Healthcare_Data_Analysis.ipynb

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      "source": [
        "from io import IncrementalNewlineDecoder\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import datetime\n",
        "from time import strftime\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "my_data= pd.read_csv('Data.csv')"
      ],
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        "id": "AqQQLvV8HuLx"
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      "outputs": []
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      "source": [
        "my_data"
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        "outputId": "2e55f0dd-9181-42f5-ad38-902a6a0980be"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "           PatientId  AppointmentID Gender          ScheduledDay  \\\n",
              "0       2.987250e+13        5642903      F  2016-04-29T18:38:08Z   \n",
              "1       5.589978e+14        5642503      M  2016-04-29T16:08:27Z   \n",
              "2       4.262962e+12        5642549      F  2016-04-29T16:19:04Z   \n",
              "3       8.679512e+11        5642828      F  2016-04-29T17:29:31Z   \n",
              "4       8.841186e+12        5642494      F  2016-04-29T16:07:23Z   \n",
              "...              ...            ...    ...                   ...   \n",
              "110522  2.572134e+12        5651768      F  2016-05-03T09:15:35Z   \n",
              "110523  3.596266e+12        5650093      F  2016-05-03T07:27:33Z   \n",
              "110524  1.557663e+13        5630692      F  2016-04-27T16:03:52Z   \n",
              "110525  9.213493e+13        5630323      F  2016-04-27T15:09:23Z   \n",
              "110526  3.775115e+14        5629448      F  2016-04-27T13:30:56Z   \n",
              "\n",
              "              AppointmentDay  Age      Neighbourhood  Scholarship  \\\n",
              "0       2016-04-29T00:00:00Z   62    JARDIM DA PENHA            0   \n",
              "1       2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0   \n",
              "2       2016-04-29T00:00:00Z   62      MATA DA PRAIA            0   \n",
              "3       2016-04-29T00:00:00Z    8  PONTAL DE CAMBURI            0   \n",
              "4       2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0   \n",
              "...                      ...  ...                ...          ...   \n",
              "110522  2016-06-07T00:00:00Z   56        MARIA ORTIZ            0   \n",
              "110523  2016-06-07T00:00:00Z   51        MARIA ORTIZ            0   \n",
              "110524  2016-06-07T00:00:00Z   21        MARIA ORTIZ            0   \n",
              "110525  2016-06-07T00:00:00Z   38        MARIA ORTIZ            0   \n",
              "110526  2016-06-07T00:00:00Z   54        MARIA ORTIZ            0   \n",
              "\n",
              "        Hipertension  Diabetes  Alcoholism  Handcap  SMS_received No-show  \n",
              "0                  1         0           0        0             0      No  \n",
              "1                  0         0           0        0             0      No  \n",
              "2                  0         0           0        0             0      No  \n",
              "3                  0         0           0        0             0      No  \n",
              "4                  1         1           0        0             0      No  \n",
              "...              ...       ...         ...      ...           ...     ...  \n",
              "110522             0         0           0        0             1      No  \n",
              "110523             0         0           0        0             1      No  \n",
              "110524             0         0           0        0             1      No  \n",
              "110525             0         0           0        0             1      No  \n",
              "110526             0         0           0        0             1      No  \n",
              "\n",
              "[110527 rows x 14 columns]"
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              "      <th>PatientId</th>\n",
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              "      <th>0</th>\n",
              "      <td>2.987250e+13</td>\n",
              "      <td>5642903</td>\n",
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              "      <td>2016-04-29T18:38:08Z</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>2016-04-29T00:00:00Z</td>\n",
              "      <td>62</td>\n",
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              "      <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",
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              "      <th>4</th>\n",
              "      <td>8.841186e+12</td>\n",
              "      <td>5642494</td>\n",
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              "      <td>2016-04-29T16:07:23Z</td>\n",
              "      <td>2016-04-29T00:00:00Z</td>\n",
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              "      <td>JARDIM DA PENHA</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "        document.querySelector('#df-401c7fdd-ad6e-4d61-a185-7945b5ebbc60 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "\n",
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              "      .colab-df-generate {\n",
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              "        fill: #1967D2;\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('my_data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('my_data');\n",
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              "\n",
