Detecting_Parkinsons_Disease.ipynb

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        "Parkinson Disease is a brain neurological disorder. It leads to shaking of the body, hands and provides stiffness to the body. No proper cure or treatment is available yet at the advanced stage. Treatment is possible only when done at the early or onset of the disease. These will not only reduce the cost of the disease but will also possibly save a life. Most methods available can detect Parkinson in an advanced stage; which means loss of approx.. 60% dopamine in basal ganglia and is responsible for controlling the movement of the body with a small amount of dopamine. More than 145,000 people have been found alone suffering in the U.K and in India, almost one million population suffers from this disease and it’s spreading fast in the entire world.\n",
        "\n",
        "A person diagnosed with Parkinson’s disease can have other symptoms that include-\n",
        "\n",
        "1. Depression\n",
        "\n",
        "2. Anxiety\n",
        "\n",
        "3. Sleeping, and memory-related issues\n",
        "\n",
        "4. Loss of sense of smell along with balance problems.\n",
        "\n",
        "What causes Parkinson’s disease is still unclear, but researchers have research that several factors are responsible for triggering the disease. It includes –\n",
        "1. Genes- Certain mutation genes have been found by research that are very rare. The gene variants often increase the risk of Parkinson’s disease but have a lesser effect on each genetic marker.\n",
        "\n",
        "2. Environment- Due to certain harmful toxins or chemical substances found in the environment can trigger the disease but have a lesser effect\n",
        "\n",
        "Although it develops at age of 65 15% can be found at young age people less than 50. We will make use of XGBoost, KNN, SVMs, and Random Forest Algorithm to check which is the best algorithm for detection of the onset of disease."
      ],
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CVfePt-tCUbb"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv('parkinsons.csv')\n",
        "df"
      ],
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          "height": 488
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        "id": "16ekq2ATCkeW",
        "outputId": "8189a018-6e5d-424f-ebd3-9c05fe1c62ba"
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      "outputs": [
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              "               name MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  \\\n",
              "0    phon_R01_S01_1     119.992       157.302        74.997         0.00784   \n",
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              "2    phon_R01_S01_3     116.682       131.111       111.555         0.01050   \n",
              "3    phon_R01_S01_4     116.676       137.871       111.366         0.00997   \n",
              "4    phon_R01_S01_5     116.014       141.781       110.655         0.01284   \n",
              "..              ...         ...           ...           ...             ...   \n",
              "190  phon_R01_S50_2     174.188       230.978        94.261         0.00459   \n",
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              "194  phon_R01_S50_6     214.289       260.277        77.973         0.00567   \n",
              "\n",
              "     MDVP:Jitter(Abs)  MDVP:RAP  MDVP:PPQ  Jitter:DDP  MDVP:Shimmer  ...  \\\n",
              "0             0.00007   0.00370   0.00554     0.01109       0.04374  ...   \n",
              "1             0.00008   0.00465   0.00696     0.01394       0.06134  ...   \n",
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              "4             0.00011   0.00655   0.00908     0.01966       0.06425  ...   \n",
              "..                ...       ...       ...         ...           ...  ...   \n",
              "190           0.00003   0.00263   0.00259     0.00790       0.04087  ...   \n",
              "191           0.00003   0.00331   0.00292     0.00994       0.02751  ...   \n",
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              "193           0.00004   0.00370   0.00390     0.01109       0.02296  ...   \n",
              "194           0.00003   0.00295   0.00317     0.00885       0.01884  ...   \n",
              "\n",
              "     Shimmer:DDA      NHR     HNR  status      RPDE       DFA   spread1  \\\n",
              "0        0.06545  0.02211  21.033       1  0.414783  0.815285 -4.813031   \n",
              "1        0.09403  0.01929  19.085       1  0.458359  0.819521 -4.075192   \n",
              "2        0.08270  0.01309  20.651       1  0.429895  0.825288 -4.443179   \n",
              "3        0.08771  0.01353  20.644       1  0.434969  0.819235 -4.117501   \n",
              "4        0.10470  0.01767  19.649       1  0.417356  0.823484 -3.747787   \n",
              "..           ...      ...     ...     ...       ...       ...       ...   \n",
              "190      0.07008  0.02764  19.517       0  0.448439  0.657899 -6.538586   \n",
              "191      0.04812  0.01810  19.147       0  0.431674  0.683244 -6.195325   \n",
              "192      0.03804  0.10715  17.883       0  0.407567  0.655683 -6.787197   \n",
              "193      0.03794  0.07223  19.020       0  0.451221  0.643956 -6.744577   \n",
              "194      0.03078  0.04398  21.209       0  0.462803  0.664357 -5.724056   \n",
              "\n",
              "      spread2        D2       PPE  \n",
              "0    0.266482  2.301442  0.284654  \n",
              "1    0.335590  2.486855  0.368674  \n",
              "2    0.311173  2.342259  0.332634  \n",
              "3    0.334147  2.405554  0.368975  \n",
              "4    0.234513  2.332180  0.410335  \n",
              "..        ...       ...       ...  \n",
              "190  0.121952  2.657476  0.133050  \n",
              "191  0.129303  2.784312  0.168895  \n",
              "192  0.158453  2.679772  0.131728  \n",
              "193  0.207454  2.138608  0.123306  \n",
              "194  0.190667  2.555477  0.148569  \n",
              "\n",
              "[195 rows x 24 columns]"
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              "      <th>MDVP:Fo(Hz)</th>\n",
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              "      <th>1</th>\n",
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              "      <td>122.4</td>\n",
              "      <td>148.650</td>\n",
              "      <td>113.819</td>\n",
              "      <td>0.00968</td>\n",
              "      <td>0.00008</td>\n",
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              "      <td>0.335590</td>\n",
              "      <td>2.486855</td>\n",
              "      <td>0.368674</td>\n",
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              "      <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>...</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",
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              "      <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>...</td>\n",
              "      <td>0.08771</td>\n",
              "      <td>0.01353</td>\n",
              "      <td>20.644</td>\n",
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              "      <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",
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              "      <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",
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              "      <td>0.01767</td>\n",
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              "      <td>-3.747787</td>\n",
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              "      <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",
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              "      <td>0.04087</td>\n",
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              "      <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",
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              "      <th>191</th>\n",
              "      <td>phon_R01_S50_3</td>\n",
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              "      <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>...</td>\n",
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              "      <td>0.01810</td>\n",
              "      <td>19.147</td>\n",
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              "      <td>-6.195325</td>\n",
              "      <td>0.129303</td>\n",
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              "      <th>192</th>\n",
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              "      <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>...</td>\n",
              "      <td>0.03804</td>\n",
              "      <td>0.10715</td>\n",
              "      <td>17.883</td>\n",
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              "      <td>0.655683</td>\n",
              "      <td>-6.787197</td>\n",
              "      <td>0.158453</td>\n",
              "      <td>2.679772</td>\n",
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              "      <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>...</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",
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              "      <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>...</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",
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              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-33d78751-e809-440c-9d94-fb3b4bf8a7f6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-33d78751-e809-440c-9d94-fb3b4bf8a7f6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z_EIk0RdOa95",
        "outputId": "1690f061-4de8-4402-b95b-9680f1ebfffb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(195, 24)"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "Dy3z3e00PVIX",
        "outputId": "379d4a28-d12a-4454-b2ee-e98cbbb30855"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  MDVP:Jitter(Abs)  \\\n",
              "count    193.000000    195.000000      195.000000        195.000000   \n",
              "mean     197.135394    116.324631        0.006220          0.000044   \n",
              "std       91.966286     43.521413        0.004848          0.000035   \n",
              "min      102.145000     65.476000        0.001680          0.000007   \n",
              "25%      134.656000     84.291000        0.003460          0.000020   \n",
              "50%      172.975000    104.315000        0.004940          0.000030   \n",
              "75%      224.429000    140.018500        0.007365          0.000060   \n",
              "max      592.030000    239.170000        0.033160          0.000260   \n",
              "\n",
              "         MDVP:RAP    MDVP:PPQ  Jitter:DDP  MDVP:Shimmer  MDVP:Shimmer(dB)  \\\n",
              "count  195.000000  195.000000  195.000000    195.000000        195.000000   \n",
              "mean     0.003306    0.003446    0.009920      0.029709          0.282251   \n",
              "std      0.002968    0.002759    0.008903      0.018857          0.194877   \n",
              "min      0.000680    0.000920    0.002040      0.009540          0.085000   \n",
              "25%      0.001660    0.001860    0.004985      0.016505          0.148500   \n",
              "50%      0.002500    0.002690    0.007490      0.022970          0.221000   \n",
              "75%      0.003835    0.003955    0.011505      0.037885          0.350000   \n",
              "max      0.021440    0.019580    0.064330      0.119080          1.302000   \n",
              "\n",
              "       Shimmer:APQ3  ...    MDVP:APQ  Shimmer:DDA         NHR         HNR  \\\n",
              "count    195.000000  ...  195.000000   195.000000  195.000000  195.000000   \n",
              "mean       0.015664  ...    0.024081     0.046993    0.024847   21.885974   \n",
              "std        0.010153  ...    0.016947     0.030459    0.040418    4.425764   \n",
              "min        0.004550  ...    0.007190     0.013640    0.000650    8.441000   \n",
              "25%        0.008245  ...    0.013080     0.024735    0.005925   19.198000   \n",
              "50%        0.012790  ...    0.018260     0.038360    0.011660   22.085000   \n",
              "75%        0.020265  ...    0.029400     0.060795    0.025640   25.075500   \n",
              "max        0.056470  ...    0.137780     0.169420    0.314820   33.047000   \n",
              "\n",
              "             RPDE         DFA     spread1     spread2          D2         PPE  \n",
              "count  195.000000  195.000000  195.000000  195.000000  195.000000  195.000000  \n",
              "mean     0.498536    0.718099   -5.684397    0.226510    2.381826    0.206552  \n",
              "std      0.103942    0.055336    1.090208    0.083406    0.382799    0.090119  \n",
              "min      0.256570    0.574282   -7.964984    0.006274    1.423287    0.044539  \n",
              "25%      0.421306    0.674758   -6.450096    0.174351    2.099125    0.137451  \n",
              "50%      0.495954    0.722254   -5.720868    0.218885    2.361532    0.194052  \n",
              "75%      0.587562    0.761881   -5.046192    0.279234    2.636456    0.252980  \n",
              "max      0.685151    0.825288   -2.434031    0.450493    3.671155    0.527367  \n",
              "\n",
              "[8 rows x 21 columns]"
            ],
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              "\n",
              "  <div id=\"df-1d19b451-cadd-49f4-8596-d3e7d8707792\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></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>...</th>\n",
              "      <th>MDVP:APQ</th>\n",
              "      <th>Shimmer:DDA</th>\n",
              "      <th>NHR</th>\n",
              "      <th>HNR</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>count</th>\n",
              "      <td>193.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>195.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>197.135394</td>\n",
              "      <td>116.324631</td>\n",
              "      <td>0.006220</td>\n",
              "      <td>0.000044</td>\n",
              "      <td>0.003306</td>\n",
              "      <td>0.003446</td>\n",
              "      <td>0.009920</td>\n",
              "      <td>0.029709</td>\n",
              "      <td>0.282251</td>\n",
              "      <td>0.015664</td>\n",
              "      <td>...</td>\n",
              "      <td>0.024081</td>\n",
              "      <td>0.046993</td>\n",
              "      <td>0.024847</td>\n",
              "      <td>21.885974</td>\n",
              "      <td>0.498536</td>\n",
              "      <td>0.718099</td>\n",
              "      <td>-5.684397</td>\n",
              "      <td>0.226510</td>\n",
              "      <td>2.381826</td>\n",
              "      <td>0.206552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>91.966286</td>\n",
              "      <td>43.521413</td>\n",
              "      <td>0.004848</td>\n",
              "      <td>0.000035</td>\n",
              "      <td>0.002968</td>\n",
              "      <td>0.002759</td>\n",
              "      <td>0.008903</td>\n",
              "      <td>0.018857</td>\n",
              "      <td>0.194877</td>\n",
              "      <td>0.010153</td>\n",
              "      <td>...</td>\n",
              "      <td>0.016947</td>\n",
              "      <td>0.030459</td>\n",
              "      <td>0.040418</td>\n",
              "      <td>4.425764</td>\n",
              "      <td>0.103942</td>\n",
              "      <td>0.055336</td>\n",
              "      <td>1.090208</td>\n",
              "      <td>0.083406</td>\n",
              "      <td>0.382799</td>\n",
              "      <td>0.090119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>102.145000</td>\n",
              "      <td>65.476000</td>\n",
              "      <td>0.001680</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.000680</td>\n",
              "      <td>0.000920</td>\n",
              "      <td>0.002040</td>\n",
              "      <td>0.009540</td>\n",
              "      <td>0.085000</td>\n",
              "      <td>0.004550</td>\n",
              "      <td>...</td>\n",
              "      <td>0.007190</td>\n",
              "      <td>0.013640</td>\n",
              "      <td>0.000650</td>\n",
              "      <td>8.441000</td>\n",
              "      <td>0.256570</td>\n",
              "      <td>0.574282</td>\n",
              "      <td>-7.964984</td>\n",
              "      <td>0.006274</td>\n",
              "      <td>1.423287</td>\n",
              "      <td>0.044539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>134.656000</td>\n",
              "      <td>84.291000</td>\n",
              "      <td>0.003460</td>\n",
              "      <td>0.000020</td>\n",
              "      <td>0.001660</td>\n",
              "      <td>0.001860</td>\n",
              "      <td>0.004985</td>\n",
              "      <td>0.016505</td>\n",
              "      <td>0.148500</td>\n",
              "      <td>0.008245</td>\n",
              "      <td>...</td>\n",
              "      <td>0.013080</td>\n",
              "      <td>0.024735</td>\n",
              "      <td>0.005925</td>\n",
              "      <td>19.198000</td>\n",
              "      <td>0.421306</td>\n",
              "      <td>0.674758</td>\n",
              "      <td>-6.450096</td>\n",
              "      <td>0.174351</td>\n",
              "      <td>2.099125</td>\n",
              "      <td>0.137451</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>172.975000</td>\n",
              "      <td>104.315000</td>\n",
              "      <td>0.004940</td>\n",
              "      <td>0.000030</td>\n",
              "      <td>0.002500</td>\n",
              "      <td>0.002690</td>\n",
              "      <td>0.007490</td>\n",
              "      <td>0.022970</td>\n",
              "      <td>0.221000</td>\n",
              "      <td>0.012790</td>\n",
              "      <td>...</td>\n",
              "      <td>0.018260</td>\n",
              "      <td>0.038360</td>\n",
              "      <td>0.011660</td>\n",
              "      <td>22.085000</td>\n",
              "      <td>0.495954</td>\n",
              "      <td>0.722254</td>\n",
              "      <td>-5.720868</td>\n",
              "      <td>0.218885</td>\n",
              "      <td>2.361532</td>\n",
              "      <td>0.194052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>224.429000</td>\n",
              "      <td>140.018500</td>\n",
              "      <td>0.007365</td>\n",
              "      <td>0.000060</td>\n",
              "      <td>0.003835</td>\n",
              "      <td>0.003955</td>\n",
              "      <td>0.011505</td>\n",
              "      <td>0.037885</td>\n",
              "      <td>0.350000</td>\n",
              "      <td>0.020265</td>\n",
              "      <td>...</td>\n",
              "      <td>0.029400</td>\n",
              "      <td>0.060795</td>\n",
              "      <td>0.025640</td>\n",
              "      <td>25.075500</td>\n",
              "      <td>0.587562</td>\n",
              "      <td>0.761881</td>\n",
              "      <td>-5.046192</td>\n",
              "      <td>0.279234</td>\n",
              "      <td>2.636456</td>\n",
              "      <td>0.252980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>592.030000</td>\n",
              "      <td>239.170000</td>\n",
              "      <td>0.033160</td>\n",
              "      <td>0.000260</td>\n",
              "      <td>0.021440</td>\n",
              "      <td>0.019580</td>\n",
              "      <td>0.064330</td>\n",
              "      <td>0.119080</td>\n",
              "      <td>1.302000</td>\n",
              "      <td>0.056470</td>\n",
              "      <td>...</td>\n",
              "      <td>0.137780</td>\n",
              "      <td>0.169420</td>\n",
              "      <td>0.314820</td>\n",
              "      <td>33.047000</td>\n",
              "      <td>0.685151</td>\n",
              "      <td>0.825288</td>\n",
              "      <td>-2.434031</td>\n",
              "      <td>0.450493</td>\n",
              "      <td>3.671155</td>\n",
              "      <td>0.527367</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1d19b451-cadd-49f4-8596-d3e7d8707792')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
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              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1d19b451-cadd-49f4-8596-d3e7d8707792 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1d19b451-cadd-49f4-8596-d3e7d8707792');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eAooXqNGPXF_",
        "outputId": "25a1fb09-11c3-4bfd-b388-63091e68cc59"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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)       193 non-null    object \n",
            " 2   MDVP:Fhi(Hz)      193 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    object \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(21), object(3)\n",
