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Iowa Liquor Sales Forecast
==========================

This repository contains the functions created to generate a sales forecasting
model that predicts sales based on the historical data of liquor purchases from
the state of Iowa.

The created model consists of a multivariate ARIMA model that includes
relevant features such as moving averages of key columns from the dataset,
lag columns and weather forecast information.

All data used to train the model was obtained from the library
of `BigQuery public datasets <https://cloud.google.com/bigquery/public-data>`_.

All the datasets and models created are stored inside **BigQuery**.
Therefore, to run this solution and generate the sales forecasts,
you need to `register an account in Google Cloud <https://console.cloud.google.com/>`_.
Then you have to `create a new project <https://developers.google.com/workspace/guides/create-project>`_,
`enable the BigQuery service <https://cloud.google.com/bigquery/docs/enable-transfer-service>`_
to your account and configure your credentials.

Forecast Results
================

A report with the latest forecast results can be found at:
`Iowa Liquor Sales Forecast Report <https://lookerstudio.google.com/reporting/df348e6b-5d25-47bd-ae51-d7d40906a73b>`_


Code Walkthrough
================

You can find a step-by-step walkthrough of the entire solution, including
the data extraction, feature engineering, and transformation, model training
and evaluation, as well as forecasting future sales at:
`notebooks/Walkthrough.ipynb <../notebooks/Walkthrough.ipynb>`_

Pipelines
=========

The `pipelines <../pipelines>`_ folder contains scripts that can be used as
entrypoints to perform several tasks related to the solution.

Additional Information
=======================

Docker Container
----------------

The `Dockerfile <../Dockerfile>`_ defines the Docker container configuration to
replicate the environment used to develop and run the forecasting model.
By using this Docker container, you can ensure that the code runs consistently
across different environments.

To build and run the Docker container, you can use the following commands:

* **Build the Docker image:**

.. code-block:: bash
docker build -t iowa-liquor-sales-forecast .
* **Run the Docker container:**

.. code-block:: bash
docker run -it --rm iowa-liquor-sales-forecast
Environment Variables
---------------------

The solution relies on a few environment variables that need to be set up for proper operation.
These include:

- ``GOOGLE_APPLICATION_CREDENTIALS``: Path to the JSON file that contains your Google Cloud service account credentials.
- ``PROJECT_ID``: The ID of your Google Cloud project.
- ``DATASET_ID``: The ID of the BigQuery dataset where the data is stored.

You can set these environment variables in your shell or define them in a ``.env`` file,
which will be automatically loaded when running the Docker container or scripts.

Testing
-------

The repository includes a suite of tests to ensure that the code behaves as expected.
You can run the tests using ``pytest``:

.. code-block:: bash
# Run tests
pytest tests/
Continuous Integration (CI)
---------------------------

This repository is set up with a Continuous Integration (CI) pipeline using GitHub Actions.
The CI pipeline is configured to run the tests automatically whenever code is pushed to the
repository or a pull request is created. This helps to ensure that new changes do not break existing
functionality. It also contains a pipeline that recreates the documentation
for the project and generates a new release of the documentation on GitHub
Pages.

Here's the list of currently available pipelines for the project:

* `deploy-docs.yml <../.github/workflows/deploy-docs.yml>`_: deploy
documentation to GitHub Pages.
* `test-code.yml <../.github/workflows/test-code.yml>`_: run the unit-tests
from the `tests <../tests>`_ directory and generate a test coverage report
for the project.

License
=======

This project is licensed under the MIT License. See the `LICENSE <../LICENSE>`_ file for more details.

Codebase Static Test Results
============================

The ``iowa_forecast`` package received the following pylint scores:

* ``iowa_forecast/__init__.py``: 10.0
* ``iowa_forecast/models_configs.py``: 10.0
* ``iowa_forecast/ml_train.py``: 10.0
* ``iowa_forecast/plots.py``: 9.8
* ``iowa_forecast/utils.py``: 8.99
* ``iowa_forecast/load_data.py``: 9.31
* ``iowa_forecast/ml_eval.py``: 8.41

