script_parkinson.ipynb

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      "version": "3.6.10"
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    "colab": {
      "name": "script_parkinson.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Espipi/DataInsightProgram/blob/master/script_parkinson.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CTmXz5AFI1op",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "outputId": "586cfadd-4932-4243-f266-79676dd6f2cd"
      },
      "source": [
        "#Load package\n",
        "import os, sys\n",
        "import pandas as pd\n",
        "import random\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "print(os.listdir())"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['.config', 'sample_data']\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T56tuPl5I1o7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        },
        "outputId": "d2f6a7b1-a7a8-4b51-be30-8f0c1c28b5b7"
      },
      "source": [
        "##Load the dataset and the 3 first lines \n",
        "df = pd.read_csv('/content/sample_data/parkinsons.data')\n",
        "df.head(3)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>MDVP:Fo(Hz)</th>\n",
              "      <th>MDVP:Fhi(Hz)</th>\n",
              "      <th>MDVP:Flo(Hz)</th>\n",
              "      <th>MDVP:Jitter(%)</th>\n",
              "      <th>MDVP:Jitter(Abs)</th>\n",
              "      <th>MDVP:RAP</th>\n",
              "      <th>MDVP:PPQ</th>\n",
              "      <th>Jitter:DDP</th>\n",
              "      <th>MDVP:Shimmer</th>\n",
              "      <th>MDVP:Shimmer(dB)</th>\n",
              "      <th>Shimmer:APQ3</th>\n",
              "      <th>Shimmer:APQ5</th>\n",
              "      <th>MDVP:APQ</th>\n",
              "      <th>Shimmer:DDA</th>\n",
              "      <th>NHR</th>\n",
              "      <th>HNR</th>\n",
              "      <th>status</th>\n",
              "      <th>RPDE</th>\n",
              "      <th>DFA</th>\n",
              "      <th>spread1</th>\n",
              "      <th>spread2</th>\n",
              "      <th>D2</th>\n",
              "      <th>PPE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>phon_R01_S01_1</td>\n",
              "      <td>119.992</td>\n",
              "      <td>157.302</td>\n",
              "      <td>74.997</td>\n",
              "      <td>0.00784</td>\n",
              "      <td>0.00007</td>\n",
              "      <td>0.00370</td>\n",
              "      <td>0.00554</td>\n",
              "      <td>0.01109</td>\n",
              "      <td>0.04374</td>\n",
              "      <td>0.426</td>\n",
              "      <td>0.02182</td>\n",
              "      <td>0.03130</td>\n",
              "      <td>0.02971</td>\n",
              "      <td>0.06545</td>\n",
              "      <td>0.02211</td>\n",
              "      <td>21.033</td>\n",
              "      <td>1</td>\n",
              "      <td>0.414783</td>\n",
              "      <td>0.815285</td>\n",
              "      <td>-4.813031</td>\n",
              "      <td>0.266482</td>\n",
              "      <td>2.301442</td>\n",
              "      <td>0.284654</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>phon_R01_S01_2</td>\n",
              "      <td>122.400</td>\n",
              "      <td>148.650</td>\n",
              "      <td>113.819</td>\n",
              "      <td>0.00968</td>\n",
              "      <td>0.00008</td>\n",
              "      <td>0.00465</td>\n",
              "      <td>0.00696</td>\n",
              "      <td>0.01394</td>\n",
              "      <td>0.06134</td>\n",
              "      <td>0.626</td>\n",
              "      <td>0.03134</td>\n",
              "      <td>0.04518</td>\n",
              "      <td>0.04368</td>\n",
              "      <td>0.09403</td>\n",
              "      <td>0.01929</td>\n",
              "      <td>19.085</td>\n",
              "      <td>1</td>\n",
              "      <td>0.458359</td>\n",
              "      <td>0.819521</td>\n",
              "      <td>-4.075192</td>\n",
              "      <td>0.335590</td>\n",
              "      <td>2.486855</td>\n",
              "      <td>0.368674</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>phon_R01_S01_3</td>\n",
              "      <td>116.682</td>\n",
              "      <td>131.111</td>\n",
              "      <td>111.555</td>\n",
              "      <td>0.01050</td>\n",
              "      <td>0.00009</td>\n",
              "      <td>0.00544</td>\n",
              "      <td>0.00781</td>\n",
              "      <td>0.01633</td>\n",
              "      <td>0.05233</td>\n",
              "      <td>0.482</td>\n",
              "      <td>0.02757</td>\n",
              "      <td>0.03858</td>\n",
              "      <td>0.03590</td>\n",
              "      <td>0.08270</td>\n",
              "      <td>0.01309</td>\n",
              "      <td>20.651</td>\n",
              "      <td>1</td>\n",
              "      <td>0.429895</td>\n",
              "      <td>0.825288</td>\n",
              "      <td>-4.443179</td>\n",
              "      <td>0.311173</td>\n",
              "      <td>2.342259</td>\n",
              "      <td>0.332634</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "             name  MDVP:Fo(Hz)  MDVP:Fhi(Hz)  ...   spread2        D2       PPE\n",
              "0  phon_R01_S01_1      119.992       157.302  ...  0.266482  2.301442  0.284654\n",
              "1  phon_R01_S01_2      122.400       148.650  ...  0.335590  2.486855  0.368674\n",
              "2  phon_R01_S01_3      116.682       131.111  ...  0.311173  2.342259  0.332634\n",
              "\n",
              "[3 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zsU5s_t7I1pM",
        "colab_type": "text"
      },
      "source": [
        "## **A. Description of the data set**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hE41HlKRI1pP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "with open('/content/sample_data/parkinsons.names', 'r') as f:\n",
        "    data = f.read()\n",
        "    \n",
        "print(data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8pH0I651bC0K",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "**1**.   **Data Set Information**\\\n",
        "This dataset is composed of a range of biomedical voice measurements from \n",
        "31 people, 23 with Parkinson's disease (PD). Each column in the table is a \n",
        "particular voice measure, and each row corresponds one of 195 voice \n",
