{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "b4b9c2c3-e43e-416f-9fa0-bc12501adcb7",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" price | \n",
" area | \n",
" bedrooms | \n",
" bathrooms | \n",
" stories | \n",
" mainroad | \n",
" guestroom | \n",
" basement | \n",
" hotwaterheating | \n",
" airconditioning | \n",
" parking | \n",
" prefarea | \n",
" furnishingstatus | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 13300000 | \n",
" 7420 | \n",
" 4 | \n",
" 2 | \n",
" 3 | \n",
" yes | \n",
" no | \n",
" no | \n",
" no | \n",
" yes | \n",
" 2 | \n",
" yes | \n",
" furnished | \n",
"
\n",
" \n",
" | 1 | \n",
" 12250000 | \n",
" 8960 | \n",
" 4 | \n",
" 4 | \n",
" 4 | \n",
" yes | \n",
" no | \n",
" no | \n",
" no | \n",
" yes | \n",
" 3 | \n",
" no | \n",
" furnished | \n",
"
\n",
" \n",
" | 2 | \n",
" 12250000 | \n",
" 9960 | \n",
" 3 | \n",
" 2 | \n",
" 2 | \n",
" yes | \n",
" no | \n",
" yes | \n",
" no | \n",
" no | \n",
" 2 | \n",
" yes | \n",
" semi-furnished | \n",
"
\n",
" \n",
" | 3 | \n",
" 12215000 | \n",
" 7500 | \n",
" 4 | \n",
" 2 | \n",
" 2 | \n",
" yes | \n",
" no | \n",
" yes | \n",
" no | \n",
" yes | \n",
" 3 | \n",
" yes | \n",
" furnished | \n",
"
\n",
" \n",
" | 4 | \n",
" 11410000 | \n",
" 7420 | \n",
" 4 | \n",
" 1 | \n",
" 2 | \n",
" yes | \n",
" yes | \n",
" yes | \n",
" no | \n",
" yes | \n",
" 2 | \n",
" no | \n",
" furnished | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" price | \n",
" area | \n",
" bedrooms | \n",
" bathrooms | \n",
" stories | \n",
" parking | \n",
"
\n",
" \n",
" \n",
" \n",
" | count | \n",
" 5.450000e+02 | \n",
" 545.000000 | \n",
" 545.000000 | \n",
" 545.000000 | \n",
" 545.000000 | \n",
" 545.000000 | \n",
"
\n",
" \n",
" | mean | \n",
" 4.766729e+06 | \n",
" 5150.541284 | \n",
" 2.965138 | \n",
" 1.286239 | \n",
" 1.805505 | \n",
" 0.693578 | \n",
"
\n",
" \n",
" | std | \n",
" 1.870440e+06 | \n",
" 2170.141023 | \n",
" 0.738064 | \n",
" 0.502470 | \n",
" 0.867492 | \n",
" 0.861586 | \n",
"
\n",
" \n",
" | min | \n",
" 1.750000e+06 | \n",
" 1650.000000 | \n",
" 1.000000 | \n",
" 1.000000 | \n",
" 1.000000 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" | 25% | \n",
" 3.430000e+06 | \n",
" 3600.000000 | \n",
" 2.000000 | \n",
" 1.000000 | \n",
" 1.000000 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" | 50% | \n",
" 4.340000e+06 | \n",
" 4600.000000 | \n",
" 3.000000 | \n",
" 1.000000 | \n",
" 2.000000 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" | 75% | \n",
" 5.740000e+06 | \n",
" 6360.000000 | \n",
" 3.000000 | \n",
" 2.000000 | \n",
" 2.000000 | \n",
" 1.000000 | \n",
"
\n",
" \n",
" | max | \n",
" 1.330000e+07 | \n",
" 16200.000000 | \n",
" 6.000000 | \n",
" 4.000000 | \n",
" 4.000000 | \n",
" 3.000000 | \n",
"
\n",
" \n",
"
\n",
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Box Plots for numerical columns to identify potential outliers\n",
"plt.figure(figsize=(15, 10))\n",
"numerical_columns = ['price', 'area', 'bedrooms', 'bathrooms', 'stories', 'parking']\n",
"\n",
"for i, column in enumerate(numerical_columns, 1):\n",
