diff --git "a/IOT ML Model.ipynb" "b/IOT ML Model.ipynb"
new file mode 100644--- /dev/null
+++ "b/IOT ML Model.ipynb"
@@ -0,0 +1,2272 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4729389b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np \n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt \n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "90bbdf2a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"IOT.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "38f7eaae",
+   "metadata": {},
+   "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>x axis</th>\n",
+       "      <th>y axis</th>\n",
+       "      <th>z axis</th>\n",
+       "      <th>activities</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2.70664</td>\n",
+       "      <td>2.31437</td>\n",
+       "      <td>10.19892</td>\n",
+       "      <td>eating</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.58896</td>\n",
+       "      <td>2.35360</td>\n",
+       "      <td>10.31660</td>\n",
+       "      <td>eating</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3.41271</td>\n",
+       "      <td>6.15858</td>\n",
+       "      <td>10.12046</td>\n",
+       "      <td>eating</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.27514</td>\n",
+       "      <td>1.60829</td>\n",
+       "      <td>9.76742</td>\n",
+       "      <td>eating</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2.19669</td>\n",
+       "      <td>2.78509</td>\n",
+       "      <td>9.33593</td>\n",
+       "      <td>eating</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    x axis   y axis    z axis activities\n",
+       "0  2.70664  2.31437  10.19892     eating\n",
+       "1  2.58896  2.35360  10.31660     eating\n",
+       "2  3.41271  6.15858  10.12046     eating\n",
+       "3  2.27514  1.60829   9.76742     eating\n",
+       "4  2.19669  2.78509   9.33593     eating"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "1f077578",
+   "metadata": {},
+   "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>x axis</th>\n",
+       "      <th>y axis</th>\n",
+       "      <th>z axis</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>325.000000</td>\n",
+       "      <td>325.00000</td>\n",
+       "      <td>325.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>-0.459736</td>\n",
+       "      <td>0.74784</td>\n",
+       "      <td>10.146895</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>2.875754</td>\n",
+       "      <td>3.22656</td>\n",
+       "      <td>1.096561</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>-8.080680</td>\n",
+       "      <td>-5.25636</td>\n",
+       "      <td>6.707750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>-2.745860</td>\n",
+       "      <td>-1.37293</td>\n",
+       "      <td>9.532060</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.196130</td>\n",
+       "      <td>-0.23536</td>\n",
+       "      <td>10.277370</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>1.647520</td>\n",
+       "      <td>2.78509</td>\n",
+       "      <td>10.944220</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>6.668520</td>\n",
+       "      <td>9.13980</td>\n",
+       "      <td>14.749200</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           x axis     y axis      z axis\n",
+       "count  325.000000  325.00000  325.000000\n",
+       "mean    -0.459736    0.74784   10.146895\n",
+       "std      2.875754    3.22656    1.096561\n",
+       "min     -8.080680   -5.25636    6.707750\n",
+       "25%     -2.745860   -1.37293    9.532060\n",
+       "50%      0.196130   -0.23536   10.277370\n",
+       "75%      1.647520    2.78509   10.944220\n",
+       "max      6.668520    9.13980   14.749200"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b702bebf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='activities', ylabel='count'>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_style('whitegrid')\n",
+    "sns.countplot(x='activities',data=df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1aef93f",
+   "metadata": {},
+   "source": [
+    "# Data Visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e6a97b88",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('IOT.csv')\n",
+    "plt.subplot(2,1,1)\n",
+    "plt.title('Complete dataset')\n",
+    "plt.rcParams[\"figure.figsize\"] = (8,8)\n",
+    "plt.plot(df['x axis'],label = 'aceleration in x direction')\n",
+    "plt.plot(df['y axis'],label = 'aceleration in y direction')\n",
+    "plt.plot(df['z axis'],label = 'aceleration in z direction')\n",
+    "plt.legend()\n",
+    "plt.ylabel('Acceleration values')\n",
+    "plt.show()\n",
+    "plt.subplot(2,1,2)\n",
+    "plt.title('Complete dataset')\n",
+    "plt.ylabel('Acceleration values')\n",
+    "plt.scatter(np.arange(325),df['x axis'],label = 'aceleration in x direction')\n",
+    "plt.scatter(np.arange(325),df['y axis'],label = 'aceleration in y direction')\n",
+    "plt.scatter(np.arange(325),df['z axis'],label = 'aceleration in z direction')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4cf677fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df1 = pd.read_csv('EAT.csv')\n",
+    "plt.rcParams[\"figure.figsize\"] = (8,8)\n",
+    "plt.subplot(2,1,1)\n",
+    "plt.title('Eating dataset')\n",
+    "plt.plot(df1['x axis'],label = 'aceleration in x direction')\n",
+    "plt.plot(df1['y axis'],label = 'aceleration in y direction')\n",
+    "plt.plot(df1['z axis'],label = 'aceleration in z direction')\n",
+    "plt.legend()\n",
+    "plt.ylabel('Acceleration values')\n",
+    "plt.show()\n",
+    "plt.subplot(2,1,2)\n",
+    "plt.title('Eating dataset')\n",
+    "plt.ylabel('Acceleration values')\n",
+    "plt.scatter(np.arange(105),df1['x axis'],label = 'aceleration in x direction')\n",
+    "plt.scatter(np.arange(105),df1['y axis'],label = 'aceleration in y direction')\n",
+    "plt.scatter(np.arange(105),df1['z axis'],label = 'aceleration in z direction')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "be189c7c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df2 = pd.read_csv('STAND.csv')\n",
+    "plt.rcParams[\"figure.figsize\"] = (8,8)\n",
+    "plt.subplot(2,1,1)\n",
+    "plt.title('Standing dataset')\n",
+    "plt.plot(df1['x axis'],label = 'aceleration in x direction')\n",
+    "plt.plot(df1['y axis'],label = 'aceleration in y direction')\n",
+    "plt.plot(df1['z axis'],label = 'aceleration in z direction')\n",
+    "plt.legend()\n",
+    "plt.ylabel('Acceleration values')\n",
+    "plt.show()\n",
+    "plt.subplot(2,1,2)\n",
+    "plt.title('Standing dataset')\n",
+    "plt.scatter(np.arange(105),df1['x axis'],label = 'aceleration in x direction')\n",
+    "plt.scatter(np.arange(105),df1['y axis'],label = 'aceleration in y direction')\n",
+    "plt.scatter(np.arange(105),df1['z axis'],label = 'aceleration in z direction')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a7fdd21e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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SFkqStNHy+yRJkr6SJGmLJEn/kCRpRDcLf9n9F/5Y/Ef21u7l+inX895l7/GjWT/i8/LPufyDy/nk5Cc+9XLKWsv4wWc/IFgTzLPnP8ujKx4lLiSOuzbeRWtPK5sqN7EgZQGh2tBBz1Wr1CxNW8r26u2jrsocTUpbhGJ5uAa7z9RHfXc9qRGpRAdHMyVuCjtP+14M5C0vHn2Rqo6qUb0QQr9CfDQMNnDG5LHbe9upaK9gctxkpsZPdelhl7aU8sWpLwDYXeNa1NXQNdhgK0a2vM17g202m6loq3BaN69Wqbk452K2V28ftXRda0+ryw5myWHJAGdkWNxvRlOSpHuAfwMhlpv+DvxGluVlgAr4hr/O7Ygr8q7gl3m/5Murv+SueXeRF5vHD2f+kLcueYu0iDTu3nw3j+551Cfnqmir4NbPb8WMmWfPf5aMyAxiQ2J5dMWj1HbWcseXd1DRXuEwHK6wJHUJLT0tHGs85pM1jTfae9up6xY75OEa7LquOkxmExPCJwCwcMJCDtUfoquva9jrHC71XfW8fOxlwnXhFDUV2anZfUVNZw0bKza63JAqBjs3JtfnaxgKxdicKXlsJWc9OW4yUxOmUtpaOuRn7bkjzxGsCebq/Ks51njMZSi3obuBUG0o4bpw620pYSloVJphedgtPS2097UPEpzZcknOJRjNRtadXOf1ebyl29CN3qh36WEHDLZ3lABX2vx/LqC05FkHnOvHcw9ictxkFsQuGJSbyYvN45WLXuEa6RpePvYybxa9OazzbKncwjUfXUNXXxfPnPeMnbcyK2kWv5j3Cw7VHwIY0mAvTl2MChVfVfs/zz4WUYzHwN+H4mD9QYqbiwfdXt1RDWBnsA1mA3tq9/hgpcPj6YNP02fq4y/L/wLAxsqNPj/HPw7+gx+v/zF/3fPXIUVJpS2lhGpDSQlP8fkahiIiKIKE0IQzprRLEZwVxBUwLX4aJrPJqhofSFVHFR+VfsQ387/JBVkXYDQb2Ve3b8jj13fXEx8SbydW1ag1JAcnDyutoBj7gSVdtuTG5FIQV+Cwda2/sXY5c+FhJ4WfuSFxvw3/kGX5XUmSsmxuUsmyrGzx2wHHle829PT0UFjo+IPuDXq93unxLo+6nOKYYv6484+Ymk3Mipnl0bFNZhPvV7/PW1VvMTF0Infl3YWp1kRhrf355jKXpfFLaextpLWilVacC6pyw3P5/PjnrAxa6dFafM1Q75u/2Fq/FYCssCzketnl+TsNndx58E4mhk3kdwW/s7tvX4O4AHbVdFHYWkiYKQydSse6o+tIbE/0zwvA9ftW2V3Ju8XvckHyBSS2JzIhZAIfFX7ELPMsn65jf9V+gtXBvHLsFU7VnuKH2T9Eqx781T9cdZgJQROQi9xTNfuSRG0ihTWFFBYWjsrnzZfsOLmDaF00DacaCOoLAmD90fWENQ0eBflc2XMAnBV0FiFNIWhVWpefy/LGcsIJH/QeJeoSKa4v9vq929EgdB199X0Udjg/xoKIBbxU/hLr969nQsgEr87lDaWdYuPeUd9BoWHo1xipjaSoqohCnev3Yjx93kZyWpft1j4SaHH1hODgYAoKCny2gMLCwiGP93Te09y47kYeP/k4r6x+hUmxk9w6bldfF7/a8is2Vm3k4pyLeXDxgw5z09bzFDwN4LIJxXk95/GvQ/9iQvYEl6UM/sTV++YPPu78mCB1EOflnsdzR54jJz+HYE2w08c/feBpOo2dVOormTx5st17u/ngZgCWzFhCiFZkaOZUzuF4z3G/vi5X79s/1v+DMF0Y9628j7iQOC7ovIBXC18lIzeDiKAIn6yhz9RH5Z5Kriu4jghdBE8eeBJVqIq/rvjroM9ozZEa5qfMH/G/NcCU5ilsqNhAQUHBqHzefEnN8RqmJU6zvoaU4hTqNfWDXlNDdwMb9mzgG5O+wbJZotvhrIpZlPSWDPn6u+VucuJzBj0m7VQaxY3Fgz7/7rL+wHpUqFg+czlBmiCnjzMkGHip/CXUCWoKMgavc1PFJp459AxPn/u0045k3tBc1QxHYVruNAqSh/58pB5PpS+oz63P0Vj7vO3du9fpfSMp/NovSdJKy++rgTEngQ7XhfPUOU8Rog3hzvV30tjtXuegl46+xMaKjdy74F4eWfrIkMYasNa4umJJ2hJMZtOYUjQ7o76rnmcPPcu31nyLL8u/dOs5pa2lfGftdxwKWEpbS8mMzmRSzCRMZtOQ4dLWnlZeOfYKYdow2vvarW1IFU53niYuJM5qrEGExeVm2e2/sa/ZV7uPDRUbuHnazcSFxAGiOYXBZGBr9VafnaestYw+Ux9SnMRtM2/jNwt/w+bKzdy96W67x3X0dlDbVTvi+WuFrKgsmvRNtPW2jcr5fUWvsZeSlhK7CVLT4qc5FJ69cuwVDGYDN0+72XrbgpQFFDUVDVnKqITEB5ISkkK3oZtG/eDP9F92/4XH9z0+5NrL28qZED5hSGMNrlXYO2t2cqjhEA/veHjI43iKqz7itpyptdgjabDvAn4nSdJ2IAgYk628UsJTeGLVEzR0N/DAtgdcCnX0Bj1vyG+wPH051xVc59PWjdPipxEVFOXX8q5PTn7CzZ/e7FbpiSP21OzhJ+t/wnnvnMfj+x+npKWEt2X3hgRsrNjIkcYjbK0abKBKWkrIjc4lOzobGFp49tLRl+js6+SX838JMCiPfbrz9KBGEAsnLATEyEhHf2O9Qc+26m1+6em+6/QuHtz2IImhiVxfcL319pmJM4kJjmFjxUafnUvJp06OFQbk25O/zR2z7mBT5SY7Vbby/irv90hj7SneOr7z2CdaTmAwG5gc32+wpyZMpby93M4IN+ubeVN+k/Mzz7cro5qfMh8zZqf6il5jL229bXYKcYWUYKE9GKgUN5lNfHD8A54/8jxVHVVO117RXmEtDxuK+NB41Cq1U4Ot3P5J2Sd8XPqxy+O5S0tPC4DLOmwQ3c4CBttDZFkuk2V5keX3YlmWV8iyvFiW5ZtlWR6z9UrTEqbx87k/Z3PlZt45PvS+4qPSj8QUoym+nWIEQkhyVupZbK3a6hfD0Wfs4297/8bumt1c9/F1/H3v39Eb9G4/f3v1dm757BYO1h/kxqk3svaKtVxbcC07a3a61bTgcL1oXjJQZNNt6Ka6o5qc6ByyorNQoXIqPGvWN/Na4WtckHUBF2VfBDg22BMi7HNt0xKmcc7Ec/jHwX9w/1f323Vv2le7j2+u+Sa3fX4b/zr4L9dvhJscrj/MrZ/dyvc/+z7dhm4eXvowYbr+vKZWrWV5+nI2V27GYDL45JzFzcUEqYPIis6y3nZV3lVoVBo+OPGB9TarQjx6dDzszOgzQyluKzhTmBo/FbBvoPLE/ifQG/TcNuM2u+fPSJxBsCbYaXmXEo1KDBuc404JsRjsAUrx8rZy6wS2F4+86HTt5e3lZEY6rsG2RavWEh8S79Rg13bWMidpDjMTZ/Lwzod9NjmrWd+MChVRQVEuH5sUlkSTvumMasYDX6PGKZ7yncnfYdGERfx191+paHOsvDSZTbx87GUmx0122ADFFyxNW0qjvtGh+nm4rC1dS01nDX9d/leumHQFLxx5gavXXO1Wt6XSllLu2ngX2dHZfHTFR/xi7i/IjMr0KKyrdBvbV2tvsMtayzBjJicmh1BtKKkRqU497BeOvkC3oZvbZ95OuC6c9Ih0u1aQZrOZ0x2nrQpxBbVKzd9X/p07Z93JR6UfccPHNyA3yTyy8xG++8l3MZgMLElbwj8O/oNt1dtcvhZX/GnXn7j242uRm2Tunnc3H135EWelnjXocSszVtLW28b+uv3DPicIAzIpdpKdyCwxLJGlaUtZU7LGujEoaS1Bp9aRHpnuk/N6SkZEBhqVZtwrxYuaigjThtnVMk9NsBjsBmGwCxsLeaf4Hb4z+TuDdDJBmiBmJc1iV80uh8d31DRFISEoQZR2DfCwjzSK6NmMxBm8d/w9hymo1p5WWntahyzpsiUpLGlIDzs1IpU/Lv0jBpOB3279rU8cjpaeFqKDo9GoNS4fmxImNi/K3PAzhYDBdoJapeYPS/6AVq3lvq/uc+jxfFX1FaWtpdw45UafTzFSUC7qvm6jajQZef7I8xTEFXBB1gU8dNZDPHPeM/SZ+rjls1us3q8jmvXN/OjLHxGkCeKpc56yE0i5G9at66qjtquWlPAUytrK7C4iAxt4ZEVnOTTYDd0NvFH0BhflXGRtpynFSXabm5aeFvRG/SCDDeJvfNvM23jqnKc43Xmab675Jv8p+g/XFlzLe5e9x99X/J3cmFzu23IftZ3eh9e2VG7htcLXuCrvKtZdtY4bp97oVEB3VupZ6NQ6n4TFzWYzcpNsl09VuHzS5dR111k3I6UtpWRGZTpUj48EOo2OtIi0M8LDluIk1Kr+S2tUUBSZUZkcbTyK2WzmkV2PEBMcw+2zbnd4jAUpCzjefNxhB8ahDLZWrSUtIm2Qh3204SghmhD+cNYfMJgNvHLslUHPVcrBnDVNGUhSWJK1T4ItJrOJuu46ksOSmRg1kbvn382O0zv4T9F/3DruULT0tLiswbZdHzCs7+1YJGCwhyAlPIXfLPwNB+sP8sKRFwbd//LRl0kKS+LCbN9PMVJIDEskIzLDGmrzFV+Uf0FZWxm3TL/FutlYnLqY1y9+nYTQBH68/scOm3j0Gnv52YafUddVx+OrHic1wj437G5YV8mZKzlcW4+ypKUEjUpjze3lROdQ1lo2aJf+0tGX6DX2cvvM/gtffmw+5e3l1hB3daelBjvCefnJsvRlvHHJG1yZdyUvXvgi9y64lzBdGGG6MP628m/oDXru3nw3fSbPB4Z09XXx8I6HyYnO4f6F99s1u3BEuC6cBRMWsKFiw7A779V319Pc00x+bP6g+1akryA2ONYaFi9tLR3xDmcDyYzKHNcetslscrpBmhI/hSMNR/j45Mfsr9vPT+f81GloV5k5vqdmcB57KIMNokXpQA/7aONRJsdNJicmh/Mzz+dN+c1BojbrlK4harBtceZhN+mbMJgMVoP5zbxvsjx9OX/Z/ReeP/L8sD7TLfoWlzXYCkrzlDOtFjtgsF1wUc5FXJh1IU8feJp3i9+1GqGipiJ21uzkuoLrhpzN6guyorJ82rbRbDbz78P/Jisqi3MmnmN3X1xIHE+d8xQ9xh7uXH8nnX2d1vsq2yv5+cafs69uH/+z9H+YkTjD4fHdCesebjiMVqXlyrwrCdGE2IXFT7aeJCMyw6pWzYnOQW/U26m/jSYja0rWsGriKjvRjhQrYTKbrH3IazpE/syRh21LRmQGvzvrd8xJnmN3e050Dg+d9RD76/bz511/ZnfNbuu/Q/WHaOhuGPIi9OSBJ6nurObBxQ+6VN8qnJ1+NhXtFcPu8GYVnDkwIDqNjktyL2FDxQZqOmuobK8cNYW4gmKwx8ogFE8pbyuny9Bll79WmBY/jdquWv68689MiZ/C5ZMud3qcqQlTCdWGOgyLN3Q3oEJlrSwYyMTIiVS0V1jfQ4PJQGFjIdMSpgFwy/Rb6Ozr5I2iN+zXbvHK3U2JJIUl0drTOkjzohhIxWCqVCr+uvyvnDvxXB7b+xh3bbrL7priCc09rvuIW9dnaZ5ypgnPAgbbDX6z6DdMSZjCQ9sf4or/XsEnZZ/w0tGXCNWGclXeVX4/f1Z0FqfaTvlMePZV1VcUNRVx87SbHeaDcmNy+duKv1HSUsKvNv+Kpt4mHt7xMJd+cCk7T+/kV/N/NWRUwZ2w7uGGw+TF5hEZFMn0xOnsre2vPSxpLbHz9hTlstJbHIRQrVHfyPlZ59sdV/EmlbC44mEPVIl7wurs1VwjXcOb8pvc/OnN1n/XfXwdZ791NgtfX8jlH1zOr7/6tZ2RLeko4bXC1/hW/rcGbQSGYkXGCkBEQYaD8h448rBBhMUNJgNPH3haaAZG2cPOjs6m29BNc1/zqK7DW5QNkhQnDbpPMZjNPc3ct+C+IfOwOrWOOclzHArP6rvriQ2JdZq6mBg5kY6+DmsJVGlrKXqjninxU6xrW56+nFcLX7Vrl1rRVkFyWLJd6eNQKB70wOlgSgg6OTzZeluYLoxHVzzKL+f9ki/Lv+Taj651u3uhLZ542JG6SEK1oQGD/XUkOjiaV1e/yv+e/b9o1Vru3nQ3a0vXcmXelT5tDOCMrKgs9Ea9z/Ix/z78b1LCU4YcmXhW2lnct+A+NlVu4vYDt/Nu8btcOelKPrriI66fcr3T54HrsK7JbOJow1GmJ0wHYE7SHORmmY7eDvqMfVS0Vdh5e4ohsf2Sf1b2GSGaEJan2bd3TYtMI0wbZhWene48Tag2dNh/p/sW3scrq1/hufOfs/57ctWT3LfgPq7Ov5qJURP5/NTnXPHfK3hw24NUtFfwr7J/ER8Sz8/m/syjc6WEpzArcdaw+zUXNRWRFpFGZFCkw/vzY/OZGj+V/5b8F8BurOZooERKqvXVo7oObylqKkKr0jIpZnDDpclxkwnRhHBpzqXMSprl8lgLUhZQ2lo6SCDW0N3gNBwOWEVjSlhcEbopGwaAW6ffSktPC7/Z+hvWl6+nvbed8vZytwVnYFOLPSCPrXjYyv0KKpWKm6bexLPnPUuzvpn7t9zv9rnoasJs6PXIw1apVCSHJZ9xOezRUZiMQ1QqFedMPIeV6Sv5+OTHfFb2Gd+d+t0RObe1Frnt5JC5WHfYV7uPfXX7uHfBvQ5n3try7cnfprW3lcMVh7l72d0efaHPTj+bh3c+zMnWk4MMQVlbGR19HdaLyJzkOZgOmThYf5CU8BQMZoNdPXBsSCwxwTFW79VoMvJF+RcsS19mVxYFQkiWF5tn9S4VhfhwRYFqldrlhbaxu5F/H/43b8pv8t7x9wB4bOVjTg3mUKzOXs0jux7hePNx8mLzvFmy03yqLZdPupyjO4+iVqmtU7NGC18bbL1Bz9/2/I0bp9zoVn3xcClqKiI3Jtdh6iNMF8bbl749SPPhjHnJ8wCh7Tgv8zzr7Y3djUMabEU0VtFewaykWRxpOEKELsIubTQraRbXTr6W90+8z+enPketUqNCNWSYfiBJoY6bp9R21aJRaRw2dgFYMGEBF+dczLvH38VsNrv+XprN8PQiuhbcQp+pz60abIXksORADvvrjkat4dLcS3ninCdGbEiCJ+MHu/q6+Mvuv1gb5Q/kvePvEaGL4Mq8Kx3eP5AfzPgBd+Tc4ZGxhv6wrqP5vorgTPGwZyXOQqPSsLd2r9OJUTnROVaDvb9uPw3dDZyfaR8OV8iPzUdulkVJV+fgki5/ER8az68W/IqPrviIq/Ov5uLkizk307sZN+dnnY9apfbay+7q6+JU2ymH4VlbVmevJkgdZKcZGC2SwpII1YZyuts3E8teLXyVN+Q3+Oehf/rkeEPR3tvOnto9zE6a7fQxWdFZbr/HBXEFBKmDOFh30O72+u76IQ12WkQaapXampM+0niEqfFT7VTrICJGX13zFc9f8Dy3Tr+V+SnznX6fHOFswEZtVy0JoQlDhvwnhE+g29A9ZDc3Kx210FFLs6Whjictmr3pdra9ejvvH3/fo+eMJAGDPQ5ICE0gXBfuVsnLlqotvHLsFd4qfmvQfT3GHr4s/5JVE1e5bJ86XFLCU5gSP8VhHvtQ/SHCtGFWLzpMF8bkuMnsq9tnFYsN9Payo7OtxvzzU58TrAl2Ou1MipVo722ntqvWYdMUfzMhYgIPLH6AmzK9b6aTEJrAgpQFfFLm3Zz24y3HMWNGih3aYEcHR/ODGT8YES2GK9QqNZlRmZzWD99gN+ubee7wc2hVWj45+YnDEqmBFDUV8cyhZ7yaQf/FqS/oMfZwSa7zNJMn6DQ6psRP4UD9AettZrPZZUg8SBPEhPAJnGo7Ra+xl+LmYqYkTHH62Pkp87lz9p08e/6znJU2uC+AM5zliGu7au3y145QogwDWwg7pLkMgJaedsC9LmcKyeHJ1HfVe6T9+efBf/K3vX9z+/EjTcBgjwNUKpXbSnGlm9IHJz4YdKH/qvIrOvo6rB3B/M3KjJUcrD84qF/3kYYjTEuYZrcLn5M8h8P1h5GbZNIi0gaFurOjs2npaaGxu5EvTn3B0rSlgx6jkB8nRFYH6w/SpG8aMQ/b11yUfREV7RUO+1C7wnYmsytum3kb35v2PY/P4Q8yozKp0jtvn+kuzxx6hi5DF39e/md6Tb3WFIUzdp3exXc/+S5P7H+Cnad3eny+NaVryIzKZEaC48oJb5iVNItjjces3bpae1oxmAwkhg49YS4jMoOKtgqKm4sxmAxMi5825OO9QaVSkRSWNEh0VtdVZ1WIO0P5Pjoz2B+XftzfvKlZeNbNBtE50RMtSlJYEgazwa3NGojOj0cbj9La00qzfmwKHwMGe5yQFZ3llod9tOEoGpWGivYKO+U1wLqydcSFxFn7aPubszPOxoyZ90/0h5h6jD3IzbKdCAZgbtJcek29bK7c7LCftSI8e//E+9R11w0ZvsuLETnfzZViStd4NdirJq5Cq9by8UnP+zHLTTKRQZHj7rXnxeRR11PndekPiPLDN+Q3uHzS5ZyfdT4LJyzkTflNp30Bvjj1BT/84odMCJ9ApC6SNaWezXo+3XGa3TW7uTjnYp82UJqZOJM+Ux/HGo8BrmuwFTKjMilvL7emngZ+13xFYmji4JB4Z61rgx0xtMF+ZNcj/POgJY2heNgGoWh3VyUO/aVl7obFi5qK6DH2AGO3RW7AYI8TsqKyON152q7n9UBMZhPHGo9xcc7FhOvC7QxlV18Xmyo2cV7meSPWzUqKlViVsYqnDzxtFYHJTTIGk8Gav1aYnSxyf72mXof9rBXh2stHXyZIHWTNkTsiIiiCtIg0tlSKoSljwmiVboQyz7rVRQdHszRtKZ+e/NTjkj65WUaKlfzWgc9fTI6bjBkzx5uPu/X494+/z4Zy+2qEx/c/jlal5Y6ZdwCizXBNZw2bKjYNev67xe9y16a7mBo/lRcvfJHzs87ny/Iv7UqeXPHRyY8AuCTbN+FwhZmJMwERKQJo0AuDHR/qWNClkBGZQVtvG9uqtxEbHOu3z//AHHFHbwddhq5BCvGBxAbHEqIJcdiYqa23jZaeFuRmS3vhFuFhtxjFdc9dlTjYGGw3leLK+wzu6YVGg4DBHicowxsGdjGyRVFfz0uex4VZF/L5qc+tnsr6ivXojXpWZ68eieUCImz24FkPEhkUyf1b7qfX2GvtHz5w1x8XEmf1rB2VF00In0CIJoTmnmaWpi112TFMipWstajuKnP9yhcPwcd3u3zYQC7Kvoi67rpB0ZKhMJqMFDcXuxUOH2soIjl3Ovt9Wf4lD2x7gJ9s+AnXfXwdO07v4GjjUdadXMcNU26w5lJXpq9kQvgEXi963fpcpXnQQ9sf4qzUs3jm/GeIDo7m0txL6TZ0uz0i1mw2s7ZkLbMSZ/lciZ4YlkhaRJrVkCjhZ1chcaVb2VdVXzE1YarfNm3JYSJHrGyWFOPtysNWqVSkhKdYeyTYorRIbehuEBEFi4fdbOxBo9J4VHGh/P3dVYofqD9AUlgSWrWWk23Da1rkLwIGe5yQHdVf2uUM25rLK/KuoNvQzadlnwJijGZyWPKQKlZ/EBcSx+/O+h1ys8zTB57mcMNhEkMTHX6p5ySJ5iKOGnioVWrrpmVgsxRHKHlstUrtcLLRiKNvhbpj0OVePk1hRfoKQrWhfHLyE7efU9FeQbeh22nDlLFMclgyEZqIfg/ro1/CBz8a9LiG7gZ+t+13FMQV8NDih6jvrufWz27llk9vISY4xi4nr1Fr+Lb0bXbV7OJE8wlMZhOP7nmU/9v3f1ycczGPr3rcKsKcnTSbtIg01paudWu9hU2FlLSWcGnupcN/8Q6YkTiDg3UHMZvNVi2Iq5C4UtHRZ+rzWzgcxIai19RrVXsrBtuVhw1iA+5oipftoKXipuL+kLipj+jg6EFq96GIC4lDq9K6HRI/WH+QOUlzmBg5MeBhBxgeypdwqA/S0cajhGpDyY7OZkbCDLKjs/ngxAe09rSytXorq7NXe/SB9xUrM1ZyVd5VPH/keTZXbmZ6wnSHu/4Lsi4gJzrHqaHJic5Bp9axIt15OFxBUUcnhSX5vXWsW+gtJSwVngmawnRhrExfyWenPnO7l7nSEnY8etgqlYrMsMz+iWtVe+CUfSrBbDbzu22/o7Ovk0eWPcJV+Vex9oq1/Gr+r4gKiuIXc38xyBO7Ku8qgjXBvFr4Kr/d+ltePvYy1xVcxx+X/tHu86FWqbk452J2nN4xSFDliDUla9CpdVyQdcHwX7wDZiXOoq67jprOGuq76wnRhLiMLqVHpqNCfL+U0Z7+wDpgw2IQHXU5c0ZqRCrVHc49bICihiPQJh7Tgmc12NC/WXfHw67prKGms4aZiTOFwDeQww4wHEK1oUwInzDkB+lIwxEK4grQqrWoVCqumHQF++v28+yhZzGYDH4dUuKKu+ffTWpEKu297UxPnO7wMYtTF/Pfy//rVP19x6w7eHLVk3bTwZyhGP0xkb82m0HfJn4/5fmoztXZq2npaWFH9Y4hH1fWWsY9m+7hgW0PMCF8wqj3BveWrLAsjjcfF+VVXU3QWgWm/hz+e8ffY2PlRn4292fW1xisCeb6Kdfz6Tc/5Yq8KwYdMyYkhtXZq3n3+Lt8WPIhd866k1/N/5XDDeylOZdiMptciv0MJgPrTq5jefpyv3U8nJnUn8dWSrpchbiDNcFWo+lPD3vggA1nXc4ckRKeQqO+0SryUihvLycxNJGU8BTkuoOAGTTBNGPyqAZbISksybqRMJvNPHXgKa768KpBokYl7TAraRZZ0VlUtFf4bCa9LwkY7HHEUKVdfaY+ipqKrLN3AS7NvRSNSsNLx14iMyqTKXGO6zFHgnBdOI8se4To4GgWpy726hiZUZlu14qmR6YTpg0bGwbboAfFOy7f7vHTl6QtITIo0mkTla6+Lh7a9hCX//dyNlZu5Nbpt/LOZe+MeiMUb8kKF614T7Wfgu4W8d51iItuRVsFf979ZxamLOS6gus8Ou6NU24kITSB3y76LbfNvM2p4cuKzmJ6wnTWlAytFt9evZ1GfSOX5vgnHA5i4xmiCeFA/QGXXc5syYzMJDks2e3He4O1n7hl5nRdVx2xwbFOR8faonwvB4bFK9oryIjMQIqV+tMiifm0YPLYwwaxqajtqsVoMvL7Hb/nnwf/SXFzMR+VfmT3uIP1BwnWBCPFSmRFZWEwGajqGH55oa8JGOxxhFLa5aiRRklLCT3GHruay4TQBJalLQOElzbaiuHZSbPZ8u0tfg3TKahVav68/M/cOv1Wv5/LJYp3HRID1fuh130FMogGF+dOPFcIBwdMRwJ48eiLvHf8Pa6ZfA0fX/kxP5nzE6ejG8cDmWGijabccAyUblitIlT68M6H0aq0/GHJHzxO7+TF5rH+6vV8S/qWy8deknMJcrNsN1t9IB+c+ICooCiWpS/zaB2eoFPrmJYwjYN1B6nvrndbj/GzuT/jD0v+4Ld1Qb/4zRoS76p1y7sG581TKtosBjtO4mR3HXqVCpKm0Kz2rMuZgqJkv3vz3bxT/A63Tr+V/Nh83il+x+5xB+sOMjV+KjqNzip+HYt57BEz2JIk6SRJel2SpG2SJG2RJGn8JdhGmayoLDr7OgcNBIB+wZmthw2ipCVUGzrkoI+RZCQ3DSszVjIpdvAghhGnx2Kw884Dk0HkZT1kdfZqOvs62VK1xe52g8nAu8ff5azUs7h3wb1+9ahGivSQdLRqLXL9of4bWyto7G5ke/V2rptyndfd69z9/K3OXo1WpWVtiWPxWUlLCZ+f+pxv5n/T75GMmYkzKWoqoqazxmmP7oFMS5jmdSTLXXQaHXEhcXYhcXfy14C1rbNtaVe3oZu67jomRk1kctxkTJgpCQnDHJNFi1pNbJDnaYeU8BS6Dd18fupz7pl/Dz+Z8xOuzr+awqZC6zWz19TLsaZj1vSDtRX0GMxjj6SHfRGglWX5LOD3wP+M4LnPCBSVtKMP0pHGI0QGRQ4aQH9W2lls/852u+b/AUYYxcOedB6gglOeh8Xnp8wnLiRuUFj8q6qvqOuq4+r8q32w0LGBVq0lNzrXvrSrpYLNlZsxY2ZVxiq/ryE2JJalaUtZW7rWYU32Pw7+g1Bt6IgMAJqZOBOD2UCXoWvMbciSwpKsBtsTDzslLAUVKjsPu7K9EsAaEgcoik6mXavDqFIR40Js54i82Dx0ah1/XPpHbphyAwAX51xMqDaUt4vfBuBk50kMJoO17j0mJMZu2NBYYiQNdjGglSRJDUQB7kleA1ixlnY5+CAdbTjK1HjHNZdDNeIPMAIoYd2YDEieCuWeC8+0ai0XZF3ApopNdPR2WG9/u/htEkITWJ7huK/6eEWKkyi2nZncWsnGio2khKeMmPr9u9O+S0N3A3/f+3e724ubi/m07FOuK7jOo85b3qJ4fsDYKFG0ITE0kfquenqNvTTpm1zWYCvoNDoSQxOFwW4ug5YKq0J8YuREoUExgxwWQavFSsVqHItRh+Ks1LPYce0Ou7K7yKBILsy6kI9PfkxHbwdyh8iVKwYbGLNK8ZEcr9kBZAFFQALgMkbb09NDYWGhzxag1+t9eryRxmQ2EaQOYt/JfUw39Sute02iyf+lKZf65fWN9/dttFDet8iKQtKB0upGYiInE1P2EfLRw+Bhx7kpqin8x/QfXtv5GssTltPQ08CWyi1ckXoFJ+QT/nkRo4BeryemL4b63lYa1GrizSoaq46wNbSBlQkrKSpy3VTFF4QRxiUpl/Cm/CY55hxmx4geBo8ef5RQTSiLdYtH7HuREpxCTU8N3Q3dFBocn3M0vqdBvUFUt1Wz/bCIGhlbjW6vIUYTw4m6E3TvuQazWsOeqd8AoOt0F3K9TF5vH0eCDRxvFKK2rtomCtW+eX3zgubxvuF9ntv+HEWtRSQHJ1NfVk894lwx5hj2N+0fc9e9kTTYPwc+lWX5PkmSMoD1kiRNl2V5sIrGQnBwMAUFBT5bQGFhoU+PNxpkn8imTdtm9zoO1R/CaDayQlpBQabvX9+Z8L6NBtb3rXs3ADkFsyCyF068Q0FML6Q5Lm9zxmTzZP5R/g8O9BzgtoLbeHL/kwDctvi2sdHNzUcUFhayYvIKXi5/meLgIM6KzOGYuYkeUw9XzriSgrSR+yw+mP8ghWsLebbiWd6f/T6nO0+za9cu7ph5B/Onzx+xdSxoXMCHJR8yK38WBfGOX/9ofE+lHokv678kJDkEgNm5s93+++TU5XCs4QihLcLD7Q1aRXRwtHhfu5uZ/IWetaE9qBNioQWmpGdRkO+b1zfZPJmXT7/MV21fUdNdw5KMJXbv3SzDLDbu20h6bvqQ3dUq2yuJC4lzWorqDXv3Ou9qOJIh8WZAGYDaBOiAQKzWQxwNAfF3k/8Aw0RpmhISBRMtZWle5LFVKhUXZF/AjuodNHQ38P7x91mStuSMMtYKSotSOUgHKTPYYGwhXBfOvJR5I7qOYE0wf1r2J1p6Wvj9jt/z9IGniQyK5Pop14/oOhZNWESQOoi08LQRPa8rlJy1MlHO3Rw2iCEgpztrRJ98s4ny+iNkRFjauzaXMbm3l05TH0f1Fq9X5Tv/UqVScXX+1RQ1FdHS12IXDgcbvdAQSvH23na+ueabvCm/6bN1uWIkDfZjwBxJkrYA64H7ZVn2fiTP15SsqCyqOqqsI/dAfFniQ+Ldzh8FGGH0bYAKgiIhagLEZnlVjw2it7jBbOCBrQ9Q131mic1siQ6OJkUTSlFQMKbEyWwK1rAkZcGo1JZLcRI/mvUjPj/1ORsrN/Ldqd/1qKe1L7gk5xI+/eanXpU2+RMlp644De6qxEHUYveZDTRp1KANoaKjioxIxWCfQuoRMqcdbSUAxPrQYANclHORtSXtrMRZdvcpeqGh8tiflH1CZ18n85JHbhM5YiFxWZY7ANcFkAGGJCs6C5PZREV7hbXL05GGI35t8h9gmPS0QXAkqC3744mL4fhnogOah38zKVYiOzqbLVVbSApNYnn6mSU2s2WyOpzikE6OBWmp12pZGTt6jX++N/V7bKncQllbmccNW3yBSqUacwpx6O92drjhMKHaUCJ0rrsQKqSGi8hQdUIu0RETOW08zkVR/R72pL4+1Co1h9vL0JrNhJsG958YDpFBkVyScwnrStaRF5tnd19GZAYalWawwLezUXxnw+L44MQH5EbnjmhkcyRz2AF8gLLz+7DkQ+JC4qhor+Bk60kuzBq9tqMBXKBvg2CbRiYTF8PB/0BDMSRKHh1KpVKxOms1Tx98mivyrhixUamjQb5ZyxaNmk86T6Exm1keNHoRJI1aw7PnP0tHX4fLXt5fJ5QQeE1nDVlRWR45DdZa7IRcYhKmYSw/TobWErloLiM0JJasqCxKW0tJNBpRDTFa2Ft+teBXrApbNeh7pNPoSItIG+xhv/wNqC+iNH8Vh3oK+eXcX4yoo+Ty2y5JUhoQDRiAXwFPyLJ8wM/rCuCErOgsdGodzx95HhC7xKnxUzk389xRXlkAp/S0ify1QqaSx97mscEGuDLvSoqbi7lm8jU+WuDYZLLBiFEFb5/ezGx9D9GdgxsGjSRBmiDiNHHOH9DTAWoN6EJHblGjTExwDDq1jj5Tn8cpudQeYYBPRyUSnpAJ5TCxzTJwpeUUxGYhxUqUtpYSYzJ53CHQHYKrDpB/Yg3MXDrovkF6od4uqD0CKdP4oPEg2nAdF3/5d4ieBhkjI0B0Z3v+OvAQ8CPgHUQu+mw/rinAEITrwnnjkjcwmAykRaT5behAAB+ib7X3sOMnQWQqrH8YDD0w73ugdd1/WSE5PJnHzn7MDwsdW0j6bgiBLkM3K/W90Fo52ksamje+A6Gx8K2XR3slI4ZKpSIpLImqjqr+/HXVXtjzAkxcBNkrRP8BB0TWHCHCZOJ0cBjBWmGKMmosJXvNZTBhJlKcxLqydcQaTeCggc2w2fM8SYffgPN/DpEpdndlRWWx8/ROTGaTaIPbIANm+pb+gg+P/h/LghNJCAkGm74I/sYd0ZkJ2AzEyLL8huX/AUaR/Nh8psRPCRjr8cJAD1ulgmvfhKQC+ORX8MRc2PcKGMfedKDRJL2zjTBLIcnZmhhrP/ExS2MJyJ9A79dLS6uExZPCksTm9K2bYP+r8N8fwf9Og8fnwFf/O/iJFbuYYDRTbeymvL2CUNQklG0DkxFaKoSHbakWiDEaoc/3IXHqLRuEk5sH3ZUVnUWPsae/G1vtMQC2qnpo1DdxxZwfwTWvQe7I+a/uGGwd8BdgsyRJZwPjcwRQgACjhb4NQgZsribMgO+uhRs+gPBE+PBO2P7EqCxvrKLubmKKLoZJMZOYGJnhGw/b0Ov6Md5gNkNXIxh7oGTD8I/X1QRf/gH6nLapGDMoBjs5LBnW3QttVfD9z+D2bXDBI+Lz/cWDUHPY/okVu5igi6Kmq4bK9krSg+NQtVfDyU1iQltslrWrXazJ5HuDbTJBw3Hxe+mmQXdbe4orpV11x0ATzPu124kLiWNp+uAwur9xx2B/DygB/gQkAjf5dUUBApxp9AwQndmSezbcuh6Spznc5X9t6dNDXxcPJ6/k8VWPQ8xE4XUNh9pj8MdUMTHN1/R1iTGqAPLQc7TdYtsTsOVRKFk//GP5GWVqV1JjGRx8HZb9EjIWiDa8i++Aa98AXRhsf7r/Sd0tUF/EhMg0qjuqKW8vZ6IyqGefJaUQk0lCaAKrs1Zzlr7P9yHxtkro68Ss0kDpRrHpssE6tUvJY9cV0piYx+bKLVyWexk6tc6363EDdwx2KdAL/AaoA9r8uqIAAc4kzGYRJgwZYtylSgWps6H6wKCLxteW7mYA0qIyRG1udDq0nwbjMEYQyB8Lz63S82lpLulqFD81wVD8iQjrekufHva9JH6v3DX8tfkZRWyWvONZmDALVtxj/4DQWJh9PRx+G9os4eWqPYCZCfGTaetto7ytnIw4CaIzoNAyIS02C4C/rPgLqwwa33vY9WJ0alvGOcJ4N5Xa3R0fEk+ELoLjzcdp622jrf4Y78fEYjAbuHzS5b5di5u4Y7D/BUwEzgMiga+PoiJAgOHS1y1GajrzsBVSZ0N309jP044UFoNNmEWVHZ0BmEW41VtOWsKeTW5OYWoug8PvuHwY0G+wp1wmfq/c7fHyrBx9TxwjOAoqvDTYlXt9E5p3g/kp85lBCNn6TrjyGdA48DwX3S6+B7ueEf+v2A0qNRMmzAHAYDaQETURclaITZVKIzZpCrpQ33vYlvx1c56l+VDpRru7VSoVOdE5vHv8XZb8ZwlL4nX8X88ppidMt/bAGGncMdi5siw/AHTLsrwGUeIVIEAAd1BmYQ/lYQOkzhI//RGuHY90N4mfoZZpWMrF29s8dl83lO8UvzeVuPecPS/Au7e4l/dWDPaMb4uhLt6Gxc1m2PkvSJBg1rVQtc+7qMKmP8Gan3q3Bg+Z2ljOayeLCTvnQedlinE5UHAJ7HleiPIqd0HSVFJtDF9GZAZkrxT/iU6zN/y6UN972A0yhCXQHT8NotL7N3Q2/Hbxb7ln/j3ck3s19zQ2c0/WN3h46cO+XYcHuGOwtZIkJQBIkhRJQCUeYLzwypWw9fHRXYMyC9uVoj9pqrjQVx/w+5LGBV2KwbZ42DGWOe/e5rErdgpBWEi0UHO7Q3cTYIaOWtePVdYbmw1ZS0FeN/TjnVG5B04fgAW3ijywoVvU/npKVxO0lPtHWT2Q6v2gUsO8m4d+3OIfg75FVERU7oGM+dbmKaAYbEvnPks43IouzA8etiw2GCqV8OxPbhZCNBsmx03mhik3cEPQBG5oa+eGWbeTE53j23V4gDsG+zfAVmAesAP4vV9XFCCALzAZxY65YuforsNdD1sXAklTAh62ghISVzzsKMvQC2897NKNYkM0/WoR6nanhE5ZQ3uN68cqHnZYHEgXiS52DV6MPN31jAiFz/wOpC8Qt3kTFte3AGZoHIGxq40nxIbKVS+BiQshfT5s+KP4XqQvIDE0Ea1Ki1atFcY7MhnyL4SclfbP9bWHbTb3G2wQ9eLdzVBzyPHj6wrFpjtqdAftuDTYsixvkmVZAnKBabIsf+7/ZQVwitEAz50Pn/56eAKcM52OOpEzc8c78ifKpC5XOWwQYfHTB1wLz4wGkVsdjrBprKOExJUcti4EwpOgtdy745VuEsYiZYbIkbqjFehuET873DDYnQ0i7xoSIwwOQLGHXnZHHRx9X4TCgyNEGiAy1TuDray9Xvb8uZ7ScFw0A3KHxXdCj+U7kbEAjVpDcngyaRFp/e1Br30Tlt1l/zxf57A76sSmJsFisHNWiJ8OwuKAMNjJUzzu/e9rXBpsSZI2SJK0HngT+NLye4Bh8MLWk9z99kHvntxWJbzG7U+Kvrbto2yQhoOhF45+4B/Do3hiI/X+tFVD+Y7Bt1s9bDekH6mzxS6/xYVROvE5vPt9KP7U83WOF7qbheLads5wdLp3HnZ3s9gIZa+AeEvOdIAi2PHzWsRPdz3ssDgx4CU2U5TpeRoW3/ui2EzMv1X8X6USLS89VYqbzRYPG+Hp+xOzWaQY4vNcPxag4FKIyYSweJHXBuYmz2V+iovWnr4OiTdYNjKKhx2ZAomTHdZjYzZD3VHR6GiUcSck/kPgduAO4DnA+XTtAG6xraSRTcX13j1Z8QzmflcIUp5Z4b2SdLQ58Bq8fZMYhOFrlPepo3ZkSqU+uQ9e//bg2/VuhsRBlMSA67B4nei4RNlXbi9v3NHVJMLhth5NTIZ3Oeyyr8BsEl5UnCcGWwmJn3ZjvY3CCClIq8UIVSW37QpjnxBk5Z4DCTbeasZCsYFzZ9Og0Nspokvgf4PdXgN9nf0bIVeoNXDls/CNp61/2/9Z+j88uPjBoZ/n65B4/QCDDWJDd2qbaBdsS/tpESlLGr1pcQruhMQVimRZfg2YOwLrOqNp1/fRrveyDaXiYSz+MdzyucgbvXCRY+9urKN4IJv+7PvwvlL+Y+zp9zb8hbFPNLjQt4gBELZ4EhJPngpqnfAGh8JSP8qpM9hgdzf3h8MVoi3dzjzdgJVuEh5a2jzhSenC3BOeKZ8bd6I0XU2DDbbZJMaoukPFLmEY5n7X/nZv8ti2n/d6PxtsJUfubkgcRC5b8nC6oC7M9wY7OAoiJ/TflrNSiPwGluQpG+TxYLAlSfqBzb8HAfcHngZwSLveQHefEYPRC8G94mFEp0HKdPjBRggKF7vz8URvpxACJU8XHsSB13x7fNvQaUedb489kPLt/aHvgTnznjZABUFufG20wSJP5koprvQ/rjncvyE40+hu7hecKURniAuqIvByl5ObIHMJaIOEVxeX47q0y9DbP9TBbQ/bZoMxYbZoyeluLbTStjNjgf3tE2aAJsgz8aQSyo/JFAbVn1qHRktrT08MtjcE+TgkXl8ECfn2EZysJULtPjAsXqsY7PEREp9g808PfMuvK/oaoHjXnT1efJFaK8SFQBnhFxormjUUfeSX8XN+o2SD8H4veFh4PpsfHRyKGg52BtvPeWzbXPLA0KUyC1vtzlcNERav3u/ci1T6HydNFR7ceIysuIMSErfFWovtQVi8rVqEhRVREQiD7crDtvVS3c5h23jYarX4W7pbklVzWHyvIwaMqNQGC22DJ41YlLVnLBDfsZZT7j/XUxpLQBvar+L3F74OiTcUi5y1LSHRkDoHjn9q//2rK4SIlMERn1HA6VVEkqR8SZLygf/Y/HufwPCPYdPRIwx2m96LMHBrhX0HIIBpVwlvwN3w21hAXifKJDKXwNn3i9e1/xXfHb+1sr9219/Cs+OficYLMNgbGzipyxWps8QF19lF1tL/mNnXi/D5mZrHdhQSV8Y0epLHVrylbBuDHZ8r3t+hSrsULzU42rVKXBn8YWuwAVKmCU/OnY1ozSEhVHOkQk6fL6Iu7m5olbVnLBQ//RkWbzwh3k93N6TeoojOfKFH6W4Wm/jE/MH3zb4eTh+019XUHRORrzHAUO/yv5z8+6e3J5Mk6T5JkrZLkrRXkqTve3scv9B8anD+0Q+YzWbaLYZaMdwe0VppadNoQ9YysTM/4qCNYl2hEEN1NnixWj9hMop+y3nniW5GuasgYxFs/pvvphO1VUGaRW7hTw+76aTYrc+5QfzfmYftLqmzxU9nYXHl4ps6S7y+U1s9We34wGwWZV2OQuLgmVL85CZhSJOn9d8WlytEWUOViCmCs6QCYYyHMpb6VjAbHRjs6eI8rkqrjH3CsKdMd3x/xkLhKZ92UiM8aD0t4me6RXnd4OL8w0Ex2P5GiSgafHB9UL5DAz1sgDk3iff70/vFNdNkFH+bMZC/hiEMtizLZzv6B3ioFhBIkrQSOAtYAqwAHE81Hw3MZnj2bDEhx8/0GEz0GcUu0WODbTYL72KgwVZrYOoVUPyZfU7TbIZ1vxLGcev/Dm/h7tJS4VpAVrkHuhqEMAeEV7Hq19Be3T/0YDj0dUNnvQgba4Ldq6P1FiWqMf1q0IYMPpenHnbSFOE5O1OKK/nrBEnk3KoPQE+7/WOMfe538xqDqIx6MPb2dzlTCI0FXbj7IXGzWXjYWcvsPUDFwDQOoRRXjF6S5aI+1KbP2jRloMGeIX4OHCs5kIZi8XqdGmxLXtvd8i7Fw47NErXr/lKKG/tEExp/56+hv7zPF2Fx5TvkqI2qWg2XPi6ct0/uE6/PoB8T+WtwT3R2myRJsiRJpZIknQS86JMHwAXAYURYfQ2w1svj+J6+LvGla/PBvF0X2IbB2z0NiXc1CdFNjIO9zrSrxC68yKaHccmXwsOISIFd//Z/aLhehsdnw1s3Dh26kj8WXacmndt/W/ZycWHd8rfhzyxuqxY/o9NF5MGforPiT8UFKz5XKJAHedit7tVgKyjCM2dKcUv/Y8LjRTrBbBwsSPr8QXh6cX9J2ThDozTWGOhhq1TCCLmbF24uE5vA7GX2t1vqf4cUnikedqLlQj1UHtuZwY7LEfldV+utsdzvzGBHpkD0RPeV4voWQCUiOwn5/guJN58SEQR3a7CHg+Jh93YO/1gNxeLvEj3R8f1Jk2HZL+DwW/1O3Fj3sG34EbASWIeYjX3My3MlINqbXo2o7X5NkqTRbRujoHzhlJ2pH7Et5/K4tEsJ4Q3MYYMIf8VM7A+Lm0zw+UNCKXrjf8UO/qvHvFu0O5jNsO4ewCwM8lDRCnmdMDahMfa3z/yO8GSGO7FKCZlGp4tWh/4Kifd2ihxy3gXi/xEODPZQs7CdMdSoTdt2ihkLRXetMpuweFs17P632Ly5O+RijKHptRhsRyIfabV4z90RgimelOLpKkQkC9X+UFEI5VqgeNjeGGy1RpTqufKwaw6JSNBQhi9jgfsGu7tFbBLVapGnbZD904vAm5Iub/G1h52QN3TefekvxN9j7wuAynH4fBTQuvGYalmWT0uSFCnL8kZLaZc3NAJFsiz3ArIkSXogETFj2yE9PT0UFhZ6ebrB6PV6h8cLaSoiG+hsqqbch+dzhFzfn4M5UVZJYbD7XlBk5Q7SgZNNBvQO1pk4YSXxRa9xfP82wmt2kFZ7mKrFf6Ct0cyErIuI2v0cJUmrMYQlebRmZ++bLRGVm8go3UjN7J8TVn+AyC8e4pQxke7EWXaP07WXM6lBpibjIpoHHDO8uYeJQNmxvXQneO9lR5/cRSpwokFPsjkMXUM5J/3wd42o2kyGsYdTIZPpKiwkjXCCG49TajmXXq/H0NlMW7eRWg/OH6NKYYK+hRN7vqQvwkZ9azaTX1tIW8Y51FiOlxU7GXPh55xKE8UbyXsfJc4o8q1Vh7fQ1hrio1c7cmg7hN6irK6dbuzft6CI+eSaTdR+8SRNk68b8jhxhVtIBuQmMHXYHyc7LBVDxSEqnPxdEiqKSQSOt2jIA2pOHKBZ5digRpceEZ+30830tdkfLyU4naiKLyk+dsxpW8uJpTtQR2VTVnzc6WuJ1WWQ0l5N8f6vMIbEO3yM8j1NrSsnVBNOSWEhscZoUvStFB/Y6vR53hInbxXvb6Nx0PvrayJqG8kAThYfRd8wvDK13OojdCfMoNrme+ro+hY64xdkbbid3og0Skr8qLT3AHcMdqskSZcDZkmSbkN4yt7wFfBTSZL+jigRC0cYcacEBwdTUOC73EFhYaHj450QTTbCVX0+PZ8jGrT1gAjZhscmUFDggWCjRdR0Zs9aLkKiA4n9ARS+TL5+PxQ9DxNmkXbenaSp1ZDyMDyxjryaNXDxox6t2en7ptDXDZ9+CxILSLnkN0LF/MxKsnb/Dm7bAhGJ/Y/d9iUAKcu/S8rAiTzRetgMWYkRMHkYf4e6NQBMmr0caj+GpsP++bue+CcERZK59BpR41uWD7W7rOcqPHYMbV8HcSmZxHly/mg97PkTk0LboMAmbdBeC71txOYvJlY5XuW5sP1pCnIzhad38kOYcQ0ceoO04G7S/Px59geV5V8AkDV5FiQPXH8BHJxDcs1Gkq9wMeawqBkiUpBmLhh83+EpUHPI+efipBZCosmbtQTWakkJM5Pi7LFNQscwafpC0QPclo5lUPoBBamRjlNZZjOsOQnSRUN/Ro3TYT/kpyX0e/0DsH5P95khKlH8rlsG+x8jP8YM2T7+LJxog9A4pFmLfHtcRwSdhq8gOz0FMofxOno6oKuGoEm3EK18T51d3woKQFVNkC7M73bBlr17nTcTdSckfgtwCrgPyAd+7M0iZFleC+wHdiFy2D+SZXlsTC9Q2gf6uyMW9mHwDo9D4pUiNOSsHjB5mhAjffE7EVY+/w/9YZ/YLFGysO8l70cUOmPbk6L5yeo/g0Zc6PjWyyIP+O737fNO8johBhtorEHkZkEIxoZDa4UQ22iDRZi6u2n4efGBmM1w/HPIXSmMNYjwe2+7VQSmMupFjtkT0RmI9ycoEkoHNN1Q1L4JNuUomUtF/+nKXSL/bzbDqt+IMrNxGxK3RJ2cfc5nXgO1h6H26NAHapCdz2eOzxU5WGcCSX2LGOShVlt0EC5EZ5pg0cBoIK6EZ+2nxfOd5a8VFB2EO41yulv6003K6/eHUryxRISWRwKd5b0dbvMUpdFLgpPPxUCW/xIW3zG8c/oQdwz2ywiP+LQsy3fJsrzR25PJsnyPLMvzZVmeK8vy2JlcoOSgRqBrlK3QzGOVeGu5yMs6mxijUsH0b4r85aTz+mfLKiz7pbigb/mbh6seak2V4nhTvmHfnCJlOlz0VyF6+2Mq/HUSPHuO6AqmqMMHEm4x2F3DLEFrrerP80dYwv+dPhae1R4RpWNK/hr62xxaxH2aPkuZoKc5bG0Q5J8vBIS2Xaqs/Y9tPKyJi0R3poNvwP5XYe5NwpOLzx2Z0Yp+wJrDHig6U5h2lRAtHnzD+UHMZiG2cmaw43LFZsrZoBXbTmuRKUN3O1NqsB19L5OmACrnwjNXgjOFkBjx051rlLLZANHQRBcumu34msYTI5O/hn7R2XBz2I6+Q+MIdwz2w8BFwH5Jkh6SJGnslGP5CsVg97T5fWSh4mFHBms9b5ziqAZ7ILOuFTXNF/zP4PtiMsQFff8r0Olhe0dnfP4AYIbzHYQnZ98A178L5zwgZgQHhYsL2MxrHB9LFyouLsNdW2ulaN0K4mILvheeyZ+In3nn9d9mPZcQKKkVg+2JSlxh8iVi42Lbycza/zil/7aQKOHFHfyPMNxLfyFuVwz2SAw+8TGanjbxOXA2Xzk8QVQYHH7b+fe1rVpEO5wabItS3JnwzNZLdSQmtGVgH3FbgiPEuZzNWVZuT57q/PjgvYetUgkv2NdjNnvaxSZmJGqwwXeis8rd4rMVlz38NY0C7gz/2CvL8p3A2cBkYHxu24fCtjexn71sxWCnRId4HhJvcdDlbCDR6fD9T51fqGZdK0oxBoZbvcFsFl7grOv6u4rZolKJC+uyu+Cyx+GmD+H2r4YOo4UnDM/DNpvtNzaKh+3rkrZj/xWDGWyNZ4Tld8vFXa2kAjz1sMHSVCYYimyqH+uLxN91oCeXtVT8nPe9/o1K/CTxWXZ3WtQYQtPb6ty7VpjxbWEwTm52fL9tvbojXI3ZHORhD2GwOxuGbluZMs15SLzmsKjkcLWpsxrslqEfp4zWVDxsEJ8ZX9diKxudEfewhxkSL1kvyvw0uuGvaRRwpw57mSRJzwGbgKPACG2pRhDbLmBK/aWfaNcbCA/SEBOm8ywk3tctDJkj4YonTJgtvIHjnw/vOCDeN0O3fU51uIQnDK8rm75FiN6sIXFLb2ZfetiNJSKHOvVy+9sV420Jn2qG42EHR0Lu2VC4tt9Lbih2bICmXin6Vi/9ef9t1jGS4y+PrelthTAXBltaLTZCh95yfL9ioJyFPsMThU7A2ftja/QiJ1h0EE66nTlqS2pLynRRE+6oLr72iOtwOPRv+lw5FH3dlqYzMf23JeSL9M3ABjvDwVrSNVI5bB+ExJtOig1a7irfrGkUcCck/jPgbWCmLMt/kGXZ/91FRhpbL8TPwrN2fR+RIToigrWe1WFba4uHabDVavGBLflS1GoPB6UufLibCFvCEoYnOlPeJ2UYQbjFw/Zl85RjH4ifU75hf3tItGjI0D4wJO6Fhw0iLN5aLnobW/sfOzDY6XPhtk323r7i+YzDPLamp21wl7OB6ELF+1/4oeOhN/VFwkNWdBEDUakg3skQELN5gIdt2fQ587JdGmyL8GygSK63U5zfHYOtCxGd9FwZbOX6ZethKxtqX+axG0sA1ciFlq0h8WE0TlGiimeywZZl+SpZlj+RZXn8JcPcpauxX6E8AiHxyBAtESEeethKMxFXIXF3mHSuMIrO8mq2VOwmtP6A4/usoz59aLDDEzwfn2hLq2UOtrImbZC4+PuyPenRD0SjmoF/C5VKXNwtF3avRWcK0kUiL1201qb/sZvq1thM0VRlHLYo1fS2uQ6Jg9BC9HaISXUDqbdMY3Im0AQRhXDkYfd2iLSR1WBbxISOojRGgzCSQxlspY/5QOFZ7THA7J7BBrEhdHV9Uhq+2HrYVqW4D8PijcfFd0zxfP2NNhhQDc/DLtkgqidGKozvB/w8YmWc0NXYn9Pyc7ez9p4+IkO0RIaMkocNkHuO+Hnii6EfZ+iFt24gZe9fnazJYrB96mHHi5C4t2Ip68bGpuFIZIrvPOymUrHRGehdW881wcbDtngD3nrYSvvRwrVD9z92hEYnjPZ49LB729wbZTjxLBFJUSIetijzjociPleoxAeW/A00egNSHfaPtaTQnHnyAFGpYtM4cINsFZxNG/wcR7hjsK0etk0aJi5HqOp9KTwbqaEfCiqVZWKXlwbbaBAVK7lnD72JG+MEDLYyGk/J+fk9JG4gIkRHZLDWs17iLRXC24pKHf4iIhJFztOVwT76PrSfJqij0nH4vKVC5AFtw2/DJTxRlKX1ejk5ra1KDM8It+nmFpHkXitLdzj6gfjp1GCnWL15TV+7+JsFRTh+rDtMvgTqC0W716H6HzsiftL4y2Gbze572Go15J0PpRvt88udDSLn7Kp0Jy5HzBQfOMpUMcLKGgaICe2wtiUdYoOhUlmEZwM87JrDYnynI8GmIzzxsG2/kxodxGb3b/qGi9k8sjXYCrpQ70Vn1fvFezeOw+HgnugsQ5KkeyRJekD5NxILGzGU0XjxljIPf3vYSkg8WEuPwUSvwc08cmul8N58pW6cdK7oTezs9ZrNsF30A1cbexyHlFsrhHftyx2r4ql4m8durRSbGts+wb4cAHLsv2KspbOLrE0JkLq3Q4jHhvP+FFwifhZ/4rr/8UDicsVEqoHRik1/hXfG1nRbK/pWVGaj6xy2Qv4FYnN3alv/bdZohAsPOyZT/BxosAfmgcPihYc6pMF20fYzZYaYq2w7g1sRnLn7+fDEwx7Ypz9jgXiPfFG22lkvSmBHOrQcNAwPu2Q9oIKclb5c0Yjjzrf/bSAKqLX5d+agfOGi0kETNCKisyhLSByg0908dqsbJV2eMOlcsVE5ucnx/Sc3Cw9gyuXi/00nBz/G0ajP4WLtduZlHttRrbrSqWq4NclNJ8UULeU9cURkijAgPe0iJB7shULcluj0/hnZ7obDFeJzhUjH1tCYzbDnOTjyrt8rIrxCWZM7IXEQzYE0wf1jTsH95hhKKmdg57+BHrZa7bwW212DnTxNjGlUauMbTggRWoqb4XDw3sMG4VnqW5zPWfcEJc0SN4IhcbCExL30sEs3iBny7n6uxijuGOx2WZZ/I8vyv5R/fl/VSKIoxMMTxIfczx52m94gVOIhwlN2O4/d6mPjmD5fGBNnYfHtTwnjufJe8X9H9aqt5b7NX0N/j3Rva7Ftu5wpRCSLMPtwN2PH/it+OguHg123M01fh/f5a1smW7xsbww22Oexaw5ZcrFmMfVqrNFt+T66ExIH0Ywne5mIQCjUyyINEZXm/HkAkalCmKfoQ6xraLGsIcbmscmOc9jK59Slh20Rlq39GTw2DZ6cK4xP5llDP88Wb3PY0O9Zlqx3/3zOUMouFfX8SKEL9c7D1reJaOI4D4eDewb7iCRJ10iCfEmSfFh0OwawfuHi3PtCDIMeg5Feg4nIYBESByFCc4nJ5NgQDQeNVvTBPv7FYM+zXobjn8KCWyE+D7NKA80DPGx9m3ivfO1hh1sGhXhTi20yihx29IALtbUD2RBh8cYSeGYlfHyP8x7Vxz6A1DlCzOUMawnQaVHW5U0N9kCmXiFKejI8HLKghCxt89jFFk9UGyJyv2MNq3frgSeUd4HYUDZYNiYNshCcuQo1a7QifTJwnOtADxvsxIR2KB62q/UmSuKzXV8EaXPg4r/Bj/cNvfkbiHJ9GipS1N0iNuJqjf3t4QkiLO+LhklKPbe31Q/e4q3orGyLiCbmnO37NY0w7kzrmmX5p2AGxv9WRcEa0koQO2o/hsSVzmaRIVqiLCFxtzzsjlox4MHX3uykc4XXWFcIyTYD2rc/JS7o828BjZa+8AkEDQyJ+0MhDv0hcW887I5a8cUc5GEr3c5qHHupRgO8f5vYqNQehV3/Ennqmd/pv2j3dgrhyrm/G3oNVg+7Bk1fJwSnDP14d4jPhXsr+oeMuEtUuggX23rYxz8Vm46weCh1kg4ZTbocGEtX5J8P6+4Wry1hkvg7untxjs5wHBLXBPXX/oLY9J3ayiC6moQ3r3MxxlSjg58fFYJIT3QItoREi+tAX7fI5zpC3wKhTjaJuatg+5PC4AZHercGEPlr8E30yBN0od6lcUrWi3akGQ6mto0z3KnDPhv4JvAr4GpZls8cYw32OSg/h8StfcRDdERYDLZb7UmtpUp+MNhgHxbvqBdDFWZeYxWA9UakDQ6JW2uwPVAtu0NQmLhQeuNhW5umOAiJg3MPe+tjosfwZU/AXTJc8Igw0B//Ukwbe/f7sOYnQng09Yqh12BTAqT2VUgcPDfWIAxDXI4QnoF4Tyv3CKFWzgpRS9tW7Zv1+QpPc9ggJr8lTobiT4UH2n7ateBMISZjsIetdDmz9dAjUsTa+vT2j+1qdH+t2mDvjTW410+8u8V51UbuKlFfPjAVYjTAOze7rhpRUDq2BQ3D6HuDtyHxkg2ifa+z3vTjCJcetiRJVyMGgBQC0yRJekiW5Vf9vrKRwnY0XmhM//g1P9Bu42ErIXG3mqf4y2BHpYpRjoffEjv35jLhRRp7YNGPrA/rjUiHCkvoXLmI+cvDBku3s2EYbEc5bHDc+KL6AGz8k5gANf2b4rbFd8Ci28X7YTt+MWTA4A1HBEeJDUdHrTDYIx02HEh8bn+HqxNfAGZRCqVUG5RuglnfGbXlDULJYXtaKph3Puz4B1RZZgm7O40pOkNsWowGESIH+y5nCraDXWxHw7rqcuZLbA121ATHj9G3DFaIK0xcJEoDSzbYT8w79oEQIUak9G/ih0IZzqJxJ0DrQ3Rh9qN63aG5TKSEFtzqlyWNNO5s934BzJVl+XJgNvBTv65opLEdjed3D1tc/CNDdERaRWdu5LCt3qwPc9gKky8SavAvfy9mVWtDxOQtGw+lLyINelrtw1Et5SJsaFvv7CvC470LiVsN9oAcdki02JQNLE3r64b3fiByixc9an+fytJ2MTG//58rY608LyIZ2qpFSHykw4YDic8V+gOTUXig4UmiBj9pqiUsvnF01zeQriaMugjPjUH+BWLTudOiiXW3v31MhkijtNtEGmynXSkMGJ3av16bLon+ZrgetjYYspbYC8/MZtjyd8txW9xbh751dD7X3njYlXvEz6xlvl/PKODOt8Iky3IHgCzL7ZIk6V09YVxhOxovNEZ8GE2m4YWunNBm42ErZV3tbnnYleLL6o8vyfJ7xOSjqFQRZXBAb4Rlo9B0sj/8p5SZ+eF9IjzRu7rp1krh0Q4UeilGdOAxv/y9ECjd8L5vyz0iJ0BTqagnHnUPe5IYBtFcJvrHT760/2+WvVyU9dlGTkab7maMQVFoXD/SnoyFQmxV/InYnNl6wUOhRK1aKvpr67ubBzcoshET2tHV6HwimK9xZyb2UB42iLD4p/dbXm+G2MTVWUSW7jorPe2j87n2RnSmVAEpUbZxjjtX21JJkv4mSdI3JEn6GzDOWie5wDYHFRINmMUcXT/Q72FrCdaq0apV7uewfR0OV9AGiYYcTow12Bpsmzy2P2qwFbwNibdVOS/liUy2D4nXHoUdT8P8W31f7hGZ0h+GHm0PW6mVPfC6uNDnn99/X85KYYB8ORRiuOhbMHqTG9XoYJLl75iQN1gl7QzFSNvmsfUtDkLi/WJCO4aahe1rhuthQ/9nvXSDxbt+VOhQMha6XyHT0zY80Zq3KHXYnvRT6LG8ptH+HvoIdwz294BS4DyEsT4zkgEKtjko5YPup7C4rehMpVK510+8t1N4R/4yjm7QF27xNmxLu5QuZ/5ACYl79MXsEPlLZ55VRLJ9OPOrx4S69+z7h7VUh0Sm9E8VGgseNsCe54VC2VY9nb1C/BxLYXF9KyZvxUx5F4ifntSrK2kmW6W4I6MXGifeP9u0Sp9eNMkZqWYcLmZiqwx6oT8ZysNOnCw2HyXrhfiscjcs+YnYJLsdEm8bvZC42WivK3GFvk2k+c4AwRkMYbAlSZpn+XUVcBz4EDgBDKuYTZKkJEmSKiRJclMV4mc6G+xD4uC30i5b0RlARIjWsehsxz/g3+fCX/Pgj6midjMuxy9rcgezNkQ0mVBKu/r0wlv1tUJcISxBdIXyRGCy4Y/CW1z6M8f3R9h42E2lQmQz72b/XGxtc92+7LPuDRFJYmPS3QSZi+0vtHHZwsN01u1uNOhuwajz0hjknSd0Fe5OvwJhBMIT+0fFGg3CgxzoYavVlk2fjcF2t8uZr3AxE1vTZ4kMDvWZU6nEpq10I2z+q9A0zL5eXPvcDom3jV5IHDzrdqZv9U0vhDHCUDnsc4A9wEAJqRn4bPDDXSNJkg74FzCMGWk+ZOBoPG897PZa0Xd71QNDlt909PQRolOj04h9UkSwzrGHvfVxUUIkXSj6HcdmiYvRaBKX0x8Sb7OMsPSbh21Tix3sxuCMqr2w8x/CAE900lwkIlkYLUMvbP0/4S0t/pHjxw6XSBsF72iH4lQqITw7fbDfA7UlZ6WoxTcZ3Q8j+xN9C8YoL3PC4Qlw+zbPxZm2tdiKMXTkpUam2OewR9pgu5iJrVZSeUN52CDC4gdfFxu1c38nNi2eNI3qaR89DxtEHtvVa1QYrc2Fn3DqYcuy/GfLr1tlWf6e8g84MIzzPQr8ExgbxZ/Wms9hethH3oVtT0D59iEf1m5pS6ogQuIDwjtGgwi7zbxG1AUv/6UoNxrtXWJcVn9IvMXijfgrTO9JtzNjH3z4U+EpnPuQ88cpzVNOHxT53NnXu6f69gZbgctYuFgoeex8BwY7e4W4UJ8+MKJLcspwQuIg8teezmi2rcV21OVMITKlf946jLzBhiENq6ZXaWgSM/QxlDalIdFik6s8p7fdfjiJM/TjycMepfC9n3DqYUuS9B3gMuBsSZIUVY4amA487umJJEn6LlAvy/KnkiTd58VafY/yhQsfpoetqCyr9oqGFE5QJnUpRAZrqWkbILrvqBEj/3wxRtOXxOWIkHJPh39rsMFmAIgbBnv7U1B7GL71ytCbGsU4f/6A8CaX/GT463R6rjHkYQNMvVyIshxNV7LNY6fNHclVDaZPDwa99yFxb4m2qKXN5sGTumyZuBiK1sL+V8WGb8wZbDc97IhEmHOTGIahfD6tzkpr//XQEUaD0GeMisG28bDd5WsUEv8EOA3EI8LYACa8V4nfDJglSToX0er0ZUmSLpNl2emg4p6eHgoLC7083WD0er3d8ULr9pMFnGropKuwEHVfJxJQW36cpjD3z5t1ah+hQFvRRqoSVjt93OnGFnRmk3UNxp4umtoHrKnhMFlAeZuJTh++9uGg1+up7AoiHSjdt57Iiv0kqNQUVbdDje/XqOtoYRJQfeIwrSbnfbt1HZXkbPgjnWkrqFTlwRDvV0hjF9kA5dtoyVzN6Zpuv6wdsH6OAIrKTmPW+a8/vVuo8qEgH4ocz0POjs7FcPQzKhIuGuGF2aPtbiAP6FGF+PR774pYfRApBj3FB7YS0iwzEThZ24reOGAN0SuZmDyP0LW/oEwfRVj9MVKA4qpGjI0js95McxCmxmoqHLw/YZ2ihOlEVSN9bS7Wk3e7+Gk5TlRTJ2nAiaN76Yt0vhFX97QiATWt3TSP8PUpvKaBiUDZ8WN0N7uXvslpq6dHHU3VEGsdaBfGMk4NtizLzcBGYKMkSRMAHaACMvEipC3L8nLld0mSNgI/HMpYAwQHB1NQUODpqZxSWFhofzyzKGfJnDwbUgrEDvt9DclRQSS7e16TEd4VoeKotuNEDfE80/omkmK11jWkywYO1dXYr+mouKhOnLoIkn332odDYWEh6dOXwXbIiQZquiFyAgVTPRD3eEJPBnwEqdE6Up29n2YzvPpr0AQR+e1/UuAqItEaBZbOizEXP0RMkh81j2YzrAnDbOhh8vQ5Y6fG2RlHZ0Plbp9+17zCMhZTHR43smtRzYf9kJ8YChoRjs8umC36kg8k8z/wz2Xk7H4I8i8EIH/GwpHr+rU3BbqaHL4/NcVvAjBp2jzPxZTqU7ATJqXFQ9oQ731zGQApE/NIGenPS2gTbIastCTIcfPcH+kJTkwb8ro8yC6MMnv37nV6n8uyLkmSngO+BLYgRGiP+Wxlo83AkJZKJcInnoTEm8vA0A3J00W3pCF6M7fr++xC4hHBusF12EqObMyFxLPFz+aT/q3BBlETrg0ZutuZvE40Ajn7fvfeq/BEIeSbfAn401iD+BxFpmDShY99Yw1C1Nha6V7+0p9YvndG3QjX+CqpndZyx6M1bYlIgqtfEN/7nf8Uue6RbNHpVg7bixCw8npdXfuUSV2jLTpzF33bGRUSd6cOeyYwFfgUKACG3elMluWVsiw7js+NJI5G43k6sUsZxTjnRvGzyvnuqF1vIDLYXnTWazTRYzD2P6itWvTpHe1yoIGERIuNTVOpf+Zg26JSCQM7IIf9zOYSvvXP7SLX+el9oqbU3R7B2iC49i24ZIT2m5ETMOqcN6MZU8RmivrWtkrXj/Unlu+dMciNygBfYtvtTBGdDfX9yzwLznlAvGcjmb8G1zns4Cjv1P4uarytKIM/RqNxitLcyV3RmaFXOFPBXy+D3SjLshkIl2XZi/ZTYxhHo/E87SdeexRQCSW3WuvaYNuKzhyN2GyrEh7jWPTMYrPF3Oi2av83cgmLH2Sw95e3sKusie7N/yc8nAv/1D/Ewh0mndOvFvc3OSvpShplEZe7KM1mLOHOUcNiiExBI+y9hcYIQ9daIQxWUKRrr/msn8D0bwkh2kgSEi1KlRw0FRLT4WK8PK7lea5Ku5TRmuNBdDZaY0D9iDuxnL2SJP0SqJYk6Q3Aw5qJMUxXw+Bcj6cedt1RUecaFgfJ05wa7D6jie4+o3WsJtA/sUtvICHC0olHMdhjkbhsKFwrRvT508MGUVM7ICTe2NFLCo0EbXsMCi6F3DE8kH7FPZxOKiRmtNfhDlaDfWpUlzFqIXHor8UOjXFvFrdaDVc96/dlDSIkWvSGN+gHla9petucz8J2hbshcf0wwu7DxVrW5abBVjYfZ1BI3B2D/RJCZNYNrAZ2+XVFI4lNW9KvjjcwKSmClJDo/jpjd6g9BslTxe/p8+Dgmw6Hh3T29LclVXA4YrOtur/UZqwRlyNCTOC/LmcKYQlQX2x3U0NHD/frXsdsNsH5/+Pf83+diEoT0aFR97BbALzrJT5cbGuxvTV6I4FtP/FBBrsdImK8O642RHSJc+WsjAkP282Q+Giu1U+4ExJ/TpbldlmWDbIsr5Fl2cFQ4XGKxWDXteu56YVdPPqZ7FlIvLdT5HQVg502VzQfcDBTe2BbUvG7MN5tSvMUo0G0Phw4HnKsEJvd//tIeNid9XY3ZXbs5zLNdj6J/rbIuwbwDWqN8DBH3WC3Cv2GeoTnLEO/h+1oFvZYYogBIOredvc7gA1EGS/sdkh8FDZVWg9D4l9TD7tTkqTHABlRh40sy8/4dVUjRVcjJOTz4YFqjCYz2040YJ4Xg0rf4t7IwfoiwGxvsEHMYB0wgEAxylEOcthWpXhnnRCyjNmQuE0/c3/M5rYlPEF4872dEBROj8HIdcb/UquO4e9dF3GJf8/+9SM2C1rGQEjcW4MzXGIyxGSnllOQsWB01uAOQxhsTV/78MSq7vQT17cJT9xW9zNSqNUiEuCuh20J3++tNTI3y3/LGknc8bC3AS1AMjDB8u/MoKsJwhJ4f38VKhVUt+ppMoWJHK07gydqj4mfSVPEz/g8IVhxkMe2ndSlMCgkbi3pGqMetlLaFRY/5DhOnzCg21lzUwPL1IfZFLSC0lYTTZ29/j3/143YzDHgYbeMnjekiCjbT4+9Cg1bhhCHiRx2zDCOHe1eSHw0Q8y6UI9FZz/9oJTjtf4ZmTzSuDTYsiz/DtiK6Hr2PvDnoZ8xTrCMxmswRXC0uo2bFmcBUNxmKYlwR3hWe1QIIZRQsVoNabMHG+x9r5C9XnQWGlIl3jZGa7AVwhOFqn4kRn3aDgABDMc+IlhloC3nYgAOV41y97AzjdgsEXHqGcULm7519IxljI0mYzyGxA09qI09w3v/3AmJj3Zvbl0Y9LrrYYvX0mYOo7i2w4+LGjncaZzyR+BGxBzs2cAL/l7UiNAt2vjtrlehUau4c9Uk0mJCOdxoCYO7k8euOwpJBfYCs7S5UHtEbAgA6grho7tIrvyUFBrtPeyQAR620nRlrHrYKhWkzYEJM/x/LquHLWrlg4+vpdocx6TZQpB35GtksCubu7jmme38Y2MJ3b1G10/whhiLJmA0leKjGRK33YSO1hrcwVm9tKuGL+7gTki8p30MeNiehcQ7CKWk/mtisIGlsizfCHTIsvwSkO3qCeMCS9OUTRUmVuQnkhARzFm58eypMYn7Xe00zWbhYSvhcIW0uSKkXnNYFO6/19/YY7b6hJ2HHazVEKRR23vY2tBBO/z73jvEfw9UMSa47h24+O/+P4/iYXfWg76NuNNbWGdcSE5iFFnxYRyu/HoY7B6DkR+9to89Zc38+ZMiVvx1A6/sOEWvweTbE42FWuzRDImHJ4LGUlo5lj1sZzOxhxpa4i5uh8RHQXCm4EFI3NjdQoc5FBPqr5XB1kqSFIIY3KEB/LTFH2EsudHSzmCumC082iWTEqjusXxpXX1wO+qE0U+eZn972jzxs2oPbPqTMNxX/guDKohZAww2DBix6aBpSmtXH//ZVcF97x2mqmUMjBHXBnvWrMRbbEPixZ+gMfXykXEhCZFBTEuL/tqExP/4USEHK1t58trZvHXbYjLjw/jtB0c49++bkGt8GL5WDPZoCs9GMySuVvcLKcdyDtvZTGxfeNhKSNxBUxYro93qUxfutofd3tJIK2Fo1KqvlcF+DNgLTAN2Ak/7dUUjhcXD1gfFct4UMb/4rEnxtGIRUw0MDdXLor5aQRmpmTzAw46aAJGpsO8V+OoxMYZv6hXUhEvM1ZwgWGvfNjAiRGsfEh+Qvz5aLb6YXb1GHvjgCOahvkxnEkERwuPpbICjH9CmS6RIKxEWpGVGejRVLd00dvSM9ir9ypqD1by0/RS3LM3mwmkTWJAdx1u3LebF781H32fk+ud2crJhsDhS32f0fHMXGiu8t9HysE1G4b2NZjhaKVUcyx42OG5PavWwh7H20Bgx2ncoHcM4Ep11tDXRbg5j6aQESus7z4hrpzuis7eBpcDFwIWyLL/m91X5gXZ9H83d/Q1KetuFhz1nci4hOmFEkyJDSEwUxtvOw64rgqcWwMd39e8+lR7iSVMHnyxtjjDo0elwwSMAlIVMYZrqJBj77B4aEay1CYlXDyqXOmIx2HeszOXLojo+Onza05c+PlGphJfdfBJOfMH+iBXERYhSkmlpYod/JnvZJfUd3PvuIeZmxvKr1f3DSlQqFSulJF67ZSFGk5nr/73TzjjvLG3kwv/dzDl/29hf3+8OKtXoKsXHQs1s9Dg22D7xsN3oJz4WRGduGuy+jma6VGGcW5BEV6+RmjbXYzD+s6ucH72+j1d2nKK0vmPMGXmnddiSJP0HGLRaSZKQZflav67KD/xuzTHe21fJ2Yf0XLNgIullpygAzp9nP7lpZu5ETPtUmDqb+t+ckvXi557nRenW4jtESVdEiuNh7xMXg/wxXPEv64e7WDeZpfQKQVrqbOtDI0O0og7bZHToYR+paiM1OoRfnJfPluMNPPThMZZNSiQ6bATC0qNNWDwUfwrGXjbplpCgFekKxWAfqWplpTRCvcFHkLo2Pbe/updgnYYnr52NTjN4X52XHMnLNy/gO8/u4Lpnd/DC9xbwwtaTvLz9FBHBWvR9Jo7XtjM304Mxi7FZg7rLjRi+yMEOF0UpPpZFZ+DCwx7GhsdVP3Gz2asc9vaSRvaUNfHjc/K8X5uCB6Izk74Nc0gcuUlimExJXScTop131v70aA33v3+YMJ2Gjw4Jxyg1OoQ7zp7E9YvGRqOmoTzsfwL/cvJv3HHPBRJXT4vhUFUrt768h13HjtNGBItyk+0etyQviXZCqa+v67+xbItQ0RZcCp/eD/InwoMeGA5XWPADuHOPmOpj4aja8mGt3GP30IhgHe09BpETd9A05Uh1K9PSotFq1Dxy5XSau3p5ZN3Qw9YbzpRQcXii6JscOYEdvbkkRAQBEBWiIzsh/Iz0sHeXNXHxE19R0dTNE9+ZPeQFZlpaNC9+bwF17T2s+ttGXtlxiu8tyeK9O8TnTq7xMG8Xkyly2CYfC9rcwRce4nCZfAnMul6ktMYyQ3nYwzHYrvqJ93YAZo9D4m/sLuexL4rR9/lA/uRmSNxoMqPta0cXFsOkRIvBHiKPfaSqlZ+9cYAZ6THs/e15bPjlSh6+fBpqtYq391QMf90+wqnBlmV5kyzLm4B9wHnATUA8MEbkyp6RFBXCd+fEse3eVTxzw1ymxhhQhcejVtt3M1uYE0cb4TQ1WtpimoxQthWylwuPecJMeOdmUa6V7CAcDmKUY3yu3U1lfXE0q+MGGWyr6Mxa0tUfEu/oMXCyodPqUU5Li+aWpdm8sbuCXSebHJ56Z2kj8//nC/aUOb5/XKEIzwouo6Gzr39ACjA9LfqMUoqbzWZe3HqS7zyzg4hgLR/8aAlLJiW4fN7czFieu2k+K/ITefu2xTx46VQmJUYQHqRBrmnzbBGxWWKoRMcodB/2hYc4XJKnwOVPjex8a29w4mEbtWHDE4S6ColbB394ZrBPNXZhMoufw0YX5paHfbKhg0g6CYuOIzEymMhgrVOD3dBp4Psv7SYuPIhnb5xLiE5DdkI41y/KZG5mLC3dHqSW/Iw7orPngVIgD6gBnvPrivyMTqPm/KkpzEs0ERmXMuj+yBAdvbooutssk6JqDomWhdnLRXev77zRPzHHUf7aCe09BspCC6By94DzWURnDpqmFJ5uw2yGaWn9X5CfnZtPYmQwT2044fA8L28/hdkMu84Eg22pxTYVfIOmzl7iLR42CINd3ao/Y4Rn9713mIfWHGOllMR/71yClOJ+2HFxbjwvfm8B87JE+FutVpGXHInsaXcnpQHQaCjFrTnsmJE/93jDkcFuLMEQluz48W4fN0b8dBYS93KYRnmTMLAn6nyg1HYzJH6kspVIuomJjUelUpGTFOHQYHf1GnhofQ0degP/vmkeSZH2LVdjw4JoHkNdFd0x2PGyLD8P9MmyvM3N54w9DL0EN8tw7L+w9XHRB9zJ8HlteCwqfasQ7ZzcIm7MWiZ+Rk2Aa9+EzKWQ4/5UrXa9gerwadBUIlqiWogIFjlsc2ul5fj9TVOU5iDTUvu9jtAgDdctnMim4vpBCuH69h4+PVoDwNEqD72rsUjeuTDlcloT52IwmYkP7/ewzyTh2Ym6Dt7YXcF3z8rimRvmEhUyfH3C5JRI5Jp2z0QzykCV0RCejYWQ+HhBMdjK39ZshsrddMdPG/p5rnAVEvfCw27X91nbCPvGYIcJZ8loGPJhRRV16FRGYmPFpj83MZzS+sEVFU+uP8HJ5l6euHY2BRMGv67oUB1tegNG09gQn7llfCVJmmz5mQ4M/U6NVdbdTc5nN8FbN8LnvxWhv9xVDh8aEZ1AFJ08tf4EnfIGzPGThKFWmDADvveRRy1E2/UG6mOmi//YtC6NCNFiMJkxtFSJ+kqb+dxHqtpIjAwmKcp+13ftgolo1Spe2W7vCb2ztxKDyUzBhCirunxck7sKvvUSjV0iJJUQaWuwxZfrTAiLrzlYjUoFt6/MHZSi8Zb85Eiau/qo9yQCEZ0BqEbHYI+FkPh4wXYmNoiJgd1NdMe7H/FzSFAkoHIeElfKvTzwsBXvGobOIbuNMmLTMHQe+1S1SDFqwmIAyE2M4HSr3n6UMfD5sVpmpoSyarLj6ESMRdzbNkbC4u4Y7J8g2pHOAd4B7vLrivzFkp9RedYf4bbN8KtTcG85LLjV4UNj4hKJ13bx783HMZ3azvvNOfzy7YO0dHkXGjGazHT0GGiPmw4qtV1YXGlVamypHNQ05Wh1K9NSB385kqJCuGj6BN7eW2Gds20ymfnPrnIWZsdxyYwJnGrsonWMfMiGS327eN8TwvtD4pEhOnISwjnkQw/b593D3MBsNrPmYDWLsuNJjvLdBKTJlpB6sSfCM10IRE4Ynfak+lZQ64QHFWBoBvYTt+hihm2w1WrH4XaFHsvtnhhsS946KTLYNx52kOXzMYTwzGQyU1Vj0WFY1pqbKPprnLTxsqtaujle18G8NOfCTsVgj5U8tjsG+zhwhyzLMcCfgMPenEiSJJ0kSa9IkrRFkqRdkiRd5s1xvCYum/aMVUI05iLspgmLJU7VxdYb4olUddOSvJD39lXy/FcnvTq1sqsLDY8SeW9bg22Z2GVuq7YLh+v7jByv67CGfgdy01mZtOsNvL9f5L63ljRQ3tTFtQsnWp9zrPoMCIsDjZ3CS7T1sAEW5cazubieWjfqK12xUa5j2oOfcvajG/n1+4f5+PDpEcldHa1uo7Shk8tm+VaZnG8x2EXeCM8GetgHXheNg/yJ0kfc1UjbAIMNdtUe0IXTE5Xj/DnuMlQ/cS9C4qcsHvbZUhKlDR2YhhtaVjZ0Q+SxK5q70PYqaxXvVa4DpfjmYiEsnpvmfJMYEyachGYvnTVf447Bfg2YZfk9H3jJy3NdDzTKsrwMuBB40svj+J+QaDD2kFIv8tc3X3cji3PjWXPotFeF9IrBjgzRQvo8qNxrLZ1RRmxq2u1rsItq2jGazExNdWyw50yMZVpaFC9vL8NsFt51bJiOC6elMNXilR89E8LiQEO7MNjxNh42wA+X52IwmfnHxpJhHV+uaefO1/eTGR9GdkI4H+yv4o7X9rHkz+sp9XNLwzUHq9GqVayeNlgAORwSIoKJDw+i2GPhWZa96Oz45/DB7UL34U9Gs4/4eGOQh71bNGtSa5w/x5NjOw2Jey46O9XYRWyYjlkTY9D3mYbfXlkJiQ/hYR+paiNSZbnf8l5NjB/conSjXEdqdAgTo51rRmJCxX2tXePHw06TZfkFAFmW/4L387DfBn5r+V3FWM6FKx544VpInAwRSVw6I5WTDZ0ccSDmatP38frOcqfCBKVXeESwThjsnlZoFCrvyBAtKkxoO2vsDLZVcJbm+MuhUqm4cXEWxbUdrDl0ms+O1vLNuekEazUkRAQzITrkjJlo1djZi1olFJu2TIwP4+q56by+s5zTrd5dCOrbe7j5xd2EBWl4+fsLeP678znw4Pm8fstCunqNfOzHznImkwiHL89PtO7kfYmUEons6VjB2ExRYtinh54OWPsLcXvdMZ+vz47R7CM+3rBVc/d1i3kF6fN9d2xnIXF9m0jpBYW7fbjypk4mxoczydK85MRwN8ADPOy6Nj1L/7yeZzb3b9qPVLcSo7ZcDyybi2CtholxYVaD3Wc0sfVEIyukJFRDRHWU72VL99jwsN0pODRLkpQvy3KxJEm5gFfbOFmWOwAkSYpE5MJ/4+o5PT09FBYO3STEE/R6vVvHi2rqIg2g9jBNk75JbWEhOUFGtGp4Yf1hbp1vry5/dncj7x1rpbmhhlU5g8txjtSKkG1zXTUlkXHkAtW7/0tr9iXUN/WQQBtqs4GaLg3NlvV9dbSeyGA1bafLKKxx/IHKDzYRFazm7rcPYDCZWRDfZ319mVFq9p6s98n75+775i+OV9QTHaxBlosG3XfhRHhnr4mH39vDnYtc1y3b0mMwce9np2lo7+UvF6bSUl1Gi6UcPhbIiw/mo/2nOGfC4L3llyXtvHm4hccvSSNE63jf6+p9O1qnp7pVz3UzIv3y/iYG9bHvVDtHjx1D7WaoOao7iDTMlOzbSEzJ+8S3ltMVP52Q2mPIx46KC7YfyGquwRgURUVh4ah/3sY6QW0N5AJVJcfoO91IlslABSk+ed/S+jQEt9VS6uA4yTWniNaGU1w0+HvojBM1rRQkBmNsFl+sbYdLSDE1er2+sNp6MoFTJ4roagvjy5J2Kpu7+ePHRRSfOs3Nc+PYWVzDslA99MHxyjoMjcKRSgoxc6yyicLCQg7XdNPRYyA3rAe93uD0fWvTi2Yv8slKCkNGcVa8BXcM9s+BNyVJSgaqgR96ezJJkjKA94GnZVl+3dXjg4ODKSgo8PZ0gygsLHTveLoq2C5+jZtzGXGW56w82M32qjYevX6yVc3b3NnLutdFCPG/xd3cvnr+IKXvaVUtUM3U/Bxy02fD+mhSjVWkFhQQ3thFyloRek/Jn0vKZHGuqi8amZkRx5QpTrqpWbi2UsM/N5WwKCeO8xbNtN6+qErD/315nMzcPMKChtcIwu33zU8Yd3eSHIPDNRQA365U8daeCu67fC7pse6JlsxmMz994wBF9T388/o5XDhtcODookoNj68/TvLEXOIGhOPvXb+VitY+ThliuHy64/nlrt63/xQfIUSn5rvnziE82PfNOha1l/PfwsNEJmcxMd5NMVd4K+yEXP1hOP4WzLuZsAmzYM1PKEgJhTgf5Ekd8bkeEgooKCgY9c+bLzCbzdz4/C7On5LMDYuzfHvwjnhYB2nx4dAn+kVkLLycjsqm4b9vx9Oh5ajj4xRpISzW7XP0GkzUd5byrQVZLJotEbf2NB3q8OGtMbITNkJmahLkFfBy4SEiQ7R8Y1Yqr+4oRxUSSVmrkVsS1FALedPmWiMCs07CC9vKyJcms+aUjFat4tsrZ1J58oTTNRlNZlRvnSIoMo6Cgnzv1+0Be/fudXqfO9vlA8DNsiynAg8DB71ZhMXgfwb8ylLXPXaxnXiTudT666UzUzndqmfPqWbrbS9sK6O7z8iPzs6luLaDLwoHd4lShntEhuiEEjN9Lpz4AjrqiAzRkqqy7DgtIfFegwm5pp2pTsLhttywOJOYMB23LrO/kE5LjcZsFs1XbGnT97H2ULVv2gSOEA0dPXZdzgbyo7MnoULltJmMI042dPLhwWruPHuSQ2MNsGpyEmYzbCqus7u9vLGLgxUtALzlZdtCg9HEx4dPc87kZL8Ya8DafMWjBioxllrsjX+E8CQ496H+jn51fvR6z7CQ+Im6DrYcb2Cnk46Ew8J2JnblbtEDPXKYTVMUXIXEPRCcVbV0YzLDxDixWcxNDB++UtyawxYh8V0nm5iXGcsfvjGNn56Tx9t7K2nq7CUj3AAqjV3VQU5iOL0GE1XN3WyU65mbGWut0nGGRq0iKkRH69dQdHY/ItL4W0mSNlr+OdfTjyZKDjt5mt1wj3MLkgnRqVlzUIR3OnoMvLj1JBdMTebn5+aTERfKUxtLBgnT2iwGO0qZhb3052Js5PMXEN5VRYrK8qW2qMSP17XTazTZNUxxRlpMKAceOJ9zCuy/sP0DMuwN9t8/K+bO1/dz9qMbeWNXOQajb0uZdpQ2+rycrLHDvsvZQFJjQvnOggze3lNpLSNxxcHKFgAunuFckjE9LZqEiGC+LLQ32GsOib//NfMz2FbSSEWTe+e0ZXtpIw0dvVw60399q/MseUOPWpRGJIt+AGYTXPRXIdpJlMR9tX7KY5vN/SrxM4TPLRv3Jn9UGtjOxK7c47v8NYi/gUEvNAwD8XC0plKDnRkvPNxJSRGUOGhe4hGKwe7torGjh5L6ThZki25mPz8vn99dNpWYMB0ZYX3is2uTClKU4ttLGzh2uo0VUqJbp4wJ042rsi6fiM5kWf6pLMspsiyvtPk3TMmgn1B2+kp3MwvhwVrOLUjm48OnMRhNvLbjFG16A3esnIRWo+aHK3I5WNHCthL7HI0iOrPu5rKXw43/ha5Ggl66kOWaIxhUOmvvbKVLmbOSLndIjgomISLITnjW0WPgnb2VLM6JJyU6hHvfO8x5j23mkyM1Xp/HlvVFtVzzzA5+8eYBj56n7zNyoq6DDUV1bJTrBt3f6MLDBrjj7Elo1CoufnwLC/7nC+u/V3Y4rik+VNlKiE5tNWqOUKtVrJQS2Vxcb7ex+fBANfMyY/nxOXmoVKJhjad8eKCayGAtK928aHhDZIiOtJhQz4RnajWkzoGpV8IUS+VlcKTw4vwlPOvtEINvziCV+BfHhMFu7PCTZxYSLUrt2iohbZ4PjxsjfjpSins4qau8URjnzHjFw46gqbN3eJsYG9HZ7jIR6VyQ3R8RvemsLPb/9jxiVN2DogGKwX5haxkAK/LdNNihOlrGkUrcLElSPoAkSZPwUnQ2rgiPhwv/JMZoDuDSmak0dvayvqiOZ7ecZOmkBGZmxABw1Zx0kgb0+e7qNbCnrBmtWkWIzubtnrgQvvcJqFSco95Lqy7Ruhs8Ut1KRLCWzDjvm0ioVCqmpkZzxKYW+/19lXT0GLjnQon3bj+Lf984j2Ctmh++upe1Fq/RWxo7erjnncOE6jR8WVTHhqLBhncge081sewv65n820849++b+N6Lu/nuC7vtWq529xrp7DUO6WEDJEeF8NerZ3LJzAmcU5DEOQVJ6DRq3nViTA9VtjItVUxBG4pVk5No0xvYa0mDyDXtyLXtXDozlbSYUJZOSuCdvZUe1ZeaTGa+KKzlvCnJ1lns/mJySiTFNR6KZb67Fq4aMDIgaar/QuJnWB/x+vYe9le0oFaJCge/EBLd3zbZlx72wJIxWzwMiZ9q7CJYqybRstlWxlwOKyyuePhdTewuayJYq2Z6WozdQ1QqlcNoQGx4EHHhQRTVtJMYGcwUB61IHREdFuR10yxf447BVkRn1cCnwOf+XdIYYdHt/fNxbVgpJRIZouW+9w7T0NHDHWf3T+UK0Wm4dVkO20oa2VfezLYTDVzwv5tZX1THHStzB5cPJE+Bmz+lXJVKtS4LfZ+RD/ZX8fmxWqakRg27TeW0tCiO17aj7zNiNpt5afspZqZHM3tiLCqVinOnJPPfO5cwLzOWu946yP7yZtcHdYDZbObX7x+hrbuPN29bRE5iOL9fe2zIzmENHT3c8do+zGb4+bn5PPbtmfzfNbMAMWLS9nGASw8b4LKZqTxy5Qzrv2/MSuVIVStdvfYqb4PRxNHqVqanu/boluYloFWrWG/x/NccrEatgoumi0DT1fMyqGrpZnupfVSlrk1PTbvjXXlxXTvNXX2c5cY0ruGSnxJJSX2HZ13c1BrhaduSVACNx8HghwvXGdZHfENRHWYzrJSSaO7qHX6zEEeERENfJ2iCRKtkXzFUP3EPQ+KnmrqYGBdmvY65M+bSJUFholdA7RF2lzUxKyOGIEdVGvpWhxEbpePZivzEIcu5bIkdTyFxWZZ3ArcBXwDhgI/UDeOTYK2GC6em0NjZy+yJMSzOsS/xunbhRKJDddz+6l6u/fdONCoVb/5gEb84X3J8wNhMfhr7ND/Q/5iFf/ySn715AJ1GzY9XTRr2WqelRmMwmSmubWdbSSMn6jq4cYBiNVir4V83zCUpKphbX95LZbPn+dj39lXxydEafnF+PjPSY3jgkimcbOjk+a2OO8MZTWZ++sZ+Wrr6eOaGefz03DyumJ3OpTNSiQ7Vsbesf+PQb7A9r1Oenx2HwWTmQHmL3e3H6zrQ95mYmR7j8hhRITrmZ8VZLsJmPjxYzZJJCSRauq6dPyWZqBCtnfhsf3kz5//vZu77zHGjnZ2lYkOyMDtu0H2+RkqOxGAyDxoU4zFJU8BkEEbbA0rqO/jeC7uG9lDOsD7inxfWkhodwpJJCRhNZv+0CFbeq5QZoHW9mXX/uJbw8sCQuNnssYdd3thlDYeD0NuE6NTDF54lT8NUc5ij1W0scPYd0rc5Mdhi0+BuOBzGSUhckqQgSZJukiRpF/A3YCaQLcvynSO2ujHKVXPFzOqfrMobtEsLD9byg+U51Lf3cOuybNb9dDkLBxj1gUyIj6JRr2J5fiKv3bKQjb9cybK84ec2bYVnL20rIz48yKHIKj4imOdvmk9Pn5FbXtozqEH+UFQ2d/Hgh0dZkBVnVaqvlJI4tyCZJ7487rBt6P99eZytJxr5/TemMsWmV7parWJuZix7TvV72EoO0HZSl7vMzYxFpRo8avSQRXA2ww0PG0RYvLi2g3VHaihv6uLSGf1CsRCdhm/MSuOTIzW0dvex5Xg91/17J109Rmo6DA69iZ0nG0mLCSVjGCkPd/FKKe6IZEt5oYdh8Q1FdWyQ63l7zxB5/jMoJK7vM7LleD3nTkm2bjL9EhZXjFG6D/PXtscd6GEb9GDqg+BITCYzR6pah+z6aDabKW/qYmJcf5MVtVpFTkLE8A12ygxUTaUEm7qZn+XMYLc6jAbMSI8hPEjDsjz3o1vRYUG06fvGxMSuoTzsMmAGcJ2lnWj1mBWJjTCLcuLZ/etzOXtyksP771iZy+5fn8uvL55CaJDrHOXfvzWLPb89lye+M5slkxJ8NrEpPTaUqBAtnxyt4YvCWq5ZkOE0Z5qXHMnT18/heF2HR6Kx+947jNls5m/fmonGZt2/vaSAPqOZP6+zb7KwubieJ9Yf56o56XxrXsag483LiqWkvtMqTLF62JGeG+yoEB0FKVF2IXaAg5WtRAZryYp3r2OT8nd+8MOjBGnUXDCgjejV89JFE5Z3D3Hzi7uZGBfGm7ctAmCjXG/3WLPZzK6TTSPiXYMoZdGoVValeH17D39Ye4zfrTnqWag2Pg/UWqg96tH5ldao/9lV7vwCrxiHM8DD3nqiAX2fiXMLkq2bTL/MbLcabMf56/f3V/Lw2mN093pYvqmExAfmsG0mde082cQlT3zF4186L6Osb++hu89o52GDohT3zGD/+v3DfHbURhibMh0VZgrU5czJjHX8pB7HHva352fw1a9WedRZMCZUh9k8NiZ2DVUA+r/AdUCWJEn/RrQTDWAhcQgDolKpiHcj56oQotP4RXykUqmYlhbN5uJ6NGoV1y3MHPLxy/ISufsCiT+tK2JzcT3LXYSNlFrT+1ZPHuQtZsaHc+vybJ7aUEJFc5c1ElF4uo38pEgevnyawxzSvExhyPaeaua8KclW72RgH3F3WZAdx5u7K+gzmtBZBGaHK0X+2t2NUW5iOBPjwihv6uK8KclEh9rXbk5Pi2ZySiTrjtSwICuOZ2+aR3SojoxoHZuK67nFpka+pL6Dho5eFuaMjMEO1mrITghnf3kLf/20iOe/KkNvMGI2i8/dry6c7N6BtEHCaHvoYR+v6yBIo6a0oZMdpU0sznUQbVLCr2dADvuLwloigrUszImjpE6kIfxS2uXCw37+qzIOV7WytaSRf10/1/3GOVbRWYv97dbBH9HWNsCPfVFMdmI4lzkoTVRKugaeNzcxgjWHqunuNbrlzFS3dPPaznLWHjrNnMxYoWVJEXO/V8XWW2cx2GEyiQ2Gg/C9Rq0i1sNrSWx4/8QuT5/ra5x62LIs/0WW5ZnA48C1wHxJkv4sSdIwp6QHGEmUsPj5U5JJjXFd9v69JVmkx4byp3VFLj2w9/dXolbBFXMcd/q6Y+UkrpyThkatQq0CtUrkbf95w1ynX9YZ6dHoNCprWLy+vYfIYK3XG5r5WXF09xk5alHL9xiMFNW0McON/LWCSqVilcXLdnRxUqlU3H9RAd9bksXL319gNejz0sLYebLJzsvZYc1fD50m8SVSSiTbShp5akMJ505J5stfrOA7Cybyj40lnjV+SSrwqLTLbDZzoraDK2anERWi5fVd5Y4fqG8FVBA8vj1sof6vY0V+oqWnv7i4N/jDYBdcCovv7G90Y4PRoltZkB1HdUs3lzyxhQ0OyiUdotGBLnxwSNxmtKayAZmeFs0v3z5oraCw5ZSlH8LASpdJSRGYze4Lz5ToWGt3H79fIz57veFptJrDWRha5fhJPW2A2WcRm5hQSz/xMaAUd9liSZblTcAmSZJigBuAV4DZfl5XAB8xZ2IMIOoT3SFYq+HuCyR++sYB/nuwiitmpzt8nMlk5oP91SzLSyQp0vEc5/BgLX//1iyP1hui0zAtLdoqPGvsHLppiivmW2o0d58UitLC0+30Gc3MdDN/rXDdwok0d/VyboFjzeXy/MRBEYl5aaG8f6yVHaWN1rD6zpNNJEcFDwoV+pNr5mcQrFHz/WXZ1ulvv//GVCqauvj1+4eZGBfGIhc6C0AIz46+J7wXN+pxa9r0tPcYmJYeTWiQhtd2nqKxY8rg6FN3i8g3DlSmjzMOVbVS397DuVPE31rxxpr8UYudNlf8c0BZYyc9BhNXz01nYXY8t726l5tf3M1vL57CzUuzXR87NGZwSNxmtGZTZy8atYoXvzefK/+xjdte2cP7dyyxi7KdaupCpWJQq+DcJJGGKql3PjrYll0nm4gI1vK9JVk8sf4EV8xJIypER595IpNNTsYdezFVbCiix9BMbLe/IbIst8iy/IQsywFjPY44f0oKn/18uXsXZAuXzkhlelo0j35a7LSF6a6yJqpaurnSiXc9HOZlxnKoqpUeg5HGjh6P0gsDSYoMISs+zCo8UwRn7pR02ZKXHMn/XTPbrTCewrTkEEJ0ajZZ5u6azWZ2ljZaOzONFMvyEvn7t2fZjWrVadQ8dd0cJsaF8cNX97qnIleEZ27Oxi62NGzJT4rguoUT6TOaeXefA/GZvgVCx7d3DaJZikat4mxJGGydRk10qM46z90Wo8nM/3x0zO3OfJ4gW+ruJ6dEMTE+jPduP4vzpyTzh4+O8dXxBtcHCIkZHBK3GsFImrt6iQ0LIj4imOdumk+vwcT3X9ptVz5Z3thJanTooJKr7IRw1CoocVN4trusiTmZsdy5ahKTkiL4zftH2FRczzFTJtFtx8Hk4PrkxdzuoVBGbI4FD3t8b2kDuEStVpGf7H53IuU5962eTFVLN69sd9wp7P19VYQHaTh/im/nOAPMzYyj12DiSFWrpY/48PJG87Pi2FPWhMlk5lBlK/HhQaS5kR4YLkEaNYtz4q0Gu6yxi7r2nhETnLkiOlTH898VoqXfrXFDTJZkGZDgZlj8uEVwlpccSV5yJPOzYvnProrB4rMzoI94d6+R9/dXMS8z1k7QFB8e5FAlfrKhg2e3nOTZLaU+X0tRTTtqFeQlixKm0CANj317FnlJEfz0jf3UtDpoO2pLSLSDkHi/6Kyps9eqKZmUFMFT1wmx6h8/7tc3KDXYA1HGXLozZrO5s5fi2g4WZMUSrNXwyJXTqWrp5ukNJ6iPyEdl6IImB++fterAN5tAZazvWCjtChjsAA45a1ICK/ITeXLDCdp77Hex+j4xJ/qCaSkeeZzuMtei/NxT1mzpIz68OtP52XE0d/VRUt/BocoWZqRHj5iHuyI/kZMNnZxq7GSnpbnKohESnLlDZnw4q6Qkq1c2JDFZojWkmz3Fj9d2EG/pLgWiR8HJhk62D2jdS3fLuFeI/++XxVS1dPPz8+wnOsVHBDkMiVe3CKO57kiNz8uFik63kZUQbqf7CAvS8vR1c+juM/Lj/+yjb6gZAm6ExBUhFogIzi1Ls3l1R7k1Vz6wBtuW/GQh0Lz48S388eNCNsp1DiN5ypAlpXRrflYc1y2ciMFkJjTdMpmw5vDgE/g4JB5l9bADBjvAGObe1ZNp0/fx6gF7UckXhbW09xi40kl+e7gkRgaTnRDOrpNNNHX1utXlbCgWWL7wG+V6TtR1MN0DwdlwWWkJj24qrmfnySYSIoKszRvGCpnx4Zxu1bue4KZWQ+LkwR62wXHZ0vG6dquXB7B62gSiQ3WDxWf6FrcV4kaTeUzUw9pypKqVf285ybfnZQxKPcWFBzkMiStK64aOnkFlh8NFrm2nIGWwsZqUFMkjV05nd1kzj346RFpjyJC4MNgDx83edb6ElBzJPe8coqKpi8bOXqfK9AcuncLPz80nIljLi1vL+O4Lu/nRa/sGPW53WRNBGrW19TPAr1ZPZlleAgsWnCXKDB0ZbBtFuy8QE7u0/mmA4yEBgx3AKQUTorh+YSYfFrXxyLpCayjzg/1VJEcFOy7R8RFzM2P56kQDZrN3Xc5syYwPIzEymJd3lGEy47HgbDhkJYSTGR/GRrnekr+OG9H8tTtkJYgLa7k7U8eSpvSXdpnNsP0peCQddv7L7mFms5njdR3kJfWnY0J0Gq6ak84XR6to09tc/DwIiV/37x38+n0HF+lRwmgyc997h4kNC+L+iwbPVI6PCHZY1lXdokelgmCtmnWHT/tsPZ09Bk41dlkb5gzkG7PSuH7RRP61udQ6dXAQjkLi+jahHldraO7qG2SwQ3Qi7N7S1cttr4h5zplxjvscpMeG8ZNz8njztsUcfPB8bl2WzZdFdZQOCJPvOtnEjPRou0hBVIiOV76/kEX5qZAgQe2RwSfwcUgcIGaM9BP3zyBeP9LX10dlZSV6vYs8jJPnFhb6cabvGch38jVckJZKd1832/cdIipExzV5Gm6ZnkSxXOT6AF5yTZ6aC9KEIjs+rGPYf7fHzk+ksL6bJ3b2eFTS5QtW5Cfy+s5yDCYzt41gOZe7KA1kyho6XesdkgrgwP+3d97xUZT5439vkt00QgKhpNDbgBRpoiKgZxcO2ykenn4tZ/15HliwFyxYTk4P9RSVpugJglgRECuKPYCAhqFJCTUQElI2dff3xxZ2NzOzs5PZljzv18uXZGb2mWeeeeb5PM/n+ZQ3oXQXfPYIbFwMadmw4j7IGwKdRwBw4GgN5dX1fitsgCssnzAp6d+s3/Q5owa7jdh0qsTrGxys2VnKhqIypp7fP+yJU/Qwd/UfbNhTxgsTh3itiX3JTrdRUumKJ+7r97+31E77VskM7dKGZRv38/D4/qYETPIEqlET2AAP/vk4CveVc+vba9lysILJZ/T2v3dqFtSWQ0M9JLpFRI0rLGmDw8mRqlraKgQeOS6vNbed1Yd/LXet3vV4QqTaErl+TA/mrt7Bgp93eyc9VbX1bNxTxvVjeqj/OGcg/PF14+M+Lmhm0SbNypEYUInHncAuKioiIyODbt26hbxSsdvtpKbGZgruWMZut1NWZ+Hg0WqSEhPokOmgd8cMUsM4YFbXNXgHnx7tWykHSAiB4vJqUlsfYIo1UTPoTTg4tU973nAb70UqYEooeAT2Tj0Wyx5L8ddOd+V0P/1BOOHv8MqpsOhquPEbSM9my0G3wZnPCpvy/XRf/xwWSxWWn2fB4Gdd6vR6uy6V+I7DVdQ2OKhtcEUUC8wBH2l2l1Tx7083c3rfDvxZJa96droNh9PlEuS7Kt1XVk1uVipjB+Wy/Lf9/LLziHpc7BDw2CIoqcQ9JCcl8tZ1J/Lg+xt5/vMtrC8q5T+XDT5mLOfRdtQchbS2x/6d3Joyex1OJ6oBRG4c05MvCg9SsOuI7mAtHTJSOOu4jiwuKOKOs/uQnJTIul2l1Duc3u0sRXIGwvoFrn6Y7hNqtLrMlS88ybwgJ5lptvhy64oVqquryc6OrFuMAHJap5CXlUptg4MUa2JYhTW4VIWeUKdJJqw8WiUnkZTWmm5ZjVdB4ebkntnYEhPISrPSp0NoFvuRIDPNSlaalR2Hg7t2Vbd1RUarra2FK96FMXdCahuY8DpUFsOS68Hh8Lp0+a2wP5uKpaGWzUl9GLh3EdTZG8UR37T/KAV7lCcOm33ioa/4zZwc7k3h2ZWbsVjgMZWofQBtWymHJ91bZicvM4Uz+nYgOSmBTxTU4jX1DWwvruAr+SDzv9/BG9/vCJp1bdP+ctJsiXRqo70wSbEm8q9LBjHtogGs3nqI8S9+e8y1zxtP3Md2xZ34w6PeD1SJe0hMsDDzymHM+r/htE7R/61NHNGFkspaVvzmyiP+044SLBbUQ4+CN+JZo31slcQfTSEr1UpZDKjE405gA0JYR4l2rZLp0b6VoruG2VgsFtJtrlW1GQI7xZpIenISaWGwag9Gmi2JC4fkceHgfNPixJtN1+x0XSvsrVXpXF07hZszZkCvM46dyBviyiG/7XP45t9sPVhO23TbMYPB3T/Br2/Dyf/gp16Tae08St2a//nEEc8C4OEPfuOpVQcV445vPlCOxeKK2vdZ4UHqtSydI8BPf5Rwet8Omi6CHvcnX9cup9PJvtJqcjNTSU9O4jSpPcs27vOLLPje2iIGTv2U0//9NVfP/ZkHP/iNhz74jevf+KVRulhfNu0/Sp+OGbr6mcXiCle88MaTOVRey5xv3YFIlOKJ1xyF5IygAhtc40So2o9RvdrRpW0a//vRpYn6eUcJfXNaNwoD7EfHga7/BwrsENOA6iErRlJsxqXAjmd+/PFHbrvttiaVUVpaykcffQTAq6++yvr16w2XtWrVKhYuXKj7+lY6w4Tedttt/PjjjyGX70tmqpVUayJv/+8tQ3X1xWKx0KtDBmm26OwC/euS45l6fv+o3FsP3bLTdAVPkfeX85VjCJ/vtTUOLzn8Whh4KXz1BAO3vYbU3h0Bz+GAT6ZARi6MvoOOA89gvaM79atfALvbQjo1iyOVtfyy8wgVtQ52lzTOM7TlQAVd2qZx4ZB8Sipr+XnHkUbXRIqSylr2lNoZGCRalydK32Ef164yex32ugbyslztM3ZgLgeO1lDgzke/bMM+7njnVwZ3zuLZCcez+KaT+fG+M3jy4oF8s6WYK2b9qGgA5XQ62bS/nH65oWlxhnZpw6BOmWzcG5A1zddSvPqoX1jSNiEkz9BDQoKFv47ozA/bS9h8oJw1O0sZ0U1jdQ2Qng0ZeY0Nz1RyYTeFrFQrZfboZ+yK6OglSVIC8BKuVJ01wHWyLKunfBEoIssyX3zxBePHj+eGG25oUlljxowxqVbml98m3UabdBtXz5zJlVdeGfa6tmS6Zqfz4a97qalvIDlJfUImHyjHmmihweHk/bV7uMM3z7vFAuNn4Gyo4/Lf3+C0hu9h3xzYt87138WzILkVw7vZeLh+HM8ffRHWv+P6bUoWX20+6B0Q1+8pbbQHKh8op3eHDE7t0x5bUgIrftsfVk8FLX5zC7dg4TU9K9ESH9cujw+2J7b/Gf06YktKYOn6fVTU1PPPBWsZ0qUNc68+gXQf242JI7rQJs3KP99ex4RXvueNa08kJ/NYWOASewOlVXVIIQZK8jzHWz/upL7BQZJSik13Mo0j7olCU8IFq3HJsE48++lmHnh/I/a6Bk7Qs6efM1BFJW72CtuG0wnl1XUhZfoym0gvNy4EUmRZPlmSpJNw5dm+wGhh7xYUhZS8wOFwkBAkXvGE4Z29+a6VqKio4P7776e8vJyDBw9y+eWXc/nll/Prr7/yxBNP4HA46NixI9OnT2fnzp08/vjjAGRlZfHEE0/4lbVs2TLmzZtHQkICw4YN48477+SFF15g7dq1VFVVMW3aNN5//302btxIaWkpffv25cknn2TmzJls2rSJhQsXsnbtWsaOHcvJJ5/MvffeS1FREQ0NDVxzzTWMHTuWK6+8kr59+7JlyxYqKiqYMWMG+fnHwokuWbKE7du389e//pU77riDnJwcdu/ezcCBA3nkkUe819XX13PFFVdwyy230K9fP6666ipmzZpFbu4xY5u33nqLRYsW0b59ew4fPtyo/JtvvpmsrCzGjBnDmDFjGrVNq1ateOyxx1i/fj11dXXceuutbNmyhbKyMqZOncqgQYPYvn07d955J3PmzGHp0qUkJSUxfPhwpkyZwgsvvEBRURGHDx9m79693HvvvYwePVpn72jZdMtOw+mE3SV2enVQ9xOX97uEZnYrG++t3cNtZ/bxV7/a0jl47is8tK4b/7HNh9f+BEmp0PkkGHgJ4JqIbWl3OocrF5K95g3X71Iy+ez3g7RrlUxpVQ0b9pTxZ5+847X1DnYcquTs4zqSnpzEmN7tWfn7AR4ef1xUtsg27nH5+vbP0xYMHmvqQz4rbI8Pdq5b2LZKTuLUPu15f90e3v5pF306ZjAnQFh7OHdALvOusXL9G78w4ZXvWT55tFdr9McR1z365oYurAbkt6a6zsH2Q5X0SXWvbPcUwICLXf+uCe8KG1zGZ2f378gnG1z2Caq5rn3JGeDahqmrBmvKsbpmmhsjIivtWPCUaArsSKvERwHLAWRZ/gEwOft6+Nm5cyfjxo1jzpw5zJ49m3nz5gHw0EMP8cQTT7Bo0SJOPfVUtm3bxoMPPsjDDz/M/PnzGTNmDLNmzfKWU1paygsvvMC8efN4++23OXDgAKtXrwagR48eLFiwgI4dO9K6dWvmzp3Lu+++y7p16zhw4AA33XQTJ510Epdddpm3vIULF9K2bVsWLFjA3Llz+c9//kNJiUvdOGjQIObNm8cpp5zC0qVLVZ9tx44dTJs2jUWLFrFq1SqKi4/lck5KSmL69Ok8/fTTTJkyhbvuustPWB86dIg33niDd955h5deeom6usb7PcXFxcyePZvrr79esW0+++wzjhw5wuLFi3njjTfYuHEjN998M5mZmUydOtVbjizLLFu2jAULFrBgwQJ27tzJl19+CYDNZmPWrFncf//93ncjCE5Xr6W4tlp884FypJwMLhqST9ERuzcalS9bDlSwwjGCDRd86lKROx0w9l+uFbibId07MLf+HHC4+kmNNYOvNxdz1nEd6d7GxoYi/0hbfxyqpN7h9LorndO/I3tK7V7BGWk27i2jU5vUoIN3ktvY0NcXe2+Z/wobYNzAXEqr6ujSNo35fz9Rc+92ZK92vHbVcHaVVB3bdwZ2eAS2hkuXGgPcceY37imDjBzofxF8/6LLda+hDuqqICWTkspa0mzhSQcMcPkIV/axLm3T6NhaOamQHzkDwVEPxT4upuFQicdIApBIr7BbA75fYoMkSUmyLCtaUdTU1DTyv62rq8Nud81Qxx6Xzdjj9KvEnE6nrtm4p3wl0tPTWbFiBcuWLSM9PZ3a2lrsdjvFxcXk5eVht9v585//DMC2bdt46KGHANcKtUuXLtTU1NDQ0MDmzZspKSnh73//OwBVVVVs27aNuro6OnXqhN1ux+FwcPDgQSZNmkRqaiqVlZWUl5d7y7Db7dTX11NbW4ssy5x00knY7XYSExPp3r07W7dupaGhgR49emC328nOzubQoUN+z1dbW0t9fT3V1dV06tSJxMREamtryc7OpqysjFatWuF0Or2/P/7441m/fj0nnHCCXzlbtmyhe/fuNDS4omX179+fmpoav/Lz8vK89VZqG5vNRv/+/bHb7dhsNm688Ubsdrv3/p6yNm3aRP/+/amvr6e+vp5BgwZRWFhIXV0dvXr1wm6306ZNG+x2e6N3GUlf/Orq6rjx+6+rdr23H3//gzyUI29V1Dawr6yaNgl2uiWVkZxkYc4XG8kY6Z+l7NtC1ydeV2OnsO8k6HMLlCZB6bG2yLfZmVlzGpPSl2BtqOLdn3ZQUVNP34xaDmcm8d3uI/z+++/e7/WrP1z75UmVxRQWHqVLYgMJFnjz641cPTTyrnJr/yimexubrvebYYUd+w55r924rYRECxTv3k6JWzvR3erkuuFt+VP3VhzYtY0DQcpsA5zcOY3/frmFYW1qyEpJZNshO9mpiezbuY1QQ7E0OJwkJ1pYteEP+qWWw3F3kmN30ObbZynbuZ5MYH9pFX8cKCbDZglbv27jdNK9jY1BHZN03SPJnkkvSwJHl09j70mPgMWCZC/lSGU9B3XWUc93WnrQNclav2kbyRWRy7QXSKQF9lHAd/qXoCasAZKTk+nXzz96UGFhoWFfajP8sN9++22GDRvG5Zdfzg8//MDq1atJTU2lY8eOHDhwgG7duvHqq6/SvXt3unfvzvTp08nLy6OgoIDi4mKSk5NJTEykV69e5Obm8vrrr2O1WlmyZAn9+vXjs88+Izk5mdTUVL777juKi4uZMWMGJSUlnHfeed5zFouF1NRUkpKSsNlsSJLE+vXrGTduHBUVFWzdupWePXuSmJhISkoKqampWK1WrFarXxvYbDaSkpJISUkhMTHRey4hIcH7O0+7rVu3ju3btzNixAjefvtt72QDQJIk/vjjDywWC1arlc2bN3PRRRf5lZ+UlOQtX6ltkpKSWL58OampqZSXlzN58mRmz54NQGpqqresvn378tZbb2G1WklMTOTXX3/lwgsvZNOmTdhsNlJTU73tHPi+rVZroz4VLgoLCyN2r6bidDrJ+GAP1UmtVOvsCqG5k9EDezK0bwfGFtbxWeEB/tOrj9+K643CDWSlHWXkEHV3p9a5dp75ppjfu0zk+JJP2WTPINVaxmWnDeZw1S98ur2K9I5dvSv/T3bLJCYUc8aIgd57nfRLBQUHang6wm18tLqOveXbufzkHvTr1zvo9blfl1JrwduuNb+uJSezhgH9j/O77vgBodXjsezOnPOfVazYbWHq+f3Y9WERAzq3Ndzn+ucfYW91wrHfH/c6fPEYmd/8G4CcLr1pOJxKx6zEsPbrT6W+JFosOj0q+kHVfWR+8TiZA8+BIf8HDTVk53UnW2cd9Xynye0qYNleWrfLoV8/8zMU+lJQUKB6LtICezUwHnjHvYcdOzEGdfKnP/2Jxx9/nE8++YSMjAzvivSRRx7hvvvuIyEhgfbt23P11VeTm5vL3XffTX19PRaLhWnTpnHwoCs4ftu2bbn66qu58soraWhoID8/n/POO8/vXoMGDeKll17ib3/7GxaLhc6dO3Pw4EG6dOnC5s2b/VS+EyZM4MEHH2TixInU1NTwj3/8g+xs8wxyysvLuf/++3nxxRfJy8vj0ksvZcSIEQwcOND7PNdffz1//etfadu2bdCJ0dSpUxu1Tbdu3fj++++ZOHEiDQ0N3HLLLQD07NmTO++8k5EjRwKuycF5553HxIkTcTgcDBs2jDPPPJNNm8IXea25Y7FY6Jadzg4N1y5PUI4+bpXrRUPyeW/tHr7cdJDzBh7bHtl6sJzeHVpparPys1LJz0rl1cSJvPiPaXw2/TtG925HijWR3tkuV7D1RWVegb35QDlds9P8Jgbn9M/h4Q9/Y1txRVjis3+56SBL1u5hxmWD/YTH73tdang9+ZzBZaC11Sed5N6yaq+FeFPo1aEVE4Z35s0fdnLFSV3ZVVbLWQON+/kPyM9kyZo9x6KyWSxwxkOQ1s4VyS6rsze1ZjixJoa4UzvqDtj1Iyy/15WgBsKgEvdk7IqyL7bT6YzYf3369Eno06fPzD59+nzXp0+f7/v06dNX6/rff//dGYjSMb1UVVUZ/m1Lpjm1W1P6TyzfywxueavAOeZfX6ief/D9Dc4BDy13OhwOp9PpdNY3OJwnPL7Sed3rP3uvcTgczkFTVzjvXbI+6P0mvb3GOeyxlc4NRaXOrnd/7Fz48y6n0+l0/rrhN2fv+z5xPrH0WPud9syXzhvf+MXv93tLq5xd7/7YefvCdc7quvqQntXpdDo37il1XjXnR2dpZa3i+b/P+8nZ9e6Pnb/sKPE7/tqqbc6ud3/sPHi0Wtd97luy3jnk0U+9f496+nPnrf9bE3J9lThQZnf2fWCZc9zzq5xd7/7Y+W7BbsNlLfxpl7Pr3R87txdXND5ZdcTpdDqdI5/83HnbgrWG7xE2Kg87nf8+zul8sovT+XBrp3Pd27p/quc7ratvcHa9+2PncyvlptRSF7/88ssvThWZGFGjM1mWHbIs3yTL8khZlk+W5TAGoxYIBCHRLTudoiN21dSLm/aX0ycnw7tyTkywcMHgPL6SD7Ly9wN8vbmYTzbsp8xeR28NS3MPJ3Rvy6GKGmZ/+wcWC5ze15XZzJpooW9uBhv2uPbCq+sa2Hm40ruy95Cbmco1p3Tj3TVF/Pn5b1m7S79fdnVdA/98ey1fycV8Vth4x7i+wcEP2117+YHJOX7be5Sc1im6Q9xmt0rmSFUtDQ4nDoeT/WXV5Jqwwgbo0DqF60d39xrf9dUISRqM/vmu327cU9b4pDuYypGqWtWwpFElra0r2l5tQLQ2k0hKTCAjJSnqKTZF4BSBQAC4kjU0OJwUHWlsdOl0Otl8oLxRcpC/DOtEvcPJ9W/8wlVzfuKW/7nSJOpRF3viRL+/bg9Du7TxS6M6ID+TDXvKcDqdbCuuwOGEPh0bTwIeHt+fedecQGVNPX95+TumLf0de22QNKHAU8s2sa24kjRbIl9vLm50/teiMipq6slITmLZxv1+kdc27iljQL5+wZid7vLhPVJVy6HKGuoanJrR0ULlhlN7kp1uI8ECPTsoZ8jSQ+8OGdgSE44FUAmguq6BqtoGzShnUaXTcDhnmuvfrfO0rzVAmxjI2BV3yT8EAkF46NbOnbXrcCXd2/kP/MXlNe6gHP5Cs29Oa1beNoYy+zHb0TRboi7Xol4dWnmzIJ0ZEMpyUH4m//txFzsPVx3LQKUSEOQ0qQMrbhvDU8s28do3f7Dy9wM8/ZdBnNhD2Ybj2y2HmPfdDq4e2Y2j1XV8sckVsCXRZ5/6u62HAJh0Zm8eX1rIut2lDOnShqraerYVVzDWZ88+GMeCp9R6JxO5meYJ7FbJSTz1l0F89etWzaA3wbAlJSDlZPCbiqucnrCkUefEG0E6D7K6mF60UnjSwxU1tE61hr7vbhCxwhYIBMCxdIg7FUKUbtrvSdvYeGXZq0MGw7q28f7XL7e1LvdJi8XCcPcq+6zjOvid86zQN+wpY/OBCqyJFu+EQomMFCvTLhrI/64/EYcTLnv1Bx76YCMVNf5OKGVVddy56Fd6tk/nnvP6cprUgdKqOtYXlfpdt3rbIfrntebS4Z2xJlq8yTkK95XjcOo3OINjUcEOVdQ0CppiFmcd15ErBjfdvW1Afms27i1TjOUezqApphIGYQ2uUMm+KvHqugbOfm4Vc1f/ofErcxErbIFAAED7Vsmk2xIVLcU9q1wltXRTuPKkruS0Tmlk5d2nYwa2pAQ27Clje3EF3dul61rFjOzZjuWTRzN9xWbmfvcHnxce5E9929O5TRpd2qbx4a97OVRRw6v/N5IUayKje7XDYoGvNxczpIsrwpe9toE1O0u5+pRuZKZaGdWrHZ9s2M99Y/v5hCQNRSXuUvWXVNZy8KgrRGmeiSpxM+mfl8nbP+1mT6mdTm38/Y09YUljeoUdRrLSbOwuOfZtfLftEIcra+ltIBSsUYTAFggEgGvF68ra1XiFLe8vp12rZLJb6TO00suYPu0Z06d9o+O2pAT65WSwoaiMotIqju+UpbvMNFsSD40/jnGDcnh6mczH6/f5rYxuP6sPg9zltUm3cXynLL6Si5l8Zh/A5W9e2+BgpDtO+diBuXwpr2d9URkb95SRnW4jR08ULjeeFXZJZS17S+0kJyXQJi3yaV714NEcbNxztJHAPqYSj826h5usVH+V+IqNB8hITvL2k0ggVOIRJt6zdenFjGxdHt58800gfHUVHKNbuzTFNJvygXJDIS+bwoD8TNYXlbK7xN7I2E0Pw7q25Z2bTmbdQ2ezfurZLP3nKOb/fQT/+FMvv+tOk9rza1EpR9wCafW2Q1gTLYxwJ584+7gcr1p8w56j9M/PDCl+eZs0GxaLK574vrJq8rJSYzZFcN+cDBITLF5Ngi/HBLa5k7Z4oU2aK2OXw+GkweFkZeEB/tS3Q5PsBkJFCOw4xJOtC+CGG25g0KBBhssaM2aMX0xyszGj/Jdfftm0sgTadM1OZ1dJlV+uaYdD2UI83AzqlEml20irqar41ilW+udlMrp3+0YRtE7t0x6nE75xG5qt3nqIIV3aeJNqZKZZOaVXOz5ev48tB8oZECThRyCJCRbapNkoqaxhb5nd9P1rM0mxJtK7QytF164jlbVYLGjnqG7GZHozdtXz844SSiprOad/TkTrEN8q8XVvw9o3dV9uczRAQpDZ0JArYPBE1dMtNVtXeXk5F110EStWrCAxMZFnnnmG/v37M3bsWO81IltX/NMtO416h5O9pdXe9Ja7j1RRXedAyjE/mpgWvoZd4ZwsDOqURVaala/kg4zp3Y7f9h5l8hl9/K4ZOyCXu+T1jeqll7bpNg5X1LKvtJpTerUzpd7hon9eJqu2NHZ1O1xZS1aq1c+aviWRlepJAFLLit/2Y0tK4DSp8XZOOBEr7BBpqdm6MjIyGDZsGN9++y0NDQ2sWrWKM88803teZOtqHnhCge7w2cfWshAPJx7DM1tSgrde4SAxwcLo3u1ZtfkQq7cexumEUb399yXP7t+RJLeg8mS2CoXsdBsHy2s4WG5OWNJwMiC/NcXlNRw8Wu13/EhVbYs1OINjGbtKKmv59LcDjOndTjEFajiJ7xX24Imaq+FAak1I/tGuXTtef/11Pv30U1q1akV9vctt5NChQ/Ts2ROASy+9FHBl6/KsUuvq6ujWrZu3nF27dlFSUsINN9wAQGVlJbt27QJciTHAlfykpKSE22+/nbS0NKqqqhQFoedenljbrVq1omfPnuze7coVftxxriQDOTk5HDp0SPXZunTpQqtWrlVU+/btqamp8Tt/6aWXMn/+fBwOByNHjsRmO/bx7tq1i169enmPKanpO3Xq5D2v1Dbp6ekMHjwYgMzMTCZPnqxYz+3bt3P88cdjtbo+oOHDh7NlyxbgWIKFnJwcamujHPc3Dunml2bTtXrY7BbYeqKXmYk1MYH+ea2pa3CEfVV3Wp/2fPTrXmZ9u510W6LXKM1DVpqNU3q1Y93uUjq3DX0MyW5l42u5GIczdi3EPXgNz/aWcbqPcV1JZUsX2K5n/27bYfaU2pl0ZvDEL2YjVtghMmfOHAYPHsz06dM599xzvf6KHTp0YMeOHYDLEGzlypV0796dp59+mvnz5zNlyhROO+00bzmdOnUiNzeXOXPmMH/+fK644gqvsEpIcL2WVatWsW/fPp599lluv/12qqurXfFkExJwOPzDR/bs2ZNffvkFcKntN2/eTKdOoSVxD2YIM3z4cHbv3s3ixYu55JJL/M5169aNrVu3Ul1dTUNDg2K6Os9zAYpt06NHDzZscOWDKS8v92YDC/QJ7dGjB+vXr6e+vh6n08nPP//sneTEqjFPvNCxdTIp1gS+2XKIXW7jM/lAOZ3bpkZ8NQHwr78MYvqlx4f9PqP7uNTUa3eVcmKPbEUXsscvHMCcq4cb6mPZ6cne/fhY3sMGOC63NQkWWLfbfx+7pDL8iT9iGc8K+51fdpNgoVGwn0gQ3yvsKNBSs3V5GD9+PMuXL6d3b//ZpcjW1TywWCyc0rMdn/5+gE9/P0CnNqkctdcxonvkXFd8iZSPa4eMFPrntea3vUdV95g7t02jc1tjuZB9V6axvsJOT05CymndKDZ7SWUdw7q2YIHt3sPeebiKk3tkR0XbYFGKaBMrFBYWOpXyYRvNxWpGPuyWiG+7zZo1i6ysrEYr7Hghkjmq4ykfti+u+N2VfLftEKu3HuKXHUeYfFYfrjypa0TuH612e2bFJv775TaWTx7dpCQaSrzx/Q4e+uA3ADZMPZuMFPMtrc1st/vf28AH6/by68Nnk5hgwel00uv+Zdw4pgd3ndvXlHvECnrbrb7BQa/7lwEwdfxxXH1K97DUp6CgoGDYsGHDlc6JFbZAN/fccw8HDx5k5syZ0a6KIIxYLBZ6dWhFrw6t+L+Tu0W7OhHj76N60L1dK9WY5U3BE+0sIzkpLMLabIZ1bcNbP+5iy8Fy+ua05qi9ngaHs0XvYXsydpVX13N2hN25vHWIyl0FcclTTz0V7SoIBGGjbbqNS4aFZvcRStmAaWk1w81Qd5jWNTtL6ZvTmpIWHpbUQ5s0Gz3apUdtW0MIbIFAIAgz7dzhSc3M0hVOumankZ1uo2DnES4/scuxxB8tXGBPPf+4qEZ6EwJbIBAIwoxnZRrrPtgeLBYLQ7q08RqeecOStmArcYDT+0beMtwX4dYlEAgEYSYrzUaHjGRDUdKixdCuWWw/VElJZa03znpLV4lHm4itsCVJygTeBFoDNuB2WZa/j9T9BQKBIFokJlhYfc/p3mhp8cAw9z722l1HxB52jBDJFfbtwOeyLJ8KXA38N4L3jhlEtq7QEdm6BM0Ba2JCXAX2GdQpi6QEC2t2HaGkshZbUgJptshlphI0JpJ72M8BnliXSUC1xrUCDTzZusaPH+8NbWqUMWPGmFSr8JX/8ssvc8UVV4S9rgKB4BiptkSOy2vNmp2l5LdJJTvdFlcTjuZIWAS2JEl/BwKXkdfIsvyzJEk5uFTjk4OVU1NT0yjEZV1dHXa7HYClO5by0R8fmVJnD+O7j2dct3Gq5ysqKnjkkUcoLy+nuLiYyy67jAkTJrBhwwaeeeYZHA4HHTp04IknnmD37t08/fTTOJ1OsrKymDp1KjU1NTQ0NGC32/n000958803SUhIYMiQIUyaNImXX36ZX3/9laqqKqZOncrHH3/Mb7/9RllZGX369OHRRx/lv//9L5s3b2b+/Pn8+uuvnHvuuYwYMYKHH36YoqIiHA4HV155Jeeccw5///vfkSSJbdu2UVFRwTPPPENeXp73eT744AN27NjBJZdcwr333kvHjh0pKipiwIAB3H///YArkIbdbmfy5MmUl7viSv/666/MnDmT4cOP+fcvWLCA9957j3bt2nHkyBFqampYsGCBt/xJkyaRmZnJqFGjGDVqVKO2adWqFU899RQbN26krq6Om2++ma1bt1JaWsoDDzzAgAED2LFjB5MmTeKNN95g+fLlJCUlMXToUCZPnszLL7/Mnj17OHLkCPv27fOLjuahrq5OMWxqOKiuro7YvZoTot2MEY5269bKyYqtJdTVpJCa6GiW7yWe+ltYBLYsy7OB2YHHJUkaCCwA7pRl+etg5SQnJzeKQFNYWOiNumWz2fziUwfD4XAEvd5ms2lGQ9u+fTvnn38+Z599NgcOHODKK6/kqquuYtq0aTz77LP07NmTRYsWsXfvXh5//HGeeOIJevXqxaJFi3jrrbcYOXIkiYmJ1NTU8Morr/Duu++SmprKlClTWLNmDVarld69e/PAAw9QUVFB27ZteeONN3A4HIwbN46jR49yyy23sGDBAq688kp+++03bDYbH374Ie3bt+e5556joqKCiy++mDFjxpCYmMjQoUN5+OGHee655/j888/9VuU2m42kpCRSUlLYtWsXc+fOJTU1lTPPPJOKigrat2/vjXT2yiuvAPDvf/+bE044wS915aFDh1iwYAEfffQRFouFiy++mOTkZL/yDx8+zHvvvYfNZmPChAmN2mbAgAGUl5ezZMkSysrKmDt3LpMnT2bhwoU8/vjjLFmyhKSkJHbt2sVnn33GO++8Q1JSErfeeis//PADVquVtLQ0/v3vf7N69WrmzJnDGWec4ff+rFariHQW44h2M0Y42u2Mmj18uGkdmw7VMbhzVrN8L7HW3woKClTPRdLo7DhgEXCZLMu/mlHm+T3P5/ye5+u+3ozQpC05WxfA7NmzKSkpYdq0aX7HRbYugaD5Mayry/CsoqZeGJzFAJHcw34SSAFmSJIEUCbL8gURvL8peLJ1XX755fzwww98/bVLUeDJ1tWtWzdeffVVunfv7s1IlZeXR0FBgV9+ad9sXVarlSVLltCvXz8+++yzRtm6PLmtV65cGTRb11lnnRW2bF2LFi2ioKCAF154odE532xdVquVwsJCzj/ffzKllK3Lt22SkpJYvnw54MrWNXnyZGbPnq2YrWvu3LnU19eTmJjIzz//zIUXXsimTZvEHptAYCL5Wal0yEjmYHmNENgxQMQEdjwKZyVaarau4uJiHn74YYYOHcrVV1/tvef48eO9zyOydQkEzQuLxcKwrm1YtnF/i06tGSuIbF2CoDSndhPZumIf0W7GCFe7vbZqO9M+KeSxCwdELGNbJIm1/qaVrUtEOhMIBAKBKid0bwtATuv4CKvanBGxxAUCgUCgyuDOWSy66WRvBi9B9BACWyAQCASanNCtbbSrICBOVeKxvO8uiF1EvxEIBPFM3AlsTwAOMfgKQsHpdHL48GFSUsQ+nEAgiE/iTiXeqVMnioqK/Hya9VJXV+cNtiHQT3Npt5SUlJB90wUCgSBWiDuBbbVavZHAQiXWzPfjBdFuAoFAEH3iTiUuEAgEAkFLRAhsgUAgEAjiACGwBQKBQCCIA2I6NGlBQUExsDPa9RAIBAKBIEJ0HTZsWHulEzEtsAUCgUAgELgQKnGBQCAQCOIAIbAFAoFAIIgDhMAWCAQCgSAOEAJbIBAIBII4QAhsgUAgEAjigLgLTWoESZISgJeA44Ea4DpZlrdGt1axiSRJVmAO0A1IBh4HfgfmAU5gI3CLLMuOKFUxppEkqQNQAJwF1CPaLSiSJN0LnA/YcH2nXyPaTRP3d/o6ru+0Abge0d80kSTpROBpWZZPkySpFwptJUnSw8A4XG05WZbln6JWYQVaygr7QiBFluWTgXuAf0e3OjHNFcBhWZZHA+cCLwLPAg+4j1mAC6JYv5jFPYi+Atjdh0S7BUGSpNOAkcApwKlAZ0S76WEskCTL8kjgUWAaot1UkSTpLmAW4EnX16itJEkaiqsPngj8FfhvNOqqRUsR2KOA5QCyLP8ADI9udWKaRcCD7n9bcM00h+Fa9QAsA86MQr3igenATGCv+2/RbsE5B9gAvAd8BHyMaDc9bAaS3NrD1kAdot202AZc7PO3UluNAj6VZdkpy/IuXO2rGMAkWrQUgd0aKPP5u0GSpBaxHRAqsixXyLJcLklSBrAYeACwyLLsibBTDmRGrYIxiiRJVwPFsiyv8Dks2i047XBNoC8FbgLeAhJEuwWlApc6fBPwGvA8or+pIsvyu7gmNR6U2ipQTsRcG7YUgX0UyPD5O0GW5fpoVSbWkSSpM/AlMF+W5f8BvvtgGUBpNOoV41wLnCVJ0lfAYOANoIPPedFuyhwGVsiyXCvLsgxU4z9IinZT5jZc7dYHl23O67hsADyIdtNGaUwLlBMx14YtRWCvxrXngyRJJ+FSwQkUkCSpI/ApcLcsy3Pch9e69xoBzgO+iUbdYhlZlsfIsnyqLMunAeuA/wOWiXYLyrfAuZIkWSRJygPSgc9FuwXlCMdWgyWAFfGdhoJSW60GzpEkKUGSpC64FnaHolVBJVqKWvg9XKuf73Dty14T5frEMvcBbYAHJUny7GVPAp6XJMkGFOJSlQuCcwfwmmg3dWRZ/liSpDHAT7gWELcAfyDaLRjPAXMkSfoG18r6PuAXRLvppdG3Kctyg7s9v+dYX4wpRPIPgUAgEAjigJaiEhcIBAKBIK4RAlsgEAgEgjhACGyBQCAQCOIAIbAFAoFAIIgDhMAWCAQCgSAOEAJbIBAIBII4QAhsgUAgEAjiACGwBQKBQCCIA4TAFggEAoEgDhACWyAQCASCOEAIbIEggkiStEySpMk+f/eRJMkpSdKTPsc6SJJUK0mSamo/SZK+kiTpEkmSukmSVKFwPs8dOz8sSJJUIUlStyDXnCBJ0sww3f86SZL+XzjKFghiFSGwBYLIsgw4zefv8cBHwPk+x04HVsuy7JubNyRkWd4ry/JIo783if5ApzCVPQpIC1PZAkFM0lKydQkEscIyYKokSQmyLDtwCez7gAWSJPWQZXk7cAawVJKkdOBloA/QFigHLnfnjW6EJEn9gE+A24G1wEZZlltJkjQV6AbkAl2BYuAyWZb3SpI0AngJV8anbe7zt8uy/FVA2aOBFwAn8DPuyb4kSQm4MkedhCt/sAW4DtgFPApkSpI0F/i70nWyLK+WJGkU8CyQ6C7/SVmW33VnUnoaONV9bi3wT3f7nI8rA59dluX/6m9+gSB+EStsgSCCyLK8BVf+4kGSJLUBJOAHXIL2AvdlZwBLceXpLZVl+SRZlvvgEpT/UCpXkqQBuFbq18my/J7CJaOBS2VZ7osrl/KNkiQlAe8CD8qyPAh4HhisULYNWATcIcvyEOBLINV9+kQgDzhZluXjgNeBe2RZ3g08BHwjy/I1ate5y3gEeFaW5WHAtbg0DLjP1wPDZFk+HtgLPOV+vg+B54SwFrQkhMAWCCKPRy1+HrDSvdL+GDjbsy8sy3KhLMuLgXmSJN0qSdIM929aKZSXjEuIrpNl+XOVe34ly/JR97/X4lqxD3Tfa5n7/18CGxV+OxCo85Qty/LbuFb7yLL8PfAArgnAdOASpToGue4d4L+SJL0FDMOlcQD4M65JzFpJktYBFwLHqTyfQNDsEQJbIIg8y4AxuATSx+5jX+Ba3Z6Ja3WNJEk3A7OBKuB/wNu4VMlKXAgMlSTpYpXzdp9/O93l1CuU16DwW6fCdfXuOo7z1Bf4AJipVEet62RZfgXXpGAlcA6w3m1wlwhMkmV5sCzLg4ERuAS9QNAiEQJbIIg8X+ISzqcCKwBkWa4C1uBSeXsE2znAPFmWZwMyrv3uRIXyamRZXo1LnTxTkqQcnfUoBGokSToXwL2fPRCXgPZlA2CRJGms+7rzgTbuc2cBH8my/DIulf2FPnWsB6zBrnNbsw+RZXkecAOQ5S5/BfAPSZJs7r3y14AnFcoWCFoEQmALBBFGlmU7sNn1Tz9L8KVAb+Ar99/TcamQ1wGf4xLovTTK/QpYAMzRWY964C+4jODWAncA+3Gt6H2vq8MlYB9z1+Vi4KD79EzgVEmS1gPf4zJc6+4WsN8DfSVJei/IdXcBj7rr8CXwiCzLO4DHgB24VPi/41qR3+G+7zLgn5Ik3avnWQWC5oDF6QycTAsEgpaCJEnPANNlWT4gSVJn4FeghyzLpdGtmUAgCES4dQkELZudwOeSJNVxzNWqNLpVEggESogVtkAgEAgEcYDYwxYIBAKBIA4QAlsgEAgEgjhACGyBQCAQCOKAmDY6W7dunTM5Odm08mpqajCzvHhHtIc/oj0aI9rEH9Ee/oj28MeM9qiqqjo0bNiw9krnYlpgJycn069fP9PKKywsNLW8eEe0hz+iPRoj2sQf0R7+iPbwx4z2KCgo2Kl2TqjEBQKBQCCIA4TAFggEAoEgDgirSlySpBOBp2VZPk2SpCG4Eh1scZ9+WZblheG8v0AgEAgEzYWwCWxJku4CrgQq3YeG4cp5++9w3VMgEAgEguZKOFXi23AlCfAwDBgnSdIqSZJmS5KUEcZ7CwQCgSBOWLp9KWcvPptBrw/i7MVns3T70uA/aoGENTSpJEndgAWyLJ8kSdI1wHpZlgskSbofaCPL8p1avzfbrau6upqUlBTTyot3RHv4I9qjMaJN/BHt4Y8Z7fHNoW94Zccr1DpqvcdsCTZu7HYjo9uNbmoVI4oZ7VFVVVUwbNiw4UrnIunW9Z5PUoH3gBeC/UC4dYUX0R7+iPZojGgTf0R7+GNGe0xaPMlPWAPUOmpZfGAxN4y+oUllRxqT3LpUz0XSSnyFJEkj3P8+A1CvlUAgEAhaBPsr94d0vCUTyRX2zcAL7jR++4H4mjoJBAKBwHRy0nPYV7lP8bjAn7AKbFmWdwAnuf+9BjglnPcTCAQCQXwxaegkpn43leqGau+xlMQUJg2dFMVaxSYxHZpUIBAIBM2bcT3GATBjzQz2V+4nJz2HSUMneY8LjiEEtkAgEAiiyrge44SA1oEITSoQCAQCQRwgBLZAIBAIBHGAENgCgUAgEMQBQmALBAKBQBAHCIEtEAgEAkEcIAS2QCAQCARxgBDYAoFAIBDEAUJgCwQCgUAQBwiBLRAIBAJBHCAEtgYiqbpAIBAIYgURmlSFpduX+gWk31e5j6nfTQUQIfQEAoFAEHHECluFGWtm+GWPAahuqGbGmhlRqlHzR2g0BAKBQB0hsFUQSdUji0ejsa9yH06cXo1GMKEthHzzQrxPgUAdIbBVUEueLpKqhwcjGg2jQj4UhACJHJF4nwJ/RP+OL4TAVmHS0EmkJKb4HRNJ1cOHEY1GuLctoiFAWvIAKrahIouYIMUfQmCrMK7HOKaOnEpuei4WLOSm5zJ15NQWbXAWTmFiRKMR7m2LSAuQlj6Aim2oyCImSPGHENgajOsxjk8v+ZT1V63n00s+DauwNkMYhlOghluYGNFohHvbItICJJYGUE9fuuynyyK20hfbUJFFTJDiDyGwYwAzhGG4BWq4hYkRjUa4ty0iLUBiZQCN1ko/1PfZkrcPzEBMkOIPIbBNxsggYoYwDKUMI6unSAiTUDUaZm1bqLVHpO0YYmUAjdZKP5T32dK3D8xA2OnEHyJwiokYDbZihjDUW4bROuak57Cvcp/i8Wgyrsc43QJ66falzFgzg/2V+8lJz/EOTMHaI/A34doamTR0kl9dIDoDaDRX+nrfp9akoiXbmYRCpPu3oOkIgW0iRgeRpghDjxBy4lQt2/c6pfvoqWOsCBOjqE1UUpJSNN9ZKBOCphLpAVRpAjOux7iYnZz5EsqkQu05w0Ek72UGkezfgqYjBLaJGF2ZGBWGgUIoEE8Zwa5Tq2Pg4HNBrwtYVbSqSYNRtAY0tcmUWptEat9YqT0+veTTiNxXTbMQD5MzrUmFb5u2trWmqr6KOkcdEJ4Qw2qTYRHOWGA2QmCbiNGVSSgrK9/ByGKx4HA6FMvMTc/1lnH24rM1hbVSHZUG9A+2ftAk17ZoxmcPVQBHYjVpVnsYmQRpaYM8E4ZYXimqTSrGdBrjd7ystqzRb81UnQebDAs1vcBMhMA2kaasTPSopgIHB6dTWQ1uweK3SgsmrJTqGI49wmjuO6pNpjJtmdQ01ERlNWlGe4TLbsLTHwsLC+nXr5+uuoQDtclI4CS3ta01FouFhfJCXeWG01c/XPcSRIZY3tZo8VbiZrqGBLNybeq99AwO0Hh1qLVaVLPEbYrhkdpzmmXMpNWOaufULGLvPfFew5bmet9n4HWP//A4Zy8+W3ECAaG1h1GL7lixSNcimCW4x6vgydFPUtNQQ2lNqe6yw+2rH457NQdi3RUv1r0PWvQKOxwqWrWVcij3Upvh6RkclFaHaiv/67tezw2jb1Asx6h6X+s5zTCu09onBOMW36GuJvW+T6Xrgq0CQxngI203EUn0aiD0TmQ9mO2rrzbx8rCvch9nLz7blJVarKz+/Orxm74tvEjYEzSVWPc+aNEr7Ej6m6rd675v7/ObbWrN8NQG8gRLgubqUG3lP7rdaNX6BvPRVJspa7WpUb9P3zZRwlN+sPdpZuQ6vX0n3MLE6Eo5HkLv6p2MBJucJFmSyErOCstzKvVpJcxYqSmNDfd8cw8DXx8Y0dWq3lVo4HVltWVeYe2hqeOt2Sv2WAlepEaLXmFH4uVouVMBXqMxPW5GaqsiPQOQ0sq/sLDQr46+e4FlNWW0trUmJSmFspoyv9m81upSq02Nui01dZ8wHB+bWcLEF19DQb0EWylrrchi3aVHr0ZGa5VrpE2VUFtRqvVppW9ea6WmZ+Ws9R3sq9zHA98+wFM/PdXoezUbszUfRr/PcGhIY92lsUUL7HC/HD3uVL4EczMKh59uYB19rWrLastISUzhydFPBv0QPR9ssDY1IiRC2SeM1MdmhjDxJTc915A7l1KfGNNpDDPWzOCeb+7xu9aMAS2SKlm9avumTGT1EEwwKPXpe7+5V7EsT182oioO9h3UO+u9+/jhVDebPVnVs82m1OfMUl8HvgtrgtVPExBLW0VhVYlLknSiJElfuf/dS5KkbyVJ+kaSpJclSYq6Oj7coflCVYdq4SvwzExIEqyOSiorrQ9WTUXo2cfTYyAWSLAP2vPOQnmfRgzXfM+pCeHA59SjMm1qn/PtE5OGTuKDrR8E3T4wQqQNcvSq7UMNaRrqezeydaa1VWFUVRzqxDNYaOJQjSU91+ndhtFbX62xYeDrA7nnm3sU+5wZhrGB5ZfVluF0OsO2hdJUwrbCliTpLuBKoNJ96FngAVmWv5IkaSZwAfBeuO6vhtFgIEZWFlodJ8GSoOhDHWk3Iz2dO/AardWl74qvKQZiwWa9HpRUnsHek9qK6fqu17N9+3bVegXWWY1gxm5jOo1pcgAaNcLpZtSUFY3RlblejYwRt0i9/VGtvbSMybS0A6GqioNtq+kpw0NTjCVDDayjdF2SJYlWtlaNLPpD+c70avPUCKb5rHfWk5qUyjd//SZoOYF9ugc9NH/TVCxqvrxNRZKkvwDrgfmyLJ8kSdIeoJMsy05Jki4AzpZl+RatMgoLC51m+oC++s2rvLbztZBVZ0ov2Pd3aoOR2krMI2TUyoSmq731DJCFhYVM+m1S0IEgUF0brD08aD0/KKuv1drG86GHsj8X6ntpZ2uH1WoNuc5qky+jam4l9Aq8Qa8PUg1Ta6RevpbzamVbsLD+qvWadVcbIHPTc3VPYJqqjjfSH0H9/XpQG0PU6qvnHXnItGX6qctDJfBda7VBKNfpfRdGxkZQfxceLFh4cvSThrZCtDRkvuUH9mmtbQzPvbU8b/RSUFBQMGzYsOGK9QqXwAaQJKkbsMAtsPfKspznPn46cK0sy1do/X7dunXO5OTkJtfjm0Pf8HbR2xyqPaR4vp2tHS8Nfkn19/9v3f9T/W2rxFZUO6qpd9Z7j9kSbNzY7UYAXtnxCrWO2kbnRrcb7a3X4drDZNuymdhpoqLltt7rfK/Xuq+H6upqfq74udG1vij9Tm+dLvvpMtUBHlA9l23LVmzvYO8psH5qbfDi9hdVB0wLlpDrrIYFCwtH6