        "recording from these individuals (\"name\" column). The main aim of the data \n",
        "is to discriminate healthy people from those with PD, according to \"status\" \n",
        "column which is set to 0 for healthy and 1 for PD."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KgxKRY-jcvEK",
        "colab_type": "text"
      },
      "source": [
        "**2.**   **Attribute Information**\\\n",
        "Matrix column entries (attributes):\\\n",
        "name - ASCII subject name and recording number\\\n",
        "MDVP:Fo(Hz) - Average vocal fundamental frequency\\\n",
        "MDVP:Fhi(Hz) - Maximum vocal fundamental frequency\\\n",
        "MDVP:Flo(Hz) - Minimum vocal fundamental frequency\\\n",
        "MDVP:Jitter(%),MDVP:Jitter(Abs),MDVP:RAP,MDVP:PPQ,Jitter:DDP - Several \n",
        "measures of variation in fundamental frequency\\\n",
        "MDVP:Shimmer,MDVP:Shimmer(dB),Shimmer:APQ3,Shimmer:APQ5,MDVP:APQ,Shimmer:DDA - Several measures of variation in amplitude\n",
        "NHR,HNR - Two measures of ratio of noise to tonal components in the voice\\\n",
        "status - Health status of the subject (one) - Parkinson's, (zero) - healthy\\\n",
        "RPDE,D2 - Two nonlinear dynamical complexity measures\\\n",
        "DFA - Signal fractal scaling exponent\\\n",
        "spread1,spread2,PPE - Three nonlinear measures of fundamental frequency variation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yw0SYdK8I1pc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 586
        },
        "outputId": "cb5b05d0-fbd9-4bab-e9ae-3c41430ae447"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 195 entries, 0 to 194\n",
            "Data columns (total 24 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   name              195 non-null    object \n",
            " 1   MDVP:Fo(Hz)       195 non-null    float64\n",
            " 2   MDVP:Fhi(Hz)      195 non-null    float64\n",
            " 3   MDVP:Flo(Hz)      195 non-null    float64\n",
            " 4   MDVP:Jitter(%)    195 non-null    float64\n",
            " 5   MDVP:Jitter(Abs)  195 non-null    float64\n",
            " 6   MDVP:RAP          195 non-null    float64\n",
            " 7   MDVP:PPQ          195 non-null    float64\n",
            " 8   Jitter:DDP        195 non-null    float64\n",
            " 9   MDVP:Shimmer      195 non-null    float64\n",
            " 10  MDVP:Shimmer(dB)  195 non-null    float64\n",
            " 11  Shimmer:APQ3      195 non-null    float64\n",
            " 12  Shimmer:APQ5      195 non-null    float64\n",
            " 13  MDVP:APQ          195 non-null    float64\n",
            " 14  Shimmer:DDA       195 non-null    float64\n",
            " 15  NHR               195 non-null    float64\n",
            " 16  HNR               195 non-null    float64\n",
            " 17  status            195 non-null    int64  \n",
            " 18  RPDE              195 non-null    float64\n",
            " 19  DFA               195 non-null    float64\n",
            " 20  spread1           195 non-null    float64\n",
            " 21  spread2           195 non-null    float64\n",
            " 22  D2                195 non-null    float64\n",
            " 23  PPE               195 non-null    float64\n",
            "dtypes: float64(22), int64(1), object(1)\n",
            "memory usage: 36.7+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NOksMPbuI1pn",
        "colab_type": "text"
      },
      "source": [
        "## **B. Exploratory Data Analysis**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KbiBz-xdI1po",
        "colab_type": "text"
      },
      "source": [
        "#### ***Health status of the subject***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gxic4d3jI1pq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "outputId": "92c8b5a3-3746-481f-f6ff-4b268d0f5a79"
      },
      "source": [
        "###Plotting number of healthy (0) and sick (1) persons\n",
        "sns.set_style(\"white\")\n",
        "sns.countplot(df['status'], palette=[\"red\", \"blue\"]).set(title = \"Barplot of healthy (0) and sick (1) persons\", \n",
        "                                                             xlabel = 'Health status', ylabel ='Number of person')\n",
        "#plt.xlabel('Health status')\n",
        "#plt.ylabel('Number of person')\n",
        "###Print\n",
        "h = df[df['status']==0]['status'].count()\n",
        "s = df[df['status']==1]['status'].count()\n",
        "\n",
        "print('Healthy :',h ,'Proportion:', round((h*100/(h+s)),2),\"%\")\n",
        "print('Sick:',s,'Proportion:' ,round((s*100/(h+s)), 2),\"%\" )\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Healthy : 48 Proportion: 24.62 %\n",
            "Sick: 147 Proportion: 75.38 %\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4aBTfB9Md69q",
        "colab_type": "text"
      },
      "source": [
        "The number of sick individuals is 147, i.e. nearly 75% of the total.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "19pz_VEyI1p1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "60a714cc-21dc-4c03-c936-b404cf055d39"
      },
      "source": [
        "##set plot parameters\n",
        "fig, ax = plt.subplots(1,3,figsize=(15,9))\n",
        "\n",
        "##Boxplot for the average vocal fundamental frequency\n",
        "sns.boxplot(x='status',y='MDVP:Fo(Hz)', data=df, width=0.3,\n",
        "                palette=[\"red\", \"blue\"], ax=ax[0]).set(title = 'Average vocal fundamental frequency')\n",
        "\n",
        "##Boxplot for the minimum vocal fundamental frequency\n",
        "sns.boxplot(x='status',y='MDVP:Flo(Hz)', data=df, width=0.3,\n",
        "                palette=[\"red\", \"blue\"], ax=ax[1]).set(title = \"Minimum vocal fundamental frequency\")\n",
        "\n",
        "##Boxplot for the maximum vocal fundamental frequency\n",
        "sns.boxplot(x='status',y='MDVP:Fhi(Hz)', data=df, width=0.3,\n",
