No_Show_Medical_Appointment.ipynb

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      "name": "No Show Medical Appointment.ipynb",
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        "<a href=\"https://colab.research.google.com/github/vy-phung/Data-analysis/blob/main/No_Show_Medical_Appointment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "**<h1>Data analysis Medical Appointment <h1>**\n",
        "\n",
        "**<h3>Author</h3>** \n",
        "<p>Vy K Phung</p>\n",
        "\n",
        "**<h3>Date</h3>** \n",
        "<p>2021-Nov-5</p>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7eCArbS34Xxg"
      },
      "source": [
        "## **Introduction**\n",
        "The purpose of this data analysis is to find if receiving SMS or not is associated with the customers showing up or not showing up in Medical appointment. I re-analyse the data source on Kaggle (you can go to the link below if you're also interested to analyse it) \n",
        "\n",
        "In addition, I wil analyse another factor which is scheduling in the same day or in advance the Medical appointment to know if it may effect the relationship between SMS_received and no-show appointment. \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "**Data source:** https://www.kaggle.com/joniarroba/noshowappointments/home \n",
        "\n",
        " (In this data source there is also the explanation for variable name. Moreover, this analysis for this data source refers to Simple Regression Analysis of Public Health on Coursera )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      },
      "source": [
        "##**Summary:**\n",
        "1. Data preprocessing\n",
        "2. Exploratory data analysis\n",
        "<ul>\n",
        "<li>Feature engineering</li>\n",
        "</ul>\n",
        "3. Statistical analysis in R (using Logistic regression analysis)\n",
        "<ul>\n",
        "<li>Unadjusted data analysis</li>\n",
        "<li>Adjusted data analysis</li>\n",
        "<li>Stratified analysis</li>\n",
        "</ul>\n",
        "4. Basic predicting and classifying\n",
        "<ul>\n",
        "<li>Test-train split</li>\n",
        "<li>Predicting using logistic regression and GridSearchCV</li>\n",
        "</ul>\n"
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "# Import packages \n",
        "# Package\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import metrics\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn import preprocessing\n",
        "# import the visualization package\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline "
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
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      "source": [
        "## **Data preprocessing**"
      ]
    },
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        "outputId": "3df29ab8-f740-40e0-cb18-c30b5d68335a"
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      "source": [
        "noshow = pd.read_csv('/content/KaggleV2-May-2016.csv')\n",
        "noshow.head()"
      ],
      "execution_count": 2,
      "outputs": [
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              "      <th></th>\n",
              "      <th>PatientId</th>\n",
              "      <th>AppointmentID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>ScheduledDay</th>\n",
              "      <th>AppointmentDay</th>\n",
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              "      <th>Hipertension</th>\n",
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              "      <th>Alcoholism</th>\n",
              "      <th>Handcap</th>\n",
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              "      <td>2016-04-29T00:00:00Z</td>\n",
              "      <td>62</td>\n",
              "      <td>JARDIM DA PENHA</td>\n",
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              "      <td>1</td>\n",
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              "      <td>2016-04-29T16:08:27Z</td>\n",
              "      <td>2016-04-29T00:00:00Z</td>\n",
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              "      <th>2</th>\n",
              "      <td>4.262962e+12</td>\n",
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              "      <td>2016-04-29T16:19:04Z</td>\n",
              "      <td>2016-04-29T00:00:00Z</td>\n",
              "      <td>62</td>\n",
              "      <td>MATA DA PRAIA</td>\n",
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              "      <th>3</th>\n",
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              "      <td>2016-04-29T17:29:31Z</td>\n",
              "      <td>2016-04-29T00:00:00Z</td>\n",
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              "      <th>4</th>\n",
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              "      <td>5642494</td>\n",
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              "      <td>2016-04-29T16:07:23Z</td>\n",
              "      <td>2016-04-29T00:00:00Z</td>\n",
              "      <td>56</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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            "text/plain": [
              "      PatientId  AppointmentID Gender  ... Handcap SMS_received  No-show\n",
              "0  2.987250e+13        5642903      F  ...       0            0       No\n",
              "1  5.589978e+14        5642503      M  ...       0            0       No\n",
              "2  4.262962e+12        5642549      F  ...       0            0       No\n",
              "3  8.679512e+11        5642828      F  ...       0            0       No\n",
              "4  8.841186e+12        5642494      F  ...       0            0       No\n",
              "\n",
              "[5 rows x 14 columns]"
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              "      <th>count</th>\n",
              "      <td>1.105270e+05</td>\n",
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              "      <th>mean</th>\n",
              "      <td>1.474963e+14</td>\n",
              "      <td>5.675305e+06</td>\n",
              "      <td>37.088874</td>\n",
              "      <td>0.098266</td>\n",
              "      <td>0.197246</td>\n",
              "      <td>0.071865</td>\n",
              "      <td>0.030400</td>\n",
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              "      <th>std</th>\n",
              "      <td>2.560949e+14</td>\n",
              "      <td>7.129575e+04</td>\n",
              "      <td>23.110205</td>\n",
              "      <td>0.297675</td>\n",
              "      <td>0.397921</td>\n",
              "      <td>0.258265</td>\n",
              "      <td>0.171686</td>\n",
              "      <td>0.161543</td>\n",
              "      <td>0.466873</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>3.921784e+04</td>\n",
              "      <td>5.030230e+06</td>\n",
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              "      <th>25%</th>\n",
              "      <td>4.172614e+12</td>\n",
              "      <td>5.640286e+06</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th>50%</th>\n",
              "      <td>3.173184e+13</td>\n",
              "      <td>5.680573e+06</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th>75%</th>\n",
              "      <td>9.439172e+13</td>\n",
              "      <td>5.725524e+06</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>9.999816e+14</td>\n",
              "      <td>5.790484e+06</td>\n",
              "      <td>115.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "    <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-15b1a438-f8d8-480a-85e7-c504d70d76f2 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-15b1a438-f8d8-480a-85e7-c504d70d76f2');\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",
