{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ejxfaLTW4fY7"
      },
      "source": [
        "**READ DATASET**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "RpASdZNAx48v"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "dataSepsis = pd.read_csv(\"/content/Dataset.csv\", sep=\",\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "J-wf8_5k2Jv9",
        "outputId": "cb43636f-c3b6-46bd-ea8f-af11625ea560"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0  Hour    HR  O2Sat  Temp    SBP   MAP   DBP  Resp  EtCO2  ...  \\\n",
              "0           0     0   NaN    NaN   NaN    NaN   NaN   NaN   NaN    NaN  ...   \n",
              "1           1     1  65.0  100.0   NaN    NaN  72.0   NaN  16.5    NaN  ...   \n",
              "2           2     2  78.0  100.0   NaN    NaN  42.5   NaN   NaN    NaN  ...   \n",
              "3           3     3  73.0  100.0   NaN    NaN   NaN   NaN  17.0    NaN  ...   \n",
              "4           4     4  70.0  100.0   NaN  129.0  74.0  69.0  14.0    NaN  ...   \n",
              "\n",
              "   Fibrinogen  Platelets    Age  Gender  Unit1  Unit2  HospAdmTime  ICULOS  \\\n",
              "0         NaN        NaN  68.54       0    NaN    NaN        -0.02       1   \n",
              "1         NaN        NaN  68.54       0    NaN    NaN        -0.02       2   \n",
              "2         NaN        NaN  68.54       0    NaN    NaN        -0.02       3   \n",
              "3         NaN        NaN  68.54       0    NaN    NaN        -0.02       4   \n",
              "4         NaN      330.0  68.54       0    NaN    NaN        -0.02       5   \n",
              "\n",
              "   SepsisLabel  Patient_ID  \n",
              "0            0       17072  \n",
              "1            0       17072  \n",
              "2            0       17072  \n",
              "3            0       17072  \n",
              "4            0       17072  \n",
              "\n",
              "[5 rows x 44 columns]"
            ],
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              "<style scoped>\n",
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              "      <th>Unnamed: 0</th>\n",
              "      <th>Hour</th>\n",
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              "      <th>O2Sat</th>\n",
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              "      <th>DBP</th>\n",
              "      <th>Resp</th>\n",
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              "      <th>...</th>\n",
              "      <th>Fibrinogen</th>\n",
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              "      <th>Unit2</th>\n",
              "      <th>HospAdmTime</th>\n",
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              "      <th>0</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>65.0</td>\n",
              "      <td>100.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>72.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16.5</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>68.54</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>17072</td>\n",
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              "      <th>2</th>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>68.54</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>17072</td>\n",
              "    </tr>\n",
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              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>73.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>68.54</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>17072</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>70.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>129.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>330.0</td>\n",
              "      <td>68.54</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>17072</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 44 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c86f4e2a-95be-417a-b27d-e754b2513db7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "      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",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
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              "    }\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",
              "    }\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c86f4e2a-95be-417a-b27d-e754b2513db7 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-c86f4e2a-95be-417a-b27d-e754b2513db7');\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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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "dataSepsis.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WLoL6Ym83bMV",
        "outputId": "899d3f2b-3e9d-48ac-c2c2-90299ede1ca0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1552210, 44)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "dataSepsis.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mva7KmVV2bf9",
        "outputId": "ffa87a62-d758-4c44-df31-0554682af377"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1552210 entries, 0 to 1552209\n",
            "Data columns (total 44 columns):\n",
            " #   Column            Non-Null Count    Dtype  \n",
            "---  ------            --------------    -----  \n",
            " 0   Unnamed: 0        1552210 non-null  int64  \n",
            " 1   Hour              1552210 non-null  int64  \n",
            " 2   HR                1398811 non-null  float64\n",
            " 3   O2Sat             1349474 non-null  float64\n",
            " 4   Temp              525226 non-null   float64\n",
            " 5   SBP               1325945 non-null  float64\n",
            " 6   MAP               1358940 non-null  float64\n",
            " 7   DBP               1065656 non-null  float64\n",
            " 8   Resp              1313875 non-null  float64\n",
            " 9   EtCO2             57636 non-null    float64\n",
            " 10  BaseExcess        84145 non-null    float64\n",
            " 11  HCO3              65028 non-null    float64\n",
            " 12  FiO2              129365 non-null   float64\n",
            " 13  pH                107573 non-null   float64\n",
            " 14  PaCO2             86301 non-null    float64\n",
            " 15  SaO2              53561 non-null    float64\n",
            " 16  AST               25183 non-null    float64\n",
            " 17  BUN               106568 non-null   float64\n",
            " 18  Alkalinephos      24941 non-null    float64\n",
            " 19  Calcium           91331 non-null    float64\n",
            " 20  Chloride          70466 non-null    float64\n",
            " 21  Creatinine        94616 non-null    float64\n",
            " 22  Bilirubin_direct  2990 non-null     float64\n",
            " 23  Glucose           265516 non-null   float64\n",
            " 24  Lactate           41446 non-null    float64\n",
            " 25  Magnesium         97951 non-null    float64\n",
            " 26  Phosphate         62301 non-null    float64\n",
            " 27  Potassium         144525 non-null   float64\n",
            " 28  Bilirubin_total   23141 non-null    float64\n",
            " 29  TroponinI         14781 non-null    float64\n",
            " 30  Hct               137433 non-null   float64\n",
            " 31  Hgb               114591 non-null   float64\n",
            " 32  PTT               45699 non-null    float64\n",
            " 33  WBC               99447 non-null    float64\n",
            " 34  Fibrinogen        10242 non-null    float64\n",
            " 35  Platelets         92209 non-null    float64\n",
            " 36  Age               1552210 non-null  float64\n",
            " 37  Gender            1552210 non-null  int64  \n",
            " 38  Unit1             940250 non-null   float64\n",
            " 39  Unit2             940250 non-null   float64\n",
            " 40  HospAdmTime       1552202 non-null  float64\n",
            " 41  ICULOS            1552210 non-null  int64  \n",
            " 42  SepsisLabel       1552210 non-null  int64  \n",
            " 43  Patient_ID        1552210 non-null  int64  \n",
            "dtypes: float64(38), int64(6)\n",
            "memory usage: 521.1 MB\n"
          ]
        }
      ],
      "source": [
        "dataSepsis.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OQpdIpvp44Zb"
      },
      "source": [
        "**CLEAN DATASET**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "6ARCLbJ24_mN"
      },
      "outputs": [],
      "source": [
        "import numpy as np \n",
        "from matplotlib import pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jmJ4QfXD5QWl",
        "outputId": "f254c956-18a3-48e8-ff7e-eae4a4518f87"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0                0\n",
              "Hour                      0\n",
              "HR                   153399\n",
              "O2Sat                202736\n",
              "Temp                1026984\n",
              "SBP                  226265\n",
              "MAP                  193270\n",
              "DBP                  486554\n",
              "Resp                 238335\n",
              "EtCO2               1494574\n",
              "BaseExcess          1468065\n",
              "HCO3                1487182\n",
              "FiO2                1422845\n",
              "pH                  1444637\n",
              "PaCO2               1465909\n",
              "SaO2                1498649\n",
              "AST                 1527027\n",
              "BUN                 1445642\n",
              "Alkalinephos        1527269\n",
              "Calcium             1460879\n",
              "Chloride            1481744\n",
              "Creatinine          1457594\n",
              "Bilirubin_direct    1549220\n",
              "Glucose             1286694\n",
              "Lactate             1510764\n",
              "Magnesium           1454259\n",
              "Phosphate           1489909\n",
              "Potassium           1407685\n",
              "Bilirubin_total     1529069\n",
              "TroponinI           1537429\n",
              "Hct                 1414777\n",
              "Hgb                 1437619\n",
              "PTT                 1506511\n",
              "WBC                 1452763\n",
              "Fibrinogen          1541968\n",
              "Platelets           1460001\n",
              "Age                       0\n",
              "Gender                    0\n",
              "Unit1                611960\n",
              "Unit2                611960\n",
              "HospAdmTime               8\n",
              "ICULOS                    0\n",
              "SepsisLabel               0\n",
              "Patient_ID                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "# Check for any null values\n",
        "\n",
        "dataSepsis.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qDcnk3Yi9Jzn",
        "outputId": "774d7410-e53f-4a37-915d-f759b994c87f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0            336\n",
              "Hour                  336\n",
              "HR                    343\n",
              "O2Sat                 145\n",
              "Temp                  641\n",
              "SBP                   990\n",
              "MAP                   945\n",
              "DBP                   678\n",
              "Resp                  235\n",
              "EtCO2                 139\n",
              "BaseExcess            407\n",
              "HCO3                  304\n",
              "FiO2                  104\n",
              "pH                    103\n",
              "PaCO2                 551\n",
              "SaO2                  432\n",
              "AST                  2025\n",
              "BUN                   265\n",
              "Alkalinephos          752\n",
              "Calcium               549\n",
              "Chloride              108\n",
              "Creatinine           1407\n",