            "memory usage: 36.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "**DATA CLEANING** \n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "yKbPWaJUVN6D"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Dzo-rKIajKnv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qNh4vhGiG7hm",
        "outputId": "6f0b2775-aca1-41df-db35-36c511f62e28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "name                0\n",
              "MDVP:Fo(Hz)         2\n",
              "MDVP:Fhi(Hz)        2\n",
              "MDVP:Flo(Hz)        0\n",
              "MDVP:Jitter(%)      0\n",
              "MDVP:Jitter(Abs)    0\n",
              "MDVP:RAP            0\n",
              "MDVP:PPQ            0\n",
              "Jitter:DDP          0\n",
              "MDVP:Shimmer        0\n",
              "MDVP:Shimmer(dB)    0\n",
              "Shimmer:APQ3        0\n",
              "Shimmer:APQ5        0\n",
              "MDVP:APQ            0\n",
              "Shimmer:DDA         0\n",
              "NHR                 0\n",
              "HNR                 0\n",
              "status              0\n",
              "RPDE                0\n",
              "DFA                 0\n",
              "spread1             0\n",
              "spread2             0\n",
              "D2                  0\n",
              "PPE                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['MDVP:Fo(Hz)'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "seb-iFrsKZni",
        "outputId": "e39b1fe7-cb36-49a1-92ed-8c8f25f7187a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['119.992', '122.4', '116.682', '116.676', '116.014', '120.552',\n",
              "       '120.267', '107.332', '95.73', '95.056', '88.333', '91.904',\n",
              "       '136.926', '139.173', '152.845', '142.167', '144.188', '168.778',\n",
              "       '153.046', '156.405', '??', '???', '167.93', '173.917', '163.656',\n",
              "       '104.4', '171.041', '146.845', nan, '162.568', '197.076',\n",
              "       '199.228', '198.383', '202.266', '203.184', '201.464', '177.876',\n",
              "       '176.17', '180.198', '187.733', '186.163', '184.055', '237.226',\n",
              "       '241.404', '243.439', '242.852', '245.51', '252.455', '122.188',\n",
              "       '122.964', '124.445', '126.344', '128.001', '129.336', '108.807',\n",
              "       '109.86', '110.417', '117.274', '116.879', '114.847', '209.144',\n",
              "       '223.365', '222.236', '228.832', '229.401', '228.969', '140.341',\n",
              "       '136.969', '143.533', '148.09', '142.729', '136.358', '120.08',\n",
              "       '112.014', '110.793', '110.707', '112.876', '110.568', '95.385',\n",
              "       '100.77', '96.106', '95.605', '100.96', '98.804', '176.858',\n",
              "       '180.978', '178.222', '176.281', '173.898', '179.711', '166.605',\n",
              "       '151.955', '148.272', '152.125', '157.821', '157.447', '159.116',\n",
              "       '125.036', '125.791', '126.512', '125.641', '128.451', '139.224',\n",
              "       '150.258', '149.689', '151.884', '151.989', '193.03', '200.714',\n",
              "       '208.519', '204.664', '210.141', '206.327', '151.872', '158.219',\n",
              "       '170.756', '178.285', '217.116', '128.94', '176.824', '138.19',\n",
              "       '182.018', '156.239', '145.174', '138.145', '166.888', '119.031',\n",
              "       '120.078', '120.289', '120.256', '119.056', '118.747', '106.516',\n",
              "       '110.453', '113.4', '113.166', '112.239', '116.15', '170.368',\n",
              "       '208.083', '198.458', '202.805', '202.544', '223.361', '169.774',\n",
              "       '183.52', '188.62', '202.632', '186.695', '192.818', '198.116',\n",
              "       '121.345', '119.1', '117.87', '122.336', '117.963', '126.144',\n",
              "       '127.93', '114.238', '115.322', '114.554', '112.15', '102.273',\n",
              "       '236.2', '237.323', '260.105', '197.569', '240.301', '244.99',\n",
              "       '112.547', '110.739', '113.715', '117.004', '115.38', '116.388',\n",
              "       '151.737', '148.79', '148.143', '150.44', '148.462', '149.818',\n",
              "       '117.226', '116.848', '116.286', '116.556', '116.342', '114.563',\n",
              "       '201.774', '174.188', '209.516', '174.688', '198.764', '214.289'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def isNaN(string):\n",
        "    return string != string\n",
        "sum = 0\n",
        "count = 0\n",
        "for i in df['MDVP:Fo(Hz)']:\n",
        "  if i == '??' or i == '???' or isNaN(i):\n",
        "    continue;\n",
        "  else:\n",
        "    sum = sum + float(i);\n",
        "    count = count +1;\n",
        "mean = sum/count\n",
        "mean\n",
        "df['MDVP:Fo(Hz)'].replace(np.nan,round(mean,3),inplace=True)\n",
        "df['MDVP:Fo(Hz)'].replace('??',round(mean,3),inplace=True)\n",
        "df['MDVP:Fo(Hz)'].replace('???',round(mean,3),inplace=True)"
      ],
      "metadata": {
        "id": "6ALveaFLHKYO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yL0w-pSoQSip",
        "outputId": "0578e970-3ac6-4215-b3e5-ab0214019189"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "name                0\n",
              "MDVP:Fo(Hz)         0\n",
              "MDVP:Fhi(Hz)        2\n",
              "MDVP:Flo(Hz)        0\n",
              "MDVP:Jitter(%)      0\n",
              "MDVP:Jitter(Abs)    0\n",
              "MDVP:RAP            0\n",
              "MDVP:PPQ            0\n",
              "Jitter:DDP          0\n",
              "MDVP:Shimmer        0\n",
              "MDVP:Shimmer(dB)    0\n",
              "Shimmer:APQ3        0\n",
              "Shimmer:APQ5        0\n",
              "MDVP:APQ            0\n",
              "Shimmer:DDA         0\n",
              "NHR                 0\n",
              "HNR                 0\n",
              "status              0\n",
              "RPDE                0\n",
              "DFA                 0\n",
              "spread1             0\n",
              "spread2             0\n",
              "D2                  0\n",
              "PPE                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['MDVP:Fhi(Hz)'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C_c79Qq9RISQ",
        "outputId": "b9537fe7-ada5-41c4-e6a4-d3a897ee64e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([157.302, 148.65 , 131.111, 137.871, 141.781, 131.162, 137.244,\n",
              "       113.84 , 132.068, 120.103, 112.24 , 115.871, 159.866, 179.139,\n",
              "       163.305, 217.455, 349.259, 232.181, 175.829, 189.398, 165.738,\n",
              "       172.86 ,     nan, 192.735, 200.841, 206.002, 208.313, 208.701,\n",
              "       227.383, 198.346, 206.896, 209.512, 215.203, 211.604, 211.526,\n",
              "       210.565, 192.921, 185.604, 201.249, 202.324, 197.724, 196.537,\n",
              "       247.326, 248.834, 250.912, 255.034, 262.09 , 261.487, 128.611,\n",
              "       130.049, 135.069, 134.231, 138.052, 139.867, 134.656, 126.358,\n",
              "       131.067, 129.916, 131.897, 271.314, 237.494, 238.987, 231.345,\n",
              "       234.619, 252.221, 239.541, 159.774, 166.607, 162.215, 162.824,\n",
              "       162.408, 176.595, 139.71 , 588.518, 128.101, 122.611, 148.826,\n",
              "       125.394, 102.145, 115.697, 108.664, 107.715, 110.019, 102.305,\n",
              "       205.56 , 200.125, 202.45 , 227.381, 211.35 , 225.93 , 206.008,\n",
              "       163.335, 164.989, 161.469, 172.975, 163.267, 168.913, 143.946,\n",
              "       140.557, 141.756, 141.068, 150.449, 586.567, 154.609, 160.267,\n",
              "       160.368, 163.736, 157.765, 157.339, 208.9  , 223.982, 220.315,\n",
              "       221.3  , 232.706, 226.355, 492.892, 442.557, 450.247, 442.824,\n",
              "       233.481, 479.697, 215.293, 203.522, 197.173, 198.109, 197.238,\n",
              "       198.966, 127.533, 126.632, 128.143, 125.306, 125.213, 123.723,\n",
              "       112.777, 127.611, 133.344, 130.27 , 126.609, 131.731, 268.796,\n",
              "       253.792, 219.29 , 231.508, 241.35 , 263.872, 191.759, 216.814,\n",
              "       216.302, 565.74 , 211.961, 224.429, 233.099, 139.644, 128.442,\n",
              "       127.349, 142.369, 134.209, 154.284, 138.752, 124.393, 135.738,\n",
              "       126.778, 131.669, 142.83 , 244.663, 243.709, 264.919, 217.627,\n",
              "       245.135, 272.21 , 133.374, 113.597, 116.443, 144.466, 123.109,\n",
              "       129.038, 190.204, 158.359, 155.982, 163.441, 161.078, 163.417,\n",
              "       123.925, 217.552, 177.291, 592.03 , 581.289, 119.167, 262.707,\n",
              "       230.978, 253.017, 240.005, 396.961, 260.277])"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def isNaN(string):\n",
        "    return string != string\n",
        "sum = 0\n",
        "count = 0\n",
        "for i in df['MDVP:Fhi(Hz)']:\n",
        "  if isNaN(i):\n",
        "    continue;\n",
        "  else:\n",
        "    sum = sum + float(i);\n",
        "    count = count +1;\n",
        "mean = sum/count\n",
        "mean"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RZ6cAOjCSPDS",
        "outputId": "c4363e0a-0abb-475c-b37e-c7cd27ce2973"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "197.13539378238346"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['MDVP:Fhi(Hz)'].replace(np.nan,round(mean,3),inplace=True)"
      ],
      "metadata": {
        "id": "b-vEP7xzSSql"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['MDVP:Fhi(Hz)'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q396KQrsSopr",
        "outputId": "d57048f1-4ac7-44cb-ea3e-6fac385186f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([157.302, 148.65 , 131.111, 137.871, 141.781, 131.162, 137.244,\n",
              "       113.84 , 132.068, 120.103, 112.24 , 115.871, 159.866, 179.139,\n",
              "       163.305, 217.455, 349.259, 232.181, 175.829, 189.398, 165.738,\n",
              "       172.86 , 197.135, 192.735, 200.841, 206.002, 208.313, 208.701,\n",
              "       227.383, 198.346, 206.896, 209.512, 215.203, 211.604, 211.526,\n",
              "       210.565, 192.921, 185.604, 201.249, 202.324, 197.724, 196.537,\n",
              "       247.326, 248.834, 250.912, 255.034, 262.09 , 261.487, 128.611,\n",
              "       130.049, 135.069, 134.231, 138.052, 139.867, 134.656, 126.358,\n",
              "       131.067, 129.916, 131.897, 271.314, 237.494, 238.987, 231.345,\n",
              "       234.619, 252.221, 239.541, 159.774, 166.607, 162.215, 162.824,\n",
              "       162.408, 176.595, 139.71 , 588.518, 128.101, 122.611, 148.826,\n",
              "       125.394, 102.145, 115.697, 108.664, 107.715, 110.019, 102.305,\n",
              "       205.56 , 200.125, 202.45 , 227.381, 211.35 , 225.93 , 206.008,\n",
              "       163.335, 164.989, 161.469, 172.975, 163.267, 168.913, 143.946,\n",
              "       140.557, 141.756, 141.068, 150.449, 586.567, 154.609, 160.267,\n",
              "       160.368, 163.736, 157.765, 157.339, 208.9  , 223.982, 220.315,\n",
              "       221.3  , 232.706, 226.355, 492.892, 442.557, 450.247, 442.824,\n",
              "       233.481, 479.697, 215.293, 203.522, 197.173, 198.109, 197.238,\n",
              "       198.966, 127.533, 126.632, 128.143, 125.306, 125.213, 123.723,\n",
              "       112.777, 127.611, 133.344, 130.27 , 126.609, 131.731, 268.796,\n",
              "       253.792, 219.29 , 231.508, 241.35 , 263.872, 191.759, 216.814,\n",
              "       216.302, 565.74 , 211.961, 224.429, 233.099, 139.644, 128.442,\n",
              "       127.349, 142.369, 134.209, 154.284, 138.752, 124.393, 135.738,\n",
              "       126.778, 131.669, 142.83 , 244.663, 243.709, 264.919, 217.627,\n",
              "       245.135, 272.21 , 133.374, 113.597, 116.443, 144.466, 123.109,\n",
              "       129.038, 190.204, 158.359, 155.982, 163.441, 161.078, 163.417,\n",
              "       123.925, 217.552, 177.291, 592.03 , 581.289, 119.167, 262.707,\n",
              "       230.978, 253.017, 240.005, 396.961, 260.277])"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['status'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R4nrcueaSsCG",
        "outputId": "7f72de5d-760d-4629-a7e3-d6e8ca3e247e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['1', 'one', '0', 'zero'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['status'].replace('zero','0',inplace=True)\n",
        "df['status'].replace('one','1',inplace=True)"
      ],
      "metadata": {
        "id": "EbUQEEgZTHCx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['status'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6R2js6eHTvZH",
        "outputId": "f07caded-7924-4278-cc19-aba487ac8514"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['1', '0'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "**DATA PREPROCESSING** \n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "rrQL_Yb3WEay"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "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 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   "
      ],
      "metadata": {
        "id": "vJT0_mhbXKfp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "features=df.drop([\"name\",\"status\"],axis=1)\n",
        "labels=df[\"status\"]"
      ],
      "metadata": {
        "id": "5DKzJaHZWMWZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#DataFlair - Get the count of each label (0 and 1) in labels\n",
        "print(labels[labels=='1'].shape[0], labels[labels=='0'].shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oRiDCYVigQs3",
        "outputId": "5b7b7c6e-e19a-40dd-fafc-ba9a2ea8b6c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "147 48\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "76Nsyl4dvgKW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "**NORMALISATION - MIN MAX SCALAR** \n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "kIkg5ySIzHjd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Initialize a MinMaxScaler and scale the features to between -1 and 1 to normalize them. \n",
        "#The MinMaxScaler transforms features by scaling them to a given range. \n",
        "#The fit_transform() method fits to the data and then transforms it. We don’t need to scale the labels\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler=MinMaxScaler((-1,1))\n",
        "x=scaler.fit_transform(features)\n",
        "y=labels\n",
        "y=y.astype(int)\n",
        "hm = sns.heatmap(data = x)"
      ],
      "metadata": {
        "id": "dxlapr57g0Dr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        },
        "outputId": "a80e8435-afe3-464f-d333-4558eb141f1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BY1jSz9Wlwgi",
        "outputId": "0cd512fd-7d66-489c-cc93-bfb2bc37223e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.63138346, -0.77481654, -0.89037042, ...,  0.17153026,\n",
              "        -0.21867743, -0.0053808 ],\n",
              "       [-0.6033463 , -0.81013911, -0.4433544 , ...,  0.48267409,\n",
              "        -0.05370956,  0.34265204],\n",
              "       [-0.66992292, -0.88174367, -0.46942324, ...,  0.37274182,\n",
              "        -0.18236124,  0.19336492],\n",
              "       ...,\n",
              "       [ 0.00546073, -0.43717403, -0.89854572, ..., -0.31484696,\n",
              "         0.11793486, -0.63884033],\n",
              "       [ 0.28578581,  0.20361309, -0.89144127, ..., -0.09423055,\n",
              "        -0.36355605, -0.67372646],\n",
              "       [ 0.46654868, -0.35441175, -0.85610326, ..., -0.16981039,\n",
              "         0.00734563, -0.5690805 ]])"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xW-aWu8-oQzs",
        "outputId": "642799c5-a592-4aa9-9eda-4504ae58b789"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      1\n",
              "1      1\n",
              "2      1\n",
              "3      1\n",
              "4      1\n",
              "      ..\n",
              "190    0\n",
              "191    0\n",
              "192    0\n",
              "193    0\n",
              "194    0\n",
              "Name: status, Length: 195, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "Sqg7PnsazJjU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#DataFlair - Split the dataset\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(x, y, test_size=0.2, random_state=7)"
      ],
      "metadata": {
        "id": "zw6Rc0BYl4sN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "**XGB CLASSIFIER** \n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Cc9Hl5NPzVpL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#DataFlair - Train the model\n",
        "from xgboost import XGBClassifier\n",
        "model=XGBClassifier()\n",
        "model.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "8nsHvc8anuMq",
        "outputId": "81df3f09-3452-4c4d-fd1d-58e487c04324"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "              predictor=None, random_state=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "              predictor=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "              predictor=None, random_state=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lBy_r59NqtDy",
        "outputId": "7dff543b-afdd-41d1-f40c-7d8ef0368cb0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "191    0\n",
              "120    1\n",
              "24     1\n",
              "117    1\n",
              "2      1\n",
              "101    1\n",
              "104    1\n",
              "145    1\n",
              "86     1\n",
              "22     1\n",
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              "193    0\n",
              "179    1\n",
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              "174    0\n",
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              "139    1\n",
              "66     1\n",
              "74     1\n",
              "102    1\n",
              "98     1\n",
              "132    1\n",
              "165    0\n",
              "116    1\n",
              "156    1\n",
              "11     1\n",
              "88     1\n",
              "58     1\n",
              "109    1\n",
              "122    1\n",
              "190    0\n",
              "182    1\n",
              "78     1\n",
              "108    1\n",
              "154    1\n",
              "Name: status, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=model.predict(x_test)\n",
        "from sklearn.metrics import accuracy_score\n",
        "print(accuracy_score(y_test, y_pred)*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ncfl9Bmo9Q9",
        "outputId": "ed44d857-3dbe-4907-f517-36110acbe439"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "94.87179487179486\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "l=y_test.shape[0]\n",
        "l\n",
        "TP = 0\n",
        "FP = 0\n",
        "FN = 0\n",
        "TN = 0\n",
        "\n",
        "y_test_list = []\n",
        "for index,val in enumerate(y_test):\n",
        "  y_test_list.append(val)\n",
        "y_test_list\n",
        "\n",
        "y_pred_list = []\n",
        "for index,val in enumerate(y_pred):\n",
        "  y_pred_list.append(val)\n",
        "y_pred_list\n",
        "\n",
        "for i in range(l):\n",
        "  if y_test_list[i] == 0:\n",
        "    if y_pred_list[i] == 0:\n",
        "      TP = TP+1\n",
        "    else:\n",
        "      FN = FN+1\n",
        "  else:\n",
        "    if y_pred_list[i] == 1:\n",
        "      TN = TN+1\n",
        "    else:\n",
        "      FP = FP+1\n",
        "\n",
        "pd.DataFrame(\n",
        "\n",
        "    data=[[TP,FP],[FN,TN]],\n",
        "    index=['Predicted Healthy', 'Predicted Parkinsons'],\n",
        "    columns=['True Healthy', 'True Parkinsons']\n",
        "\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "xrKfY4igpQmn",
        "outputId": "a29e5064-c827-415e-c7e7-6edaa0ab6117"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      True Healthy  True Parkinsons\n",
              "Predicted Healthy                6                1\n",
              "Predicted Parkinsons             1               31"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-60c2640d-fa0d-4c69-a2c8-7d2313da9993\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>True Healthy</th>\n",
              "      <th>True Parkinsons</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Predicted Healthy</th>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Predicted Parkinsons</th>\n",
              "      <td>1</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-60c2640d-fa0d-4c69-a2c8-7d2313da9993')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-60c2640d-fa0d-4c69-a2c8-7d2313da9993 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-60c2640d-fa0d-4c69-a2c8-7d2313da9993');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "data=[[TP,FP],[FN,TN]]\n",
        "sns.heatmap(data, cmap ='RdYlGn', linewidths = 0.30, annot = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "jI3TqYXy_c5e",