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              "  </div>\n"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "my_data"
            }
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "my_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YTkV4ajcIZbN",
        "outputId": "9fb86cca-7c79-4d02-8ddb-d148e9c63dfc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(110527, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "my_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M5wboYX1Iv0D",
        "outputId": "43349ede-7356-4307-94b8-50655272b669"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 110527 entries, 0 to 110526\n",
            "Data columns (total 13 columns):\n",
            " #   Column           Non-Null Count   Dtype         \n",
            "---  ------           --------------   -----         \n",
            " 0   Gender           110527 non-null  object        \n",
            " 1   ScheduledDay     110527 non-null  datetime64[ns]\n",
            " 2   AppointmentDay   110527 non-null  datetime64[ns]\n",
            " 3   Scholarship      110527 non-null  int64         \n",
            " 4   Hypertension     110527 non-null  int64         \n",
            " 5   Diabetes         110527 non-null  int64         \n",
            " 6   Alcoholism       110527 non-null  int64         \n",
            " 7   Handicap         110527 non-null  int64         \n",
            " 8   SMS              110527 non-null  int64         \n",
            " 9   NoShow           110527 non-null  int64         \n",
            " 10  scheduled_day    110527 non-null  int64         \n",
            " 11  appointment_day  110527 non-null  int64         \n",
            " 12  Age_group        106987 non-null  category      \n",
            "dtypes: category(1), datetime64[ns](2), int64(9), object(1)\n",
            "memory usage: 10.2+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#converting the date and time into standard format\n",
        "my_data['ScheduledDay'] = pd.to_datetime(my_data['ScheduledDay']).dt.date.astype('datetime64[ns]')\n",
        "my_data['AppointmentDay'] = pd.to_datetime(my_data['AppointmentDay']).dt.date.astype('datetime64[ns]')"
      ],
      "metadata": {
        "id": "6i9rEJSxJde3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "my_data.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        },
        "id": "tS57yYXIKZZy",
        "outputId": "fdad7751-4859-4f00-bb11-316d612bf33c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      PatientId  AppointmentID Gender ScheduledDay AppointmentDay  Age  \\\n",
              "0  2.987250e+13        5642903      F   2016-04-29     2016-04-29   62   \n",
              "1  5.589978e+14        5642503      M   2016-04-29     2016-04-29   56   \n",
              "2  4.262962e+12        5642549      F   2016-04-29     2016-04-29   62   \n",
              "3  8.679512e+11        5642828      F   2016-04-29     2016-04-29    8   \n",
              "4  8.841186e+12        5642494      F   2016-04-29     2016-04-29   56   \n",
              "5  9.598513e+13        5626772      F   2016-04-27     2016-04-29   76   \n",
              "6  7.336882e+14        5630279      F   2016-04-27     2016-04-29   23   \n",
              "7  3.449833e+12        5630575      F   2016-04-27     2016-04-29   39   \n",
              "8  5.639473e+13        5638447      F   2016-04-29     2016-04-29   21   \n",
              "9  7.812456e+13        5629123      F   2016-04-27     2016-04-29   19   \n",
              "\n",
              "       Neighbourhood  Scholarship  Hipertension  Diabetes  Alcoholism  \\\n",
              "0    JARDIM DA PENHA            0             1         0           0   \n",
              "1    JARDIM DA PENHA            0             0         0           0   \n",
              "2      MATA DA PRAIA            0             0         0           0   \n",
              "3  PONTAL DE CAMBURI            0             0         0           0   \n",
              "4    JARDIM DA PENHA            0             1         1           0   \n",
              "5          REPÚBLICA            0             1         0           0   \n",
              "6         GOIABEIRAS            0             0         0           0   \n",
              "7         GOIABEIRAS            0             0         0           0   \n",
              "8         ANDORINHAS            0             0         0           0   \n",
              "9          CONQUISTA            0             0         0           0   \n",
              "\n",
              "   Handcap  SMS_received No-show  \n",
              "0        0             0      No  \n",
              "1        0             0      No  \n",
              "2        0             0      No  \n",
              "3        0             0      No  \n",
              "4        0             0      No  \n",
              "5        0             0      No  \n",
              "6        0             0     Yes  \n",
              "7        0             0     Yes  \n",
              "8        0             0      No  \n",
              "9        0             0      No  "
            ],
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              "\n",
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              "    <div>\n",
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              "      <th></th>\n",
              "      <th>PatientId</th>\n",
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              "      <td>2.987250e+13</td>\n",
              "      <td>5642903</td>\n",
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              "      <td>2016-04-29</td>\n",
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              "      <td>5642494</td>\n",