              "Bilirubin_direct      280\n",
              "Glucose              1157\n",
              "Lactate              1340\n",
              "Magnesium             110\n",
              "Phosphate             193\n",
              "Potassium             402\n",
              "Bilirubin_total       407\n",
              "TroponinI            2423\n",
              "Hct                   725\n",
              "Hgb                   340\n",
              "PTT                  1410\n",
              "WBC                   891\n",
              "Fibrinogen            823\n",
              "Platelets             989\n",
              "Age                  5987\n",
              "Gender                  2\n",
              "Unit1                   2\n",
              "Unit2                   2\n",
              "HospAdmTime         12156\n",
              "ICULOS                336\n",
              "SepsisLabel             2\n",
              "Patient_ID          40336\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "dataSepsis.nunique()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tnQB125HU_mB",
        "outputId": "8e95b22c-a8f6-45cc-a726-cc04bd214a02"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "40336"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "# list of all the unique patients\n",
        "# total number of patients present in the dataset\n",
        "\n",
        "patients = list(dataSepsis['Patient_ID'].unique())\n",
        "len(patients)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "n3f3I53iIDKN",
        "outputId": "cf0362f6-ece3-4925-fff4-ccde5be5f75a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0  Hour    HR  O2Sat  Temp    SBP   MAP   DBP  Resp  EtCO2  ...  \\\n",
              "0           0     0   NaN    NaN   NaN    NaN   NaN   NaN   NaN    NaN  ...   \n",
              "1           1     1  65.0  100.0   NaN    NaN  72.0   NaN  16.5    NaN  ...   \n",
              "2           2     2  78.0  100.0   NaN    NaN  42.5   NaN   NaN    NaN  ...   \n",
              "3           3     3  73.0  100.0   NaN    NaN   NaN   NaN  17.0    NaN  ...   \n",
              "4           4     4  70.0  100.0   NaN  129.0  74.0  69.0  14.0    NaN  ...   \n",
              "\n",
              "   Platelets    Age  Gender  Unit1  Unit2  HospAdmTime  ICULOS  SepsisLabel  \\\n",
              "0        NaN  68.54       0    NaN    NaN        -0.02       1            0   \n",
              "1        NaN  68.54       0    NaN    NaN        -0.02       2            0   \n",
              "2        NaN  68.54       0    NaN    NaN        -0.02       3            0   \n",
              "3        NaN  68.54       0    NaN    NaN        -0.02       4            0   \n",
              "4      330.0  68.54       0    NaN    NaN        -0.02       5            0   \n",
              "\n",
              "   Patient_ID  Unit  \n",
              "0       17072   NaN  \n",
              "1       17072   NaN  \n",
              "2       17072   NaN  \n",
              "3       17072   NaN  \n",
              "4       17072   NaN  \n",
              "\n",
              "[5 rows x 45 columns]"
            ],
            "text/html": [
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              "  <div id=\"df-3bef9418-b883-41c7-80a6-f02f5957fa52\">\n",
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              "      <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>SBP</th>\n",
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              "      <th>Resp</th>\n",
              "      <th>EtCO2</th>\n",
              "      <th>...</th>\n",
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              "      <th>Gender</th>\n",
              "      <th>Unit1</th>\n",
              "      <th>Unit2</th>\n",
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              "      <th>SepsisLabel</th>\n",
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              "      <th>Unit</th>\n",
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              "      <th>0</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>68.54</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>17072</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>1</th>\n",
              "      <td>1</td>\n",
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              "      <td>65.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>72.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16.5</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>68.54</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>17072</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>78.0</td>\n",
              "      <td>100.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>42.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>68.54</td>\n",
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              "      <td>73.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>68.54</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>17072</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>70.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>129.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>330.0</td>\n",
              "      <td>68.54</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>17072</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 45 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3bef9418-b883-41c7-80a6-f02f5957fa52')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </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",
              "      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",
              "    [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-3bef9418-b883-41c7-80a6-f02f5957fa52 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-3bef9418-b883-41c7-80a6-f02f5957fa52');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "dataSepsis['Unit'] = dataSepsis['Unit1'].add(dataSepsis['Unit2'], fill_value=0)\n",
        "dataSepsis.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 804
        },