        "outputId": "cb29af11-7f6e-47d4-b6bc-24217e8a1cc1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 107
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# print precision score\n",
        "#The model precision score measures the proportion of \n",
        "#positively predicted labels that are actually correct. \n",
        "#Precision is also known as the positive predictive value\n",
        "\n",
        "precision = TP / float(TP + FP)\n",
        "\n",
        "print('Precision : {0:0.4f}'.format(precision))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AREAuNUepq68",
        "outputId": "71b13589-09f3-4b08-a62d-0eb66141e9ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision : 0.8571\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#print recall or sensitivity score\n",
        "#Recall measures the proportion of actual positive labels correctly identified by the model.\n",
        "recall = TP / float(TP + FN)\n",
        "\n",
        "print('Recall or Sensitivity : {0:0.4f}'.format(recall))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pegsozgmyEsE",
        "outputId": "15e8b24f-41f7-40b7-961e-ebbad74d1b75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recall or Sensitivity : 0.8571\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "specificity = TN / (TN + FP)\n",
        "\n",
        "print('Specificity : {0:0.4f}'.format(specificity))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5cM8xd8jyJNE",
        "outputId": "95440e8b-9b1d-4854-8b62-723fa2699a99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Specificity : 0.9688\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RyPHJ6KZ0j4R",
        "outputId": "dcc6015a-4b29-447d-ba66-ea9c57067bef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-0.6033463 , -0.81013911, -0.4433544 , -0.49174079, -0.4229249 ,\n",
              "       -0.61753372, -0.35262594, -0.6179162 , -0.05422677, -0.11092851,\n",
              "        0.03197227,  0.07137042, -0.4411517 ,  0.03209655, -0.88133813,\n",
              "       -0.13484516, -0.05833903,  0.95404891,  0.40655399,  0.48267409,\n",
              "       -0.05370956,  0.34265204])"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "in_data= np.asarray(tuple(map(float,input().rstrip().split(','))))\n",
        "#reshape and scale the input array\n",
        "in_data_re = in_data.reshape(1,-1)\n",
        "in_data_sca = scaler.transform(in_data_re)\n",
        "#print the predicted output for input array\n",
        "print(\"\\n\")\n",
        "print(\"Parkinson's Disease Detected\" if model.predict(in_data_sca) else \"No Parkinson's Disease Detected\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7odfgKy1yJXn",
        "outputId": "49c53668-b68b-4a92-f253-037f41ab403b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-0.6033463 , -0.81013911, -0.4433544 , -0.49174079, -0.4229249 ,        -0.61753372, -0.35262594, -0.6179162 , -0.05422677, -0.11092851,         0.03197227,  0.07137042, -0.4411517 ,  0.03209655, -0.88133813,        -0.13484516, -0.05833903,  0.95404891,  0.40655399,  0.48267409,        -0.05370956,  0.34265204\n",
            "\n",
            "\n",
            "Parkinson's Disease Detected\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "**SUPPORT VECTOR MACHINE** \n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "RPP_BpHO1oCS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Another algorithm for the analysis of classification and regression is the support vector machine.   \n",
        "It is a supervised machine algorithm used. Image classification and hand-written recognition\n",
        "are where the support vector machine comes to hand used. It sorts the data in one out of two\n",
        "categories and displays the output with the margin between the two as far as possible."
      ],
      "metadata": {
        "id": "ikMAUj6x1n07"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(x, y, test_size=0.2, random_state=7)\n",
        "from sklearn import svm\n",
        "clf = svm.SVC()\n",
        "clf.fit(x_train,y_train)\n",
        "y_pred=clf.predict(x_test)\n",
        "print(accuracy_score(y_test, y_pred)*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v1d6kh622swJ",
        "outputId": "9d714be2-910e-4e49-a4a3-63b505110a76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "87.17948717948718\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "l=y_test.shape[0]\n",
        "l\n",
        "TP = 0\n",
        "FP = 0\n",
        "FN = 0\n",
        "TN = 0\n",
        "\n",
        "y_test_list = []\n",
        "for index,val in enumerate(y_test):\n",
        "  y_test_list.append(val)\n",
        "y_test_list\n",
        "\n",
        "y_pred_list = []\n",
        "for index,val in enumerate(y_pred):\n",
        "  y_pred_list.append(val)\n",
        "y_pred_list\n",
        "\n",
        "for i in range(l):\n",
        "  if y_test_list[i] == 0:\n",
        "    if y_pred_list[i] == 0:\n",
        "      TP = TP+1\n",
        "    else:\n",
        "      FN = FN+1\n",
        "  else:\n",
        "    if y_pred_list[i] == 1:\n",
        "      TN = TN+1\n",
        "    else:\n",
        "      FP = FP+1\n",
        "\n",
        "pd.DataFrame(\n",
        "\n",
        "    data=[[TP,FP],[FN,TN]],\n",
        "    index=['Predicted Healthy', 'Predicted Parkinsons'],\n",
        "    columns=['True Healthy', 'True Parkinsons']\n",
        "\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "NJBE8VSx2L9i",
        "outputId": "334ae40c-2132-4cfc-b778-77190d7fca1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      True Healthy  True Parkinsons\n",
              "Predicted Healthy                2                0\n",
              "Predicted Parkinsons             5               32"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8726dc1a-5c27-4527-b975-1a0389179787\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <th>True Healthy</th>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8726dc1a-5c27-4527-b975-1a0389179787');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "data=[[TP,FP],[FN,TN]]\n",
        "sns.heatmap(data, cmap ='RdYlGn', linewidths = 0.30, annot = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
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        "id": "2JKznd5U_u95",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 115
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "in_data= np.asarray(tuple(map(float,input().rstrip().split(','))))\n",
        "#reshape and scale the input array\n",
        "in_data_re = in_data.reshape(1,-1)\n",
        "in_data_sca = scaler.transform(in_data_re)\n",
        "#print the predicted output for input array\n",
        "print(\"\\n\")\n",
        "print(\"Parkinson's Disease Detected\" if clf.predict(in_data_sca) else \"No Parkinson's Disease Detected\")"
      ],
      "metadata": {
        "id": "qUgd90n73TN5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0cfa1ef0-43f8-4ca6-cf36-466a94e75b54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-0.6033463 , -0.81013911, -0.4433544 , -0.49174079, -0.4229249 ,        -0.61753372, -0.35262594, -0.6179162 , -0.05422677, -0.11092851,         0.03197227,  0.07137042, -0.4411517 ,  0.03209655, -0.88133813,        -0.13484516, -0.05833903,  0.95404891,  0.40655399,  0.48267409,        -0.05370956,  0.34265204\n",
            "\n",
            "\n",
            "Parkinson's Disease Detected\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "**K NEAREST NEIGHBORS** \n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Ejhft5nD7WQl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2,random_state=2)\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "# Scale the features using StandardScaler\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "SoHIbruz6P1l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn = KNeighborsClassifier(n_neighbors=3)\n",
        "knn.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "7uTARlew7lg9",
        "outputId": "267fdac3-f714-4adb-b1bd-add0ed19141a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=3)"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = knn.predict(X_test)"
      ],
      "metadata": {
        "id": "c0LEhLoW8Bkk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(\"Accuracy:\", accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g7ZIEs0D8OTj",
        "outputId": "86ed6aaf-c679-48cf-bcb8-a03544847fbb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8205128205128205\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "l=y_test.shape[0]\n",
        "l\n",
        "TP = 0\n",
        "FP = 0\n",
        "FN = 0\n",
        "TN = 0\n",
        "\n",
        "y_test_list = []\n",
        "for index,val in enumerate(y_test):\n",
        "  y_test_list.append(val)\n",
        "y_test_list\n",
        "\n",
        "y_pred_list = []\n",
        "for index,val in enumerate(y_pred):\n",
        "  y_pred_list.append(val)\n",
        "y_pred_list\n",
        "\n",
        "for i in range(l):\n",
        "  if y_test_list[i] == 0:\n",
        "    if y_pred_list[i] == 0:\n",
        "      TP = TP+1\n",
        "    else:\n",
        "      FN = FN+1\n",
        "  else:\n",
        "    if y_pred_list[i] == 1:\n",
        "      TN = TN+1\n",
        "    else:\n",
        "      FP = FP+1\n",
        "\n",
        "pd.DataFrame(\n",
        "\n",
        "    data=[[TP,FP],[FN,TN]],\n",
        "    index=['Predicted Healthy', 'Predicted Parkinsons'],\n",
        "    columns=['True Healthy', 'True Parkinsons']\n",
        "\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "9nLSOFm28RQR",
        "outputId": "fad66a31-c641-426e-f3be-a54ee3d8b46c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      True Healthy  True Parkinsons\n",
              "Predicted Healthy                6                5\n",
              "Predicted Parkinsons             2               26"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8ebc53b9-05d1-44ff-aa7d-0603667b9b5b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>True Healthy</th>\n",
              "      <th>True Parkinsons</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Predicted Healthy</th>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Predicted Parkinsons</th>\n",
              "      <td>2</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8ebc53b9-05d1-44ff-aa7d-0603667b9b5b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8ebc53b9-05d1-44ff-aa7d-0603667b9b5b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8ebc53b9-05d1-44ff-aa7d-0603667b9b5b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "data=[[TP,FP],[FN,TN]]\n",
        "sns.heatmap(data, cmap ='RdYlGn', linewidths = 0.30, annot = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "bdZ8ZX-x_1Ki",
        "outputId": "21ee4dae-e84c-402a-d639-5061305ca898"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 122
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "k_values = [i for i in range (1,40)]\n",
        "scores = []\n",
        "from sklearn.model_selection import cross_val_score\n",
        "scaler = StandardScaler()\n",
        "x = scaler.fit_transform(x)\n",
        "\n",
        "for k in k_values:\n",
        "    #Classifier implementing the k-nearest neighbors vote.\n",
        "    knn = KNeighborsClassifier(n_neighbors=k)\n",
        "    #Evaluate a score by cross-validation.\n",
        "    score = cross_val_score(knn, x, y, cv=5)\n",
        "    scores.append(np.mean(score))"
      ],
      "metadata": {
        "id": "r1qeCH3n8lVk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "sns.lineplot(x = k_values, y = scores, marker = 'o')\n",
        "plt.xlabel(\"K Values\")\n",
        "plt.ylabel(\"Accuracy Score\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "o8sQRvxa8-kl",
        "outputId": "4474f17d-7877-4089-f44d-ce18cacc64ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Accuracy Score')"
            ]
          },
          "metadata": {},
          "execution_count": 124
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_new = df.copy()\n",
        "df_new['MDVP:Fo(Hz)']=df_new['MDVP:Fo(Hz)'].astype(float)\n",
        "df_new"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "zoSCmXz8jQUu",
        "outputId": "435bc6cf-50c6-48f5-ef38-26c71ba5c43b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               name  MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  \\\n",
              "0    phon_R01_S01_1      119.992       157.302        74.997         0.00784   \n",
              "1    phon_R01_S01_2      122.400       148.650       113.819         0.00968   \n",
              "2    phon_R01_S01_3      116.682       131.111       111.555         0.01050   \n",
              "3    phon_R01_S01_4      116.676       137.871       111.366         0.00997   \n",
              "4    phon_R01_S01_5      116.014       141.781       110.655         0.01284   \n",
              "..              ...          ...           ...           ...             ...   \n",
              "190  phon_R01_S50_2      174.188       230.978        94.261         0.00459   \n",
              "191  phon_R01_S50_3      209.516       253.017        89.488         0.00564   \n",
              "192  phon_R01_S50_4      174.688       240.005        74.287         0.01360   \n",
              "193  phon_R01_S50_5      198.764       396.961        74.904         0.00740   \n",
              "194  phon_R01_S50_6      214.289       260.277        77.973         0.00567   \n",
              "\n",
              "     MDVP:Jitter(Abs)  MDVP:RAP  MDVP:PPQ  Jitter:DDP  MDVP:Shimmer  ...  \\\n",
              "0             0.00007   0.00370   0.00554     0.01109       0.04374  ...   \n",
              "1             0.00008   0.00465   0.00696     0.01394       0.06134  ...   \n",
              "2             0.00009   0.00544   0.00781     0.01633       0.05233  ...   \n",
              "3             0.00009   0.00502   0.00698     0.01505       0.05492  ...   \n",
              "4             0.00011   0.00655   0.00908     0.01966       0.06425  ...   \n",
              "..                ...       ...       ...         ...           ...  ...   \n",
              "190           0.00003   0.00263   0.00259     0.00790       0.04087  ...   \n",
              "191           0.00003   0.00331   0.00292     0.00994       0.02751  ...   \n",
              "192           0.00008   0.00624   0.00564     0.01873       0.02308  ...   \n",
              "193           0.00004   0.00370   0.00390     0.01109       0.02296  ...   \n",
              "194           0.00003   0.00295   0.00317     0.00885       0.01884  ...   \n",
              "\n",
              "     Shimmer:DDA      NHR     HNR  status      RPDE       DFA   spread1  \\\n",
              "0        0.06545  0.02211  21.033       1  0.414783  0.815285 -4.813031   \n",
              "1        0.09403  0.01929  19.085       1  0.458359  0.819521 -4.075192   \n",
              "2        0.08270  0.01309  20.651       1  0.429895  0.825288 -4.443179   \n",
              "3        0.08771  0.01353  20.644       1  0.434969  0.819235 -4.117501   \n",
              "4        0.10470  0.01767  19.649       1  0.417356  0.823484 -3.747787   \n",
              "..           ...      ...     ...     ...       ...       ...       ...   \n",
              "190      0.07008  0.02764  19.517       0  0.448439  0.657899 -6.538586   \n",
              "191      0.04812  0.01810  19.147       0  0.431674  0.683244 -6.195325   \n",
              "192      0.03804  0.10715  17.883       0  0.407567  0.655683 -6.787197   \n",
              "193      0.03794  0.07223  19.020       0  0.451221  0.643956 -6.744577   \n",
              "194      0.03078  0.04398  21.209       0  0.462803  0.664357 -5.724056   \n",
              "\n",
              "      spread2        D2       PPE  \n",
              "0    0.266482  2.301442  0.284654  \n",
              "1    0.335590  2.486855  0.368674  \n",
              "2    0.311173  2.342259  0.332634  \n",
              "3    0.334147  2.405554  0.368975  \n",
              "4    0.234513  2.332180  0.410335  \n",
              "..        ...       ...       ...  \n",
              "190  0.121952  2.657476  0.133050  \n",
              "191  0.129303  2.784312  0.168895  \n",
              "192  0.158453  2.679772  0.131728  \n",
              "193  0.207454  2.138608  0.123306  \n",
              "194  0.190667  2.555477  0.148569  \n",
              "\n",
              "[195 rows x 24 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a5ad1892-144e-412b-9c00-68c751fe0119\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>...</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>...</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>...</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>...</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>...</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>...</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",
              "    </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>...</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>...</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>...</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>...</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>...</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>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a5ad1892-144e-412b-9c00-68c751fe0119')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a5ad1892-144e-412b-9c00-68c751fe0119');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "corr = df_new.corr(method = 'pearson')\n",
        "corr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        },
        "id": "J2FDVoO_4z39",
        "outputId": "0b770ee7-4219-4a00-d7a5-36c964099b69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  \\\n",
              "MDVP:Fo(Hz)          1.000000      0.401021      0.596513       -0.117829   \n",
              "MDVP:Fhi(Hz)         0.401021      1.000000      0.084667        0.102022   \n",
              "MDVP:Flo(Hz)         0.596513      0.084667      1.000000       -0.139919   \n",
              "MDVP:Jitter(%)      -0.117829      0.102022     -0.139919        1.000000   \n",
              "MDVP:Jitter(Abs)    -0.381863     -0.029300     -0.277815        0.935714   \n",
              "MDVP:RAP            -0.076040      0.097123     -0.100519        0.990276   \n",
              "MDVP:PPQ            -0.112000      0.091070     -0.095828        0.974256   \n",
              "Jitter:DDP          -0.076059      0.097096     -0.100488        0.990276   \n",
              "MDVP:Shimmer        -0.098153      0.002405     -0.144543        0.769063   \n",
              "MDVP:Shimmer(dB)    -0.073550      0.043538     -0.119089        0.804289   \n",
              "Shimmer:APQ3        -0.094503     -0.003631     -0.150747        0.746625   \n",
              "Shimmer:APQ5        -0.070464     -0.009894     -0.101095        0.725561   \n",
              "MDVP:APQ            -0.077545      0.005130     -0.107293        0.758255   \n",
              "Shimmer:DDA         -0.094517     -0.003620     -0.150737        0.746635   \n",
              "NHR                 -0.021864      0.163689     -0.108670        0.906959   \n",
              "HNR                  0.058796     -0.024934      0.210851       -0.728165   \n",
              "RPDE                -0.383624     -0.112016     -0.400143        0.360673   \n",
              "DFA                 -0.445870     -0.343230     -0.050406        0.098572   \n",
              "spread1             -0.413375     -0.076412     -0.394857        0.693577   \n",
              "spread2             -0.249489     -0.002973     -0.243829        0.385123   \n",
              "D2                   0.178143      0.176497     -0.100629        0.433434   \n",
              "PPE                 -0.372013     -0.069381     -0.340071        0.721543   \n",
              "\n",
              "                  MDVP:Jitter(Abs)  MDVP:RAP  MDVP:PPQ  Jitter:DDP  \\\n",
              "MDVP:Fo(Hz)              -0.381863 -0.076040 -0.112000   -0.076059   \n",
              "MDVP:Fhi(Hz)             -0.029300  0.097123  0.091070    0.097096   \n",
              "MDVP:Flo(Hz)             -0.277815 -0.100519 -0.095828   -0.100488   \n",
              "MDVP:Jitter(%)            0.935714  0.990276  0.974256    0.990276   \n",
              "MDVP:Jitter(Abs)          1.000000  0.922911  0.897778    0.922913   \n",
              "MDVP:RAP                  0.922911  1.000000  0.957317    1.000000   \n",
              "MDVP:PPQ                  0.897778  0.957317  1.000000    0.957319   \n",
              "Jitter:DDP                0.922913  1.000000  0.957319    1.000000   \n",
              "MDVP:Shimmer              0.703322  0.759581  0.797826    0.759555   \n",
              "MDVP:Shimmer(dB)          0.716601  0.790652  0.839239    0.790621   \n",
              "Shimmer:APQ3              0.697153  0.744912  0.763580    0.744894   \n",
              "Shimmer:APQ5              0.648961  0.709927  0.786780    0.709907   \n",
              "MDVP:APQ                  0.648793  0.737455  0.804139    0.737439   \n",
              "Shimmer:DDA               0.697170  0.744919  0.763592    0.744901   \n",
              "NHR                       0.834972  0.919521  0.844604    0.919548   \n",
