Udacity ML - Finding Donors for CharityML

June 23, 2018 in Udacity ML

This is a project submission for the Udacity Machine Learning Engineer Nanodegree programme. I’ve removed submission information sections that are not relevant. For this tutorial I was using Python 2.7. In the code, sections labeled # ToDo: are where code has been added. Getting Started In this project, you will employ several supervised algorithms of your choice to accurately model individuals’ income using data collected from the 1994 U.S. Census.

Jonathan Phelps

I am a Data Scientist working at Kantar Worldpanel

Data Scientist

London, England

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Exploring FPL Bonus Points

Aug 8, 2018

Overview Data Exploration Setup Data collection Data Visualisation Modelling Objective Logistic regression bps Playing Team Opposition Team Playing Position Final Model Final Thoughts Overview In this post, I am going to look at predicting the number of bonus points that a player will accrue throughout the 2017/18 FPL season. During a fixture, a footballer will receive bps points for events e.g. scoring a goal or making an interception.

Udacity ML - Finding Donors for CharityML

Jun 6, 2018

Tidy R code from R

May 5, 2018

This is a simplified version of a script I wrote for work. We have a large code base, built up over many years. The end result was a mismatch in formatting. This script is an example of bulk editing R scripts using regular expressions. In particular, standardising the naming convention of R functions to the piped format eg thisIsPiped <- function(){...}. Please have these packages: # packages library(magrittr) library(data.table) library(rprojroot) # I'm using an rstudio project in my root folder For this tutorial, I have saved in to a “/scripts” folder two files:

Makeover Monday - Economic Freedom Visualisation

May 5, 2018

Overview Data processing Data Visualisations Shiny App Overview Makeover Monday is a website which posts interesting data sets and related articles, each Monday. The aim is to experiment with data visualisations to extract new insights and views from the data. In this post, I am looking at data for this makeover monday: https://www.makeovermonday.co.uk/week9-2018/. Data is at https://data.world/makeovermonday/2018w9-world-economic-freedom. The original source and use of the data: https://www.