Businesses, from market giants like Amazon and Netflix to a small retail store somewhere in the heart of Ohio, strive to grow and improve their efficiency. Incorporating AI and Machine Learning into operational activity is one of the ways to achieve this. But due to the diversity of ML, it’s hard to choose the right method and clearly understand what benefits it can bring. So, in this article we’re going to overview basic Machine Learning algorithms, explain their business application, and highlight a step-by-step guide to choosing an appropriate algorithm that will meet your business needs.
Regression is a rudimental ML algorithm for finding the relationship between at least two variables. These variables can be dependent (target) and independent (predictor). An understanding of how variables affect each other allows for building forecasts, while also identifying times series,cause and effect relationships, and serving as a predictor of strength.
The goal of regression techniques is typically to explain or predict a specific numerical value while using historical data. And the variety of the regression model depends on the type and number of input data (variables). In total, there are more than 10 such models. Simple linear and multiple Linear regression are the most popular of them.
Simple linear regression consists of only one independent and one dependent variable. Multiple Linear regression is much more common in practice. It foresees numerous explanatory (independent) variables that influence one dependent variable. Here, a specific example can better illustrate the differences between simple and multiple linear regression.
Assume that we’re dealing with an ice cream business.With a Simple linear regression, we can find dependency between the number of sales (dependent variable) and the storage temperature of an ice cream (independent variable). Multiple linear regression covers clarifying deeper patterns. For instance, we can check how independent variables–the storage temperature, pricing, and number of flavors and staff–affect the sales(dependent variable).
Linear regression is easy to comprehend, yet it is rarely used in practicebecause not all of the features (variables) in the world are perfectly generalized with a linear trend. Usually, non-linear interconnections are more frequent since they depict a curvy trend in the data change occurring in real-life projects.
Time series information in such projects allows us to work with regression tasks by not only finding key factors affecting the target variable but predicting future values based on historically gathered data, including timestamps. This is one of the reasons why regression has found a wide application in areas such as retail, business processes optimization, recommendation systems, and etc.
Let’s walk through an example of applying a regression model in a restaurant business.Were you a restaurateur, you’d probably think about cost optimization. You can satisfy this need by minimizing the number of spoiled products and by leveraging precise planning of goods purchases. We can develop a regression model that will be able to predict when and how many products to buy, considering the expiration date of different products. To make a workable model, we’d need to feed it with the following historical data:
The benefits are obtained through the regression model adoption that explains or predicts a numerical value while using historical data from a previous data set. After you implement the described solution, you can plan purchases more accurately.
Classification is an ML algorithm of categorizing unstructured or structured data. Its application remains effective in such areas as spam filtering, document classification, auto-tagging, and defect detection. Classes here may be perceived as labels or targets. By analyzing the input, the model learns how to classify new information, mapping labels or targets to the data.
