03.model-development.ipynb

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  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3"
  },
  "language_info": {
   "name": "python"
  }
 },
 "cells": [
  {
   "cell_type": "code",
   "source": [
    "!pip install -Uqqq pip --progress-bar off\n",
    "!pip install -qqq scikit-learn==1.3.2 --progress-bar off\n",
    "!pip install -qqq pandas==2.1.3 --progress-bar off\n",
    "!pip install -qqq pytorch-lightning==2.1.2 --progress-bar off"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hI-iUHCtAI4P",
    "outputId": "699ed6fe-f4ae-468f-914b-7f228883b5ad"
   },
   "execution_count": 1,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "lida 0.0.10 requires fastapi, which is not installed.\n",
      "lida 0.0.10 requires kaleido, which is not installed.\n",
      "lida 0.0.10 requires python-multipart, which is not installed.\n",
      "lida 0.0.10 requires uvicorn, which is not installed.\n",
      "google-colab 1.0.0 requires pandas==1.5.3, but you have pandas 2.1.3 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1YfV_wn_GZLl",
    "outputId": "3df1dbef-d770-46d0-beaf-269265b4b07e"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1Rrh6Hn3tP2NcwiUUunmH2hhiH0jZQdul\n",
      "To: /content/bodyfat-new-features.db\n",
      "\r  0% 0.00/69.6k [00:00<?, ?B/s]\r100% 69.6k/69.6k [00:00<00:00, 83.1MB/s]\n"
     ]
    }
   ],
   "source": [
    "!gdown 1Rrh6Hn3tP2NcwiUUunmH2hhiH0jZQdul"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Load Data"
   ],
   "metadata": {
    "id": "1zV3tJ90RaIP"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "import pickle\n",
    "import sqlite3\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.colors as colors\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from pylab import rcParams\n",
    "from pytorch_lightning import seed_everything\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.ensemble import (\n",
    "    AdaBoostRegressor,\n",
    "    ExtraTreesRegressor,\n",
    "    GradientBoostingRegressor,\n",
    "    RandomForestRegressor,\n",
    ")\n",
    "from sklearn.linear_model import (\n",
    "    ElasticNet,\n",
    "    HuberRegressor,\n",
    "    Lasso,\n",
    "    LinearRegression,\n",
    "    Ridge,\n",
    ")\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import (\n",
    "    FunctionTransformer,\n",
    "    KBinsDiscretizer,\n",
    "    OneHotEncoder,\n",
    "    StandardScaler,\n",
    ")\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from tqdm import tqdm\n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='retina'\n",
    "\n",
    "COLORS = [\"#bae1ff\", \"#ffb3ba\", \"#ffdfba\", \"#ffffba\", \"#baffc9\"]\n",
    "\n",
    "sns.set(style=\"whitegrid\", palette=\"muted\", font_scale=1.2)\n",
    "sns.set_palette(sns.color_palette(COLORS))\n",
    "\n",
    "cmap = colors.LinearSegmentedColormap.from_list(\"custom_cmap\", COLORS[:2])\n",
    "\n",
    "MY_STYLE = {\n",
    "    \"figure.facecolor\": \"black\",\n",
    "    \"axes.facecolor\": \"black\",\n",
    "    \"axes.edgecolor\": \"white\",\n",
    "    \"axes.labelcolor\": \"white\",\n",
    "    \"axes.linewidth\": 0.5,\n",
    "    \"text.color\": \"white\",\n",
    "    \"xtick.color\": \"white\",\n",
    "    \"ytick.color\": \"white\",\n",
    "    \"grid.color\": \"gray\",\n",
    "    \"grid.linestyle\": \"--\",\n",
    "    \"grid.linewidth\": 0.5,\n",
    "    \"axes.grid\": True,\n",
    "    \"xtick.labelsize\": \"medium\",\n",
    "    \"ytick.labelsize\": \"medium\",\n",
    "    \"axes.titlesize\": \"large\",\n",
    "    \"axes.labelsize\": \"large\",\n",
    "    \"lines.color\": COLORS[0],\n",
    "    \"patch.edgecolor\": \"white\",\n",
    "}\n",
    "\n",
    "mpl.rcParams.update(MY_STYLE)\n",
    "seed_everything(42)"
   ],
   "metadata": {
    "id": "2E_YFNeLRTdH",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "e4dbd7c8-75c0-4a3d-8826-586b023bdc74"
   },
   "execution_count": 1,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "INFO:lightning_fabric.utilities.seed:Seed set to 42\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "metadata": {},
     "execution_count": 1
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "connection = sqlite3.connect(\"bodyfat-new-features.db\")\n",
    "df = pd.read_sql_query(\"SELECT * FROM bodyfat\", connection)\n",
    "df.head()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "5upjo1R2Os3X",
    "outputId": "c4ecfb42-f378-4cf9-ac9d-56ab6fccebf5"
   },
   "execution_count": 2,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   body_fat sex  age  weight  height  neck  chest  abdomen    hip  thigh  \\\n",
