hai_prediction_model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1X3dFDE2bCHBn4RdVvxkb6WYJzbChWruU",
      "authorship_tag": "ABX9TyOb5FrvZtIRAzUlhdadcQEF",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/stupiddint/Symposium-source-code/blob/main/hai_prediction_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "id": "nOuORTvlhwwp"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import string\n",
        "# Standardization \n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import nltk\n",
        "# remove stopwords\n",
        "from nltk.corpus import stopwords\n",
        "# lemmatization & pos tagging\n",
        "from nltk import pos_tag\n",
        "from nltk.corpus import wordnet\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "from datetime import datetime\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df= pd.read_csv('/content/drive/MyDrive/Colab Notebooks/dataset/HAI_DATASET(N).csv')\n",
        "df.head()"
      ],
      "metadata": {
        "id": "V9adov8siU0y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "outputId": "5bde47ca-e58f-4789-9658-cea581c60ca1"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PatientID  Age Sex MedicalHistory       HospitalizationData  \\\n",
              "0          1   45   M   Hypertension  2020-03-20 to 2020-03-27   \n",
              "1          2   65   F       Diabetes  2020-03-15 to 2020-03-22   \n",
              "2          3   50   M           None  2020-03-10 to 2020-03-17   \n",
              "3          4   70   F           COPD  2020-03-05 to 2020-03-12   \n",
              "4          5   55   M         Asthma  2020-04-02 to 2020-04-09   \n",
              "\n",
              "                    LaboratoryData  ImagingData       MicrobiologyData  \\\n",
              "0                           Normal        X-ray               Negative   \n",
              "1  Elevated white blood cell count      CT scan  Positive for bacteria   \n",
              "2                           Normal          NaN               Negative   \n",
              "3  Elevated white blood cell count  Chest X-ray  Positive for bacteria   \n",
              "4  Elevated white blood cell count  Chest X-ray  Positive for bacteria   \n",
              "\n",
              "                      RiskFactors                               Symptoms  \\\n",
              "0                            None  Frequent urination, burning sensation   \n",
              "1         Use of urinary catheter  Fever, pain in surgical incision site   \n",
              "2  Central venous catheterization     Fever, chills, elevated heart rate   \n",
              "3          Mechanical ventilation      Fever, cough with purulent sputum   \n",
              "4          Mechanical ventilation          Shortness of breath, wheezing   \n",
              "\n",
              "                                Signs               Treatment  \\\n",
              "0                                 NaN  Antibiotics prescribed   \n",
              "1  Redness, swelling at incision site  Antibiotics prescribed   \n",
              "2              Positive blood culture  Antibiotics prescribed   \n",
              "3       Crackles on lung auscultation  Antibiotics prescribed   \n",
              "4            Diminished breath sounds  Antibiotics prescribed   \n",
              "\n",
              "  Hospital_Acquired_Infection  \n",
              "0                         UTI  \n",
              "1                         SSI  \n",
              "2                      CLABSI  \n",
              "3                         NaN  \n",
              "4                         VAP  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6baaaf98-121c-4e2d-b1d6-68c758619483\">\n",
              "    <div 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>PatientID</th>\n",
              "      <th>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>MedicalHistory</th>\n",
              "      <th>HospitalizationData</th>\n",
              "      <th>LaboratoryData</th>\n",
              "      <th>ImagingData</th>\n",
              "      <th>MicrobiologyData</th>\n",
              "      <th>RiskFactors</th>\n",
              "      <th>Symptoms</th>\n",
              "      <th>Signs</th>\n",
              "      <th>Treatment</th>\n",
              "      <th>Hospital_Acquired_Infection</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>45</td>\n",
              "      <td>M</td>\n",
              "      <td>Hypertension</td>\n",
              "      <td>2020-03-20 to 2020-03-27</td>\n",
              "      <td>Normal</td>\n",
              "      <td>X-ray</td>\n",
              "      <td>Negative</td>\n",
              "      <td>None</td>\n",
              "      <td>Frequent urination, burning sensation</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Antibiotics prescribed</td>\n",
              "      <td>UTI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>65</td>\n",
              "      <td>F</td>\n",
              "      <td>Diabetes</td>\n",
              "      <td>2020-03-15 to 2020-03-22</td>\n",
              "      <td>Elevated white blood cell count</td>\n",
              "      <td>CT scan</td>\n",
              "      <td>Positive for bacteria</td>\n",
              "      <td>Use of urinary catheter</td>\n",
              "      <td>Fever, pain in surgical incision site</td>\n",
              "      <td>Redness, swelling at incision site</td>\n",
              "      <td>Antibiotics prescribed</td>\n",
              "      <td>SSI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>50</td>\n",
              "      <td>M</td>\n",
              "      <td>None</td>\n",
              "      <td>2020-03-10 to 2020-03-17</td>\n",
              "      <td>Normal</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Negative</td>\n",
              "      <td>Central venous catheterization</td>\n",
              "      <td>Fever, chills, elevated heart rate</td>\n",
              "      <td>Positive blood culture</td>\n",
              "      <td>Antibiotics prescribed</td>\n",
              "      <td>CLABSI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>70</td>\n",
              "      <td>F</td>\n",
              "      <td>COPD</td>\n",
              "      <td>2020-03-05 to 2020-03-12</td>\n",
