code.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled0.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOUcu8i8PKy9Xr+SB1xaWvU",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/anupamaaas/Parkinson/blob/main/code.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nwrHJSLvcDPa"
      },
      "source": [
        "#Description: This program detects if an individual has Parkinson's disease"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h1uIAu9ZeDV7"
      },
      "source": [
        "#Get the dependancies\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.model_selection import train_test_split\n",
        "import seaborn as sns"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "lFz-2QcDfXhc",
        "outputId": "9734f167-219f-45f5-f4fe-665f69c51d6d"
      },
      "source": [
        "#Load the data\n",
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-8178a30e-52b2-46a7-920a-b7890f771ee8\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-8178a30e-52b2-46a7-920a-b7890f771ee8\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving parkinsons.data to parkinsons.data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "rEHKxug7fsga",
        "outputId": "eeb0cdab-fed4-4563-bb66-ece09e15d931"
      },
      "source": [
        "#Load the data into a data frame\n",
        "df = pd.read_csv('parkinsons.data')\n",
        "df.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>MDVP:Fo(Hz)</th>\n",
              "      <th>MDVP:Fhi(Hz)</th>\n",
              "      <th>MDVP:Flo(Hz)</th>\n",
              "      <th>MDVP:Jitter(%)</th>\n",
              "      <th>MDVP:Jitter(Abs)</th>\n",
              "      <th>MDVP:RAP</th>\n",
              "      <th>MDVP:PPQ</th>\n",
              "      <th>Jitter:DDP</th>\n",
              "      <th>MDVP:Shimmer</th>\n",
              "      <th>MDVP:Shimmer(dB)</th>\n",
              "      <th>Shimmer:APQ3</th>\n",
              "      <th>Shimmer:APQ5</th>\n",
              "      <th>MDVP:APQ</th>\n",
              "      <th>Shimmer:DDA</th>\n",
              "      <th>NHR</th>\n",
              "      <th>HNR</th>\n",
              "      <th>status</th>\n",
              "      <th>RPDE</th>\n",
              "      <th>DFA</th>\n",
              "      <th>spread1</th>\n",
              "      <th>spread2</th>\n",
              "      <th>D2</th>\n",
              "      <th>PPE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>phon_R01_S01_1</td>\n",
              "      <td>119.992</td>\n",
              "      <td>157.302</td>\n",
              "      <td>74.997</td>\n",
              "      <td>0.00784</td>\n",
              "      <td>0.00007</td>\n",
              "      <td>0.00370</td>\n",
              "      <td>0.00554</td>\n",
              "      <td>0.01109</td>\n",
              "      <td>0.04374</td>\n",
              "      <td>0.426</td>\n",
              "      <td>0.02182</td>\n",
              "      <td>0.03130</td>\n",
              "      <td>0.02971</td>\n",
              "      <td>0.06545</td>\n",
              "      <td>0.02211</td>\n",
              "      <td>21.033</td>\n",
              "      <td>1</td>\n",
              "      <td>0.414783</td>\n",
              "      <td>0.815285</td>\n",
              "      <td>-4.813031</td>\n",
              "      <td>0.266482</td>\n",
              "      <td>2.301442</td>\n",
              "      <td>0.284654</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>phon_R01_S01_2</td>\n",
              "      <td>122.400</td>\n",
              "      <td>148.650</td>\n",
              "      <td>113.819</td>\n",
              "      <td>0.00968</td>\n",
              "      <td>0.00008</td>\n",
              "      <td>0.00465</td>\n",
              "      <td>0.00696</td>\n",
              "      <td>0.01394</td>\n",
              "      <td>0.06134</td>\n",
              "      <td>0.626</td>\n",
              "      <td>0.03134</td>\n",
              "      <td>0.04518</td>\n",
              "      <td>0.04368</td>\n",
              "      <td>0.09403</td>\n",
              "      <td>0.01929</td>\n",
              "      <td>19.085</td>\n",
              "      <td>1</td>\n",
              "      <td>0.458359</td>\n",
              "      <td>0.819521</td>\n",
              "      <td>-4.075192</td>\n",
              "      <td>0.335590</td>\n",
              "      <td>2.486855</td>\n",
              "      <td>0.368674</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>phon_R01_S01_3</td>\n",
              "      <td>116.682</td>\n",