       "0      12.3   M   23   69.97    1.72  36.2   93.1     85.2   94.5   59.0   \n",
       "1       6.1   M   22   78.59    1.84  38.5   93.6     83.0   98.7   58.7   \n",
       "2      25.3   M   22   69.85    1.68  34.0   95.8     87.9   99.2   59.6   \n",
       "3      10.4   M   26   83.80    1.84  37.4  101.8     86.4  101.2   60.1   \n",
       "4      28.7   M   24   83.58    1.81  34.4   97.3    100.0  101.9   63.2   \n",
       "\n",
       "   knee  ankle  biceps  forearm  wrist        BMI        BAI       WHR  \n",
       "0  37.3   21.9    32.0     27.4   17.1  23.651298  23.892776  0.901587  \n",
       "1  37.3   23.4    30.5     28.9   18.2  23.213020  21.544894  0.840932  \n",
       "2  38.9   24.0    28.8     25.2   16.6  24.748441  27.556227  0.886089  \n",
       "3  37.3   22.8    32.4     29.4   18.2  24.751890  22.546538  0.853755  \n",
       "4  42.2   24.0    32.2     27.7   17.7  25.512042  23.846228  0.981354  "
      ],
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>body_fat</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "      <th>neck</th>\n",
       "      <th>chest</th>\n",
       "      <th>abdomen</th>\n",
       "      <th>hip</th>\n",
       "      <th>thigh</th>\n",
       "      <th>knee</th>\n",
       "      <th>ankle</th>\n",
       "      <th>biceps</th>\n",
       "      <th>forearm</th>\n",
       "      <th>wrist</th>\n",
       "      <th>BMI</th>\n",
       "      <th>BAI</th>\n",
       "      <th>WHR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12.3</td>\n",
       "      <td>M</td>\n",
       "      <td>23</td>\n",
       "      <td>69.97</td>\n",
       "      <td>1.72</td>\n",
       "      <td>36.2</td>\n",
       "      <td>93.1</td>\n",
       "      <td>85.2</td>\n",
       "      <td>94.5</td>\n",
       "      <td>59.0</td>\n",
       "      <td>37.3</td>\n",
       "      <td>21.9</td>\n",
       "      <td>32.0</td>\n",
       "      <td>27.4</td>\n",
       "      <td>17.1</td>\n",
       "      <td>23.651298</td>\n",
       "      <td>23.892776</td>\n",
       "      <td>0.901587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.1</td>\n",
       "      <td>M</td>\n",
       "      <td>22</td>\n",
       "      <td>78.59</td>\n",
       "      <td>1.84</td>\n",
       "      <td>38.5</td>\n",
       "      <td>93.6</td>\n",
       "      <td>83.0</td>\n",
       "      <td>98.7</td>\n",
       "      <td>58.7</td>\n",
       "      <td>37.3</td>\n",
       "      <td>23.4</td>\n",
       "      <td>30.5</td>\n",
       "      <td>28.9</td>\n",
       "      <td>18.2</td>\n",
       "      <td>23.213020</td>\n",
       "      <td>21.544894</td>\n",
       "      <td>0.840932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25.3</td>\n",
       "      <td>M</td>\n",
       "      <td>22</td>\n",
       "      <td>69.85</td>\n",
       "      <td>1.68</td>\n",
       "      <td>34.0</td>\n",
       "      <td>95.8</td>\n",
       "      <td>87.9</td>\n",
       "      <td>99.2</td>\n",
       "      <td>59.6</td>\n",
       "      <td>38.9</td>\n",
       "      <td>24.0</td>\n",
       "      <td>28.8</td>\n",
       "      <td>25.2</td>\n",
       "      <td>16.6</td>\n",
       "      <td>24.748441</td>\n",
       "      <td>27.556227</td>\n",
       "      <td>0.886089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.4</td>\n",
       "      <td>M</td>\n",
       "      <td>26</td>\n",
       "      <td>83.80</td>\n",
       "      <td>1.84</td>\n",
       "      <td>37.4</td>\n",
       "      <td>101.8</td>\n",
       "      <td>86.4</td>\n",
       "      <td>101.2</td>\n",
       "      <td>60.1</td>\n",
       "      <td>37.3</td>\n",
       "      <td>22.8</td>\n",
       "      <td>32.4</td>\n",
       "      <td>29.4</td>\n",
       "      <td>18.2</td>\n",
       "      <td>24.751890</td>\n",
       "      <td>22.546538</td>\n",
       "      <td>0.853755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28.7</td>\n",
       "      <td>M</td>\n",
       "      <td>24</td>\n",
       "      <td>83.58</td>\n",
       "      <td>1.81</td>\n",
       "      <td>34.4</td>\n",
       "      <td>97.3</td>\n",
       "      <td>100.0</td>\n",
       "      <td>101.9</td>\n",
       "      <td>63.2</td>\n",
       "      <td>42.2</td>\n",
       "      <td>24.0</td>\n",
       "      <td>32.2</td>\n",
       "      <td>27.7</td>\n",
       "      <td>17.7</td>\n",
       "      <td>25.512042</td>\n",
       "      <td>23.846228</td>\n",
       "      <td>0.981354</td>\n",
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       "    30% {\n",
