parkinson-s-disease-detection-using-ml.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3b1158cc",
   "metadata": {
    "papermill": {
     "duration": 0.018077,
     "end_time": "2023-07-09T17:23:19.538230",
     "exception": false,
     "start_time": "2023-07-09T17:23:19.520153",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Using Machine Learning to Analyze Voice Disorders for Parkinson's Disease Detection**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dec9844c",
   "metadata": {
    "papermill": {
     "duration": 0.016957,
     "end_time": "2023-07-09T17:23:19.572711",
     "exception": false,
     "start_time": "2023-07-09T17:23:19.555754",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## The purpose of this project is to develop a machine learning model that can accurately predict the presence of Parkinson's disease in an individual based on their voice recordings. Parkinson's disease is a neurodegenerative disorder that affects movement, with symptoms that include tremors, stiffness, and difficulty with coordination.\n",
    "\n",
    "<div style=\"text-align:center\">\n",
    "<img src=\"data:image/jpeg;base64,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\" width=\"500\" alt=\"parkinson detection\">\n",
    "</div>\n",
    "\n",
    "<div style=\"text-align:center\">\n",
    "<img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMSkillsNetwork-GPXX01JCEN/images/parkinson_patient_voice.png\" width=\"500\" alt=\"parkinson detection\">\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6d48e93",
   "metadata": {
    "papermill": {
     "duration": 0.017688,
     "end_time": "2023-07-09T17:23:19.607702",
     "exception": false,
     "start_time": "2023-07-09T17:23:19.590014",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# __Table of Contents__\n",
    "\n",
    "<ol>\n",
    "    <li><a href=\"#Objectives\">Objectives</a></li>\n",
    "    <li>\n",
    "        <a href=\"#Setup\">Setup</a>\n",
    "        <ol>\n",
    "            <li><a href=\"#Installing-Required-Libraries\">Installing Required Libraries</a></li>\n",
    "        </ol>\n",
    "    </li>\n",
    "    <li>\n",
    "        <a href=\"#Parkinson Data and Voice Disorder\">Parkinson Data and Voice Disorder</a>\n",
    "    </li>\n",
    "    <li><a href=\"#Loading Data\">Loading Data</a></li>\n",
    "    <li><a href=\"#Data Preparation\">Data Preparation</a></li>\n",
    "    <li><a href=\"#Train and Test Random Forest\">Train and Test Random Forest Model</a></li>\n",
    "    <li><a href=\"#Visualizing the decision tree\">Find Strong Model</a></li>\n",
    "</ol>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad0b5520",
   "metadata": {
    "papermill": {
     "duration": 0.0171,
     "end_time": "2023-07-09T17:23:19.642132",
     "exception": false,
     "start_time": "2023-07-09T17:23:19.625032",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Objectives\n",
    "\n",
    "## After completing this lab you will be able to:\n",
    "\n",
    "- Use Python for data analysis and machine learning\n",
    "- Implement machine learning algorithms to detect Parkinson's disease in voice recordings\n",
    "- Evaluate model performance\n",
    "- Conduct grid search for tuning parameters\n",
    "- Visualize the decision tree model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13aa2c0c",
   "metadata": {
    "papermill": {
     "duration": 0.017598,
     "end_time": "2023-07-09T17:23:19.677541",
     "exception": false,
     "start_time": "2023-07-09T17:23:19.659943",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "----\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "260dd8c2",
   "metadata": {
    "papermill": {
     "duration": 0.017221,
     "end_time": "2023-07-09T17:23:19.713030",
     "exception": false,
     "start_time": "2023-07-09T17:23:19.695809",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Setup\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec5d78e2",
   "metadata": {
    "papermill": {
     "duration": 0.018121,
     "end_time": "2023-07-09T17:23:19.748596",
     "exception": false,
     "start_time": "2023-07-09T17:23:19.730475",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Installing Required Libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6ed70891",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:23:19.786260Z",
     "iopub.status.busy": "2023-07-09T17:23:19.785748Z",
     "iopub.status.idle": "2023-07-09T17:24:41.062411Z",
     "shell.execute_reply": "2023-07-09T17:24:41.061573Z"
    },
    "papermill": {
     "duration": 81.297654,
     "end_time": "2023-07-09T17:24:41.064472",
     "exception": false,
     "start_time": "2023-07-09T17:23:19.766818",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "!mamba install -- upgrade scikit-learn=1.2.1\n",
    "!mamba install seaborn --y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "38b4f051",
   "metadata": {
    "_kg_hide-output": true,
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:41.101383Z",
     "iopub.status.busy": "2023-07-09T17:24:41.101065Z",
     "iopub.status.idle": "2023-07-09T17:24:50.852556Z",
     "shell.execute_reply": "2023-07-09T17:24:50.851554Z"
    },
    "papermill": {
     "duration": 9.77219,
     "end_time": "2023-07-09T17:24:50.854745",
     "exception": false,
     "start_time": "2023-07-09T17:24:41.082555",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting dtreeviz\r\n",
      "  Downloading dtreeviz-2.2.1-py3-none-any.whl (91 kB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m91.5/91.5 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hRequirement already satisfied: graphviz>=0.9 in /opt/conda/lib/python3.10/site-packages (from dtreeviz) (0.20.1)\r\n",
      "Requirement already satisfied: pandas in /opt/conda/lib/python3.10/site-packages (from dtreeviz) (1.5.3)\r\n",
      "Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from dtreeviz) (1.23.5)\r\n",
      "Requirement already satisfied: scikit-learn in /opt/conda/lib/python3.10/site-packages (from dtreeviz) (1.2.2)\r\n",
      "Requirement already satisfied: matplotlib in /opt/conda/lib/python3.10/site-packages (from dtreeviz) (3.6.3)\r\n",
      "Collecting colour (from dtreeviz)\r\n",
      "  Downloading colour-0.1.5-py2.py3-none-any.whl (23 kB)\r\n",
      "Requirement already satisfied: pytest in /opt/conda/lib/python3.10/site-packages (from dtreeviz) (7.3.2)\r\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib->dtreeviz) (1.0.7)\r\n",
      "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib->dtreeviz) (0.11.0)\r\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib->dtreeviz) (4.39.3)\r\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib->dtreeviz) (1.4.4)\r\n",
      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib->dtreeviz) (21.3)\r\n",
      "Requirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib->dtreeviz) (9.5.0)\r\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib->dtreeviz) (3.0.9)\r\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib->dtreeviz) (2.8.2)\r\n",
      "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas->dtreeviz) (2023.3)\r\n",
      "Requirement already satisfied: iniconfig in /opt/conda/lib/python3.10/site-packages (from pytest->dtreeviz) (2.0.0)\r\n",
      "Requirement already satisfied: pluggy<2.0,>=0.12 in /opt/conda/lib/python3.10/site-packages (from pytest->dtreeviz) (1.0.0)\r\n",
      "Requirement already satisfied: exceptiongroup>=1.0.0rc8 in /opt/conda/lib/python3.10/site-packages (from pytest->dtreeviz) (1.1.1)\r\n",
      "Requirement already satisfied: tomli>=1.0.0 in /opt/conda/lib/python3.10/site-packages (from pytest->dtreeviz) (2.0.1)\r\n",
      "Requirement already satisfied: scipy>=1.3.2 in /opt/conda/lib/python3.10/site-packages (from scikit-learn->dtreeviz) (1.10.1)\r\n",
      "Requirement already satisfied: joblib>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from scikit-learn->dtreeviz) (1.2.0)\r\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.10/site-packages (from scikit-learn->dtreeviz) (3.1.0)\r\n",
      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib->dtreeviz) (1.16.0)\r\n",
      "Installing collected packages: colour, dtreeviz\r\n",
      "Successfully installed colour-0.1.5 dtreeviz-2.2.1\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install dtreeviz"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bad30f6",
   "metadata": {
    "papermill": {
     "duration": 0.01808,
     "end_time": "2023-07-09T17:24:50.891526",
     "exception": false,
     "start_time": "2023-07-09T17:24:50.873446",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Importing Required Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7f1438e1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:50.929096Z",
     "iopub.status.busy": "2023-07-09T17:24:50.928775Z",
     "iopub.status.idle": "2023-07-09T17:24:52.131745Z",
     "shell.execute_reply": "2023-07-09T17:24:52.130868Z"
    },
    "papermill": {
     "duration": 1.224406,
     "end_time": "2023-07-09T17:24:52.133778",
     "exception": false,
     "start_time": "2023-07-09T17:24:50.909372",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "\n",
    "# You can also use this section to suppress warnings generated by your code:\n",
    "def warn(*args, **kwargs):\n",
    "    pass\n",
    "import warnings\n",
    "warnings.warn = warn\n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "034be696",
   "metadata": {
    "papermill": {
     "duration": 0.018298,
     "end_time": "2023-07-09T17:24:52.170824",
     "exception": false,
     "start_time": "2023-07-09T17:24:52.152526",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Creating helper function for plotting \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4df3dc71",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:52.208599Z",
     "iopub.status.busy": "2023-07-09T17:24:52.207427Z",
     "iopub.status.idle": "2023-07-09T17:24:52.217336Z",
     "shell.execute_reply": "2023-07-09T17:24:52.216693Z"
    },
    "papermill": {
     "duration": 0.030332,
     "end_time": "2023-07-09T17:24:52.218802",
     "exception": false,
     "start_time": "2023-07-09T17:24:52.188470",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "sns.set(style=\"whitegrid\", color_codes=True)\n",
    "import itertools\n",
    "\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    thresh = 3*cm.max()/4\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, cm[i, j],\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43cb3e17",
   "metadata": {
    "papermill": {
     "duration": 0.01803,
     "end_time": "2023-07-09T17:24:52.255083",
     "exception": false,
     "start_time": "2023-07-09T17:24:52.237053",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Parkinson Data and Voice Disorder\n",
    "\n",
    "## <a href=https://archive.ics.uci.edu/ml/datasets/parkinsons>Voice disorder dataset</a> can be used to detect the presence of Parkinson's disease in an individual. While current tools have limitations in analyzing complex voice disorders, advancements in technology and research have enabled the development of new algorithms that can identify specific acoustic markers associated with Parkinson's disease in voice recordings. Therefore, the analysis of voice disorders can provide valuable information in diagnosing and monitoring Parkinson's disease.\n",
    "\n",
    "\n",
    "\n",
    "## This dataset is composed of a range of biomedical voice measurements from 31 people, 23 with Parkinson's disease (PD).  Our dataset includes voice attributes Information that can be used for detecting parkinson, these information including:\n",
    "\n",
    "\n",
    "## Matrix column entries (attributes):  \n",
    "- name - ASCII subject name and recording number\n",
    "- MDVP:Fo(Hz) - Average vocal fundamental frequency\n",
    "- MDVP:Fhi(Hz) - Maximum vocal fundamental frequency\n",
    "- MDVP:Flo(Hz) - Minimum vocal fundamental frequency\n",
    "- Five measures of variation in Frequency\n",
    "    - MDVP:Jitter(%) - Percentage of cycle-to-cycle variability of the period duration\n",
    "    - MDVP:Jitter(Abs) - Absolute value of cycle-to-cycle variability of the period duration\n",
    "    - MDVP:RAP - Relative measure of the pitch disturbance\n",
    "    - MDVP:PPQ - Pitch perturbation quotient\n",
    "    - Jitter:DDP - Average absolute difference of differences between jitter cycles\n",
    "- Six measures of variation in amplitude\n",
    "    - MDVP:Shimmer - Variations in the voice amplitdue\n",
    "    - MDVP:Shimmer(dB) - Variations in the voice amplitdue in dB\n",
    "    - Shimmer:APQ3 - Three point amplitude perturbation quotient measured against the average of the three amplitude\n",
    "    - Shimmer:APQ5 - Five point amplitude perturbation quotient measured against the average of the three amplitude\n",
    "    - MDVP:APQ - Amplitude perturbation quotient from MDVP\n",
    "    - Shimmer:DDA - Average absolute difference between the amplitudes of consecutive periods\n",
    "- Two measures of ratio of noise to tonal components in the voice\n",
    "    - NHR - Noise-to-harmonics Ratio and \n",
    "    - HNR - Harmonics-to-noise Ratio\n",
    "- status - Health status of the subject (one) - Parkinson's, (zero) - healthy\n",
    "- Two nonlinear dynamical complexity measures\n",
    "    - RPDE - Recurrence period density entropy\n",
    "    - D2 - correlation dimension\n",
    "- DFA - Signal fractal scaling exponent\n",
    "- Three nonlinear measures of fundamental frequency variation\n",
    "    - spread1 - discrete probability distribution of occurrence of relative semitone variations\n",
    "    - spread2 - Three nonlinear measures of fundamental frequency variation\n",
    "    - PPE - Entropy of the discrete probability distribution of occurrence of relative semitone variations\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7a77840",
   "metadata": {
    "papermill": {
     "duration": 0.017691,
     "end_time": "2023-07-09T17:24:52.291018",
     "exception": false,
     "start_time": "2023-07-09T17:24:52.273327",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Loading data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a35f9f91",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:52.330741Z",
     "iopub.status.busy": "2023-07-09T17:24:52.330166Z",
     "iopub.status.idle": "2023-07-09T17:24:52.373507Z",
     "shell.execute_reply": "2023-07-09T17:24:52.372459Z"
    },
    "papermill": {
     "duration": 0.065123,
     "end_time": "2023-07-09T17:24:52.375276",
     "exception": false,
     "start_time": "2023-07-09T17:24:52.310153",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "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>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>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",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>phon_R01_S01_1</th>\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>...</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>phon_R01_S01_2</th>\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>...</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>phon_R01_S01_3</th>\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>...</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>phon_R01_S01_4</th>\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>...</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>phon_R01_S01_5</th>\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>...</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",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  \\\n",
       "name                                                                      \n",
       "phon_R01_S01_1      119.992       157.302        74.997         0.00784   \n",
       "phon_R01_S01_2      122.400       148.650       113.819         0.00968   \n",
       "phon_R01_S01_3      116.682       131.111       111.555         0.01050   \n",
       "phon_R01_S01_4      116.676       137.871       111.366         0.00997   \n",
