Subina_Chapter_3_mimic_iv (1).ipynb

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    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "sg7VK1uZvPtc",
        "outputId": "ed2c687b-c346-47ea-af8b-b0bfb17bc540",
        "colab": {
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'handson-ml-mimic-iv'...\n",
            "remote: Enumerating objects: 83, done.\u001b[K\n",
            "remote: Counting objects: 100% (83/83), done.\u001b[K\n",
            "remote: Compressing objects: 100% (68/68), done.\u001b[K\n",
            "remote: Total 83 (delta 29), reused 40 (delta 13), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (83/83), 3.86 MiB | 7.55 MiB/s, done.\n",
            "Resolving deltas: 100% (29/29), done.\n"
          ]
        }
      ],
      "source": [
        "\n",
        "!git clone https://github.com/iupui-soic/handson-ml-mimic-iv.git"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "N_YtrCL5xX0v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import os\n",
        "print(os.getcwd())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fRYd55ZHxlOa",
        "outputId": "8457e28b-0ee0-4443-86e1-a11e4b015b0d"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.chdir('/content/handson-ml-mimic-iv')"
      ],
      "metadata": {
        "id": "zxqhDCGcy_P-"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HFvYKwAty_UC",
        "outputId": "924a6b98-6731-4d71-82ad-b6c8329fefc5"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import dask.dataframe as dd\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "UH8ZQ23sy_Xq"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from mimic.mimic import MIMIC, MIMICManipulations"
      ],
      "metadata": {
        "id": "FyW20VgczXj-"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mimic_instance = MIMIC('/content/drive/MyDrive/mimiv-iv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KtOd-Oe_zXsU",
        "outputId": "05c29d03-66d8-4b26-ebbe-f2a03c1b938e"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MIMIC dataset found at path : /content/drive/MyDrive/mimiv-iv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mimic_manipulation_instance = MIMICManipulations('/content/drive/MyDrive/mimiv-iv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jaVJz05IzXvz",
        "outputId": "e3cbf8a6-effd-452c-d306-b0052138cbac"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MIMIC dataset found at path : /content/drive/MyDrive/mimiv-iv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "EdNUZdG20ZDn"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!ls '/content/drive/My Drive/mimiv-iv'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XB1gnqxizh05",
        "outputId": "ff2aa6df-c646-4efa-f379-a056c49184f7"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "admissions.parquet\t d_icd_procedures.parquet  labevents.parquet\t   provider.parquet\n",
            "caregiver.parquet\t d_items.parquet\t   omr.parquet\t\t   services.parquet\n",
            "chartevents.parquet\t d_labitems.parquet\t   outputevents.parquet    transfers.parquet\n",
            "d_hcpcs.parquet\t\t drgcodes.parquet\t   patients.parquet\n",
            "diagnoses_icd.parquet\t emar.parquet\t\t   poe_detail.parquet\n",
            "d_icd_diagnoses.parquet  icustays.parquet\t   procedures_icd.parquet\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/ch_03_dataset.csv')"
      ],
      "metadata": {
        "id": "Oo5u00u64QJC"
      },
      "execution_count": 13,
      "outputs": []
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      "source": [
        "df.head()"
      ],
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      "execution_count": 14,
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              "   Unnamed: 0  subject_id   hadm_id            admittime            dischtime  \\\n",
              "0           0    10000032  22595853  2180-05-06 22:23:00  2180-05-07 17:15:00   \n",
              "1           1    10000032  22841357  2180-06-26 18:27:00  2180-06-27 18:49:00   \n",
              "2           2    10000032  25742920  2180-08-05 23:44:00  2180-08-07 17:50:00   \n",
              "3           3    10000032  29079034  2180-07-23 12:35:00  2180-07-25 17:55:00   \n",
              "4           4    10000068  25022803  2160-03-03 23:16:00  2160-03-04 06:26:00   \n",
              "\n",
              "  deathtime  admission_type admit_provider_id      admission_location  \\\n",
              "0       NaN          URGENT            P874LG  TRANSFER FROM HOSPITAL   \n",
              "1       NaN        EW EMER.            P09Q6Y          EMERGENCY ROOM   \n",
              "2       NaN        EW EMER.            P60CC5          EMERGENCY ROOM   \n",
              "3       NaN        EW EMER.            P30KEH          EMERGENCY ROOM   \n",
              "4       NaN  EU OBSERVATION            P51VDL          EMERGENCY ROOM   \n",
              "\n",
              "  discharge_location  ... marital_status   race            edregtime  \\\n",
              "0               HOME  ...        WIDOWED  WHITE  2180-05-06 19:17:00   \n",
              "1               HOME  ...        WIDOWED  WHITE  2180-06-26 15:54:00   \n",
              "2            HOSPICE  ...        WIDOWED  WHITE  2180-08-05 20:58:00   \n",
