Equity codes prediction using Naive Bayesian Classifier with scikit-learn

The aim of this article is to have an introduction to Naive baysian classification using scikit-learn. The naive Bayesian classification is a simple Bayesian type of probabilistic classification based on Bayes’ theorem with strong (so-called naive) independence of hypotheses. In this article, we will use it to build a basic text prediction system. We will predict Equity codes in a search form fashion (i.e prediction starts when user starts typing).

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Principal Component Analysis through the Happiness Index exemple

What determines happiness? Why countries are more (or less) happy than other ones? In 2017, Norway tops the global happiness ranking, made as an annual publication of the United Nations Sustainable Development Solutions Network. In this article, we use their data to show correlations of the variables used in this Index, furthermore we analyse the countries with the help of the Principal Component Analysis technic.

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Multiple correspondence analysis, Clustering and Tandem Analysis through a basic income analysis example

Okay… So there were several basic income experiments launched in 2017, Finland started a two-year experiment by giving 2,000 unemployed citizens approximately $600 a month. In the Silicon Valley, Y Combinator, announced in mid-2016 that it would begin paying out monthly salaries between $1,000 and $2,000 a month to 100 families in Oakland, while in Utrecht, Netherland 250 Dutch citizens will receive about $1,100 per month. These are just three of the already launched experiments, and their aim is to measure how basic income could provide new structure for social security and to see how people’s productivity levels change when they receive a guaranteed salary.

But how people think about basic income? Are we supportive of it or we fear it? Who is the most likely to vote for it? Is there a difference between people according to their education or job status who are more pro or contra of this idea? This study aims to answer these question by using a semi-supervised approach, Clustering and a Tandem analysis to classify people according to their characteristics and their opinion of basic income.

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