       "phon_R01_S01_5      116.014       141.781       110.655         0.01284   \n",
       "\n",
       "                MDVP:Jitter(Abs)  MDVP:RAP  MDVP:PPQ  Jitter:DDP  \\\n",
       "name                                                               \n",
       "phon_R01_S01_1           0.00007   0.00370   0.00554     0.01109   \n",
       "phon_R01_S01_2           0.00008   0.00465   0.00696     0.01394   \n",
       "phon_R01_S01_3           0.00009   0.00544   0.00781     0.01633   \n",
       "phon_R01_S01_4           0.00009   0.00502   0.00698     0.01505   \n",
       "phon_R01_S01_5           0.00011   0.00655   0.00908     0.01966   \n",
       "\n",
       "                MDVP:Shimmer  MDVP:Shimmer(dB)  ...  Shimmer:DDA      NHR  \\\n",
       "name                                            ...                         \n",
       "phon_R01_S01_1       0.04374             0.426  ...      0.06545  0.02211   \n",
       "phon_R01_S01_2       0.06134             0.626  ...      0.09403  0.01929   \n",
       "phon_R01_S01_3       0.05233             0.482  ...      0.08270  0.01309   \n",
       "phon_R01_S01_4       0.05492             0.517  ...      0.08771  0.01353   \n",
       "phon_R01_S01_5       0.06425             0.584  ...      0.10470  0.01767   \n",
       "\n",
       "                   HNR  status      RPDE       DFA   spread1   spread2  \\\n",
       "name                                                                     \n",
       "phon_R01_S01_1  21.033       1  0.414783  0.815285 -4.813031  0.266482   \n",
       "phon_R01_S01_2  19.085       1  0.458359  0.819521 -4.075192  0.335590   \n",
       "phon_R01_S01_3  20.651       1  0.429895  0.825288 -4.443179  0.311173   \n",
       "phon_R01_S01_4  20.644       1  0.434969  0.819235 -4.117501  0.334147   \n",
       "phon_R01_S01_5  19.649       1  0.417356  0.823484 -3.747787  0.234513   \n",
       "\n",
       "                      D2       PPE  \n",
       "name                                \n",
       "phon_R01_S01_1  2.301442  0.284654  \n",
       "phon_R01_S01_2  2.486855  0.368674  \n",
       "phon_R01_S01_3  2.342259  0.332634  \n",
       "phon_R01_S01_4  2.405554  0.368975  \n",
       "phon_R01_S01_5  2.332180  0.410335  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"/kaggle/input/parkinsons-disease-data-set/parkinsons.data\", sep=',', index_col='name') # assign index column to name \n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8c9e52f",
   "metadata": {
    "papermill": {
     "duration": 0.018266,
     "end_time": "2023-07-09T17:24:52.412258",
     "exception": false,
     "start_time": "2023-07-09T17:24:52.393992",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## we can see the number of samples \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2f9a116f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:52.450816Z",
     "iopub.status.busy": "2023-07-09T17:24:52.450275Z",
     "iopub.status.idle": "2023-07-09T17:24:52.454571Z",
     "shell.execute_reply": "2023-07-09T17:24:52.453701Z"
    },
    "papermill": {
     "duration": 0.025383,
     "end_time": "2023-07-09T17:24:52.456084",
     "exception": false,
     "start_time": "2023-07-09T17:24:52.430701",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(195, 23)\n"
     ]
    }
   ],
   "source": [
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6f849ca",
   "metadata": {
    "papermill": {
     "duration": 0.017916,
     "end_time": "2023-07-09T17:24:52.492043",
     "exception": false,
     "start_time": "2023-07-09T17:24:52.474127",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## To improve our understanding of the variables involved in parkinson detection, we first need to analyze the relationships within the data. Correlation diagrams can be helpful in visualizing how different variables are associated with each other and with parkinson status. Additionally, random forest models can help identify the importance of different features in predicting the target variable (parkinson).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "313f8ed0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:52.530063Z",
     "iopub.status.busy": "2023-07-09T17:24:52.529735Z",
     "iopub.status.idle": "2023-07-09T17:24:53.270569Z",
     "shell.execute_reply": "2023-07-09T17:24:53.269798Z"
    },
    "papermill": {
     "duration": 0.761927,
     "end_time": "2023-07-09T17:24:53.272286",
     "exception": false,
     "start_time": "2023-07-09T17:24:52.510359",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# creating the correlation matrix\n",
    "plt.figure(figsize=(12, 10))\n",
    "mask = np.triu(np.ones_like(data.corr(), dtype=np.bool))\n",
    "sns.heatmap(data.corr(),vmin=-1, vmax=1,cmap='BrBG', mask=mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fc2a18e9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:53.314984Z",
     "iopub.status.busy": "2023-07-09T17:24:53.314300Z",
     "iopub.status.idle": "2023-07-09T17:24:53.812837Z",
     "shell.execute_reply": "2023-07-09T17:24:53.812216Z"
    },
    "papermill": {
     "duration": 0.521814,
     "end_time": "2023-07-09T17:24:53.814499",
     "exception": false,
     "start_time": "2023-07-09T17:24:53.292685",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# modify the headmap plot to show correlation variables to the status \n",
    "plt.figure(figsize=(10, 10))\n",
    "heatmap = sns.heatmap(data.corr()[['status']].sort_values(by='status', ascending=False), vmin=-1, vmax=1, annot=True, cmap='BrBG')\n",
    "heatmap.set_title('Features Correlating with Parkinson existance', fontdict={'fontsize':18}, pad=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15361ab1",
   "metadata": {
    "papermill": {
     "duration": 0.020941,
     "end_time": "2023-07-09T17:24:53.857041",
     "exception": false,
     "start_time": "2023-07-09T17:24:53.836100",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Let's get the  features we select all columns in the dataset except for the `status` column. This is done using the ```drop``` method, which returns a new DataFrame with the specified columns (in this case, 'status') removed. The `axis=1` argument indicates that we're dropping a column, not a row.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8024f70d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:53.901835Z",
     "iopub.status.busy": "2023-07-09T17:24:53.901341Z",
     "iopub.status.idle": "2023-07-09T17:24:53.921535Z",
     "shell.execute_reply": "2023-07-09T17:24:53.920798Z"
    },
    "papermill": {
     "duration": 0.044536,
     "end_time": "2023-07-09T17:24:53.923133",
     "exception": false,
     "start_time": "2023-07-09T17:24:53.878597",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "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>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",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>phon_R01_S01_1</th>\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>...</td>\n",
       "      <td>0.02971</td>\n",
       "      <td>0.06545</td>\n",
       "      <td>0.02211</td>\n",
       "      <td>21.033</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>phon_R01_S01_2</th>\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>...</td>\n",
       "      <td>0.04368</td>\n",
       "      <td>0.09403</td>\n",
       "      <td>0.01929</td>\n",
       "      <td>19.085</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>phon_R01_S01_3</th>\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>...</td>\n",
       "      <td>0.03590</td>\n",
       "      <td>0.08270</td>\n",
       "      <td>0.01309</td>\n",
       "      <td>20.651</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>phon_R01_S01_4</th>\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>...</td>\n",
       "      <td>0.03772</td>\n",
       "      <td>0.08771</td>\n",
       "      <td>0.01353</td>\n",
       "      <td>20.644</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>phon_R01_S01_5</th>\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>...</td>\n",
       "      <td>0.04465</td>\n",
       "      <td>0.10470</td>\n",
       "      <td>0.01767</td>\n",
       "      <td>19.649</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",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  \\\n",
       "name                                                                      \n",
       "phon_R01_S01_1      119.992       157.302        74.997         0.00784   \n",
       "phon_R01_S01_2      122.400       148.650       113.819         0.00968   \n",
       "phon_R01_S01_3      116.682       131.111       111.555         0.01050   \n",
       "phon_R01_S01_4      116.676       137.871       111.366         0.00997   \n",
       "phon_R01_S01_5      116.014       141.781       110.655         0.01284   \n",
       "\n",
       "                MDVP:Jitter(Abs)  MDVP:RAP  MDVP:PPQ  Jitter:DDP  \\\n",
       "name                                                               \n",
       "phon_R01_S01_1           0.00007   0.00370   0.00554     0.01109   \n",
       "phon_R01_S01_2           0.00008   0.00465   0.00696     0.01394   \n",
       "phon_R01_S01_3           0.00009   0.00544   0.00781     0.01633   \n",
       "phon_R01_S01_4           0.00009   0.00502   0.00698     0.01505   \n",
       "phon_R01_S01_5           0.00011   0.00655   0.00908     0.01966   \n",
       "\n",
       "                MDVP:Shimmer  MDVP:Shimmer(dB)  ...  MDVP:APQ  Shimmer:DDA  \\\n",
       "name                                            ...                          \n",
       "phon_R01_S01_1       0.04374             0.426  ...   0.02971      0.06545   \n",
       "phon_R01_S01_2       0.06134             0.626  ...   0.04368      0.09403   \n",
       "phon_R01_S01_3       0.05233             0.482  ...   0.03590      0.08270   \n",
       "phon_R01_S01_4       0.05492             0.517  ...   0.03772      0.08771   \n",
       "phon_R01_S01_5       0.06425             0.584  ...   0.04465      0.10470   \n",
       "\n",
       "                    NHR     HNR      RPDE       DFA   spread1   spread2  \\\n",
       "name                                                                      \n",
       "phon_R01_S01_1  0.02211  21.033  0.414783  0.815285 -4.813031  0.266482   \n",
       "phon_R01_S01_2  0.01929  19.085  0.458359  0.819521 -4.075192  0.335590   \n",
       "phon_R01_S01_3  0.01309  20.651  0.429895  0.825288 -4.443179  0.311173   \n",
       "phon_R01_S01_4  0.01353  20.644  0.434969  0.819235 -4.117501  0.334147   \n",
       "phon_R01_S01_5  0.01767  19.649  0.417356  0.823484 -3.747787  0.234513   \n",
       "\n",
       "                      D2       PPE  \n",
       "name                                \n",
       "phon_R01_S01_1  2.301442  0.284654  \n",
       "phon_R01_S01_2  2.486855  0.368674  \n",
       "phon_R01_S01_3  2.342259  0.332634  \n",
       "phon_R01_S01_4  2.405554  0.368975  \n",
       "phon_R01_S01_5  2.332180  0.410335  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data.drop('status', axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10c8811e",
   "metadata": {
    "papermill": {
     "duration": 0.021694,
     "end_time": "2023-07-09T17:24:53.966564",
     "exception": false,
     "start_time": "2023-07-09T17:24:53.944870",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## The   Health status of the subject (one) - Parkinson's, (zero) - healthy  is given in the ```status``` column:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2d8db813",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:54.012865Z",
     "iopub.status.busy": "2023-07-09T17:24:54.012256Z",
     "iopub.status.idle": "2023-07-09T17:24:54.018453Z",
     "shell.execute_reply": "2023-07-09T17:24:54.017746Z"
    },
    "papermill": {
     "duration": 0.032014,
     "end_time": "2023-07-09T17:24:54.020398",
     "exception": false,
     "start_time": "2023-07-09T17:24:53.988384",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name\n",
       "phon_R01_S01_1    1\n",
       "phon_R01_S01_2    1\n",
       "phon_R01_S01_3    1\n",
       "phon_R01_S01_4    1\n",
       "phon_R01_S01_5    1\n",
       "Name: status, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=data['status']\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da224ba2",
   "metadata": {
    "papermill": {
     "duration": 0.020778,
     "end_time": "2023-07-09T17:24:54.063070",
     "exception": false,
     "start_time": "2023-07-09T17:24:54.042292",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### we can see that 3/4 of the samples are have Parkinson's\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5e3c6316",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:54.107501Z",
     "iopub.status.busy": "2023-07-09T17:24:54.107161Z",
     "iopub.status.idle": "2023-07-09T17:24:54.115013Z",
     "shell.execute_reply": "2023-07-09T17:24:54.114115Z"
    },
    "papermill": {
     "duration": 0.031299,
     "end_time": "2023-07-09T17:24:54.116605",
     "exception": false,
     "start_time": "2023-07-09T17:24:54.085306",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.753846\n",
       "0    0.246154\n",
       "Name: status, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70e99860",
   "metadata": {
    "papermill": {
     "duration": 0.020521,
     "end_time": "2023-07-09T17:24:54.158022",
     "exception": false,
     "start_time": "2023-07-09T17:24:54.137501",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## t-SNE (t-Distributed Stochastic Neighbor Embedding) is a machine learning technique used for dimensionality reduction and visualization of high-dimensional datasets. It is particularly useful for visualizing complex data structures, as it helps to project the data points from a high-dimensional space to a lower-dimensional space (usually 2D or 3D) while preserving the relationships between the data points as much as possible. Lets apply it to our dataset:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "04da7d91",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:54.201010Z",
     "iopub.status.busy": "2023-07-09T17:24:54.200520Z",
     "iopub.status.idle": "2023-07-09T17:24:55.498979Z",
     "shell.execute_reply": "2023-07-09T17:24:55.498209Z"
    },
    "papermill": {
     "duration": 1.322275,
     "end_time": "2023-07-09T17:24:55.500889",
     "exception": false,
     "start_time": "2023-07-09T17:24:54.178614",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "# Apply t-SNE to reduce the dimensions to 2\n",
    "tsne = TSNE(n_components=2, random_state=42)\n",
    "X_tsne = tsne.fit_transform(X)\n",
    "\n",
    "# Create a DataFrame with the t-SNE-transformed data and class labels\n",
    "tsne_df = pd.DataFrame(data=X_tsne, columns=['TSNE1', 'TSNE2'])\n",
    "tsne_df['Class'] = y.values\n",
    "\n",
    "# Visualize the data based on class using a scatter plot\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.scatterplot(data=tsne_df, x='TSNE1', y='TSNE2', hue='Class', palette='Set2')\n",
    "plt.title('t-SNE Visualization')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba254c7e",
   "metadata": {
    "papermill": {
     "duration": 0.021152,
     "end_time": "2023-07-09T17:24:55.546525",
     "exception": false,
     "start_time": "2023-07-09T17:24:55.525373",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Data preparation\n",
    "\n",
    "## Here, we are splitting a dataset into independent and dependent variables, and then splitting it further into training and testing sets.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "91deac68",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:55.593822Z",
     "iopub.status.busy": "2023-07-09T17:24:55.593233Z",
     "iopub.status.idle": "2023-07-09T17:24:55.600054Z",
     "shell.execute_reply": "2023-07-09T17:24:55.599227Z"
    },
    "papermill": {
     "duration": 0.032784,
     "end_time": "2023-07-09T17:24:55.601742",
     "exception": false,
