Learn the intuition behind PCA in Python and Sklearn by transforming a multidimensional dataset into an arbitrary number of dimensions and visualizing the reduced data with Matplotlib — As data analysts and scientists, we are often faced with complex challenges due to the growing amount of information available. It is undeniable that the accumulation of data from various sources has become a constant in our lives. Data scientist or not, everyone practically describes a phenomenon as a collection…

A guide to serving a machine learning model via APIs with as FastAPI, Pydantic, and Sklearn You have trained a predictive model on your dataset. The model performs well both in cross-validation and on the test set. That’s good. But what are the next steps now? …

Data leakage represents, together with over/underfitting, the main cause of failure of machine learning projects that go into production — Data leakage is undoubtedly a threat that preys on data scientists, regardless of the level of seniority. It is that phenomenon that can affect everyone — even professionals with years of experience in the sector. Together with over/underfitting, it represents the main cause of failure of machine learning projects that…

A list of software useful for managing data science projects and gathering ideas and content — Data science isn’t just characterized by tools related to code. VSCode, PyCharm or EMACS/VIM aren’t the only tools an analyst has to worry about using to meet his or her professional needs. In this article I will talk about management tools, for communication and knowledge organization. As a data scientist…

Going blindly for an ML-powered solution could lead to unseen perils. Explore the benefits and challenges of adopting machine learning models to make informed decisions — It’s not an obvious question. For novice data scientists, pushing machine learning models right away can be a mistake. In fact, simpler rule-based solutions can be efficient and easier to implement, saving large sums of money in the process.

A guide on how to approach categorical variables for machine learning and data science purposes — Handling categorical variables in a data science or machine learning project is no easy task. This type of work requires deep knowledge of the field of application and a broad understanding of the multiple methodologies available. For this reason, the present article will focus on explaining the following concepts

Statistical sampling is a fundamental block of statistics that allows us to obtain information on the population of interest efficiently, without studying it directly — One of the most hidden desires of any researcher is to be able to have data on the entire population that he / she intends to study. Studying the whole population allows the researcher to obtain a complete understanding of the phenomenon under study, since this allows to collect information…

An article exploring techniques for outlier detection in datasets. Learn how to use data visualization, z-scores, and clustering techniques to spot outliers in your dataset — Nassim Taleb writes how “tail” events define a large part of the success (or failure) of a phenomenon in the world. Everybody knows that you need more prevention than treatment, but few reward acts of prevention. N. Taleb — The Black Swan

How data scientists convert real-world objects in numerical representation for the development of machine learning models — Machine learning engineers leverage numerical representations of the world to build and train predictive algorithms. In the context of supervised learning, these representations allow the computer to learn the relationship between them and the target variable. Let’s imagine a vector to just be a list of numbers