notebooks/regression/linear_regression/penguim_weight_prediction.ipynb

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   "source": [
    "# Copyright 2024 Google LLC\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#     https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JAPoU8Sm5E6e"
   },
   "source": [
    "## Predict penguin weights (Multiple linear regression)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
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   },
   "source": [
    "<table align=\"left\">\n",
    "\n",
    "<a href=\"https://github.com/GoogleCloudPlatform/ai-ml-recipes/blob/main/notebooks/regression/linear_regression/penguim_weight_prediction.ipynb\">\n",
    "<img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
    "View on GitHub\n",
    "</a>\n",
    "</td>\n",
    "<td>\n",
    "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/ai-ml-recipes/main/notebooks/regression/linear_regression/penguim_weight_prediction.ipynb\">\n",
    "<img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
    "Open in Vertex AI Workbench\n",
    "</a>\n",
    "</td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "## Overview\n",
    "\n",
    "This notebook shows how to predict penguim weights fitting a multiple linear regression model using BigQuery Dataframes.\n",
    "\n",
    "#### **Steps**\n",
    "Using Bigframes (BigQuery Dataframes),\n",
    "1) It reads the [```penguins```](https://console.cloud.google.com/bigquery?p=bigquery-public-data&d=ml_datasets&t=penguins) table from BigQuery Public Datasets.  \n",
    "2) It fits a multiple linear regression model that predicts the weight of penguins based on the following features:  \n",
    "        **Features**: 'island', 'culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm', 'sex'  \n",
    "        **Target**: 'body_mass_g'  \n",
    "3) It evaluates and plot the results.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
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   },
   "source": [
    "References:\n",
    "- [BQML linear regression example](https://github.com/googleapis/python-bigquery-dataframes/blob/main/notebooks/ml/bq_dataframes_ml_linear_regression.ipynb)\n",
    "- [BQML linear regression tutorial](https://cloud.google.com/bigquery-ml/docs/linear-regression-tutorial)\n",
    "- [BigQuery DataFrames](https://cloud.google.com/python/docs/reference/bigframes/latest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aed92deeb4a0"
   },
   "source": [
    "### Costs\n",
    "\n",
    "This tutorial uses billable components of Google Cloud:\n",
    "\n",
    "* BigQuery (compute)\n",
    "* BigQuery ML\n",
    "\n",
    "Learn about [BigQuery compute pricing](https://cloud.google.com/bigquery/pricing#analysis_pricing_models)\n",
    "and [BigQuery ML pricing](https://cloud.google.com/bigquery/pricing#bqml),\n",
    "and use the [Pricing Calculator](https://cloud.google.com/products/calculator/)\n",
    "to generate a cost estimate based on your projected usage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "i7EUnXsZhAGF"
   },
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9O0Ka4W2MNF3",
    "pycharm": {
     "is_executing": true
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   },
   "outputs": [],
   "source": [
    "!pip install bigframes -q"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oDfTjfACBvJk"
   },
   "source": [
    "\n",
    "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 credit towards your compute/storage costs.\n",
    "\n",
    "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
    "\n",
    "3. [Enable the BigQuery API](https://console.cloud.google.com/flows/enableapi?apiid=bigquery.googleapis.com).\n",
    "\n",
    "4. If you are running this notebook locally, install the [Cloud SDK](https://cloud.google.com/sdk)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "960505627ddf"
   },
   "source": [
    "### Import dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "id": "PyQmSRbKA8r-"
   },
   "outputs": [],
   "source": [
    "from google.cloud import bigquery\n",
    "\n",
    "import bigframes.pandas as bpd\n",
    "from bigframes.ml.linear_model import LinearRegression as BQLinearRegression\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.linear_model import LinearRegression as SKLinearRegression\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set_theme(color_codes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "### Set BigQuery DataFrames options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "PROJECT_ID = \"<YOUR_GCP_PROJECT>\"\n",
    "REGION = \"US\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# Note: The project option is not required in all environments.\n",
    "# On BigQuery Studio, the project ID is automatically detected.\n",
    "bpd.options.bigquery.project = PROJECT_ID\n",
    "# Note: The location option is not required.\n",
    "# It defaults to the location of the first table or query\n",
    "# passed to read_gbq(). For APIs where a location can't be\n",
    "# auto-detected, the location defaults to the \"US\" location.\n",
    "bpd.options.bigquery.location = REGION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "If you want to reset the location of the created DataFrame or Series objects, reset the session by executing `bpd.close_session()`. After that, you can reuse `bpd.options.bigquery.location` to specify another location."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9EMAqR37AfLS"