" plt.subplot(2, 3, i)\n",
" sns.boxplot(y=df[column])\n",
" plt.title(f'Box Plot of {column}')\n",
"\n",
"plt.tight_layout()\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "b1a63fb2-4b7e-41ea-9ffa-ce05b3532d4f",
"metadata": {},
"outputs": [],
"source": [
"# Function to cap outliers based on the Interquartile Range (IQR)\n",
"def cap_outliers(df, column):\n",
" Q1 = df[column].quantile(0.25)\n",
" Q3 = df[column].quantile(0.75)\n",
" IQR = Q3 - Q1\n",
" lower_bound = Q1 - 1.5 * IQR\n",
" upper_bound = Q3 + 1.5 * IQR\n",
" df[column] = df[column].apply(lambda x: min(max(x, lower_bound), upper_bound))\n",
"\n",
"# Apply the function to cap outliers in specified numerical columns\n",
"for column in ['price', 'area', 'parking']:\n",
" cap_outliers(df, column)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "88a2e95d-a1cb-4fd9-abba-52803cbd1310",
"metadata": {},
"outputs": [
{
"data": {
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bm5u+//77Ox7aB0D2oc3P3TZ/z5492rVrl7p27aqHHnpIf/75p2bPnq2mTZvql19+UeHCha3q9+/fX6VKldKIESPMsccXLVqksLAwhYSEaPz48bp06ZJmz56thg0bav/+/eZ1we22ITc6deqUYmNjVadOHavynTt36ty5cxo0aFCmw8mFhoYqMjJSq1evztCb3Z7GjRtr4MCBmjFjhv7v//5PVapUkSTzv1lx+PBhs6fcG2+8oSJFimjZsmXq1KmTvvzyywztjq3j+9JLL+mLL77QgAEDVLVqVf3333/auXOnfv311wzH40bffPONypYtq/r162cp1tmzZ6tatWrq0KGDChYsqG+++Ub9+/dXWlqawsPDreoeO3ZMzz77rF566SWFhYUpMjJSnTt31rp169SyZUurugMGDJCnp6dGjRqlo0ePavbs2Tpx4oSZXJCyfg5t2LBBTz/9tKpWraqIiAj9999/ZiLBlqCgIE2dOlWHDx82rycAZB/a7Lx7n3473+lHjx5Vt27d9OKLL6pv376qVKmSze2+9dZbevfdd/Xhhx+qb9++dmNMTU1VSEiI6tWrp0mTJmnTpk2aPHmyypUrp5dfflnS9fvg9u3b68cff9TLL7+sypUra9WqVeaPC7cSHR2tlJSUDG3h2rVrlZqaqtDQ0EyXDQ0N1datW7Vu3Tr16dMnS9uTdEfnws22bNmiNm3aKCgoSCNHjpSzs7P5w8d3332nRx991Kp+586dVaFCBb377rvmj09PP/20Dh8+rFdeeUVlypTRmTNntHHjRsXGxmaaj7l06ZI2b96sxo0bq3Tp0lmKdfr06erQoYO6d++ulJQULV26VJ07d9bq1avVtm1bq7pZuadPl5VzeNy4cXr77bfVpUsX9enTR//8849mzpypxo0bm/+GJGn+/Pl68cUXVb9+fQ0ePFh//PGHOnTooBIlSsjf3z/DPgUFBWnVqlVKTEyUu7t7lo7Dfc/AfSMyMtKQlGGyWCzGggULrOquXLnSkGS88847VuXPPPOM4eTkZBw/ftwsGz58uOHs7Gzs2LHDWL58uSHJmDZt2i3j2bp1qyHJ+Pjjj41//vnH+Pvvv401a9YYZcqUMZycnIw9e/YYhmEYMTExhiQjMjLSXLZWrVqGl5eX8d9//5llP/30k+Hs7GyEhoaaZRMnTjQkGTExMbeMJyUlxfDy8jKqV69uXL582SxfvXq1IckYMWKEWZZ+LNNjtGfkyJGGJKNDhw5W5f379zckGT/99JNZJslwdnY2Dh8+nGE9koyRI0ean0NDQw1nZ2ebMaSlpRmGYRhjx441ihQpYvz2229W89944w2jQIECRmxsrN3YH3roIePpp5+2Kjt//rwhyejYsaPdZTt06GBIMhITEw3DMIywsDAjICAgQ73043OjIkWKGGFhYRnqph/3G/+eTZo0MZo0aWJ+XrRokeHs7Gx89913VsvOmTPHkGR8//33Zllmx3vQoEGGu7u7ce3aNbv7eLMnn3zSkGScO3cuS/UvXbqUoeyzzz4zJBk7duwwy273HJJk7N271yw7ceKE4erqajz55JNmWe/evQ1fX1/j33//tVpn165dDQ8PDzO2adOmGZKMZcuWmXWSkpKM8uXLG5KMrVu3ZtiHihUrGm3atMnSMQCQM2jz7cupNt/W93pUVJQhyfjkk08yrLNhw4ZWbc2FCxcMT09Po2/fvlbriIuLMzw8PKzKs9qG2LJp0yZDkvHNN99Ylad/569YsSLTZc+ePWtIMp566imz7OZrlHQBAQFW7Xn6OWOr7bi5Pbd1LjRv3tyoUaOGceXKFbMsLS3NqF+/vlGhQgWzLLPjaxiG4eHhYYSHh2e6f7YkJCRk6drnRrb+PiEhIUbZsmWtygICAgxJxpdffmm1PV9fX6N27dpmWfo+BQUFGSkpKWb5hAkTDEnGqlWrDMO4vXOoVq1ahq+vr3H+/HmzbMOGDYYkm9dsu3btMiQZn3/+eRaPAoCsoM22Ly/cp9/ud/q6desy1Jdktj+vvvqq4ezsnOHva+uYhoWFGZKMMWPGWNWtXbu2ERQUZH7+8ssvM/yNU1NTjccffzzDOm356KOPDEnGwYMHrcoHDx5sSDL279+f6bL79u0zJBlDhw7NdD/S3XzNYO9cuPk6Iv3cTL+OSEtLMypUqGCEhISYORDDuP73CgwMNFq2bGmWpf+9u3XrZrWNc+fOGZKMiRMnZrp/tvz000+GJGPQoEFZXubm8yglJcWoXr268fjjj1uVZ/WePqvn8J9//mkUKFDAGDdunFW9gwcPGgULFjTL0/+t1apVy0hOTjbrzZ0715BkdZ2WbsmSJYYkY/fu3Vk+Dvc7hna5D82aNUsbN27Uxo0btXjxYjVr1kx9+vSxeoT022+/VYECBTRw4ECrZV999VUZhmE1LuaoUaNUrVo1hYWFqX///mrSpEmG5ex54YUXVKpUKfn5+alt27bmsB43jg93o9OnT+vAgQPq2bOnSpQoYZbXrFlTLVu21Lfffpvlbd9o7969OnPmjPr372817lbbtm1VuXLlux738uZfql955RVJyhBvkyZNVLVqVbvrSktL08qVK9W+fXubxym9R9Ly5cvVqFEjFS9eXP/++685tWjRQqmpqTaH+7jRf//9p+LFi1uVXbhwQZJsPrp0o/T5iYmJdutlt+XLl6tKlSqqXLmy1T6nP4q4detWq/q2jrenp+cdDU+Svq+3Ojbpbhwr78qVK/r333/N3n22HjHP6jkUHBysoKAg83Pp0qXVsWNHrV+/XqmpqTIMQ19++aXat28vwzCsjlNISIgSEhLM7X/77bfy9fU1x0eUpMKFC2d4Ac2N0s83AI5Hm29bTrX5N36vX716Vf/995/Kly8vT09Pm9/rffv2teoVuHHjRp0/f17dunWz+m4uUKCA6tWrZ9WG3W4bcqP04V/upI13VPt+9uxZbdmyRV26dNGFCxfMY/Pff/8pJCREx44ds3rEX8p4fKXrbfzu3bv1999/Z3nbt9u+S9Z/n/Repk2aNNEff/yhhIQEq7p+fn5WvenTh2zZv3+/4uLirOr269fP6knBl19+WQULFjT/LWT1HEr/txUWFmb1QvmWLVtmeh2afr7QxgM5gzbbtrxwn3473+mBgYEKCQmxuS3DMDRgwABNnz5dixcvznJvcen6E1U3atSokf744w/z87p161SoUCGr3u3Ozs4Z9i8z9+K1wYEDB3Ts2DE999xz+u+//8w2LykpSc2bN9eOHTsyDGl783F0c3OTi4uLtm3bZnOotMzc7bXBuXPnlJCQoEaNGtm8brvVPf2NbnUOf/XVV0pLS1OXLl2srg18fHxUoUIF89og/d/aSy+9ZPUuup49e1pdK9yIa4OM7puhXXbs2KGJEycqOjpap0+f1ooVK9SpU6fbWodhGJo8ebLmzp2rEydOqGTJkurfv/899/LARx991Krx69atm2rXrq0BAwaoXbt2cnFx0YkTJ+Tn55fhSyH98dsTJ06YZS4uLvr444/1yCOPyNXVVZGRkXYfJ77ZiBEj1KhRIxUoUEAlS5ZUlSpVMn2c+MZt23pEqkqVKlq/fv0dvTTC3norV66snTt33tb6blahQgWrz+XKlZOzs3OGcbcCAwNvua5//vlHiYmJt3ys9tixY/r5558zfTQq/WUs9hg3jYOZfk6kN6iZyWrCPbsdO3ZMv/76a5b32dbx7t+/v5YtW6Y2bdrowQcfVKtWrdSlSxe1bt3a7rbTH2W6cOFCll5wc/bsWY0ePVpLly7NENfNF2RS1s+hm+tJUsWKFXXp0iX9888/cnZ21vnz5zV37lzNnTvXZmzp8Zw4cULly5fP8G86s0cUpevnzO18BwDIObT5t7/eu2nzL1++rIiICEVGRurUqVNWbait7/Wb26Bjx45JUqYvc7zxkdnbbUNsuZM23lHt+/Hjx2UYht5++229/fbbNuucOXPG6gVpttr4CRMmKCwsTP7+/goKCtITTzyh0NBQuy9Hv7F9z6rvv/9eI0eOVFRUlC5dumQ1LyEhweqG1FY7W7FiRUnXx5n18fExy29u44sWLSpfX1/zWiCr51D6vwFb1wyVKlWyeVOffr7QxgM5gzb79tebW/fpt/Odbu9+/pNPPtHFixc1e/ZsdevWLcsxurq6Zri/LV68uFXi98SJE/L19c0wjFz58uWzvB3p3ro2SG/z7P0gkZCQYPXjwM1/H4vFovHjx+vVV1+Vt7e3HnvsMbVr106hoaFW7e/N7uTaYPXq1XrnnXd04MABq3eO2fp3eat7envXBjefw8eOHZNhGDbXKcn8gT6za4NChQplep3EtUFG900iPSkpSQ8//LBeeOEFPfXUU3e0jkGDBmnDhg2aNGmSatSoobNnz+rs2bPZHGnuc3Z2VrNmzTR9+nQdO3ZM1apVu+11rF+/XtL1HlHHjh3LUjI4XY0aNTK80CI/yOyLJqtv9M6KtLQ0tWzZUq+//rrN+ek3aZl54IEHMvwq6+HhIV9fX/388892l/3555/14IMPmg1MZvt7Jy+6sSctLU01atQwxwK82c3jetk63l5eXjpw4IDWr1+vtWvXau3atYqMjFRoaKgWLlyY6bYrV64sSTp48GCm48ffqEuXLtq1a5eGDRumWrVqqWjRokpLS1Pr1q2z9DLYO22s0tf9/PPPZ3rRUbNmzTtat3T91/XMGmkAjkWbn7NeeeUVRUZGavDgwQoODpaHh4ecnJzUtWtXm9/rN7dB6XUWLVpk8+btxgTG3bQh6ePb3tzGpydifv7550w7nKS3/7d6ek7K3jY+fZ9ee+21THv63ZwssNXGd+nSRY0aNdKKFSu0YcMGTZw4UePHj9dXX32lNm3a2Fyvu7u7/Pz8dOjQoSzF+vvvv6t58+aqXLmypkyZIn9/f7m4uOjbb7/V1KlTs9TG36nbOYduV/r5kj6GLYCcRZvtGDffY93ud7q9+/kGDRrowIEDev/999WlSxernvv23MmY9rfrxmuDG9+VceO1Qa1atWwue/O1QW7e+0vSxIkTM42taNGiVp9t/X0GDx6s9u3ba+XKlVq/fr3efvttRUREaMuWLapdu7bN9ZYvX14FCxbUwYMHsxTrd999pw4dOqhx48b64IMP5Ovrq0KFCikyMlJLlizJ0jqy6ubjn5aWJicnJ61du9bmuXTzMbodXBtkdN8k0tu0aZPpxbEkJScn680339