              "      <td>Elevated white blood cell count</td>\n",
              "      <td>Chest X-ray</td>\n",
              "      <td>Positive for bacteria</td>\n",
              "      <td>Mechanical ventilation</td>\n",
              "      <td>Fever, cough with purulent sputum</td>\n",
              "      <td>Crackles on lung auscultation</td>\n",
              "      <td>Antibiotics prescribed</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>55</td>\n",
              "      <td>M</td>\n",
              "      <td>Asthma</td>\n",
              "      <td>2020-04-02 to 2020-04-09</td>\n",
              "      <td>Elevated white blood cell count</td>\n",
              "      <td>Chest X-ray</td>\n",
              "      <td>Positive for bacteria</td>\n",
              "      <td>Mechanical ventilation</td>\n",
              "      <td>Shortness of breath, wheezing</td>\n",
              "      <td>Diminished breath sounds</td>\n",
              "      <td>Antibiotics prescribed</td>\n",
              "      <td>VAP</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6baaaf98-121c-4e2d-b1d6-68c758619483')\"\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",
              "    <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",
              "  </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-6baaaf98-121c-4e2d-b1d6-68c758619483 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-6baaaf98-121c-4e2d-b1d6-68c758619483');\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": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna()\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oSdEwd85uFIY",
        "outputId": "5bb3c853-1c9e-4451-c265-a7d2a0614435"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 142 entries, 0 to 141\n",
            "Data columns (total 13 columns):\n",
            " #   Column                       Non-Null Count  Dtype \n",
            "---  ------                       --------------  ----- \n",
            " 0   PatientID                    142 non-null    int64 \n",
            " 1   Age                          142 non-null    int64 \n",
            " 2   Sex                          142 non-null    object\n",
            " 3   MedicalHistory               142 non-null    object\n",
            " 4   HospitalizationData          142 non-null    object\n",
            " 5   LaboratoryData               142 non-null    object\n",
            " 6   ImagingData                  99 non-null     object\n",
            " 7   MicrobiologyData             142 non-null    object\n",
            " 8   RiskFactors                  142 non-null    object\n",
            " 9   Symptoms                     142 non-null    object\n",
            " 10  Signs                        135 non-null    object\n",
            " 11  Treatment                    142 non-null    object\n",
            " 12  Hospital_Acquired_Infection  141 non-null    object\n",
            "dtypes: int64(2), object(11)\n",
            "memory usage: 14.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#finding Y"
      ],
      "metadata": {
        "id": "FsZIGR-JrDGc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# df['Hospital_Acquired_Infection'].unique()\n",
        "# df['Hospital_Acquired_Infection'].value_counts()\n",
        "# # df['Hospital_Acquired_Infection']= df['Hospital_Acquired_Infection'].replace(['Gastroenteritis',\n",
        "# #        'Pharyngitis', 'CAP', 'Meningitis'],'other')\n",
        "\n",
        "df['Hospital_Acquired_Infection']= df['Hospital_Acquired_Infection'].map({'other':0,'VAP':1,'UTI':2,'CLABSI':3,'SSI':4,'Respiratory Infection':5})\n",
        "df['Hospital_Acquired_Infection'] = df['Hospital_Acquired_Infection'].replace('None', -1)\n",
        "df['Hospital_Acquired_Infection'] = df['Hospital_Acquired_Infection'].fillna(-1)\n",
        "df['Hospital_Acquired_Infection'].dropna()\n",
        "print(len(df['Hospital_Acquired_Infection']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zNuYJk_Uq1HB",
        "outputId": "991a1d79-7d58-4dd4-b551-06a61422adb4"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "142\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Sex'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BEfQTxWBkHwu",
        "outputId": "179db1bc-8e28-430e-f3b4-f780b55baa18"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "M    87\n",
              "F    55\n",
              "Name: Sex, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#data preprocessing"
      ],
      "metadata": {
        "id": "JoSXt__-iKpg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Processing:\n",
        "    def __init__(self, df):\n",
        "        df1 = df.drop(['PatientID'], axis=1)\n",
        "        df2 = df1\n",
        "        for index, row in df2.iterrows():\n",
        "            try:\n",
        "                start_date_str, end_date_str = row['HospitalizationData'].split(' to ')\n",
        "                start_date = datetime.strptime(start_date_str, '%Y-%m-%d')\n",
        "                end_date = datetime.strptime(end_date_str, '%Y-%m-%d')\n",
        "                days = (end_date - start_date).days\n",
        "                df2.at[index, 'HospitalizationData'] = days\n",
        "            except ValueError:\n",
        "                print(f\"Invalid date range at index {index}: {row['HospitalizationData']}\")\n",
        "        \n",
        "        df2['Sex'] = df2['Sex'].replace(['Female'], 'F')\n",
        "        df2['Sex'] = df2['Sex'].replace(['Male'], 'M')\n",
        "        df2['Sex'] = df2['Sex'].map({'F': 1, 'M': 0}).astype(int)\n",
        "        df2 = df2.replace('None', -1)\n",
        "    \n",
        "        nc = df2.select_dtypes(include=['int', 'float']).columns\n",
        "        tc = df2.select_dtypes(include='object').columns\n",
        "        nd = df2[nc]\n",
        "        td = df2[tc]\n",
        "        scaler = StandardScaler()\n",
        "        std_nd = scaler.fit_transform(nd)\n",
        "        std_nd = pd.DataFrame(std_nd, columns=nd.columns)\n",
        "        \n",
        "        # Text preprocessing\n",
        "        # 1. Lowercase\n",
        "        # for column in td.columns:\n",
        "        #     if df2[column].dtype in [np.int64, np.float64]:\n",