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       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-9ede215f-e802-4cac-a752-37e0c938ddd8 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "    </div>\n",
       "  </div>\n"
      ]
     },
     "metadata": {},
     "execution_count": 2
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Data Preprocessing"
   ],
   "metadata": {
    "id": "RtiytBbeRq_Z"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "X = df.drop(\"body_fat\", axis=1)\n",
    "y = df[\"body_fat\"]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, stratify=df[\"sex\"]\n",
    ")"
   ],
   "metadata": {
    "id": "iMJkx3qgPq1_"
   },
   "execution_count": 3,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "X_train.head()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "nAiMADOh6DDa",
    "outputId": "ff6c1b6e-9226-4a26-98e2-7e43d68d3ff5"
   },
   "execution_count": 4,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "    sex  age   weight  height  neck  chest  abdomen    hip  thigh  knee  \\\n",
       "353   F   21  52.6176  1.6510  31.0   81.0     63.5   93.0   48.5  33.0   \n",
       "301   F   22  67.5864  1.7145  31.0   94.0     74.0  100.0   56.5  36.4   \n",
       "239   M   65  86.0700  1.6700  40.8  106.4    100.5  100.5   59.2  38.1   \n",
       "217   M   51  70.0800  1.7800  36.9   93.3     81.5   94.4   54.7  39.0   \n",
       "140   M   40  80.1700  1.8000  37.4   98.6     93.1  101.6   59.1  39.6   \n",
       "\n",
       "     ankle  biceps  forearm  wrist        BMI        BAI       WHR  \n",
       "353   20.3    25.5     23.7   16.3  19.303537  25.839172  0.682796  \n",
       "301   22.0    28.0     25.3   17.5  22.992402  26.544467  0.740000  \n",
       "239   24.0    35.9     30.5   19.1  30.861630  28.568404  1.000000  \n",
       "217   22.6    27.5     25.9   18.6  22.118419  21.750445  0.863347  \n",
       "140   21.6    30.8     27.9   16.6  24.743827  24.071205  0.916339  "
      ],
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "      <th>neck</th>\n",
       "      <th>chest</th>\n",
       "      <th>abdomen</th>\n",
       "      <th>hip</th>\n",
       "      <th>thigh</th>\n",
       "      <th>knee</th>\n",
       "      <th>ankle</th>\n",
       "      <th>biceps</th>\n",
       "      <th>forearm</th>\n",
       "      <th>wrist</th>\n",
       "      <th>BMI</th>\n",
       "      <th>BAI</th>\n",
       "      <th>WHR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>F</td>\n",
       "      <td>21</td>\n",
       "      <td>52.6176</td>\n",
       "      <td>1.6510</td>\n",
       "      <td>31.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>63.5</td>\n",
       "      <td>93.0</td>\n",
       "      <td>48.5</td>\n",
       "      <td>33.0</td>\n",
       "      <td>20.3</td>\n",
       "      <td>25.5</td>\n",
       "      <td>23.7</td>\n",
       "      <td>16.3</td>\n",
       "      <td>19.303537</td>\n",
       "      <td>25.839172</td>\n",
       "      <td>0.682796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>F</td>\n",
       "      <td>22</td>\n",
       "      <td>67.5864</td>\n",
       "      <td>1.7145</td>\n",
       "      <td>31.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>56.5</td>\n",
       "      <td>36.4</td>\n",
       "      <td>22.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>25.3</td>\n",
       "      <td>17.5</td>\n",
       "      <td>22.992402</td>\n",
       "      <td>26.544467</td>\n",
       "      <td>0.740000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>M</td>\n",
       "      <td>65</td>\n",
       "      <td>86.0700</td>\n",
       "      <td>1.6700</td>\n",
       "      <td>40.8</td>\n",
       "      <td>106.4</td>\n",
       "      <td>100.5</td>\n",
       "      <td>100.5</td>\n",
       "      <td>59.2</td>\n",
       "      <td>38.1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>35.9</td>\n",
       "      <td>30.5</td>\n",
       "      <td>19.1</td>\n",
       "      <td>30.861630</td>\n",
       "      <td>28.568404</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>M</td>\n",
       "      <td>51</td>\n",
       "      <td>70.0800</td>\n",
       "      <td>1.7800</td>\n",
       "      <td>36.9</td>\n",
       "      <td>93.3</td>\n",
       "      <td>81.5</td>\n",
       "      <td>94.4</td>\n",
       "      <td>54.7</td>\n",
       "      <td>39.0</td>\n",
       "      <td>22.6</td>\n",
       "      <td>27.5</td>\n",
       "      <td>25.9</td>\n",
       "      <td>18.6</td>\n",
       "      <td>22.118419</td>\n",
       "      <td>21.750445</td>\n",
