KNN-SMOTE-weeklyApproach.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6dfbc098",
   "metadata": {},
   "source": [
    "# Importing the necessary packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1fd236b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import os\n",
    "import tempfile\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import sklearn\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import confusion_matrix , classification_report\n",
    "from imblearn.over_sampling import SMOTE\n",
    "import logging\n",
    "logging.getLogger(\"tensorflow\").setLevel(logging.ERROR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "22ea679b",
   "metadata": {},
   "outputs": [],
   "source": [
    "mpl.rcParams['figure.figsize'] = (15, 18)\n",
    "colors = plt.rcParams['axes.prop_cycle'].by_key()['color']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3592ec4",
   "metadata": {},
   "source": [
    "# Read the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cefaf45a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nr. sessions</th>\n",
       "      <th>nr. rest days</th>\n",
       "      <th>total kms</th>\n",
       "      <th>max km one day</th>\n",
       "      <th>total km Z3-Z4-Z5-T1-T2</th>\n",
       "      <th>nr. tough sessions (effort in Z5, T1 or T2)</th>\n",
       "      <th>nr. days with interval session</th>\n",
       "      <th>total km Z3-4</th>\n",
       "      <th>max km Z3-4 one day</th>\n",
       "      <th>total km Z5-T1-T2</th>\n",
       "      <th>...</th>\n",
       "      <th>max training success.2</th>\n",
       "      <th>avg recovery.2</th>\n",
       "      <th>min recovery.2</th>\n",
       "      <th>max recovery.2</th>\n",
       "      <th>Athlete ID</th>\n",
       "      <th>injury</th>\n",
       "      <th>rel total kms week 0_1</th>\n",
       "      <th>rel total kms week 0_2</th>\n",
       "      <th>rel total kms week 1_2</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22.2</td>\n",
       "      <td>16.4</td>\n",
       "      <td>11.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.718447</td>\n",
       "      <td>1.378882</td>\n",
       "      <td>1.919255</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.6</td>\n",
       "      <td>16.4</td>\n",
       "      <td>11.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.683544</td>\n",
       "      <td>1.018868</td>\n",
       "      <td>1.490566</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.6</td>\n",
       "      <td>16.4</td>\n",
       "      <td>11.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.683544</td>\n",
       "      <td>1.018868</td>\n",
       "      <td>1.490566</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.6</td>\n",
       "      <td>16.4</td>\n",
       "      <td>11.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.683544</td>\n",
       "      <td>1.018868</td>\n",
       "      <td>1.490566</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.2</td>\n",
       "      <td>17.6</td>\n",
       "      <td>18.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.202247</td>\n",
       "      <td>1.361111</td>\n",
       "      <td>0.618056</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 72 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   nr. sessions  nr. rest days  total kms  max km one day  \\\n",
       "0             5            2.0       22.2            16.4   \n",
       "1             5            2.0       21.6            16.4   \n",
       "2             5            2.0       21.6            16.4   \n",
       "3             5            2.0       21.6            16.4   \n",
       "4             6            1.0       39.2            17.6   \n",
       "\n",
       "   total km Z3-Z4-Z5-T1-T2  nr. tough sessions (effort in Z5, T1 or T2)  \\\n",
       "0                     11.8                                          1.0   \n",
       "1                     11.7                                          1.0   \n",
       "2                     11.7                                          1.0   \n",
       "3                     11.7                                          1.0   \n",
       "4                     18.9                                          1.0   \n",
       "\n",
       "   nr. days with interval session  total km Z3-4  max km Z3-4 one day  \\\n",
       "0                             2.0           10.0                 10.0   \n",
       "1                             2.0           10.0                 10.0   \n",
       "2                             2.0           10.0                 10.0   \n",
       "3                             2.0           10.0                 10.0   \n",
       "4                             3.0           17.2                 10.0   \n",
       "\n",
       "   total km Z5-T1-T2  ...  max training success.2  avg recovery.2  \\\n",
       "0                0.6  ...                     0.0            0.18   \n",
       "1                0.5  ...                     0.0            0.18   \n",
       "2                0.5  ...                     0.0            0.17   \n",
       "3                0.5  ...                     0.0            0.18   \n",
       "4                0.5  ...                     0.0            0.17   \n",
       "\n",
       "   min recovery.2  max recovery.2  Athlete ID  injury  rel total kms week 0_1  \\\n",
       "0            0.16            0.20           0       0                0.718447   \n",
       "1            0.16            0.20           0       0                0.683544   \n",
       "2            0.16            0.18           0       0                0.683544   \n",
       "3            0.16            0.18           0       0                0.683544   \n",
       "4            0.16            0.18           0       0                2.202247   \n",
       "\n",
       "   rel total kms week 0_2  rel total kms week 1_2  Date  \n",
       "0                1.378882                1.919255     0  \n",
       "1                1.018868                1.490566     1  \n",
       "2                1.018868                1.490566     2  \n",
       "3                1.018868                1.490566     3  \n",
       "4                1.361111                0.618056     4  \n",
       "\n",
       "[5 rows x 72 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = tf.keras.utils\n",
    "raw_df = pd.read_csv('injury-prediction/week_approach_maskedID_timeseries.csv')\n",
    "convert_dict = {'nr. sessions': int } #convert nr.sessions to int \n",
    "raw_df = raw_df.astype(convert_dict)\n",
    "raw_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3ff9ceb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Examples:\n",
      "    Total: 42798\n",
      "    Positive: 575 (1.34% of total)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# This section displays the percentage of the majority class\n",
    "neg, pos = np.bincount(raw_df['injury'])\n",
    "total = neg + pos\n",
    "print('Examples:\\n    Total: {}\\n    Positive: {} ({:.2f}% of total)\\n'.format(total, pos, 100 * pos / total))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "da3bf02e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         0\n",
       "1         0\n",
       "2         0\n",
       "3         0\n",
       "4         0\n",
       "         ..\n",
       "42793    71\n",
       "42794    71\n",
       "42795    71\n",
       "42796    71\n",
       "42797    71\n",