        "        #         continue\n",
        "        #     else:\n",
        "        #         td[column] = td[column].str.lower()\n",
        "        \n",
        "        # 2. Remove punctuation\n",
        "        for column in td.columns:\n",
        "            td[column] = td[column].apply(lambda x: x.translate(str.maketrans('', '', string.punctuation)) if isinstance(x, str) else x)\n",
        "        \n",
        "        # 3. Remove stopwords\n",
        "        for column in td.columns:\n",
        "            stop_words = set(stopwords.words('english'))\n",
        "            td[column] = td[column].apply(lambda x: ' '.join([word for word in str(x).split() if word.lower() not in stop_words]))\n",
        "        \n",
        "        # 4. Lemmatizer\n",
        "        lemmatizer = WordNetLemmatizer()\n",
        "        \n",
        "        def lemmatize_word(word, pos_tag):\n",
        "            if pos_tag.startswith('J'):\n",
        "                pos = wordnet.ADJ\n",
        "            elif pos_tag.startswith('V'):\n",
        "                pos = wordnet.VERB\n",
        "            elif pos_tag.startswith('N'):\n",
        "                pos = wordnet.NOUN\n",
        "            elif pos_tag.startswith('R'):\n",
        "                pos = wordnet.ADV\n",
        "            else:\n",
        "                pos = wordnet.NOUN\n",
        "            return lemmatizer.lemmatize(word, pos)\n",
        "\n",
        "        def lemmatize_column(column):\n",
        "            if column == 'NaN':\n",
        "                return column\n",
        "            elif isinstance(column, str):\n",
        "                tokens = word_tokenize(column)\n",
        "                pos_tags = nltk.pos_tag(tokens)\n",
        "                lemmatized_words = [lemmatize_word(word, pos_tag) for word, pos_tag in pos_tags]\n",
        "                return ' '.join(lemmatized_words)\n",
        "            else:\n",
        "                return column\n",
        "        \n",
        "        for column in td.columns:\n",
        "            td.loc[:, column] = td[column].apply(lemmatize_column)\n",
        "        \n",
        "        # 5. Vectorizer\n",
        "        text_columns = ['MedicalHistory', 'LaboratoryData', 'ImagingData', 'MicrobiologyData',\n",
        "                        'RiskFactors', 'Symptoms', 'Signs', 'Treatment']\n",
        "\n",
        "        td['Combined Text'] = td[text_columns].apply(lambda x: ' '.join(x.dropna().astype(str)), axis=1)\n",
        "\n",
        "        vectorizer = TfidfVectorizer()\n",
        "        vectorized_data = vectorizer.fit_transform(td['Combined Text'])\n",
        "\n",
        "        self.vectorized_df = pd.DataFrame(vectorized_data.toarray(), columns=vectorizer.get_feature_names_out())\n",
        "        self.vectorized_text_data = pd.concat([td, self.vectorized_df], axis=1)\n",
        "        \n",
        "        # 6. Combine data\n",
        "        self.combined_data = pd.concat([nd, self.vectorized_df], axis=1)\n",
        "        self.combined_data = self.combined_data.fillna(-1)\n",
        "        # self.combined_data = self.combined_data.dropna()\n",
        "    \n",
        "    def process_data(self):\n",
        "        return self.combined_data\n"
      ],
      "metadata": {
        "id": "e3Vuc0BShyVD"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('stopwords')\n",
        "nltk.download('punkt')\n",
        "nltk.download('averaged_perceptron_tagger')\n",
        "nltk.download('wordnet')"
      ],
      "metadata": {
        "id": "Qe7hZ3XZkB59",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bfd8d6ba-ce81-41fb-bd4f-2bc8e6c5a6b5"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
            "[nltk_data]       date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "processing = Processing(df)\n",
        "combined_data = processing.process_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H8oofyWqj1Ed",
        "outputId": "608d8241-9e62-4c2c-82d8-d3706a44beb2"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-102-adab19079636>:38: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  td[column] = td[column].apply(lambda x: x.translate(str.maketrans('', '', string.punctuation)) if isinstance(x, str) else x)\n",
            "<ipython-input-102-adab19079636>:43: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  td[column] = td[column].apply(lambda x: ' '.join([word for word in str(x).split() if word.lower() not in stop_words]))\n",
            "<ipython-input-102-adab19079636>:73: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  td.loc[:, column] = td[column].apply(lemmatize_column)\n",
            "<ipython-input-102-adab19079636>:79: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  td['Combined Text'] = td[text_columns].apply(lambda x: ' '.join(x.dropna().astype(str)), axis=1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "combined_data.head()\n",
        "# combined_data.info()"
      ],
      "metadata": {
        "id": "TWH_oOxdimC2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "8250cb2c-6518-4dbf-ebc0-9e827739ab93"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Age  Sex  HospitalizationData  Hospital_Acquired_Infection  abdominal  \\\n",
              "0   45    0                    7                          2.0        0.0   \n",
              "1   65    1                    7                          4.0        0.0   \n",
              "2   50    0                    7                          3.0        0.0   \n",
              "3   70    1                    7                         -1.0        0.0   \n",
              "4   55    0                    7                          1.0        0.0   \n",
              "\n",
              "   allergens  angle  antibiotics    asthma  auscultation  ...  urine  \\\n",
              "0        0.0    0.0     0.138927  0.000000      0.000000  ...    0.0   \n",
              "1        0.0    0.0     0.086505  0.000000      0.000000  ...    0.0   \n",
              "2        0.0    0.0     0.138732  0.000000      0.000000  ...    0.0   \n",
              "3        0.0    0.0     0.118375  0.000000      0.396564  ...    0.0   \n",
              "4        0.0    0.0     0.145897  0.206068      0.000000  ...    0.0   \n",