        "                palette=[\"red\", \"blue\"], ax=ax[2]).set(title = \"Maximum vocal fundamental frequency\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vlSkvL8+nP/3pEidqu5oskrZt25alS5fmvvvuy8svv5z7779/j3/2186dO7N06dKMGjWqcaxTp06N1y4OHjw4RxxxRONNuQFo2e6+++7G15dddlkJkwDQEpkngOZw1llnpUOH3RVHhw4dctZZZ5U4UdvV5KVtH/vYxxovWxg6dGgeeOCBPd4/+eST9+uEDz30UAYMGJA+ffo0jm3ZsiXdu3dPx44ds27duqxduzb9+vXbr+MCUHoHa+UqAG2TeQIolp49e2b48OH5xS9+kc985jNutF1ETRZJs2bNOqADT5s2LatXr059fX2GDRuWKVOmZPz48fnZz36W0aNH77HvL3/5y1x33XUpLy9Phw4d8s1vftNTfgBaiTdXrr7xxhuNK1ffan9/cACgbTFPAM3lrLPOyrPPPms1UpE1WSS91ZNPPplBgwbtdfut5syZ847js2fPftvYyJEjM3LkyP2JAkALcaArV2tqarJy5cr06tUrixcvTpL80z/9U+6888707Nkzye4fJd58TPQNN9yQBQsWpEOHDrniiivyyU9+slgfCYCD6GBf4QCwNz179nzHzoGDa7+KpNtvvz1XXnnlXrcBaH8OdOXquHHjMmHChMyYMWOP8fPOOy9f+tKX9hj7wx/+kCVLlmTJkiWpq6vLxIkTc99996Vjx44HnBuA5nGg8wQALdN+PbXtz0sjJRIAb/Xkk0/uc/uthg4dmu7duxd03OXLl2f06NHp1KlT+vXrlw984AN54okn3lVWAJrf/swTALRMTRZJmzdvzre+9a2cf/75+e53v5uXX365OXIB0Ardfvvt+9wuxG233ZYxY8akpqYmL774YpKkrq5uj4c0VFVVpa6u7t2FBaDZHYx5AoDSarJIuuyyy3LooYdmwoQJefXVV61CAmCv3u3K1S9+8YtZunRpFi1alN69e7vGHaCNcYUDQOvX5D2SXnjhhVxyySVJkk9+8pM57bTTih4KgNZj8+bN+eEPf5hnn302Rx11VM4///x06dLlgI512GGHNb4eP358vvKVryTZvQJp48aNje/V1dWlqqrq3QUHoFkczHkCgNIr6B5JL774YrZu3ZqtW7dm165de2wD0L4dzJWrmzZtany9bNmyDBw4MElSXV2dJUuWZMeOHVm3bl3Wrl2bv/qrv3rX2QEoPlc4ALQtTa5Ievnll9+2CunN7bKysixfvrw4yYAW58Ybb0xtbW1Rz/Hm8Wtqaop2jgEDBmTSpElFO357c6ArV6dNm5bVq1envr4+w4YNy5QpU7J69eo89dRTSZK+fftm5syZSZKBAwfmlFNOyahRo9KxY8f8wz/8gye2AbQSrnAAaFuaLJJWrFjRHDmAVqC2tjaPP/5Mtm/vW7RzlJfvXur+8MPbi3L8iooNRTlue/fiiy+moaEhSRpXrr653aNHj3f8mzlz5rxtbPz48Xs9xwUXXJALLrjgIKQFoLkdyDwBQMvUZJE0atSojBkzJqeeemr69evXHJmAFmz79r5Zs+biUsc4YP37X1vqCG2OlasA7It5AqBtabJImjNnTpYsWZKJEyemR48eOfXUU3PKKae4ySkASaxcBWDfzBMAbUuTN9s++uijc+mll2bZsmW54oor8txzz+XMM8/MOeeckzvvvLM5MgLQgo0aNSrXX3991q1bV+ooALRA5gmAtqWgp7a9aciQIfna176Wq666Ki+99FLjTVABaL/mzJmTV199NRMnTswXvvCF3HTTTamrqyt1LABaCPMEQNvS5KVtb3riiSeyZMmS3H///Xn/+9+fM888M5/97GeLmQ2AVuDoo49uXL362GOP5Wc/+1nOPPPM9OvXL2PGjMkZZ5xR6ogAlJB5AqBtKegeST/72c/SvXv3jB49Orfffnv69OnTHNkAaGWGDBmSIUOGZPjw4Zk1a1ZmzpzpCwIAjcwTAK1fk0VSp06dMn/+/Hzwgx9shjgAtFZWrgKwL+YJgLahySLpwgsvTG1tbWbPnp3a2tokyZFHHpnx48dnwIABRQ8IQMtm5SoA+2KeAGhbmiySHn300UyZMiVnnHFG47LT3/3udzn33HPz/e9/P0OGDCl6SABaLitXAdgX8wRA29JkkfSDH/wg3/3ud3Pcccc1jn3mM5/J8ccfn+9///uZP39+UQMC0LJZuQrAvpgnANqWDk3tsG7duj1KpDd97GMfy7p164oSCoDW49FHH825556bQw89tHH1aufOnXPuuefmscceK3U8AErMPAHQtjS5Iuk973nPXt879NBDD2oYAFofK1cB2BfzBEDb0mSR9Pzzz+fKK69823hDQ0Pq6uqKEgqA1mNfK1f/z//5PyVIBEBLYp4AaFuaLJIuu+yyvb43ePDggxoGgNbHylUA9sU8AdC2NFkknXbaac2RA4BWyspVAPbFPAHQtjRZJH3lK1/Z5/s//OEPD1oYAFofK1cB2BfzBEDb0mSR9Nhjj+V973tfRo8enWOOOSYNDQ3NkQuAVsLKVQD2xTwB0LY0WST927/9W/7t3/4tS5YsyeLFi3PSSSfl1FNPzcCBA5sjHwAtnJWrAOyLeQKgbWmySOrYsWOGDRuWYcOGZceOHVm8eHHOOeecXHjhhZkwYUJzZASgBbNyFYB9MU8AtC1NFklJsmPHjqxcuTKLFy/Ohg0bcs4552TEiBHFzgZAK2DlKgD7Yp4AaFuaLJIuu+yy/P73v8+wYcNy4YUX5qijjmqOXAC0ElauArAv5gmAtqXJIuknP/lJOnfunLVr1+aWW25pHG9oaEhZWVl+/etfFzUgAC2flasA7It5AqDtaLJIeuqpp5ojBwCtlJWrAOyLeQKgbSnoHkkAsDdWrgKwL+YJgLZFkQTAu2LlKgD7Yp4AaFs6lDoAAAAAAK2DIgkAAACAgiiSAAAAACiIeyQBAAAtzmuvvZazzz47O3bsyK5duzJy5MhcdNFFWbduXaZNm5atW7dm0KBBufrqq9OpU6fs2LEjl112WZ588sn06NEj3/ve9/L+97+/1B8DoM2xIgkAAGhxOnXqlJtvvjk/+clPsnDhwvzrv/5rHnvssVxzzTU577zzsnTp0nTr1i0LFixIktx1113p1q1bli5dmvPOOy/XXHNNiT8BQNukSAIAAFqcsrKyvOc970mS7Ny5Mzt37kxZWVn+4z/+IyNHjkySnHbaaVm+fHmSZMWKFTnttNOSJCNHjsy///u/p6GhoTThAdowRRIAANAi7dq1K5///Ofz8Y9/PB//+MfTr1+/dOvWLeXlu+/Q0adPn9TV1SVJ6urq8r73vS9JUl5enq5du6a+vr5k2QHaKkUSAADQInXs2DGLFi3Kgw8+mCeeeCK1tbWljgTQ7imSAACAFq1bt2457rjj8thjj2Xbtm3ZuXNnkmTjxo2pqqpKklRVVeX5559PsvtSuJdeeimVlZUlywzQVimSAACAFmfLli3Ztm1bkmT79u156KGHcuSRR+a4447LfffdlyS59957U11dnSSprq7OvffemyS57777cvzxx6esrKw04QHasPJSBwAAAPhzmzZtyuWXX55du3aloaEhn/3sZ/PpT386f/mXf5lLLrkkc+fOzYc+9KGMHz8+SfKFL3wh06dPz4gRI9K9e/d873vfK/EnAGibFEkAAECLc/TRR2fhwoVvG+/Xr18WLFjwtvFDDjkk1113XXNEA2jXXNoGAAAAQEEUSQAAAAAURJEEAAAAQEEUSQAAAAAURJEEAAAAQEEUSQAAAAAURJEEAAAAQEEUSQAAAAAURJEEAAAAQEHKSx0AAIC24cYbb0xtbW3Rjv/msWtqaop2jiQZMGBAJk2aVNRzAEBrpUgCAOCgqK2tzeOPP5Pt2/sW5fjl5V2SJA8/vL0ox0+SiooNRTs2ALQFRSuSampqsnLlyvTq1SuLFy9OkvzTP/1T7rzzzvTs2TNJMm3atJx00klJkhtuuCELFixIhw4dcsUVV+STn/xksaIBAFAk27f3zZo1F5c6xgHr3//aUkcAgBataEXSuHHjMmHChMyYMWOP8fPOOy9f+tKX9hj7wx/+kCVLlmTJkiWpq6vLxIkTc99996Vjx47FigcAAADAfirazbaHDh2a7t27F7Tv8uXLM3r06HTq1Cn9+vXLBz7wgTzxxBPFigYAAADAAWj2p7bddtttGTNmTGpqavLiiy8mSerq6tKnT5/GfaqqqlJXV9fc0QAAAADYh2Ytkr74xS9m6dKlWbRoUXr37p3Zs2c35+kBAAAAeBeatUg67LDD0rFjx3To0CHjx4/Pb37zmyS7VyBt3Lixcb+6urpUVVU1ZzQAAAAAmtCsRdKmTZsaXy9btiwDBw5MklRXV2fJkiXZsWNH1q1bl7Vr1+av/uqvmjMaAAAAAE0o2lPbpk2bltWrV6e+vj7Dhg3LlClTsnr16jz11FNJkr59+2bmzJlJkoEDB+aUU07JqFGj0rFjx/zDP/yDJ7YBtHE1NTVZuXJlevXqlcWLFydJrrrqqjzwwAP5i7/4ixxxxBGZNWtWunXrlvXr12fUqFHp379/kuSYY45pnEMAAIDmU7Qiac6cOW8bGz9+/F73v+CCC3LBBRcUKw4ALcy4ceMyYcKEzJgxo3HsxBNPzKWXXpry8vJ85zvfyQ033JDp06cnSY444ogsWrSoVHEBAGhmK1asyNKlSwvef+vWrUmSHj167Nd5RowYkerq6v36m/as2Z/aBgBJMnTo0HTv3n2PsU984hMpL9/9G8eQIUP2uH8eAADsy5YtW7Jly5ZSx2jzirYiCQDejbvvvjunnHJK4/b69eszduzYdOnSJVOnTs2xxx5bwnQAABRbdXX1fq0UqqmpSZLMmjWrWJGIIgmAFuj6669Px44d87nPfS5J0rt37zzwwAOprKzMb3/72/zd3/1dlixZki5dupQ4KQAAtC8ubQOgRbnnnnuycuXKXHPNNSkrK0uSdOrUKZWVlUmSwYMH54gjjsiaNWtKGRMAANolRRIALcaqVasyf/78XH/99encuXPj+JYtW7Jr164kybp167J27dr069evVDEBAKDdcmkbULD6+vpUVGxO//7XljrKAauoWJ/6+l6ljkGSadOmZfXq1amvr8+wYcMyZcqUzJs3Lzt27MjEiROTJMccc0xmzpyZX/7yl7nuuutSXl6eDh065Jvf/OZ+P40DAAB49xRJAJTEnDlz3jY2fvz4d9x35MiRGTlyZLEjAQAATVAkAQWrrKzM0093zpo1F5c6ygHr3//aVFZWlDoGAABAq+QeSQAAAAAURJEEAAAAQEEUSQAAAAAURJEEAAAAQEEUSQAAAAAURJEEAAAAQEEUSQAAAAAURJEEAAAAQEEUSQAAAAAUpLzUAQAAACiOG2+8MbW1tUU7/pvHrqmpKdo5kmTAgAGZNGlSUc8BFEaRBAAA0EbV1tbmmccfT9/t24ty/C7lu79Sbn/44aIcP0k2VFQU7djA/lMkAQAAtGF9t2/PxWvWlDrGAbu2f/9SRwDewj2SAAAAACiIIgkAAACAgiiSAAAAACiIIgkAAACAgiiSAAAAACiIIgkAAACAgiiSAAAAACiIIgkAAACAgpSXOgAAcODq6+uzuaIi1/bvX+ooB2x9RUV61deXOgYAAAWwIgkAAACAgliRBACtWGVlZTo//XQuXrOm1FEO2LX9+6eisrLUMQAAKIAVSQAAAAAURJEEAAAAQEEUSQAAAAAURJEEAAAAQEHcbBsAAKCNqq+vz+aKilzbv3+poxyw9RUV6VVfX+oYwP+wIgkAAACAgliRBAAA0EZVVlam89NP5+I1a0od5YBd279/KiorSx0D+B9WJAEAAABQEEUSAAAAAAVRJAEAAABQEEUSAAAAAAVRJAEAAABQEEUSAAAAAAVRJAEAAABQEEUSAAAAAAVRJAEAAABQEEUSAAAAAAVRJAEAAABQEEUSAAAAAAVRJAEAAABQEEUSAAAAAAVRJAEAAABQEEUSAAAAAAVRJAEAAABQkPJiHbimpiYrV65Mr169snjx4iTJVVddlQceeCB/8Rd/kSOOOCKzZs1Kt27dsn79+owaNSr9+/dPkhxzzDGZOXNmsaIBAAAAcACKtiJp3LhxmT9//h5jJ554YhYvXpyf/vSn+eAHP5gbbrih8b0jjjgiixYtyqJFi5RIAAAAAC1Q0VYkDR06NOvXr99j7BOf+ETj6yFDhuQXv/hFsU4PAAAAtCA33nhjamtri3b8N49dU1NTtHMkyYABAzJp0qSinqMlK1qR1JS77747p5xySuP2+vXrM3bs2HTp0iVTp07NscceW6poAAAAwEFWW1ubxx9/Jtu39y3K8cvLuyRJHn54e1GOnyQVFRuKduzWoiRF0vXXX5+OHTvmc5/7XJKkd+/eeeCBB1JZWZnf/va3+bu/+7ssWbIkXbp0KUU8AAAAoAi2b++bNWsuLnWMA9a//7WljlByzf7UtnvuuScrV67MNddck7KysiRJp06dUllZmSQZPHhwjjjiiKxZs6a5owEAAACwD81aJK1atSrz58/P9ddfn86dOzeOb9myJbt27UqSrFu3LmvXrk2/fv2aMxoAANCCPP/88znnnHMyatSojB49OjfffHOSZOvWrZk4cWJOPvnkTJw4MS+++GKSpKGhIVdeeWVGjBiRMWPG5MknnyxlfIA2q2iXtk2bNi2rV69OfX19hg0blilTpmTevHnZsWNHJk6cmCQ55phjMnPmzPzyl7/Mddddl/Ly8nTo0CHf/OY306NHj2JFA6AFqKmpycqVK9OrV68sXrw4ye4vB5dcckk2bNiQvn37Zu7cuenevXsaGhryrW99Kw8++GAqKioye/bsDBo0qMSfAIBi6tixYy6//PIMGjQoL7/8ck4//fSceOKJueeee3LCCSdk8uTJmTdvXubNm5fp06dn1apVWbt2be6///48/vjj+cY3vpG77rqr1B8DoM0pWpE0Z86ct42NHz/+HfcdOXJkRo4cWawoALRA48aNy4QJEzJjxozGsXnz5vlyAECS3fdR7d