   },
   "source": [
    "## Read a BigQuery table into a BigQuery DataFrames DataFrame\n",
    "\n",
    "Read the [```penguins``` table](https://console.cloud.google.com/bigquery?p=bigquery-public-data&d=ml_datasets&t=penguins) into a BigQuery DataFrames DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "id": "EDAaIwHpQCDZ"
   },
   "outputs": [],
   "source": [
    "df = bpd.read_gbq(\"bigquery-public-data.ml_datasets.penguins\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
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   "outputs": [
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     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>island</th>\n",
       "      <th>culmen_length_mm</th>\n",
       "      <th>culmen_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gentoo penguin (Pygoscelis papua)</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>50.5</td>\n",
       "      <td>15.9</td>\n",
       "      <td>225.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gentoo penguin (Pygoscelis papua)</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>45.1</td>\n",
       "      <td>14.5</td>\n",
       "      <td>215.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>41.4</td>\n",
       "      <td>18.5</td>\n",
       "      <td>202.0</td>\n",
       "      <td>3875.0</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>38.6</td>\n",
       "      <td>17.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gentoo penguin (Pygoscelis papua)</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>46.5</td>\n",
       "      <td>14.8</td>\n",
       "      <td>217.0</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>35.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3725.0</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
       "      <td>Dream</td>\n",
       "      <td>37.5</td>\n",
       "      <td>18.9</td>\n",
       "      <td>179.0</td>\n",
       "      <td>2975.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Gentoo penguin (Pygoscelis papua)</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>42.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>4150.0</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Gentoo penguin (Pygoscelis papua)</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>48.5</td>\n",
       "      <td>14.1</td>\n",
       "      <td>220.0</td>\n",
       "      <td>5300.0</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>45.8</td>\n",
       "      <td>18.9</td>\n",
       "      <td>197.0</td>\n",
       "      <td>4150.0</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 7 columns</p>\n",
       "</div>[10 rows x 7 columns in total]"
      ],
      "text/plain": [
       "                               species     island  culmen_length_mm  \\\n",
       "0    Gentoo penguin (Pygoscelis papua)     Biscoe              50.5   \n",
       "1    Gentoo penguin (Pygoscelis papua)     Biscoe              45.1   \n",
       "2  Adelie Penguin (Pygoscelis adeliae)  Torgersen              41.4   \n",
       "3  Adelie Penguin (Pygoscelis adeliae)  Torgersen              38.6   \n",
       "4    Gentoo penguin (Pygoscelis papua)     Biscoe              46.5   \n",
       "5  Adelie Penguin (Pygoscelis adeliae)     Biscoe              35.0   \n",
       "6  Adelie Penguin (Pygoscelis adeliae)      Dream              37.5   \n",
       "7    Gentoo penguin (Pygoscelis papua)     Biscoe              42.0   \n",
       "8    Gentoo penguin (Pygoscelis papua)     Biscoe              48.5   \n",
       "9  Adelie Penguin (Pygoscelis adeliae)  Torgersen              45.8   \n",
       "\n",
       "   culmen_depth_mm  flipper_length_mm  body_mass_g     sex  \n",
       "0             15.9              225.0       5400.0    MALE  \n",
       "1             14.5              215.0       5000.0  FEMALE  \n",
       "2             18.5              202.0       3875.0    MALE  \n",
       "3             17.0              188.0       2900.0  FEMALE  \n",
       "4             14.8              217.0       5200.0  FEMALE  \n",
       "5             17.9              192.0       3725.0  FEMALE  \n",
       "6             18.9              179.0       2975.0    <NA>  \n",
       "7             13.5              210.0       4150.0  FEMALE  \n",
       "8             14.1              220.0       5300.0    MALE  \n",
       "9             18.9              197.0       4150.0    MALE  \n",
       "\n",
       "[10 rows x 7 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "species              string[pyarrow]\n",
       "island               string[pyarrow]\n",
       "culmen_length_mm             Float64\n",
       "culmen_depth_mm              Float64\n",
       "flipper_length_mm            Float64\n",
       "body_mass_g                  Float64\n",
       "sex                  string[pyarrow]\n",
       "dtype: object"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rwPLjqW2Ajzh"
   },
   "source": [
    "## Clean and prepare dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jhK2OlyMbY4L"
   },
   "source": [