              "\n",
              "        use  uti    venous  ventilation  vomit  wheezing     white  worsen  \\\n",
              "0  0.000000  0.0  0.000000     0.000000    0.0  0.000000  0.000000     0.0   \n",
              "1  0.193911  0.0  0.000000     0.000000    0.0  0.000000  0.104745     0.0   \n",
              "2  0.000000  0.0  0.288397     0.000000    0.0  0.000000  0.000000     0.0   \n",
              "3  0.000000  0.0  0.000000     0.193978    0.0  0.000000  0.143336     0.0   \n",
              "4  0.000000  0.0  0.000000     0.239077    0.0  0.206068  0.176661     0.0   \n",
              "\n",
              "       xray  \n",
              "0  0.194329  \n",
              "1  0.000000  \n",
              "2  0.000000  \n",
              "3  0.165582  \n",
              "4  0.204079  \n",
              "\n",
              "[5 rows x 117 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bce75989-e48a-4efd-982e-d8b7c03b490b\">\n",
              "    <div 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>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>HospitalizationData</th>\n",
              "      <th>Hospital_Acquired_Infection</th>\n",
              "      <th>abdominal</th>\n",
              "      <th>allergens</th>\n",
              "      <th>angle</th>\n",
              "      <th>antibiotics</th>\n",
              "      <th>asthma</th>\n",
              "      <th>auscultation</th>\n",
              "      <th>...</th>\n",
              "      <th>urine</th>\n",
              "      <th>use</th>\n",
              "      <th>uti</th>\n",
              "      <th>venous</th>\n",
              "      <th>ventilation</th>\n",
              "      <th>vomit</th>\n",
              "      <th>wheezing</th>\n",
              "      <th>white</th>\n",
              "      <th>worsen</th>\n",
              "      <th>xray</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>45</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.138927</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.194329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>65</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.086505</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.193911</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.104745</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>50</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.138732</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.288397</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.118375</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.396564</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.193978</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.143336</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.165582</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>55</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.145897</td>\n",
              "      <td>0.206068</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.239077</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.206068</td>\n",
              "      <td>0.176661</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.204079</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 117 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bce75989-e48a-4efd-982e-d8b7c03b490b')\"\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",
              "    <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",
              "  </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-bce75989-e48a-4efd-982e-d8b7c03b490b 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-bce75989-e48a-4efd-982e-d8b7c03b490b');\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": 105
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#model training"
      ],
      "metadata": {
        "id": "AELnT-P-m0GM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.ensemble import GradientBoostingClassifier"
      ],
      "metadata": {
        "id": "-2sQ8GEmm2zh"
      },
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "combined_data.info()"
      ],
      "metadata": {
        "id": "3rfXbOfOtDwE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c03d3133-668b-4214-c152-7b679bc7a842"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 142 entries, 0 to 141\n",
            "Columns: 117 entries, Age to xray\n",
            "dtypes: float64(114), int64(3)\n",
            "memory usage: 129.9 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "combined_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "Ov4qhoZKqJaP",
        "outputId": "9b5c6707-a99b-45df-b345-4002cef0a487"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Age  Sex  HospitalizationData  Hospital_Acquired_Infection  abdominal  \\\n",
              "0   45    0                    7                          2.0        0.0   \n",
              "1   65    1                    7                          4.0        0.0   \n",
              "2   50    0                    7                          3.0        0.0   \n",
              "3   70    1                    7                         -1.0        0.0   \n",
              "4   55    0                    7                          1.0        0.0   \n",
              "\n",
              "   allergens  angle  antibiotics    asthma  auscultation  ...  urine  \\\n",
              "0        0.0    0.0     0.138927  0.000000      0.000000  ...    0.0   \n",
              "1        0.0    0.0     0.086505  0.000000      0.000000  ...    0.0   \n",
              "2        0.0    0.0     0.138732  0.000000      0.000000  ...    0.0   \n",
              "3        0.0    0.0     0.118375  0.000000      0.396564  ...    0.0   \n",
              "4        0.0    0.0     0.145897  0.206068      0.000000  ...    0.0   \n",
              "\n",
              "        use  uti    venous  ventilation  vomit  wheezing     white  worsen  \\\n",
              "0  0.000000  0.0  0.000000     0.000000    0.0  0.000000  0.000000     0.0   \n",
              "1  0.193911  0.0  0.000000     0.000000    0.0  0.000000  0.104745     0.0   \n",
              "2  0.000000  0.0  0.288397     0.000000    0.0  0.000000  0.000000     0.0   \n",
              "3  0.000000  0.0  0.000000     0.193978    0.0  0.000000  0.143336     0.0   \n",