27d5KkS5cuGTBgQOrq6rJ8+fLccsstSZKxY8fmnHPOyfTp07N8+fKMHTs2ZWVlGTJkSLZt25ZNmzY1HgOAg6PZ75EEAEkydOjQdO/efY+xN78EJLu/HCxbtmyP8T//cgBA+7B+/fr853/+Z4455phs3ry5sRx673vfm82bNydJ6urq0qdPn8a/6dOnT+rq6kqSF6AtK8lT2wDgnezvlwO/MkPLUl9fn4qKza36iTYVFetTX9+r1DF4i1deeSUXXXRRvva1r73tqc5lZWWND/ABoHlYkQRAi+TLAQCvv/56LrrooowZMyYnn3xykqRXr16Nq1I3bdqUnj17JkmqqqqycePGxr/duHFjqqqqmj80QBtnRRIALcabXw569+7tywG0QpWVlXn66c5Zs+biUkc5YP37X5vKyopSxyC7n8L293//9xkwYEDjw3qSpLq6OgsXLszkyZOzcOHCDB8+vHH81ltvzejRo/P444+na9euVq4CFIEiqRXYUFGRa/v3L8qxt5Xv/leg286dRTl+sjv/kUU7OtCW+HIAwJt+9atfZdGiRTnqqKPy+c9/PsnuJ0NPnjw5U6dOzYIFC3L44Ydn7ty5SZKTTjopDz74YEaMGJHOnTvn29/+dinjA7RZiqQWbsCAAUU9/nO1tUmS3kU8z5Ep/ucAWp9p06Zl9erVqa+vz7BhwzJlyhRfDg6QHxyAtujYY4/Nf/3Xf73jezfffPPbxsrKyvL1r3+92LEA2j1FUgs3adKkoh6/pqYmSTJr1qyingfgz82ZM+cdx3052D9+cAAAoDkpkgCgFfODAwAAzclT2wAAAAAoiCIJAAAAgIIokgAAAAAoiCIJAAAAgIIokgAAAAAoiKe2AfulomJD+ve/tmjHLy/fliTZubNbUY5fUbEhux82DgAAwP5SJAEFGzBgQNHPUVv73P+cq3eRznBks3wOAACAtkiRBBRs0qRJRT9HTU1NkmQ7J/AwAAANkElEQVTWrFlFPxcAAAD7xz2SAAAAACiIIgkAAACAgiiSAAAAACiIIgkAAACAgiiSAAAAACiIIgkAAACAgiiSAAAAACiIIgkAAACAgiiSAAAAACiIIgkAAACAgiiSAAAAACiIIgkAAACAgiiSAAAAACiIIgkAAACAgiiSAAAAAChIeakDcHCtWLEiS5cuLXj/2traJElNTc1+nWfEiBGprq7er78BAAAAWjdFUjvXs2fPUkcAAAAAWglFUhtTXV1tpRAAAAAtTn19fSoqNqd//2tLHeWAVVSsT319r1LHKCn3SAIAAACgIFYkAQAAAEVXWVmZp5/unDVrLi51lAPWv/+1qaysKHWMkrIiCQAAAICCKJIAAAAAKIgiCQAAAICCKJIAAAAAKIgiCQAAAICCKJIAAAAAKIgiCQAAAICCKJIAAAAAKIgiCQAAAICCKJIAAAAAKIgiCQAAAICCKJIAAAAAKIgiCQAAAICClJc6AAAAbUdFxYb0739tUY5dXr4tSbJzZ7eiHD/ZnT85smjHB4DWTpEEAMBBMWDAgKIev7b2uf85T+8inuXIon8OAGjNFEkAABwUkyZNKurxa2pqkiSzZs0q6nkAgL1zjyQAAAAACqJIAgAAAKAgiiQAAAAACuIeSQAAAG3YhoqKXNu/f1GOva1891fKbjt3FuX4ye78nqUILUdRi6SampqsXLkyvXr1yuLFi5MkW7duzSWXXJINGzakb9++mTt3brp3756GhoZ861vfyoMPPpiKiorMnj07gwYNKmY8AACANq3YTyF8rrY2SdK7iOc5MsX/HEDhilokjRs3LhMmTMiMGTMax+bNm5cTTjghkydPzrx58zJv3rxMnz49q1atytq1a3P//ffn8ccfzze+8Y3cddddxYwHAADQpnmaInCwFfUeSUOHDk337t33GFu+fHnGjh2bJBk7dmyWLVu2x3hZWVmGDBmSbdu2ZdOmTcWMBwAAAMB+aPabbW/evDm9e/dOkrz3ve/N5s2bkyR1dXXp06dP4359+vRJXV1dc8cDAAAAYC9KerPtsrKylJWVlTICAAAA0EwqKjakf/9ri3Ls8vJtSZKdO7sV5fjJ7vxp57d/b/YiqVevXtm0aVN69+6dTZs2pWfPnkmSqqqqbNy4sXG/jRs3pqqqqrnjAQAAAEVQ7Jum19Y+9z/n6V3EsxzZ7m/+3uxFUnV1dRYuXJjJkydn4cKFGT58eOP4rbfemtGjR+fxxx9P165dGy+BA6D9qK2tzSWXXNK4vW7dulx00UV56aWXcueddzb+ADFt2rScdNJJpYoJAMB+cvP3tqGoRdK0adOyevXq1NfXZ9iwYZkyZUomT56cqVOnZsGCBTn88MMzd+7cJMlJJ52UBx98MCNGjEjnzp3z7W9/u5jRAGihBgwYkEWLFiVJdu3alWHDhmXEiBG55557ct555+VLX/pSiRMCAED7VdQiac6cOe84fvPNN79trKysLF//+teLGQeAVubf//3f069fv/Tt27fUUQAAgJTgqW0AUKglS5bk1FNPbdy+7bbbMmbMmNTU1OTFF18sYTIAAGifSvrUNgDYmx07dmTFihW59NJLkyRf/OIX89WvfjVlZWW59tprM3v2bNe/H4AVK1Zk6dKlBe9fW1ub5P+/50ChRowYkerq6v36GwAAWj4rkgBokVatWpVBgwblsMMOS5Icdthh6dixYzp06JDx48fnN7/5TYkTtg89e/ZsvME5AABYkQRAi7RkyZKMHj26cXvTpk2NT/NctmxZBg4cWKporVp1dbWVQgAAHDBFEgAtzquvvpqHHnooM2fObBz7zne+k6eeeipJ0rdv3z3eAwAAmociCYAW59BDD83DDz+8x9h3vvOdEqUBAADe5B5JAAAAABREkQQAAABAQRRJAAAAABREkQQAAABAQRRJAAAAABREkQQAAABAQRRJAAAAABSkvNQBAAAAaBlWrFiRpUuXFrx/bW1tkqSmpqbgvxkxYkSqq6v3OxvQMiiSAAAAOCA9e/YsdQSgmSmSAAAASJJUV1dbLQTsk3skAQAAAFAQRRIAAAAABVEkAQAAAFAQRRIAAAAABVEkAQAAAFAQRRIAAAAABVEkAQAAAFAQRRIAANDi1NTU5IQTTsipp57aOLZ169ZMnDgxJ598ciZOnJgXX3wxSdLQ0JArr7wyI0aMyJgxY/Lkk0+WKjZAm1de6gBA27VixYosXbp0v/6mtrY2ye7/eSzUiBEjUl1dvV/nAaD09neeOJA5IjFPtFbjxo3LhAkTMmPGjMaxefPm5YQTTsjkyZMzb968zJs3L9OnT8+qVauydu3a3H///Xn88cfzjW98I3fddVcJ0wMHg3miZbIiCWhRevbsmZ49e5Y6BgAtkDmifRk6dGi6d+++x9jy5cszduzYJMnYsWOzbNmyPcbLysoyZMiQbNu2LZs2bWr2zEBpmSeahxVJQNFUV1dr9gHYK/ME+2vz5s3p3bt3kuS9731vNm/enCSpq6tLnz59Gvfr06dP6urqGvcFWifzRMtkRRIAANDqlJWVpaysrNQxANodRRIAANAq9OrVq/GStU2bNjVewlJVVZWNGzc27rdx48ZUVVWVJCNAW6dIAgAAWoXq6uosXLgwSbJw4cIMHz58j/GGhoY89thj6dq1q8vaAIrEPZIAAIAWZ9q0aVm9enXq6+szbNiwTJkyJZMnT87UqVOzYMGCHH744Zk7d26S5KSTTsqDDz6YESNGpHPnzvn2t79d4vQAbZciCQAAaHHmzJnzjuM333zz28bKysry9a9/vdiRAIhL2wAAAAAokCIJAAAAgIIokgAAAAAoiCIJAAAAgIIokgAAAAAoiCIJAAAAgIIokgAAAAAoiCIJAAAAgIIokgAAAAAoiCIJAAAAgIIokgAAAAAoiCIJAAAAgIIokgAAAAAoiCIJAAAAgIIokgAAAAAoSHmpA7wbGzZsyLhx40odA6BF2rBhQ6kjlJx5AuCdmSN2M08AvLN9zRNlDQ0NDc2YBQAAAIBWyqVtAAAAABREkQQAAABAQRRJAAAAABREkQQAAABAQRRJAAAAABREkdTOrVq1KiNHjsyIESMyb968UsehHaupqckJJ5yQU089tdRRgLcwT9BSmCegZTJP0FKYJ5qPIqkd27VrV2bOnJn58+dnyZIlWbx4cf7whz+UOhbt1Lhx4zJ//vxSxwDewjxBS2KegJbHPEFLYp5oPoqkduyJJ57IBz7wgfTr1y+dOnXK6NGjs3z58lLHop0aOnRounfvXuoYwFuYJ2hJzBPQ8pgnaEnME81HkdSO1dXVpU+fPo3bVVVVqaurK2EiAFoS8wQA+2KegPZJkQQAAABAQRRJ7VhVVVU2btzYuF1XV5eqqqoSJgKgJTFPALAv5glonxRJ7dj/+l//K2vXrs26deuyY8eOLFmyJNXV1aWOBUALYZ4AYF/ME9A+lTU0NDSUOgSl8+CDD+bb3/52du3aldNPPz0XXHBBqSPRTk2bNi2rV69OfX19evXqlSlTpmT8+PGljgXtnnmClsI8AS2TeYKWwjzRfBRJAAAAABTEpW0AAAAAFESRBAAAAEBBFEkAAAAAFESRBAAAAEBBFEkAAAAAFESRBAW46aab8qc//emg7QdA22KeAGBvzBG0NYokKMCPf/zjgv6jXuh+ALQt5gkA9sYcQVtTXuoA0NK8+uqrmTp1ajZu3Jg33ngjn/3sZ7Np06b87d/+bXr06JFbbrklX//61/Ob3/wmr732WkaOHJmLLrooP/7xj9+230c+8pE8+uijSZJf/OIXWblyZWbPnp2f//zn+cEPfpAOHTqka9euue2220r8qQEolHkCgL0xR9AeKJLgz/zrv/5revfunXnz5iVJXnrppdxzzz25+eab07NnzyTJJZdckh49emTXrl0577zz8tRTT+Xcc8/NTTfdtMd+e/PP//zP+dGPfpSqqqps27at6J8JgIPHPAHA3pgjaA9c2gZ/5qijjspDDz2U73znO3nkkUfStWvXt+3z85//PKeddlrGjh2b3//+93nmmWf26xwf+chHcvnll+fOO+/Mrl27DlZ0AJqBeQKAvTFH0B4okuDP9O/fP/fcc0+OOuqozJ07N9///vf3eH/dunX5v//3/+amm27KT3/603zqU5/Ka6+91uRx37rPzJkzM3Xq1Dz//PM5/fTTU19ff9A/BwDFYZ4AYG/MEbQHiiT4M3V1dencuXM+//nP50tf+lJ+97vf5T3veU9eeeWVJMkrr7ySzp07p2vXrvnv//7vrFq1qvFv37pfkhx22GF55pln8sYbb2TZsmWN488++2yOOeaYXHzxxamsrMzGjRub7wMC8K6YJwDYG3ME7YF7JMGfefrpp3P11VenQ4cOKS8vzze+8Y089thj+fKXv5zevXvnlltuyYc//OGccsop6dOnT/76r/+68W/POOOMPfa79NJLc/7556dnz54ZPHhwXn311STJ1VdfnT/+8Y9paGjI8ccfn6OPPrpUHxeA/WSeAGBvzBG0B2UNDQ0NpQ4BAAAAQMvn0jYAAAAACqJIAgAAAKAgiiQAAAAACqJIAgAAAKAgiiQAAAAACqJIAgAAAKAgiiQAAAAACqJIAgAAAKAg/x+2+Ef5e/yvRQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1440x648 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PjBcZzP1fq-1",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "For the average vocal fundamental frequency (MDVP:Fo(Hz)), the median value is around 199 Hz for people who are normal. For people who are affected with Parkinsons the median value comes around 145 Hz. For the maximum vocal fundamental frequency (MDVP:Fhi(Hz)), there are probably many outliers such for the 2 kind of status.\\\n",
        "For the 3 parameters we can note that the number of healthy people are higher than the number of sick people."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "INMLCCYYI1p_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "01764a60-8980-4a51-e8fa-cf3bc5c40eab"
      },
      "source": [
        "fig, axs = plt.subplots(ncols = 2, figsize = (20,9))\n",
        "col_names = [\"NHR\",\"HNR\"]\n",