              "      <td>131.111</td>\n",
              "      <td>111.555</td>\n",
              "      <td>0.01050</td>\n",
              "      <td>0.00009</td>\n",
              "      <td>0.00544</td>\n",
              "      <td>0.00781</td>\n",
              "      <td>0.01633</td>\n",
              "      <td>0.05233</td>\n",
              "      <td>0.482</td>\n",
              "      <td>0.02757</td>\n",
              "      <td>0.03858</td>\n",
              "      <td>0.03590</td>\n",
              "      <td>0.08270</td>\n",
              "      <td>0.01309</td>\n",
              "      <td>20.651</td>\n",
              "      <td>1</td>\n",
              "      <td>0.429895</td>\n",
              "      <td>0.825288</td>\n",
              "      <td>-4.443179</td>\n",
              "      <td>0.311173</td>\n",
              "      <td>2.342259</td>\n",
              "      <td>0.332634</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>phon_R01_S01_4</td>\n",
              "      <td>116.676</td>\n",
              "      <td>137.871</td>\n",
              "      <td>111.366</td>\n",
              "      <td>0.00997</td>\n",
              "      <td>0.00009</td>\n",
              "      <td>0.00502</td>\n",
              "      <td>0.00698</td>\n",
              "      <td>0.01505</td>\n",
              "      <td>0.05492</td>\n",
              "      <td>0.517</td>\n",
              "      <td>0.02924</td>\n",
              "      <td>0.04005</td>\n",
              "      <td>0.03772</td>\n",
              "      <td>0.08771</td>\n",
              "      <td>0.01353</td>\n",
              "      <td>20.644</td>\n",
              "      <td>1</td>\n",
              "      <td>0.434969</td>\n",
              "      <td>0.819235</td>\n",
              "      <td>-4.117501</td>\n",
              "      <td>0.334147</td>\n",
              "      <td>2.405554</td>\n",
              "      <td>0.368975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>phon_R01_S01_5</td>\n",
              "      <td>116.014</td>\n",
              "      <td>141.781</td>\n",
              "      <td>110.655</td>\n",
              "      <td>0.01284</td>\n",
              "      <td>0.00011</td>\n",
              "      <td>0.00655</td>\n",
              "      <td>0.00908</td>\n",
              "      <td>0.01966</td>\n",
              "      <td>0.06425</td>\n",
              "      <td>0.584</td>\n",
              "      <td>0.03490</td>\n",
              "      <td>0.04825</td>\n",
              "      <td>0.04465</td>\n",
              "      <td>0.10470</td>\n",
              "      <td>0.01767</td>\n",
              "      <td>19.649</td>\n",
              "      <td>1</td>\n",
              "      <td>0.417356</td>\n",
              "      <td>0.823484</td>\n",
              "      <td>-3.747787</td>\n",
              "      <td>0.234513</td>\n",
              "      <td>2.332180</td>\n",
              "      <td>0.410335</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             name  MDVP:Fo(Hz)  MDVP:Fhi(Hz)  ...   spread2        D2       PPE\n",
              "0  phon_R01_S01_1      119.992       157.302  ...  0.266482  2.301442  0.284654\n",
              "1  phon_R01_S01_2      122.400       148.650  ...  0.335590  2.486855  0.368674\n",
              "2  phon_R01_S01_3      116.682       131.111  ...  0.311173  2.342259  0.332634\n",
              "3  phon_R01_S01_4      116.676       137.871  ...  0.334147  2.405554  0.368975\n",
              "4  phon_R01_S01_5      116.014       141.781  ...  0.234513  2.332180  0.410335\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P2hnETfmiKAP",
        "outputId": "b7771b2a-47d8-45eb-ade6-ccc8cf2b5261"
      },
      "source": [
        "#Check data for missing values\n",
        "df.isnull().values.any()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tokNORUViSdH",
        "outputId": "c7df2ffd-a152-48ff-d54b-e50d6fd3a643"
      },
      "source": [
        "#Get the number of rows and columns in data set\n",
        "df.shape"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(195, 24)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GL-a7RrKiZZg",
        "outputId": "8e44a66b-ff10-441e-a257-82933c4367e1"
      },
      "source": [
        "#Get the target count\n",
        "df['status'].value_counts()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    147\n",
              "0     48\n",
              "Name: status, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BcpiGvJqkg9R",
        "outputId": "a3135013-cf3e-41f5-d570-4a7c9833051e"
      },
      "source": [