              "  "
            ],
            "text/plain": [
              "          PatientId  AppointmentID  ...        Handcap   SMS_received\n",
              "count  1.105270e+05   1.105270e+05  ...  110527.000000  110527.000000\n",
              "mean   1.474963e+14   5.675305e+06  ...       0.022248       0.321026\n",
              "std    2.560949e+14   7.129575e+04  ...       0.161543       0.466873\n",
              "min    3.921784e+04   5.030230e+06  ...       0.000000       0.000000\n",
              "25%    4.172614e+12   5.640286e+06  ...       0.000000       0.000000\n",
              "50%    3.173184e+13   5.680573e+06  ...       0.000000       0.000000\n",
              "75%    9.439172e+13   5.725524e+06  ...       0.000000       1.000000\n",
              "max    9.999816e+14   5.790484e+06  ...       4.000000       1.000000\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "heg3nYASAkI7",
        "outputId": "9a631b52-49ac-4d47-b12b-a69b686163be"
      },
      "source": [
        "print(noshow.dtypes)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PatientId         float64\n",
            "AppointmentID       int64\n",
            "Gender             object\n",
            "ScheduledDay       object\n",
            "AppointmentDay     object\n",
            "Age                 int64\n",
            "Neighbourhood      object\n",
            "Scholarship         int64\n",
            "Hipertension        int64\n",
            "Diabetes            int64\n",
            "Alcoholism          int64\n",
            "Handcap             int64\n",
            "SMS_received        int64\n",
            "No-show            object\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(noshow.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D8PUn4HMqb0K",
        "outputId": "6dceebd4-bd62-4e43-8a48-d1e97677e970"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PatientId         0\n",
            "AppointmentID     0\n",
            "Gender            0\n",
            "ScheduledDay      0\n",
            "AppointmentDay    0\n",
            "Age               0\n",
            "Neighbourhood     0\n",
            "Scholarship       0\n",
            "Hipertension      0\n",
            "Diabetes          0\n",
            "Alcoholism        0\n",
            "Handcap           0\n",
            "SMS_received      0\n",
            "No-show           0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RebgFarL-nEr"
      },
      "source": [
        "## **Exploratory data analysis**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Vo_RYBpQd18M",
        "outputId": "cafbbf78-ad5a-4028-c6b3-20f0473c0b04"
      },
      "source": [
        "no_show = []\n",
        "for term in noshow['No-show']:\n",
        "  if term == 'No':\n",
        "    no_show.append('0')\n",
        "  elif term == 'Yes':\n",
        "    no_show.append('1')\n",
        "df = pd.Series(no_show, name='noshow')\n",
        "noshow = pd.concat([noshow, df], axis=1)\n",
        "noshow.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-1dd99952-0b67-481d-b61d-52ed67287847\">\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>AppointmentID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>ScheduledDay</th>\n",
              "      <th>AppointmentDay</th>\n",
              "      <th>Age</th>\n",
              "      <th>Neighbourhood</th>\n",
              "      <th>Scholarship</th>\n",
              "      <th>Hipertension</th>\n",
              "      <th>Diabetes</th>\n",
              "      <th>Alcoholism</th>\n",
              "      <th>Handcap</th>\n",
              "      <th>SMS_received</th>\n",
              "      <th>No-show</th>\n",
              "      <th>noshow</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.987250e+13</td>\n",
              "      <td>5642903</td>\n",
              "      <td>F</td>\n",
              "      <td>2016-04-29T18:38:08Z</td>\n",
              "      <td>2016-04-29T00:00:00Z</td>\n",
              "      <td>62</td>\n",
              "      <td>JARDIM DA PENHA</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.589978e+14</td>\n",
              "      <td>5642503</td>\n",
              "      <td>M</td>\n",
              "      <td>2016-04-29T16:08:27Z</td>\n",
              "      <td>2016-04-29T00:00:00Z</td>\n",
              "      <td>56</td>\n",
              "      <td>JARDIM DA PENHA</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.262962e+12</td>\n",
              "      <td>5642549</td>\n",
              "      <td>F</td>\n",
              "      <td>2016-04-29T16:19:04Z</td>\n",
              "      <td>2016-04-29T00:00:00Z</td>\n",
              "      <td>62</td>\n",
              "      <td>MATA DA PRAIA</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8.679512e+11</td>\n",
              "      <td>5642828</td>\n",
              "      <td>F</td>\n",
              "      <td>2016-04-29T17:29:31Z</td>\n",
              "      <td>2016-04-29T00:00:00Z</td>\n",
              "      <td>8</td>\n",
              "      <td>PONTAL DE CAMBURI</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8.841186e+12</td>\n",
              "      <td>5642494</td>\n",
              "      <td>F</td>\n",
              "      <td>2016-04-29T16:07:23Z</td>\n",
              "      <td>2016-04-29T00:00:00Z</td>\n",
              "      <td>56</td>\n",
              "      <td>JARDIM DA PENHA</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1dd99952-0b67-481d-b61d-52ed67287847')\"\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",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      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-1dd99952-0b67-481d-b61d-52ed67287847 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-1dd99952-0b67-481d-b61d-52ed67287847');\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",
              "  "
            ],
            "text/plain": [
              "      PatientId  AppointmentID Gender  ... SMS_received No-show  noshow\n",
              "0  2.987250e+13        5642903      F  ...            0      No       0\n",
              "1  5.589978e+14        5642503      M  ...            0      No       0\n",
              "2  4.262962e+12        5642549      F  ...            0      No       0\n",
              "3  8.679512e+11        5642828      F  ...            0      No       0\n",
              "4  8.841186e+12        5642494      F  ...            0      No       0\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "h_MqC6ihh_ye",
        "outputId": "7210c278-ec83-4491-b2f7-56e1653a7f6f"
      },
      "source": [
        "print(noshow['No-show'].value_counts())\n",
        "sns.countplot(data=noshow,x = 'No-show')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No     88208\n",
            "Yes    22319\n",
            "Name: No-show, dtype: int64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5b167d77d0>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "8oWBbY6_iq13",
        "outputId": "f8ebfc23-64e6-4db5-c332-bd40af232f20"
      },
      "source": [
        "print(noshow['SMS_received'].value_counts())\n",
        "sns.countplot(data=noshow,x = 'SMS_received')"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    75045\n",
            "1    35482\n",
            "Name: SMS_received, dtype: int64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5b15524910>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Because I also want to analyse other factors especially the factor of scheduling in the same day or in advance the Medical appointment may effect the relationship between SMS_received and no-show appointment, I will do data wrangling and exploratory data analysis with Appointment day and Scheduled day columns"
      ],
      "metadata": {