    "Drop rows with `NULL` values in order to create a BigQuery DataFrames DataFrame for the training data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "id": "0am3hdlXZfxZ"
   },
   "outputs": [],
   "source": [
    "# Drop rows with nulls\n",
    "dataset = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "training_data = dataset.sample(frac=0.9,random_state=200)\n",
    "test_data = dataset.drop(training_data.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
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       "Query job 4b65acba-e4fb-480a-a67c-3721ac8c619a is DONE. 28.9 kB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=dataproc-workspaces-notebooks&j=bq:US:4b65acba-e4fb-480a-a67c-3721ac8c619a&page=queryresults\">Open Job</a>"
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data sample size:  2107\n"
     ]
    },
    {
     "data": {
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       "Query job 7b25557c-b0a7-4e27-b990-5849e31c829f is DONE. 28.9 kB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=dataproc-workspaces-notebooks&j=bq:US:7b25557c-b0a7-4e27-b990-5849e31c829f&page=queryresults\">Open Job</a>"
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     "text": [
      "Test data sample size:  231\n"
     ]
    }
   ],
   "source": [
    "print(\"Training data sample size: \", training_data.size)\n",
    "print(\"Test data sample size: \", test_data.size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M_-0X7NxYK5f"
   },
   "source": [
    "Select features and labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_columns = ['island', 'culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm', 'sex']\n",
    "label_colum = ['body_mass_g']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "features_training = training_data[features_columns]\n",
    "labels_training = training_data[label_colum]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_test = test_data[features_columns]\n",
    "labels_test = test_data[label_colum]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "#### Visualize the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "Query job e3218e82-a291-4660-88b3-c11db0983cd8 is DONE. 28.9 kB processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=dataproc-workspaces-notebooks&j=bq:US:e3218e82-a291-4660-88b3-c11db0983cd8&page=queryresults\">Open Job</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_to_plot = training_data.to_pandas().sample(n=200, random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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59thjW8VdopfVhx9+mD/96U85/PDDFynqaK0+/PDDvP766znwwANzyy235LLLLssbb7yRM888s9ChrXBf/epXc99992XMmDGprq7OK6+8kt/85jdJ5t/9sjX517/+lR/+8If5yle+kh133HGxeemCv1vjr2Yu3H9K19L2WbNnz06SxR53OeZqmqXtM2fPnr3Y3NHYL5/G7reNf/Np7Jxh7JtXQ+P/0Ucf5dhjj82cOXPqjs2Nf/NZ0tj36NEjf/7zn3Puuefmb3/7Wy666KIkxn5pdt9994wdOzbvvvtukuSxxx5Lt27dsvHGG9dr9/DDD+e9996rK5Lfbbfd0q5du/zxj39s9Hs+88wzdXUYn/134IEHNr1D0Abdeeedi2xPRxxxRN3z//nPfxa7zQ0dOnSR1+rZs2f69OlTd5HMvHnzcu+99y5ygUwy/7uMMWPGZOjQoenQoUO6dOmS7bbbLnfcccdy7V/32muvxcY5adKkRr8W0DpVLL0JTbXgjksLfob3C1/4Qt1zC+7a+p3vfKfeMp9++mldEf2WW26ZSZMm5Ze//GVef/31TJkyJa+99toiX1x99nVXXnnlJMn6669f91iHDh1K/i4wUEy+9a1vZdiwYUnmJ3FTpkzJJZdcUnfy+rMOPPDA3H///dlhhx2y0UYbZdttt82ee+6Z1VdfPcn8L6kXTg6/8Y1vJJl/F6+OHTumT58+dc+VlZVliy22yKOPPppk/r7khRdeyK233lrXZsEXvJMmTcq6667bzL2H1mtx2+PgwYPrnuvVq1eSxc/ngwYNqnussrIyAwYMqNdmypQp9dosMGnSpGy11VaLvG6SnHDCCTn//PNz0003ZfDgwRkyZEj22muvlJeX56WXXkptbW122mmnesvMnTu33gHk5z73uXTu3Lnu744dO7a6L8OLyYLPyHnnnZfnn38+v/vd73LeeeflO9/5Trp27Zpk/i8HrbHGGvnGN76RcePGLfJzzTSfO+64I88880zuvPPOxT7foUOHRQokFmw/q6yyygqPry1b2rr57ne/a7spoIqKijz55JNp165dOnTokCTp169fJkyYkGuvvda2U0BLWzejRo2y7bBYq622WjbeeONsvPHGmTdvXk466aSccsopWWeddQod2goxZ86cnHzyyfnud7+bDTfcsNDhtLi2PI9WVFRklVVWycUXX5yVVlopSdK5c+cccMABGTduXL3jtNbs/vvvz9y5c7PffvsVOpQWM3LkyMycOTOXX355kmSTTTZJ586dM3z48AwfPjwbbbRRgSNccb72ta/l3//+d84888ycdtpp+fznP58jjzwyP/nJT9KxY8dCh9ds7r///px88snZbLPN6oqXKisrF8lLF/y94HuS1mJx/ad0LW2ftSDXnzt3bt3/J/NznNb22W5pS9tnLu54NzH2y2N59tvGv3ksz5xh7JtPQ+P/3nvv5eijj87bb7+da6+9tu473IbG37m2ZdfQ2K+++upZffXVs+GGG2batGm58sor83//938++0vRr1+/rLfeern33ntz8MEH5+67715s0ettt92W1VdfPVtvvXWS+d9Nbr311hkzZkxOOeWURn2O+/Xrt9h9V1u50Bua29ChQ3PyySfXe+yzxxjdu3fPjTfeuMhyi7vTezL/Ypd77703xxxzTJ566ql06NAhgwYNqqtfWuChhx7K+++/X2+fseeee+Yf//hH/va3v+VrX/tao/oxatSorLnmmos8vvAvqwBtl2L4FvDSSy9lgw02yKqrrpqk/oSyoFj197//fd3zCyyYVO68886cfvrp2XvvvbPZZpvlW9/6VsaPH59zzjmnXvuKikVXZ1lZWbP2Bfifzp071yta7dmzZzp37pzvfOc7+ec//1mv7QYbbJD77rsvTz31VB577LE8+OCDGT16dC644ILsu+++i91+F1jSXctqa2vrlqupqckRRxyRfffdd5F2a6yxxvJ0D9qsxW1zCy5A++y2+tn5vF27dvXaLU5NTU323nvvHHPMMYs8161bt8W+bjL/YprddtstDz30UB5//PFcfvnl+dWvfpU77rgjNTU1WW211XL77bcv8pqfvYtFW7sLZiFMmzYtjz/+eHbddde6z0l5eXl69eqVqVOnpry8vK4QaYHevXsnmf+z1625GKnQbrvttnzwwQeL3H3n7LPPzt1335211lorU6dOrffcgr8Xd0KF5rO0dXPNNdfYbgps4WPUZP46ePTRR207BdbQujHnsLBnnnkm7du3r1cA3Ldv3yTzt9vWWgz//PPPZ8KECbnyyivzy1/+Msn8m09UV1dn0KBBGT16dLbYYosCR7nitOV9wVprrZXa2tq6Qvjkf31/++2321Qx/A477FB3w5W24Nlnn10kt1zwWX/jjTdadTF8knzve9/LMccck/fffz9rrLFGHnnkkbRr1y5rr712oUNrFgsuNN9tt93ys5/9rO5cx+c///nF5qWrrLJKq7oQYEn9p3QtbZ+1IEebOnVqvRtfTZ06tS6XY/k1tM9ca621MmPGjMydO7fetjZ16lTHu42wvPtt4990yztnGPvm0dD4T5o0KUcccURqamry+9//vu44JZk//vfff3+915o7d25mzJiR7t27t1j8pWxJY//www9n7bXXrruRUTL/vMiC8fXZX7rdd98999xzT775zW/mgQceWOTGgNOmTcuDDz6YTz/9NP379697vKamJrW1tbnrrrvqbgS4LDp06LDIzcOA5bfqqqs2uE1VVFQ0apvbY489cvnll2fKlCm5++67s