              "4  0.000000  0.0  0.000000     0.239077    0.0  0.206068  0.176661     0.0   \n",
              "\n",
              "       xray  \n",
              "0  0.194329  \n",
              "1  0.000000  \n",
              "2  0.000000  \n",
              "3  0.165582  \n",
              "4  0.204079  \n",
              "\n",
              "[5 rows x 117 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f07b026a-9a1e-4715-8c7c-1ff013977fff\">\n",
              "    <div 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>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>HospitalizationData</th>\n",
              "      <th>Hospital_Acquired_Infection</th>\n",
              "      <th>abdominal</th>\n",
              "      <th>allergens</th>\n",
              "      <th>angle</th>\n",
              "      <th>antibiotics</th>\n",
              "      <th>asthma</th>\n",
              "      <th>auscultation</th>\n",
              "      <th>...</th>\n",
              "      <th>urine</th>\n",
              "      <th>use</th>\n",
              "      <th>uti</th>\n",
              "      <th>venous</th>\n",
              "      <th>ventilation</th>\n",
              "      <th>vomit</th>\n",
              "      <th>wheezing</th>\n",
              "      <th>white</th>\n",
              "      <th>worsen</th>\n",
              "      <th>xray</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>45</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.138927</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.194329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>65</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.086505</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.193911</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.104745</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>50</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.138732</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.288397</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.118375</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.396564</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.193978</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.143336</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.165582</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>55</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.145897</td>\n",
              "      <td>0.206068</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.239077</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.206068</td>\n",
              "      <td>0.176661</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.204079</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 117 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f07b026a-9a1e-4715-8c7c-1ff013977fff')\"\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",
              "    <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",
              "  </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-f07b026a-9a1e-4715-8c7c-1ff013977fff 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-f07b026a-9a1e-4715-8c7c-1ff013977fff');\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": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "combined_data['Hospital_Acquired_Infection'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eXMFfJEYtpfO",
        "outputId": "28cda812-d347-4c70-e5e9-a15be3a65229"
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " 5.0    38\n",
              " 1.0    35\n",
              " 2.0    23\n",
              "-1.0    20\n",
              " 3.0    18\n",
              " 4.0     8\n",
              "Name: Hospital_Acquired_Infection, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Hospital_Acquired_Infection'].info()"
      ],
      "metadata": {
        "id": "WDLmyCm-tJFc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "837a3eb1-cf97-46fe-99e7-f7697a09100b"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.series.Series'>\n",
            "RangeIndex: 142 entries, 0 to 141\n",
            "Series name: Hospital_Acquired_Infection\n",
            "Non-Null Count  Dtype  \n",
            "--------------  -----  \n",
            "142 non-null    float64\n",
            "dtypes: float64(1)\n",
            "memory usage: 1.2 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = combined_data.drop('Hospital_Acquired_Infection', axis=1)\n",
        "\n",
        "y = combined_data['Hospital_Acquired_Infection']\n",
        "X.info()\n",
        "print(len(X)\n",
        ")\n",
        "print(len(y))\n",
        "# len(X) == len(y)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6wlBcGn1m3MP",
        "outputId": "b842bb22-66eb-4446-c5b8-3e59e5485e21"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 142 entries, 0 to 141\n",
            "Columns: 116 entries, Age to xray\n",
            "dtypes: float64(113), int64(3)\n",
            "memory usage: 128.8 KB\n",
            "142\n",
            "142\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# X_train.to_csv('hai_X_train.csv', index=False)\n"
      ],
      "metadata": {
        "id": "G_oHueOV8e0X"
      },
      "execution_count": 151,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Read the CSV file\n",
        "df = pd.read_csv('/content/hai_X_train.csv')\n",
        "\n",
        "# Get the first row as a separate DataFrame\n",
        "# first_row = df.head(1)\n",
        "df.head(1)"
      ],
      "metadata": {
        "id": "kx_lR7Li9Wb2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "outputId": "f35981e5-3cd9-4220-ee2e-d7a46a36dd2a"
      },
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Age  Sex  HospitalizationData  abdominal  allergens  angle  antibiotics  \\\n",
              "0   48    0                    7        0.0   0.220517    0.0          0.0   \n",
              "\n",
              "     asthma  auscultation  bacteria  ...  urine  use  uti  venous  \\\n",
              "0  0.393376           0.0       0.0  ...    0.0  0.0  0.0     0.0   \n",
              "\n",
              "   ventilation  vomit  wheezing  white  worsen      xray  \n",
              "0          0.0    0.0  0.196688    0.0     0.0  0.194789  \n",
              "\n",
              "[1 rows x 116 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5437d227-7901-4fdc-bfc7-0db3d4b7d159\">\n",