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              "\n",
              "<div id=\"df-96b0c681-7b09-4db8-b48a-8cdb02cb1878\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-96b0c681-7b09-4db8-b48a-8cdb02cb1878')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-96b0c681-7b09-4db8-b48a-8cdb02cb1878 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "my_data"
            }
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#0 is for Monday and so on 5 is Saturday and 6 is Sunday\n",
        "my_data['scheduled_day'] = my_data['ScheduledDay'].dt.dayofweek"
      ],
      "metadata": {
        "id": "N8RW_2wMMHO9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "my_data['appointment_day'] = my_data['AppointmentDay'].dt.dayofweek"
      ],
      "metadata": {
        "id": "PjHkgsODM-Ts"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "my_data['scheduled_day'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Ulb7yGpNSMw",
        "outputId": "ea64d908-1fdc-48b9-c308-df94ec717905"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    26168\n",
              "2    24262\n",
              "0    23085\n",
              "4    18915\n",
              "3    18073\n",
              "5       24\n",
              "Name: scheduled_day, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "my_data['appointment_day'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yKusyelJOCRR",
        "outputId": "b052d4ba-3e01-4ed2-be5f-c3f0159f81e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    25867\n",
              "1    25640\n",
              "0    22715\n",
              "4    19019\n",
              "3    17247\n",
              "5       39\n",
              "Name: appointment_day, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "my_data.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6wWvfJxzOWSQ",
        "outputId": "d592b78a-6766-45a0-89fa-377d6ffbd7e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['PatientId', 'AppointmentID', 'Gender', 'ScheduledDay',\n",
              "       'AppointmentDay', 'Age', 'Neighbourhood', 'Scholarship', 'Hipertension',\n",
              "       'Diabetes', 'Alcoholism', 'Handcap', 'SMS_received', 'No-show',\n",
              "       'scheduled_day', 'appointment_day'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Rename the column names\n",
        "my_data=my_data.rename(columns={'Hipertension':'Hypertension','Handcap':'Handicap','SMS_received':'SMS','No-show': 'NoShow'})"
      ],
      "metadata": {
        "id": "XSv5gsuJOa-7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "my_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S4AA-AJbPLLx",
        "outputId": "2f904162-b924-403c-bf00-30aa456cca0c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 110527 entries, 0 to 110526\n",
            "Data columns (total 16 columns):\n",
            " #   Column           Non-Null Count   Dtype         \n",
            "---  ------           --------------   -----         \n",
            " 0   PatientId        110527 non-null  float64       \n",
            " 1   AppointmentID    110527 non-null  int64         \n",
            " 2   Gender           110527 non-null  object        \n",
            " 3   ScheduledDay     110527 non-null  datetime64[ns]\n",
            " 4   AppointmentDay   110527 non-null  datetime64[ns]\n",
            " 5   Age              110527 non-null  int64         \n",
            " 6   Neighbourhood    110527 non-null  object        \n",
            " 7   Scholarship      110527 non-null  int64         \n",
            " 8   Hypertension     110527 non-null  int64         \n",
            " 9   Diabetes         110527 non-null  int64         \n",
            " 10  Alcoholism       110527 non-null  int64         \n",
            " 11  Handicap         110527 non-null  int64         \n",
            " 12  SMS              110527 non-null  int64         \n",
            " 13  NoShow           110527 non-null  object        \n",
            " 14  scheduled_day    110527 non-null  int64         \n",
            " 15  appointment_day  110527 non-null  int64         \n",
            "dtypes: datetime64[ns](2), float64(1), int64(10), object(3)\n",
            "memory usage: 13.5+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Let us now drop some columns which are not required\n",
        "my_data.drop(['PatientId', 'AppointmentID','Neighbourhood'],axis=1,inplace =True)"
      ],
      "metadata": {
        "id": "NAZ3Fd0-PUli"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "my_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "EMNfwpUVP8H9",
        "outputId": "1370e1cc-db4d-409b-9b82-b1bb7ae7f6d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Gender ScheduledDay AppointmentDay  Age  Scholarship  Hypertension  \\\n",
              "0           F   2016-04-29     2016-04-29   62            0             1   \n",
              "1           M   2016-04-29     2016-04-29   56            0             0   \n",
              "2           F   2016-04-29     2016-04-29   62            0             0   \n",
              "3           F   2016-04-29     2016-04-29    8            0             0   \n",
              "4           F   2016-04-29     2016-04-29   56            0             1   \n",
              "...       ...          ...            ...  ...          ...           ...   \n",
              "110522      F   2016-05-03     2016-06-07   56            0             0   \n",
              "110523      F   2016-05-03     2016-06-07   51            0             0   \n",
              "110524      F   2016-04-27     2016-06-07   21            0             0   \n",
              "110525      F   2016-04-27     2016-06-07   38            0             0   \n",