       "      <td>0.863347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>M</td>\n",
       "      <td>40</td>\n",
       "      <td>80.1700</td>\n",
       "      <td>1.8000</td>\n",
       "      <td>37.4</td>\n",
       "      <td>98.6</td>\n",
       "      <td>93.1</td>\n",
       "      <td>101.6</td>\n",
       "      <td>59.1</td>\n",
       "      <td>39.6</td>\n",
       "      <td>21.6</td>\n",
       "      <td>30.8</td>\n",
       "      <td>27.9</td>\n",
       "      <td>16.6</td>\n",
       "      <td>24.743827</td>\n",
       "      <td>24.071205</td>\n",
       "      <td>0.916339</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
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       "\n",
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       "\n",
       "    .colab-df-convert {\n",
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       "      border: none;\n",
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       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
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       "      fill: #D2E3FC;\n",
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       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
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       "      fill: #FFFFFF;\n",
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       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-93dbf037-31e5-432e-a106-6eb18f2687de button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-93dbf037-31e5-432e-a106-6eb18f2687de');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
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       "\n",
       "\n",
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       "\n",
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       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
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       "\n",
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       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
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       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-ddb05068-d8b9-4cab-b70f-b56c20307b80 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "    </div>\n",
       "  </div>\n"
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "categorical_features = [\"sex\"]\n",
    "numerical_features = [col for col in X.columns if col not in categorical_features]\n",
    "\n",
    "categorical_transformer = OneHotEncoder(handle_unknown=\"ignore\")\n",
    "numerical_transformer = StandardScaler()\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        (\"num\", numerical_transformer, numerical_features),\n",
    "        (\"cat\", categorical_transformer, categorical_features),\n",
    "    ]\n",
    ")"
   ],
   "metadata": {
    "id": "JEkUkgCIfYge"
   },
   "execution_count": 5,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "X_train = preprocessor.fit_transform(X_train)\n",
    "X_test = preprocessor.transform(X_test)"
   ],
   "metadata": {
    "id": "HNSQ4MCtfaYF"
   },
   "execution_count": 6,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "X_train[0].shape"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RxO7F6EXgnmT",
    "outputId": "563a198b-d36c-4993-a6cf-4f22baa26cc1"
   },
   "execution_count": 7,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(18,)"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "X_train[0]"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Rka3juyAgNrz",
    "outputId": "8dd1409a-b64f-42c5-c7c8-f01391f08d79"
   },
   "execution_count": 8,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([-0.86457355, -1.32605609, -0.85309475, -1.12112437, -1.23348165,\n",
       "       -1.33140665, -0.86900364, -1.25710466, -1.61173649, -1.14731999,\n",
       "       -1.11527746, -0.87830853, -0.51817294, -0.57197435,  0.04306472,\n",
       "       -1.32016578,  1.        ,  0.        ])"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Training Pipeline"
   ],
   "metadata": {
    "id": "PDjOIF6JsO0f"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "regressors = {\n",
    "    \"Linear Regression\": LinearRegression(),\n",
    "    \"Random Forest\": RandomForestRegressor(),\n",
    "    \"Gradient Boosting\": GradientBoostingRegressor(),\n",
    "    \"Support Vector Regressor\": SVR(),\n",
    "    \"Decision Tree\": DecisionTreeRegressor(),\n",
    "    \"K-Nearest Neighbors\": KNeighborsRegressor(),\n",
    "    \"Lasso\": Lasso(),\n",
    "    \"Ridge\": Ridge(),\n",
    "    \"ElasticNet\": ElasticNet(),\n",
