Modeling time series data can be challenging (and fascinating) due to its inherent complexity and unpredictability. Long-term trends in time series can change drastically due to certain events, for example. Recall the beginning of the global pandemic, when businesses such as airlines or brick-and-mortar shops saw a quick decline in the number of customers and sales. In contrast…
]]>Linear regression is a powerful statistical tool used to model the relationship between a dependent variable and one or more independent variables (features). An important, and often forgotten, concept in regression analysis is that of interaction terms. In short, interaction terms enable you to examine whether the relationship between the target and the independent variable changes depending on…
]]>As a data scientist, evaluating machine learning model performance is a crucial aspect of your work. To do so effectively, you have a wide range of statistical metrics at your disposal, each with its own unique strengths and weaknesses. By developing a solid understanding of these metrics, you are not only better equipped to choose the best one for optimizing your model but also to explain your…
]]>Linear regression is one of the simplest machine learning models out there. It is often the starting point not only for learning about data science but also for building quick and simple minimum viable products (MVPs), which then serve as benchmarks for more complex algorithms. In general, linear regression fits a line (in two dimensions) or a hyperplane (in three and more dimensions) that…
]]>Imagine you have just started a new data science project. The goal is to build a model predicting Y, the target variable. You have already received some data from the stakeholders/data engineers, did a thorough EDA, and selected some variables you believe are relevant for the problem at hand. Then you finally built your first model. The score is acceptable, but you believe you can do much better.
]]>