        "for i in range(0, len(col_names)):\n",
        "    sns.boxplot(x='status', y=col_names[i], data=df, width=0.3, palette=[\"red\", \"blue\"], ax = axs[i])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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oKNZUAgAAAKAwpRIAAAAAhSmVAAAAAChMqQQAAABAYUolAAAAAApTKgEAAABQmFIJAAAAgMKUSgC0lUajkXXr1mVsbKzsKAAA0NGUSgC0lXq9npGRkdTr9bKjAABAR1MqAdA2Go1GBgcHMzk5mYGBAdNKAABQIqUSAG2jXq9nYmIiSTIxMWFaCQAASqRUAqBtDA8Pp9lsJkmazWaGhoZKTgQAAJ1LqQRA21i2bFmq1WqSpFqtZvny5SUnAgCAzqVUAqBt1Gq1dHW9+q2rq6srtVqt5EQAANC5lEoAtI2enp709/enUqlkxYoV6e7uLjsSAAB0rGrZAQCgiFqtlv3795tSgjfhhRdeyK/92q/lpZdeSqVSyS233JL3ve99+fjHP54//uM/Tk9PT5LknnvuydKlS0tOCwDMVEolANpKT09PNm/eXHYMaGuzZs3KunXr8o53vCPf+973ctNNN+XKK69Mkrz//e/Pr/zKr5ScEABoB0olAIAOM2/evMybNy9JcuaZZ2bx4sUZHR0tORWvZ2xsLC/NmZMtixaVHaUtPDdnTs4ZGys7BkDHsKYSAEAHe+655/Ltb387P/MzP5Mk+dznPpfrr78+69evz+HDh0tOBwDMZCaVAAA61Pe///3cdddd+fVf//WceeaZee9735sPfOADqVQq2bJlSzZv3pxNmzaVHbOjdXd354y///vc/eyzZUdpC1sWLcocN3EAOGVMKgEAdKAf/vCHueuuu3L99dfn6quvTpL8xE/8RGbNmpWurq7cfPPN+du//duSUwIAM5lSCQCgw0xOTuY3fuM3snjx4tx6661T+1988cWpxwMDA1myZEkZ8QCANuHyNwCADvNXf/VX2b59ey688MKsXr06SXLPPffkT//0T/Od73wnSbJgwYJs3LixzJgAwAynVAIA6DA/93M/l7/7u797zf6lS5eWkAYAaFcufwMAAACgMKUSAAAAAIUplQAAAAAoTKkEAAAAQGFKJQAAAAAKUyoBAAAAUJhSCQAAAIDClEoAAAAAFFZt5cl3796d3/7t387ExERuvvnm3HHHHdOe37p1ax599NHMmjUrPT09+Z3f+Z0sWLAgSfL444/nwQcfTJLceeedufHGG1sZFQAAAEr3qU99Knv37i07xmscy7R+/fqSk7zW4sWLc/vtt5cdoyO1rFQaHx/Pxo0bs3Xr1vT29mbt2rXp6+vLBRdcMHXMRRddlC9+8Ys544wz8vnPfz4f+9jH8sADD+TQoUP5xCc+kS9+8YupVCpZs2ZN+vr6cvbZZ7cqLgAAAJRu7969+eY3n8mRIwvKjjJNtXpmkuRrXztScpLp5sx5vuwIHa1lpdKePXty/vnnZ+HChUmSa6+9NoODg9NKpcsvv3zq8bve9a586UtfSpI89dRTufLKKzN37twkyZVXXpknn3wy1113XaviAgAAwIxw5MiCPPvs3WXHaAuLFm0pO0JHa9maSqOjo5k/f/7Udm9vb0ZHR9/w+Mceeyzvec97fqTXAgAAAHBqtXRNpRO1ffv2fOtb38pnP/vZsqMAAAAAcAJaNqnU29ubgwcPTm2Pjo6mt7f3Ncc9/fTTeeihh/Lggw9m9uzZhV4LAAAAQDlaVipdeuml2bdvXw4cOJCjR49mx44d6evrm3bMyMhINmzYkAcffDDnnHPO1P6rrroqTz31VA4fPpzDhw/nqaeeylVXXdWqqAAAAAAU1LLL36rVajZs2JDbbrst4+Pjuemmm7JkyZJs2bIll1xySfr7+3PffffllVdeyd13v7oA2bnnnpuHHnooc+fOzQc+8IGsXbs2SfLBD35watFuAAAAAMrX0jWVli5dmqVLl07bd6xASpLPfOYzb/jatWvXTpVKAAAAAMwsLbv8DQAAAIDTl1IJAAAAgMKUSgAAAAAUplQCAAAAoDClEgAAAACFKZUAAAAAKEypBAAAAEBhSiUAAAAAClMqAQAAAFCYUgkAAACAwpRKAAAAABSmVAIAAACgMKUSAAAAAIVVyw4AAAC8sefnzMmWRYvKjjHNy9VXf4w4q9ksOcl0z8+Zk7eXHQKggyiVAABghlq8eHHZEV7X/9q7N0kyb4ble3tm7v8ZwOlIqQQAADPU7bffXnaE17V+/fokyaZNm0pOAkCZrKkEAAAAQGFKJQAAAAAKUyoBAAAAUJhSCQAAAIDClEoAAAAAFKZUAgAAAKAwpRIAAAAAhSmVAAAAAChMqQQAAABAYUolAAAAAApTKgEAAABQmFIJAAAAgMKUSgAAAAAUplQCAAAAoDClEgAAAACFKZUAAAAAKEypBAAAAEBhSiUAAAAAClMqAQAAAFCYUgkAAACAwpRKALSVRqORdevWZWxsrOwoAADQ0ZRKALSVer2ekZGR1Ov1sqMAAEBHUyoB0DYajUYGBwczOTmZgYEB00oAAFAipRIAbaNer2diYiJJMjExYVoJAABKpFQCoG0MDw+n2WwmSZrNZoaGhkpOBAAAnatadgAAOFHLli3Lzp0702w2U61Ws3z58rIjAQCcVGNjY5kz56UsWrSl7ChtYc6c5zI2dk7ZMTqWSSUA2katVktX16vfurq6ulKr1UpOBAAAncukEgBto6enJ1deeWWGhobyi7/4i+nu7i47EgDASdXd3Z2///sz8uyzd5cdpS0sWrQl3d1zyo7RsUwqAdBWKpVK2REAAIAolQBoI41GI0899VSS5Mknn8zY2FjJiQAAoHP9SKXSxMREvvSlL53sLABwXPV6PRMTE0le/V5Ur9dLTgQzj/dpAMCpctxS6Xvf+14++clPZuPGjXnqqacyOTmZP/qjP8qKFSvy3/7bfztVGQEgSTI8PJxms5kkaTabGRoaKjkRlMf7NACgbMddqPujH/1ozj777LzrXe/Ko48+mk9+8pOZnJzMf/7P/zkXXXTRqcoIAEmSZcuWZefOnWk2m6lWq1m+fHnZkaA03qcBAGU7bqn03HPP5cEHH0yS3HzzzbnqqqsyPDyct771rackHAD8Q7VaLYODg0mSrq6u1Gq1khNBebxPAwDKdtzL36rV/79zmjVrVubPn++NCgCl6enpSX9/fyqVSlasWJHu7u6yI0FpvE8DAMp23Eml73znO7nsssuSJJOTk/nBD36Qyy67LJOTk6lUKvnGN75xSkICwDG1Wi379+83pUTH8z4NACjbcUulb3/726cqBwCckJ6enmzevLnsGFA679MAgLIdt1Q6dOjQcV88d+7ckxoGAAAAgPZw3FJpzZo1qVQqmZyczP/+3/878+bNy+TkZJKkUqlMLZYKAMCp9bM/+7NT79OOqVQqGR8fzw9/+MOMjIyUmA4A6ATHLZV27do19fiGG27Itm3bWh4IAIB/2l//9V9P2/7+97+fz33uc/nCF76QlStXlpQKAOgkxy2V/qFKpdLKHAAA/AhefvnlPPLII9m2bVuuu+66PPbYY+6MCACcEidcKgEAMHM0Go1s3bo1f/Znf5abbrop27Zty4//+I+XHQsA6CDHLZW2bt069fill16atp0kt956a2tSAQBwXH19fenp6cmaNWtyxhln5LHHHpv2vPdpAECrHbdU+v73vz/1+JZbbpm2DQBAeX7lV35lankC79EAgDIct1T60Ic+dKpyAABQwIc//OGyIwAAHe64pdInPvGJN3yuUqnkgx/84EkPBADAP+0//sf/eNznf/M3f/MUJQEAOlXX8Z5829ve9po/SfLFL34xn/70p//Jk+/evTurVq3KypUr8/DDD7/m+a9//eu58cYbc/HFF+eJJ56Y9txFF12U1atXZ/Xq1fl3/+7fFfk3AQCc9t7xjndM/dm1a9e07Xe84x1lxwMAOsBxJ5X+7b/9t1OPv/e97+UP//AP8yd/8if5pV/6pWnPvZ7x8fFs3LgxW7duTW9vb9auXZu+vr5ccMEFU8ece+652bRpU/7gD/7gNa+fM2dOtm/fXvTfAwDQEW688capx4888si0bQCAU+G4pVKSHDp0KFu3bs2Xv/zl3HjjjXn88cdz9tln/5Mn3rNnT84///wsXLgwSXLttddmcHBwWqn0Uz/1U0mSrq7jDkwBAHAcxxbsPlEvvPBCfu3Xfi0vvfRSKpVKbrnllrzvfe/LoUOH8qu/+qt5/vnns2DBgjzwwAMn9L4PAOhMx21zfvd3fzdr167Nj/3Yj+XLX/5yPvzhD5/wG4vR0dHMnz9/aru3tzejo6MnHOwHP/hB1qxZk1tuuSUDAwMn/DoAAI5v1qxZWbduXf7sz/4sX/jCF/L5z38+3/3ud/Pwww/niiuuyJ//+Z/niiuueN3lCwAAjjnupNLWrVsze/bsPPjgg3nooYem9k9OTqZSqeQb3/hGy4INDQ2lt7c3Bw4cyPve975ceOGFOe+881r29wEAtJOf/dmfnZpQOnLkSC677LIkJ/Y+bd68eZk3b16S5Mwzz8zixYszOjqawcHB/NEf/VGS5IYbbsi/+Tf/Jh/96Edb/C8BANrVcUul73znOz/yiXt7e3Pw4MGp7dHR0fT29hZ6fZIsXLgwv/ALv5CRkRGlEgDA//PXf/3XJ+U8zz33XL797W/nZ37mZ/LSSy9NlU0/+ZM/mZdeeumk/B0AwOmpZYsZXXrppdm3b18OHDiQo0ePZseOHenr6zuh1x4+fDhHjx5NkjQajXzjG9+YthYTAABv3ve///3cdddd+fVf//WceeaZ056rVCqF12oCADrLP7lQ94984mo1GzZsyG233Zbx8fHcdNNNWbJkSbZs2ZJLLrkk/f392bNnTz70oQ/l5ZdfztDQUD7+8Y9nx44deeaZZ/Jbv/VbqVQqmZyczO23365UAgA4iX74wx/mrrvuyvXXX5+rr746SXLOOefkxRdfzLx58/Liiy+mp6en5JQAwEzWslIpSZYuXZqlS5dO23f33XdPPX7nO9+Z3bt3v+Z1l112Wb785S+3MhoAQMeanJzMb/zGb2Tx4sW59dZbp/b39fVl27ZtueOOO7Jt27b09/eXmBIAmOlaWioBADDz/NVf/VW2b9+eCy+8MKtXr06S3HPPPbnjjjvykY98JI899lj+2T/7Z3nggQdKTgoAzGRKJQCADvNzP/dz+bu/+7vXfe6RRx45xWkAgHbVsoW6AQAAADh9KZUAAAAAKEypBAAAAEBhSiUAAAAAClMqAQAAAFCYUgkAAACAwpRKAAAAABSmVAIAAACgMKUSAAAAAIUplQAAAAAoTKkEAAAAQGHVsgMAAACtt2vXruzcufOknGvv3r1JkvXr15+U861cuTJ9fX0n5VwAnDpKJQAAoJCenp6yIwAwAyiVAACgA/T19ZkGAuCksqYSAAAAAIUplQAAAAAozOVvAAAAMIPMmfN8Fi3aUnaMaarVl5MkzeZZJSeZbs6c55O8vewYHUupBAAAADPE4sWLy47wuvbu/V9JksWL55Wc5B97+4z9P+sESiUAAACYIW6//fayI7yu9evXJ0k2bdpUchJmEmsqAQAAAFCYUgkAAACAwpRKAAAAABSmVAIAAACgMKUSAAAAAIUplQAAAAAoTKkEAAAAQGFKJQAAAAAKUyoBAAAAUJhSCQAAAIDClEoAAEAhjUYj69aty9jYWNlRACiRUgkAACikXq9nZGQk9Xq97CgAlEipBAAAnLBGo5HBwcFMTk5mYGDAtBJAB1MqAQAAJ6xer2diYiJJMjExYVoJoIMplQBoK9bxACjX8PBwms1mkqTZbGZoaKjkRACURakEQFuxjgdAuZYtW5ZqtZokqVarWb58ecmJACiLUgmAtmEdD4Dy1Wq1dHW9+mNEV1dXarVayYkAKItSCYC2YR0PgPL19PSkv78/lUolK1asSHd3d9mRACiJUgmAtmEdD4CZoVar5eKLLzalBNDhlEoAtA3reADMDD09Pdm8ebMpJYAOp1QCoG1YxwMAAGYOpRIAbcM6HgAAMHNUyw4AAEXUarXs37/flBIAAJRMqQRAWzm2jgcAAFAul78BAAAAUJhSCQAAAIDClEoAtJVGo5F169ZlbGys7CgAANDRlEoAtJV6vZ6RkZHU6/WyowAAQEdTKgHQNhqNRgYHBzM5OZmBgQHTSgAAUCKlEgBto16vZ3x8PEkyPj5uWqZqohEAABqcSURBVAkAAEqkVKKtWVsFOsvw8PC0UmloaKjkRAAA0LmUSrQ1a6tAZ7n88sunbf/zf/7PS0oCAAAolWhb1laBzlOpVMqOAAAA/D9KJdpWvV7PxMREkmRiYsK0EnSAr371q9O2n3766ZKSAAAASiXa1vDwcJrNZpKk2WxaWwU6wLJly1KtVpMk1Wo1y5cvLzkRAAB0LqUSbcsPl9B5arXa1CVwlUoltVqt5EQAANC5lEq0rVqtlq6uVz+Eu7q6/HAJHaCnpyfz589Pkpx77rnp7u4uOREAAHQupRJtq6enJ/39/alUKlmxYoUfLqEDNBqNHDx4MEnywgsvWKAfAABKpFSirdVqtVx88cWmlKBD1Ov1TE5OJkkmJyct0A8AACVSKtHWenp6snnzZlNK0CEs0A8AADOHUgmAtmGBfgAAmDlaWirt3r07q1atysqVK/Pwww+/5vmvf/3rufHGG3PxxRfniSeemPbc448/nquvvjpXX311Hn/88VbGBKBNWKAfAABmjpaVSuPj49m4cWM+/elPZ8eOHfnTP/3TfPe73512zLnnnptNmzbluuuum7b/0KFD+cQnPpE//uM/zqOPPppPfOITOXz4cKuiAtAmLNAPAAAzR8tKpT179uT888/PwoULM3v27Fx77bUZHBycdsxP/dRP5ad/+qenfut8zFNPPZUrr7wyc+fOzdlnn50rr7wyTz75ZKuiAtBGLNAPAAAzQ8tKpdHR0cyfP39qu7e3N6Ojoy1/LQCnNwv0AwDAzGChbgAAAAAKa1mp1Nvbm4MHD05tj46Opre3t+WvBQAAAKD1WlYqXXrppdm3b18OHDiQo0ePZseOHenr6zuh11511VV56qmncvjw4Rw+fDhPPfVUrrrqqlZFBQAAAKCgastOXK1mw4YNue222zI+Pp6bbropS5YsyZYtW3LJJZekv78/e/bsyYc+9KG8/PLLGRoaysc//vHs2LEjc+fOzQc+8IGsXbs2SfLBD34wc+fObVVUAAAAAApqWamUJEuXLs3SpUun7bv77runHr/zne/M7t27X/e1a9eunSqVAAAAAJhZLNQNAAAAQGFKJQAAAAAKUyoBAAAAUJhSCQAAAIDClEoAAAAAFKZUAgAAAKAwpRIAAAAAhSmVAAAAAChMqQRAW2k0Glm3bl3GxsbKjgIAAB1NqQRAW6nX6xkZGUm9Xi87CgAAdDSlEgBto9FoZHBwMJOTkxkYGDCtBAAAJVIqAdA26vV6JiYmkiQTExOmlQAAoERKJQDaxvDwcJrNZpKk2WxmaGio5EQAANC5lEoAtI1ly5alWq0mSarVapYvX15yImhf69evzxVXXJHrrrtuat/HP/7x/OIv/mJWr16d1atX5y/+4i9KTAgAzHRKJQDaRq1WS1fXq9+6urq6UqvVSk4E7WvNmjX59Kc//Zr973//+7N9+/Zs3749S5cuLSEZANAulEoAtI2enp709/enUqlkxYoV6e7uLjsStK2f//mfz9lnn112DACgjSmVAGgr11xzTc4444xcc801ZUeB09LnPve5XH/99Vm/fn0OHz5cdhwAYAZTKgHQVrZt25ZXXnkl27dvLzsKnHbe+973ZufOndm+fXvmzZuXzZs3lx0JAJjBlEoAtI1GozG1cPDQ0FDGxsZKTgSnl5/4iZ/IrFmz0tXVlZtvvjl/+7d/W3YkAGAGUyoB0DY+85nPZGJiIkkyMTGRRx55pOREcHp58cUXpx4PDAxkyZIlJaYBAGa6atkBAOBE7d69e9r28PBwPvKRj5SUBtrbPffck7/8y7/M2NhY3vOe9+TDH/5w/vIv/zLf+c53kiQLFizIxo0bS04JAMxkSiUA2kalUjnuNnDifu/3fu81+26++eYSkgAA7crlbwC0jXe/+93Tti+//PKSkgAAAEolANrGW9/61uNuAwAAp45SCYC28dWvfnXa9tNPP11SEgAAQKkEQNtYtmxZZs2alSSZNWtWli9fXnIiAADoXEolANpGrVbL5ORkkmRycjK1Wq3kRAAA0LmUSgAAAAAUplQCoG3U6/VUKpUkSaVSSb1eLzkRAAB0LqUSba3RaGTdunUZGxsrOwpwCgwPD2d8fDxJMj4+nqGhoZITAQBA51Iq0dbq9XpGRkZMK0CHWLZs2bRJJQt1AwBAeZRKtK1Go5HBwcFMTk5mYGDAtBJ0gGuuuWbaQt3XXHNNyYkAAKBzKZVoW/V6PRMTE0mSiYkJ00rQAZ544olpk0pPPPFEyYkAAKBzKZVoW8PDw2k2m0mSZrNpbRXoAMPDw9MmlXzeAwBAeZRKtK1ly5alWq0mSarVqrVVoAP4vAcAgJlDqUTbqtVq6ep69UO4q6srtVqt5ERAq/m8BwCAmUOpRNvq6elJf39/KpVKVqxYke7u7rIjAS3m8x4AAGaOatkB4M2o1WrZv3+/aQXoID7vAQBgZlAq0dZ6enqyefPmsmMAp5DPewAAmBlc/gYAAABAYUolAAAAAApTKgEAAABQmFIJAAAAgMKUSgAAAAAUplQCAAAAoDClEgAAAACFKZUAaCt79+7Nv/yX/zLPPvts2VEAAKCjKZUAaCv3339/Xnnlldx///1lRwEAgI6mVAKgbezduzcHDhxIkuzfv9+0EgAAlEipBEDb+MfTSaaVAACgPEolANrGsSmlY/bv319SEgAAQKkEQNtYuHDhtO3zzjuvpCQAAIBSCYC2ce+99x53GwAAOHWUSgC0jcWLF09NK5133nlZtGhRyYkAAKBzKZUAaCv33ntv3va2t5lSAgCAklXLDgAARSxevDhf+MIXyo4BAAAdz6QSAAAAAIWZVAKg5Xbt2pWdO3eelHMdOnQoSTJ37tyTcr6VK1emr6/vpJwLAAA6iVIJgLbSaDSSnLxSCQAA+NEolQBoub6+vpM2DbR+/fokyaZNm07K+QAAgB+NNZUAAAAAKKylpdLu3buzatWqrFy5Mg8//PBrnj969Gg+8pGPZOXKlbn55pvz3HPPJUmee+65vPOd78zq1auzevXqbNiwoZUxAQAAACioZZe/jY+PZ+PGjdm6dWt6e3uzdu3a9PX15YILLpg65tFHH81ZZ52VnTt3ZseOHbn//vvzwAMPJEnOO++8bN++vVXxAAAAAHgTWjaptGfPnpx//vlZuHBhZs+enWuvvTaDg4PTjtm1a1duvPHGJMmqVavy1a9+NZOTk62KBAAAAMBJ0rJSaXR0NPPnz5/a7u3tzejo6GuOOffcc5Mk1Wo1P/7jP56xsbEkr14Cd8MNN+Rf/+t/nf/5P/9nq2ICAAAA8COYkXd/mzdvXoaGhtLd3Z1vfetb+eAHP5gdO3bkzDPPLDsaAAAAAGnhpFJvb28OHjw4tT06Opre3t7XHPPCCy8kSZrNZv7P//k/6e7uzuzZs9Pd3Z0kueSSS3Leeefl2WefbVVUAAAAAApqWal06aWXZt++fTlw4ECOHj2aHTt2pK+vb9oxfX19efzxx5MkX/nKV3L55ZenUqmk0WhkfHw8SXLgwIHs27cvCxcubFVUAAAAAApq2eVv1Wo1GzZsyG233Zbx8fHcdNNNWbJkSbZs2ZJLLrkk/f39Wbt2bT760Y9m5cqVOfvss/Of/tN/SpJ8/etfz+///u+nWq2mq6sr/+E//IfMnTu3VVEBAAAAKKilayotXbo0S5cunbbv7rvvnnr81re+Nb//+7//mtetWrUqq1atamU0ThN79+7N+vXrs3nz5ixatKjsOAAAANAxWnb5G5wK999/f1555ZXcf//9ZUcBAACAjqJUom3t3bs3Bw4cSJLs37/fYu4AAABwCimVaFv/eDrJtBIAAACcOkol2taxKaVj9u/fX1ISAAAA6DwtXagbWmnhwoXTiqXzzjuvxDQAAAAzy65du7Jz586Tcq69e/cmSdavX39Szrdy5cr09fWdlHNRHpNKtK177733uNsAAACcHD09Penp6Sk7BjOMSSXa1uLFi6emlc4777wsWrSo7EgAAAAzRl9fn2kgWsqkEm3t3nvvzdve9jZTSgAAAHCKmVSirS1evDhf+MIXyo4BAAAAHcekEgAAAACFKZUAAAAAKEypBAAAAEBhSiUAAAAAClMqAQAAAFCYUgkAAACAwpRKAAAAABSmVAIAAACgMKUSAAAAAIUplQAAAAAoTKkEAAAAQGFKJQAAAAAKUyoBAAAAUJhSibbWaDSybt26jI2NlR0FAAAAOopSibZWr9czMjKSer1edhQAAADoKEol2laj0cjg4GAmJyczMDBgWgkAAKBFXCXC66mWHQB+VPV6PRMTE0mSiYmJ1Ov13HnnnSWngtPHpz71qezdu7fsGK9xLNP69etLTvJaixcvzu233152DACAk+4fXiXi5y6OUSrRtoaHh9NsNpMkzWYzQ0NDvrjBSbR3795885vP5MiRBWVHmaZaPTNJ8rWvHSk5yXRz5jxfdgQAgJb4x1eJ1Gq1dHd3lx2LGUCpRNtatmxZdu7cmWazmWq1muXLl5cdCU47R44syLPP3l12jLawaNGWsiMAALSEq0R4I9ZUom3VarV0db36IdzV1ZVarVZyIgAAgNPP610lAolSiTbW09OT/v7+VCqVrFixwvglAABACyxbtizV6qsXOrlKhH9IqURbq9Vqufjii00pAQAAtIirRHgjSiXaWk9PTzZv3mxKCQAAoEVcJcIbsVA3AAAAcFy1Wi379+83pcQ0JpUAADrQ+vXrc8UVV+S6666b2nfo0KHceuutufrqq3Prrbfm8OHDJSYEYCZxlQivR6kEANCB1qxZk09/+tPT9j388MO54oor8ud//ue54oor8vDDD5eUDgBoB0olAIAO9PM///M5++yzp+0bHBzMDTfckCS54YYbMjAwUEY0AKBNKJVoa41GI+vWrcvY2FjZUQCg7b300kuZN29ekuQnf/In89JLL5WcCACYyZRKtLV6vZ6RkZHU6/WyowDAaaVSqaRSqZQdAwCYwZRKtK1Go5HBwcFMTk5mYGDAtBIAvEnnnHNOXnzxxSTJiy++mJ6enpITAQAzmVKJtlWv1zMxMZEkmZiYMK0EAG9SX19ftm3bliTZtm1b+vv7S04EAMxkSiXa1vDwcJrNZpKk2WxmaGio5EQA0D7uueee1Gq1PPvss3nPe96TRx99NHfccUf++3//77n66qvz9NNP54477ig7JgAwg1XLDgA/qmXLlmXnzp1pNpupVqtZvnx52ZHgtDI2NpY5c17KokVbyo7SFubMeS5jY+eUHQNO2O/93u+97v5HHnnkFCcBANqVSSXaVq1WS1fXqx/CXV1dqdVqJScCAAA4PbnzNq/HpBJtq6enJ/39/XniiSeyYsWKdHd3lx0JTivd3d35+78/I88+e3fZUdrCokVb0t09p+wYAAAt8Q/vvH3nnXeWHYcZwqQSba1Wq+Xiiy82pQQAANAi7rzNGzGpxCm3a9eu7Ny586Sc69ChQ0mS++6776Scb+XKlenr6zsp5wIAADgdvN6dt00rkZhUos01Go00Go2yYwAAAJy23HmbN2JSiVOur6/vpE0DrV+/PkmyadOmk3I+AAAApnPnbd6ISSUAAADgDbnzNm9EqQQAAAC8oWN33q5UKu68zTQufwMAAACOq1arZf/+/aaUmEapxAn51Kc+lb1795Yd4zWOZTq2ttJMsnjx4tx+++1lxwAAAHjTenp6snnz5rJjMMMolTghe/fuzTPf/GYWHDlSdpRpzqy++iF85GtfKznJdM/PmVN2BAAAAGgppRInbMGRI7n72WfLjtEWtixaVHYEOCnmzHk+ixZtKTvGNNXqy0mSZvOskpNMN2fO80neXnYMAICWaDQaue+++/Lv//2/t6YSU5RKwP/X3v2FZln3fwB/bw5RYqkrvO1ApH4pEVpYBHam0zHNoPJP7Kg/JITQHyuCOrHYQYUFGVSQWJkSjAI7qOFkzr/QQQSFgUSpmMvH3dHPQaLi2Hb/Dh6SfMwn719b1z33ep1du7/Xvfe1MfbhzfW9bvhTN910U9ER/tTRo/9Kktx00/SCk/yn/6nZnxkAwN/V0dGRQ4cOpaOjI2vXri06DjVCqQTAn6rVZ4L9/gy1V199teAkAADjw6lTp9LT05NKpZJdu3alra3N3UokSeqLDgAAAADUro6OjgwPDydJhoeH09HRUXAiaoVSCQAAALisvXv3ZnBwMEkyODiYPXv2FJyIWmH7G1ekv78//ztpkgdQX6GfJ03Kdf39RccAAAD42xYuXJju7u4MDg6moaEhixYtKjoSNUKpxBUbqK/Pz5MmFR3jIkN1dUmSCZVKwUkuNlDvJkAAAODq0NbWlp6eniRJfX192traCk5ErVAqcUXuvPPOHK3BB7EdPXo0SW1+SlUtZgIAAKhWU1NTFi9enK6urixZssRDurlAqcQV8SlQwN+xe/fudHd3j8h7/V4m//73/3e1tLSkubl5RN4LAOBqtXTp0uzbty9Lly4tOgo1ZFT36Ozfvz+tra1paWnJpk2bLnl9YGAg69atS0tLS1avXp2ff/75wmvvvfdeWlpa0tramgMHDoxmTADGkKampjQ1NRUdAwBgXOnq6sq5c+fS1dVVdBRqyKjdqTQ0NJT29vZ8+OGHKZVKWbVqVZqbm3PzzTdfWPPpp5/m2muvTXd3dzo7O/PGG29k48aNOXz4cDo7O9PZ2ZlyuZxHH300O3fuzIQJE0YrLgCjqLm52d1AAABj1KlTp9LT05NKpZJdu3alra3NFjiSjGKpdPDgwcyaNSszZ85Mkixfvjw9PT0XlUq7d+/OE088kSRpbW1Ne3t7KpVKenp6snz58kycODEzZ87MrFmzcvDgwcyfP3+04vIPsg0GAABg7Ojo6Mjw8HCSZHh4OB0dHVm7dm3BqagFo7b9rVwuZ8aMGReOS6VSyuXyJWtuuOGGJElDQ0MaGxvT399/RedCYhsMAADAaNu7d28GBweTJIODg9mzZ0/BiagVHtTNP842GAAAgLFj4cKF6e7uzuDgYBoaGrJo0aKiI1EjRu1OpVKplL6+vgvH5XI5pVLpkjUnT55M8u+28/Tp05k2bdoVnQsAAACMvra2ttTX/7s+qK+vT1tbW8GJqBWjVirNmzcvx44dS29vbwYGBtLZ2XnJ3SnNzc357LPPkiQ7d+7MggULUldXl+bm5nR2dmZgYCC9vb05duxYbrvtttGKCgAAAFxGU1NTFi9enLq6uixZssRDurlg1La/NTQ0ZP369VmzZk2GhoaycuXKzJ49O2+99Vbmzp2bxYsXZ9WqVXn++efT0tKSKVOm5M0330ySzJ49O8uWLcs999yTCRMmZP369T75DQAAAArS1taW48ePu0uJi9RVKpVK0SFGwooVK7J9+/aiYwAAo8j/+9rjdwIAV7f/9r9+1La/AQAAAHD1UioBAAAAUDWlEgAAAABVUyoBAAAAUDWlEgAAAABVUyoBAAAAUDWlEgAAAABVUyoBAAAAUDWlEgAAAABVUyoBAAAAUDWlEgAAAABVUyoBAAAAUDWlEgAAAABVUyoBAAAAUDWlEgAAAABVUyoBAAAAULWGogOMlBMnTmTFihVFxwAARtGJEyeKjsB/MIMBwNXtv81fdZVKpfIPZgEAAADgKmD7GwAAAABVUyoBAAAAUDWlEgAAAABVUyoBAAAAUDWlEgAAAABVUyoxpu3fvz+tra1paWnJpk2bio4DjLIXX3wxd999d+69996iowCMW+YvGH/MYFyOUokxa2hoKO3t7dm8eXM6OzvzxRdf5PDhw0XHAkbRihUrsnnz5qJjAIxb5i8Yn8xgXI5SiTHr4MGDmTVrVmbOnJmJEydm+fLl6enpKToWMIruuuuuTJkypegYAOOW+QvGJzMYl6NUYswql8uZMWPGheNSqZRyuVxgIgCAq5v5C4A/UioBAAAAUDWlEmNWqVRKX1/fheNyuZxSqVRgIgCAq5v5C4A/UioxZs2bNy/Hjh1Lb29vBgYG0tnZmebm5qJjAQBctcxfAPxRXaVSqRQdAv6/9u3bl1deeSVDQ0NZuXJl1q5dW3QkYBQ9++yz+eqrr9Lf35/rrrsuTz75ZFavXl10LIBxxfwF448ZjMtRKgEAAABQNdvfAAAAAKiaUgkAAACAqimVAAAAAKiaUgkAAACAqimVAAAAAKiaUgmoOVu2bMm5c+dGbB0AAH/NDAZUS6kE1JytW7de0aBypesAAPhrZjCgWg1FBwDGt7Nnz2bdunXp6+vL8PBwli5dml9++SUPP/xwpk6dmm3btuWll17Kd999l/Pnz6e1tTVPPfVUtm7desm6+fPn55tvvkmSdHV1Ze/evXnttdeyY8eOvPPOO6mvr09jY2M+/vjjgq8aAKBYZjBgJCiVgEIdOHAg06dPz6ZNm5Ikp0+fzvbt2/PRRx+lqakpSfLMM89k6tSpGRoayiOPPJLvv/8+Dz30ULZs2XLRust599138/7776dUKuW3334b9WsCAKh1ZjBgJNj+BhRqzpw5+fLLL/P666/n66+/TmNj4yVrduzYkQceeCD3339/fvzxxxw5cqSq7zF//vy88MIL+eSTTzI0NDRS0QEAxiwzGDASlEpAoW688cZs3749c+bMycaNG/P2229f9Hpvb28++OCDbNmyJZ9//nkWLlyY8+fP/+X7/nFNe3t71q1bl5MnT2blypXp7+8f8esAABhLzGDASFAqAYUql8uZPHly7rvvvjz22GM5dOhQrrnmmpw5cyZJcubMmUyePDmNjY359ddfs3///gvn/nFdklx//fU5cuRIhoeHs2vXrgtfP378eG6//fY8/fTTmTZtWvr6+v65CwQAqEFmMGAkeKYSUKgffvghGzZsSH19fRoaGvLyyy/n22+/zZo1azJ9+vRs27Ytt956a5YtW5YZM2bkjjvuuHDugw8+eNG65557Lo8//niampoyd+7cnD17NkmyYcOG/PTTT6lUKlmwYEFuueWWoi4XAKAmmMGAkVBXqVQqRYcAAAAAYGyx/Q0AAACAqimVAAAAAKiaUgkAAACAqimVAAAAAKiaUgkAAACAqimVAAAAAKiaUgkAAACAqimVAAAAAKja/wHGLIfVwiTLPwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1440x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QU6cTgREf6It",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "People who have the disease have higher levels of Noise to Harmonic ratio. Looking to the second boxplot, the HNR ratio for people who have PD are lower levels than healthy individuals.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KJ3tYCD8I1qH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "outputId": "443d7999-001b-4cfe-b14e-0a5f158551c0"
      },
      "source": [
        "plt.figure(figsize=(20,9))\n",
        "\n",
        "plt.subplot(2,2,1)\n",
        "sns.distplot(df[df['status']==0]['spread1'], label=\"Health\",color='red')\n",
        "sns.distplot(df[df['status']==1]['spread1'], label=\"Sick\", color='darkblue')\n",
        "plt.legend()\n",
        "plt.subplot(2,2,2)\n",
        "sns.distplot(df[df['status']==0]['spread2'], label=\"Health\", color='red')\n",
        "sns.distplot(df[df['status']==1]['spread2'], label=\"Sick\", color='green')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FTMCIPUaDcxm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "726e0492-8cbd-4a16-9e9d-ef72ace3c5a9"
      },
      "source": [
        "# Shapiro-Wilk Test\n",
        "from scipy.stats import shapiro\n",
        "# normality test for spread1\n",
        "stat_s1_0, p_s1_0 = shapiro(df[df['status']==0]['spread1'])\n",
        "stat_s1_1, p_s1_1 = shapiro(df[df['status']==1]['spread1'])\n",
        "\n",
        "# normality test for spread2\n",
        "\n",
        "stat_s2_0, p_s2_0 = shapiro(df[df['status']==0]['spread2'])\n",
        "stat_s2_1, p_s2_1 = shapiro(df[df['status']==1]['spread2'])\n",
        "\n",
        "print('s1_0:','Statistics=%.3f, p=%.3f' % (stat_s1_0, p_s1_0))\n",
        "print('s1_1:','Statistics=%.3f, p=%.3f' % (stat_s1_1, p_s1_1))\n",
        "print('s2_0:','Statistics=%.3f, p=%.3f' % (stat_s2_0, p_s2_0))\n",
        "print('s2_1:','Statistics=%.3f, p=%.3f' % (stat_s2_1, p_s2_1))\n",
        "# interpret\n",
        "# if p > 0.05,Sample looks Gaussian\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "s1_0: Statistics=0.974, p=0.371\n",
            "s1_1: Statistics=0.975, p=0.009\n",
            "s2_0: Statistics=0.981, p=0.640\n",
            "s2_1: Statistics=0.991, p=0.520\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1GZKFR2PgKiG",
        "colab_type": "text"
      },
      "source": [
        "If `p_value > 0.005`, data simple looks Gaussian so is normally distributed.\\\n",
        "Spread1 is normally distributed between person  who is normal (***p=0.371***). People who have spread1 between - 8.5 and -7.5 are more and they are normal. People whose spread1 is between -6.5 and -5 are having PD.\\\n",
        "Spread2 data for people get PD and healthy person is normally distributed (respectively ***p=0.640, p=0.520***)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ijFXrIbCI1qR",
        "colab_type": "text"
      },
      "source": [
        "#### ***Correlation between parameters***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IRzA9SDKI1qS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 860
        },
        "outputId": "b60f9dbb-ab7a-46c1-de25-b4260a00d740"
      },
      "source": [
        "correlation = round(df.corr(), 2)\n",
        "mask = np.zeros_like(correlation)\n",
        "mask[np.triu_indices_from(mask)] = True\n",
        "with sns.axes_style(\"white\"):\n",
        "    f, ax = plt.subplots(figsize=(20, 14))\n",
        "    ax = sns.heatmap(correlation, cmap=\"YlGnBu\", annot = True,\n",
        "            mask=mask, square=True, vmin=0, vmax=1).set(title = 'Correlation matrix')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1008 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GLbi9VkOgN30",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "MDVP:Jitter(%) has a very high correlation with MDVP:Jitter(Abs),MDVP:RAP,MDVP:PPQ,Jitter:DDP.\\\n",
        "MDVP:Shimmer has a very correlation with MDVP:Shimmer(dB),Shimmer:APQ3,Shimmer:APQ5,MDVP:APQ,Shimmer:DDA this may be because they are related to each other.\\\n",
        "The target variable status has a weak positive corelation with all the other variables\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B9nIzP_WI1qc",