AvkoYXe9wnafVXrd2pUV1eTkpKiWXZTvh+9dQylDdQw0h/1EkrfDLU91O43sdNE7zeYnpiuOg75to9WGywcsTDoWBnYp337Ryg09V142jvUsVHr3krle1Dqf0pkW7N5ecjLmtcEo6qqSlVgR9LozHeKmgGUBvtBcnJyk6MsLd2+tNGqOpDDtYfp16+f6mzw8E+HVX9b0VDR6Fito5bFBxbz6SWfkp+frzoT7Uc/bkB7NhZY/0O1h3ht52vk5+erziInLZ7UqGN56uQ7+yssLOSGITf41dHXSlxr5qyn7jm/aaus1M6pqSA970kPWm2gpkrTWmFr1VltBZaTnmNKlDC97xPgzuQ7VVezmbZMLBYLL25/kcUHFutaofqusJXKTklM4c4T76RfD/Xn1Pp+lFB6tlDaQA0j/dEXrZV2KH1TqR3VVMVqHK49zA2jb/D7BrVWvZ5zWkmCtidvDzpWBvZpo5HwmvIufPtc4Di0dPtSJq2ZpLnyV7u3UvmhbkeU1JU0+ZsvKChQPRdJgb1WkqTTZFn+CjgP+DISN9WzX+RrCBJKwA8tAkM8GsXIvmGoxhjhcu8JtteltcfXVGtvrTZQU6VN7DSR/Pz8kOt8Qa8L+GDrB2GzOwjlfap5EgTWXctmQG2gM+ql0JTvR+tZtY4rEWp/DMTpdNLO1k5x9RlK39RqR73qcqX76Q3aFIjevXUz+7TRd6Hlpqd3f17p3krlh+rlA5Bty9Z9rREiKbDvAF6TJMkGFAKLI3FTvXG0gwX8CPXFhTv0odZzxYovoZ4BXu1cU6Nw6TWM8zMYqenhXSmGWuchHYaEzd0p1PepNHArJYDxtSJWG+gCjWgCy/ZY22qt6owYS+l1j2uqoPS4wHm0SylJKaqr3Jz0HC7peImiHUyogkxNuOqZ3IR6Py1B7Cug1FzRAq8zg6aMDWroXdzonXgaCXo0sdNE3dcbIax72E3FDKMzLQMD304YzKAmlMHHTB9QvUYivug1Cot2YgctmmpgpLcNfIm19tDqc6H2Ma3+rSYkctNzmdF/BtuTtyu+C602Bn0W9UooPZuR9xkMtTLVNCZTR06lR00P1fbQc79QPRjAmMGlL3qNBY2MNdH4ZkI15vMYqIX6zoJpO5TeS4+aHmaoxGNiDzsq6A2qoDfgRzg+KCP115phhyPASqRpqpo+2m0QjgmHB49l9Yw1M7j3m3t1la/Vv7W0ON8c+sZvRem7+g7mn6ylcgzVzS0c71Ot/quKVjF15FTFexUWFhrqm3rVteF4Tr3aCSNjTaQxsnXZ2tbaUEQ0IxH0PNEjw0WzX2GD8ZltqO4a4SJc9zNzdhwrSQmaglntYcZq0KhLoFr5WnVSW8XnpudSV1enuGebm57L/sr9hrwAtFzAIokRNzWjfcTI6tUszBrbFP2Om6BxMIKR70JtmyNY20dLS9eiV9igL7dvKDPbcBlpqRHp+4VKOGL6xjNmhEzUWvUaKT9Y/1ZbWWmF2FRbgThxkmBJUMzXHmk7Ci0iaesRzaQSZoxtat/4mLZjWFWyKmLffii5CjweL2o2CcHaPtpaOiVahMDWS6wLxljFrJi+zQUzBmejKmwt1Pq31sA0/cfpqlbRWsaYSu5PsaZejaQKONqGoOHyVvms+DMcOBodD9e335StS7XfaBFrMiHq8bwF8U80Vw+xiN5Yy1poxUU3o/xAxvVQjlE/sdNE1XqM63EshrcawVK/RhPf+oe7juHOWxBu1L7lQGEd7PqmorcdI+miFknEClvQZKK9eog1zFi5GVVhm83odqM1g/94ViBq+8FOpzNm9qyViNQKKtLqVbNtStS+8QQSFIV2uL59ve2oNWEw20UtkgiBLWgy8WBdGknMGpyNqLDDgR6hJiZtwYnU5CAcNiVq33jgHrbneDi//ab0x0gY+YUTIbAFTSYWjTOiTbgH51jbWxOTttghHDYlat94j5oenN739Jj79ptrfxQCW2AKsSZABJFFTNpih3DZlCh940b90sNNc+2PQmALBAJTiMWBuyUitidcNMf+KKzEBQKBoBkRbYt0T3z5Qa8P4uzFZ7N0+9KI3LclIFbYAoFA0IyIpjpYBFEKL0JgCwQCQTMjWupgEUQpvAiVuEAgEAhMQQRRCi9CYAsEAoHAFMIRhU9wDCGwBQKBQGAK0TZ4a+6IPWyBQCAQmEJz9X+OFYTAFggEAoFpNEf/51hBqMQFAoFAIIgDhMAWCAQCgSAOEAJbIBAIBII4QAhsgUAgEAjiACGwBQKBQCCIA4TAFggEAoEgDhACWyAQCASCOED4YQsEAoFB6urqKCoqorq6OvjFzZC6ujoKCwujXY2YIZT2SElJoVOnTlitVt3lC4EtEAgEBikqKiIjI4Nu3bphsViiXZ2IY7fbSU1NjXY1Yga97eF0Ojl8+DBFRUV0795dd/lCJS4QCAQGqa6uJjs7u0UKa4FxLBYL2dnZIWtmhMAWCASCJiCEtcAIRvqNENgCgUAgEMQBQmALBAJBC+XHH3/ktttua1IZpaWlfPTRRwC8+uqrrF+/3nBZq1atYuHChU2qjxK33XYbP/74oynlv/nmm0D46qqFMDoTCASCCPH+2j08s0Jmb6mdvKxUppwjceGQ/GhXq0nIsswXX3zB+PHjueGGG5pU1pgxY0yqVfjKf/nll7niiivCXlclIi6wJUlaAxx1//mHLMvXRLoOAoFAEGneX7uHe5dswF7XAMCeUjv3LtkAYFhoV1RUcP/991NeXs7Bgwe5/PLLufzyy/n111954okncDgcdOzYkenTp7Nz504ef/xxALKysnjiiSf8ylq2bBnz5s0jISGBYcOGceedd/LCCy+wdu1aqqqqmDZtGu+//z4bN26ktLSUvn378tBDDzFz5kw2bdrEwoULWbt2LWPHjuXkk0/m3nvvpaioiIaGBq655hrGjh3LlVdeSd++fdmyZQsVFRXMmDGD/Pxjz75kyRK2b9/OX//6V+644w5ycnLYvXs3AwcO5JFHHvFeV19fzxVXXMEtt9xCv379uOqqq5g1axa5ubnea9566y0WLVpE+/btOXz4cKPyb775ZrKyshgzZgxjxoxp1DatWrXiscceY/369dTV1XHrrbeyZcsWysrKmDp1KoMGDWL79u3ceeedzJkzh6VLl5KQkMCIESOYMmUKL7zwAkVFRRw+fJi9e/dy7733Mnr0aEPv2UNEBbYkSSmARZbl0yJ5X4FAIIg2z6yQvcLag72ugWdWyIYF9s6dOxk3bhxnn302Bw4c4Morr+Tyyy/noYce4tlnn6Vnz54sWrSIbdu28cgjj/DEE0/Qq1cvFi1axKxZsxg5ciTgUmu/8MILvPvuu6SmpjJlyhRWr14NQI8ePXjggQeoqKigdevWzJ07F4fDwbhx4zhw4AA33XQTCxYs4LLLLmPt2rUALFy4kLZt2zJ9+nQqKiq4+OKLOemkkwAYNGgQ999/P8899xxLly5VXZXv2LGD2bNnk5qayplnnklxcTHt27cHICkpienTp3PTTTfRvn177rrrLj9hfejQId544w0++ugjLBYLF198caPyi4uLeffdd7HZbEyYMKFR2wwYMIAjR46wePFiysrKmDt3LpMnT+bNN99k6tSpLFmyBHBpGJYtW8aCBQuoq6vjrrvu4ssvvwTAZrMxa9YsVq9ezZw5c+JLYAPHA2mSJH3qvvd9siz/EOE6CAQCQcTZW2oP6bge2rVrx+uvv86nn35Kq1atqK+vB1wCq2fPngBceumlAF6hDa4AH926dfOWs2vXLkpKSrzCs7Kykl27dgF4/YSTk5MpKSnh9ttvJy0tjaqqKu/9Atm2bZt3MtCqVSt69uzJ7t27ATjuuOMAyMnJ4dChQ6rP1qVLF1q1agVA+/btqamp8TvfqVMnhg4dyrp16xqpp3ft2kWvXr2w2WyAa5IQSKdOnbznldomPT2dwYMHA5CZmcnkyZMV67l9+3aOP/54rFYr9fX1DB8+nC1btgDQr18/77PW1taqPqteIi2wq4DpwCygN7BMkiRJlmXFt15TU2NqFJ3q6moRlccH0R7+iPZojGgTfwLbo66uDrtdn8DNzUxmb1mN4nG9ZQTy6quv0r9/fyZMmMDPP//MV199hd1up127dmzatImuXbsyd+5cunbtSteuXXnkkUfIzc1l7dq1HDp0iJqaGhoaGsjOzqZjx47897//xWq18sEHHyBJEl9++SUNDQ3Y7Xa++uorioqK+Ne//kVJSQkrV67E6XRSV1fnbYf6+npqa2vp0qULP/zwA6NGjaKyshJZlmnXrh0NDQ3U1NRgt9v9fuehtraW+vp6qqurcTqd3nMOh4Pq6mq/a9evX48sywwZMoRXXnmFq666ynuuY8eObN68mSNHjmC1Wvntt98455xz/MoHvOUptU1SUhIrV65kwoQJlJeXc9ddd/Hyyy/jcDiw2+3esvLz85k9ezbl5eUkJCTwww8/MH78eGRZpr6+Hrvd7m3nwPccaqS4SAvszcBWWZadwGZJkg4DucBupYuTk5O9MxQzKCwsNLW8eEe0hz+iPRoj2sSfwPYoLCzUHenrrnP7+e1hA6RaE7nr3H6Go4WdddZZPP7446xcuZKMjAySkpJITEzkscce49FHHyUhIYH27dtz3XXX0bVrVx5++GHq6+uxWCxMmzaNgwcPkpiYSH5+Ptdeey033HADDQ0N5Ofnc8EFF/Dtt99itVpJTU1l+PDhzJo1i+uuuw6LxULnzp0pLi6md+/ebNu2jYULF5KUlITNZuNvf/sbDz74INdeey01NTXceuut5Ofnk5iYSHJyMqmpqVitVm/ZHmw2G0lJSaSkpJCQkOA9l5CQQEpKivfv8vJyHn30UV588UXy8vK49NJLOeWUUxg4cCAA+fn53HjjjVxzzTW0bduW9PR0kpOTVct/9NFHG7VNt27dKCgo4Nprr6WhoYFbbrmF1NRUevXqxYMPPsjIkSNJSkpi0KBBjBs3jmuvvZb6+npOOOEExo4dy/bt273Pl5ycTGJiYqP3bLVaG31fBQUFqu/b4nQ6DXUUI0iSdDMwUJbl/ydJUh7wBTBAbYVdWFjoFAI7fIj28Ee0R2NEm/ijJLBDaZ/mZiUuQpP6E2p7KPWfgoKCgmHDhg1Xuj7SK+zZwDxJkr4FnMC1asJaIBAImhsXDsmPawEtiC4RFdiyLNcCl0fyngKBQCAQNAdEpDOBQCAQCOIAIbAFAoFAIIgDhMAWCAQCgSAOEAJbIBAIBII4QAhsgUAgaKGIbF2hI7J1CQQCQUtg/Tvw+aNQVgSZneCMh2DQhGjXqkmIbF2RQwhsgUAgiATr34GP/gl17vCUZbtdf4Nhod1Ss3WVl5dz0UUXsWLFChITE3nmmWfo378/Y8eO9V7THLN1CZW4QCAQRILPHz0mrD3U2V3HDeLJ1jVnzhxmz57NvHnzAHjooYd44oknWLRoEaeeeirbtm3jwQcf5OGHH2b+/PmMGTOGWbNmecvxZOuaN28eb7/9NgcOHPDL1rVgwQI6duzozdb17rvvsm7dOm+2rpNOOonLLrvMW54nW9eCBQuYO3cu//nPfygpKQFciTjmzZvHKaecwtKlS1WfbceOHUybNo1FixaxatUqiouLvecyMjIYNmwY3377LQ0NDaxatYozzzzTe96Treudd97hpZdeoq6urlH5xcXFzJ49m+uvv16xbT777DNvtq433niDjRs3cvPNN5OZmcnUqVO95fhm63r99dfZuXNno2xd999/v/fdNAWxwhYIBPFBvKuTy4pCO66Dlpyt69JLL2X+/Pk4HA5GjhzpzbzleZ7mmK1LrLAFAkHs41Enl+0GnMfUyevfiXbN9JPZKbTjOpgzZw6DBw9m+vTpnHvuuXhyQ3To0IEdO3YALkOwlStX0r17d55++mnmz5/PlClTOO2007zldOrUidzcXObMmcP8+fO54oorvMIqIcElJlatWsW+fft49tlnuf32270ZrxISEnA4HH716tmzJ7/88gvgUttv3ryZTp1Ce06LxaJ5fvjw4ezevZvFixdzySWX+J3r1q0bW7dupbq6moaGBsWMWJ7nAhTbpkePHmzYsAFwqeD//ve/AxCYf6NHjx6sX7+e+vp6nE4nP//8s3eSE+wZQkWssAUCQeyjpU6Ol1X2GQ/572EDWFNdxw3ypz/9iccff5xPPvmEjIwMEhMTqa2t5ZFHHuG+++7zZuu6+uqryc3N5e67726UrQugbdu2XH311Vx55ZXebF3nnXee370GDRrESy+9xN/+9jdvtq6DBw/Ss2dPNm/e7KfynTBhAg8++CATJ06kpqaGf/zjH2RnZxt+TjXGjx/P8uXL6d27t9/xtm3bcv311/PXv/6Vtm3bBk3IMXXq1EZt061bN77//nsmTpzozdYFrsnInXfe6dUgSJLEeeedx8SJE73Zus4880w2bdpk+vNGNFtXqLSobF1RUPfFdHtEgbC1RxyrcmOmj0zNwpUvKBALTC2NWDWamq0rnvuCEtHO1jVr1iyysrIarbCjRXPL1iVQIgzWo4IYQbxbc8js5FaHKxyPJwZNEO/dJO655x4OHjzIzJkzo12ViCH2sGOBMFiPCmIE8W7N4YyHXOpjX/Sqk9e/A88NcK3SnxsQX/veAlWeeuop5syZ42ds1twRK+xYIAzWo4IYQbxbc/CsSkNVJwsNh6AZIQR2LNBc1H2Cxoh3ax5G1MlmGau59577NpO9Z0F8IlTiegmnWq0p6j5BbCPebXQxQ8Ph41JmiVeXMkGzQAhsPYTbB3TQBBj/PGR2Biyu/49/XszgmwPi3UYXM3yfhR2CIEYQAlsPkfhgB02A2za6XFRu2ygG9OaEeLfRwwwNRzO2QxDZukJHZOuKdZrxBysQNGuMGqv5YqIdwtLtS5mxZgb7K/eTk57DpKGTGNdjXMjlmEZNOVSXwd61kGiDjFxIaxtSESJbV+QQAlsPwnBIIIgM4Qgs0lTfZ5MilC3dvpSp302lusEV0nNf5T6mfjcVwLDQblK2rgemQMVBcLrCii77fBXzFi0lwZbKsBNOjOlsXQA333wzFRUVAKxZs4a5c+cyYsQI73mRraulIgyHBILwE6vxwn3sEJxNsEOYsWaGV1h7qG6oZsaaGYar1qRsXa/NBFzCuvRoBS/MW8S8fz/A289PjflsXeBa6c6fP5/Bgwdz3XXX+Qlrka2rJWOGWk3Q/IhkmMlmFtJSkViOF+5epW9qQqjW/ZX7QzquhyZl6+qQ4S1n1579lJQe5YZ7ngKgsj4h5rN1AcyePZuSkhKmTZvmd7y5ZusSAlsvIqSgwJdIBuRoKcE/mrmtSE5aB/ZVHWh8PD3HcJmebF2XX345P/zwA19//TVwLFtXt27dePXVV+nevbs3I1VeXh4FBQUUb13jLadTbgdyO2QzZ/r9WJPTWLJapl+/fnz22WeNsnV5VssrV64EtLN1nXXWWWHL1rVo0SIKCgp44YUXGp3zzdZltVopLCzk/PPP97tGKVuXt22Ki0lKSmL58uWAK1vX5MmTmT17tmK2rrlz5/pl67rwwgvZtGmTyNYlEMQETVkNhrpajuWVp5k0Z1uRqhIm9biYqb/PptpxbKWVkpjMpKGTDBfbpGxdD97FwT9+B6BtVmuuvnQcV05+lAZLEvmdu8Z0tq7i4mIefvhhhg4dytVXX+295/jx413PI7J1RZ4Wla0rCoTaHu+v3cMzK2T2ltrJy0plyjkSFw7JD/7DOCGk9jCaPSpwtQwuewitPdEoZqqK6DdjpG0ijOFsXQd+g4Zalu77jhlbF7G/+jA5KdlM6jORcUNuDGONg1BVAuX7oKHWkJV4tLN1xRoiW1e8Y/LeY7SE5vtr93Dvkg3Y6xoA2FNq594lruTuzUlo6ybYalDtvRtZLTfnlacvzdlWpMG1qh6XO5JxuSOjXBkf0tqG7MYliB5CYIcTk/ceoyk0n1khe+/rwV7XwDMr5JYpsLVcfbTeu5F9WpPciuICo7YiRifGkTLmS7R5hXaj4wKBToTADicm7z0qCc2zGr7mpA/+AR8c0h5wlAYm60Dd995bag/peLNHazX43AD1925ktdycV55mYHRiHMLv/DVb+/w0W06nM7hxUUauq3ynj3GWJcF1vKXSRHV8vGNkO1oI7HBistVroHA8P+FbnrLOIg33zF1twFEZmFoPuxt07k/mZaWyR0E452W14P0rldWgs6wIpeHbWVaE5eJXja2WtVaeZqwSw11GOFeyRifGOn+npdk6PiuFw4cPk52drS20PYKoqQKquQi5qhL/CUxDLY7SXRSVVFGV2JqOmSm0SdOhfYjT9nA6nRw+fJiUlJSQficEdjgxee8xUGjelfQOaZYANZvSQKUyMLVfPxPO1RdHeMo5kt+gBZBqTWTKOVLIz9HcOUA7cihWPm7WatkrAHcDFrxGaUa2XbRWmnq1MFplQHjd0oxOjHX+Tms76Ks7RlNUVNQoqIc6CYB7kC45ADR281KlthLsJeC7MrPsgdS2YEvXX46J1NXVYbVa9f+gttIVCtWh7L8NB6gnkULSSU5rRZpNQ0TFeXukpKSE7OoWUYEtSVIC8BJwPFADXCfL8tZI1sEohoy9lPYesbgGrOcGhKa+HjSBKedIfPveS0xmAXmWQ4qrOKDxQKQyMFk9PqE6Vj+eZ9XVBi08oMiTtZfypHWW32SqymnjybpLmQH692nVnq2RNXWAai3UbRe1leZ7N9HX6dC51aIwMfVNkBNOtzSjE2Odv9PaDrJard7AIo0IpW/qufa5ASr17exKKhMFQvIiWP8OfBw4HipT5bTxL+v/Y+oDj6hfFO/tYYBIr7AvBFJkWT5ZkqSTgH8DF0S4DiFj2NjLbzWlcyWksVK5MBH+bJ1FUkB4w0YEDlQqA1NdWkdswVZGPoPIhWc8xIX36PAxbuEBRX5pfRb3HHVpQPIsh9nrzOZf9RMoaH1W8B/rWTkrCdhAQtl2UbvW2eCaFOrtq6HWw6yAKEaN8nT+ztB2UCh9U++1sRRYxt1P+4YyUdbTb92kWWq5rvZNQENgx1J7RIhIxxIfBSwHkGX5B0DR1yzW0FKJBcWTWjGzM6orIV80VjssuT64sFYaqFRioRcPukn9fsvuNhbXWa28Jde7ZsRmxoWOpTzF699xPd/ULFZa/h+2pARG1T5Pj5q3GFX7PCsTTw2+feAXSxtU+4ueASmUbRc91+rtq0plB8tJ7dN2hvqI0ZzjWr8LeJ+X2L7z+2nQ7aBQ+qbea83I7W0GPv3UEsrYEKIgzUs4rH1BrLRHBIlo4BRJkmYB78qyvMz99y6ghyzLihsa69atcyYnJ5t2/+rq6pA3+QHGvr5dLWwFn1zVQ1cZfRee7OrcATixsOmy74Nep4XTXZu6tI4UD7qJo13PbXRN653Lab9+JtaqA97rDnY8jcEf/EmlXigbTlkSwOlUvVew+jsSU9h3wj2KdQwVvW2qF6P9o/XO5eT+/BQJPpOpuoRkHnXewJv2k2lls2CxWCivcdA+PYmrhrbh9B4Zjcrp+dGF2Kq040o73e9Z6zq1NlbqA0e7nqtYf7V7h9JXPfUAGpWv55wZfcQIwd6n1jv0EErf1HutUr2i0VZq/bQ2LYdt498P+XdqY81RWwf2XPShanmx0h6+GB1DfKmqqoqZwClHAd9enqAmrMEVbD6qkc7cap9tKUXsdbjUmh86RnlP52Wl6i9PRS1tyezkX4bavpoGFveejQ3Id/+nuCd2rksj4LnuaGEhFrV6qd3LbdVpq9pPfsHT5Ofl+69kgtQ/oaGa/MLZ5Os0dtNEb5vqxPD+0/JLIUDYWR01PJb5PsPOv9m1nVLr0tAcrKznxR9KyM/Lb7ydsjC4AZIlsxM2NdsInJDZmYQzHiJ/0AT8Sl//DhQ87f2N3/s79zbIyz/WXywJ4PTXKHnurbuv+tQD8C8/s9Oxc88NaNR2pvaRQHRZsjd+Js/7vHzC3/T1kVD6pt5r+/VTbceIRkJQ6ae2qgPabVP3mOIWhOX4y6lf+5af9rA+MYXWf55Ga63yYqU9fDBjD7ugoED1XKQF9mpgPPCOew97Q0Tuqne/xfdjTm0DtRXQUEsC0CnhEE9ZZ4E7S9vd1nfIqz4Mz+ncv9G7z6Y4GGugVEYo+2dq9UpKdVlgaqFkOKSn/h7VWODg2fts2PKpfgOypgQU0fJLD9WQTWMvLaSAM8Ema55nM2JpHsyFydcQTi1EqCcojO83EhgQRC2UqJqhXST3IUOxZNeqk57+EUrfDOXaWEhCZNTIT6PfJnU5ye94kl7j0VhojwgSaYH9HnCWJEnf4VoSXBP2O/p8pCEZ0CgIqzRLLQ8nvUFaQh2puFO96TV0Uuqsvc92/b3khsYffpDVDuDac1Pq2KH4pap9RKBv4hA4sDYytFOqdyflwfOX2T7l6mhXoy5SWn7pdRtCN2TTGMD2Hggh4EyQlbPfs4U6UIUiGH3a1VlWhCW1jevvJdfjZwhnL4EEq8uNxn7EmO+10RCvRgi2V6xjP771zuV+mgrKdlP/wa08/uFvvF4xwsd7IoS+GW+BcZoyUVbrty1M8Bql+Sf/0Gv6r3adXkJ1JdCb6CCMySKCqm98B0sjEweluidYITkj+OpdT/lGUXnXTsBiSVR+Tq33q/GOTvmknaKFcX5WKqvvOV25rHAM3AZdYPYsf458XwGlhFIZZvRvMDcZiNZ3ASrn/O9bu/xBxX3YIkc7RtW66pxqTeTJiwc275C97n7qLCtybavF8gQjgpikElfdw460lXjk0buyaKoKLtTf67UMNWIBa5b1pMfCfWopXDSzsaW5BzUr0cC6p7YFiyU0Ya1VfjDUrI9V3pUF1CclWu9X4x1NOUci1Zrod7mmhbFvm9+20bxBUMVTINiqqP36mcZcyMzo38HKCNW6XOu70Po2fOpkVchnDZBnOWbRrNuDJJI01RI/EHc/3XTZ9+b2U4EmzT/SmV6Vmx7LbK293VCFYagqylA+iHAkiwim6tZSuXuOPTcgdGEdrHw1tPYrDRj26dqfU6hbSAFnwkmIaldPoKBv7PvVLRA9KLWNVv9W0iIorfKDlaEzfsDPPW9l8u+9GX50PE/ZZh/bzgL/70LHal7NQn+v0z/Xc5Ni7JutZYnReAVhJ9YTwhig+QvsULIqBeJV3/rsz4Gymre20jV71fuCTQpb6huB7apWP3GXdSFp9v0ug6CkVMW9Ra1EBpp4hJKaatFoKEi9hPJ7rdWZGYZ9IXDhEAWLcBVMT58azCr6uQGNzvkGCtpra0cnyyH18tXaRq1/p7bRLzy0vhGt+AH1dr/yBxQ8wLC66/jAMQpnrdtg1HJYWZUbZKAuHnRToy2CKqeNf9X7Xxc0xr7eKHZmCFeN2AhVyx7iX3WXBey/NwNVfgQSwkSD5i+wAw1ogmVV8hBs3zTQmtyzctT7gk1YBfsOrOcnfMtddbNIq3db7NpLXOVd/KpfPUxJ0Wl2KEhwtbfXSlzDWE0vWqszPYZxlkRXYoIIJrEwPX2qwfjez6xo563Dv+onuBLM+MWsVzGE80Wtf4N+g0itb2TJDcrPrKDBSbXUclfSO3xYO4oPHaP4sGaUy4bgtgAbAh2arKNdz3W5wrn7QFVqDg9V/oUPHcdyXAcNqqL1XkzO8OcqX32im2bfx13OlyhJqOXD0lHmp+uN1mo1HAlhPOe1vFpCyIBohOa/hw3q+y2qHdmivS/ju89oS2+c51ZPtK2Avbuq1FymOm+k+//SOeWpL3h/7Z6gj+XrMqSZCETlNx789tz07HUZ3A9V/d3Fr7na88/Puv5/8WvGyvcl2D6+5x2q3euimY33kf0ikYUQ4UknTYqop4TW4KNxzled+6FjFPfUXUeRox0Op3uP+eJXYWpZ8G9EaW/afkT5erWtILX97RA1Ub57zNBElbXP95929yZGXfT/yM9KxYLLmDCowZnWewmHm1uQtkpzT2jA5P33pnwvTd1zD6Udfe+lNoH31N33WX6Z3ejZWu9cHlo9Q6T5r7C1MEMt3ZQPzD2jN7qy8h108tTUlgH10MxrrVcdZNQNRe/vzHBz0avB0NLABBKO1Y8PpuccN9I3y4oaxc7+0DGKD2tVVqVaKK1Y1bQaat+c2qo3xPgBgXvMZqaFDWXLA9B+L6GMSXpXrzq2gHwnNHr6m65tNaPfS7BxSE/wGzWbpMB21BMPH1wat2DX1IWWAdEILVtgm2GcZYLQDym4hg++A+tep8peY0A9NBMZfH53aP7bge45CvuhjdBrQKd1nZ6BSknoq/m9u//bFMwlQ2WgdZQWMfqpLwzt//kOfAkWCw0KbpaGhUuwvqlybsppYUylapZBpNqkDhqVbw/YY456Wlit96K3fULZa9WxBeQ7oQnW33QvMIwuZoKppY0Gv1FqRz3x8K2puu1d1LwIzKJlqMTVMOIyFYhR9bAPRldWvi5D/6qfQJXTFrQemm5GRj8wk1TF76/dwylPfUH3e5Y23hbwqq0yXQJXz718ty7OeAh+/V/T6qgyCdvrzPYOWnq2Mnyf994lG9hTascJisK6ScJFq29qnLtwSD5PXjwwNDWvXsz45nzLCnSBUyh/47DHKWh9lvnPokRTt5T0tk+oiW80toB8jeb09DfdWzdG3Uu1xqFQt3m89wxoR+8CQ8tbxKf9Mztr19lTlbSOuq4zSsteYUPTI+wEzvS9UaFuOGaRHKR8Q+n78HcZ+qh0FG2ttmNW4iorT003o68MagtCUH2pWUFrztoTV+vP/6y2+jZDna2w+vEd7PRoRXxRGvgAEi0WHE5n06129WwtqJzzqHnDkt+3qd9ciOWfAKw+P3y382LWlpKe9jE6uQ64d1VqDv+qu4yPakaQH6y/ub+tb+y72Wtr1yi3QqMFhlFtipYGwtBzW1z39GjXfMJOq9dBIRhQMNW5OwNiOG3shcA2A88HZtAlYMo5xlWQ/vtn49DMHxvwm0aDsdEPTOdHpCWUNbcFknWorYL55jZFe+A7sB5/OWz5FEdpkTfPteagpcHeUrvLuj/pHfIsh9jrdA2CHzlG8cdT43SXo+kKpjX4h1twmkzgc/6pb3u+3FQcXf92X0INCdyUtm/KVpzPvdOAqe7/NPH5thIs0MlyLLeCp/83WmAYtUXRGoeC2UDocSUMFgsiiK2LlpX4UetAIbDjBoOruHgNruFF5+ChJZTVBN2eUjuOlKLgezdavrmfPxrSAOcRDMOPrvQPtFG226VWH/88o1VCjoay33xVq59crnhu637PINjWasM1+Wpcp5A0E83Bn9aN0nO++cMu7/mYeO4gNg6mTjDCERxJC4VvK83HXc5vgdFUV65BE/h5xxE6r3mGDs5DHLS0Y/fAKZzgKUPrufW6EqoR6KoY6rMUFuq7j0GEwDaTJliMh2xpGiJKA76klLbVyMxfYfCwk8w9xeP5xccYS2uvXm1bAGCvI5tOCTqCd6j55pYVuVyRdAxwvoJhoe0d/6hY4J0ATDlnhWGtiOddLKx9k7QEf7VcmqWWu6wL8dWUhKqZOKvha0764B/wwaGYi9RkFLXtA19C3ZIwHZVJocfGwdQJhhmeFKGgMoblWQ77q9JNCDzy/to93PtzV+x1M7zHUn9O5MnOe/QlVQk8pzYuBBKoBo/BICpCYJtJkFWc6VGsdKI24P/jpLaYsj0Z4Bq115nN03VuVbHPwKS1V6+0LeBBd/AOLXWZzgHOVzBoucoZ1Yr4vou8ZOXy0+z+oS9D0Uycn/Ctq61wt1UMDDJNwfPNqE3mAmmSf3VTCWLjEEiTJxiR3NJQGdvq0zuy+txD8Pk/4YMi5SRBIdqKBPWaCXWbRytAkge9FuQmunEaoWVbiZuNhvVnoEWwEatio6h9AK+vUQliYQS3FeqolCWcUvO8376u52NTslC34GqLZ1bI/GVYPvkKKmXf4B3eIDNJk+he/T9OqXme9xtOcV0YzGJfyao4AN8Bf6+znfKzuidgFw7JZ/U9p/PHU+NYfc/pugZe33cRrHylOgUeD1TB6w2gEw/4fjN6Mcu/WtNjQQ0fC2+H00KRox331F3n9y0EEtUJRiiofFsVuSP9PURUkuc4Sot0t6PSJPRb2z/5xn6RsSAqCnVvsCRRSgYOp4X9tOfngY80Hg8imatdJ0Jgm4mGS4bpUaxCQG1QKK6sBwwOTiHea2+p3c9dCPwyK7On1M67BXuYco6kKrQvS3uN9y/4jWEV/2FexYjGEx8TXIZ8B3y9rnKh4Ns+estXE0KeVb3vJEhNKxDKgBkr6FGD+2KWf3WTJtfuSeHo1CWMqn1eU1iDuQFcTMnIpVaGyrfVat93uvaHlVwf1cYd3zbxaIw6JRwiwYIxd8yAulel5nJ3w00Mrn6FHjVvcVL1DP7v566N369ZWQ9NRAhss1FZxZkexSoE1AaF9ulJpq/8tYQLHFuV5melNopFpLUS9wzGQSc+OlbRWvje27Oy3+NshzPIBEDvpMe3ffzCfmqUr9UegT7TBy3tFe9r1Fc8mmh9G/lZqVxxUpew+IqbMblWemeB6J5g6BHEZsRCCFaGwrelJ1CIkuuj1rjj23amaYx86n6W8yUW1470O634fk2IsWE2LW8PO8LB6D17cGrJO5UEnNl73YH7w+cnfOvKWNRwmAMftOOshkv5kMYqbCP31OuiFmwlDsr7w7ctXKf6OzMIvHdB67P4+Zx/aLZFKJbage3zoWMUK52n8uQF6sIm2H65n8Hi+kpTfcXNwkifVrN5yM9KZfU9IYRIDREzJtdK78yQlbhewycz9lsNlKGWbhRLIg6HQ9X1UWtS5Hm3z6yQybPrC7kcCrrfb6QN+3TQogR2653LwTc1XhCDnKYKzsCBPBAlQRYONx3fwSPQVSmH4kb+lGBcAOo1xgoWLEbNat5okJlQCNViP5TQskaN1XTXKcAlpqm+4mYQSp/2/eYyU61YEy3UNRyb7upZlRr9bo1MrrUwxfNDrxA1Y7/VQBlK6UY9ecS1XB+DCU1v2z3X2XRD3pDGkBiLVdCiBHb79TNDisjVVMGptQenFlXIaFzxYBz7AP4JZf6uSr7+lB6aIgD1DFRGg8WE8jutj1m3m5sOQl2RhdOFT8klJhBT9011oLdPB35zpfY6rAkW2qRZKa2q0zUgG/1u9U+uj+p6ZtPQK0RVU9c6XWp0E+Mp+BKYbtR3FTqloXGb+m5t6RKaGv7mRt91UwJVRZsWJbDV9lscZUX0vGep34BghuBUG7AtoKrSa4o6TtdsU8Of0kMkOm9TVpp6fqf1MQOmurlFYtWvl2CGWtEYmPT2aaW61zmcpNmSWPvQ2bruZfS71Tu5LiyMsMDWEKK+3/tVrf7CA4kzSWqobnytXtc+o8FYVFahatsCHmHta3QKKn1TQy39zFNfGHrXgfXKTLViscBtC9fxzAo59iLo+dCiBLbafsteR7af4QOYs48VbCBXErBGB38lAXXbwnVMXrjOfzWvMgActLTDAhHtoEZXmnp+F8xwSM3N7Rb9EUG9xNKMPZihltnvVs8kUa1PO4FTdAbW0YvRMoxMrr2YYBej2o4qQvTnnrf69bl5FSOosNXzaPq7pNn3Nb6Bnv1sE/IiBOL7rQaOUU6OeYpo9k2VCUFT+ounXnERQc+HFmUlXjzoJs1MNXBsUA9m7awHLeteNSvJP/Vtr55NSwMlAeXrMuW1DlaxfMy5+ImQ/IljHa2POZibW6iENbtViKj1T4+hltnCWo+HgZbFtO9vzPjmjJZh+N4mWGdrtqOKO9Xk33s3+t4X145kYOmzONQ24d3aNU2PBm9Wr1eh3u6Ou208A58vamOU0b5pRn8JJYJeLNAiBLang476qg9TnTdSlZoLqAc22Ftq105DqVJ+4AegNZCrrQC/3FRsaPAPNqv0djqfASCYq5IZmOnjHQpaH7OWm5tRjARRCQeh9Numotf9KdD/PhA97nx6MVqG4XuHmuZSgVBcFd8/bQWnqBhzgStFq1ZAHt1unCY8VyBmu7YqvTNrgoWq2nrd443ee8dKgJtmrxIPVHnMqxjBQuvJPHnxQE3DBzP2Sj1qF6XBO5hbk5HZZrCIUN57ulVMm8KROtGHaCamCKamVjp31dA2ps6b5wAADchJREFUYa1TJIhkIplQBmBPn+5+z1JFK+xg7nx6CbdtRCOCJPzQU4bedgxmGOdBMZSvey/6mU907vGHIcqX2bYeSnvRlbX1HKmqA/SNN3rGzabU0WyavcDWmr0qDeqeUJmevbVg+1dq5U92GzCofbChdF49+4Rasbi1yg4n4bJ414OeAbixlXiEDYrCRDit0H0xMgAbdecLhXDaRjQiSMIPPe5rCRYLDc7G05jAdtQb+e1Dxyiow5229TAJWcf21ff+b6nibxpNGpqSvlOFcNh6+L6zU576glJ7nd/5YOONnnEzlizIm73A1hugI9BqUe9qUEtVEkoADWiaX7bWs6iVHYym+qFHIrqbVh21BmClcxG3AA6C2QF0QnVzC3bOyAAcSwZ6YEIbB0n4ocd9TUlY+7aJngQoiQFC/0PHKD6sHeXaH77t2KJD9yQrDOk7w639MTLeqFmyCyvxKKF3Rn/KU180uk7PajCYSqWpATRCDcqhZwDWgxnq7HC7OzXnXNBmP5sRNzeAPXvLefGHnZr1UHOPUepzkVTZB8OUNvaxrHaUFukKVKO2Uk60WHA4nZr5zpXwWFjrmQjpnjCFKcpXOLU/RsebSGmkzKDZC2wzQmWGWr7eMvR0FKP1amonNEOdHe7VVDRV7uHG7Gcz4ub2zAqZuro6zXpouceoCb9YGSBNa2O3TchohUk/NBYYat+uw+nkj6f8/Qr1+tWH4lucmWolxZoQPBhNjEX5CkasaW/CQbMX2Hpn9E3ZU/7LsHy+3FSsutJuyooyWkE5whVPOZoqsGjlIzdCsGcL9VmMvM9QzsXj5CkcVst6BEYo33QofvV6fYtL7XWkWhN57rLBMftujGBE4xNvNHuBDcc6cqGGVXRT9pTfLdjDkxcPBJStj5syw4vWrNGsiUKsqMDiTX2u9WxGniVYW6mdq6ur46CCf7reVWOsuMMoEW6r5VAMRNW+aSMJUELxLQ51m8yscL7hwojGRy+Bz6+01x3u9mgRfth68PUV1fJ/DraSMDuARjjK1EMk/XmNEkod9foMxwpaz2bkWbTK0zp31dA2jc75elIo5TD2RY/wi5avfjj6+IVDgvvjB/umfdujsqYea6IlpDqGw7dYzX/7i+3lusuIJGZ/70rP/+YPuyLeHhFbYUuSZAGKgC3uQ9/LsnxvpO6vBzP2lMOxoozGnl+o6uxoqJtDqaNZK8BIPafWsxlJMWrEze3CIfkUphwlPy8/qCeFUU2QWSshI+8lmgZwat+0GQlQwuFbrCYAjYbzDTdma3z0ai3C3R6RVIn3BNbIsjw+gvdsRFMH3FhK9BBu9E4Uoqlu1lvHYCrmY31in2qfiPRzqj1bOKxh9ZzT8qTwzWEcyrdlxt53U95LrBjAeTAjAUo4fIvNDucbbswep/UK+nC3RyRV4sOAfEmSvpQk6RNJkiKuW/1ie7m+sHwaxIOqOBL4qu3ueOfXmFc3q723P/Vtr7tPxIpaPVp9UI92yVcdDARVdZuxEoqV92IGZhl7BqrcrzipS5O21cIRzjecmP2N6BX04W6PsJQuSdLfgdsCDt8CPCnL8iJJkkYBbwInhOP+ary+5kiTZ/Ox5EcaDvRoIPQEfoDYMjhSe2+hrPBixbAqWn0wHEZ+ZqyEQnkvse4pEKvGnmpbHrEaztfsb0Sv1iLc7WFxqgy2ZiNJUhpQL8tyrfvvPUAnWZZVK7Bu3TpncnKyaXUY+/p2xTjGFuCTq3qYdp94obq6mpSUY2aNX2wv5/nvDlHTcKyVkhMt/HNkO07vkeE9dtXiXYqWw4F0SE/i9Uu6mFtpkwmlT6g9dzw8p1F8+4je/gH62yqUMtWI5L0CvxmzMaOO4eKL7eW8vuYIxZX1tE9P4qqhbRiZZw1re5iFUt1Dbc/AMk7olMrPRXbT26Oqqqpg2LBhw5XORVKf8TBwGPiXJEnHA7u1hDVAcnKyqckp2qcrf9h5WalhTYIRqwS6uV33wRd+AwVATYOT/22o4JZxI7zHiiu3By071ZrIfX8eQL9+sbN6USIva5/qiiawT9z359aKq4x4eE6j+PaRfv0gP0/fClWtjxRX1vu1ayhlqqH3vejt31pouYaagRntEc66BRpUhbs9lAhVS/L+2j1+0foOVtbz4g8l5OeFpoVQev5AzGiPgoIC1XORFNhPAW9KkjQOqAeujuC9AbhqaBte/KGkWUfCaQp6VYtqajul0IqxTijWzc19O0QPZhj5GS1Tq04Q/L3EypZGMGLNEC6WMGJgGI9BfdSImMCWZfkIEFUHgNN7ZHhdVFrqgKuF3kFWTchFwj/cbEIVwmIw1UekA/7oeS8tycOjuWJE+MbLRE0PsWniF0bEgKuO3kG2ua009UTCE4RGLPaRlhBrurljRPg2p4laixPYAnVCGWTFxEcQjFjrI2ZNImLd0rw5Y0T4NmWiFmvvWghsgR+xNsgKwkesDUaRoKn9+4vtwdONCsKHEeFrdKIWi/kHhMAWCFogsTgYxQNmxHIQGMeo8DUyUYtFYzUhsAUCDZrrKjQWB6N4QC30ZDwaMMUrkdICxqKxmsjWJRCooJahKFLZpMJJLA5G8YBa6Ml4NGASaNOUDHThQghsgUCF5hSjOpBYHIziAaV0o8LSvHkSi3kjhEpc0KwwU4XdnFehwsXJGM09lkNz3QIyQiy6JgqBLWg2mG1I1Zz8NwOJxcEoXmiunhRGv59wC/loTiJi7V0LgS1oNphtSNXcV6GxNhgJoouR7yfcbm7Cm8EfsYctaDaYrcJWyiscj+FXBQI9GPl+tNzczKA525EYQaywBc2GcKiwxSpU0FJQ+36cwClPfaGoig63m1tztiMxglhhC+KC99fu4ZSnvqD7PUs55akvFF2rYtGqUyCIF5S+Hw9qLo3hdnMT3gz+CIEtiHn0+kMLFbZAYBzf70cJJVV0uN3cxCTcH6ESF8Q8oRjDCBW2QGAcz/fT/Z6lOBXOB6qiw+3mJrwZ/BECWxDziH0sgSCyhGIPEu5JspiEH0OoxAUxj9jHEggii1BFxyZCYAtiHjF4CASRpSn2IHoMRAXGECpxQcwj9rEEgshjRBUtAp2EFyGwBXGB2McSCGIfkbY1vAiVuEAgEAhMQRiIhhchsAUCgUBgCsJANLwIgS0QCAQCUxAGouFF7GELBAKBwBSEgWh4EQJbIBAIBKYhDETDh1CJCwQCgUAQBwiBLRAIBAJBHCAEtkAgEAgEcYAQ2AKBQCAQxAEWp1MpiVpsUFBQUAzsjHY9BAKBQCCIEF2HDRvWXulETAtsgUAgEAgELoRKXCAQCASCOEAIbIFAIBAI4gAhsAUCgUAgiAOEwBYIBAKBIA4QAlsgEAgEgjigRcQSlyQpAXgJOB6oAa6TZXlrdGsVWSRJsgJzgG5AMvA48DswD3ACG4FbZFl2RKmKUUGSpA5AAXAWUE8Lbg9Jku4FzgdsuL6Xr2nZ7WEFXsf1zTQA19NC+4gkSScCT8uyfJokSb1QaANJkh4GxuFqo8myLP8UtQqHmYD2GAy8gKuP1AD/J8vyAUmSrgduxNUej8uy/HFT79tSVtgXAimyLJ8M3AP8O7rViQpXAIdlWR4NnAu8CDwLPOA+ZgEuiGL9Io57QH4FsLsPtdj2kCTpNGAkcApwKtCZFtwebsYCSbIsjwQeBabRAttEkqS7gFlAivtQozaQJGkorn5zIvBX4L/RqGskUGiPGcCtsiyfBiwB7pYkKQf4J67v6RzgSUmSkpt675YisEcBywFkWf4BGB7d6kSFRcCD7n9bcM36huFaRQEsA86MQr2iyXRgJrDX/XdLbo9zgA3Ae8BHwMe07PYA2AwkuTV0rYE6WmabbAMu9vlbqQ1GAZ/KsuyUZXkXrnZTDP7RDAhsj7/KsrzO/e8koBoYAayWZblGluUyYCswqKk3bikCuzVQ5vN3gyRJLWI7wIMsyxWyLJdLkpQBLAYeACyyLHsi55QDmVGrYISRJOlqoFiW5RU+h1tsewDtcE1kLwVuAt4CElpwewBU4FKHbwJeA56nBfYRWZbfxTVZ8aDUBoFjbLNtm8D2kGV5H4AkSSOBfwDPEab2aCkC+yiQ4fN3gizL9dGqTLSQJKkz8CUwX5bl/wG+e28ZQGk06hUlrgXOkiTpK2Aw8AbQwed8S2uPw8AKWZZrZVmWca0SfAeYltYeALfhapM+uOxfXse1v++hJbYJKI8bgWNsi2obSZIuw6WtGyfLcjFhao+WIrBX49qPQpKkk3Cp/loUkiR1BD4F7pZleY778Fr33iXAecA30ahbNJBleYwsy6e6953WAf8HLGup7QF8C5wrSZJFkqQ8IB34vAW3B8ARjq2SSgArLfib8UGpDVYD50iSlCBJUhdci6JD0apgJJEk6QpcK+vTZFne7j78EzBakqQUSZIygX64DPSaREtRC7+HazX1Ha7922uiXJ9ocB/QBnhQkiTPXvYk4HlJkmxAIS5VeUvmDuC1ltgesix/LEnSGFwDTQJwC/AHLbQ93DwHzJEk6RtcK+v7gF9o2W0CCt+JLMsN7nb6nmP9p9kjSVIirq2SXcASSZIAvpZl+WFJkp7HNZlJAO6XZbm6qfcTyT8EAoFAIIgDWopKXCAQCASCuEYIbIFAIBAI4gAhsAUCgUAgiAOEwBYIBAKBIA4QAlsgEAgEgjhACGyBQCAQCOIAIbAFAoFAIIgDhMAWCAQCgSAO+P/o3YYkjx6qFwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 576x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df3 = pd.read_csv('WALK.csv')\n",
