Parkinson_Disease.ipynb

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  "metadata": {
    "colab": {
      "name": "Parkinson_Disease.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMS3KzU8z5bGE3444a/mco1",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/alexandreleitetrevisani/DATA_SCIENCE_MACHINE_LEARNING/blob/master/Parkinson_Disease.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b_N9C1Xai-sD",
        "colab_type": "code",
        "outputId": "456c5535-1993-4d52-859f-4fc11811e6d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.model_selection import train_test_split\n",
        "import seaborn as sns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "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": "vB_4Rlswn4BJ",
        "colab_type": "code",
        "outputId": "26a3b9f2-cc94-4f05-def1-7593f58aeb17",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        }
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-0ad98e1294ac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0muploaded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'https://archive.ics.uci.edu/ml/machine-learning-databases/parkinsons/parkinsons.data'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: upload() takes 0 positional arguments but 1 was given"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "69YZbdJWoRVR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/parkinsons/parkinsons.data')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gW1ZtrtbpQDc",
        "colab_type": "code",
        "outputId": "a2d0a382-2b63-435e-8298-63fd0cc2d358",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<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",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>phon_R01_S01_4</td>\n",
              "      <td>116.676</td>\n",
              "      <td>137.871</td>\n",
              "      <td>111.366</td>\n",
              "      <td>0.00997</td>\n",
              "      <td>0.00009</td>\n",
              "      <td>0.00502</td>\n",
              "      <td>0.00698</td>\n",
              "      <td>0.01505</td>\n",
              "      <td>0.05492</td>\n",
              "      <td>0.517</td>\n",
              "      <td>0.02924</td>\n",
              "      <td>0.04005</td>\n",
              "      <td>0.03772</td>\n",
              "      <td>0.08771</td>\n",
              "      <td>0.01353</td>\n",
              "      <td>20.644</td>\n",
              "      <td>1</td>\n",
              "      <td>0.434969</td>\n",
              "      <td>0.819235</td>\n",
              "      <td>-4.117501</td>\n",
              "      <td>0.334147</td>\n",
              "      <td>2.405554</td>\n",
              "      <td>0.368975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>phon_R01_S01_5</td>\n",
              "      <td>116.014</td>\n",
              "      <td>141.781</td>\n",
              "      <td>110.655</td>\n",
              "      <td>0.01284</td>\n",
              "      <td>0.00011</td>\n",
              "      <td>0.00655</td>\n",
              "      <td>0.00908</td>\n",
              "      <td>0.01966</td>\n",
              "      <td>0.06425</td>\n",
              "      <td>0.584</td>\n",
              "      <td>0.03490</td>\n",
              "      <td>0.04825</td>\n",
              "      <td>0.04465</td>\n",
              "      <td>0.10470</td>\n",
              "      <td>0.01767</td>\n",
              "      <td>19.649</td>\n",
              "      <td>1</td>\n",
              "      <td>0.417356</td>\n",
              "      <td>0.823484</td>\n",
              "      <td>-3.747787</td>\n",
              "      <td>0.234513</td>\n",
              "      <td>2.332180</td>\n",
              "      <td>0.410335</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",
              "      <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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>190</th>\n",
              "      <td>phon_R01_S50_2</td>\n",
              "      <td>174.188</td>\n",
              "      <td>230.978</td>\n",
              "      <td>94.261</td>\n",
              "      <td>0.00459</td>\n",
              "      <td>0.00003</td>\n",
              "      <td>0.00263</td>\n",
              "      <td>0.00259</td>\n",
              "      <td>0.00790</td>\n",
              "      <td>0.04087</td>\n",
              "      <td>0.405</td>\n",
              "      <td>0.02336</td>\n",
              "      <td>0.02498</td>\n",
              "      <td>0.02745</td>\n",
              "      <td>0.07008</td>\n",
              "      <td>0.02764</td>\n",
              "      <td>19.517</td>\n",
              "      <td>0</td>\n",
              "      <td>0.448439</td>\n",
              "      <td>0.657899</td>\n",
              "      <td>-6.538586</td>\n",
              "      <td>0.121952</td>\n",
              "      <td>2.657476</td>\n",
              "      <td>0.133050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>191</th>\n",
              "      <td>phon_R01_S50_3</td>\n",
              "      <td>209.516</td>\n",
              "      <td>253.017</td>\n",
              "      <td>89.488</td>\n",
              "      <td>0.00564</td>\n",
              "      <td>0.00003</td>\n",
              "      <td>0.00331</td>\n",
              "      <td>0.00292</td>\n",
              "      <td>0.00994</td>\n",
              "      <td>0.02751</td>\n",
              "      <td>0.263</td>\n",
              "      <td>0.01604</td>\n",
              "      <td>0.01657</td>\n",
              "      <td>0.01879</td>\n",
              "      <td>0.04812</td>\n",
              "      <td>0.01810</td>\n",
              "      <td>19.147</td>\n",
              "      <td>0</td>\n",
              "      <td>0.431674</td>\n",
              "      <td>0.683244</td>\n",
              "      <td>-6.195325</td>\n",
              "      <td>0.129303</td>\n",
              "      <td>2.784312</td>\n",
              "      <td>0.168895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>192</th>\n",
