Abstract: The Optimization provides a way through which an optimum can be achieved. It is all about designing accurate and optimal output for a given problems with using minimum available resources. It is a task which refers to minimizing an objective function f(x) parameterized by x or it is the task which refers minimizing the cost function using the model’s parameters. In machine learning optimization is slightly different. Usually most of the problems, are very much aware about shape, size and type of data. Such information helps us to know where need improve. In case of machine learning optimization works perfectly when there is no knowledge about new data. The method proposed in this paper is named as Optimization with adaptive learning which is used to minimize the cost in term of number of iterations for linear regression to fit the correct line for given dataset to reduce residual error. In regression analysis a curve or line fit in such a way for the data objects, that the differences of distances between the data points and curve or line is always minimum. Proposed approach Initialize random values for parameters of linear model and calculate Error (SSE). Our objective is minimizing the values of SSE, if SSE is large, need to adjust the selected initial values. The size of the step used in each iteration is direction movement to reach the local minimum for optimal value. After performing certain repetitions of the deviation, minimum value for SSE has found and it has a stable value with no change. Real life data set have been used for expositional analysis.

Vijay Kumar Verma, Umesh Banodha and Kamlesh Malpani. “Optimization with Adaptive Learning: A Better Approach for Reducing SSE to Fit Accurate Linear Regression Model for Prediction”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.10 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0151019

@article{Verma2024,
title = {Optimization with Adaptive Learning: A Better Approach for Reducing SSE to Fit Accurate Linear Regression Model for Prediction},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151019},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151019},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {10},
author = {Vijay Kumar Verma and Umesh Banodha and Kamlesh Malpani}
}