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In India, the agriculture is main profession for more than sixty percent of the population. The stakeholders of agriculture in India, facing plenty of problems that leads the people of the country to shift their profession and lets them migrate towards urban area. So, need of implementing technology in agriculture is must in future days because as population is increasing in exponential form as the result huge requirement of food and agricultural product. The data analytics will play a significant role in agricultural dataset for implementing prediction and recommendation system in the sector. Yield is one of the factors to be considered in the agriculture that determines the wellness and prosperity of the farmer. In this paper deals with prediction system to predict yield of areca nut product in Puttur taluk, Dakshin Kannda District, Karnataka state in India. The time series data analytics model known as Auto Regressive Integrated Moving Average (ARIMA) model is used for yield prediction system. The research is mainly focused on forecasting of areca nut production for next four or five years in Puttur taluk. It compares various ARIMA models with performance criteria and selects best model for prediction purpose. The diagnostic check is carried out to test the system performance After the prediction, then the actual values and predicted values are compared and presented in the form graphical representation. [ABSTRACT FROM AUTHOR]

In India, agriculture represents the primary occupation of more than 60% of the population. In terms of GDP, economic growth, traditional aspects, and social aspects, agriculture is essential for the country's development. The Indian farmers experienced numerous issues that have an impact on their way of life because the expansion in the agronomy business has not been as expected during the past two decades. Price fluctuation is one of the major issues faced by farmers, and as a result, they cannot get a reasonable price for their commodity. Also, it is very problematic to decide today without knowing the future price. So, this paper focused on finding a solution to the uncertainty problem in price faced by farmers that helps them take appropriate decisions during the farming process. The paper mainly concerns predictive data analytics using the ARIMA model, which predicts the price of areca nut products for the next 4 years using the past ten-year price dataset. The ARIMA model is a time series approach and a very appropriate framework for predicting future prices compared to other models. This paper includes a step-by-step procedure for the ARIMA techniques for forecasting price of agriculture commodity, and the outcomes are represented in the form of tables and graphical representations. [ABSTRACT FROM AUTHOR]