              "3               HOME  ...        WIDOWED  WHITE  2180-07-23 05:54:00   \n",
              "4                NaN  ...         SINGLE  WHITE  2160-03-03 21:55:00   \n",
              "\n",
              "             edouttime hospital_expire_flag gender  anchor_age anchor_year  \\\n",
              "0  2180-05-06 23:30:00                    0      F          52        2180   \n",
              "1  2180-06-26 21:31:00                    0      F          52        2180   \n",
              "2  2180-08-06 01:44:00                    0      F          52        2180   \n",
              "3  2180-07-23 14:00:00                    0      F          52        2180   \n",
              "4  2160-03-04 06:26:00                    0      F          19        2160   \n",
              "\n",
              "   anchor_year_group         dod  \n",
              "0        2014 - 2016  2180-09-09  \n",
              "1        2014 - 2016  2180-09-09  \n",
              "2        2014 - 2016  2180-09-09  \n",
              "3        2014 - 2016  2180-09-09  \n",
              "4        2008 - 2010         NaN  \n",
              "\n",
              "[5 rows x 22 columns]"
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              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    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",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\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-8932ba54-b2a8-4ad5-b6ce-5b24deea1f88 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C2kMapigzuNJ",
        "outputId": "e975c16d-cbe4-44f1-d94a-02ad76ca87f1"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 431231 entries, 0 to 431230\n",
            "Data columns (total 22 columns):\n",
            " #   Column                Non-Null Count   Dtype \n",
            "---  ------                --------------   ----- \n",
            " 0   Unnamed: 0            431231 non-null  int64 \n",
            " 1   subject_id            431231 non-null  int64 \n",
            " 2   hadm_id               431231 non-null  int64 \n",
            " 3   admittime             431231 non-null  object\n",
            " 4   dischtime             431231 non-null  object\n",
            " 5   deathtime             8598 non-null    object\n",
            " 6   admission_type        431231 non-null  object\n",
            " 7   admit_provider_id     431227 non-null  object\n",
            " 8   admission_location    431231 non-null  object\n",
            " 9   discharge_location    312076 non-null  object\n",
            " 10  insurance             431231 non-null  object\n",
            " 11  language              431231 non-null  object\n",
            " 12  marital_status        421998 non-null  object\n",
            " 13  race                  431231 non-null  object\n",
            " 14  edregtime             299282 non-null  object\n",
            " 15  edouttime             299282 non-null  object\n",
            " 16  hospital_expire_flag  431231 non-null  int64 \n",
            " 17  gender                431231 non-null  object\n",
            " 18  anchor_age            431231 non-null  int64 \n",
            " 19  anchor_year           431231 non-null  int64 \n",
            " 20  anchor_year_group     431231 non-null  object\n",
            " 21  dod                   106218 non-null  object\n",
            "dtypes: int64(6), object(16)\n",
            "memory usage: 72.4+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "ypmabcaO0G7d",
        "outputId": "6af5d329-e889-46ce-d5e1-aff1361792b1"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Unnamed: 0    subject_id       hadm_id  hospital_expire_flag  \\\n",
              "count  431231.000000  4.312310e+05  4.312310e+05         431231.000000   \n",
              "mean   215615.000000  1.500766e+07  2.500314e+07              0.019964   \n",
              "std    124485.811304  2.877498e+06  2.888180e+06              0.139876   \n",
              "min         0.000000  1.000003e+07  2.000002e+07              0.000000   \n",
              "25%    107807.500000  1.251557e+07  2.250155e+07              0.000000   \n",
              "50%    215615.000000  1.501288e+07  2.500669e+07              0.000000   \n",
              "75%    323422.500000  1.749622e+07  2.750470e+07              0.000000   \n",
              "max    431230.000000  1.999999e+07  2.999993e+07              1.000000   \n",
              "\n",
              "          anchor_age    anchor_year  \n",
              "count  431231.000000  431231.000000  \n",
              "mean       56.875394    2152.764136  \n",
              "std        19.051639      23.666988  \n",
              "min        18.000000    2110.000000  \n",
              "25%        43.000000    2133.000000  \n",
              "50%        58.000000    2153.000000  \n",
              "75%        72.000000    2173.000000  \n",
              "max        91.000000    2208.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ed3df952-1a39-462d-893a-122ce4594c23\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>subject_id</th>\n",
              "      <th>hadm_id</th>\n",
              "      <th>hospital_expire_flag</th>\n",
              "      <th>anchor_age</th>\n",
              "      <th>anchor_year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>431231.000000</td>\n",