8ceeyy23YIahu9///uLPHfzzTc3uhh+7bXXdiNQoEGLv4SHZvPOO+/kgQceyN57773Y5xcc4Lz33nv5whe+UPfv9ttvr5sURo0alf333z8XXnhhDjzwwGy55ZZ56623kiy5SBYojAXb5MIFsTfccEPuu+++bLvttjn11FNz5513Zptttsndd9+dZH4h/bhx4+otc8EFF+S4445L3759M2vWrIwfP77e+zz77LN1B+29e/fO5MmT6+1H3nnnnfz85z/PRx99tCK7DK1O3759869//aveY88880yS+dvqkpYpKyvL2LFj6x6bO3duXnrppbq/e/funYkTJ9bbTqurq3PBBRfkv//972Jf94MPPsg555yTTz/9NF//+tczcuTIjBkzJu+9916eeuqp9OnTJx9++GE+/fTTeq87evToPPDAA00cCRrj/fffz4knnpjHH3+87rFPP/00L7/8cnr27JlTTz01w4cPr7fMgv3+Z0/A0vwuuuii3H333bnjjjvq/iXJcccdl/POOy9bbrllnn322cybN69umSeeeCI9evSo+wUXVoylrRvbTWFNmDAhm222WZ588sl6j7/44ovp1auXbaeAlrZubDss7IYbbsj5559f77Hnn38+FRUV2WCDDQoTVAsYMGBA7rvvvvzlL3+pm2e+9a1vpXv37rnjjjvSr1+/Qoe4QrXlfcGWW26ZV199NZ988kndYwvOqbSlL9OfeeaZbLPNNoUOo0Wtueaaee211+o9tuDvHj16FCKkFvO73/0uP/3pT9OuXbusueaaKS8vz7333ptBgwYt9iK6UnPTTTflpz/9aQ488MD84he/qFektMUWW+Spp56q1/6JJ57IZptttsS72JWahvpP6VraPmvDDTfMaqutVi/vr6qqyssvv5wtt9yyRWNtbZa2z9x8881TU1OTZ599tm6ZyZMn59133zX2y6gp+23j3zRNmTOMfdM1NP5vvfVWDjnkkKy88sq5+eab6xXCJ/OPY955551MmTKl7rEF28rmm2/eMh0oYQ2N/aWXXpqrrrqqXvvnn38+Xbp0yec+9zmf/WWw++6751//+lduu+22rLfeeot8XzpmzJh8+umn+eUvf1nvfP8dd9yRbt265eabby5Q5MCKsOB45e67785999232F+L+OCDD/LQQw/l61//+iL7hf322y/PPfdcvRoogObQOs4EFomPP/447733Xt5777289dZbuf/++3PEEUdk3XXXzaGHHrrYZXr37p2ddtopZ599dv7f//t/eeuttzJ69OhcffXVdXd6+PznP59//etfeemll/Lmm2/muuuuy+9+97skWezPNQEt47Pb/NSpU/PMM8/k/PPPT/fu3Rf5snHatGk555xz8sADD+Tf//53HnnkkbzyyisZNGhQkuSoo47K3XffnRtvvDFvvvlm7rzzzvzhD3/I0KFDs91222WjjTbKSSedlKeeeiqTJk3KOeeck/Hjx+eQQw5Jkhx55JG59957c+WVV2by5Ml5/PHH88Mf/jCzZs1yZ3hopCOOOCL33XdfrrrqqkyePDn/+Mc/8tOf/jQ77bTTEovh11tvvey+++756U9/mscffzwTJ07Mj3/847zzzjt1v9Jy2GGH5eWXX86IESMyadKkPPfccznppJPyxhtvLLEIqXPnznnwwQdzxhln5JVXXslbb72Vm2++OSuttFL69euXIUOGZKONNsoJJ5yQJ554IlOmTMkFF1yQ22+/fYmxsmL06dMn22+/fc4999w8/fTTGT9+fE4//fRUVVVl+PDh2XXXXfP444/nyiuvzJtvvpmHHnooP/rRj7LXXntZVyvYmmuuWe9ikQUFUKuvvnrWXHPN7Lfffvnwww/z4x//OBMnTsztt9+e6667LkcffXSBI2/9lrZubDeF1bNnz3zxi1/MOeeck2eeeSaTJk3KBRdckLFjx+a73/2ubaeAlrZubDssbPjw4XnhhRdyySWXZMqUKfnb3/6WkSNH5uCDD17kzuGtyYK7iH32X+fOnevudLTwLzK1Nm15X/Ctb30r7dq1y0knnZQJEybk2WefzRlnnJGtttoqm2yySaHDaxH//e9/M3369Gy44YaFDqVFDR8+PI888kguvfTSvPnmm3Xnx3bcccdWPxY9e/bMzTffnDvuuCNvv/12Ro0alTFjxuT//u//Ch1ak02ePDnnn39+dtlllxx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7d8/o0aNz+umnZ8MNN8yGG27YqDEfOXJk9tprr1x55ZX5xz/+keuvvz6PPvpoNtxww1x00UUZO3ZsrrjiivTo0SNHHHFEk5cDgLboyiuvzKWXXpqXX345V155Zf75z3/msssua3CZm266KV/4whdywQUXZNq0abn44oszZcqU3HzzzXX5zSuvvJLXX389J554Yjp37pzLL788Bx10UO6+++507969Ued5rrjiivz4xz/Oaqutlk033XS5+rmsOVBNTU2OP/74DB8+PMcff3xuvfXW/PznP0+fPn0yZMiQzJ07N4ccckg+/vjj/OhHP8pqq62WUaNG5ZVXXskaa6yRZP55qVNOOaXu/3v16pWXXnopSXLBBRfkmGOOyRFHHJEHHnggo0ePzlprrZWDDjqoUf1Znty0KcsBAK3fxIkTM3369Bx88MHZbLPNkiRf/OIX88c//jEfffRRLr744vTo0SNXX3112rVrlyTZdNNNs+eee+a2227LgQcemF69euUHP/hBLr744uy88875yU9+kh133DHf+c53Ctk1oJVRDA/QRFtuuWUuueSS7LXXXtl1112zww47ZLvttssOO+yw2Pbbbbddtttuu3z66aeZOHFipkyZkvHjx2fatGnp0qVLvbYDBw6s9/daa62V2bNnJ5l/l4gPPvggO+20U702u+++u2J4AGgDqqqqcsMNN+Sggw6q+yLtS1/6Ut577708/fTTy/261dXVufLKK9O5c+ckyX333ZdHHnkk999/f9Zbb70kydixY/OXv/wlyfwL9C666KIMGTIkF110Ud3rbLDBBhk+fHgeeuih7LjjjkmS2bNn57zzzss222xT12annXbKQw89tNzF8Ndff31mzJiRP/zhD1lnnXWSJNtvv3322GOPXHbZZfWK0XfYYYecdtppSZJtttkmjz32WB588MGcdNJJefPNN/PnP/85p512Wg499NAkyZAhQ/L+++/n0UcfTTI/F1tQAL9wnta/f//8/Oc/r3vt559/Pg899FCj+zN79uycffbZ2X777ZPMP8l48cUX57zzzsv++++fJPn4449z3HHHZfLkydloo42atBwAtBVtNXeaMGFC7r333vzkJz/Jt7/97STzc5y99947EydOrGu3LF9cLjBs2LB873vfq3utfffdN7/85S+zww471OVIa6211iL50rnnnluXq6y//vrZZZdd8tRTTzW6GL5Xr14555xzkiSDBw/OLbfcUvcLPxUVFdluu+1y77335l//+lezLAcAbdHGG2+cbt26pX379hk4cGBef/31pS5TXl6e3/72t+nYsWOSpFu3bvne976XRx55pC4HmDVrVn79619niy22SJIMGDAgO++8c2644YacfPLJjTrPs/vuu2e//fZrUj+XNQeqra3NscceW3ex3+abb56///3vefDBBzNkyJCMGTMmr7/+em677bb069cvSbL11ltn5513rnuvXr161d0ka+E86eCDD86xxx5bt9z999+fJ554otHF8I3NTZu6HADQ+vXu3TvdunXLMccck9122y1DhgzJtttum1NOOSWzZ8/O888/n8MPPzy1tbV1N5xYb7310rNnzzz22GN1+dThhx+e+++/P8cdd1y6du2a888/v5DdAlqh8kIHAFDqBg0alFGjRmW99dbLb3/72xx44IHZfvvtc+ONNy62fU1NTS666KIMHjw4e+65Z37605/mlVdeSWVl5SJtV1555Xp/l5eXp7a2Nkkyc+bMJEnXrl3rtVlwhwkAoHUbO3Zsqqur85WvfKXe42eccUauueaa5X7dnj171n3xlSSf+9zn0rVr17ovvpL5v4Iza9asJPMv0HvnnXcydOjQVFdX1/3bcssts9pqq+Wxxx6r9/qf/bJvQWH5xx9/vNzxPv7449loo42y5ppr1r13eXl5tt9++/zzn/9c4nsveP8F7/3kk0+mtrY2u+22W702e+211zLFsfnmm9f7e911101VVVUjezPfgjtrJPPHP0m9O5wtuIBy4ddf3uUAoC1oq7nTM888kyQZOnRo3WPl5eXZdddd6/5e8MXlDjvsUPfFZXV1db0vLj9r3333rfv/srKy7LLLLnnhhRfyySefNBjLgqK3ZH6ulCxfXjJo0KC6/2/Xrl26du2aTTbZJBUV/7v3z2fHvKnLAQDLZujQoXWF8Av+rqioqHfh4brrrlsvJ+jevXsGDRpU16Yx53maeqF/Y3Ogz+YS7du3T7du3erysieeeCLrrbdeXSF8kqy22mqL3NBqST47JmVlZVlnnXWWK09qbG7a1OUAgNZv1VVXze9///vssMMO+dvf/pbvf//72WabbXLWWWdl2rRpqampyejRo7PJJpvU+zd+/PhMnTq17nXatWuXffbZJzU1NRkwYEBWX331AvYKaI3cGR6gGQwZMiRDhgzJ7Nmz88QTT+SGG27Iueeem0033TQDBgyo13bUqFG57rrrMmLEiHzlK1+pOzG44I6dy2pBEfwHH3xQ7/EZM2Ysf0cAgJKxYM7v1q1bs77ugjtUfdYqq6yy1DhGjBiRESNGLPL8Z090JfUv9isvn3999oKL/ZbHjBkzMmXKlGyyySaLfX7Br+os/N4L3n/Be0+bNi1JFjn5tqwn4xYeo8++dmMtbh0sHHtzLgcAbUFbzZ2W5WYKVVVVdV9cjh49epHXWPgGDt27d6/39+qrr57a2tpUVVWlQ4cOS4zls+PSlDywsWPe1OUAgGWz5ppr1vu7vLw8Xbt2rctHFtcmmZ9LvPTSS0kad56nqfN4Y3OghfOcz577mT59+mLPIS3reaWGzlk1hjwJAFgRvvjFL2bkyJGZN29eXnjhhfzlL3/J/9fe/cdEXcdxHH8dhzQOuzCLU6clzFrQwJQ6ocWYNvlDmdWi9YNpzGViggYaAeV1NclsapRI08FJ5AalE24ZY/6AMhsLM3OzGjV/zC6NlbuyFkIc1x+NGxcGKKcIPB/bbXef7/v7+X4+/PXh+31/35+qqipZLBYZDAZlZGRo/vz5vc7rucb55ZdftHnzZkVHR6uxsVH19fW9ClQBwGCQDA8Ag7R+/Xo1