+    "plt.rcParams[\"figure.figsize\"] = (8,8)\n",
+    "plt.subplot(2,1,1)\n",
+    "plt.title('Walking dataset')\n",
+    "plt.plot(df1['x axis'],label = 'aceleration in x direction')\n",
+    "plt.plot(df1['y axis'],label = 'aceleration in y direction')\n",
+    "plt.plot(df1['z axis'],label = 'aceleration in z direction')\n",
+    "plt.legend()\n",
+    "plt.ylabel('Acceleration values')\n",
+    "plt.subplot(2,1,2)\n",
+    "plt.title('Walking dataset')\n",
+    "plt.scatter(np.arange(120),df3['x axis'],label = 'aceleration in x direction')\n",
+    "plt.scatter(np.arange(120),df3['y axis'],label = 'aceleration in y direction')\n",
+    "plt.scatter(np.arange(120),df3['z axis'],label = 'aceleration in z direction')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86c09f40",
+   "metadata": {},
+   "source": [
+    "# Visualising the missing data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "bde21031",
+   "metadata": {},
+   "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>x axis</th>\n",
+       "      <th>y axis</th>\n",
+       "      <th>z axis</th>\n",
+       "      <th>activities</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>320</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>321</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>322</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>323</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>324</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>325 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     x axis  y axis  z axis  activities\n",
+       "0     False   False   False       False\n",
+       "1     False   False   False       False\n",
+       "2     False   False   False       False\n",
+       "3     False   False   False       False\n",
+       "4     False   False   False       False\n",
+       "..      ...     ...     ...         ...\n",
+       "320   False   False   False       False\n",
+       "321   False   False   False       False\n",
+       "322   False   False   False       False\n",
+       "323   False   False   False       False\n",
+       "324   False   False   False       False\n",
+       "\n",
+       "[325 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "b7eb0dcb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "x axis        0\n",
+       "y axis        0\n",
+       "z axis        0\n",
+       "activities    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "c62cbf85",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(df.isnull(),yticklabels=False,cbar=False,cmap='viridis')\n",
+    "plt.rcParams[\"figure.figsize\"] = (5,5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "405a448b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      " [-2.82432  0.07845 10.3166 ]\n",
+      " [-5.33482 -1.52984 10.27737]\n",
+      " [-4.15802 -0.11768 11.02267]\n",
+      " [ 0.82376 -0.31381 11.84643]\n",
+      " [-5.13868 -0.86299 10.39505]\n",
+      " [-4.98178 -1.80442  8.90444]\n",
+      " [-2.39282  0.07845 10.70886]\n",
+      " [-3.84421 -0.19613 10.08124]\n",
+      " [-2.03978 -0.86299 11.10113]\n",
+      " [-2.66741 -0.54917 10.12046]\n",
+      " [-1.9221  -0.54917 12.31715]\n",
+      " [-2.31437 -0.82376  9.92433]\n",
+      " [-7.45305 -1.9221  10.51273]\n",
+      " [-2.58896 -1.72597 10.66964]\n",
+      " [-4.15802  0.03923 12.19947]\n",
+      " [-6.7862  -1.96133 10.74809]\n",
+      " [-3.33426 -0.94144  9.80665]\n",
+      " [-4.55029 -1.7652  11.37571]\n",
+      " [-3.21658 -0.5884  10.00278]\n",
+      " [-5.09946 -0.86299 11.41494]\n",
+      " [-2.27514 -0.54917 10.08124]\n",
+      " [-6.51162 -2.11824 12.12102]\n",
+      " [-4.98178 -1.37293 10.12046]\n",
+      " [-1.80442  1.1768  10.66964]\n",
+      " [-2.942   -0.19613 10.08124]\n",
+      " [ 0.39227  0.70608  9.06135]\n",
+      " [-0.31381  2.62818 10.70886]\n",
+      " [-2.43205  0.94144 10.70886]\n",
+      " [-1.72597 -2.11824 11.0619 ]\n",
+      " [-1.25525 -0.11768 12.00334]\n",
+      " [-4.19725  0.       9.21825]\n",
+      " [-2.31437  1.1768   9.80665]\n",
+      " [-4.51106  0.15691  9.10057]\n",
+      " [-2.07901  0.31381 10.90499]\n",
+      " [-3.13813 -2.3536  10.23814]\n",
+      " [-2.70664 -1.29448 10.3166 ]\n",
+      " [-4.19725 -0.31381  9.61052]\n",
+      " [-6.15858 -1.25525 11.17958]\n",
+      " [-4.15802  0.23536  9.41438]\n",
+      " [-1.09834  0.      11.72875]\n",
+      " [-4.19725 -0.74531  9.1398 ]\n",
+      " [-4.82487 -0.5884  10.15969]\n",
+      " [-3.49117  0.07845  9.2967 ]\n",
+      " [-4.31493  0.23536  8.74753]\n",
+      " [-5.17791 -0.70608  9.33593]\n",
+      " [-3.02045  0.31381  9.8851 ]\n",
+      " [-3.49117 -1.09834  9.84588]\n",
+      " [-4.74642 -0.23536  9.80665]\n",
+      " [-3.13813  1.45138 12.27793]\n",
+      " [-2.47128 -0.70608 10.82654]\n",
+      " [-2.11824  2.98122  9.76742]\n",
+      " [-2.19669 -0.94144 11.53262]\n",
+      " [-4.98178  0.54917 10.86577]\n",
+      " [-3.13813 -1.1768  11.0619 ]\n",
+      " [-1.1768   2.3536  11.17958]\n",
+      " [-5.33482 -0.50995 10.00278]\n",
+      " [-4.19725  0.54917  9.33593]\n",
+      " [-3.0989  -0.47072 10.04201]\n",
+      " [-2.58896 -0.27459  9.84588]\n",
+      " [ 0.47072  2.11824 10.27737]\n",
+      " [-6.08012 -0.66685  9.8851 ]\n",
+      " [-0.74531 -3.53039 11.10113]\n",
+      " [-3.92266 -1.84365 11.21881]\n",
+      " [-2.31437  0.03923 10.55196]\n",
+      " [-2.31437  1.01989  9.21825]\n",
+      " [-4.07957 -1.21602 10.15969]\n",
+      " [-3.49117  0.07845 10.35582]\n",
+      " [-4.78565 -1.13757  9.84588]\n",
+      " [-4.19725 -2.90277  9.8851 ]\n",
+      " [-3.76575  0.27459 10.51273]\n",
+      " [-3.45194 -1.64752 10.4735 ]\n",
+      " [-3.60885 -2.70664 10.3166 ]\n",
+      " [-4.43261 -3.21658  8.47295]\n",
+      " [-4.43261 -0.94144  9.61052]\n",
+      " [-3.6873  -1.21602 10.08124]\n",
+      " [-1.25525  1.49061 12.04257]\n",
+      " [-6.7862  -2.74586  9.68897]\n",
+      " [-7.3746  -3.49117 10.08124]\n",
+      " [-4.62874 -1.96133  8.94366]\n",
+      " [-1.60829 -0.03923 10.55196]\n",
+      " [-6.90388 -2.54973  8.86521]\n",
+      " [-8.08068 -2.23592  7.80609]\n",
+      " [-2.62818 -1.37293 10.55196]\n",
+      " [-3.37349  0.11768 10.74809]\n",
+      " [-2.82432  0.35304 10.12046]\n",
+      " [ 0.27459  0.11768 14.7492 ]\n",
+      " [-2.23592 -1.45138 10.08124]\n",
+      " [-7.02156 -2.62818 10.23814]\n",
+      " [-5.53095 -1.64752  9.17902]\n",
+      " [-3.13813  0.31381 10.4735 ]\n",
+      " [-3.84421 -1.64752  9.61052]\n",
+      " [-4.55029 -1.05912  8.82598]\n",
+      " [-5.49172 -2.00056  9.1398 ]\n",
+      " [-2.66741 -1.84365 10.00278]\n",
+      " [-3.45194 -1.45138 10.19892]\n",
+      " [-3.41271 -1.05912 10.86577]\n",
+      " [-6.0409  -2.27514  9.53206]\n",
+      " [-4.70719 -1.41216  9.84588]\n",
+      " [-4.66797 -1.3337   9.8851 ]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Seperating the independent features\n",
+    "X = df.iloc[ : , :-1].values\n",
+    "print(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "b6a3b540",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['eating' 'eating' 'eating' 'eating' 'eating' 'eating' 'eating' 'eating'\n",
+      " 'eating' 'eating' 'eating' 'eating' 'eating' 'eating' 'eating' 'eating'\n",
+      " 'eating' 'eating' 'eating' 'eating' 'eating' 'eating' 'eating' 'eating'\n",
+      " 'eating' 'eating' 'eating' 'eating' 'eating' 'eating' 'eating' 'eating'\n",
+      " 'eating' 'eating' 'eating' 'eating' 'eating' 'eating' 'eating' 'eating'\n",
+      " 'eating' 'eating' 'eating' 'eating' 'eating' 'eating' 'eating' 'eating'\n",
+      " 'eating' 'eating' 'eating' 'eating' 'eating' 'eating' 'eating' 'eating'\n",
+      " 'eating' 'eating' 'eating' 'eating' 'eating' 'eating' 'eating' 'eating'\n",
+      " 'eating' 'eating' 'eating' 'eating' 'eating' 'eating' 'eating' 'eating'\n",
+      " 'eating' 'eating' 'eating' 'eating' 'eating' 'eating' 'eating' 'eating'\n",
+      " 'eating' 'eating' 'eating' 'eating' 'eating' 'eating' 'eating' 'eating'\n",
+      " 'eating' 'eating' 'eating' 'eating' 'eating' 'eating' 'eating' 'eating'\n",
+      " 'eating' 'eating' 'eating' 'eating' 'eating' 'eating' 'eating' 'eating'\n",
+      " 'eating' 'standing' 'standing' 'standing' 'standing' 'standing'\n",
+      " 'standing' 'standing' 'standing' 'standing' 'standing' 'standing'\n",
+      " 'standing' 'standing' 'standing' 'standing' 'standing' 'standing'\n",
+      " 'standing' 'standing' 'standing' 'standing' 'standing' 'standing'\n",
+      " 'standing' 'standing' 'standing' 'standing' 'standing' 'standing'\n",
+      " 'standing' 'standing' 'standing' 'standing' 'standing' 'standing'\n",
+      " 'standing' 'standing' 'standing' 'standing' 'standing' 'standing'\n",
+      " 'standing' 'standing' 'standing' 'standing' 'standing' 'standing'\n",
+      " 'standing' 'standing' 'standing' 'standing' 'standing' 'standing'\n",
+      " 'standing' 'standing' 'standing' 'standing' 'standing' 'standing'\n",
+      " 'standing' 'standing' 'standing' 'standing' 'standing' 'standing'\n",
+      " 'standing' 'standing' 'standing' 'standing' 'standing' 'standing'\n",
+      " 'standing' 'standing' 'standing' 'standing' 'standing' 'standing'\n",
+      " 'standing' 'standing' 'standing' 'standing' 'standing' 'standing'\n",
+      " 'standing' 'standing' 'standing' 'standing' 'standing' 'standing'\n",
+      " 'standing' 'standing' 'standing' 'standing' 'standing' 'standing'\n",
+      " 'standing' 'standing' 'standing' 'standing' 'standing' 'walking'\n",
+      " 'walking' 'walking' 'walking' 'walking' 'walking' 'walking' 'walking'\n",
+      " 'walking' 'walking' 'walking' 'walking' 'walking' 'walking' 'walking'\n",
+      " 'walking' 'walking' 'walking' 'walking' 'walking' 'walking' 'walking'\n",
+      " 'walking' 'walking' 'walking' 'walking' 'walking' 'walking' 'walking'\n",
+      " 'walking' 'walking' 'walking' 'walking' 'walking' 'walking' 'walking'\n",
+      " 'walking' 'walking' 'walking' 'walking' 'walking' 'walking' 'walking'\n",
+      " 'walking' 'walking' 'walking' 'walking' 'walking' 'walking' 'walking'\n",
+      " 'walking' 'walking' 'walking' 'walking' 'walking' 'walking' 'walking'\n",
+      " 'walking' 'walking' 'walking' 'walking' 'walking' 'walking' 'walking'\n",
+      " 'walking' 'walking' 'walking' 'walking' 'walking' 'walking' 'walking'\n",
+      " 'walking' 'walking' 'walking' 'walking' 'walking' 'walking' 'walking'\n",
+      " 'walking' 'walking' 'walking' 'walking' 'walking' 'walking' 'walking'\n",
+      " 'walking' 'walking' 'walking' 'walking' 'walking' 'walking' 'walking'\n",
+      " 'walking' 'walking' 'walking' 'walking' 'walking' 'walking' 'walking'\n",
+      " 'walking' 'walking' 'walking' 'walking' 'walking' 'walking' 'walking'\n",
+      " 'walking' 'walking' 'walking' 'walking' 'walking' 'walking' 'walking'\n",
+      " 'walking' 'walking' 'walking' 'walking' 'walking' 'walking' 'walking']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Seperating the dependent feature\n",
+    "y = df.iloc[ : , -1].values\n",
+    "print(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "9ef64121",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Mean encoding to fill the NAN values\n",
+    "from sklearn.impute import SimpleImputer  # We are importing SimpleImputer class from sklearn.impute module.\n",
+    "imputer = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
+    "imputer.fit(X[:, :2])  # Applying this imputer object on the matrix of features\n",
+    "imputer.transform(X[:, :2]) # Calling the transformer object so that the missing values will be replaced by the mean of the respective feature\n",
+    "X[:, :2] = imputer.transform(X[:, :2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "cc32e215",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 2.70664  2.31437 10.19892]\n",
+      " [ 2.58896  2.3536  10.3166 ]\n",
+      " [ 3.41271  6.15858 10.12046]\n",
+      " [ 2.27514  1.60829  9.76742]\n",
+      " [ 2.19669  2.78509  9.33593]\n",
+      " [ 1.3337   2.43205  9.17902]\n",
+      " [ 1.96133  4.11879  9.84588]\n",
+      " [ 1.3337   2.70664 10.08124]\n",
+      " [ 1.52984  3.13813 10.39505]\n",
+      " [ 6.66852  1.7652  10.00278]\n",
+      " [ 1.3337   1.49061 11.02267]\n",
+      " [ 1.45138  4.19725 10.55196]\n",
+      " [ 2.98122  4.23647  9.49284]\n",
+      " [ 2.5105   6.74698  7.53151]\n",
+      " [ 1.80442  6.51162  7.57073]\n",
+      " [ 2.54973  5.64863  9.02212]\n",
+      " [ 1.01989  6.1978   8.23759]\n",
+      " [ 1.72597  5.6094   9.10057]\n",
+      " [ 1.56906  4.55029  9.7282 ]\n",
+      " [ 1.21602  4.58951  9.53206]\n",
+      " [ 0.66685  5.25636  9.17902]\n",
+      " [ 0.39227  3.0989   9.33593]\n",
+      " [ 1.21602  5.57018  9.68897]\n",
+      " [ 1.64752  6.08012  9.80665]\n",
+      " [ 1.13757  5.68786  8.82598]\n",
+      " [ 1.13757  5.64863  8.39449]\n",
+      " [ 1.52984  6.6293   9.80665]\n",
+      " [ 1.56906  6.0409   8.5514 ]\n",
+      " [ 0.27459  2.43205 10.39505]\n",
+      " [ 3.6873   3.33426  8.51217]\n",
+      " [ 2.19669  3.84421  9.41438]\n",
+      " [ 1.60829  4.31493 10.00278]\n",
+      " [ 1.60829  6.90388  8.35527]\n",
+      " [ 0.35304  3.76575 10.86577]\n",
+      " [ 1.80442  4.39338  9.7282 ]\n",
+      " [ 2.15746  4.82487  9.49284]\n",
+      " [ 2.07901  4.70719  9.68897]\n",
+      " [ 1.05912  4.70719  9.2967 ]\n",
+      " [ 1.9221   5.6094   9.2967 ]\n",
+      " [ 1.13757  6.6293   9.25748]\n",
+      " [ 1.41216  3.64807  9.10057]\n",
+      " [ 0.35304  8.70831  9.57129]\n",
+      " [ 1.56906  7.45305  9.7282 ]\n",
+      " [ 2.43205  7.29615 10.27737]\n",
+      " [ 2.66741  5.80554  9.2967 ]\n",
+      " [ 3.05967  4.8641   8.43372]\n",
+      " [ 2.90277  6.70775  7.57073]\n",
+      " [ 2.58896  5.49172  8.31604]\n",
+      " [ 2.78509  6.08012  8.47295]\n",
+      " [ 1.05912  4.04034 10.08124]\n",
+      " [ 1.21602  5.06023  9.80665]\n",
+      " [ 0.86299  3.92266  9.02212]\n",
+      " [-1.25525  1.64752 10.86577]\n",
+      " [-1.3337   2.11824 10.4735 ]\n",
+      " [ 0.39227  0.39227 11.14035]\n",
+      " [-0.43149  0.62763 11.0619 ]\n",
+      " [-0.43149  0.23536 11.0619 ]\n",
+      " [ 1.37293  0.03923 10.98345]\n",
+      " [ 0.62763  0.54917 11.25803]\n",
+      " [-0.62763 -0.11768 10.90499]\n",
+      " [ 0.35304  4.47183  8.90444]\n",
+      " [-1.13757  8.59063  7.29615]\n",
+      " [ 0.62763  6.08012  8.27681]\n",
+      " [-0.31381  5.25636  8.70831]\n",
+      " [-0.50995  4.23647  8.5514 ]\n",
+      " [-0.70608  6.08012  9.10057]\n",
+      " [-0.74531  7.21769  8.39449]\n",
+      " [-0.31381  5.13868  8.66908]\n",
+      " [-1.37293  2.66741  8.94366]\n",
+      " [-0.23536  7.02156  8.11991]\n",
+      " [-1.13757  4.04034  7.68841]\n",
+      " [ 0.39227  3.64807  8.27681]\n",
+      " [ 0.86299  7.45305  9.10057]\n",
+      " [ 1.84365  6.47239 10.66964]\n",
+      " [-1.72597  4.8641   9.21825]\n",
+      " [ 0.90221  4.94255  9.7282 ]\n",
+      " [ 0.       6.55084 10.35582]\n",
+      " [ 0.31381  7.25692  9.17902]\n",
+      " [ 0.35304  5.92322  7.84532]\n",
+      " [ 0.07845  1.13757 10.98345]\n",
+      " [-0.47072  2.5105  10.4735 ]\n",
+      " [ 1.21602  7.02156  8.35527]\n",
+      " [ 0.98067  5.13868  9.80665]\n",
+      " [ 0.5884   4.04034  9.17902]\n",
+      " [ 0.35304  4.66797  9.10057]\n",
+      " [-1.1768   0.07845 11.21881]\n",
+      " [-2.5105  -0.19613 10.78732]\n",
+      " [ 1.25525  4.98178  9.96356]\n",
+      " [ 0.90221  6.98234  7.10001]\n",
+      " [ 0.47072  7.41383  9.8851 ]\n",
+      " [ 2.66741  7.33537  9.45361]\n",
+      " [ 0.78453  6.66852  9.21825]\n",
+      " [ 0.70608  8.00223  6.70775]\n",