     "start_time": "2023-07-09T17:24:55.568958",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape:  (136, 22)\n",
      "y_train shape:  (136,)\n",
      "X_test shape:  (59, 22)\n",
      "y_test shape:  (59,)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=11)\n",
    "\n",
    "\n",
    "# print the shape of train and test data\n",
    "print(\"X_train shape: \", X_train.shape)\n",
    "print(\"y_train shape: \", y_train.shape)\n",
    "print(\"X_test shape: \", X_test.shape)\n",
    "print(\"y_test shape: \", y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92a58736",
   "metadata": {
    "papermill": {
     "duration": 0.022203,
     "end_time": "2023-07-09T17:24:55.646463",
     "exception": false,
     "start_time": "2023-07-09T17:24:55.624260",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# k-Nearest Neighbors (k-NN)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36a827bf",
   "metadata": {
    "papermill": {
     "duration": 0.021707,
     "end_time": "2023-07-09T17:24:55.690648",
     "exception": false,
     "start_time": "2023-07-09T17:24:55.668941",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## K-NN \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7eb05c95",
   "metadata": {
    "papermill": {
     "duration": 0.022094,
     "end_time": "2023-07-09T17:24:55.734958",
     "exception": false,
     "start_time": "2023-07-09T17:24:55.712864",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## In k-NN, the basic idea is to predict the label of a new instance based on the labels of its k nearest neighbors in the training data. \n",
    "\n",
    "## We import the library \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0fb44a95",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:55.780592Z",
     "iopub.status.busy": "2023-07-09T17:24:55.780014Z",
     "iopub.status.idle": "2023-07-09T17:24:55.784048Z",
     "shell.execute_reply": "2023-07-09T17:24:55.783353Z"
    },
    "papermill": {
     "duration": 0.028593,
     "end_time": "2023-07-09T17:24:55.785631",
     "exception": false,
     "start_time": "2023-07-09T17:24:55.757038",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae4bab1f",
   "metadata": {
    "papermill": {
     "duration": 0.021909,
     "end_time": "2023-07-09T17:24:55.829843",
     "exception": false,
     "start_time": "2023-07-09T17:24:55.807934",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## we create a knn object \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bf468090",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:55.875737Z",
     "iopub.status.busy": "2023-07-09T17:24:55.875150Z",
     "iopub.status.idle": "2023-07-09T17:24:55.879065Z",
     "shell.execute_reply": "2023-07-09T17:24:55.878443Z"
    },
    "papermill": {
     "duration": 0.028941,
     "end_time": "2023-07-09T17:24:55.880867",
     "exception": false,
     "start_time": "2023-07-09T17:24:55.851926",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adb2aafc",
   "metadata": {
    "papermill": {
     "duration": 0.021887,
     "end_time": "2023-07-09T17:24:55.926830",
     "exception": false,
     "start_time": "2023-07-09T17:24:55.904943",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## we fit the object\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "77d7a405",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:55.974010Z",
     "iopub.status.busy": "2023-07-09T17:24:55.973161Z",
     "iopub.status.idle": "2023-07-09T17:24:55.985214Z",
     "shell.execute_reply": "2023-07-09T17:24:55.984595Z"
    },
    "papermill": {
     "duration": 0.037657,
     "end_time": "2023-07-09T17:24:55.986844",
     "exception": false,
     "start_time": "2023-07-09T17:24:55.949187",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" 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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" 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>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fbb5788",
   "metadata": {
    "papermill": {
     "duration": 0.02247,
     "end_time": "2023-07-09T17:24:56.031993",
     "exception": false,
     "start_time": "2023-07-09T17:24:56.009523",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Make predictions on the testing data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7820543c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:56.079142Z",
     "iopub.status.busy": "2023-07-09T17:24:56.078540Z",
     "iopub.status.idle": "2023-07-09T17:24:56.095569Z",
     "shell.execute_reply": "2023-07-09T17:24:56.094713Z"
    },
    "papermill": {
     "duration": 0.042458,
     "end_time": "2023-07-09T17:24:56.097263",
     "exception": false,
     "start_time": "2023-07-09T17:24:56.054805",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 1, 1, 1, 1, 0, 0, 0])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = knn.predict(X_test)\n",
    "y_pred[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9a91e71",
   "metadata": {
    "papermill": {
     "duration": 0.021674,
     "end_time": "2023-07-09T17:24:56.141810",
     "exception": false,
     "start_time": "2023-07-09T17:24:56.120136",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## We can  get a probability of belonging to a each class for each sample, here are the first ten\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "aaf2111e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:56.187221Z",
     "iopub.status.busy": "2023-07-09T17:24:56.186893Z",
     "iopub.status.idle": "2023-07-09T17:24:56.196283Z",
     "shell.execute_reply": "2023-07-09T17:24:56.195490Z"
    },
    "papermill": {
     "duration": 0.036723,
     "end_time": "2023-07-09T17:24:56.200659",
     "exception": false,
     "start_time": "2023-07-09T17:24:56.163936",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.33333333, 0.66666667],\n",
       "       [0.66666667, 0.33333333],\n",
       "       [0.33333333, 0.66666667],\n",
       "       [0.33333333, 0.66666667],\n",
       "       [0.        , 1.        ],\n",
       "       [0.33333333, 0.66666667],\n",
       "       [0.        , 1.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.predict_proba(X_test)[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "024aae81",
   "metadata": {
    "papermill": {
     "duration": 0.021553,
     "end_time": "2023-07-09T17:24:56.244174",
     "exception": false,
     "start_time": "2023-07-09T17:24:56.222621",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## The confusion matrix is a table that shows the number of true positive, true negative, false positive, and false negative predictions. The classification report provides the precision, recall, F1-score, and support for each class, which are important metrics for evaluating the performance of a classifier. The accuracy score is a simple metric that calculates the proportion of correct predictions out of the total predictions made by the model. These three elements together provide a comprehensive evaluation of the classifier's performance on the test dataset. Let's prints the confusion matrix, classification report, and accuracy score for a given classification model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3fe7a47f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:56.289885Z",
     "iopub.status.busy": "2023-07-09T17:24:56.289052Z",
     "iopub.status.idle": "2023-07-09T17:24:56.293000Z",
     "shell.execute_reply": "2023-07-09T17:24:56.292355Z"
    },
    "papermill": {
     "duration": 0.028545,
     "end_time": "2023-07-09T17:24:56.294741",
     "exception": false,
     "start_time": "2023-07-09T17:24:56.266196",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0d89b984",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:56.341499Z",
     "iopub.status.busy": "2023-07-09T17:24:56.340974Z",
     "iopub.status.idle": "2023-07-09T17:24:56.617411Z",
     "shell.execute_reply": "2023-07-09T17:24:56.616490Z"
    },
    "papermill": {
     "duration": 0.301185,
     "end_time": "2023-07-09T17:24:56.619089",
     "exception": false,
     "start_time": "2023-07-09T17:24:56.317904",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[ 9  3]\n",
      " [ 5 42]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(confusion_matrix(y_test, y_pred),classes=[ \"Not Parkinson\", \" Parkinson\"],title='Confusion matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1e2216f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:56.665275Z",
     "iopub.status.busy": "2023-07-09T17:24:56.664995Z",
     "iopub.status.idle": "2023-07-09T17:24:56.677379Z",
     "shell.execute_reply": "2023-07-09T17:24:56.676256Z"
    },
    "papermill": {
     "duration": 0.037572,
     "end_time": "2023-07-09T17:24:56.679111",
     "exception": false,
     "start_time": "2023-07-09T17:24:56.641539",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.64      0.75      0.69        12\n",
      "           1       0.93      0.89      0.91        47\n",
      "\n",
      "    accuracy                           0.86        59\n",
      "   macro avg       0.79      0.82      0.80        59\n",
      "weighted avg       0.87      0.86      0.87        59\n",
      "\n",
      "\n",
      "Accuracy Score: 0.864406779661017\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))\n",
    "print(\"\\nAccuracy Score:\", accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "920aef47",
   "metadata": {
    "papermill": {
     "duration": 0.021878,
     "end_time": "2023-07-09T17:24:56.723682",
     "exception": false,
     "start_time": "2023-07-09T17:24:56.701804",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# GridSearchCV\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4bb577f",
   "metadata": {
    "papermill": {
     "duration": 0.022234,
     "end_time": "2023-07-09T17:24:56.768135",
     "exception": false,
     "start_time": "2023-07-09T17:24:56.745901",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### In the code above, we randomly selected the hyperparameter ```K``` for our KNN classifier. However, in order to find the optimal hyperparameters that maximize the performance metric, we will perform a grid search. To do this, we define a parameter grid that we will search over for ```K```.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e1ece9ba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:56.814349Z",
     "iopub.status.busy": "2023-07-09T17:24:56.814026Z",
     "iopub.status.idle": "2023-07-09T17:24:56.817707Z",
     "shell.execute_reply": "2023-07-09T17:24:56.816909Z"
    },
    "papermill": {
     "duration": 0.029105,
     "end_time": "2023-07-09T17:24:56.819691",
     "exception": false,
     "start_time": "2023-07-09T17:24:56.790586",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "    'n_neighbors': [3, 5, 7, 9],\n",
    "\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e26e22f",
   "metadata": {
    "papermill": {
     "duration": 0.024537,
     "end_time": "2023-07-09T17:24:56.868312",
     "exception": false,
     "start_time": "2023-07-09T17:24:56.843775",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Define the KNN classifier object\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "99e401c3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:56.917694Z",
     "iopub.status.busy": "2023-07-09T17:24:56.917117Z",
     "iopub.status.idle": "2023-07-09T17:24:56.921160Z",
     "shell.execute_reply": "2023-07-09T17:24:56.920284Z"
    },
    "papermill": {
     "duration": 0.030893,
     "end_time": "2023-07-09T17:24:56.923110",
     "exception": false,
     "start_time": "2023-07-09T17:24:56.892217",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "knn = KNeighborsClassifier()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3d422f9",
   "metadata": {
    "papermill": {
     "duration": 0.022381,
     "end_time": "2023-07-09T17:24:56.969032",
     "exception": false,
     "start_time": "2023-07-09T17:24:56.946651",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### In medical testing, recall is a measure of the ability of a test to correctly identify true positive cases, or the proportion of actual positive cases that are correctly identified by the test. A high recall means that the test is able to correctly identify a large proportion of true positive cases, even if it may also produce some false positive results, therefore for ```scoring``` we select recall . The choice of performance metric for a machine learning model may depend on several factors. \n",
    "\n",
    "### The next cell  performs a grid search over the hyperparameters of a KNN classifier using ```GridSearchCV()``` function from scikit-learn library. The param_grid parameter specifies a dictionary of hyperparameters to search over, while the scoring parameter specifies the performance metric to optimize for, which in this case is the recall. The cv parameter specifies the number of folds for cross-validationv ```cv``` . The resulting grid_search object can be used to fit the data and obtain the best hyperparameters that maximize the performance metric.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0b4de7a2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:57.016285Z",
     "iopub.status.busy": "2023-07-09T17:24:57.015630Z",
     "iopub.status.idle": "2023-07-09T17:24:57.023334Z",
     "shell.execute_reply": "2023-07-09T17:24:57.022721Z"
    },
    "papermill": {
     "duration": 0.03316,
     "end_time": "2023-07-09T17:24:57.024929",
     "exception": false,
     "start_time": "2023-07-09T17:24:56.991769",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;n_neighbors&#x27;: [3, 5, 7, 9]}, scoring=&#x27;recall&#x27;)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;n_neighbors&#x27;: [3, 5, 7, 9]}, scoring=&#x27;recall&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div><div class=\"sk-serial\"><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\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
       "             param_grid={'n_neighbors': [3, 5, 7, 9]}, scoring='recall')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "\n",
    "grid_search = GridSearchCV(knn, param_grid, scoring='recall', cv=5)\n",
    "\n",
    "grid_search "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0706fe6d",
   "metadata": {
    "papermill": {
     "duration": 0.022996,
     "end_time": "2023-07-09T17:24:57.070888",
     "exception": false,
     "start_time": "2023-07-09T17:24:57.047892",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Fit the grid search to the training data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ddfd2e65",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:57.120160Z",
     "iopub.status.busy": "2023-07-09T17:24:57.119868Z",
     "iopub.status.idle": "2023-07-09T17:24:57.251883Z",
     "shell.execute_reply": "2023-07-09T17:24:57.251077Z"
    },
    "papermill": {
     "duration": 0.157928,
     "end_time": "2023-07-09T17:24:57.253463",
     "exception": false,
     "start_time": "2023-07-09T17:24:57.095535",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;n_neighbors&#x27;: [3, 5, 7, 9]}, scoring=&#x27;recall&#x27;)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;n_neighbors&#x27;: [3, 5, 7, 9]}, scoring=&#x27;recall&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
       "             param_grid={'n_neighbors': [3, 5, 7, 9]}, scoring='recall')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3c44dc2",
   "metadata": {
    "papermill": {
     "duration": 0.023902,
     "end_time": "2023-07-09T17:24:57.301467",
     "exception": false,
     "start_time": "2023-07-09T17:24:57.277565",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Print the best parameters and score on validation data \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b666474b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:57.351093Z",
     "iopub.status.busy": "2023-07-09T17:24:57.350577Z",
     "iopub.status.idle": "2023-07-09T17:24:57.354410Z",
     "shell.execute_reply": "2023-07-09T17:24:57.353855Z"
    },
    "papermill": {
     "duration": 0.030441,
     "end_time": "2023-07-09T17:24:57.355944",