        "id": "KJsL6Tovy2oR"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "QnffBL1JHbI2",
        "outputId": "a3dfe645-4c19-4d67-bdce-611d73c69cdf"
      },
      "source": [
        "day = noshow[['AppointmentDay','ScheduledDay']]\n",
        "day "
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-d7db29c1-bae9-4237-bb00-541187001682\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AppointmentDay</th>\n",
              "      <th>ScheduledDay</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2016-04-29T00:00:00Z</td>\n",
              "      <td>2016-04-29T18:38:08Z</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2016-04-29T00:00:00Z</td>\n",
              "      <td>2016-04-29T16:08:27Z</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2016-04-29T00:00:00Z</td>\n",
              "      <td>2016-04-29T16:19:04Z</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016-04-29T00:00:00Z</td>\n",
              "      <td>2016-04-29T17:29:31Z</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016-04-29T00:00:00Z</td>\n",
              "      <td>2016-04-29T16:07:23Z</td>\n",
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              "      <th>...</th>\n",
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              "      <td>2016-06-07T00:00:00Z</td>\n",
              "      <td>2016-05-03T09:15:35Z</td>\n",
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              "    <tr>\n",
              "      <th>110523</th>\n",
              "      <td>2016-06-07T00:00:00Z</td>\n",
              "      <td>2016-05-03T07:27:33Z</td>\n",
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              "    <tr>\n",
              "      <th>110524</th>\n",
              "      <td>2016-06-07T00:00:00Z</td>\n",
              "      <td>2016-04-27T16:03:52Z</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110525</th>\n",
              "      <td>2016-06-07T00:00:00Z</td>\n",
              "      <td>2016-04-27T15:09:23Z</td>\n",
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              "    <tr>\n",
              "      <th>110526</th>\n",
              "      <td>2016-06-07T00:00:00Z</td>\n",
              "      <td>2016-04-27T13:30:56Z</td>\n",
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              "\n",
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              "\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-d7db29c1-bae9-4237-bb00-541187001682 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-d7db29c1-bae9-4237-bb00-541187001682');\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",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              AppointmentDay          ScheduledDay\n",
              "0       2016-04-29T00:00:00Z  2016-04-29T18:38:08Z\n",
              "1       2016-04-29T00:00:00Z  2016-04-29T16:08:27Z\n",
              "2       2016-04-29T00:00:00Z  2016-04-29T16:19:04Z\n",
              "3       2016-04-29T00:00:00Z  2016-04-29T17:29:31Z\n",
              "4       2016-04-29T00:00:00Z  2016-04-29T16:07:23Z\n",
              "...                      ...                   ...\n",
              "110522  2016-06-07T00:00:00Z  2016-05-03T09:15:35Z\n",
              "110523  2016-06-07T00:00:00Z  2016-05-03T07:27:33Z\n",
              "110524  2016-06-07T00:00:00Z  2016-04-27T16:03:52Z\n",
              "110525  2016-06-07T00:00:00Z  2016-04-27T15:09:23Z\n",
              "110526  2016-06-07T00:00:00Z  2016-04-27T13:30:56Z\n",
              "\n",
              "[110527 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "month = []\n",
        "year = []\n",
        "month1 = []\n",
        "year1 = []\n",
        "for s,a in zip(day['ScheduledDay'].values,day['AppointmentDay'].values):\n",
        "  schedule = s.split('-')\n",
        "  appoint = a.split('-')\n",
        "  year.append(schedule[0]) \n",
        "  month.append(schedule[1]) \n",
        "  month1.append(appoint[1]) \n",
        "  year1.append(appoint[0]) \n",
        "Scheduled1 = pd.Series(month, name='Month_Sched')\n",
        "Scheduled2 = pd.Series(year, name='Year_Sched')\n",
        "Scheduled3 = pd.Series(month1, name='Month_App')\n",
        "Scheduled4 = pd.Series(year1, name='Year_App')\n",
        "day = pd.concat([day, Scheduled1, Scheduled2, Scheduled3, Scheduled4], axis=1)\n",
        "day"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "h8fXeCz_mL_j",
        "outputId": "38d99ad0-d43c-41dd-c7cc-897c4768ab7e"
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      "execution_count": 11,
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              "      <td>2016-04-29T00:00:00Z</td>\n",
              "      <td>2016-04-29T17:29:31Z</td>\n",
              "      <td>04</td>\n",
              "      <td>2016</td>\n",
              "      <td>04</td>\n",
              "      <td>2016</td>\n",
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              "      <td>04</td>\n",
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              "      <td>2016</td>\n",
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              "      <td>2016-06-07T00:00:00Z</td>\n",
              "      <td>2016-05-03T07:27:33Z</td>\n",
              "      <td>05</td>\n",
              "      <td>2016</td>\n",
              "      <td>06</td>\n",
              "      <td>2016</td>\n",
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              "    <tr>\n",
              "      <th>110524</th>\n",
              "      <td>2016-06-07T00:00:00Z</td>\n",
              "      <td>2016-04-27T16:03:52Z</td>\n",
              "      <td>04</td>\n",
              "      <td>2016</td>\n",
              "      <td>06</td>\n",
              "      <td>2016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110525</th>\n",
              "      <td>2016-06-07T00:00:00Z</td>\n",
              "      <td>2016-04-27T15:09:23Z</td>\n",
              "      <td>04</td>\n",
              "      <td>2016</td>\n",
              "      <td>06</td>\n",
              "      <td>2016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110526</th>\n",
              "      <td>2016-06-07T00:00:00Z</td>\n",
              "      <td>2016-04-27T13:30:56Z</td>\n",
              "      <td>04</td>\n",
              "      <td>2016</td>\n",
              "      <td>06</td>\n",
              "      <td>2016</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>110527 rows × 6 columns</p>\n",
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              "\n",
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              "          const element = document.querySelector('#df-71a7d856-e49d-4081-ad63-ea7dd774caf1');\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",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              AppointmentDay          ScheduledDay  ... Month_App Year_App\n",
              "0       2016-04-29T00:00:00Z  2016-04-29T18:38:08Z  ...        04     2016\n",
              "1       2016-04-29T00:00:00Z  2016-04-29T16:08:27Z  ...        04     2016\n",
              "2       2016-04-29T00:00:00Z  2016-04-29T16:19:04Z  ...        04     2016\n",
              "3       2016-04-29T00:00:00Z  2016-04-29T17:29:31Z  ...        04     2016\n",
              "4       2016-04-29T00:00:00Z  2016-04-29T16:07:23Z  ...        04     2016\n",
              "...                      ...                   ...  ...       ...      ...\n",
              "110522  2016-06-07T00:00:00Z  2016-05-03T09:15:35Z  ...        06     2016\n",
              "110523  2016-06-07T00:00:00Z  2016-05-03T07:27:33Z  ...        06     2016\n",
              "110524  2016-06-07T00:00:00Z  2016-04-27T16:03:52Z  ...        06     2016\n",
              "110525  2016-06-07T00:00:00Z  2016-04-27T15:09:23Z  ...        06     2016\n",
              "110526  2016-06-07T00:00:00Z  2016-04-27T13:30:56Z  ...        06     2016\n",
