Abstract
Agriculture is one of India’s crucial sectors in terms of its contribution to employment and the country’s (Gross Domestic Product) GDP. It has primarily emerged as an essential - growing sector in the global economy since independence. [21]However, the non-realization of the reasonable price for agricultural crop production leads to the introduction of loan waivers, which impact the credit culture and weaken the farming economy and growth. The presented work aims to perform exploratory data analytics on the GDP data in agriculture public domain by performing feature engineering on the factors affecting the agricultural GDP using the data for the period 1961 to 2019. It further builds a multi-linear prediction model to forecast the Agriculture Sector’s economic performance in terms of GDP and NPAs generated by the Agricultural Sector using Machine Learning Techniques. Keywords: Multiple Linear Regression, Ordinary least Squared, Agricultural GDP, Non-Performing assets, Indian Economy.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IOP Conference Series: Materials Science and Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