     "exception": false,
     "start_time": "2023-07-09T17:24:57.325503",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters:  {'n_neighbors': 9}\n",
      "Best Score:  0.97\n"
     ]
    }
   ],
   "source": [
    "print(\"Best Parameters: \", grid_search.best_params_)\n",
    "print(\"Best Score: \", grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "268eda48",
   "metadata": {
    "papermill": {
     "duration": 0.022353,
     "end_time": "2023-07-09T17:24:57.402318",
     "exception": false,
     "start_time": "2023-07-09T17:24:57.379965",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Get the predicted labels on the test data using the best estimator:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "91505fe0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:57.449088Z",
     "iopub.status.busy": "2023-07-09T17:24:57.448577Z",
     "iopub.status.idle": "2023-07-09T17:24:57.455588Z",
     "shell.execute_reply": "2023-07-09T17:24:57.455042Z"
    },
    "papermill": {
     "duration": 0.032768,
     "end_time": "2023-07-09T17:24:57.457591",
     "exception": false,
     "start_time": "2023-07-09T17:24:57.424823",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_pred = grid_search.best_estimator_.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f1b3307",
   "metadata": {
    "papermill": {
     "duration": 0.022774,
     "end_time": "2023-07-09T17:24:57.504296",
     "exception": false,
     "start_time": "2023-07-09T17:24:57.481522",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Calculate the recall score on the test data using the predicted labels:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "01f84f79",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:57.551644Z",
     "iopub.status.busy": "2023-07-09T17:24:57.551371Z",
     "iopub.status.idle": "2023-07-09T17:24:57.559379Z",
     "shell.execute_reply": "2023-07-09T17:24:57.558502Z"
    },
    "papermill": {
     "duration": 0.033477,
     "end_time": "2023-07-09T17:24:57.560959",
     "exception": false,
     "start_time": "2023-07-09T17:24:57.527482",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9148936170212766"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import recall_score\n",
    "recall = recall_score(y_test, y_pred)\n",
    "\n",
    "recall"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7599fbd4",
   "metadata": {
    "papermill": {
     "duration": 0.022294,
     "end_time": "2023-07-09T17:24:57.605876",
     "exception": false,
     "start_time": "2023-07-09T17:24:57.583582",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### we can make a prediction \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e6a4d668",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:57.652867Z",
     "iopub.status.busy": "2023-07-09T17:24:57.652527Z",
     "iopub.status.idle": "2023-07-09T17:24:57.668916Z",
     "shell.execute_reply": "2023-07-09T17:24:57.668113Z"
    },
    "papermill": {
     "duration": 0.041889,
     "end_time": "2023-07-09T17:24:57.670591",
     "exception": false,
     "start_time": "2023-07-09T17:24:57.628702",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_hat = grid_search.best_estimator_.predict(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2f0f6fc",
   "metadata": {
    "papermill": {
     "duration": 0.022327,
     "end_time": "2023-07-09T17:24:57.715871",
     "exception": false,
     "start_time": "2023-07-09T17:24:57.693544",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Now we use all our data to train the model \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0f6ceee6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:57.762996Z",
     "iopub.status.busy": "2023-07-09T17:24:57.762462Z",
     "iopub.status.idle": "2023-07-09T17:24:57.769327Z",
     "shell.execute_reply": "2023-07-09T17:24:57.768794Z"
    },
    "papermill": {
     "duration": 0.032189,
     "end_time": "2023-07-09T17:24:57.770746",
     "exception": false,
     "start_time": "2023-07-09T17:24:57.738557",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "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>KNeighborsClassifier()</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-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier()\n",
    "knn.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef51b6ae",
   "metadata": {
    "papermill": {
     "duration": 0.022984,
     "end_time": "2023-07-09T17:24:57.817021",
     "exception": false,
     "start_time": "2023-07-09T17:24:57.794037",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### let's study our predictions  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "48786897",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:57.864882Z",
     "iopub.status.busy": "2023-07-09T17:24:57.863937Z",
     "iopub.status.idle": "2023-07-09T17:24:57.879051Z",
     "shell.execute_reply": "2023-07-09T17:24:57.878302Z"
    },
    "papermill": {
     "duration": 0.040955,
     "end_time": "2023-07-09T17:24:57.880990",
     "exception": false,
     "start_time": "2023-07-09T17:24:57.840035",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_hat =knn.predict(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9463e409",
   "metadata": {
    "papermill": {
     "duration": 0.022924,
     "end_time": "2023-07-09T17:24:57.927390",
     "exception": false,
     "start_time": "2023-07-09T17:24:57.904466",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### we can plot the confusion matrix \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "99c18b74",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:57.974843Z",
     "iopub.status.busy": "2023-07-09T17:24:57.974106Z",
     "iopub.status.idle": "2023-07-09T17:24:58.250649Z",
     "shell.execute_reply": "2023-07-09T17:24:58.249835Z"
    },
    "papermill": {
     "duration": 0.302081,
     "end_time": "2023-07-09T17:24:58.252338",
     "exception": false,
     "start_time": "2023-07-09T17:24:57.950257",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[ 32  16]\n",
      " [  5 142]]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjgAAAHkCAYAAAAzYVU/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABqBElEQVR4nO3dd3yN99/H8dfJOAkiCCGxZ0IIQhGjYrb9qVkULWrUVrtGqaJGtVaJPapUW1WjaKnSosMstVpq1QxiBCEyz/1H7hxOE5rkhMTl/ewj96/nmp9z7qs573y/3+t7mSwWiwURERERA3FI7wJERERE0poCjoiIiBiOAo6IiIgYjgKOiIiIGI4CjoiIiBiOAo6IiIgYjgKOiIiIGI4CjoiIiBiOAo6IiIgYjgKOiIEcPXqUYcOGUadOHfz9/QkICKBZs2bMnz+fsLCwx3ruP//8k7Zt21KxYkV8fX1ZvHhxmp/D19eXGTNmpPlxM5I5c+awefPmFO2zatUqfH19OX/+/GOqSuTpY9KjGkSM4auvvmL06NEUKVKENm3aULx4cWJiYjh8+DBfffUVJUuWZObMmY/t/E2bNiUiIoJ33nmHbNmykS9fPjw9PdP0HH/88QdeXl54eXml6XEzkoCAAF588UU++OCDZO9z/fp1zp49i5+fH2az+TFWJ/L0cErvAkTEfvv372fUqFFUq1aNWbNm2XzJVa9enY4dO/Lzzz8/1hqOHz9Oy5YtCQoKemznKF++/GM79tPo3r17uLi44OHhgYeHR3qXI5KhqItKxADmzp2LyWTi/fffT/IveLPZTN26da2v4+LimD9/Pi+99BJlypShatWqDB48mEuXLtns165dOxo2bMjBgwd57bXXKFeuHHXr1mXevHnExcUB97tHYmJi+OKLL/D19cXX1xeAGTNmWP/9QUl1qezYsYN27dpRpUoVypYtS61atXjrrbeIiIiwbpNUF9Xff/9Njx49qFSpEv7+/jRp0oTVq1fbbLNr1y58fX1Zv349U6dOpUaNGlSoUIEOHTpw6tSp//x8E97H0aNH6dOnDxUrVqRy5cpMmDCBmJgYTp06RefOnQkICKBOnTrMnz/fZv/IyEg++OADmjRpYt23VatWibqifH19uXv3LqtXr7Z+ju3atbP5zH755ReGDRtGYGAg5cqVIyoqKtHn+c8//1ChQgX69Oljc/wdO3ZQqlQppk2b9p/vWeRppxYckadcbGwsO3fupHTp0nh7eydrn1GjRrF8+XLatm1LrVq1uHDhAh9//DG7d+9m1apVNq0BoaGhvP3223Ts2JHevXvzww8/MHnyZHLnzk3Tpk2pVasWy5cvp1WrVrz44ot06tQpxe/h/PnzdOvWjeeee45x48bh7u7O5cuX+fnnn4mOjiZTpkxJ7nfq1Clat25Nzpw5GT58ODly5GDt2rUMHTqUq1ev0qVLF5vtp0yZQoUKFRg3bhzh4eFMmjSJHj168N133+Ho6Pifdfbr14/GjRvTunVrfv31VxYsWEBMTAy//fYbr732Gp07d2bdunVMmjSJQoUK8cILLwAQFRXFzZs36dSpE3ny5CE6OprffvuNt956iwkTJtC0aVMAli9fzhtvvEGVKlXo2bMnAG5ubjY1vPPOO9SqVYsPP/yQiIgInJwS/xovXLgwY8eOpX///ixZsoT27dsTGhrKoEGDeO6553jrrbf+872KPO0UcESecjdu3CAiIoL8+fMna/uTJ0+yfPlyXnvtNd59913rcj8/P1q2bMmnn35K//79rcvDwsKYP38+ZcuWBaBatWrs3r2bdevW0bRpU5vukVy5cqWqG+nIkSNERkYyePBgSpYsaV3eqFGjR+4XHBxMdHQ0S5YssYa7oKAgbt26xcyZM2ndujVZs2a1bl+8eHEmTZpkfe3g4EC/fv04dOhQsupu1aoVHTt2BOI/h19//ZXPPvuM4OBg6tevD0DlypXZunUr69atswacrFmzMmHCBOtxYmNjqVq1Krdu3eLTTz+1Bpzy5cvj4OCAh4fHQ+upWrUqY8aM+c9aGzRowO7du/nwww8pW7YsU6dOxWKxMHny5GSFOZGnnbqoRJ4xu3btAqBZs2Y2y8uWLUuxYsXYsWOHzXJPT09ruEng6+vLxYsX06ymUqVK4ezszLvvvsvq1as5d+5csvbbuXMnVatWTdRy1axZMyIiIti/f7/N8jp16ti8Tug+S+57qVWrls3rYsWKYTKZqFmzpnWZk5MThQoV4sKFCzbbbtiwgdatWxMQEICfnx+lS5fm66+/5uTJk8k6d4KE0JQc77zzDiVKlKB9+/bs3r2bjz76iNy5c6fofCJPKwUckadcjhw5yJQpU7JvEU64XTypL7rcuXMnup08e/bsibYzm81ERkamtNSHKliwIIsXLyZnzpyMGTOGevXqUa9ePT799NNH7hcWFpbknVoJ7+2/3kvCeKV79+4lq85s2bLZvHZ2diZTpky4uLgkWh4VFWV9vWnTJvr160eePHn46KOPWL58OV9//TXNmzdP8eeYkjvTzGYzDRs2JDIyklKlSlG9evUUnUvkaaaAI/KUc3R0JDAwkCNHjiQaJJyUhC/5K1euJFp35coVcuTIkWa1JXzxP/hlD/Hdav/23HPPMWfOHPbu3ctXX31F+fLlGT9+PN9+++1Dj589e3ZCQ0MTLU94b2n5Xuyxdu1a8ufPz7Rp06hXrx7ly5fH398/0eeSHCaTKdnb/v3330yfPh1/f3+OHDnCJ598kuLziTytFHBEDKBbt25YLBZGjBiR5JdmdHQ0P/74IwCBgYFA/Jfugw4ePMjJkyet69NCvnz5gPgJCB/0008/PXQfR0dHypUrx3vvvQfEj895mKpVq7Jz504uX75ss/ybb74hU6ZMGea2cpPJhLOzs004CQ0NZcuWLYm2NZvNyW5RepS7d+/St29f8uXLx5IlS2jbti2TJ0/mwIEDdh9b5GmgQcYiBhAQEMCoUaMYPXo0zZs3p3Xr1pQoUYKYmBj+/PNPvvrqK0qUKEGdOnUoWrQorVq14rPPPsPBwYGaNWta76Ly9vamQ4cOaVZXUFAQ2bNnZ/jw4fTt2xdHR0dWr15NSEiIzXZffPEFO3fupFatWnh7exMZGcnKlSuB+MG8D9OrVy9++ukn2rdvT69evciWLRvr1q1j69atvP322zYDjNNTrVq12LRpE6NGjeLFF1/k0qVLzJo1i9y5c/PPP//YbOvj48Pu3bv58ccf8fT0JEuWLBQtWjTF53zvvfcICQlhxYoVZM6cmSFDhrB//3769+/PmjVrcHd3T6N3J5IxKeCIGMSrr75K2bJlWbx4MQsWLCA0NBRnZ2cKFy5Mw4YNadu2rXXbUaNGUaBAAb7++ms+//xz3NzceP755xk4cGCaduu4ubkxf/58xo8fbw0cLVu25Pnnn2fEiBHW7UqVKsWvv/7KjBkzCA0NJXPmzPj4+DB79mxq1Kjx0OMXLVqUL7/8kilTpjBmzBju3btHsWLFmDBhAq+88kqavQ97NW/enGvXrvHll1+ycuVKChQoQNeuXbl06RLBwcE22w4fPpzRo0czYMAAIiIiqFy5MkuXLk3R+VasWMHatWuZMGECJUqUAOJbhqZNm0azZs0YNmzYY53VWiQj0KMaRERExHA0BkdEREQMRwFHREREDEcBR0RERAxHAUdEREQMRwFHREREDEcBR0RERAxH8+BImtu/fz8WiwVnZ+f0LkVEJN1FR0djMpkICAh4bOc4duxYqh798W9ms9n6ENqUOHPmDAsXLuTAgQMcP36cokWLsn79+oduf/jwYVq2bImrq2uih+ICLFy4kGXLlhEaGoqPjw+DBw+mSpUqKapJAUfSnMViwWKxEBWjKZZSzwJxMeDgBCT/2UNiy1Ft1HaLjYnG0Ul/rNjjSUw3FxUVxd2Ie1y5fifVx8jtkSXV+x4/fpxt27ZRrlw54uLiHvmeLRYL77//Ph4eHty9ezfR+oULFzJ16lT69++Pn58fK1asoEuXLqxYsSJF4UsBR9Kcs7MzUTEWnHIWSe9SnlqW6HvEhp3F0T0vJmfX9C7nqZUnm8t/byQPFXUvgtDzJ/DwKoTZNVN6l/PUuvTPURwdHv8fKleu3+HVd1alev+vxr9C4Xyp+31Tp04d6tWrB8DQoUM5fPjwQ7dduXIlN27coHnz5olm6Y6KimL27Nm0b9+ezp07A1C5cmUaNWrEnDlzmDp1arJr0t83IiIiRmEypf7HDg4OyYsTt27dYvLkyQwbNizJYQz79u3j9u3bNGzY0LrM0dGRBg0asG3bthS1hingiIiIyBMxbdo0SpcuTe3atZNcf/LkSYBED5gtVqwYd+7c4fLly8k+l7qoREREjMJkX7tFSEgI/fr1e+j6LVu2pPrYf/31F19//TWrV69+6Da3bt3CbDbj6mrbVZYtWzYAwsLC8PLyStb5FHBERESMws6upsfFYrEwZswYXnvtNYoVK/bIbU1JvIeErqmk1j2MAo6IiIgRmEz2teCYTHh7e9vVSvMw3333HSdPnmTSpEncunULgMjISCC+1cbFxQUXFxfc3d2JjIwkMjISF5f7Nwkk7JPQkpMcCjgiIiJGkUFbcE6dOsXNmzepU6dOonWVKlWiS5cuDBo0yNq6c/LkSfz8/KzbnDx5kixZspAnT55kn1MBR0RERB6rZs2aUblyZZtlq1ev5rvvvmP+/PnkzZsXgAoVKpA1a1a+++47a8CJjY1lw4YNBAUFqYtKRETkmWTnIOPUioiIYNu2bQBcuHCB8PBwNm7cCMTPY5M/f37y589vs8/u3btxdHS0maHYbDbTo0cPpk6dioeHh3Wiv3PnzjFlypQU1aSAIyIiYhTp1EV17do1+vbta7Ms4fWSJUtS9JiFTp06YbFYWLp0KVevXsXHx4d58+al+BESCjgiIiKGYOcgYzseC5M/f36OHTuWon3eeust3nrrrcRVmEy8+eabvPnmm6muBxRwREREjCODDjJOD5rJWERERAxHLTgiIiJGkU6DjDMiBRwRERGjUBeVlQKOiIiIEZiwcybjNKskQ1BbloiIiBiOWnBEREQMIf1uE8+IFHBERESMwsFYIcUeCjgiIiJGobuorBRwREREjEJ3UVkp6omIiIjhqAVHRETEKNRFZaWAIyIiYhTqorJSwBERETECk523iRssHCngiIiIGIXBQoo91FknIiIihqMWHBEREaPQIGMrBRwRERGjUBeVlQKOiIiIIehZVA9SW5aIiIgYjlpwREREjEJdVFYKOCIiIkahQcZWCjgiIiJGYMLOif7SrJIMQQFHRETEKNRFZaW2LBERETEcteCIiIgYgm4Tf5ACjoiIiFGoi8pKAUdERMQodBeVlQKOiIiIUagFx0pRT0RERAxHLTgiIiIGYVILjpUCjoiIiEEo4NyngCMiImIEJuy709tg2UgBR0RExBBMdrbgGCvhaJCxiIiIGI5acERERAwivcbgnDlzhoULF3LgwAGOHz9O0aJFWb9+vXV9bGwsixYtYtu2bZw4cYLY2Fh8fHzo3bs3VatWTXS8hQsXsmzZMkJDQ/Hx8WHw4MFUqVIlRTWpBUdERMQgTCZTqn/scfz4cbZt20ahQoUoVqxYovX37t1j7ty5lCxZkgkTJjBlyhTy5MlDx44d+emnn2y2XbhwIVOnTuX1119n3rx5FCpUiC5dunDs2LEU1aQWHBEREYNIrxacOnXqUK9ePQCGDh3K4cOHbda7urqyZcsWsmXLZl1Wo0YN/vnnHxYtWkTt2rUBiIqKYvbs2bRv357OnTsDULlyZRo1asScOXOYOnVqsmtSC46IiIjYxcHh0XHC0dHRJtxAfBgrWbIkV65csS7bt28ft2/fpmHDhjb7NmjQgG3btmGxWJJdk1pwREREjMLOBpyQkBD69ev30PVbtmyx7wQPiIuLY//+/TZdWidPngSgaNGiNtsWK1aMO3fucPnyZby8vJJ1fLXgiIiIGER6jcFJjaVLl3L69Gk6duxoXXbr1i3MZjOurq422ya0/oSFhSX7+GrBERERMQCTyb4xOCYTeHt7p2krzcPs3r2bjz76iE6dOlGpUqV/1ZH4PSR0TaXk/SngiIiIGMTT8KiGo0eP0rNnT+rVq8fbb79ts87d3Z3IyEgiIyNxcXGxLr916xZAonE8j6IuKhEREXkizp49y5tvvomfnx8ffvhhokCWMB4nYSxOgpMnT5IlSxby5MmT7HMp4IiIiBhERh6DExoaSqdOnciVKxezZs3CbDYn2qZChQpkzZqV7777zrosNjaWDRs2EBQUpC4qERGRZ1I69VBFRESwbds2AC5cuEB4eDgbN24E4uexyZw5M2+++SbXrl1j6NChnDhxwmb/8uXLA2A2m+nRowdTp07Fw8MDPz8/VqxYwblz55gyZUqKalLAERERMYT0e9jmtWvX6Nu3r82yhNdLliwhX758HD16FIBevXol2v/BWYo7deqExWJh6dKlXL16FR8fH+bNm4evr2+KalLAERERMYj0GmScP3/+/3yUQnIftWAymXjzzTd588037apJY3BERETEcNSCIyIiYhBPw23iT4oCjoiIiFEo31gp4IiIiBiBnTMZGy0caQyOiIiIGI5acERERAzAhJ3Pokq7UjIEBRwRERGD0CDj+xRwREREDEIB5z4FHBEREaNQvrHSIGMRERExHAUckXRw9MhBendowUtV/ajik5uaZQvSvmldvl31pXWb2NhYli6cTc/2zXihSkkCffPQrM5zfPzBe9y6GZZ+xYuhhN++zfsjh9GqWQNKF8uHd3YXJk14P8lto6OjmRM8jdrVKlDEKxu+BXPT6IUg9uza8YSrlqSl/kni8V1bxmr+UReVSDq4fesmXnnz87/GLcjtlZeIiDt8t+YrhvfvysXzZ3mzRx8iIyOZO/0jXmrSkmat3iC7R06OHj7A/Bkfsm3zBj5fvw1X10zp/VbkKXfj+jU+W7wQvzJleenlxny+ZFGS28XGxtKpbUt27/iNnn0HUqlyIHfv3uHgH/u5e/fOE65aHkZjcO5TwBFJB5WqPk+lqs/bLKtZ939cOHeGlV8s5s0efXBxcWH91r3kyJ3XZj+vvPl5u2d7tnz3DS+/0vpJly4Gk79gIY6euYzJZOLatasPDTgL587kxx++Z+33W6lYqYp1eb0XGzypUuW/aKI/G+qiEslAsufIiaNj/N8djo6OZM/hkWibMuUrAnAp5MITrU2M6X73xKMtmBNMYLXnbcKNZEAmO34MRgFHJB3FxcURExPD9WtXWb5kPju2b6Fjj36P3Gf3b9sBKOZT6glUKAIXL5zn3NkzlPIrzfgx7+JfogD5c2YmKLA8X32+NL3LE0mSuqhE0tH4EQP4ell8l4Cz2czgUR/S4vVOWKLvJbn95UsXmT7xPfzKBlCz7ktPslR5hl26FALAV19+hnfefIz/aBpZ3d1Z9uki+vZ8k6joKNq+0TmdqxTQGJwHpXsLzowZM/D19eX1119Pcl1AQECKj7lq1SrWrVuXrG3btWuHr68vvr6+lCxZklq1ajFw4EAuXEib5v/kvIfUvk95+nXuNZBl67Yy45MVNH21HR+MHMSnc6cnue3NsOu81aEFFouFD2cuxsEh3f/zlWdEXFwcAJH37vHZV9/QqGlzatWpz7zFn+NfLoCpH45P5wolgX13URlLhmnB2bt3Lzt27KBq1ap2H2v16tVkzpyZRo0aJWv7ChUqMGTIEGJjY/n777+ZNm0aBw4cYN26dWTK9PjvUmnZsiVBQUGP/TyS8XjnK4B3vgIAPF/nRQBmfDiKhk1eIZvj/e1u3bxB99ebcuXSReZ9sZ78BYukR7nyjMrx/2PBipfwpUDBQtblJpOJWnXrM2PKh1wNvUIuz9zpVaKgZ1H9W4b4EzBz5syUK1eOmTNnpsv53d3dKV++PBUrVqRNmzYMGzaMc+fOsW3btlQfMyoqyvpXz3/x8vKibNmyqT6XGEfpchWJiYnhwrkz1mW3bt6g22tNuHDuH+Z89g0+pcqkY4XyLCpUuAiZMmdOcp3FYgHApBbFDEEtOPdlmCuyV69e7Nmzh127dj1yu7CwMIYPH05gYCBly5alRYsW/PLLL9b17dq1Y/fu3WzdutXa9TRjxowU1VKmTPwXyPnz57l79y5jxozhxRdfpFy5ctSpU4eRI0dy+/Ztm33q1KnDmDFjWLBgAbVr16ZcuXKEhYUlefzZs2dTtmxZfvrpJyBxF9WuXbvw9fXll19+YeDAgQQEBFC7dm3mz59vc5zjx4/TpUsXqlSpQrly5XjxxRcTbbN582aaNm2Kv78/1atXZ/To0dy5cyfF55InY++O7Tg4OJCvQPxfybduhtHttSacP/sPsz9bQ8ky5dK5QnkWOTk58VKDRhz/+yjnzvxjXW6xWNi6eROFixQlZ85c6VegSBIyTBdVUFAQ/v7+BAcHU6VK0rchxsbG0qVLF86ePcuAAQPw8vLiiy++oGvXrixatIjAwEDee+893n77bVxdXRkyZAgQ30KSEufPnwcgd+7c3Lt3j9jYWPr374+HhwchISHMmTOHXr16sWTJEpv9Nm3aROHChRk+fDgODg64uromOvakSZNYtmwZc+fO/c/uuFGjRtGkSRNmzpzJpk2bmDRpEr6+vtSsWROAHj16kDNnTsaNG4ebmxtnz57l0qVL1v23bNlC7969eemll+jfvz/nz59n8uTJnD59msWLF6foXClneehAWYH3hw8ki5sbZcpWwCOXJ2E3rvHDhnVs+nYNb3TpRY5s7ty9ep2ePTpz9MhBBo0YS8y9uxzYfT/M5/DISYFC6qp6lKh7yWtFfdb9tOUH7t69y53w+D/cjv55mNUrviQ2JooA/1JER92j/6ChbPlhI22aN6TfoKG4uWXly8+XcOTwQWbOW0zUvYh0fhcZV3wr1xNqITFeQ0yqZZiAA/GtON27d2f37t1Urlw50fqtW7dy8OBB5s2bZx2z8vzzz9OwYUNmzpxJYGAgxYsXx83NjcyZM1O+fPlknddisRATE0NcXBx///03H330Ee7u7lSrVg0PDw9Gjx5t3TYmJob8+fPz2muvcfr0aYoUKWKzbv78+UmO27FYLIwePZpvv/2WRYsWJWtQ8QsvvMBbb70FQGBgIFu3buX777+nZs2aXL9+nXPnzvHOO+9Qp04d6zYPCg4Oxt/fn2nTplmXZcuWjYEDB7Jr1y6bIPmoc6VKXAyxYWdTt+8zoIxvEdatW8e6lV9w+/ZtMmfOTIkSJRgzZgwNGjQgLvwS169f58ihAwB89P7wRMdo2LAho0aNesKVP11Cw9K7gqfDsEF9CAkJsb7+dt0avl23BoC1a9fi6nqezE4wb+5cgoODGTqwDzExMfj4+DBlyhQqB5Qm9PyJdKr+KeFofiKnMWJXU2plqIBTu3ZtSpcuTXBwcKLWEYgfiJwlSxabAbkODg7873//Y+7cucTGxuLo6Jhov/+ybds2SpcubX1duHBhZsyYQa5c8U2ua9asYfHixZw5c4a7d+9at/vnn39sAk7lypUfGm6GDBnCL7/8wpIlSyhVKnnzl9SoUcPmfRYtWtTaQpMjRw7y5cvHlClTuHnzJlWrVrVpqbpz5w5//fUXgwcPtjnmSy+9xJAhQ9i7d69NwHnUuVLFwQlH97z/vd0zqln73jRr3/uh6y0xUeTNC/uOnsfk9GR+MRqRh5s+u+TYuf+vJJdHR90j7Mp5sufOj7PZFc/8xalaS9MTpNS1kH+ezInsHUtjsHCUoQIOQM+ePenVqxd79+5NtO7WrVvW0PGgXLlyER0dzd27d8maNWuKz1mxYkWGDRuGo6MjefLkIWfOnNZ1P/zwA0OGDKFVq1b079+f7NmzExoaSq9evYiMjLQ5zoP7PSg6OpotW7ZQrVo1fH19k13Xv9+Ls7OzNWCZTCYWLFjAtGnTGDNmDHfv3qV06dIMGzaMSpUqcfv2bSwWS6LPy8nJiezZs3Pz5s1knyt1TJicE3fRScqYnMz6HO1gdnVJ7xIMwdnsilnPPUs1taqkjwwzyDhBvXr1KFWqFMHBwYnWZcuWjatXryZafvXqVZydncn8kFH+/yVr1qz4+/vj5+eXKKRs3LiRUqVKMWbMGIKCgihXrhzu7u5JHudhF7HZbGbevHn88ssvvPfee9a7DuxVtGhRpk+fzu7du1m6dClms5nu3btz584dsmbN+v/Plrlms09MTAxhYWFky5YtTWoQEZGMw2RK/Y/RZLiAA/FjcXbs2MHvv/9us7xixYrcuXOH7du3W5fFxcWxceNGAgICrN1Tzs7OiVpXUuvevXs4OzvbLEvuJIIPqlixIrNmzeKbb75h3LhxaVJbAmdnZypXrkzXrl0JDw/nypUrZMmShVKlSrFhwwabbTdt2kRMTAzPPfdcmtYgIiLpT7eJ35fhuqggvhXH19eXHTt22LTK1KpVi7JlyzJ48GAGDBhAnjx5+PLLLzl9+jQjR460ble0aFHWrFnDjz/+iKenJ7lz5yZPnjypqqVatWqMGTOG4OBgKlSowPbt29mxY0eqjlW1alVmzJhBr169cHFx4e23307VcQCOHj3KxIkTadCgAQUKFCA8PJy5c+eSL18+ChYsCEDv3r3p1asXAwYMoGnTppw7d44pU6ZQtWrVh96pJiIiTy8D5pRUy5AtOCaTiV69eiVa7ujoyPz586lTpw6TJ0+md+/ehIaGMnfuXJsv7C5dulhnJ27RogVfffVVqmtp3bo1nTp1YtmyZfTu3ZuLFy8yefLkVB8vKCiIadOmsXjx4hTPz/MgT09PcuXKxdy5c+nSpQsjR47E29ubRYsWWVuy6taty4wZMzh16hQ9e/Zk5syZNG7cON0mVBQRkccnYSbjVP+k9xtIYyZLWg0IEfl/hw4dIirGglNOzdGSWpboe8SGncUxe0ENMrZDnmwaZGyPqHsRhJ4/gWf+4hpkbIdL/xzF0cGEv7//YzvHoUOHOH89gn7f3Uj1MaY1yEF+j0yPtc4nKUN2UYmIiEjKqYvqPgUcERERIzCBg4M98+CkXSkZgQKOiIiIQagF5z4FHBEREYMw4u3eqZUh76ISERERsYcCjoiIiEGk10zGZ86cYeTIkTRp0gQ/Pz8aNmyY5Hbbtm2jadOm+Pv7U79+fZYtW5bkdgsXLqROnTr4+/vTvHlzdu3aleKaFHBEREQMIr1mMj5+/Djbtm2jUKFCFCtWLMlt9u/fT8+ePfHz82P+/Pk0a9aMsWPHsmLFCpvtFi5cyNSpU3n99deZN28ehQoVokuXLhw7dixFNWkMjoiIiAEkTPRnz/6pVadOHerVqwfA0KFDOXz4cKJtZs6ciZ+fH+PHjwcgMDCQkJAQPv74Y5o3b46DgwNRUVHMnj2b9u3b07lzZwAqV65Mo0aNmDNnDlOnTk12TWrBEREREbs4ODw6TkRFRbFz505efvllm+WNGjUiNDSUP//8E4B9+/Zx+/Ztmy4uR0dHGjRowLZt21L0sGq14IiIiBiEvWNpQkJC6Nev30PXb9myJVXHPXv2LNHR0RQtWtRmefHixQE4efIkZcqU4eTJkwCJtitWrBh37tzh8uXLeHl5JeucasERERExBDueQ2Uy8Thn+rt58yYA7u7uNssTXiesv3XrFmazGVdX20fUZMuWDYCwsLBkn1MtOCIiIkZg791QJvD29k51K02yTvGQAh9cntQ2CV1TKRljpIAjIiJiEBl1or+EFpiElpoEt27dAu635Li7uxMZGUlkZCQuLi6Jtks4TnKoi0pEREQeq4IFC+Ls7MypU6dslp84cQLAemt5wv8mjMVJcPLkSbJkyUKePHmSfU4FHBEREYNIr4n+/ovZbCYwMJANGzbYLF+/fj2enp74+fkBUKFCBbJmzcp3331n3SY2NpYNGzYQFBSkLioREZFnUXp1UUVERLBt2zYALly4QHh4OBs3bgTi57Hx8PCgV69etG3blhEjRtCoUSP27dvHihUrGDNmjPU2c7PZTI8ePZg6dSoeHh74+fmxYsUKzp07x5QpU1JUkwKOiIiIAcRP9Gff/ql17do1+vbta7Ms4fWSJUuoUqUKAQEBzJo1iylTprBmzRq8vLwYMWIELVu2tNmvU6dOWCwWli5dytWrV/Hx8WHevHn4+vqmqCYFHBEREYNIrxac/PnzJ+tRCkFBQQQFBT1yG5PJxJtvvsmbb75pV00agyMiIiKGoxYcERERg8igd4mnCwUcERERg8io8+CkBwUcERERI0iDmYyNRAFHRETEINSCc58GGYuIiIjhqAVHRETEINSAc58CjoiIiEGoi+o+BRwREREDiJ/JOPUBx2jRSGNwRERExHDUgiMiImIQ6qG6TwFHRETEEEx2jsExVjpSwBERETECTfRnQwFHRETEIHQX1X0aZCwiIiKGoxYcERERg1ADzn0KOCIiIgbhoIRjlayAM2zYsGQf0GQyMX78+FQXJCIiIikXP9GfffsbSbICzq5du5J9QA1wEhERSR/6Dr4vWQHnxx9/fNx1iIiIiKQZjcERERExCAc14FilOuD8/PPP7N69mxs3btCzZ0/y5s3LwYMHyZ8/Px4eHmlZo4iIiCSDuqjuS3HAiYiIoGfPnuzYscP6QbZp04a8efOyaNEivL29GTJkSJoXKiIiIo+gmYxtpHiiv6lTp3L48GFmzJjB3r17sVgs1nXVq1fnt99+S9MCRURERFIqxS04GzdupG/fvtSvX5/Y2FibdXnz5iUkJCTNihMREZHkMxmtGcYOKQ44169fp3jx4kmuc3Bw4N69e3YXJSIiIiljwr5BxkaLRinuosqTJw9///13kuuOHTtG/vz57S5KREREUs5kMqX6x2hSHHBeeOEF5syZw59//mldZjKZuHDhAosXL+all15K0wJFREQkeUym1P8YTYq7qHr16sWOHTto2bIlJUqUwGQyMWzYMM6ePUuRIkXo2rXr46hTREREJNlS3ILj5ubGl19+Sd++fcmcOTMFCxYkU6ZMdOvWjWXLluHq6vo46hQREZFHMuFgSv2P0UbhpGqiP1dXV7p27arWGhERkQzEiF1NqZXqmYwjIyM5cuQIYWFhZM+endKlS+Pi4pKWtYmIiEgK2DdY2PLfmzxFUhVwPvnkE2bNmkV4eDgWiwWTyUSWLFno2bMnnTp1SusaRURE5D/YO1jYaK0/KQ44S5cuZeLEiVSvXp2GDRuSK1curl69yrp16/joo49wcnKiffv2j6NWERERkWRJccD59NNPady4MR9++KHN8mbNmjFo0CCWLFmigCMiIpIOHNKxi2rz5s3MnTuXkydP4urqSoUKFRgwYABFixa12W7btm1MnTqVkydP4uXlRYcOHXj99dftOndSUnwX1ZUrV2jUqFGS65o0acKVK1fsLkpERERSzmTHjz1+++03evfuTZEiRZgxYwbvvvsup0+fpmPHjoSHh1u3279/Pz179sTPz4/58+fTrFkzxo4dy4oVK+ysILEUt+AULlyYa9euJbkuNDSUQoUK2V2UiIiIpFx6zUj87bffkjdvXiZOnGitIV++fLRs2ZLff/+doKAgAGbOnImfnx/jx48HIDAwkJCQED7++GOaN2+Og0OK210eKsVH6tOnD9OnT0/0uIajR48SHBxMnz590qw4ERERST4HU+p/7BETE0OWLFlsAlbWrFlttomKimLnzp28/PLLNssbNWpEaGiozRMS0kKyWnC6d+9u8zo2NpamTZtSvHhxPD09CQ0N5cSJE+TOnZtVq1ZRv379NC1SREREMq4WLVrQoUMHli5dSpMmTbh16xYTJ06kWLFiVK1aFYCzZ88SHR2daExOwgO8T548SZkyZdKspmQFnH+31jg6OuLl5UV4eLi1b83LyyvJbUVEROTxM2FfF5UJCAkJoV+/fg/dZsuWLUkur1SpEsHBwQwcOJCxY8cC8cFl0aJFmM1mAG7evAmAu7u7zb4JrxPWp5VkBZwff/wxTU8qIiIiaS+95rLZt28fb7/9Ns2bN6dOnTqEh4czZ84cunTpwhdffIGbm9sDNSZdZFqPH0r1TMYiIiKSsdgbEry9vR/aSvMoY8eOJTAwkOHDh1uXVaxYkZo1a7JixQo6duxItmzZgMQtNbdu3QISt+zYy66Ac/36de7du5doed68ee05rIiIiDxFTp48SZ06dWyWeXh4kDt3bs6ePQtAwYIFcXZ25tSpU9SsWdO63YkTJwAoVqxYmtaUqoAza9Ysli5dSlhYWJLr//rrL3tqEhERkVSw926o1MqbNy9HjhyxWRYaGsqVK1fIly8fAGazmcDAQDZs2ECHDh2s261fvx5PT0/8/PzStKYU3yb+9ddfM3/+fNq1a4fFYqFbt2507doVLy8vChUqZB1cJCIiIk+QKb6LKrU/9sz29/rrr/Pjjz8yZswYfv31VzZs2ECXLl3InDkzjRs3tm7Xq1cvDh8+zIgRI9i1axezZ89mxYoV9O3bN03nwIFUtOB8/vnndOvWjS5dujB9+nTq169P6dKl6dGjB6+//jo3btxI0wJFREQkedLreZmvv/46zs7OfP7556xevZrMmTPj7+/PxIkTyZ07t3W7gIAAZs2axZQpU1izZg1eXl6MGDGCli1bpnlNKQ44Z86coVy5ctakFR0dDYCrqyudOnXi448/5s0330zbKkVEROSRTNj3LCp7wpHJZKJVq1a0atXqP7cNCgqyzmz8OKW4PcjJKT4TmUwm3NzcuHTpknVdjhw5uHz5ctpVJyIiIpIKKQ44hQoVsoYaf39/VqxYQXR0NLGxsSxfvtw6mEhERESeLJMp9T9Gk+KAU7NmTfbs2QNA165d2blzJ5UqVaJy5cps2rSJLl26pHmRIiIi8l9SP8A4fv4cY6WcFI/B6d27t/Xfq1atyhdffMF3332HyWQiKCiIwMDANC1QREREkseILTGpZfdMxmXLlqVs2bJpUYuIiIjYwZ5BxkaTtjedi4iIiGQAyWrBad++fbIPaDKZ+PTTT1NdkIiIiKSOGnDuS1bAsVgsyT5gSrYVERGRtGEy2fewTaOFo2QFnKVLlz7uOsRgnJ1M+OTNmt5lPLUi7jpyKgwKeWYhU+bM6V3OUytHpd7/vZE8lE/BnCwa2ZhGXafw99lr6V3OU+urCS0onC/nEzmXxp3cp89CREREDMfuu6hEREQkY7Cni8poFHBEREQMwkH5xkoBR0RExCAUcO5TwBERETEAE3beRZV2pWQIGmQsIiIihpPqFpyTJ0+yZ88ebty4QYsWLfD09OTy5ctky5YNV1fXtKxRREREkkFdVPelOODExsby7rvvsnr1aiwWCyaTiZo1a+Lp6cl7771HqVKl6Nu37+OoVURERB5BN1Hdl+IuqtmzZ7N+/XoGDx7M+vXrbWYufv755/n555/TtEARERFJHgeTKdU/RpPiFpzVq1fTs2dPOnbsSGxsrM26/Pnzc/78+TQrTkRERJLHhH0Da40WcVL8WVy+fJny5csnuc7FxYU7d+7YW5OIiIiIXVIccHLmzMm5c+eSXHf69Gm8vLzsLkpERERSyJTwwM3U/RitCSfFAScoKIg5c+Zw+fJl6zKTycTt27dZunQptWvXTtMCRUREJHk0Bue+FI/B6dOnD9u3b6dBgwZUqVIFk8nElClTOH78OE5OTvTs2fNx1CkiIiL/wYA5JdVS3IKTK1cuvv76a15++WWOHDmCo6MjR48epWbNmnz55Zdkz579MZQpIiIi/8XBlPofo0nVRH+5cuVizJgxaV2LiIiISJrQs6hEREQMwAR2jaUxWiNOigPOsGHDHrneZDIxfvz4VBckIiIiqaMxOPelOODs2rUr0bKwsDDu3r2Lu7s7WbNmTZPCREREJGWMOJYmtVIccH788cckl+/YsYPRo0fz8ccf212UiIiIiD3smdXZRtWqVWnbti3jxo1Lq0OKiIhIspns+sdoo3DSdJBxsWLFOHToUFoeUkRERJLD3tu9jZVv0jbg7Nmzhxw5cqTlIUVERCQZ4u+ism9/I0lxwAkODk60LDo6mmPHjrF9+3Y6d+6cJoWJiIhIyph0G5VVmgQcs9lMvnz56NOnjwKOiIiIpLsUB5yjR48+jjpERETETrpN/L4U3UV17949Bg4cyN69ex9XPSIiIpJKJlPqf9LCihUraNy4Mf7+/lStWpXu3bvbrN+2bRtNmzbF39+f+vXrs2zZsrQ5cRJS1ILj6urKli1baN269eOqR0RERFLJnkc12GvGjBksXryY7t27U65cOW7evMnPP/9sXb9//3569uxJkyZNGDp0KPv27WPs2LGYzWZatmyZ5vWkuIuqZMmS/P3331SqVCnNixEREZHUSc+7qE6ePMns2bOZN28eNWrUsC6vX7++9d9nzpyJn5+f9XFOgYGBhISE8PHHH9O8eXMcHNJsaj4gFRP9DRo0iIULF7J79+40LURERESeTqtWraJAgQI24eZBUVFR7Ny5k5dfftlmeaNGjQgNDeXPP/9M85qS1YKzZ88e/Pz8yJIlC6NHj+bOnTu88cYbuLu7kzt3bpttTSYTa9euTfNCRURE5NHs7aEKCQmhX79+D12/ZcuWJJcfOHAAHx8fZs6cyWeffcbt27cpX748w4cPp1SpUpw9e5bo6GiKFi1qs1/x4sWB+BagMmXK2Ff8vyQr4LRv357ly5dTtmxZsmfPTvbs2dO0CBEREbGfQzpN1xcaGsqRI0c4fvw4o0ePxtnZmeDgYDp27MimTZu4efMmAO7u7jb7JbxOWJ+WkhVwLBaL9d+XLl2a5kWIiIiIney9G8oE3t7eD22leRSLxcLdu3eZMWMGJUqUAKB06dLUrVuX5cuXU6FChfhTPKTAxzFBYdqO6BEREZFnTrZs2ciVK5c13ADkzp2bokWLcuLECbJlywYkbqm5desWkLhlJy0o4IiIiBiEgyn1P/YoVqxYksstFgsODg4ULFgQZ2dnTp06ZbP+xIkTj9zfHsm+TfyNN95IVhOSyWTi999/t6soERERSZn428RTn1TsyTi1atVi9erV/P333/j4+ABw+fJlTp06xSuvvILZbCYwMJANGzbQoUMH637r16/H09MTPz8/O86etGQHnMqVK+Ph4ZHmBYiIiEjaSK95/urXr0/p0qV566236Nu3L2azmZkzZ+Lh4cGrr74KQK9evWjbti0jRoygUaNG7Nu3jxUrVjBmzJg0nwMHUhBwevXqRdmyZdO8ABEREUkb6TWTsaOjI/Pnz2f8+PGMHDmSmJgYKlWqxOTJk8mcOTMAAQEBzJo1iylTprBmzRq8vLwYMWLEY5nFGFIxk7GIiIjIv+XMmZPJkyc/cpugoCCCgoKeSD0KOCIiIgaRjo+iynAUcERERAzAhH23RhstGyUr4Bw9evRx1yEiIiJ2ehwT5j2t1IIjIiJiEIo392miPxERETEcteCIiIgYgclk58M2jdX+o4AjIiJiEMaKKPZRwBERETEIu8YYW9KsjAxBAUdERMQg7LqLymABR4OMRURExHDUgiMiImIAmujPlgKOiIiIQWiiv/sUcERERAxC8eY+jcERERERw1ELjoiIiEGoi+o+BRwRERGDULfMfQo4IiIiBmDCvhYco7X9KOCIiIgYhNFCij3UmiUiIiKGoxYcERERg9AY4/sUcERERAzCQZ1UVgo4IiIiBqEWnPsUcERERAzCpBYcKw0yFhEREcNRC46IiIgRmOzsojJY448CjoiIiAGYsG+QscHyjQKOiIiIUWiQ8X0agyMiIiKGoxYcERERg1ALzn0KOCIiIoZgsvM2cWOlIwUckQxi+7atvFivdpLrtv68gyqBgU+4IjESt8wuDOvyEmV981OuZH48c2Rl7JzvGDf3u0fu5+Pjw4FvRjPny230n7jCurx4wdx0bl6dms+VoGiBXMTFWTh2+hIzlv3E6s1/POZ3Iw/jYKyMYhcFHJEMZszY8VQJrMrFc/+Qt0BhXFxcKV2mTHqXJU85j2xZ6NS8Oof+vsC6nw7S6ZXq/7lP6waVcXFxSXJdvaolealGaT7/dje/HzmLk5MDLV6owOcfvcmY2euZMG9jWr8F+Q8m7Jvoz2jZSAFHJIMpVrwElSpV5lT2rBQtUYpMmTOnd0liAGdDruNdczAAObNn+c+AU9Dbgz7t63Hu3DmKFSuWaP2K739nzvLtNss2/fonObO7MbBDfSZ/spmo6Ji0ewMiKaS7qEREJJGZ77Zh5x8nCQsLS3L9tbA7SS7fe+QfsmRywSObgnl6MJlS/2M0CjgiGUz/Pr3w9HAnKCiI5s0a8+svv6R3SfKM6dCsKs+VKcSEeY8en5OUoOd8uHL9Nleu334Mlcl/MdnxT1q5c+cONWvWxNfXl0OHDtms27ZtG02bNsXf35/69euzbNmyNDvvv6mLSiSDcHfPRq+3+lIzqBZuWbKwa8cvfLn8K16sV4vVa7+l/gsvpneJ8gzI65mNCf2bMXzaN4SmMKR0aFaVoEo+DPxwBXFxlsdUoTxKRhhkPGvWLGJjYxMt379/Pz179qRJkyYMHTqUffv2MXbsWMxmMy1btkzzOp7pFpyhQ4fi6+tr/alRowbdu3fn2LFjaXL8VatW4evry/Xr1+3aRp4N5QMCmDRlGo2bNKVqteo0btyY7zdtwcvbm+FDB6d3efKMmD68NYf+vsCiVb+maL8XqvsxbeirrPphH7O+2PaYqpP/kt4tOCdPnuTzzz/nrbfeSrRu5syZ+Pn5MX78eAIDA+nZsyctWrTg448/Ji4uLk3O/6BnOuAAFChQgOXLl/Pll18yaNAg/v77b9q2bUtoaOgTOX+tWrVYvnw57u7uT+R88nTJlj07DRo05NChg0RERKR3OWJwzeqV54VqfgyftoZsbpnImsUVR0dHAJydncjmlgknp8RfG/WqluLLSW+yZecxOrzz6ZMuWzKQcePG0bp1a4oUKWKzPCoqip07d/Lyyy/bLG/UqBGhoaH8+eefaV7LM99F5erqSvny5QEICAggf/78vP7666xdu5bOnTun6pixsbHJTqMeHh54eHik6jzybLBY4pv6TUYcBSgZil+xvDg7O7J96duJ1nVuXp3Ozavzav95rNt60Lq8XtVSfDWlCz//foI2gxYQHZO4a0KenPT8NbFx40aOHj3K9OnTOXLkiM26s2fPEh0dTdGiRW2WFy9eHIhv+SmTxtNhPPMB599Kly4NwPnz5wGYNGkS27Zt4/z587i5uVGpUiWGDh1K7ty5rfu0a9eOzJkz89JLLzFnzhzOnTvHl19+meTx16xZw4gRI3j33Xdp1aoVq1atYtiwYezYsQMPDw/Onz9P3bp1+fDDDzlw4ADr1q3DxcWFRo0aMXDgQJyc4v9fdunSJSZMmMCePXu4ffs2np6e1KtXj3feecd6rr179zJ58mSOHDlCpkyZqFmzJkOGDCFXrlzW95icc0n6Cbtxg+++W0+5cuVxdXVN73LE4Jau3cn2vcetrwvkcWdw+2r4+vqy9scDBH++lT9PXrSurxtYkq+mdOG3P07x6oB5ui08A7A334SEhNCvX7+Hrt+yZUuSyyMiIvjggw8YMGAAbm5uidbfvHkTIFFvRcLrhPVpSd9g/5IQbBICzLVr1+jWrRu5c+fm+vXrfPLJJ7Rr145vv/3WJgAcPnyYixcv0rdvX9zd3fH29ubEiRM2x162bBkTJkxg/PjxNG7c+JF1TJs2jbp16zJt2jT27dtHcHAwBQsWpE2bNgAMHjyYK1euMGLECHLmzElISAiHDx+2qadDhw4899xzTJs2jZs3bzJ58mTeeOMNVq1aZTN513+dKzUsFgsRd++mev9nUZfOHcifvwDlAyrgntWNfb/v4cvly7ly+TLBM+fo80wFn4I507uEDKV6heJkcjWTJZMZgEplCtKz9fMA/LL3OPeiorkcen88oItjLOHh4QDcjbjH5dDr5HR3Jae7KwGlCjJ7dDuuh93hi/U7aFiztM25Tp0L5U5E5BN6ZxmbcxLdeo+DCXCwownHnnA0e/ZscubMySuvvPLoczykvsfRQq2AA8TExGCxWDh79iyjRo3C2dmZunXrAjBhwgTrdrGxsQQEBFCzZk127txJjRo1rOtu3rzJypUr8fLySvIc8+bNY8aMGUybNo169er9Z01ly5ZlxIgRAFSvXp3ffvuN77//3ho6Dh06xIABA2jQoIF1n6ZNm1r/fc6cOeTMmZN58+ZhNsf/MitcuDCtW7fm22+/tbkI/+tcqRETHc2p43+lev9nkbdXHjZ8t56FC+YRERGBu7s75cuXZ+S771Iwv7c+z1RYNPLRf0g8a8qUKWPzx82LNcrwYo34boFDhw4RFRX10H3rVCpCCa/7n6e3tzeZXMzky2NmwdiOibY/duyYNRzJk2NvTPD29n5oK83DXLhwgUWLFjFz5sz7gfj//yC7e/cud+7cIVu2bEDilppbt24BiVt20sIzH3COHz9u7ZaC+Jabjz76CB8fHyD+nv3Zs2dz/Phxm/9Y//nnH5uA4+vr+9BwM23aNNatW8fcuXOpVq1asup68NgQ30+5Z88e62s/Pz8WLVqEo6Mj1atXp1ChQjbb7927l5dfftkabiB+jFG+fPnYu3evTcD5r3OlhpOzMwULF7frGM+aMeMmMmbcRAAi70Vw4dw/5CtQGBfXTOlc2dOrVruJ6V1CBrM2RVsX9MrGqK5BNOo2hbOX0r4L4Vkx8a265Mtj/u8Nn1Lnz58nOjqarl27JlrXvn17ypUrx2effYazszOnTp2iZs2a1vUJPR1JzZZtr2c+4BQsWJApU6ZgMpnw9PQkd+7c1qaygwcP0rNnT+rWrUuXLl3ImTMnJpOJV199lchI26bXnDkf3hT+/fff4+PjQ8WKFZNdV9asWW1eOzs72/x1NXXqVKZOncq0adMYPXo0RYoUYcCAAbzwwgtAfCpOGGvzoFy5ciVK0P91rtQwmUx6xEAacHHNpM/RDn+fvZbeJRjC2Us39VnaITom7W+BTpIJ+5pwUrlvqVKlWLJkic2yv/76iwkTJjB69Gj8/f0xm80EBgayYcMGOnToYN1u/fr1eHp64ufnZ0fhSXvmA46Liwv+/v5Jrtu8eTNubm5MmzYNB4f4PtQLFy4kue2j+g9nzZpFnz59eOuttwgODrZpVUmt3LlzM2HCBOLi4jh8+DCzZ8+mf//+bNy4kQIFCpAtWzauXUv8C+nq1avWUesiImIsaTkjcXK5u7tTpUqVJNeVLl3a2kvSq1cv2rZty4gRI2jUqBH79u1jxYoVjBkzxvodm5ae+XlwHuXevXs4OzvbhJd169al+DhFihRh0aJFHDhwgAEDBhATk3Z3Gjg4OFC2bFn69etHTEwMZ86cAaBixYps3ryZ6Oho67YHDhzgwoULPPfcc2l2fhERyTgy8rOoAgICmDVrFocOHaJz586sXLmSESNGPJZZjEEtOI9UvXp1Pv30U95//33q16/P/v37+eabb1J1LF9fXxYtWsQbb7zBkCFD+Oijj1KdWG/fvk3nzp1p0qQJRYoUITo6mqVLl+Lu7m5t5uvevTutW7ema9eutG/f3noXVfHixRNNtCQiIpKWqlSpkuRTAYKCgggKCnoiNagF5xGCgoIYNGgQW7ZsoUePHuzdu5e5c+em+nilS5dmwYIF/PjjjwwfPtw6gVtKubi44OPjw9KlS+nRoweDBw/GYrGwcOFC66SBZcqU4ZNPPiEiIoI+ffowfvx4qlSpwuLFi23uohAREeMw2fFjNCZLar9lRR7i0KFDWACfUkmPbZL/FnH3LqeO/0XREqU0yNgOOSr1Tu8Snmo+BXOyaGRjOo1Zq0HGdvhqQgsK58v50PGeaeHQoUNExsRhyV441ccwhf2Di5PDY63zSVIXlYiIiEGkxyDjjEoBR0RExABM1v8joDE4IiIiYkBqwRERETEIexpwjDYgVwFHRETEKNRFZaWAIyIiYggmuwYZWwyWjhRwREREDOJJzEj8tNAgYxERETEcteCIiIgYhBpw7lPAERERMQolHCsFHBEREYPQTMb3KeCIiIgYgcnOQcYGy0YaZCwiIiKGoxYcERERgzBYI4xdFHBERESMQgnHSgFHRETEAEzYN8jYaNlIY3BERETEcNSCIyIiYhB6VMN9CjgiIiIGoXxznwKOiIiIUSjhWCngiIiIGIRmMr5Pg4xFRETEcNSCIyIiYhAaZHyfAo6IiIhBKN/cp4AjIiJiFEo4Vgo4IiIiBqFBxvdpkLGIiIgYjlpwREREDEKDjO9TwBERETEAE/YNwTFaNlLAERERMQqjpRQ7aAyOiIiIGI5acERERAxCd1Hdp4AjIiJiBCY7BxkbLBupi0pERMQgTHb82GPDhg307NmToKAgypcvT6NGjfj888+Ji4uz2W7btm00bdoUf39/6tevz7Jly+w888OpBUdERMQo0qkV5pNPPiFv3rwMHjyYnDlzsmvXLsaNG8e5c+cYMmQIAPv376dnz540adKEoUOHsm/fPsaOHYvZbKZly5ZpXpMCjoiIiNhlzpw5eHh4WF8HBgZy9+5dli1bRv/+/TGbzcycORM/Pz/Gjx9v3SYkJISPP/6Y5s2b4+CQtp1K6qISERExCJMd/9jjwXCToFSpUkRGRhIWFkZUVBQ7d+7k5ZdfttmmUaNGhIaG8ueff9p1/qSoBUdERMQg7J3JOCQkhH79+j10/ZYtW5J9rN9//53s2bOTM2dOTp8+TXR0NEWLFrXZpnjx4gCcPHmSMmXKpKrmh1ELjoiIiEGk1yDjfzt06BCrVq3ijTfewNHRkZs3bwLg7u5us13C64T1aUktOCIiIgZgwr4WHBPg7e2dolaapISGhtKnTx/8/f3p0qWL7TkeUuDDlttDLTgiIiKSJm7fvk2XLl1wdXVl9uzZODs7A5AtWzYgcUvNrVu3gMQtO2lBAUdERMQw0q+TKjIykh49enD16lUWLFhAjhw5rOsKFiyIs7Mzp06dstnnxIkTABQrVszu8/+bAo6IiIhBmEyp/7FHTEwMffv25ejRoyxYsIB8+fLZrDebzQQGBrJhwwab5evXr8fT0xM/Pz/7CkiCxuCIiIgYRHo9bWHMmDH89NNPvP3229y7d48//vjDuq548eK4ubnRq1cv2rZty4gRI2jUqBH79u1jxYoVjBkzJs3nwAEFHBEREcN4DGN1k+WXX34B4KOPPkq0bsmSJVSpUoWAgABmzZrFlClTWLNmDV5eXowYMeKxzGIMCjgiIiJipx9//DFZ2wUFBREUFPSYq4mngCMiImII9s5IbKzHiSvgiIiIGIWxMopdFHBEREQMQvnmPt0mLiIiIoajFhwREREjsHc+G4M1/yjgiIiIGIR9g4yNRQFHRETEKJRvrBRwREREDMDeJ0oZLRtpkLGIiIgYjlpwREREDCK9HtWQESngiIiIGIQGGd+ngCMiImIQasG5T2NwRERExHAUcERERMRw1EUlIiJiEOqiuk8BR0RExCA0yPg+BRwRERGDUAvOfRqDIyIiIoajFhwRERED0KMabCngiIiIGIXRUoodFHBEREQMQoOM71PAERERMQgNMr5Pg4xFRETEcNSCIyIiYhBqwLlPAUdERMQIdBuVDQUcERERg9Ag4/sUcERERAxCg4zvM1ksFkt6FyHGsm/fPiwWC85mc3qX8tSyxFmIiYnGyckZk4N+Y6XWmQvX0ruEp5qzkwOeObIQeuMO0TFx6V3OUyu3RxacnRypUKHCYzvHoUOHiIqKsuv3bnRUFGazGX9//zSsLP2oBUfSnOn//4TQ13LqmRxMmBUQ7VY4X870LsEQ8uXRtWiP6Oho6+/FxyUtfl+YzWZD/d5RC46IiIgYjubBEREREcNRwBERERHDUcARERERw1HAEREREcNRwBERERHDUcARERERw1HAEREREcNRwBERERHDUcARERERw1HAEREREcNRwBERERHDUcARERERw1HAEREREcNRwBERERHDUcARERsWiyW9SxCDi4uLS+8S5BmggCMiVrGxsZhMJgCuX79OXFycAo+kqdjYWBwc4r96wsLCiImJSeeKxKhMFv32EhHiW24Sws0HH3zA/v37MZvNvPjiizRp0oSsWbOmc4XytPv3NbZnzx7c3d2pXr06b775ZjpXJ0ajFhwRIS4uzvrFM3LkSNavX0/JkiW5c+cO8+bNY/z48YSFhaVvkfJUe/Aae++991i/fj3+/v7cuXOHpUuXMnDgwHSuUIxGAUdErF0G169fJywsjAkTJjB69GhWrVpFo0aN+P3333n//fcVciTVEq6xs2fPcu3aNcaNG8eoUaNYunQpLVq0YO/evfTp0yedqxQjUcARESC+5ea1114jJCSEwoULW5cPGDCA//3vfxw8eFAhR+wyYsQI+vbty9WrVylZsiQALi4udOjQgVatWvHHH3/Qt2/fdK5SjEIBR0SwWCxUqVKFqKgoTp48yeXLl4H4bgVHR0f69u1LgwYN+PPPPxk6dCg3b95M54rlaVS1alXOnj3LkSNHOHnypHV51qxZad++Pa1bt+bQoUN07tw5HasUo1DAEXkG/fs2XZPJRJ06dRg+fDjOzs7MmzeP69ev4+DggMViwcHBgb59+1KjRg0uX77MvXv30qlyeVo8eI0l3Mvy8ssvM3nyZBwcHPjss884d+6cdRs3Nzfat29Pw4YNuXTpkjVki6SW7qISecbExsbi6OgIxI+5iYiIIE+ePDg5OQHwww8/MHToUKpUqcLYsWPx8PCw3v0SFxdHWFgYHh4e6fkWJIN78Bq7desWt27dIn/+/MTFxeHg4MD333/PoEGDqFevHgMGDKBAgQLWfcPDw4mKitI1JnZzSu8CROTJefCL5/3332f37t2cP3+evHnz8sYbb1C7dm3q168PwNChQxkxYoRNyHFwcNAXjzxSUtfY6dOnKVasGK+88goNGzbkxRdfBGDQoEEANiHHzc0tfQoXw1ELjsgzaODAgezbt482bdrg6urKjh072L59O6+++irdu3cnT548bN68mREjRuDj48O0adMUbCRFBg4cyP79+2nRogWenp589913HDt2jKCgIAYMGICnpyebNm1iyJAhPPfcc7z33nvkz58/vcsWA1ELjsgzZv/+/ezZs4d33nmHl156CYD27dszZcoUFi9ejLe3N127dqVmzZqMHDmSSZMmERUVlc5Vy9Nk//797Nq1i5EjR1K/fn1MJhMtW7Zk4sSJrF+/nrx589K1a1deeOEFYmJiGDduHM7OzuldthiMBhmLPGNCQ0O5evUqpUuXBrCGl4TbwT/99FOuX79uncV43bp1eHl5pWfJ8pS5cuUKN27coFy5cphMJus1NmTIEAICAli1ahWRkZEANGjQgE2bNpEnT570LFkMSAFHxMCSeqhhjhw5cHFxYe/evQCYzWbrF9Drr79OWFgY+/fvB8DR0ZEsWbI8uYLlqZPUNebl5YXJZGLHjh2A7TXWu3dvQkJC+P33363b6xqTx0EBR8SgHnyo4e7du7l69SoAxYoVI1euXHz99dccP34ci8WC2WwG4h9+6O7uTu7cudOtbnl6PHiN7dy5k4iICAA8PT0pXLgwa9as4fDhwwDWaywkJIQcOXKoVVAeOwUcEQN68E6WYcOGMWXKFFauXElkZCQeHh5MmjSJP//8kw8++IDt27cD8VPob9y4kRw5cuDt7Z2e5ctT4N/X2AcffMAnn3xCbGwsefPmZciQIezevZvp06ezZcsWAM6cOcOmTZvIkSMHnp6e6Vm+PAN0F5WIgQ0YMIA//viDd955B39/f/LkyWOd02bXrl3069ePmJgYnJycyJEjBzdu3GDRokWUKlUqvUuXp0TCNfbuu+9SsmRJvL29rdfYTz/9xOjRo7l58yaZM2fGzc2NW7du6RqTJ0J3UYkY1Pr169m7dy9TpkyhYsWKmEwmwsPDuX79OiaTiSpVqrBq1Sp+/vlnzpw5Q/78+alRo4bNpGsij/LNN9+wf/9+Jk+eTPny5XFwcODOnTuEhITg5uZG7dq1KVSoEIcPH+bIkSMULlxY15g8MQo4IgZ148YNMmXKRNGiRYmLi2Pfvn2MGTOGmzdvWp/m3LRpU1q2bInJZErvcuUpFB4ejtlsJl++fDg4OLB7927ef/99bt68ye3btxk1ahRNmjShaNGiNG7cOL3LlWeMAo6IASR0CTzIw8ODM2fOMHnyZG7evMm2bduoW7cu