Predicting Olympics medalists

A #TidyTuesday Tidymodels adventure

After a few weeks of hiatus from #TidyTuesday, I am back with 27-July’21 dataset which is about Olympics. In this exercise, I will try to develop a model to predict medalists using a bagged tree model. This post is intended to workflow through a bare-bones model. So, we are not... [Read More]
Tags: Tidyverse, R, Tidymodels, TidyTuesday, tutorial

Tidymodels with GLMNet for Kaggle competition

Trying out #Sliced S01E02 datasets

Introduction Lately I have been indulged in learning all things tidymodels in my after office hours. But I was missing something - the effectiveness of my learning journey. Committing to large scale competition was unwieldy but then came #Sliced - a data science problem solving 2-hour sprint with small datasets.... [Read More]
Tags: R, Tidyverse, Tidymodels, tutorial

#TidyTuesday Water sources in Nigeria over the years

100+ years of water sourcing history

This week’s #TidyTuesday dataset is about Water Access Points sourced from Water point data exchange. TidyTuesday is a weekly data project aimed at the R ecosystem. The data was curtailed to select nations for this week. I only focused on Nigeria. The Outcome After 1980s, boreholes increased drastically in Nigeria... [Read More]
Tags: Tidyverse, R, Nigeria, Water, TidyTuesday, tutorial, gganimate

#TidyTuesday CEO exits of S&P1500

A look over two decades of data

This week’s #TidyTuesday dataset is about CEO departures in S&P 1500 firms.TidyTuesday is a weekly data project aimed at the R ecosystem. I have taken 2000-2019 data and tried to plot the two decades of reason on why CEOs leave. The Outcome CEO departures have increased significantly in recent years.... [Read More]
Tags: Tidyverse, R, CEO, exits, TidyTuesday, Tutorial, Patchwork