![]() Application Frontend Monitor Shares Stock Exchange In this way, the Data Scientist will be able to customize the new Data Science App for new business models, using this article as a basis.įor more complex environments, we offer alternatives to Streamlit.io, which can be used for more robust business solutions. We have detailed a roadmap for the implementation of the app on the Web, which extracts data from the stock exchange through the InvestPy Python Library, with the resources of the Python Pandas library, we process this data and make them available in interactive candlestick format through the Python’s Plotly library. ![]() In this article, we will detail the need for data scientists to quickly develop a Data Science App, with the objective of presenting to their users and customers, the results of Machine Learning experiments.
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