1atXZ9++fbz//vtUr15dYyEkWZK6xsxmM1euXGH69Oncvn2brVu3UqtWLerUqcPPP//MhAkTqFWrFtmyZUunquVZpoAj8pR7cLDn3bt3yZw5MxD/YMOzZ8/yzTff4OPjw8iRI2nZsiUQ/2X1xx9/WJ8/JfIoD15jkZGRuLi4ANCyZUtOnTrF7t278fb25t1337VeYwBHjhwhOjo6XWoW0W83kadYXFyc9Ytn6tSpHD58GA8PD2rVqsXLL79Mjx49eP3113FxcbF+KYWFhbFv3z48PT01e6z8p39fY3/88Qd58uQhMDCQV155hSFDhhAeHo6joyOZMmUC4rtHf/vtNzw9PXF1dU3P8uUZpruoRJ5SD3YZDB06lG3btlGpUiUOHDiAo6MjDRo0sD7MMMHPP//Mhg0b+OGHH1i2bBk+Pj7pUbo8JR68xoYNG8b27dupUKECf//9N/fu3aNRo0aJrrGdO3fyzTff8OOPP7J06VJdY5Ju1IIj8pRJmFwt4Yvn0qVLXL16lSlTplC1alXCw8N5//332bhxI7GxsQwZMgSAjRs3Mm3aNLJkycJnn32mLx55pAfDzcmTJ7l27RqTJk2iatWqhISEMHfuXFauXElcXByDBw8GYM2aNUydOpVcuXKxZMkSXWOSrhRwRJ4SCWMfEroLIP6v6pCQECwWC76+vgC4ubkxZMgQPvroI3744Qcg/hlAL730ElmzZqV48eJ67o8kKSoqyjrjcEK4eeedd7h27RpRUVH4+fkBWB/I6ujoyOrVqwEYPHgwTZs2JVu2bPj5+ekak3SnmYxFngJRUVG88sorzJo1y2ZZ4cKFOXDgAKdPn+bGjRtA/JgJDw8P3n77bSpVqsSPP/7IqFGjAKhevbq+eCRJUVFRNGjQgMWLF9ssz5s3L9u2beP48eOEhITYLO/cuTMvv/wya9euZeTIkQDUrl1b15hkCAo4Ik+BiIgIunTpQvv27a3LzGYzbdq0YfDgwVy/fp3PPvuMiIgIHBwcsFgs1pBTsmRJ/vjjD65du5aO70AyusjISPr370/z5s1tlvfu3Zt3332Xa9eu8fXXX9tcRwkh5/nnn2fnzp26xiRD0SBjkafMqFGjuHjxIvPmzQPiJ1tbuXIlEydOpH379vTr1w9XV1frGIobN24QHR2tB2hKsg0fPpzIyEgmTZpkXbZw4UI++ugjOnfuTKdOnciZM6d13aVLl3ByciJXrlzpUa5IkjQGRySDS3hWFMT/lZ01a1b27t3L4MGD+fDDD3Fzc7P+1T1x4kQAm5CTI0eOdKtdng4PXmMxMTHkzp2befPmkT17dkaMGAFA586dsVgs1tDTuXNnPDw8APRkcMmQFHBEMriEL56EWWI7duxI1qxZmT17NoMGDWLSpEk2IWfy5MncvXuX4cOHW+e+EXmUhGvsp59+4vnnn6ddu3ZkzZqVSZMmERcXZx1f8+abb2IymZg6dSoRERH07t3bGnJEMhqNwRHJoGJiYqz/PmvWLKZMmWIdW9OyZUu6d+/Oli1brPOQuLm50aJFC3r37s2mTZsIDw9Pr9LlKfHgCIXg4GCmTJmCk5MTHh4eNG/enIEDB/Lll18yZswY63adO3eme/furF+/Ho1wkIxMAUckA4mIiKBPnz5cv34dJycna8gJDw8nX758mEwm4uLiyJEjB6+++mqikJMlSxZef/11vv/+e5sxEiIJIiIiGDx4MLdv37ZeTxDf/VmoUCEgPvhky5btoSEnIUTrGpOMTAFHJAPZu3cvv/zyC506dSIsLMzadRAbG0v27NkBcHCI/882IeR069aNn3/+mR49egDxIUcPN5SHOXToEN9++y1du3YlPDzcej3FxsaSNWtW4H7LTvbs2WnRogUDBw5k5cqVDB061HqchOtRJKNSwBHJQKpVq8bYsWOJiIigXbt2XL9+HYDQ0FDu3bsHxHddxcTEWAcQt2zZkjZt2nDkyBEuX76cnuXLU6B8+fLMmDGDixcv0qlTJ27fvg3A5cuXuXPnDhAfcBIekpktWzaaNm1Kly5d+OmnnwgNDU232kVSQgFHJIOIiorC0dGRF154gQEDBlhDjsVioUCBAkRFRQHxA0KdnJysM83mzJmTnj178s0332iCNXmk6OhozGYz1apVY+TIkVy5coWOHTtaJ4dMmCXb0dHR5kGsHh4e9OzZk40bN+Lp6Zle5YukiObBEckAHnzuzyeffELLli3Zvn07kydPxmw24+XlxcWLF61jHsxmM3FxccTExFC8eHFGjBhhnWJfJCkPXmNffPEFNWrU4NixY4waNYo8efLg7e3NwYMHKVy4MJGRkZjNZhwdHbl37x5FixZl1KhRusbkqaLbxEXSWVxcnHUcxJgxY/j888+pVq0aL7zwAnFxcXzyySfs2LGDVq1akTdvXm7evAnAzZs3cXR05LXXXtMXjzzSg9fYiBEjWL9+PZUrV6ZGjRqMGjWKqVOnsnnzZtq1a4fZbCYmJgYHBwdu3bpFXFwcb7zxhq4xeeoo4Iiks4QvnoMHDxIWFkZwcDBFihTBycmJ//3vf8TFxbFw4UL++OMPBgwYoAHEkiIWi8V6jf3111/ExcUxdepUChcujKOjIzVr1rRO4Hf06FFmz56Nm5tbOlctYj91UYlkABMmTGDPnj3cuXOHxYsX4+3tTXR0NM7OzsTGxrJx40amTZtG5syZWbRokW7PlRRLaKW5ffs2n332GQULFiQ2NhZHR0eioqLYvn07o0ePJl++fMyfP996R5XI00qDjEUygKJFi3LhwgXOnDnDkSNHAKzhxtHRkZdeeon+/ftz6dIlevXqZZ27RCS5smXLRkxMDFevXuXChQtAfOthXFwcZrOZmjVrMnr0aP766y/69OmjSfzkqacWHJEn7MHBng8+A2jTpk288847lCpVioEDB1K+fHkAa8iJjY1ly5Yt+Pr6WidkE0nKw66x1atXM3nyZLy9vXnvvfcoU6YMcH+MTlRUFDt27KBQoUIULlw4vcoXSRMKOCJPUEJYSRAREUGmTJmsr7/55hsmTpxI+fLl6datG+XKlQNsv6REHuXf19i9e/dwdXW1vl6+fDmffPIJ+fLlo3///olCjohRKOCIPCEPfvEEBwdz+PBhQkJCqFu3Lm3btrU+tHDNmjV8+OGHlC9fnu7du1O2bNn0LFueIg9eYzNmzOCvv/7i5MmTNG/enBdeeMHaKvPFF1/w6aefki9fPgYOHIifn186Vi3yeCjgiDwBD/513LdvX/766y9q1qxJ/vz5+eCDD2jevDkdOnSgRIkSQHzImTJlCoUKFWLIkCHWv7JFHubBbql+/frx559/EhQURJ48eZg8eTLNmjWjTZs2+Pv7A/Eh5/PPPydTpkyMGTOGkiVLpmf5ImlO7ZEiT0BCuJk2bRp///03EydOZMSIEdb133zzDVOmTOHEiRMANG3alN69e3PlyhVy5cqVLjXL0yUh3EybNo1jx47x0UcfMXz4cNzc3LBYLKxevZrZs2dz+PBhANq0aUPz5s2xWCy4u7unZ+kij4U69UUeg8jISHbu3Mn58+cpU6YMRYoUwdnZmfPnz9OiRQsCAgJYvHgxkyZNYsaMGZhMJvr160fmzJnp2rUrvr6+vPrqq/zvf//T7bqSpMjISPbs2cO5c+coV64cXl5euLm5ceLECdq0aUO5cuX45JNPmDRpEgsXLuT27dv069cPFxcXOnbsSNmyZenQoQOvvPKKAo4YkrqoRNJYeHg4PXr04MyZM1y5cgUnJyeaN29Ot27duHPnDlmzZuXy5ct0796dPn360LJlS6Kjo+nbty/bt2/n+eefZ9iwYRQtWtSm20EkQcI1dvr0aa5evYrZbKZRo0YMGjSI0NBQMmXKxLVr1+jevTv9+/enWbNmmM1m3nrrLbZu3UrVqlXp378/pUqVSu+3IvLYqAVHJA2Fh4fTuHFj8ufPz/jx48mcOTM//PADn3zyCbly5eKtt94C4m8Jd3d3p169etaHZ+bJk4fAwEBOnDhhvetF4Ub+LeEaK1iwIOPGjSNPnjwsX76cL774gpw5czJgwAAAtm/fTrZs2ahTp471MQtubm6ULl2aAwcOkD179nR8FyKPnwKOSBq5e/cur776Knny5GHOnDlkzpwZgHLlynH16lUWLVpEixYt8Pb2xtHRkStXrnD16lU8PT25cuUK165d44033qBGjRo2T3IWSRAREUHz5s0pUKAAM2fOJHPmzJhMJt577z2uX7/O8uXLadeuHZ6ensTFxXHmzBliY2MBuHr1KuHh4YwYMQIfHx89W0oMTwFHJI0sX76cU6dO0aRJE2JiYgCIiorCbDZTokQJtm3bRnR0NAD+/v4UKVKEYcOG8dxzz3Hp0iUOHjzIsGHDFG7koVasWMGFCxcoXbo0WbJkAe7PpeTv78+OHTu4ffs2np6eVKpUCT8/P9q2bUv9+vU5ffo0Bw8exN3dXeFGngm6i0okjbRv35433niD33//nUmTJnH9+nXrF8mpU6fIkSOHda6bsmXL0q1bN/LmzctPP/3E9evXWbBgAQUKFEjPtyAZXJMmTejUqRN//PEHw4YNIyYmxjpR5LFjx8iePTu5c+cGoGTJkvTo0YOSJUuyadMmwsPDWbRoEQULFkzPtyDyxGiQsUgaSJhgLS4ujgkTJvDjjz9SvXp1xowZw4IFC5g2bRpLly4lICDA+hBNiB9PERcXh8lk0t1S8kgJcyndunWLefPm8e233xIYGMiECROYP38+06dPZ+nSpZQvX97mGouKiuL27du4uLjoKeHyTFHAEUkj/w45W7duJWvWrBw7dozg4GBq165tvSvq3/8rkhxJhRw3NzdOnz7Nxx9/TN26dXVtifw/jcERSSMJD8R0dHRk2LBhODg4sHr1aipVqkTFihWB+7PNJnzx6AtIUiLh6d/u7u507doVgLVr11K+fHnq1q0LxIcgR0dHXVvyzNMYHJE0lBByHBwcGDJkCE2bNuX8+fNMmjSJmzdvWr+gRFLr3yGncePGXLhwgWHDhgH3r0GRZ50CjkgqPOoL5MGQM3ToUGrXrs2vv/7K1KlTCQsL0xObxW7/Djkvv/wyO3fu5J133gGweZq4yLNKXVQiyRQXF8evv/6Kq6srlSpVAuKfCh4YGMhzzz1ns+2/u6scHR35+uuvMZvNDB06VCFH7PbvkOPo6MiSJUtwcXHhvffeS+/yRNKdAo5IMoWGhvLNN99w6tQphgwZwqeffsqhQ4do2LBhkts/GHLefvttnJ2dadGihcKNpJkHQ06nTp1wdnZ+6PUo8qzRXVQiKfDzzz8zdepULly4gLOzM0uWLKFo0aKP3Cch5Ig8Lgl3V+nOKZH79KekSAo8//zzuLu7c+fOHXLkyMG5c+es6x72t4LCjTxuCa2CCjci9yngiCRTwt1P1apVY8iQITg5OTFz5ky2bt0KYJ175EFqIBURSR/qohJ5hEd1L23ZsoXg4GCcnZ3p0aMHtWvXBuJnjj137hzFihV7kqWKiMgDNMhY5CEeDDfLli3jn3/+ISoqCl9fX1577TXrxGrBwcHMnTuXuLg4nn/+ecaNG8e1a9eYOHGi9YGIIiLyZKkFRyQJDw7W7NOnD3v37iVfvnyEhoYSFhZGyZIlWbRoEZkzZ2bz5s3MmTOHs2fPki9fPs6ePcuSJUsoXbp0Or8LEZFnlwKOyAMS7kZJsHDhQpYuXcrkyZOpUKECsbGxrFmzhunTp+Pp6cnKlSsB2Lt3L/v27eP69eu0atWKIkWKpNdbEBERFHBEgPhxM2azGbDtmhoyZAgXL15k3rx5ZMqUybrt5s2bGTFiBK1bt2bw4MHW4+g2XRGRjEF3UckzLzo6mvbt29O7d28g/rbuqKgo4uLiuHjxIiaTiUyZMhEXF0dsbCxms5l69erh7+/PX3/9RUxMjPVYCjciIhmDAo488yIiIqhYsaLNs3zMZjMODg40bNiQ3bt3s3nzZhwcHKy3gpvNZgoXLkxYWJhNwBERkYxBd1HJM81iseDu7k7nzp3JkiUL8+fPx2KxMGHCBAAqVqxItWrVGDduHE5OTtSqVQuAGzducObMGYoWLaqJ/EREMiCNwZFn2oODiq9fv86XX37J/Pnzeemll6whZ9u2bSxatIg9e/bQpk0bnJycuHDhAjt37uSLL76gRIkS6fkWREQkCWrBkWfO3bt3mTp1KnXq1KFQoULkzZsXAA8PD1q2bAlgnddm4sSJBAUFkSNHDrZu3cqqVavIlCkTBQsW5PPPP1e4ERHJoNSCI8+UmJgYevXqxbZt2zCbzZQsWZKCBQvSqFEjypYtS44cObh37x6LFi1i/vz51K1bl0mTJln3Dw8PJ1OmTERHR+Pq6pqO70RERB5FLTjyTImMjKRcuXJcvHiRixcvUqtWLTZv3sygQYNwcXGhQYMGVK9enSZNmuDk5MTixYt55513GD9+PAAuLi44OjrazJUjIiIZj1pw5Jlz+/ZtVq5cyeLFi/H392fGjBkcOnSItWvXsnPnTk6ePEnZsmXJnDkzJpOJX3/9lebNmzNu3Lj0Ll1ERJJJAUeeSQkhZ/r06dSoUYPp06cDcOnSJS5fvszKlSs5duwYBw8etD4R/JdffiFXrlzpWbaIiCSTAo48s8LDw/n666+ZNWsWAQEBzJ0717ouYTbjzZs3888//1C7dm09HVxE5CmigCPPtISQM3v2bAICApgzZw5g++gGPX5BROTpo4Ajz7yHhZyYmBicnDQOX0TkaaSAI8L9kDN//nyKFi3K0qVL07skERGxg/48FQHc3Nxo0aIFkZGRfPXVV4SEhODt7Z3eZYmISCqpBUfkAeHh4cTExJA9e/b0LkVEROyggCMiIiKGo+lYRURExHAUcERERMRwFHBERETEcBRwRERExHAUcERERMRwFHBERETEcBRwRERExHAUcESeEatWrcLX19f64+fnR82aNRk2bBiXL19+IjXUqVOHoUOHWl/v2rULX19fdu3alaLj7Nu3jxkzZnDr1q20LpGhQ4dSp06d/9yuXbt2tGvXLlXnqFOnDt26dUvVvo865oOfrcizTo9qEHnGTJgwgaJFi3Lv3j327t3L3Llz2b17N+vWrSNz5sxPtJbSpUuzfPlyihcvnqL99u/fT3BwMM2aNcPd3f0xVSciTzMFHJFnTIkSJfD39wcgMDCQ2NhYZs2axebNm2ncuHGS+0RERJApU6Y0r8XNzY3y5cun+XFFRNRFJfKMSwgYFy9eBOK7aAICAjh27BidOnUiICCADh06ABAVFcWsWbN46aWXKFOmDIGBgQwbNozr16/bHDM6OpoPP/yQ6tWrU65cOdq0acPBgwcTnfthXVQHDhyge/fuVKlSBX9/f+rVq8e4ceMAmDFjBh9++CEAdevWtXa5PXiM7777jlatWlG+fHkCAgLo3Lkzf/75Z6Lzr1q1ihdffJEyZcrwv//9jzVr1qTqM0wQHBxMy5YtqVy5MhUqVKBZs2asWLGChz0R54cffqBRo0b4+/tTt25dlixZkmib8PBwJk6cSJ06dShTpgzPP/8848aN4+7du3bVKmJ0asERecadOXMGAA8PD+uy6OhoevToQevWrenSpQuxsbHExcXRs2dPfv/9dzp37kyFChW4cOECM2bM4ODBg6xcuRJXV1cA3n33XdasWUOnTp2oXr06x48fp3fv3ty5c+c/6/n555/p0aMHRYsWZejQoXh7e3PhwgV+/fVXAFq2bMnNmzdZunQpwcHBeHp6Ali7uebMmcO0adN45ZVX6NGjB9HR0SxcuJDXX3+dFStWWLdbtWoVw4YNo27dugwdOpTbt28THBxMVFQUDg6p+9vvwoULtGrVirx58wLwxx9/MHbsWC5fvkzv3r1ttv3rr78YP348vXv3JleuXKxbt45x48YRHR1N586dgfiWs7Zt23Lp0iW6d++Or68vx48fZ/r06fz9998sXrwYk8mUqlpFDM8iIs+ElStXWnx8fCx//PGHJTo62hIeHm756aefLIGBgZaAgABLaGioxWKxWIYMGWLx8fGxfP311zb7r1+/3uLj42P5/vvvbZYfPHjQ4uPjY1m2bJnFYrFYTpw4YfHx8bGMHz/eZru1a9dafHx8LEOGDLEu27lzp8XHx8eyc+dO67J69epZ6tWrZ7l3795D38uCBQssPj4+lnPnztksv3jxosXPz8/y/vvv2ywPDw+3VK9e3dK3b1+LxWKxxMbGWmrUqGFp1qyZJS4uzrrd+fPnLaVLl7bUrl37oedO0LZtW0vbtm0fuj42NtYSHR1tCQ4OtlSuXNnmPLVr17b4+vpa/vrrL5t9OnbsaKlQoYLl7t27FovFYpk7d66lZMmSloMHD9pst3HjRouPj49l69atNsd88LMVedapi0rkGfPqq69SunRpKlSoQLdu3ciVKxfz588nV65cNtu9+OKLNq9/+ukn3N3dqV27NjExMdafUqVK4enpye7duwGsXUWNGjWy2f9///sfTk6PbjQ+ffo0Z8+epUWLFri4uKT4vf3yyy/ExMTQpEkTmxpdXFyoVKmStcbTp09z5coVGjZsaNMCki9fPgICAlJ83gQ7duygQ4cOVKxYkVKlSlG6dGmmT59OWFgY165ds9m2RIkSlCxZ0mZZw4YNCQ8P58iRI0D8Z16iRAlKlSpl835q1KiByWSyvh8RSUxdVCLPmIkTJ1KsWDGcnJzImTMnuXPnTrRNpkyZcHNzs1l27do1bt26RZkyZZI87o0bNwAICwsDsHYdJXByciJ79uyPrC1hLE+ePHmS81YSuXr1KgAtWrRIcn1C11NCrf8OdQnLLly4kOJzHzx4kM6dO1O5cmXef/99vLy8cHZ2ZvPmzcyZM4d79+4lOk9S54b7n+G1a9c4c+YMpUuXTvKcCe9DRBJTwBF5xhQrVsx6F9XDJDWuI0eOHGTPnp0FCxYkuU+WLFkArCEmNDTUJqjExMRYv7gfJmEcUGrn5cmRIwcA06dPt46DedR2CYHoQUktS45vv/0WJycn5s6da9P6tHnz5iS3f9S5Ez7DHDly4OLiwvjx45M8RsL7EJHEFHBEJFlq1arFt99+S1xcHOXKlXvodlWqVAFg3bp1Nq09GzZsICYm5pHnKFKkCAULFmTlypV07NgRs9mc5HYJyyMjI22W16hRAycnJ86ePZuoi+3f5/H09GT9+vV07NjRGuguXLjA/v37k2zV+i8mkwlHR0ebAcr37t1j7dq1SW5//Phxjh49atNNtX79erJkyWJtsalVqxZz584le/bsFChQIMU1iTzLFHBEJFlefvll1q1bR9euXWnXrh1ly5bF2dmZS5cusWvXLurWrUv9+vUpVqwYjRs35tNPP8XJyYlq1apx/PhxFi5cmKjbKykjR46kR48evPrqq3To0AFvb29CQkL4+eefmTx5MgA+Pj4AfPrppzRr1gwnJyeKFClC/vz56dOnD9OmTePcuXPUrFkTd3d3rl69yqFDh8iUKRN9+vTBwcGBvn37MmLECHr16sWrr77KrVu3CA4OTrLrKDmCgoL45JNPGDhwIK1atSIsLIyFCxc+NKTlzp2bHj160Lt3bzw9PVm7di2//vorgwYNss459MYbb7Bp0ybatm1Lhw4d8PX1JS4ujpCQEH755Rc6der0yLAp8ixTwBGRZHF0dGT27NksWbKEb775hnnz5uHo6IiXlxeVKlWyhg6AcePGkStXLlavXs3SpUspVaoUM2bMYMCAAf95nueff57PPvuMmTNnMnbsWCIjI/Hy8rJ5fEKVKlXo1q0bq1evZsWKFcTFxbFkyRLr8mLFirFkyRK+/fZboqKi8PT0pEyZMrRp08Z6jJYtWwKwYMECevfuTb58+ejWrRt79uxJ1eDdqlWrMn78eObPn0/37t3JkycPr776Kh4eHgwfPjzR9qVKleKVV15hxowZ/PPPP+TOnZthw4ZZ5xwCyJw5M8uWLWPevHksX76c8+fP4+rqire3N9WqVSNfvnwprlPkWWGyWB4yA5WIiIjIU0q3iYuIiIjhKOCIiIiI4SjgiIiIiOEo4IiIiIjhKOCIiIiI4SjgiIiIiOEo4IiIiIjhKOCIiIiI4SjgiIiIiOEo4IiIiIjhKOCIiIiI4fwfNSzFQhHE8CsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(confusion_matrix(y, y_hat),classes=[ \"Not Parkinson\", \" Parkinson\"],title='Confusion matrix')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5e7521c",
   "metadata": {
    "papermill": {
     "duration": 0.025392,
     "end_time": "2023-07-09T17:24:58.302183",
     "exception": false,
     "start_time": "2023-07-09T17:24:58.276791",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### let's label true positives, false positives, true negatives, and false negatives \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d212c18",
   "metadata": {
    "papermill": {
     "duration": 0.024066,
     "end_time": "2023-07-09T17:24:58.350930",
     "exception": false,
     "start_time": "2023-07-09T17:24:58.326864",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Machine Learning Interpretability\n",
    "## Machine learning interpretability refers to the ability to understand and explain how a machine learning model arrives at its predictions or decisions. It is an important aspect of machine learning because it enables users to gain insight into how a model works, assess its strengths and limitations, and identify potential issues such as bias, errors, or overfitting. Interpretability can be achieved through a variety of techniques such as visualizations, feature importance scores, model-agnostic methods, and explanations of specific decisions. \n",
    "## T-SNE can be used for machine learning interpretability by visualizing high-dimensional data in a two-dimensional space.  For example, in the context of clustering, t-SNE can be used to visualize the clusters in a two-dimensional space, making it easier to identify patterns and relationships between the data points. This can help to identify outliers, clusters that are not well-separated, and potentially interesting subsets of the data that may warrant further investigation. Let's label our predictions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "44b4229a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:58.402506Z",
     "iopub.status.busy": "2023-07-09T17:24:58.402125Z",
     "iopub.status.idle": "2023-07-09T17:24:58.546246Z",
     "shell.execute_reply": "2023-07-09T17:24:58.545418Z"
    },
    "papermill": {
     "duration": 0.17156,
     "end_time": "2023-07-09T17:24:58.547783",
     "exception": false,
     "start_time": "2023-07-09T17:24:58.376223",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# find true positives(tp)\n",
    "tsne_df['label']=''\n",
    "tsne_df['label'].iloc[np.where((y_hat == 1) & (y == 1))]=\"TP\"\n",
    "# find true negatives (tn)\n",
    "tsne_df['label'].iloc[np.where((y_hat == 0) & (y == 0))]=\"TN\"\n",
    "# find false positives (fp)\n",
    "tsne_df['label'].iloc[np.where((y_hat == 1) & (y == 0))]=\"FP\" \n",
    "# find false negatives (fp)\n",
    "tsne_df['label'].iloc[np.where((y_hat == 0) & (y == 1))]=\"FN\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45711854",
   "metadata": {
    "papermill": {
     "duration": 0.023154,
     "end_time": "2023-07-09T17:24:58.595184",
     "exception": false,
     "start_time": "2023-07-09T17:24:58.572030",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Let's plot the data, with the labeled predictions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8bd5c4b3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:58.643794Z",
     "iopub.status.busy": "2023-07-09T17:24:58.643048Z",
     "iopub.status.idle": "2023-07-09T17:24:59.020042Z",
     "shell.execute_reply": "2023-07-09T17:24:59.019197Z"
    },
    "papermill": {
     "duration": 0.403282,
     "end_time": "2023-07-09T17:24:59.021857",
     "exception": false,
     "start_time": "2023-07-09T17:24:58.618575",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "sns.scatterplot(data=tsne_df, x='TSNE1', y='TSNE2', hue='Class', palette='Set2',style='label')\n",
    "plt.title('t-SNE Visualization')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93cc9234",
   "metadata": {
    "papermill": {
     "duration": 0.025303,
     "end_time": "2023-07-09T17:24:59.072833",
     "exception": false,
     "start_time": "2023-07-09T17:24:59.047530",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## From the plot, we see most of the errors are confined to specific clusters. We can apply clustering to our data before we classify the data, but let's try several other models. Here are several more libraries for Machine Learning Interpretability\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47802226",
   "metadata": {
    "papermill": {
     "duration": 0.026406,
     "end_time": "2023-07-09T17:24:59.124650",
     "exception": false,
     "start_time": "2023-07-09T17:24:59.098244",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<ul>\n",
    "  <li><a href=\"https://github.com/slundberg/shap\">SHAP</a></li>\n",
    "  <li><a href=\"https://github.com/marcotcr/lime\">LIME</a></li>\n",
    "  <li><a href=\"https://github.com/oracle/Skater\">Skater</a></li>\n",
    "  <li><a href=\"https://github.com/microsoft/interpret\">Microsoft InterpretML</a></li>\n",
    "  <li><a href=\"https://github.com/interpretml/interpret-text\">Interpret-text</a></li>\n",
    "  <li><a href=\"https://github.com/interpretml/interpret-core\">Interpret-core</a></li>\n",
    "  <li><a href=\"https://github.com/surrey-unplugged/saliency\">Saliency</a></li>\n",
    "  <li><a href=\"https://github.com/PAIR-code/facets\">Facets</a></li>\n",
    "  <li><a href=\"https://github.com/marcotcr/photonai\">Photonai</a></li>\n",
    "</ul>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0b5f12e",
   "metadata": {
    "papermill": {
     "duration": 0.024585,
     "end_time": "2023-07-09T17:24:59.174595",
     "exception": false,
     "start_time": "2023-07-09T17:24:59.150010",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Logistic Regression\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6df62fcd",
   "metadata": {
    "papermill": {