Nzdr165dCg0N1ezZszVx4kQ99NBDOnv2bK9k+CNHjmjatGl+Wze2trbq+++/V2xs7ICvO3XqVE2cOFH19fV6+OGHfe2NjY2DnhMAALj+mc1mSf8mcUdFRfnaz549qzNnzkiSPB6P3zmDqcDe3zjy8vJktVp7He9ZVepquPHGG2W1WpWXl3fJ4yEhIQPqp/th7K+//qpJkyb52ruT5AEAwPA2WtdO3Unw/13j9CymEBYWNuAHl93ndu9C09230Wj07UIDAABGJ7fb7ffb4/HI7Xb7vYz43xjp37VEd9J4oO7zDMTlroH6YrFYdPr06V7t/y1oBQAAMNzU19fLbrfro48+0q233qoZM2ZoxowZ+vjjj3X+/HnFxMTo5MmTfvlOFy9e1IoVK5ScnKxp06ZJkmw2m4xGoyoqKlRYWKhXX31VVqs14IUrAIxeQUM9AAAY7hISEvTNN98oPz9fn3/+uT755BOtXbtW4eHhSkhI6BUfFxenlpYWbdu2Tc3Nzdq5c6fS09PV0dHhV9GiPwaDQatXr1ZjY6NefvllHTp0SCUlJaqqqgrk9AAAwHUqLi5OY8aM6fUinMPhUG5ursLCwvTzzz/7HTty5EjAxxEVFaXx48fL5XIpNjbW97FYLNq4caO+/fbbgF+zJ6vVqlOnTikyMtLv+k6nU7t27ZLRaBxQP/Hx8TIajdq3b59f+969e/1+d1cxBQAAw8toXTt135uqr6/3a+/5dxg7dqzfg8vuzx133KHNmzfriy++8Dt3//79vu9er1d79+5VfHy8LzmN9RIAAKPTwYMH1dHR4ft94MABdXZ2KjEx0dd2+vRpnThxwve7tbVVR48e9cUE6j7PQFzuGqgvVqtVLpdL3333na/t4sWL+uyzz/ziWCcBAIDhZubMmerq6tLy5cu1f/9+NTU1yWaz6Y8//lBKSopyc3N16NAhrVq1Sp9++qkaGhr0zDPPqKmpybfbT21trRoaGlRYWKjw8HDZbDa1t7fLbrcP7eQAjChUhgeAQUpOTtaGDRvkcDiUlZUlg8Gg+Ph4VVZWXrIi1tKlS+V2u1VZWaktW7b4qsgbDAZt3bpVFy5c8FUJ609qaqqCgoJUWloqp9OpO++8U6+99ppyc3MDPEsAAHC9ufnmm7Vo0SJVVFQoJCREVqtVx44dU1VVlfLy8hQaGqrGxkatW7dOc+bM0Zdffqna2tqAj8NoNConJ8dX0WH27Nm6cOGCSktL1dra+r/bWgdKRkaGnE6nMjIytHjxYo0bN051dXX68MMPVVBQMOB+pkyZokcffVSbNm3S33//rbvuukv79u3zJYp1P6zsXqft2bNH06dP15QpUwI/KQAAEHCjde10++236/HHH9dbb72lzs5ORUdHy+l0qqWlxS8uNzdXzz77rFatWqUFCxbI4/HI4XDo2LFjeu655/xi33zzTbW3tysyMlI7d+7UiRMn9N577/mOm81mffXVVzp8+LDuvffegM4HAABcv86dO6dly5Zp0aJFOnfunDZt2qSkpCTNmjXLF+P1epWZmamcnBwZjUaVlJTopptu0sKFCyUF7j7PQF3OGqgvqamp2rZtm5YvX66VK1fKbDZr+/btOn/+vN/uPGazWUePHlVTU5NiYmICPh8AAIBAi4iIUFlZmd5++2299NJLamtr87082F2Eoby8XCUlJVqxYoXGjBmju+++W9u3b9c999yj1tZWFRUVKTk5WampqZKkCRMmKCcnR2vXrtWePXt87QAwGCTDA8AVamho8H1PTU3tc3HW8wFjSEiIbDabbDZbr7isrKxLntMtOztb2dnZfm3z5s3TvHnz/NoutZ0jAAAYeV544QWNHz9e1dXVKisr0+TJk7VmzRo98cQT8ng8OnPmjGpqalRdXa377rtP77zzjp588smAj+Oxxx5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      "text/plain": [
       "<Figure size 3000x1500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, len(features_columns), sharey=True, constrained_layout=True, figsize=(30,15))\n",
    "\n",
    "for i, e in enumerate(features_columns):\n",
    "  axes[i].set_xlabel(str(e))\n",
    "  axes[i].set_ylabel('body_mass_g')\n",
    "  axes[i].scatter(sample_to_plot[e], sample_to_plot[\"body_mass_g\"], color='g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Fx4lsNqMorJ-"
   },
   "source": [
    "## Multiple Linear Regression using BigQuery Dataframes\n",
    "\n",
    "BigQuery DataFrames ML lets you move from exploring data to creating machine learning models through its scikit-learn-like API, `bigframes.ml`. BigQuery DataFrames ML supports several types of [ML models](https://cloud.google.com/python/docs/reference/bigframes/latest#ml-capabilities).\n",
    "\n",
    "In this notebook, you create a linear regression model, a type of regression model that generates a continuous value from a linear combination of input features.\n",
    "\n",
    "When you create a model with BigQuery DataFrames ML, it is saved locally and limited to the BigQuery session. However, as you'll see in the next section, you can use `to_gbq` to save the model permanently to your BigQuery project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EloGtMnverFF"
   },
   "source": [
    "### Train the model using `bigframes.ml`\n",
    "\n",
    "When you pass the feature columns without transforms, BigQuery ML uses [automatic preprocessing](https://cloud.google.com/bigquery/docs/auto-preprocessing) to encode string values and scale numeric values.  \n",
    "BigQuery ML also [automatically splits the data for training and evaluation](https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-glm#data_split_method).  \n",
    "Since there are between 500 and 50_000 rows in our training dataset, 20% of the data is used as evaluation data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "GskyyUQPowBT",
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "bq_model = BQLinearRegression(fit_intercept=True, enable_global_explain=True)\n",
    "bq_model.fit(features_training, labels_training)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'calculate_p_values': False,\n",
       " 'early_stop': True,\n",
       " 'enable_global_explain': True,\n",
       " 'fit_intercept': True,\n",
       " 'l2_reg': 0.0,\n",
       " 'learn_rate_strategy': 'line_search',\n",
       " 'ls_init_learn_rate': 0.1,\n",
       " 'max_iterations': 20,\n",
       " 'min_rel_progress': 0.01,\n",
       " 'optimize_strategy': 'normal_equation'}"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bq_model.get_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UGjeMPC2caKK"
   },
   "source": [
    "### Score the model\n",
    "\n",
    "Check how the model performed by using the `score` method. More information on model scoring can be found [here](https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-evaluate#mlevaluate_output)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "id": "kGBJKafpo0dl"
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_absolute_error</th>\n",
       "      <th>mean_squared_error</th>\n",
       "      <th>mean_squared_log_error</th>\n",
       "      <th>median_absolute_error</th>\n",
       "      <th>r2_score</th>\n",
