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Create a dashboard to visualize housing market in San Francisco neighborhoods. Parallel categories, coordinates and neighborhood maps are created on annual housing units, average gross rent and sales prices. Visuals in line and scaled bar charts are also created using Matplotlib and hvplot libraries.
A visualization index designed to analyze fluctuations in crypto exchange data as it correlates with global crypto-specific news stories sentiment data
The data Martha will be working with is not ideal, so it will need to be processed to fit the machine learning models. Since there is no known output for what Martha is looking for, she has decided to use unsupervised learning. To group the cryptocurrencies, Martha decided on a clustering algorithm. She’ll use data visualizations to share her fi…
Uses Python, Scikit-Learn, and unsupervised learning - specifically KMeans Algorithm - to predict if cryptocurrencies such as Bitcoin, Ethereum, and Ripple are affected by 24-hour or 7-day price changes.
The project explored Melbourne's public transport use and housing sales. Python tools visualized data on transport footfall and housing by council areas. Sources included PTV for transport data and housing sales records, along with the VISTA survey for travel activity. Analysis combined these datasets to examine trends and correlations, with ma
Generated analysis of cryptocurrencies is available on the trading market and how they can be grouped using classification. To do this I used unsupervised learning and Amazon SageMaker by clustering cryptocurrencies and creating plots to present results.
Using Sklearn, Python, & Plotly, I analyzed a dataset of cryptocurrencies with unsupervised machine learning algorithms to spot any trends that would make a client want to invest in them.