A NOVEL METHOD FOR COMPUTATIONALLY EFFICACIOUS LINEAR AND POLYNOMIAL REGRESSION ANALYTICS OF BIGDATA

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In propose paper author is saying machine learning algorithms exists from centuries and often almost all algorithms models are not accurate and perform wrong prediction and to overcome from this problem author is suggesting to apply optimization techniques to algorithm to perform accurate prediction. In propose paper author is applying Linear & Polynomial optimization technique to Regression algorithms and then compare its performance without optimized Regression algorithm and evaluate its performance in terms of SUM OF SQUARE ERROR (SSE0. For any Regression algorithm the lower the SSE the better is the algorithm.
Optimization algorithms means tuning algorithm to get better result and in propose paper author using Regression with LINEAR and POLYNOMIAL. Optimized regression giving less SSE compare to pre or without optimize Regression algorithm.
In propose paper author applying Regression on medicine dataset called EMBASE but this dataset not available so we are using medicine SALES and MANUFACTURING dataset which will predict manufacturing quantity medicines for sales.