     "duration": 0.024469,
     "end_time": "2023-07-09T17:24:59.223706",
     "exception": false,
     "start_time": "2023-07-09T17:24:59.199237",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## here's an example code for fitting a logistic regression model using scikit-learn, it's almost identical to K-NN , first we import ```LogisticRegression```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "25b5a800",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:59.275368Z",
     "iopub.status.busy": "2023-07-09T17:24:59.275033Z",
     "iopub.status.idle": "2023-07-09T17:24:59.279133Z",
     "shell.execute_reply": "2023-07-09T17:24:59.277963Z"
    },
    "papermill": {
     "duration": 0.032405,
     "end_time": "2023-07-09T17:24:59.281110",
     "exception": false,
     "start_time": "2023-07-09T17:24:59.248705",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "154a46a6",
   "metadata": {
    "papermill": {
     "duration": 0.024,
     "end_time": "2023-07-09T17:24:59.330469",
     "exception": false,
     "start_time": "2023-07-09T17:24:59.306469",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    " ## We create and fit logistic regression model ```lr``` and fit it \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1f55883d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:59.380294Z",
     "iopub.status.busy": "2023-07-09T17:24:59.379988Z",
     "iopub.status.idle": "2023-07-09T17:24:59.402510Z",
     "shell.execute_reply": "2023-07-09T17:24:59.401721Z"
    },
    "papermill": {
     "duration": 0.049522,
     "end_time": "2023-07-09T17:24:59.404115",
     "exception": false,
     "start_time": "2023-07-09T17:24:59.354593",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "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>LogisticRegression()</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-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LogisticRegression()\n",
    "lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41f2bfb3",
   "metadata": {
    "papermill": {
     "duration": 0.024016,
     "end_time": "2023-07-09T17:24:59.453137",
     "exception": false,
     "start_time": "2023-07-09T17:24:59.429121",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## we can make a prediction on the first ten samples\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "44ce34b1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:59.503762Z",
     "iopub.status.busy": "2023-07-09T17:24:59.503048Z",
     "iopub.status.idle": "2023-07-09T17:24:59.511104Z",
     "shell.execute_reply": "2023-07-09T17:24:59.510155Z"
    },
    "papermill": {
     "duration": 0.035,
     "end_time": "2023-07-09T17:24:59.512609",
     "exception": false,
     "start_time": "2023-07-09T17:24:59.477609",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 1, 1, 1, 1, 0, 0, 0])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = lr.predict(X_test)\n",
    "y_pred[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "864451a5",
   "metadata": {
    "papermill": {
     "duration": 0.023936,
     "end_time": "2023-07-09T17:24:59.561012",
     "exception": false,
     "start_time": "2023-07-09T17:24:59.537076",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## We can get a probability of belonging to a each class for each sample, here are the first ten \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e0ea6e40",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:59.611341Z",
     "iopub.status.busy": "2023-07-09T17:24:59.610329Z",
     "iopub.status.idle": "2023-07-09T17:24:59.617217Z",
     "shell.execute_reply": "2023-07-09T17:24:59.616694Z"
    },
    "papermill": {
     "duration": 0.033602,
     "end_time": "2023-07-09T17:24:59.618625",
     "exception": false,
     "start_time": "2023-07-09T17:24:59.585023",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.26796849, 0.73203151],\n",
       "       [0.69161219, 0.30838781],\n",
       "       [0.06764952, 0.93235048],\n",
       "       [0.38588988, 0.61411012],\n",
       "       [0.07237225, 0.92762775],\n",
       "       [0.26252443, 0.73747557],\n",
       "       [0.00912184, 0.99087816],\n",
       "       [0.5108703 , 0.4891297 ],\n",
       "       [0.85915321, 0.14084679],\n",
       "       [0.90263876, 0.09736124]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.predict_proba(X_test)[0:10]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87dca5f0",
   "metadata": {
    "papermill": {
     "duration": 0.024259,
     "end_time": "2023-07-09T17:24:59.667561",
     "exception": false,
     "start_time": "2023-07-09T17:24:59.643302",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## We can display the confusion matrix as well as a classification report \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "28801628",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:24:59.720617Z",
     "iopub.status.busy": "2023-07-09T17:24:59.720040Z",
     "iopub.status.idle": "2023-07-09T17:25:00.007137Z",
     "shell.execute_reply": "2023-07-09T17:25:00.006081Z"
    },
    "papermill": {
     "duration": 0.317716,
     "end_time": "2023-07-09T17:25:00.009735",
     "exception": false,
     "start_time": "2023-07-09T17:24:59.692019",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[ 9  3]\n",
      " [ 6 41]]\n",
      "\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.60      0.75      0.67        12\n",
      "           1       0.93      0.87      0.90        47\n",
      "\n",
      "    accuracy                           0.85        59\n",
      "   macro avg       0.77      0.81      0.78        59\n",
      "weighted avg       0.86      0.85      0.85        59\n",
      "\n",
      "\n",
      "Accuracy Score: 0.847457627118644\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(confusion_matrix(y_test, y_pred),classes=[ \"Not Parkinson\", \" Parkinson\"],title='Confusion matrix')\n",
    "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))\n",
    "print(\"\\nAccuracy Score:\", accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84d2d8d5",
   "metadata": {
    "papermill": {
     "duration": 0.025166,
     "end_time": "2023-07-09T17:25:00.060964",
     "exception": false,
     "start_time": "2023-07-09T17:25:00.035798",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## While grid search can be used to optimize hyperparameters for many machine learning algorithms, the hyperparameters in logistic regression may not be particularly insightful. Instead, we can use logistic regression to identify feature importance, as the coefficients learned during training can reveal which features are most strongly associated with the target variable. We can obtain the coefficients.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "35cbf704",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:25:00.113906Z",
     "iopub.status.busy": "2023-07-09T17:25:00.112871Z",
     "iopub.status.idle": "2023-07-09T17:25:00.116950Z",
     "shell.execute_reply": "2023-07-09T17:25:00.116379Z"
    },
    "papermill": {
     "duration": 0.031966,
     "end_time": "2023-07-09T17:25:00.118464",
     "exception": false,
     "start_time": "2023-07-09T17:25:00.086498",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "coef=abs(lr.coef_[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12dc2689",
   "metadata": {
    "papermill": {
     "duration": 0.024521,
     "end_time": "2023-07-09T17:25:00.168338",
     "exception": false,
     "start_time": "2023-07-09T17:25:00.143817",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## the coefficients represent the impact of each feature on the probability of the positive class, So larger the magnitude  of the coefficients  are more important the feature\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5dea807f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:25:00.220682Z",
     "iopub.status.busy": "2023-07-09T17:25:00.219625Z",
     "iopub.status.idle": "2023-07-09T17:25:00.521711Z",
     "shell.execute_reply": "2023-07-09T17:25:00.520913Z"
    },
    "papermill": {
     "duration": 0.330301,
     "end_time": "2023-07-09T17:25:00.523466",
     "exception": false,
     "start_time": "2023-07-09T17:25:00.193165",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.barh(X.columns, coef)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76ae24c0",
   "metadata": {
    "papermill": {
     "duration": 0.026328,
     "end_time": "2023-07-09T17:25:00.575838",
     "exception": false,
     "start_time": "2023-07-09T17:25:00.549510",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## We can select the features with the highest magnitude coefficients \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e0276ff6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:25:00.627574Z",
     "iopub.status.busy": "2023-07-09T17:25:00.627252Z",
     "iopub.status.idle": "2023-07-09T17:25:00.632818Z",
     "shell.execute_reply": "2023-07-09T17:25:00.632030Z"
    },
    "papermill": {
     "duration": 0.033561,
     "end_time": "2023-07-09T17:25:00.634478",
     "exception": false,
     "start_time": "2023-07-09T17:25:00.600917",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['D2', 'MDVP:Shimmer(dB)', 'spread1', 'DFA', 'spread2'], dtype='object')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_columns=X.columns[np.argsort(-1*coef)[0:5]]\n",
    "best_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3035099a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:25:00.687173Z",
     "iopub.status.busy": "2023-07-09T17:25:00.686691Z",
     "iopub.status.idle": "2023-07-09T17:25:00.980833Z",
     "shell.execute_reply": "2023-07-09T17:25:00.980014Z"
    },
    "papermill": {
     "duration": 0.322403,
     "end_time": "2023-07-09T17:25:00.982565",
     "exception": false,
     "start_time": "2023-07-09T17:25:00.660162",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[ 9  3]\n",
      " [ 6 41]]\n",
      "\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.60      0.75      0.67        12\n",
      "           1       0.93      0.87      0.90        47\n",
      "\n",
      "    accuracy                           0.85        59\n",
      "   macro avg       0.77      0.81      0.78        59\n",
      "weighted avg       0.86      0.85      0.85        59\n",
      "\n",
      "\n",
      "Accuracy Score: 0.847457627118644\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_reg = LogisticRegression(max_iter=10000)\n",
    "log_reg.fit(X_train[best_columns], y_train)\n",
    "\n",
    "# Get the predicted probabilities for each class\n",
    "y_pred_proba = log_reg.predict_proba(X_test[best_columns])\n",
    "\n",
    "plot_confusion_matrix(confusion_matrix(y_test, y_pred),classes=[ \"Not Parkinson\", \" Parkinson\"],title='Confusion matrix')\n",
    "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))\n",
    "print(\"\\nAccuracy Score:\", accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d2ca988",
   "metadata": {
    "papermill": {
     "duration": 0.026196,
     "end_time": "2023-07-09T17:25:01.035745",
     "exception": false,
     "start_time": "2023-07-09T17:25:01.009549",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### We see many of the  features are redundant, identifying and removing redundant features can potentially help in a few ways.\n",
    "\n",
    "### First, it can reduce the amount of data required for the model, which can reduce the cost of storing and processing data.\n",
    "\n",
    "### Second, it can improve the performance of the model, allowing it to make more accurate predictions with fewer resources. This can help reduce the cost of misclassifications or errors in the predictions.\n",
    "\n",
    "### Finally, it can also reduce the cost of model development and maintenance. Removing redundant features can simplify the model, making it easier to develop and maintain over time. It can also reduce the need for frequent updates and retraining, which can be costly in terms of time and resources\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "152483ed",
   "metadata": {
    "papermill": {
     "duration": 0.026178,
     "end_time": "2023-07-09T17:25:01.088158",
     "exception": false,
     "start_time": "2023-07-09T17:25:01.061980",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Support Vector Machines (SVM)\n",
    "\n",
    "\n",
    "### SVM stands for Support Vector Machines. It is a type of supervised machine learning algorithm used for classification. The algorithm identifies a hyperplane (or a set of hyperplanes) in an n-dimensional space that maximally separates the different classes in the dataset. The hyperplane that is selected is the one that has the maximum margin between the closest points of different classes, known as support vectors. SVM is particularly effective in high-dimensional datasets where other algorithms may have difficulty in identifying a clear boundary between the classes. SVM is also versatile in the type of kernel functions that can be used to transform the data into higher dimensions, such as linear, polynomial, and radial basis function (RBF) kernels.\n",
    "\n",
    "## Like Logistic Regression SVM is a linear classifier but there are some Pros and Cons\n",
    "\n",
    "## Pros of SVM over Logistic Regression\n",
    "<ul>\n",
    "<li>Effective in high dimensional spaces and with datasets that have a lot of features</li>\n",
    "<li>Can handle non-linearly separable data using kernel trick</li>\n",
    "<li>Robust to overfitting due to the regularization parameter</li>\n",
    "</ul>\n",
    "\n",
    "## Cons of SVM compared to Logistic Regression<b>\n",
    "<ul>\n",
    "<li>Can be computationally expensive to train, especially with large datasets</li>\n",
    "<li>Difficult to interpret the results and understand the impact of each feature on the model</li>\n",
    "<li>Requires careful selection of hyperparameters to achieve optimal performance</li>\n",
    "</ul>\n",
    "\n",
    "You can train a SVM just like any other model \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71017ccf",
   "metadata": {
    "papermill": {
     "duration": 0.026043,
     "end_time": "2023-07-09T17:25:01.141883",
     "exception": false,
     "start_time": "2023-07-09T17:25:01.115840",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Train and Test SVM Model using the SVC constructor and display the confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4aadd33b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:25:01.196436Z",
     "iopub.status.busy": "2023-07-09T17:25:01.195894Z",
     "iopub.status.idle": "2023-07-09T17:25:01.482548Z",
     "shell.execute_reply": "2023-07-09T17:25:01.481803Z"
    },
    "papermill": {
     "duration": 0.316021,
     "end_time": "2023-07-09T17:25:01.484253",
     "exception": false,
     "start_time": "2023-07-09T17:25:01.168232",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[ 4  8]\n",
      " [ 2 45]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "# Train the SVM classifier\n",
    "svm = SVC()\n",
    "svm.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_hat = svm.predict(X_test)\n",
    "\n",
    "# confusion_matri\n",
    "plot_confusion_matrix(confusion_matrix(y_test, y_hat),classes=[ \"Not Parkinson\", \" Parkinson\"],title='Confusion matrix')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1b45a71",
   "metadata": {
    "papermill": {
     "duration": 0.026419,
     "end_time": "2023-07-09T17:25:01.538179",
     "exception": false,
     "start_time": "2023-07-09T17:25:01.511760",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Here are the explanations for some of the important parameters: \n",
    "    \n",
    "## ```C:``` Regularization parameter. A smaller value of C creates a wider margin but may lead to more misclassifications, while a larger value of C creates a narrower margin but may lead to overfitting\n",
    "\n",
    "## ```kernel:``` Type of kernel function used to transform the data into a higher-dimensional space where it can be linearly separated. Common choices include linear, polynomial, and radial basis function (RBF) kernels\n",
