Lab7.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RkapEQVfrJpt",
        "outputId": "a4149ae5-4423-4219-ae02-a30ab7b990ca"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  StudentPerfDataset.zip\n",
            "  inflating: StudentsPerformance.csv  \n"
          ]
        }
      ],
      "source": [
        "!unzip StudentPerfDataset.zip"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from pandas.plotting import scatter_matrix\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.impute import SimpleImputer"
      ],
      "metadata": {
        "id": "jE57UjgwrMSq"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('StudentsPerformance.csv')\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "KTlyLmppvInX",
        "outputId": "38fbc205-8dc7-4b6c-b265-e51cb338a7ad"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     gender race/ethnicity parental level of education         lunch  \\\n",
              "0    female        group B           bachelor's degree      standard   \n",
              "1    female        group C                some college      standard   \n",
              "2    female        group B             master's degree      standard   \n",
              "3      male        group A          associate's degree  free/reduced   \n",
              "4      male        group C                some college      standard   \n",
              "..      ...            ...                         ...           ...   \n",
              "995  female        group E             master's degree      standard   \n",
              "996    male        group C                 high school  free/reduced   \n",
              "997  female        group C                 high school  free/reduced   \n",
              "998  female        group D                some college      standard   \n",
              "999  female        group D                some college  free/reduced   \n",
              "\n",
              "    test preparation course  math score  reading score  writing score  \n",
              "0                      none          72             72             74  \n",
              "1                 completed          69             90             88  \n",
              "2                      none          90             95             93  \n",
              "3                      none          47             57             44  \n",
              "4                      none          76             78             75  \n",
              "..                      ...         ...            ...            ...  \n",
              "995               completed          88             99             95  \n",
              "996                    none          62             55             55  \n",
              "997               completed          59             71             65  \n",
              "998               completed          68             78             77  \n",
              "999                    none          77             86             86  \n",
              "\n",
              "[1000 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f42a1b90-6301-4157-adf5-e0e9de79b846\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\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>gender</th>\n",
              "      <th>race/ethnicity</th>\n",
              "      <th>parental level of education</th>\n",
              "      <th>lunch</th>\n",
              "      <th>test preparation course</th>\n",
              "      <th>math score</th>\n",
              "      <th>reading score</th>\n",
              "      <th>writing score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>female</td>\n",
              "      <td>group B</td>\n",
              "      <td>bachelor's degree</td>\n",
              "      <td>standard</td>\n",
              "      <td>none</td>\n",
              "      <td>72</td>\n",
              "      <td>72</td>\n",
              "      <td>74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>female</td>\n",
              "      <td>group C</td>\n",
              "      <td>some college</td>\n",
              "      <td>standard</td>\n",
              "      <td>completed</td>\n",
              "      <td>69</td>\n",
              "      <td>90</td>\n",
              "      <td>88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>female</td>\n",
              "      <td>group B</td>\n",
              "      <td>master's degree</td>\n",
              "      <td>standard</td>\n",
              "      <td>none</td>\n",
              "      <td>90</td>\n",
              "      <td>95</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>male</td>\n",
              "      <td>group A</td>\n",
              "      <td>associate's degree</td>\n",
              "      <td>free/reduced</td>\n",
              "      <td>none</td>\n",
              "      <td>47</td>\n",
              "      <td>57</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>male</td>\n",
              "      <td>group C</td>\n",
              "      <td>some college</td>\n",
              "      <td>standard</td>\n",
              "      <td>none</td>\n",
              "      <td>76</td>\n",
              "      <td>78</td>\n",
              "      <td>75</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>female</td>\n",
              "      <td>group E</td>\n",
              "      <td>master's degree</td>\n",
              "      <td>standard</td>\n",
              "      <td>completed</td>\n",
              "      <td>88</td>\n",
              "      <td>99</td>\n",
              "      <td>95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>male</td>\n",
              "      <td>group C</td>\n",
              "      <td>high school</td>\n",
              "      <td>free/reduced</td>\n",
              "      <td>none</td>\n",
              "      <td>62</td>\n",
              "      <td>55</td>\n",
              "      <td>55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>female</td>\n",
              "      <td>group C</td>\n",
              "      <td>high school</td>\n",
              "      <td>free/reduced</td>\n",
              "      <td>completed</td>\n",
              "      <td>59</td>\n",