              "110526      F   2016-04-27     2016-06-07   54            0             0   \n",
              "\n",
              "        Diabetes  Alcoholism  Handicap  SMS NoShow  scheduled_day  \\\n",
              "0              0           0         0    0     No              4   \n",
              "1              0           0         0    0     No              4   \n",
              "2              0           0         0    0     No              4   \n",
              "3              0           0         0    0     No              4   \n",
              "4              1           0         0    0     No              4   \n",
              "...          ...         ...       ...  ...    ...            ...   \n",
              "110522         0           0         0    1     No              1   \n",
              "110523         0           0         0    1     No              1   \n",
              "110524         0           0         0    1     No              2   \n",
              "110525         0           0         0    1     No              2   \n",
              "110526         0           0         0    1     No              2   \n",
              "\n",
              "        appointment_day  \n",
              "0                     4  \n",
              "1                     4  \n",
              "2                     4  \n",
              "3                     4  \n",
              "4                     4  \n",
              "...                 ...  \n",
              "110522                1  \n",
              "110523                1  \n",
              "110524                1  \n",
              "110525                1  \n",
              "110526                1  \n",
              "\n",
              "[110527 rows x 13 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ff01711b-e79b-4441-a591-b40a360c1a4b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gender</th>\n",
              "      <th>ScheduledDay</th>\n",
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              "      <th>Hypertension</th>\n",
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              "      <th>Handicap</th>\n",
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              "      <th>0</th>\n",
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              "      <th>2</th>\n",
              "      <td>F</td>\n",
              "      <td>2016-04-29</td>\n",
              "      <td>2016-04-29</td>\n",
              "      <td>62</td>\n",
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              "      <th>3</th>\n",
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              "      <th>...</th>\n",
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              "      <th>110522</th>\n",
              "      <td>F</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>110523</th>\n",
              "      <td>F</td>\n",
              "      <td>2016-05-03</td>\n",
              "      <td>2016-06-07</td>\n",
              "      <td>51</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <th>110524</th>\n",
              "      <td>F</td>\n",
              "      <td>2016-04-27</td>\n",
              "      <td>2016-06-07</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <th>110525</th>\n",
              "      <td>F</td>\n",
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              "      <td>38</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>110527 rows × 13 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ff01711b-e79b-4441-a591-b40a360c1a4b')\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ff01711b-e79b-4441-a591-b40a360c1a4b 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-ff01711b-e79b-4441-a591-b40a360c1a4b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-de48e72a-b36c-4567-b31a-ae3454e76731 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_6f01b051-39e5-4441-827a-dc1690f8293f\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('my_data')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_6f01b051-39e5-4441-827a-dc1690f8293f button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('my_data');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "my_data"
            }
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#descriptive statistics woeking only on numerical values not categorical values\n",
        "my_data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "rvIrEZanQAva",
        "outputId": "8908243a-51e0-4aa2-db50-bfc43ead1b02"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Age    Scholarship   Hypertension       Diabetes  \\\n",
              "count  110527.000000  110527.000000  110527.000000  110527.000000   \n",
              "mean       37.088874       0.098266       0.197246       0.071865   \n",
              "std        23.110205       0.297675       0.397921       0.258265   \n",
              "min        -1.000000       0.000000       0.000000       0.000000   \n",
              "25%        18.000000       0.000000       0.000000       0.000000   \n",
              "50%        37.000000       0.000000       0.000000       0.000000   \n",
              "75%        55.000000       0.000000       0.000000       0.000000   \n",
              "max       115.000000       1.000000       1.000000       1.000000   \n",
              "\n",
              "          Alcoholism       Handicap            SMS  scheduled_day  \\\n",
              "count  110527.000000  110527.000000  110527.000000  110527.000000   \n",
              "mean        0.030400       0.022248       0.321026       1.851955   \n",
              "std         0.171686       0.161543       0.466873       1.378520   \n",