              "\n",
              "[110527 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "year = day['Year_Sched'].value_counts().reset_index()\n",
        "year['Year_App'] = day['Year_App'].value_counts().reset_index()[['Year_App']]\n",
        "year['Year_App'].replace(to_replace= np.nan, value=0, inplace=True)\n",
        "year"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "2AqNe-EXvTWJ",
        "outputId": "85cd293e-af4d-4f8f-da96-52cf03be1fde"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fa694235-ad2b-4db4-aa55-ffce65533795')\"\n",
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              "          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",
              "  "
            ],
            "text/plain": [
              "  index  Year_Sched  Year_App\n",
              "0  2016      110465  110527.0\n",
              "1  2015          62       0.0"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "month = day['Month_Sched'].value_counts().reset_index()\n",
        "month['Month_App'] = day['Month_App'].value_counts().reset_index()[['Month_App']]\n",
        "month['Month_App'].replace(to_replace= np.nan, value=0, inplace=True)\n",
        "month = month.set_index(\"index\")\n",
        "month.index.names = [None]\n",
        "month"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "ofhpwUMBwWJh",
        "outputId": "56f90750-704d-4761-a1f3-f8cd8dc800e2"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-13d83fda-8b5d-4356-b5d5-7d17bde92a18\">\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>Month_Sched</th>\n",
              "      <th>Month_App</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>05</th>\n",
              "      <td>67421</td>\n",
              "      <td>80841.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>04</th>\n",
              "      <td>25339</td>\n",
              "      <td>26451.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>06</th>\n",
              "      <td>13750</td>\n",
              "      <td>3235.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>03</th>\n",
              "      <td>3614</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>02</th>\n",
              "      <td>281</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>61</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>01</th>\n",
              "      <td>60</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-13d83fda-8b5d-4356-b5d5-7d17bde92a18 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-13d83fda-8b5d-4356-b5d5-7d17bde92a18');\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",
              "  "
            ],
            "text/plain": [
              "    Month_Sched  Month_App\n",
              "05        67421    80841.0\n",
              "04        25339    26451.0\n",
              "06        13750     3235.0\n",
              "03         3614        0.0\n",
              "02          281        0.0\n",
              "12           61        0.0\n",
              "01           60        0.0\n",
              "11            1        0.0"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "month.plot(kind='bar', figsize = (20,6))\n",
        "plt.xlabel('Month')\n",
        "plt.ylabel('The number of appointment booked in each month')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        },
        "id": "7qCSQavjxJdd",
        "outputId": "2deac095-10cc-455c-d470-2d7d7498a6ac"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'The number of appointment booked in each month')"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that mostly people booked their Medical appointment in summer (around April, May and June)"
      ],
      "metadata": {
        "id": "7zAc-FrSyWsp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Feature engineering**"
      ],
      "metadata": {
        "id": "jy53pWZ8x3IW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will select the features that will be used in statistical analysis and prediction in the future module. <br>\n",
        "Firstly, creating \"in advance\" column (people booking in advance the appointment day) and \"same day\" column (people booking in the same day with the appointment day)"
      ],
      "metadata": {
        "id": "HPN6uh2ByikF"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jRe8b1r-jLuV"
      },
      "source": [
        "booking = []\n",
        "for i in range(0,len(day)):\n",
        "  if day['Year_Sched'][i] < day['Year_App'][i]:\n",
        "    booking.append('in advance')\n",
        "  elif day['Year_Sched'][i] == day['Year_App'][i]:\n",
        "    if day['Month_Sched'][i] == day['Month_App'][i]:\n",
        "      booking.append('same day')\n",
        "    elif day['Month_Sched'][i] < day['Month_App'][i]:\n",
        "      booking.append('in advance')\n",
        "Booking = pd.Series(booking, name='Reservation')\n",
        "day = pd.concat([day, Booking], axis=1)   \n"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5yqg3o-tQLny"
      },
      "source": [
        "In_adv = []\n",
        "for i in day['Reservation']:\n",
        "  if i == 'same day':\n",
        "    In_adv.append('0')\n",
        "  elif i == 'in advance':\n",
        "    In_adv.append('1')\n",
        "inadv = pd.Series(In_adv, name='In_advance')\n",
        "day = pd.concat([day, inadv], axis=1)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will do data transform for \"In_advance\" column so that in the next section we will easily see the frequency of no-show (Yes or No) in \"In_advance\" variable. "
      ],
      "metadata": {
        "id": "tLioosdOGx0X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "in_adv_trans = preprocessing.StandardScaler().fit(day[['In_advance']].values).transform(day[['In_advance']].values)\n",
        "day['transform'] = pd.DataFrame(in_adv_trans)[[0]]\n",
        "day.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "CDQ11OegF6au",
        "outputId": "628483db-a5a0-43f8-c07b-b2a1119bfa89"
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      "execution_count": 18,
      "outputs": [
        {
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              "      <th></th>\n",
              "      <th>AppointmentDay</th>\n",
              "      <th>ScheduledDay</th>\n",
              "      <th>Month_Sched</th>\n",
              "      <th>Year_Sched</th>\n",
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              "      <td>2016-04-29T00:00:00Z</td>\n",
              "      <td>2016-04-29T16:19:04Z</td>\n",
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              "      <td>2016</td>\n",
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              "      <th>3</th>\n",
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              "      <td>2016-04-29T17:29:31Z</td>\n",
              "      <td>04</td>\n",
              "      <td>2016</td>\n",
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              "      <td>2016</td>\n",
              "      <td>same day</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016-04-29T00:00:00Z</td>\n",
              "      <td>2016-04-29T16:07:23Z</td>\n",
              "      <td>04</td>\n",
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              "      <td>same day</td>\n",
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              "                                                     [key], {});\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         AppointmentDay          ScheduledDay  ... In_advance transform\n",
              "0  2016-04-29T00:00:00Z  2016-04-29T18:38:08Z  ...          0 -0.715843\n",
              "1  2016-04-29T00:00:00Z  2016-04-29T16:08:27Z  ...          0 -0.715843\n",
              "2  2016-04-29T00:00:00Z  2016-04-29T16:19:04Z  ...          0 -0.715843\n",
