In-depth explanation of Naive Bayes with Python implementation — If you are interested in machine learning, you must be aware of the significance of making assumptions. Developing machine learning systems and other risk-based models is conducted based on predetermined conventions and assumptions that enable developers to obtain the desired outcomes. One of the most well-known and often-used instances of…

Making your model less complex and easy to learn the pattern — The term “dimensionality” describes a dataset’s number of input variables or characteristics. You can reduce the number of input variables in a dataset via dimensionality reduction methods. The curse of dimensionality is the phenomenon wherein an increase in input features makes a predictive modeling assignment more challenging to model.

Matplotlib is a data visualization and graphical plotting toolkit that can be used with Python and its numerical extension NumPy. It was first presented by John Hunter in 2002 and has become fundamental in the world of data visualization. One of the most significant advantages of data visualization is that…

Normalization or standardization, which should you use? — In data processing, we attempt to alter the data so that the model can handle it without encountering too many difficulties. Feature scaling is one process that often improves our data usability. In feature scaling, a dataset’s characteristics are normalized into a finite range. In this article, we will go…

Using Support Vector Machines — The Support Vector Machine, often known as SVM, is one of the most widely used supervised learning algorithms. It may be used for issues involving classification as well as regression. However, its primary use is in machine learning for classification difficulties. The Support Vector Machine (SVM) technique aims to generate…