              "      <td>phon_R01_S50_4</td>\n",
              "      <td>174.688</td>\n",
              "      <td>240.005</td>\n",
              "      <td>74.287</td>\n",
              "      <td>0.01360</td>\n",
              "      <td>0.00008</td>\n",
              "      <td>0.00624</td>\n",
              "      <td>0.00564</td>\n",
              "      <td>0.01873</td>\n",
              "      <td>0.02308</td>\n",
              "      <td>0.256</td>\n",
              "      <td>0.01268</td>\n",
              "      <td>0.01365</td>\n",
              "      <td>0.01667</td>\n",
              "      <td>0.03804</td>\n",
              "      <td>0.10715</td>\n",
              "      <td>17.883</td>\n",
              "      <td>0</td>\n",
              "      <td>0.407567</td>\n",
              "      <td>0.655683</td>\n",
              "      <td>-6.787197</td>\n",
              "      <td>0.158453</td>\n",
              "      <td>2.679772</td>\n",
              "      <td>0.131728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>phon_R01_S50_5</td>\n",
              "      <td>198.764</td>\n",
              "      <td>396.961</td>\n",
              "      <td>74.904</td>\n",
              "      <td>0.00740</td>\n",
              "      <td>0.00004</td>\n",
              "      <td>0.00370</td>\n",
              "      <td>0.00390</td>\n",
              "      <td>0.01109</td>\n",
              "      <td>0.02296</td>\n",
              "      <td>0.241</td>\n",
              "      <td>0.01265</td>\n",
              "      <td>0.01321</td>\n",
              "      <td>0.01588</td>\n",
              "      <td>0.03794</td>\n",
              "      <td>0.07223</td>\n",
              "      <td>19.020</td>\n",
              "      <td>0</td>\n",
              "      <td>0.451221</td>\n",
              "      <td>0.643956</td>\n",
              "      <td>-6.744577</td>\n",
              "      <td>0.207454</td>\n",
              "      <td>2.138608</td>\n",
              "      <td>0.123306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>194</th>\n",
              "      <td>phon_R01_S50_6</td>\n",
              "      <td>214.289</td>\n",
              "      <td>260.277</td>\n",
              "      <td>77.973</td>\n",
              "      <td>0.00567</td>\n",
              "      <td>0.00003</td>\n",
              "      <td>0.00295</td>\n",
              "      <td>0.00317</td>\n",
              "      <td>0.00885</td>\n",
              "      <td>0.01884</td>\n",
              "      <td>0.190</td>\n",
              "      <td>0.01026</td>\n",
              "      <td>0.01161</td>\n",
              "      <td>0.01373</td>\n",
              "      <td>0.03078</td>\n",
              "      <td>0.04398</td>\n",
              "      <td>21.209</td>\n",
              "      <td>0</td>\n",
              "      <td>0.462803</td>\n",
              "      <td>0.664357</td>\n",
              "      <td>-5.724056</td>\n",
              "      <td>0.190667</td>\n",
              "      <td>2.555477</td>\n",
              "      <td>0.148569</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>195 rows × 24 columns</p>\n",
              "</div>"
            ],
            "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",
              "3    phon_R01_S01_4      116.676       137.871  ...  0.334147  2.405554  0.368975\n",
              "4    phon_R01_S01_5      116.014       141.781  ...  0.234513  2.332180  0.410335\n",
              "..              ...          ...           ...  ...       ...       ...       ...\n",
              "190  phon_R01_S50_2      174.188       230.978  ...  0.121952  2.657476  0.133050\n",
              "191  phon_R01_S50_3      209.516       253.017  ...  0.129303  2.784312  0.168895\n",
              "192  phon_R01_S50_4      174.688       240.005  ...  0.158453  2.679772  0.131728\n",
              "193  phon_R01_S50_5      198.764       396.961  ...  0.207454  2.138608  0.123306\n",
              "194  phon_R01_S50_6      214.289       260.277  ...  0.190667  2.555477  0.148569\n",
              "\n",
              "[195 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pQJkoiM-pR6n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_name = pd.read_fwf('https://archive.ics.uci.edu/ml/machine-learning-databases/parkinsons/parkinsons.names')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ex9Rh50DpiKh",
        "colab_type": "code",
        "outputId": "9d3e46a1-e89f-4be6-d463-330bcf76e8fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "df_name"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Title: Parkinsons Disease Data Set</th>\n",
              "      <th>Unnamed: 1</th>\n",
              "      <th>Unnamed: 2</th>\n",
              "      <th>Unnamed: 3</th>\n",
              "      <th>Unnamed: 4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Abstract: Oxford Parkinson's Disease Detection...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>----------------------------------------------...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>'Exploiting Nonlinear Recurrence and Fractal S...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>Little MA, McSharry PE, Roberts SJ, Costello D...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>BioMedical Engineering OnLine 2007, 6:23 (26 J...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>74 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                   Title: Parkinsons Disease Data Set  ... Unnamed: 4\n",
              "0                                                 NaN  ...        NaN\n",
              "1   Abstract: Oxford Parkinson's Disease Detection...  ...        NaN\n",
              "2                                                 NaN  ...        NaN\n",
              "3   ----------------------------------------------...  ...        NaN\n",
              "4                                                 NaN  ...        NaN\n",
