An article on what one-hot encoding is, why to use it, and how to do it (in Python) — When working with real data, you often have datasets with “mixed” values: strings and numbers. If you are a novice in the Data Science and Machine Learning world you typically find “perfectly beautiful” columns (at most, with some Nans to deal with), often with scaled values. But when it comes…

An overview of Gradient Descent and Regularization for a better understanding — When moving the first steps into Machine Learning, there are a lot of things to study and understand. Also, some ML models may seem very similar to each other; sometimes, it can be difficult to really understand the difference between some of them. This was my case when I first…

Or…what does “training an ML model” mean? — When talking about Machine Learning we always talk about “trained model” or “training a model” but…what exactly does that mean? What is a trained model? What do we do when we train models? Do we always have to train a model? In this article, we will answer all these questions…

Three practical ways to deal with missing values in a DS project — When dealing with real-world data, you may often find missing values in your data frame. This can happen for several reasons, for example: some measurements may be missing lack of information transcript errors So the question is: how to deal with missing data? Can we accept 0 as a value…

See how easy and fast is to extract any data you want from the web and analyze them in Python There is a lot of hype around data; data is everywhere and more and more industries are beginning to analyze data; but if you are in Analytics — or, if…

A clarifying article on one of the most used Machine Learning model — Logistic Regression is a widely used model in Machine Learning, so we have to have a deep understanding of how it works and when to apply it. …

A study on a fetal dataset using Machine Learning — This article relies on the analysis I made for the final project of my Data Science immersive course. I’ve analyzed a dataset on fetal diseases and found the “best” Machine Learning model to predict fetal diseases. The analysis is pretty long, so, in this article, I will only give an…

A couple of techniques to guess if you can use or not a linear model in your ML problem — An essential part of a regression analysis is to understand if we can use a linear model or not for solving our ML problem. There are many ways to do this, and, generally, we have to use multiple ways to understand if our data are really linear distributed. In this…

Four methods and a whole process for Feature Selection, with examples in Python — Feature selection is an essential part of a Data Science project. When you work with a (very) large dataset you should always ask yourself two questions: What do these features represent? Are all these features important? The answer to the second question leads us to features selection; in fact, you…