              "min         0.000000       0.000000       0.000000       0.000000   \n",
              "25%         0.000000       0.000000       0.000000       1.000000   \n",
              "50%         0.000000       0.000000       0.000000       2.000000   \n",
              "75%         0.000000       0.000000       1.000000       3.000000   \n",
              "max         1.000000       4.000000       1.000000       5.000000   \n",
              "\n",
              "       appointment_day  \n",
              "count    110527.000000  \n",
              "mean          1.858243  \n",
              "std           1.371672  \n",
              "min           0.000000  \n",
              "25%           1.000000  \n",
              "50%           2.000000  \n",
              "75%           3.000000  \n",
              "max           5.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-44f8a98d-8399-48d6-bb94-4da253eb57fe\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Scholarship</th>\n",
              "      <th>Hypertension</th>\n",
              "      <th>Diabetes</th>\n",
              "      <th>Alcoholism</th>\n",
              "      <th>Handicap</th>\n",
              "      <th>SMS</th>\n",
              "      <th>scheduled_day</th>\n",
              "      <th>appointment_day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>110527.000000</td>\n",
              "      <td>110527.000000</td>\n",
              "      <td>110527.000000</td>\n",
              "      <td>110527.000000</td>\n",
              "      <td>110527.000000</td>\n",
              "      <td>110527.000000</td>\n",
              "      <td>110527.000000</td>\n",
              "      <td>110527.000000</td>\n",
              "      <td>110527.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\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",
              "      <td>0.022248</td>\n",
              "      <td>0.321026</td>\n",
              "      <td>1.851955</td>\n",
              "      <td>1.858243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\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",
              "      <td>1.378520</td>\n",
              "      <td>1.371672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-1.000000</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",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\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",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\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",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>55.000000</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>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\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",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "    }\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-44f8a98d-8399-48d6-bb94-4da253eb57fe 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-44f8a98d-8399-48d6-bb94-4da253eb57fe');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-964f30f6-06f4-4249-8190-ef98f4329b21\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-964f30f6-06f4-4249-8190-ef98f4329b21')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-964f30f6-06f4-4249-8190-ef98f4329b21 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"my_data\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39062.856585792935,\n        \"min\": -1.0,\n        \"max\": 110527.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          37.08887421173107,\n          37.0,\n          110527.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Scholarship\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39077.1250979673,\n        \"min\": 0.0,\n        \"max\": 110527.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.09826558216544373,\n          1.0,\n          0.2976747541093071\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hypertension\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39077.11503551283,\n        \"min\": 0.0,\n        \"max\": 110527.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.1972459218109602,\n          1.0,\n          0.397921349947084\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Diabetes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39077.12842189655,\n        \"min\": 0.0,\n        \"max\": 110527.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.07186479321794674,\n          1.0,\n          0.25826507350746697\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Alcoholism\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39077.13488912927,\n        \"min\": 0.0,\n        \"max\": 110527.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.030399811810688793,\n          1.0,\n          0.17168555541424485\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Handicap\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39076.98431348664,\n        \"min\": 0.0,\n        \"max\": 110527.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.022247957512643968,\n          4.0,\n          0.16154272581427898\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SMS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39077.054794184485,\n        \"min\": 0.0,\n        \"max\": 110527.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.32102563174608917,\n          1.0,\n          0.46687273170186816\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"scheduled_day\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39076.47688273183,\n        \"min\": 0.0,\n        \"max\": 110527.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.8519547259945535,\n          2.0,\n          110527.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"appointment_day\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39076.476911028316,\n        \"min\": 0.0,\n        \"max\": 110527.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.8582427823065857,\n          2.0,\n          110527.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#plotting our target variable by using a bar plot graph\n",