    "\n",
    "## ```gamma:``` Kernel coefficient for RBF, polynomial, and sigmoid kernels. A smaller value of gamma creates a larger influence radius for the support vectors, while a larger value of gamma creates a smaller influence radius and can lead to overfitting\n",
    "\n",
    "## ```degree:``` Degree of the polynomial kernel function. Only used for polynomial kernel.    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0d3917e",
   "metadata": {
    "papermill": {
     "duration": 0.025916,
     "end_time": "2023-07-09T17:25:01.590641",
     "exception": false,
     "start_time": "2023-07-09T17:25:01.564725",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## here is a dictionary for grid search with some of the specified parameters:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "13bc77b9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:25:01.644726Z",
     "iopub.status.busy": "2023-07-09T17:25:01.644374Z",
     "iopub.status.idle": "2023-07-09T17:25:01.648775Z",
     "shell.execute_reply": "2023-07-09T17:25:01.647824Z"
    },
    "papermill": {
     "duration": 0.033166,
     "end_time": "2023-07-09T17:25:01.650333",
     "exception": false,
     "start_time": "2023-07-09T17:25:01.617167",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "    'C': [0.1, 1,],\n",
    "    'gamma': [0.1, 1, 10],\n",
    "\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39c5d10d",
   "metadata": {
    "papermill": {
     "duration": 0.026249,
     "end_time": "2023-07-09T17:25:01.703407",
     "exception": false,
     "start_time": "2023-07-09T17:25:01.677158",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Performing grid search on an SVM classifier with the specified hyperparameters and using recall as the scoring metric to find the best hyperparameters and the corresponding recall score:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "bdc8f883",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:25:01.762949Z",
     "iopub.status.busy": "2023-07-09T17:25:01.762073Z",
     "iopub.status.idle": "2023-07-09T17:25:01.854275Z",
     "shell.execute_reply": "2023-07-09T17:25:01.853307Z"
    },
    "papermill": {
     "duration": 0.122431,
     "end_time": "2023-07-09T17:25:01.856246",
     "exception": false,
     "start_time": "2023-07-09T17:25:01.733815",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best hyperparameters:  {'C': 0.1, 'gamma': 0.1}\n",
      "Best recall score:  1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "# Train the SVM classifier\n",
    "svm = SVC()\n",
    "# Create a GridSearchCV object\n",
    "grid_search = GridSearchCV(svm, param_grid, scoring='recall', cv=2)\n",
    "\n",
    "# Fit the GridSearchCV object to the training data\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Print the best hyperparameters and the corresponding recall score\n",
    "print(\"Best hyperparameters: \", grid_search.best_params_)\n",
    "print(\"Best recall score: \", grid_search.best_score_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcd5c6e2",
   "metadata": {
    "papermill": {
     "duration": 0.026648,
     "end_time": "2023-07-09T17:25:01.910883",
     "exception": false,
     "start_time": "2023-07-09T17:25:01.884235",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#  Random Forest Model\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77afdd91",
   "metadata": {
    "papermill": {
     "duration": 0.026211,
     "end_time": "2023-07-09T17:25:01.964104",
     "exception": false,
     "start_time": "2023-07-09T17:25:01.937893",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Random Forest is a good choice for various machine learning tasks due to its high accuracy in classifying complex data patterns. The ability to interpret the results of Random Forest models also makes it easier to validate and understand the decisions it makes.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f7639525",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:25:02.019195Z",
     "iopub.status.busy": "2023-07-09T17:25:02.018835Z",
     "iopub.status.idle": "2023-07-09T17:25:02.257530Z",
     "shell.execute_reply": "2023-07-09T17:25:02.256712Z"
    },
    "papermill": {
     "duration": 0.268155,
     "end_time": "2023-07-09T17:25:02.259180",
     "exception": false,
     "start_time": "2023-07-09T17:25:01.991025",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 89.83%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "# Create a Random Forest model\n",
    "rf = RandomForestClassifier(random_state=123)\n",
    "\n",
    "# Train the model on the training data\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test data\n",
    "y_pred = rf.predict(X_test)\n",
    "\n",
    "# Evaluate the model's performance\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy: {:.2f}%\".format(accuracy * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03a50060",
   "metadata": {
    "papermill": {
     "duration": 0.026657,
     "end_time": "2023-07-09T17:25:02.313581",
     "exception": false,
     "start_time": "2023-07-09T17:25:02.286924",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Perform grid search for RandomForest to tune the parameters\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b8dbff5a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:25:02.369652Z",
     "iopub.status.busy": "2023-07-09T17:25:02.369161Z",
     "iopub.status.idle": "2023-07-09T17:25:17.238735Z",
     "shell.execute_reply": "2023-07-09T17:25:17.237910Z"
    },
    "papermill": {
     "duration": 14.899741,
     "end_time": "2023-07-09T17:25:17.240457",
     "exception": false,
     "start_time": "2023-07-09T17:25:02.340716",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'max_depth': 5, 'max_features': 'sqrt', 'n_estimators': 100}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Define the parameter grid to search over\n",
    "param_grid = {\n",
    "    'n_estimators': [50, 100, 200],\n",
    "    'max_depth': [5, 10, 15],\n",
    "    'max_features': ['sqrt', 'log2']\n",
    "}\n",
    "\n",
    "# Create a random forest classifier\n",
    "rf = RandomForestClassifier(random_state=42)\n",
    "\n",
    "# Create the GridSearchCV object\n",
    "grid_search = GridSearchCV(estimator=rf, param_grid=param_grid, cv=5)\n",
    "\n",
    "# Fit the GridSearchCV object to the data\n",
    "grid_search.fit(X_train, y_train)\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "# Print the best hyperparameters\n",
    "print(\"Best parameters:\", grid_search.best_params_)\n",
    "\n",
    "# Get the best model\n",
    "best_model = grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "fd9df347",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:25:17.296114Z",
     "iopub.status.busy": "2023-07-09T17:25:17.295771Z",
     "iopub.status.idle": "2023-07-09T17:25:17.621848Z",
     "shell.execute_reply": "2023-07-09T17:25:17.620102Z"
    },
    "papermill": {
     "duration": 0.356281,
     "end_time": "2023-07-09T17:25:17.623932",
     "exception": true,
     "start_time": "2023-07-09T17:25:17.267651",
     "status": "failed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unexpected exception formatting exception. Falling back to standard exception\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3508, in run_code\n",
      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "  File \"/tmp/ipykernel_20/3398676756.py\", line 3, in <module>\n",
      "    feat_importances.sort_values().plot(kind='barh',color=['g','b'])\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/pandas/plotting/_core.py\", line 920, in __call__\n",
      "    return plot_backend.plot(data, x=x, y=y, kind=kind, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/pandas/plotting/_core.py\", line 1886, in _get_plot_backend\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/pandas/plotting/_core.py\", line 1817, in _load_backend\n",
      "    # Fall back to unregistered, module name approach.\n",
      "ImportError: matplotlib is required for plotting when the default backend \"matplotlib\" is selected.\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 2105, in showtraceback\n",
      "    stb = self.InteractiveTB.structured_traceback(\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/IPython/core/ultratb.py\", line 1396, in structured_traceback\n",
      "    return FormattedTB.structured_traceback(\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/IPython/core/ultratb.py\", line 1287, in structured_traceback\n",
      "    return VerboseTB.structured_traceback(\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/IPython/core/ultratb.py\", line 1140, in structured_traceback\n",
      "    formatted_exception = self.format_exception_as_a_whole(etype, evalue, etb, number_of_lines_of_context,\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/IPython/core/ultratb.py\", line 1055, in format_exception_as_a_whole\n",
      "    frames.append(self.format_record(record))\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/IPython/core/ultratb.py\", line 955, in format_record\n",
      "    frame_info.lines, Colors, self.has_colors, lvals\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/IPython/core/ultratb.py\", line 778, in lines\n",
      "    return self._sd.lines\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/stack_data/utils.py\", line 144, in cached_property_wrapper\n",
      "    value = obj.__dict__[self.func.__name__] = self.func(obj)\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/stack_data/core.py\", line 734, in lines\n",
      "    pieces = self.included_pieces\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/stack_data/utils.py\", line 144, in cached_property_wrapper\n",
      "    value = obj.__dict__[self.func.__name__] = self.func(obj)\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/stack_data/core.py\", line 681, in included_pieces\n",
      "    pos = scope_pieces.index(self.executing_piece)\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/stack_data/utils.py\", line 144, in cached_property_wrapper\n",
      "    value = obj.__dict__[self.func.__name__] = self.func(obj)\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/stack_data/core.py\", line 660, in executing_piece\n",
      "    return only(\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/executing/executing.py\", line 190, in only\n",
      "    raise NotOneValueFound('Expected one value, found 0')\n",
      "executing.executing.NotOneValueFound: Expected one value, found 0\n"
     ]
    }
   ],
   "source": [
    "# plot the important features\n",
    "feat_importances = pd.Series(rf.feature_importances_, index=X.columns)\n",
    "feat_importances.sort_values().plot(kind='barh',color=['g','b'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "939d01b4",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "### To save the trained model we utilize liberary `pickle`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59943727",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:00:42.093492Z",
     "iopub.status.busy": "2023-07-09T17:00:42.093125Z",
     "iopub.status.idle": "2023-07-09T17:00:42.105132Z",
     "shell.execute_reply": "2023-07-09T17:00:42.103941Z",
     "shell.execute_reply.started": "2023-07-09T17:00:42.093465Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# Save the model to a file\n",
    "filename = 'rf_model_parkinson'\n",
    "pickle.dump(rf, open(filename, \"wb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15137830",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "# Visualizing the Decision Trees\n",
    "\n",
    "## using the `dtreeviz` library, we are able to visualize the decision tree. This can contribute to better the understanding of the role of our variables in identifying Parkinson.\n",
    "\n",
    "## At first, let's tune the parameters for the decision tree classifier:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd0b84df",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:00:42.106686Z",
     "iopub.status.busy": "2023-07-09T17:00:42.106352Z",
     "iopub.status.idle": "2023-07-09T17:00:42.463979Z",
     "shell.execute_reply": "2023-07-09T17:00:42.463271Z",
     "shell.execute_reply.started": "2023-07-09T17:00:42.106661Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Define the parameter grid\n",
    "param_grid = {\n",
    "    'criterion': ['gini', 'entropy'],\n",
    "    'max_depth': [2, 4],\n",
    "    'min_samples_split': [2, 4],\n",
    "    'min_samples_leaf': [1, 2]\n",
    "}\n",
    "\n",
    "# Create a decision tree classifier\n",
    "dt = DecisionTreeClassifier()\n",
    "\n",
    "# Use GridSearchCV to search for the best parameters\n",
    "grid_search = GridSearchCV(dt, param_grid, cv=5, scoring='accuracy')\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Print the best parameters and best score\n",
    "print(\"Best parameters:\", grid_search.best_params_)\n",
    "print(\"Best recall score:\", grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9871ade6",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "### Apply the parameters to model:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d759b2f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:01:05.172169Z",
     "iopub.status.busy": "2023-07-09T17:01:05.171789Z",
     "iopub.status.idle": "2023-07-09T17:01:05.178783Z",
     "shell.execute_reply": "2023-07-09T17:01:05.178063Z",
     "shell.execute_reply.started": "2023-07-09T17:01:05.172137Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "clf=grid_search.best_estimator_\n",
    "#same as\n",
    "#clf = DecisionTreeClassifier(max_depth=2, min_samples_leaf=1, min_samples_split=2, criterion= 'entropy')\n",
    "#clf.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test data\n",
    "y_pred = clf.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b20542b",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "### Next, we plot the tree:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "584467bf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:01:59.013278Z",
     "iopub.status.busy": "2023-07-09T17:01:59.012881Z",
     "iopub.status.idle": "2023-07-09T17:02:01.480743Z",
     "shell.execute_reply": "2023-07-09T17:02:01.479477Z",
     "shell.execute_reply.started": "2023-07-09T17:01:59.013246Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# plot the tree \n",
    "import dtreeviz\n",
    "\n",
    "# Getting the list of variables\n",
    "feature_names = list(X_train.columns)\n",
    "\n",
    "# visualizing the tree\n",
    "viz_model = dtreeviz.model(clf,\n",
    "                           X_train=X_train, y_train=y_train, \n",
    "                           feature_names=feature_names)\n",
    "\n",
    "v = viz_model.view(fancy=True)     # render as SVG into internal object \n",
    "v"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cef2c304",
   "metadata": {
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "### Let's choose a random point and visualize the decision path:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d371c52e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-09T17:02:07.828167Z",
     "iopub.status.busy": "2023-07-09T17:02:07.827782Z",
     "iopub.status.idle": "2023-07-09T17:02:08.762925Z",
     "shell.execute_reply": "2023-07-09T17:02:08.761851Z",
     "shell.execute_reply.started": "2023-07-09T17:02:07.828140Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# get a random point\n",
    "rand = np.random.randint(0, len(X))\n",
    "sample_point = X.iloc[rand,:].values\n",
    "\n",
    "# visualizing the path for the point\n",
    "v = viz_model.view(fancy=True,scale=1.5,x=sample_point,show_just_path=True)\n",
    "v"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 126.954627,
   "end_time": "2023-07-09T17:25:18.574054",
   "environment_variables": {},
   "exception": true,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2023-07-09T17:23:11.619427",
   "version": "2.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}