       "Name: Athlete ID, Length: 42798, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dropping the unnecessary columns/features\n",
    "cleaned_df = raw_df.copy()\n",
    "cleaned_df.pop('Date')\n",
    "cleaned_df.pop('Athlete ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6d0dfc27-27a1-4a80-ada1-4f80b4676a63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['nr. sessions',\n",
       " 'nr. rest days',\n",
       " 'total kms',\n",
       " 'max km one day',\n",
       " 'total km Z3-Z4-Z5-T1-T2',\n",
       " 'nr. tough sessions (effort in Z5, T1 or T2)',\n",
       " 'nr. days with interval session',\n",
       " 'total km Z3-4',\n",
       " 'max km Z3-4 one day',\n",
       " 'total km Z5-T1-T2',\n",
       " 'max km Z5-T1-T2 one day',\n",
       " 'total hours alternative training',\n",
       " 'nr. strength trainings',\n",
       " 'avg exertion',\n",
       " 'min exertion',\n",
       " 'max exertion',\n",
       " 'avg training success',\n",
       " 'min training success',\n",
       " 'max training success',\n",
       " 'avg recovery',\n",
       " 'min recovery',\n",
       " 'max recovery',\n",
       " 'nr. sessions.1',\n",
       " 'nr. rest days.1',\n",
       " 'total kms.1',\n",
       " 'max km one day.1',\n",
       " 'total km Z3-Z4-Z5-T1-T2.1',\n",
       " 'nr. tough sessions (effort in Z5, T1 or T2).1',\n",
       " 'nr. days with interval session.1',\n",
       " 'total km Z3-4.1',\n",
       " 'max km Z3-4 one day.1',\n",
       " 'total km Z5-T1-T2.1',\n",
       " 'max km Z5-T1-T2 one day.1',\n",
       " 'total hours alternative training.1',\n",
       " 'nr. strength trainings.1',\n",
       " 'avg exertion.1',\n",
       " 'min exertion.1',\n",
       " 'max exertion.1',\n",
       " 'avg training success.1',\n",
       " 'min training success.1',\n",
       " 'max training success.1',\n",
       " 'avg recovery.1',\n",
       " 'min recovery.1',\n",
       " 'max recovery.1',\n",
       " 'nr. sessions.2',\n",
       " 'nr. rest days.2',\n",
       " 'total kms.2',\n",
       " 'max km one day.2',\n",
       " 'total km Z3-Z4-Z5-T1-T2.2',\n",
       " 'nr. tough sessions (effort in Z5, T1 or T2).2',\n",
       " 'nr. days with interval session.2',\n",
       " 'total km Z3-4.2',\n",
       " 'max km Z3-4 one day.2',\n",
       " 'total km Z5-T1-T2.2',\n",
       " 'max km Z5-T1-T2 one day.2',\n",
       " 'total hours alternative training.2',\n",
       " 'nr. strength trainings.2',\n",
       " 'avg exertion.2',\n",
       " 'min exertion.2',\n",
       " 'max exertion.2',\n",
       " 'avg training success.2',\n",
       " 'min training success.2',\n",
       " 'max training success.2',\n",
       " 'avg recovery.2',\n",
       " 'min recovery.2',\n",
       " 'max recovery.2',\n",
       " 'injury',\n",
       " 'rel total kms week 0_1',\n",
       " 'rel total kms week 0_2',\n",
       " 'rel total kms week 1_2']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# confirm drop\n",
    "cleaned_df.columns.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9e71874d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = cleaned_df.drop('injury', axis = 1)\n",
    "y = cleaned_df['injury']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1a5a64fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42798, 69)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e0fc169",
   "metadata": {},
   "source": [
    "# Applying Synthetic Minority Oversampling Technique (SMOTE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "33554ac1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#This is because the data is so imbalanced. The minority class is oversampled with new similar data is generated to compensate its minimal numbers\n",
    "smote_technique = SMOTE(sampling_strategy='minority')\n",
    "X_smt, y_smt = smote_technique.fit_resample(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9945a7b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    42223\n",
       "1    42223\n",
       "Name: injury, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_smt.value_counts() #class counts now match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "01270776",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data is split to Train and test \n",
    "X_train_smt, X_test_smt, y_train_smt, y_test_smt = train_test_split(X_smt, y_smt, test_size=0.3, random_state=15, stratify=y_smt)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "015f4775-d0fb-4c93-9973-cbf8fecf69b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data size:  (59112, 69)\n",
      "Test data size:  (25334, 69)\n"
     ]
    }
   ],
   "source": [
    "# check shape of training and test data\n",
    "print(\"Training data size: \", X_train_smt.shape)\n",
    "print(\"Test data size: \", X_test_smt.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53a226a1",
   "metadata": {},
   "source": [
    "# Defining KNN Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bbb0f0ba",
   "metadata": {},
   "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()</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()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_model = KNeighborsClassifier()\n",
    "knn_model.fit(X_train_smt, y_train_smt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9c18b91f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using the trained model, the prediction is done on the test set\n",
    "knn_preds_smt = knn_model.predict(X_test_smt)\n",
    "knn_preds_smt = np.round(knn_preds_smt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "407605b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.87      0.92     12667\n",
      "           1       0.89      0.99      0.93     12667\n",
      "\n",
      "    accuracy                           0.93     25334\n",
      "   macro avg       0.93      0.93      0.93     25334\n",
      "weighted avg       0.93      0.93      0.93     25334\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Display the classification report of the model out of prediction using the test data\n",
    "print(classification_report(y_test_smt, knn_preds_smt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3db5e7a7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "815b13c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to display the confusion matrix\n",
    "def plot_cm(labels, predictions, p=0.5):\n",
    "    cm = confusion_matrix(labels, predictions > p)\n",
    "    plt.figure(figsize=(5,5))\n",
    "    sns.heatmap(cm, annot=True, fmt=\"d\")\n",
    "    plt.title('Confusion matrix @{:.2f}'.format(p))\n",
    "    plt.ylabel('Actual label')\n",
    "    plt.xlabel('Predicted label')\n",
    "\n",
    "    print('Correct Non-injury predictions (True Negatives): ', cm[0][0])\n",
    "    print('Incorrect Non-injury predictions (False Positives): ', cm[0][1])\n",
    "    print('Correct injury predictions (False Negatives): ', cm[1][0])\n",
    "    print('Incorrect injury predictions (True Positives): ', cm[1][1])\n",
    "    print('Total injury predictions: ', np.sum(cm[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a42a9d33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correct Non-injury predictions (True Negatives):  11050\n",
      "Incorrect Non-injury predictions (False Positives):  1617\n",
      "Correct injury predictions (False Negatives):  189\n",
      "Incorrect injury predictions (True Positives):  12478\n",
