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    "# Python Basics\n",
    "\n",
    "This tutorial is a quick introduction to training and testing your model with Vowpal Wabbit using Python. We explore passing some data to Vowpal Wabbit to learn a model and get a prediction.\n",
    "\n",
    "For more advanced Vowpal Wabbit tutorials, including how to format data and understand results, see [Tutorials](https://vowpalwabbit.org/tutorials.html).\n",
    "\n",
    "```{admonition} Prerequisites\n",
    "To install Vowpal Wabbit see [Get Started](https://vowpalwabbit.org/start.html).\n",
    "```\n",
    "\n",
    "## Getting started\n",
    "\n",
    "First, import the [Vowpal Wabbit Python package](https://github.com/VowpalWabbit/vowpal_wabbit/tree/master/python) for this tutorial:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import vowpalwabbit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we create an instance of Vowpal Wabbit, and pass the `quiet=True` option to avoid diagnostic information output to `stdout` location:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = vowpalwabbit.Workspace(quiet=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training scenario and dataset\n",
    "\n",
    "For this tutorial scenario, we want Vowpal Wabbit to help us predict whether or not our house will require a new roof in the next 10 years.\n",
    "\n",
    "To create some examples, we use the Vowpal Wabbit text format and then learn on them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "train_examples = [\n",
    "    \"0 | price:.23 sqft:.25 age:.05 2006\",\n",
    "    \"1 | price:.18 sqft:.15 age:.35 1976\",\n",
    "    \"0 | price:.53 sqft:.32 age:.87 1924\",\n",
    "]\n",
    "\n",
    "for example in train_examples:\n",
    "    model.learn(example)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Note:** For more details on Vowpal Wabbit input format and feature hashing techniques see the [Linear Regression Tutorial](cmd_linear_regression.md).\n",
    "\n",
    "Now, we create a `test_example` to use for prediction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_example = \"| price:.46 sqft:.4 age:.10 1924\"\n",
    "\n",
    "prediction = model.predict(test_example)\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vowpal Wabbit results\n",
    "\n",
    "The model predicted a value of **0**. According to our learning model, our house will not need a new roof in the next 10 years (at least that is the result from just three examples we used in our training dataset).\n",
    "\n",
    "## More to explore\n",
    "\n",
    "- To learn how to approach a contextual bandits problem using Vowpal Wabbit — including how to  work with different contextual bandits approaches, how to format data, and understand the results — see the [Contextual Bandit Reinforcement Learning Tutorial](python_Contextual_bandits_and_Vowpal_Wabbit.ipynb).\n",
    "- For more on the contextual bandits approach to reinforcement learning, including a content personalization scenario, see the [Contextual Bandit Simulation Tutorial](python_Simulating_a_news_personalization_scenario_using_Contextual_Bandits.ipynb).\n",
    "- See the [Linear Regression Tutorial](cmd_linear_regression.md) for a different look at the roof replacement problem and learn more about Vowpal Wabbit's format and understanding the results.\n"
   ]
  }
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