Introduction Spatial clustering is a valuable technique that can be used in various fields such as urban planning, ecology, and data analysis. It helps us identify patterns, group similar data points together, and gain insights into spatial distributions. In this article, we will explore how to perform spatial clustering using, scikit-learn…

Introduction Continuing on my previus post, in this second part I will focus on techniques of feature extraction for text data. Feature extraction are techniques that encode text data into numerical data such that machine learning models can interpret it. Techniques covered: Bag of words (BoW); Term Frequency Inverse Document Frequency…

Introduction Text is everywhere and it can give us many insights about a company, product and service. However, extracting such insights is not an easy task. Text data is an unstructured data and, as the name suggest, we have to process it to a more “structured” form to be able to…

Introduction In data science, an EDA (Exploratory Data Analysis) is a fundamental step of a project. It gives you better understanding of the data you are working with. It helps to identify outliers and extract insights through hypothesis testing. …

Introduction In this post I will show how to aggregate information from two different GeoDataFrames (layers) using GeoPandas. I will show one of many ways that we can do that with code example. But first, let’s see a situation when we might need to do this kind of operation. Obs 01…

Introduction It is said that wherever that is data, there is work for data scientist. And in the geospatial field that are lots of data being generated that can be analysed to extract insights from. As Sergio J. Rey, Dani Arribas-Bel and Levi J. …

When we think of problems that machine learning can solve, the first thing that comes to our minds are classification, regression, NLP, clustering. We rarely see someone talking about Learning to Rank. So in this post you are going to see: What is Learning to Rank; How I applied Learning…

Hi everyone! As my first data science project I’ve learned a lot doing it and I would like to share some things, as it may help others and is a way to register my progress. This post do not have any technical knowledge and is not the intend of it…