              "      <td>4.312310e+05</td>\n",
              "      <td>4.312310e+05</td>\n",
              "      <td>431231.000000</td>\n",
              "      <td>431231.000000</td>\n",
              "      <td>431231.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>215615.000000</td>\n",
              "      <td>1.500766e+07</td>\n",
              "      <td>2.500314e+07</td>\n",
              "      <td>0.019964</td>\n",
              "      <td>56.875394</td>\n",
              "      <td>2152.764136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>124485.811304</td>\n",
              "      <td>2.877498e+06</td>\n",
              "      <td>2.888180e+06</td>\n",
              "      <td>0.139876</td>\n",
              "      <td>19.051639</td>\n",
              "      <td>23.666988</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000003e+07</td>\n",
              "      <td>2.000002e+07</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>2110.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>107807.500000</td>\n",
              "      <td>1.251557e+07</td>\n",
              "      <td>2.250155e+07</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>43.000000</td>\n",
              "      <td>2133.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>215615.000000</td>\n",
              "      <td>1.501288e+07</td>\n",
              "      <td>2.500669e+07</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>2153.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>323422.500000</td>\n",
              "      <td>1.749622e+07</td>\n",
              "      <td>2.750470e+07</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>2173.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>431230.000000</td>\n",
              "      <td>1.999999e+07</td>\n",
              "      <td>2.999993e+07</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>91.000000</td>\n",
              "      <td>2208.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ed3df952-1a39-462d-893a-122ce4594c23')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\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-ed3df952-1a39-462d-893a-122ce4594c23 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-ed3df952-1a39-462d-893a-122ce4594c23');\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",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a9a0d6ba-def8-4a1b-a653-37763ce37d01\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a9a0d6ba-def8-4a1b-a653-37763ce37d01')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    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",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\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-a9a0d6ba-def8-4a1b-a653-37763ce37d01 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 155417.51982382554,\n        \"min\": 0.0,\n        \"max\": 431231.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          431231.0,\n          215615.0,\n          323422.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"subject_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6894824.276515122,\n        \"min\": 431231.0,\n        \"max\": 19999987.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          15007664.969607936,\n          15012885.0,\n          431231.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hadm_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11230533.526033193,\n        \"min\": 431231.0,\n        \"max\": 29999928.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          25003138.264725868,\n          25006691.0,\n          431231.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hospital_expire_flag\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 152463.1235985651,\n        \"min\": 0.0,\n        \"max\": 431231.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.019963778114282137,\n          1.0,\n          0.13987591303876895\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"anchor_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 152445.10614765954,\n        \"min\": 18.0,\n        \"max\": 431231.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          56.875393930399255,\n          58.0,\n          431231.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"anchor_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 151810.7697096908,\n        \"min\": 23.666988214012818,\n        \"max\": 431231.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2152.764135695254,\n          2153.0,\n          431231.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.hist(bins=50, figsize=(20,15))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 654
        },
        "id": "GjYMpOid0MfZ",
        "outputId": "e6353bf9-c56e-487c-8603-d491a4468ea5"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1500 with 6 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split, cross_val_score\n",
        "from sklearn.preprocessing import StandardScaler, OrdinalEncoder\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report"
      ],
      "metadata": {
        "id": "HDcTwKwg1i96"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[['marital_status', 'hospital_expire_flag']]"
      ],
      "metadata": {
        "id": "mCVw8Ziz1jBZ"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna(inplace=True)"