      "Total injury predictions:  12667\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cm(y_test_smt, knn_preds_smt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd454041-071d-4f0c-8057-7c2349788e52",
   "metadata": {},
   "source": [
    "# Optimize KNN Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "50941886-a2bc-453c-bc43-38e888fdea0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tmTNnToFlvvvuu5kwYQLbtm3Dz8+7t1nROtdFgbWwf93/inbSLEg/CsYPKjX93xSSKi0hINjttCIiIj6tKK1z/e9//5vw8HDuv//+k+9lZmYSEFB0xk+zs7OJjY2lUqVKPPvss3Tu3Nkj98nKysLfv+Cn2p7rOtfe/aNDcWEMRNeGVrfCtZ/DQ0lw48/Q8UHwC4DfX4WPe8FzsfBpf5jzJuxZ6ZRyERER8Xk33HAD9913H126dOGhhx5iwYIFtG3blqZNm9K2bVvWrl0LwPTp0+nVqxfgFPMbb7yRzp07U7169b+MZoeHh588v3PnzgwYMIA6deowaNAg/hyY/eGHH6hTpw7t27fnrrvuOnndU02bNo0GDRowYsQIxo0bd/L9PXv20LdvXxo3bkzjxo1PjpSPGTOGRo0a0bhxY6677rqTX1/ubdRz5+vSpQvXXnstDRs2BKBPnz40b96c+vXrM3LkyJOf+emnn2jWrBmNGzemW7duZGdnU7NmTfbt2wc4PwTUqFHjjDtK5kfR+bFG/sc/0HnosWpr6PIIpCVD0uz/rUTyy2POeWHlnFHt5jdAbDs3E4uIiPimHx+G3csL9poVGsIlz57zx9atW8eUKVPw9/cnJSWFmTNnEhAQwJQpU3j00Uf56quv/vaZNWvWMG3aNI4cOULt2rUZMWIEgYF/3RhvyZIlrFy5kkqVKtGuXTtmz55NQkICt956KzNnziQuLu6MW5GPGzeOgQMH0rt3bx599FEyMjIIDAzkrrvuolOnTkycOJGsrCyOHj3KypUr+c9//sPs2bOJiori4MGDZ/26FyxYwIoVK4iLiwNg9OjRlClThuPHj9OiRQv69+9PdnY2w4YNO5n34MGD+Pn5MXjwYMaOHcs999zDlClTaNy4MVFRUef4nf8rleviIKTUX5f8S97+vykkG39zlv0bMcdZqURERER80pVXXnlyWkRycjLXX38969evxxhDRkZGnp+57LLLCA4OJjg4mHLlyrFnzx5iYmL+ck7Lli1PvtekSROSkpIIDw+nevXqJwvtwIED/zJK/Kf09HR++OEHXnnlFSIiImjVqhW//PILl112GVOnTmXMmDEA+Pv7U6pUKcaMGcOAAQNOFtwyZcqc9etu2bLlyRwAr7/+OhMnTgRg27ZtrF+/nn379tGxY8eT5/153RtvvJHevXtzzz33MHr0aIYOHXrW+52NynVxVCoGmg52/iRvh7daw7d3w5DJ2hlSRESkMJ3HCLOnhIWFnfznxx9/nC5dujBx4kSSkpJOO885OPh/z3L5+/uTmZmZr3Py+8zeTz/9RHJy8skpG8eOHSM0NJTLLrssz/OttZg8ukxAQADZ2dknz0lPTz95LPfXPX36dKZMmcLcuXMJDQ2lc+fOpKWlnfa6VapUoXz58kydOpX58+czduzYfH1dZ6I518VdqRjo8X+weQYsHuN2GhERESkCkpOTqVy5MgAfffRRgV+/Tp06bNq0iaSkJADGjx+f53njxo1j1KhRJCUlkZSUxObNm/nll184duwY3bp145133gGchxFTUlLo1q0bX3zxBQcOHAA4OS0kNjaWRYsWATB58uTTjsQnJydTunRpQkNDWbNmDfPmzQOgTZs2zJgxg82bN//lugA333wzgwcP5qqrriqQByJVrr1B86FQrT388k9I2el2GhEREXHZgw8+yCOPPEK7du3Iysoq8OuXKFGCt99+m549e9K+fXvKly9PqVKl/nLOsWPH+Pnnn/8ySh0WFkb79u359ttvee2115g2bRoNGzakefPmrFy5kvr16/PYY4/RqVMnGjduzH333QfAsGHDmDFjBi1btmT+/Pl/Ga3OrWfPnmRmZtKoUSMef/xxWrduDUB0dDQjR46kX79+NG7cmKuvvvrkZ6644gqOHj1aIFNCQEvxeY8DG+Gdds4DjgPHaXqIiIiIhxSlpfjcdPToUcLDw7HWcvvtt1OzZk3uvfdet2Ods8TERO69915mzZqV53EtxeerysZD18dg3Y+w4u9PA4uIiIgUpPfff58mTZpQv359kpOTufXWW92OdM6effZZ+vfvzzPPPFNg19TItTfJzoIPesChJLh9AYRd2FIyIiIi8ncaufYtGrn2ZX7+0PstSEuBHx90O42IiIiIz1G59jbl6kLHB5ypIWt+cDuNiIiIV/Km3/zL6Z3P37PKtTdqfy+Uqw/f3QvHD7udRkRExKuEhIRw4MABFWwvZ63lwIEDhISEnNPntImMNwoIgt5vwqhuzvJ8vd90O5GIiIjXiImJYfv27ezbt8/tKOJhISEhf9ux8mxUrr1V5WbQ9k6Y/Ro06A/xXdxOJCIi4hUCAwP/st22SG6aFuLNOj8CZeLh27vgxFG304iIiIh4PZVrbxZYwpkScngrTH3K7TQiIiIiXk/l2ttVawsthsH892DrPLfTiIiIiHg1lWtf0P1fUCoGJt8BGWlupxERERHxWirXviA4Ai5/DQ6shxnPuZ1GRERExGupXPuKGt2gySBn9ZCdS91OIyIiIuKVVK59ycX/gbAo+OYOyMpwO42IiIiI11G59iUlSsNlL8Hu5TD7VbfTiIiIiHgdlWtfU/dyqNcHZjwPe9e4nUZERETEq6hc+6JLX4CgMGd6SHaW22lEREREvIbKtS8KLwc9n4PtC531r0VERESkQKhc+6pGV0HNi5ydGw9udjuNiIiIiFdQufZVxkCvV8EvAL69C6x1O5GIiIhIsady7ctKVYYeT8LmmbD4Y7fTiIiIiBR7Kte+rvkNENsBfnkckne4nUZERESkWFO59nXGwBWvO5vKfH+fpoeIiIiIXACVa4Ey1aHb47DuJ1g+we00IiIiIsWWyrU4Wg2HmBbw44NwdJ/baURERESKJZVrcfj5wxVvQvpRp2CLiIiIyDlTuZb/KVcHOj4IK7+GNd+7nUZERESk2FG5lr9qfw+Ubwjf3QfHD7udRkRERKRYUbmWv/IPhN5vQuo++OUxt9OIiIiIFCsq1/J3lZpAu7tgyaewcarbaURERESKDY+Wa2NMT2PMWmPMBmPMw3kcL22MmWiM+cMYs8AY0yDXsXuNMSuNMSuMMeOMMSGezCqn6PQwlK0J39wNJ466nUZERESkWPBYuTbG+ANvAZcA9YCBxph6p5z2KLDUWtsIGAK8lvPZysBdQIK1tgHgD1zjqaySh8AQZ3pI8jb47Um304iIiIgUC54cuW4JbLDWbrLWpgOfA71POace8BuAtXYNEGuMKZ9zLAAoYYwJAEKBnR7MKnmp2hpa3gILRsLWeW6nERERESnyPFmuKwPbcr3envNebsuAfgDGmJZANSDGWrsDeBHYCuwCkq21v3gwq5xOtycgsgpMvgMy0txOIyIiIlKkebJcmzzes6e8fhYobYxZCtwJLAEyjTGlcUa544BKQJgxZnCeNzHmFmNMojEmcd8+7SxY4ILD4fLX4MB6mPGs22lEREREijRPluvtQJVcr2M4ZWqHtTbFWjvUWtsEZ851NLAZ6A5sttbus9ZmAF8DbfO6ibV2pLU2wVqbEB0d7YEvQ4jvCk0Hw+zXYedSt9OIiIiIFFmeLNcLgZrGmDhjTBDOA4nf5D7BGBOZcwzgZmCmtTYFZzpIa2NMqDHGAN2A1R7MKmdz0X8gLNqZHpKV4XYaERERkSLJY+XaWpsJ3AH8jFOMv7DWrjTGDDfGDM85rS6w0hizBmdVkbtzPjsfmAAsBpbn5BzpqaySDyUiodfLsGc5/P6q22lEREREiiRj7anToIuvhIQEm5iY6HYM7/blUFjzHdw6C8rVcTuNiIiISKEzxiyy1ibkdUw7NMq5ufQFCAqHybdDdpbbaURERESKFJVrOTdhUXDJ87AjEea/63YaERERkSJF5VrOXcMBUKsn/PYUHNjodhoRERGRIkPlWs6dMdDrFfAPhG/vhuxstxOJiIiIFAkBbgeQYqpkJbjoKadc//ZvqNQU/AJy/fEHv8BTXgc4hTz3a7+AnPP8T/l8APjpZz8REREpXlSu5fw1ux5WfwuzX/PM9Y3fmQu4f87/Vu8ClzznjKiLiIiIuEjlWs6fMXDtF3BwM2RnQHZmzp8s53+zMv76Ojsz57xcr/NzTnYmZGX+9XV2lnPekd2w4D2odRHU6O72d0RERER8nMq1XBg/f4iq4d79M9Phzebw25NQvaumkoiIiIir1ESkeAsIgs6Pwq5lsPobt9OIiIiIj9PI9QV68ee1rN1zhCB/P4IC/Aj0NwT6+xHo70dwgN/Jf/7zWFDOe0H+fgQG+BGU672/f845FvTnsZP/bDCaX/w/ja6C2a/C1KehTi9nLraIiIiIC9RCLtDBY+lsO3iMjKxsMrIsGVnZpGdmk56VffKfsz2ww3ygvzlZ0LvXLc+LVzYu+JsUF37+0PWfMH4wLBsHza5zO5GIiIj4KJXrC/Tfvg3Pek5Wdk7pzinbGVnZZGTav77+SynPu6T/WeBPvp+Zzfq9R5mwaDtD2lSjUUyk57/goqpOL6jcHKY/Cw2vhMAQtxOJiIiID1K5LgT+fgZ/P39CAv0L/NopaRm0e3Yqb0/byLvXNS/w6xcbxkC3J2BMb0gcDW1uczuRiIiI+CA90FjMlQwJ5Ia2sfy0cjfr9xxxO467qneGuE4w60U44ePfCxEREXGFyrUXGNoujhKB/rw9faPbUdzX7V9w7ADMe8ftJCIiIuKDVK69QJmwIAa1qso3y3ay9cAxt+O4K6a5M/96zhtw7KDbaURERMTHqFx7iWEdq+NvDO/O1Og1Xf/pTAv5/WW3k4iIiIiPUbn2EuVLhjAgIYYJidvZnZzmdhx3lasLja+BBe9Dyk6304iIiIgPUbn2IsM7xpNlLe/P2uR2FPd1fgSys2DGc24nERERER+icu1FqpYN5YrGlfhs/lYOpqa7HcddpatBwlBY/Akc0FQZERERKRwq117mts7xHM/I4sPZm92O4r4O90NAMEz7r9tJRERExEeoXHuZmuUj6Fm/Ah/NSSIlLcPtOO6KKA+thsOKCbB7udtpRERExAeoXHuh27vU4EhaJp/M3eJ2FPe1uwtCSsFvT7mdRERERHyAyrUXahhTio61ohn9+2aOp2e5HcddJUpDu3tg/c+wdZ7baURERMTLqVx7qTu61OBAajqfL9zqdhT3tRoO4eVhyv+BtW6nERERES+mcu2lWsaVoWVsGUbO3ER6ZrbbcdwVFAodH4Ctc2DDFLfTiIiIiBdTufZit3etwa7kNL5evN3tKO5rdj1EVoPfnoRsH/9hQ0RERDxG5dqLdawZRcPKpXhnxkYys3y8UAYEQZdHYfcfsGqS22lERETES6lcezFjDLd3iWfLgWN8v3yX23Hc1/BKiK4L0/4DWZlupxEREREvpHLt5S6qV4Ga5cJ5e9pGsrN9/GE+P3/o9jgc2ABLx7qdRkRERLyQyrWX8/Mz3NYlnrV7jjBl9R6347iv9qUQ0wJmPAcZaW6nERERES+jcu0DLm9UiSplSvDWtA1YX1+Kzhjo9gSk7ICFo9xOIyIiIl5G5doHBPj7MbxTPMu2JzN7wwG347gvriNU7wKzXoK0FLfTiIiIiBdRufYRA5rHUL5kMG9OW+92lKKh2+Nw/CDMe9vtJCIiIuJFVK59RHCAP8M6VGfepoMs2nLQ7Tjuq9wc6l4Oc96EVI3mi4iISMFQufYh17aqSunQQN6attHtKEVD18chIxV+f9ntJCIiIuIlVK59SGhQADe2i2Pqmr2s3Jnsdhz3RdeGxgNhwfuQrF0sRURE5MKpXPuYIW1jiQgO4G2NXjs6PwxYZ2k+ERERkQukcu1jSpUI5Lo21fhhxS427D3qdhz3RVaFhBthyVjYv8HtNCIiIlLMqVz7oBvbxxEc4Me7MzR6DUCHf0BAiLMtuoiIiMgFULn2QVHhwVzToiqTluxg+6FjbsdxX3g5aD0CVn4Nu5a5nUZERESKMZVrH3Vrp+oYA+/N2OR2lKKh7Z0QEgm/PeV2EhERESnGVK59VMVSJejfLIbxidvYm5Lmdhz3lYiE9vfChl9hyxy304iIiEgxpXLtw4Z3iiczK5sPft/sdpSioeUtEF4BfnsSrHU7jYiIiBRDKtc+LDYqjF6NKvHpvC0cPpbudhz3BYVCpwdh61xY/6vbaURERKQYUrn2cbd3qUFqehYfzk5yO0rR0PQ6KB3rjF5nZ7udRkRERIoZlWsfV7tCBD3qleejOUkcPZHpdhz3BQRBl8dgz3Jn9RARERGRc6ByLdzepQbJxzMYO2+L21GKhgYDoFx9Z93rrAy304iIiEgx4tFybYzpaYxZa4zZYIx5OI/jpY0xE40xfxhjFhhjGuQ6FmmMmWCMWWOMWW2MaePJrL6sSZVI2teI4v1Zm0nLyHI7jvv8/KDb43BwEyz51O00IiIiUox4rFwbY/yBt4BLgHrAQGNMvVNOexRYaq1tBAwBXst17DXgJ2ttHaAxsNpTWcUZvd5/9ARfJG5zO0rRUKsnxLSEGc9DxnG304iIiEgx4cmR65bABmvtJmttOvA50PuUc+oBvwFYa9cAscaY8saYkkBH4IOcY+nW2sMezOrzWlcvQ/NqpXlvxiYysvQgH8ZA93/BkZ2wcJTbaURERKSY8GS5rgzkHgbdnvNebsuAfgDGmJZANSAGqA7sAz40xiwxxowyxoTldRNjzC3GmERjTOK+ffsK+mvwGcYYbu8Sz47Dx5m0ZIfbcYqG2PYQ3xVmvQxpKW6nERERkWLAk+Xa5PHeqTtzPAuUNsYsBe4ElgCZQADQDHjHWtsUSAX+NmcbwFo70lqbYK1NiI6OLqjsPqlL7XLUq1iSd6ZvJCtbm6gA0O0JOH4Q5r7pdhIREREpBjxZrrcDVXK9jgF25j7BWptirR1qrW2CM+c6Gtic89nt1tr5OadOwCnb4kHO6HUNNu1P5ccVu9yOUzRUagr1esPctyB1v9tpREREpIjzZLleCNQ0xsQZY4KAa4Bvcp+QsyJIUM7Lm4GZOYV7N7DNGFM751g3YJUHs0qOng0qUD06jLembcRqC3BHl39CxjGY9ZLbSURERKSI81i5ttZmAncAP+Os9PGFtXalMWa4MWZ4zml1gZXGmDU4q4rcnesSdwJjjTF/AE2A/3oqq/yPv59hRKd4Vu9KYdravW7HKRqia0GTa50HGw9rNRURERE5PeNNo5MJCQk2MTHR7RjFXkZWNp1fmE75ksF8NaItxuQ1fd7HHN4GbzSDRldDb82/FhER8WXGmEXW2oS8jmmHRvmbQH8/hneqzuKth5m76YDbcYqGyCqQcBMsHQv717udRkRERIoolWvJ05UJVYgKD+ataRvcjlJ0dPgHBJSAqU+7myM7C3Yuhblvw+eD4MuhznsiIiLiugC3A0jRFBLoz7AOcTzz4xqWbD1E06ql3Y7kvvBoaHM7zHzeKbeVmhTOfbMynPttme382ToPTuSsux1REY7sgvgu0GxI4eQRERGR09KcazmtoycyaffsVFrElmHU9XlOK/I9acnwWmOo1Ayu+9oz98g8ATsWQVJOmd62ADJSnWNRtaBaW6jW3vnfkpXgg4vg8Ba4cxEER3gmk4iIiJx0pjnXGrmW0woPDmBou1henbKeNbtTqFOhpNuR3BdSCtrfB78+Dkm/O7s4Xqj0Y7B9AWyZ4xTq7Qsh64RzrFx9aDoop1C3g/Byf/98z2dgVDeY/Rp0/eeF5xEREZHzppFrOaPDx9Jp9+xUutYtzxsDm7odp2jIOA6vN4XIqnDjz3Cuq6mkpTij0Vt+d8r0ziWQnQHGDyo0cgp7tbZQtQ2ElsnfNb+6GVZ/64xel4o5969JRERE8k0j13LeIkODGNy6Gu/P2sR9PWoRFxXmdiT3BZaATg/Cd/fCup+hds8zn3/8EGyZ+78507uWgc0GvwBnekmb251CXaWlMzJ+Prr9yynXvz0J/Uae3zVERETkgmnkWs5q75E02j83jb5NKvPcgEZuxykasjLgrZYQGAq3zgK/XAvvHN0HW+f8b870npWABf9giElwpndUa+uU6aAC/GHltyedXSSHTYXKzQvuuiIiIvIXGrmWC1IuIoRrWlRh3IKt3N29JpUiS7gdyX3+gdDlMfjqJmfnxtAyzhzsLXNg/1rnnMBQp0B3ecwp05WbQ2CI5zK1vxcWj4GfH4OhP577dBURERG5YCrXki+3dKzOZ/O3MnLmJv59RX234xQN9fvB76/Cjw84r4NLQtXW0GSgs5pHpSZOCS8swRHOA43f3g2rv4F6vQvv3iIiIgKoXEs+xZQOpU/Tyny+cCt3dK1BVHiw25Hc5+cH/UfB5pnOCHWFhuDn726mptfB/JHw6xNQqycE6O9JRESkMGmHRsm3EZ3jOZGZzQe/b3Y7StFRrg60usUZpXa7WIOT4eKn4VASLNCDjSIiIoVN5VryLT46nEsbVuSTuVtIPpbhapa0jCzmbzrAW9M28MXCba5mKXLiu0LNi2DGC5B6wO00IiIiPkXTQuSc3NY5nu//2MWYuUnc2a1mod33YGo6i7YcIjHpIAuTDrJ8RzIZWc5KN34GWlcvS9WyoYWWp8jr8RS80xZmPAuXvuB2GhEREZ+hci3npH6lUnStU47RszdzY/s4woIL/l8hay3bDh5nYdJBErccZGHSITbsPQpAkL8fjWJKcVP76rSILU2VMqH0ev133p+1iaf6NCjwLMVWuTqQMBQWfgAthkF0LbcTiYiI+ASVazlnt3epQf935jBuwVZu7lD9gq+XmZXNmt1HnDKddIiFSQfZe8TZ/rtkSAAJsWXo16wyLWLL0LByKUIC/zq3uV+zynyRuI27u9fUg5a5dX4E/vjC2ar92vFupxEREfEJKtdyzppXK02b6mUZOXMTg1tX+1vZPZtj6Zks3XqYhUmHSNxykMVbDpGangVA5cgStI0vS0JsGVrElqFmuXD8/M68XvMtHaszPnEbH81O4v6La5/31+V1wqKg4/3OyiEbp0F8F7cTiYiIeD2Vazkvt3epweAP5vPV4u0MalXtjOfuO3KCRTnTOxKTDrJiZwpZ2RZjoE6FkvRvHkNCbBkSqpU+rw1qqkeH07N+BcbMTWJ453jCPTBVpdhqeaszNeSXf8KtM4vGiiYiIiJeTC1Ezku7GmVpXCWSd2ds5OqEKgT4OwvPWGvZvD/15PSOxC2H2Lw/FYDgAD+aVIlkRKd4EmJL06xaaUqGFMwmK8M7xfPjit2Mm7+VYR0vfKqK1wgMgR7/B1/eAEs+hebXu51IRETEq6lcy3kxxnBHlxoMG5PIO9M3UiLIn8ScaR77j6YDUDo0kObVyjCwZRUSYsvQoFIpggI8s/pj4yqRtI0vy6jfNzGkbTWCAzRCe1K9PlClFUx9Ghr0c3ZyFBEREY9QuZbz1q1OOepUiOClX9cBULVMKB1rRdMitgwtYktTPers86UL0vBO8QwZvYDJS3ZyVYsqhXbfIs8YuPi/MKqbs117t8fdTiQiIuK1VK7lvPn5Gd4d3JzVu1JoVq005UuGuJqnQ80o6lcqybszNzKgeUyhFvsiLyYBGl4Jc9+E5jdApH74EBER8QTt0CgXJDYqjEsaVnS9WIMzVWV4p3g27Uvll1V73I5T9HT7l/O/vz3pbg4REREvpnItXuWSBhWoVjaUd2ZsxFrrdpyiJbIKtLkdln8B2xe5nUZERMQrqVyLVwnw9+OWjtVZtu0wczcdcDtO0dP+XgiLhp8fBf3wISIiUuBUrsXr9G8WQ1R4MO/O2OR2lKInOAK6/hO2zYNVk91OIyIi4nVUrsXrhAT6c2P7WGau28eKHcluxyl6ml4H5eo7OzdmnnA7jYiIiFdRuRavNKhVNcKDA3hvpkav/8bPHy5+Gg5vgfnvuZ1GRETEq6hci1cqVSKQQa2r8v0fO9lyINXtOEVPfFeoeTHMfAFS97udRkRExGuoXIvXuqldHAF+frw/S6PXebroKUhPhenPup1ERETEa6hci9cqVzKE/s0r80XidvYd0dziv4muDQk3QuJo2LfW7TQiIiJeQeVavNqwDtXJyMrmozmb3Y5SNHV+GILC4RdtiS4iIlIQVK7Fq1WPDueSBhUYM3cLR9Iy3I5T9IRFQcf7Yf3PsHGq22lERESKPZVr8XrDO8VzJC2TcQu2uh2laGp1K0RWg5//CdlZbqcREREp1lSuxes1iomkXY2yjJq1mROZKo9/ExAMPZ6EvSthyadupxERESnWVK7FJwzvFM/eIyeYtGSH21GKpnq9oUprmPo0nDjidhoREZFiS+VafEL7GlE0qFyS92ZsIivbuh2n6DEGLv4vpO6F319xO42IiEixpXItPsEYw4hONdi0P5VfVu52O07RFNMcGl4Fc96Ew5qfLiIicj5UrsVn9GxQgdiyobw7YyPWavQ6T92ecEaxf3vS7SQiIiLFksq1+Ax/P8MtHeNZtj2ZuRsPuB2naIqsAm3ugOVfwvZEt9OIiIgUOyrX4lP6NatMVHgw78zY6HaUoqv9PRBWDn5+FDTCLyIick5UrsWnhAT6c1P7OGat38+KHcluxymagiOg6z9h23xYNcntNCIiIsWKyrX4nEGtqxIRHMC7Gr0+vaaDoVx9+PVfkJHmdhoREZFiQ+VafE7JkEAGta7GD8t3seVAqttxiiY/f7j4P3B4Cyx4z+00IiIixYbKtfikG9vFEuDnx8iZm9yOUnTFd4GaF8PMFyF1v9tpREREigWVa/FJ5UqG0L95DF8u2s7eI5r2cFoXPQXpqTD9GbeTiIiIFAsq1+KzbulYnYysbD6aneR2lKIrujYk3AiJH8LeNW6nERERKfI8Wq6NMT2NMWuNMRuMMQ/ncby0MWaiMeYPY8wCY0yDU477G2OWGGO+82RO8U1xUWFc2qAin8zbwpG0DLfjFF2dH4agcPj1cbeT/JW1sHMJrJ/idhIREZGTPFaujTH+wFvAJUA9YKAxpt4ppz0KLLXWNgKGAK+dcvxuYLWnMooM7xTPkbRMPpuv7b5PKywKOt4P63+BDb+5m8Va2L4IfvknvNYIRnaGsf1h03R3c4mIiOTw5Mh1S2CDtXaTtTYd+Bzofco59YDfAKy1a4BYY0x5AGNMDHAZMMqDGcXHNYwpRfsaUYz6fTNpGVluxym6Wt0KkdWcUptdyN+n7GzYOh9+ehReaQCjusK8dyGqNlzxBpSpDt/dpyUDRUSkSPBkua4MbMv1envOe7ktA/oBGGNaAtWAmJxjrwIPAtkezCjCiM7x7DtygolLdrgdpegKCIYeT8LeVbDkE8/fLzsLkmbDDw/CK/Vg9EWw8H2o0BD6vAsPrIfBE6DZELj0RTi4EWaf+osvERGRwhfgwWubPN47dS/lZ4HXjDFLgeXAEiDTGNML2GutXWSM6XzGmxhzC3ALQNWqVS8wsviitvFlaVi5FCNnbuKqhCr4++X1r65QrzdUbQNTn4YG/Z2dHAtSViZsmQ2rJsPqbyF1LwSEQI3uUK8P1LoYQkr+/XM1ujl5Zr0EDQdA2fiCzSUiInIOPDlyvR2okut1DLAz9wnW2hRr7VBrbROcOdfRwGagHXCFMSYJZzpJV2PMp3ndxFo70lqbYK1NiI6OLvivQryeMYYRnePZvD+Vn1fudjtO0WWMs7FM6j74/ZWCuWZWBmycCt/eDS/VhjFXwLJxUK0tDPgQHtgI14yFRlfmXaz/dPF/ndH17//hzMsWERFxiSdHrhcCNY0xccAO4Brg2twnGGMigWM5c7JvBmZaa1OAR3L+kDNyfb+1drAHs4qPu7h+BeKiwnh3xkYuaVABYzR6nafKzaHR1TDnTWh+A0Sex2+LMtNh8wxYNQnWfA/HDzmrkdS62Bkdr9EdgsLO7ZoRFaDbE/DD/bDiK2cEW0RExAUeK9fW2kxjzB3Az4A/MNpau9IYMzzn+LtAXWCMMSYLWAXc5Kk8Imfi72e4pWN1Hvl6OXM2HqBdjSi3IxVd3Z5wpm5M+T8Y8EH+PpORBpumOZ9b8wOcSIbgklD7EqdQx3eFwBIXlivhRlg6Fn5+1CnoJSIv7HoiIiLnwVgv+hVqQkKCTUxMdDuGFFNpGVl0eH4adSpE8MlNrdyOU7RNfRpmvgA3TYEqLfI+J+O4s3Tfqkmw9idIPwIhpaBOL6dQV+/sTOUoSDuXwPtdIeEmuOzFgr22iIhIDmPMImttQl7HPDktRKRYCQn056b2cTz74xqWb0+mYUwptyMVXe3ugcVjnFHim35x5mODs1X6+l+dEep1P0NGKpQoDfX7OA8lxnWEgCDP5arUFFreAvPfgybXQuVmnruXiIhIHjRyLZJLSloG7Z6ZSsfa0bx1rYrZGS0eA9/cCVe86UzpWDXZKdaZxyE0Cupe7oxQx7YH/8DCy5WWAm+2gIjyMGwa+PkX3r1FRMQnaORaJJ9KhgQyuE013puxkaT9qcRGneODdb6kySBnhPibO5zX4eWh6WCnUFdr616pDSkJPZ+BCUNh4ShnAxwREZFC4sml+ESKpaHtYgnw92PkrE1uRyna/Pyh77vQ/l4Y+hPct9qZ5xzXwf3R4vp9Ib4b/PYUpOxyN4uIiPgUlWuRU5SLCGFA8xgmLNrO3iPaUvuMKjSE7v+Gam3cL9S5GeMU/ax0+PkRt9OIiIgPUbkWycMtHaqTmZXNh7OT3I4i56tMdej4AKycCOunuJ1GRER8hMq1SB5io8K4pGFFPp27hZS0DLfjyPlqdxeUrQk//MNZGlBERMTDVK5FTmNEp3iOnMhk7LytbkeR8xUQDL1ehkNJMOslt9OIiIgPULkWOY0GlUvRoWYUo2dvJi0jy+04cr7iOkKja+D3V2HfOrfTiIiIl1O5FjmDEZ3i2XfkBF8v3uF2FLkQFz0NQaHw/X3gRWv7i4hI0aNyLXIGbeLL0iimFCNnbiQrW6Ws2AqPhu7/B0mz4I/xbqcREREvpnItcgbGGEZ0iifpwDF+WrHb7Th/s+PwcdbsTnE7RvHQ7HqIaQE/PwbHD7mdRkREvNRZy7UxppcxRiVcfNZF9SsQFxXGuzM2YovAlILsbMvMdfsYNiaRDs9N5Yo3ZrPt4DG3YxV9fn7Q6xWnWE/5P7fTiIiIl8pPab4GWG+Med4YU9fTgUSKGn8/w60dq7N8RzKzNxxwLUfysQw++H0z3V6ewZDRC1i85RA3tY/DGHhlih7Uy5cKDaH1CFj0IWxb4HYaERHxQgFnO8FaO9gYUxIYCHxojLHAh8A4a+0RTwcUKQr6NqvMy7+u490ZG2lfM6pQ771iRzKfztvCpKU7SMvIplnVSO6+ugmXNKxAcIA/fsYwctYmbulYnToVShZqtmKp88POxjLf3Qu3zAD/s/5nUEREJN/yNd3DWpsCfAV8DlQE+gKLjTF3ejCbSJERHODPTe3j+H3DfpZvT/b4/U5kZjFpyQ76vT2bXm/8zqSlO+jTpDLf3dmer29rR5+mlQkOcLYbH9E5nvDgAF78ea3Hc3mF4Ai45DnYswLmv+t2GhER8TL5mXN9uTFmIjAVCARaWmsvARoD93s4n0iRcW2rqkSEBPDujI0eu8eOw8d5/qc1tH1mKveMX8rB1HT+eVld5j/SnWf7N6JB5VJ/+0xkaBDDO8UzZfVeFiYd9Fg2r1KnF9TqCdP+C8nb3U4jIiJeJD+/D70SeMVaOzP3m9baY8aYGz0TS6ToiQgJ5LrW1XhnxkY2708lLiqsQK6bnW35fcN+xszdwtQ1ewDoVrc817WuRvsaUfj5mbNe48Z2cXw8J4nnflzDl8PbYMzZP+PTjIFLnoe3WsGPD8E1Y91OJCIiXiI/00L+BZx88scYU8IYEwtgrf3NQ7lEiqSh7eII9Pdj5MxNF3yt5GMZjJq16eQDiku2HmJE53hmPtiF94ck0LFWdL6KNUCJIH/u7l6TxC2H+G313gvO5hNKV4POD8Ga72Dtj26nERERL5Gfcv0lkJ3rdVbOeyI+JzoimCubx/DVou3sTUk7r2us2JHMQxP+oNUzU3j6+9WUCQvi1aubMOeRrjxwcR1iSoee13WvSqhCXFQYL/y8Vhve5FebOyC6LvzwIKSnup1GRES8QH7KdYC1Nv3PFzn/HOS5SCJF2y0dq5OZnc0Hszfn+zMnMrOYuGQ7fXMeUPxm2U76Nq3M93e156sRbf/ygOL5CvT34x8X1WLtniNMWqLt2vPFPxB6vQzJW2HG826nERERL5CfOdf7jDFXWGu/ATDG9Ab2ezaWSNFVrWwYlzasyGfztnJ7lxqUDAk87bnbDx1j7PytjF+4jYOp6VSPCuOJXvXo3zyGUiVO/7nzdWmDijSsvImXf11Hr8YVL7iw+4RqbaHpYJj7JjS6GsrXczuRiIgUY/kZuR4OPGqM2WqM2QY8BNzq2VgiRdvwTvEcOZHJp/O2/O1YdrZlxrp93PzxQjo+P433ZmwkoVppPrmpJVPu68SN7eM8UqwB/PwMD/Wsw47Dx/l03laP3MMrdX8SgkvC9/dBdvbZzxcRETmN/GwisxFobYwJB4w2jhGBBpVL0aFmFKN/T+LGdnGEBPqTfCyDLxdt49N5W0g6cIyyYUGM6BzPta2qUTmyRKFla18zivY1onhr2gauSogh4gwj65IjrCz0eBK+uQOWfeaMZIuIiJwHY+3ZH3wyxlwG1AdC/nzPWvukB3Odl4SEBJuYmOh2DPERczbu59r353Nrp+ocTs1g8jJnB8WEaqW5rk01ejao4Nq0jD+2H+aKN2dzV7ea3NejlisZip3sbPjoUti3Fu5IdAq3iIhIHowxi6y1CXkdO+vItTHmXSAU6AKMAgaQa2k+EV/VpnpZGseU4r0ZmygR6E/fpjEMbl2V+pX+vtFLYWsUE8llDSsyatYmrmtdjeiIYLcjFX1+fnDZy/BeB5jyBPR+y+1EIiJSDOVnznVba+0Q4JC19v+ANkAVz8YSKfqMMbx4ZWOe6deQeY9245l+DYtEsf7TPy6qxYnMbN6cut7tKMVH+XrO8nxLPoUtc9xOIyIixVB+yvWfi/keM8ZUAjKAOM9FEik+apaPYGDLqh57QPFCVI8O56qEKny2YCtbDxxzO07x0elBKFUVvrsPMtPPfr6IiEgu+SnX3xpjIoEXgMVAEjDOg5lEpIDc070m/n6Gl39d63aU4iMoDC59AfathnmaGiIiIufmjOXaGOMH/GatPWyt/QqoBtSx1j5RKOlE5IKULxnC0HZxTF62k1U7U9yOU3zU7gl1esH05+DQ35dbFBEROZ0zlmtrbTbwUq7XJ6y1yR5PJSIFZnineEqGBPL8z2vcjlK8XPIcGD/44QHIx6pKIiIikL9pIb8YY/obY4zH04hIgStVIpARneOZvnYf8zYdcDtO8VEqBro8Cut/hjXfuZ3m9LTpjYhIkZKfcn0f8CVwwhiTYow5YozR75dFipEb2sZSoWQIz/20hvysbS85Wg2H8g3hx4fgxFG30/zP0X0w7x0Y2RmeqwZ79VsJEZGi4qzl2lobYa31s9YGWWtL5rwuWRjhRKRghAT6c0/3mizZephfVu1xO07x4R8AvV6GlB0w/Rl3s6Qfg+UT4NMB8FJt+OlhyM4CvwCYeCtkZbibT0REgPxtItMxr/ettTMLPo6IeMqA5jGMnLWJF35eS7c65Qjwz88vroQqLaH5Dc5IceNroELDwrt3dhYkzYJl42H1N5B+FErGQLu7oNHVUK4urPoGvrgOZr4IXR4pvGwiIpKns5Zr4IFc/xwCtAQWAV09kkhEPCLA348HLqrNiLGL+XrxDq5qob2g8q3bv2D1d/DdvXDjL85ujp60ewX8Md4ZqT6yE4JLQv0+0OgaqNbur/evd4Xz/swXoNZFULm5Z7OJiMgZmXOdf2mMqQI8b60d6JlI5y8hIcEmJia6HUOkyLLW0uftOexNSWPa/Z0JCfR3O1LxsexzZ/pFr1chYWjBXz9lJyz/Ev74AvascKZ71OgBja6C2pdAYInTf/b4YXinrbNG960zz3yuiIhcMGPMImttQl7Hzmf4ZTvQ4MIiiYgbjDE81LM2u5LT+GSu1m8+J42uhtgOMOVfzgOFBeHEEVj6GXx8BbxcD359AgJC4NIX4R9r4drPoUG/s5flEpHQ+y3Yvw5+e7JgsomIyHnJz5zrN4A/h7f9gCbAMg9mEhEPahsfRcda0bw1fQNXt6xCyZCit3V7kWQMXPayM0L8yz+h33vnd52sTNg0zRkJX/M9ZB6H0rHOtuuNroay8ed33fgu0PJWmPe2M9Idl+fjMiIi4mFnnRZijLk+18tMIMlaO9ujqc6TpoWI5M+KHcn0euN3bu8SzwMX13E7TvEy9WlnfvP13+a/wFoLO5c4Uz5WTIDUfRAS6YxKN7rGeWiyILYSSD8G73WAzBMwYjaElLrwa4qIyN+caVpIfsp1GJBmrc3Kee0PBFtrjxV40gukci2Sf3eOW8KUVXuY8UBnypUMcTtO8ZFxHN5uDX6BToENCD79uYe2wPIvnFK9fx34B0Gtns4Idc2LICCo4PNtT4QPekDjgdDn7YK/voiIXPCc69+A3BP+SgBTCiKYiLjnHz1qkZGVzetT17sdpXgJLAGXvgQH1sPs1/9+/PhhWPQRjL4EXmvkjHSHRsHlr8H96+DqT6BuL88Ua4CYBOjwD1g61pl2IiIihSo/S/GFWGtPbk1mrT1qjAn1YCYRKQSxUWFc07IKny/Yxs3tqxMbFeZ2pOKjZneo39eZHtKgH5SqAht+deZRr/sJstKhbE3o+k9oeBWUrla4+To+COt+hm/ugpiWEB5duPcXEfFh+Rm5TjXGNPvzhTGmOXDcc5FEpLDc1a0mgf5+vPjLWrejFD8XP+NM8xh7JbxUCz6/FrbOhYSbYNg0uGMhdHyg8Is1OKPi/UY6q5F8d48z51tERApFfsr1PcCXxphZxphZwHjgDo+mEpFCUS4ihJvax/HdH7tYsSPZ7TjFS8mKcNFTcGQ3xHeDa7+E+1bDJc9C5WYF84DihShX1xk5X/OdM6IuIiKFIl+byBhjAoHagAHWWGszPB3sfOiBRpFzl5KWQcfnp9Gwcik+uamV23GkIGVnwUe9nE1pRsyBSO3KKSJSEC7ogUZjzO1AmLV2hbV2ORBujLmtoEOKiDtKhgRye+cazFq/nzkb9rsdRwqSn7+zYojNhsm3QXa224lERLxefqaFDLPWHv7zhbX2EDDMY4lEpNBd16YalUqF8NxPa8jPb7OkGCkTBxf/FzbPhIXvu51GRMTr5adc+xnzv8mDOetc52sNKWNMT2PMWmPMBmPMw3kcL22MmWiM+cMYs8AY0yDn/SrGmGnGmNXGmJXGmLvz+wWJyLkLCfTnnh61WLY9mZ9W7HY7jhS0ZkOg5sXO9ur71rmdRkTEq+WnXP8MfGGM6WaM6QqMA34824dySvhbwCVAPWCgMabeKac9Ciy11jYChgCv5byfCfzDWlsXaA3cnsdnRaQA9W8WQ81y4bzwy1oyszR9wKsYA1e87qzRPfFWZwt2ERHxiPyU64dwNpIZAdwO/MFfN5U5nZbABmvtJmttOvA50PuUc+rlXBtr7Rog1hhT3lq7y1q7OOf9I8BqoHI+7iki58nfz3D/xbXZtC+VLxdtdzuOFLSICtDrFdi5GH5/2e00IiJe66zl2lqbDcwDNgEJQDecsns2lYFtuV5v5+8FeRnQD8AY0xKoBsTkPsEYEws0Bebn454icgEuqleeZlUjeXXKOo6nZ7kdRwpa/b7Q8EqY8RzsXOp2GhERr3Tacm2MqWWMecIYsxp4k5yibK3tYq19Mx/XzmuR11OflHoWKG2MWQrcCSzBmRLyZ4Zw4CvgHmttymly3mKMSTTGJO7bty8fsUTkdIwxPNSzDntSTvDx3CS344gnXPoChEU700My0txOIyLidc40cr0GZ5T6cmtte2vtG8C5DGVtB3IvqhoD7Mx9grU2xVo71FrbBGfOdTSwGU6urf0VMNZa+/XpbmKtHWmtTbDWJkRHa4tfkQvVqnpZutSO5u1pG0g+ViSXtJcLUaI09H4L9q2BqU+5nUZExOucqVz3B3YD04wx7xtjupH3aPTpLARqGmPijDFBwDXAN7lPMMZE5hwDuBmYaa1NyVmd5ANgtbVWkwNFCtkDF9fhyIlM3pmx0e0o4gk1ujnbtM99C5J+dzuNiIhXOW25ttZOtNZeDdQBpgP3AuWNMe8YYy4624WttZk426T/jDNH+wtr7UpjzHBjzPCc0+oCK40xa3BWFflzyb12wHVAV2PM0pw/l57flygi56pepZL0blyJD2dvZneypg54pYuegtKxMGkEnDjidhoREa+Rr+3PT55sTBngSuBqa21Xj6U6T9r+XKTgbD1wjG4vT2dA8yo806+h23H+JjvbMmX1HjbvT+WWjtXJtRy/5NfW+fBhT2g6GK54w+00IiLFxpm2Pw84lwtZaw8C7+X8EREvVrVsKNe2rMqn87dyc4c44qPD3Y4EQGZWNt/9sYu3p29g3Z6jAERHBNOvWcxZPil/U7UVtLvHWZqv9mVQu6fbiUREir38rHMtIj7qjq41CQ7w46Vf1rodhbSMLD6dt4UuL03nnvFLMRhevboJTapE8t8fVpN8XA9fnpfOj0D5hvDNnZB6wO00IiLFnsq1iJxWdEQwN3eozg/Ld7Ns22FXMhw9kcnImRvp8Pw0/jlpBWXDgnl/SAI/3t2BPk0r83SfBhxITeeVX7Wt93kJCIK+70LaYfjuHjiHqYIiIvJ3KtcickbDOsRRJiyI539eU6j3PZSazsu/rqPds1P57w9rqF0+gs+GtWLibW3pUa88fn7OHOsGlUsxuFU1xsxNYuXO5ELN6DUqNIAuj8Lqb2D5l26nEREp1lSuReSMIkICub1LDWZvOMCs9Z7fqGl3chpPf7eKds9N5fXf1tMqrgyTbm/Hpze3om18VJ4PLt5/UW1KhwbxxOSVZGdr5PW8tL0LqrSCH+6H5B1upxERKbZUrkXkrAa3rkrlyBI899Maj5XXLQdSeeTr5XR8fhofzkmiZ/0K/HJvR0YOSaBJlcgzfrZUaCAPX1KHRVsOMWHxdo/k83p+/s70kKxMmHy7poeIiJwnlWsROavgAH/u61GLFTtS+H75rgK99prdKdw1bgldXpzOV4u3c1WLGKbf35mXr25CrfIR+b5O/2YxNK9Wmmd/XKOdJc9Xmepw8dOwaRosHOV2GhGRYknlWkTypU/TytQuH8FLv6wlIyv7gq+3eOshbv54IT1fncVvq/cwrEN1fn+wC0/3aUiVMqHnfD0/P8NTvRtw+Fg6LxaB1U2KreZDoUZ3+OVxOKAdOkVEzpXKtYjki7+f4YGLa5N04BjjF247r2tYa5m1fh8DR86j39tzSNxyiHu712L2w1155NK6lCsZckEZ61UqyZA2sXw6fwvLt+vhxvNiDFzxJgQEw8RbnWkiIiKSbyrXIpJv3eqWI6FaaV77bT3H0vNfurKzLT+t2E3vt2Zz3QcL2LT/KP+8rC6zH+rK3d1rEhkaVGAZ77uoFmXDgvnn5BV6uPF8lawIl70E2xfCnNfcTpO31P0w9y348gY4ssftNCIiJ53TDo0i4tuMMTx0SR2ufHcuH85O4vYuNc54fkZWNt8u28nb0zeyYe9RqpUN5Zl+DenXrDLBAf4eyVgyJJDHLqvDveOX8UXiNq5pWdUj9/F6DQfAmu9h2jNQowdUbOR2IsjOgo1TYfEYWPsjZGeA8YOj+2DIZPDX/6WJiPuM9aInwhMSEmxiYqLbMUS83k0fLWTB5oPMfLALpcP+PuqclpHFl4nbeHfGJnYcPk6dChGM6BzPZQ0rEuDv+V+YWWu5+r15rN97hKn/6JxnRsmHYwfh7TYQWgZume5MFXHDwU2wZCws/QyO7ITQstB4IDQdDDuXwqThzjbuPf7PnXwi4nOMMYustQl5HdO0EBE5Zw/0rM3R9EzemfHXB96OpGXw7oyNtH9uGo9PXkm5ksF8cL2zm2LvJpULpViDM8L+ZJ/6pKRl8vzPerjxvIWWgd5vwt5VMO0/hXvv9GOwbDx81Atebwq/v+xsdnPVGLhvDVz8HyhXF5oMhOY3wOxXYc0PhZtRRCQP+h2aiJyzOhVK0rdpZT6ak8QNbWMJCfTno9mb+WhOEilpmXSoGcVtnZvSunqZPDd9KayMQ9vG8sHszVzdospZ18qW06jZI6e8vg61LoFqbTx3L2th5xJY8gks/wpOJEPpWOj6T2h8LZSqnPfnej7nfG7icLh1urOkoIiISzQtRETOy7aDx+j20gyqR4ex5cAxjmdk0bN+BW7rEk+jmEi34wHOSHq3l2ZQvmQIk25vh7+fO0W/2DtxFN5t5/zz8NkQHF6w1z92EP4YD0s+hT0rICAE6vWGptdBtXbgl4/feBxKgvc6QWQVuOlXCCxRsBlFRHLRtBARKXBVyoQytF0s6/ce5ZKGFfj13o68e13zIlOswdm6/bHL6rJ8RzLjFmx1O07xFRwOfd6FQ1vgl38WzDWzs2DDFGe1j5dqw08Pg38gXPYy/GMt9BsJcR3yV6zBGeHuNxJ2L4cfHiiYjCIi50Ej1yJy3rKzLUdOZFKqRKDbUU7LWsu1789n1a4Upv6jE2XDXXoozxv8+gTMfg0GTXCmi5yPQ0nOg4lLxkLKdihRGhpd4zycWKHBhWf87SmY9SL0fsu5poiIB5xp5FrlWkS83vo9R7jktVn0a1aZ5wc0djtO8ZV5AkZ2dqZx3DbXeeAxPzLSYPW3zlzqzTMAA/FdnfJb57KCXYUkOws+6Qvb5jvTQ4rCEoIi4nU0LUREfFrN8hHc1D6OLxK3s2jLIbfjFF8BwdD3PTh2AL7/x9nP37UMvr8fXqoFX98MhzZD50fhnuVw3dfQoF/BL+/n5w/9P4ASZeCLIXD8cMFeX0TkLFSuRcQn3NWtJhVKhvD4pBVkaefG81exEXR+GFZ+Dcsn/P348UMwfyS82x7e6+hs+FKjh7PJy13LoPNDzkOHnhQeDVd+BMnbYNJtziokIiKFROVaRHxCWHAAj/eqx6pdKXw6b4vbcYq3dvdATAtn9DplF2Rnw8ZpMOFGeLE2/PgAYODSF+H+tTDgA6jeOf8PJxaEqq2gx1Ow9ntnnriISCHROtci4jMubViB9jWiePGXtVzasCLREXq48bz4BzjTQ95tD2OvhLRkSN4KIZHQ/HpnLnXFIjC3vfUIZ+71b/8HMQkQ297tRCLiAzRyLSI+wxjD//WuT1pGFs/+uMbtOMVb2Xhnl8Q9K6BsdWee8z/WwqUvFI1iDWAMXPGGs6nMl0PhyG63E4mID1C5FhGfEh8dzrAO1flq8XYWJh10O07xlnAjPLrTmU/dcAAEhrid6O9CSsJVn0D6UWfaSlam24lExMupXIuIz7mjaw0qR5bg8UkryMzKdjtO8RYU6naCsytfD3q9CltmO1NEREQ8SOVaRHxOaJDzcOOa3UcYM1cPN/qExlc7I+1zXofV37mdRkS8mMq1iPiki+uXp1OtaF75dR17U9LcjiOFoeezUKkpTBoBBza6nUZEvJTKtYj4JGMM/76iPicys/nvD6vdjiOFISAYrvzY2Wjmi+sh47jbiUTEC6lci4jPiosKY3in6kxaupN5mw64HUcKQ+lq0O99Z5WT7+93O42IeCGVaxHxaSM61yCmdAmemLyCDD3c6Btq9oCOD8DST50dJEVECpDKtYj4tBJB/vzr8vqs23OUj2YnuR1HCkvnh6F6F2f0etcyt9OIiBdRuRYRn9ejXnm61SnHq1PWsTtZDzf6BD9/6D8KwqLgiyFw/JDbiUTES6hci4gA/7q8PhnZlv/o4UbfERYFV34Eydth4gjI1rQgEblwKtciIkDVsqHc1jmeb5ftZPaG/W7HkcJSpSVc9B9Y9yPMftXtNCLiBVSuRURyDO8UT9UyoTwxeQXpmRrF9BmtboX6fWHqU7B5pttpRKSYU7kWEckREujPv6+ox8Z9qYyevdntOFJYjIEr3oCyNWDCjZCyy+1EIlKMqVyLiOTStU55etQrz2tT1rPzsDYZ8RnBEXDVGEhPhQlDISvD7UQiUkypXIuInOKJXvWwWJ7+fpXbUc7IWkvycZXAAlOuLlz+OmydC1P+7XYaESmmVK5FRE5RpUwod3SpwQ/LdzNz3T634/xNemY2ExZtp+ers2jx9BSWbNUycgWm0ZXQYhjMfRNWfeN2GhEphlSuRUTyMKxjdeKiwvjXNys5kZnldhwAjqRlMHLmRjo+P437v3Q2PikbHsR9XyzjWHqmy+m8yMX/gcrNYfLtcGCj22lEpJhRuRYRyUNwgD//vqI+m/enMmqWuw837klJ45kfV9P2man894c1xEWF8eHQFvx0TwdeuboJSQdSefp7rc9dYAKC4cqPwS8Axl8H6cfcTvRX1sKelTD1PzCmN+xb63YiEcklwO0AIiJFVada0VzSoAJvTF1P7yaViCkdWqj3X7fnCCNnbmLy0h1kZVsuaViRWztWp1FM5MlzWlcvyy0dq/PejE10q1OObnXLF2pGrxVZBfq/D58OgO//AX3edlYVcYu1sHcVrJwEKyfCgfVg/CAgBMYNhGFToUSke/lE5CRjrXU7Q4FJSEiwiYmJbscQES+y8/Bxur00gw41oxg5JMHj97PWMn/zQUbO3MTUNXsJCfTj6oQq3NS+OlXL5l3uT2Rm0eetOew7ksZP93QkKjzY4zl9xrRnYMazcPlr0PyGwr33yUI90SnVfxbqau2gfh+oewUc2AAfXw7x3WDg5+CnX0iLFAZjzCJrbZ7/p6ByLSJyFu9M38hzP63hwxta0KVOOY/cIyvb8vPK3bw3cxPLth2mTFgQ17eJ5bo21SgTFnTWz6/bc4Reb/xOx5pRvD8kAePmKKs3yc6CsQMgaTbc9DNUaurZ+/055WPVpDwKdV+oezmEn/Lv4IL34Yf7oeMD0PWfns0nIoDKtYjIBUnPzKbnazOdAnxPR0IC/Qvs2mkZWXy5aDujZm1iy4FjVCsbys0dqjOgWQwlgs7tPqN/38yT363imX4NGdiyaoFl9HmpB+C9js6o8C0zILRMwV7/L4V6ojMabfwgtj3U65N3oT7189/cAUs+has+gXpXFGw+EfkblWsRkQs0e8N+Bo2az73da3F395oXfL2Dqel8MncLH89N4mBqOo2rRDK8Y3Uuql8Bf7/zG3XOzrYMGb2ARVsO8cPdHYiLCrvgnJJjeyKM7gnxXQtm+sVZC/UVEB6d/+tlpMFHlzoPN978G5Src2H5ROSMVK5FRArAHZ8t5tdVe/j13k6nnf98NlsPHGPU75v4InEbaRnZdKtTjls6VqdlXJkCmcqxOzmNi1+dSVxUGBOGtyHAX3NwC8z8kfDjA9D1ceh4/7l//s9CvXKiU6pzF+r6faHO5edWqE+VvANGdoLgknrAUcTDVK5FRArA7uQ0ur40nbbxZRl1fYtz+uwf2w/z3sxN/Lh8F/5+hj5NKnNLx+rULB9R4Dm/+2Mnd3y2hHu61+Se7rUK/Po+y1r46ianHF83Eap3zt9n9qxw5k//pVB3cB5KvNBCfaotc3IecPxzhL3gpjCJyP+cqVx7dCk+Y0xP4DXAHxhlrX32lOOlgdFAPJAG3GitXZGfz4qIFLYKpUK4p3tN/vvDGqas2kP3emde9s5ay/R1+xg5YxNzNx0gIjiAWzrGM7RdLOVLhngsZ69GlZi6ei9vTN1Ap1rRNK1a2mP38inGONuj714BE26C4bOgZKW/n5e7UK+cCAc3/q9Qt7m94At1btXaQs9nnQccpz+jBxxFXOCxkWtjjD+wDugBbAcWAgOttatynfMCcNRa+3/GmDrAW9babvn5bF40ci0inpaRlc2lr83ieEYWU+7rlOfDjemZ2Xy7bCfvz9rEmt1HqFAyhJvax3FNyypEhAQWSs6UtAwueXUWgf6G7+/qQFiwtjUoMPvWwsguUKEh3PAd+AfmKtQ5y+blLtR/rvIRFlU4+fSAo4jHuTVy3RLYYK3dlBPic6A3kLsg1wOeAbDWrjHGxBpjygPV8/FZEZFCF+jvx5O9GzDw/Xm8PX0j9/X437SLI2kZfL5gGx/8vpndKWnULh/BS1c25vLGlQgKKNy5zyVDAnn5qsZc8/48nv5+Nc/0a1io9/dq0bWh9xsw4Ub47l5nJY/chTquI7S9s3ALdW7GwKUvwd7VMGkERNXSA44ihciT5boysC3X6+1Aq1POWQb0A343xrQEqgEx+fysiIgr2sSXpXeTSrw7YyP9mlamRJA/o2dv5rN5WzlyIpM21cvyTP+GdK4V7ep6062ql+XWjvG8O2Mj3eqUO+s0FjkHDfrD1vmw4D0w/hDXwd1CfarAELj6U3ivE3x+rR5wFClEnizXef0/yqlzUJ4FXjPGLAWWA0uAzHx+1rmJMbcAtwBUrap1XUWkcDx2aV1+W72XwR/MZ09K2mm3J3fbfT1qMXPdPh766g9+qtKR6Ajt3lhgLv6P8+BgTELRKNSnKlkJrhoDH/eCr4fpAUeRQuLJ31NuB6rkeh0D7Mx9grU2xVo71FrbBBgCRAOb8/PZXNcYaa1NsNYmREd76AEREZFTlCsZwsOX1OFgajrXtqzK9Pu78Na1zYpUsQYICvDj1WuacOREJg9/9QfetEKU6/wDoXbPolms/1StDVzyHKz/Bab91+00Ij7BkyPXC4Gaxpg4YAdwDXBt7hOMMZHAMWttOnAzMNNam2KMOetnRUTcNrh1NQa1qlrktxqvVT6CRy6pw/99u4pxC7ZxbSv9ls+nJNwEO5fCrBehYmM94CjiYR4bubbWZgJ3AD8Dq4EvrLUrjTHDjTHDc06rC6w0xqwBLgHuPtNnPZVVROR8FfVi/afr28TSoWYUT323is37U92OI4XJGLjsJaicABOHOw86iojHaBMZEREf8efujbE5uzcGavdG35Ky03nAMTg85wFHrX8ucr7OtBSf/ssqIuIjKpQK4Zl+DVm27TBvTt3gdhwpbH8+4Hh4K3w1DLKz3E4k4pVUrkVEfMilDSvSr1ll3py2gcVbD7kdRwrbnw84bvgVpv3H7TQiXknlWkTEx/z7ivpUKBnCveOXknoi0+04UtgSboKm18Gsl2DVZLfTiHgdlWsRER9TMiSQV65uwtaDx3j6e21863P+8oDjCNijfwdECpLKtYiID2oZV4bhneIZt2Abv67a43YcKWwBwXD1J87DjZ9fC8c1RUikoKhci4j4qHu716J+pZI8/NUf7Dtywu04Utj+fMAxeTt8dbMecBQpICrXIiI+KijAj1evbsLRE5k8pN0bfVPV1jkPOE6BqU+7nUbEK6hci4j4sJo5uzdOXbOXzxZsdTuOuCHhRmg2BH5/GVZOcjuNSLGnci0i4uOG5Oze+PR3q9m076jbcaSwGQOXvggxLWDSbXrAUeQCqVyLiPg4Pz/Di1c2JjjQj3vHLyUjK9vtSFLYAoLhKj3gKFIQVK5FRITyJUP4b9+GLNuezBvavdE3layoBxxFCoDKtYiIAM7ujf2bxfDWtA0s2lK0Ry4Ppabz8i9r6f3WbDbsPeJ2HO9RHB5wtBZ2LoUfHoDXmsDaH91OJPIXxpueDk9ISLCJiYluxxARKbaOpGVwyWuz8Pcz/HBXB8KCA9yO9Bd7j6QxatZmPp23hWPpWYQG+RMVHsyk29tRJizI7XjewVr49i5YPAau/Bjq93E7kSN1P/zxBSwdC3tWgH8whEXB8cNw409QsZHbCcWHGGMWWWsT8jymci0iIrkt2HyQq0fO5eqEKjzbv2gUlh2Hj/PejI18vnAbmVnZXN64Erd1rkFqeibXjJxHk5hIPrm5JcEB/m5H9Q6ZJ+Cjy5yHG2+eAuXruZMjKxM2/ApLPoV1P0N2BlRqBk0HQYP+Ts73uwIGhk2FiPLu5BSfo3ItIiLn5Pmf1vD29I2MvK45F9Wv4FqOpP2pvDN9I18t3g5A/2YxjOgcT2xU2Mlzvlm2k7vGLaFfs8q8dGVjjDFuxfUuKbtgZCcIDIVbpkGJ0oV3771rYOmnsGw8pO6FsGhodDU0GfT3or9rGYzuCeXqwg3fQ2CJwsspPutM5bpo/b5PRESKhHu612Lm+n08/PVymlSNpFxESKHef92eI7w1bQPfLttJgL8fg1pV5ZZO8VSO/HtxuqJxJTbvS+WVKeuIjw7n9i41CjWr1ypZ0VlB5KPLYMJNMOhL8PPgbwbSkmHFV7BkLOxIBL8AqHmxM0pd8yLwD8z7cxUbQ7/3YfxgmHw79P/AWV5QxCUauRYRkTxt2HuEy17/nbbxZRl9Q4tCGRFevj2ZN6et5+eVewgN8mdw62rc3D6OciXPXO6ttdwzfimTl+7k7UHNuLRhRY9n9RmJo+G7e6H9vdD93wV77exs2DzDmUe9+lvITINy9ZwR6kZXQ3h0/q/1+ysw5d/Q+RHo/HDB5hQ5hUauRUTknNUoF8Gjl9blX9+sZOz8rQxuXc1j90pMOsgbUzcwY90+IkICuKtrDYa2i6N0Ph9SNMbwXP9GbDt4jPu+WErlyBI0rhLpsbw+JeFGZ3WO319xRonr973wax7cDEs/g2XjIHkbhJSCpoOdUl2p6fmNPLe7B/atg+nPQFRNZ062iAs0ci0iIqdlreX6DxeyYPMBvr+rA/HR4QV67dkbDvDmtPXM23SQMmFB3NQ+juvaVKNkyGmmAJzF/qMn6PPWbE5kZjP59nZUymMaiZyHzBPwUS9nlY6bp0D5+ud+jfRUWPWNM0qdNAswEN/FKdR1ekFgAUw9yjwBY/rAzsVwww8Q0/zCrymSBz3QKCIi521PShoXvzqTqmVC+WpEWwL9L2yLBGstv63ey5vTNrB022HKlwzmlo7xDGxZhdCgC/+F6ro9R+j39hyqlAllwvA2RW45wWLr5AOOJWDYNAgtc/bPWAvb5jurfaycBOlHoHScM4+68UAoFVPwOVMPwPtdIOO48yCmJ+4hPk/lWkRELshPK3Yx/NPF3NW1BvddVPu8rpGVbflpxW7enLaB1btSiCldguGd4hnQPIaQwIJ9UG762r3c+NFCutYpx3vXJeDvpwfcCsTW+c4DjnEdYNCE0z/gmLLLmfKxdCwc2ACBYc50kqaDoGobzz9wuHcNfNADIqs5a2AHF9xvXERA5VpERArA/V8u4+vF2/lyeBuaV8vHqGWOjKxsvlm6k7emb2DTvlSqR4dxW+ca9G5S6YJHwc9kzNwknpi8kmEd4njsMpfWafZGiR/Cd/c4c5x7/N//3s88AWt/cFb72Pgb2Gyo2tYp1PX6FH7BXT8FPrsSal0CV38KftqUWgqOHmgUEZEL9q/L6zFv0wHuHb+MH+7uQPhZplucyMxiwqLtvDtjI9sOHqdOhQjevLYplzSoWCgjyUPaxLJx71Hen7WZ6tHhDGxZ1eP39AkJQ2HnEpj9qvOAY5nqzgj18i/h+CEoWRna3wdNroWy8e7lrNkdej4LPz4Iv/0bejzpXhbxKSrXIiKSLxEhgbxydROufm8uT327iucG5L174/H0LMYt2MrImZvYnZJG4yqR/KtXfbrVLVfoG7w83qseSQeO8fikFVQtE0q7GlGFen+vdekLsHc1TLgRsM5W5HUuc1b8qN7Zs+thn4uWt8C+tTD7NYiq7Yyii3iYpoWIiMg5eeHnNbw1bSPvXdeci3Pt3ngkLYNP5m3hg1mbOZCaTqu4MtzRtQbta0S5umtiSloGA96Zw+7kNCbe3q5AVzzxaSm74NfHoUoraDigcHdwPBdZGTB2ACTNhiGTIbad24nEC2jOtYiIFJj0zGz6vTObnYfT+OmeDgT5+zF6dhIfzd5MSlomnWpFc0fXGrSIzf+8bE/bdvAYfd6aTXhIAJNua5fv9bPFSxw/DKO6w7EDMOw3ZyqLyAVQuRYRkQL15+6NVcqEsuvwcVLTs7ioXnnu6FqDRjGRbsfL06ItBxn4/nyaVInk05taERSgB9x8yoGNMKobhJWDm391Nq4ROU9nKtf6L4uIiJyzGuUieLxXPTbvT6Vb3fL8fE9HRg5JKLLFGqB5tTK8MKARCzYf5NGJy/GmwSXJh7LxcNUncHAjfHkDZGW6nUi8lB5oFBGR8zK4dTWPrFHtSb2bVGbTvlRe+2098dHhjOjs4moWUvjiOkCvV+CbO+HnR5wHM0UKmMq1iIict+JUrP90T/eabNqfynM/rSEuKpSeDSq6HUkKU7Mhzgoic9+EqFrQcpjbicTLaFqIiIj4FGMMLwxoRNOqkdwzfinLtye7HUkKW48noVZP+PEh2DjV7TTiZVSuRUTE54QE+jPyugTKhgVz08cL2ZV83O1IUpj8/KH/KChXF764AfatczuReBGVaxER8UnREcGMvqEFx9KzuOmjRFJP6AE3nxIcAQPHQUAQfHYVHDvodiLxEirXIiLis2pXiOCNgU1ZszuFe8YvJTtbK4j4lMiqcM04SNkJ4wdDZrrbicQLqFyLiIhP61KnHI/3qsevq/bw3E9r3I4jha1KC+j9FmyZDd/fC1qiUS6QVgsRERGfd0PbWDbtS+W9mZuoHh3G1S2quh1JClOjK2H/Opj5PETVhnZ3uZ1IijGVaxER8XnGGP51eT2SDqTy2MQVVCkTStv4KLdjSWHq/IhTsH99AsrWgDqXup1IiilNCxEREQEC/P14a1Az4qLCGPHpYjbtO+p2pDPalXycb5ftJD0z2+0o3sHPD/q8A5WawFc3w+7lbic6vT2r4OfH4LXGsHSc22nkFMabtn9NSEiwiYmJbscQEZFibOuBY/R5ezalSgQy8ba2RIYGuR3ppOTjGfy0YheTluxk3uYDWAv9mlXmpSsbY4xxO553OLIbRnYB4wfDpkJEebcTOY4fguUTYOlY2LkE/AKhZEXnYczrJkJcR7cT+hRjzCJrbUKex1SuRURE/iox6SDXvj+fZtUiGXNjK4IC3PtF74nMLKat2cfkpTv4bc1e0jOziYsKo0+TyqSmZzJy5ib+0aMWd3ar6VpGr7NrGYzuCeXqwQ3fQWAJd3JkZ8Gm6bDkU1jzPWSdgPINoMkgaHQV+AXA6IvhyC64+TeI0r8DhUXlWkRE5BxNWrKDe8Yv5aqEGJ7r36hQR4azsy0Lkg4yeekOvv9jFylpmUSFB3F540r0aVKZRjGlMMZgreW+L5YxcckOXh/YlCsaVyq0jF5v1TfwxXXQYICz4Uxh/mbgwEZY+hksGwcpOyAk0inTTQZBxcZ/zXIoCd7v5qzbffNvEFa28HL6sDOVaz3QKCIikoc+TSuzad9RXp+6gfjocG7tFO/xe67ZncKkJTv5ZukOdianERrkz8X1K9CnaWXaxZclwP+vI+jGGJ7t35Adh45z/5fLqBxZgubVSns8p0+odwV0ewJ+exKiakHnhzx7vxNHYdUkWDIWts5xpqXEd4OL/wO1L4WA4Lw/VzrW2Qzno14wfhAMmXz6c6VQaORaRETkNLKzLXd+voQflu/i3cHNubh+hQK/x87Dx/lm2U4mLdnBmt1H8PczdKwZRZ+mlelRrzyhQWcfBzuUmk7ft2dzJC2Tibe1o2rZ0ALP6ZOshUkjnBHkAR9Cg34Ff/0tc5x51CsnQUYqlImHpoOg8UAoeQ6/iVjxFUy4ERpdDX3fK9yRdh+kaSEiIiLnKS0ji6tHzmPd7iN8ObwNDSqXuuBrJh/P4Mflu5i4ZAcLkg5iLTStGkmfJpW5rFFFosLPfeRx076j9H17DtERwXw1oi2lSgRecE4BMk/Ax1fArqVwww8Q0/