        "id": "S4NDD_vz5eb9",
        "outputId": "226d1198-ed42-4370-e729-43ef92bbb872"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2400x2000 with 2 Axes>"
            ],
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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "correlation_features = [\"HR\", \"O2Sat\", \"Temp\", \"SBP\", \"MAP\", \"DBP\", \"Resp\", \"BaseExcess\", \"HCO3\", \"pH\", \"PaCO2\", \"AST\",\n",
        "                       \"BUN\", \"Alkalinephos\", \"Calcium\", \"Chloride\", \"Creatinine\", \"Bilirubin_direct\", \"Glucose\", \"Lactate\",\n",
        "                       \"Magnesium\", \"Phosphate\", \"Potassium\", \"Bilirubin_total\", \"Hct\", \"Hgb\", \"PTT\", \"WBC\",\n",
        "                       \"Fibrinogen\", \"Platelets\", \"Age\"]\n",
        "\n",
        "mat_corr = dataSepsis[correlation_features].corr()\n",
        "\n",
        "mask = np.zeros_like(mat_corr)\n",
        "mask[np.triu_indices_from(mask)] = True\n",
        "plt.figure(figsize=(24,20))\n",
        "sns.heatmap(mat_corr, mask=mask, square=True, annot=True, fmt=\".2f\", center=0, linewidths=.5, cmap=\"RdBu\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "8S9Iuf5iKbls",
        "outputId": "30806969-fb46-4259-85ee-8eba92a4cb9b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Hour    HR  O2Sat  Temp   MAP  Resp  FiO2  SaO2  AST   BUN  ...  Hgb   WBC  \\\n",
              "0     0   NaN    NaN   NaN   NaN   NaN   NaN   NaN  NaN   NaN  ...  NaN   NaN   \n",
              "1     1  65.0  100.0   NaN  72.0  16.5   0.4   NaN  NaN   NaN  ...  NaN   NaN   \n",
              "2     2  78.0  100.0   NaN  42.5   NaN   NaN   NaN  NaN   NaN  ...  NaN   NaN   \n",
              "3     3  73.0  100.0   NaN   NaN  17.0   NaN   NaN  NaN   NaN  ...  NaN   NaN   \n",
              "4     4  70.0  100.0   NaN  74.0  14.0   0.4   NaN  NaN  23.0  ...  9.5  11.3   \n",
              "\n",
              "   Platelets    Age  Gender  HospAdmTime  ICULOS  SepsisLabel  Patient_ID  \\\n",
              "0        NaN  68.54       0        -0.02       1            0       17072   \n",
              "1        NaN  68.54       0        -0.02       2            0       17072   \n",
              "2        NaN  68.54       0        -0.02       3            0       17072   \n",
              "3        NaN  68.54       0        -0.02       4            0       17072   \n",
              "4      330.0  68.54       0        -0.02       5            0       17072   \n",
              "\n",
              "   Unit  \n",
              "0   NaN  \n",
              "1   NaN  \n",
              "2   NaN  \n",
              "3   NaN  \n",
              "4   NaN  \n",
              "\n",
              "[5 rows x 26 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f9d310e0-6ef2-4bf2-89ed-56ea9d9ea168\">\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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              "      <th></th>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>0</td>\n",
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              "      <td>100.0</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>17072</td>\n",
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              "      <td>78.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>42.5</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>68.54</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>3</td>\n",
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              "      <td>17072</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>73.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>68.54</td>\n",
              "      <td>0</td>\n",
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              "      <td>4</td>\n",
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              "      <td>17072</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>70.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>74.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>23.0</td>\n",
              "      <td>...</td>\n",
              "      <td>9.5</td>\n",
              "      <td>11.3</td>\n",
              "      <td>330.0</td>\n",
              "      <td>68.54</td>\n",
              "      <td>0</td>\n",
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              "      <td>17072</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 26 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f9d310e0-6ef2-4bf2-89ed-56ea9d9ea168')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "          document.querySelector('#df-f9d310e0-6ef2-4bf2-89ed-56ea9d9ea168 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "columns_drop={'Unnamed: 0','SBP','DBP','EtCO2','BaseExcess', 'HCO3','pH','PaCO2','Alkalinephos', 'Calcium','Magnesium', \n",
        "'Phosphate','Potassium','PTT','Fibrinogen','Bilirubin_direct','Hct','Unit1','Unit2'}\n",
        "dataSepsis.drop(columns=columns_drop, inplace=True)\n",
        "dataSepsis.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "zCvbVjoOLf34",
        "outputId": "39097cfe-db1c-4b61-dd85-75e82f61ae7c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Hour     HR  O2Sat  Temp    MAP  Resp  FiO2  SaO2  AST   BUN  ...  Hgb  \\\n",
              "0      0    0.0    0.0   0.0   0.00   0.0   0.0   0.0  0.0   0.0  ...  0.0   \n",
              "1      1   65.0  100.0   0.0  72.00  16.5   0.4   0.0  0.0   0.0  ...  0.0   \n",
              "2      2   78.0  100.0   0.0  42.50   0.0   0.0   0.0  0.0   0.0  ...  0.0   \n",
              "3      3   73.0  100.0   0.0   0.00  17.0   0.0   0.0  0.0   0.0  ...  0.0   \n",
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              "..   ...    ...    ...   ...    ...   ...   ...   ...  ...   ...  ...  ...   \n",
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              "99     4  147.0   99.0   0.0   0.00   0.0   0.0   0.0  0.0   0.0  ...  0.0   \n",
              "\n",
              "     WBC  Platelets    Age  Gender  HospAdmTime  ICULOS  SepsisLabel  \\\n",
              "0    0.0        0.0  68.54       0        -0.02       1            0   \n",