        "my_data['NoShow'].value_counts().plot(kind='barh',figsize=(8,6))\n",
        "plt.xlabel(\"Count\",labelpad=12)\n",
        "plt.ylabel(\"Target Variable\",labelpad=12)\n",
        "plt.title(\"Count of Target Variable per Category\",y=1.02);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        },
        "id": "5oCrU2T8Q2j1",
        "outputId": "1d3c8205-a97b-4c5a-ac93-0bbe5461521b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the percentage of Appointments shown or not\n",
        "100*my_data['NoShow'].value_counts()/len(my_data['NoShow'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VWInCLA6SpGu",
        "outputId": "9482b5bf-3792-4aa8-ee29-7bdbeecc840c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "No     79.806744\n",
              "Yes    20.193256\n",
              "Name: NoShow, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Let us now check is the data has missing values or not\n",
        "missing_data = pd.DataFrame((my_data.isnull().sum())*100/my_data.shape[0]).reset_index()\n",
        "plt.figure(figsize=(16,5))\n",
        "ax = sns.pointplot(data=missing_data, x='index', y=0)\n",
        "plt.xticks(rotation = 90,fontsize = 10)\n",
        "plt.title(\"Percentage of Missing Values\")\n",
        "plt.ylabel(\"PERCENTAGE\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 659
        },
        "id": "J1nlOo6wTRw1",
        "outputId": "da4bafe8-7940-4428-c335-19770435d435"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Data Cleaning:**"
      ],
      "metadata": {
        "id": "9DUJjrMDVBMQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Let us now create a copy of my_data and use it for manipulating and processing our data"
      ],
      "metadata": {
        "id": "uNK3pwbtVHFt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "newmy_data = my_data.copy()"
      ],
      "metadata": {
        "id": "IPWQlmQfVQ5l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "newmy_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Rmy2r97VWrT",
        "outputId": "ee373781-578a-4e84-dad1-bd98dfcae498"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 110527 entries, 0 to 110526\n",
            "Data columns (total 13 columns):\n",
            " #   Column           Non-Null Count   Dtype         \n",
            "---  ------           --------------   -----         \n",
            " 0   Gender           110527 non-null  object        \n",
            " 1   ScheduledDay     110527 non-null  datetime64[ns]\n",
            " 2   AppointmentDay   110527 non-null  datetime64[ns]\n",
            " 3   Age              110527 non-null  int64         \n",
            " 4   Scholarship      110527 non-null  int64         \n",
            " 5   Hypertension     110527 non-null  int64         \n",
            " 6   Diabetes         110527 non-null  int64         \n",
            " 7   Alcoholism       110527 non-null  int64         \n",
            " 8   Handicap         110527 non-null  int64         \n",
            " 9   SMS              110527 non-null  int64         \n",
            " 10  NoShow           110527 non-null  object        \n",
            " 11  scheduled_day    110527 non-null  int64         \n",
            " 12  appointment_day  110527 non-null  int64         \n",
            "dtypes: datetime64[ns](2), int64(9), object(2)\n",
            "memory usage: 11.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. We can clearly see that there are no NULL records in our data so no data cleaning is required!"
      ],
      "metadata": {
        "id": "kGk5gXWZVffF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Get the maximum age of the patients\n",
        "print(my_data['Age'].max())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pifwXRuvVvyi",
        "outputId": "939169dd-46a6-49e4-d6f1-5f88a7eb1f45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "115\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Now group the age in bins or groups according to 12 months\n",
        "bins = [\"{0} - {1}\".format(i,i + 20) for i in range(1, 118, 20)]\n",
        "my_data['Age_group'] = pd.cut(my_data.Age, range(1,130,20), right= False, labels = bins)"
      ],
      "metadata": {
        "id": "8r3y4uAwV8YA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "my_data.drop(['Age'], axis=1, inplace = True)"
      ],
      "metadata": {
        "id": "12H2SUq3Xunx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Data Exploration:**"
      ],
      "metadata": {
        "id": "ZI231tDXX3I2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "list(my_data.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k-kW6cr6X9k7",
        "outputId": "07d86df0-cc22-436e-a3b1-420722e074b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Gender',\n",
              " 'ScheduledDay',\n",
              " 'AppointmentDay',\n",
              " 'Scholarship',\n",
              " 'Hypertension',\n",
              " 'Diabetes',\n",
              " 'Alcoholism',\n",
              " 'Handicap',\n",
              " 'SMS',\n",
              " 'NoShow',\n",
              " 'scheduled_day',\n",
              " 'appointment_day',\n",
              " 'Age_group']"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "my_data['NoShow'] = np.where(my_data.NoShow == 'Yes',1 , 0)"
      ],
      "metadata": {