      ],
      "metadata": {
        "id": "ap5sAGnVBDnS"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GZrTz9BYH63b",
        "outputId": "a12e3bb8-087f-4d90-8b4e-431fb897d22b"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 421998 entries, 0 to 431229\n",
            "Data columns (total 2 columns):\n",
            " #   Column                Non-Null Count   Dtype\n",
            "---  ------                --------------   -----\n",
            " 0   marital_status        421998 non-null  int8 \n",
            " 1   hospital_expire_flag  421998 non-null  int64\n",
            "dtypes: int64(1), int8(1)\n",
            "memory usage: 6.8 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['marital_status'] = df['marital_status'].astype('category').cat.codes"
      ],
      "metadata": {
        "id": "Lup9XqEOBDyi"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "yI24X9vTIECe",
        "outputId": "07743f01-ac74-4db8-9320-fc9213983be1"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       marital_status  hospital_expire_flag\n",
              "count   421998.000000         421998.000000\n",
              "mean         1.528931              0.017758\n",
              "std          0.785129              0.132072\n",
              "min          0.000000              0.000000\n",
              "25%          1.000000              0.000000\n",
              "50%          1.000000              0.000000\n",
              "75%          2.000000              0.000000\n",
              "max          3.000000              1.000000"
            ],
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              "      <th>marital_status</th>\n",
              "      <th>hospital_expire_flag</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>421998.000000</td>\n",
              "      <td>421998.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.528931</td>\n",
              "      <td>0.017758</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.785129</td>\n",
              "      <td>0.132072</td>\n",
              "    </tr>\n",
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              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>1.000000</td>\n",
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              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
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              "        const element = document.querySelector('#df-29c9c753-9b69-4cdc-8f11-f1f0df8b69d3');\n",
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              "                                                    [key], {});\n",
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              "\n",
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              "  }\n",
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              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
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              "    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-ec4d25a3-4277-42ad-87ca-7f2af2c7ecb0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"marital_status\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 149198.35329517396,\n        \"min\": 0.0,\n        \"max\": 421998.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          421998.0,\n          1.5289314167365722,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hospital_expire_flag\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 149198.76564876424,\n        \"min\": 0.0,\n        \"max\": 421998.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.01775837800179148,\n          1.0,\n          0.1320721747643943\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Splitting data into train and test sets\n",
        "X = df[['marital_status']]\n",
        "y = df['hospital_expire_flag']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "N1N-SicZBWG9"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing necessary modules\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.compose import ColumnTransformer # Import ColumnTransformer\n",
        "\n",
        "# Pipeline construction\n",
        "num_pipeline = Pipeline([\n",
        "    ('imputer', SimpleImputer(strategy='median')),\n",
        "    ('std_scaler', StandardScaler())\n",
        "])\n",
        "\n",
        "full_pipeline = ColumnTransformer([ # Now ColumnTransformer should be recognized\n",
        "    ('num', num_pipeline, X.columns)\n",
        "])\n",
        "\n",
        "X_train_prepared = full_pipeline.fit_transform(X_train)\n",
        "X_test_prepared = full_pipeline.transform(X_test)\n"
      ],
      "metadata": {
        "id": "8RvOjLYfBWKk"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Training and Evaluation:\n",
        "\n",
        "Logistic Regression:\n"
      ],
      "metadata": {
        "id": "-MiR3gn2HZec"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf = LogisticRegression(random_state=42)\n",
        "clf.fit(X_train_prepared, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "kKv7OdjmBWX7",
        "outputId": "b601cb4c-9fe6-4031-bda7-e8882087d11c"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(random_state=42)"
            ],