* **Average Score:** 9.50

---

Modules
=======

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<article role="main" id="furo-main-content">
<section id="iowa-liquor-sales-forecast">
<h1>Iowa Liquor Sales Forecast<a class="headerlink" href="#iowa-liquor-sales-forecast" title="Permalink to this heading"></a></h1>
<p>This repository contains the functions created to generate a sales forecasting
model that predicts sales based on the historical data of liquor purchases from
the state of Iowa.</p>
<p>The created model consists of a multivariate ARIMA model that includes
relevant features such as moving averages of key columns from the dataset,
lag columns and weather forecast information.</p>
<p>All data used to train the model was obtained from the library
of <a class="reference external" href="https://cloud.google.com/bigquery/public-data">BigQuery public datasets</a>.</p>
<p>All the datasets and models created are stored inside <strong>BigQuery</strong>.
Therefore, to run this solution and generate the sales forecasts,
you need to <a class="reference external" href="https://console.cloud.google.com/">register an account in Google Cloud</a>.
Then you have to <a class="reference external" href="https://developers.google.com/workspace/guides/create-project">create a new project</a>,
<a class="reference external" href="https://cloud.google.com/bigquery/docs/enable-transfer-service">enable the BigQuery service</a>
to your account and configure your credentials.</p>
</section>
<section id="forecast-results">
<h1>Forecast Results<a class="headerlink" href="#forecast-results" title="Permalink to this heading"></a></h1>
<p>A report with the latest forecast results can be found at:
<a class="reference external" href="https://lookerstudio.google.com/reporting/df348e6b-5d25-47bd-ae51-d7d40906a73b">Iowa Liquor Sales Forecast Report</a></p>
</section>
<section id="code-walkthrough">
<h1>Code Walkthrough<a class="headerlink" href="#code-walkthrough" title="Permalink to this heading"></a></h1>
<p>You can find a step-by-step walkthrough of the entire solution, including
the data extraction, feature engineering, and transformation, model training
and evaluation, as well as forecasting future sales at:
<a class="reference external" href="../notebooks/Walkthrough.ipynb">notebooks/Walkthrough.ipynb</a></p>
</section>
<section id="pipelines">
<h1>Pipelines<a class="headerlink" href="#pipelines" title="Permalink to this heading"></a></h1>
<p>The <a class="reference external" href="../pipelines">pipelines</a> folder contains scripts that can be used as
entrypoints to perform several tasks related to the solution.</p>
</section>
<section id="additional-information">
<h1>Additional Information<a class="headerlink" href="#additional-information" title="Permalink to this heading"></a></h1>
<section id="docker-container">
<h2>Docker Container<a class="headerlink" href="#docker-container" title="Permalink to this heading"></a></h2>
<p>The <a class="reference external" href="../Dockerfile">Dockerfile</a> defines the Docker container configuration to
replicate the environment used to develop and run the forecasting model.
By using this Docker container, you can ensure that the code runs consistently
across different environments.</p>
<p>To build and run the Docker container, you can use the following commands:</p>
<ul>
<li><p><strong>Build the Docker image:</strong></p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>docker<span class="w"> </span>build<span class="w"> </span>-t<span class="w"> </span>iowa-liquor-sales-forecast<span class="w"> </span>.
</pre></div>
</div>
</li>
<li><p><strong>Run the Docker container:</strong></p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>docker<span class="w"> </span>run<span class="w"> </span>-it<span class="w"> </span>--rm<span class="w"> </span>iowa-liquor-sales-forecast
</pre></div>
</div>
</li>
</ul>
</section>
<section id="environment-variables">
<h2>Environment Variables<a class="headerlink" href="#environment-variables" title="Permalink to this heading"></a></h2>
<p>The solution relies on a few environment variables that need to be set up for proper operation.
These include:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">GOOGLE_APPLICATION_CREDENTIALS</span></code>: Path to the JSON file that contains your Google Cloud service account credentials.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">PROJECT_ID</span></code>: The ID of your Google Cloud project.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">DATASET_ID</span></code>: The ID of the BigQuery dataset where the data is stored.</p></li>
</ul>
<p>You can set these environment variables in your shell or define them in a <code class="docutils literal notranslate"><span class="pre">.env</span></code> file,
which will be automatically loaded when running the Docker container or scripts.</p>
</section>
<section id="testing">
<h2>Testing<a class="headerlink" href="#testing" title="Permalink to this heading"></a></h2>
<p>The repository includes a suite of tests to ensure that the code behaves as expected.
You can run the tests using <code class="docutils literal notranslate"><span class="pre">pytest</span></code>:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># Run tests</span>
pytest<span class="w"> </span>tests/
</pre></div>
</div>
</section>
<section id="continuous-integration-ci">
<h2>Continuous Integration (CI)<a class="headerlink" href="#continuous-integration-ci" title="Permalink to this heading"></a></h2>
<p>This repository is set up with a Continuous Integration (CI) pipeline using GitHub Actions.
The CI pipeline is configured to run the tests automatically whenever code is pushed to the
repository or a pull request is created. This helps to ensure that new changes do not break existing
functionality. It also contains a pipeline that recreates the documentation
for the project and generates a new release of the documentation on GitHub
Pages.</p>
<p>Here’s the list of currently available pipelines for the project:</p>
<ul class="simple">
<li><p><a class="reference external" href="../.github/workflows/deploy-docs.yml">deploy-docs.yml</a>: deploy
documentation to GitHub Pages.</p></li>
<li><p><a class="reference external" href="../.github/workflows/test-code.yml">test-code.yml</a>: run the unit-tests
from the <a class="reference external" href="../tests">tests</a> directory and generate a test coverage report
for the project.</p></li>
</ul>
</section>
</section>
<section id="license">
<h1>License<a class="headerlink" href="#license" title="Permalink to this heading"></a></h1>
<p>This project is licensed under the MIT License. See the <a class="reference external" href="../LICENSE">LICENSE</a> file for more details.</p>
</section>
<section id="codebase-static-test-results">
<h1>Codebase Static Test Results<a class="headerlink" href="#codebase-static-test-results" title="Permalink to this heading"></a></h1>
<p>The <code class="docutils literal notranslate"><span class="pre">iowa_forecast</span></code> package received the following pylint scores:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">iowa_forecast/__init__.py</span></code>: 10.0</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">iowa_forecast/models_configs.py</span></code>: 10.0</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">iowa_forecast/ml_train.py</span></code>: 10.0</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">iowa_forecast/plots.py</span></code>: 9.8</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">iowa_forecast/utils.py</span></code>: 8.99</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">iowa_forecast/load_data.py</span></code>: 9.31</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">iowa_forecast/ml_eval.py</span></code>: 8.41</p></li>
<li><p><strong>Average Score:</strong> 9.50</p></li>
</ul>
<p>—</p>
</section>
<section id="modules">
<h1>Modules<a class="headerlink" href="#modules" title="Permalink to this heading"></a></h1>
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