Rnn32m8+fPq3r16ho/fryaNm0q6fqLBmfPnq1Dhw6Zv4TeTiOU1127dk2SdPHiRUlSQECANm3apAsXLlj9qnjkyBFzfrqff/5ZY8aMUa9evXTgwAH16dNHBw8ezPTRj7uVvu2jR49mmHfkyBGVLFnS/JX7dhKNN6735l48R48etdrnO3Hzhcvx48eVlpaW6csr7ClVqpTc3d1veUNXrlw5Xbx48Y4vgCpXrmzzrezt2rXTvHnztHPnTvNN1zf67rvv9Oeff+rFF180y4oXL67z589nqHtjr4l0d/NrZrly5fTTTz+pefPmd7UeFxcXtW/fXu3bt1daWpr69++vDz/8UG+//Xamv+q3b99eERERWrx48S0T6efOndPmzZs1evRojRgxwixP/1XdlqyeQ7bW8dtvv6lw4cJmT4ZixYopNTX1ludGQECADh06lKGXua1/f9L175KTJ0+qQ4cOdtcLwHFo83Ouzf/iiy8UFhamyZMnm2VXrlyx2f7Zkv5SNC8vL7vfz3fShtwo/Yffm9v4hg0bytPTU0uWLNGbb75p82Yr/cVV7dq1M8tstfEpKSk6ffq0VdndtMvpPaEKFSp014kdX19f9e/fX/3799eZM2dUp04djRs3zu69Qrt27TR37lxFRUUpODjY7vq/+eYbJScn6+uvv7Z6AdnNw8ulS+9tf+Px+e233yTJZhvfrFkz8/PFixd1+vRpPfHEE5Kyfg6ln+O2zpnM2vj08+V2XhAL4O7QZjv+Pv12v9PtKV++vCZMmKCmTZuqdevW2rx5c7b14g4ICNDWrVt16dIlq17px48fz9LyN14b1KhRwyxv06aNChQooEWLFmX6wtFPPvlEBQsWNJ/gTu8BfvO1QU7c+0vXf/C+22uDcuXK6dVXX9Wrr76qY8eOqVatWpo8ebIWL15ss37hwoX1+OOPa8uWLTp58qTNF3He6Msvv5Srq6vWr18vi8VilkdGRtqsn5V7+hvr2juHy5UrJ8MwFBgYaLcz5Y3XBjf+W7t69apiYmL08MMPZ1gmJiZGzs7Ot+ykmZ/kmzHSBwwYoKioKC1dulQ///yzOnfurNatW5sn7zfffKOyZctq9erVCgwMVJkyZdSnT5/7okf61atXtWHDBrm4uJgXxk888YRSU1P1/vvvW9WdOnWqnJyczBuNq1evqmfPnvLz89P06dO1YMECxcfHa8iQITkWr6+vr2rVqqWFCxdafTEfOnRIGzZsMG8kJJkNdVZuYOvWrSsvLy/NmTPH6jGbtWvX6tdff83wFuXbNWvWLKvPM2fOlCS7N22ZcXZ2VqdOnfTNN99o7969GeanP17TpUsXRUVFmT0RbnT+/HnzwiwzwcHBOnTokNXxkKRhw4bJzc1NL774ojmWWrqzZ8/qpZdeUuHChTVs2DCzvFy5ckpISLDqyZ4+zNLNihQpkuWkw826dOmiU6dOad68eRnmXb58WUlJSbdcx8375OzsbPbQvvlY3Cg4OFitW7fWRx99pJUrV2aYn5KSotdee03S/+9VcPOjc9OmTct0/Vk9h6KioqweyT558qRWrVqlVq1aqUCBAipQoICefvppffnllzZ/jPnnn3/M/3/iiSf0999/64svvjDLLl26lOmQML/88ouuXLmi+vXrZ7ofAByHNv+6nGrzCxQokOF7febMmVnugRUSEiJ3d3e9++67unr1aob56d/Pd9KG3OjBBx+Uv79/hmuIwoUL67XXXtPRo0dtDl24Zs0aLViwQCEhIeZ47NL1Nv7m967MnTs3w37fzt/oZl5eXmratKk+/PDDDAl6ybrtykxqamqGYW+8vLzk5+dnt32XpNdff11FihRRnz59FB8fn2H+77//runTp0uy/fdJSEjI9Gb577//troeSkxM1CeffKJatWpl6FU+d+5cq3Nj9uzZunbtmvnvNKvn0I3/tm48Jhs3btQvv/xiM87o6Gh5eHjcUa9YALePNvs6R9+n3+53+q3UrFlT3377rX799Ve1b99ely9fvqP13CwkJERXr161ug9OS0vLsH+ZCQoKkouLS4ZrA39/f/Xq1UubNm3S7NmzMyw3Z84cbdmyRb179zZ7sru7u6tkyZIZrg0++OCDDMvfzbVBUFCQypUrp0mTJpk/Nt0oK9cGly5d0pUrV6zKypUrp2LFit3y2mDkyJEyDEM9evSwuf3o6GjzifYCBQrIycnJ6trozz//tJk3kG59T3+jW53DTz31lAoUKKDRo0dnuHY0DMPMf9StW1elSpXSnDlzlJKSYtZZsGBBpn+f6OhoVatWLcd+oLsX3Tc90u2JjY1VZGSkYmNj5efnJ+n6Y6Pr1q1TZGSk3n33Xf3xxx86ceKEli9frk8++USpqakaMmSInnnmGW3ZssXBe3B71q5da/5ifebMGS1ZskTHjh3TG2+8YQ7D0b59ezVr1kxvvvmm/vzzTz388MPasGGDVq1apcGDB5u//KWP75T+S2rNmjU1YsQIvfXWW3rmmWesGsvsNHHiRLVp00bBwcHq3bu3Ll++rJkzZ8rDw0OjRo0y66W/nOHNN99U165dVahQIbVv397muGyFChXS+PHj1atXLzVp0kTdunVTfHy8pk+frjJlytz1RUdMTIw6dOig1q1bKyoqSosXL9Zzzz1n81e9rHj33Xe1YcMGNWnSRP369VOVKlV0+vRpLV++XDt37pSnp6eGDRumr7/+Wu3atVPPnj0VFBSkpKQkHTx4UF988YX+/PNPu4/gdOzYUWPHjtX27dvVqlUrs7xChQpauHChunfvrho1aqh3794KDAzUn3/+qfnz5+vff//VZ599Zp4nktS1a1f973//05NPPqmBAwfq0qVLmj17tipWrJhhHM6goCBt2rRJU6ZMkZ+fnwIDA1WvXr0sHZcePXpo2bJleumll7R161Y1aNBAqampOnLkiJYtW6b169dn+oKcdOk/kj3++ON66KGHdOLECc2cOVO1atW6ZS+sTz75RK1atdJTTz2l9u3bq3nz5ipSpIiOHTumpUuX6vTp05o0aZLc3d3VuHFjTZgwQVevXtWDDz6oDRs22HwCIF1Wz6Hq1asrJCREAwcOlMViMS9YRo8ebdZ57733tHXrVtWrV099+/ZV1apVdfbsWe3bt0+bNm0yfyTs27ev3n//fYWGhio6Olq+vr5atGhRhrH30m3cuFGFCxdWy5Yt7R4nALmDNj932/x27dpp0aJF8vDwUNWqVRUVFaVNmzaZj0vfiru7u2bPnq0ePXqoTp066tq1q0qVKqXY2FitWbNGDRo00Pvvv39HbcjNOnbsqBUrVmToCf3GG29o//79Gj9+vKKiovT000/Lzc1NO3fu1OLFi1WlSpUMw5z16dNHL730kp5++mm1bNlSP/30k9avX5/hGqNWrVoqUKCAxo8fr4SEBFksFj3++OPy8vLKUsyzZs1Sw4YNVaNGDfXt21dly5ZVfHy8oqKi9Ndff+mnn36yu/yFCxf00EMP6ZlnntHDDz+sokWLatOmTdqzZ4/VUwS2lCtXTkuWLNGzzz6rKlWqKDQ0VNWrV1dKSop27dql5cuXq2fPnpKkVq1amU+2vfjii7p48aLmzZsnLy8vmz8CVKxYUb1799aePXvk7e2tjz/+WPHx8TaTNCkpKWrevLm6dOmio0eP6oMPPlDDhg3NJ8Gyeg5JUkREhNq2bauGDRvqhRde0NmzZzVz5kxVq1bNZkJg48aNat++PeOgAjmENjtv3qff7nd6Vjz22GNatWqVnnjiCT3zzDNauXKl1Yuk70SnTp306KOP6tVXX9Xx48dVuXJlff311+Z93a2+u11dXdWqVStt2rRJY8aMsZo3depUHTlyRP3799e6devMnufr16/XqlWr1KRJkwztaJ8+ffTee++pT58+qlu3rnbs2GE+bXWj2zkXbubs7KyPPvpIbdq0UbVq1dSrVy89+OCDOnXqlLZu3Sp3d3d98803dtfx22+/me1q1apVVbBgQa1YsULx8fHq2rWr3WXr16+vWbNmqX///qpcubJ69OihChUq6MKFC9q2bZu+/vprvfPOO5Kuvxx3ypQpat26tZ577jmdOXNGs2bNUvny5W0Om5uVe/p0tzqHy5Urp3feeUfDhw/Xn3/+qU6dOqlYsWKKiYnRihUr1K9fP7322msqVKiQ3nnnHb344ot6/PHH9eyzzyomJkaRkZE2x0i/evWqtm/frv79+9s9TvmOcR+SZKxYscL8vHr1akOSUaRIEaupYMGCRpcuXQzDMIy+ffsakoyjR4+ay0VHRxuSjCNHjuT2LtyRyMhIQ5LV5OrqatSqVcuYPXu2kZaWZlX/woULxpAhQww/Pz+jUKFCRoUKFYyJEyea9aKjo42CBQsar7zyitVy165dMx555BHDz8/POHfuXKbxbN261ZBkLF++3G7cMTExhiQjMjLSqnzTpk1GgwYNDDc3N8Pd3d1o37698csvv2RYfuzYscaDDz5oODs7G5KMmJgYu9v7/PPPjdq1axsWi8UoUaKE0b17d+Ovv/6yqpN+LPfs2WN3XYZhGCNHjjQkGb/88ovxzDPPGMWKFTOKFy9uDBgwwLh8+bJVXUlGeHi4zfVIMkaOHGlVduLECSM0NNQoVaqUYbFYjLJlyxrh4eFGcnKyWefChQvG8OHDjfLlyxsuLi5GyZIljfr16xuTJk0yUlJSbhl/zZo1jd69e9uc9/PPPxvdunUzfH19jUKFChk+Pj5Gt27djIMHD9qsv2HDBqN69eqGi4uLUalSJWP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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Replot Box Plots to verify outlier treatment\n",
"plt.figure(figsize=(15, 5))\n",
"for i, column in enumerate(['price', 'area', 'parking'], 1):\n",
" plt.subplot(1, 3, i)\n",
" sns.boxplot(y=df[column])\n",
" plt.title(f'Box Plot of {column} (Outliers Capped)')\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "2bda5904-0817-4770-b83a-c4944a41bc35",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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fjmeffRY7d+7ElVdeiYsuusi9xOS1115D//79sX79ehQUFODUU0/Fr371K84EEZnc+++/73fnNFc8i2tQ5NJLL0V8fDzuuusun1F8198nJCR4/RsAnE4nHnvsMZ/3T09P97skytVpaGho8Hr8zDPPxIABA/DAAw+gsbHR5++OHDkS8BjV0qdPH4wcORJPP/20V3l3796Nd955Bz/96U/djwU6Ln9GjRqF3Nxc/P3vf0dbW5v78TfffBNffPEFpkyZEnZZW1pa8PLLL2Pq1Km44oorfP6bO3cujh8/HnKb5tzcXJx77rl44okn3B1kT57nISEhwadtvfDCC2HFvFx11VX473//i5UrV+Lo0aNeS+EAoK6uzuvf8fHxGD58OAB41Z0WzjjjDBQUFOChhx7yOc+uegin/oiI1GSamaBgampqsGrVKtTU1Li3JL3pppvw1ltvYdWqVViyZAm++uorHDhwAC+88AL+/e9/o7OzE7///e9xxRVXYOPGjTofARGpZd68eWhubsa0adMwePBgOJ1ObNu2Dc899xxOPfVUzJ49G8CJJTq33norFi9ejLFjx+Kyyy5DcnIyduzYgb59+2Lp0qUYM2YMsrKyMHPmTNxwww2Ii4vD//t//89vJ+vMM8/Ec889hwULFuCss85CRkYGLr74YgwYMACZmZn4+9//jh49eiA9PR3nnHMOCgoK8K9//QuTJ0/G0KFDMXv2bJx88sn47rvv8P7778NqteK1117Tuvpw//33Y/LkyRg9ejSuvfZa9xbZNpsNd9xxh9fxAie2B//5z3+OpKQkXHzxxX4TuCYlJeHee+/F7NmzMX78eMyYMcO9Rfapp56K3//+92GX89VXX8Xx48fxs5/9zO/zP/nJT9CrVy+sXr3ap6PR3aOPPorS0lIUFRXh17/+Nfr3749Dhw5h+/bt+Pbbb915gKZOnYq77roLs2fPxpgxY7Br1y6sXr3aPRsix/Tp03HTTTfhpptuQs+ePX1mT1yDdRMmTMApp5yCAwcOYMWKFRg5ciROP/102Z+jhPj4eDz++OO4+OKLMXLkSMyePRt9+vTB//73P3z++ed4++23AcivPyIiVem2L52KAEhr1651/9u1rWp6errXf4mJidL06dMlSZKkX//61xIAae/eve6/+/jjjyUA0v/+9z+tD4GINPLmm29KZWVl0uDBg6WMjAzJYrFIhYWF0rx586RDhw75vH7lypVScXGxlJycLGVlZUnjx493b7csSZJUUVEh/eQnP5FSU1Olvn37urfchsdWwpIkSY2NjdLVV18tZWZmSgC8tst+5ZVXpCFDhkiJiYk+W1JXVlZKl112mZSdnS0lJydL/fr1k6ZPny6999577te4tsg+cuSIrDpwbXX8wgsvBH2dvy2yJUmS3n33XamkpERKTU2VrFardPHFF0t79uzx+fvFixdLJ598shQfHy9ru+znnnvOXdc9e/aUrrnmGunbb7/1eo3cLbIvvvhiKSUlRWpqagr4mlmzZklJSUnS0aNH3cd6//33+33t/v37pV/+8pfSSSedJCUlJUknn3yyNHXqVOnFF190v6a1tVX6wx/+IPXp00dKTU2VSkpKpO3bt0vjx4+Xxo8fH7S8nkpKSiQA0q9+9Suf51588UXpggsukHJzcyWLxSLl5+dL1113nXTw4MGQ7wtAuv7664O+Jljb6L5Ftkt5ebk0adIkqUePHlJ6ero0fPhwn62+5dQfEZGa4iQpggx6gouLi8PatWtx6aWXAgCee+45XHPNNfj888/dy1VcMjIycNJJJ+H222/HkiVL0N7e7n6upaUFaWlpeOeddzBp0iQtD4GIiIiIiFQSE8vhiouL0dnZicOHD7szsHdXUlKCjo4O7N+/3x3c+eWXXwJAxEG4REREREQkHtPMBDU2NqKqqgrAiU7Pgw8+iPPOOw89e/ZEfn4+fvGLX6CiogLLli1DcXExjhw5gvfeew/Dhw/HlClT0NXV5V6X/9BDD6GrqwvXX389rFYr3nnnHZ2PjoiIiIiIlGKaTtCmTZvcGcA9zZw5E0899RTa29vx17/+Ff/+97/x3XffIScnBz/5yU9w5513oqioCABQW1uLefPm4Z133kF6ejomT56MZcuWeWVCJyIiIiIiYzNNJ4iIiIiIiEiOmMkTREREREREBLATREREREREMcbQu8N1dXWhtrYWPXr0QFxcnN7FISIiIiIinUiShOPHj6Nv376Ijw8+12PoTlBtbS3y8vL0LgYREREREQnim2++wSmnnBL0NYbuBPXo0QPAiQO1Wq06l4aIiIiIiPTicDiQl5fn7iMEY+hOkGsJnNVqZSeIiIiIiIhkhclwYwQiIiIiIoop7AQREREREVFMYSeIiIiIiIhiCjtBREREREQUU9gJIiIiIiKimMJOEBERERERxRR2goiIiIiIKKawE0RERERERDGFnSAiIiIiIoop7AQREREREVFMYSeIiIiIiIhiCjtBREREREQUU9gJIiIiIiKimMJOEBERERERxZREvQtARN7szU4cbXTC0doOa2oSctItsKVZ9C4WUdiUbstGujaMVFYtHXK0or7JCUdrB6ypichKs6C3NSXg40pR+3yIer71Kpeo9WEURqo/ta9dNbETRCSQ2oYW/Pmlndi676j7sXEDc3DP5cPRNzNVx5IRhUfptmyka8NIZdVSTV0TFq7dhYqqOvdjEwf3wm0XD8Wta3eh3OPx0sJsLJlWhPzs9Kg/V+3zIer51qtcotaHURip/vxd00peu2rjcjgiQdibnT43PgDYsu8obn5pJ+zNTp1KRhQepduyka4NI5VVS4ccrT4/lgDg9L423NKtAwQA5VV1uGXtLhxytEb1uWqfD1HPt17lErU+jMJI9Rfomlbq2tUCO0FEgjja6PS58bls2XcURxvFufkRBaN0WzbStWGksmqpvsnp82MJAIrzMv0+Dpz4MVXfFF19qX0+RD3fepVL1PowCiPVX6BrGlDm2tUCO0FEgnC0tgd9/niI54lEoXRbNtK1YaSyasnR2uH38baOroj+Tv7nqns+RD3fepVL1PowCiPVX6hrM9prVwvsBBEJwpqSFPT5HiGeJxKF0m3ZSNeGkcqqJWuK/xDk5MTgP0MC/Z38z1X3fIh6vvUql6j1YRRGqr9Q12a0164W2AkiEkROhgXjBub4fW7cwBzkZIi5MwxRd0q35ZwMCyadnou5Ewrx5MxReOyaM7By1lmYO6EQk07PFera8HfsaZYEzJ1QiGd+dQ7sLU7sP9Io1Np+LWSlW1BamO3zeOU3DX4fB04EWGelR3du1b6vinrf1qtcotaHURip/gJd04Ay164W4iRJkvQuRKQcDgdsNhvsdjusVqvexSGKWm1DC25+aSe2dNsV5t7Lh6OPYLvCEAWjdFs20i5EnseeZknA8hnFWFVR7VV2UXd7UlNNXZPPJgha7Q6n5n1V1Pu2XuUStT6Mwkj15++a1vu+HE7fgJ0gIsG48gMcb21Hj5Qk5GSImx+AKBil2rK92Ym5ayr9BgyPG5iDFTOKhbtGXMfeJUm467XPsdVPALGoZVeTV06RlERkpfvJE+TxuFLUvq+Ket/Wq1yi1odRGKn+1L52wxVO30D8BXtEMcaWJu7NjigcSrVlOTsmiXbNuI59/+FGvx0gQNyyq6m3NcXvD6RAjytF7fuqqPdtvcolan0YhZHqT+1rV02MCSIiIqEZacek7oxcdiIiM9O1E3T8+HHMnz8f/fr1Q2pqKsaMGYMdO3boWSQiIhKMkXZM6s7IZSciMjNdO0G/+tWvsGHDBvy///f/sGvXLlxwwQU4//zz8d133+lZLCIiEoiRdkzqzshlJyIyM902RmhpaUGPHj3wyiuvYMqUKe7HzzzzTEyePBl//etfQ74HN0YgIooNRtoxqTsjl52IyEgMsTFCR0