              "HNR                      -0.656810 -0.721543 -0.731510   -0.721494   \n",
              "RPDE                      0.441839  0.342140  0.333274    0.342079   \n",
              "DFA                       0.175036  0.064083  0.196301    0.064026   \n",
              "spread1                   0.735779  0.648328  0.716489    0.648328   \n",
              "spread2                   0.388543  0.324407  0.407605    0.324377   \n",
              "D2                        0.310694  0.426605  0.412524    0.426556   \n",
              "PPE                       0.748162  0.670999  0.769647    0.671005   \n",
              "\n",
              "                  MDVP:Shimmer  MDVP:Shimmer(dB)  ...  MDVP:APQ  Shimmer:DDA  \\\n",
              "MDVP:Fo(Hz)          -0.098153         -0.073550  ... -0.077545    -0.094517   \n",
              "MDVP:Fhi(Hz)          0.002405          0.043538  ...  0.005130    -0.003620   \n",
              "MDVP:Flo(Hz)         -0.144543         -0.119089  ... -0.107293    -0.150737   \n",
              "MDVP:Jitter(%)        0.769063          0.804289  ...  0.758255     0.746635   \n",
              "MDVP:Jitter(Abs)      0.703322          0.716601  ...  0.648793     0.697170   \n",
              "MDVP:RAP              0.759581          0.790652  ...  0.737455     0.744919   \n",
              "MDVP:PPQ              0.797826          0.839239  ...  0.804139     0.763592   \n",
              "Jitter:DDP            0.759555          0.790621  ...  0.737439     0.744901   \n",
              "MDVP:Shimmer          1.000000          0.987258  ...  0.950083     0.987626   \n",
              "MDVP:Shimmer(dB)      0.987258          1.000000  ...  0.960977     0.963202   \n",
              "Shimmer:APQ3          0.987625          0.963198  ...  0.896645     1.000000   \n",
              "Shimmer:APQ5          0.982835          0.973751  ...  0.949146     0.960072   \n",
              "MDVP:APQ              0.950083          0.960977  ...  1.000000     0.896647   \n",
              "Shimmer:DDA           0.987626          0.963202  ...  0.896647     1.000000   \n",
              "NHR                   0.722194          0.744477  ...  0.694019     0.716215   \n",
              "HNR                  -0.835271         -0.827805  ... -0.800407    -0.827130   \n",
              "RPDE                  0.447424          0.410684  ...  0.451379     0.435237   \n",
              "DFA                   0.159954          0.165157  ...  0.157276     0.151132   \n",
              "spread1               0.654734          0.652547  ...  0.673158     0.610971   \n",
              "spread2               0.452025          0.454314  ...  0.502188     0.402223   \n",
              "D2                    0.507088          0.512233  ...  0.536869     0.467261   \n",
              "PPE                   0.693771          0.695058  ...  0.721694     0.645389   \n",
              "\n",
              "                       NHR       HNR      RPDE       DFA   spread1   spread2  \\\n",
              "MDVP:Fo(Hz)      -0.021864  0.058796 -0.383624 -0.445870 -0.413375 -0.249489   \n",
              "MDVP:Fhi(Hz)      0.163689 -0.024934 -0.112016 -0.343230 -0.076412 -0.002973   \n",
              "MDVP:Flo(Hz)     -0.108670  0.210851 -0.400143 -0.050406 -0.394857 -0.243829   \n",
              "MDVP:Jitter(%)    0.906959 -0.728165  0.360673  0.098572  0.693577  0.385123   \n",
              "MDVP:Jitter(Abs)  0.834972 -0.656810  0.441839  0.175036  0.735779  0.388543   \n",
              "MDVP:RAP          0.919521 -0.721543  0.342140  0.064083  0.648328  0.324407   \n",
              "MDVP:PPQ          0.844604 -0.731510  0.333274  0.196301  0.716489  0.407605   \n",
              "Jitter:DDP        0.919548 -0.721494  0.342079  0.064026  0.648328  0.324377   \n",
              "MDVP:Shimmer      0.722194 -0.835271  0.447424  0.159954  0.654734  0.452025   \n",
              "MDVP:Shimmer(dB)  0.744477 -0.827805  0.410684  0.165157  0.652547  0.454314   \n",
              "Shimmer:APQ3      0.716207 -0.827123  0.435242  0.151124  0.610967  0.402243   \n",
              "Shimmer:APQ5      0.658080 -0.813753  0.399903  0.213873  0.646809  0.457195   \n",
              "MDVP:APQ          0.694019 -0.800407  0.451379  0.157276  0.673158  0.502188   \n",
              "Shimmer:DDA       0.716215 -0.827130  0.435237  0.151132  0.610971  0.402223   \n",
              "NHR               1.000000 -0.714072  0.370890 -0.131882  0.540865  0.318099   \n",
              "HNR              -0.714072  1.000000 -0.598736 -0.008665 -0.673210 -0.431564   \n",
              "RPDE              0.370890 -0.598736  1.000000 -0.110950  0.591117  0.479905   \n",
              "DFA              -0.131882 -0.008665 -0.110950  1.000000  0.195668  0.166548   \n",
              "spread1           0.540865 -0.673210  0.591117  0.195668  1.000000  0.652358   \n",
              "spread2           0.318099 -0.431564  0.479905  0.166548  0.652358  1.000000   \n",
              "D2                0.470949 -0.601401  0.236931 -0.165381  0.495123  0.523532   \n",
              "PPE               0.552591 -0.692876  0.545886  0.270445  0.962435  0.644711   \n",
              "\n",
              "                        D2       PPE  \n",
              "MDVP:Fo(Hz)       0.178143 -0.372013  \n",
              "MDVP:Fhi(Hz)      0.176497 -0.069381  \n",
              "MDVP:Flo(Hz)     -0.100629 -0.340071  \n",
              "MDVP:Jitter(%)    0.433434  0.721543  \n",
              "MDVP:Jitter(Abs)  0.310694  0.748162  \n",
              "MDVP:RAP          0.426605  0.670999  \n",
              "MDVP:PPQ          0.412524  0.769647  \n",
              "Jitter:DDP        0.426556  0.671005  \n",
              "MDVP:Shimmer      0.507088  0.693771  \n",
              "MDVP:Shimmer(dB)  0.512233  0.695058  \n",
              "Shimmer:APQ3      0.467265  0.645377  \n",
              "Shimmer:APQ5      0.502174  0.702456  \n",
              "MDVP:APQ          0.536869  0.721694  \n",
              "Shimmer:DDA       0.467261  0.645389  \n",
              "NHR               0.470949  0.552591  \n",
              "HNR              -0.601401 -0.692876  \n",
              "RPDE              0.236931  0.545886  \n",
              "DFA              -0.165381  0.270445  \n",
              "spread1           0.495123  0.962435  \n",
              "spread2           0.523532  0.644711  \n",
              "D2                1.000000  0.480585  \n",
              "PPE               0.480585  1.000000  \n",
              "\n",
              "[22 rows x 22 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fc058a86-030f-4050-9ff6-3935dd8e88ce\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>...</th>\n",
              "      <th>MDVP:APQ</th>\n",
              "      <th>Shimmer:DDA</th>\n",
              "      <th>NHR</th>\n",
              "      <th>HNR</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>MDVP:Fo(Hz)</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.401021</td>\n",
              "      <td>0.596513</td>\n",
              "      <td>-0.117829</td>\n",
              "      <td>-0.381863</td>\n",
              "      <td>-0.076040</td>\n",
              "      <td>-0.112000</td>\n",
              "      <td>-0.076059</td>\n",
              "      <td>-0.098153</td>\n",
              "      <td>-0.073550</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.077545</td>\n",
              "      <td>-0.094517</td>\n",
              "      <td>-0.021864</td>\n",
              "      <td>0.058796</td>\n",
              "      <td>-0.383624</td>\n",
              "      <td>-0.445870</td>\n",
              "      <td>-0.413375</td>\n",
              "      <td>-0.249489</td>\n",
              "      <td>0.178143</td>\n",
              "      <td>-0.372013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MDVP:Fhi(Hz)</th>\n",
              "      <td>0.401021</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.084667</td>\n",
              "      <td>0.102022</td>\n",
              "      <td>-0.029300</td>\n",
              "      <td>0.097123</td>\n",
              "      <td>0.091070</td>\n",
              "      <td>0.097096</td>\n",
              "      <td>0.002405</td>\n",
              "      <td>0.043538</td>\n",
              "      <td>...</td>\n",
              "      <td>0.005130</td>\n",
              "      <td>-0.003620</td>\n",
              "      <td>0.163689</td>\n",
              "      <td>-0.024934</td>\n",
              "      <td>-0.112016</td>\n",
              "      <td>-0.343230</td>\n",
              "      <td>-0.076412</td>\n",
              "      <td>-0.002973</td>\n",
              "      <td>0.176497</td>\n",
              "      <td>-0.069381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MDVP:Flo(Hz)</th>\n",
              "      <td>0.596513</td>\n",
              "      <td>0.084667</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.139919</td>\n",
              "      <td>-0.277815</td>\n",
              "      <td>-0.100519</td>\n",
              "      <td>-0.095828</td>\n",
              "      <td>-0.100488</td>\n",
              "      <td>-0.144543</td>\n",
              "      <td>-0.119089</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.107293</td>\n",
              "      <td>-0.150737</td>\n",
              "      <td>-0.108670</td>\n",
              "      <td>0.210851</td>\n",
              "      <td>-0.400143</td>\n",
              "      <td>-0.050406</td>\n",
              "      <td>-0.394857</td>\n",
              "      <td>-0.243829</td>\n",
              "      <td>-0.100629</td>\n",
              "      <td>-0.340071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MDVP:Jitter(%)</th>\n",
              "      <td>-0.117829</td>\n",
              "      <td>0.102022</td>\n",
              "      <td>-0.139919</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.935714</td>\n",
              "      <td>0.990276</td>\n",
              "      <td>0.974256</td>\n",
              "      <td>0.990276</td>\n",
              "      <td>0.769063</td>\n",
              "      <td>0.804289</td>\n",
              "      <td>...</td>\n",
              "      <td>0.758255</td>\n",
              "      <td>0.746635</td>\n",
              "      <td>0.906959</td>\n",
              "      <td>-0.728165</td>\n",
              "      <td>0.360673</td>\n",
              "      <td>0.098572</td>\n",
              "      <td>0.693577</td>\n",
              "      <td>0.385123</td>\n",
              "      <td>0.433434</td>\n",
              "      <td>0.721543</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MDVP:Jitter(Abs)</th>\n",
              "      <td>-0.381863</td>\n",
              "      <td>-0.029300</td>\n",
              "      <td>-0.277815</td>\n",
              "      <td>0.935714</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.922911</td>\n",
              "      <td>0.897778</td>\n",
              "      <td>0.922913</td>\n",
              "      <td>0.703322</td>\n",
              "      <td>0.716601</td>\n",
              "      <td>...</td>\n",
              "      <td>0.648793</td>\n",
              "      <td>0.697170</td>\n",
              "      <td>0.834972</td>\n",
              "      <td>-0.656810</td>\n",
              "      <td>0.441839</td>\n",
              "      <td>0.175036</td>\n",
              "      <td>0.735779</td>\n",
              "      <td>0.388543</td>\n",
              "      <td>0.310694</td>\n",
              "      <td>0.748162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MDVP:RAP</th>\n",
              "      <td>-0.076040</td>\n",
              "      <td>0.097123</td>\n",
              "      <td>-0.100519</td>\n",
              "      <td>0.990276</td>\n",
              "      <td>0.922911</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.957317</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.759581</td>\n",
              "      <td>0.790652</td>\n",
              "      <td>...</td>\n",
              "      <td>0.737455</td>\n",
              "      <td>0.744919</td>\n",
              "      <td>0.919521</td>\n",
              "      <td>-0.721543</td>\n",
              "      <td>0.342140</td>\n",
              "      <td>0.064083</td>\n",
              "      <td>0.648328</td>\n",
              "      <td>0.324407</td>\n",
              "      <td>0.426605</td>\n",
              "      <td>0.670999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MDVP:PPQ</th>\n",
              "      <td>-0.112000</td>\n",
              "      <td>0.091070</td>\n",
              "      <td>-0.095828</td>\n",
              "      <td>0.974256</td>\n",
              "      <td>0.897778</td>\n",
              "      <td>0.957317</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.957319</td>\n",
              "      <td>0.797826</td>\n",
              "      <td>0.839239</td>\n",
              "      <td>...</td>\n",
              "      <td>0.804139</td>\n",
              "      <td>0.763592</td>\n",
              "      <td>0.844604</td>\n",
              "      <td>-0.731510</td>\n",
              "      <td>0.333274</td>\n",
              "      <td>0.196301</td>\n",
              "      <td>0.716489</td>\n",
              "      <td>0.407605</td>\n",
              "      <td>0.412524</td>\n",
              "      <td>0.769647</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jitter:DDP</th>\n",
              "      <td>-0.076059</td>\n",
              "      <td>0.097096</td>\n",
              "      <td>-0.100488</td>\n",
              "      <td>0.990276</td>\n",
              "      <td>0.922913</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.957319</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.759555</td>\n",
              "      <td>0.790621</td>\n",
              "      <td>...</td>\n",
              "      <td>0.737439</td>\n",
              "      <td>0.744901</td>\n",
              "      <td>0.919548</td>\n",
              "      <td>-0.721494</td>\n",
              "      <td>0.342079</td>\n",
              "      <td>0.064026</td>\n",
              "      <td>0.648328</td>\n",
              "      <td>0.324377</td>\n",
              "      <td>0.426556</td>\n",
              "      <td>0.671005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MDVP:Shimmer</th>\n",
              "      <td>-0.098153</td>\n",
              "      <td>0.002405</td>\n",
              "      <td>-0.144543</td>\n",
              "      <td>0.769063</td>\n",
              "      <td>0.703322</td>\n",
              "      <td>0.759581</td>\n",
              "      <td>0.797826</td>\n",
              "      <td>0.759555</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.987258</td>\n",
              "      <td>...</td>\n",
              "      <td>0.950083</td>\n",
              "      <td>0.987626</td>\n",
              "      <td>0.722194</td>\n",
              "      <td>-0.835271</td>\n",
              "      <td>0.447424</td>\n",
              "      <td>0.159954</td>\n",
              "      <td>0.654734</td>\n",
              "      <td>0.452025</td>\n",
              "      <td>0.507088</td>\n",
              "      <td>0.693771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MDVP:Shimmer(dB)</th>\n",
              "      <td>-0.073550</td>\n",
              "      <td>0.043538</td>\n",
              "      <td>-0.119089</td>\n",
              "      <td>0.804289</td>\n",
              "      <td>0.716601</td>\n",
              "      <td>0.790652</td>\n",
              "      <td>0.839239</td>\n",
              "      <td>0.790621</td>\n",
              "      <td>0.987258</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.960977</td>\n",
              "      <td>0.963202</td>\n",
              "      <td>0.744477</td>\n",
              "      <td>-0.827805</td>\n",
              "      <td>0.410684</td>\n",
              "      <td>0.165157</td>\n",
              "      <td>0.652547</td>\n",
              "      <td>0.454314</td>\n",
              "      <td>0.512233</td>\n",
              "      <td>0.695058</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Shimmer:APQ3</th>\n",
              "      <td>-0.094503</td>\n",
              "      <td>-0.003631</td>\n",
              "      <td>-0.150747</td>\n",
              "      <td>0.746625</td>\n",
              "      <td>0.697153</td>\n",
              "      <td>0.744912</td>\n",
              "      <td>0.763580</td>\n",
              "      <td>0.744894</td>\n",
              "      <td>0.987625</td>\n",
              "      <td>0.963198</td>\n",
              "      <td>...</td>\n",
              "      <td>0.896645</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.716207</td>\n",
              "      <td>-0.827123</td>\n",
              "      <td>0.435242</td>\n",
              "      <td>0.151124</td>\n",
              "      <td>0.610967</td>\n",
              "      <td>0.402243</td>\n",
              "      <td>0.467265</td>\n",
              "      <td>0.645377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Shimmer:APQ5</th>\n",
              "      <td>-0.070464</td>\n",
              "      <td>-0.009894</td>\n",
              "      <td>-0.101095</td>\n",
              "      <td>0.725561</td>\n",
              "      <td>0.648961</td>\n",
              "      <td>0.709927</td>\n",
              "      <td>0.786780</td>\n",
              "      <td>0.709907</td>\n",
              "      <td>0.982835</td>\n",
              "      <td>0.973751</td>\n",
              "      <td>...</td>\n",
              "      <td>0.949146</td>\n",
              "      <td>0.960072</td>\n",
              "      <td>0.658080</td>\n",
              "      <td>-0.813753</td>\n",
              "      <td>0.399903</td>\n",
              "      <td>0.213873</td>\n",
              "      <td>0.646809</td>\n",
              "      <td>0.457195</td>\n",
              "      <td>0.502174</td>\n",
              "      <td>0.702456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MDVP:APQ</th>\n",
              "      <td>-0.077545</td>\n",
              "      <td>0.005130</td>\n",
              "      <td>-0.107293</td>\n",
              "      <td>0.758255</td>\n",
              "      <td>0.648793</td>\n",
              "      <td>0.737455</td>\n",
              "      <td>0.804139</td>\n",
              "      <td>0.737439</td>\n",
              "      <td>0.950083</td>\n",
              "      <td>0.960977</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.896647</td>\n",
              "      <td>0.694019</td>\n",
              "      <td>-0.800407</td>\n",
              "      <td>0.451379</td>\n",
              "      <td>0.157276</td>\n",
              "      <td>0.673158</td>\n",
              "      <td>0.502188</td>\n",
              "      <td>0.536869</td>\n",
              "      <td>0.721694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Shimmer:DDA</th>\n",
              "      <td>-0.094517</td>\n",
              "      <td>-0.003620</td>\n",
              "      <td>-0.150737</td>\n",
              "      <td>0.746635</td>\n",
              "      <td>0.697170</td>\n",
              "      <td>0.744919</td>\n",
              "      <td>0.763592</td>\n",
              "      <td>0.744901</td>\n",
              "      <td>0.987626</td>\n",
              "      <td>0.963202</td>\n",
              "      <td>...</td>\n",
              "      <td>0.896647</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.716215</td>\n",
              "      <td>-0.827130</td>\n",
              "      <td>0.435237</td>\n",
              "      <td>0.151132</td>\n",
              "      <td>0.610971</td>\n",
              "      <td>0.402223</td>\n",
              "      <td>0.467261</td>\n",
              "      <td>0.645389</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NHR</th>\n",
              "      <td>-0.021864</td>\n",
              "      <td>0.163689</td>\n",
              "      <td>-0.108670</td>\n",
              "      <td>0.906959</td>\n",
              "      <td>0.834972</td>\n",
              "      <td>0.919521</td>\n",
              "      <td>0.844604</td>\n",
              "      <td>0.919548</td>\n",
              "      <td>0.722194</td>\n",
              "      <td>0.744477</td>\n",
              "      <td>...</td>\n",
              "      <td>0.694019</td>\n",
              "      <td>0.716215</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.714072</td>\n",
              "      <td>0.370890</td>\n",
              "      <td>-0.131882</td>\n",
              "      <td>0.540865</td>\n",
              "      <td>0.318099</td>\n",
              "      <td>0.470949</td>\n",
              "      <td>0.552591</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HNR</th>\n",
              "      <td>0.058796</td>\n",
              "      <td>-0.024934</td>\n",
              "      <td>0.210851</td>\n",
              "      <td>-0.728165</td>\n",
              "      <td>-0.656810</td>\n",
              "      <td>-0.721543</td>\n",
              "      <td>-0.731510</td>\n",
              "      <td>-0.721494</td>\n",
              "      <td>-0.835271</td>\n",
              "      <td>-0.827805</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.800407</td>\n",
              "      <td>-0.827130</td>\n",