              "      <td>71</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>female</td>\n",
              "      <td>group D</td>\n",
              "      <td>some college</td>\n",
              "      <td>standard</td>\n",
              "      <td>completed</td>\n",
              "      <td>68</td>\n",
              "      <td>78</td>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>female</td>\n",
              "      <td>group D</td>\n",
              "      <td>some college</td>\n",
              "      <td>free/reduced</td>\n",
              "      <td>none</td>\n",
              "      <td>77</td>\n",
              "      <td>86</td>\n",
              "      <td>86</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f42a1b90-6301-4157-adf5-e0e9de79b846')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\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",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f42a1b90-6301-4157-adf5-e0e9de79b846 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-f42a1b90-6301-4157-adf5-e0e9de79b846');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scatter_matrix(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 636
        },
        "id": "LP9eYw19vy68",
        "outputId": "e0427e26-6b25-45c1-f060-cd38f2c96f94"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: xlabel='math score', ylabel='math score'>,\n",
              "        <Axes: xlabel='reading score', ylabel='math score'>,\n",
              "        <Axes: xlabel='writing score', ylabel='math score'>],\n",
              "       [<Axes: xlabel='math score', ylabel='reading score'>,\n",
              "        <Axes: xlabel='reading score', ylabel='reading score'>,\n",
              "        <Axes: xlabel='writing score', ylabel='reading score'>],\n",
              "       [<Axes: xlabel='math score', ylabel='writing score'>,\n",
              "        <Axes: xlabel='reading score', ylabel='writing score'>,\n",
              "        <Axes: xlabel='writing score', ylabel='writing score'>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "Oo8Ggexa6t2w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "num_features = ['math score', 'reading score']\n",
        "cat_features = ['gender', 'race/ethnicity', 'parental level of education', 'lunch', 'test preparation course']\n",
        "\n"
      ],
      "metadata": {
        "id": "mF5coiuJwtwt"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df, test_df = train_test_split(df, test_size=0.2)"
      ],
      "metadata": {
        "id": "hBQjO2hH2gD1"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_pipeline = Pipeline([\n",
        "    ('imputer', SimpleImputer(strategy=\"median\")),\n",
        "    #('scaler', StandardScaler()),\n",
        "])\n",
        "\n",
        "pipeline = ColumnTransformer([\n",
        "    (\"num\", num_pipeline, num_features),\n",
        "    (\"cat\", OneHotEncoder(), cat_features)\n",
        "])\n",
        "\n",
        "score_transform = Pipeline([('pass', 'passthrough')])\n",
        "\n",
        "X_train = pipeline.fit_transform(train_df)\n",
        "Y_train = score_transform.fit_transform(train_df[['writing score']])\n",
        "\n",
        "X_test = pipeline.transform(test_df)\n",
        "Y_test = score_transform.transform(test_df[['writing score']])\n"
      ],
      "metadata": {
        "id": "6-sOmK8uxMKZ"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, Y_train"
      ],
      "metadata": {
        "id": "TAW9rv3c7IVz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "33a8eb3d-ffca-4ac2-a226-4e85ba586de9"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[59., 71.,  1., ...,  1.,  0.,  1.],\n",
              "        [56., 65.,  1., ...,  0.,  0.,  1.],\n",
              "        [60., 68.,  0., ...,  1.,  0.,  1.],\n",
              "        ...,\n",
              "        [38., 60.,  1., ...,  0.,  0.,  1.],\n",
              "        [72., 64.,  0., ...,  1.,  0.,  1.],\n",
              "        [65., 78.,  1., ...,  1.,  1.,  0.]]),\n",
              "      writing score\n",
              " 354             70\n",
              " 319             63\n",
              " 681             60\n",
              " 375             41\n",
              " 392             67\n",
              " ..             ...\n",
              " 695             86\n",
              " 741             56\n",
              " 9               50\n",
              " 83              63\n",
              " 899             82\n",
              " \n",
              " [800 rows x 1 columns])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = DecisionTreeClassifier()\n",
        "model.fit(X_train, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "whcTJl2B1FTO",
        "outputId": "0b04af1c-4e04-493f-f3bf-24eab6d888b9"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#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>DecisionTreeClassifier()</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 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 sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_hat = model.predict(X_test)"
      ],
      "metadata": {
        "id": "cDbaWQsf1u6n"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred = score_transform.inverse_transform(Y_hat)\n",
        "Y_target = score_transform.inverse_transform(Y_test)"
      ],
      "metadata": {
        "id": "CN7_CdRH11Kk"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "error = mean_squared_error(Y_pred, Y_target)\n",
        "error"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mHVVPvw02kDN",
        "outputId": "f4c290f2-f5ee-42a5-c975-3dde6ad2d2c4"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "35.78"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "list(zip(Y_pred, Y_target.values.flatten()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V2tQz4K-Egw5",
        "outputId": "46d16b8f-b605-459a-cf3d-dd761d03701f"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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    {
      "cell_type": "code",
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      "metadata": {
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      },
      "execution_count": null,
      "outputs": []
    }
  ]
}