+      " [-0.03923  6.86466  8.86521]\n",
+      " [ 1.21602  9.1398   7.25692]\n",
+      " [ 0.74531  1.56906 11.10113]\n",
+      " [ 2.00056 -0.03923 10.94422]\n",
+      " [ 1.72597 -0.43149 10.94422]\n",
+      " [ 3.25581  4.55029  8.15913]\n",
+      " [ 1.3337   5.64863  8.43372]\n",
+      " [ 2.39282  7.49228  7.68841]\n",
+      " [ 2.74586  6.35471  9.02212]\n",
+      " [ 2.66741  5.88399  7.06079]\n",
+      " [-1.52984  7.60996  7.06079]\n",
+      " [-0.5884   7.49228  7.84532]\n",
+      " [ 0.90221 -0.98067 11.10113]\n",
+      " [ 0.31381 -1.1768  11.0619 ]\n",
+      " [ 0.15691 -0.98067 11.14035]\n",
+      " [ 0.19613 -0.90221 11.17958]\n",
+      " [ 0.86299 -0.39227 11.14035]\n",
+      " [ 1.88288 -1.1768  11.0619 ]\n",
+      " [ 1.64752 -1.3337  11.0619 ]\n",
+      " [ 0.86299 -1.21602 11.02267]\n",
+      " [ 0.23536 -1.05912 11.14035]\n",
+      " [ 0.50995 -1.09834 11.02267]\n",
+      " [ 0.47072 -2.07901 10.98345]\n",
+      " [ 1.56906 -1.64752 10.98345]\n",
+      " [ 1.49061 -1.56906 11.0619 ]\n",
+      " [ 0.78453 -0.94144 11.14035]\n",
+      " [ 2.54973 -1.13757 10.86577]\n",
+      " [ 1.56906 -1.49061 10.94422]\n",
+      " [ 1.3337  -1.56906 10.90499]\n",
+      " [-1.13757 -2.82432 10.51273]\n",
+      " [ 0.19613 -2.78509 10.82654]\n",
+      " [ 2.03978 -0.82376 11.10113]\n",
+      " [ 1.3337  -0.27459 11.02267]\n",
+      " [ 1.1768   0.      11.17958]\n",
+      " [ 2.66741 -0.35304 10.90499]\n",
+      " [ 1.3337  -0.98067 10.98345]\n",
+      " [ 1.56906 -0.54917 11.0619 ]\n",
+      " [ 1.37293 -0.50995 11.14035]\n",
+      " [ 3.41271 -0.47072 10.55196]\n",
+      " [ 2.07901 -1.37293 10.94422]\n",
+      " [ 2.54973 -0.90221 10.86577]\n",
+      " [ 3.49117 -0.90221 10.78732]\n",
+      " [ 2.78509 -1.68674 10.82654]\n",
+      " [ 0.70608 -1.49061 11.0619 ]\n",
+      " [ 4.78565  0.31381 10.12046]\n",
+      " [ 3.45194  0.43149 10.63041]\n",
+      " [ 2.62818  0.03923 10.82654]\n",
+      " [-0.5884   0.03923 11.21881]\n",
+      " [ 0.50995 -0.03923 11.10113]\n",
+      " [ 1.52984 -0.78453 11.02267]\n",
+      " [ 3.29503 -1.9221  10.4735 ]\n",
+      " [ 3.13813 -2.5105  10.55196]\n",
+      " [ 2.66741 -2.58896 10.70886]\n",
+      " [ 2.19669 -1.01989 10.86577]\n",
+      " [ 2.62818 -1.09834 10.94422]\n",
+      " [ 6.31548 -3.02045  8.74753]\n",
+      " [ 3.88343 -2.15746 10.08124]\n",
+      " [ 4.11879 -2.3536  10.23814]\n",
+      " [ 4.31493 -2.3536  10.12046]\n",
+      " [ 4.90333 -2.942    9.76742]\n",
+      " [ 4.90333 -2.78509  9.64974]\n",
+      " [ 2.31437  0.      11.0619 ]\n",
+      " [ 1.96133 -0.78453 10.94422]\n",
+      " [ 3.0989  -0.15691 10.55196]\n",
+      " [ 4.66797 -1.88288 10.08124]\n",
+      " [ 0.07845 -2.15746 11.0619 ]\n",
+      " [ 0.03923 -1.29448 11.14035]\n",
+      " [-3.96189  0.      10.19892]\n",
+      " [-0.50995 -1.37293 11.0619 ]\n",
+      " [-1.56906 -0.23536 10.94422]\n",
+      " [-1.45138 -0.43149 10.94422]\n",
+      " [-1.29448 -0.39227 11.10113]\n",
+      " [ 2.00056  1.13757 14.0039 ]\n",
+      " [-0.23536 -1.9221  10.86577]\n",
+      " [ 1.41216 -1.64752 10.90499]\n",
+      " [ 1.29448 -1.80442 11.0619 ]\n",
+      " [ 2.74586 -5.09946  9.7282 ]\n",
+      " [-0.19613 -1.29448 11.10113]\n",
+      " [-1.88288 -0.15691 10.63041]\n",
+      " [ 1.68674 -2.62818 10.90499]\n",
+      " [ 3.41271 -3.29503 10.23814]\n",
+      " [ 2.27514 -3.84421 10.35582]\n",
+      " [ 2.942   -3.29503 10.35582]\n",
+      " [ 0.86299 -5.13868 10.08124]\n",
+      " [ 2.58896 -3.92266 10.27737]\n",
+      " [ 2.31437 -4.43261 10.15969]\n",
+      " [ 2.31437 -5.25636  9.64974]\n",
+      " [ 2.66741 -4.78565  9.96356]\n",
+      " [ 2.90277 -4.55029 10.00278]\n",
+      " [ 2.942   -4.2757  10.04201]\n",
+      " [ 2.31437 -1.21602 11.10113]\n",
+      " [ 2.86354 -1.80442 10.70886]\n",
+      " [ 1.72597  1.25525 10.86577]\n",
+      " [-2.19669 -0.07845 10.74809]\n",
+      " [-1.84365 -0.86299 10.98345]\n",
+      " [-2.74586 -1.01989 10.59118]\n",
+      " [ 1.13757 -0.03923 10.94422]\n",
+      " [ 1.41216 -3.92266 10.63041]\n",
+      " [ 2.5105  -3.29503 10.35582]\n",
+      " [ 4.82487 -3.41271  9.76742]\n",
+      " [ 4.94255 -3.56962  9.45361]\n",
+      " [ 3.33426 -2.70664 10.27737]\n",
+      " [ 4.35415 -1.68674 12.74864]\n",
+      " [-1.68674 -3.60885 10.35582]\n",
+      " [-0.78453 -4.31493 10.39505]\n",
+      " [-0.35304 -1.80442 10.70886]\n",
+      " [-0.86299 -1.52984 11.14035]\n",
+      " [-0.62763 -0.86299 11.45417]\n",
+      " [ 0.86299 -2.00056 11.53262]\n",
+      " [-0.11768 -2.15746 11.02267]\n",
+      " [-1.05912 -1.05912 11.10113]\n",
+      " [ 3.49117 -0.70608 10.70886]\n",
+      " [-3.80498 -0.66685  9.64974]\n",
+      " [-5.09946 -3.05967  9.92433]\n",
+      " [-3.37349 -1.05912 10.74809]\n",
+      " [-4.04034 -0.62763  9.53206]\n",
+      " [-3.17735 -0.43149 12.55251]\n",
+      " [ 0.03923  1.96133  9.96356]\n",
+      " [-4.58951 -0.62763  9.7282 ]\n",
+      " [-3.84421 -1.29448  9.80665]\n",
+      " [-4.04034 -1.96133 11.49339]\n",
+      " [-3.05967  0.66685 10.3166 ]\n",
+      " [-2.47128  0.      10.70886]\n",
+      " [-3.53039 -0.74531 10.39505]\n",
+      " [-1.49061  2.54973 11.76798]\n",
+      " [-3.02045  0.07845 10.51273]\n",
+      " [-4.07957 -0.27459  9.68897]\n",
+      " [ 0.70608  0.03923 10.94422]\n",
+      " [-1.29448  0.47072 11.21881]\n",
+      " [-3.45194 -1.29448 11.76798]\n",
+      " [-3.56962 -0.50995  9.84588]\n",
+      " [-2.3536   0.39227 11.25803]\n",
+      " [-1.29448 -3.05967 11.41494]\n",
+      " [-2.82432  0.07845 10.3166 ]\n",
+      " [-5.33482 -1.52984 10.27737]\n",
+      " [-4.15802 -0.11768 11.02267]\n",
+      " [ 0.82376 -0.31381 11.84643]\n",
+      " [-5.13868 -0.86299 10.39505]\n",
+      " [-4.98178 -1.80442  8.90444]\n",
+      " [-2.39282  0.07845 10.70886]\n",
+      " [-3.84421 -0.19613 10.08124]\n",
+      " [-2.03978 -0.86299 11.10113]\n",
+      " [-2.66741 -0.54917 10.12046]\n",
+      " [-1.9221  -0.54917 12.31715]\n",
+      " [-2.31437 -0.82376  9.92433]\n",
+      " [-7.45305 -1.9221  10.51273]\n",
+      " [-2.58896 -1.72597 10.66964]\n",
+      " [-4.15802  0.03923 12.19947]\n",
+      " [-6.7862  -1.96133 10.74809]\n",
+      " [-3.33426 -0.94144  9.80665]\n",
+      " [-4.55029 -1.7652  11.37571]\n",
+      " [-3.21658 -0.5884  10.00278]\n",
+      " [-5.09946 -0.86299 11.41494]\n",
+      " [-2.27514 -0.54917 10.08124]\n",
+      " [-6.51162 -2.11824 12.12102]\n",
+      " [-4.98178 -1.37293 10.12046]\n",
+      " [-1.80442  1.1768  10.66964]\n",
+      " [-2.942   -0.19613 10.08124]\n",
+      " [ 0.39227  0.70608  9.06135]\n",
+      " [-0.31381  2.62818 10.70886]\n",
+      " [-2.43205  0.94144 10.70886]\n",
+      " [-1.72597 -2.11824 11.0619 ]\n",
+      " [-1.25525 -0.11768 12.00334]\n",
+      " [-4.19725  0.       9.21825]\n",
+      " [-2.31437  1.1768   9.80665]\n",
+      " [-4.51106  0.15691  9.10057]\n",
+      " [-2.07901  0.31381 10.90499]\n",
+      " [-3.13813 -2.3536  10.23814]\n",
+      " [-2.70664 -1.29448 10.3166 ]\n",
+      " [-4.19725 -0.31381  9.61052]\n",
+      " [-6.15858 -1.25525 11.17958]\n",
+      " [-4.15802  0.23536  9.41438]\n",
+      " [-1.09834  0.      11.72875]\n",
+      " [-4.19725 -0.74531  9.1398 ]\n",
+      " [-4.82487 -0.5884  10.15969]\n",
+      " [-3.49117  0.07845  9.2967 ]\n",
+      " [-4.31493  0.23536  8.74753]\n",
+      " [-5.17791 -0.70608  9.33593]\n",
+      " [-3.02045  0.31381  9.8851 ]\n",
+      " [-3.49117 -1.09834  9.84588]\n",
+      " [-4.74642 -0.23536  9.80665]\n",
+      " [-3.13813  1.45138 12.27793]\n",
+      " [-2.47128 -0.70608 10.82654]\n",
+      " [-2.11824  2.98122  9.76742]\n",
+      " [-2.19669 -0.94144 11.53262]\n",
+      " [-4.98178  0.54917 10.86577]\n",
+      " [-3.13813 -1.1768  11.0619 ]\n",
+      " [-1.1768   2.3536  11.17958]\n",
+      " [-5.33482 -0.50995 10.00278]\n",
+      " [-4.19725  0.54917  9.33593]\n",
+      " [-3.0989  -0.47072 10.04201]\n",
+      " [-2.58896 -0.27459  9.84588]\n",
+      " [ 0.47072  2.11824 10.27737]\n",
+      " [-6.08012 -0.66685  9.8851 ]\n",
+      " [-0.74531 -3.53039 11.10113]\n",
+      " [-3.92266 -1.84365 11.21881]\n",
+      " [-2.31437  0.03923 10.55196]\n",
+      " [-2.31437  1.01989  9.21825]\n",
+      " [-4.07957 -1.21602 10.15969]\n",
+      " [-3.49117  0.07845 10.35582]\n",
+      " [-4.78565 -1.13757  9.84588]\n",
+      " [-4.19725 -2.90277  9.8851 ]\n",
+      " [-3.76575  0.27459 10.51273]\n",
+      " [-3.45194 -1.64752 10.4735 ]\n",
+      " [-3.60885 -2.70664 10.3166 ]\n",
+      " [-4.43261 -3.21658  8.47295]\n",
+      " [-4.43261 -0.94144  9.61052]\n",
+      " [-3.6873  -1.21602 10.08124]\n",
+      " [-1.25525  1.49061 12.04257]\n",
+      " [-6.7862  -2.74586  9.68897]\n",
+      " [-7.3746  -3.49117 10.08124]\n",
+      " [-4.62874 -1.96133  8.94366]\n",
+      " [-1.60829 -0.03923 10.55196]\n",
+      " [-6.90388 -2.54973  8.86521]\n",
+      " [-8.08068 -2.23592  7.80609]\n",
+      " [-2.62818 -1.37293 10.55196]\n",
+      " [-3.37349  0.11768 10.74809]\n",
+      " [-2.82432  0.35304 10.12046]\n",
+      " [ 0.27459  0.11768 14.7492 ]\n",
+      " [-2.23592 -1.45138 10.08124]\n",
+      " [-7.02156 -2.62818 10.23814]\n",
+      " [-5.53095 -1.64752  9.17902]\n",
+      " [-3.13813  0.31381 10.4735 ]\n",
+      " [-3.84421 -1.64752  9.61052]\n",
+      " [-4.55029 -1.05912  8.82598]\n",
+      " [-5.49172 -2.00056  9.1398 ]\n",
+      " [-2.66741 -1.84365 10.00278]\n",
+      " [-3.45194 -1.45138 10.19892]\n",
+      " [-3.41271 -1.05912 10.86577]\n",
+      " [-6.0409  -2.27514  9.53206]\n",
+      " [-4.70719 -1.41216  9.84588]\n",
+      " [-4.66797 -1.3337   9.8851 ]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "a64f7708",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Encoding the dependent variable\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "le = LabelEncoder()\n",
+    "y = le.fit_transform(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "84a21665",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1\n",
+      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
+      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
+      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
+      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
+      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
+      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bb285649",
+   "metadata": {},
+   "source": [
+    "# Splitting the data into training test and test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "93f8ce4a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train , X_test , y_train , y_test = train_test_split( X , y , test_size = 0.25 , random_state = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "f4f252ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 2.62818  0.03923 10.82654]\n",
+      " [-2.39282  0.07845 10.70886]\n",
+      " [-4.04034 -1.96133 11.49339]\n",
+      " [ 2.86354 -1.80442 10.70886]\n",
+      " [ 3.45194  0.43149 10.63041]\n",
+      " [ 0.74531  1.56906 11.10113]\n",
+      " [-6.51162 -2.11824 12.12102]\n",
+      " [-4.74642 -0.23536  9.80665]\n",
+      " [ 2.19669  2.78509  9.33593]\n",
+      " [ 1.64752 -1.3337  11.0619 ]\n",
+      " [ 2.62818 -1.09834 10.94422]\n",
+      " [ 0.62763  0.54917 11.25803]\n",
+      " [-4.07957 -1.21602 10.15969]\n",
+      " [ 3.41271 -3.29503 10.23814]\n",
+      " [-2.3536   0.39227 11.25803]\n",
+      " [ 1.96133  4.11879  9.84588]\n",
+      " [-0.62763 -0.11768 10.90499]\n",
+      " [ 2.19669 -1.01989 10.86577]\n",
+      " [-3.13813 -1.1768  11.0619 ]\n",
+      " [-2.23592 -1.45138 10.08124]\n",
+      " [-5.09946 -3.05967  9.92433]\n",
+      " [ 1.56906  4.55029  9.7282 ]\n",
+      " [ 1.3337  -0.27459 11.02267]\n",
+      " [-0.50995 -1.37293 11.0619 ]\n",
+      " [ 0.90221 -0.98067 11.10113]\n",
+      " [ 0.70608  8.00223  6.70775]\n",
+      " [-1.13757 -2.82432 10.51273]\n",
+      " [-7.45305 -1.9221  10.51273]\n",
+      " [-4.43261 -0.94144  9.61052]\n",
+      " [ 1.45138  4.19725 10.55196]\n",
+      " [-3.6873  -1.21602 10.08124]\n",
+      " [-4.19725  0.       9.21825]\n",
+      " [-4.98178  0.54917 10.86577]\n",
+      " [-1.1768   0.07845 11.21881]\n",
+      " [ 2.31437 -5.25636  9.64974]\n",
+      " [-0.70608  6.08012  9.10057]\n",
+      " [-2.31437  1.1768   9.80665]\n",
+      " [-3.37349 -1.05912 10.74809]\n",
+      " [-3.13813  1.45138 12.27793]\n",
+      " [ 2.74586 -5.09946  9.7282 ]\n",
+      " [ 2.98122  4.23647  9.49284]\n",
+      " [ 1.56906  6.0409   8.5514 ]\n",
+      " [-4.58951 -0.62763  9.7282 ]\n",
+      " [-4.66797 -1.3337   9.8851 ]\n",
+      " [ 1.84365  6.47239 10.66964]\n",
+      " [ 3.6873   3.33426  8.51217]\n",
+      " [-0.47072  2.5105  10.4735 ]\n",
+      " [ 3.41271 -0.47072 10.55196]\n",
+      " [ 2.66741  5.88399  7.06079]\n",
+      " [ 0.82376 -0.31381 11.84643]\n",
+      " [-4.70719 -1.41216  9.84588]\n",
+      " [ 0.78453  6.66852  9.21825]\n",
+      " [ 2.66741 -0.35304 10.90499]\n",
+      " [-4.55029 -1.05912  8.82598]\n",
+      " [-6.0409  -2.27514  9.53206]\n",
+      " [-1.84365 -0.86299 10.98345]\n",
+      " [ 0.47072  7.41383  9.8851 ]\n",
+      " [ 2.54973 -1.13757 10.86577]\n",
+      " [-2.19669 -0.07845 10.74809]\n",
+      " [-2.31437  1.01989  9.21825]\n",
+      " [ 2.66741  7.33537  9.45361]\n",
+      " [ 0.07845 -2.15746 11.0619 ]\n",
+      " [-1.80442  1.1768  10.66964]\n",
+      " [-7.02156 -2.62818 10.23814]\n",
+      " [ 1.80442  6.51162  7.57073]\n",
+      " [-3.84421 -0.19613 10.08124]\n",
+      " [-6.15858 -1.25525 11.17958]\n",
+      " [ 0.86299  3.92266  9.02212]\n",
+      " [ 6.66852  1.7652  10.00278]\n",
+      " [ 0.86299 -1.21602 11.02267]\n",
+      " [ 1.01989  6.1978   8.23759]\n",
+      " [ 2.70664  2.31437 10.19892]\n",
+      " [ 1.72597  1.25525 10.86577]\n",
+      " [ 0.27459  0.11768 14.7492 ]\n",
+      " [ 0.62763  6.08012  8.27681]\n",
+      " [ 2.942   -3.29503 10.35582]\n",
+      " [ 4.11879 -2.3536  10.23814]\n",
+      " [-2.74586 -1.01989 10.59118]\n",
+      " [-1.13757  4.04034  7.68841]\n",
+      " [ 1.29448 -1.80442 11.0619 ]\n",
+      " [-3.45194 -1.29448 11.76798]\n",
+      " [ 0.35304  8.70831  9.57129]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "90044de0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 1 2 1 0 0 0 2 2 2 0 2 0 1 1 0 1 1 1 1 0 2 1 0 2 1 2 2 0 1 2 1 0 0 2 2\n",
+      " 1 2 1 1 2 2 1 2 1 0 2 0 1 1 1 1 2 0 2 1 0 2 0 2 2 2 0 0 0 0 1 0 2 2 0 0 2\n",
+      " 0 1 0 0 1 1 2 2 2 2 0 1 2 0 0 0 1 0 0 1 0 2 1 2 1 2 1 2 1 2 0 0 2 2 2 0 1\n",
+      " 2 1 2 2 2 1 2 1 1 2 2 0 1 2 1 1 2 2 1 2 0 2 1 0 2 0 2 0 0 1 1 1 1 2 1 1 2\n",
+      " 1 0 1 0 0 0 0 2 0 2 0 2 0 0 2 0 2 2 0 1 0 2 2 1 2 2 0 0 2 2 1 1 2 1 0 2 0\n",
+      " 2 1 2 1 1 0 1 1 1 2 2 0 1 2 0 1 0 0 1 0 0 0 2 0 0 2 0 1 1 2 2 2 2 0 0 0 1\n",
+      " 0 2 2 0 0 1 2 2 2 1 2 2 0 1 1 1 1 2 0 2 0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "0ee2ddfa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 2 1 1 0 2 2 0 1 1 0 2 1 2 0 0 1 2 2 2 0 1 1 1 0 1 2 2 0 2 2 2 0 1 0 2\n",
+      " 2 2 1 0 0 2 2 0 0 0 1 0 2 2 0 1 2 2 1 0 1 1 2 0 1 2 2 0 2 2 0 0 1 0 0 1 2\n",
+      " 0 1 1 1 0 1 2 0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bb2be99b",
+   "metadata": {},
+   "source": [
+    "# Feature scaling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "c9df15ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "59c3798c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc = StandardScaler() # We should not apply feature scaling on the dummy variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "f81d2fb3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train[ : , :3 ] = sc.fit_transform(X_train[ : , :3 ])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "1cfe0636",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_test[ : , :3 ] = sc.fit_transform(X_test[ : , :3 ])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "742b0b80",
+   "metadata": {},
+   "source": [
+    "# Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "dc3b8fac",
+   "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>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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" 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": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "lr = LogisticRegression()\n",
+    "lr.fit(X_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "9e8f39bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_predicted_1 = lr.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "4ca8844b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 2, 1, 1, 0, 2, 2, 0, 1, 1, 1, 2, 1, 2, 0, 1, 1, 2, 2, 2, 0,\n",
+       "       1, 1, 1, 0, 1, 2, 2, 0, 2, 2, 2, 2, 1, 0, 2, 2, 2, 1, 0, 0, 2, 2,\n",
+       "       0, 0, 0, 1, 0, 1, 2, 0, 1, 2, 2, 2, 0, 1, 2, 2, 0, 1, 2, 2, 0, 2,\n",
+       "       2, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 2, 0, 1, 2, 0])"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predicted_1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "87df4cbe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8902439024390244"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_score_1 = lr.score(X_test , y_test)\n",
+    "accuracy_score_1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f4d2bd6",
+   "metadata": {},
+   "source": [
+    "# Support Vector Classification"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "59b1c995",
+   "metadata": {},
+   "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>SVC()</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "SVC()"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "svc = SVC()\n",
+    "svc.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "e4bf51df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_predicted = svc.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "b86fe72b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 2, 1, 1, 0, 2, 2, 0, 1, 1, 1, 2, 1, 2, 0, 1, 1, 2, 2, 2, 0,\n",
+       "       1, 1, 1, 0, 1, 2, 2, 0, 2, 2, 2, 2, 1, 0, 2, 2, 2, 1, 0, 0, 2, 2,\n",
+       "       0, 0, 0, 1, 0, 1, 2, 0, 1, 2, 2, 2, 0, 1, 2, 2, 0, 1, 2, 2, 0, 2,\n",
