This file accompanies the new Real Estate Dashboard being provided to Harold's company.
The goal of these analysis tools is to provide your customers with a more complete set of portfolio options through better dashboard information.
With this planner, your customers can update as needed the real estate datasets that are being analyzed.
This dashboard provides all the available information in a clear, easy-to-use format.
This Jupyter Lab notebook utilizes the following libraries:
os
Pandas
Numpy
dotenv
JSON
Pathlib
Alpaca Trade API
MCForecastTools
matplotlib
Besides the elements related to those libraries, additional data to be input by the user includes their currency holdings, their stocks and bonds amounts, and their monthly income amounts.
I would like to first acknowledge the guidance and teaching of our FinTech Boot Camp Instructor, Garth Mortensen, our TA, Alejandro Esquivel, and out Student Success Manager, Angelica Baraona. I also found the collective Stack Overflow wisdom essential as ever. Regarding PyViz and the visualization process overall I utilized information from Holoviews.com, hvplot.holoviz.org, geeks for geeks.org, towardsdatascience.com, and the tech website collective in general. Finally, regarding Python and pandas, "Python Data Science Handbook" by Jake VanderPlas was as always helpful, the Pandas documentation website and other sites like Data Science Parichay, as I continued my learning process.