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Machine Learning offers a vast variety of algorithms to choose from, to train your data. Linear regression is one of the simplest, beginner-friendly and yet one of the most effective algorithms. This method is prevalent in predicting outputs, data forecasting, analyzing the time series, and finding the causal effect dependencies between the variables, like in businesses where estimations and evaluation of trends take place. So let us learn more about it by understanding how it works and how we can use it to predict house prices in Boston!
What is Regression?
A regression problem is one in which we predict an exact numeric value based on some given factors. The value to be predicted is known as the dependent variable (or target variable) while the factors are called the independent variables. Suppose, we need to find the price of a house on the basis of the number of rooms. Then, a simple approach would be plotting the (x,y) pair on a 2D graph and fitting a line which covers most of the points.
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