+       "       2, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 2, 0, 1, 2, 0])"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predicted"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "440ea8b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8780487804878049"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score = svc.score(X_test , y_test)\n",
+    "score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "b57d72c4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8780487804878049"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score\n",
+    "accuracy_score_2 = accuracy_score(y_test, y_predicted)\n",
+    "accuracy_score_2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dfa085c0",
+   "metadata": {},
+   "source": [
+    "# Making the Confusion matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "c8347f60",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[23,  3,  1],\n",
+       "       [ 1, 21,  3],\n",
+       "       [ 0,  2, 28]], dtype=int64)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn import metrics\n",
+    "cm = metrics.confusion_matrix(y_test , y_predicted) \n",
+    "cm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "3030d86e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Confusion Matrix')"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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8swCQJCmp9O//PQQgSVIpMgGQJCmhIggATAAkSSpFJgCSJCXUXgTnAZoASJJUgkwAJElKKP3f/00AJEkqSSYAkiQlVARTAEwAJEkqRSYAkiQlVAyXAjYBkCSpBJkASJKUUHv6AwATAEmSSpEJgCRJCTkHQJIkpZIJgCRJCfX3dQBCCBXAdGA4MAg4G5gNXEv2QoWzgKNjjO3dbcMEQJKk9DkYeDfGuAOwG3AFcDFwWq6tDNirpw2YAEiSlFABzAH4A/DH3PMyoA3YErg/13YH8BXg5u42YAEgSVLKxBjrAUIIdWQLgdOAn8UYl1Umi4GhPW3DAkCSpIT64joAIYRxwLhOTVNjjFM7LV+X7Df8KTHGG0MIF3bqWwcs6Gn7FgCSJCXUF4cAcjv7qV0tCyGsDvwNmBBjvDvX/GQIYccY433A7sC9PW3fAkCSpPSZBKwCTA4hTM61HQtcFkKoBJ7ngzkCXbIAkCQpof4+DTDGeCzZHf6Hfam32/A0QEmSSpAJgCRJCfV3ArAymABIklSCTAAkSUqovf8vBLTCTAAkSSpBJgCSJCXkHABJkpRKJgCSJCVUADcDWmEmAJIklSATAEmSEnIOgCRJSiUTAEmSEvI6AJIkKZVMACRJSsg5AJIkKZVMACRJSqgIAgATAEmSSpEJgCRJCWWKYBKACYAkSSXIBECSpITa+3sAK4EFgCRJCXkIQJIkpZIJgCRJCaX/+78JgCRJJckEQJKkhJwDIEmSUskEQJKkhIrhNEATAEmSSpAJgCRJCTkHQJIkpZIJgCRJCRVBAGACIElSKTIBkCQpoSIIAEwAJEkqRWUFOJOx4AYkSUqVsny/wXWPvZr3fdW3t1o3r5+jIA8BVI2a1N9DUIFpmnkuD89d0N/DUIHZbsQwqvac0t/DUIFpunV8fw8hFQqyAJAkqZAVQ1TtHABJkkqQCYAkSQkV4Py5xEwAJEkqQSYAkiQlVAx3A7QAkCQpoSI4AuAhAEmSSpEJgCRJCbUXQQRgAiBJUgkyAZAkKaEiCABMACRJKkUmAJIkJeQcAEmSlEomAJIkJdSe/gDABECSpFJkAiBJUkJFMAXABECSpFJkAiBJUkLtpD8CMAGQJKkEmQBIkpSQcwAkSVIqmQBIkpSQ1wGQJEmpZAIgSVJC3gtAkiSlkgmAJEkJFUEAYAEgSVJSTgKUJEmpZAIgSVJCmSI4BmACIElSCTIBkCQpIecASJKkVDIBkCQpIRMASZKUSiYAkiQllCH9EYAJgCRJJcgEQJKkhJwDIEmSUskEQJKkhIrgQoAmAJIklaJuE4AQwgbdLYsxvpCf4UiSVPjaiyAC6OkQwC+7ac8AY/MwFkmS1Ee6LQBijDstex5CGAoMB16MMdb3wbgkSSpYJXEWQAjhG8B9wG+A40MIp+V7UJIkKb96cxbACcB2wJ3A2cBjuT8lSSpJhTIFIISwLXBBjHHHEMIWwG3Av3OLr4ox/q67dXtTACyNMTaHEDIxxkwIoWEljFmSJK2AEMLJwCHAsv3ylsDFMcaLerN+bwqAB0IINwHrhBB+ATz6iUYqSVKRKJCzAF4E9gWuz73eEgghhL3IpgDHxRgXd7fyx84BiDFOAn4NXA3cFmM8cYWHLElSimUy+X+EEMaFEB7r9BjXeQwxxv8DWjs1/Qv4YYzxi8BLwOk9fYaPTQBCCJ8CdgEC8D8hhH/GGBcm/cuSJEm9F2OcCkxNsMrNMcYFy54Dl/fUuTdXAryObJTwI+B1smmAJEklq70PHp/AXSGEbXLPvww83lPn3swBGBxj/EXu+dO50wIlSVJhOQq4PITQCrwJjOupc28uBTw/hPC/wD+BbYD/rKSBSpKUSgUyCZAY48tkT9UnxvgEMLq36/b2UsDjcw/IXgpYkiSlWK8uBdxZCKEif8ORJKnwFUgAsEJ6cxbAkWSvBlgBlJE95aDbOwVKkqTC15uzAI4GdgTuAA4DZudzQJIkFbr2TP4f+dabAuCNGOM8oC7GeB8wNL9DkiRJ+dab0wAXhhD2BjK5wwH/L79DkiSpsGWKYBJAbxKAI4D/AqeSPfZ/TF5HJEmS8q6n6wB85UNNqwJ3AZV5HZEkSQWuL47R51tPhwAO6KY9A/wtD2MpGVtvtA5nj9+NXSdMW659j9EbMunwsbQtbefXtz3GNX95jMGVA7nm9G+y6iq1LG5s5ntn/5H5Cxq67Kv0aGtr41eX/oT5b8+jtbWVr+9/GF/Y7osA3DD1EtZcZ33G7rHvcuu0t7dz3ZQLeeU//6aiopLDfzCJ1ddal7lznuWGX17CgPJyNvnCtuxz4BHd9lV6DBhQxpQJO7LB2sPIAMdceT+zX3mvY/keW6/PpAO2zv4OmPE81/zteQZXlnPNiTuz6tAqFje18r1L7mb+oiVd9pV6ug7AYX05kFJxwkE7cMBuW9DY1LJc+8DyAVx47FcZ890raWhq5d5fHsnt/5zDt3bdnFkvvcU5v7qR/915MyYeuhMTL/9rl33ffr++nz6Vkpp57x3UDhnKkSedSf3ihUw+5hBGfH5Tpl50Jm++/gprrrP+R9Z54qH7aW1p4ccX/Yq5c57lpmk/57gf/4xfX3EBE350PqutsTYXn3E8/30x8s6bb3TZV+nx1W2GAzD2lJvZYZO1OOOQbfnmOXcAud8XR4xhzAl/pKG5lXsv3Jfb//Uy3/rSBsx6+T3OuelR/neHEUzcfysmTp/ZZd+3FzT146dLv2JIAHozB0Ar0Uuvv8e3Tr3hI+0bDl+NF197lwWLl9DatpSZT7/MmJHDGbXZcGY8/AIAdz0U2Wmrz3bbV+mxzZgvs+/BR2ZfZKB8QDnNTU3sc+ARjB67e5frvDD7aTbdcjsARmy4Kf+ZO4emxnpaW1tZfc11KCsrY9MvbMdzT/2ry75Kl1sf/g9HX3EfAOutVsfChuaOZRuuuwovzlvIgoZmWtvamTl7HmM2XotRG63JjCdeAeCux19hp5HrdNtXsgDoY3++7zla25Z+pH1IzSAW1S/peL24sYUhtYOpqxnEwlz74sYWhtYO7rav0mNwVTVV1TU0NTZw+bkT+ca3v8+qa6zFZzfcpNt1mhobqKqp7Xg9YMCAbFt1zXLbbWxo6LLv0qVt+fkwypul7RmuPm4sFx+5A7+9/98d7UOqK1jU+EGKuLiphSE1ldRVV7CwoaWjbWh1Zbd9tWIymUzeH/mWZBJghxijcwBWskUNzdRWD+p4XVddycL6JSxuaKYu115XXcmC+iXd9lW6vPvOW1x29sl8+avfYPsdd/3Y/lXVNSxpaux4nWlvz7U1dLQtaWqkuqaWluYlH+lbXt6bs35VaL536T2cdu1D/OOi/dhi/E00NrexqLGV2qoPrspeV1XJwvpmFje2Updrr6uqZEFDc7d9pZ4SgAO6eXyrD8ZVcua8/DYj1v0Uq9RVUTGwnNEjP80jz77CQ8/+l11HBQB23T7w4FMvd9tX6bHw/Xf56Wk/4JuHTeCLX/l6r9b53Eab8cyjMwGYO+dZ1hk+gqrqWgYOrOCtea+RyWR49omHCRuP7LKv0uWAnTbgpP2+AEBjcxvtmUzHHejmvPo+I9Yayiq1g6gYOIDRG6/JI3Pe4qHn57HrVtn5I7tuuR4PPjev275aMe198Mi3xJMAQwhr9rTBEMK9wKAPNZcBmRjjqMQjLHL777I5NdWVTL/lUU657K/ceulhlJWVcd1tj/PG/EVM/dMjTJu8H3dfNY6WtqUcevrvaFva3mVfpcetv7+WxvpF/OW30/nLb6cDcOKZl1A56KOHcn550Rnsd8j32XL7HXnuyX/xkxOPIEOGI46bDMB3JpzCL3/6Y9rb29lki2357Iab8On29i77Kj1umfkSU48by4zz9qZi4AB+ePUD7LX9Z6gZXMH0u2ZzyrQHufWsPSkrg+tmzOGN9xqYesdzTDtuLHdfsA8trUs59Gczsr8vuugrlX3ccYYQwlnAUWTP/68GXogxbtxD/22Bq4F9gOUOOsYY/9uLMWWqRk3qRTeVkqaZ5/Lw3AX9PQwVmO1GDKNqzyn9PQwVmKZbx5fl+z3G/2l23g/ST9l3o7x+jt4cFPw6sA5wCXAx0ONPW4zxkRDC9cBmMcabV3yIkiRpZetNATAvxtgcQqiLMc4NIXzs9NEY409XwtgkSSpIRXArgF4VAK+FEA4HGkII5wHD8jskSZIKW3sRVAC9uQ7AkcDdwA+BN4AD8zoiSZKUd71JAA7u9HwhsBUwOz/DkSSp8BVBANCrAuDzuT/LgJHAe8B1+RqQJEnKv48tAGKMpy57HkIoA27L64gkSSpwfXGp3nz72ALgQ7P+1wQ+nb/hSJKkvtCbQwARyJA9BNAEXJjXEUmSVOCKIADoVQHwzRjjo8tehBC+lMfxSJKkPtDT3QB3ADYCjg8hXJxrHgBMALq/Z6kkSUWuGK4D0FMC8D6wBtkb+6xB9hBAO3ByH4xLkiTlUU93A5wFzAohXA2sFmN8KoSwNzCjrwYnSVIhSv/3/95dCfAyYIvc8w2AX+dvOJIkqS/0pgBYO8Z4DUCM8UKypwJKklSyMplM3h/51psCIBNC2AAghDACKM/vkCRJUr715jTA44HfhRBWJ3sdgGvzOiJJkgpcexFMAvjYBCDG+AjZOwL+HagBVs/3oCRJUn71dB2ASuAA4GigGRgCfDrG2NRHY5MkqSAVw70AekoAXgY2Aw6KMe4AvOHOX5Kk4tDTHIBLgYOA4SGEaWQvBCRJUskrggCg+wQgxnhhjHFzstcBOBDYOoRwQQjBywBLkpRyH3sWQIzxfuD+EMIw4BDgej64MJAkSSWnGOYA9OY0QABijAuAy3MPSZJKVkmcBihJkopPrxMASZKUVQyHAEwAJEkqQSYAkiQllP7v/yYAkiSVJBMASZISancOgCRJSiMTAEmSEiqCAMAEQJKkUmQCIElSQl4HQJIkpZIJgCRJCRVBAGACIElSKTIBkCQpIa8DIEmSUskEQJKkhIogADABkCSpFJkASJKUkNcBkCRJqWQCIElSQu3pDwAsACRJSipD+isADwFIklSCTAAkSUqoCOYAmgBIklSKTAAkSUrI0wAlSVIqmQBIkpRQMZwGaAIgSVIJMgGQJCkh5wBIkqRUMgGQJCmhIggATAAkSSpFJgCSJCXUXgQRgAmAJEklyARAkqSEiiAAMAGQJKkUmQBIkpSQ1wGQJEmpZAIgSVJCRRAAWABIkpRWIYRtgQtijDuGEEYA1wIZYBZwdIyxvbt1C7IAaJp5bn8PQQVouxHD+nsIKkBNt47v7yGoBBXCHIAQwsnAIUBDruli4LQY430hhF8AewE3d7d+QRYAVVtM6O8hqMA0PXkFVTuf39/DUIFp+vtElrT19yhUaAb3wZ6tAPb/AC8C+wLX515vCdyfe34H8BV6KACcBChJUgrFGP8PaO3UVBZjXFaaLAaG9rR+QSYAkiQVsr44BBBCGAeM69Q0NcY4tYdVOh/vrwMW9LR9CwBJkgpQbmff0w7/w54MIewYY7wP2B24t6fOFgCSJCVUCJMAu3AicHUIoRJ4HvhjT50tACRJSqkY48vAdrnnLwBf6u26FgCSJCVUmAFAMp4FIElSCTIBkCQpoQKdA5CICYAkSSXIBECSpISKIAAwAZAkqRSZAEiSlJBzACRJUiqZAEiSlFARBAAmAJIklSITAEmSEnIOgCRJSiUTAEmSEiqCAMAEQJKkUmQCIElSQs4BkCRJqWQCIElSQkUQAFgASJKUlIcAJElSKpkASJKUUBEEACYAkiSVIhMASZIScg6AJElKJRMASZISKoIAwARAkqRSZAIgSVJCzgGQJEmpZAIgSVJCJgCSJCmVTAAkSUqoCAIAEwBJkkqRCYAkSQk5B0CSJKWSCYAkSQkVQQBgAiBJUikyAZAkKaH29vRHACYAkiSVIBMASZISKoY5ABYAkiQl5GmAkiQplUwAJElKqAgCABMASZJKkQmAJEkJOQdAkiSlkgmAJEkJFUEAYAIgSVIpMgGQJCkh5wBIkqRUMgGQJCkhEwBJkpRKJgCSJCWV/gDABECSpFJkAiBJUkLOAZAkSalkAiBJUkLFkABYAPShsrIyfj5pfzbbYG2aW9o46qwbeOnV+R3LD9tnFN/dbwxL25Zy/rS7uOOfs/jUsBquPfdQqgZVMO+dhYw74zc0LWntsq/SaWD5AH550h6sv8ZQBlWUc/4NM7n9obkdy/fYbgSTDhlN29J2fn3nM1zz16cZXDmQa07dk1WHVbO4sYXvXXgb8xc2ddlX6dHa2srpkyfxxuuv09LSwrgjj2KNNdfi7LNOp7y8nPWHD+eMs85hwIAPwtv29nbO+ckZvBAjlZWVnH7m2ay3/vo88/RTXHDeOQwsL2f70WP4/vgJ3fZVafIQQB/6+k6bMbhyIDt+5yImX3YL55+wb8ey1T9Vx/gDdmTsoRez59FXctYxX6eyYiCTxu3O7+54jJ2/eylPx9c44htjuu2rdDpg5415b1ETOx9/A18/9fdccswuHcsGlg/gwqO+zNdO+S27nHAD3/3qSFYbVs24Pbdg1n/eYefjb+DGGbOYeNDobvsqPW6/7S8MGzqMa6+/kat+OY3zzvkJv7jqCo486mh+/ZubaG1p4R/337fcOvfc/Xdamlu4/sbfcezxJ3LRT88H4OwzT+f8Cy/i2t/cxLPPPM3zz8/utq+Sy2QyeX/kmwVAHxq1xWeZMfN5AP717MtsudF6Hcu22mQ4Dz39Ei2tbSyqX8JLr77Dpp9bi+1HfpYZM2cDcNeDs9lp29BtX6XTn+6fw5nX/hOAsjJoW/rBD/6G632KF994nwX1zbS2tTNz1muM2WxdRm2yDjMefQmAux59iZ2+sH63fZUeX/nKbhz9g2MByJChfGA5G274eRYuXEAmk6GhoYGKgcsX+08+8TijxuwAwGabj+S552ZRX19PS2sL6663HmVlZYwaPYZHHprZZV+Vrj4pAEIIg/rifQpdXc1gFtY3dbxeurSd8vIBHcsWLf5g2eLGJQypq2JIzWAW1i/JtjUsYWhtVbd9lU4NS1qpb2qhtqqSG3+8D2de84+OZUNqBrGoobnj9eLGFobUDKKuZhALc+2LG5sZWjOo275Kj+qaGmpqamloqOfE437AhGOOY/31h3PBueew95678+6777LVNtsut05DQz11dbUdr8sHlNNQX09NTe1y261fvLjLvm1tbfn/YEXIBOBDQgh7hhD+G0KYG0LYv9OiO1bm+6TV4oYl1FV/8At5wIAyli5t71hW2+mXdV31YBYubmJRp3XqagazYHFTt32VXuusWsedPzuAG//+HL+7Z3ZH+6KGZmqrKjte11VXsrC+mcUNzdTl2uuqB7GgvrnbvkqXN+fN44jDvs3Xvr4Xe3xtTy44/xyuuf4GbrntTvb8+t5cdOHysX22YGjoeN2eaaemtpbGTm2NDQ3UDRnSZd+BAz18+Ilk+uCRZys7AfgRMBLYFjgyhPCdXHvZSn6fVHroqZfYdczGAGyz6XBmzX2jY9ljs15m9BYjGFQ5kCG1gwmfXp3n5r7Bw09/sM6uozdi5pNzu+2rdFptWDW3nr8/p027j+vufGa5ZXNeeZcRa6/CKnWDqRg4gNGbrssjs1/noedeZ9dtPwvArlt/hgdnvdptX6XHu/Pn8/1xh3PcCT9kn333A2Do0KHU5r7Nr7raaixatGi5dbbY4gs88I9savTM00/xuc9tQG1tLRUVFbz6yitkMhlmPvgAW2y5VZd9VbpWdunXEmN8HyCEsBdwTwjhFYriookr7pZ7nmbsdhty77UnUFZWxrjTf8MPDh7Li6++w+33P8uUm+7j7unHU1ZWxhlX3kZzSxvnX30nV591CIfvO4r5Cxo49NRraVzS0mVfpdPJB45iWN1gTj14NKcePBqAa25/iuqqCqbf/jSn/OIebj1/f8rKyrjuzmd44916pt76BNNO/hp3X3oQLa3tHHruX2hb2t5lX6XHtKt/waKFi5j6iylM/cUUAE4/82xOOel4ygcOpKKigh+f+RMAfnTqyUw45jjG7rwLDz30IN8+6FtkMhnOOvtcAE47/UxOPeUk2pcuZfvRY9hss81p32TTLvsquWI4DbBsZX6IEMJ1wHxgcoyxIYSwLnAXMCzG2NtZapmqLSastDGpODQ9eQVVOztjWctr+vtEllj76kMGD8x/6rzO+D/nvQJ4bcreef0cK/sQwOHAM+S+8ccYXwV2An6/kt9HkqR+UwyTAFfqIYAYYxtw7Yfa3gKOW5nvI0mSVozTPyVJSqgY5gB4ISBJkkqQCYAkSUmlPwAwAZAkqRSZAEiSlJBzACRJUiqZAEiSlJAJgCRJSiUTAEmSEjIBkCRJqWQCIElSQiYAkiQplUwAJElKKv0BgAmAJEmlyARAkqSEimEOgAWAJEkJFUIBEEJ4AliUe/mfGONhSda3AJAkKWVCCIOBshjjjp90GxYAkiQlVAAJwOZAdQjhb2T35ZNijA8n2YAFgCRJBSiEMA4Y16lpaoxxau55I/AzYBrwOeCOEEKIMbb1dvsWAJIkJdUHAUBuZz+1m8UvAHNjjBnghRDCu8CawKu93b6nAUqSlD6HAxcBhBDWAoYA85JswARAkqSECmAOwK+Aa0MID5DNIw5PEv+DBYAkSakTY2wBDlyRbVgASJKUUAEkACvMOQCSJJUgEwBJkhIyAZAkSalkAiBJUkImAJIkKZVMACRJSir9AYAJgCRJpcgEQJKkhJwDIEmSUskEQJKkhEwAJElSKpkASJKUkAmAJElKJRMASZISKoYEwAJAkqSk0r//9xCAJEmlyARAkqSEiuEQgAmAJEklyARAkqSETAAkSVIqmQBIkpSUCYAkSUojEwBJkpLKtPf3CFaYCYAkSSXIBECSpKScAyBJktLIBECSpKScAyBJktLIBECSpKScAyBJktLIBECSpKScAyBJktLIBECSpKRMACRJUhqZAEiSlFQRnAVgASBJUlIeApAkSWlkAiBJUlIeAsiLsqYnr+jvMagANf19Yn8PQQVocCH+FpNSwB8dSZKScg6AJElKIxMASZKSKoI5ACYAkiSVIBMASZKScg6AJElKIxOAAhRCGABMATYHmoEjYoxz+3dUKgQhhG2BC2KMO/b3WNT/QggVwHRgODAIODvG+Jd+HVSpcA6A8mRvYHCMcXtgInBR/w5HhSCEcDIwDRjc32NRwTgYeDfGuAOwG+BFVNRrFgCFaQxwJ0CM8WFgq/4djgrEi8C+/T0IFZQ/AJNzz8uAtn4cS2nJtOf/kWcWAIVpCLCw0+ulIQQP15S4GOP/Aa39PQ4VjhhjfYxxcQihDvgjcFp/j0np4U6lMC0C6jq9HhBjtLKX9BEhhHWBm4EpMcYb+3s8JcM5AMqTB4E9AEII2wHP9u9wJBWiEMLqwN+AU2KM0/t7PEoXE4DCdDOwSwhhJtnjeof183gkFaZJwCrA5BDCsrkAu8cYm/pxTKWhCK4DUJYpghhDkqS+VLXTT/K+82y6d3JZPrdvAiBJUlLt6f/y7BwASZJKkAmAJElJFcEcAAsASZKSKoICwEMAkiSVIBMAKYEQwo7A74HZQAaoAm6IMV7+CbZ1PjAHeAr4eozxrG767QM8EmN8oxfb3A34Vozx0A+N+fsxxm91s86hwIYxxom92H6v+0pFrQjOoLMAkJK7Z9nONIQwCIghhOtjjAs+ycZijE+RLQK6cyzwfeBjCwBJ6i0LAGnF1AFLgbYQwn3A28D/AF8le0vnz5E91HZajPG+EMI3yF6v/R2gEpjT+Rt6COG7wFFAOfAX4F/ASOC6EMIY4EjgQLLpw29jjJeFED5P9pawDbnH+90NNoQwgewNhWqA+cA+uUXbhxDuJnsfijNijLeHEL4EnJP7fC/m3lsSOAdAKlFjQwj3hRDuAW4Ajokx1ueW3RRj3Bk4HJgfY/wisBdwZe7e7RcDOwO7Ao2dNxpCWI3s7Z93AL5A9v7u95NNB74NjAD2J3u3yB2AvUMIAfgp8OPc+87sbtAhhAHAp4CdY4zbkv0CsHVucUNuXF8FrgghlANXA/vGGL8EvA4cmvyvSlKhMgGQkrunu+PpQMz9uSmwQwhh29zrgcCawHsxxncBcpd67uwzwKxOl3GdmOu3bPkmwPrA3bnXq5BNGDYgmxRA9j4Sn+9yYDG2hxBagJtCCPXAOkBFbvEDMcYM8HYIYSHw/3Lj/X3u/auAGcDcbj63VFqKYA6ACYC0ci3LBeeQTQN2BHYne9/2N4FhIYRVc322/tC6LwIb5uYVEEL4Ywhh7dw2B5AtLp4Ddspt91rgGbITErfvZpsdQgibAXvHGPcHjslts6zzeiGENYBasocHXgP2yr3XOcA9vf9rkFToLACk/Pgl2Z35/WRj+f/GGFuACcBdIYS/k50D0CHG+A5wAXB/COEh4IkY4+u59a8DXiX77f+BEMJjZL/9vw6cCJyWO4a/Ld2bCzSEEB4k+21+HrBWbllV7pDGX4AjY4xLyU4+vD2XVIwHZq3Q34hUTDLt+X/kmTcDkiQpoartJ+b/ZkAPne/NgCRJKihF8OXZQwCSJJUgEwBJkpLyOgCSJCmNTAAkSUrKOQCSJCmNTAAkSUrKOQCSJCmNTAAkSUrKOQCSJCmNTAAkSUqqCOYAWABIkpRUERwCsACQJCllQggDgCnA5kAzcESMcW6SbTgHQJKkpPr/dsB7A4NjjNsDE4GLkn4ECwBJktJnDHAnQIzxYWCrpBvwEIAkSQk1PXlFWb7fI4QwDhjXqWlqjHFq7vkQYGGnZUtDCANjjG293b4FgCRJBSi3s5/azeJFQF2n1wOS7PzBQwCSJKXRg8AeACGE7YBnk27ABECSpPS5GdglhDATKAMOS7qBskwRnMsoSZKS8RCAJEklyAJAkqQSZAEgSVIJsgCQJKkEWQBIklSCLAAkSSpBFgCSJJUgCwBJkkrQ/wcXrPISbmH/pgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 648x648 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(9,9))\n",
+    "sns.heatmap(cm, annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r')\n",
+    "plt.ylabel('Actual label')\n",
+    "plt.xlabel('Predicted label')\n",
+    "plt.title('Confusion Matrix')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3920a649",
+   "metadata": {},
+   "source": [
+    "# KNN Classification"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "879c2cf5",
+   "metadata": {},
+   "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>KNeighborsClassifier(n_neighbors=11)</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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=11)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier(n_neighbors=11)"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "knn = KNeighborsClassifier(n_neighbors=11)\n",
+    "knn.fit(X_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "b04cd5b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = knn.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "b33bc4b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 2, 1, 1, 0, 2, 2, 0, 1, 1, 0, 2, 1, 2, 0, 1, 1, 2, 2, 2, 0,\n",
+       "       1, 1, 1, 0, 1, 2, 2, 0, 2, 2, 2, 1, 1, 0, 2, 2, 2, 1, 0, 0, 2, 2,\n",
+       "       0, 0, 0, 1, 0, 1, 2, 0, 1, 2, 2, 1, 0, 1, 2, 2, 0, 1, 2, 2, 0, 2,\n",
+       "       2, 0, 1, 1, 0, 0, 0, 2, 0, 1, 1, 2, 0, 1, 2, 0])"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "34fd0375",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9146341463414634"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score\n",
+    "accuracy_score_3 = accuracy_score(y_test, y_pred)\n",
+    "accuracy_score_3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "7a696adc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "accuracy_scores = [accuracy_score_1 , accuracy_score_2 , accuracy_score_3]\n",
+    "plt.rcParams[\"figure.figsize\"] = (5,5)\n",
+    "ml_model = ['Logistic' , 'SVM' , 'Knn']\n",
+    "plt.bar(ml_model, accuracy_scores, color ='g',width = 0.4)\n",
+    "plt.xlabel(\"Machine Learning Model\")\n",
+    "plt.ylabel(\"Accuracy Score\")\n",
+    "plt.title(\"Accuracy scores comparision\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "500429fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Walking\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Prediction using logistic regression algorithm\n",
+    "p = lr.predict([[-6.15858,-1.25525,11.17958]])\n",
+    "if p == 0:\n",
+    "    print('Eating')\n",
+    "elif p == 1:\n",
+    "    print('Standing')\n",
+    "else:\n",
+    "    print('Walking')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "de320f91",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Walking\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Prediction using using Support Vector Machine algorithm\n",
+    "q = svc.predict([[-6.15858,-1.25525,11.17958]])\n",
+    "if q == 0:\n",
+    "    print('Eating')\n",
+    "elif q == 1:\n",
+    "    print('Standing')\n",
+    "else:\n",
+    "    print('Walking')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "b5484cf1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Walking\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Prediction using K nearest neighbours algorithm\n",
+    "r = knn.predict([[-6.15858,-1.25525,11.17958]])\n",
+    "if r == 0:\n",
+    "    print('Eating')\n",
+    "elif r == 1:\n",
+    "    print('Standing')\n",
+    "else:\n",
+    "    print('Walking')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "d6f80ade",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7861/\n",
+      "Running on public URL: https://18331.gradio.app\n",
+      "\n",
+      "This share link expires in 72 hours. For free permanent hosting, check out Spaces (https://huggingface.co/spaces)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"https://18331.gradio.app\" width=\"900\" height=\"500\" allow=\"autoplay; camera; microphone;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<gradio.routes.App at 0x2c8dcae14b0>,\n",
+       " 'http://127.0.0.1:7861/',\n",
+       " 'https://18331.gradio.app')"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import gradio as gr\n",
+    "\n",
+    "def pre(x_axis ,y_axis ,z_axis):\n",
+    "    if (knn.predict([[x_axis,y_axis,z_axis]])) == 1:\n",
+    "        return(\"Eating\");\n",
+    "    elif(knn.predict([[x_axis,y_axis,z_axis]])) == 2:\n",
+    "        return(\"Standing\");\n",
+    "    else:\n",
+    "        return(\"Walking\")\n",
+    "\n",
+    "demo = gr.Interface(\n",
+    "    pre,\n",
+    "    [\"number\",\"number\", \"number\"],\n",
+    "    \"text\",\n",
+    "    title=\"Activity classification of your cattle\",\n",
+    "    description=\"Know what your cattle is doing anytime, anywhere!\",\n",
+    ")\n",
+    "\n",
+    "demo.launch(share = True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "747a4245",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Accuracy of logistic regression\n",
+    "LR_accuracy = accuracy_score_1*100\n",
+    "#Accuracy of Support Vector Machine\n",
+    "SVM_accuracy = accuracy_score_2*100\n",
+    "#Accurcy of K-Neasrest Neighbor\n",
+    "KNN_accuracy = accuracy_score_3*100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "8aea184e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The accuracy of LOGISTIC REGRSSION algorithm is 89.02439024390245\n",
+      "The accuracy of SUPPORT VECTOR MACHINE algorithm is 87.8048780487805\n",
+      "The accuracy of K-NEAREST NEIGHBOR is 91.46341463414635\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"The accuracy of LOGISTIC REGRSSION algorithm is\",LR_accuracy)\n",
+    "print(\"The accuracy of SUPPORT VECTOR MACHINE algorithm is\",SVM_accuracy)\n",
+    "print(\"The accuracy of K-NEAREST NEIGHBOR is\",KNN_accuracy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "46ed2179",
+   "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": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}