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README.rst
Spot_Price_Forecasting.ipynb

README.rst

Spot Price Forcasting Notebook

A notebook that forecasts Spot Pricing for an instance in Amazon Web Services (AWS). This uses boto3 to retrieve data from Amazon Web Service, organize the data into a dataframe, predict and plot observed and future data using Prophet and Plotly.

Python requirements

This notebook was created in Python 3 environment. Make sure the notebook's kernel is set to Python 3. It's preferable to use the latest version of Python (3.6 at the time of this readme).

Instalation

Installing Boto3

The notebook requires boto3 in order to retrieve data from AWS. To install and configure boto, use the following command:

pip install boto3

and follow the directions in order to setup the proper configurations.

Installing nodejs & ipywidgets

Before installing ipywidgets, you must have nodejs installed. This is especially for JupyterLab users. To install ipywidgets with pip, use the following commands:

pip install ipywidgets
jupyter nbextension enable --py widgetsnbextension

with conda:

conda install -c conda-forge ipywidgets

JupyterLab users will need to use an additional command in order for the input widgets to display:

jupyter labextension install @jupyter-widgets/jupyterlab-manager

Users who are having issues installing the widgets for JupyterLab or those who aren't using the latest version should refer to version compatiblity page and select the command that is compaitible to your version of JupyterLab.

Installing fbprophet

Before installing fbprophet, PyStan must be installed first along with a compiler. Refer to the installation page for more details depending on the operating system.

Installing Plotly

To install Plotly, use the following command:

pip install plotly

JupyterLab users will need to use an additional command in order for the graphs to display:

jupyter labextension install @jupyterlab/plotly-extension