    "    \"AdaBoost Regressor\": AdaBoostRegressor(),\n",
    "    \"XGBoost Regressor\": XGBRegressor(),\n",
    "    \"Huber Regressor\": HuberRegressor(max_iter=200),\n",
    "}"
   ],
   "metadata": {
    "id": "PuuX54qytd9t"
   },
   "execution_count": 9,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "result = []\n",
    "\n",
    "for name, model in tqdm(regressors.items()):\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    rmse = mean_squared_error(y_pred, y_test, squared=False)\n",
    "    r2 = r2_score(y_test, y_pred)\n",
    "    result.append({\"model\": name, \"RMSE\": rmse, \"R2\": r2})"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YmO3VLnyu_79",
    "outputId": "d87d8ffc-55cd-433d-85ea-5cab8558f922"
   },
   "execution_count": 10,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|██████████| 12/12 [00:03<00:00,  3.86it/s]\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "pd.DataFrame(result).sort_values(by=\"R2\", ascending=False)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 426
    },
    "id": "xNpEZsOyqVAc",
    "outputId": "92d94af5-6d1a-4d72-c387-df1ed69bc362"
   },
   "execution_count": 11,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                       model      RMSE        R2\n",
       "0          Linear Regression  4.798754  0.688889\n",
       "2          Gradient Boosting  4.800084  0.688717\n",
       "11           Huber Regressor  4.816429  0.686593\n",
       "7                      Ridge  4.934344  0.671060\n",
       "1              Random Forest  4.973306  0.665845\n",
       "9         AdaBoost Regressor  5.105892  0.647790\n",
       "10         XGBoost Regressor  5.285572  0.622565\n",
       "5        K-Nearest Neighbors  5.327401  0.616567\n",
       "3   Support Vector Regressor  5.747334  0.553737\n",
       "8                 ElasticNet  6.886714  0.359259\n",
       "6                      Lasso  6.924959  0.352123\n",
       "4              Decision Tree  7.297489  0.280543"
      ],
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       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>4.798754</td>\n",
       "      <td>0.688889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>4.800084</td>\n",
       "      <td>0.688717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Huber Regressor</td>\n",
       "      <td>4.816429</td>\n",
       "      <td>0.686593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>4.934344</td>\n",
       "      <td>0.671060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>4.973306</td>\n",
       "      <td>0.665845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AdaBoost Regressor</td>\n",
       "      <td>5.105892</td>\n",
       "      <td>0.647790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>XGBoost Regressor</td>\n",
       "      <td>5.285572</td>\n",
       "      <td>0.622565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>K-Nearest Neighbors</td>\n",
       "      <td>5.327401</td>\n",
       "      <td>0.616567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Support Vector Regressor</td>\n",
       "      <td>5.747334</td>\n",
       "      <td>0.553737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ElasticNet</td>\n",
       "      <td>6.886714</td>\n",
       "      <td>0.359259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Lasso</td>\n",
       "      <td>6.924959</td>\n",
       "      <td>0.352123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>7.297489</td>\n",
       "      <td>0.280543</td>\n",
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       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
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       "      border-bottom-color: var(--fill-color);\n",
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       "    60% {\n",
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       "      border-right-color: var(--fill-color);\n",
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       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
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       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
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       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
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       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-61383424-e8cc-405d-af7c-765270978570 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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       "</div>\n",
       "    </div>\n",
       "  </div>\n"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Evaluation"
