What are Linear and Logistic Regression?
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Linear and logistic regression are both statistical modelling techniques used in data analysis. Linear regression is used when there is a linear relationship between two variables, and the goal is to predict a continuous value, such as the price of a house based on its size and location. Logistic regression, on the other hand, is used when the outcome is categorical, such as predicting whether a customer will purchase a product based on their demographic information. Logistic regression uses a special curve called the sigmoid curve to model the probability of the outcome variable.
Both linear and logistic regression involves fitting a line or curve to the data, to minimize the error between the predicted values and the actual values. These techniques are widely used in fields such as finance, marketing, and healthcare to make predictions and inform decision-making.
