I have a real passion for analysis and visualisation of data, as well as an enjoyment for bringing out new insights and discoveries.
I'm a certified Data Scientist and have diplomas in Business Analysis and Insurance.
The below are examples of what I've created throughout my career and development in the world of Data Science and Data Visualisation.
You can view my full github repository here.
In this project, I delve into AB Testing to see if a new website redesign will see an uplift in customer conversion. The company has run a randomised test, where visitors will be split in four groups, seeing
I utilse both frequentism and bayesian methods to establish the probabity that a change will create an uplit in conversion, whether the uplift could be caused by random chance and what the mean uplift will likely be.
In this project, I delve into the world of natural language processing (NLP), where I create a class that will
I achieve this by creating two models, through the use of a single custom class and pipelines to predict if a given text may contain fake news or hate speech. I then take an extractive text summarisation approach to summarise the article.
Humans are really good at understanding the emotion behind spoken words. Does someone sound happy, or angry? Can we build an AI to do this and if so why would we want to when we're already so good at it?
We're good at understanding emotion because it's vital to our communication. We react and respond differently to people depending on their emotional state, be it happy, sad, angry or another emotion.
Having an AI complete this task can provide great advantage in so many different ways, many of which wouldn't be possible by us; from having digital assistance tools (think Siri and Alexa) understand the emotional intent of a request, to a business analysing their calls to identify potential complaints or the general mood of their customers. You could also apply this in a call center telephony routing, where the system picks up angry or upset customers and transfers them to a human to prevent further agitation.
This is also where AI can really help because we can process audio in bulk or without human intervention. Rather than having a person listen to a sample of calls, an AI can listen to a large volume without an invasion of privacy, whilst outputting the general positive or negative nature of their customers.
DataCamp's analysis of traffic incidents, asking
How can we reduce fatalities?
The Scenario: You work for the road safety team within the department of transport and are looking into how they can reduce the number of major incidents. The safety team classes major incidents as fatal accidents involving 3+ casualties. They are trying to learn more about the characteristics of these major incidents so they can brainstorm interventions that could lower the number of deaths. They have asked for your assistance with answering a number of questions.
DataCamp's first competition asked the question
Where should a drinks company run promotions?
The Scenario: Your company owns a chain of stores across Russia that sell a variety of alcoholic drinks. The company recently ran a wine promotion in Saint Petersburg that was very successful. Due to the cost to the business, it isn’t possible to run the promotion in all regions. The marketing team would like to target 10 other regions that have similar buying habits to Saint Petersburg where they would expect the promotion to be similarly successful.
A selection of my public projects.
Can we establish if a new website design will increase customer conversion? Could this increase be due to random chance? What is the likely uplift we will see?
Can we summarise news articles and warn users when it may (or may not) contain fake news or hate speech?
Can AI understand the emotional intent behind speech? What uses and benefits could this provide?
Can we predict the value of a house? Detailed EDA of the Ames housing dataset.
Using data from the UK department of transport, I look into how we can reduce the number of major incidents.
Recognising hand writen numbers, through the use of PCA and SVC machine learning techniques on the MNIST ("Modified National Institute of Standards and Technology") dataset.
Following a recent wine promotion, we use clustering techniques to identify 10 regions to target for our next wine marketing campaign.
DataCamp Professional Certificate in Data Science project.
The sales team wants to know whether you can make predictions for the price of used cars.
Through the University of Edinburgh, this was my submission for the 10-credit online course; Data Visualisation for Professionals.
My girlfriend and I have recently started watching a few TV sitcoms that have recently finished. I thought it’ll be a good idea to visualise these shows and see how they stand up over their many seasons.
Kaggle Competition.
Can we predict the value of a house?