Implementation of Data Analysis Algorithm Using Julia

Registration ID: IJNRD_196485

Published ID: IJNRD2305646

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Mr. Hojiwala Robin, Dr. Hitesh Joshi, Mrs. Ridhdhi

Linear Regression, Logistic Regression, NaiveBayes Regression, Classification Regression Tree (CART), K – Mean Clustering, XGBoost

Now a days cloud computing rapidly grow in computer and IT field. Every second so many data generated by multiple devices. Most of devices connect through internet. Now a days it’s require the data store and analyze for the future decision. There are many algorithms are available for the data analysis. The generated data is in the huge amount. The Big data is available in various form like images, strings, numbers etc. We need algorithm that analyze data and give prediction about future decision. The gradient boosting algorithm is one of them that analyze different format data and give result. Gradient boosting algorithm analyze big data and select effective algorithm related to best outcome. Gradient Boosting Algorithm combine all the algorithm and give efficient and accurate data. Gradient Boosting Algorithm mainly associate with the server based analytics algorithm and we can use them for real time data. In our research Julia programming language is used because Julia is so fast as compared to Python language. Julia is reliable and accuracy is more in compare to Python. Julia is useful for server based real time data analysis and it is very fast as compare to python. Here we are using medical data of America in different location.

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Mr. Hojiwala Robin, Dr. Hitesh Joshi, & Mrs. Ridhdhi (May-2023). Implementation of Data Analysis Algorithm Using Julia. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(5), g376-g384. https://ijnrd.org/papers/IJNRD2305646.pdf

Volume 8 Issue 5, May-2023

Pages : g376-g384

Paper Reg. ID: IJNRD_196485

Published Paper Id: IJNRD2305646

Research Area: Computer Engineering 

Author Type: Indian Author

Country: Surat, Gujarat, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2305646.pdf

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