              "3  2016-04-29T00:00:00Z  2016-04-29T17:29:31Z  ...          0 -0.715843\n",
              "4  2016-04-29T00:00:00Z  2016-04-29T16:07:23Z  ...          0 -0.715843\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "noshow = pd.concat([noshow, day[['Reservation']], day[['In_advance']],day[['transform']]], axis=1)\n",
        "noshow.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "yI5hXjTzFrRI",
        "outputId": "584e3d1c-97de-4148-8ef2-689b5e69a3a4"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-fb95c2d1-bdf0-429b-aeac-51ff5d86dbcb\">\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PatientId</th>\n",
              "      <th>AppointmentID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>ScheduledDay</th>\n",
              "      <th>AppointmentDay</th>\n",
              "      <th>Age</th>\n",
              "      <th>Neighbourhood</th>\n",
              "      <th>Scholarship</th>\n",
              "      <th>Hipertension</th>\n",
              "      <th>Diabetes</th>\n",
              "      <th>Alcoholism</th>\n",
              "      <th>Handcap</th>\n",
              "      <th>SMS_received</th>\n",
              "      <th>No-show</th>\n",
              "      <th>noshow</th>\n",
              "      <th>Reservation</th>\n",
              "      <th>In_advance</th>\n",
              "      <th>transform</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.987250e+13</td>\n",
              "      <td>5642903</td>\n",
              "      <td>F</td>\n",
              "      <td>2016-04-29T18:38:08Z</td>\n",
              "      <td>2016-04-29T00:00:00Z</td>\n",
              "      <td>62</td>\n",
              "      <td>JARDIM DA PENHA</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.589978e+14</td>\n",
              "      <td>5642503</td>\n",
              "      <td>M</td>\n",
              "      <td>2016-04-29T16:08:27Z</td>\n",
              "      <td>2016-04-29T00:00:00Z</td>\n",
              "      <td>56</td>\n",
              "      <td>JARDIM DA PENHA</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>same day</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.715843</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.262962e+12</td>\n",
              "      <td>5642549</td>\n",
              "      <td>F</td>\n",
              "      <td>2016-04-29T16:19:04Z</td>\n",
              "      <td>2016-04-29T00:00:00Z</td>\n",
              "      <td>62</td>\n",
              "      <td>MATA DA PRAIA</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>same day</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.715843</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8.679512e+11</td>\n",
              "      <td>5642828</td>\n",
              "      <td>F</td>\n",
              "      <td>2016-04-29T17:29:31Z</td>\n",
              "      <td>2016-04-29T00:00:00Z</td>\n",
              "      <td>8</td>\n",
              "      <td>PONTAL DE CAMBURI</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>same day</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.715843</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8.841186e+12</td>\n",
              "      <td>5642494</td>\n",
              "      <td>F</td>\n",
              "      <td>2016-04-29T16:07:23Z</td>\n",
              "      <td>2016-04-29T00:00:00Z</td>\n",
              "      <td>56</td>\n",
              "      <td>JARDIM DA PENHA</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>same day</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.715843</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fb95c2d1-bdf0-429b-aeac-51ff5d86dbcb');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      PatientId  AppointmentID Gender  ... Reservation In_advance  transform\n",
              "0  2.987250e+13        5642903      F  ...    same day          0  -0.715843\n",
              "1  5.589978e+14        5642503      M  ...    same day          0  -0.715843\n",
              "2  4.262962e+12        5642549      F  ...    same day          0  -0.715843\n",
              "3  8.679512e+11        5642828      F  ...    same day          0  -0.715843\n",
              "4  8.841186e+12        5642494      F  ...    same day          0  -0.715843\n",
              "\n",
              "[5 rows x 18 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "id": "wL-mZ6IDPD3L",
        "outputId": "719f7b76-1fe0-43f0-e5fa-022dc658d3bd"
      },
      "source": [
        "sns.catplot(data=noshow,x= 'transform',y='SMS_received' ,hue='No-show',color='blue', aspect = 4)\n",
        "plt.xlabel(\"In_advance which near 0 is No and near 1 is Yes\")"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 20.80000000000002, 'In_advance which near 0 is No and near 1 is Yes')"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1492x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that for people booking not in advance (same day), they have the high frequency of showing up in appointment although they even didn't receive SMS. However, for people booking in advance, the frequency of not showing up in appointment is still high in both receiving and not receiving SMS.      "
      ],
      "metadata": {
        "id": "smOFoNNC8eTF"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mJESIxSG-xKi"
      },
      "source": [
        "# save dataset\n",
        "noshow.to_csv('noshow.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uP8GTJk7RF3c"
      },
      "source": [
        "## **Statistical analysis in R**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j-ZJat7BRFOZ"
      },
      "source": [
        "%load_ext rpy2.ipython"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P_qQb9z3ST3-",
        "outputId": "fc5d6219-f918-45f3-9024-1addad5a0869"
      },
      "source": [
        "%%R\n",
        "noshow <- read.csv(\"/content/noshow.csv\")\n",
        "head(noshow)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  X    PatientId AppointmentID Gender         ScheduledDay       AppointmentDay\n",
            "1 0 2.987250e+13       5642903      F 2016-04-29T18:38:08Z 2016-04-29T00:00:00Z\n",
            "2 1 5.589978e+14       5642503      M 2016-04-29T16:08:27Z 2016-04-29T00:00:00Z\n",
            "3 2 4.262962e+12       5642549      F 2016-04-29T16:19:04Z 2016-04-29T00:00:00Z\n",
            "4 3 8.679512e+11       5642828      F 2016-04-29T17:29:31Z 2016-04-29T00:00:00Z\n",
            "5 4 8.841186e+12       5642494      F 2016-04-29T16:07:23Z 2016-04-29T00:00:00Z\n",
            "6 5 9.598513e+13       5626772      F 2016-04-27T08:36:51Z 2016-04-29T00:00:00Z\n",
            "  Age     Neighbourhood Scholarship Hipertension Diabetes Alcoholism Handcap\n",
            "1  62   JARDIM DA PENHA           0            1        0          0       0\n",
            "2  56   JARDIM DA PENHA           0            0        0          0       0\n",
            "3  62     MATA DA PRAIA           0            0        0          0       0\n",
            "4   8 PONTAL DE CAMBURI           0            0        0          0       0\n",
            "5  56   JARDIM DA PENHA           0            1        1          0       0\n",
            "6  76         REPÚBLICA           0            1        0          0       0\n",
            "  SMS_received No.show noshow Reservation In_advance  transform\n",
            "1            0      No      0    same day          0 -0.7158434\n",
            "2            0      No      0    same day          0 -0.7158434\n",
            "3            0      No      0    same day          0 -0.7158434\n",
            "4            0      No      0    same day          0 -0.7158434\n",
            "5            0      No      0    same day          0 -0.7158434\n",
            "6            0      No      0    same day          0 -0.7158434\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gZgfWITRVEPP"