       "      <th>explained_variance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>262.79297</td>\n",
       "      <td>105406.044309</td>\n",
       "      <td>0.006682</td>\n",
       "      <td>219.307948</td>\n",
       "      <td>0.833374</td>\n",
       "      <td>0.833374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 6 columns</p>\n",
       "</div>[1 rows x 6 columns in total]"
      ],
      "text/plain": [
       "   mean_absolute_error  mean_squared_error  mean_squared_log_error  \\\n",
       "0            262.79297       105406.044309                0.006682   \n",
       "\n",
       "   median_absolute_error  r2_score  explained_variance  \n",
       "0             219.307948  0.833374            0.833374  \n",
       "\n",
       "[1 rows x 6 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bq_model.score(features_training, labels_training)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "### Predict using the trained model\n",
    "\n",
    "Use the model to predict the body mass of the data row you saved earlier to the `test_data` DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
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   "outputs": [
    {
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       "Query job 1590cc83-186a-4585-9e70-cac5530153a9 is DONE. 550 Bytes processed. <a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=dataproc-workspaces-notebooks&j=bq:US:1590cc83-186a-4585-9e70-cac5530153a9&page=queryresults\">Open Job</a>"
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predicted_body_mass_g</th>\n",
       "      <th>species</th>\n",
       "      <th>island</th>\n",
       "      <th>culmen_length_mm</th>\n",
       "      <th>culmen_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>3483.093747</td>\n",
       "      <td>Chinstrap penguin (Pygoscelis antarctica)</td>\n",
       "      <td>Dream</td>\n",
       "      <td>46.6</td>\n",
       "      <td>17.8</td>\n",
       "      <td>193.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>3921.654298</td>\n",
       "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>40.6</td>\n",
       "      <td>18.6</td>\n",
       "      <td>183.0</td>\n",
       "      <td>3550.0</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2972.345633</td>\n",
       "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>40.2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>3450.0</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>3488.070871</td>\n",
       "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>40.9</td>\n",
       "      <td>16.8</td>\n",
       "      <td>191.0</td>\n",
       "      <td>3700.0</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>5235.107126</td>\n",
       "      <td>Gentoo penguin (Pygoscelis papua)</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>47.6</td>\n",
       "      <td>14.5</td>\n",
       "      <td>215.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 8 columns</p>\n",
       "</div>[5 rows x 8 columns in total]"
      ],
      "text/plain": [
       "    predicted_body_mass_g                                    species  \\\n",
       "30            3483.093747  Chinstrap penguin (Pygoscelis antarctica)   \n",
       "38            3921.654298        Adelie Penguin (Pygoscelis adeliae)   \n",
       "41            2972.345633        Adelie Penguin (Pygoscelis adeliae)   \n",
       "55            3488.070871        Adelie Penguin (Pygoscelis adeliae)   \n",
       "67            5235.107126          Gentoo penguin (Pygoscelis papua)   \n",
       "\n",
       "       island  culmen_length_mm  culmen_depth_mm  flipper_length_mm  \\\n",
       "30      Dream              46.6             17.8              193.0   \n",
       "38     Biscoe              40.6             18.6              183.0   \n",
       "41  Torgersen              40.2             17.0              176.0   \n",
       "55  Torgersen              40.9             16.8              191.0   \n",
       "67     Biscoe              47.6             14.5              215.0   \n",
       "\n",
       "    body_mass_g     sex  \n",
       "30       3800.0  FEMALE  \n",
       "38       3550.0    MALE  \n",
       "41       3450.0  FEMALE  \n",
       "55       3700.0  FEMALE  \n",
       "67       5400.0    MALE  \n",
       "\n",
       "[5 rows x 8 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bq_predictions = bq_model.predict(test_data)\n",
    "bq_predictions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can, we get an additional column, predicted_body_mass_g, which can be compared to the labal body_mass_g.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(bq_predictions[\"body_mass_g\"], bq_predictions[\"predicted_body_mass_g\"], color='k')\n",
    "plt.plot(bq_predictions[\"body_mass_g\"], bq_predictions[\"body_mass_g\"], color='red')  # Line of perfect fit\n",
    "plt.xlabel(\"Actual Values\")\n",
    "plt.ylabel(\"Predicted Values\")\n",
    "plt.title(\"Actual vs. Predicted Values\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GTRdUw-Ro5R1"
   },
   "source": [
    "### Save the results in BigQuery\n",
    "\n",
    "The model is saved locally within this session. You can save the model permanently to BigQuery for use in future sessions, and to make the model sharable with others."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "K0mPaoGpcwwy"