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              "98   0.0        0.0  58.18       0        -2.43       4            0   \n",
              "99   0.0        0.0  58.18       0        -2.43       5            0   \n",
              "\n",
              "    Patient_ID  Unit  \n",
              "0        17072   0.0  \n",
              "1        17072   0.0  \n",
              "2        17072   0.0  \n",
              "3        17072   0.0  \n",
              "4        17072   0.0  \n",
              "..         ...   ...  \n",
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              "[100 rows x 26 columns]"
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>58.18</td>\n",
              "      <td>0</td>\n",
              "      <td>-2.43</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>9891</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>1</td>\n",
              "      <td>119.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>87.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>58.18</td>\n",
              "      <td>0</td>\n",
              "      <td>-2.43</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>9891</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>2</td>\n",
              "      <td>120.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>95.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>58.18</td>\n",
              "      <td>0</td>\n",
              "      <td>-2.43</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>9891</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>3</td>\n",
              "      <td>116.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>84.33</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>58.18</td>\n",
              "      <td>0</td>\n",
              "      <td>-2.43</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>9891</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>4</td>\n",
              "      <td>147.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>58.18</td>\n",
              "      <td>0</td>\n",
              "      <td>-2.43</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>9891</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 26 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b34b6f32-d4ee-4a7c-acc6-b2fee929c496')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "          document.querySelector('#df-b34b6f32-d4ee-4a7c-acc6-b2fee929c496 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b34b6f32-d4ee-4a7c-acc6-b2fee929c496');\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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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "dataSepsis.fillna(0, inplace=True)\n",
        "dataSepsis.head(100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "onM_wt42NktN",
        "outputId": "95c30d41-4590-40cc-d451-cb2e30aed1fc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Hour, HR, O2Sat, Temp, MAP, Resp, FiO2, SaO2, AST, BUN, Chloride, Creatinine, Glucose, Lactate, Bilirubin_total, TroponinI, Hgb, WBC, Platelets, Age, Gender, HospAdmTime, ICULOS, SepsisLabel, Patient_ID, Unit]\n",
              "Index: []\n",
              "\n",
              "[0 rows x 26 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fdf55993-c367-4815-a7e8-23a54621bd8d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Hour</th>\n",
              "      <th>HR</th>\n",
              "      <th>O2Sat</th>\n",
              "      <th>Temp</th>\n",
              "      <th>MAP</th>\n",
              "      <th>Resp</th>\n",
              "      <th>FiO2</th>\n",
              "      <th>SaO2</th>\n",
              "      <th>AST</th>\n",
              "      <th>BUN</th>\n",
              "      <th>...</th>\n",
              "      <th>Hgb</th>\n",
              "      <th>WBC</th>\n",
              "      <th>Platelets</th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>HospAdmTime</th>\n",
              "      <th>ICULOS</th>\n",
              "      <th>SepsisLabel</th>\n",
              "      <th>Patient_ID</th>\n",
              "      <th>Unit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>0 rows × 26 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fdf55993-c367-4815-a7e8-23a54621bd8d')\"\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 0 24 24\"\n",
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              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
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              "      \n",
              "  <style>\n",
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              "\n",
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              "\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fdf55993-c367-4815-a7e8-23a54621bd8d 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-fdf55993-c367-4815-a7e8-23a54621bd8d');\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",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "# Identified duplicates in the dataset\n",
        "\n",
        "duplicated = dataSepsis[dataSepsis.duplicated(keep=False)]\n",
        "duplicated.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cVupWaWvPnmj"
      },
      "source": [
        "CHECK OUTLIERS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GjvK8oe2PrCW",
        "outputId": "5d0a4a0c-eacd-4525-a5f0-a6616544a2f5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 26)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "# Determining Heart rate in the range of 10 and 200\n",
        "target_dataSepsis = dataSepsis [(dataSepsis['HR'] >= 200) & (dataSepsis['HR'] <= 10 )]\n",
        "target_dataSepsis.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n4gJpZs6RNRP",
        "outputId": "ac776d6c-a381-404b-aa32-28f8e45775c4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 26)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "# Determining Temperature in the range of 20 and 40\n",
        "target_dataSepsis = dataSepsis [(dataSepsis['Temp'] >= 40) & (dataSepsis['Temp'] <= 20 )]\n",
