Final_Project_ (1).ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aanmMlS5RLqL",
        "outputId": "a0b6459a-b1e1-4966-cb3b-a71dca7551fa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Machine Learning/Final Project\n"
          ]
        }
      ],
      "source": [
        "%cd /content/drive/MyDrive/Machine Learning/Final Project"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "lRdk9vJTRnvx"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gIzkmy-tje_9",
        "outputId": "c4882526-4727-4353-903b-40cfb509bcfd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "do0jiTbGTV-4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fa293a46-e982-42b4-e96b-4e8536bc5034"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: xlrd in /usr/local/lib/python3.10/dist-packages (2.0.1)\n"
          ]
        }
      ],
      "source": [
        "!pip install xlrd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "ncG-xIYr-Mj2"
      },
      "outputs": [],
      "source": [
        "raw_df = pd.read_excel(\"/content/drive/MyDrive/Machine Learning/Final Project/PS206767-553247439.xlsx\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "zi6WqDtsDvgc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 956
        },
        "outputId": "67b8acfd-e2cc-44f1-9381-30c6f2a6b9f5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PharmGKB Subject ID PharmGKB Sample ID  Project Site  Gender  \\\n",
              "0            PA135312261        PA135312629             1    male   \n",
              "1            PA135312262        PA135312630             1  female   \n",
              "2            PA135312263        PA135312631             1  female   \n",
              "3            PA135312264        PA135312632             1    male   \n",
              "4            PA135312265        PA135312633             1    male   \n",
              "...                  ...                ...           ...     ...   \n",
              "5695         PA152407681        PA152407969            21    male   \n",
              "5696         PA152407682        PA152407970            21  female   \n",
              "5697         PA152407683        PA152407971            21    male   \n",
              "5698         PA152407684        PA152407972            21    male   \n",
              "5699         PA152407685        PA152407973            21    male   \n",
              "\n",
              "     Race (Reported) Race (OMB)    Ethnicity (Reported)  \\\n",
              "0              White      White  not Hispanic or Latino   \n",
              "1              White      White  not Hispanic or Latino   \n",
              "2              White      White  not Hispanic or Latino   \n",
              "3              White      White  not Hispanic or Latino   \n",
              "4              White      White  not Hispanic or Latino   \n",
              "...              ...        ...                     ...   \n",
              "5695           White      White  not Hispanic or Latino   \n",
              "5696           White      White  not Hispanic or Latino   \n",
              "5697           White      White  not Hispanic or Latino   \n",
              "5698           White      White  not Hispanic or Latino   \n",
              "5699           White      White  not Hispanic or Latino   \n",
              "\n",
              "             Ethnicity (OMB)      Age  Height (cm)  ...  \\\n",
              "0     not Hispanic or Latino  60 - 69      193.040  ...   \n",
              "1     not Hispanic or Latino  50 - 59      176.530  ...   \n",
              "2     not Hispanic or Latino  40 - 49      162.560  ...   \n",
              "3     not Hispanic or Latino  60 - 69      182.245  ...   \n",
              "4     not Hispanic or Latino  50 - 59      167.640  ...   \n",
              "...                      ...      ...          ...  ...   \n",
              "5695  not Hispanic or Latino  20 - 29      185.420  ...   \n",
              "5696  not Hispanic or Latino  70 - 79      160.020  ...   \n",
              "5697  not Hispanic or Latino  60 - 69      187.960  ...   \n",
              "5698  not Hispanic or Latino  60 - 69      177.800  ...   \n",
              "5699  not Hispanic or Latino  70 - 79      190.500  ...   \n",
              "\n",
              "      VKORC1 QC genotype: -4451 C>A (861); Chr16:31018002; rs17880887; A/C  \\\n",
              "0                                                   NaN                      \n",
              "1                                                   C/C                      \n",
              "2                                                   NaN                      \n",
              "3                                                   NaN                      \n",
              "4                                                   NaN                      \n",
              "...                                                 ...                      \n",
              "5695                                                NaN                      \n",
              "5696                                                NaN                      \n",
              "5697                                                NaN                      \n",
              "5698                                                NaN                      \n",
              "5699                                                NaN                      \n",
              "\n",
              "     CYP2C9 consensus VKORC1 -1639 consensus  VKORC1 497 consensus  \\\n",
              "0               *1/*1                    A/G                   G/T   \n",
              "1               *1/*1                    A/A                   G/T   \n",
              "2               *1/*1                    G/G                   T/T   \n",
              "3               *1/*1                    A/G                   G/T   \n",
              "4               *1/*3                    A/G                   T/T   \n",
              "...               ...                    ...                   ...   \n",
              "5695            *1/*1                    NaN                   NaN   \n",
              "5696            *1/*3                    NaN                   NaN   \n",
              "5697            *1/*1                    NaN                   NaN   \n",
              "5698              NaN                    NaN                   NaN   \n",
              "5699              NaN                    NaN                   NaN   \n",
              "\n",
              "      VKORC1 1173 consensus  VKORC1 1542 consensus VKORC1 3730 consensus  \\\n",
              "0                       NaN                    C/G                   A/G   \n",
              "1                       T/T                    C/C                   G/G   \n",
              "2                       NaN                    G/G                   A/G   \n",
              "3                       NaN                    C/G                   G/G   \n",
              "4                       NaN                    C/G                   A/G   \n",
              "...                     ...                    ...                   ...   \n",
              "5695                    NaN                    NaN                   NaN   \n",
              "5696                    NaN                    NaN                   NaN   \n",
              "5697                    NaN                    NaN                   NaN   \n",
              "5698                    NaN                    NaN                   NaN   \n",
              "5699                    NaN                    NaN                   NaN   \n",
              "\n",
              "      VKORC1 2255 consensus  VKORC1 -4451 consensus  \\\n",
              "0                       NaN                     NaN   \n",
              "1                       T/T                     C/C   \n",
              "2                       NaN                     NaN   \n",
              "3                       NaN                     NaN   \n",
              "4                       NaN                     NaN   \n",
              "...                     ...                     ...   \n",
              "5695                    NaN                     NaN   \n",
              "5696                    NaN                     NaN   \n",
              "5697                    NaN                     NaN   \n",
              "5698                    NaN                     NaN   \n",
              "5699                    NaN                     NaN   \n",
              "\n",
              "                Comments regarding Project Site Dataset  \n",
              "0                                            Project 1:  \n",
              "1                 Warfarin Therapeutic Dose Definition:  \n",
              "2     The dose (unchanged for 6 days) that yielded a...  \n",
              "3                                                   NaN  \n",
              "4                                                   NaN  \n",
              "...                                                 ...  \n",
              "5695                                                NaN  \n",
              "5696                                                NaN  \n",
              "5697                                                NaN  \n",
              "5698                                                NaN  \n",
              "5699                                                NaN  \n",
              "\n",
              "[5700 rows x 68 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PharmGKB Subject ID</th>\n",
              "      <th>PharmGKB Sample ID</th>\n",
              "      <th>Project Site</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Race (Reported)</th>\n",
              "      <th>Race (OMB)</th>\n",
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              "      <th>Ethnicity (OMB)</th>\n",
              "      <th>Age</th>\n",
              "      <th>Height (cm)</th>\n",
              "      <th>...</th>\n",
              "      <th>VKORC1 QC genotype: -4451 C&gt;A (861); Chr16:31018002; rs17880887; A/C</th>\n",
              "      <th>CYP2C9 consensus</th>\n",
              "      <th>VKORC1 -1639 consensus</th>\n",
              "      <th>VKORC1 497 consensus</th>\n",
              "      <th>VKORC1 1173 consensus</th>\n",
              "      <th>VKORC1 1542 consensus</th>\n",
              "      <th>VKORC1 3730 consensus</th>\n",
              "      <th>VKORC1 2255 consensus</th>\n",
              "      <th>VKORC1 -4451 consensus</th>\n",
              "      <th>Comments regarding Project Site Dataset</th>\n",
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              "      <th>0</th>\n",
              "      <td>PA135312261</td>\n",
              "      <td>PA135312629</td>\n",
              "      <td>1</td>\n",
              "      <td>male</td>\n",
              "      <td>White</td>\n",
              "      <td>White</td>\n",
              "      <td>not Hispanic or Latino</td>\n",
              "      <td>not Hispanic or Latino</td>\n",
              "      <td>60 - 69</td>\n",
              "      <td>193.040</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>*1/*1</td>\n",
              "      <td>A/G</td>\n",
              "      <td>G/T</td>\n",
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              "      <td>C/G</td>\n",
              "      <td>A/G</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Project 1:</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>PA135312262</td>\n",
              "      <td>PA135312630</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>White</td>\n",
              "      <td>White</td>\n",
              "      <td>not Hispanic or Latino</td>\n",
              "      <td>not Hispanic or Latino</td>\n",
              "      <td>50 - 59</td>\n",
              "      <td>176.530</td>\n",
              "      <td>...</td>\n",
              "      <td>C/C</td>\n",
              "      <td>*1/*1</td>\n",
              "      <td>A/A</td>\n",
              "      <td>G/T</td>\n",
              "      <td>T/T</td>\n",
              "      <td>C/C</td>\n",
              "      <td>G/G</td>\n",
              "      <td>T/T</td>\n",
              "      <td>C/C</td>\n",
              "      <td>Warfarin Therapeutic Dose Definition:</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>PA135312263</td>\n",
              "      <td>PA135312631</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>White</td>\n",
              "      <td>White</td>\n",
              "      <td>not Hispanic or Latino</td>\n",
              "      <td>not Hispanic or Latino</td>\n",
              "      <td>40 - 49</td>\n",
              "      <td>162.560</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>*1/*1</td>\n",
              "      <td>G/G</td>\n",
              "      <td>T/T</td>\n",
              "      <td>NaN</td>\n",
              "      <td>G/G</td>\n",
              "      <td>A/G</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The dose (unchanged for 6 days) that yielded a...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>PA135312264</td>\n",
              "      <td>PA135312632</td>\n",
              "      <td>1</td>\n",
              "      <td>male</td>\n",
              "      <td>White</td>\n",
              "      <td>White</td>\n",
              "      <td>not Hispanic or Latino</td>\n",
              "      <td>not Hispanic or Latino</td>\n",
              "      <td>60 - 69</td>\n",
              "      <td>182.245</td>\n",
              "      <td>...</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>PA135312265</td>\n",
              "      <td>PA135312633</td>\n",
              "      <td>1</td>\n",
              "      <td>male</td>\n",
              "      <td>White</td>\n",
              "      <td>White</td>\n",
              "      <td>not Hispanic or Latino</td>\n",
              "      <td>not Hispanic or Latino</td>\n",
              "      <td>50 - 59</td>\n",
              "      <td>167.640</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>*1/*3</td>\n",
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              "      <td>PA152407681</td>\n",
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              "      <td>White</td>\n",
              "      <td>White</td>\n",
              "      <td>not Hispanic or Latino</td>\n",
              "      <td>not Hispanic or Latino</td>\n",
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              "      <td>185.420</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>PA152407682</td>\n",
              "      <td>PA152407970</td>\n",
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              "      <td>female</td>\n",
              "      <td>White</td>\n",
              "      <td>White</td>\n",
              "      <td>not Hispanic or Latino</td>\n",
              "      <td>not Hispanic or Latino</td>\n",
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              "      <td>White</td>\n",
              "      <td>not Hispanic or Latino</td>\n",
              "      <td>not Hispanic or Latino</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>White</td>\n",
              "      <td>White</td>\n",
              "      <td>not Hispanic or Latino</td>\n",
              "      <td>not Hispanic or Latino</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "\n",
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              "          const element = document.querySelector('#df-aea21a35-35b3-4c9f-bdd9-45fa64b59ef5');\n",
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              "            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",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "raw_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "WNkNiVYZcl1h"
      },
      "outputs": [],
      "source": [