zCr5m83SnsS8bCoc0QFA71+0LTwVCl1fkX4xkvwLSnoctj0OnBC88pp6VyLSIicgH2Hkmjz5uzybKWybe3p0KpkHO+RlpGFtPX7mXikh1MW7OP9KxsqkeF0adpZXo3qUS1smEXnHPepgNc98F8WsaV4aOhLQn014q7BSJ1P7zfxSnaw6ZCqZhzv0ZGGqz5zhml3jgNsBDbwZlHXe8KCLrwv3+shYnD4Y/Pof8H0HDAhV9T8qRyLSIicoFW70phwDtziIsO44tb2+RrukZ2tmX+5oNMWrKDH1bs4khaJlHhwVzeuCJ9m1amYeVSBf6g5JeJ23hgwh9c06IKz/RrqCX6Csre1TCqB5SJhaE/QXD42T9jLexc7IxQr5gAaclQqooz5aPJtVAmruBzZp6AMX1gxyK4/luo2qrg7yEq1yIiIgVh6po93PxxIj3qleedQc3x88u7uK7elcKkJTv4ZtlOduU8mNgz58HEtnk8mFjQXvh5DW9N28ijl9bhlo6efxDTZ6z/FT67ynnA8KpPnI1n8nJ0H/wx3hml3rsKAkKg7uXOKHVcp9N/rqAcOwijukFaCtw8xTMl3sepXIuIiBSQD37fzFPfrWJ4p3gevqTOyfd3HD7O5KU7mLxkJ2v3OA8mdqoVTe8mlfL9YGJByf0g5juDmtOzQcE/iOmz5r0DPz0M7e6BHv/3v/ezMmD9L84o9fqfITsTKjd35lHX7wclIgs35/71MKo7hJeHm34p/Pt7OdeW4jPG9AReA/yBUdbaZ085Xgr4FKiak+VFa+2HOcfuBW4GLLAcGGqtTfNkXhERkbO5sV0sm/Yd5d0ZG6lYKoSgAD/nwcTNBwFoVjWSJ3vX57KGFSl7Hg8mFgQ/P8NLVzZm5+Hj3DN+CV9EtqFRTKQrWbxOq+Gwby3MftVZoq9SU2eE+o/xkLoPwspB6xHQZDCUq3PWy3lMVE24+lP4pA98eT0MmgD+esi1MHhs5NoY4w+sA3oA24GFwEBr7apc5zwKlLLWPmSMiQbWAhWAaOB3oJ619rgx5gvgB2vtR2e6p0auRUSkMGRkZXPjRwuZtX4/ANWjnR0TC+rBxIKy78gJ+r49mxOZ2Uy6vR2VI13aadDbZGXAp/1h80zAOjsl1urpjFLX6F60SuyST2Hy7dDserj8NS3RV0DcGrluCWyw1m7KCfE50BtYlescC0QY52mLcOAgkJkrWwljTAYQCuz0YFYREZF8C/T3461Bzfh8wVZaVy/rkQcTC0J0RDCjb2hB/7fncNNHC/lyeBsiQopQ8Suu/APhqo/hp0ehQgNoeBWER7udKm9NBzs7Pv7+sjOa3fZOtxN5PU/OqK8MbMv1envOe7m9CdTFKc7LgbuttdnW2h3Ai8BWYBeQbK39xYNZRUREzknJkEBu6RhPo5jIIlms/1SrfARvD27G+r1HuXPcEjKzst2O5B1KlIa+70Cb24tusf5T18ehXm/45XFY/Z3babyeJ8t1Xv+lOXUOysXAUqAS0AR40xhT0hhTGmeUOy7nWJgxZnCeNzHmFmNMojEmcd++fQWVXURExGt0qBnNU70bMH3tPp76btXZPyDexc/P2bWxcjP4ehjsXOJ2Iq/myXK9HaiS63UMf5/aMRT42jo2AJuBOkB3YLO1dp+1NgP4Gmib102stSOttQnW2oTo6CL+k6OIiIhLrm1VlWEd4vh47hY+mr3Z7ThS2AJLwDXjILQsfHYNJO9wO5HX8mS5XgjUNMbEGWOCgGuAb045ZyvQDcAYUx6oDWzKeb+1MSY0Zz52N2C1B7OKiIh4vYcvqUuPeuV58rtVTF2zx+04UtgiysO14yE9FT67Gk4cdTuRV/JYubbWZgJ3AD/jFOMvrLUrjTHDjTHDc057CmhrjFkO/AY8ZK3db62dD0wAFuPMxfYDRnoqq4iIiC/w9zO8dk0T6lUqyR2fLWHVzhS3I0lhK18frvwI9q6Er26C7Cy3E3kdbSIjIiLiY/akpNHnrdkATLq9HeVLhricSArdgvfhh/uh9W3Q8xm30xQ7Z1qKz8P7b4qIiEhRU75kCB9c34KU4xnc9PFCjqVnnv1D4l1aDoNWI2De207RlgKjci0iIuKD6lUqyRvXNmXVzhTu/nwpWdne85tsyaeL/wM1L4YfH4L1U9xO4zVUrkVERHxU1zrlebxXPX5dtYdnf9S6AT7Hzx8GfADl6sGXN8AeLdNYEFSuRUREfNjQdnFc36Ya78/azNj5W9yOI4UtOAKu/RyCwuCzq+CIVpG5UCrXIiIiPu7xXvXoXDuaJyavZNZ6bcjmc0rFOAX72AH4fCBkHHc70ZllZcLqb+HjK2D5BLfT/I3KtYiIiI8L8PfjzWubUbNcOLd9uph1e464HemM9qak8ebU9fR+83dmrtMPAwWiUlPo9z7sWAwTh0N2ttuJ/u7oPpj5IrzWGMYPhgMbwRa9nFqKT0RERADYcfg4fd6aTZC/H5Nub0d0RLDbkU7KzrbM2XiAzxZs4ZeVe8jMtpQODeR4Rhaf3NSKFrFl3I7oHWa/Dr8+Dh3+Ad2ecDsNWAs7FsGCkbByImSlQ1wnaHkL1OoJ/gGuxDrTUnwq1yIiInLSH9sPc9V7c6lbsSTjhrUmJNDf1TwHU9OZsGgbn83fStKBY5QODeTKhCoMbFmViJAArn5vLntTTvDZsNY0jCnlalavYC18ezcs/hh6vw1NB7mTI+M4rPjaKdW7lkJQBDQZCC1uhuja7mTKReVaRERE8u2nFbsZMXYRlzasyBvXNMXPzxTq/a21JG45xNh5W/hh+W7Ss7JpEVuaQa2q0bNBhb8U/l3Jx7ny3bmknshk/K1tqFU+olCzeqWsDPi0P2yZA9dNhLgOhXfvQ1sg8QNYPAaOH4LoOk6hbnyN8/BlEaFyLSIiIufkvRkbeebHNdzRpQb3X1w4I4UpaRlMXLyDsfO3sG7PUSKCA+jXrDLXtqpG7QqnL1ZbDqRy5btzAfhyeBuqlQ0rlLxe7fhh+KAHHN0LN/8GUTU8d6/sbNg0FRaMgnU/gfGDOpc5G93EdgBTuD/c5YfKtYiIiJwTay2PfL2czxdu44UBjbgyoYrH7vXH9sOMnbeVb5bt5HhGFo1jSnFtq6pc3rgSoUH5m1O7bs8Rrn5vLmHBAXw5vA0VS5XwWF6fcXAzjOoGIaWcgh1awPPajx+GpZ/BwlFwcCOERUPzG6D5UChVuWDvVcBUrkVEROScZWRlc8OHC1iw+SBjbmxFm/iyBXbt1BOZfLNsJ2Pnb2HFjhRKBPrTp2klrm1Z7bznTi/fnszA9+dRrmQwX9zahqjwovNAZrG1dT58fDlUbg5DJkFAAXxPd6+Ahe/DH19AxjGIaek8oFjvioK5fiFQuRYREZHzknw8g/7vzGHfkRNMvK0t1aPDL+h6a3anMHbeViYu2cHRE5nULh/B4NZV6d20MiVDAi8474LNBxkyej5xUeF8Pqw1pUIv/Jo+b/kE+OomaDwQ+rxzftM0sjKctakXvA9b50BACDQcAC2GQaUmBR7Z01SuRURE5LxtO3iMPm/NJiIkgK9va0eZsKBz+nxaRhY/LN/F2PlbWbTlEEEBfvRqWJFBravSrGppTAHPqZ25bh83f5xIg8ol+eSmVoQFu7Ncm1eZ/hxM/y90/Sd0fCD/nzuyGxZ9BIkfwtHdEFnNeUCx6eCCn2ZSiFSuRURE5IIs2nKQge/Pp0lMJJ/c3JLggLMv0bdx31HGzd/KhMXbOXwsg+pRYVzbqir9m8VQ+hwL+rn6acVubv9sMa2rl+GD61u4vqRgsWctTLwV/hgPA0ZDg/5nPnfrXGeUevU3kJ0JNbo7Uz9qdAe/4v93oXItIiIiF+ybZTu5a9wS+jatzMtXNc5zxDk9M5tfV+1h7PwtzNl4gAA/w8UNKjCoVVXaVC9b4KPUZ/L14u3c98UyutctxzuDmxPor42pL0jmCRjT29nF8YbvoErLvx5PT3XmUS8cBXtWOA9CNhkMLW6CsvHuZPaQM5Vr/Z5ERERE8uWKxpXYsj+Vl35dR1xUGHd1q3ny2LaDxxi3YCtfJG5j/9F0KkeW4IGLa3NlQgzlIkJcyduvWQypJzJ5fPJK/vHFMl65ugn+hbxmt1cJCIarxzoriIwbCMN+g9KxzjbkC0fBkrFwIhnKN4TLX4OGV0KQ7y2LqHItIiIi+XZH1xpsPpDKy7+uo0qZEoQHBzJ2/hZmrNuHAbrWKc+g1lXpWDO6SBTZ69rEcvREFs/9tIawYH/+27dhoY6ee52wsjDoS6dgj70KSsXAxt/ALwDq9XYeUKzaukiuTV1YVK5FREQk34wxPNOvIdsPHefe8csAKBcRzJ1da3JNiypUiix660uP6BxP6olM3py2gbCgAB67rK4K9oWIqglXfwqf9IO0ZOj8iLM+dUQFt5MVCSrXIiIick6CA/x5b3BzXp+6nlZxZelWt1yRn8/8j4tqcfREJqN+30x4SAD3dK/ldqTiLa4j3LvSWfHDX8sd5qZyLSIiIuesdFgQ/7q8vtsx8s0YwxO96nH0RCavTllPeHAAN3eo7nas4i2ivNsJiiSVaxEREfEJfn6GZ/s15Fh6Jk9/v5qw4AAGtqzqdizxMirXIiIi4jMC/P149eqmHEtP5NGJywkLDuCKxpXcjiVepGhPkBIREREpYEEBfrw7uDktY8tw3/ilTFm1x+1I4kVUrkVERMTnhAT6M+r6BOpXKsltny1m9ob9bkcSL6FyLSIiIj4pIiSQj4a2JK5sGMPGJLJoyyG3I4kXULkWERERn1U6LIhPbm5JuYhgbvhwASt3JrsdSYo5lWsRERHxaeUiQvj05lZEBAcw5IMFbNh71O1IZ5SRlc3UNXt4bOJylm/XDwNFjbHWup2hwCQkJNjExES3Y4iIiEgxtGnfUa56by4Bfn58ObwNVcqEuh3pJGstK3ak8PWS7XyzdCcHUtMBiAgO4MOhLUiILeNyQt9ijFlkrU3I85jKtYiIiIhj9a4Urn5vLpGhQXw5vA3lS4a4mmfH4eNMWrKDiUt2sGHvUYL8/ehWtxx9m1amToWSXP/hAvakpPHB9S1oE1/W1ay+ROVaREREJJ+WbD3EoFHzqRxZgvG3tqFMWFCh3v9IWgY/rtjNxMU7mLf5ANZCi9jS9G0aw2UNK1Iq9H/bje9NSWPQqPlsPXiMkUMS6FQrulCz+iqVaxEREZFzMGfjfoZ+uJBa5SMYO6wVJUMCz/6hC5CZlc2s9fv5eskOflm5mxOZ2cSWDaVv0xj6Nq1M1bKnn6Jy4OgJrsuZK/7WoGb0qKdtyT1N5VpERETkHE1ds4dbxiyiadVIxtzYihJB/gV6fWstK3em8PXiHXyzbCf7j54gMjSQyxtVom+zyjStEokxJl/XSj6WwZDR81m5M4XXrmnKZY0qFmhW+SuVaxEREZHz8N0fO7lr3BLa1Yhi1PUJBAdceMHeefg4k5buYOLiHazPmUfdtU45+jarTJfa5QgKOL/F3I6kZTD0w4Us3nqIl65qTN+mMRecVfJ2pnIdUNhhRERERIqLXo0qcexEFg9+9Qd3jVvCW9c2I8D/3Mvv0ROZ/Lh8F1/nmkedUK00/+nbgMsaViQy9MLndUeEBPLxjS25+eNE7vtiGScysrmmZdULvq6cG5VrERERkTO4qkUVjpzI5KnvVvHghD948crG+PmdfbpGZlY2szbsZ+LiHfyyajdpGdlUKxvK3d1q0rdpZaqVDSvwrGE5S/Pd+skiHv56OelZ2QxpE1vg95HTU7kWEREROYub2seReiKTl39dR1hwAE/2rp/nfOg/51FPXLKDyUv/N496QPMY+jaNoVnV/M+jPl8hgf6MHNKcOz5bwhOTV5KWkcUtHeM9ek/5H5VrERERkXy4s2sNUk9k8t7MTYQFB/BQz9oni/Ku5ONMWrKTiUu2s27PUQL9DV3rlKNfs5gLmkd9voID/Hl7UDPuHb+U//6whhMZ2dzZrWahZvBVKtciIiIi+WCM4eFL6nD0RCbvzthISKAfMaVDmbhkO3M2OvOom1crzdN9GtCrUcHMo74Qgf5+vHZNU4IC/Hjp13WkZWZx/0W1PT5y7utUrkVERETyyRjDU70bkHoik1enrAegWtlQ7urqzKOOjSr4edQXwt/P8OKAxgQH+PPWtI0cT8/m8V51VbA9SOVaRERE5Bz4+RleuLIxzWPLUK9iBM2qli7SZdXPz/Dfvg0IDvBj9OzNnMjM4qneDfL1UKacO5VrERERkXMU6O/Hda2ruR0j34wx/OvyeoQE+vPujI2cyMzmuf6N8FfBLnAq1yIiIiI+wBjDQz1rExLox6tT1pOemc1LVzUm8DzW7ZbTU7kWERER8RHGGO7pXovgAH+e+2kNJzKzeGNgs0JfzcSb6TspIiIi4mNGdI7niV71+HnlHoZ/uoi0jCy3I3kNlWsRERERH3Rj+zj+07cBU9fs5eaPEzmWnul2pHzJzrZMX7uXYWMS+XH5Lrfj/I2mhYiIiIj4qEGtqhEc4M+DE5Zxw+iFjB7agvDgolkP9x05wZeLtjFuwVa2HTxO2bAgetQt73asvyma3z0RERERKRQDmscQHODHPeOXct0H8/loaEtKlQh0OxbgbCc/d9MBxs7fyi8rd5ORZWlTvSwP9azDRfUqFMm54h4t18aYnsBrgD8wylr77CnHSwGfAlVzsrxorf0w51gkMApoAFjgRmvtXE/mFREREfFFlzeuRFCAH3d8tphBo+bxyY2tKB3m3g6Th4+lM2HRdj5bsJVN+1IpVSKQIW1iGdiyKjXKhbuWKz+MtdYzFzbGH1gH9AC2AwuBgdbaVbnOeRQoZa19yBgTDawFKlhr040xHwOzrLWjjDFBQKi19vCZ7pmQkGATExM98vWIiIiIeLtpa/cy/JNFxJYN49ObWxEdEVxo97bWsnjrYcbO38J3f+wiPTObZlUjGdSqGpc1qkhIoH+hZTkbY8wia21CXsc8OXLdEthgrd2UE+JzoDewKtc5FogwzrZG4cBBINMYUxLoCNwAYK1NB9I9mFVERETE53WpXY4Pb2jB/7d358FSlWcex78/UGMBCiiLIJsSoqMxEAUuLonMJGOiYURTkeUyCq5xJjJkapzSqaQMlapYGsNUZkLGBeMWQTFmXGrKJM44auKMXBACStxlX8IiyjqAF57547zXajvdd+Geu9G/T1XX7X7Pe855+6m3Tz/37fecc/WDrzDxnpeZd80YTuh+dIvuc+fej3hy6QbmLljNm3/cSbdPHcGEkQOoHj2Y0/of26L7bgktmVyfCKwteL0OqCqqMxt4GtgAHANMjIiDkk4GtgD3SxoOLAZmRMTu4p1Iug64DmDQoEG5vwkzMzOzSnLOp3vx0NWjufL+RUy4+2XmXVvFgJ5dct/P8vXbmVuzmqeWbmDP/gOc3v9Ybr30DMaP6E/XdnpSZWO05CzwUvfTLJ6D8hVgKdAfGAHMTqPWRwBnAndGxOeB3cDNpXYSEfdExMiIGNm7d++cmm5mZmZWuUYNOY6Hr6niwz37mXDXy6za+ifjm4dkz/5aHlu0lvGzX2LcT17iid+vZ9zn+vHkt87lP6afR3XVoA6dWEPLjlyvAwYWvB5ANkJd6Ergtsgmfr8raSVwKrAGWBcRNane45RJrs3MzMwsfyMG9mDetWO4/Gc1H49gf7rPMYe0rbc37WRezRp+uWQdO/fWMqxPN2b+1WlceuaAdnNlkry0ZHK9CBgm6SRgPTAJqC6qswb4EvA7SX2BU4AVEbFV0lpJp0TEW6nO65iZmZlZq/nsid2Z/82zqZ5Tw8S7F/DwNVX8Wb/GzYPeV3uAXy//I3MXrGHhqm0c1bkTF55xAlOqBjNqSE+yU+4OPy12tRAASRcBPya7FN99EfEDSdcDRMRdkvoDDwD9yKaR3BYRD6d1R5Bdiu8oYAVwZUR8UN/+fLUQMzMzs/yt2LKL6jk17K09wM+vquKMAd3L1l25dTePLFzD44vXsW33fgYf34Xq0YP4xlkDOL5b6119pCXVd7WQFk2uW5uTazMzM7OWseb9PVTfu4Dtez7igatGc9bgnh8v++jAQf7r9U3MrVnDS+9upXMnccFpfamuGsS5Q3vRqdPhNUrt5NrMzMzMmm3Dh/9H9ZwFbN65j/umjWLgcV14dOEaHl20li0799G/+9FMHj2ICaMG0vfYlr2EX1tycm1mZmZmudi8Yy/V99aw5v091B48SJBdH3tK1SDGntKHzofZKHUpbXUTGTMzMzM7zPQ59mjmXzeG7z65nKG9uzFp9MAWuQ52R+Xk2szMzMya5Phun+LOvz6rrZvRLrXkTWTMzMzMzCqKk2szMzMzs5w4uTYzMzMzy4mTazMzMzOznDi5NjMzMzPLiZNrMzMzM7OcOLk2MzMzM8uJk2szMzMzs5w4uTYzMzMzy4mTazMzMzOznDi5NjMzMzPLiZNrMzMzM7OcOLk2MzMzM8uJk2szMzMzs5w4uTYzMzMzy4mTazMzMzOznDi5NjMzMzPLiZNrMzMzM7OcKCLaug25kbQFWN3W7eiAegFb27oRHZjj1zyOX/M4fs3j+DWfY9g8jl/ztFX8BkdE71ILDqvk2g6NpFciYmRbt6Ojcvyax/FrHseveRy/5nMMm8fxa572GD9PCzEzMzMzy4mTazMzMzOznDi5NoB72roBHZzj1zyOX/M4fs3j+DWfY9g8jl/ztLv4ec61mZmZmVlOPHJtZmZmZpYTJ9cVQtJASc9LekPSHyTNKFFnrKTtkpamxy1t0db2StIqSa+l2LxSYrkk/aukdyW9KunMtmhneyTplIJ+tVTSDknfLqrj/ldA0n2SNktaXlB2nKT/lPRO+tuzzLpflfRW6os3t16r248y8btD0pvp8/mEpB5l1q33s14JysRvpqT1BZ/Ri8qsW/H9D8rGcH5B/FZJWlpm3Yrug+Vylo5yDPS0kAohqR/QLyKWSDoGWAxcEhGvF9QZC9wYEePappXtm6RVwMiIKHk9zfRFMx24CKgC/iUiqlqvhR2DpM7AeqAqIlYXlI/F/e9jkr4I7AIeiojPprIfAtsi4rb0hdEzIm4qWq8z8Dbwl8A6YBEwufCzXgnKxO8C4L8jolbS7QDF8Uv1VlHPZ70SlInfTGBXRPyonvXc/5JSMSxaPgvYHhHfL7FsFRXcB8vlLMA0OsAx0CPXFSIiNkbEkvR8J/AGcGLbtuqwM57sIBoRsQDokQ4Q9klfAt4rTKztT0XEb4FtRcXjgQfT8wfJvmyKjQbejYgVEbEfeDStV1FKxS8ino2I2vRyATCg1RvWQZTpf43h/pfUF0NJAiYAj7RqozqIenKWDnEMdHJdgSQNAT4P1JRYfLakZZJ+Jen01m1ZuxfAs5IWS7quxPITgbUFr9fhf2BKmUT5LxT3v/r1jYiNkH35AH1K1HE/bJyrgF+VWdbQZ72S3ZCm1dxX5id597/G+QKwKSLeKbPcfTApylk6xDHQyXWFkdQN+CXw7YjYUbR4CdntPIcDPwGebOXmtXfnRsSZwIXAt9JPfoVUYh3Puyog6SjgYuAXJRa7/+XD/bABkr4D1AJzy1Rp6LNeqe4EhgIjgI3ArBJ13P8aZzL1j1q7D9JgzlJ2tRJlrdoHnVxXEElHknXSuRHx78XLI2JHROxKz58BjpTUq5Wb2W5FxIb0dzPwBNlPT4XWAQMLXg8ANrRO6zqMC4ElEbGpeIH7X6NsqptqlP5uLlHH/bAekqYC44ApUeako0Z81itSRGyKiAMRcRCYQ+m4uP81QNIRwNeB+eXquA+WzVk6xDHQyXWFSPO7fga8ERH/XKbOCakekkaT9Y/3W6+V7ZekrumkCiR1BS4AlhdVexq4QpkxZCeqbGzlprZ3ZUdr3P8a5Wlgano+FXiqRJ1FwDBJJ6VfCial9SqepK8CNwEXR8SeMnUa81mvSEXnkFxK6bi4/zXsy8CbEbGu1EL3wXpzlo5xDIwIPyrgAZxH9rPIq8DS9LgIuB64PtW5AfgDsIzsZJ9z2rrd7eUBnJzisizF6DupvDB+An4KvAe8Rnamd5u3vb08gC5kyXL3gjL3v/LxeoTsp/ePyEZirgaOB54D3kl/j0t1+wPPFKx7EdnZ8u/V9dVKe5SJ37tkczHrjoF3Fcev3Ge90h5l4vfzdGx7lSxZ6ef+17QYpvIH6o57BXXdBz8Zj3I5S4c4BvpSfGZmZmZmOfG0EDMzMzOznDi5NjMzMzPLiZNrMzMzM7OcOLk2MzMzM8uJk2szMzMzs5w4uTYzawJJIWlWwesbJc3MadsPSPpGHttqYD+XSXpD0vNF5UPS+5teUDZb0rQGtne9pCsaqDNN0uwyy3Y1oflmZu2ak2szs6bZB3y9vd09UlLnJlS/GvjbiPjzEss2AzPSzRcaJSLuioiHmrD/3KS73ZmZtRtOrs3MmqYWuAf4++IFxSPPdSOyksZKelHSY5LelnSbpCmSFkp6TdLQgs18WdLvUr1xaf3Oku6QtEjSq5K+WbDd5yXNI7u5R3F7JqftL5d0eyq7hewGDXdJuqPE+9tCdnOGqcULJA2V9GtJi1MbT03lMyXdmJ6PSm18ObW58M5y/dP670j6YdG2Z0laIuk5Sb1T2QhJC9L2npDUM5W/IOlWSS+S/SNwWXqPyyT9tsR7MjNrNU6uzcya7qfAFEndm7DOcGAGcAZwOfCZiBgN3AtML6g3BDgf+BpZAnw02Ujz9ogYBYwCrpV0Uqo/muwOZKcV7kxSf+B24C+AEcAoSZdExPeBV4ApEfGPZdp6G/APJUbD7wGmR8RZwI3Av5VY936yu8+dDRwoWjYCmJhiMFHSwFTeFVgSEWcCLwLfS+UPATdFxOfI/nn4XsG2ekTE+RExC7gF+EpEDAcuLvOezMxahZNrM7MmiogdZInf3zVhtUURsTEi9pHdkvfZVP4aWUJd57GIOBgR7wArgFOBC4ArJC0FashuATws1V8YEStL7G8U8EJEbImIWmAu8MVGvr+VwEKguq5MUjfgHOAXqR13A/0K15PUAzgmIv43Fc0r2vRzEbE9IvYCrwODU/lBYH56/jBwXvrHpUdEvJjKHyxq//yC5/8DPCDpWqAp02PMzHLnuWpmZofmx8ASspHaOrWkQQtJAgrnLe8reH6w4PVBPnksjqL9BCCyEePfFC6QNBbYXaZ9aqD9DbkVeByom2bRCfgwIkbUs05D+yyMwQHKfwcVx6CUj993RFwvqYpstH+ppBER8X4jtmFmljuPXJuZHYKI2AY8RjZlo84q4Kz0fDxw5CFs+jJJndI87JOBt4DfAH8j6UgASZ+R1LWB7dQA50vqlaZ3TCabctEoEfEm2ejyuPR6B7BS0mWpDZI0vGidD4CdksakokmN3F0noG6uejXwUkRsBz6Q9IVUfnm59ksaGhE1EXELsBUYWKqemVlr8Mi1mdmhmwXcUPB6DvCUpIVkJwWWG1Wuz1tkSWRfsrnLeyXdSzZ1ZEkaEd8CXFLfRiJio6R/Ap4nG1F+JiKeamJbfgD8vuD1FOBOSd8l+8fhUWBZ0TpXA3Mk7QZeALY3Yj+7gdMlLU71J6byqWTzzruQTZG5ssz6d0gaRvY+nyvRJjOzVqOIxvz6ZmZm1jBJ3SKi7iopNwP9ImJGGzfLzKzVeOTazMzy9LU0Yn4EsBqY1rbNMTNrXR65NjMzMzPLiU9oNDMzMzPLiZNrMzMzM7OcOLk2MzMzM8uJk2szMzMzs5w4uTYzMzMzy4mTazMzMzOznPw/bg+QaO0V+uUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best accuracy is 0.9745401436804295 with n = 1\n"
     ]
    }
   ],
   "source": [
    "# set range of n values\n",
    "neighbor = range(1, 21)\n",
    "train_acc = []\n",
    "test_acc = []\n",
    "\n",
    "# Loop through different values of n\n",
    "for i, n in enumerate(neighbor):\n",
    "    # n from 1 to 20\n",
    "    knn_model = KNeighborsClassifier(n_neighbors=n)\n",
    "    \n",
    "    # Fit with knn\n",
    "    knn_model.fit(X_train_smt,y_train_smt)\n",
    "    \n",
    "    # train accuracy\n",
    "    train_acc.append(knn_model.score(X_train_smt, y_train_smt))\n",
    "    \n",
    "    # test accuracy\n",
    "    test_acc.append(knn_model.score(X_test_smt, y_test_smt))\n",
    "\n",
    "# line plot\n",
    "plt.figure(figsize=[12, 8])\n",
    "plt.plot(neighbor, test_acc, label = 'Test Accuracy')\n",
    "plt.plot(neighbor, train_acc, label = 'Training Accuracy')\n",
    "plt.legend()\n",
    "plt.title('Value VS Accuracy')\n",
    "plt.xlabel('Number of Neighbors')\n",
    "plt.ylabel('Accuracy')\n",
    "#plt.xticks(neig)\n",
    "#plt.savefig('graph.png')\n",
    "plt.show()\n",
    "\n",
    "# print best score\n",
    "print(\"Best accuracy is {} with n = {}\".format(np.max(test_acc), 1+test_acc.index(np.max(test_acc))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "dd27b34d-fde7-4479-b8a3-0af3cc4e16ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.96      0.97     12667\n",
      "           1       0.96      0.99      0.97     12667\n",
      "\n",
      "    accuracy                           0.97     25334\n",
      "   macro avg       0.97      0.97      0.97     25334\n",
      "weighted avg       0.97      0.97      0.97     25334\n",
      "\n"
     ]
    }
   ],
   "source": [
    "knn_model_imp = KNeighborsClassifier(n_neighbors=1)\n",
    "knn_model_imp.fit(X_train_smt, y_train_smt)\n",
    "\n",
    "knn_preds_smt_imp = knn_model_imp.predict(X_test_smt)\n",
    "knn_preds_smt_imp = np.round(knn_preds_smt_imp)\n",
    "\n",
    "#Display the classification report of the model out of prediction using the test data\n",
    "print(classification_report(y_test_smt, knn_preds_smt_imp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8d364666-0a0c-4ff5-86d9-be399d7d90d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correct Non-injury predictions (True Negatives):  12161\n",
      "Incorrect Non-injury predictions (False Positives):  506\n",
      "Correct injury predictions (False Negatives):  164\n",
      "Incorrect injury predictions (True Positives):  12503\n",
      "Total injury predictions:  12667\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cm(y_test_smt, knn_preds_smt_imp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21d3a6d2-f275-42b4-ba61-b4c112d03065",
   "metadata": {},
   "source": [
    "# Saving Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "43aafafd-0f8d-4682-85fb-ea917cd4a562",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "file_name = \"knn_model_week.pkl\"\n",
    "\n",
    "# save\n",
    "pickle.dump(knn_model_imp, open(file_name, \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a14226f-6064-4385-9ed8-8d62d3fd2af1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}