Linear Regression is one of the simplest and most commonly used algorithms in Machine Learning. It is a type of supervised learning used for predicting a continuous numerical value based on input features. The main idea is to find a straight line (linear relationship) that best fits the data.<\/p>\n\n\n\n
Linear Regression assumes that there is a linear relationship between the input variables (features) and the output variable (target). The relationship can be represented by the equation:<\/p>\n\n\n\n
Where:<\/p>\n\n\n\n The model tries to find the values of coefficients that minimize the difference between the predicted values and the actual values. This difference is measured using a method called Mean Squared Error (MSE)<\/strong>.<\/p>\n\n\n\n Linear Regression is a foundational algorithm in Machine Learning for predicting continuous values. It is easy to implement and interpret, making it an excellent starting point for understanding supervised learning and regression problems.<\/p>\n\n\ny = b0 + b1*x1 + b2*x2 + ... + bn*xn<\/code><\/p>\n\n\n\n\n
y<\/code> is the predicted output<\/li>\n\n\n\nb0<\/code> is the intercept<\/li>\n\n\n\nb1, b2, ..., bn<\/code> are the coefficients for each feature<\/li>\n\n\n\nx1, x2, ..., xn<\/code> are the input features<\/li>\n<\/ul>\n\n\n\nSteps in Linear Regression<\/h2>\n\n\n\n
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Applications of Linear Regression<\/h2>\n\n\n\n
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Advantages<\/h2>\n\n\n\n
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Limitations<\/h2>\n\n\n\n
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Conclusion<\/h2>\n\n\n\n