## Understanding Agent Based Model with Python

When we want to model a given phenomenon, we could employ a mathematical underlying structure which we believe describes the scenario. Then, we can parametrize and calibrate it so that our model is able to reproduce patterns seen in data. This technique is appealing since it provides us with a set of parameters which can …

## Interactive analytics and predictions on Restaurant tips

Imagine you own a restaurant and you want to analyze not only the trend of your revenue, but also the reason behind periods of particularly high earnings, moment of the day where a particular kind of clients comes to your restaurant, why some days tips are higher than others and so on. Knowing all those …

## 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 …

## Interactive Convolutional Neural Network

Image recognition is one of the main topics Deep Learning is focusing on. Indeed, the family of algorithms entitled to deal with image recognition belongs to the class of Neural Networks, typical multi-layers algorithms employed in deep learning tasks. More specifically, image recognition employs Convolutional Neural Networks (CNNs), which I’ve been explaining in my previous …

## Sports Analytics: an exploratory analysis of international football matches-Part 2

In my previous article (Part 1 of this series), I’ve been implementing some interesting visualization tools for a meaningful exploratory analysis. Then, with the Python package Streamlit, I made them interactive in the form of a web app. In this article, I’m going to continue working on the same dataset as before, this time focusing …

## Optimization algorithms: the Newton Method

Predictive Statistics and Machine Learning aim at building models with parameters such that the final output/prediction is as close as possible to the actual value. This implies the optimization of an objective function, which might be either minimized (like loss functions) or maximized (like Maximum Likelihood function). The idea behind optimization routine is starting from …

## Sports Analytics: an exploratory analysis of international football matches-Part 1

Data Science and Analytics have a huge variety of fields of applications, basically every time pieces of information are delivered in the form of data. The sports industry makes no exception. There is a great business all around, and having the possibility to study the market of sports via powerful analytics tools is a great …

## Building Machine Learning Apps with Streamlit

Streamlit is an open-source Python library that makes it easy to build beautiful apps for machine learning. You can easily install it via pip in your terminal and then start writing your web app in Python. In this article, I’m going to show some interesting features about Streamlit, building an app with the purpose of …

## Markov Chain Montecarlo

A Markov chain can be defined as a stochastic process Y in which the value at each point at time t depends only on the value at time t-1. It means that the probability for our stochastic process to have state x at time t, given all its past states, is equal to the probability …

## Building a ML model in 3 lines of code? Yes you can

Machine Learning as a subject is not easy. It is indeed a set of tools (mainly algorithms and optimization procedures) whose comprehension involves, inevitably, a deep understanding of Maths and Stats. Nevertheless, the implementation of a ML model to a real scenario might be easier than expected. Indeed, once you got familiar with theoretical concepts, …