        "raw_df.loc[raw_df[\"Race (Reported)\"] == \"other\", \"Race (Reported)\"] = \"Other\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "u8zz-aPJz14V"
      },
      "outputs": [],
      "source": [
        "df = raw_df[[\"Gender\", \"Race (Reported)\", \"Age\", \"Height (cm)\", \"Weight (kg)\", \"Diabetes\", \"Simvastatin (Zocor)\", \"Amiodarone (Cordarone)\",\"INR on Reported Therapeutic Dose of Warfarin\",\"VKORC1 genotype: -1639 G>A (3673); chr16:31015190; rs9923231; C/T\", \"Therapeutic Dose of Warfarin\"  ]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
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          "height": 710
        },
        "outputId": "87e0bac1-63dc-49b7-fc65-ddbdadeb7928"
      },
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        {
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          "data": {
            "text/plain": [
              "      Gender Race (Reported)      Age  Height (cm)  Weight (kg)  Diabetes  \\\n",
              "0       male           White  60 - 69      193.040       115.70       NaN   \n",
              "1     female           White  50 - 59      176.530       144.20       NaN   \n",
              "2     female           White  40 - 49      162.560        77.10       NaN   \n",
              "3       male           White  60 - 69      182.245        90.70       NaN   \n",
              "4       male           White  50 - 59      167.640        72.60       NaN   \n",
              "...      ...             ...      ...          ...          ...       ...   \n",
              "5695    male           White  20 - 29      185.420       113.64       0.0   \n",
              "5696  female           White  70 - 79      160.020        55.91       0.0   \n",
              "5697    male           White  60 - 69      187.960        97.73       0.0   \n",
              "5698    male           White  60 - 69      177.800        87.27       0.0   \n",
              "5699    male           White  70 - 79      190.500        79.55       0.0   \n",
              "\n",
              "      Simvastatin (Zocor)  Amiodarone (Cordarone)  \\\n",
              "0                     0.0                     0.0   \n",
              "1                     0.0                     0.0   \n",
              "2                     0.0                     0.0   \n",
              "3                     0.0                     0.0   \n",
              "4                     0.0                     0.0   \n",
              "...                   ...                     ...   \n",
              "5695                  0.0                     0.0   \n",
              "5696                  0.0                     0.0   \n",
              "5697                  0.0                     0.0   \n",
              "5698                  0.0                     0.0   \n",
              "5699                  0.0                     0.0   \n",
              "\n",
              "      INR on Reported Therapeutic Dose of Warfarin  \\\n",
              "0                                             2.60   \n",
              "1                                             2.15   \n",
              "2                                             1.90   \n",
              "3                                             2.40   \n",
              "4                                             1.90   \n",
              "...                                            ...   \n",
              "5695                                          2.80   \n",
              "5696                                          2.80   \n",
              "5697                                          2.00   \n",
              "5698                                          2.00   \n",
              "5699                                          3.00   \n",
              "\n",
              "     VKORC1 genotype: -1639 G>A (3673); chr16:31015190; rs9923231; C/T  \\\n",
              "0                                                   A/G                  \n",
              "1                                                   A/A                  \n",
              "2                                                   G/G                  \n",
              "3                                                   A/G                  \n",
              "4                                                   A/G                  \n",
              "...                                                 ...                  \n",
              "5695                                                NaN                  \n",
              "5696                                                NaN                  \n",
              "5697                                                NaN                  \n",
              "5698                                                NaN                  \n",
              "5699                                                NaN                  \n",
              "\n",
              "      Therapeutic Dose of Warfarin  \n",
              "0                            49.00  \n",
              "1                            42.00  \n",
              "2                            53.00  \n",
              "3                            28.00  \n",
              "4                            42.00  \n",
              "...                            ...  \n",
              "5695                         52.50  \n",
              "5696                         27.51  \n",
              "5697                         57.47  \n",
              "5698                         70.00  \n",
              "5699                         24.01  \n",
              "\n",
              "[5700 rows x 11 columns]"
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              "<p>5700 rows × 11 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-83dcf261-0569-4919-b7b4-972f422a1359')\"\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-83dcf261-0569-4919-b7b4-972f422a1359');\n",
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              "            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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            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "fs5CDBbzRejl",
        "colab": {
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          "height": 1000
        },
        "outputId": "2288d356-0932-4428-a119-1b6e72f4f762"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "counts = df[\"Gender\"].value_counts()\n",
        "counts.plot(kind='bar')\n",
        "plt.title('Bar Chart for Gender')\n",
        "plt.show()\n",
        "\n",
        "counts = df[\"Race (Reported)\"].value_counts()\n",
        "counts.plot(kind='bar')\n",
        "plt.title('Bar Chart for Race')\n",
        "plt.show()\n",
        "\n",
        "counts = df[\"Age\"].value_counts()\n",
        "counts.plot(kind='bar')\n",
        "plt.title('Bar Chart for Age')\n",
        "plt.show()\n",
        "\n",
        "counts = df[\"Diabetes\"].value_counts()\n",
        "counts.plot(kind='bar')\n",
        "plt.title('Bar Chart for Diabetes')\n",
        "plt.show()\n",
        "\n",
        "counts = df[\"Simvastatin (Zocor)\"].value_counts()\n",
        "counts.plot(kind='bar')\n",
        "plt.title('Bar Chart for Simvastatin (Zocor)')\n",
        "plt.show()\n",
        "\n",
        "counts = df[\"Amiodarone (Cordarone)\"].value_counts()\n",
        "counts.plot(kind='bar')\n",
        "plt.title('Bar Chart for Amiodarone (Cordarone)')\n",
        "plt.show()\n",
        "\n",
        "counts = df[\"VKORC1 genotype: -1639 G>A (3673); chr16:31015190; rs9923231; C/T\"].value_counts()\n",
        "counts.plot(kind='bar')\n",
        "plt.title('Bar Chart for VKORC1 genotype: -1639 G>A (3673); chr16:31015190; rs9923231; C/T')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AZQIk9Lyh3TF"
      },
      "source": [
        "#From the bar charts, we can come up with a few conclusions about our data. We have more  males than females. For race, White is predominantly more common. Most of the patients are b/t the ages of 50-80. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "S4gM8dSeRhdl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0b37b2b4-e6a0-4538-b13a-3cc8e9eefc0d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.hist(df[\"Height (cm)\"], bins=5)\n",
        "plt.title('Histogram for Height')\n",
        "plt.show()\n",
        "\n",
        "plt.hist(df[\"Weight (kg)\"], bins=5)\n",
        "plt.title('Histogram for Weight')\n",
        "plt.show()\n",
        "\n",
        "plt.hist(df[\"INR on Reported Therapeutic Dose of Warfarin\"], bins=5)\n",
        "plt.title('Histogram for INR on Reported Therapeutic Dose of Warfarin')\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "JikbO43BsNjd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "24f599d5-4379-41ba-e16a-6e179c2e7581"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5700 entries, 0 to 5699\n",
            "Data columns (total 11 columns):\n",
            " #   Column                                                             Non-Null Count  Dtype  \n",
            "---  ------                                                             --------------  -----  \n",
            " 0   Gender                                                             5696 non-null   object \n",
            " 1   Race (Reported)                                                    5194 non-null   object \n",
            " 2   Age                                                                5658 non-null   object \n",
            " 3   Height (cm)                                                        4554 non-null   float64\n",
            " 4   Weight (kg)                                                        5413 non-null   float64\n",
            " 5   Diabetes                                                           3283 non-null   float64\n",
            " 6   Simvastatin (Zocor)                                                3861 non-null   float64\n",
            " 7   Amiodarone (Cordarone)                                             4182 non-null   float64\n",
            " 8   INR on Reported Therapeutic Dose of Warfarin                       4968 non-null   float64\n",
            " 9   VKORC1 genotype: -1639 G>A (3673); chr16:31015190; rs9923231; C/T  4046 non-null   object \n",
            " 10  Therapeutic Dose of Warfarin                                       5528 non-null   float64\n",
            "dtypes: float64(7), object(4)\n",
            "memory usage: 490.0+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "invB-BQsSoEF"
      },
      "source": [
        "We are going to use regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4pWUWm9Ihole"
      },
      "source": [
        "#We have to make sure that the categorical values are converted to numerical equivalents. Hence, we will be using LabelEncoder"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "DbQOUr_R0PXm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "35facd4b-fc5a-488d-ff29-eb28615be96b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-22-a8ac4a0b807c>:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['Gender'] = le.fit_transform(df['Gender'])\n",
            "<ipython-input-22-a8ac4a0b807c>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['Race (Reported)'] = le.fit_transform(df['Race (Reported)'])\n",
            "<ipython-input-22-a8ac4a0b807c>:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['Age'] = le.fit_transform(df['Age'])\n",
            "<ipython-input-22-a8ac4a0b807c>:7: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['VKORC1 genotype: -1639 G>A (3673); chr16:31015190; rs9923231; C/T'] = le.fit_transform(df['VKORC1 genotype: -1639 G>A (3673); chr16:31015190; rs9923231; C/T'])\n",
            "<ipython-input-22-a8ac4a0b807c>:8: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['Diabetes'] = le.fit_transform(df['Diabetes'])\n",
            "<ipython-input-22-a8ac4a0b807c>:9: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['Simvastatin (Zocor)'] = le.fit_transform(df['Simvastatin (Zocor)'])\n",
            "<ipython-input-22-a8ac4a0b807c>:10: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['Amiodarone (Cordarone)'] = le.fit_transform(df['Amiodarone (Cordarone)'])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Gender  Race (Reported)  Age  Height (cm)  Weight (kg)  Diabetes  \\\n",
              "0          1               20    5      193.040       115.70         2   \n",
              "1          0               20    4      176.530       144.20         2   \n",
              "2          0               20    3      162.560        77.10         2   \n",
              "3          1               20    5      182.245        90.70         2   \n",
              "4          1               20    4      167.640        72.60         2   \n",
              "...      ...              ...  ...          ...          ...       ...   \n",
              "5695       1               20    1      185.420       113.64         0   \n",
              "5696       0               20    6      160.020        55.91         0   \n",
              "5697       1               20    5      187.960        97.73         0   \n",
              "5698       1               20    5      177.800        87.27         0   \n",
              "5699       1               20    6      190.500        79.55         0   \n",
              "\n",
              "      Simvastatin (Zocor)  Amiodarone (Cordarone)  \\\n",
              "0                       0                       0   \n",
              "1                       0                       0   \n",
              "2                       0                       0   \n",
              "3                       0                       0   \n",
              "4                       0                       0   \n",
              "...                   ...                     ...   \n",
              "5695                    0                       0   \n",
              "5696                    0                       0   \n",
              "5697                    0                       0   \n",
              "5698                    0                       0   \n",
              "5699                    0                       0   \n",
              "\n",
              "      INR on Reported Therapeutic Dose of Warfarin  \\\n",
              "0                                             2.60   \n",
              "1                                             2.15   \n",
              "2                                             1.90   \n",
              "3                                             2.40   \n",
              "4                                             1.90   \n",
              "...                                            ...   \n",
              "5695                                          2.80   \n",
              "5696                                          2.80   \n",
              "5697                                          2.00   \n",
              "5698                                          2.00   \n",
              "5699                                          3.00   \n",
              "\n",
              "      VKORC1 genotype: -1639 G>A (3673); chr16:31015190; rs9923231; C/T  \\\n",
              "0                                                     1                   \n",
              "1                                                     0                   \n",
              "2                                                     2                   \n",
              "3                                                     1                   \n",
              "4                                                     1                   \n",
              "...                                                 ...                   \n",
              "5695                                                  3                   \n",
              "5696                                                  3                   \n",