      },
      "source": [
        "### **Unadjusted data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zrrrB8YfRLuC",
        "outputId": "bf317acd-54bd-4a38-c02e-345018eed6f6"
      },
      "source": [
        "%%R\n",
        "result_un = glm(noshow$noshow ~ noshow$SMS_received, family = binomial)\n",
        "summary(result_un)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Call:\n",
            "glm(formula = noshow$noshow ~ noshow$SMS_received, family = binomial)\n",
            "\n",
            "Deviance Residuals: \n",
            "    Min       1Q   Median       3Q      Max  \n",
            "-0.8033  -0.6046  -0.6046  -0.6046   1.8919  \n",
            "\n",
            "Coefficients:\n",
            "                     Estimate Std. Error z value Pr(>|z|)    \n",
            "(Intercept)         -1.606802   0.009786 -164.19   <2e-16 ***\n",
            "noshow$SMS_received  0.641137   0.015391   41.66   <2e-16 ***\n",
            "---\n",
            "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
            "\n",
            "(Dispersion parameter for binomial family taken to be 1)\n",
            "\n",
            "    Null deviance: 111206  on 110526  degrees of freedom\n",
            "Residual deviance: 109503  on 110525  degrees of freedom\n",
            "AIC: 109507\n",
            "\n",
            "Number of Fisher Scoring iterations: 4\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lxDwjv6GT78r",
        "outputId": "2a8781c7-66b9-49dd-981f-8b16f7adde52"
      },
      "source": [
        "%%R\n",
        "coef <- c(coef(result_un)[1],coef(result_un)[2]) \n",
        "odd <- c(exp(coef(result_un)[1]), exp(coef(result_un)[2]))\n",
        "prob <- c(odd[1]/(odd[1]+1), (odd[2] * odd[1])/((odd[2] * odd[1])+1))\n",
        "table <- data.frame(coef, odd, prob)\n",
        "table\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                         coef       odd      prob\n",
            "(Intercept)         -1.606802 0.2005279 0.1670331\n",
            "noshow$SMS_received  0.641137 1.8986385 0.2757454\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yxvXvb6yEThf"
      },
      "source": [
        "According to above summary, ln(odds of being a \"no-show\") = **-1.606802** + **0.641137**x where x = 1 if the patients received a SMS (a reference group), and 0 if the patient did not receive a SMS. The standard error of the slope estimate for x and intercept respectively is  0.015391, 0.009786\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For patients not receiving SMS: ln(odd of being \"no-show\") = **-1.606802** (Intercept) <br>\n",
        "Odd ratio of patients not receiving SMS and no show = **0.2** and the probability of them is **0.167** <br>\n",
        "For the difference of ln(odd of being 'no-show') in patients receiving SMS to patients not receiving SMS = **0.641137** <br>\n",
        "Unadjusted odds ratio (and 95% CI) of being \"no-show\" for those who received a SMS compared to those who did not: **1.899(1.841, 1.958)** <br>\n",
        "Odd ratio of patients receiving SMS but no show = 0.2 * 1.899 = **0.3798** and the probability of them is 0.275\n"
      ],
      "metadata": {
        "id": "0TZOvPLFgEww"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iFMQAu5Ln-GH"
      },
      "source": [
        "### **Adjusted data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T_GuleupoLZ-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "63116bac-bd79-4d01-f4e1-d54fcaa4e762"
      },
      "source": [
        "%%R\n",
        "result_ad = glm(noshow$noshow ~ noshow$SMS_received + noshow$In_advance, family = binomial)\n",
        "summary(result_ad)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Call:\n",
            "glm(formula = noshow$noshow ~ noshow$SMS_received + noshow$In_advance, \n",
            "    family = binomial)\n",
            "\n",
            "Deviance Residuals: \n",
            "    Min       1Q   Median       3Q      Max  \n",
            "-0.8896  -0.6166  -0.5539  -0.5539   1.9751  \n",
            "\n",
            "Coefficients:\n",
            "                    Estimate Std. Error z value Pr(>|z|)    \n",
            "(Intercept)         -1.79707    0.01094 -164.20   <2e-16 ***\n",
            "noshow$SMS_received  0.23348    0.01773   13.17   <2e-16 ***\n",
            "noshow$In_advance    0.84091    0.01743   48.23   <2e-16 ***\n",
            "---\n",
            "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
            "\n",
            "(Dispersion parameter for binomial family taken to be 1)\n",
            "\n",
            "    Null deviance: 111206  on 110526  degrees of freedom\n",
            "Residual deviance: 107188  on 110524  degrees of freedom\n",
            "AIC: 107194\n",
            "\n",
            "Number of Fisher Scoring iterations: 4\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WLsIeL6l_Yh6",
        "outputId": "13259f41-6c7b-4fa4-99a8-3d416d61a80f"
      },
      "source": [
        "%%R\n",
        "coef(result_ad)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        (Intercept) noshow$SMS_received   noshow$In_advance \n",
            "         -1.7970683           0.2334772           0.8409114 \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "SMS <- c(0,0,1,1)\n",
        "Booking <- c(0,1,0,1)\n",
        "ln_odds_of_noshow <- c(-1.7970683 + 0.2334772*SMS[1] + 0.8409114*Booking[1],\n",
        "                        -1.7970683 + 0.2334772*SMS[2] + 0.8409114*Booking[2],\n",
        "                        -1.7970683 + 0.2334772*SMS[3] + 0.8409114*Booking[3],\n",
        "                        -1.7970683 + 0.2334772*SMS[4] + 0.8409114*Booking[4])\n",
        "odd <- c(exp(ln_odds_of_noshow[1]),\n",
        "         exp(ln_odds_of_noshow[2]),\n",
        "         exp(ln_odds_of_noshow[3]),\n",
        "         exp(ln_odds_of_noshow[4]))\n",
        "prob <- c(odd[1]/(odd[1]+1),\n",
        "          odd[2]/(odd[2]+1),\n",
        "          odd[3]/(odd[3]+1),\n",
        "          odd[4]/(odd[4]+1))\n",
        "table <- data.frame(SMS, Booking, ln_odds_of_noshow, odd, prob)\n",
        "table"
      ],
      "metadata": {
        "id": "lCeLyGn_raAA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f2f09c8-b956-41e1-87a3-6014ccc09c68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  SMS Booking ln_odds_of_noshow       odd      prob\n",
            "1   0       0        -1.7970683 0.1657842 0.1422083\n",
            "2   0       1        -0.9561569 0.3843672 0.2776483\n",
            "3   1       0        -1.5635911 0.2093828 0.1731320\n",
            "4   1       1        -0.7226797 0.4854497 0.3268032\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xNiWrfmDBg6X"
      },
      "source": [
        "According to above summary having adjustment, ln(odds of being a \"no-show\") = **- 1.7970683** + **0.2334772**x1 + **0.8409114**x2 where: <br>\n",
        "**x1(SMS)** = 1 if the patients received a SMS, and 0 if the patient did not receive a SMS. <br>\n",
        "**x2(Booking)** = 1 if patients booking in advance, and 0 if patients booking same day to appointment. <br>\n",
        "The standard errors of the intercept, slope estimate for x1, and slope estimate for x2 respectively are 0.01094, 0.01773, 0.01743."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-CMIKANn_ieH"
      },
      "source": [
        "### **Stratified analysis**\n",
        "In this section, I will analyse separatedly 2 groups (same day or in advance) in order to see if the association between SMS_received with no showing up can be different between these 2 groups    "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gnzpyj7-APOC"
      },
      "source": [