              "    <div 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>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>HospitalizationData</th>\n",
              "      <th>abdominal</th>\n",
              "      <th>allergens</th>\n",
              "      <th>angle</th>\n",
              "      <th>antibiotics</th>\n",
              "      <th>asthma</th>\n",
              "      <th>auscultation</th>\n",
              "      <th>bacteria</th>\n",
              "      <th>...</th>\n",
              "      <th>urine</th>\n",
              "      <th>use</th>\n",
              "      <th>uti</th>\n",
              "      <th>venous</th>\n",
              "      <th>ventilation</th>\n",
              "      <th>vomit</th>\n",
              "      <th>wheezing</th>\n",
              "      <th>white</th>\n",
              "      <th>worsen</th>\n",
              "      <th>xray</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>48</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.220517</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.393376</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.196688</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.194789</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1 rows × 116 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5437d227-7901-4fdc-bfc7-0db3d4b7d159')\"\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",
              "    <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",
              "  </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-5437d227-7901-4fdc-bfc7-0db3d4b7d159 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-5437d227-7901-4fdc-bfc7-0db3d4b7d159');\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": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # svm\n",
        "svm = SVC()\n",
        "svm.fit(X_train, y_train)\n",
        "\n",
        "y_pred = svm.predict(X_test)\n",
        "svm_accuracy = accuracy_score(y_test, y_pred)*100\n",
        "print(\"Accuracy:\", svm_accuracy, \"%\")\n",
        "\n",
        "svm_score = cross_val_score(svm, X_train, y_train, cv=6)\n",
        "print('svm cross validation score',round(svm_score.mean()*100,2).astype(str),'%' )\n",
        "\n"
      ],
      "metadata": {
        "id": "S1oQKpr3m7l9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "52e83dc2-cc9d-4dc3-92bc-48a749c0128a"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 34.48275862068966 %\n",
            "svm cross validation score 31.92 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Random Forest Classifer\n",
        "rf = RandomForestClassifier(n_estimators=100, random_state=47)\n",
        "rf.fit(X_train, y_train)\n",
        "\n",
        "y_pred = rf.predict(X_test)\n",
        "rf_accuracy = accuracy_score(y_test, y_pred)*100\n",
        "print(\"Accuracy:\", rf_accuracy, \"%\")\n",
        "\n",
        "# rf_score = cross_val_score(rf, X_train, y_train, cv=6)\n",
        "print('rfc cross validation score',round(rf_score.mean()*100,2).astype(str),'%' )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "id": "p-ErnXdinFmf",
        "outputId": "2fa3c71b-726f-4a55-dac2-69c8392d0e28"
      },
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 100.0 %\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-157-981ee7c715c7>\u001b[0m in \u001b[0;36m<cell line: 9>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Accuracy:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrf_accuracy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"%\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mrf_score\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'rfc cross validation score'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrf_score\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'%'\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: 'list' object is not callable"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#  Naive bayes\n",
        "nb = GaussianNB()\n",
        "nb.fit(X_train, y_train)\n",
        "\n",
        "y_pred = nb.predict(X_test)\n",
        "nb_accuracy = accuracy_score(y_test, y_pred)*100\n",
        "print(\"Accuracy:\", nb_accuracy, \"%\")\n",
        "\n",
        "nb_score= cross_val_score(nb,X_train,y_train,cv=6)\n",
        "print('naive bayes cross validation score',round(nb_score.mean()*100,2).astype(str),'%' )"
      ],
      "metadata": {
        "id": "0j1sprZsnF1s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0fdea0a5-56d9-43ab-d51f-448b3a601880"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 100.0 %\n",
            "naive bayes cross validation score 95.57 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# GridSearchCV\n",
        "svc_params = {'C': [0.5, 0.7, 0.9, 1], 'kernel': ['rbf', 'poly', 'sigmoid', 'linear']}\n",
        "grid_svc = GridSearchCV(SVC(), svc_params)\n",
        "grid_svc.fit(X_train, y_train)\n",
        "svc = grid_svc.best_estimator_\n",
        "\n",
        "y_pred = grid_svc.predict(X_test)\n",
        "svc_accuracy = accuracy_score(y_test, y_pred)*100\n",
        "print(\"Accuracy:\", nb_accuracy, \"%\")\n",
        "\n",
        "svc_score= cross_val_score(svc,X_train,y_train,cv=6)\n",
        "print('svc cross validation score',round(svc_score.mean()*100,2).astype(str),'%' )"
      ],
      "metadata": {
        "id": "W1Po5cmJnGGT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0023f596-1463-4c8f-826c-602b3077004f"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 100.0 %\n",
            "svc cross validation score 96.44 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # Gradient boost classifier\n",
        "# xgb= GradientBoostingClassifier(n_estimators = 300,max_depth=1,subsample=0.8,max_features=0.2,random_state=42)\n",
        "# xgb.fit(X_train,y_train)\n",
        "# y_pred= xgb.predict(X_test)\n",
        "# xgb_accuracy= accuracy_score(y_test,y_pred)*100\n",
        "# print(\"Accuracy\",xgb_accuracy)\n",
        "\n",
        "\n",
        "# xgb_score= cross_val_score(xgb,X_train,y_train,cv=4)\n",
        "# print('Gradient Xboost cross validation score', round(xgb_score.mean()*100,2).astype(str),'%')\n"
      ],
      "metadata": {
        "id": "wfocYHw4JPXB"
      },
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Visualization"
      ],
      "metadata": {