        "target_dataSepsis.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "_ehX3TBaVgCK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fe22ed88-6f51-4439-d7d4-6898ff7d599e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-31-a23b072c40f7>:10: FutureWarning: Index.ravel returning ndarray is deprecated; in a future version this will return a view on self.\n",
            "  grouped_data.columns = [\"_\".join(x) for x in grouped_data.columns.ravel()]\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# Group the data by patient ID and calculate mean, min, max, and std for each column\n",
        "grouped_data = dataSepsis.groupby(['Patient_ID']).agg({'Hour': 'mean', 'HR': ['mean', 'min', 'max', 'std'], 'O2Sat': ['mean', 'min', 'max', 'std'], \n",
        "                                               'Temp': ['mean', 'min', 'max', 'std'], 'MAP': ['mean', 'min', 'max', 'std'], 'Resp': ['mean', 'min', 'max', 'std'],\n",
        "                                               'FiO2': ['mean', 'min', 'max', 'std'], 'SaO2': ['mean', 'min', 'max', 'std'], 'AST': ['mean', 'min', 'max', 'std'],\n",
        "                                               'BUN': ['mean', 'min', 'max', 'std'], 'Creatinine': ['mean', 'min', 'max', 'std'], 'Glucose': ['mean', 'min', 'max', 'std'],\n",
        "                                               'Hgb': ['mean', 'min', 'max', 'std'], 'WBC': ['mean', 'min', 'max', 'std'], 'Platelets': ['mean', 'min', 'max', 'std'],\n",
        "                                               'Age': 'mean', 'Gender': 'first', 'HospAdmTime': 'first', 'ICULOS': 'max', 'SepsisLabel': 'max', 'Unit': 'first'})\n",
        "\n",
        "# Flatten the column names of the resulting DataFrame\n",
        "grouped_data.columns = [\"_\".join(x) for x in grouped_data.columns.ravel()]\n",
        "# Add patient_ID to the data frame\n",
        "grouped_data = grouped_data.reset_index()\n",
        "# Write the aggregated data to a new CSV file\n",
        "grouped_data.to_csv('C:\\\\Users\\\\Azmat\\\\OneDrive\\\\Desktop\\\\big data\\\\aggregated_data.csv', index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q38gy2a3qrj-"
      },
      "source": [
        "CHECK FOR DATA IMBALANCE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LmZJcejWoQv8",
        "outputId": "0ddb808a-9187-495f-adbd-05210b698676"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1    55.945061\n",
            "0    44.054939\n",
            "Name: Gender_first, dtype: float64\n",
            "0    92.731059\n",
            "1     7.268941\n",
            "Name: SepsisLabel_max, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "data = pd.read_csv(\"/content/C:\\\\Users\\\\Azmat\\\\OneDrive\\\\Desktop\\\\big data\\\\aggregated_data.csv\", sep=\",\")\n",
        "# Count the number of instances for each target class\n",
        "Gender_counts = data['Gender_first'].value_counts()\n",
        "Sepsis_counts = data['SepsisLabel_max'].value_counts()\n",
        "# Calculate the percentage of each target class\n",
        "Gender_percentages = Gender_counts / len(data) * 100\n",
        "Sepsis_percentages = Sepsis_counts / len(data) * 100\n",
        "\n",
        "# Print the class percentages\n",
        "print(Gender_percentages)\n",
        "print(Sepsis_percentages)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "nOWEV6Fmxzgg",
        "outputId": "98f05c88-436f-4613-ee2f-68a061c5e1bb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Create a bar chart\n",
        "fig, ax = plt.subplots()\n",
        "ax.bar(Sepsis_percentages.index, Sepsis_percentages.values, tick_label=Sepsis_percentages.index, width=0.4, align='center', alpha=0.8)\n",
        "ax.set_ylabel('Percentage')\n",
        "ax.set_title('Sepsis Label Distribution')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print (dataSepsis.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TegpLfbXNMSa",
        "outputId": "353ac08e-c28c-473f-ea74-b72e2ef22594"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['Hour', 'HR', 'O2Sat', 'Temp', 'MAP', 'Resp', 'FiO2', 'SaO2', 'AST',\n",
            "       'BUN', 'Chloride', 'Creatinine', 'Glucose', 'Lactate',\n",
            "       'Bilirubin_total', 'TroponinI', 'Hgb', 'WBC', 'Platelets', 'Age',\n",
            "       'Gender', 'HospAdmTime', 'ICULOS', 'SepsisLabel', 'Patient_ID', 'Unit'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***UNDER SAMPLING***"
      ],
      "metadata": {
        "id": "RIGPcLYrbSo7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(\"/content/C:\\\\Users\\\\Azmat\\\\OneDrive\\\\Desktop\\\\big data\\\\aggregated_data.csv\", sep=\",\")\n",
        "majority_class = data[data['SepsisLabel_max'] == 0]\n",
        "minority_class = data[data['SepsisLabel_max'] == 1]\n",
        "print('number of sepsis label 1 is {}'.format(len(minority_class)))\n",
        "print('while number of sepsis label 0 is {}'.format(len(majority_class)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RwBLQrdqME_W",
        "outputId": "ef6b6257-9dee-4740-db7c-a5e652d6a605"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of sepsis label 1 is 2932\n",
            "while number of sepsis label 0 is 37404\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Undersampling\n",
        "\n",
        "majority_class_subset = majority_class.sample(n=2*len(minority_class))\n",
        "data = pd.concat([majority_class_subset, minority_class])"
      ],
      "metadata": {
        "id": "zta3acd2NwYb"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Sepsis_counts = data['SepsisLabel_max'].value_counts()\n",
        "Sepsis_percentages = Sepsis_counts / len(data) * 100\n",
        "print(Sepsis_percentages)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9jP_SHEAXNTn",
        "outputId": "fb91a103-1810-401c-e252-a916a3063fe1"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    66.666667\n",
            "1    33.333333\n",
            "Name: SepsisLabel_max, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.bar(Sepsis_counts.index, Sepsis_percentages)\n",
