Machine learning

• Project using a survey providing personal predictions to get the conversation going.
• Machine learning models: Logistic Regression, Random Forest and Sequential.
• Completed using Python, Javascript, Plotly and Pandas.
• Deployed using Flask via Heroku.

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Tableau

Analysis and visualisation of Citybike New York. I chose the period October to December 2020. The data was cleaned using Python with Pandas by removing anomalies to get a better representation of the use of the Citybikes.

This is a fully interactive embedded window, but you are also free to visit the workbook on the Tableau public site.

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Data Visualisation Project

• Project about raising awareness of the UV Index values and the importance of protection.
• Completed as part of a group of 4. I was responsible for the visualization and presentation, non-geo graphs including the extraction of the information from the database and through an API.
• Completed using Javascript including D3 and Plotly, HTML including bootstrap, CSS.

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Geo-mapping with Leaflet

• Project using Leaflet to visualise earthquakes and tectonic plates. The tectonic plate information is a given GeoJson file.
• The earthquake data is collected from US Geological Survey (USGS) and is updated dynamically when opening the webpage.
• Creating a map with the circle diameter and colour showing the magnitude of the earthquakes by size.
• The map has several map options, a legend and a pop-up menu appears with related data when clicking on a circle.

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D3 Challenge

• Project using D3 to visualise the health risks facing a particular demographics using information from the U.S. Census Bureau and the Behavioral Risk Factor Surveillance System.
• Creating a dynamic visualisation using a scatter plot.
• The information changes depending on the x and y values that are chosen. The pop-up information changes accordingly.

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Plotly dashboard

• Project with a given dataset to create a dashboard visualising operational taxonomic units (OTU's) in subjects belly buttons.
• The dashboard is created using HTML, CSS, Bootstrap, Plotly, D3 and jQuery.
• The dashboard includes a demographic card, horizontal bar chart, bubble chart and gauge.

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JavaScript & D3 Challenge

• Project using a given dataset with UFO sightings in 2010.
• Created a website showing the information in a table format.
• Provided a filter search with multiple filter options using D3 and jQuery.

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Extract, Transform and Load (ETL)

• Project to find different datasets available online and combine them into a database.
• Reading the data of LEGO sets using Python with Pandas.
• Creating a report including diagrams to visualise the information using Pandas and Matplotlib.
• Creating a schema using an Entity Relational Diagram (ERD).
• Entering the information into a new PostgreSQL database using PGAdmin.

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Web Scraping Challenge

• Using Python with Pandas and Splinter to collect information from several websites.
• Create a scrape function that collects the information as a Python dictionary in a MongoDB database.
• Create a website that displays the collected information.
• Create a script that executes on the click of a button that refreshes the information.

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Web Design Challenge

• Building a website using a previous analysis.
• Completed using HTML, CSS and Bootstrap.

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Python Data Visualisation

• Project about the impact of the Covid-19 crisis on the residential construction and real estate market.
• Completed as part of a group of 4. I was responsible for the analysis of the issued permits, construction value and correlation.
• Completed using Python, Pandas, Matplotlib and data analytics to find the answer to our hypothesis, that Covid-19 does not affect all parts of the market.

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