              "      <td>-0.714072</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.598736</td>\n",
              "      <td>-0.008665</td>\n",
              "      <td>-0.673210</td>\n",
              "      <td>-0.431564</td>\n",
              "      <td>-0.601401</td>\n",
              "      <td>-0.692876</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RPDE</th>\n",
              "      <td>-0.383624</td>\n",
              "      <td>-0.112016</td>\n",
              "      <td>-0.400143</td>\n",
              "      <td>0.360673</td>\n",
              "      <td>0.441839</td>\n",
              "      <td>0.342140</td>\n",
              "      <td>0.333274</td>\n",
              "      <td>0.342079</td>\n",
              "      <td>0.447424</td>\n",
              "      <td>0.410684</td>\n",
              "      <td>...</td>\n",
              "      <td>0.451379</td>\n",
              "      <td>0.435237</td>\n",
              "      <td>0.370890</td>\n",
              "      <td>-0.598736</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.110950</td>\n",
              "      <td>0.591117</td>\n",
              "      <td>0.479905</td>\n",
              "      <td>0.236931</td>\n",
              "      <td>0.545886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DFA</th>\n",
              "      <td>-0.445870</td>\n",
              "      <td>-0.343230</td>\n",
              "      <td>-0.050406</td>\n",
              "      <td>0.098572</td>\n",
              "      <td>0.175036</td>\n",
              "      <td>0.064083</td>\n",
              "      <td>0.196301</td>\n",
              "      <td>0.064026</td>\n",
              "      <td>0.159954</td>\n",
              "      <td>0.165157</td>\n",
              "      <td>...</td>\n",
              "      <td>0.157276</td>\n",
              "      <td>0.151132</td>\n",
              "      <td>-0.131882</td>\n",
              "      <td>-0.008665</td>\n",
              "      <td>-0.110950</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.195668</td>\n",
              "      <td>0.166548</td>\n",
              "      <td>-0.165381</td>\n",
              "      <td>0.270445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spread1</th>\n",
              "      <td>-0.413375</td>\n",
              "      <td>-0.076412</td>\n",
              "      <td>-0.394857</td>\n",
              "      <td>0.693577</td>\n",
              "      <td>0.735779</td>\n",
              "      <td>0.648328</td>\n",
              "      <td>0.716489</td>\n",
              "      <td>0.648328</td>\n",
              "      <td>0.654734</td>\n",
              "      <td>0.652547</td>\n",
              "      <td>...</td>\n",
              "      <td>0.673158</td>\n",
              "      <td>0.610971</td>\n",
              "      <td>0.540865</td>\n",
              "      <td>-0.673210</td>\n",
              "      <td>0.591117</td>\n",
              "      <td>0.195668</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.652358</td>\n",
              "      <td>0.495123</td>\n",
              "      <td>0.962435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spread2</th>\n",
              "      <td>-0.249489</td>\n",
              "      <td>-0.002973</td>\n",
              "      <td>-0.243829</td>\n",
              "      <td>0.385123</td>\n",
              "      <td>0.388543</td>\n",
              "      <td>0.324407</td>\n",
              "      <td>0.407605</td>\n",
              "      <td>0.324377</td>\n",
              "      <td>0.452025</td>\n",
              "      <td>0.454314</td>\n",
              "      <td>...</td>\n",
              "      <td>0.502188</td>\n",
              "      <td>0.402223</td>\n",
              "      <td>0.318099</td>\n",
              "      <td>-0.431564</td>\n",
              "      <td>0.479905</td>\n",
              "      <td>0.166548</td>\n",
              "      <td>0.652358</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.523532</td>\n",
              "      <td>0.644711</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D2</th>\n",
              "      <td>0.178143</td>\n",
              "      <td>0.176497</td>\n",
              "      <td>-0.100629</td>\n",
              "      <td>0.433434</td>\n",
              "      <td>0.310694</td>\n",
              "      <td>0.426605</td>\n",
              "      <td>0.412524</td>\n",
              "      <td>0.426556</td>\n",
              "      <td>0.507088</td>\n",
              "      <td>0.512233</td>\n",
              "      <td>...</td>\n",
              "      <td>0.536869</td>\n",
              "      <td>0.467261</td>\n",
              "      <td>0.470949</td>\n",
              "      <td>-0.601401</td>\n",
              "      <td>0.236931</td>\n",
              "      <td>-0.165381</td>\n",
              "      <td>0.495123</td>\n",
              "      <td>0.523532</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.480585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PPE</th>\n",
              "      <td>-0.372013</td>\n",
              "      <td>-0.069381</td>\n",
              "      <td>-0.340071</td>\n",
              "      <td>0.721543</td>\n",
              "      <td>0.748162</td>\n",
              "      <td>0.670999</td>\n",
              "      <td>0.769647</td>\n",
              "      <td>0.671005</td>\n",
              "      <td>0.693771</td>\n",
              "      <td>0.695058</td>\n",
              "      <td>...</td>\n",
              "      <td>0.721694</td>\n",
              "      <td>0.645389</td>\n",
              "      <td>0.552591</td>\n",
              "      <td>-0.692876</td>\n",
              "      <td>0.545886</td>\n",
              "      <td>0.270445</td>\n",
              "      <td>0.962435</td>\n",
              "      <td>0.644711</td>\n",
              "      <td>0.480585</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>22 rows × 22 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "corr=df_new.corr()['MDVP:Fo(Hz)'].abs().sort_values()\n",
        "corr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d6OWu_uhgv_p",
        "outputId": "f394ec9c-0208-4d75-ba31-3e617f412d68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NHR                 0.021864\n",
              "HNR                 0.058796\n",
              "Shimmer:APQ5        0.070464\n",
              "MDVP:Shimmer(dB)    0.073550\n",
              "MDVP:RAP            0.076040\n",
              "Jitter:DDP          0.076059\n",
              "MDVP:APQ            0.077545\n",
              "Shimmer:APQ3        0.094503\n",
              "Shimmer:DDA         0.094517\n",
              "MDVP:Shimmer        0.098153\n",
              "MDVP:PPQ            0.112000\n",
              "MDVP:Jitter(%)      0.117829\n",
              "D2                  0.178143\n",
              "spread2             0.249489\n",
              "PPE                 0.372013\n",
              "MDVP:Jitter(Abs)    0.381863\n",
              "RPDE                0.383624\n",
              "MDVP:Fhi(Hz)        0.401021\n",
              "spread1             0.413375\n",
              "DFA                 0.445870\n",
              "MDVP:Flo(Hz)        0.596513\n",
              "MDVP:Fo(Hz)         1.000000\n",
              "Name: MDVP:Fo(Hz), dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "list = ['NHR','HNR','Shimmer:APQ5','MDVP:Shimmer(dB)','MDVP:RAP','MDVP:Fo(Hz)','status']\n",
        "\n",
        "list_new =  []\n",
        "list_new = df.columns.tolist()\n",
        "list_new.remove('NHR')\n",
        "list_new.remove('HNR')\n",
        "list_new.remove('Shimmer:APQ5')\n",
        "list_new.remove('MDVP:Shimmer(dB)')\n",
        "list_new.remove('MDVP:RAP')\n",
        "list_new.remove('MDVP:Fo(Hz)')\n",
        "list_new.remove('status')\n",
        "df_new"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "sI8l1cJWj1r2",
        "outputId": "dc008401-a374-4798-fe9e-471bb5e25cf6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               name  MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  \\\n",
              "0    phon_R01_S01_1      119.992       157.302        74.997         0.00784   \n",
              "1    phon_R01_S01_2      122.400       148.650       113.819         0.00968   \n",
              "2    phon_R01_S01_3      116.682       131.111       111.555         0.01050   \n",
              "3    phon_R01_S01_4      116.676       137.871       111.366         0.00997   \n",
              "4    phon_R01_S01_5      116.014       141.781       110.655         0.01284   \n",
              "..              ...          ...           ...           ...             ...   \n",
              "190  phon_R01_S50_2      174.188       230.978        94.261         0.00459   \n",
              "191  phon_R01_S50_3      209.516       253.017        89.488         0.00564   \n",
              "192  phon_R01_S50_4      174.688       240.005        74.287         0.01360   \n",
              "193  phon_R01_S50_5      198.764       396.961        74.904         0.00740   \n",
              "194  phon_R01_S50_6      214.289       260.277        77.973         0.00567   \n",
              "\n",
              "     MDVP:Jitter(Abs)  MDVP:RAP  MDVP:PPQ  Jitter:DDP  MDVP:Shimmer  ...  \\\n",
              "0             0.00007   0.00370   0.00554     0.01109       0.04374  ...   \n",
              "1             0.00008   0.00465   0.00696     0.01394       0.06134  ...   \n",
              "2             0.00009   0.00544   0.00781     0.01633       0.05233  ...   \n",
              "3             0.00009   0.00502   0.00698     0.01505       0.05492  ...   \n",
              "4             0.00011   0.00655   0.00908     0.01966       0.06425  ...   \n",
              "..                ...       ...       ...         ...           ...  ...   \n",
              "190           0.00003   0.00263   0.00259     0.00790       0.04087  ...   \n",
              "191           0.00003   0.00331   0.00292     0.00994       0.02751  ...   \n",
              "192           0.00008   0.00624   0.00564     0.01873       0.02308  ...   \n",
              "193           0.00004   0.00370   0.00390     0.01109       0.02296  ...   \n",
              "194           0.00003   0.00295   0.00317     0.00885       0.01884  ...   \n",
              "\n",
              "     Shimmer:DDA      NHR     HNR  status      RPDE       DFA   spread1  \\\n",
              "0        0.06545  0.02211  21.033       1  0.414783  0.815285 -4.813031   \n",
              "1        0.09403  0.01929  19.085       1  0.458359  0.819521 -4.075192   \n",
              "2        0.08270  0.01309  20.651       1  0.429895  0.825288 -4.443179   \n",
              "3        0.08771  0.01353  20.644       1  0.434969  0.819235 -4.117501   \n",
              "4        0.10470  0.01767  19.649       1  0.417356  0.823484 -3.747787   \n",
              "..           ...      ...     ...     ...       ...       ...       ...   \n",
              "190      0.07008  0.02764  19.517       0  0.448439  0.657899 -6.538586   \n",
              "191      0.04812  0.01810  19.147       0  0.431674  0.683244 -6.195325   \n",
              "192      0.03804  0.10715  17.883       0  0.407567  0.655683 -6.787197   \n",
              "193      0.03794  0.07223  19.020       0  0.451221  0.643956 -6.744577   \n",
              "194      0.03078  0.04398  21.209       0  0.462803  0.664357 -5.724056   \n",
              "\n",
              "      spread2        D2       PPE  \n",
              "0    0.266482  2.301442  0.284654  \n",
              "1    0.335590  2.486855  0.368674  \n",
              "2    0.311173  2.342259  0.332634  \n",
              "3    0.334147  2.405554  0.368975  \n",
              "4    0.234513  2.332180  0.410335  \n",
              "..        ...       ...       ...  \n",
              "190  0.121952  2.657476  0.133050  \n",
              "191  0.129303  2.784312  0.168895  \n",
              "192  0.158453  2.679772  0.131728  \n",
              "193  0.207454  2.138608  0.123306  \n",
              "194  0.190667  2.555477  0.148569  \n",
              "\n",
              "[195 rows x 24 columns]"
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              "      <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",
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              "      <th>0</th>\n",
              "      <td>phon_R01_S01_1</td>\n",
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              "      <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>...</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>...</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>...</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>...</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",
              "    </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>...</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>...</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>...</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>...</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>...</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>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_new_1=df.drop(list_new,axis=1)\n",
        "df_new_1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "7wkm-rWppYfa",
        "outputId": "c1f45eca-ee1f-464f-8064-ed0d26a1f4c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    MDVP:Fo(Hz)  MDVP:RAP  MDVP:Shimmer(dB)  Shimmer:APQ5      NHR     HNR  \\\n",
              "0       119.992   0.00370             0.426       0.03130  0.02211  21.033   \n",
              "1         122.4   0.00465             0.626       0.04518  0.01929  19.085   \n",
              "2       116.682   0.00544             0.482       0.03858  0.01309  20.651   \n",
              "3       116.676   0.00502             0.517       0.04005  0.01353  20.644   \n",
              "4       116.014   0.00655             0.584       0.04825  0.01767  19.649   \n",
              "..          ...       ...               ...           ...      ...     ...   \n",
              "190     174.188   0.00263             0.405       0.02498  0.02764  19.517   \n",
              "191     209.516   0.00331             0.263       0.01657  0.01810  19.147   \n",
              "192     174.688   0.00624             0.256       0.01365  0.10715  17.883   \n",
              "193     198.764   0.00370             0.241       0.01321  0.07223  19.020   \n",
              "194     214.289   0.00295             0.190       0.01161  0.04398  21.209   \n",
              "\n",
              "    status  \n",
              "0        1  \n",
              "1        1  \n",
              "2        1  \n",
              "3        1  \n",
              "4        1  \n",
              "..     ...  \n",
              "190      0  \n",
              "191      0  \n",
              "192      0  \n",
              "193      0  \n",
              "194      0  \n",
              "\n",
              "[195 rows x 7 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MDVP:Fo(Hz)</th>\n",
              "      <th>MDVP:RAP</th>\n",
              "      <th>MDVP:Shimmer(dB)</th>\n",
              "      <th>Shimmer:APQ5</th>\n",
              "      <th>NHR</th>\n",
              "      <th>HNR</th>\n",
              "      <th>status</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>119.992</td>\n",
              "      <td>0.00370</td>\n",
              "      <td>0.426</td>\n",
              "      <td>0.03130</td>\n",
              "      <td>0.02211</td>\n",
              "      <td>21.033</td>\n",
              "      <td>1</td>\n",
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              "      <th>1</th>\n",
              "      <td>122.4</td>\n",
              "      <td>0.00465</td>\n",
              "      <td>0.626</td>\n",
              "      <td>0.04518</td>\n",
              "      <td>0.01929</td>\n",
              "      <td>19.085</td>\n",
              "      <td>1</td>\n",
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              "      <td>116.682</td>\n",
              "      <td>0.00544</td>\n",
              "      <td>0.482</td>\n",
              "      <td>0.03858</td>\n",
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              "      <td>1</td>\n",
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              "      <th>4</th>\n",
              "      <td>116.014</td>\n",
              "      <td>0.00655</td>\n",
              "      <td>0.584</td>\n",
              "      <td>0.04825</td>\n",
              "      <td>0.01767</td>\n",
              "      <td>19.649</td>\n",
              "      <td>1</td>\n",
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              "      <td>0.00263</td>\n",
              "      <td>0.405</td>\n",
              "      <td>0.02498</td>\n",
              "      <td>0.02764</td>\n",
              "      <td>19.517</td>\n",
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              "      <td>209.516</td>\n",
              "      <td>0.00331</td>\n",
              "      <td>0.263</td>\n",
              "      <td>0.01657</td>\n",
              "      <td>0.01810</td>\n",
              "      <td>19.147</td>\n",
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              "    <tr>\n",
              "      <th>192</th>\n",
              "      <td>174.688</td>\n",
              "      <td>0.00624</td>\n",
              "      <td>0.256</td>\n",
              "      <td>0.01365</td>\n",
              "      <td>0.10715</td>\n",
              "      <td>17.883</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>198.764</td>\n",
              "      <td>0.00370</td>\n",
              "      <td>0.241</td>\n",
              "      <td>0.01321</td>\n",
              "      <td>0.07223</td>\n",
              "      <td>19.020</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>194</th>\n",
              "      <td>214.289</td>\n",
              "      <td>0.00295</td>\n",
              "      <td>0.190</td>\n",
              "      <td>0.01161</td>\n",
              "      <td>0.04398</td>\n",
              "      <td>21.209</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>195 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-041e7600-3d80-4905-a6ac-4e6524d36f84')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-041e7600-3d80-4905-a6ac-4e6524d36f84 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-041e7600-3d80-4905-a6ac-4e6524d36f84');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x1=df_new_1.drop('status',axis=1)\n",
        "y1=df_new_1['status']"
      ],
      "metadata": {
        "id": "fF3SgVzPsWzk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X1_train, X1_test, y1_train, y1_test = train_test_split(x1, y1, test_size=0.2,random_state=3)\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "# Scale the features using StandardScaler\n",
        "scaler = StandardScaler()\n",
        "X1_train = scaler.fit_transform(X1_train)\n",
        "X1_test = scaler.transform(X1_test)"
      ],
      "metadata": {
        "id": "u5xe_4vYqrIw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn = KNeighborsClassifier(n_neighbors=3)\n",
        "knn.fit(X1_train, y1_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "Q2RivOXzr8VQ",
        "outputId": "0c4f8456-68da-4293-9c0a-3b84eabd744f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=3)"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y1_pred = knn.predict(X1_test)"
      ],
      "metadata": {
        "id": "ZBBU8pQvsA8C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = accuracy_score(y1_test, y1_pred)\n",
        "print(\"Accuracy:\", accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D_LroaCSsFcI",
        "outputId": "efaeb984-b1e5-4a91-82b6-b1f2c09f588d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9230769230769231\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(\n",
        "\n",
        "confusion_matrix(y1_test, y1_pred),\n",
        "\n",
        "columns=['Predicted Healthy', 'Predicted Parkinsons'],\n",
        "\n",
        "index=['True Healthy', 'True Parkinsons']\n",
        "\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "mzrXoXEn14VY",
        "outputId": "99893bc7-2724-47aa-d5a3-185d80278913"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Predicted Healthy  Predicted Parkinsons\n",
              "True Healthy                     6                     1\n",
              "True Parkinsons                  2                    30"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d79967ff-dcc9-427b-b30e-8841690d13bc\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Predicted Healthy</th>\n",
              "      <th>Predicted Parkinsons</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>True Healthy</th>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>True Parkinsons</th>\n",
              "      <td>2</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d79967ff-dcc9-427b-b30e-8841690d13bc')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d79967ff-dcc9-427b-b30e-8841690d13bc button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d79967ff-dcc9-427b-b30e-8841690d13bc');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "**DECISION TREE** \n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "uvht_kolgsQR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "classifier = DecisionTreeClassifier(criterion = 'gini',random_state=0)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2,random_state=1)\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "# Scale the features using StandardScaler\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "#perform training\n",