   ],
   "metadata": {
    "id": "MFoSxIHSshp5"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "model = LinearRegression()\n",
    "model"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 75
    },
    "id": "WrSo6q2bsIWV",
    "outputId": "d29a5ed7-90ab-4399-8f28-550bbb8a8d7b"
   },
   "execution_count": 12,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "LinearRegression()"
      ],
      "text/html": [
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      ]
     },
     "metadata": {},
     "execution_count": 12
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)"
   ],
   "metadata": {
    "id": "i5iluBk1iEOy"
   },
   "execution_count": 13,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "pd.DataFrame({\"prediction\": y_pred, \"label\": y_test}).head(n=10)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "id": "qZhNquvEt_md",
    "outputId": "93ed5c03-e1c2-4208-9ea6-ff403f2d540e"
   },
   "execution_count": 14,
   "outputs": [
    {
     "output_type": "execute_result",
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      "text/plain": [
       "     prediction  label\n",
       "61    23.551926  29.80\n",
       "97    16.951391  11.30\n",
       "425   20.518403  29.47\n",
       "128   18.735429  20.80\n",
       "304   22.278719  25.44\n",
       "115   15.819718  16.70\n",
       "95    17.906050  17.40\n",
       "179   25.089668  16.90\n",
       "53    11.373259   6.30\n",
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       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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       "\n",
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       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
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       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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       "</div>\n",
       "    </div>\n",
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      ]
     },
     "metadata": {},
     "execution_count": 14
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "r2_score(y_test, y_pred)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_aidDdxOkQZj",
    "outputId": "4bcdc9b4-beed-4f8e-df78-32d59cd2d906"
   },
   "execution_count": 15,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.6888891620486111"
      ]
     },
     "metadata": {},
     "execution_count": 15
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "rmse = mean_squared_error(y_test, y_pred, squared=False)\n",
    "rmse"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ihYtyxqukSF7",
    "outputId": "a4860090-a36f-4048-9666-f07bf1026686"
   },
   "execution_count": 16,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "4.798754048152829"
      ]
     },
     "metadata": {},
     "execution_count": 16
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "plt.scatter(y_test, y_pred, alpha=0.5)\n",
    "plt.title(\"Actual vs. Predicted Body Fat Percentage\")\n",
    "plt.xlabel(\"Actual Body Fat (%)\")\n",
    "plt.ylabel(\"Predicted Body Fat (%)\")\n",
    "\n",
    "plt.plot(\n",
    "    [min(y_test), max(y_test)],\n",
    "    [min(y_test), max(y_test)],\n",
    "    color=COLORS[1],\n",
    "    linestyle=\"--\",\n",
    ")\n",
    "plt.show()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 511
    },
    "id": "_UpBCEWdsSt_",
    "outputId": "0fc192fb-a089-4767-bf11-de1e31d6ecbb"
   },
   "execution_count": 17,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "image/png": {
       "width": 707,
       "height": 494
      }
     }
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "pred_difference = y_test - y_pred\n",
    "plt.hist(pred_difference)\n",
    "plt.title(\"Actual vs. Predicted Body Fat (%)\")\n",
    "plt.xlabel(\"Body Fat (%)\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.show();"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 495
    },
    "id": "BjwkuENZkZdL",
    "outputId": "47f30620-530b-4aa5-92ea-6ba631127460"
   },
   "execution_count": 18,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "image/png": {
       "width": 602,
       "height": 478
      }
     }
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Save the Pipeline"
   ],
   "metadata": {
    "id": "_2iNmPvtTC4Z"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "model = Pipeline(\n",
    "    steps=[(\"preprocessor\", preprocessor), (\"regressor\", LinearRegression())]\n",
    ")"
   ],
   "metadata": {
    "id": "XIy5K175tIg9"
   },
   "execution_count": 19,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "model.fit(X, y)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 192