        "plt.title(\"Percentage of Sepsis Labels after Undersampling\")\n",
        "plt.xlabel(\"Sepsis Label\")\n",
        "plt.ylabel(\"Percentage\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "B7PsFD_rX8GK",
        "outputId": "f4506bb4-18da-4718-d884-76273e423d4f"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***LOGISTIC REGRESSION***"
      ],
      "metadata": {
        "id": "NRdLfcsupw1l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split, cross_val_score\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# Separate features and target variables\n",
        "X = data.drop('SepsisLabel_max', axis=1)\n",
        "y = data['SepsisLabel_max']\n",
        "\n",
        "# Split data into training, validation, and testing sets\n",
        "X_trainval, X_test, y_trainval, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
        "X_train, X_val, y_train, y_val = train_test_split(X_trainval, y_trainval, test_size=0.2, random_state=42)\n",
        "\n",
        "# Define the logistic regression model\n",
        "clf = LogisticRegression(random_state=42)\n",
        "\n",
        "# Define the hyperparameter grid to search over\n",
        "param_grid = {'C': [0.01, 0.1, 1, 10, 100]}\n",
        "\n",
        "# Perform grid search using 5-fold cross-validation on the training set\n",
        "grid_search = GridSearchCV(clf, param_grid, cv=5)\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "# Print the best hyperparameters and score\n",
        "print('Best hyperparameters:', grid_search.best_params_)\n",
        "print('Best score:', grid_search.best_score_)\n",
        "\n",
        "# Train the model on the training set with the best hyperparameters\n",
        "clf_best = LogisticRegression(C=grid_search.best_params_['C'], random_state=42)\n",
        "clf_best.fit(X_train, y_train)\n",
        "\n",
        "# Evaluate the model performance on the validation set\n",
        "y_pred_val = clf_best.predict(X_val)\n",
        "LR_acc_val = accuracy_score(y_val, y_pred_val)\n",
        "print('Accuracy on validation set:', LR_acc_val)\n",
        "print(classification_report(y_val, y_pred_val))\n",
        "\n",
        "# Evaluate the model performance on the testing set\n",
        "y_pred_test = clf_best.predict(X_test)\n",
        "LR_acc_test = accuracy_score(y_test, y_pred_test)\n",
        "print('Accuracy on testing set:', LR_acc_test)\n",
        "print(classification_report(y_test, y_pred_test))\n"
      ],
      "metadata": {
        "id": "57-fjQqrJhg5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b0cb3f5d-ccd6-431b-b858-53e65ad54949"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best hyperparameters: {'C': 0.01}\n",
            "Best score: 0.7356345177664975\n",
            "Accuracy on validation set: 0.7086038961038961\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.71      0.92      0.80       782\n",
            "           1       0.70      0.35      0.47       450\n",
            "\n",
            "    accuracy                           0.71      1232\n",
            "   macro avg       0.71      0.63      0.63      1232\n",
            "weighted avg       0.71      0.71      0.68      1232\n",
            "\n",
            "Accuracy on testing set: 0.7366426676771505\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.75      0.91      0.82      1759\n",
            "           1       0.69      0.38      0.49       880\n",
            "\n",
            "    accuracy                           0.74      2639\n",
            "   macro avg       0.72      0.65      0.66      2639\n",
            "weighted avg       0.73      0.74      0.71      2639\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***NAIVE BAYES***"
      ],
      "metadata": {
        "id": "eBKi7HacqPlq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# Define the Naive Bayes classifier\n",
        "clf = GaussianNB()\n",
        "\n",
        "# Define the hyperparameter grid to search over\n",
        "param_grid = {'var_smoothing': [1e-10, 1e-9, 1e-8]}\n",
        "\n",
        "# Perform grid search using 5-fold cross-validation\n",
        "grid_search = GridSearchCV(clf, param_grid, cv=5)\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "# Print the best hyperparameters and score\n",
        "print('Best hyperparameters:', grid_search.best_params_)\n",
        "print('Best score:', grid_search.best_score_)\n",
        "\n",
        "# Train the model on the training data with the best hyperparameters\n",
        "clf_best = GaussianNB(var_smoothing=grid_search.best_params_['var_smoothing'])\n",
        "clf_best.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the validation set\n",
        "y_pred_val = clf_best.predict(X_val)\n",
        "\n",
        "# Evaluate the model performance on the validation set\n",
        "NB_acc_val = accuracy_score(y_val, y_pred_val)\n",
        "\n",
        "# Make predictions on the testing data\n",
        "y_pred_test = clf_best.predict(X_test)\n",
        "\n",
        "# Evaluate the model performance on the testing set\n",
        "NB_acc_test = accuracy_score(y_test, y_pred_test)\n",
        "\n",
        "# Print the validation and testing accuracy and classification report\n",
        "print('Validation Accuracy:',NB_acc_val)\n",
        "print(classification_report(y_val, y_pred_val))\n",
        "\n",
        "print('Testing Accuracy:', NB_acc_test)\n",
        "print(classification_report(y_test, y_pred_test))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qhbGBLFzLUXK",
        "outputId": "4205665c-1e24-4c85-830a-d1ccf5b73615"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best hyperparameters: {'var_smoothing': 1e-10}\n",
            "Best score: 0.7650761421319796\n",
            "Validation Accuracy: 0.7532467532467533\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.75      0.91      0.82       782\n",
            "           1       0.75      0.49      0.59       450\n",
            "\n",
            "    accuracy                           0.75      1232\n",