        "classifier = classifier.fit(X_train , y_train)"
      ],
      "metadata": {
        "id": "Aic6Y4Jl9OCC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = classifier.predict(X_test)\n",
        "print(y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r87uw6KTYvcl",
        "outputId": "25890969-6b37-4e22-9e1d-a50a9139c0a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 1 1 0 1 0 1 1 0 1 0 0 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 0\n",
            " 1 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import tree\n",
        "fig = plt.subplots(figsize = (30,20))\n",
        "tree.plot_tree(classifier,fontsize =10,filled = True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "sEdaYrgiYz2D",
        "outputId": "b635e8d4-5246-467a-f175-f6137eac0b10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3000x2000 with 1 Axes>"
            ],
            "image/png": 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Yr776qjp37mx0HCBR8uTJo08//VRz5szRqVOnjI4DAACQrOhJCQAAAAAAZChXrlyRl5eX3nnnHY0fP97oOMAL+fDDDzVjxgz5+vqqYMGCRscBgHTtq6++0qBBg3TgwAG98sorRscBEi0yMlKlS5dWnjx59Pvvv8vMzMzoSAAAAMmCIiUAAAAAAJChtG7dWvv27ZOfn58cHR2NjgO8kODgYHl6eqp69epatWqV0XEAIN0KDAyUh4eH3nzzTS1atMjoOMAL+/XXX9WoUSOtWrVKbdq0MToOAABAsqBICQAAAAAAZBjbt29XvXr1tHz5cnXs2NHoOECSLF++XJ07d9bvv/+uOnXqGB0HANKlfv36acWKFfL391fOnDmNjgMkSfPmzXXs2DH5+vrK3t7e6DgAAAAvjSIlAAAAAACQIURFRalcuXJycXHR7t27GRYD6ZbJZFL16tUVFBSkY8eOydLS0uhIAJCuHDt2TBUqVNC0adM0ZMgQo+MASXbhwgWVLFlSH3zwgUaPHm10HAAAgJdmbnQAAAAAAACA5DBnzhz5+Pho1qxZFCghXTMzM9OsWbN05swZzZ071+g4AJCumEwmDRo0SMWLF1f//v2NjgO8lKJFi2rYsGGaOHGiLl26ZHQcAACAl0ZPSgAAAAAAIN27c+eOPDw81L59e82bN8/oOECy6NOnj1atWiV/f3/lyJHD6DgAkC58//336tixo3777TfVq1fP6DjASwsJCZGnp6cqVaqktWvXGh0HAADgpVCkBAAAAAAA0r1evXrpxx9/lL+/v1xdXY2OAySLu3fvysPDQ23atNGCBQuMjgMAad7jx4/l6empV199VT/++KPRcYBk88MPP6hDhw7aunWr6tevb3QcAACAJKNICQAAAAAApGuHDx9WpUqVNGvWLA0YMMDoOECymj17tgYPHqxDhw6pQoUKRscBgDRtxIgRmj59us6ePatChQoZHQdINiaTSbVr19bdu3d14sQJWVlZGR0JAAAgSShSAgAAAAAA6VZsbKyqVaumkJAQHT16VJaWlkZHApJVdHS0ypUrJycnJ+3du1dmZmZGRwKANOncuXMqVaqURowYoVGjRhkdB0h2J06cUPny5TV58mQNHTrU6DgAAABJQpESAAAAAABIt5YtW6auXbtqx44dqlWrltFxgBSxY8cO1alTR8uWLVPnzp2NjgMAaVKzZs108uRJnT17Vvb29kbHAVLEgAEDtHz5cvn7+ytXrlxGxwEAAHhhFCkBAAAAAIB0KSgoSJ6enqpVq5Z++OEHo+MAKapdu3batWuX/P395eTkZHQcAEhTfvnlFzVp0kRr1qxRq1atjI4DpJj79+/L3d1dzZs31+LFi42OAwAA8MIoUgIAAAAAAOnS+++/r6+++kp+fn4qUKCA0XGAFBUQECAvLy8NGjRIEydONDoOAKQZERER8vb2VoECBbRt2zaGxUSGN2/ePPXr108HDhxQpUqVjI4DAADwQihSAgAAAAAA6Y6vr6+8vb312Wef6ZNPPjE6DpAqPv/8c33++ec6ffq0PDw8jI4DAGnCl19+qY8++kgnTpxQyZIljY4DpLiYmBhVqFBBNjY22rdvn8zNzY2OBAAAkGgUKQEAAAAAgHTFZDKpUaNG8vf3l4+Pj2xtbY2OBKSKsLAwlShRQsWLF9emTZvoLQRApnfjxg15enqqR48emj59utFxgFSze/du1axZU0uWLFG3bt2MjgMAAJBoFCkBAAAAAIB05aefflLz5s21fv16NW/e3Og4QKpav369WrZsqZ9//llNmzY1Og4AGKpz58769ddfde7cObm4uBgdB0hVHTt21LZt2+Tv768sWbIYHQcAACBRKFICAAAAAADpRnh4uEqWLKlixYrp119/pScZZDomk0mvv/66Ll68qDNnzsjGxsboSABgiL1796p69epauHChevbsaXQcINVdv35dnp6e6tOnj6ZMmWJ0HAAAgERhoFoAAAAAAJBuTJ06VQEBAZoxYwYFSsiUzMzMNGPGDF25ckVTp041Og4AGCImJkaDBw9WhQoV1L17d6PjAIbIly+fPv74Y82cOVNnz541Og4AAECi0JMSAAAAAABIF65evSovLy/169dPkydPNjoOYKj33ntP8+bNk5+fn/Lnz290HABIVQsWLFCfPn30559/qkqVKkbHAQwTERGhkiVLqkiRItqyZQtF/AAAIM2jSAkAAAAAAKQLHTp00B9//CF/f385OzsbHQcw1KNHj+Th4aF69erpu+++MzoOAKSaBw8eyN3dXU2aNNHSpUuNjgMYbuPGjWrWrJnWrVunFi1aGB0HAADguShSAgAAAAAAad6uXbtUq1YtLVmyRN26dTM6DpAmLFmyRG+//bZ2796t6tWrGx0HAFLF4MGD9c0338jPz0958uQxOg5gOJPJpCZNmujs2bPy8fGRnZ2d0ZEAAACeiSIlAAAAAACQpkVHR6tChQqys7PTn3/+KXNzc6MjAWlCbGysqlSposjISB0+fFgWFhZGRwKAFHXq1CmVK1dOEyZM0LBhw4yOA6QZ/v7+KlWqlD799FN98sknRscBAAB4Ju7qAQAAAACANG3BggU6efKkZs2aRYES8A/m5uaaNWuWjh8/roULFxodBwBSlMlk0uDBg1W0aFENHjzY6DhAmuLh4aGhQ4dq3LhxCggIMDoOAADAM9GTEgAAAAAASLPu3bsnd3d3tWzZUl9//bXRcYA06e2339aGDRt07tw5ZcuWzeg4AJAiVq9erbZt22rz5s1q2LCh0XGANCc4OFgeHh6qWbOmVq5caXQcAACABPHzQwAAAAAAkKZ06dIlrleYkSNHKiYmRuPGjTM4FZB2jR8/XtHR0Ro5cqTRUQAg2dy7d09eXl66du2aQkND9d5776lZs2YUKAHP4OTkpC+//FKrVq3Sjh07jI4DAACQIIqUAAAAAABAmrJz505duXJFx48f1/z58zV69GjlypXL6FhAmpUrVy6NGjVK8+bN04kTJ4yOAwDJ4sKFC/Lz89O9e/c0YcIE3b59W9OmTTM6FpCmderUSVWqVNHgwYMVHR1tdBwAAICnUKQEAAAAAADSlAcPHsjFxUWDBg2Sp6enBgwYoKCgIDFiPfBsAwcOlKenpwYPHsyxAiBDePDggaQnQ1h9+eWXGjZsmAoXLqzg4GCDkwFpl5mZmWbNmqXTp09r3rx5RscBAAB4CkVKAAAAAAAgzYiKilJwcLDOnTunPXv26Msvv9SIESOUNWtWHTx40Oh4QJplZWWlGTNmaNeuXVq1apXRcQDgpd2/f1+SNHHiRLm6uqp169aqXbu2SpUqRTEm8BwVKlRQz549NXLkSAUGBhodBwAAIB6KlAAAAAAAQJrx8OFDSdLq1atVo0YNvfPOO5o9e7bGjRunihUrGhsOSOPq16+vli1batiwYQoJCTE6DgC8lAcPHsjCwkIbN25UzZo1VbVqVd24cUPLli2TmZmZ0fGANG3s2LGSpI8//tjgJAAAAPFRpAQAAAAAANKMv4Z2efjwoXbv3q28efPq5MmT+uCDD2RhYWFwOiDtmzJliu7evavx48cbHQUAXkpgYKBiY2Pl6OiolStXauDAgTp58qRq1apldDQgzcuRI4fGjBmjhQsX6ujRo0bHAQAAiEOREgAAAAAASDMuXrwo6cnQVV999ZV27NghDw8Pg1MB6UfhwoU1fPhwTZo0SRcuXDA6DgAk2Z9//imTyaTcuXNr3759mjRpkuzt7Y2OBaQb/fr1U8mSJTVo0CCGSAQAAGmGmYkzEwAAAAAAkEbcu3dPHTp00KxZs+Tp6Wl0HCBdCg0NlZeXl8qVK6cNGzYYHQcAkmTFihXasmWLFixYIGtra6PjAOnSH3/8obp162r58uXq2LGj0XEAAAAoUgIAAAAAAAAymtWrV6tt27bavHmzGjZsqOHDh6t06dLq1KmT0dEAAEAqatu2rfbs2SM/Pz+Zm5urSZMm+v7775U3b16jowEAgEyI4d4AAAAAAACADKZ169aqXbu23nnnHUVGRmrPnj3atm2b0bEAAEAqmzRpkh4+fKixY8fq4cOH2rlzp44fP250LAAAkElZGh0AAAAAAICMJCAgQIGBgUbHQDJwdXWVm5ub0TGAJDEzM9PMmTNVrlw5zZo1S1mzZtWDBw+MjgVkGpwPZCycEyA9K1iwoD788EN98cUXeuuttySJcwIAAGAYipQAAAAAAEgmAQEB8ipeXGGhoUZHQTKws7eX79mzPJREujNlyhTNmjVLc+bMUf/+/TV69Gg1aNBAd+7cMToakCkEBATI06u4wsM4H8gobO3s5efLOQHSn6pVqypLliyaPn26lixZog8//FA2NjYUKQEAAMNQpAQAAAAAQDIJDAxUWGio6r0/V1kLeBgdBy/hwVV/bZvUT4GBgTyQRLrToUMH/fbbb2rSpInatm0rS0tLnTlzRpaW3AoEUkNgYKDCw0JVvM9sOeQtZnQcvKSQG+d1dv5AzgmQLo0ZM0bdu3dXhQoV1KFDBy1atIjeFQEAgKG4MwEAAAAAQDLLWsBDOYqVMToGgEwqb9682rx5s5YtW6ahQ4cqOjpavr6+cnV1NToakKk45C0mp0KljY4BIBOrV6+ezpw5ow8//FBz585VlixZFBQUpLt37xodDQAAZFLmRgcAAAAAAAAAkLzMzMzUtWtX+fj4qH79+pKk+/fvG5wKAACkNmdnZ82ZM0c7duxQlixZFBMTo/379xsdCwAAZFIUKQEAAAAAAAAZVO7cufXjjz9q6tSpqlGjhtFxAACAQWrVqiVfX1+1aNFCjRs3NjoOAADIpBjuDQAAAAAAAMjghg4dqqFDhxodAwAAGMjOzk7r1q0zOgYAAMjE6EkJAAAAAIA04ODyiZrT2FVzGrvqxPp5L7TuX+stalMkhdIBAIDUdmndZP3RNa/+6JpXV7csTPR6YXevxq13aGS9FEwIAAAAAC+GnpQAAAAAAEgjshX00htjf5SVvVPctND7t/Xn16N09fhORYU+lkv+oqrQ7l0Vrd4sbpluy8/o3K71OvTdRCNiv5BTP3+t4z/OVuiDO8peuKRq9JugXJ7ln7n8+g/e0I1Tfz413e2Vemo6+gdJUlTYY+1b8rku7ftF4cEP5JzLTd5v9FKpJt2f206JRl1Ve9CUZNoz4NkCAgIUGBhodAwkgqurq9zc3IyOAcRxyOepMsNXytLOMW5aTGS4LvwwWrf3/yRTdISyedeWR5fxss6SQ5Jkmz2vqs44rqub5+qBz26joj/loe9+BWyeo+DLpxT58LZKDf5aOSo0Svz6/gd1fHwrOeT31Cufb4s379q2Jbq6ea4iH92VQ4ES8uj0hZyLloubH3b7ss7/MEaPzh1UbFSksnnXkUfnL+LeMyC1cE6QfnBOAABAyqBICQAAAACANMLMwlL22XLFm7ZtygBFhjxS40+Xy9Y5m87t+FFbJ/RQ6xnblKNoaUmSfbZcsnFwfuntB9+9Lqcc+V66nWc5t3Od9i4cqVoDJyuXVwWdXD9PG0e2UYcF+2XvkvBDwoafLFVsVGTc6/DgB1o5oJaKVW8eN23vwpG6dmKP6r0/V0653HT16B/a9dVwOWTPrcKv/v3ws0TDzqrU6cO415a29imwl0B8AQEBKu7lqdCwcKOjIBHs7Wx11tePh5JIM8wsLGTjkjPetPPfj9K9E9tUauB8Wdo5y//bj3VqZg9VGPnTk3XMn6xjYevwUts2xcYq8uFt2WTL81Lt/CUmIlSOBUoqT40OOj2rxwutGxXySGcXDJFLieqKCrobb97tAxt0fsVoeXadIOei5XV1y0KdmPyWKk/cLWtnV8VEhOr4pA5ydCuhsh+sliRdWvulTk7rqgqfbpSZOQNOIHVwTpC+cE4AAEDKoEgJAAAAAIAUFvYoUD/0r6nSb/RShXZDJUk3fQ5qw4fN1XTMSuUvW/OZ6946e0i1BkyK622oYof3dGL9PN09dyKuSOllBN+5Jr/fV8lv2w/KVtBLjUYue+k2n+XEurkq0bCzijd4S5JUa+AUXTn0m3y3fq/ybYckuI6tU9Z4r8/tWidLGzsVrfFG3LRbZw/J67V2yle6uiSpZKOu8tm8VHf8jsUrUrK0sX+qCAxIaYGBgQoNC9esVsXk7mpndBw8x7nAMA368bwCAwN5IIkUFxl0T4c+qat89XuoULPBkqRH5w7p2PjWKv3ecmUrWSPB9aJDg3Rz1wqV6PeVspZ48r3n1XOqDo6opUfnjyhLsQovnS3kxjnd2rNat//8UTleaSr3jqNfuk1Jyl6mrrKXqZukdf2XfqBcVVrKzNxcgUd/jTfv6q8LlLfWW8pTs70kybPbRN07sV03d61QwaaD9Mj/oMIDr+qVz7fK0u5Jb5XFe83Q7v7F9eDsHmUr+ezzMCA5cU6QfnBOAABAyqFICQAAAACAFGaXxVV135mhzZ93UYFyteWSv5i2T+4v76Y9nlugJEm5i7+i87vWqWCl+rJxyKLzu9crJjJC+UpXS3KeqPAQXdy7SX7bf9D1k3uUy7Oiyr7ZX0VrtIhbxv+P1doxa9hz22k65gflLVUlUduMiYrU3fMnVL7tO3HTzMzNlb9sLd3yPZTo7Ge3fCf3Wi1l9Y/eIXIXf0WXDvwqrwYd5ZA9t26c3KOH1y+oWq8v4q3r/8ca+f+xWvZZc6pgpddVscN7sqI3JaQSd1c7eed1/O8FAWQK1s7Z5dVjqk7NeFvZStWSfe6i8pk/WPnqdX9mgZIkBV8+KVNMlLKW+HsZh7zussmeT0EvUaQUFfJQd/Zv0K29qxV86YRcSlRXkTYfKUfFv4t9L/88UwE/z3xuO5XG75Bt9vxJyvAsN3f9oLA7ASreZ7au/DQ93rzY6Eg9vnxSBZsOjJtmZm6ubCVrKOj8kbhlzMzMZG5pHbeMuZWNzMzM9cj/IEVKSHWcEwAAgMyMIiUAAAAAAFJBwVfqq8TrnbVtUl/lcC8rS1t7vdp95H+u9/qIr7V1Qk8tbucucwtLWdrYqeHIpcqSt8gLZ7h+aq/8tq3UhT0bZJclhzzqtlatQVOUJU/hp5YtVLmh2nk+/0GnQ/bED/8SHnRPptgY2WeNP6ybnUsOPbh6LlFt3PY7qvtXzqrOOzPiTa/Rb4J2zHxXy7p4y9zCUjIzV+0h05TXu2rcMu61W8kpZwE5ZMute5fPaN/iMXp4/bwafbI00fsAAEByyl7mNeWt/ZZ85g2Qc+EysrCxV9E2I567TuSjOzKztJaVQ5Z4062dcyjy0Z0X2r4pNlb3Tv6uW3tW697xrbLLVUS5q7VSqcFfy8bl6Z4H89XprJyVmj23TWuX3C+U4b+E3rqoC6vHqfzH6558x/9LVPB9mWJjZJ0l/vmFVRZXhdw8L0lyLlpB5jb2urBqrIq0fjLs64VVY2WKjVHkwxd7zwAAAAC8HIqUAAAAAABIJVV7jtYP/Wrowp6f1GbGdllY2fznOge/Ha+Ix4/0xri1snXOpkv7ftHW8T3U8suNyl64xAttf8MHzWVpY6dqvT5Xycbdnrustb2TrO2dXqj9v9w4vU8bP20f97r2oMlxQ7G9jLNblytboRJxQ9/95eRPC3Xb97Aaf7ZcjjkL6Obpfdo9Z7gcsuVWgXK1JD0ZAu4v2QuXkH3WXPrpozf16OalBIu0AABIDUXbf6p7H9XVnYMbVXH0rzJPxLlBcgm/d12npnWRpYOLSvSdE6/XpIRYOWaVlWPW5y6TnEyxMfKZN0CFWw6Tfe6iSW7H2jm7Sg2YL7+lI3Ttt69lZmaunK+2kGNBb8nMPBkTAwAAAPgvFCkBAAAAAJBKHt28rJD7t2SKjVXwnYD/LDJ6dPOSTv28SO3n7lG2gl6SJNcipXTzzH6d2vi1ag+a8kLbb/zZd/LbvlJ75n+sM5uXyrNuW7nXelP22Z7uLeFlhnvL6V5W7Wb/EffaziWHLKxsZGZuodAHd+MtG/bwruyz5fzP7FHhITq/c51e6fRhvOnREWE6sHSsGn6yVIUqNZAkuRYuqcALp3R87VdxRUr/lsvrSS9Rj25QpAQAME7YnSuKfHhbMsUqPPCqHAsUf+7y1llyyhQdqaiQR/F6U4oMuivrLP/9ffpPNtnyqES/Obq1Z7XOzOkj56IVlLtaa+V4pelTPTVJqT/cW3TYYwVfOqHHV07r3LcfS5JMpljJZNKO7gVU5v0VyuJRSWbmFop8FP/8IupRoGz+0btSNu/aqjJ5nyKD78nM3FJWDlm0d3AZ2eV0S5asAAAAABKHIiUAAAAAAFJBTFSktk3up2I1W8glXzH9MWOo2s2pIHuXHM9cJzo87Mk//vUrfzNzC8kU+8IZClV+XYUqv67w4Ic6v3OtfLf9oD+/HqUC5WrLo24bFa7SWFa29v9fNunDvVna2CU4HF2OYmV0/cQuFanaWNKTYWauHd8l72Y9/zP7hd0/KSYqUp5128SbHhsTrdjoKJn9+z2ysJAp9tnvUeCF05KUYIEWkFFUnnZUPV/No15VEjc045+XHqnNNz7y+fAVZbHjtiGQ0mKjI3V2/kDlrPyG7HMXle/iYao09ndZO7s+cx2nQqVlZmGlBz57lPOVJpKk0JvnFXHvupyLPf97+9/MLSyV69UWyvVqC0U8vK1be9fo6paFOrf8E2UvW1+5q7ZSttJ1ZW5pJSn1h3uztHPSK2N/jzft+valenh2j0oOXCi7HG4yt7SWY6HSeuCzRzkqPOkJyhQbqwc+e5SvXren8zlllyQ98NmjyKBAuZZrkGx5gbSMcwIAAJBWcGYBAAAAAEAqOLBsrCJDglSj73hZ2Too4PA2/TFtsJqMXvHMdVwKuCtL3sLaOetdVe05RrbOWXVp3y+6emyHmoz6PslZbJ1cVKrp2yrV9G09uHpOvttWaN/i0bq4d6MafvKNpJcb7u1ZyrTsp9+nDlQO97LK6VFeJzfMU3REqLzqd4hbZtvk/nLInkdVuo+Mt+7Zrd+pcJVGsnXOFm+6tb2T8npX1b7Fo2RpYyunnAV049Sf8tu+StV6jZH0pEeqc3/8KLdX6snWOZvuXTqjvQtGKm+pKnItXDJZ9xFIS37p7S17q8QPZVSxgJOODasgZ1uLFMsUHhWrMVsua8Ppe4qMiVXtoi4a17SwcjhaP3Mdk8mkyX9c1fdH7igoPFoV3Zw1vmlhFclul2I5gdRwcc0ERYcGy73T57KwcdC9k9vlu+hdlX532TPXsbR3Vp6aHXR+xShZObrI0tZJ/ss/lnOxCsrygkVK/2TjkksFmwxQwSYDFHTphG7tXinfr99V7uptVazDZ5Jefri36PAQhd2+FPc6/O5VBV85LStHl7jely6sGqeIB7dUos9MmZmbyzG/V7w2rJ2zy9zKJt70Ag17y3fhO3IqXEbORcrp2paFiokIVZ4afw89e3PXD7LP6y5rp+x6dP6Izn33qQq83lv2eYoleX+A9IRzAgAAkFZQpAQAAAD8j727ju+q+uM4/v6uO2AbPXIFjO4OaZCQFEQQEIMWAxAppRsbJaRFQBQRRQEFpLu20UjXWLCN1ff3B/6mkw0HbNzF6/l4+HjsnnvPue+LD/Y97H52DgBksEuHt+nwt5+p9cRvkwp/Gg79WF+/XldHf5in0i1eSrGfpZW1WoxZrp3zx2n9mK6Ki74r1/xF1XDIRypcuVG6ZHMv5KPqPd9TtRffVdjlM+kyZmp86rZVTPgt7V40UVGh1+VRrLRajv1aDu5/b08TeeOiTBbJX6CEXjypK8d2qtX736Q4buO352rngvf1y5RXFBNxR85eBVW1+3CVat5TkmRhZaOLB3/TobWfKT4mSk6e+VWsZktV6vJGxj0skAnkdrR+pOttrCzk5Zz6i8H0MHrDOf16MlSfdfSVi52lRvxwVr2Xh2ht79Kp9vl422XN23VVM9uWUCE3W03Z9Ke6Ljqhza+Xk90jvHAFMpPQE3/o4s9fqNw7K2Vlf39uEPDyHO0Z+Ywu/bpQBRq+mGrfEs+PlsnCpKNz+igx7p5yBdaTb/cJ6ZbNpWhZuRQtqxLPj9a921fSbdyIs4d0cGL7pONTy0ZLkvLW6qiAPjMlSbFh13Xv9qVHGjdP1daKC7+ls6unKDbshpy8S6nM0CWy+cd2b1FXT+vMNxMUF3lHdh6FVPjZASrU5OUnfiYgq2BOAAAAMguKlAAAAAAAyGAFytTSK99fTdbmksdbvb85m0qPv7kVKJ60ulFGMllYyK1gxq8mENiq90O3d2sz6bsH2twL+ui19TdT7eOQK48aDJmT6nlnzwJqM/n7RwsKZHKR9xL0zvdntCHotpxtLfVqzfz6OThUJfM6aGyzopIe3NqlwKgdmvJsMf0aEqotp8OU19lGo5oUVmP/+yuUZfTWLuEx8Vp+4Lo+fM5HtYq5SpJmtCmhuh8e1L4/I1Sx0IOrt5nNZn2x84oG1imoJn/lnNWuhMpN2aufgm6rdWDq22IBmZl7QA3Vm3chWZu9ZyHV+TT4P/ta2tjJt/uEdC1MSomFlY3svQqn23juATVUf+Hlh17z/2Kl1BRtO1RF2w59oL1go5dUsFHKRd+SVLzjCBXvOCJNOYGshjkBcwIAALISipQAAAAAAMgkbp87rs/bFVaNl0apdMvUX7T92+ftCsucEC9LG9sMTAcgMxmz4Zz2/Bmh+c/7y9PRWlM3/6kjV+6qZF6Hh/abvuWi3m1cWO82Lqz5u66q36qT2jW4gtwd0rbCQrdFJ7TrQniq5wu62mpzv3Ipnjt8+a7iEsyq/dfLSEkq4WmvAq42qb6QvBB6T9cj45JeYEqSi52Vyhdw0r4/I3ghiWwv8s8g/f5yCRXv9K4KNOyRpj4xty5q97B6SoyPk2MBn4wNCMBwzAmYEwAAkJVQpAQAAAAAQCZQpvXL8mvQQZJk5/poP2Dv9OFmSZLJwjLdcwHIfCLvJWjloRv68DmfpJd709sUV4Wp+/6zb8dynmrz10u8d57x1pe7rurgpUjV93FP072ntC6mmLjEVM9bW6a+1cqNyFjZWJoeWJHB08laNyLjUuxz/a92T6fkL0w9nGySzgHZVcFGvZSnxnOSJBvn3GnuZ+OWV5XGbZR0fzUkANkXcwLmBAAAZDUUKQEAAAAAkAnYObvLzjltLwT+zTV/sXROAyAzOx8ao7gEs8oXcEpqc7GzUnEP+//sG5DXMelrBxtLOdta6ubd+DTfO58LK7YBT4u1k7usnR59bmBhaSWHPEUzIBGAzIY5AQAAyGooUgIAAAAAAAByCGsLU7Jjk0lKNJvT3P9JtnbxdLJRbIJZYdHxyVZOuBEZ98CqCP/n9Vf7jcg45XH+e0WYm5GxKvWPl6sAAODRMCcAAABGoEgJAAAAAIAsblGP8irTpq/KtnklTddfOrxNa99po15fn5atk2sGpwOQ3gq728na0qSDlyNVwO3+KgbhMfE6cytaVQs7Z+i9n2RrlzL5HWVtadK2s2FqUfL+1lWnbkbrUlisKhZKObe3u628nKy17UyYSue7/wIyIiZeBy5FqnvlvE/wJEDWt+ONKirYuI8KNemTputDT/yhgxPbq9bHJ2TtyOc/kB0wJ2BOAABAVkOREgAAAAAAWVz7WRtlZeuQ5uvzBlRRj8XHZOPokmGZ4mNj9Mfc93Ty9zVKiIuVd4X6qvP6ZDm4e6XaZ/fiSTr1+xpF3rgsS2treZYoq6rdRyiPf0VJUvi1C9q7bJouHdqqqNDrcsyVV74N2qtipyGytLZJdVwgu3GytVSHsp56/+fzcrO3koejtaZu/lMWJpNMMv33AE/gSbZ2cbGzUufyXhqz4Zzc7K3kbGupd9efVcVCTsleSNaZc0DDnvFWs4DcMplM6l0tn2b/flHFctupkLutpmz6U3mcbdTEP1d6PBKQZVUc9aMsbf97S6f/c/WppBqzDsrKIeM+/xNiY3R6+Rhd2/mdzPH3lCuwnny7T5CNq2eqfcxms86umaIrW5YqPipcrj6V5PviRDnkvb+d7f+Lq1JScdR6uRQrp7Nrpurct9MfOG9hY6+6c0+nz8MBmRBzAuYEAABkNRQpAQAAAACQxdm7ejzS9ZbWNnLIlSeD0ty3/fN3dX7PRjUZ9qVsHF209ZN3tOH9Hmo3bX2qfdwKFFftVyfJJW9hJcTG6NCaT/T9u+3V9cs9snf10J0/T0qJiarbf5pc8xXV7fMntGX2EMXFRKlm77EZ+jxAZjOqaRG98/0ZvbgkSM62lnq1Zn5dCYuVnVXGvpB8UqObFpGFSXp5RbDuxZtVr4Sbxrcomuya0zdjFB6TkHT8Wq38iopL0Fvfn1F4TLwqe7tocbcA2VmnvkIDkBPYuOR+pOstrGxk65Z6sXB6OLV0tG4d+kWl+30mK3sXhSwaoSOze6niyO9S7XNh/Ue6tHGe/PvMlL2Ht86unqxDU59XlfFbZGljl1Rc9U9nV09W6PFtci5aVpJUqNmryl+/e7JrDk7qKJdi5dL7EYFMhzkBcwIAALISipQAAAAAAMjEYqMi9NuHQ3V2x4+ycXBS+fb9dXbnj/IoFqhafT+Q9OB2bx8391C9ATN0fs9G/bl/sxxz51WN3mNVtFozSRm/3du9u+E68fMSNXrrMxUsV0eS1GDwHC3rW11Xg/Yqr3+lFPv51k++SkLNl9/XiZ+X6NbZ4ypYro68KzWUd6WGSedd8xXRnYundGz9AoqUkOM42Vrqw/Y+ScdRsQma8dtFda34dwHCrsEVkvW5NKb6A+OcGFYl6esaRV1TvCY92VlbaHzLYhrfsliq1/w7g8lk0psNvPVmA+8MzQZkJvHRkQpZ+LZu7NsgK3tneTd/TTcP/CQn71Ly6Xr/M+/f271tfjG//HpO0a1Dv+r20S2ydc+nEp3fk0eFJpIyfru3+KhwXfl9mUq++pHcS9aSJPn3nq7dw+oq7NQ+uZao+EAfs9msiz99ocKtBsqzQlNJUsDLs7V9QFnd3L9Beaq1eaC4KjE+Tjf3/6SCjV6SyXS/CMPKzlFWdo5J10ReOKaoyyHy6zEp3Z8TyGyYEwAAgKyEIiUAAAAAADKx7XNH6urx3Wo+arHs3Ty1e/FE3Th1WB7FAh/ab8/SKarx0ijV6DVah7/7Qr9MeUUvLDgoO2f3NN133chOunxsZ6rnnb0Kqsun21M8d+PkQSXGx6lgubpJbe6FfOTkWVDXTuxJtUjpnxLiYnXsx4WycXRR7qKlUr0u9m6EbJ3c/nM8ILs5euWuTt2MVrkCToqIideM3y5KEtudANnEqWWjFXZyjwIHLZCNq6fOrp6iiHNH5OSd+meiJJ1bO13FO76r4p1H6tLGeTr+WT9Vn7Zb1k5p+/w/NLWrwkJ2pXreNndBVZ2wJcVzEecOy5wQJ/eStZPaHPP7yDZ3AYWnUqQUc+OCYsOuy73U332sHFzkXKy8wk/tU55qbR7oc/PAz4qLDFXe2p1SzXn5t6Wyz1tMbn5VU70GyC6YEwAAgKyEIiUAAAAAADKp2KgIBf+64oEViRZ2K/2fff2f6SKfes9Jkqr1GKEj332u68H7k61E9DD1Bs5UQmx0quctLK1TPRcVev3+qgf/WqXJwd1TUaHXH3rfc7t+0s+TXlb8vSg55sqjVh98I3vXlLezCbt8Rke+n6savcc8dEwgu/p0+2WdvhUtG0uTAvM5afVLpZTLMfW/mwCyhvjoSF3dtlIlX/1Iuf4q3gnoPUPbB5b/z755a3VSnuptJUnFOgzTxY1fKvzMQeUuUz9N9/Z/aaoS4mJSPf+wz//YsOsyWdk8sEqTjYunYsNS/vz/f7uNq2ea+1z5fZlyBdaTXa78KZ5PiI3RtR1r5N3i9VSzAtkNcwIAAJBVUKQEAAAAAEAmFX71vBLj4+Tl+/f2DLaOLnIrWPw/++YuWjLpa2s7R9k4OCsq7Gaa7+3kke/RwqaTAmVrqdOHmxUdflvHNyzSzxN667kZP8nBLfnLy8ibV/T9yE4qXutZlWza3ZCsgJFK53PUhlfKGB0DQAaIuXFe5oQ4uRQrl9Rm5eAih3z//fnvVCgg6WtLWwdZ2jsrLjztn/+2uYz5/E+rmNuXdfvIFpV6/bNUr7m570clxEQqX62OTy8YYCDmBAAAICuhSAkAAAAAgGzogZUOTCYpMTHN/Z9kuzcHdy8lxsfqXmRYstWUokJvyMHd66H3tbZzlGv+YnLNX0x5/StpSe/KOvHTElXsNCjpmru3rmjtO62VN6Cy6g2YkeZnAgAguzNZJv+Rv8lkktmc9s//J9nuzcbVS+b4WMXdDUu2mlJs+A3ZuKb8+f//9tiwG7J1y5Osj3MKW9td3bpC1k7u8ijfONWMl39fptxln3lgdSYAAAAAxqNICQAAAACATMolb2FZWFnresgBOXsVlCTduxuuO5fOKH/pGhl67yfZ7s3Tp5wsrKx18eDvKl6rlSQp9OJJRd64qDwBlR8phznRrIS4e0nHkTfvFyh5+pRVg8FzZLKweKTxADyeQWtOKTwmXvO6+BsdBcj27DwLy2RprfAzh2SX+/7nf3xUuKKvnpGbX7UMvfeTbPfmXKSMTJbWCj2+TV6VW0iSoq6c0r1bl+RSomKKfew8vWXj6qXQ49vkXPj+drbx0RGKOHNABRokXynRbDbrytYVyluzvSysUs4RfeOC7pzYrsBBCx72mACeAHMCAADwJChSAgAAAAAgk7JxcJZfw07aMW+07JzdZO/mqd2LJ8lkMkmmjL33k2z3ZuvoooDGXbV97kjZObvJ2sFZWz8dpjwBlZXXv1LSdUtfrqZqPUaqWI0Wiou5q33LZ6hItaZydM+j6PBbOrpunu7euqIStVtL+n+B0rNy9iqkGr3GKuYf29c55MrzQA4AOUfVGft18c69B9pfrJxH41sWMyAR8Pis7J2Ut1YHnV4xTtZObrJx9tDZNVMlk4UyegLwJNu9WTm4KF+dLjq1bLSsndxkZeeskMUj5FKiolz/UaS0653aKtZ+uDwrNZPJZFLBJr11/rtZcshTVHae3jq7erJs3PLIo0LTZOOHHt+mmBsXlK/u86lmuPL7ctm45lHuMg0e+zkAZG1zfr+kH0/c0qmb0bKztlClQs4a3qiwSnjYGx0NAACIIiUAAAAAADK1mn3G6bcPh+qH0V1l4+Ck8u37K/LGJVla2xkd7aFqvvy+TCYLbfigpxLiYlWoYn3VfW1ysmvuXDyl2LvhkiSThaVCL55U8AfLFR12W3Yu7vLyLa82U75XrsL3f0v74oEtCrt8VmGXz+qr7oHJxnpt/U0ByLnWvxyohERz0nHQ9Sh1+eqEWpbKbWAq4PGV6DJaIQvf1uHp3WVl7yzv5q8p5vZlWVjbGh3toUo8P1omC5OOzumjxLh7yhVYT77dJyS7JurKacVHhycdezd/XQn3ohS84C3FR4XL1aeyyg5dIkub5HOdK78vk0uJSnLM75Pivc2Jibq6bYXy1e4ok4Vl+j8cgCxh5/kwvVglr8oVcFJ8olkTf7mg5786ri39ysnBhu8NAAAYjSIlAAAAAAAyMRsHZzV667Ok47iYu9qzdIpKNvt7C5QXFhxI1ielgp3eK88kfV2gTK0ML+qxsrFTndcnq87rk1O95p8ZrGzs1OzdhQ8d079RF/k36pJuGYHMaN2xW5qx5U+dux0jO2tLlc7nqPld/ORgY6mDlyI18ZcLOnr1ruITzCqV10GjmxZRYH6npP4FRu3QxJbFtDHktrafDVdBV1tNa1NcuR2s9OZ3Z3TwUqRK5nXQ7HY+KpLrfgHAtM1/akPQbXWvlFezfr+o0Oh4PePrrinPFpOLXco/PkxMNOujbZe1ZN813YiMVdHc9hpUt2BSUdCd6Hi9+8NZ/Xb6jqJiE5TXxVYD6hRQp/JeGfLnltsx+dZPH267pCK5bFW9iEuG3A/IaFb2Tir5ykdJxwn3onRu7XTlr9c1qa36tN3J+tRfePmBcWp/EpT0tXtAjRSvSU+WNnby7T7hgcKkf/p3BpPJpGLt3lKxdm89dOxSr3780PMmCwvVmLEv7WGBTI45weNZ8kLJZMcz25ZQmcl7dfjyXVVjXgAAgOEoUgIAAAAAIBO7cfqw7vx5Ul5+FRR7N1x7l06VJBWt1szgZADS27WIWL3+zUmNaOStZgG5FBmboF3nI2T+a4GgyHsJ6lDOU+/nLyqzzPrsjyt6YUmQtg0oLyfbv1cGmPn7RY1qUlijmhTR+I3n1e+bk/J2t1W/2gVUwNVGQ749rXd/OKvFLwQk9Tl3O0bfH7upBc/7K/Jegt5Ye1rD153Vh+1TXrFkztZLWn34pia2Kqaiuey083y4Bqw+qdyOVqpexFVTNl1QyI0oLe4WoFwOVjp7O0Yx8YmpPvvs3y9qztZLD/3z2fJ6ORVw++9VZGLjE7X68E29XD3f/e0xgSwo4vwRRV0+JZdi5RUfHa5za2dIkjwqNDE4GYCngTlB+swJJCk8Jl6S5GbPK1EAADIDPpEBAAAAAMjkDq7+SKEXT8vSylqeJcqq7eR1sndlCyMgu7keEav4RLOal8ytgn+9eAvI45h0vlYx12TXT25VTAET92jHuXA18nNPau9UzlPPlvaQJL1Wq4Ce/eKoBtUtqHol3CRJvavl05BvTyUb6158oma1K6F8Lvfv+37zIuq+JEjvNSksL2ebB66ds/WSlr9YUpUKOUuSCuey054LEVq895qqF3HVpbBYlc7nqLIF7q/oUMj94VtUvlApj1r9x9Zsef6VIzUbgm4rPCZeHctlzAoNwNNy4cdPFX31tExWNnIuEqjyw9fIxpnPfyAnYE6QPnOCxESzRm04p8rezvLP45CmPgAAIGNRpAQAAAAAQCbmWbyMOszeZHQMAE9BybyOqlXMVQ0/PqS6xV1Vt7ibWpTKnfSb/zciYzX51z/1x7lw3bobpwSzWdFxiboUdi/ZOAF5/36J6el0fxu0f76Y83CyVky8WREx8XL+a+uWAq62SS8jJaliIWclmqXTt6IfeCF57naMouMS1eWr48na4xLMKv3XvbtXzqM+K0J05Mpd1S3upib+uVTZ2znVZ3d3sJa7g3Wq5x/F8v3XVb+Eu/K6pO0FJpAZORcOVOWxPxkdA4BBmBOkz5xg+A9nFXw9WmteKpUu4wEAgCdHkRIAAAAAAACQCVhamLS8e4D2/hmh306Faf7uq5q06YLW9QmUt7udBq05pdCoeI1tVkQF3WxlY2nSs18cVVyCOdk41hZ/b3H2/6+sUmhLTN4tze7GJkiSvurqr7z/ellpY2UhSWrg467dgyvo15Oh2no6TJ0XHtOLVfLqvSZFUhwzvbZ2uXjnnraeCdMXnf3S+DQAAGQ+zAmefE4w4ocz+iUkVKtfKqX8rmnbGg4AAGQ8ipQAAAAAAMBD/Tq9n2Ijw9TsvUVGRwGyPZPJpMreLqrs7aLB9Qqqyoz9+vHEbfWtkV97LkRofMtiauh7fxuXS2H3dDsqPl3ueynsnq6GxyatPrT/YqQsTFLx3PYPXOvr6SBbK5MuhcWqehHXB87/X25Ha3Us56WO5bxUxdtF7288n+oLyfTa2mXFgevycLRWQx/3/7wWwMOdmDtI8VFhChw43+goQI7EnCB1D5sTmM1mvbv+rDacuK2VPUvJ+z+2lwMAAE8XRUoAAAAAACBL2714kvYunZKsza1gCT3/+U6DEgGPZ//FCG07E6a6xd3k4Wit/ZcidftunHw8778ULJrbXqsO3VDZ/I6KuJeg938+Lztri3S5t62VhQatOaWRTQor8l6CRq4/q1alcj+wrYskOdlaqm+N/Bq94ZwSzWZV8XZRREy89lyIkJOdpTqW89KUTRdUJr+TfD3tFZtg1i8hofLxePDl5v+lx9YuiYlmrThwXR3KecrK0vTfHQBkaee/n6Mb+9Yr6sopWVjbydWnkop3HCGHfCWSrjkw4TndCdqRrF/++i/Ir8ekpx0XeCTMCR5/TjD8h7P69shNzeviJycbS12PiJUkOdtZyt7a8rHHBQAA6YMiJQAAAAAAkOXlKuyvZz9YlXRssuRHHsh6nG0ttet8uL7YeUWR9xJUwNVW7zUprAZ/rQo0rXVxvfXdaTX99LDyudrqnYbeGvfz+XS5d5FcdmoWkEvdF5/Qneh4NfR11/iWxVK9/q0GhZTbwVofbr2kC6Fn5GJnqcB8jupfu6AkydrSQhN+uaA/79yTnZWFqhZ21scdfNIla2q2ngnTpbBYdSrvlaH3AZA53AneoQINe8ilaDmZE+N1+puJOjili6pO+E2Wtg5J1+Wr21VF272ZdGxpm3pxBJBZMCd4fF/tuSZJaj//eLL26W2KM0cAACAT4Cd2AAAAAABkEqe3fac9S6Yo7MpZWdnay7N4oJq9t0jWdo66FrJfuxZ8oJtnjigxPk4exUqr5svvy7NE2aT+Hzf3UN1+U3Vu10+6dHibnLwKqsGg2bJ3za3NswbpeshB5S5WSs8M/Viu+YpKur8K0dmdP6p08x7au3y67kWEqnDlxqo3cIZsHV1SzGlOTNT+lbN1fMNXigq9LrcCxVWpyxsqXutZSVJMxB1t/eRt/bl/i+Ji7srJI58qdBysgMbPZ9ifncnSSg658mTY+MDT4OPpoCUvlEz1fOl8jlrft0yytpb/2g7l0pjqyY4Luds90FajqOsDbZL0YpW8erFK3hTvPbNtiWTHJpNJvavnU+/q+VK8flDdghpUt2DKD5JB6pZwS/G5gMzu+p51OvftdEVfOycLG3s5Fy6lwEELZGnroPAzB3XmmwmKPH9UiQnxcvIuJZ/nR8u5yN/fCza/mF++PSbp1oGNCj2xTXa5C8q/93RZO+dW8LyhCj9zUE7eJVXy5Tmyz1NEknR2zVTd3L9B+Ru8qPPfzVRcZKhyl20k/5emyMoh9c//Cz98pMtbFis27Ibs8xZTkdaD5FW5pSQp7u4dnVw0QreP/qaEmCjZ5sqnwi37K1+dzhny51Z26NJkxwG9Z2p7/0BFnD0sN/9qSe2WtvaydaMwAVkLc4LHx1wAAIDMjSIlAAAAAAAygbu3r2rjpJdV/aVRKlqjheKiInXl2E7JbJYkxUVFyu+ZTqrtM1Fms1mHVn+kdaO6qOvcXbJxcE4aZ++yaarZZ5xq9hmnHfPHauPkvnLJW1gVOg6Sk2dBbZ45QFs/fkctx61I6hN2+axObV2rFqOWKDYqQptnDdTvH72pRm99lmLWfV/PVMjmlarbb6pc8xfTlaM79MuUV2XnmlsFAmtq96IJCr0QopZjl8vONbfCLp9VQmx0qs++b8UM7Vsx86F/Pl0+3S5nr9RfboRdOqMF3UrJysZOefwrqVqPkQ+9HgCAzODenWs6/slrKt7xXXlUbKaEmEiFheyS+a/P/4SYSOWt2VHO3d6XJP3546c6PP0FVZ20XVb2TknjnF87UyW6jFKJ50fp9IoPdPyT12XnVVjeLfvLLncBBX0xRCGLRqjs0CVJfaKvndP13d8rcNBCJURHKmjeGwr5aphKvvJRilnPr5uja3+skm+PSXLIU1R3gnfqxGf9Ze2cW+7+1XV21WTdvRSiMm8skbVTLkVfP6vE2JhUn/3c97N14fvZD/3zqTJhi+xyp+3zPD46XJJk5eSWrP3ajtW69scq2bh6KXe5RirSelCylZYAAAAAPD0UKQEAAAAAkAlE3b6mxIR4FavRUs55CkmSchf9+7enC5ark+z6egNm6IsOxXT5yB8qUrVJUrt/o+dVok4bSVL5DgO0ekhTVeryhrwrNpAklWn9sjbNGJBsrITYGDV842M5edz/7efar0zUD6O7qGbvsQ+sTpQQd0/7V8zUs+NXKW9AZUmSa74iunJsp46vX6gCgTUVeeOiPIoHysu3vCTJJY/3Q5+9VPMeKlG79UOvccyd8m9yS1Iev4pqMGSO3AqWUNTta9qzdIrWvNlSnT/ZmqyACwCAzCb2zjWZE+LlWam57DzuF+M4FQpIOu9eslay6/16TtHWV/11J3iHPMo1SmrPW7uTvKreX9HQu8Xr2j+ulYq0HqTcgfUkSQUb91LQF0OSjZUYd08l+8ySba77n/8+3d7X4ekvqHjnUQ+sPJQYd0/nv5+tcm+vkGuJSpIke6/CCgvZrcubF8ndv7pibl2SU+HScil6f5VHe89CD332AvVfkFeVVg+9xsYt9c//fzInJurUklFy9aksp4L+Se15qrWVrUdB2brlUeSfJ3Tm6w8UdfW0Agd8maZxAQAAAKQvipQAAAAAAMgEchctrYLl6mj5a7XlXbGBClWop2I1n5Wds5skKSr0unZ9NV6Xj2xX9J2bSkxMVPy9KEXeuPSvcf4ubHJw85Qk5SoSkKwtITZGsVERSQU8zl