Les comptes de la campagne #FaisonsLesComptes sur Twitter

  Dimanche, 18 Décembre 2016, l’annonce de Noel Kokou Tadegnon sur Twitter était solennelle : des activistes Togolais lancent la campagne #FaisonsLesComptes pour exiger les comptes de la CAN 2013 de foot … S’en suivront des salves de publication avec l’hashtag #FaisonsLesComptes. Facebook et Twitter étaient principalement utilisés pour cette cause. Des activistes #Togo lais lancent … More Les comptes de la campagne #FaisonsLesComptes sur Twitter

About Degninou Yehadji, MPH

English In 2015, Degninou Yehadji, MPH launched http://www.degninou.net  to showcase his scholar and professional activities. As soon as November 2015, a few months after his return from the United States, he published his first blog entries which obtain a favorable echo before his readers. This site dedicated to be a platform for sharing his academic and professional passions … More About Degninou Yehadji, MPH

Machine Learning: k-means cluster analysis with Python

A k-means cluster analysis was conducted to identify underlying subgroups of adolescents based on their similarity of responses on 18 variables that represent characteristics that could have an impact adolescents self-esteem. Clustering variables included gender, ethnicity (Hispanic, White, Black, Non american, Asian), age, two binary variables measuring whether or not the adolescent had ever used … More Machine Learning: k-means cluster analysis with Python

Machine Learning: Lasso Regression Analysis with Python

A lasso regression analysis was conducted to identify a subset of variables from a pool of 23 categorical and quantitative predictor variables that best predicted a quantitative response variable measuring adolescents’ grade point average (GPA). Categorical predictors included gender and a series of 5 binary categorical variables for race and ethnicity (Hispanic, White, Black, Native … More Machine Learning: Lasso Regression Analysis with Python

Machine Learning: Building a Random Forest with Python

Random forest analysis was performed to evaluate the importance of a series of explanatory variables in predicting regular smoking among adolescent – a binary categorical response variable. The following explanatory variables were included as possible contributors to a random forest evaluating the response variable: gender, age, (race/ethnicity) Hispanic, White, Black, Native American and Asian, alcohol … More Machine Learning: Building a Random Forest with Python

Machine Learning: Growing a Decision Tree with Python

Decision tree analysis was performed to test nonlinear relationships among a series of explanatory variables and a binary, categorical response variable. The training sample and the test sample were set at a ratio of 40/60. For the present analyses, the maximum number of nodes was limited to 5. The following explanatory variables were included as … More Machine Learning: Growing a Decision Tree with Python

Logistic regression model with Python

Objective: Assess the association between income per person, alcohol consumption and cancer rate using logistic regression. Independent variables: Income per person and alcohol consumption. Income per person: 2010 Gross Domestic Product per capita in constant 2000 US$. Alcohol consumption: 2008 alcohol consumption per adult (age 15+) liters, recorded and estimated average alcohol consumption, adult (15+) … More Logistic regression model with Python

Multiple Regression and Regression Diagnostics with Python

Objective: Perform a multivariate regression modeling to identify indicators associated with breast cancer, and conduct a regression diagnostic of our model. Indicators of interest are: urbanization rate, life expectancy, CO2 emission, income per person, alcohol consumption and employment rate. The dependent variable is breast cancer rate, which is the 2002 breast cancer new cases per … More Multiple Regression and Regression Diagnostics with Python

Simple linear regression with Python

The Coursera course I am taking this week is dedicated to the Regression Modeling in Practice, Week2 -Basics of Linear Regression. I decided to use The GapMinder dataset and run linear regression models to assess the association between urbanicity and breast cancers rate. Urbanicity is 2008 urban population (% of total). Urban population refers to people living … More Simple linear regression with Python

A look at GapMinder data

Data source: GapMinder data are comprised of global development indicators curated by the Gapminder Foundation. The foundation is a non-profit venture registered in Stockholm, Sweden, aiming at promoting sustainable global development and achievement of the United Nations Millennium Development Goals by increased use and understanding of statistics and other information about social, economic and environmental … More A look at GapMinder data