              "5697                                                  3                   \n",
              "5698                                                  3                   \n",
              "5699                                                  3                   \n",
              "\n",
              "      Therapeutic Dose of Warfarin  \n",
              "0                            49.00  \n",
              "1                            42.00  \n",
              "2                            53.00  \n",
              "3                            28.00  \n",
              "4                            42.00  \n",
              "...                            ...  \n",
              "5695                         52.50  \n",
              "5696                         27.51  \n",
              "5697                         57.47  \n",
              "5698                         70.00  \n",
              "5699                         24.01  \n",
              "\n",
              "[5700 rows x 11 columns]"
            ],
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              "      <th>Gender</th>\n",
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              "<p>5700 rows × 11 columns</p>\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "le = LabelEncoder()\n",
        "df['Gender'] = le.fit_transform(df['Gender'])\n",
        "df['Race (Reported)'] = le.fit_transform(df['Race (Reported)'])\n",
        "df['Age'] = le.fit_transform(df['Age'])\n",
        "df['VKORC1 genotype: -1639 G>A (3673); chr16:31015190; rs9923231; C/T'] = le.fit_transform(df['VKORC1 genotype: -1639 G>A (3673); chr16:31015190; rs9923231; C/T'])\n",
        "df['Diabetes'] = le.fit_transform(df['Diabetes'])\n",
        "df['Simvastatin (Zocor)'] = le.fit_transform(df['Simvastatin (Zocor)'])\n",
        "df['Amiodarone (Cordarone)'] = le.fit_transform(df['Amiodarone (Cordarone)'])\n",
        "\n",
        "\n",
        "\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "37wnobYCKCiv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a074be9a-73c8-43cb-d25a-f58001b7e1cb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5700 entries, 0 to 5699\n",
            "Data columns (total 11 columns):\n",
            " #   Column                                                             Non-Null Count  Dtype  \n",
            "---  ------                                                             --------------  -----  \n",
            " 0   Gender                                                             5700 non-null   int64  \n",
            " 1   Race (Reported)                                                    5700 non-null   int64  \n",
            " 2   Age                                                                5700 non-null   int64  \n",
            " 3   Height (cm)                                                        4554 non-null   float64\n",
            " 4   Weight (kg)                                                        5413 non-null   float64\n",
            " 5   Diabetes                                                           5700 non-null   int64  \n",
            " 6   Simvastatin (Zocor)                                                5700 non-null   int64  \n",
            " 7   Amiodarone (Cordarone)                                             5700 non-null   int64  \n",
            " 8   INR on Reported Therapeutic Dose of Warfarin                       4968 non-null   float64\n",
            " 9   VKORC1 genotype: -1639 G>A (3673); chr16:31015190; rs9923231; C/T  5700 non-null   int64  \n",
            " 10  Therapeutic Dose of Warfarin                                       5528 non-null   float64\n",
            "dtypes: float64(4), int64(7)\n",
            "memory usage: 490.0 KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xXs9mC-Ahef1"
      },
      "source": [
        "#Here, I am trying to find the numerical equivalent of the categorical values. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "XfYeuasJXOs0"
      },
      "outputs": [],
      "source": [
        "# from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "# le = LabelEncoder()\n",
        "# df['Gender'] = le.fit_transform(raw_df['Gender'])\n",
        "\n",
        "# # Print the mapping of encoded values to original categorical values\n",
        "# print(dict(zip(le.classes_, range(len(le.classes_)))))\n",
        "\n",
        "\n",
        "# # To get the original categorical value back for a particular encoded value:\n",
        "# encoded_value = 1\n",
        "# original_value = le.inverse_transform([encoded_value])\n",
        "# print(original_value[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KrmnvQIZnCnH"
      },
      "source": [
        "#Using simple imputation with mean values to have the same number of entries in each column. This will ensure for a better model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "uUbLxv_2dEdv"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from sklearn.impute import SimpleImputer\n",
        "import pandas as pd\n",
        "\n",
        "# Step 1: Simple Imputation with Mean values\n",
        "step1_imputer = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
        "step1_impute_mean = step1_imputer.fit_transform(df)\n",
        "step1_impute_df = pd.DataFrame(step1_impute_mean, columns=df.columns + '_impute')\n",
        "step1_impute_df = pd.concat([df, step1_impute_df], axis=1)\n",
        "step1_impute_df\n",
        "\n",
        "new_df = step1_impute_df.drop(['Gender', 'Race (Reported)', 'Age', 'VKORC1 genotype: -1639 G>A (3673); chr16:31015190; rs9923231; C/T', 'Height (cm)', 'Weight (kg)', 'Diabetes', 'Amiodarone (Cordarone)', 'INR on Reported Therapeutic Dose of Warfarin', 'Therapeutic Dose of Warfarin', 'Simvastatin (Zocor)'], axis = 1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "WzeELr7jdjSi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6023e711-1106-43fb-b107-da133d294995"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5700 entries, 0 to 5699\n",
            "Data columns (total 11 columns):\n",
            " #   Column                                                                    Non-Null Count  Dtype  \n",
            "---  ------                                                                    --------------  -----  \n",
            " 0   Gender_impute                                                             5700 non-null   float64\n",
            " 1   Race (Reported)_impute                                                    5700 non-null   float64\n",
            " 2   Age_impute                                                                5700 non-null   float64\n",
            " 3   Height (cm)_impute                                                        5700 non-null   float64\n",
            " 4   Weight (kg)_impute                                                        5700 non-null   float64\n",
            " 5   Diabetes_impute                                                           5700 non-null   float64\n",
            " 6   Simvastatin (Zocor)_impute                                                5700 non-null   float64\n",
            " 7   Amiodarone (Cordarone)_impute                                             5700 non-null   float64\n",
            " 8   INR on Reported Therapeutic Dose of Warfarin_impute                       5700 non-null   float64\n",
            " 9   VKORC1 genotype: -1639 G>A (3673); chr16:31015190; rs9923231; C/T_impute  5700 non-null   float64\n",
            " 10  Therapeutic Dose of Warfarin_impute                                       5700 non-null   float64\n",
            "dtypes: float64(11)\n",
            "memory usage: 490.0 KB\n"
          ]
        }
      ],
      "source": [
        "new_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "GBT1aGE0N1xP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "outputId": "986aab90-9a77-4ed7-9873-e0b9712321f7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Gender_impute  Race (Reported)_impute  Age_impute  Height (cm)_impute  \\\n",
              "0               1.0                    20.0         5.0             193.040   \n",
              "1               0.0                    20.0         4.0             176.530   \n",
              "2               0.0                    20.0         3.0             162.560   \n",
              "3               1.0                    20.0         5.0             182.245   \n",
              "4               1.0                    20.0         4.0             167.640   \n",
              "...             ...                     ...         ...                 ...   \n",
              "5695            1.0                    20.0         1.0             185.420   \n",
              "5696            0.0                    20.0         6.0             160.020   \n",
              "5697            1.0                    20.0         5.0             187.960   \n",
              "5698            1.0                    20.0         5.0             177.800   \n",
              "5699            1.0                    20.0         6.0             190.500   \n",
              "\n",
              "      Weight (kg)_impute  Diabetes_impute  Simvastatin (Zocor)_impute  \\\n",
              "0                 115.70              2.0                         0.0   \n",
              "1                 144.20              2.0                         0.0   \n",
              "2                  77.10              2.0                         0.0   \n",
              "3                  90.70              2.0                         0.0   \n",
              "4                  72.60              2.0                         0.0   \n",
              "...                  ...              ...                         ...   \n",
              "5695              113.64              0.0                         0.0   \n",
              "5696               55.91              0.0                         0.0   \n",
              "5697               97.73              0.0                         0.0   \n",
              "5698               87.27              0.0                         0.0   \n",
              "5699               79.55              0.0                         0.0   \n",
              "\n",
              "      Amiodarone (Cordarone)_impute  \\\n",
              "0                               0.0   \n",
              "1                               0.0   \n",
              "2                               0.0   \n",
              "3                               0.0   \n",
              "4                               0.0   \n",
              "...                             ...   \n",
              "5695                            0.0   \n",
              "5696                            0.0   \n",
              "5697                            0.0   \n",
              "5698                            0.0   \n",
              "5699                            0.0   \n",
              "\n",
              "      INR on Reported Therapeutic Dose of Warfarin_impute  \\\n",
              "0                                                  2.60     \n",
              "1                                                  2.15     \n",
              "2                                                  1.90     \n",
              "3                                                  2.40     \n",
              "4                                                  1.90     \n",
              "...                                                 ...     \n",
              "5695                                               2.80     \n",
              "5696                                               2.80     \n",
              "5697                                               2.00     \n",
              "5698                                               2.00     \n",
              "5699                                               3.00     \n",
              "\n",
              "      VKORC1 genotype: -1639 G>A (3673); chr16:31015190; rs9923231; C/T_impute  \\\n",
              "0                                                   1.0                          \n",
              "1                                                   0.0                          \n",
              "2                                                   2.0                          \n",
              "3                                                   1.0                          \n",
              "4                                                   1.0                          \n",
              "...                                                 ...                          \n",
              "5695                                                3.0                          \n",
              "5696                                                3.0                          \n",
              "5697                                                3.0                          \n",
              "5698                                                3.0                          \n",
              "5699                                                3.0                          \n",
              "\n",
              "      Therapeutic Dose of Warfarin_impute  \n",
              "0                                   49.00  \n",
              "1                                   42.00  \n",
              "2                                   53.00  \n",
              "3                                   28.00  \n",
              "4                                   42.00  \n",
              "...                                   ...  \n",
              "5695                                52.50  \n",
              "5696                                27.51  \n",
              "5697                                57.47  \n",
              "5698                                70.00  \n",
              "5699                                24.01  \n",
              "\n",
              "[5700 rows x 11 columns]"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gender_impute</th>\n",
              "      <th>Race (Reported)_impute</th>\n",
              "      <th>Age_impute</th>\n",
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              "      <th>INR on Reported Therapeutic Dose of Warfarin_impute</th>\n",
              "      <th>VKORC1 genotype: -1639 G&gt;A (3673); chr16:31015190; rs9923231; C/T_impute</th>\n",
              "      <th>Therapeutic Dose of Warfarin_impute</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>20.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>162.560</td>\n",
              "      <td>77.10</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.90</td>\n",
              "      <td>2.0</td>\n",
              "      <td>53.00</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>182.245</td>\n",
              "      <td>90.70</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.40</td>\n",
              "      <td>1.0</td>\n",
              "      <td>28.00</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>167.640</td>\n",
              "      <td>72.60</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.90</td>\n",
              "      <td>1.0</td>\n",
              "      <td>42.00</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>5695</th>\n",
              "      <td>1.0</td>\n",
              "      <td>20.0</td>\n",
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              "      <td>185.420</td>\n",
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              "      <td>55.91</td>\n",
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              "      <td>27.51</td>\n",
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              "      <th>5697</th>\n",
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              "      <td>187.960</td>\n",
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              "<p>5700 rows × 11 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "new_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "diH7H1pY7SlM"