        "percent_has_disease = 147/(147+48)*100\n",
        "percent_dont_have_disease = 48/(147+48)*100\n",
        "print('If I guess the individual did not have Parkisons disease, I would be correct ',percent_dont_have_disease,'% of the time.')\n",
        "print('If I guess the individual has Parkisons disease, I would be correct ',percent_has_disease,'% of the time.')"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "If I guess the individual did not have Parkisons disease, I would be correct  24.615384615384617 % of the time.\n",
            "If I guess the individual has Parkisons disease, I would be correct  75.38461538461539 % of the time.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "A2QRgcDulc4G",
        "outputId": "6cfc0003-1014-4aba-ddc5-80355dec510a"
      },
      "source": [
        "#Visualise the count\n",
        "sns.countplot(df['status'])"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb0aca537d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mTA0wD4clkNm",
        "outputId": "7de34ac4-7912-4caa-e7c2-7e039c569925"
      },
      "source": [
        "#Get the data types\n",
        "df.dtypes"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "name                 object\n",
              "MDVP:Fo(Hz)         float64\n",
              "MDVP:Fhi(Hz)        float64\n",
              "MDVP:Flo(Hz)        float64\n",
              "MDVP:Jitter(%)      float64\n",
              "MDVP:Jitter(Abs)    float64\n",
              "MDVP:RAP            float64\n",
              "MDVP:PPQ            float64\n",
              "Jitter:DDP          float64\n",
              "MDVP:Shimmer        float64\n",
              "MDVP:Shimmer(dB)    float64\n",
              "Shimmer:APQ3        float64\n",
              "Shimmer:APQ5        float64\n",
              "MDVP:APQ            float64\n",
              "Shimmer:DDA         float64\n",
              "NHR                 float64\n",
              "HNR                 float64\n",
              "status                int64\n",
              "RPDE                float64\n",
              "DFA                 float64\n",
              "spread1             float64\n",
              "spread2             float64\n",
              "D2                  float64\n",
              "PPE                 float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4EKYLJO1lvPI"
      },
      "source": [
        "#Create the feature data set\n",
        "X = df.drop(['name'],1)\n",
        "X = np.array(X.drop(['status'],1))\n",
        "#Create the target data set\n",
        "y = np.array(df['status'])"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KqxZVmGNn0E1"
      },
      "source": [
        "#Split the data into 80% training and 20% testing data sets\n",
        "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xhmMekesoYQe"
      },
      "source": [
        "#Transform the feature data to be values between 0 and 1\n",
        "sc = MinMaxScaler(feature_range=(0,1))\n",
        "x_train = sc.fit_transform(x_train)\n",
        "x_test = sc.transform(x_test)"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rvO11fF8p2Vf"
      },
      "source": [
        "#Create the XGBClassifier\n",
        "model = XGBClassifier().fit(x_train, y_train)"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BfWKT2SCqD2i",
        "outputId": "f1b4459f-485a-4341-ca0e-de9c1b69b60d"
      },
      "source": [
        "#Get the model's prediction\n",
        "predictions = model.predict(x_test)\n",
        "predictions"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1,\n",
              "       0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J-c0Cc6RqYu4",
        "outputId": "74219a40-81d4-4733-9f40-d173c89fbf17"
      },
      "source": [
        "y_test"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1,\n",
              "       0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5PdPi8Igqgz8",
        "outputId": "b4905029-cb3f-46d8-a1c0-12472b73499b"
      },
      "source": [
        "#Get the model's accuracy, precision, recall and f1-score\n",
        "print(classification_report(y_test, predictions))"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      0.80      0.80        10\n",
            "           1       0.93      0.93      0.93        29\n",
            "\n",
            "    accuracy                           0.90        39\n",
            "   macro avg       0.87      0.87      0.87        39\n",
            "weighted avg       0.90      0.90      0.90        39\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}