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            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predictions and Evaluation\n",
        "y_pred = clf.predict(X_test_prepared)\n"
      ],
      "metadata": {
        "id": "iz4qiDI0CcbM"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Confusion Matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False)\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "qmG4txUfD_Sw",
        "outputId": "4f1a7c8d-8a35-4d92-e718-aa1e067533dd"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluating the model\n",
        "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
        "\n",
        "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pnoHpAGhCcow",
        "outputId": "d5e77052-ff92-4845-aebe-3eff6f72fc77"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9816706161137441\n",
            "\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      1.00      0.99     82853\n",
            "           1       0.00      0.00      0.00      1547\n",
            "\n",
            "    accuracy                           0.98     84400\n",
            "   macro avg       0.49      0.50      0.50     84400\n",
            "weighted avg       0.96      0.98      0.97     84400\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Optional: Cross-validation\n",
        "from sklearn.model_selection import cross_val_score\n",
        "scores = cross_val_score(clf, X_train_prepared, y_train, cv=5) # Perform cross-validation\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(range(1, 6), scores, marker='o', linestyle='-', color='b', alpha=0.7)\n",
        "plt.title('Cross-validation Scores')\n",
        "plt.xlabel('Fold')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "PSoA99fX_bkx",
        "outputId": "f3f8a145-590b-44a1-c095-673eb501a0c4"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import precision_recall_curve, roc_curve, auc\n",
        "\n",
        "# Training the classifier (Logistic Regression in this case)\n",
        "clf = LogisticRegression(random_state=42)\n",
        "clf.fit(X_train_prepared, y_train)\n",
        "\n",
        "# Predicting probabilities and calculating Precision/Recall metrics\n",
        "y_scores = clf.decision_function(X_test_prepared)\n",
        "precisions, recalls, _ = precision_recall_curve(y_test, y_scores)"
      ],
      "metadata": {
        "id": "Pnerp5RYNtIB"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Plotting Precision/Recall trade-off curve"
      ],
      "metadata": {
        "id": "_Butgw9HHlsT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting Precision/Recall trade-off curve\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(recalls, precisions, color='b', alpha=0.8)\n",
        "plt.fill_between(recalls, precisions, alpha=0.2, color='b')\n",
        "plt.xlabel('Recall')\n",
        "plt.ylabel('Precision')\n",
        "plt.title('Precision-Recall Curve')\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "5Bq8BVaUNvJG",
        "outputId": "6ab023d3-6443-4cb2-d0bb-18dcbd41d519"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculating ROC curve and AUC\n",
        "fpr, tpr, thresholds = roc_curve(y_test, y_scores)\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "# Plotting ROC curve\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (AUC = %0.2f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "xMBua7ubOM8i",
        "outputId": "a21c17f5-0695-4634-b2e1-4b446acbc754"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import SGDClassifier\n",
        "\n",
        "sgd_clf = SGDClassifier(random_state=42)\n",
        "sgd_clf.fit(X_train_prepared, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "LqjLM3cvxZ1B",
        "outputId": "55496404-172d-45cf-cec8-d9d99d625d7f"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SGDClassifier(random_state=42)"
            ],
            "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>SGDClassifier(random_state=42)</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\">SGDClassifier</label><div class=\"sk-toggleable__content\"><pre>SGDClassifier(random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Making predictions and evaluating the model's performance."
      ],
      "metadata": {
        "id": "UrR32r8gFIoW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_sgd = sgd_clf.predict(X_test_prepared)\n"
      ],
      "metadata": {
        "id": "NLBHbrEFFLDc"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Evaluation:\n",
        "\n",
        "Confusion Matrix: Plotting the confusion matrix for the SGD classifier."
      ],
      "metadata": {
        "id": "El-Ms_BtFZoI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cm_sgd = confusion_matrix(y_test, y_pred_sgd)\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm_sgd, annot=True, fmt='d', cmap='Blues', cbar=False)\n",
        "plt.title('Confusion Matrix - SGD Classifier')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "BWv9bfB3xaFt",
        "outputId": "c186ea6c-2e27-4aa2-e8e6-d02d7c2ed889"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tErnKjf_xaQF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AuVqdTzNxaW9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kEJKgK5rxaZn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6diglBAfxadD"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}