            "   macro avg       0.75      0.70      0.71      1232\n",
            "weighted avg       0.75      0.75      0.74      1232\n",
            "\n",
            "Testing Accuracy: 0.7635467980295566\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.93      0.84      1759\n",
            "           1       0.75      0.43      0.55       880\n",
            "\n",
            "    accuracy                           0.76      2639\n",
            "   macro avg       0.76      0.68      0.69      2639\n",
            "weighted avg       0.76      0.76      0.74      2639\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***SUPPORT VECTOR MACHINE***"
      ],
      "metadata": {
        "id": "3OlEnN7Dqham"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import pandas as pd\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "clf = SVC(kernel='rbf',C=5)\n",
        "clf.fit(X_train, y_train)\n",
        "y_pred = clf.predict(X_test)\n",
        "\n",
        "# Make predictions on the validation set\n",
        "y_pred_val = clf.predict(X_val)\n",
        "# Evaluate the model performance on the validation set\n",
        "SVM_acc_val = accuracy_score(y_val, y_pred_val)\n",
        "# Make predictions on the testing data\n",
        "y_pred_test = clf.predict(X_test)\n",
        "# Evaluate the model performance on the testing set\n",
        "SVM_acc_test = accuracy_score(y_test, y_pred_test)\n",
        "print('Validation Accuracy:', SVM_acc_val)\n",
        "print(classification_report(y_val, y_pred_val))\n",
        "\n",
        "print('Testing Accuracy:', SVM_acc_test)\n",
        "print(classification_report(y_test, y_pred_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y4LeBlm5gdW8",
        "outputId": "d153d28f-a464-4c3d-ec7d-1a5182196ad9"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation Accuracy: 0.6339285714285714\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.63      1.00      0.78       782\n",
            "           1       0.00      0.00      0.00       450\n",
            "\n",
            "    accuracy                           0.63      1232\n",
            "   macro avg       0.32      0.50      0.39      1232\n",
            "weighted avg       0.40      0.63      0.49      1232\n",
            "\n",
            "Testing Accuracy: 0.6657824933687002\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.67      1.00      0.80      1759\n",
            "           1       0.00      0.00      0.00       880\n",
            "\n",
            "    accuracy                           0.67      2639\n",
            "   macro avg       0.33      0.50      0.40      2639\n",
            "weighted avg       0.44      0.67      0.53      2639\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***RANDOM FOREST***"
      ],
      "metadata": {
        "id": "YNKMLcX8qnd0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Train a Random Forest classifier on the training data\n",
        "rfc = RandomForestClassifier(n_estimators=100, max_depth=10, random_state=42)\n",
        "rfc.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the validation set\n",
        "y_pred_val = rfc.predict(X_val)\n",
        "\n",
        "# Evaluate the model performance on the validation set\n",
        "RF_acc_val = accuracy_score(y_val, y_pred_val)\n",
        "print('Validation Accuracy:', RF_acc_val)\n",
        "print(classification_report(y_val, y_pred_val))\n",
        "\n",
        "# Make predictions on the testing set\n",
        "y_pred_test = rfc.predict(X_test)\n",
        "\n",
        "# Evaluate the model performance on the testing set\n",
        "RF_acc_test = accuracy_score(y_test, y_pred_test)\n",
        "print('Testing Accuracy:', RF_acc_test)\n",
        "print(classification_report(y_test, y_pred_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iO6H6koAkLiK",
        "outputId": "a757a9e0-3bcb-4397-e4f5-f4092ea8ec6a"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation Accuracy: 0.8587662337662337\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.97      0.90       782\n",
            "           1       0.92      0.67      0.78       450\n",
            "\n",
            "    accuracy                           0.86      1232\n",
            "   macro avg       0.88      0.82      0.84      1232\n",
            "weighted avg       0.87      0.86      0.85      1232\n",
            "\n",
            "Testing Accuracy: 0.8609321712769988\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.85      0.96      0.90      1759\n",
            "           1       0.89      0.66      0.76       880\n",
            "\n",
            "    accuracy                           0.86      2639\n",
            "   macro avg       0.87      0.81      0.83      2639\n",
            "weighted avg       0.86      0.86      0.85      2639\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***COMPARISON OF ACCURACY***"
      ],
      "metadata": {
        "id": "E2XkEq3TquI0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "classifiers = ['LogisticRegression', 'SVM', 'naive_bayes','RF']\n",
        "accuracies = [LR_acc_test, SVM_acc_test, NB_acc_test, RF_acc_test]\n",
        "\n",
        "plt.bar(classifiers, accuracies)\n",
        "plt.ylim([0,1])\n",
        "plt.ylabel('Accuracies')\n",
        "plt.title('Sepsis Prediction Accuracy for Different Classifiers')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "EDn1C33ZoCcP",
        "outputId": "5703f13c-c5a5-4f86-c0cc-c0046a55d716"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classifiers = ['LogisticRegression', 'SVM', 'naive_bayes','RF']\n",
        "accuracies = [LR_acc_val, SVM_acc_val, NB_acc_val, RF_acc_val]\n",
        "\n",
        "plt.bar(classifiers, accuracies)\n",
        "plt.ylim([0,1])\n",
        "plt.ylabel('Accuracies')\n",
        "plt.title('Sepsis Prediction Validation Accuracy for Different Classifiers')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "9r6K5wHvVOQv",
        "outputId": "bf6772d2-3c9f-44d8-9c5c-6a2e394c57e8"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}