        "#### **In advance group** (people had scheduled the day before appointment day)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sWT1kQrxAfia",
        "outputId": "381249f1-c859-4a0a-d0cf-40cc5ecbaadd"
      },
      "source": [
        "%%R\n",
        "x_in = noshow[noshow$Reservation == 'in advance',]\n",
        "head(x_in)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      X    PatientId AppointmentID Gender         ScheduledDay\n",
            "50   49 9.223559e+13       5534656      F 2016-03-31T17:11:17Z\n",
            "51   50 1.827172e+14       5534661      F 2016-03-31T17:12:45Z\n",
            "52   51 4.694699e+13       5534635      F 2016-03-31T17:05:31Z\n",
            "53   52 7.987570e+14       5534639      F 2016-03-31T17:06:17Z\n",
            "178 177 7.271247e+12       5482953      M 2016-03-17T10:33:33Z\n",
            "184 183 7.254972e+11       5524189      M 2016-03-30T07:32:47Z\n",
            "          AppointmentDay Age  Neighbourhood Scholarship Hipertension Diabetes\n",
            "50  2016-04-29T00:00:00Z  27 GRANDE VITÓRIA           0            0        0\n",
            "51  2016-04-29T00:00:00Z  19 GRANDE VITÓRIA           0            0        0\n",
            "52  2016-04-29T00:00:00Z  23 GRANDE VITÓRIA           1            0        0\n",
            "53  2016-04-29T00:00:00Z  23 GRANDE VITÓRIA           1            0        0\n",
            "178 2016-04-29T00:00:00Z  11 NOVA PALESTINA           0            0        0\n",
            "184 2016-04-29T00:00:00Z  69      CONQUISTA           0            0        0\n",
            "    Alcoholism Handcap SMS_received No.show noshow Reservation In_advance\n",
            "50           0       0            1     Yes      1  in advance          1\n",
            "51           0       0            1     Yes      1  in advance          1\n",
            "52           0       0            1     Yes      1  in advance          1\n",
            "53           0       0            1     Yes      1  in advance          1\n",
            "178          0       0            0      No      0  in advance          1\n",
            "184          0       0            1      No      0  in advance          1\n",
            "    transform\n",
            "50   1.396954\n",
            "51   1.396954\n",
            "52   1.396954\n",
            "53   1.396954\n",
            "178  1.396954\n",
            "184  1.396954\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CzLdL19vDICP",
        "outputId": "248b7504-59b1-4559-c683-6b9d50fd99f8"
      },
      "source": [
        "%%R\n",
        "result_in = glm(x_in$noshow ~ x_in$SMS_received, family = binomial)\n",
        "summary(result_in)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Call:\n",
            "glm(formula = x_in$noshow ~ x_in$SMS_received, family = binomial)\n",
            "\n",
            "Deviance Residuals: \n",
            "    Min       1Q   Median       3Q      Max  \n",
            "-0.9227  -0.8220  -0.8220   1.4555   1.5807  \n",
            "\n",
            "Coefficients:\n",
            "                  Estimate Std. Error z value Pr(>|z|)    \n",
            "(Intercept)       -0.63353    0.01792  -35.36   <2e-16 ***\n",
            "x_in$SMS_received -0.27789    0.02296  -12.10   <2e-16 ***\n",
            "---\n",
            "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
            "\n",
            "(Dispersion parameter for binomial family taken to be 1)\n",
            "\n",
            "    Null deviance: 46293  on 37447  degrees of freedom\n",
            "Residual deviance: 46147  on 37446  degrees of freedom\n",
            "AIC: 46151\n",
            "\n",
            "Number of Fisher Scoring iterations: 4\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "coef <- c(coef(result_in)[1],coef(result_in)[2]) \n",
        "odd <- c(exp(coef(result_in)[1]), exp(coef(result_in)[2]))\n",
        "prob <- c(odd[1]/(odd[1]+1), (odd[2] * odd[1])/((odd[2] * odd[1])+1))\n",
        "table <- data.frame(coef, odd, prob)\n",
        "table"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M425rUGCx17F",
        "outputId": "e708fd5a-7146-47c1-dd04-594e4d91b2ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                        coef       odd      prob\n",
            "(Intercept)       -0.6335312 0.5307144 0.3467103\n",
            "x_in$SMS_received -0.2778898 0.7573803 0.2867092\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4EtA0XBLHLOm",
        "outputId": "2e036265-f633-4870-fe34-9c07154109ff"
      },
      "source": [
        "%%R \n",
        "dim(x_in)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1] 37448    19\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PLasRrLQGrz6"
      },
      "source": [
        "According to above summary of 37448 appointments booked in advance, this is the following logistic regression which show association between no-shows and SMS_received in this in advance group:\n",
        "\n",
        "ln(odds of being a \"no-show\") = **- 0.6335312** + **- 0.2778898**x where x = 1 if the patients received a SMS, and 0 if the patient did not receive a SMS. \n",
        "The standard errors of the intercept and slope estimate for x respectively are 0.01792, 0.02296.\n",
        "\n",
        "Odds ratio (and 95% CI) of being a \"no-show\" for those who booking in advance received a SMS compared to those who booking in advance but did not received: \n",
        "0.757 (0.723, 0.793) \n",
        "\n",
        "**p(SMS) = 0.287**; **p(noSMS) = 0.347** <br>\n",
        "RD (Risk difference) = p(SMS) - p(noSMS) = 0.287 - 0.347 = **-0.06** <br>\n",
        "**Conclusion:** For the patients **booking before appointment day**, there is **6% reduction of risk no showing up** in Medical appointment **if they received SMS** compared to those who didn't receive SMS "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HycQ_PsEBoHT"
      },
      "source": [
        "#### **Same day group** (people had scheduled the same day to appointment day)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uybjm0sgB_0u",
        "outputId": "35ee9bef-6ccd-4127-b4e0-28aba5c3aada"
      },
      "source": [
        "%%R\n",
        "x_sam = noshow[noshow$Reservation == 'same day',]\n",
        "head(x_sam)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  X    PatientId AppointmentID Gender         ScheduledDay       AppointmentDay\n",
            "1 0 2.987250e+13       5642903      F 2016-04-29T18:38:08Z 2016-04-29T00:00:00Z\n",
            "2 1 5.589978e+14       5642503      M 2016-04-29T16:08:27Z 2016-04-29T00:00:00Z\n",
            "3 2 4.262962e+12       5642549      F 2016-04-29T16:19:04Z 2016-04-29T00:00:00Z\n",
            "4 3 8.679512e+11       5642828      F 2016-04-29T17:29:31Z 2016-04-29T00:00:00Z\n",
            "5 4 8.841186e+12       5642494      F 2016-04-29T16:07:23Z 2016-04-29T00:00:00Z\n",
            "6 5 9.598513e+13       5626772      F 2016-04-27T08:36:51Z 2016-04-29T00:00:00Z\n",
            "  Age     Neighbourhood Scholarship Hipertension Diabetes Alcoholism Handcap\n",
            "1  62   JARDIM DA PENHA           0            1        0          0       0\n",
            "2  56   JARDIM DA PENHA           0            0        0          0       0\n",
            "3  62     MATA DA PRAIA           0            0        0          0       0\n",
            "4   8 PONTAL DE CAMBURI           0            0        0          0       0\n",
            "5  56   JARDIM DA PENHA           0            1        1          0       0\n",
            "6  76         REPÚBLICA           0            1        0          0       0\n",
            "  SMS_received No.show noshow Reservation In_advance  transform\n",
            "1            0      No      0    same day          0 -0.7158434\n",
            "2            0      No      0    same day          0 -0.7158434\n",
