Regression is a supervised machine learning algorithm approach to determine the relationship between variables and predict the dependent variable (y) based on the independent variable (x). Prediction, time series modelling, forecasting, and identifying the causal-effect connection between variables are all common applications.
The regression and machine learning methods are implemented in Python using the Scikit package.
In machine learning, there are two types of regression algorithms:
When the variables are continuous and numeric, linear regression is used.
When the variables are continuous and categorical, logistic regression is used.
It is a statistical method that is used to analyse the relationship between a dependent variable and independent variable (s). It models the relationship for better understanding of how the variables involved relate to each other.
Considering a simple linear regression y =mx + c + error. Y is called the dependent variable because it’s value depends on x which is the independent variable.
Logistic regression is a type of statistical analysis that is used to predict the probability of a binary outcome. In eCommerce, this can be used to predict whether a customer is likely to make a purchase, based on their past behavior. For example, logistic regression can take into account whether a customer has visited the site before, how long they spend on each page, and what products they have viewed.
By analyzing this data, eCommerce businesses can better understand which customers are most likely to make a purchase and can tailor their marketing and advertising accordingly. Additionally, logistic regression can be used to test different hypotheses about customer behavior and can help to improve the overall effectiveness of an eCommerce business.