rate, you may make a significant profit. In this research, we use the machine learning algorithm Nave Bayes to suggest the crop with the highest production rate, taking into account environmental, physical, and economic aspects. The main goal of this project is to provide a method by which farmers of all levels, from novice to expert, may maximize their profits from agriculture while also simplifying their farming practices.

Agriculture, Algorithm, Machine Learning, Naive Bayes, Polynomial Regression.

"Machine Learning Recommendations for Crops Based on Rain Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 1, page no.415-420, January-2019, Available :http://www.jetir.org/papers/JETIRFT06079.pdf

"Machine Learning Recommendations for Crops Based on Rain Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 1, page no. pp415-420, January-2019, Available at : http://www.jetir.org/papers/JETIRFT06079.pdf