Below you can find some of my data science projects. I have integrated different open-source programming languages such as R and Python, with web applications and platforms such as Jupyter Notebook, R Shiny, Power BI, Tableau and Streamlit, dash and ploty, to name a few.

January 5, 2023

Case Study - Analyze smart device usage data to gain insights into how consumers use smart devices.

This case study is part of the Google Data Analytics Professional Certificate. The case study centres on Bellabeat, a high-tech manufacturer of health-focused smart products for women. The business task is to analyze smart device usage data in order to gain insight into how consumers use non-Bellabeat smart devices. Then select one Bellabeat product to apply these insights to and using this information, establish high-level recommendations for how these trends can inform Bellabeat marketing strategy.

November 9, 2021

Exploratory Data Analysis of Sales Data with Jupyter Notebook and Power BI

For a business to remain competitive it’s critical that it leverages data analysis and AL/ML to improve forecasting and future sales models. This involves analysis of historical sales data, exploring customer demand, analysing the competition, seasonality patterns, the effect of holidays and promotions, and much more. The objective of this project is twofold. To carry out analysis of the datasets in order to determine the feature characteristics, data reliability, critical details and any weaknesses and/or missing data in the datasets.

July 1, 2021

Human Resources Attrition Prediction App

This machine learning application predicts which employees are more likely to stay or quit the business. Using models to predict if a staff member is likely to quit can support HR’s ability to be proactive and to take actions to prevent or at least minimise the probability of the employee leaving. Simply input some details and the app will make a prediction and it’s probability. This application is built with Python & deployed using streamlit and Render.

April 30, 2021

Interactive Machine Learning Project with Streamlit Deployment

Check out this machine learning interactive application that predicts the strength of concrete using different models. Choose from different Exploratory Data Analysis (EDA) options, apply feature engineering, and tweak a few hyperparameters tuning options. Test the various models and determine their accuracy. This application is deployed using streamlit, a versatile interactive open source framework ideal for model exploration and machine learning applications. Interactive Machine Learning Project with Streamlit Deployment

February 9, 2021

Python Libraries for Visualisation

There are many python visualisation libraries available to plot scatter plots, bar charts, heatmaps and histograms. Some can be a little more sophisticated and interactive than others but it really comes down to what’s appropriate for the project and your visualisation requirements, i.e. a static chart may be suffice or maybe something more interactive is required. There are of course many other options, like Boheh, Pygal and Altair, to name just a few.

November 11, 2020

Manufacturing Quality Performance Interactive Dashboard

This Manufacturing Quality Dashboard visualises manufacturing, supplier performance and customer satisfaction for a manufacturing facility. The Web Application is built using Python, Plotly and Dash libaries. The App pulls data from three different sources and visualizes the data in various interactive charts and tables. Tableau is an excellent data visualisation tool, particularly for interactive dashboards. Check out the the equivalent tableau manufacturing dashboard below. Plotly Manufacturing Dashboard Web Application

October 10, 2020

ShinyApp Titanic Survival Predictor

Titanic Survival Predictor ShinyApp. Created using interactive web apps straight from R. Would you Survive the sinking of the Titanic? Simply enter a few details and the Shiny App will predict your probability of survival with a 94% Accuracy. Experiment with different scenarios and see if your chances improve or deteriorate. Titanic Survival Predictor ShinyApp The full source code for this Shiny App can be found on my GitHub page

May 21, 2020

South Australia Dive Site Locator

Locate your local South Australian dive sites. Created using R Markdown and Leaflet. Click on a site for the dive site name and maximum depth. The full source code for this App can be found on my GitHub page

April 9, 2020

Bike Rental Visualisation

Count of bikes rented vs hour for working & non-working days. The Visualiation is built using R, R Markdown & Plotly. Check out how time of the day, temperature, and working/non-working days, impact bike usage. Link to GitHub Depository

May 7, 2019

Predict Exercise Performance with R

There are many devices that record key fitness performance metrics making it possible to collect a large amount of data about personal activity. Usually the goal is to measure regularly so to improve fitness, health and find new patterns in behaviour. One thing that people regularly do is quantify how much of a particular activity they do, but they rarely quantify how well they do it. This project will use data from accelerometers on the belt, forearm, arm, and dumbbell of 6 participants.