    },
    "id": "ciLr15yJtglc",
    "outputId": "4de173da-fcb6-40a0-ba30-b217bec0ace6"
   },
   "execution_count": 20,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num', StandardScaler(),\n",
       "                                                  ['age', 'weight', 'height',\n",
       "                                                   'neck', 'chest', 'abdomen',\n",
       "                                                   'hip', 'thigh', 'knee',\n",
       "                                                   'ankle', 'biceps', 'forearm',\n",
       "                                                   'wrist', 'BMI', 'BAI',\n",
       "                                                   'WHR']),\n",
       "                                                 ('cat',\n",
       "                                                  OneHotEncoder(handle_unknown='ignore'),\n",
       "                                                  ['sex'])])),\n",
       "                ('regressor', LinearRegression())])"
      ],
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
       "                                                  [&#x27;age&#x27;, &#x27;weight&#x27;, &#x27;height&#x27;,\n",
       "                                                   &#x27;neck&#x27;, &#x27;chest&#x27;, &#x27;abdomen&#x27;,\n",
       "                                                   &#x27;hip&#x27;, &#x27;thigh&#x27;, &#x27;knee&#x27;,\n",
       "                                                   &#x27;ankle&#x27;, &#x27;biceps&#x27;, &#x27;forearm&#x27;,\n",
       "                                                   &#x27;wrist&#x27;, &#x27;BMI&#x27;, &#x27;BAI&#x27;,\n",
       "                                                   &#x27;WHR&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                                  [&#x27;sex&#x27;])])),\n",
       "                (&#x27;regressor&#x27;, 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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
       "                                                  [&#x27;age&#x27;, &#x27;weight&#x27;, &#x27;height&#x27;,\n",
       "                                                   &#x27;neck&#x27;, &#x27;chest&#x27;, &#x27;abdomen&#x27;,\n",
       "                                                   &#x27;hip&#x27;, &#x27;thigh&#x27;, &#x27;knee&#x27;,\n",
       "                                                   &#x27;ankle&#x27;, &#x27;biceps&#x27;, &#x27;forearm&#x27;,\n",
       "                                                   &#x27;wrist&#x27;, &#x27;BMI&#x27;, &#x27;BAI&#x27;,\n",
       "                                                   &#x27;WHR&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                                  [&#x27;sex&#x27;])])),\n",
       "                (&#x27;regressor&#x27;, LinearRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
       "                                 [&#x27;age&#x27;, &#x27;weight&#x27;, &#x27;height&#x27;, &#x27;neck&#x27;, &#x27;chest&#x27;,\n",
       "                                  &#x27;abdomen&#x27;, &#x27;hip&#x27;, &#x27;thigh&#x27;, &#x27;knee&#x27;, &#x27;ankle&#x27;,\n",
       "                                  &#x27;biceps&#x27;, &#x27;forearm&#x27;, &#x27;wrist&#x27;, &#x27;BMI&#x27;, &#x27;BAI&#x27;,\n",
       "                                  &#x27;WHR&#x27;]),\n",
       "                                (&#x27;cat&#x27;, OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                 [&#x27;sex&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>[&#x27;age&#x27;, &#x27;weight&#x27;, &#x27;height&#x27;, &#x27;neck&#x27;, &#x27;chest&#x27;, &#x27;abdomen&#x27;, &#x27;hip&#x27;, &#x27;thigh&#x27;, &#x27;knee&#x27;, &#x27;ankle&#x27;, &#x27;biceps&#x27;, &#x27;forearm&#x27;, &#x27;wrist&#x27;, &#x27;BMI&#x27;, &#x27;BAI&#x27;, &#x27;WHR&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>[&#x27;sex&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div></div></div>"
      ]
     },
     "metadata": {},
     "execution_count": 20
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "model_path = Path(\"model.pkl\")\n",
    "\n",
    "with model_path.open(\"wb\") as f:\n",
    "    pickle.dump(model, f)"
   ],
   "metadata": {
    "id": "O5GXO_XUTpwo"
   },
   "execution_count": 21,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "with model_path.open(\"rb\") as f:\n",
    "    model = pickle.load(f)"
   ],
   "metadata": {
    "id": "yJn0_Oq5m4Zp"
   },
   "execution_count": 22,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "model"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 192