      },
      "source": [
        "##Removed all of the outliers (except last column)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "FT7ethWb5CNL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f5bfbbfa-4ac0-4d7d-d2c4-c20e29be21a4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Outliers: [   7   10   17   27   32  187  188  200  201  215  261  262  274  281\n",
            "  291  295  296  300  324  330  338  341  342  384  412  423  434  445\n",
            "  456  460  482  498  509  553  573  575  580  592  708  710  853 1011\n",
            " 1188 1336 1555 1572 1636 1657 1685 1690 1692 1708 1716 1742 1744 1777\n",
            " 1779 1796 1805 1806 1834 1852 1868 1885 1888 1891 1892 1928 1958 1998\n",
            " 2039 2066 2101 2141 2215 2241 2246 2300 2398 2438 2442 2480 2527 2552\n",
            " 2562 2571 2622 2649 2657 2680 2682 2733 2822 3006 3010 3016 3018 3028\n",
            " 3033 3050 3051 3056 3061 3066 3107 3127 3138 3141 3144 3145 3164 3166\n",
            " 3175 3181 3199 3206 3216 3221 3223 3227 3249 3250 3254 3258 3265 3281\n",
            " 3285 3286 3292 3322 3336 3353 3356 3376 3397 3406 3420 3425 3430 3441\n",
            " 3453 3459 3460 3467 3470 3473 3475 3479 3486 3492 3503 3625 3644 3653\n",
            " 3656 3663 3701 3710 3739 3805 3812 3820 3834 3840 3856 3876 3902 3919\n",
            " 3940 3945 3963 3974 3991 4006 4008 4033 4060 4070 4083 4085 4113 4149\n",
            " 4160 4162 4167 4168 4186 4189 4191 4243 4246 4247 4256 4262 4273 4280\n",
            " 4286 4308 4310 4312 4316 4319 4323 4334 4343 4346 4348 4352 4356 4361\n",
            " 4364 4376 4386 4387 4388 4389 4392 4393 4394 4396 4399 4415 4425 4428\n",
            " 4440 4442 4444 4447 4453 4464 4471 4518 4537 4585 4608 4821 4834 4835\n",
            " 4846 4887 4905 4933 4959 4961 5010 5085 5086 5096 5225 5305 5311 5326\n",
            " 5338 5341 5353 5365 5368 5369 5381 5408 5436 5442 5447 5466 5504 5523\n",
            " 5526 5530 5551 5576 5587 5635]\n"
          ]
        }
      ],
      "source": [
        "import statsmodels.api as sm\n",
        "\n",
        "outliers = []\n",
        "\n",
        "    # fit OLS model\n",
        "    #model = sm.OLS(new_df[col], sm.add_constant(new_df.drop([col, new_df.columns[-1]], axis=1))).fit()\n",
        "model = sm.OLS(new_df['Therapeutic Dose of Warfarin_impute'], sm.add_constant(new_df.drop(['Therapeutic Dose of Warfarin_impute'], axis=1))).fit()\n",
        "\n",
        "    # get influence measures\n",
        "influence = model.get_influence()\n",
        "\n",
        "    # calculate Cook's distance\n",
        "(c, p) = influence.cooks_distance\n",
        "\n",
        "    # identify outliers\n",
        "col_outliers = np.where(c > 4/len(new_df))[0]\n",
        "threshold = 4 / len(new_df)\n",
        "\n",
        "\n",
        "    # store outliers for this column\n",
        "outliers.append(col_outliers)\n",
        "\n",
        "# merge outlier indices and remove duplicates\n",
        "outliers = np.unique(np.concatenate(outliers))\n",
        "\n",
        "new_df_cook = new_df.drop(outliers[:-1])\n",
        "\n",
        "# print outliers\n",
        "print(\"Outliers:\", outliers)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df_cook"
      ],
      "metadata": {
        "id": "mZM3fR6OOqRI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "outputId": "9afbb8d7-d2ec-4344-efb8-fb01b26cfa80"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Gender_impute  Race (Reported)_impute  Age_impute  Height (cm)_impute  \\\n",
              "0               1.0                    20.0         5.0             193.040   \n",
              "1               0.0                    20.0         4.0             176.530   \n",
              "2               0.0                    20.0         3.0             162.560   \n",
              "3               1.0                    20.0         5.0             182.245   \n",
              "4               1.0                    20.0         4.0             167.640   \n",
              "...             ...                     ...         ...                 ...   \n",
              "5695            1.0                    20.0         1.0             185.420   \n",
              "5696            0.0                    20.0         6.0             160.020   \n",
              "5697            1.0                    20.0         5.0             187.960   \n",
              "5698            1.0                    20.0         5.0             177.800   \n",
              "5699            1.0                    20.0         6.0             190.500   \n",
              "\n",
              "      Weight (kg)_impute  Diabetes_impute  Simvastatin (Zocor)_impute  \\\n",
              "0                 115.70              2.0                         0.0   \n",
              "1                 144.20              2.0                         0.0   \n",
              "2                  77.10              2.0                         0.0   \n",
              "3                  90.70              2.0                         0.0   \n",
              "4                  72.60              2.0                         0.0   \n",
              "...                  ...              ...                         ...   \n",
              "5695              113.64              0.0                         0.0   \n",
              "5696               55.91              0.0                         0.0   \n",
              "5697               97.73              0.0                         0.0   \n",
              "5698               87.27              0.0                         0.0   \n",
              "5699               79.55              0.0                         0.0   \n",
              "\n",
              "      Amiodarone (Cordarone)_impute  \\\n",
              "0                               0.0   \n",
              "1                               0.0   \n",
              "2                               0.0   \n",
              "3                               0.0   \n",
              "4                               0.0   \n",
              "...                             ...   \n",
              "5695                            0.0   \n",
              "5696                            0.0   \n",
              "5697                            0.0   \n",
              "5698                            0.0   \n",
              "5699                            0.0   \n",
              "\n",
              "      INR on Reported Therapeutic Dose of Warfarin_impute  \\\n",
              "0                                                  2.60     \n",
              "1                                                  2.15     \n",
              "2                                                  1.90     \n",
              "3                                                  2.40     \n",
              "4                                                  1.90     \n",
              "...                                                 ...     \n",
              "5695                                               2.80     \n",
              "5696                                               2.80     \n",
              "5697                                               2.00     \n",
              "5698                                               2.00     \n",
              "5699                                               3.00     \n",
              "\n",
              "      VKORC1 genotype: -1639 G>A (3673); chr16:31015190; rs9923231; C/T_impute  \\\n",
              "0                                                   1.0                          \n",
              "1                                                   0.0                          \n",
              "2                                                   2.0                          \n",
              "3                                                   1.0                          \n",
              "4                                                   1.0                          \n",
              "...                                                 ...                          \n",
              "5695                                                3.0                          \n",
              "5696                                                3.0                          \n",
              "5697                                                3.0                          \n",
              "5698                                                3.0                          \n",
              "5699                                                3.0                          \n",
              "\n",
              "      Therapeutic Dose of Warfarin_impute  \n",
              "0                                   49.00  \n",
              "1                                   42.00  \n",
              "2                                   53.00  \n",
              "3                                   28.00  \n",
              "4                                   42.00  \n",
              "...                                   ...  \n",
              "5695                                52.50  \n",
              "5696                                27.51  \n",
              "5697                                57.47  \n",
              "5698                                70.00  \n",
              "5699                                24.01  \n",
              "\n",
              "[5429 rows x 11 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6f3acbd0-f9ea-403f-9db7-0be2d607d904\">\n",
              "    <div class=\"colab-df-container\">\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>Gender_impute</th>\n",
              "      <th>Race (Reported)_impute</th>\n",
              "      <th>Age_impute</th>\n",
              "      <th>Height (cm)_impute</th>\n",
              "      <th>Weight (kg)_impute</th>\n",
              "      <th>Diabetes_impute</th>\n",
              "      <th>Simvastatin (Zocor)_impute</th>\n",
              "      <th>Amiodarone (Cordarone)_impute</th>\n",
              "      <th>INR on Reported Therapeutic Dose of Warfarin_impute</th>\n",
              "      <th>VKORC1 genotype: -1639 G&gt;A (3673); chr16:31015190; rs9923231; C/T_impute</th>\n",
              "      <th>Therapeutic Dose of Warfarin_impute</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>193.040</td>\n",
              "      <td>115.70</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>2.60</td>\n",
              "      <td>1.0</td>\n",
              "      <td>49.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>176.530</td>\n",
              "      <td>144.20</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.15</td>\n",
              "      <td>0.0</td>\n",
              "      <td>42.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>162.560</td>\n",
              "      <td>77.10</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.90</td>\n",
              "      <td>2.0</td>\n",
              "      <td>53.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>182.245</td>\n",
              "      <td>90.70</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.40</td>\n",
              "      <td>1.0</td>\n",
              "      <td>28.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>167.640</td>\n",
              "      <td>72.60</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.90</td>\n",
              "      <td>1.0</td>\n",
              "      <td>42.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5695</th>\n",
              "      <td>1.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>185.420</td>\n",
              "      <td>113.64</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>3.0</td>\n",
              "      <td>52.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5696</th>\n",
              "      <td>0.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>160.020</td>\n",
              "      <td>55.91</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>3.0</td>\n",
              "      <td>27.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5697</th>\n",
              "      <td>1.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>187.960</td>\n",
              "      <td>97.73</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>57.47</td>\n",
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              "    <tr>\n",
              "      <th>5698</th>\n",
              "      <td>1.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>5.0</td>\n",
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              "      <td>87.27</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>5699</th>\n",
              "      <td>1.0</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5429 rows × 11 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6f3acbd0-f9ea-403f-9db7-0be2d607d904')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display: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",
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              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
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              "\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",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6f3acbd0-f9ea-403f-9db7-0be2d607d904 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-6f3acbd0-f9ea-403f-9db7-0be2d607d904');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CgSf49lXFe_K"
      },
      "source": [
        "#Now, we are going to do visualization on our prepped data!