chore: import upstream snapshot with attribution

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+Sebastian Raschka, 2015
+
+Python Machine Learning - Code Examples
+
+## Chapter 9 - Embedding a Machine Learning Model into a Web Application
+
+- Serializing fitted scikit-learn estimators
+- Setting up a SQLite database for data storage
+- Developing a web application with Flask
+- Our first Flask web application
+  - Form validation and rendering
+  - Turning the movie classifier into a web application
+- Deploying the web application to a public server
+  - Updating the movie review classifier
+- Summary
+
+---
+
+The code for the Flask web applications can be found in the following directories:
+
+- `1st_flask_app_1/`: A simple Flask web app
+- `1st_flask_app_2/`: `1st_flask_app_1` extended with flexible form validation and rendering
+- `movieclassifier/`: The movie classifier embedded in a web application
+- `movieclassifier_with_update/`: same as `movieclassifier` but with update from sqlite database upon start
+
+
+To run the web applications locally, `cd` into the respective directory (as listed above) and execute the main-application script, for example,
+
+    cd ./1st_flask_app_1
+    python3 app.py
+
+Now, you should see something like
+
+     * Running on http://127.0.0.1:5000/
+     * Restarting with reloader
+
+in your terminal.
+Next, open a web browser and enter the address displayed in your terminal (typically http://127.0.0.1:5000/) to view the web application.
+
+
+**Link to a live example application built with this tutorial: http://raschkas.pythonanywhere.com/**.