   },
   "source": [
    "Create a BigQuery dataset, adding a name for your dataset as the `DATASET_ID` variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "id": "ZSP7gt13QrQt"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset penguim created.\n"
     ]
    }
   ],
   "source": [
    "DATASET_ID = \"penguim\"  # @param {type:\"string\"}\n",
    "\n",
    "client = bigquery.Client(project=PROJECT_ID)\n",
    "dataset = bigquery.Dataset(PROJECT_ID + \".\" + DATASET_ID)\n",
    "dataset.location = REGION\n",
    "dataset = client.create_dataset(dataset, exists_ok=True)\n",
    "print(f\"Dataset {dataset.dataset_id} created.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save the predictions table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "QE_GD4Byo_jb",
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "bq_predictions.to_gbq(DATASET_ID + \".penguin_weight_predictions\" , if_exists=\"replace\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zqAIWWgJczp-"
   },
   "source": [
    "#### Save the model itself"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "bq_model.to_gbq(DATASET_ID + \".penguin_weight_model\", replace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "## Multiple Linear Regression using scikit-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we use scikit-learn to compare the results with BigQuery ML.  \n",
    "As you will see, you need to perform more feature transformations with scikit-learn, which are automatically performed by BigQuery ML.  \n",
    "Additionally, the dataframes are put in to memory and the training perform on the environment you are in, instead of leveraging BigQuery's compute and storage, which could handle larger datasets and models.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bigframes dataframes are converted back to pandas dataframes.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "features_training_pandas = features_training.to_pandas()\n",
    "labels_training_pandas = labels_training.to_pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encoding categorial features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unlike in BigQuery ML, where feature columns are [automatic preprocessed](https://cloud.google.com/bigquery/docs/auto-preprocessing), we need to encode our categorical features for sklearn model.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "encoder = OneHotEncoder()\n",
    "encoded_features = encoder.fit_transform(features_training_pandas[['sex','island']])\n",
    "encoded_df = pd.DataFrame(encoded_features.toarray(),\n",
    "                          columns=encoder.get_feature_names_out(['sex','island']),\n",
    "                          index=features_training_pandas.index)  # Use the original index\n",
    "\n",
    "# Concatenate encoded features with numerical features\n",
    "features_training_encoded_pandas = pd.concat([features_training_pandas.drop(['sex','island'], axis=1), encoded_df], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More feature processing could also be performed such as numberical feature standartization, etc  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>culmen_length_mm</th>\n",
       "      <th>culmen_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>sex_.</th>\n",
       "      <th>sex_FEMALE</th>\n",
       "      <th>sex_MALE</th>\n",
       "      <th>island_Biscoe</th>\n",
       "      <th>island_Dream</th>\n",
       "      <th>island_Torgersen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>46.6</td>\n",
       "      <td>14.2</td>\n",
       "      <td>210.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>36.7</td>\n",
       "      <td>18.8</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>40.7</td>\n",
       "      <td>17.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>35.7</td>\n",
       "      <td>17.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>42.5</td>\n",
       "      <td>20.7</td>\n",
       "      <td>197.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>48.1</td>\n",
       "      <td>16.4</td>\n",
       "      <td>199.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>49.6</td>\n",
       "      <td>16.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>43.4</td>\n",
       "      <td>14.4</td>\n",
       "      <td>218.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>59.6</td>\n",
       "      <td>17.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>39.2</td>\n",
       "      <td>21.1</td>\n",
       "      <td>196.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>301 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     culmen_length_mm  culmen_depth_mm  flipper_length_mm  sex_.  sex_FEMALE  \\\n",
       "146              46.6             14.2              210.0    0.0         1.0   \n",
       "150              36.7             18.8              187.0    0.0         1.0   \n",
       "66               40.7             17.0              190.0    0.0         0.0   \n",
       "233              35.7             17.0              189.0    0.0         1.0   \n",
       "44               42.5             20.7              197.0    0.0         0.0   \n",