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "CkY9vax9QsrB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "dc525217-a23c-4e08-9ce5-5cecffbb0396"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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KisaPH6/vvvtOvr6+l+2tsLBQQUFBKigo4DZqAAAsoqJ/v116DUxpaanWrl2rP/3pT4qLi1NoaKiio6Od3mbKyspSSUmJYmNjHduaNWumxo0bKzMzU5KUmZmp1q1bO8KLJMXFxamwsFA5OTnlHruoqEiFhYVOCwAA8EwuDTB5eXk6deqUpk+frl69emnDhg36y1/+ojvvvFNbt26VJNntdvn6+io4ONjpsWFhYbLb7Y6aX4aXsvGysfIkJycrKCjIsfA1AgAAeC6Xn4GRpDvuuEOjR49WmzZtNGHCBPXt21cpKSmuPNQFJk6cqIKCAsdy7NixKj0eAABwH5cGmPr168vb21stWrRw2t68eXPHXUjh4eEqLi5Wfn6+U01ubq7Cw8MdNb++K6lsvazm1/z8/BxfG8DXBwAA4NlcGmB8fX3VoUMHHTx40Gn7559/rsjISElSu3bt5OPjo40bNzrGDx48qKNHjyomJkaSFBMTo3379ikvL89Rk5GRocDAwAvCEQAA+OOp9Jc5njp1Sl988YVj/fDhw8rOzlZISIgaN26ssWPH6u6771aXLl3UvXt3paen67333tOWLVskSUFBQRo6dKjGjBmjkJAQBQYGauTIkYqJiVHHjh0lST179lSLFi00ePBgzZgxQ3a7XZMmTdKIESPk5+fnmpkDAADrMpW0efNmI+mCZciQIY6al19+2TRt2tT4+/ubG2+80axevdppHz/99JN59NFHTd26dU3NmjXNX/7yF/Ptt9861Rw5csT07t3bBAQEmPr165snnnjClJSUVLjPgoICI8kUFBRUdooAAMBNKvr3+3d9Dkx19ls+B6bJhLVV3JV0ZHp8lR8DAACrcsvnwAAAAFwJBBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5BBgAAGA5lQ4w27ZtU79+/RQRESGbzabVq1dftPbhhx+WzWbT7NmznbafOHFCiYmJCgwMVHBwsIYOHapTp0451XzyySe69dZb5e/vr0aNGmnGjBmVbRUAAHioSgeY06dP68Ybb9SCBQsuWbdq1Sp9/PHHioiIuGAsMTFROTk5ysjI0Jo1a7Rt2zYNHz7cMV5YWKiePXsqMjJSWVlZeuGFF/TUU09p8eLFlW0XAAB4IO/KPqB3797q3bv3JWu++eYbjRw5UuvXr1d8fLzT2IEDB5Senq7du3erffv2kqR58+apT58+mjlzpiIiIpSWlqbi4mK98sor8vX1VcuWLZWdna1Zs2Y5BZ1fKioqUlFRkWO9sLCwslMDAAAW4fJrYEpLSzV48GCNHTtWLVu2vGA8MzNTwcHBjvAiSbGxsfLy8tLOnTsdNV26dJGvr6+jJi4uTgcPHtTJkyfLPW5ycrKCgoIcS6NGjVw8MwAAUF24PMA8//zz8vb21mOPPVbuuN1uV2hoqNM2b29vhYSEyG63O2rCwsKcasrWy2p+beLEiSooKHAsx44d+71TAQAA1VSl30K6lKysLM2ZM0d79+6VzWZz5a4vy8/PT35+flf0mAAAwD1cegZm+/btysvLU+PGjeXt7S1vb2999dVXeuKJJ9SkSRNJUnh4uPLy8pwed+7cOZ04cULh4eGOmtzcXKeasvWyGgAA8Mfl0jMwgwcPVmxsrNO2uLg4DR48WA888IAkKSYmRvn5+crKylK7du0kSZs2bVJpaamio6MdNX//+99VUlIiHx8fSVJGRoauv/561a1b15Ute6QmE9ZW+TGOTI+/fBEAAFWk0gHm1KlT+uKLLxzrhw8fVnZ2tkJCQtS4cWPVq1fPqd7Hx0fh4eG6/vrrJUnNmzdXr169NGzYMKWkpKikpERJSUlKSEhw3HI9aNAgPf300xo6dKjGjx+vTz/9VHPmzNFLL730e+YKAAA8RKUDzJ49e9S9e3fH+pgxYyRJQ4YMUWpqaoX2kZaWpqSkJPXo0UNeXl4aMGCA5s6d6xgPCgrShg0bNGLECLVr107169fXlClTLnoLNQAA+GOpdIDp1q2bjDEVrj9y5MgF20JCQrR8+fJLPu6GG27Q9u3bK9seAAD4A+C7kAAAgOUQYAAAgOUQYAAAgOUQYAAAgOUQYAAAgOUQYAAAgOUQYAAAgOUQYAAAgOUQYAAAgOUQYAAAgOUQYAAAgOUQYAAAgOUQYAAAgOUQYAAAgOUQYAAAgOUQYAAAgOUQYAAAgOUQYAAAgOUQYAAAgOUQYAAAgOUQYAAAgOUQYAAAgOUQYAAAgOUQYAAAgOUQYAAAgOUQYAAAgOUQYAAAgOUQYAAAgOUQYAAAgOUQYAAAgOUQYAAAgOUQYAAAgOUQYAAAgOVUOsBs27ZN/fr1U0REhGw2m1avXu0YKykp0fjx49W6dWvVqlVLERERuu+++3T8+HGnfZw4cUKJiYkKDAxUcHCwhg4dqlOnTjnVfPLJJ7r11lvl7++vRo0aacaMGb9thgAAwONUOsCcPn1aN954oxYsWHDB2JkzZ7R3715NnjxZe/fu1dtvv62DBw/q9ttvd6pLTExUTk6OMjIytGbNGm3btk3Dhw93jBcWFqpnz56KjIxUVlaWXnjhBT311FNavHjxb5giAADwNN6VfUDv3r3Vu3fvcseCgoKUkZHhtG3+/Pm6+eabdfToUTVu3FgHDhxQenq6du/erfbt20uS5s2bpz59+mjmzJmKiIhQWlqaiouL9corr8jX11ctW7ZUdna2Zs2a5RR0AADAH1OVXwNTUFAgm82m4OBgSVJmZqaCg4Md4UWSYmNj5eXlpZ07dzpqunTpIl9fX0dNXFycDh48qJMnT5Z7nKKiIhUWFjotAADAM1VpgDl79qzGjx+ve+65R4GBgZIku92u0NBQpzpvb2+FhITIbrc7asLCwpxqytbLan4tOTlZQUFBjqVRo0aung4AAKgmqizAlJSUaODAgTLGaNGiRVV1GIeJEyeqoKDAsRw7dqzKjwkAANyj0tfAVERZePnqq6+0adMmx9kXSQoPD1deXp5T/blz53TixAmFh4c7anJzc51qytbLan7Nz89Pfn5+rpwGAACoplx+BqYsvBw6dEgffPCB6tWr5zQeExOj/Px8ZWVlObZt2rRJpaWlio6OdtRs27ZNJSUljpqMjAxdf/31qlu3rqtbBgAAFlPpAHPq1CllZ2crOztbknT48GFlZ2fr6NGjKikp0V133aU9e/YoLS1N58+fl91ul91uV3FxsSSpefPm6tWrl4YNG6Zdu3bpo48+UlJSkhISEhQRESFJGjRokHx9fTV06FDl5ORoxYoVmjNnjsaMGeO6mQMAAMuq9FtIe/bsUffu3R3rZaFiyJAheuqpp/Tuu+9Kktq0aeP0uM2bN6tbt26SpLS0NCUlJalHjx7y8vLSgAEDNHfuXEdtUFCQNmzYoBEjRqhdu3aqX7++pkyZwi3UAABA0m8IMN26dZMx5qLjlxorExISouXLl1+y5oYbbtD27dsr2x4AAPgD4LuQAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5RBgAACA5Xi7uwHgYppMWFul+z8yPb5K9w8AqDqcgQEAAJZDgAEAAJZDgAEAAJZDgAEAAJZDgAEAAJZDgAEAAJZDgAEAAJZDgAEAAJZDgAEAAJZDgAEAAJZDgAEAAJZDgAEAAJZDgAEAAJZDgAEAAJZT6QCzbds29evXTxEREbLZbFq9erXTuDFGU6ZMUcOGDRUQEKDY2FgdOnTIqebEiRNKTExUYGCggoODNXToUJ06dcqp5pNPPtGtt94qf39/NWrUSDNmzKj87AAAgEeqdIA5ffq0brzxRi1YsKDc8RkzZmju3LlKSUnRzp07VatWLcXFxens2bOOmsTEROXk5CgjI0Nr1qzRtm3bNHz4cMd4YWGhevbsqcjISGVlZemFF17QU089pcWLF/+GKQIAAE/jXdkH9O7dW7179y53zBij2bNna9KkSbrjjjskSa+++qrCwsK0evVqJSQk6MCBA0pPT9fu3bvVvn17SdK8efPUp08fzZw5UxEREUpLS1NxcbFeeeUV+fr6qmXLlsrOztasWbOcgg4AAPhjcuk1MIcPH5bdbldsbKxjW1BQkKKjo5WZmSlJyszMVHBwsCO8SFJsbKy8vLy0c+dOR02XLl3k6+vrqImLi9PBgwd18uTJco9dVFSkwsJCpwUAAHgmlwYYu90uSQoLC3PaHhYW5hiz2+0KDQ11Gvf29lZISIhTTXn7+OUxfi05OVlBQUGOpVGjRr9/QgAAoFrymLuQJk6cqIKCAsdy7Ngxd7cEAACqiEsDTHh4uCQpNzfXaXtubq5jLDw8XHl5eU7j586d04kTJ5xqytvHL4/xa35+fgoMDHRaAACAZ3JpgImKilJ4eLg2btzo2FZYWKidO3cqJiZGkhQTE6P8/HxlZWU5ajZt2qTS0lJFR0c7arZt26aSkhJHTUZGhq6//nrVrVvXlS0DAAALqnSAOXXqlLKzs5WdnS3p5wt3s7OzdfToUdlsNo0aNUrPPvus3n33Xe3bt0/33XefIiIi1L9/f0lS8+bN1atXLw0bNky7du3SRx99pKSkJCUkJCgiIkKSNGjQIPn6+mro0KHKycnRihUrNGfOHI0ZM8ZlEwcAANZV6duo9+zZo+7duzvWy0LFkCFDlJqaqnHjxun06dMaPny48vPzdcsttyg9PV3+/v6Ox6SlpSkpKUk9evSQl5eXBgwYoLlz5zrGg4KCtGHDBo0YMULt2rVT/fr1NWXKFG6hBgAAkn5DgOnWrZuMMRcdt9lsmjZtmqZNm3bRmpCQEC1fvvySx7nhhhu0ffv2yrYHAAD+ADzmLiQAAPDHQYABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACWQ4ABAACW4/IAc/78eU2ePFlRUVEKCAjQtddeq2eeeUbGGEeNMUZTpkxRw4YNFRAQoNjYWB06dMhpPydOnFBiYqICAwMVHBysoUOH6tSpU65uFwAAWJDLA8zzzz+vRYsWaf78+Tpw4ICef/55zZgxQ/PmzXPUzJgxQ3PnzlVKSop27typWrVqKS4uTmfPnnXUJCYmKicnRxkZGVqzZo22bdum4cOHu7pdAABgQd6u3uGOHTt0xx13KD4+XpLUpEkTvf7669q1a5ekn8++zJ49W5MmTdIdd9whSXr11VcVFham1atXKyEhQQcOHFB6erp2796t9u3bS5LmzZunPn36aObMmYqIiHB12wAAwEJcfgamU6dO2rhxoz7//HNJ0n//+199+OGH6t27tyTp8OHDstvtio2NdTwmKChI0dHRyszMlCRlZmYqODjYEV4kKTY2Vl5eXtq5c2e5xy0qKlJhYaHTAgAAPJPLz8BMmDBBhYWFatasmWrUqKHz58/rH//4hxITEyVJdrtdkhQWFub0uLCwMMeY3W5XaGioc6Pe3goJCXHU/FpycrKefvppV08HAABUQy4/A/Pmm28qLS1Ny5cv1969e7V06VLNnDlTS5cudfWhnEycOFEFBQWO5dixY1V6PAAA4D4uPwMzduxYTZgwQQkJCZKk1q1b66uvvlJycrKGDBmi8PBwSVJubq4aNmzoeFxubq7atGkjSQoPD1deXp7Tfs+dO6cTJ044Hv9rfn5+8vPzc/V0AABANeTyMzBnzpyRl5fzbmvUqKHS0lJJUlRUlMLDw7Vx40bHeGFhoXbu3KmYmBhJUkxMjPLz85WVleWo2bRpk0pLSxUdHe3qlgEAgMW4/AxMv3799I9//EONGzdWy5Yt9X//93+aNWuW/va3v0mSbDabRo0apWeffVbXXXedoqKiNHnyZEVERKh///6SpObNm6tXr14aNmyYUlJSVFJSoqSkJCUkJHAHEgAAcH2AmTdvniZPnqxHH31UeXl5ioiI0EMPPaQpU6Y4asaNG6fTp09r+PDhys/P1y233KL09HT5+/s7atLS0pSUlKQePXrIy8tLAwYM0Ny5c13dLgAAsCCb+eVH5HqQwsJCBQUFqaCgQIGBgRV6TJMJa6u4K+nI9PgqPwbzqJgrMQcAQOVU9O8334UEAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAshwADAAAsx9vdDQCerMmEtVV+jCPT46v8GABQ3XAGBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWE6VBJhvvvlG9957r+rVq6eAgAC1bt1ae/bscYwbYzRlyhQ1bNhQAQEBio2N1aFDh5z2ceLECSUmJiowMFDBwcEaOnSoTp06VRXtAgAAi3F5gDl58qQ6d+4sHx8frVu3Tvv379eLL76ounXrOmpmzJihuXPnKiUlRTt37lStWrUUFxens2fPOmoSExOVk5OjjIwMrVmzRtu2bdPw4cNd3S4AALAgl38b9fPPP69GjRppyZIljm1RUVGO/zbGaPbs2Zo0aZLuuOMOSdKrr76qsLAwrV69WgkJCTpw4IDS09O1e/dutW/fXpI0b9489enTRzNnzlRERISr2wYAABbi8jMw7777rtq3b6+//vWvCg0NVdu2bfWvf/3LMX748GHZ7XbFxsY6tgUFBSk6OlqZmZmSpMzMTAUHBzvCiyTFxsbKy8tLO3fuLPe4RUVFKiwsdFoAAIBncnmA+fLLL7Vo0SJdd911Wr9+vR555BE99thjWrp0qSTJbrdLksLCwpweFxYW5hiz2+0KDQ11Gvf29lZISIij5teSk5MVFBTkWBo1auTqqQEAgGrC5QGmtLRUN910k5577jm1bdtWw4cP17Bhw5SSkuLqQzmZOHGiCgoKHMuxY8eq9HgAAMB9XB5gGjZsqBYtWjhta968uY4ePSpJCg8PlyTl5uY61eTm5jrGwsPDlZeX5zR+7tw5nThxwlHza35+fgoMDHRaAACAZ3J5gOncubMOHjzotO3zzz9XZGSkpJ8v6A0PD9fGjRsd44WFhdq5c6diYmIkSTExMcrPz1dWVpajZtOmTSotLVV0dLSrWwYAABbj8ruQRo8erU6dOum5557TwIEDtWvXLi1evFiLFy+WJNlsNo0aNUrPPvusrrvuOkVFRWny5MmKiIhQ//79Jf18xqZXr16Ot55KSkqUlJSkhIQE7kACAACuDzAdOnTQqlWrNHHiRE2bNk1RUVGaPXu2EhMTHTXjxo3T6dOnNXz4cOXn5+uWW25Renq6/P39HTVpaWlKSkpSjx495OXlpQEDBmju3LmubhcAAFiQywOMJPXt21d9+/a96LjNZtO0adM0bdq0i9aEhIRo+fLlVdEeAACwOL4LCQAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWA4BBgAAWE6V3EYNwLM0mbC2yo9xZHp8lR8DgOfgDAwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALAcAgwAALCcKg8w06dPl81m06hRoxzbzp49qxEjRqhevXqqXbu2BgwYoNzcXKfHHT16VPHx8apZs6ZCQ0M1duxYnTt3rqrbBQAAFlClAWb37t365z//qRtuuMFp++jRo/Xee+9p5cqV2rp1q44fP64777zTMX7+/HnFx8eruLhYO3bs0NKlS5WamqopU6ZUZbsAAMAiqizAnDp1SomJifrXv/6lunXrOrYXFBTo5Zdf1qxZs3TbbbepXbt2WrJkiXbs2KGPP/5YkrRhwwbt379fr732mtq0aaPevXvrmWee0YIFC1RcXFxVLQMAAIuosgAzYsQIxcfHKzY21ml7VlaWSkpKnLY3a9ZMjRs3VmZmpiQpMzNTrVu3VlhYmKMmLi5OhYWFysnJKfd4RUVFKiwsdFoAAIBn8q6Knb7xxhvau3evdu/efcGY3W6Xr6+vgoODnbaHhYXJbrc7an4ZXsrGy8bKk5ycrKefftoF3QMAgOrO5Wdgjh07pscff1xpaWny9/d39e4vauLEiSooKHAsx44du2LHBgAAV5bLA0xWVpby8vJ00003ydvbW97e3tq6davmzp0rb29vhYWFqbi4WPn5+U6Py83NVXh4uCQpPDz8gruSytbLan7Nz89PgYGBTgsAAPBMLg8wPXr00L59+5Sdne1Y2rdvr8TERMd/+/j4aOPGjY7HHDx4UEePHlVMTIwkKSYmRvv27VNeXp6jJiMjQ4GBgWrRooWrWwYAABbj8mtg6tSpo1atWjltq1WrlurVq+fYPnToUI0ZM0YhISEKDAzUyJEjFRMTo44dO0qSevbsqRYtWmjw4MGaMWOG7Ha7Jk2apBEjRsjPz8/VLQMAAIupkot4L+ell16Sl5eXBgwYoKKiIsXFxWnhwoWO8Ro1amjNmjV65JFHFBMTo1q1amnIkCGaNm2aO9oFAADVzBUJMFu2bHFa9/f314IFC7RgwYKLPiYyMlLvv/9+FXcGAACsiO9CAgAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAlkOAAQAAluPt7gYA4EpoMmFtlR/jyPT4Kj8GgJ9xBgYAAFgOAQYAAFiOywNMcnKyOnTooDp16ig0NFT9+/fXwYMHnWrOnj2rESNGqF69eqpdu7YGDBig3Nxcp5qjR48qPj5eNWvWVGhoqMaOHatz5865ul0AAGBBLg8wW7du1YgRI/Txxx8rIyNDJSUl6tmzp06fPu2oGT16tN577z2tXLlSW7du1fHjx3XnnXc6xs+fP6/4+HgVFxdrx44dWrp0qVJTUzVlyhRXtwsAACzI5RfxpqenO62npqYqNDRUWVlZ6tKliwoKCvTyyy9r+fLluu222yRJS5YsUfPmzfXxxx+rY8eO2rBhg/bv368PPvhAYWFhatOmjZ555hmNHz9eTz31lHx9fS84blFRkYqKihzrhYWFrp4aAACoJqr8GpiCggJJUkhIiCQpKytLJSUlio2NddQ0a9ZMjRs3VmZmpiQpMzNTrVu3VlhYmKMmLi5OhYWFysnJKfc4ycnJCgoKciyNGjWqqikBAAA3q9IAU1paqlGjRqlz585q1aqVJMlut8vX11fBwcFOtWFhYbLb7Y6aX4aXsvGysfJMnDhRBQUFjuXYsWMung0AAKguqvRzYEaMGKFPP/1UH374YVUeRpLk5+cnPz+/Kj8OAABwvyo7A5OUlKQ1a9Zo8+bNuvrqqx3bw8PDVVxcrPz8fKf63NxchYeHO2p+fVdS2XpZDQAA+ONyeYAxxigpKUmrVq3Spk2bFBUV5TTerl07+fj4aOPGjY5tBw8e1NGjRxUTEyNJiomJ0b59+5SXl+eoycjIUGBgoFq0aOHqlgEAgMW4/C2kESNGaPny5XrnnXdUp04dxzUrQUFBCggIUFBQkIYOHaoxY8YoJCREgYGBGjlypGJiYtSxY0dJUs+ePdWiRQsNHjxYM2bMkN1u16RJkzRixAjeJgIAAK4PMIsWLZIkdevWzWn7kiVLdP/990uSXnrpJXl5eWnAgAEqKipSXFycFi5c6KitUaOG1qxZo0ceeUQxMTGqVauWhgwZomnTprm6XQAAYEEuDzDGmMvW+Pv7a8GCBVqwYMFFayIjI/X++++7sjUAAOAh+C4kAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOVX6bdQAANdqMmFtlR/jyPT4Kj8G8HtxBgYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFgOAQYAAFiOt7sbAAD88TSZsLZK939kenyV7h/uR4ABAOA3qOoQJhHELoW3kAAAgOUQYAAAgOUQYAAAgOVU6wCzYMECNWnSRP7+/oqOjtauXbvc3RIAAKgGqu1FvCtWrNCYMWOUkpKi6OhozZ49W3FxcTp48KBCQ0Pd3R4AAB7BqhcjV9szMLNmzdKwYcP0wAMPqEWLFkpJSVHNmjX1yiuvuLs1AADgZtXyDExxcbGysrI0ceJExzYvLy/FxsYqMzOz3McUFRWpqKjIsV5QUCBJKiwsrPBxS4vO/MaOK64y/fxWzKNiPGEOEvOoKE+Yg8Q8KsoT5iD9MedRVmuMuXShqYa++eYbI8ns2LHDafvYsWPNzTffXO5jpk6daiSxsLCwsLCweMBy7NixS2aFankG5reYOHGixowZ41gvLS3ViRMnVK9ePdlstio5ZmFhoRo1aqRjx44pMDCwSo5R1TxhDhLzqE48YQ6SZ8zDE+YgMY/q5ErMwRijH3/8UREREZesq5YBpn79+qpRo4Zyc3Odtufm5io8PLzcx/j5+cnPz89pW3BwcFW16CQwMNCyL8YynjAHiXlUJ54wB8kz5uEJc5CYR3VS1XMICgq6bE21vIjX19dX7dq108aNGx3bSktLtXHjRsXExLixMwAAUB1UyzMwkjRmzBgNGTJE7du3180336zZs2fr9OnTeuCBB9zdGgAAcLNqG2Duvvtufffdd5oyZYrsdrvatGmj9PR0hYWFubs1Bz8/P02dOvWCt66sxBPmIDGP6sQT5iB5xjw8YQ4S86hOqtMcbMZc7j4lAACA6qVaXgMDAABwKQQYAABgOQQYAABgOQQYAABgOQQYAABgOQQYAABgOQSY3+jX334NAHA9T/ld6ynzqE4IMJWQkZGhPn36qG7duqpZs6Zq1qypunXrqk+fPvrggw/c3d4fyv79+/Xoo4+qbdu2atiwoRo2bKi2bdvq0Ucf1f79+93dHizIE19TVv2j6Sm/az1hHtX554IPsqugpUuX6sEHH9Rdd92luLg4xycC5+bmasOGDXrrrbf08ssva/DgwW7u9NL279+v+fPnKzMzU3a7XZIUHh6umJgYJSUlqUWLFm7u8PLWrVun/v3766abbrrgucjIyFBWVpbeeecdxcXFubnTy/OE58MT5uBJr6mMjAy99NJLyszMVGFhoaSfv3gvJiZGY8aMUWxsrJs7vDRP+V3rCfOo9j8XBhVy3XXXmfnz5190fMGCBaZp06ZXsKPKe//9942vr6/p2LGjmTp1qlm4cKFZuHChmTp1qunUqZPx8/Mz6enp7m7zsm644QYzefLki45PnTrVtG7d+gp29Nt4wvPhCXMwxnNeU6mpqcbb29skJCSYJUuWmPfff9+8//77ZsmSJeaee+4xPj4+5tVXX3V3m5fkCb9rjfGMeVT3nwsCTAX5+fmZzz777KLjn332mfH397+CHVVedX8xVpS/v7/lnwtjPOP58IQ5GOM5rylP+KPpCb9rjfGMeVT3nwuugamgli1b6uWXX77o+CuvvFLtT5V//vnnSkxMvOj4Pffco0OHDl3Bjn6bJk2aaO3atRcdX7t2rSIjI69gR7+NJzwfnjAHyXNeU0ePHr3kW0Q9evTQ119/fQU7qjxP+F0recY8qvvPRbX9Nurq5sUXX1Tfvn2Vnp6u2NhYp/cCN27cqC+//PKST3R1UPZivP7668sdd/eLsaKmTZumQYMGacuWLeU+F+np6Vq+fLmbu7w8T3g+PGEOkue8psr+aM6YMaPccSv80fSE37WSZ8yjuv9ccBFvJRw5ckSLFi3Sxx9/fMHFig8//LCaNGni3gYvY+XKlRo0aJB69+59yRfjgAED3Nzp5e3YsUNz584t98LRxx9/XDExMW7u8PI84fnwhDmU8YTX1JYtW9S3b19dc801l/yj2aVLFzd3emlW/11bxhPmUZ1/LggwfzDV+cX4R+QJz4cnzMGTeMIfTaAiCDAAAMByuIjXRYYMGaLbbrvN3W1A0pNPPqm//e1v7m4DHoTXVPXhKb9rPWEe7v65IMC4yFVXXWWJixUvxd0vRlf55ptvdOTIEXe38bt5wvPhCXOQPOc15Ql/ND3hd63kGfP4+uuv3fpzwVtIcLjvvvv09ddfa9OmTe5u5Tcxxshms7m7DZcZMmSIjh07ZtnnQ7L+a8rTTJw4UXa7XUuWLHF3K8DvRoCBx/D19dV///tfNW/e3N2tAPgdvv32Wy1atEgffvihvv32W3l5eemaa65R//79df/996tGjRrubvEP4/vvv9crr7xywUX6nTp10v33368GDRq4rTcCTAXt3btXdevWVVRUlCRp2bJlSklJ0dGjRxUZGamkpCQlJCS4ucvKOX36tN5880198cUXatiwoe655x7Vq1fP3W1d1pgxY8rdPmfOHN17772OOcyaNetKtlVpI0eO1MCBA3Xrrbe6u5Xf5cCBA/r4448VExOjZs2a6bPPPtOcOXNUVFSke++91zJvWfz000/KyspSSEjIBZ+VcvbsWb355pu677773NSdaxw7dkxTp07VK6+84u5WLmrPnj2KjY1V06ZNFRAQoMzMTA0aNEjFxcVav369WrRoofT0dNWpU8fdrV7W/PnztWvXLvXp00cJCQlatmyZkpOTVVpaqjvvvFPTpk2Tt3f1/Ti23bt3Ky4uTjVr1iz3tvwzZ85o/fr1at++vXsadMvn/1rQDTfcYDIyMowxxvzrX/8yAQEB5rHHHjOLFi0yo0aNMrVr1zYvv/yym7u8tObNm5sffvjBGGPM0aNHTZMmTUxQUJDp0KGDCQkJMaGhoebLL790c5eXZ7PZTJs2bUy3bt2cFpvNZjp06GC6detmunfv7u42L8tmsxkvLy9z3XXXmenTp5tvv/3W3S1V2rp164yvr68JCQkx/v7+Zt26daZBgwYmNjbW3HbbbaZGjRpm48aN7m7zsg4ePGgiIyMdz0mXLl3M8ePHHeN2u914eXm5sUPXyM7Orvbz6Ny5s3nqqacc68uWLTPR0dHGGGNOnDhh2rRpYx577DF3tVdhzzzzjKlTp44ZMGCACQ8PN9OnTzf16tUzzz77rHnuuedMgwYNzJQpU9zd5iVFR0eb4cOHm9LS0gvGSktLzfDhw03Hjh3d0NnPCDAVFBAQYI4cOWKMMaZt27Zm8eLFTuNpaWmmRYsW7mitwmw2m8nNzTXGGJOYmGg6depk8vPzjTHG/PjjjyY2Ntbcc8897myxQpKTk01UVNQFfxi9vb1NTk6Om7qqPJvNZj744APz+OOPm/r16xsfHx9z++23m/fee8+cP3/e3e1VSExMjPn73/9ujDHm9ddfN3Xr1jVPPvmkY3zChAnmz3/+s7vaq7D+/fub+Ph4891335lDhw6Z+Ph4ExUVZb766itjjHUCzDvvvHPJ5aWXXqr28wgICDD/+9//HOvnz583Pj4+xm63G2OM2bBhg4mIiHBXexV27bXXmv/85z/GmJ+DY40aNcxrr73mGH/77ber/fdS+fv7mwMHDlx0/MCBA279LiQCTAXVq1fP7NmzxxhjTGhoqMnOznYa/+KLL0xAQIA7WquwXwaYa665xmzYsMFp/KOPPjKNGjVyR2uVtmvXLvOnP/3JPPHEE6a4uNgYY80AU/Z8FBcXmxUrVpi4uDhTo0YNExERYZ588klz6NAhN3d5aYGBgY4ez58/b7y9vc3evXsd4/v27TNhYWHuaq/CQkNDzSeffOJYLy0tNQ8//LBp3Lix+d///meZAFN2Bslms110qe7ziIyMNB9++KFj/fjx48Zms5kzZ84YY4w5fPhwtf8SRGN+DmJlAdgYY3x8fMynn37qWD9y5IipWbOmO1qrsCZNmpilS5dedHzp0qUmMjLyyjX0K9xGXUG9e/fWokWLJEldu3bVW2+95TT+5ptvqmnTpu5orVLK7tI5e/asGjZs6DR21VVX6bvvvnNHW5XWoUMHZWVl6bvvvlP79u316aefWvoOJB8fHw0cOFDp6en68ssvNWzYMKWlpV30O4aqk7J/dy8vL/n7+ysoKMgxVqdOHRUUFLirtQr76aefnK5FsNlsWrRokfr166euXbvq888/d2N3FdewYUO9/fbbKi0tLXfZu3evu1u8rP79++vhhx9Wenq6Nm/erMTERHXt2lUBAQGSpIMHD+qqq65yc5eXFx4erv3790uSDh06pPPnzzvWJSknJ0ehoaHuaq9C/t//+38aPny4Hn/8cb377rvauXOndu7cqXfffVePP/64Hn74YY0bN859DbotOlnMN998Y5o0aWK6dOlixowZYwICAswtt9xihg0bZrp06WJ8fX3N2rVr3d3mJdlsNtO6dWvTtm1bU7t2bfPWW285jW/dutVcddVVburut3v99ddNWFiY8fLysuwZmPKUlpZecJasurnhhhvMunXrHOv79u0zJSUljvVt27aZqKgod7RWKR06dDCvvvpquWMjRowwwcHB1f7MhTHG9OvXz0yePPmi49nZ2cZms13Bjirvxx9/NAMHDjTe3t7GZrOZTp06OV2bt379evPmm2+6scOKmTRpkmnQoIF58MEHTVRUlJkwYYJp3LixWbRokUlJSTGNGjUyo0ePdnebl/XGG2+Y6Ohox/Nhs9mMt7e3iY6ONitWrHBrb9yFVAn5+fmaPn263nvvPX355ZcqLS1Vw4YN1blzZ40ePdp9V2JX0NNPP+203rFjR8XFxTnWx44dq6+//lqvv/76lW7td/v666+VlZWl2NhY1apVy93tVEhUVJT27NljiTu/LiYlJUWNGjVSfHx8ueNPPvmk8vLy9O9///sKd1Y5ycnJ2r59u95///1yxx999FGlpKSotLT0CndWOdu3b9fp06fVq1evcsdPnz6tPXv2qGvXrle4s8o7e/aszp07p9q1a7u7ld+ktLRU06dPV2Zmpjp16qQJEyZoxYoVGjdunM6cOaN+/fpp/vz5lvl9VVJSou+//16SVL9+ffn4+Li5I26jBgAAFsQ1MAAAwHIIMAAAwHIIMAAAwHIIMAAAwHIIMAAAwHIIMAAAwHIIMAAAwHL+P+mt7PVJcNJEAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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aY4xJTk5Oc05kpHXr1sbX19cKphlxfFD68ssv05QFBgaap59+2mlZVs/FhQsXmrp16xofHx8TEBBgGjdubFavXm2VO4L35s2bTd26dY2Xl5cpVaqUWbBggdN6HO83GzZsMP369TNFihQxAQEBabZ3q+Bdv359U69ePWPMnx1RGT1Ghw8fNocPH87UflatWtXUr18/3fKMgs7MmTONJLNnzx6n5YsWLTKSzObNm13eL7PB+9KlSxl2uN0oo+A9aNAgIynNsXeEpbffftta9tBDD5mgoKA0rx19+/Y1+fLlczqfT548afbt22eFwvTc6n152rRppnLlyta5Fh4e7vThs0CBAuahhx5Ks962bdsaT09P64PyG2+84fJ4OM6t559/PsN2GmNM7dq1M9WpcObMGSPJjBgx4pZ1hw0bZiS57Hy42V85H43J/Ln/2muvZfpDvsPu3buNJJevy381eN/WxZWJiYk6c+aMTp8+rT179qhfv35W9/2N3nrrLdWqVUvjx4/Xq6++Kg8PDz300ENasWJFmnWuW7dOQ4cOVZcuXfTWW2+lO67n66+/1tmzZ9WtW7csfV07aNAgff/99xozZoz69eunZcuWaeDAgU515s+fr/z582vYsGF66623FB4ertGjR+u5555Ls77ff/9drVu3Vs2aNTV16lQ1b95cU6dOVbFixVSxYkUtXLhQCxcu1AsvvOCyPZUqVdLChQtVuHBh1axZ06pfpEgRXb58Wc2aNdPChQvVvXt3vf766/L399djjz2mt956K8265s2bp+nTp6tv37564403FBgY6HKbpUqV0j333KOPPvpI169fdypzDDPp1q1bmvslJCTo3nvvVXx8vFavXq26detaZZcuXdLatWvVuHFjlSpVyuV2u3TpIi8vLy1fvtxl+Y0c48gKFiwo6faP9+3auXOnWrVqpd9//13jxo1T7969NX78eC1dujRN3VdeeUU9e/ZUuXLlNGXKFA0ZMsQaknDzeLFz586pVatWqlGjht544w1VrFhRI0aMcLo2IjPHvUiRItZYxQceeMA6bx588EF17txZPj4+io6OTtPW6OhoNWvWTP/617+c2tSmTRuFh4dr0qRJKlasmPr166e5c+dadVJTU9W+fXtNnjxZ7dq10/Tp09WxY0e9+eab6tKli9M2ZsyYoUqVKmnr1q1Zftyz6quvvpKfn59+/fVXVahQQfnz55efn5/69eunK1eupKlvjNGZM2cUHx+vzZs36+mnn1aePHmchuokJyfLx8cnzX0dwyXi4uLSbc+lS5e0bt06NWvWTMWKFcv0fnTr1k3du3fX4sWLVa9ePVWvXl1vvfXWLYfZSNLJkye1fv16PfLII5KkRx55RJ988olSUlJued+rV69q27Zt1lAxV86fP6/Tp0/rhx9+0L///W8lJSWpRYsWVvnXX3+tK1euqGzZsurcubPy5csnHx8fNWzYMN1hAA6nT59WTEyMOnbsaA31udG1a9d05swZnTx5UmvWrNGLL76oAgUKqF69emnq1q5dO81Y0ayci+PGjVOPHj2UN29ejR8/XuPGjVNYWFiaa0cc4//vu+8+vfHGGypYsKAee+wx7dmzJ806+/fvr71796b7/pGRpKQkbd26VXXr1tXzzz8vf39/5c+fX6VLl9ZHH32Upn6LFi2cjsvN6zpz5oz279+v559/Xj/++GO6dW9l586d8vX1dfpqXZJ1THbu3Hlb65X+fO/19fWVj4+PKleubL0f3Y7k5GTlyZNHnp6eTstdPY937typ2rVrW8OXHOrVq6dLly45DZEZOXKkKlWqpF9//TVT7XD1vvzOO+/o6aefVuXKlTV16lSNGzdONWvW1JYtW5zan97rUEpKinWtheOi4pvrZub1SpI1VLhw4cIuy3///XedOnVK27dvt4aNZObciY+PV758+ax23Cg7z0dHezJz/y+++EI+Pj7q3Llzpte9cuVKFS1aVHXq1Lnt9qUrKynd8Wn+5puXl1ea3lhjjPVVk0NKSoqpWrWquffee52WSzLu7u5pPrm58tZbbxlJ6faIp9fmyMhIp2EKQ4cONXny5DHnz59Pt73GGPPkk0+m+eTbtGlTI8nMmTMnTf2sDjVx9RXm1KlTjSTz/vvvW8tSUlJMRESEyZ8/v/VJ0vHJ2s/PL03vfXocvRY39uZcv37d/Otf/zIRERFOdR29vCVKlDB+fn4uh6js2rXLSDKDBw/OcLvVq1c3gYGBadZ94MABc/r0aXP06FEzd+5c4+PjY4oUKWL1VmT1eN/odnq827VrZ/Lly2d+/fVXa9mhQ4eMh4eHU6/N0aNHTZ48ecwrr7zidP8ffvjBeHh4OC13nC/vvfeetSw5OdkEBwebTp06Wcsye9wz2q9HHnnEhIaGOvXg7NixI03vmqNNb7zxhlObatasaYoWLWr16ixcuNC4u7un6c2aM2eOkWS++eYba5njmN5uL8DNMnouVa9e3eTLl8/ky5fPDBo0yHz66adWT1fXrl3T1P/tt9+cXrOKFStmFi9e7FSnXbt2JiAgIE1PTUREhJFkJk+enG5bv//+e2s41M1+//13c/r0aevm6ivny5cvm+joaNOiRQvj5uZmvLy8TJcuXcyaNWvS7cmfPHmy8fHxsdp78ODBTD9XDh8+bCSZ6dOnp1unQoUK1uOVP39+8+KLLzq1ZcqUKUaSKVSokKlXr56Jjo42s2bNMkFBQaZgwYLm5MmT6a57+vTpRpJZuXKly/LY2Fin41WhQoV0z6u+ffsaHx8fp2WZPRcPHTpk3N3dXQ5ZuvH9wvFt5qZNm6xlp06dMl5eXuaZZ56xljnebxo1apThN3QZ9Xg7nq+FChUyQUFBZtasWSY6OtrUq1fPuLm5pen1L1GiRLq9glFRUdZj6OnpaZ588skMe5Uz6mFs27atKV26dJrlFy9eNJLMc8895/J+t+rxvueee8zUqVPN559/bmbPnm2qVq1qJJlZs2ale5+MerwdPcE3v2Y999xzRpK5//77rWW+vr7miSeeSLOOFStWGElm1apV1rJevXqlu80bZfS+3KFDB1OlSpUM71+tWjVTvnx5p/MnOTnZFC9e3Egyn3zyiTHGmE8//dRIMgsXLnS6v+O1uWrVqhluxzHU6+YhOQ5eXl7WuVOoUCEzbdq0DNdnzJ/PJ29vb9OjRw+X5dl5PhqT8bl/o4IFC5oaNWrcst6NGjdubHr16uWyLFd6vGfOnKmYmBjFxMTo/fffV/PmzfXvf/9bn332mVO9Gz+JnTt3TomJiWrcuLHLK4abNm2qypUr33LbSUlJkqQCBQpkqc19+/Z1uvimcePGun79uo4dO+ayvX/88YfOnDmjxo0b69KlS9q/f7/T+ry8vJwuHshOK1euVHBwsNWTJUl58+bV008/rQsXLmjjxo1O9Tt16qQiRYpkat1dunRR3rx5nXoUNm7cqF9//VXdu3d3eZ+EhATlz59fISEhacr++OMPSbc+HgUKFLCO3Y0qVKigIkWKqGTJknriiSdUtmxZffnll9an5ds93rfj+vXr+uqrr9SxY0enizrKli2r1q1bO9X97LPPlJqaqocfflhnzpyxbsHBwSpXrlyamTDy58/v9I2Qp6en6tWr5zTLRVaPuys9e/a0ekIdoqOj5ePjo06dOjnV9fDw0JNPPunUpieffFKnTp2yeks+/vhjVapUSRUrVnTaz3vvvVeSnLYzduxYGWNsueDzwoULunTpknr27Klp06bpwQcf1LRp0/Tkk0/qww8/1KFDh5zqBwYGKiYmRsuWLdP48eNVuHDhNLNk9OvXT+fPn1eXLl20c+dOHTx4UEOGDLGmIr18+XK67XGcp/nz509TVrp0aRUpUsS6ffHFF2nqeHt7q1u3bvrqq6905MgRjRw5Ulu2bFHLli1VunRpTZgwIc19oqOj1bZtW+u5Ua5cOYWHh7v8xuNmjh51xzdLrsybN0+rVq3SrFmzVKlSJV2+fNnpmzLH4+fm5qa1a9eqW7du6tevn5YuXapz585p5syZ6a570aJFKlKkiO677z6X5ZUrV1ZMTIyWLl2qZ599Vr6+vmmOl0PBggV1+fJlp1lUMnsuLl26VKmpqRo9enSaXs+bL9asXLmyGjdubP1fpEgRVahQweVMNX369Lntb+gc+/n777/r888/V79+/dStWzetXbtWhQoVSnNx/NGjR9Od3eO1117TmjVr9O6776pBgwZKSUlJM+NFZl2+fFleXl5plnt7e1vlt+Obb77R4MGD1b59ez311FOKi4tT1apV9fzzz9/WOrt16yZ/f3898cQTiomJ0dGjR/X2229r1qxZadqZlX2aP3++jDGZnmXD1ftyQECAfvnlF23bti3d+/Xv318HDx5U7969tXfvXv3444/q2bOnfvvtN6c2tWnTRiVKlNB//vMfffbZZzp27Jg++ugjvfDCC/Lw8Mjwsdu/f78GDBigiIgI9erVy2WdL7/8UitXrtQbb7yh4sWL6+LFixnu76VLl/TQQw/Jx8cnzYw0Dtl5PkoZn/s3SkpKylKGOH/+vGJjY9W2bdvbbltGbit416tXT5GRkYqMjFT37t21YsUKVa5cWQMHDnT6mnP58uVq0KCBvL29FRgYaH1NnpiYmGad6Q1TuJmfn5+k/wt8mVW8eHGn/x1vOOfOnbOW7dmzRw888ID8/f3l5+enIkWKWGHp5jb/61//SvNVVnY5duyYypUrl+aNwPEV340fFqTMP3aSVKhQIUVFRWnJkiXWV/KLFi2Sh4eHHn74YZf3ef/993X27Fndd999aa6od5zMtzoef/zxh8sT/9NPP1VMTIwWLVqkBg0a6NSpU04fgG73eN+OU6dO6fLlyypbtmyaspuXHTp0SMYYlStXzilUFSlSRPv27UvzOBUrVizNG3nBggWdzr+sHndX7rvvPoWEhFjhKzU1VR988IE6dOiQ5vEPDQ1N8zV/+fLlJf3fkJ9Dhw5pz549afbRUS8zMyzc7OzZs4qPj7durl4PbsVxjtz4IUX6v6FSsbGxTss9PT0VGRmp+++/X6NGjdLMmTPVu3dvp+FPrVu31vTp07Vp0ybVrl1bFSpU0IoVK/TKK69Ich2qHRyPratw+PnnnysmJkaTJ0/O1L6VKFFCY8aM0Xfffad27drp2LFjmjhxolOdffv2aefOnWrYsKEOHz5s3Zo1a6bly5e7/JDrijEm3bKIiAhFRUWpX79+Wr16td5//32NHDnSKnccg3bt2jk9Ng0aNFCpUqX07bffulzvzz//rNjYWHXp0kUeHq4n1vLz81NkZKQ6dOigiRMn6plnnlGHDh1cTqXn2IfbmdXkp59+kru7e6Y6fW5+D5HSPocdsvKafDPH41qqVCnVr1/fWp4/f361a9dOW7duzXRYqVmzpu677z4rhG7dujXTM265aperOdMd7yOuhkfcDk9PTw0cOFDnz5+/5XAJV4KDg/XFF18oOTlZLVu2VKlSpTR8+HBNnz5dkvPzOCf3ydU5MGLECOXPn1/16tVTuXLlNGDAgDTDpJ566ik9//zzWrRokapUqaJq1arpp59+0rPPPuvUfm9vb61YsUKFChVSp06dVLJkSfXs2VOjR49WYGBguq9X8fHxatu2rfz9/fXJJ5+k+wGxefPmat26tYYNG6aPP/5Y48aN04wZM1zWvX79urp27aq9e/fqk08+SXc2kuw8H7PCz88vSxnCMRvazTMuZZds+QEdd3d3NW/eXL/99pvV07R582a1b99e3t7emjVrllauXKmYmBh169bN5Yt9Zk/wihUrSvpzyqusSO/kcrTl/Pnzatq0qb7//nuNHz9ey5YtU0xMjPWGl5qaelvttUNW2/Loo48qKSlJy5cvV0pKij799FO1bNky3V7zpk2b6qOPPtKRI0cUFRXlFJTKli0rDw8P7d69O93tJScn68CBAy7f3Jo0aaLIyEg98sgjiomJkY+Pj7p372493rd7vHNaamqq3NzctGrVKuvbnxtvN88tfKvzL7vkyZNH3bp106effqorV65o/fr1OnnyZIZTRGYkNTVV1apVc7mPMTEx6t+/f5bX+eCDDyokJMS6DR48OMvrcLywBwUFOS0vWrSoJLkMQze65557nD6gOAwcOFAJCQn69ttvtX37du3fv1/+/v6S/u9DiSuO54Grua6bNm2qyMhIa/qqjFy7dk1ffPGFHnjgAYWFhenLL79Ux44d04x5ff/99yX9Oed1uXLlrNsbb7yhK1eu6NNPP81wO4UKFZJ068fJoWDBgrr33nudHq/0joH053FIb92OfUnvGzZXHnzwQUnShx9+mKbs3Llz1vjynJSV5/BfacutHterV6/esvfRFU9PT7Vv316fffbZbfUkh4SEKD4+Ps3+Onpis3Pqt7CwMEl/fki/HU2aNNHPP/+snTt36uuvv9avv/6qBg0aSHJ+HoeEhFjtv1F27JOrc6BSpUo6cOCAPvzwQzVq1EiffvqpGjVqlGY64VdeeUUJCQnavHmzdu/erW3btlnviTe2v0qVKvrxxx/1448/avPmzTp58qT69OmjM2fOuHy9SkxMVOvWrXX+/HmtWrUq0/tXpkwZ1apVK91v0/r06aPly5dr/vz51reht/JXz8esqFixog4ePJip61+kP799btiwofXan91uax5vVxyfwB09Pp9++qm8vb21evVqp69y5s2b95e206hRIxUsWFAffPCBnn/++Wy74G7Dhg36/fff9dlnnznN13vkyJEsrSc75pItUaKEdu/erdTUVKfeT8dwlxIlSvyl9bdv314FChTQokWLlDdvXp07d+6Wb4Lt2rXT3Llz1atXL91///1as2aNfHx8rB8cWLdunY4dO+aybR999JGSk5NvOcd7/vz5NWbMGD3++OP66KOP1LVr1xw73q4ULVpU3t7eOnz4cJqym5eVKVNGxhiVKlUqw0CWFZk97rc6x3r27Kk33nhDy5Yt05dffqkiRYooKioqTb2TJ0/q4sWLTr3ejouJHF+nlilTRt9//71atGiRbfMkv/HGG06h7Hbe3MLDwxUTE2NdXOlw8uRJScrU0KsrV6647G339fVVRESE9f9XX31lXTSYHl9fXzVr1swatnXjRayZsXfvXs2bN08LFy5UQkKCypcvr5deekmPPfZYmgBmjNGiRYvUvHlzlx98XnrpJUVHR2c4FK548eLy8fHJ0uvb5cuXnR4vxwcJVxebnTx50vrQfLNFixapTJkyVhDKjOTkZKWmpro8XkeOHElzwV9mlSlTRqmpqdq7d69q1qx5W+vIbqGhoQoODk73cfX29r7toXeXL1+WMUZ//PFHlj8c1KxZU//73/+0b98+p04Ux4WB2fn4OYbvZHYIpSt58uRxapPjh10iIyOtZTVr1tTmzZvTvOZu2bJF+fLly7bX9hv5+vqqS5cu6tKli1JSUvTggw/qlVde0ciRI60hLtKfH3YbNWrk1H7H5A03cnNzU5UqVaz/V65cqdTUVKf9lP58vWvXrp0OHjyor776KlPf8tzo8uXLLr8dGD58uObNm6epU6em+QYyM+u83fMxK9q1a6fY2Fh9+umnt2yjMUarVq3Sf/7znxxrT7ZMJ5iSkmLKlStnPD09TWJiojHmzyll8uXL5zSlz5EjR0y+fPnSXGghZW4eTwfH1DDPPPOMy3mdFy5ceMt5vB3T+TkGx3/xxRfWNFAOjovNdNMgesdUbK7Ur18/S4P4M7q40jGlnjF/zgPasGFDlxdXvv7665nenkPPnj2Nl5eXiYqKSndKL1dT/jkudmzdurV1Ad7GjRuNu7u7adasWZoLVH/++WcTHByc6Xm8U1JSTLFixUzNmjWtZVk53je6nYsr77///kxdXHn48GGTJ08e061btzRtSk1NdZqDNb3zpVevXk4XhmT2uF+6dOmWF7RWr17dtGzZ0vj5+bmcPjKjiyuLFCliHdv58+cbSea///1vmnVcunTJ6byxczpBxwVoN/92wCOPPGI8PDys43fhwgWX7XFMT3fzHLg3c8zNPXDgwFu2d9OmTdbzwNXc2I65tm+cUnX9+vWmfv36RpLx8fExPXr0MBs3bsxwO5s3b05zse6NHFNH3ngOu9K4cWPTuHHjNMtvnOfY4ciRI6ZAgQJp6teoUcP4+fk5PY9Xr15tJJlJkyalWY/juKX3uJ87d87ldG2OebxdXQgWGBjo8hzPjKxcXOlqHu+mTZs6naOZ/d2IW00nOHjwYCPJrFmzxlp2+vRp4+fnZ00f6eBqSjVXx/DcuXMmLCzMhIWFpduujC5mO3HiRLrzeP/rX/9K92LSjC6udDUpQFJSkilTpowpXLhwunNfZ3RxZXrbKV68uKlevbrTcf7www/TPCdPnz5tAgICnKbOzIqM3pdvfF9wGD58uHF3d89w+j1HOzO6wNuYP1+Ta9eubUJCQpzWd+3aNdO+fXvj4eFhVqxYke79r1696vQ+7bBlyxaTJ0+eNBdNTpo0ySgTUxfmxPmYFWfPnjUhISEmJCTEHDhwwGX7HFMpb9myxcjFNI03+qsXV95Wj/eXX35p9cKdOnVKixYt0qFDh/Tcc89ZY3Lbtm2rKVOmqFWrVurWrZtOnTqlmTNnqmzZshkOS8iM4cOHa8+ePXrjjTe0fv16de7cWcHBwYqPj9fSpUu1devWdMcXpueee+5RwYIF1atXLz399NNyc3PTwoULszwUIDw8XLNnz9bLL7+ssmXLqmjRopn+6sWhb9+++u9//6vHHntMcXFxKlmypD755BN98803mjp1arZcaPjoo4/qvffe0+rVq9W9e3eXU3q58vTTT+vs2bMaN26cevbsqejoaDVp0kSTJ0/WsGHDVL16dT322GMKCQnR/v379c477yg1NVUrV67M8EIuh7x582rw4MEaPny4Vq1apVatWmX5eC9cuFDHjh2zLrbatGmTdUFSjx49MvzGYOzYsVqzZo0aNmyofv366fr165oxY4aqVq3qNEVamTJl9PLLL2vkyJE6evSoOnbsqAIFCujIkSNasmSJ+vbtm+VPzJk97o4ptxYvXqzy5csrMDBQVatWdfr1sZ49e1rbT2+YSWhoqCZOnKijR4+qfPnyWrx4sXbt2qW3337b+hnvHj166KOPPtJTTz2l9evXq2HDhrp+/br279+vjz76SKtXr7amW5oxY4bGjRun9evX3/YFlps2bdKmTZsk/Tnl3MWLF61j16RJE+vbqFq1aumJJ57Q3Llzde3aNTVt2lQbNmzQxx9/rJEjR1q96IcOHVJkZKS6dOmiihUryt3dXdu3b9f777+vkiVLOg1zOXbsmB5++GG1b99ewcHB2rNnj+bMmaPq1avr1VdfvWXbGzdurBkzZmjQoEEqV66c9cuVKSkpOnjwoKKjo+Xp6ang4GDrPhs3btTVq1c1a9Ys66KwW4mOjlaePHnSvfCnffv2euGFF/Thhx9q2LBh6a6nQ4cOeuGFF5SUlGS9bktStWrV1KJFC9WsWVMFCxbUoUOH9O677+rq1atpLpp68803dd9996lRo0Z68sknlZiYqClTpqh8+fLq16+fy7ZL6Q8z2bBhg55++ml17txZ5cqVU0pKijZv3qzPPvtMderUSXMux8XF6ezZs+rQoYPT8rFjx2bqXCxbtqxeeOEFvfTSS2rcuLEefPBBeXl5adu2bQoNDXV5UetfMWPGDJ0/f976ZmbZsmXWL60OGjTIOv4jR47URx99pE6dOmnYsGHy9/fXnDlzdPXq1TTnomM6tRsvMmvdurWKFSum+vXrq2jRojp+/LjmzZunkydPavHixU733717t3XB7+HDh5WYmGg952rUqKF27dpJ+vM6lSFDhuj111/X1atXVbduXS1dulSbN2+2zkmHY8eOaeHChZJkXZzsWGeJEiXUo0cPSX9O1LB06VK1a9dOxYsX12+//aa5c+fq+PHjWrhwodN1VImJidY4bce46BkzZiggIEABAQFO0wM3bdpUERERKlu2rOLj4/X222/rwoULWr58uVPPdufOndWgQQM9/vjj2rt3r/XLldevX7d+Hdjhscce04IFC3TkyJHb/hnzli1bKjg4WA0bNlRQUJD27dunGTNmOF0kvWnTJo0fP14tW7ZUoUKF9N1332nevHlq1apVmmF5Dz/8sEJDQ1W5cmUlJSVp7ty5+vnnn7VixQqnnPDMM8/oiy++ULt27XT27FlrqJqD43l14cIFhYWFqUuXLtbPy//www+aN2+e/P39NWrUKOs+S5Ys0bPPPqty5cqpUqVKadZ53333Wd/W5cT5KP3fN7O3usCyYMGCWrJkidq0aaOaNWs6/XLljh079MEHH1jfcq5YsUIlS5bM8jcCWZKVlO5qOkFvb29Ts2ZNM3v27DQ9f++++64pV66c8fLyMhUrVjTz5s1z+UMkymKPt8Mnn3xiWrZsaQIDA42Hh4cJCQkxXbp0ceq1zmyPtzF/9m41aNDA+oGTZ5991uq9yWyPd3x8vGnbtq0pUKCAkdL/AR2H9HpSEhISzOOPP24KFy5sPD09TbVq1dL0jvyVHu9r166ZkJCQDKf0Sq9X2pj/+5GCp556ylq2adMm06FDB1O4cGGTN29eU7x4cdOnTx9z9OjRLK07MTHR+Pv7p3nsMnO8jfm/3lxXt8x8Ql27dq2pVauW8fT0NGXKlDH/+9//zDPPPGO8vb3T1P30009No0aNjK+vr/H19TUVK1Y0AwYMcPpUndkeb2Myd9yNMebbb7814eHhxtPT02Wv/m+//Wby5Mljypcv73IfXf2ATokSJcyMGTPS1E1JSTETJ040VapUMV5eXqZgwYImPDzcjBs3zvqGy5jsmU7QsQ5Xt5v3MSUlxYwdO9aUKFHC5M2b15QtW9a8+eabTnVOnz5t+vbtaypWrGh8fX2Np6enKVeunBkyZEiac+/s2bOmQ4cOJjg42Hh6eppSpUqZESNGZOqHIG60c+dO07NnT1O8eHHj6elpfH19TfXq1c0zzzyTpmcyMz8ec/M+FypUyGVP9Y1KlSplatWqlWGdhIQE4+HhkWY6sjFjxpg6deqYggULGg8PDxMaGmq6du1qdu/e7XI9MTExpkGDBsbb29sEBgaaHj16WL/QeCPHtKUZ/WDH4cOHTc+ePU3p0qWNj4+P8fb2NlWqVDFjxoxx+ViNGDHCFC9ePM17j+PX+1z9Sp0rc+fONbVq1bLO76ZNm5qYmBirPLt6vF39yJrjdnPv7U8//WQeeOAB4+fnZ3x8fMy9997r8sc8XE2pNmPGDNOoUSNTuHBh4+HhYYoUKWLatWvnNB3izW12dbt5OrXr16+bV1991ZQoUcJ4enqaKlWqOE1/6pDeD8Td/J64Zs0ac99995ng4GCTN29eExAQYFq2bOnyl0jT+/EwSWn2f+jQoaZ06dLGy8vLFClSxHTr1s389NNPadZpzJ/P+969e5tChQqZfPnymaZNm7o8fp06dTI+Pj7m3LlzLtdzcztdvS//97//NU2aNDGFChUyXl5epkyZMmb48OFOr6OHDx82LVu2NIULF7ay04QJE1z2/k+cONFUrFjR+gG39u3bu/wBvYzeE2/MY8nJyWbw4MGmevXqxs/Pz+TNm9eUKFHC9O7dO835mdFr9c3vAzl1PhYuXNg0aNAgnSOR1smTJ83QoUNN+fLljbe3t8mXL58JDw83r7zyinUM6tSpY/r375/hev5qj7ebMdl8dRdwF+rYsaP27NmTZpq6v6szZ84oJCREo0ePduqlcGjWrJnOnDnj8mJA/HP07t1bBw8e1ObNm3O7KVmWnJyskiVL6rnnnkvTE1ivXj2VKFFCH3/8cS61DneboKAg9ezZU6+//npuNwX687qYKlWqaPny5dk27V9CQoJCQkK0fPlytWnTJlvW6Uq2zGoC3E1uvsL60KFDWrlypS3zU2eX+fPn6/r169ZXuoArY8aM0bZt29JMaXYnmDdvnvLmzaunnnrKaXlSUpI1OxWQHfbs2aPLly9rxIgRud0U/H/r169XREREts61nZiYqNGjR6t58+bZtk5X6PEGbhISEqLHHntMpUuX1rFjxzR79mwlJydr586dKleuXG43L0Pr1q3T3r17NWrUKDVv3jzNj1o50OMNAID9sm06QeBu0apVK33wwQeKj4+Xl5eXIiIi9Oqrr/7tQ7ckjR8/Xt9++60aNmxoXYgEAAD+HujxBgAAAGzAGG8AAADABgRvAAAAwAaM8b5Lpaam6uTJkypQoEC2/dQ3AADIWeb//4x6aGio0w/+4O5A8L5LnTx5UmFhYbndDAAAcBtOnDihYsWK5XYzkM0I3ncpx8/FnjhxwunnoAEAwN9XUlKSwsLCnH72HXcPgvddyjG8xM/Pj+ANAMAdhmGidycGDwEAAAA2IHgDAAAANiB4AwAAADYgeAMAAAA2IHgDAAAANiB4AwAAADYgeAMAAAA2IHgDAAAANiB4AwAAADYgeAMAAAA2IHgDAAAANiB4AwAAADYgeAMAAAA2IHgDAAAANvDI7Qbg7lTyuRW53YRccfS1trndBAAA8DdFjzcAAABgA4I3AAAAYAOCNwAAAGADgjcAAABgA4I3AAAAYAOCNwAAAGADgjcAAABgA4J3Fm3atEnt2rVTaGio3NzctHTp0jR19u3bp/bt28vf31++vr6qW7eujh8/bpVfuXJFAwYMUKFChZQ/f3516tRJCQkJTus4fvy42rZtq3z58qlo0aIaPny4rl27ltO7BwAAgBxC8M6iixcvqkaNGpo5c6bL8p9++kmNGjVSxYoVtWHDBu3evVujRo2St7e3VWfo0KFatmyZPv74Y23cuFEnT57Ugw8+aJVfv35dbdu2VUpKir799lstWLBA8+fP1+jRo3N8/wAAAJAz3IwxJrcbcadyc3PTkiVL1LFjR2tZ165dlTdvXi1cuNDlfRITE1WkSBEtWrRInTt3liTt379flSpVUmxsrBo0aKAvv/xS999/v06ePKmgoCBJ0pw5czRixAidPn1anp6et2xbUlKS/P39lZiYKD8/v7++s1nEL1cCAJB1uf3+jZxFj3c2Sk1N1YoVK1S+fHlFRUWpaNGiql+/vtNwlLi4OF29elWRkZHWsooVK6p48eKKjY2VJMXGxqpatWpW6JakqKgoJSUlac+ePS63nZycrKSkJKcbAAAA/j4I3tno1KlTunDhgl577TW1atVKa9as0QMPPKAHH3xQGzdulCTFx8fL09NTAQEBTvcNCgpSfHy8VefG0O0od5S5MmHCBPn7+1u3sLCwbN47AAAA/BUE72yUmpoqSerQoYOGDh2qmjVr6rnnntP999+vOXPm5Oi2R44cqcTEROt24sSJHN0eAAAAsobgnY0KFy4sDw8PVa5c2Wl5pUqVrFlNgoODlZKSovPnzzvVSUhIUHBwsFXn5llOHP876tzMy8tLfn5+TjcAAAD8fRC8s5Gnp6fq1q2rAwcOOC0/ePCgSpQoIUkKDw9X3rx5tXbtWqv8wIEDOn78uCIiIiRJERER+uGHH3Tq1CmrTkxMjPz8/NKEegAAANwZPHK7AXeaCxcu6PDhw9b/R44c0a5duxQYGKjixYtr+PDh6tKli5o0aaLmzZtr1apVWrZsmTZs2CBJ8vf3V+/evTVs2DAFBgbKz89PgwYNUkREhBo0aCBJatmypSpXrqwePXpo0qRJio+P14svvqgBAwbIy8srN3YbAAAAfxHBO4u2b9+u5s2bW/8PGzZMktSrVy/Nnz9fDzzwgObMmaMJEybo6aefVoUKFfTpp5+qUaNG1n3efPNNubu7q1OnTkpOTlZUVJRmzZpllefJk0fLly9Xv379FBERIV9fX/Xq1Uvjx4+3b0cBAACQrZjH+y6V2/OAMo83AABZl9vv38hZjPEGAAAAbEDwBgAAAGxA8AYAAABsQPAGAAAAbEDwBgAAAGxA8AYAAABsQPAGAAAAbEDwBgAAAGxA8AYAAABsQPAGAAAAbEDwBgAAAGxA8AYAAABsQPAGAAAAbEDwBgAAAGxA8AYAAABsQPAGAAAAbEDwBgAAAGxA8AYAAABsQPAGAAAAbEDwBgAAAGxA8AYAAABsQPAGAAAAbEDwBgAAAGzgkdsNAHDnK/ncitxuQq44+lrb3G4CAOAOQo83AAAAYAOCNwAAAGADgjcAAABgA4I3AAAAYAOCNwAAAGADgjcAAABgA6YTBABkCdNHAsDtocc7izZt2qR27dopNDRUbm5uWrp0abp1n3rqKbm5uWnq1KlOy8+ePavu3bvLz89PAQEB6t27ty5cuOBUZ/fu3WrcuLG8vb0VFhamSZMm5cDeAAAAwC4E7yy6ePGiatSooZkzZ2ZYb8mSJfruu+8UGhqapqx79+7as2ePYmJitHz5cm3atEl9+/a1ypOSktSyZUuVKFFCcXFxev311zV27Fi9/fbb2b4/AAAAsAdDTbKodevWat26dYZ1fv31Vw0aNEirV69W27bOX03u27dPq1at0rZt21SnTh1J0vTp09WmTRtNnjxZoaGhio6OVkpKiubOnStPT09VqVJFu3bt0pQpU5wCOgAAAO4c9Hhns9TUVPXo0UPDhw9XlSpV0pTHxsYqICDACt2SFBkZKXd3d23ZssWq06RJE3l6elp1oqKidODAAZ07d87ldpOTk5WUlOR0AwAAwN8HwTubTZw4UR4eHnr66addlsfHx6to0aJOyzw8PBQYGKj4+HirTlBQkFMdx/+OOjebMGGC/P39rVtYWNhf3RUAAABkI4J3NoqLi9Nbb72l+fPny83NzdZtjxw5UomJidbtxIkTtm4fAAAAGSN4Z6PNmzfr1KlTKl68uDw8POTh4aFjx47pmWeeUcmSJSVJwcHBOnXqlNP9rl27prNnzyo4ONiqk5CQ4FTH8b+jzs28vLzk5+fndAMAAMDfB8E7G/Xo0UO7d+/Wrl27rFtoaKiGDx+u1atXS5IiIiJ0/vx5xcXFWfdbt26dUlNTVb9+favOpk2bdPXqVatOTEyMKlSooIIFC9q7UwAAAMgWzGqSRRcuXNDhw4et/48cOaJdu3YpMDBQxYsXV6FChZzq582bV8HBwapQoYIkqVKlSmrVqpX69OmjOXPm6OrVqxo4cKC6du1qTT3YrVs3jRs3Tr1799aIESP0448/6q233tKbb75p344CAAAgWxG8s2j79u1q3ry59f+wYcMkSb169dL8+fMztY7o6GgNHDhQLVq0kLu7uzp16qRp06ZZ5f7+/lqzZo0GDBig8PBwFS5cWKNHj2YqQQAAgDsYwTuLmjVrJmNMpusfPXo0zbLAwEAtWrQow/tVr15dmzdvzmrzAAAA8DfFGG8AAADABgRvAAAAwAYEbwAAAMAGBG8AAADABgRvAAAAwAYEbwAAAMAGBG8AAADABgRvAAAAwAYEbwAAAMAGBG8AAADABgRvAAAAwAYEbwAAAMAGBG8AAADABgRvAAAAwAYEbwAAAMAGBG8AAADABgRvAAAAwAYEbwAAAMAGBG8AAADABgRvAAAAwAYEbwAAAMAGBG8AAADABgRvAAAAwAYEbwAAAMAGBG8AAADABgRvAAAAwAYEbwAAAMAGBG8AAADABgRvAAAAwAYEbwAAAMAGBG8AAADABgRvAAAAwAYE7yzatGmT2rVrp9DQULm5uWnp0qVW2dWrVzVixAhVq1ZNvr6+Cg0NVc+ePXXy5EmndZw9e1bdu3eXn5+fAgIC1Lt3b124cMGpzu7du9W4cWN5e3srLCxMkyZNsmP3AAAAkEMI3ll08eJF1ahRQzNnzkxTdunSJe3YsUOjRo3Sjh079Nlnn+nAgQNq3769U73u3btrz549iomJ0fLly7Vp0yb17dvXKk9KSlLLli1VokQJxcXF6fXXX9fYsWP19ttv5/j+AQAAIGd45HYD7jStW7dW69atXZb5+/srJibGadmMGTNUr149HT9+XMWLF9e+ffu0atUqbdu2TXXq1JEkTZ8+XW3atNHkyZMVGhqq6OhopaSkaO7cufL09FSVKlW0a9cuTZkyxSmgAwAA4M5Bj3cOS0xMlJubmwICAiRJsbGxCggIsEK3JEVGRsrd3V1btmyx6jRp0kSenp5WnaioKB04cEDnzp1zuZ3k5GQlJSU53QAAAPD3QfDOQVeuXNGIESP0yCOPyM/PT5IUHx+vokWLOtXz8PBQYGCg4uPjrTpBQUFOdRz/O+rcbMKECfL397duYWFh2b07AAAA+AsI3jnk6tWrevjhh2WM0ezZs3N8eyNHjlRiYqJ1O3HiRI5vEwAAAJnHGO8c4Ajdx44d07p166zebkkKDg7WqVOnnOpfu3ZNZ8+eVXBwsFUnISHBqY7jf0edm3l5ecnLyys7dwMAAADZiB7vbOYI3YcOHdJXX32lQoUKOZVHRETo/PnziouLs5atW7dOqampql+/vlVn06ZNunr1qlUnJiZGFSpUUMGCBe3ZEQAAAGQrgncWXbhwQbt27dKuXbskSUeOHNGuXbt0/PhxXb16VZ07d9b27dsVHR2t69evKz4+XvHx8UpJSZEkVapUSa1atVKfPn20detWffPNNxo4cKC6du2q0NBQSVK3bt3k6emp3r17a8+ePVq8eLHeeustDRs2LLd2GwAAAH8RQ02yaPv27WrevLn1vyMM9+rVS2PHjtUXX3whSapZs6bT/davX69mzZpJkqKjozVw4EC1aNFC7u7u6tSpk6ZNm2bV9ff315o1azRgwACFh4ercOHCGj16NFMJAgAA3MEI3lnUrFkzGWPSLc+ozCEwMFCLFi3KsE716tW1efPmLLcPAAAAf08MNQEAAABsQI83AABIV8nnVuR2E3LF0dfa5nYTcBeixxsAAACwAcEbAAAAsAHBGwAAALABwRsAAACwAcEbAAAAsAHBGwAAALABwRsAAACwAcEbAAAAsAHBGwAAALABwRsAAACwAcEbAAAAsAHBGwAAALABwRsAAACwAcEbAAAAsAHBGwAAALABwRsAAACwAcEbAAAAsAHBGwAAALABwRsAAACwAcEbAAAAsAHBGwAAALABwRsAAACwAcEbAAAAsAHBGwAAALABwRsAAACwAcEbAAAAsAHBGwAAALABwRsAAACwAcEbAAAAsAHBGwAAALABwTuLNm3apHbt2ik0NFRubm5aunSpU7kxRqNHj1ZISIh8fHwUGRmpQ4cOOdU5e/asunfvLj8/PwUEBKh37966cOGCU53du3ercePG8vb2VlhYmCZNmpTTuwYAAIAcRPDOoosXL6pGjRqaOXOmy/JJkyZp2rRpmjNnjrZs2SJfX19FRUXpypUrVp3u3btrz549iomJ0fLly7Vp0yb17dvXKk9KSlLLli1VokQJxcXF6fXXX9fYsWP19ttv5/j+AQAAIGd45HYD7jStW7dW69atXZYZYzR16lS9+OKL6tChgyTpvffeU1BQkJYuXaquXbtq3759WrVqlbZt26Y6depIkqZPn642bdpo8uTJCg0NVXR0tFJSUjR37lx5enqqSpUq2rVrl6ZMmeIU0G+UnJys5ORk6/+kpKRs3nMAAAD8FfR4Z6MjR44oPj5ekZGR1jJ/f3/Vr19fsbGxkqTY2FgFBARYoVuSIiMj5e7uri1btlh1mjRpIk9PT6tOVFSUDhw4oHPnzrnc9oQJE+Tv72/dwsLCcmIXAQAAcJsI3tkoPj5ekhQUFOS0PCgoyCqLj49X0aJFnco9PDwUGBjoVMfVOm7cxs1GjhypxMRE63bixIm/vkMAAADINgw1uUt4eXnJy8srt5sBAACAdNDjnY2Cg4MlSQkJCU7LExISrLLg4GCdOnXKqfzatWs6e/asUx1X67hxGwAAALizELyzUalSpRQcHKy1a9day5KSkrRlyxZFRERIkiIiInT+/HnFxcVZddatW6fU1FTVr1/fqrNp0yZdvXrVqhMTE6MKFSqoYMGCNu0NAAAAshPBO4suXLigXbt2adeuXZL+vKBy165dOn78uNzc3DRkyBC9/PLL+uKLL/TDDz+oZ8+eCg0NVceOHSVJlSpVUqtWrdSnTx9t3bpV33zzjQYOHKiuXbsqNDRUktStWzd5enqqd+/e2rNnjxYvXqy33npLw4YNy6W9BgAAwF/FGO8s2r59u5o3b2797wjDvXr10vz58/Xss8/q4sWL6tu3r86fP69GjRpp1apV8vb2tu4THR2tgQMHqkWLFnJ3d1enTp00bdo0q9zf319r1qzRgAEDFB4ersKFC2v06NHpTiUIAACAvz+CdxY1a9ZMxph0y93c3DR+/HiNHz8+3TqBgYFatGhRhtupXr26Nm/efNvtBAAAwN8LQ00AAAAAGxC8AQAAABsQvAEAAAAbELwBAAAAGxC8AQAAABsQvAEAAAAbELwBAAAAGxC8AQAAABsQvAEAAAAbELwBAAAAGxC8AQAAABsQvAEAAAAbELwBAAAAGxC8AQAAABsQvAEAAAAbELwBAAAAGxC8AQAAABsQvAEAAAAbELwBAAAAGxC8AQAAABsQvAEAAAAbELwBAAAAGxC8AQAAABsQvAEAAAAbELwBAAAAGxC8AQAAABsQvAEAAAAbELwBAAAAGxC8AQAAABsQvAEAAAAbELwBAAAAGxC8s9n169c1atQolSpVSj4+PipTpoxeeuklGWOsOsYYjR49WiEhIfLx8VFkZKQOHTrktJ6zZ8+qe/fu8vPzU0BAgHr37q0LFy7YvTsAAADIJgTvbDZx4kTNnj1bM2bM0L59+zRx4kRNmjRJ06dPt+pMmjRJ06ZN05w5c7Rlyxb5+voqKipKV65csep0795de/bsUUxMjJYvX65Nmzapb9++ubFLAAAAyAYeud2Au823336rDh06qG3btpKkkiVL6oMPPtDWrVsl/dnbPXXqVL344ovq0KGDJOm9995TUFCQli5dqq5du2rfvn1atWqVtm3bpjp16kiSpk+frjZt2mjy5MkKDQ3NnZ0DAADAbaPHO5vdc889Wrt2rQ4ePChJ+v777/X111+rdevWkqQjR44oPj5ekZGR1n38/f1Vv359xcbGSpJiY2MVEBBghW5JioyMlLu7u7Zs2eJyu8nJyUpKSnK6AQAA4O+DHu9s9txzzykpKUkVK1ZUnjx5dP36db3yyivq3r27JCk+Pl6SFBQU5HS/oKAgqyw+Pl5FixZ1Kvfw8FBgYKBV52YTJkzQuHHjsnt3AAAAkE3o8c5mH330kaKjo7Vo0SLt2LFDCxYs0OTJk7VgwYIc3e7IkSOVmJho3U6cOJGj2wMAAEDW0OOdzYYPH67nnntOXbt2lSRVq1ZNx44d04QJE9SrVy8FBwdLkhISEhQSEmLdLyEhQTVr1pQkBQcH69SpU07rvXbtms6ePWvd/2ZeXl7y8vLKgT0CAABAdqDHO5tdunRJ7u7OD2uePHmUmpoqSSpVqpSCg4O1du1aqzwpKUlbtmxRRESEJCkiIkLnz59XXFycVWfdunVKTU1V/fr1bdgLAAAAZDd6vLNZu3bt9Morr6h48eKqUqWKdu7cqSlTpuiJJ56QJLm5uWnIkCF6+eWXVa5cOZUqVUqjRo1SaGioOnbsKEmqVKmSWrVqpT59+mjOnDm6evWqBg4cqK5duzKjCQAAwB2K4J3Npk+frlGjRql///46deqUQkND9eSTT2r06NFWnWeffVYXL15U3759df78eTVq1EirVq2St7e3VSc6OloDBw5UixYt5O7urk6dOmnatGm5sUsAAADIBgTvbFagQAFNnTpVU6dOTbeOm5ubxo8fr/Hjx6dbJzAwUIsWLcqBFgIAACA3MMYbAAAAsAHBGwAAALABwRsAAACwAcEbAAAAsAHBGwAAALABwRsAAACwAcEbAAAAsAHBGwAAALABwRsAAACwAcEbAAAAsAHBGwAAALABwRsAAACwAcEbAAAAsAHBGwAAALABwRsAAACwAcEbAAAAsAHBGwAAALABwRsAAACwAcEbAAAAsAHBGwAAALABwRsAAACwAcEbAAAAsAHBGwAAALABwRsAAACwAcEbAAAAsAHBGwAAALABwRsAAACwAcEbAAAAsAHBGwAAALABwRsAAACwAcEbAAAAsAHBGwAAALABwTsH/Prrr3r00UdVqFAh+fj4qFq1atq+fbtVbozR6NGjFRISIh8fH0VGRurQoUNO6zh79qy6d+8uPz8/BQQEqHfv3rpw4YLduwIAAIBsQvDOZufOnVPDhg2VN29effnll9q7d6/eeOMNFSxY0KozadIkTZs2TXPmzNGWLVvk6+urqKgoXblyxarTvXt37dmzRzExMVq+fLk2bdqkvn375sYuAQAAIBt45HYD7jYTJ05UWFiY5s2bZy0rVaqU9bcxRlOnTtWLL76oDh06SJLee+89BQUFaenSperatav27dunVatWadu2bapTp44kafr06WrTpo0mT56s0NBQe3cKAAAAfxk93tnsiy++UJ06dfTQQw+paNGiqlWrlt555x2r/MiRI4qPj1dkZKS1zN/fX/Xr11dsbKwkKTY2VgEBAVbolqTIyEi5u7try5YtLrebnJyspKQkpxsAAAD+Pgje2eznn3/W7NmzVa5cOa1evVr9+vXT008/rQULFkiS4uPjJUlBQUFO9wsKCrLK4uPjVbRoUadyDw8PBQYGWnVuNmHCBPn7+1u3sLCw7N41AAAA/AUE72yWmpqq2rVr69VXX1WtWrXUt29f9enTR3PmzMnR7Y4cOVKJiYnW7cSJEzm6PQAAAGQNwTubhYSEqHLlyk7LKlWqpOPHj0uSgoODJUkJCQlOdRISEqyy4OBgnTp1yqn82rVrOnv2rFXnZl5eXvLz83O6AQAA4O+D4J3NGjZsqAMHDjgtO3jwoEqUKCHpzwstg4ODtXbtWqs8KSlJW7ZsUUREhCQpIiJC58+fV1xcnFVn3bp1Sk1NVf369W3YCwAAAGQ3ZjXJZkOHDtU999yjV199VQ8//LC2bt2qt99+W2+//bYkyc3NTUOGDNHLL7+scuXKqVSpUho1apRCQ0PVsWNHSX/2kLdq1coaonL16lUNHDhQXbt2ZUYTAACAOxTBO5vVrVtXS5Ys0ciRIzV+/HiVKlVKU6dOVffu3a06zz77rC5evKi+ffvq/PnzatSokVatWiVvb2+rTnR0tAYOHKgWLVrI3d1dnTp10rRp03JjlwAAAJANCN454P7779f999+fbrmbm5vGjx+v8ePHp1snMDBQixYtyonmAQAAIBcwxhsAAACwAcEbAAAAsAHBGwAAALABwRsAAACwAcEbAAAAsAHBGwAAALABwRsAAACwAcEbAAAAsAHBGwAAALABwRsAAACwAcEbAAAAsAHBGwAAALABwRsAAACwAcEbAAAAsAHBGwAAALABwRsAAACwAcEbAAAAsAHBGwAAALABwRsAAACwAcEbAAAAsAHBGwAAALABwRsAAACwAcEbAAAAsAHBGwAAALABwRsAAACwAcEbAAAAsAHBGwAAALABwRsAAACwAcEbAAAAsAHBGwAAALABwRsAAACwAcE7h7322mtyc3PTkCFDrGVXrlzRgAEDVKhQIeXPn1+dOnVSQkKC0/2OHz+utm3bKl++fCpatKiGDx+ua9eu2dx6AAAAZBeCdw7atm2b/vvf/6p69epOy4cOHaply5bp448/1saNG3Xy5Ek9+OCDVvn169fVtm1bpaSk6Ntvv9WCBQs0f/58jR492u5dAAAAQDYheOeQCxcuqHv37nrnnXdUsGBBa3liYqLeffddTZkyRffee6/Cw8M1b948ffvtt/ruu+8kSWvWrNHevXv1/vvvq2bNmmrdurVeeuklzZw5UykpKbm1SwAAAPgLCN45ZMCAAWrbtq0iIyOdlsfFxenq1atOyytWrKjixYsrNjZWkhQbG6tq1aopKCjIqhMVFaWkpCTt2bPH5faSk5OVlJTkdAMAAMDfh0duN+Bu9OGHH2rHjh3atm1bmrL4+Hh5enoqICDAaXlQUJDi4+OtOjeGbke5o8yVCRMmaNy4cdnQegAAAOQEeryz2YkTJzR48GBFR0fL29vbtu2OHDlSiYmJ1u3EiRO2bRsAAAC3RvDOZnFxcTp16pRq164tDw8PeXh4aOPGjZo2bZo8PDwUFBSklJQUnT9/3ul+CQkJCg4OliQFBwenmeXE8b+jzs28vLzk5+fndAMAAMDfB8E7m7Vo0UI//PCDdu3aZd3q1Kmj7t27W3/nzZtXa9eute5z4MABHT9+XBEREZKkiIgI/fDDDzp16pRVJyYmRn5+fqpcubLt+wQAAIC/jjHe2axAgQKqWrWq0zJfX18VKlTIWt67d28NGzZMgYGB8vPz06BBgxQREaEGDRpIklq2bKnKlSurR48emjRpkuLj4/Xiiy9qwIAB8vLysn2fAAAA8NcRvHPBm2++KXd3d3Xq1EnJycmKiorSrFmzrPI8efJo+fLl6tevnyIiIuTr66tevXpp/PjxudhqAAAA/BUEbxts2LDB6X9vb2/NnDlTM2fOTPc+JUqU0MqVK3O4ZQAAALALY7wBAAAAGxC8AQAAABsQvAEAAAAbELwBAAAAGxC8AQAAABsQvAEAAAAbELwBAAAAGxC8AQAAABsQvAEAAAAbELwBAAAAGxC8AQAAABsQvAEAAAAbELwBAAAAGxC8AQAAABsQvAEAAAAbELwBAAAAGxC8AQAAABsQvAEAAAAbELwBAAAAGxC8AQAAABsQvAEAAAAbELwBAAAAGxC8AQAAABsQvAEAAAAbELwBAAAAGxC8AQAAABsQvAEAAAAbELwBAAAAGxC8AQAAABsQvAEAAAAbELwBAAAAGxC8AQAAABsQvLPZhAkTVLduXRUoUEBFixZVx44ddeDAAac6V65c0YABA1SoUCHlz59fnTp1UkJCglOd48ePq23btsqXL5+KFi2q4cOH69q1a3buCgAAALIRwTubbdy4UQMGDNB3332nmJgYXb16VS1bttTFixetOkOHDtWyZcv08ccfa+PGjTp58qQefPBBq/z69etq27atUlJS9O2332rBggWaP3++Ro8enRu7BAAAgGzgkdsNuNusWrXK6f/58+eraNGiiouLU5MmTZSYmKh3331XixYt0r333itJmjdvnipVqqTvvvtODRo00Jo1a7R371599dVXCgoKUs2aNfXSSy9pxIgRGjt2rDw9PXNj1wAAAPAX0OOdwxITEyVJgYGBkqS4uDhdvXpVkZGRVp2KFSuqePHiio2NlSTFxsaqWrVqCgoKsupERUUpKSlJe/bscbmd5ORkJSUlOd0AAADw90HwzkGpqakaMmSIGjZsqKpVq0qS4uPj5enpqYCAAKe6QUFBio+Pt+rcGLod5Y4yVyZMmCB/f3/rFhYWls17AwAAgL+C4J2DBgwYoB9//FEffvhhjm9r5MiRSkxMtG4nTpzI8W0CAAAg8xjjnUMGDhyo5cuXa9OmTSpWrJi1PDg4WCkpKTp//rxTr3dCQoKCg4OtOlu3bnVan2PWE0edm3l5ecnLyyub9wIAAADZhR7vbGaM0cCBA7VkyRKtW7dOpUqVcioPDw9X3rx5tXbtWmvZgQMHdPz4cUVEREiSIiIi9MMPP+jUqVNWnZiYGPn5+aly5cr27AgAAACyFT3e2WzAgAFatGiRPv/8cxUoUMAak+3v7y8fHx/5+/urd+/eGjZsmAIDA+Xn56dBgwYpIiJCDRo0kCS1bNlSlStXVo8ePTRp0iTFx8frxRdf1IABA+jVBgAAuEMRvLPZ7NmzJUnNmjVzWj5v3jw99thjkqQ333xT7u7u6tSpk5KTkxUVFaVZs2ZZdfPkyaPly5erX79+ioiIkK+vr3r16qXx48fbtRsAAADIZgTvbGaMuWUdb29vzZw5UzNnzky3TokSJbRy5crsbBoAAAByEWO8AQAAABsQvAEAAAAbELwBAAAAGxC8AQAAABsQvAEAAAAbELwBAAAAGxC8AQAAABsQvAEAAAAbELwBAAAAGxC8AQAAABsQvAEAAAAbELwBAAAAGxC8AQAAABsQvAEAAAAbELwBAAAAGxC8AQAAABsQvAEAAAAbELwBAAAAGxC8AQAAABsQvAEAAAAbELwBAAAAGxC8AQAAABsQvAEAAAAbELwBAAAAGxC8AQAAABsQvAEAAAAbELwBAAAAGxC8AQAAABsQvAEAAAAbELwBAAAAGxC8AQAAABsQvP/mZs6cqZIlS8rb21v169fX1q1bc7tJAAAAuA0E77+xxYsXa9iwYRozZox27NihGjVqKCoqSqdOncrtpgEAACCLCN5/Y1OmTFGfPn30+OOPq3LlypozZ47y5cunuXPn5nbTAAAAkEUeud0AuJaSkqK4uDiNHDnSWubu7q7IyEjFxsamqZ+cnKzk5GTr/8TERElSUlJSzjfWhdTkS7my3dyWW493buN4/7NwvP9ZON65s11jTK5sHzmL4P03debMGV2/fl1BQUFOy4OCgrR///409SdMmKBx48alWR4WFpZjbURa/lNzuwWwE8f7n4Xj/c+S28f7jz/+kL+/f+42AtmO4H2XGDlypIYNG2b9n5qaqrNnz6pQoUJyc3PLxZbZKykpSWFhYTpx4oT8/PxyuznIYRzvfxaO9z/LP/V4G2P0xx9/KDQ0NLebghxA8P6bKly4sPLkyaOEhASn5QkJCQoODk5T38vLS15eXk7LAgICcrKJf2t+fn7/qBfqfzqO9z8Lx/uf5Z94vOnpvntxceXflKenp8LDw7V27VprWWpqqtauXauIiIhcbBkAAABuBz3ef2PDhg1Tr169VKdOHdWrV09Tp07VxYsX9fjjj+d20wAAAJBFBO+/sS5duuj06dMaPXq04uPjVbNmTa1atSrNBZf4P15eXhozZkyaYTe4O3G8/1k43v8sHG/cjdwM89UAAAAAOY4x3gAAAIANCN4AAACADQjeAAAAgA0I3gAAAIANCN4AAACADQjeAAAAgA0I3gAAAIANCN4A7hh79+5V//79VatWLYWEhCgkJES1atVS//79tXfv3txuHnJYcnKykpOTc7sZyCE8v/FPwA/o4I62d+9ezZgxQ7GxsYqPj5ckBQcHKyIiQgMHDlTlypVzuYXILl9++aU6duyo2rVrKyoqyvoF14SEBMXExCguLk6ff/65oqKicrmlyE4xMTF68803FRsbq6SkJEmSn5+fIiIiNGzYMEVGRuZyC5EdeH7jn4LgjTsWL9T/LDVq1FCHDh00fvx4l+Vjx47VZ599pt27d9vcMuSUBQsW6N///rc6d+6c5jm+Zs0affLJJ3r33XfVo0ePXG4p/iqe3/inIHjjjsUL9T+Lj4+Pdu3apQoVKrgsP3DggGrWrKnLly/b3DLklPLly2vw4MEaMGCAy/JZs2bpzTff1KFDh2xuGbIbz2/8UzDGG3esgwcPqnv37umWP/LII7wh30VKliypFStWpFu+YsUKlShRwsYWIacdP348w6EkLVq00C+//GJji5BTeH7jn8IjtxsA3C7HC3V6PSS8UN9dxo8fr27dumnDhg2KjIx0Gnawdu1arVq1SosWLcrlViI7ValSRe+++64mTZrksnzu3Llcx3GX4PmNfwqGmuCO9fHHH6tbt25q3bp1hi/UnTp1yuWWIrt8++23mjZtmsuLaQcPHqyIiIhcbiGy04YNG3T//ferdOnSLp/jP//8s1asWKEmTZrkckuRHXh+45+A4I07Gi/UwN3t6NGjmj17tr777rs0z/GnnnpKJUuWzN0GAkAWELwBAAAAG3BxJYC7wvPPP68nnngit5sBIAfw/MbdguCNuxYv1P8sv/zyi44ePZrbzYCNevXqpXvvvTe3mwEb/Prrrzy/cVdgVhPctX755RemGvsHee+993K7CbBZaGio3N3pP/onWLBgQW43AcgWjPEGcMc4c+aM5s6dm+Zi2nvuuUePPfaYihQpksstBAAgfXQV4I62b98+zZs3T/v375ck7d+/X/369dMTTzyhdevW5XLrkJ22bdum8uXLa9q0afL391eTJk3UpEkT+fv7a9q0aapYsaK2b9+e282EjU6cOMFwsrvI5cuX9fXXX2vv3r1pyq5cucK3Wrgr0OONO9aqVavUoUMH5c+fX5cuXdKSJUvUs2dP1ahRQ6mpqdq4caPWrFnDGNC7RIMGDVSjRg3NmTNHbm5uTmXGGD311FPavXu3YmNjc6mFsNv333+v2rVr6/r167ndFPxFBw8eVMuWLXX8+HG5ubmpUaNG+vDDDxUSEiLpz7nbQ0NDOda44xG8cce65557dO+99+rll1/Whx9+qP79+6tfv3565ZVXJEkjR45UXFyc1qxZk8stRXbw8fHRzp07VbFiRZfl+/fvV61atXT58mWbW4ac8sUXX2RY/vPPP+uZZ54hjN0FHnjgAV29elXz58/X+fPnNWTIEO3du1cbNmxQ8eLFCd64axC8ccfy9/dXXFycypYtq9TUVHl5eWnr1q2qVauWJOnHH39UZGSkNRYYd7ZSpUpp3Lhx6tmzp8vy9957T6NHj2bmg7uIu7u73NzclNHblJubG2HsLhAUFKSvvvpK1apVk/Tnt1j9+/fXypUrtX79evn6+hK8cVdgVhPc0RxDDtzd3eXt7S1/f3+rrECBAkpMTMytpiGb/ec//1Hfvn0VFxenFi1apPn58HfeeUeTJ0/O5VYiO4WEhGjWrFnq0KGDy/Jdu3YpPDzc5lYhJ1y+fFkeHv8XSdzc3DR79mwNHDhQTZs21aJFi3KxdUD2IXjjjlWyZEkdOnRIZcqUkSTFxsaqePHiVvnx48et8YG48w0YMECFCxfWm2++qVmzZlk9X3ny5FF4eLjmz5+vhx9+OJdbiewUHh6uuLi4dIP3rXrDcedwXBxdqVIlp+UzZsyQJLVv3z43mgVkO4I37lj9+vVz+tqxatWqTuVffvklF1beZbp06aIuXbro6tWrOnPmjCSpcOHCyps3by63DDlh+PDhunjxYrrlZcuW1fr1621sEXLKAw88oA8++EA9evRIUzZjxgylpqZqzpw5udAyIHsxxhsAAACwAfN4AwAAADYgeAMAAAA2IHgDAAAANiB4AwAAADYgeAMAAAA2IHgDAAAANiB4AwAAADb4f3Q9mTqXKqu4AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "counts = new_df[\"Gender_impute\"].value_counts()\n",
        "counts.plot(kind='bar')\n",
        "plt.title('Bar Chart for Gender')\n",
        "plt.show()\n",
        "\n",
        "counts = new_df[\"Race (Reported)_impute\"].value_counts()\n",
        "counts.plot(kind='bar')\n",
        "plt.title('Bar Chart for Race')\n",
        "plt.show()\n",
        "\n",
        "counts = new_df[\"Age_impute\"].value_counts()\n",
        "counts.plot(kind='bar')\n",
        "plt.title('Bar Chart for Age')\n",
        "plt.show()\n",
        "\n",
        "counts = new_df[\"VKORC1 genotype: -1639 G>A (3673); chr16:31015190; rs9923231; C/T_impute\"].value_counts()\n",
        "counts.plot(kind='bar')\n",
        "plt.title('Bar Chart for VKORC1 genotype: -1639 G>A (3673); chr16:31015190; rs9923231; C/T')\n",
        "plt.show()\n",
        "\n",
        "counts = new_df[\"Diabetes_impute\"].value_counts()\n",
        "counts.plot(kind='bar')\n",
        "plt.title('Bar Chart for Diabetes')\n",
        "plt.show()\n",
        "\n",
        "counts = new_df[\"Simvastatin (Zocor)_impute\"].value_counts()\n",
        "counts.plot(kind='bar')\n",
        "plt.title('Bar Chart for Simvastatin')\n",
        "plt.show()\n",
        "\n",
        "counts =new_df[\"Amiodarone (Cordarone)_impute\"].value_counts()\n",
        "counts.plot(kind='bar')\n",
        "plt.title('Bar Chart for Amiodarone')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "Po3XAO1s9fQ0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "30590d4b-dff8-4e74-8807-60174f738e19"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.hist(new_df[\"Height (cm)_impute\"], bins=5)\n",
        "plt.title('Histogram for Height')\n",
        "plt.show()\n",
        "\n",
        "plt.hist(new_df[\"Weight (kg)_impute\"], bins=5)\n",
        "plt.title('Histogram for Weight')\n",
        "plt.show()\n",
        "\n",
        "plt.hist(new_df[\"INR on Reported Therapeutic Dose of Warfarin_impute\"], bins=5)\n",
        "plt.title('Histogram for INR on Reported Therapeutic Dose of Warfarin')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "a1Vs5Ljm9KeI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 969
        },
        "outputId": "16cd8946-96a3-4d82-ff95-31c034dd0c9c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "\n",
        "corr_matrix = new_df.corr()\n",
        "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "SAfFCUFT-o5b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "279ee34a-d448-4eec-d749-ef42e3e97ff3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(3206, 10)\n",
            "(1069, 10)\n",
            "(1425, 10)\n",
            "(3206,)\n",
            "(1069,)\n",
            "(1425,)\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "#Do not do feature scaling on outputs(target variable)\n",
        "X_train, X_test, y_train, y_test = train_test_split(new_df.drop(['Therapeutic Dose of Warfarin_impute'], axis=1), new_df['Therapeutic Dose of Warfarin_impute'], test_size=0.25, random_state=42)\n",
        "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=42)\n",
        "\n",
        "print(X_train.shape)\n",
        "print(X_val.shape)\n",
        "print(X_test.shape)\n",
        "print(y_train.shape)\n",
        "print(y_val.shape)\n",
        "print(y_test.shape)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "3SgThUyWxKz0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "03622dde-6aee-4460-9e5c-cfad8d2515fe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1069, 10)\n",
            "(3206,)\n",
            "(1069,)\n",
            "(1425,)\n"
          ]
        }
      ],
      "source": [
        "print(X_val.shape)\n",
        "print(y_train.shape)\n",
        "print(y_val.shape)\n",
        "print(y_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "4K3MPl73Ic8B"
      },
      "outputs": [],
      "source": [
        "#Method 1: Linear Regression\n",
        "\n",
        "import pandas as pd\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "import joblib\n",
        "\n",
        "lr_model = LinearRegression()\n",
        "lr_model.fit(X_train, y_train)\n",
        "\n",
        "# save the model using joblib\n",
        "joblib.dump(lr_model, 'linear_regression_model.joblib')\n",
        "\n",
        "# load the model from file\n",
        "lr_model_loaded = joblib.load('linear_regression_model.joblib')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "P8tfOG6W68CM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd4a1ae9-b14e-4365-983d-e3e8e6601d83"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cross-validation scores for Linear Regression: [-161.49743134 -172.07952875 -208.85620271 -202.63688704 -223.65732545]\n",
            "Mean CV score: -193.74547505777437\n"
          ]
        }
      ],
      "source": [
        "#Cross-Validation Score\n",
        "\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "# calculate cross-validation score for the linear regression model\n",
        "lr_cv_scores = cross_val_score(lr_model_loaded, X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n",
        "\n",
        "print(\"Cross-validation scores for Linear Regression:\", lr_cv_scores)\n",
        "print('Mean CV score:', lr_cv_scores.mean())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "vHpwgHmaWKbl"
      },
      "outputs": [],
      "source": [
        "#Method 2: Lasso \n",
        "\n",
        "from sklearn.linear_model import Lasso\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# define the parameter grid for Lasso model\n",
        "lasso_param_grid = {'alpha': [0.01, 0.1, 1, 10]}\n",
        "\n",
        "# create a Lasso model object\n",
        "lasso = Lasso()\n",
        "\n",
        "# create a GridSearchCV object to search over the parameter grid\n",
        "lasso_grid_search = GridSearchCV(estimator=lasso, param_grid=lasso_param_grid, cv=5, scoring='neg_mean_squared_error')\n",
        "\n",
        "# fit the GridSearchCV object to the training data\n",
        "lasso_grid_search.fit(X_train, y_train)\n",
        "\n",
        "# get the best Lasso model and its hyperparameters\n",
        "best_lasso_model = lasso_grid_search.best_estimator_\n",
        "best_lasso_model.fit(X_train, y_train)\n",
        "joblib.dump(best_lasso_model, 'lasso_model.joblib')\n",
        "\n",
        "filename = 'lasso_model.joblib'\n",
        "lasso_model = joblib.load(filename)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "ipbEs7xT7Bwi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "72181c28-54c9-4faf-e11b-a7c124dc4c6c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean CV score: -193.721162798255\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "# get the cross-validation score for the best Lasso model\n",
        "lasso_cv_score = cross_val_score(best_lasso_model, X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n",
        "\n",
        "# print the mean of the cross-validation score\n",
        "print('Mean CV score:', lasso_cv_score.mean())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "x9J6bcJ9YMDB"
      },
      "outputs": [],
      "source": [
        "from sklearn.neural_network import MLPRegressor\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "import joblib\n",
        "\n",
        "# define the parameter grid for the MLPRegressor\n",
        "mlp_param_grid = {'hidden_layer_sizes': [(50,)],\n",
        "                  'activation': ['relu'],\n",
        "                  'alpha': [0.0001],\n",
        "                  'learning_rate': ['constant']}\n",
        "\n",
        "# create an MLPRegressor object\n",
        "mlp = MLPRegressor(max_iter=1000)\n",
        "\n",
        "# create a GridSearchCV object to search over the parameter grid\n",
        "mlp_grid_search = GridSearchCV(estimator=mlp, param_grid=mlp_param_grid, cv=5, scoring='neg_mean_squared_error')\n",
        "\n",
        "# fit the GridSearchCV object to the training data\n",
        "mlp_grid_search.fit(X_train, y_train)\n",
        "\n",
        "\n",
        "# get the best MLPRegressor model and its hyperparameters\n",
        "best_mlp_model = mlp_grid_search.best_estimator_\n",
        "best_mlp_model.fit(X_train, y_train)\n",
        "joblib.dump(best_mlp_model, 'mlp_model.joblib')\n",
        "\n",
        "filename = 'mlp_model.joblib'\n",