        "id": "o1t_jdSwKnXU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "algorithms = [\"SVM\",\"Random Forest\", \"Gaussian NB\",\"GridSearchCV\"]\n",
        "accuracy = [svm_accuracy, rf_accuracy, nb_accuracy,svc_accuracy]\n",
        "# cross_val_score = [svm_score,rf_score,nb_score,xgb_score,svc_score]\n",
        "cross_val_score = [round(svm_score.mean()*100,2),round(rf_score.mean()*100,2),round(nb_score.mean()*100,2),round(svc_score.mean()*100,2)]\n",
        "\n",
        "results = pd.DataFrame({\"Algorithm\": algorithms, \"Accuracy\": accuracy, \"cross_val_score\": cross_val_score})\n",
        "results.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "hM7MO4e8KmzQ",
        "outputId": "bc994995-96b4-4a62-a1da-402ebb051552"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Algorithm    Accuracy  cross_val_score\n",
              "0            SVM   34.482759            31.92\n",
              "1  Random Forest  100.000000            95.57\n",
              "2    Gaussian NB  100.000000            95.57\n",
              "3   GridSearchCV  100.000000            96.44"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0c534a2c-e87c-401b-a051-d1369dde4867\">\n",
              "    <div 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>Algorithm</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>cross_val_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>SVM</td>\n",
              "      <td>34.482759</td>\n",
              "      <td>31.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>95.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Gaussian NB</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>95.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>GridSearchCV</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>96.44</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0c534a2c-e87c-401b-a051-d1369dde4867')\"\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",
              "    <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",
              "  </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-0c534a2c-e87c-401b-a051-d1369dde4867 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-0c534a2c-e87c-401b-a051-d1369dde4867');\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": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# x-axis values (e.g., number of iterations or epochs)\n",
        "x_values = [1, 2, 3, 4]\n",
        "acc= results['Accuracy']\n",
        "cv_scores= results['cross_val_score']\n",
        "\n",
        "plt.plot(x_values, results['Accuracy'], marker='o', label='Accuracy')\n",
        "plt.plot(x_values, results['cross_val_score'], marker='o', label='Cross-Validation')\n",
        "\n",
        "# Adding labels and title\n",
        "plt.xlabel('Iterations')\n",
        "plt.ylabel('Score')\n",
        "plt.title('Accuracy Scores vs. Cross-Validation Scores')\n",
        "\n",
        "for i in range(len(x_values)):\n",
        "    plt.annotate(f'{acc[i]:.2f}', (x_values[i], acc[i]), \n",
        "                 textcoords=\"offset points\", xytext=(0, 10), ha='center')\n",
        "    plt.annotate(f'{cv_scores[i]:.2f}', (x_values[i], cv_scores[i]), \n",
        "                 textcoords=\"offset points\", xytext=(0, -20), ha='center')\n",
        "\n",
        "# Adding a legend\n",
        "plt.legend()\n",
        "\n",
        "# Display the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "iqR7vVv2K8ty",
        "outputId": "ddf9c1e9-a064-4d93-cb63-ad88ff033d42"
      },
      "execution_count": 122,
      "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": [
        "# grouped bar chart\n",
        "algorithms = [\"SVM\",\"Random Forest\", \"Gaussian NB\",\"GridSearchCV\"]\n",
        "accuracy = [svm_accuracy, rf_accuracy, nb_accuracy,svc_accuracy]\n",
        "# cross_val_score = [svm_score,rf_score,nb_score,xgb_score,svc_score]\n",
        "cross_val_score = [round(svm_score.mean()*100,2),round(rf_score.mean()*100,2),round(nb_score.mean()*100,2), round(svc_score.mean()*100,2)]\n",
        "\n",
        "bar_width = 0.35\n",
        "index = np.arange(len(algorithms))\n",
        "\n",
        "plt.bar(index, accuracy, bar_width, label='Accuracy')\n",
        "plt.bar(index + bar_width, cross_val_score, bar_width, label='Cross-Val Accuracy')\n",
        "\n",
        "plt.xlabel('Algorithms')\n",
        "plt.ylabel('Accuracy and Cross-val')\n",
        "plt.title('Accuracy and Cross-Val Accuracy for Different Algorithms')\n",
        "plt.xticks(index + bar_width / 2, algorithms, rotation='vertical')\n",
        "plt.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "3lAifoVlNSkN",
        "outputId": "f5fd16de-42c7-4c8c-938b-0b43b009d627"
      },
      "execution_count": 123,
      "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": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "\n",
        "y_pred_svm = svm.predict(X_test)\n",
        "y_pred_rf = rf.predict(X_test)\n",
        "y_pred_nb = nb.predict(X_test)\n",
        "y_pred_xgb = xgb.predict(X_test)\n",
        "\n",
        "print('Support Vector Classifier:')\n",
        "print(classification_report(y_test, y_pred_svm))\n",
        "\n",
        "print('Random Forest Classifier:')\n",
        "print(classification_report(y_test, y_pred_rf))\n",
        "\n",
        "print('Naive Bayes:')\n",
        "print(classification_report(y_test, y_pred_nb))\n",
        "\n",
        "# print('Xgboost:')\n",
        "# print(classification_report(y_test, y_pred_xgb))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GbORqK-MLZr4",
        "outputId": "a1a440d0-3490-4080-f3c5-bbf3a21e4987"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Support Vector Classifier:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "        -1.0       0.00      0.00      0.00         8\n",
            "         1.0       0.33      0.43      0.38         7\n",
            "         2.0       0.00      0.00      0.00         3\n",
            "         3.0       0.00      0.00      0.00         4\n",
            "         5.0       0.35      1.00      0.52         7\n",
            "\n",
            "    accuracy                           0.34        29\n",
            "   macro avg       0.14      0.29      0.18        29\n",
            "weighted avg       0.16      0.34      0.22        29\n",
