## How to make animated charts with Plotly

In most of my previous articles, I’ve often been stretching the importance of visualizing the results obtained by a technical analysis. Ideally, your charts should be able to summarize in a glimpse what you have been working on for days. Plus, those charts have to do so in a way which is clear and comprehensible …

## Analyzing U.S. exports with Plotly

In my previous article, I’ve been providing an introduction to some useful graphical tools available in Plotly, an opensource library which can be used both in Python and R. Here, I’m going to play a bit more with Plotly’s functionalities, using as input some data about USA exports in 2011. So let’s import and explore …

## Ensemble Methods for Machine Learning: AdaBoost

In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could not be obtained from any of the constituent learning algorithms alone. The idea of combining multiple algorithms was first developed by computer scientist and Professor Michael Kerns, who was wondering whether “weakly learnability is equivalent to strong learnability”. The goal was turning a weak …

## Visualizing the Deposits Multiplier with Python

In this article I’m going to propose a visual interpretation with Python of the so-called deposits multiplier. The latter is a macroeconomics indicator which describes how an initial deposit leads to a greater final increase in the total money supply. To fully understand how it works, we have to consider three actors in the market: …

## Hypothesis tests with Python

In my previous article, I’ve been talking about statistical Hypothesis tests. Those are pivotal in Statistics and Data Science since we are always asked to ‘summarize’ the huge amount of data we want to analyze in samples. Once provided with samples, which can be arranged with different techniques, like Bootstrap sampling, the general purpose is …

## Handling missing values with Missingo

Whenever you are about to inspect and manage some data, one of the first inconvenient which might arises is the presence of some missing values. Together with eventual outliers, they might affect the robustness of your Machine Learning model, it is worth spending some extra time during your cleaning procedure and investigating about the nature …

## Building your first chatbot with Python

Today, if you are about to order some foods on a restaurant’s website or you need assistance because your router is not working properly, you will probably get in touch with a chatbot. They appear to you like instant messaging chats, in one of the corners of the screen, and gently ask you whether you …

## Natural Language Processing with TextBlob

Natural Language Processing (NPL) is a field of Artificial Intelligence whose purpose is finding computational methods to interpret human language as it is spoken or written. The idea of NLP goes beyond a mere classification task which could be carried on by ML algorithms or Deep Learning NNs. Indeed, NLP is about interpretation: you want to …