            "3            0      No      0    same day          0 -0.7158434\n",
            "4            0      No      0    same day          0 -0.7158434\n",
            "5            0      No      0    same day          0 -0.7158434\n",
            "6            0      No      0    same day          0 -0.7158434\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jd6m4KY5DmKq",
        "outputId": "7eced0a1-a658-481c-b810-d2b8a37ac58d"
      },
      "source": [
        "%%R\n",
        "result_sam = glm(x_sam$noshow ~ x_sam$SMS_received, family = binomial)\n",
        "summary(result_sam)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Call:\n",
            "glm(formula = x_sam$noshow ~ x_sam$SMS_received, family = binomial)\n",
            "\n",
            "Deviance Residuals: \n",
            "    Min       1Q   Median       3Q      Max  \n",
            "-0.7650  -0.5205  -0.5205  -0.5205   2.0327  \n",
            "\n",
            "Coefficients:\n",
            "                   Estimate Std. Error z value Pr(>|z|)    \n",
            "(Intercept)        -1.93037    0.01214 -158.97   <2e-16 ***\n",
            "x_sam$SMS_received  0.85146    0.02440   34.89   <2e-16 ***\n",
            "---\n",
            "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
            "\n",
            "(Dispersion parameter for binomial family taken to be 1)\n",
            "\n",
            "    Null deviance: 61067  on 73078  degrees of freedom\n",
            "Residual deviance: 59945  on 73077  degrees of freedom\n",
            "AIC: 59949\n",
            "\n",
            "Number of Fisher Scoring iterations: 4\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D6cYw9YeDmxG",
        "outputId": "1a4c0eb4-83ea-4b85-8546-8aec94c9c8d2"
      },
      "source": [
        "%%R\n",
        "coef(result_sam)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       (Intercept) x_sam$SMS_received \n",
            "        -1.9303749          0.8514583 \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "coef <- c(coef(result_sam)[1],coef(result_sam)[2]) \n",
        "odd <- c(exp(coef(result_sam)[1]), exp(coef(result_sam)[2]))\n",
        "prob <- c(odd[1]/(odd[1]+1), (odd[2] * odd[1])/((odd[2] * odd[1])+1))\n",
        "table <- data.frame(coef, odd, prob)\n",
        "table"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dvxxKToZyBX5",
        "outputId": "e17fd42d-d889-404b-e8ad-fb14ee5897c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                         coef       odd      prob\n",
            "(Intercept)        -1.9303749 0.1450938 0.1267091\n",
            "x_sam$SMS_received  0.8514583 2.3430612 0.2537111\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zTL0vdObHSsO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a31f5c4c-5bcb-4b33-a563-3832bca5efce"
      },
      "source": [
        "%%R \n",
        "dim(x_sam)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1] 73079    19\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d55lJAW-fbr5"
      },
      "source": [
        "According to above summary of 73079 appointments booked in the same day, this is the following logistic regression which show association between no-shows and SMS_received in this same day group:\n",
        "\n",
        "ln(odds of being a \"no-show\") = **-1.9303749** + **0.8514583**x where x = 1 if the patients received a SMS, and 0 if the patient did not receive a SMS. \n",
        "The standard errors of the intercept and slope estimate for x respectively are 0.01214, 0.02440.\n",
        "\n",
        "Odds ratio (and 95% CI) of being no show for those who booking the same day received a SMS compared to those who booking the same day but did not received: \n",
        "2.343 (2.231, 2.46)\n",
        "\n",
        "**p(SMS) = 0.254**; **p(noSMS) = 0.127** <br>\n",
        "RD (Risk difference) = p(SMS) - p(noSMS) = 0.254 - 0.127 = 0.127 <br>\n",
        "**Conclusion:** For the patients **booking in the same day** to appointment day, there is **12.7% greater of risk no showing up** in Medical appointment **even when they received SMS** compared to those who didn't receive SMS"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7mbQE88bqibP"
      },
      "source": [
        "## **Predicting and classifying**"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Test-train split**"
      ],
      "metadata": {
        "id": "PnzDajj51XAT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = noshow[['SMS_received','In_advance']].to_numpy()\n",
        "y = noshow[['noshow']].to_numpy()"
      ],
      "metadata": {
        "id": "w-QNFsWw1uNW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sw7o-_41U-j1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4c6ab318-5bae-4ee4-e89e-d4995818ba45"
      },
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.20, random_state=2)\n",
        "print(\"x_train and x_test shape:\", x_train.shape, x_test.shape)\n",
        "print(\"y_train and y_test shape:\", y_train.shape, y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x_train and x_test shape: (88421, 2) (22106, 2)\n",
            "y_train and y_test shape: (88421, 1) (22106, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('x:',x[0:5])\n",
        "print('y:',y[0:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CqKn1-knMj7D",
        "outputId": "9973d85c-4e73-47af-dbf5-31e1f188bedc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x: [[0 '0']\n",
            " [0 '0']\n",
            " [0 '0']\n",
            " [0 '0']\n",
            " [0 '0']]\n",
            "y: [['0']\n",
            " ['0']\n",
            " ['0']\n",
            " ['0']\n",
            " ['0']]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lWisRD_Wk8yS"
      },
      "source": [
        "#### **Predicting noshow in appointment by using logistic regression and GridSearchCV for finding best parameters**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# parameters\n",
        "parameters ={'C':[0.01,0.1,1],\n",
        "             'penalty':['l2'],\n",
        "             'solver':['lbfgs']}"
      ],
      "metadata": {
        "id": "yC-T6veG0_u9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iOglMYrjO7WJ"
      },
      "source": [
        "lr=LogisticRegression()\n",
        "logreg_cv = GridSearchCV(lr, parameters, cv=10)\n",
        "logreg_cv.fit(x_train,y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"best parameters \",logreg_cv.best_params_)\n",
        "print(\"first 5 predicted value:\",logreg_cv.predict(x_test)[0:5])\n",
        "print('first 5 actual value:',y_test[0:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VYI3d6kSMvN5",
        "outputId": "8550dc76-8317-4885-e5e4-012c7bffb6f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "best parameters  {'C': 0.01, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
            "first 5 predicted value: ['0' '0' '0' '0' '0']\n",
            "first 5 actual value: [['1']\n",
            " ['0']\n",
            " ['0']\n",
            " ['0']\n",
            " ['0']]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "logreg_cv.score(x_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LxCF1bbC3kRa",
        "outputId": "48f9f880-d158-4970-9c20-d874b723ac05"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7968424862028408"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "btWpZPTfsSG_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5830af97-f837-413c-81c1-343bc49944dd"
      },
      "source": [
        "print(\"confusion matrix:\",metrics.confusion_matrix(y_test, logreg_cv.predict(x_test)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "confusion matrix: [[17615     0]\n",
            " [ 4491     0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "cP3SnQPTM8SB"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}