            "\n",
            "Random Forest Classifier:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "        -1.0       1.00      1.00      1.00         8\n",
            "         1.0       1.00      1.00      1.00         7\n",
            "         2.0       1.00      1.00      1.00         3\n",
            "         3.0       1.00      1.00      1.00         4\n",
            "         5.0       1.00      1.00      1.00         7\n",
            "\n",
            "    accuracy                           1.00        29\n",
            "   macro avg       1.00      1.00      1.00        29\n",
            "weighted avg       1.00      1.00      1.00        29\n",
            "\n",
            "Naive Bayes:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "        -1.0       1.00      1.00      1.00         8\n",
            "         1.0       1.00      1.00      1.00         7\n",
            "         2.0       1.00      1.00      1.00         3\n",
            "         3.0       1.00      1.00      1.00         4\n",
            "         5.0       1.00      1.00      1.00         7\n",
            "\n",
            "    accuracy                           1.00        29\n",
            "   macro avg       1.00      1.00      1.00        29\n",
            "weighted avg       1.00      1.00      1.00        29\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": "markdown",
      "source": [
        "#output predicting"
      ],
      "metadata": {
        "id": "Enb961F7xwsY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_list = [1, 45, 'M', 'Asthma, Hypertension', '2023-01-05 to 2023-01-12', 'Blood Test: Normal, X-ray: Clear', 'MRI: No abnormalities', 'NonNasal swab: Positive for influenzae', 'Obesity, Smoking', 'Cough, Fever', 'Elevated heart rate', 'Antibiotics']\n",
        "columns = ['PatientID','Age', 'Sex','MedicalHistory', 'HospitalizationData', 'LaboratoryData', 'ImagingData', 'MicrobiologyData', 'RiskFactors', 'Symptoms', 'Signs', 'Treatment']\n",
        "df_new = pd.DataFrame([data_list], columns=columns)\n",
        "test_df = Processing(df_new)\n",
        "X_data = test_df.process_data()\n",
        "X_data.head()\n",
        "\n",
        "# Step 1: Get feature names used during training\n",
        "feature_names_training = X_train.columns.tolist()\n",
        "\n",
        "# Step 2: Align feature names in test data\n",
        "feature_names_test = X_data.columns.tolist()\n",
        "\n",
        "# Step 3: Identify unseen features in test data\n",
        "unseen_features = set(feature_names_test) - set(feature_names_training)\n",
        "\n",
        "# Step 4: Drop unseen features from test data\n",
        "X_data = X_data.drop(unseen_features, axis=1)\n",
        "\n",
        "# Step 5: Reorder features in test data to match the order in training data\n",
        "X_data = X_data.reindex(columns=feature_names_training, fill_value=0)\n",
        "\n",
        "# # Step 6: Make predictions using the Random Forest classifier\n",
        "# predict = rf.predict(X_data)\n",
        "# print(predict)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Tj0aqEfktqV",
        "outputId": "ac052e12-2f49-4a5c-b4a3-8c7b5b1277b2"
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-102-adab19079636>:38: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  td[column] = td[column].apply(lambda x: x.translate(str.maketrans('', '', string.punctuation)) if isinstance(x, str) else x)\n",
            "<ipython-input-102-adab19079636>:43: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  td[column] = td[column].apply(lambda x: ' '.join([word for word in str(x).split() if word.lower() not in stop_words]))\n",
            "<ipython-input-102-adab19079636>:73: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  td.loc[:, column] = td[column].apply(lemmatize_column)\n",
            "<ipython-input-102-adab19079636>:79: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  td['Combined Text'] = td[text_columns].apply(lambda x: ' '.join(x.dropna().astype(str)), axis=1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# predict = grid_svc.predict(X_data)\n",
        "predict = rf.predict(X_data)\n",
        "predict = predict.astype(int)\n",
        "print(predict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R9RMCB2lauwC",
        "outputId": "4d5eff3f-67b8-41db-81cb-da9f16f47dd6"
      },
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[3]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a dictionary to map the numeric labels to their corresponding strings\n",
        "label_mapping = {-1: 'None', 0: 'other' , 1 : 'VAP', 2 : 'UTI', 3 : 'CLABSI', 4: 'SSI', 5 : 'Respiratory Infection'}\n",
        "predict = [3.]\n",
        "# Map the predicted values to their corresponding strings\n",
        "predict_strings = [label_mapping[label] for label in predict]\n",
        "# Print the predicted strings\n",
        "for prediction in predict_strings:\n",
        "    print(prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-7y_oyIZk8B7",
        "outputId": "9797bed1-4878-4939-9e32-cd61e8f61c80"
      },
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CLABSI\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#saving the model"
      ],
      "metadata": {
        "id": "fEd2yZVhnNlj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install joblib"
      ],
      "metadata": {
        "id": "cA6KL2SOnZo5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6709eac2-f133-4ec4-ffa5-9094555a8d18"
      },
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (1.2.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import pickle\n",
        "\n",
        "# filename = 'hai_pred_model.sav'\n",
        "# pickle.dump(rf, open(filename, 'wb'))\n",
        "\n"
      ],
      "metadata": {
        "id": "RBdpzxlRnLtb"
      },
      "execution_count": 149,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# loading the model\n",
        "model = pickle.load(open('hai_pred_model.sav', 'rb'))\n",
        "\n",
        "predict = model.predict(X_data)\n",
        "print(predict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dwezrbFUpFQ3",
        "outputId": "aad51598-55a5-4cf7-ce09-9ea5f5522fa9"
      },
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[3.]\n"
          ]
        }
      ]
    }
  ]
}