• N.Sendhil Kumar
• S.Karthick
• P.Haritha Reddy
• V. Pavani

Scalability is a key feature for big data analysis and machine learning frameworks and for applications that need to analyze very large and real-time data available from data repositories, social media, sensor networks, smart phones, and the Web. Scalable big data analysis today can be achieved by parallel implementations that are able to exploit the computing and storage facilities of high performance computing (HPC) systems and clouds, whereas in the near future Exascale systems will be used to implement extreme-scale data analysis. n. The platform supports the collection of social media data and applies many filters for cleaning and further use for machine learning (ML) which is random forest algorithm based systems. Our focus has been primarily on healthcare-related research, which shows the strength of the presented platform. However, the platform itself is malleable to any topic of interest. Data collected and processed are suitable for further ML analysis. We present our platform using a specific healthcare search topic to emphasize the power of our system for future research endeavors in the healthcare field.

Twitter, Machine Learning, Random Forest classifier

"A Scalable Platform to Collect and Analyse Big Data in Twitter using Random forest Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 10, page no.d391-d395, October-2022, Available :http://www.jetir.org/papers/JETIR2210361.pdf

"A Scalable Platform to Collect and Analyse Big Data in Twitter using Random forest Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 10, page no. ppd391-d395, October-2022, Available at : http://www.jetir.org/papers/JETIR2210361.pdf