       "..                ...              ...                ...    ...         ...   \n",
       "186              48.1             16.4              199.0    0.0         1.0   \n",
       "77               49.6             16.0              225.0    0.0         0.0   \n",
       "123              43.4             14.4              218.0    0.0         1.0   \n",
       "271              59.6             17.0              230.0    0.0         0.0   \n",
       "117              39.2             21.1              196.0    0.0         0.0   \n",
       "\n",
       "     sex_MALE  island_Biscoe  island_Dream  island_Torgersen  \n",
       "146       0.0            1.0           0.0               0.0  \n",
       "150       0.0            0.0           0.0               1.0  \n",
       "66        1.0            0.0           1.0               0.0  \n",
       "233       0.0            0.0           0.0               1.0  \n",
       "44        1.0            0.0           0.0               1.0  \n",
       "..        ...            ...           ...               ...  \n",
       "186       0.0            0.0           1.0               0.0  \n",
       "77        1.0            1.0           0.0               0.0  \n",
       "123       0.0            1.0           0.0               0.0  \n",
       "271       1.0            1.0           0.0               0.0  \n",
       "117       1.0            0.0           1.0               0.0  \n",
       "\n",
       "[301 rows x 9 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_training_encoded_pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train the model using `scikit-learn`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sk_model = SKLinearRegression()\n",
    "sk_model.fit(features_training_encoded_pandas, labels_training_pandas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   5.92130074,  -49.17396307,   33.44970232,  -60.53698475,\n",
       "        -228.56777289,  289.10475764,  201.35001892, -128.90199892,\n",
       "         -72.44802   ]])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sk_model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2015.86510063])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sk_model.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Predict using the trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "features_test_pandas = features_test.to_pandas()\n",
    "labels_test_pandas = labels_test.to_pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The same feature processing is required for prediction.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "encoded_features = encoder.transform(features_test_pandas[['sex','island']])\n",
    "encoded_df = pd.DataFrame(encoded_features.toarray(),\n",
    "                          columns=encoder.get_feature_names_out(['sex','island']),\n",
    "                          index=features_test_pandas.index)  # Use the original index\n",
    "features_test_encoded = pd.concat([features_test_pandas.drop(['sex','island'], axis=1), encoded_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "sk_predictions = sk_model.predict(features_test_encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3483.09374744],\n",
       "       [3921.6542977 ],\n",
       "       [2972.34563262],\n",
       "       [3488.07087057],\n",
       "       [5235.10712572],\n",
       "       [4482.4120673 ],\n",
       "       [3531.32747129],\n",
       "       [3547.34548978],\n",
       "       [2987.77211986],\n",
       "       [3935.02264044],\n",
       "       [3283.02816009],\n",
       "       [3879.8981787 ],\n",
       "       [5215.28901739],\n",
       "       [4203.74348862],\n",
       "       [3175.30951369],\n",
       "       [5496.47379505],\n",
       "       [3991.1390815 ],\n",
       "       [5647.58627156],\n",
       "       [3886.50979698],\n",
       "       [5631.05769241],\n",
       "       [4693.55643423],\n",
       "       [5315.24558032],\n",
       "       [3548.94815233],\n",
       "       [5468.72657712],\n",
       "       [5654.24822548],\n",
       "       [3212.73710039],\n",
       "       [3616.26859404],\n",
       "       [3364.19768604],\n",
       "       [3315.06145633],\n",
       "       [3254.05151982],\n",
       "       [4383.68734835],\n",
       "       [5212.27067606],\n",
       "       [3466.91327744]])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sk_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error 263.84080065881744\n",
      "Mean Squared Error 91767.10714231782\n"
     ]
    }
   ],
   "source": [
    "mae, mse = mean_absolute_error(labels_test_pandas, sk_predictions), mean_squared_error(labels_test_pandas, sk_predictions)\n",
    "\n",
    "print(\"Mean Absolute Error\", mae)\n",
    "print(\"Mean Squared Error\", mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(labels_test_pandas, sk_predictions, color='k')\n",
    "plt.plot(labels_test_pandas, labels_test_pandas, color='red')  # Line of perfect fit\n",
    "plt.xlabel(\"Actual Values\")\n",
    "plt.ylabel(\"Predicted Values\")\n",
    "plt.title(\"Actual vs. Predicted Values\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the results are similar to the BigQuery results.  "
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel) (Local)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}