        "mlp_model = joblib.load(filename)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "cNHTozFW7RoJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "631ce12e-ccd2-47cb-ac23-8a1400cc145e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cross-Validation Scores:\n",
            " [-161.98321519 -173.3150464  -208.75414356 -204.1455538  -223.50141054]\n",
            "Mean CV Score: -194.3398738980962\n",
            "Std CV Score: 22.992670489353802\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "# get the cross-validation score for the MLPRegressor model\n",
        "cv_scores = cross_val_score(best_mlp_model, X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n",
        "\n",
        "# print the mean and standard deviation of the cross-validation scores\n",
        "print(\"Cross-Validation Scores:\\n\", cv_scores)\n",
        "print(\"Mean CV Score:\", cv_scores.mean())\n",
        "print(\"Std CV Score:\", cv_scores.std())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "Y1GWlFf1yWZ8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2c718918-e220-4ffe-ae53-792eca996759"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Linear Regression accuracy:  0.2902266441727134\n",
            "Lasso accuracy:  0.29022009866220666\n",
            "MLP train accuracy:  0.28548570852449584\n"
          ]
        }
      ],
      "source": [
        "print(\"Linear Regression accuracy: \", lr_model.score(X_train, y_train))\n",
        "\n",
        "print(\"Lasso accuracy: \", best_lasso_model.score(X_train, y_train))\n",
        "\n",
        "print(\"MLP train accuracy: \", best_mlp_model.score(X_train, y_train))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "5-89QbzI4DsY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0897392d-c4a7-43ff-e225-bc2bfa02c0bd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE for Linear Regression: 9.721641757996629\n",
            "MSE for Linear Regression: 220.98774578976648\n",
            "R2 score for Linear Regression: 0.25449178515847926\n"
          ]
        }
      ],
      "source": [
        "#Metrics for Linear Regression\n",
        "\n",
        "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
        "\n",
        "# make predictions on the test set\n",
        "y_pred = lr_model_loaded.predict(X_test)\n",
        "\n",
        "# calculate the mean absolute error (MAE)\n",
        "mae = mean_absolute_error(y_test, y_pred)\n",
        "\n",
        "# calculate the mean squared error (MSE)\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "\n",
        "# calculate the R-squared (R2) score\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "print(\"MAE for Linear Regression:\", mae)\n",
        "print(\"MSE for Linear Regression:\", mse)\n",
        "print(\"R2 score for Linear Regression:\", r2)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "70GqfPRw3q55",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3012e0dc-6c0e-446d-d94f-542243a44fc7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE for Lasso: 9.719049971322603\n",
            "MSE for Lasso: 220.93079008406607\n",
            "R2 score for Lasso: 0.25468392679207974\n"
          ]
        }
      ],
      "source": [
        "#Metrics for Lasso\n",
        "\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
        "\n",
        "# make predictions on the test set\n",
        "y_pred = lasso_model.predict(X_test)\n",
        "\n",
        "# calculate the mean absolute error (MAE)\n",
        "mae = mean_absolute_error(y_test, y_pred)\n",
        "\n",
        "# calculate the mean squared error (MSE)\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "\n",
        "# calculate the R-squared (R2) score\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "print(\"MAE for Lasso:\", mae)\n",
        "print(\"MSE for Lasso:\", mse)\n",
        "print(\"R2 score for Lasso:\", r2)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "Os1fbCew3Vvi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "77b87edd-0ac2-45a2-91a4-c86c4d3ae5cd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE for MLP: 9.534745989547938\n",
            "MSE for MLP: 221.0865203676121\n",
            "R2 score for MLP: 0.2541585664139816\n"
          ]
        }
      ],
      "source": [
        "#Metrics for MLP\n",
        "\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
        "\n",
        "# make predictions on the test set\n",
        "y_pred = mlp_model.predict(X_test)\n",
        "\n",
        "# calculate the mean absolute error (MAE)\n",
        "mae = mean_absolute_error(y_test, y_pred)\n",
        "\n",
        "# calculate the mean squared error (MSE)\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "\n",
        "# calculate the R-squared (R2) score\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "print(\"MAE for MLP:\", mae)\n",
        "print(\"MSE for MLP:\", mse)\n",
        "print(\"R2 score for MLP:\", r2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vs5UkdFCpG1h"
      },
      "source": [
        "#Based on the tables in the paper, our values are relatively close. Hence, we will move on to the next step."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "6felre6e4urg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "3977e236-5382-4ec4-b851-f8718277eebd"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Learning Curve for linear regression\n",
        "\n",
        "from sklearn.model_selection import learning_curve\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# define the training set sizes to use in the learning curve\n",
        "train_sizes = np.linspace(0.1, 1.0, 10)\n",
        "\n",
        "# calculate the learning curve\n",
        "train_sizes, train_scores, test_scores = learning_curve(lr_model_loaded, X_train, y_train, train_sizes=train_sizes, cv=5, scoring='neg_mean_squared_error')\n",
        "\n",
        "# convert the negative MSE scores to positive values\n",
        "train_scores = -train_scores\n",
        "test_scores = -test_scores\n",
        "\n",
        "# calculate the mean and standard deviation of the training and test scores\n",
        "train_mean = np.mean(train_scores, axis=1)\n",
        "train_std = np.std(train_scores, axis=1)\n",
        "test_mean = np.mean(test_scores, axis=1)\n",
        "test_std = np.std(test_scores, axis=1)\n",
        "\n",
        "# plot the learning curve\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(train_sizes, train_mean, label='Training error')\n",
        "plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, alpha=0.1)\n",
        "plt.plot(train_sizes, test_mean, label='Cross-validation error')\n",
        "plt.fill_between(train_sizes, test_mean - test_std, test_mean + test_std, alpha=0.1)\n",
        "plt.xlabel('Number of training examples')\n",
        "plt.ylabel('MSE')\n",
        "plt.title('Learning curve for Linear Regression')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "NTpmVK205Qi-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "7bc4ca2b-014a-4ddd-a423-cc3da3335e3e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Learning Curve for Lasso\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn.model_selection import learning_curve\n",
        "\n",
        "train_sizes, train_scores, test_scores = learning_curve(best_lasso_model, X_train, y_train, cv=5)\n",
        "\n",
        "# Calculate mean and standard deviation for training scores\n",
        "train_mean = np.mean(train_scores, axis=1)\n",
        "train_std = np.std(train_scores, axis=1)\n",
        "\n",
        "# Calculate mean and standard deviation for test scores\n",
        "test_mean = np.mean(test_scores, axis=1)\n",
        "test_std = np.std(test_scores, axis=1)\n",
        "\n",
        "# Plot the learning curve\n",
        "plt.plot(train_sizes, train_mean, label='Training score')\n",
        "plt.plot(train_sizes, test_mean, label='Cross-validation score')\n",
        "\n",
        "# Draw the bands\n",
        "plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, alpha=0.1)\n",
        "plt.fill_between(train_sizes, test_mean - test_std, test_mean + test_std, alpha=0.1)\n",
        "\n",
        "# Add labels and legend\n",
        "plt.xlabel('Number of training samples')\n",
        "plt.ylabel('MSE')\n",
        "plt.legend()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "cL-TEobF5wfU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "9ab03cc6-f4e5-4481-c2e4-1ee9007d7381"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Learning curve for MLP\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn.model_selection import learning_curve\n",
        "\n",
        "train_sizes, train_scores, test_scores = learning_curve(\n",
        "    mlp_model, X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n",
        "\n",
        "train_scores_mean = -np.mean(train_scores, axis=1)\n",
        "test_scores_mean = -np.mean(test_scores, axis=1)\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.title('MLPRegressor Learning Curve')\n",
        "plt.xlabel('Training Examples')\n",
        "plt.ylabel('Neg MSE')\n",
        "plt.grid()\n",
        "plt.plot(train_sizes, train_scores_mean, label='Training error')\n",
        "plt.plot(train_sizes, test_scores_mean, label='Cross-validation error')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZjvXQPXk7uq7"
      },
      "source": [
        "#Gradio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "NlKaWy3Y6vhu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d65aa18b-4d14-46c6-eb36-23a4ecd41bb2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m20.0/20.0 MB\u001b[0m \u001b[31m74.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m74.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.0/57.0 kB\u001b[0m \u001b[31m6.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m288.3/288.3 kB\u001b[0m \u001b[31m27.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.4/75.4 kB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m236.8/236.8 kB\u001b[0m \u001b[31m29.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.5/50.5 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m137.0/137.0 kB\u001b[0m \u001b[31m16.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.7/45.7 kB\u001b[0m \u001b[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m8.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m129.9/129.9 kB\u001b[0m \u001b[31m16.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.5/114.5 kB\u001b[0m \u001b[31m13.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m18.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.6/149.6 kB\u001b[0m \u001b[31m18.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.0/67.0 kB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m72.5/72.5 kB\u001b[0m \u001b[31m10.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "# Step 1: install Gradio\n",
        "!pip install --quiet gradio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "enTHRsOm75fK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8ad96138-eb60-4a4e-eec9-34ec4540702d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gradio/inputs.py:27: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/gradio/inputs.py:30: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
            "  super().__init__(\n",
            "/usr/local/lib/python3.10/dist-packages/gradio/inputs.py:30: UserWarning: `numeric` parameter is deprecated, and it has no effect\n",
            "  super().__init__(\n",
            "/usr/local/lib/python3.10/dist-packages/gradio/inputs.py:219: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/gradio/inputs.py:222: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
            "  super().__init__(\n"
          ]
        }
      ],
      "source": [
        "import gradio as gr\n",
        "import tensorflow as tf\n",
        "import cv2\n",
        "\n",
        "input1 = input_module1 = gr.inputs.Textbox(label=\"Input Features. Please enter them in array form. Please use this template: [Gender, Race, Age, Height (cm), Weight (kg), Diabetes, Simvastatin (Zocor), Amiodarone (Cordarone), INR on Reported Therapeutic Dose of Warfarin, VKORC1 genotype: -1639 G>A (3673); chr16:31015190; rs9923231; C/T]. Please convert your values accordingly: \\n {'African-American': 0, 'Asian': 1, 'Black': 2, 'Black African': 3, 'Black Caribbean': 4, 'Black or African American': 5, 'Black other': 6, 'Caucasian': 7, 'Chinese': 8, 'Han Chinese': 9, 'Hispanic': 10, 'Indian': 11, 'Intermediate': 12, 'Japanese': 13, 'Korean': 14, 'Malay': 15, 'Other': 16, 'Other (Black British)': 17, 'Other (Hungarian)': 18, 'Other Mixed Race': 19, 'White': 20, nan: 21} \\n {'female': 0, 'male': 1, nan: 2} \\n For age: {'10 - 19': 0, '20 - 29': 1, '30 - 39': 2, '40 - 49': 3, '50 - 59': 4, '60 - 69': 5, '70 - 79': 6, '80 - 89': 7, '90+': 8, nan: 9} /n For Diabetes, Simvastatin (Zocor), and Amiodarone (Cordarone): {0.0: 0, 1.0: 1, nan: 2} \\n For VKORC1: {'A/A': 0, 'A/G': 1, 'G/G': 2, nan: 3}\", lines=10)\n",
        "\n",
        "input2 = input_module1 = gr.inputs.Dropdown(choices=[\"Linear Regression\", \"Lasso\", \"MLP\"], label = \"Method\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "ZNrjc87581sI"
      },
      "outputs": [],
      "source": [
        "import joblib\n",
        "\n",
        "lr_model_loaded = joblib.load('linear_regression_model.joblib')\n",
        "\n",
        "filename = 'lasso_model.joblib'\n",
        "lasso_model = joblib.load(filename)\n",
        "\n",
        "filename = 'mlp_model.joblib'\n",
        "mlp_model = joblib.load(filename)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "pCqJbO4Kep1v",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b0606ea8-c554-4d8e-c785-215cc18465f7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gradio/outputs.py:197: UserWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/gradio/outputs.py:200: UserWarning: The 'type' parameter has been deprecated. Use the Number component instead.\n",
            "  super().__init__(num_top_classes=num_top_classes, type=type, label=label)\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "def predict(input1, input2):\n",
        "    # Convert input data to a numpy array\n",
        "    input_arr = np.array(eval(input1))\n",
        "    # Reshape input data to match model input dimensions\n",
        "    input_arr = input_arr.reshape(1, -1)\n",
        "    \n",
        "    # Select the appropriate model\n",
        "    if input2 == \"Linear Regression\":\n",
        "        preds = lr_model_loaded.predict(input_arr)\n",
        "    elif input2 == \"Lasso\":\n",
        "        preds = lasso_model.predict(input_arr)\n",
        "    elif input2 == \"MLP\":\n",
        "        preds = mlp_model.predict(input_arr)\n",
        "    else:\n",
        "        preds = None\n",
        "\n",
        "    if preds is not None:\n",
        "        output_label = preds[0]\n",
        "    else:\n",
        "        output_label = \"Unknown\"\n",
        "    \n",
        "    return output_label\n",
        "\n",
        "label = gr.outputs.Label(label = \"Required Amount\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "rDOMzs4bAm3Q"
      },
      "outputs": [],
      "source": [
        "#[1.0,20.0,5.0,193.04,115.70,2.0,0.0,0.0,2.60,1.0]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "interface = gr.Interface(fn=predict, inputs=[input1, input2], examples=[\n",
        "        [\"[1.0,20.0,5.0,193.04,115.70,2.0,0.0,0.0,2.60,1.0]\"]\n",
        "    ], outputs=label).launch(debug = True, share = True)"
      ],
      "metadata": {
        "id": "6d0M7W6rLtt8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "outputId": "d01f734b-fd3e-4479-e248-27a19d024127"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
            "Running on public URL: https://ac8a04225c89fb1915.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://ac8a04225c89fb1915.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MLPRegressor was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}