              "..                                                ...  ...        ...\n",
              "69  'Exploiting Nonlinear Recurrence and Fractal S...  ...        NaN\n",
              "70  Little MA, McSharry PE, Roberts SJ, Costello D...  ...        NaN\n",
              "71  BioMedical Engineering OnLine 2007, 6:23 (26 J...  ...        NaN\n",
              "72                                                NaN  ...        NaN\n",
              "73                                                NaN  ...        NaN\n",
              "\n",
              "[74 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2cb7A1pmp6oY",
        "colab_type": "code",
        "outputId": "e7dbe07d-98b7-4fa0-8a59-64d84b52efff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "df.isnull().values.any()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6XsDNltjq2L5",
        "colab_type": "code",
        "outputId": "4edb7973-a6ed-416a-c7de-2542dc34beca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(195, 24)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NNiFFkdOroC3",
        "colab_type": "code",
        "outputId": "8bb9c3b6-b6e4-4c45-d0a9-22e9f4def8df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "df['status'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    147\n",
              "0     48\n",
              "Name: status, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KH2TNBlHsJXZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "percent_has_disease = 147 / (147 + 48) * 100\n",
        "percent_dont_have_disease = 48 / (147 + 48) * 100"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hc0vc753r2VS",
        "colab_type": "code",
        "outputId": "873ee554-227d-4daf-a93e-34f983ffe76b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "print('if i guess the individual has Parkinsons, i would be correct', percent_has_disease, '% of the time')\n",
        "print('if i guess the individual did not have Parkinsons, i would be correct', percent_dont_have_disease, '% of the time')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "if i guess the individual has Parkinsons, i would be correct 75.38461538461539 % of the time\n",
            "if i guess the individual did not have Parkinsons, i would be correct 24.615384615384617 % of the time\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5XBjPdqRsyoP",
        "colab_type": "code",
        "outputId": "f810e77a-dd5c-4af6-e630-7b08d89a7cf9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "sns.countplot(df['status'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb08562b940>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IhU6_ZcMtkev",
        "colab_type": "code",
        "outputId": "fa42bbd0-5579-42f1-ee1f-bdde29add784",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        }
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "name                 object\n",
              "MDVP:Fo(Hz)         float64\n",
              "MDVP:Fhi(Hz)        float64\n",
              "MDVP:Flo(Hz)        float64\n",
              "MDVP:Jitter(%)      float64\n",
              "MDVP:Jitter(Abs)    float64\n",
              "MDVP:RAP            float64\n",
              "MDVP:PPQ            float64\n",
              "Jitter:DDP          float64\n",
              "MDVP:Shimmer        float64\n",
              "MDVP:Shimmer(dB)    float64\n",
              "Shimmer:APQ3        float64\n",
              "Shimmer:APQ5        float64\n",
              "MDVP:APQ            float64\n",
              "Shimmer:DDA         float64\n",
              "NHR                 float64\n",
              "HNR                 float64\n",
              "status                int64\n",
              "RPDE                float64\n",
              "DFA                 float64\n",
              "spread1             float64\n",
              "spread2             float64\n",
              "D2                  float64\n",
              "PPE                 float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w5s4CgU4trxq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = df.drop(['name'],1)\n",
        "x = np.array(x.drop(['status'],1))\n",
        "y =  np.array(df['status'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3AGRalF_uG8-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i2MBZje2ulnO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "x_train = scaler.fit_transform(x_train)\n",
        "x_test = scaler.transform(x_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JCV114gRvC89",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = XGBClassifier().fit(x_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BQeEm2ggvTDr",
        "colab_type": "code",
        "outputId": "b034cd38-5dcd-4d6c-a4ab-2e6b89045056",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "predictions = model.predict(x_test)\n",
        "predictions"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0,\n",
              "       1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fvdkh4KEvapJ",
        "colab_type": "code",
        "outputId": "47203e7c-5ff7-4642-aa1f-c842eb9af36a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "y_test"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0,\n",
              "       1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TWGQKCgXvfEO",
        "colab_type": "code",
        "outputId": "8d0e70c8-d1c2-45ad-be89-d2e638b38bdf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        }
      },
      "source": [
        "print(classification_report(y_test, predictions))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      0.89      0.84         9\n",
            "           1       0.97      0.93      0.95        30\n",
            "\n",
            "    accuracy                           0.92        39\n",
            "   macro avg       0.88      0.91      0.90        39\n",
            "weighted avg       0.93      0.92      0.92        39\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D7_ZNptKvsMm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}