Machine Learning using Simple Linear Regression
الملخص
This paper illustrates linear regression as a basic method of machine learning for predicting continuous outcomes. It is one of the most widely used models for prediction in statistics and machine learning. Supervised learning has become an area of much research activity in the field of machine learning. The linear regression algorithm is perhaps one of the most common, comprehensive and widely used statistical and machine learning algorithms, as it is used to find the linear relationship between two variables, we have a comprehensive discussion of the theoretical and mathematical basis for this algorithm Where our study included defining the linear regression and analyzing the data used from the Kaggle_platform for regression analysis and the steps used in describing methods and functions, including calculating the cost function, the Gradient descent function and the error rate in order to minimize the differences between expected values and real values, and we evaluated a set of data for analysis represented by population and income with a focus on modeling the relationships between dependent (Y) and independent (X) variables. This paper is a small contribution to the understanding and simplification of the theory and practice of machine learning using linear regression.
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