4FkwqUJClPQGWZExMVeunUA0VKYZfPKv5elL4b0T5Ze2J8rDyKBUqSSjXvqZ/G99SN04dVqHw9Fa3eXPlKVkn12e2c3WXn7J7GP6kHFa78zN8HRUspj19FLepRTqe2rlXJJt0ee1wgp3ijfiG9Uf/hxQQAMoaTdym5l6yl3SMaKFdgPeUqXVeelVvI2tFNkhQbdkNnVk3SnaAdig2/KSUmKCE2WvduJf/8/2dhk43r/c9/x4LJ2xLjYhQfHSEr+/uf/7a5CyQVKEmSa4mKkjlRUVdPP1CkFH3tnBJjo3VocvKt2xLj4+RUuLQkqUCDF3X0w96KPH9E7qXryrNCU7n6VE712a2d3GXt9Pif//8U8tVw3b0UpPIjvk3Wnr/+3/MAp0IBsnXz0sFJHRV97VzS1ncA/sacAAAAZDSKlAAAAAAAyAQsLC3V6oNVunp8t/48sFmHv/tCuxaO13MzfpJL3sL6dXo/xYTfVq2+4+XsVUiW1jZaNaSZEuJj/zWO9d8HJpMkyTKFNnNi4mPljIu+K0lqMWapnHLnS3bOwtpW0v2ioRcWHND5Pb/ozwNb9N3wdird8iXV7D02xTHTY7u3f7J1cpVrgeIKu3w2TdcDAGAUk4Wlyr61QmEn9yj06G+6uHGeznwzURVH/SB7T2+dmDtQcZGh8uk6Vna5C8pkbaP941opMT4u+TiW//xRvynVtsf9/I+/d//zP3DIItm6J1/dyMLKRpKUu2wDVZ+2R7cO/6rQo7/r4KROKtDwRZXoMirFMdNru7eQr4br1qGNKj98jexy5X/otS7FK0iSoq5TpAQAAAAYgSIlAAAAAAAyCZPJpHylqipfqaqq1OVNLepRTmf++EHl2r2mq8d3qc5rU1S48v2tXSJuXFJM+K10uW/E9Yu6e+uKHP8qOroWtFcmCwu5FyjxwLXu3r6ytLZV5PVLKhBYM9Ux7V095P9MZ/k/01nHSi3QH1+OTrVI6Um3e/u3uOhIhV85J8cGHdPcB8jKqs7Yr97V8qlP9Xz/fbFBVhy4riHfnpYk9aqWV2ObFX2q95+2+U9N33JRkjS6aZFM/WeFnMdkMsnNt4rcfKuoSJsh2jGkim7s+1HeTfsq7OQe+XafoNxlG0qSYm5dUlzE7XS5771bl3Qv9GpS0VHY6f2SyUIOeYs/cK1jfl9ZWNvq3q1LcvevnuqYNi65la9WR+Wr1VGXNi/S6eXjUi1SetLt3sxms04uGqEb+zao/LBvZO/58O1lJSni/FFJkq2r139cCWRNzAn+G3MCAACMRZESAAAAAACZwLWgfbp46HcVKl9P9m6euha8T9Fht+ReyFeS5Jq/mII3fS1Pn3KKi4rQH1+OkpWtfbrc29LGTr9O66cavccoNipC2z4dpuK1Wz+w1Zsk2Tg4q1y717V97rsymxOVr1RVxd4N15Xju2Xj4Cz/Zzpr96IJ8ixRVrkK+yshLlbndv+c9BwpedLt3rZ/8Z6KVG0iZ69CunvrqvYsniSThaV86rV77DEBpD9nW0v93r+cHGwsk9oKjNqR4rXvNvLWq7UKpHns41fvasQPZ3XocqRyOVjrpap59do/+r9SI79eqJRHzT8/8vgPAGSAsNP7FXp8m3KVrisbl9wKP31AsRG35JjPR5Jkn6eorm7/Rs5Fyyo+OkKnl4+ThY1dutzbwtpWJ+YOVInO7yk+OlInF78rryqtHtjqTZKs7J1UqOkrOrV0lMzmRLn5VFF8dLjCTu6Rpb2z8tXqqDOrJ8u5SBk5FvCTOS5Wtw5ulEN+n1Tv/6TbvYV8NVzXd65R6YHzZWnnpHt3rt/P6uAsSxt7RV87p2s71yh3mYaycnLX3T+P6+TS0XL1qyYn75L/MTqAjPTvOUFcQqIm//qnNp0M1fnQe3Kxs1StYq4a/kxh5XWxeaSxmRMAAJC5UaQEAAAAAEAmYO3grMtHd+jwt58pNipCzl4FVbPPWBWu/Iwkqf6gWdoye4hWDmggJ4/8qtbjXf3xRcorEzwq1/xFVaxmC/0wqrNiIu6oSJXGqvPalFSvr9J9mOxcc2v/1zMVfvW8bB1d5VGijCp2HCTp/rYvOxe8r4jrf8rSxk75S1VT43fmpkvWlNy9eVkbJ72smPBQ2bvmVr5SVfXcjA2yd/XIsHsCeHQmk+TlnPxF44GhFZMdbz51R2+sPa3mJXOnedyImHg9v+iEahdz1cRWxRR0LUpD1p6Wi52VulW6X2zpaGspR1tLWVqYnvxBgHRkZe+ssOCduvjTXCXERMo2dwGV6PyecpdtIEny7zVNwfPf0t73msg2V34V6/COTi9PeWXCR2Wfp4g8KzbXoWkvKP7uHeUu94x8X5yQ6vVFn3tL1i65dWHdHAVfvyArBxc5FwlU4ZYDJEkWljY6s3KCYm7+KQtre7n5VVGp1z5Jl6wpubxpoSTp4ITnkrX7956hfLU7yWRlrdvHturPn75QYmyUbHPll2fl5iry7KAMywQgbf49J4iOS9SRK3c1sG5BlczrqLDoeI368Zx6LgvSj33LpHlc5gQAAGR+FCkBAAAAAJAJ5PL2VatxX6d63rN4GXWY9UuytuK1nk12/Nr6m8mOXfJ4P9BWoEytB9okqXSLl1S6xUsp3rvhkA+THZtMJpVt01dl2/RN8fpKXd5QpS5vpPwgGaDxO188tXsB6Wnx3muavuVP7R1SURb/eFHWc2mQ3B2sNL1NCZ27HaMxG85p/8VIRcUlyMfDXu884606xd1SHPPP0BhVm3lAP71SRqXzOUqSwqLjVXLiHq3sUVI1irpKkoKuRen9n89r14VwOVhbqk5xV41pWkS5HK0z/Ln/6d9FSz8F3VaNIi4qnCvtK8WsPnxTcQmJmta6uGysLOTn5aBjV+/q8x2Xk15IApmVY34flR26NNXzzoUDVWn0j8navCq3THZcf+HlZMf2noUeaHMPqPFAmyQVaPiiCjR8McV7B/SZmezYZDKpUOPeKtS4d4rXF2k9SEVaD0rxXEZI6Xn+yS53AVUYvvoppQGeTE6fE7jYWWn5i8lXOHu/RVG1+PyILt25pwJutmkahzkBAACZH0VKAAAAAAAAgAFalsqtkevPavu5cNUudv9FYWhUnLacuqOvugVIku7GJqiBj7vebugtGyuTvjl4Qz2XBun3/uXT/MLu38Ki49Vx4TF1qZBHo5sWUUx8oj7YeF59V4ZoZY9SKfa5dOee6n108KHj9q9dQAPqFHysTJJ0IzJWv4bc0cy2xR+p376LEapa2EU2VhZJbXVLuOmjbZd1Jzpebvb8CBQAkLkxJ3hQeEy8TCbJxc7yvy/+C3MCAAAyPz6NAQAAAAAAAAO42Vupvo+bvj18I+mF5A/HbyuXg5VqFnGRJJXK66hSeR2T+rzV0Fsbgm7r5+Db6lk132Pdd/7uqyqd11HDnvFOapvWurgqT9+v0zejVdzD/oE+eZxt9PMrD99u5Ulf/K08eENOthZqFpD2rd4k6UZknAr96+Ws51+rP9yIjOWFJAAg02NOkFxMXKLGb7ygNqU95GyX9rGYEwAAkPnxaQwAAAAAQA5WpdvbqtLtbaNjADlW2zKeeuu70xrfMlG2VhZac/iGni3tkbTVy917CZq25U/9GnJH1yNjFZ9oVkxcoi6FxT72PY9fvas/zoXL54NdD5w7HxqT4gtJK0uTiuZ+sD09LT9wXW0DPWVnbfHfFwN4IkXbDlXRtkONjgHgH5gT3BeXkKhXVobILGlCy6IZdh8AAGAMipQAAAAAAAAAgzTydZfZLP0aEqqyBZy060KERjctknR+7M/ntfX0HY1sUlhFctnJzspCL38dotiExBTHszCZHmiLTzQnO46KTVQjX3cNb+T9wLV5nG1SHDejt3bZdT5cp2/G6JMOXo/c19PJWjfvxiVru/HXsadTys8DAEBmw5zgrwKlr0N08c49fd2j5COtoiQxJwAAICugSAkAAAAAgCxqUY/yKtOmr8q2ecXoKKkK2rhMm2b0lySVad1Xtfp+kKPuD/wXO2sLNQvIpTWHb+rc7RgVz22vwPxOSef3XghXh3JeSVug3b2XoIt37qU6Xi7H+z/uuxYRq9L57m8Jc+zq3WTXlM7nqPUnbqmQm52sLB98gZmSjN7aZdn+6yqTP/k2NmlVsaCzJm+6oLiERFlb3l+F6ffTYSruYce2Lsi2drxRRQUb91GhJn2MjpKqK1tXKOiLwZKkgo17y6frWIMTPejE3EG6uu1rSVLpAV/Ks2IzgxMhJ8vpc4L/FyidvR2jlT1KKZeD9SOPwZwAAIDMj09kAAAAAACQoWwcnPX85ztlZeeQ1HZ6+zodW79AN04d0r2IUHWcs1kexQMf+x4x4be14vW6unvrinp9fVq2Tq6SpBJ12si7YgP9+EGPJ30MIMO0LeOhHkuDFHwjSu3KeCY7VzS3vX48cUuN/NxlMklTNv2pfy2CkIy9taUqFHTSR9suydvdVjfvxmnyr38mu6ZHlbxauv+aXvsmRK/VKiA3eyudux2jtUduamrr4rK0ePAlZUZu7RIRE691x27pvSaFH6t/2zIemvHbRb2x9rRer1VAQdej9OXOK8lWnwBgDEt7Z1WduFWWtn/PAW7sXa9Lm75SxLkjir8bqkpjf5Zz4dKPNG7khWM6v+5DhZ3crbiIUNl5FFT+Bt1VqHHvFK+/E7JbByc8J8eCfqo87pekdp+uY1Wsw3D9MbDcYz0fkN5y6pwgLiFRL68I0ZErd7Wwq78SEs26HnF/Gzs3eyvZWKVtK1jmBAAAZH5s8A4AAAAAADKWySSHXHlk4+Cc1BQfE6V8paqqes/30uUWm2cOVO6ipR5ot7K1l0OuPLK0evTfxAaellpFXeVmb6XTN2PUNtAj2blRTQrL1d5Krb88qh5Lg1SvuJsC8z18taHpbYorPtGspp8d0agfz+mthoWSnc/rYqNve5VWoll6/qvjavjxIY368Zxc7KyUwrvIDLf26C2ZJbX517P/X/v5xzRozalU+7vYWWnpCwH6M/Semn12WGN/Oq/BdQuqW6U8GZQYQFqZTCbZunnJyv7v1WAS7kXJ1beKincc/tjjRpw7LBsXDwX0/VBVxm9W4VYDdWbleF3cOO+Ba+PuhunE5wPlVrLWA+esHFxk6/bo20wCGSWnzgmuhsfq5+BQXQmPVeNPDqv81H1J/+39MyLpOuYEAABkfaykBAAAAADAU3bsx4Xas2SyXvzqiEwWf//+0Pqx3WTnnEsNBs9W2JWz2j53pK4F7VNcTJTcC/moWo+RKlS+bopjhl+7oMU9KyRbkeheZJi+7FhcrSd+qwJl7r+Yu3XuhHZ8OVqXj+2UtZ2DCpWvp5ovvy9719wZ/tz/5NewY1LuJ3X0h3m6dzdMlboM1YW9v/x3ByCTsbAwaf/QSimeK+Rup5U9khfg9aiaN9nxrsEVkh37eDrou97JVya7NKZ6suNiue31RWe/x42crrpVyvPQl4cXQmPUoZxnquclqWReR63p9WgrsQBGuLx5sc5+O001ZuxLNgc4MrOHrJzcFdB7hqKvndOpZaMVdnq/Eu9FySG/j4p1GKZcpeqkOGb0jT+1c2jVZCsSxd0N07bXAlTunW/kHlBDkhR5MUinl49TWMguWdg6KFfpuirx/GjZOD/dOUDemu2Tcj+ufHW6JDu29yqs8NN7dWPfjyrY6KVk50IWvq081dvKZGGhm/s3PPY9gachp84JCrnbPZArJcwJAADI+ihSAgAAAADgKSteq7W2fjJMlw5vU8Fy9184xkSE6sLeTWo5drkkKS76rgpXekZVu4+QpbWNgn/9WuvHdNXzn++Us1fBx7rvvcgwfTesrQKadFPNl99XfGy0dswbq58n9FLrid+m2Cfi+kUte6XmQ8et2GmQKnYa/FiZntTtC8Hau3Sqnpvxs8KvnjMkA4C0CY9JkM8Hu9Sjcl6NaJy2rd2Cr0fJxc5KHco+/IXkf5n9+0XN2XpJ0XGJTzQO8KQ8q7RUyOJ3FXpiu3KVqi1JiosM1a0jW1RmyCJJUsK9u8pVtqGKtn9HFlY2urr9Gx2Z0UNVJ/0uu9yPNweIuxumgxM7KF/d51Xi+dFKjIvR6RUf6NhHr6j8OytT7BNz66J2D6v30HG9Ww1QkVYDHitTeouPipC1o1uytiu/L1f09QsK6Puhzn8305BcAB7EnAAAgJyLIiUAAAAAAJ4yO2c3Fa7UUCFbViUVKZ3e9p3sXXMlrXjkUay0PIr9/RvAVbsP09kdP+jcrg0KbNX7se575Psv5FE8UNV6vJvU1mDwbH3VvYzuXDwlt4IlHujjmDuvOn24+aHj2jq7P1aeJ5UQd08bJ72s6r1Gy9mrIEVKQCbWomRuVfG+v+Wjq13afyTp5+WgX14r+8T3f6FSHrUqdX+1mNyObP8I41g7uil3mfq6vnNNUpHSjT0/yNopl9wD7hcFO3mXkpP336ulFHvuLd3c96Nu7v/5gVWC0urSL/PlVLi0incYltTm33u6dgyupKirp+WQt/gDfWzc8qrSuI3/+TyZQdjJPbq++zuVGfxVUlvU1TM6vXK8KoxYIwtLXoUAmQVzAgAAcjZm5gAAAAAAGMCnfnttmT1YdV+fLEtrW53c/I1K1GmbtPVLXHSkdi+ZrPN7Nirq9jUlJiQoITZaEdcvPvY9b509pkuHt+nzdg/+tnLYlXMpFilZWFrJNX+xx75nRto5f5zcC/nIr0FHo6MA+A9OtpZysrU37P7uDtZyd+BFJDKHPNXbKXj+m/LtPkEW1ra6umO18lRrnTQHiI+5q3NrpurWoV8VG3Zd5oR4JcTGKOb2pce+Z+Sfx3XnxB/6/eUHP+ujr51PsUjJwtJKDnmKPvY9n5bIi0E6MqunirQeolyB9SRJ5sQEHf/0dRVtOzTFZwNgHOYEAADkbBQpAQAAAABggCJVm0hms87t3igv3/K6fGynar78ftL57V+M0sUDv6lG7zFyzVdUVrZ22jD+JSXGx6U4nsl0/8WmWeaktsSE5NfGRd9VkSpNVP2l9x7o75ArT4rjZubt3i4e3qbb547rk5b/z37/2ed19lXFzoNVpds7Tz0TAAD/JXe5RjKbzbp16Fc5Fy2rsJBd8nl+dNL508vH6vbR31Wi83uyz1NEFjZ2OvZhH5lTmwNYmP766u85gDkhPtk1CTF35VG+kYp1HPFAf1u3lOcAWWG7t7uXQnRwUkflr9dNRVoPSmqPj45UxNlDijx/VCcX3X9mszlRMpu1pWchlX1zmdxL1jIoNQAAAJBzUaQEAAAAAIABrGzsVKxGS53c/I3CL5+RW8ES8izx9/YFV4/vlv8znVWsRgtJ91dWirh2QQpMuWDI3vX+lgVRt69Jfy0YcPP00WTXeJQoozPbv5dzHu80b3uSmbd7azpivuLvxSQdXw85oM0zB6jtlHVyyVfEkEwAAPwXSxs7eVZspms7Vivq2lk55C0u5yJlks6HndyjfLU7yrNSM0n3V1aKuZn6SorWzvfnALF3rkt/LZYYeSH5HMC5SKBu7P1Bdh6F0jwHyOzbvd29GKwDkzoob60OKtY+eWGylb2zKn+wKVnbpV8X6s6JbSrVb67sPb2fZlQAAAAAf6FICQAAAAAAg/jUb6/1o5/X7QtB8q3fIdk51wLFdOaPH+6vuGQyafeiCTInJqY6lpWtvfL4V9L+r2fJOU9hRYfd0K6vxie7JrDlSzqxYZE2TnpZ5dr3k52zu8Iun9Wp39ao3sCZsrC0fGDcjNruLSYiVJHXL+ru7auSpNCLpyRJDu5eqa7q9G+u+ZJvQRMTfkuS5F7IV7ZOrumYFgCA9JWnRjsdmfGi7l4KVp7qzyU7Z5+nqG7sXa/c5RrJZDLpzKrJD50DWNrYy6V4RZ1f96HsPL0VG35TZ1ZNTnZNgYY9dHnLEh3/5DV5N39N1o5uirp+Ttd3fiv/XtNkskh5DpAR273FRYYq5tYlxd65JkmKunpakmTj6iVbN680jRF5MUgHJ3ZQrsB6KtSkr+7duS5JMllYysYlt0wWFnIq6J+sj41LbllY2z7QDgAAAODpoUgJAAAAAACDFCxbW7bObrpz8ZR86iV/QVmzzzhtnjFAq4c2l51LLpVvP0CxUREPHa/+oNnaPGugvhnYUG4FSqj6S6P0/bvtk8475s6ntlN/0I75Y7Xu3Q5KiIuVk1dBeVdsIJOFRYY8Y2rO7dygTTP6Jx1vnNRHklTp+TdVpdvbkqRfp/dTxLULajPpu6eaDQCAjOYeUEtWjm6KunJaeaq3TXauRJfRCvpyiPa//6ysnXLJu8XrSoiJfOh4/r2nK+jLIdo7qokc8hZX8U7v6tCULknnbd3zqsK7a3X66w90aEoXJcbfk13ugsoVWF8yPd05wM0DPyvoi7+3iT3+8auSpCJthqho26GSpBNzBynm5p8qP2xVimPc2LNOcRG3dO2PVbr2x9/X2HkUVPVpuzMwPQAAAIAnQZESAAAAAAAGMVlYqMfiYymec8njrdYTv03WFtiqV7LjFxYcSHacy9tXz037MVnba+tvJjt2K1Bczd5d+JiJ049/oy7yb9TloddEXD2v/GVqpXnMAmVqPfC8QGZx8ma00RHwH/h/hKfJZGGhmrMOpHjO3rOQyr+zMllbwWd6Jjv+dyGOY34fVRz5fbK2+gsvJzt2yFtMgQO+fNzI6SZf7U7KV7vTQ6+JvnFB7gE1Uj1ftO3QpIKmtHqcPkBG4PMm8+P/EQAAGYciJQAAAAAAkKFi74br83aFFdjyJVV/aVSa+ty7G66wK+fUYsyyJ7p3yOaV2jJnqBJio+VRLPCJxgIeh4eHhxzs7dR/1SmjoyANHOzt5OHhYXQMINuIjwrX7y+XUIGGPVS807tp7hNz/bwKDVmUwemk4AVvJ1uJCchIzAmyFuYEAABkDIqUAAAAAABAhilWs6XylaoqSbJxdE1zP1tHF7246MgT379I1abq5Ffxke8PpBdvb2+dCArWzZus8pUVeHh4yNvb2+gYQLbgWamFXH2rSJKsHFzS3M/KwUU1Zu7LqFjJFG33pgo1e0WSZOuW56ncEzkXc4KshTkBAAAZgyIlAAAAAACQYWwcnGXj4Jxj7w9I919K8pILQE5jZe8kK3sno2M8lI2Lh2xcWCkFTw9zAgAAkNNZGB0AAAAAAAAAAAAAAAAAQPZGkRIAAAAAAAAAAAAAAACADEWREgAAAAAAAAAAAAAAAIAMRZESAAAAAAAAAAAAAAAAgAxlZXQAAAAAAACym9A/Q4yOgCfE/0MAwJO6e/mU0RGQDvj/CAAAAKQfipQAAAAAAEgnHh4esndw0C9TXjU6CtKBvYODPDw8jI4BAMhiPDw8ZGfvoBOf9TM6CtKJnT1zAgAAACA9mMxms9noEAAAAAAAZBcXLlzQzZs3Dc1w5coVtWvXTl26dNGAAQMMzZJWcXFx6tixo7y8vPTpp5/KZDIZHUkeHh7y9vY2OgYAIAvKDPMBSfr66681adIkffXVVypVqpTRcdLk6NGjevHFFzVs2DC1b9/e6DiSmBMAAAAA6YUiJQAAAAAAspmOHTtq27ZtCg4OlrOzs9Fx0mzDhg1q1qyZVq5cmWleSgIAkFXdunVLPj4+ateunb744guj4zySXr166dtvv1VISIhy585tdBwAAAAA6YQiJQAAAAAAspFNmzapYcOGWrRokbp162Z0nEf27LPP6tChQzpx4oQcHByMjgMAQJb12muvacmSJTp58qS8vLyMjvNIrl27Jl9fX3Xr1k0fffSR0XEAAAAApBOKlAAAAAAAyCbi4+NVrlw5ubq6atu2bZliy7RHderUKZUqVUrvvPOOxowZY3QcAACypIMHD6pixYqaNm2aBg0aZHScxzJjxgwNHTpU+/fvV9myZY2OAwAAACAdUKQEAAAAAEA2MWfOHA0cOFB79+5VhQoVjI7z2IYPH67p06frxIkTKlq0qNFxAADIUsxms+rUqaPbt2/r4MGDsra2NjrSY4mLi1PZsmXl4eGh3377LUsWXwMAAABIjiIlAAAAAACygRs3bsjX11cdO3bUZ599ZnScJxIZGSl/f39VrVpVq1atMjoOAABZyrJly/T8889r48aNeuaZZ4yO80Q2btyoxo0ba9myZercubPRcQAAAAA8IYqUAAAAAADIBl5++WWtXLlSISEh8vT0NDrOE8tOL1gBAHhasmOhb7t27bR7924FBwfL0dHR6DgAAAAAnoCF0QEAAAAAAMCT2bdvn7744guNGzcuWxQoSVLnzp1Vu3ZtDRgwQHFxcUbHAQAgSxg/frxu3bqladOmGR0l3UybNk23bt3ShAkTjI4CAAAA4AmxkhIAAAAAAFlYYmKiatWqpYiICB04cEBWVlZGR0o3Bw8eVMWKFTVt2jQNGjTI6DgAAGRqp06dUqlSpTRs2DCNHj3a6DjpatSoUZo4caKOHz+u4sWLGx0HAAAAwGOiSAkAAAAAgCxs0aJF6t69uzZv3qx69eoZHSfdvfbaa1qyZIlOnjwpLy8vo+MAAJBpPfvsszp06JBOnDghBwcHo+Okq6ioKPn7+6t8+fJau3at0XEAAAAAPCaKlAAAAAAAyKIiIiLk6+urOnXqaMWKFUbHyRC3bt2Sr6+v2rRpoy+//NLoOAAAZEo//vijmjdvrpUrV6p9+/ZGx8kQK1euVMeOHfXjjz+qadOmRscBAAAA8BgoUgIAAAAAIIt666239OGHHyooKEje3t5Gx8kwn3zyiV577TXt2rVLVapUMToOAACZSmxsrEqXLq1ChQrpl19+kclkMjpShjCbzWrYsKEuXbqkI0eOyMbGxuhIAAAAAB6RhdEBAAAAAADAowsODtbMmTM1bNiwbF2gJEkvv/yyypYtqwEDBigxMdHoOAAAZCqzZs3SmTNnNGvWrGxboCRJJpNJs2bN0unTpzV79myj4wAAAAB4DKykBAAAAABAFmM2m9W8eXMFBwfr+PHjsrOzMzpShvv9999Vt25dzZ8/Xz169DA6DgAAmcLly5fl5+enl156SbNmzTI6zlMxYMAAzZ8/XyEhIcqXL5/RcQAAAAA8AoqUAAAAAADIYtatW6dWrVppzZo1atOmjdFxnpouXbpo8+bNCg4Olqurq9FxAAAwXPfu3fXjjz8qJCRE7u7uRsd5KkJDQ+Xr66vmzZtr4cKFRscBAAAA8AgoUgIAAAAAIAuJiYlR6dKlVaxYMf3000/ZeluXf7t48aL8/Pz06quvaurUqUbHAQDAUH/88Ydq1qypuXPnqnfv3kbHearmzp2rl19+WX/88YeqV69udBwAAAAAaUSREgAAAAAAWciECRP03nvv6fDhwwoICDA6zlM3fvx4jRo1Ksc+PwAAkpSQkKAqVarIZDJp165dsrS0NDrSU/XP59+9e7csLCyMjgQAAAAgDZi5AwAAAACQRVy8eFHvv/++BgwYkGMLdIYMGSJvb28NGjRI/N4VACCnmjdvnvbv3685c+bkuAIlSbK0tNScOXO0b98+zZ8/3+g4AAAAANKIlZQAAAAAAMginn/+eW3atEnBwcFydXU1Oo5hvv/+ez377LP69ttv1bp1a6PjAADwVIWGhsrX11fNmzfXwoULjY5jqO7du2vDhg0KCQmRm5ub0XEAAAAA/AeKlAAAAAAAyAK2bt2qOnXqaP78+erRo4fRcQxlNpvVvHlzBQcH69ixY7K3tzc6EgAAT82AAQM0f/58hYSEKF++fEbHMdTly5fl5+enXr16aebMmUbHAQAAAPAfKFICAAAAACCTS0hIUMWKFWVra6sdO3bIwoLd24ODgxUYGKj33ntP7777rtFxAAB4Ko4cOaLy5ctrwoQJevPNN42OkylMnjxZw4cP16FDh1SqVCmj4wAAAAB4CIqUAAAAAADI5D755BO99tpr2rVrl6pUqWJ0nEzjrbfe0ocffqjg4GAVKlTI6DgAAGQos9mshg0b6tKlSzpy5IhsbGyMjpQpxMbGKjAwUAULFtQvv/wik8lkdCQAAAAAqaBICQAAAACATOzWrVvy9fVV69atNW/ePKPjZCrh4eHy8/NT3bp1tXz5cqPjAACQob755ht16NBBP/74o5o2bWp0nEzlxx9/VPPmzfXNN9/oueeeMzoOAAAAgFRQpAQAAAAAQCb2+uuva/HixQoJCVGePHmMjpPpfPXVV3rxxRe1efNm1atXz+g4AABkiKioKAUEBKhs2bL67rvvjI6TKbVq1UqHDx/WiRMn5ODgYHQcAAAAACmwMDoAAAAAAABI2aFDh/Tpp59q9OjRFCilolu3bqpWrZoGDBig+Ph4o+MAAJAhJk2apKtXr2r69OlGR8m0ZsyYoatXr2ry5MlGRwEAAACQClZSAgAAAAAgEzKbzapbt65u3rypQ4cOydra2uhImdbevXtVpUoVzZkzR6+//rrRcQAASFdnz55VQECA3njjDX3wwQdGx8nUhg8frhkzZujEiRMqUqSI0XEAAAAA/AtFSgAAAAAAZELLly9Xly5d9PPPP6tRo0ZGx8n0+vTpo1WrVikkJEQeHh5GxwEAIN20a9dOu3fvVlBQkJycnIyOk6lFRkbK399f1apV0zfffGN0HAAAAAD/QpESAAAAAACZzN27d+Xn56cqVapo9erVRsfJEq5fvy5fX1917txZn376qdFxAABIFxs3blTjxo21dOlSdenSxeg4WcKyZcv0/PPP65dfflHDhg2NjgMAAADgHyhSAgAAAAAgkxkxYoSmT5+u48ePq2jRokbHyTJmz56tQYMGad++fSpfvrzRcQAAeCJxcXEqW7asPDw89Ntvv8lkMhkdKUswm82qU6eObt++rYMHD7JlLgAAAJCJWBgdAAAAAAAA/O306dOaOnWq3nrrLQqUHtGrr76qkiVLqn///uJ3sgAAWd2HH36o4OBgzZ49mwKlR2AymTRnzhwFBQXp448/NjoOAAAAgH9gJSUAAAAAADKR1q1b68CBAwoKCpKDg4PRcbKcTZs2qWHDhlq8eLG6du1qdBwAAB7LtWvX5Ovrq65du1Jo85heffVVLVu2TCEhIfLy8jI6DgAAAABRpAQAAAAAQKaxYcMGNWvWTF9//bU6dOhgdJwsq0OHDtq+fbuCg4Pl7OxsdBwAAB5Zr1699O233yokJES5c+c2Ok6WdOvWLfn4+Khdu3b64osvjI4DAAAAQBQpAQAAAACQKcTGxiowMFAFChTQr7/+yrYuT+D8+fPy9/fXwIEDNXHiRKPjAADwSHbv3q2qVavq448/1quvvmp0nCzt448/Vr9+/bRr1y5VrlzZ6DgAAABAjkeREgAAAAAAmcDUqVP1zjvv6MCBAwoMDDQ6TpY3ZswYffDBBzp27Jh8fHyMjgMAQJokJiaqevXqunfvnvbt2ydLS0ujI2Vp8fHxqlixohwcHLR9+3ZZWFgYHQkAAADI0ShSAgAAAADAYFeuXJGvr6969uyp2bNnGx0nW4iOjlZAQIBKly6tdevWGR0HAIA0WbBggXr27KnffvtNderUMTpOtvD777+rbt26WrBggV588UWj4wAAAAA5GkVKAAAAAAAY7MUXX9T69esVEhIid3d3o+NkG6tXr9Zzzz2ndevWqUWLFkbHAQDgocLCwuTr66uGDRtq6dKlRsfJVrp06aLNmzcrJCRELi4uRscBAAAAciyKlAAAAAAAMNCOHTtUo0YNff755+rTp4/RcbIVs9msRo0a6fz58zp69KhsbW2NjgQAQKreeOMNffrppwoODlbBggWNjpOtXLx4UX5+fnrttdc0ZcoUo+MAAAAAORZFSgAAAAAAGCQxMVFVqlSR2WzW7t27ZWlpaXSkbOfYsWMqW7asPvjgA7399ttGxwEAIEUnTpxQmTJlNGbMGA0fPtzoONnS+PHjNWrUKB05ckT+/v5GxwEAAAByJIqUAAAAAAAwyBdffKE+ffpo+/btqlGjhtFxsq3Bgwdr7ty5CgkJUf78+Y2OAwBAMmazWU2bNtXp06d19OhR2dnZGR0pW4qJiVGpUqVUokQJbdiwQSaTyehIAAAAQI5DkRIAAAAAAAa4c+eOfHx81KxZM3311VdGx8nW7ty5I19fXzVu3FiLFy82Og4AAMl8++23atu2rb777ju1atXK6DjZ2nfffafWrVtr7dq1evbZZ42OAwAAAOQ4FCkBAAAAAGCAQYMG6csvv1RwcDCr+zwFX375pXr37q1t27apZs2aRscBAECSFB0drZIlS8rf31/r169ndZ8MZjab1axZM508eVLHjh1j1SoAAADgKbMwOgAAAAAAADnN0aNH9eGHH2rkyJEUKD0lPXv2VKVKldS/f38lJCQYHQcAAEnStGnTdOnSJc2cOZMCpafAZDJp1qxZunDhgqZNm2Z0HAAAACDHYSUlAAAAAACeIrPZrGeeeUYXL17UkSNHZGNjY3SkHGPnzp2qXr26Pv30U/Xt29foOACAHO7ChQvy9/dXv379NHnyZKPj5ChvvvmmPv74YwUFBalQoUJGxwEAAAByDIqUAAAAAAB4ilatWqX27dtr/fr1atasmdFxcpwePXpo3bp1CgkJUa5cuYyOAwDIwTp16qTff/9dwcHBcnFxMTpOjhIeHi4/Pz/Vq1dPy5YtMzoOAAAAkGNQpAQAAAAAwFMSFRWlgIAAlSlTRt9//73RcXKkq1evytfXVy+++KLmzJljdBwAQA61ZcsW1a9fXwsXLlT37t2NjpMjffXVV3rxxRe1ZcsW1a1b1+g4AAAAQI5AkRIAAAAAAE/J6NGjNWHCBB07dkwlSpQwOk6ONW3aNL311ls6cOCAypQpY3QcAEAOEx8frwoVKsjJyUnbtm2ThYWF0ZFypMTERNWsWVNRUVHat2+frKysjI4EAAAAZHv86wcAAAAAgKfg3LlzmjRpkt544w0KlAzWv39/+fj4aMCAAeJ3twAAT9unn36qo0ePavbs2RQoGcjCwkJz5szRkSNH9PnnnxsdBwAAAMgRWEkJAAAAAICnoH379tq5c6eCgoLk5ORkdJwc76efflLTpk21YsUKdezY0eg4AIAc4ubNm/Lx8VH79u01d+5co+NAUp8+fbRq1SqFhITIw8PD6DgAAABAtkaREgAAAAAAGezXX3/VM888o6VLl6pLly5Gx8Ff2rRpo3379ikoKEiOjo5GxwEA5AB9+/bVihUrdPLkSXl6ehodB5KuX78uX19fdenSRZ988onRcQAAAIBsjSIlAAAAAAAyUFxcnMqVK6dcuXLp999/l8lkMjoS/nLmzBmVLFlSb775psaNG2d0HABANrd//35VqlRJM2fO1IABA4yOg3+YNWuWBg8erH379ql8+fJGxwEAAACyLYqUAAAAAADIQLNmzdKQIUO0b98+lStXzug4+Jd3331XU6dO1fHjx1WsWDGj4wAAsimz2axatWopLCxMBw4ckLW1tdGR8A9xcXEqX7683N3dKSoHAAAAMhBFSgAAAAAAZBC2D8n87t69K39/f1WqVElr1qwxOg4AIJtasmSJunXrpl9//VUNGjQwOg5S8P/teZcsWaLnn3/e6DgAAABAtkSREgAAAAAAGaR3795avXq1Tp48qdy5cxsdB6lYsWKFOnfurJ9++kmNGzc2Og4AIJuJiIiQn5+fatasqZUrVxodBw/Rvn177dixQ8HBwXJycjI6DgAAAJDtWBgdAAAAAACA7GjPnj2aN2+e3n//fQqUMrmOHTuqTp06GjBggGJjY42OAwDIZj744AOFhoZq6tSpRkfBf5g2bZpu376t8ePHGx0FAAAAyJZYSQkAAAAAgHSWmJiomjVrKioqSvv27ZOVlZXRkfAfDh06pAoVKmjKlCkaMmSI0XEAANnEyZMnVapUKY0YMUKjRo0yOg7SYMyYMRo/fryOHTumEiVKGB0HAAAAyFYoUgIAAAAAIJ0tXLhQPXr00G+//aY6deoYHQdp1K9fP3311VcKCQlR3rx5jY4DAMgGWrRooWPHjunEiROyt7c3Og7SIDo6WgEBAQoMDNT3339vdBwAAAAgW6FICQAAAACAdBQeHi5fX1/Vr19fy5YtMzoOHsHt27fl4+OjVq1aacGCBUbHAQBkcT/88INatmypVatWqV27dkbHwSNYtWqV2rdvrx9++EHNmzc3Og4AAACQbVCkBAAAAABAOnrzzTf18ccfKygoSIUKFTI6Dh7Rp59+qldffVU7d+5U1apVjY4DAMii7t27p9KlS6tw4cLauHGjTCaT0ZHwCMxmsxo1aqQLFy7oyJEjsrW1NToSAAAAkC1YGB0AAAAAAIDsIigoSDNnztSIESMoUMqi+vTpo3Llyql///5KTEw0Og4AIIuaMWOGzp49q9mzZ1OglAWZTCbNmjVLZ86c0axZs4yOAwAAAGQbrKQEAAAAAEA6MJvNatq0qU6dOqVjx47Jzs7O6Eh4TNu2bVPt2rX15Zdf6qWXXjI6DgAgi7l06ZL8/PzUp08fzZgxw+g4eAKDBg3Sl19+qeDgYOXPn9/oOAAAAECWR5ESAAAAAADp4LvvvlPr1q21du1aPfvss0bHwRPq2rWrNm7cqJCQELm5uRkdBwCQhXTr1k0///wznyHZwJ07d+Tr66smTZpo0aJFRscBAAAAsjyKlAAAAAAAeEIxMTEqVaqUfH19tX79erZ1yQZYBQMA8Di2b9+uWrVqsRpfNvLll1+qd+/e2rZtm2rWrGl0HAAAACBLo0gJAAAAAIAn9MEHH2j06NE6evSo/Pz8jI6DdDJx4kS9++67Onz4sEqWLGl0HABAJpeQkKBKlSrJyspKu3btkoWFhdGRkA4SExNVtWpVJSYmavfu3bK0tDQ6EgAAAJBl8a8kAAAAAACewJ9//qnx48dr0KBBFChlM4MHD1aRIkU0YMAA8TteAID/8sUXX+jgwYOaM2cOBUrZiIWFhebMmaP9+/dr3rx5RscBAAAAsjRWUgIAAAAA4Al06dJFW7ZsUXBwsFxcXIyOg3S2bt06tWrVSqtXr1bbtm2NjgMAyKRu374tX19ftWzZUgsWLDA6DjJAjx499MMPPygkJETu7u5GxwEAAACyJIqUAAAAAAB4TL/99pvq1aunhQsXqnv37kbHQQZp0aKFjh8/ruPHj8ve3t7oOACATKhfv3766quvFBISorx58xodBxngypUr8vPzU48ePTR79myj4wAAAABZEkVKAAAAAAA8hvj4eFWsWFEODg7avn0727pkYyEhISpdurTeffddvffee0bHAQBkMocPH1b58uU1efJkvfHGG0bHQQaaOnWq3nnnHR04cECBgYFGxwEAAACyHIqUAAAAAAB4DB9//LH69eun3bt3q1KlSkbHQQZ7++23NXv2bAUFBalw4cJGxwEAZBJms1n169fX1atXdfjwYdnY2BgdCRkoNjZWZcqUUb58+bRp0yaZTCajIwEAAABZCkVKAAAAAAA8ops3b8rX11fPPfec5s6da3QcPAURERHy8/NTzZo1tXLlSqPjAAAyiRUrVqhz58766aef1LhxY6Pj4CnYsGGDmjVrpq+//lodOnQwOg4AAACQpVCkBAAAAADAI3r11Ve1bNkyhYSEyMvLy+g4eEoWL16sF154Qb/++qsaNGhgdBwAgMHu3r0rf39/VaxYUd9++63RcfAUtW7dWgcOHFBQUJAcHByMjgMAAABkGRZGBwAAAAAAICs5cOCAPvvsM40ZM4YCpRyma9euqlGjhgYMGKD4+Hij4wAADDZx4kTduHFD06dPNzoKnrLp06fr+vXrmjhxotFRAAAAgCyFlZQAAAAAAEgjs9msOnXqKDQ0VAcOHJC1tbXRkfCU7d+/X5UqVdLMmTM1YMAAo+MAAAxy5swZlSxZUm+++abGjRtndBwY4N1339XUqVN14sQJFS1a1Og4AAAAQJZAkRIAAAAAAGm0dOlSde3aVb/88osaNmxodBwYpG/fvlqxYoVOnjwpT09Po+MAAAzQpk0b7du3T0FBQXJ0dDQ6Dgxw9+5d+fn5qUqVKlq9erXRcQAAAIAsgSIlAAAAAADSIDIyUn5+fqpevbq++eYbo+PAQDdu3JCvr686dOigzz//3Og4AICn7KefflLTpk21fPlyderUyeg4MNDy5cvVpUsX/fzzz2rUqJHRcQAAAIBMjyIlAAAAAADSYPjw4ZoxY4ZOnDihIkWKGB0HBpszZ44GDhyoPXv2qGLFikbHAQA8JbGxsSpTpozy5s2rzZs3y2QyGR0JBjKbzapXr56uX7+uw4cPsxUwAAAA8B8sjA4AAAAAAEBmd+rUKU2bNk3vvPMOBUqQJL366qsqVaqU+vfvr8TERKPjAACekjlz5ujkyZOaPXs2BUqQyWTS7NmzFRISog8//NDoOAAAAECmx0pKAAAAAAD8h1atWunIkSM6ceKE7O3tjY6DTGLz5s1q0KCBvvrqK73wwgtGxwEAZLCrV6/K19dX3bt3pyAFybz++utavHixQkJClCdPHqPjAAAAAJkWRUoAAAAAADzE+vXr1aJFC33zzTd67rnnjI6DTKZjx47aunWrQkJC5OzsbHQcAEAG6tmzp77//nuFhIQoV65cRsdBJnL79m35+PiodevWmjdvntFxAAAAgEyLIiUAAAAAAFJx7949BQYGytvbWxs3bmRbFzzgwoUL8vf3V79+/TR58mSj4wAAMsiuXbtUrVo1ffrpp+rbt6/RcZAJffrpp3r11Ve1a9cuValSxeg4AAAAQKZEkRIAAAAAAKmYPHmyhg8frkOHDqlUqVJGx0EmNW7cOI0bN05HjhyRn5+f0XEAAOksMTFR1apVU1xcnPbu3StLS0ujIyETSkhIUMWKFWVra6sdO3bIwsLC6EgAAABApkOREgAAAAAAKbh8+bL8/PzUq1cvzZw50+g4yMSio6NVsmRJ+fv7a/369ay4BQDZzLx589SrVy9t3bpVtWrVMjoOMrGtW7eqTp06mjdvnnr27Gl0HAAAACDToUgJAAAAAIAUvPDCC/rpp58UEhIiNzc3o+Mgk/v222/Vtm1bfffdd2rVqpXRcQAA6eTOnTvy9fVV48aNtXjxYqPjIAvo2rWrfvnlF4WEhMjV1dXoOAAAAECmQpESAAAAAAD/sn37dtWqVUtffPGFevXqZXQcZAFms1lNmjTR6dOndezYMdnZ2RkdCQCQDgYPHqy5c+cqODhYBQoUMDoOsoBLly7Jz89Pffv21bRp04yOAwAAAGQqFCkBAAAAAPAPCQkJqlKliiwsLLRr1y5ZWFgYHQlZxIkTJ1SmTBmNHTtWw4YNMzoOAOAJHT9+XGXKlNH777+vd955x+g4yEImTJig9957T4cPH1ZAQIDRcQAAAIBMgyIlAAAAAAD+Ye7cuXr55Ze1Y8cOVatWzeg4yGKGDBmizz77TMHBwSpYsKDRcQAAj8lsNqtRo0Y6f/68jh49KltbW6MjIQu5d++eSpUqpaJFi+rnn3+WyWQyOhIAAACQKVCkBAAAAADAX0JDQ+Xr66sWLVpowYIFRsdBFhQWFiZfX181bNhQS5cuNToOAOAxrVmzRu3atdO6devUokULo+MgC1q3bp1atWqlNWvWqE2bNkbHAQAAADIFipQAAAAAAPjLgAEDtGDBAgUHBytfvnxGx0EWNX/+fL300kv6/fffVbt2baPjAAAeUXR0tEqWLKmSJUvqhx9+MDoOsiiz2awWLVroxIkTOn78uOzt7Y2OBAAAABjOwugAAAAAAABkBkeOHNHHH3+s9957jwIlPJEXX3xRVapUUf/+/ZWQkGB0HADAI5oyZYouXbqkGTNmGB0FWZjJZNLMmTN16dIlTZ061eg4AAAAQKbASkoAAAAAgBzPbDarQYMGunLlig4fPiwbGxujIyGL2717t6pWraqPP/5Yr776qtFxAABpdP78efn7+2vgwIGaOHGi0XGQDbz99tuaM2eOgoKC5O3tbXQcAAAAwFAUKQEAAAAAcryVK1eqY8eO+vHHH9W0aVOj4yCbeOmll7R27VqFhIQod+7cRscBAKRBhw4dtH37dgUHB8vZ2dnoOMgGIiIi5Ovrq9q1a+vrr782Og4AAABgKIqUAAAAAAA5WlRUlPz9/VW+fHmtXbvW6DjIRq5duyZfX19169ZNH330kdFxAAD/YdOmTWrYsKEWLVqkbt26GR0H2ciiRYvUvXt3bdq0SfXr1zc6DgAAAGAYipQAAAAAADnae++9p8mTJ+v48eMqVqyY0XGQzcyYMUNDhw7V/v37VbZsWaPjAABSER8fr3LlysnV1VXbtm2TyWQyOhKyEbPZrJo1ayoiIkIHDhyQlZWV0ZEAAAAAQ1gYHQAAAAAAAKOcPXtWkydP1tChQylQQobo16+f/Pz81L9/f/F7YgCQeX3yySc6fvy45syZQ4ES0p3JZNKcOXN07Ngxffrpp0bHAQAAAAzDSkoAAAAAgByrXbt22r17t4KDg+Xo6Gh0HGRTGzduVOPGjbVs2TJ17tzZ6DgAgH+5ceOGfH191bFjR3322WdGx0E29vLLL2vlypUKCQmRp6en0XEAAACAp44iJQAAAABAjkThCJ6mtm3bas+ePRTEAUAmROEInhYK4gAAAJDTUaQEAAAAAMhx4uLiVLZsWXl6emrLli1s64IMd/bsWQUEBOiNN97QBx98YHQcAMBf9u3bp8qVK2v27Nnq16+f0XGQA8yZM0cDBw7U3r17VaFCBaPjAAAAAE8VRUoAAAAAgBxnxowZGjp0qPbv36+yZcsaHQc5xHvvvadJkybp+PHjKl68uNFxACDHS0xMVK1atRQREaEDBw7IysrK6EjIAeLj41WuXDm5urpq27ZtFMsDAAAgR6FICQAAAACQo1y7dk2+vr7q1q2bPvroI6PjIAeJioqSv7+/ypcvr7Vr1xodBwByvEWLFql79+7avHmz6tWrZ3Qc5CCbN29WgwYNtGjRInXr1s3oOAAAAMBTQ5ESAAAAACBHeemll7R27VqdPHlSuXLlMjoOcpiVK1eqY8eO+vHHH9W0aVOj4wBAjhURESFfX1/VqVNHK1asMDoOcqCOHTtq27ZtCg4OlrOzs9FxAAAAgKeCIiUAAAAAQI6xe/duVa1aVZ988oleeeUVo+MgBzKbzWrQoIEuX76sI0eOyMbGxuhIAJAjvf3225ozZ46CgoLk7e1tdBzkQOfPn1dAQID69++vSZMmGR0HAAAAeCooUgIAAAAA5AiJiYmqXr26YmNjtXfvXllaWhodCTnUkSNHVL58eU2cOFFDhw41Og4A5DjBwcEKDAzUyJEjNXLkSKPjIAcbO3as3n//fR09elS+vr5GxwEAAAAyHEVKAAAAAIAcYf78+XrppZe0detW1apVy+g4yOEGDBig+fPnKyQkRPny5TM6DgDkGGazWc2bN1dwcLCOHz8uOzs7oyMhB4uOjlbJkiVVsmRJ/fDDD0bHAQAAADIcRUoAAAAAgGwvLCxMvr6+euaZZ7RkyRKj4wAKDQ2Vj4+PWrRooYULFxodBwByjHXr1qlVq1Zas2aN2rRpY3QcQGvWrFG7du30/fffq2XLlkbHAQAAADIURUoAAAAAgGzvjTfe0Geffabg4GAVKFDA6DiAJOnzzz9X37599ccff6h69epGxwGAbC8mJkalS5dWsWLF9NNPP8lkMhkdCZDZbFbjxo119uxZHTt2TLa2tkZHAgAAADKMhdEBAAAAAADISCdOnNDs2bM1YsQICpSQqfTq1UsVKlRQ//79lZiYaHQcAMj2ZsyYofPnz2vWrFkUKCHTMJlMmj17ts6fP68ZM2YYHQcAAADIUKykBAAAAADItsxms5o0aaIzZ87wm+nIlLZv365atWpp7ty56t27t9FxACDbunjxovz8/PTKK69o2rRpRscBHjBkyBB9/vnnrPwJAACAbI0iJQAAAABAtvXtt9+qbdu2+v7779WyZUuj4wApeuGFF7RhwwadPHlSbm5uRscBgGypa9eu+uWXXxQSEiJXV1ej4wAPCAsLk6+vr5555hktWbLE6DgAAABAhqBICQAAAACQLUVHR6tkyZIKCAjQDz/8wLYuyLQuX74sPz8/9erVSzNnzjQ6DgBkO1u3blWdOnU0b9489ezZ0+g4QKrmzZunXr16aevWrapVq5bRcQAAAIB0R5ESAAAAACBbGjdunMaNG6ejR4/K19fX6DjAQ02ePFnDhw/XwYMHVbp0aaPjAEC2kZCQoIoVK8rW1lY7duyQhYWF0ZGAVCUmJqpatWqKi4vT3r17ZWlpaXQkAAAAIF3xLzIAAAAAQLZz4cIFTZgwQYMHD6ZACVnCwIEDVaxYMQ0cOFD8PhkApJ+5c+fq0KFDmjNnDgVKyPQsLCw0Z84cHTx4UHPnzjU6DgAAAJDuWEkJAAAAAJDtdOrUSVu3blVwcLCcnZ2NjgOkyfr169WiRQt98803eu6554yOAwBZ3q1bt+Tr66vWrVtr3rx5RscB0qxnz5767rvvdPLkSeXKlcvoOAAAAEC6oUgJAAAAAJCtbN68WQ0aNNCiRYvUrVs3o+MAj6RVq1Y6fPiwTpw4IQcHB6PjAECW9vrrr2vx4sUKCQlRnjx5jI4DpNm1a9fk6+urF154QR9++KHRcQAAAIB0Q5ESAAAAACDbiI+PV/ny5eXs7Kzt27fLZDIZHQl4JKdOnVKpUqU0bNgwjR492ug4AJBlHTp0SBUqVNDUqVM1ePBgo+MAj2z69Ol68803tX//fpUtW9boOAAAAEC6oEgJAAAAAJBtfPjhhxowYID27NmjihUrGh0HeCzDhg3TzJkzdeLECRUpUsToOACQ5ZjNZtWrV083btzQoUOHZG1tbXQk4JHFxsaqbNmy8vLy0pYtWyi+BwAAQLZAkRIAAAAAIFu4efOmfHx81KFDB33++edGxwEeW2RkpPz8/FS9enV98803RscBgCxn+fLl6tKli37++Wc1atTI6DjAY9u4caMaN26s5cuXq1OnTkbHAQAAAJ4YRUoAAAAAgGyhb9+++vrrrxUSEiJPT0+j4wBPZOnSperatat++eUXNWzY0Og4AJBl3L17V35+fqpSpYpWr15tdBzgibVt21Z79+5VUFCQHB0djY4DAAAAPBELowMAAAAAAPCk9u/fr7lz52rs2LEUKCFb6NKli2rWrKkBAwYoLi7O6DgAkGVMmDBBN2/e1LRp04yOAqSLadOm6caNG5owYYLRUQAAAIAnxkpKAAAAAIAszWw2q1atWgoPD9eBAwdkZWVldCQgXRw4cEAVK1bUjBkzNHDgQKPjAECmd/r0aZUsWVJvv/22xo4da3QcIN2MHDlSkydP1vHjx1W8eHGj4wAAAACPjSIlAAAAAECWtnjxYr3wwgvatGmT6tevb3QcIF29+uqrWrZsmUJCQuTl5WV0HADI1Fq3bq0DBw4oKChIDg4ORscB0k1UVJT8/f1VoUIFffvtt0bHAQAAAB4bRUoAAAAAgCwrIiJCfn5+qlWrlr7++muj4wDp7ubNm/L19VW7du30xRdfGB0HADKtDRs2qFmzZvr666/VoUMHo+MA6e7rr79Wp06dtGHDBjVp0sToOAAAAMBjoUgJAAAAAJBlvfPOO5o9e7aCgoLk7e1tdBwgQ3z00Ufq37+/du3apcqVKxsdBwAyndjYWAUGBqpAgQL69ddfZTKZjI4EpDuz2awGDRroypUrOnz4sGxsbIyOBAAAADwyC6MDAAAAAADwOEJCQjR9+nQNGzaMAiVka3379lVgYKD69++vxMREo+MAQKYze/ZsnT59WrNmzaJACdmWyWTS7NmzdfLkSc2ZM8foOAAAAMBjYSUlAAAAAECW1KJFCx0/flzHjx+Xvb290XGADPXbb7+pXr16WrBggV588UWj4wBApnHlyhX5+vqqZ8+emj17ttFxgAzXv39/LVy4UMHBwcqXL5/RcQAAAIBHQpESAAAAACDL+eGHH9SyZUutXr1abdu2NToO8FR07txZW7ZsUUhIiFxcXIyOAwCZQo8ePbRu3TqdPHlS7u7uRscBMtzt27fl6+urli1basGCBUbHAQAAAB4JRUoAAAAAgCzl3r17Kl26tIoUKaKff/6ZbV2QY/z555/y9/fXa6+9pilTphgdBwAMt2PHDtWoUUOfffaZXn75ZaPjAE/N559/rr59+2rHjh2qVq2a0XEAAACANKNICQAAAACQpUycOFEjR47U4cOHFRAQYHQc4Kn64IMPNHr0aB05ckT+/v5GxwEAwyQmJqpKlSoym83avXu3LC0tjY4EPDUJCQmqXLmyLC0ttWvXLllYWBgdCQAAAEgTZq4AAAAAgCzj0qVLev/999W/f38KlJAjvfHGGypUqJAGDhwofu8MQE42f/587du3T7Nnz6ZACTmOpaWl5syZo71792r+/PlGxwEAAADSjJWUAAAAAABZRrdu3bRx40aFhITI1dXV6DiAIdauXas2bdpo7dq1evbZZ42OAwBP3Z07d+Tr66smTZpo0aJFRscBDNOtWzf9/PPPCgkJkZubm9FxAAAAgP9EkRIAAAAAIEvYtm2bateurXnz5qlnz55GxwEMYzab1axZM508eVLHjh2TnZ2d0ZEA4KkaNGiQvvzySwUHByt//vxGxwEMc/nyZfn6+qp3796aOXOm0XEAAACA/0SREgAAAAAg00tISFClSpVkbW2tnTt3ysKC3cuRswUFBSkwMFCjR4/WiBEjjI4DAE/NsWPHVLZsWY0fP15vvfWW0XEAw02aNEkjRozQoUOHVKpUKaPjAAAAAA9FkRIAAAAAINP77LPP9Morr2jnzp2qWrWq0XGATGHo0KH65JNPFBQUpEKFChkdBwAynNlsVqNGjXThwgUdOXJEtra2RkcCDHfv3j0FBgbK29tbGzdulMlkMjoSAAAAkCqKlAAAAAAAmdrt27fl6+urZ599VvPmzTM6DpBphIeHy9fXV/Xr19eyZcuMjgMAGW7VqlVq37691q9fr2bNmhkdB8g01q9frxYtWmjVqlVq166d0XEAAACAVFGkBAAAAADI1Pr166dFixYpJCREefLkMToOkKksXLhQPXr00JYtW1S3bl2j4wBAhomKilJAQIDKlCmj77//3ug4QKbTsmVLHT16VMePH5eDg4PRcQAAAIAUWRgdAAAAAACA1Bw+fFiffPKJRo0aRYESkIIXXnhBVatW1YABAxQfH290HADIMFOmTNHVq1c1Y8YMo6MAmdKMGTN0+fJlTZkyxegoAAAAQKooUgIAAAAAZEpms1kDBgyQr6+v+vXrZ3QcIFOysLDQnDlzdOTIEX3++edGxwGADHHu3DlNnDhRQ4YMUYkSJYyOA2RKPj4+GjJkiCZOnKjz588rLi5OZcqU0cGDB42OBgAAACRhuzcAAAAAQKa0YsUKde7cWT///LMaNWpkdBwgU+vdu7dWr16tkJAQeXh4GB0HANJV+/bttWPHDgUHB8vJycnoOECmFRkZKT8/P1WvXl3Lli2TjY2N5s+frx49ehgdDQAAAJDESkoAAAAAgEzo7t27Gjp0qNq0aUOBEpAG48ePV0JCgkaOHGl0FABIV7/++qtWrVqlKVOmUKAEPMTdu3dlZ2enyZMna9WqVfr999/l6Oio0NBQo6MBAAAASShSAgAAAABkOhMnTtSNGzc0bdo0o6MAWYKXl5fGjBmjzz77TAcOHDA6DgCki7i4OA0cOFC1atVSly5djI4DZGrt27dXYGCgihQpopo1a2rgwIFyd3fX7du3jY4GAAAAJKFICQAAAACQqZw5c0ZTpkzRW2+9pWLFihkdB8gyXn/9dQUEBGjAgAEym81GxwGAJ/bxxx/rxIkTmjNnjkwmk9FxgExt2rRpcnV1Ve3atVWoUCEdO3ZMiYmJrKQEAACATMVk5qdWAAAAAIBMpE2bNtq/f7+CgoLk4OBgdBwgS/nll1/UqFEjLVmyRM8//7zRcQDgsV2/fl2+vr7q0qWLPvnkE6PjAFlCQkKCZs+erREjRsjS0lJ3795V27ZttWrVKqOjAQAAAJIoUgIAAAAAZCI//fSTmjZtqhUrVqhjx45GxwGypOeee047d+5UcHCwnJycjI4DAI+lT58+WrVqlU6ePKncuXMbHQfIUs6cOaMePXpo69atKlKkiM6ePWt0JAAAAEASRUoAAAAAgEwiNjZWZcqUUb58+bRp0ya2dQEe07lz5xQQEKDBgwdr/PjxRscBgEe2d+9eValSRR9++KFee+01o+MAWZLZbNaIESPk6OioESNGGB0HAAAAkESREgAAAADAQOPGjZOXl5f69u2radOm6a233tLBgwcVGBhodDQgSxs9erQmTJigY8eOqUSJEmrfvr3eeOMNVa9e3ehoAPCAe/fuqVWrVvroo49UvHhx1axZU1FRUdq3b5+srKyMjgcAAAAASCcWRgcAAAAAAORc3333nfbt26erV69qzJgxeu211yhQAtLBW2+9pbx582rw4MGSpI0bN2rbtm0GpwKAlF2+fFkbN27UuXPntHjxYu3cuVOzZ8+mQAkAAAAAshn+lQcAAAAAMExoaKjc3d01bNgw2djYaMyYMUZHArIFBwcHTZs2TR06dND69evl7u6u0NBQo2MBQIr+//3J2tpab7/9tjp16qS6desanAp4PBcuXNDNmzeNjoEM4uHhIW9vb6NjAAAAZFkUKQEAAAAADBMaGqrIyEgtWLBAn3zyib755hsNGzZMS5YsUdOmTY2OB2RJvXv31v79+zV37lw1aNBAgwYNokgJQKb2/+9PS5YsUXh4uEaMGKFevXrp+++/16VLl2RtbW1wQiBtLly4oICAAEVFRRkdBRnEwcFBJ06coFAJAADgMVGkBAAAAAAwRGJiou7cuaMffvhBJUuW1PLly/Xbb7+pV69eql27ttHxgCxr4MCBeuGFF1S1alX16NFDW7ZsUdGiRSlSApBp/f/70/z589WpUyc1btxYUVFRmjlzJgVKyFJu3rypqKgoLV68WAEBAUbHQTo7ceKEunXrpps3b1KkBAAA8JgoUgIAAAAAGCI8PFyJiYk6f/68bG1tFR0drV9++UUNGzY0OhqQpQUGBmrPnj2aMmWKxowZIxcXF509e1YFChQwOhoApOjWrVuSJDs7Oy1dulStWrXSJ598wvctZFkBAQGqUKGC0TEAAACATMfC6AAAAAAAgJzp6tWrSV+/+uqrOnLkCAVKQDqxtrbW8OHDdfDgQfn4+CgxMVHHjx83OhYApGjPnj2SJEtLSy1btkxr166lQAkAAAAAsiFWUgIAAAAAGKJgwYIqV66cxo0bp5YtWxodB8iWAgICtGPHDg0aNIjt3gBkWg0bNlRQUJBWr14tLy8vo+MAAAAAADIIRUoAAAAAAEM4OTnpwIEDRscAsj1LS0vNmTPH6BgAkKouXbqoS5cuRscAAAAAAGQwtnsDAAAAAAAAAAAw2OjRo2UymWQymTRz5sx0HXvLli1JY7dp0yZdxwYAAADSipWUAAAAACADXbhwQTdv3jQ6BtLAw8ND3t7eRsdANsT3gayD7wNIL/y9z1743oCnqVSpUvrll1/k4uKS1Pb5559r6dKl2r9/vyIiIhQaGio3N7dk/YoUKaLz588na5swYYLeeecdSVKNGjV05coVDRw4UPfu3cvw50grs9msUaNGae7cubpz545q1qypTz75RD4+Pqn2mTBhglavXq2goCDZ29urRo0amjRpkvz8/JJdt2PHDo0YMUK7du2SpaWlypUrp59++kn29vaSpGeffVYHDx7U9evX5e7urmeeeUaTJk1S/vz5M/SZAQAAcjKKlAAAAAAgg1y4cEEB/n6Kio4xOgrSwMHeTieCgnkJiXR14cIF+fkHKCY6yugoSAM7ewcFB53g+wCeCH/vsx++N+BpsrKyUt68eZO1RUVFqWnTpmratKmGDRuWat+xY8eqT58+ScfOzs5JX9vY2Chv3ryyt7d/oiKl2NhY3b59+4GMj2vy5MmaPXu2Fi5cqKJFi2rkyJFq0qSJjh8/Ljs7uxT7/Pbbb3r99ddVuXJlxcfHa/jw4WrcuLGOHz8uR0dHSfcLlP7/5zVnzhxZWVnp0KFDsrD4e4OR+vXra/jw4cqXL58uXbqkoUOHqn379vrjjz/S5dkAAADwIIqUAAAAACCD3Lx5U1HRMZrzXAn5eNgbHQcPcfJmtPqvOqWbN2/yAhLp6ubNm4qJjlKJPnNkny/1FQFgvOgrJ3Vqbn++D+CJ8fc+e+F7A9LLjRs3FBgYqAEDBmj48OGSpD/++EP16tXTjz/+qIYNG6bad9CgQZLub9n2MM7OzulWPPRv+/bt04IFC7Rs2TKNHDlSAwcOfOIxzWazZs6cqXfffVetW7eWJH311VfKkyePvv32W3Xu3DnFfhs2bEh2vGDBAnl5eWnfvn2qU6eOJGnw4MEaMGBA0kpSkh5YaWnw4MFJXxcuXFjvvPOO2rRpo7i4OFlbWz/x8wEAAOBBFCkBAAAAQAbz8bBXYH4no2MAMJB9Ph85FQ40OgaAp4i/9wD+ydPTU/PmzVObNm3UuHFj+fn56YUXXlC/fv0eWqD0KCZOnKhx48bJ29tbzz//vAYPHiwrq8d/DXTlyhUtXrxYCxcu1MmTJ9WiRQt98cUXatGiRdI1r7zyihYvXvzQcSIjI1NsP3v2rK5evapnnnkmqc3V1VVVq1bVjh07Ui1S+rewsDBJUq5cuSRJ169f165du9S1a1fVqFFDp0+flr+/vz744APVqlUrxTFu376tJUuWqEaNGhQoAQAAZCCKlAAAAAAAAAAAADJY8+bN1adPH3Xt2lWVKlWSo6OjJkyYkC5jDxgwQBUqVFCuXLn0xx9/aNiwYbpy5YqmT5/+SOPExsZqzZo1WrhwoTZu3KhKlSrp9ddfV+fOneXu7v7A9WPHjtXQoUMfK/PVq1clSXny5EnWnidPnqRz/yUxMVGDBg1SzZo1Vbp0aUnSmTNnJEmjR4/W1KlTVa5cOX311Vdq2LChjh49Kh+fv1e5e/vtt/Xhhx8qKipK1apV07p16x7rWQAAAJA2FCkBAAAAAAAAAAA8BVOnTlXp0qW1cuVK7du3T7a2tuky7pAhQ5K+LlOmjGxsbNS3b19NmDDhke7xxx9/qHPnzipUqJA2bdqk2rVrP/R6Ly8veXl5PXbuJ/X666/r6NGj2rZtW1JbYmKiJKlv377q2bOnJKl8+fL69ddfNW/evGSFYW+++aZ69eql8+fPa8yYMerevbvWrVsnk8n0dB8EAAAgh7AwOgAAAAAAAAAAAEBOcPr0aV2+fFmJiYk6d+5cht2natWqio+Pf+R7VKlSRXPnzlXhwoXVoEEDNWvWTEuXLlVUVFSK17/yyitycnJ66H+pyZs3ryTp2rVrydqvXbuWdO5h+vXrp3Xr1mnz5s0qWLBgUnu+fPkkSSVLlkx2fUBAgC5cuJCszcPDQ76+vmrUqJGWL1+u9evXa+fOnf95bwAAADweipQAAAAAIJupOmO/5u64kubr/zgbpgKjdigsOj4DUwF42va/VVVXNs5N8/VhQX9oR68Cio8Ky8BUAJ4mvg8AmUtsbKy6deumTp06ady4cerdu7euX7+eIfc6ePCgLCwsHnmVIwcHB/Xu3Vtbt25VUFCQKleurBEjRihv3rzq2bOnNm3alLRSkXR/u7eDBw8+9L/UFC1aVHnz5tWvv/6a1BYeHq5du3apevXqqfYzm83q16+f1qxZo02bNqlo0aLJzhcpUkT58+dXcHBwsvaQkBAVLlw41XH//1z37t1L9RoAAAA8GbZ7AwAAAIBsZv3LgXKwTvvvpFQq5KwDQyvKxc4ywzLFxCVq7E/ntPboLcUmJKpecTeNb1lUnk42qfYxm82auvlPLd13XeEx8ark7aIJLYuqWG77DMsJZCeBI9fLwsYhzdc7l6ikitMPyNLeJcMyJcbF6NyKsbq1e60S42PlVqqeinYbLxtXz1T7mM1m/bl2qq7/vlTxUeFyKVFJRV+YIPs8xTIsJ5Bd8H0AyFxGjBihsLAwzZ49W05OTlq/fr1eeuklrVu37qH9rl69qqtXr+rUqVOSpCNHjsjZ2Vne3t7KlSuXduzYoV27dql+/fpydnbWjh07NHjwYHXr1k3u7u6Pnbd48eIaO3asxowZo99++00LFixQ69atNX78ePXv31/Sk233ZjKZNGjQIL3//vvy8fFR0aJFNXLkSOXPn19t2rRJuq5hw4Zq27at+vXrJ+n+Fm9Lly7V2rVr5ezsrKtXr0qSXF1dZW9vL5PJpDfffFOjRo1S2bJlVa5cOS1cuFBBQUH65ptvJEm7du3Snj17VKtWLbm7u+v06dMaOXKkihcv/tACKQAAADwZVlICAAAAgGwmt6O17G3SXnBkY2UhL2cbmUymDMs0esM5bQwJ1WcdfbWqZyldjYhV7+UhD+3z8bbLmrfrqia2Kqbv+9wvvOq66IRi4hIf2g/AfdbOuWVpm/aiPgsrG9m4emXo94Jzy0cr9ND/2Lvv8Jzu/4/jrzt39p6EkIQkCIJYrVZrF63u0qWlP91F6fBF7SqtPVrddKBVrdFWKdVS1N4rQogQKxGyd+7fH9q0qURDEieJ5+O6XJf7cz7jdcI5F/L2+axSnRc+VINB3ynr4hlFznr6imNOLZ+lM7/MVu0n3lbYGz/Iys5RB6c8rrzsjDLLCVQWvAeA8mPNmjWaNm2avvzyS7m6usrKykpffvml1q1bp/fff/+KYz/44AOFh4frmWeekSTdfvvtCg8P1/fffy9JsrOz09dff602bdqoQYMGeuuttzRw4EB99NFHpZLdZDKpbdu2+uyzz3TmzJkCBUQlNWjQIPXr10/PPvusWrRooZSUFK1YsUL29vb5faKiohQfH5//+f3331diYqLatm2ratWq5f9YsGBBfp8BAwZoyJAhGjhwoBo3bqzVq1dr1apVCgoKknRpx6hFixapQ4cOqlu3rvr06aNGjRpp7dq1srOzK7X7AwAAQEHspAQAAAAAFUhKZq4G/3BUKyIS5GJn1gu3VtfKQxdU39dRY7peOubgpqk79PTN1fRMq2qSJL+RGzXxntpaHXlBa6IS5etiq5GdA3RHPU9Jl4576/7ZAR0Y3EJuDqX/18SkjBx9vfOc3n0wRK1ru0mSpt4XrDbv7tL2E8lqVtPlsjEWi0WfbDqtl2+voc5/5pz+QLCaTNymnyMSdG+Yd6nnBCqS3PQUHf1ysBJ2rpDZwUXVu7ygC7tWyrFmfdV6dIykS8c8Vev0tKp1uvQNzY19/FS710Rd2LNaifvXyNbdVwEPj5RnkzskXTrm6cDE7mox84CsHd1KPXNOWpLOrftaIc++K7fQ1pKk4P+bql3D2ig5artcgppdNsZisej0L5+oRreX5Rne+dKYPtO1bWATJez4Wd433VvqOYGKgvcA7wFULG3btlV2dnaBtsDAQCUm/vfxiqNGjdKoUaOKvN60aVNt2rSppBGLxcnJSU5OTqU2n8lk0pgxYzRmzJgi+0RHRxf4bLFYijX34MGDNXjw4EKvhYWF6ddffy12TgAAAJQOipQAAAAAoAIZvSJaW08ka85j9eTjZKNJv53Q3tOpqu975aNcpqw5qWF3BGjYHQGas/mM+n53WJsHNpWHo02x1u355UFtjkkq8noNNzv91rdJodf2nEpVdq5Ft9X++5udwT4O8nOzLbJIKeZCps6lZOcXNUmSq721wv2ctf1EMkVKuOFFLxit5CNbVa/fHNm4+ujE0klKPb5XjjXrX3Hcye+nKKD7MAV0H6Yzq+fo8Ed91XTCZtk4F+8omINTeyrp8OYir9t51VCTN38r9Frq8T2y5GbLrf5t+W0O1YJl6+lXZHFCZnyMshPPya1+6/w2a0dXOdcOV3LUdooTcEPjPcB7AJXT3r175ezsrAkTJujFF18stXnXrVunrl27KjMzU3fddVepzQsAAABcDYqUAAAAAKCCSMnM1cLdcXr3wZD8gp8p9wWp6aTt/zm2RxMf3fdnYc/gjv76dPMZ7YpNUbuQ4n1DcuK9ta94zJqNuejTxONSsmRrNl22S5OPs43iUrILHXPuz3Yf54JFVN7OtvnXgBtVbnqK4v5YeGknkj+/0R/01BRtf7Xpf471ubWHvG+6T5Lk/+BgnVn9qVKO7ZJHWLtirV2790TlZRV9vJKVuejCx6zEOJmsbS/bncXGzUfZiXGFjslOPHepj6tPgXZbV29lJ50rVmagMuI9wHsAlVP//v3Vs2dPSZKPj89/9L46zZs3165duyRJzs7OpTo3AAAAUFwUKQEAAABABXH8Qoaycy0K9/v7mwqu9tYK8nb4z7Ghvn8fyeBoa5aLnVnxqTnFXruaq93VhQVQZjLij8uSmy3nWuH5bdaOrnLwDfrPsU41QvN/brZzlNnBRTnJ8cVe286j2tWFBVAmeA8AlZOnp6c8PT3LZG4HBwcFBweXydwAAABAcVGkBAAAAAA3ABsrU4HPJpOUZ7EUe3xJjnvzcbZVVq5Fiek5BXZTikvJvmynpL9U+bM9LiVbVV1s89vjU7LU4B8FVwCujumyHU5MsuQVvUvav5XkmCdbNx9ZcrKUk5ZYYBeV7MQ42bgVvluEjVuVS32S4mTrXjW/PSspXk41GxQ7N4C/8R4AAAAAABiFIiUAAAAAqCACPOxlYzZp16kU+blf2tkoKSNHR8+n66YAlzJduyTHvTWq7iQbs0nrjyXqrvpekqQj8emKTcxSs5qF5/b3sFMVZxutP5qohtUuFSUlZ+RoZ2yKnmzhW4I7ASo+e+8Amcw2Sjm2S3ZefpKknLQkpZ89Kpc6N5Xp2iU55skpoJFMZhslHlgvr+Z3SZLSzxxRVkKsXIKaFTrGzttfNm5VlHhwvZz8G0qSctKTlXJ0p3zbPlmCOwEqNt4DvAeA4goMDNSAAQM0YMCAYvVfs2aN2rVrpwsXLsjd3b1MswEAAODGQ5ESAAAAAFQQznZmdW/so7Erj8vdwVreTjaa9NsJWZlMMsn03xOUQEmOe3O1t9Yj4VU0ekW03B2s5WJn1rCfjqlZTecCRUq3z9ypIR391TXUSyaTSU/fXE0zfj+p2l72qulhp4m/nlBVF1t1rlc2R2AAFYXZwVk+t3TX8YVjZe3sLhsXb51YOkkmk5VMprJ9F5TkmCdrR1dVue0RRS8YLWtnd5ntXXRs/jA5BzUrUJyw843b5f/gEHk17SqTyaRqHZ/WyR9nyL5qbdl519SJxRNl615Vnk07l8YtARUS7wHeA0Bxbd26VU5Oxd+J9JZbbtHp06fl5ub2352vUUZGhl599VV9/fXXyszMVOfOnTVr1ixVrVq1yDG9e/fW559/XqCtc+fOWrFiRZnlBAAAQOmjSAkAAAAAKpCRXQI1+Iej6jUvQi52Zr1wa3WdTsySvXXZfkOypEZ1CZSVSXp2wSFl5ljUNthd4+6qVaBPVHyGkjJy8z+/2Lq60rJzNeiHo0rKyFELf1fN7Rkqe5uid20CbhSBD4/U0S8HK2J6L5kdXFS9ywvKSjgtk4290dGuKPCRUZLJSofee1aWnEy5N2yrWj3HFeiTcSZKuWl/Hy9ZveuLys1K09HPByknLUmuIS0UOnCurMr5vQJljfcA7wGgOHx8Cj9KsSi2trby9S3bnUsHDhyoZcuWaeHChXJzc1Pfvn31wAMPaMOGDVcc16VLF82ZMyf/s53dtf9HCgAAABjDZLFYLEaHAAAAAIDKaMeOHWrWrJlWPBemsOrOZbJGWlaumk3erhF3BOjRZkX/z2Nc2d5TKery4V5t375dTZs2NToOKpG/3gNhI1bIOSCszNbJzUzT9lebKeDhEap626Nltk5llnJ8r/aO6cJ7ACV2vZ77f+M9UDZ4N+Bq/PX8X6/fL8nJyXr++ee1ZMkSubq6atCgQVq6dKmaNGmiadOmSbr8uDeTyaSPP/5Yy5Yt088//yw/Pz9NnjxZ99xzj6SyP+4tMTFRPj4+mj9/vh566CFJUkREhEJDQ7Vx40bdfPPNhY7r3bu3Ll68qCVLlpR6puK63r++AAAAlRH//RQAAAAAKpB9p1O1ZG+8ohMytPdUivp+d1iSOAINuMGkHt+n+M1LlHEuWinH9+rwR30lSZ5NOPoIuFHwHgDwyiuvaMOGDfr++++1atUqrVu3Tjt27PjPcaNHj1aPHj20Z88e3XnnnXr88ceVkJBQ7HW7du0qZ2fnIn80aNCgyLHbt29Xdna2OnbsmN9Wr149+fv7a+PGjVdcd82aNapSpYrq1q2rF154QefPny92ZgAAAJQPHPcGAAAAABXMBxtOKep8umzNJoVVc9ai/2sgTycbo2MBuM5O/fyB0s9EyWS2lXNgmBoMXiQbFwoWgRsJ7wHgxpWcnKzPP/9c8+fPV4cOHSRJc+bMUfXq1f9zbO/evfXoo5d2XBs3bpxmzJihLVu2qEuXLsVa+5NPPlF6enqR121siv67yZkzZ2Rra3vZLk1Vq1bVmTNnihzXpUsXPfDAA6pVq5aioqI0dOhQde3aVRs3bpTZbC5WbgAAABiPIiUAAAAAqEAaVnPSiucbGR0DgMGcAhqq0YgVRscAYCDeA8CN7ejRo8rOzlbLli3z29zc3FS3bt3/HNuo0d9/n3BycpKrq6vOnTtX7LX9/PyuLmwpeOSRR/J/HhYWpkaNGikoKEhr1qzJL9ICAABA+cdxbwAAAAAAAAAAADeIf+90ZDKZlJeXV+zxJTnuzdfXV1lZWbp48WKB9rNnz8rX17fYGWrXri1vb28dOXKk2GMAAABgPHZSAgAAAABc0YDFR5SUkaPZj9YzOgoAgxz5dIBy0pJUr99so6MAuE547oHyrXbt2rKxsdHWrVvl7+8vSUpMTFRkZKRuv/32Ml27JMe9NWvWTDY2Nlq9erUefPBBSdKhQ4cUExOjVq1aFTvDyZMndf78eVWrVq34wQEAAGA4ipQAAAAAABXapugkvb/hlPaeTtHZ5Gx9+khddQn1NDoWAIPE/vSuYr4bL9+OfVTr0TFGxwFQRmKXzdT5HcuVfvqIrGzt5RLUXAHdh8rBNzi/T9QXg5R4YL2yLp6V2c5RLsHNFfDQG3KoFnyFmYGKwcXFRb169dLrr78uT09PValSRSNHjpSVlZVMJlOZrl2S497c3NzUp08fvfLKK/L09JSrq6v69eunVq1a6eabb87vV69ePY0fP17333+/UlJSNHr0aD344IPy9fVVVFSUBg0apODgYHXu3Lk0bgkAAADXCce9AQAAAAAqtLTsXNX3ddRbd9UyOgoAg6Uc26Wza+fKsUao0VEAlLHEyE3ybddLYW/8oPqvfiVLbrYOTH5MuZlp+X2cAxop+KkpajJ2jUJfmS9ZLDow5VFZ8nINTA6UnilTpqhVq1bq1q2bOnbsqFtvvVWhoaGyt7c3OtoVTZ06Vd26ddODDz6o22+/Xb6+vlq0aFGBPocOHVJiYqIkyWw2a8+ePbrnnntUp04d9enTR82aNdO6detkZ2dnxC0AAADgGrGTEgAAAACUEz/uP6+pa04oOiFD9jZmNazmpDmP1pWjrVm7YlP09i8x2ncmVTm5FjXwddSoLoEKq+6cP95v5Ea93a22VkUmaMOxJNVws9Pk+4Lk5Wit178/ql2xKarv66gZD4Qo0PPSNy4m/3ZCKyIS9GRzX03//aQupOeoYx0PTbyntlztC/8rY16eRe+tP6V5288qLiVLtbwcNKBNDXVr4CVJupieo2HLjmlt1EWlZeXK19VO/W/308PhVcrk69Y+xEPtQzzKZG7geju/7Ued+H6qMs5Fy2xrLyf/hqrbb47Mdo5KObZLMYveVmrMPllyc+RYs4ECHxkl54Cw/PEb+/ip9hNvK2H3KiVFbJCdVw0F9Z4saxcvHf38daUc2yXHmvUV8vQM2VcJlCSdWDpZCTtXyLftkzr543TlpF6QR6OOqt1roqwdXQvNacnL06nl7+ns7/OUlRgnh6q1VOPuAfJq3k2SlJN6UcfmDdPF/WuVm5kmOw9f+d3VX1VaP1xmX7vcjFQd/rivaveaoNgfZ5TZOkBp47m/NvUHzivwObjPNG0b0Eip0XvkWvfSbixV2/T8u4N3TdW8f5D2jOqkzPgT+V8LoCJzcXHRvHl/PwupqakaPXq0nn322fy26OjoAmMsFstl81y8eDH/523bti20T2myt7fXe++9p/fee6/IPv/M4ODgoJ9//rlMMwEAAOD6oEgJAAAAAMqBs8lZeunbw3qjk7+6hnoqJStXm48n669/m0/JzFX3Jj4aW72WLLLowz9O64l5EVrfP1zOdub8eab9flIjOwdoZOdAjVt1XH2/PSx/Dzv1vc1Pfm62emVJlIYtO6a5T/y9y0h0QoZ+2B+vzx6rp5TMXL26NEpDfzymdx8KKTTrzHWxWrQnXm/fXVu1PO216XiS+i86LC8na7UKdNPEX2MUGZemuT1D5elorWMJGcrIySvy3mf8flIz18Ve8euz5qUm8nPnf0mjcsu6eFaHP3pJ/g+9Ic+mXZWbkaLkyM3660WQm5Ein1u6q9ZjY2WxWHR65YeKmPaEwsetl9nh74LFkz9OU8DDIxX48Egd/3acDn/cV3be/vK7s69sPf0UNecVHZs3TKED5+aPyTgXrfitP6he/8+Um56iqM9e1bG5QxXy7LuFZo39aabiNy1S7Sfeln3VWko6tEmHP+4vaxcvudVtpZglE5V2KlKhA+fK2tlTGeeOKS8ro8h7P7lshmKXzbzi16fJm2tk51X08TLH5g2VR6MOcq9/O0VKqDB47kv23P9TTlqSJMnayb3Q67mZaYrbsEB23v6y9axerDmB8m7nzp2KiIhQy5YtlZiYqDFjLh1zeu+99xqcDAAAACgcRUoAAAAAUA6cS85STp5Fd9b3Uo0/i3FCqzrlX29d261A/wl311bo21u1MTpJner+vYvQw018dE9Db0nSi639dM8n+zSgTQ21DXaXJD19czW9suRIgbkyc/I0/YFgVXO9tO7YOwP15LwIjegcoCoutpf1nbkuVl/3qq/mNV0kSQGe9toak6y5286qVaCbYhOz1LCakxr7XfrmaU2PKx838UTzqrr7z12YilL1XzmAyigr8ZwsuTnyanqn7LxrSJKc/nFsmVto6wL9az85QVv7hSopcqM8GnfKb/e59WF5t7hHkuTX9UXtG3ePanQbIPeGbSVJ1To+rSNzXikwV152poKfni47j2qSpMDHxipi+pMKeHiEbN2qXNY3dtlM1X/1a7kEN5ck2fsEKPnIVp1dO1dudVsp63ysnPwbyjmw8aXr3jWveO9V2zwhr+Z3X7GPrXvVIq/Fb16qlOP71Gj4sivOAZQ3PPfX/tz/kyUvT9Ffj5RLcAs51qhX4NqZXz/T8W/fUl5mmux9g1T/1a9kZc2fK1B5TJo0SYcOHZKtrW3+EWje3t5GxwIAAAAKRZESAAAAAJQD9X2d1Lq2mzrM2q02QW5qE+Suuxp4yd3h0l/b4lKyNGH1Cf0RnaTzqdnKtViUnp2n2MTMAvOE+v5d2OTjbCNJqlfVMb/N29lGGTkWJWfkyOXP49z83OzyC5QkqVlNF+VZpKjz6ZcVKUUnZCg9O0+PfnGgQHt2rkUN/1z7yRZV9cyCSO09nao2Qe7qXM9TLfxdirx3D0cbeTjaFPtrBVRWTjXryy20tXaP7CC3Bm3k3qCNvJrflb8rSFZinE4snqCkQ38oO/m8LHm5ystKV+b5gjuR/bPAwcbVR5IKfNPexs1bluwM5aQny9rh0rNp5+mXX6ggSS5BzSRLntLPRF1WrJBxLlp5Wek6MOXRAu2WnGw5+TeUJFVt96QiZz2j1Ji9cm/QRp7hneUS3KLIe7dx9pCN87Ud25iZEKvor0co9JWvZGVz5aJIoLzhuS+d41qPzRuq9NhDajB48WXXvG9+QG4Nblf2xXM69fMHivzgeTUcsoT3BSqF8PBwbd++3egYAAAAQLFRpAQAAAAA5YDZyqSvnwzVthPJWnskUXO2nNE7v8box2fC5O9hrwGLj+hCWo7GdA1UDXc72ZpNuueTfcrOtRSYx8bKlP/zv35mXUhbXsFhxZaalStJ+uLxevL9VwGTrbWVJKl9iIe2DGyq1YcvaF1Uoh75fL96tfTViM6Bhc7JcW/AJSYrs0Jf/VrJR7Ypcf9anfl1jmIWv6OwN36UvY+/jsweoJyUCwp8dIzsvGrIZG2rfePukSU3u+A85n8U/ZlMf7b985+A/nwTWIo+hvFKcjNTJUn1Xv5Ctu6+Ba5Z2Vx6L3iEtVfTCVt0Yc9qJR5Yp/2THpFvu14KfHhEoXOW5Nin1Oi9yk6K154xXf5uzMtVUuQmnfn1M9384TGZrMyXjQPKA577kh/3dnTeG7qw+xc1+N8i2RVyjJu1o6usHV3lULW2nIOaamu/+krYsULeN913xXkBAAAAAKWPIiUAAAAAKCdMJpNa+Luqhb+rBratoZZTd2j5wQQ9d0t1bY1J1rhutdWhzqUdB2ITM5WQllMq68YmZupMUpZ8XS99k3HHyRRZmaQgL4fL+tbxcZSdtUmxiVlqFeh22fW/eDnZqEeTKurRpIpa+rtq7KrjRRYpcdwb8DeTySTXkBZyDWmhGvcM1I5BLZWwY7mqd35OyYe3qnbPcfJo1EHSpR2EclISSmXdzIRYZV04I1uPS8UHKUd3SCYrOfgGXdbXsVodmaztlHU+Vm51WxU5p42Ll6rc2kNVbu0h15CWOr5wbJHFCiU59skttLUaj15doO3InFfk4Bskv64vUaCEco/nvmhXOu7NYrHo2PxhStixQg0GLZS9j/8V5/pzkCSL8rIz/7MrgML17t1bFy9e1JIlS4yOAgAAgAqIIiUAAAAAKAd2nEzW+qOJahPkLm8nG+2ITVFCarZCfC4VCtXyctB3u+PUuLqTkjNzNXblcdnbWJXK2nbWVhqw+IiGdw5QSmauhv90THc38LrsqDdJcrYz67lbqmvUimjlWSxq6e+q5IwcbY1JlrO9WT2aVNHEX2PUqLqz6vg4KCvXol8iLyjE+/KCp7+U9Li31MxcHUvIyP8ccyFD+06nysPBmt2XUKEkH92hxIPr5d6gjWxcvJVydIeykxPkUD1EkuRQtZbiNn4np8DGys1I1vFvxsrKtnSOK7KysdOR2QMU0GO4ctNTdGz+cHm1uPuyI58kyezgrOqdn1P0glGyWPLkGtJSOenJSj6yVWZ7Z1W5tYdilkyUc0AjOVSvI0tOli7s+UUO1UKKXL8kxz6ZHZwLHGslSWY7R1k7e1zWDpQ3PPfXftzbsblDFb95ier2my2zvbOyEs/9mdVFZlsHZcQd1/kt38utQRvZuHgp68Ipxf70nqxs7POLvgBUPuPHj9eiRYsUEREhBwcH3XLLLXrnnXdUt25do6MBAABAFCkBAAAAQLngYmfW5uNJ+mTTaaVk5srPzU4jOgeofcilb95NvjdIg76PUpcP9qiam50Gd/DXmyuPl8ragZ726hrqqSfnHtTF9Bx1qOOhcd1qF9l/UPua8nK00bvrYhVz4ahc7c0Kq+akfrfVkCTZmK00/pcYnbiYKXtrK90U4KJZ3Yv+JmVJ7T6Vou6fHcj/PPrnS1+X7k18NO3+4DJbFyhtZnsXJUVu1ulVnyg3PUV2Xn4K6DFCHmHtJUlBvScr6otB2jOmi+w8q8n/gcE6/s2bpbK2fZVAeTbtqoPTnlRO6kV5NOqg2j3HFdm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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "**RANDOM FOREST** \n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "AwB7t7Ucd99U"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Random forests are an ensemble version of many choice bushes, wherein each tree will specialize its focus on a specific feature while maintaining a top-level view of all capabilities.\n",
        "\n",
        "Each tree within the random wooded area will do its own random train/check break up of the information, referred to as bootstrap aggregation and the samples no longer covered are called the ‘out-of-bag samples. Moreover, every tree will do characteristic bagging at every node-branch split to lessen the results of a characteristic mostly correlated with the response.\n",
        "While an individual tree is probably touchy to outliers, the ensemble version will no longer be the same"
      ],
      "metadata": {
        "id": "tn1wpqO4eFBK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop('status', axis=1)\n",
        "\n",
        "\n",
        "X = X.drop('name', axis=1)\n",
        "\n",
        "y = df['status']\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=7,test_size=0.2)\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "random_forest = RandomForestClassifier(n_estimators=30, max_depth=10, random_state=1)\n",
        "random_forest.fit(X_train, y_train)\n",
        "\n",
        "\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "y_predict = random_forest.predict(X_test)\n",
        "\n",
        "accuracy_score(y_test, y_predict)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nvGY4sbUeEvq",
        "outputId": "ee6723c2-0351-4a95-d8db-d9b3bc04029b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9230769230769231"
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(\n",
        "\n",
        "confusion_matrix(y_test, y_predict),\n",
        "\n",
        "columns=['Predicted Healthy', 'Predicted Parkinsons'],\n",
        "\n",
        "index=['True Healthy', 'True Parkinsons']\n",
        "\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "5KrRClDqepyX",
        "outputId": "372d645f-b16d-4efc-a4c7-6ce8b950ee49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Predicted Healthy  Predicted Parkinsons\n",
              "True Healthy                     4                     3\n",
              "True Parkinsons                  0                    32"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Predicted Healthy</th>\n",
              "      <th>Predicted Parkinsons</th>\n",
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              "      <th>True Healthy</th>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
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              "      <th>True Parkinsons</th>\n",
              "      <td>0</td>\n",
              "      <td>32</td>\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ee3b2cc7-e136-4b7b-8949-572671a4edc8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ee3b2cc7-e136-4b7b-8949-572671a4edc8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(confusion_matrix(y_test, y_predict), cmap ='RdYlGn', linewidths = 0.30, annot = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "HxoQw6Rh2ZdZ",
        "outputId": "d363bf95-afdb-4072-c914-4a5269cd2cfe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 141
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "aKCjHL9v2xtC"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}