    },
    "id": "KCM69pgTnt52",
    "outputId": "42fab5d9-ed73-4c04-d5f6-c42185a01a62"
   },
   "execution_count": 23,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num', StandardScaler(),\n",
       "                                                  ['age', 'weight', 'height',\n",
       "                                                   'neck', 'chest', 'abdomen',\n",
       "                                                   'hip', 'thigh', 'knee',\n",
       "                                                   'ankle', 'biceps', 'forearm',\n",
       "                                                   'wrist', 'BMI', 'BAI',\n",
       "                                                   'WHR']),\n",
       "                                                 ('cat',\n",
       "                                                  OneHotEncoder(handle_unknown='ignore'),\n",
       "                                                  ['sex'])])),\n",
       "                ('regressor', LinearRegression())])"
      ],
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
       "                                                  [&#x27;age&#x27;, &#x27;weight&#x27;, &#x27;height&#x27;,\n",
       "                                                   &#x27;neck&#x27;, &#x27;chest&#x27;, &#x27;abdomen&#x27;,\n",
       "                                                   &#x27;hip&#x27;, &#x27;thigh&#x27;, &#x27;knee&#x27;,\n",
       "                                                   &#x27;ankle&#x27;, &#x27;biceps&#x27;, &#x27;forearm&#x27;,\n",
       "                                                   &#x27;wrist&#x27;, &#x27;BMI&#x27;, &#x27;BAI&#x27;,\n",
       "                                                   &#x27;WHR&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                                  [&#x27;sex&#x27;])])),\n",
       "                (&#x27;regressor&#x27;, 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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
       "                                                  [&#x27;age&#x27;, &#x27;weight&#x27;, &#x27;height&#x27;,\n",
       "                                                   &#x27;neck&#x27;, &#x27;chest&#x27;, &#x27;abdomen&#x27;,\n",
       "                                                   &#x27;hip&#x27;, &#x27;thigh&#x27;, &#x27;knee&#x27;,\n",
       "                                                   &#x27;ankle&#x27;, &#x27;biceps&#x27;, &#x27;forearm&#x27;,\n",
       "                                                   &#x27;wrist&#x27;, &#x27;BMI&#x27;, &#x27;BAI&#x27;,\n",
       "                                                   &#x27;WHR&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                                  [&#x27;sex&#x27;])])),\n",
       "                (&#x27;regressor&#x27;, LinearRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
       "                                 [&#x27;age&#x27;, &#x27;weight&#x27;, &#x27;height&#x27;, &#x27;neck&#x27;, &#x27;chest&#x27;,\n",
       "                                  &#x27;abdomen&#x27;, &#x27;hip&#x27;, &#x27;thigh&#x27;, &#x27;knee&#x27;, &#x27;ankle&#x27;,\n",
       "                                  &#x27;biceps&#x27;, &#x27;forearm&#x27;, &#x27;wrist&#x27;, &#x27;BMI&#x27;, &#x27;BAI&#x27;,\n",
       "                                  &#x27;WHR&#x27;]),\n",
       "                                (&#x27;cat&#x27;, OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                 [&#x27;sex&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>[&#x27;age&#x27;, &#x27;weight&#x27;, &#x27;height&#x27;, &#x27;neck&#x27;, &#x27;chest&#x27;, &#x27;abdomen&#x27;, &#x27;hip&#x27;, &#x27;thigh&#x27;, &#x27;knee&#x27;, &#x27;ankle&#x27;, &#x27;biceps&#x27;, &#x27;forearm&#x27;, &#x27;wrist&#x27;, &#x27;BMI&#x27;, &#x27;BAI&#x27;, &#x27;WHR&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>[&#x27;sex&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div></div></div>"
      ]
     },
     "metadata": {},
     "execution_count": 23
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "pred = model.predict(X)\n",
    "rmse = mean_squared_error(y, pred, squared=False)\n",
    "rmse"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sJuEZAXy9RYo",
    "outputId": "35e17af3-ca98-41c4-86ab-52f0d5625514"
   },
   "execution_count": 24,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
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     "execution_count": 24
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 ]
}