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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
 =======