        "colab_type": "text"
      },
      "source": [
        "## **C. Model Building**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zsoRbF6yg_LM",
        "colab_type": "text"
      },
      "source": [
        "In this section, we test a range of models:  ***KNeighborsClassifier***, ***RandomForestClassifier***, ***Naives vayes***, ***XgboostClassifier*** et ***DecisionTreeClassifier***.\\\n",
        "The best model chosen will be the one with better accuracy and better f1_score."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gsvgCFZFI1qd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Load packages\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "import xgboost as xgb\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.metrics import accuracy_score, f1_score\n"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yv8nTmDCI1qm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6lTrKLR2I1qu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "f65b73a0-88fb-421b-cfac-def8f7b74598"
      },
      "source": [
        "#Data preprocessing\n",
        "df = df.dropna()\n",
        "X = df.drop(['name', 'status'],axis=1)\n",
        "y = df[\"status\"].astype(\"bool\")\n",
        "print(X.shape)\n",
        "print(y.shape)\n",
        "\n",
        "#Rescaling and transforming data\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "#Data splitting and selection\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, \n",
        "                                                    test_size = 0.3, random_state = 150)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(195, 22)\n",
            "(195,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NEMvuG8dI1q4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "##RandoForestClassification Model\n",
        "model = RandomForestClassifier(n_jobs=-1, random_state=50)\n",
        "parameters={'max_depth':[1,2,3,4,5,10],\n",
        "           'n_estimators':[1,2,3,4,5,10],\n",
        "              'max_features':[1,2,3,4,5,10],\n",
        "               'criterion':['gini'],\n",
        "               'bootstrap':[True],\n",
        "               'min_samples_leaf':[1,2,3,4]\n",
        "             }"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AHHfhyAnI1q_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## GridSearchCV for finding the best parameters\n",
        "mod_forest = GridSearchCV(estimator=model,param_grid=parameters, cv=5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m3IkrgSvI1rJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c14943fc-b3c4-4b29-b3b4-2565d5341163"
      },
      "source": [
        "##Train the RDF model\n",
        "%%timeit\n",
        "mod_forest.fit(X_train,y_train)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1 loop, best of 3: 7min 23s per loop\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DxCzetTPI1rR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "a1962dea-116e-486f-a142-0d127f30e5ba"
      },
      "source": [
        "#get best parameter of RDF model\n",
        "print(mod_forest.best_params_)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'bootstrap': True, 'criterion': 'gini', 'max_depth': 5, 'max_features': 10, 'min_samples_leaf': 2, 'n_estimators': 10}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ph-Xmy-qI1rY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "f7ab8e51-12b8-4101-be1f-a2a9a776beed"
      },
      "source": [
        "%%timeit\n",
        "##Add best parameters of RandomForest \n",
        "##Test others models: Xgboost, KNN, Naive bayes, Decision Tree\n",
        "\n",
        "ESTIMATORS = {\"RandomForestClassifier\": RandomForestClassifier(bootstrap=True, criterion='gini', \n",
        "                                     max_depth=5, max_features=10, \n",
        "                                     min_samples_leaf=2, n_estimators=10, random_state=50),\n",
        "    \"XgbClassifier\": xgb.XGBClassifier(max_depth = 3, \n",
        "                                       n_estimators = 10, objective = 'binary:logistic'),\n",
        "    \"K-nn\": KNeighborsClassifier(n_neighbors=3),\n",
        "    \"Naive-bayes\": GaussianNB(),                     \n",
        "    \"DecisionTreeClassifier\": DecisionTreeClassifier(criterion='entropy', random_state=10, max_depth=6)\n",
        "}"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The slowest run took 11.98 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
            "100000 loops, best of 3: 12.8 µs per loop\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BffzodPvjOQh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        },
        "outputId": "ca27c0a5-0d1e-4c6a-aa30-19b8c7ff73d8"
      },
      "source": [
        "##Get accuracy and f1_score\n",
        "y_test_predict = dict()\n",
        "accur = dict()\n",
        "f_score = dict()\n",
        "\n",
        "for name, estimator in ESTIMATORS.items():     \n",
        "    estimator.fit(X_train, y_train)                  \n",
        "    y_test_predict[name] = estimator.predict(X_test)   \n",
        "    accur[name] = accuracy_score(y_test, estimator.predict(X_test))\n",
        "    print(\"Accuracy of \",name,\" \", round(accur[name]*100,2),\"%\")\n",
        "    f_score[name] = f1_score(y_test, estimator.predict(X_test))\n",
        "    print(\"f1_score \",name,\" \", round(f_score[name]*100,2),\"%\")\n",
        "    "
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of  RandomForestClassifier   88.14 %\n",
            "f1_score  RandomForestClassifier   92.78 %\n",
            "Accuracy of  XgbClassifier   94.92 %\n",
            "f1_score  XgbClassifier   96.97 %\n",
            "Accuracy of  K-nn   94.92 %\n",
            "f1_score  K-nn   97.03 %\n",
            "Accuracy of  Naive-bayes   67.8 %\n",
            "f1_score  Naive-bayes   77.65 %\n",
            "Accuracy of  DecisionTreeClassifier   86.44 %\n",
            "f1_score  DecisionTreeClassifier   91.67 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SPqkvkC9I1rh",
        "colab_type": "text"
      },
      "source": [
        "## ***Model choice***\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fHsidHQAI1rj",
        "colab_type": "text"
      },
      "source": [
        "##### As KNN and Xgboost model accuracy are equal, the choice is base on their F1 score. \n",
        "##### For this study, model with KNN present most accuracy and F1_score."
      ]
    }
  ]
}