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.

This Repo contains challenge 10 creating clusters from crypto currency data using unsupervised machine learning

Time Series Analysis and FB Prophet Analysis.

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…

Using Python and unsupervised machine learning to predict if cryptocurrencies are affected by 24-hour or 7-day price changes.

Dashboarding with Panel and the Holoviz Ecosystem

Dashboarding with Panel and the Holoviz Ecosystem

Financial database and web application designed to analyze the performance of a fintech ETF.

Unsupervised machine learning to preform principal component analysis, and use clustering to determine trends in different crytocurrencies.

Retrieved data from API's, analysed it and produced maps

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

Analyzing and Visualizing Large Datasets

Analyzing a database of cryptocurrencies using unsupervised machine learning models with Python

Using unsupervised machine learning to analyze cryptocurrency datasets.

Data Science Driven Cryptocurrency Investment Portfolio

This is an An application that pulls and analyzes ETF data from a database

This app helps you determine the fund with the most investment potential based on key risk-management metrics: the daily returns, standard deviations, Sharpe ratios, beta, etc.