        "id": "w02nwbvwYh0Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "my_data.NoShow.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FiooSxSIZyZu",
        "outputId": "001d2c78-e345-4b85-cdd2-885ea199fb37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    88208\n",
              "1    22319\n",
              "Name: NoShow, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert all the categorical variables into dummy variables\n",
        "my_data_dummies = pd.get_dummies(my_data)\n",
        "my_data_dummies.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "SWAOuUcvZ4CS",
        "outputId": "676e1442-f51f-43fb-a369-50479c6ae963"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  ScheduledDay AppointmentDay  Scholarship  Hypertension  Diabetes  \\\n",
              "0   2016-04-29     2016-04-29            0             1         0   \n",
              "1   2016-04-29     2016-04-29            0             0         0   \n",
              "2   2016-04-29     2016-04-29            0             0         0   \n",
              "3   2016-04-29     2016-04-29            0             0         0   \n",
              "4   2016-04-29     2016-04-29            0             1         1   \n",
              "\n",
              "   Alcoholism  Handicap  SMS  NoShow  scheduled_day  appointment_day  \\\n",
              "0           0         0    0       0              4                4   \n",
              "1           0         0    0       0              4                4   \n",
              "2           0         0    0       0              4                4   \n",
              "3           0         0    0       0              4                4   \n",
              "4           0         0    0       0              4                4   \n",
              "\n",
              "   Gender_F  Gender_M  Age_group_1 - 21  Age_group_21 - 41  Age_group_41 - 61  \\\n",
              "0         1         0                 0                  0                  0   \n",
              "1         0         1                 0                  0                  1   \n",
              "2         1         0                 0                  0                  0   \n",
              "3         1         0                 1                  0                  0   \n",
              "4         1         0                 0                  0                  1   \n",
              "\n",
              "   Age_group_61 - 81  Age_group_81 - 101  Age_group_101 - 121  \n",
              "0                  1                   0                    0  \n",
              "1                  0                   0                    0  \n",
              "2                  1                   0                    0  \n",
              "3                  0                   0                    0  \n",
              "4                  0                   0                    0  "
            ],
            "text/html": [
              "\n",
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              "\n",
              "  @keyframes spin {\n",
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              "\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "my_data_dummies"
            }
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Building a corelation graph of all predictors with 'NoShow'\n",
        "plt.figure(figsize=(20,8))\n",
        "my_data_dummies.corr()['NoShow'].sort_values(ascending = False).plot(kind='bar')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 855
        },
        "id": "hRQWNViEaC0a",
        "outputId": "25fa6c1f-e33a-4fb9-9e6f-6d18494589e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-86-781d46987de6>:3: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  my_data_dummies.corr()['NoShow'].sort_values(ascending = False).plot(kind='bar')\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 86
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x800 with 1 Axes>"
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "sns.heatmap(my_data_dummies.corr(), cmap=\"Paired\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "-281f4ZMaVgo",
        "outputId": "c44eda8e-7e2b-4b63-b5bc-b7c768af0a30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-87-7009a70148b6>:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  sns.heatmap(my_data_dummies.corr(), cmap=\"Paired\")\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 87
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Project Findings:**\n",
        "\n"
      ],
      "metadata": {
        "id": "rTr02WGEGCb6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Ratio of NoShow and Show is almost equal for age group except Age 0 and Age 1 with 80% show rate for each age group\n",
        "* There are 99666 patients without Scholarship and out of them around 80% have come for the visit and out of the 21801 patients with Scholarship around 75% of them have come for the visit\n",
        "* There are around 88,726 patients without Hypertension and out of them around 78% have come for the visit and Out of the 21801 patients with Hypertension around 85% of them have come for the visit.\n",
        "* There are around 102,584 patients without Diabetes and out of them around 80% have come for the visit and Out of the 7,943 patients with Diabetes around 83% of them have come for the visit.\n",
        "* There are around 75,045 patients who have not received SMS and out of them around 84% have come for the visit and out of the 35,482 patients who have received SMS around 72% of them have come for the visit.\n",
        "* There is no appointments on sunday and on saturday appointments are very less in comparision to other week days.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "al55lRJoGIJf"
      }
    }
  ]
}