cHOjs7kZLiHUyVmpqK8vJyv3/T1taGtrY2978dDoeqZSQiIjH0zUzFihnFhtkxyZORy07G4NpNzNHaDmtqEnLS2b6IQtGtE9SjRw+MHj0aixcvxumnn47evXtjzZo12L59OwoLC/3+zdKlS3HnnXdqXFIiIhKBkXZM6s7IZSex1Ta04M8v7fTaQTEW81ARhUvXPEH79+9HWVkZtmzZgoSEBJxxxhkYNGgQPv74Y3zxxRc+r/c3E5SXl8flcERERBRzjJhDi0hNhlgOBwADBgzA5s2b0dTUBIfDgT59+uCqq65C//79/b4+OTkZycnJGpeSiMyCS0aIyEyMmEOLSBRCJEtNT09Heno66uvr8fbbb+O+++7Tu0hEZDJcMkJEZsM8VESR03WL7LfffhtvvfUWqqursWHDBpx33nkYPHgwZs+erWexiMhk7M1Onw4QcGKk9OaXdsLe7NSpZGQ09mYn9h9uRGVNPfYfaWTbIV0xDxVR5HSdCbLb7Vi4cCG+/fZb9OzZE5dffjnuvvtuJCXxoiUi5XDJCCmBs4kkGlceqi0BYoKYh4ooMF1ngqZPn479+/ejra0NBw8exCOPPAKbzaZnkYjIhLhkhKLF2UQSkS3NgnsuH+6TkNeVh4qDO0SBCRETRESkJi4ZoWhxNpFExTxURJFhJ4iITI9LRihanE0kkTEPFVH4dF0OR0SkBS4ZoWhxNpGIyFw4E0REwlEjnw+XjFA0OJtIRGQu7AQRkVDU3IGLS0YoUq7ZxJtf2unVEeJsIhGRMcVJkiTpXYhIORwO2Gw22O12WK1WvYtDRFGyNzsxd02l3wD0cQNzsGJGMX9skq5cs5ScTSQiEk84fQPOBBGRMLgDF4mOs4lERObAThARCYM7cBGJQ43YPCIiUbATRETC4A5cRGJQMzaPiEgE3CKbiITh2oHLn0h34LI3O7H/cCMqa+qx/0gj7M3OaItJZGr2ZqdPBwg4sST15pd28hoC7ytEZsCZICIShtI7cHE0myh8jM0LjvcVInNgJ4iIhKJUPp9Qo9ncaY7IP8bmBcb7CpF5sBNERMJRYgcujmYTRYaxeYHxvkJkHowJIiJT4mg2UWTUiM0zi1i9rzAGisyIM0FEZEoczSaKjNKxeWYSi/cVxkCRWbETRESm5BrN3uJn6Uqsj2aT+oyeY0ep2DyzibX7CmOgyMzYCSIiU+JoNunFLCPnSsTmmU2s3VcYA0Vmxk4QEZkWR7NJaxw5N79Yuq/EagwUxQZ2gojI1DiaTVriyHlsiJX7SizGQFHs4O5wRERECuHIOZkJdwokM2MniIiIDMEI2/Ry5JzMxBUD1b0jZNYYKIotXA5HRETCM8pmA7G2exiZXyzFQFFs4UwQEREJLdRmAyLNCHHknMzIlmbBgNwMjMzPwoDcDLZjMgXOBBEREQBxc9sYbbMBEUfORT23LJexsZ7IyNgJIiIioZebGXGzAZF2DxP13LJcxsZ6IqPjcjgiohgn+nIzbjYQOVHPLctlbKwnMgN2goiIYpyc5WZ64ja9kRP13LJcxsZ6IjNgJ4iIKMaJvtyMmw1ETtRzy3IZG+uJzIAxQUREMc4Iy81E3GzACEQ9tyyXsbGeyAw4E0REpAAjJPIMxCjLzbhNb/hEPbcsl7GxnsgM2AkiIopSbUML5q6pxMQHN2PaY9swcdlmzFtTidqGFr2LJguXm5mXqOeW5TI21hOZQZwkSZLehYiUw+GAzWaD3W6H1WrVuzhEFIPszU7MXVPpN0h43MAcrJhRbJgfBK6cH1xuZj6inluWy9hYTySacPoGjAkiIoqC0RJ5BiNSbhtSlqjnluUyNtYTGRmXwxERRYG7JBERERkPO0FERFHgLklERETGw04QEVEUuEuSeoy84x4REYmNMUFERFFw7ZJ080s7scUjNoi7JEWntqEFf35pp1e81biBObjn8uHom5mqY8mIiMgMuDscEZECuEuScsy04x4REWmHu8MREWmMuyQpx0w77hERkZjYCSIiIqFwx73QXDOPjtZ2WFOTkJPOTjgRUTjYCSIiIqFwx73gGC9FRBQ97g5HRERC4Y57gdmbnT4dIODEMsGbX9oZcAc97rRHROSNM0FERCQU7rgXWCTxUpw5IiLyxU4QEREJp29mKlbMKOaOe92EGy8VauZIhJ32GN9ERHpgJ4iIiITEHfd8hRsvJfpOe5ylIiK9MCaIiIjIIMKNlxJ5p71I45uIiJSgayeos7MTixYtQkFBAVJTUzFgwAAsXrwYBs7fSkREpBpXvFT3jlCgeCmRd9qTM0tFRKQWXZfD3XvvvXj88cfx9NNPY+jQofjoo48we/Zs2Gw23HDDDXoWjYiISEjhxEu5Zo62+Ols6L3TnsizVKQsxn2RiHTtBG3btg2XXHIJpkyZAgA49dRTsWbNGnz44Yd6FouIiATEH1I/khsvJfJOeyLPUpFyGPdFotK1EzRmzBj84x//wJdffolBgwbhs88+Q3l5OR588EG/r29ra0NbW5v73w6HQ6uiEhGRjvhDKnKi7rQn8iwVKcMIuxNS7NI1Jujmm2/Gz3/+cwwePBhJSUkoLi7G/Pnzcc011/h9/dKlS2Gz2dz/5eXlaVxiIiLSGgPoo2dLs2BAbgZG5mdhQG6GED88w41vIuNh3BeJTNeZoOeffx6rV6/GM888g6FDh+LTTz/F/Pnz0bdvX8ycOdPn9QsXLsSCBQvc/3Y4HOwIERGZ3NFGJz4+UI+5EwpRnJeJto4upCQl4JOaeqwsr9Z9m2eKnKizVKQMxn2RyHTtBP3xj390zwYBQFFREQ4cOIClS5f67QQlJycjOTlZ62ISEZGOGtvasXxGMVZVVOORjVXux0sKs7F8RjGa2vhDysiYD8q8GPdFItN1OVxzczPi472LkJCQgK6uLp1KREREoslMtWBVRTUqquq8Hq+oqsOqimrYUsX7AW1vdmL/4UZU1tRj/5FGLtmjmBRuXisiLek6E3TxxRfj7rvvRn5+PoYOHYrKyko8+OCDKCsr07NYREQkEGdnl08HyKWiqg7OTrEGzriJA9EJIu9OSKRrJ2jFihVYtGgR5syZg8OHD6Nv37647rrrcNttt+lZLCIiEkhjW0fQ55tCPB+pSLbk5m5YRN4Y90Wi0rUT1KNHDzz00EN46KGH9CwGEREJTI+4gkhnc+TshsUffxRrGPdFItI1JoiIiCgUreMKotmSm7thEREZAztBREQkNK3zyUST24S7YRERGYOuy+GIiIjk0DKuIJrZHNes1RY/nSjuhkVEJA7OBBERkSHY0iwYkJuBkflZGJCboVqMQTSzOVrPWhERUWQ4E0REROQh2tkc7oZFRCQ+zgQRERF5UGI2R6tZKyIiigxngoiIyBAiydsTqXBmc7QsFxERKYOdICIiEl6keXuiISe3iR7lIiKi6HE5HBERCS2avD2xWC4iIgqNnSAiIhJaNHl71CRquYiIKDR2goiISGjR5O1Rk6jlIiKi0NgJIiIioUWTt0dNopaLiIhCYyeIiIiE5srb44+cvD1qEbVcREQUWpwkSZLehYiUw+GAzWaD3W6H1WrVuzhERKSS7xta8PWxZqQnJ6CxtRM9UhLR2NaBgp5p6K3jLmy1DS24+aWdXolVXfmE+nB3OCIiTYXTN+AW2UREJDxnZxdWbNyHiqo692OlhdlYMq1Ix1KFl0+IiIjEwU4QEREJ7ZCjFXe89jmK87NQVlKAto4upCQl4JOaetz52udYctlw9Lam6FY+OfmEiIhILOwEERGR0Bqanbj6nH5YVVGNRzZWuR8vKczG7JICNDQ7de0EERGR8bATREREQpMkYFVFtddSOADufy+aMkSPYoXF3uzE0UYnHK3tsKYmISeds0dERHpiJ4iIiIQmAT4dIJeKqjqIvrtPbUML/vzSTq/EquMG5uCey4ejLzdPICLSBbfIJiJh2Jud2H+4EZU19dh/pBH2ZqfeRSIBNDs7onpeT/Zmp08HCAC27DuKm1/ayTZORKQTzgQRkRA4Wk6BZKYGXzYW6nk9HW10+nSAXLbsO4qjjU4uiyMi0gFngohIdxwtp2C0Tkqq5Iyko7U96PPHQzxvRoccrfjfQQc+rD6G/33vwCFHq95FIqIYxJkgItIdR8spGFuaBfdcPjxgUlIl24bSM5LWlKSgz/cI8bzZ1NQ1YeHaXX7zPeVnp+tYMiKKNewEEZHuOFpOoWiRlDTUjOSKGcVhf55rFmuLn06+GrNYIjvkaPXpAAFAeVUdblm7C8umj+RW50SkGS6HIyLdcbSc5LClWTAgNwMj87MwIDdD8dlBOTOS4XLNYnVfzqfGLJbo6pucAXf5K6+qQ30Tl70SkXY4E0REuuNouX/MLeOttqEF9pZ2OFraYUtNgjU1SdFNM9SakdRiFssIHK3Bd/EL9TwRkZLYCSIi3WkZ82EU3C3P24G6JtziJ5bk7mlF6KdQLImaM5K2tNjr9HRnTQn+kyPU80QkHiMP1vGOQ0RC4Gj5j9SITTGy2oYWnw4QcGIJ1a1rd+G+K0Yo0jHkjKS6stItKC3MRrmfJXGlhdnISmf9EhmJ0QfrGBNERMJQO+bDKNSITTEye0t70FgSe4syG2cwfkddva0pWDKtCKWF2V6Pu3aH46YIRMZhhtQWnAkiIhIMd8vz5gjRyVGyPjgjqa787HQsmz4S9U1OOFo7YE1JRFa6hR0gIoMxQ2oLdoKIiATD3fK8WVO1rQ/G76irtzWFnR4igzPDYB2XwxERCcYVm+JPLMam2FKTfJZQuZQWZsMWopNERPqyNzux/3AjKmvqsf9IoyGWSlFwZhisYyeIiEgwjE3x1jczFXcHiCW5e1qRIQJwiWJVbUML5q6pxMQHN2PaY9swcdlmzFtTidqGFr2LRlEww2BdnCRJkt6FiJTD4YDNZoPdbofVatW7OEREinJtPcrYlBO+q2+Go7UDjpYTW7FaUxJxclaa3sUiHRl5e95YYG92Yu6aSr+xI+MG5sTcTpdmU9vQEjC1RR+dBqfC6RswJoiISFCMTflRbUMLbn55l2G3YiXlGX173lhghuB5CszoG8lwORwREQnNDFuxkrLYJozBDMHzFJyRU1uwE0REREJj3iTqjm3CGMwQPE/mxeVwREQkNEdrO9IsCSgrLUBxXibaOrqQkpSAT2rqsbK8mqPJMYgzDMbgCp7fEiAmyAjB82Re7AQREZHQbKlJWD6jGKsqqvHIxir34yWF2Vg+ozhkHiEyH84wGINrp8tAwfNGWjpF5sNOEBERCS09ORGrKqpRUVXn9XhFVR3iACybPlKXcpF+OMNgHEYPnifzYkwQEREJrbG1w6cD5FJeVYfG1g6NS0R6Yy4tYzFy8DyZF2eCiIhIaGrEfzC/jPFxhkF/vI7IyNgJIiIioSkd/8H8MubBXFr64XVERsflcEREJDRX/Ic/4cZ/hJNfxt7sxP7Djaisqcf+I43MPUP0A+ZpIjPgTBAREQlNyR2m5Gaw5yg3UWByryMikenaCTr11FNx4MABn8fnzJmDRx99VIcSERGRiJSK/5ATXxRqlHvFjGL+wKOYxjxN5GLkuDBdO0E7duxAZ2en+9+7d+/GpEmTcOWVV+pYKiIiEpES8R9y4os4yk0UHPM0EWD8uDBdY4J69eqFk046yf3f+vXrMWDAAIwfP17PYhERkUnJiS/iKDdRcErG6ZExmSEuTJiNEZxOJ/7zn/+grKwMcXFxfl/T1tYGh8Ph9R8REZFccvLLcJSbKDjmaSI5M+aiE2ZjhHXr1qGhoQGzZs0K+JqlS5fizjvv1K5QRERkOqHii1yj3Fv8fMFzlJvohL6Zqbj/yhGob3LC0doBa2oistIs6G1N0btopAFHazvSLAkoKy1AcV4m2jq6kJKUgE9q6rGyvNoQM+ZxkiRJehcCAC688EJYLBa89tprAV/T1taGtrY2978dDgfy8vJgt9thtVq1KCYREcWA2oaWgLvR9THAWncitRk9HoSi89WRRnx1tAmrKqpRUVXnfrykMBuzSwrQPycd/XtlaF4uh8MBm80mq28gxEzQgQMH8O677+Lll18O+rrk5GQkJydrVCoiIopVSu1GR9ow8g5VRsQdFCk9OdGnAwQAFVV1iAOwbPpIXcoVDiE6QatWrUJubi6mTJmid1GIiIgAKLMbHamPMxLa4w6K1Nja4dMBcimvqkNjawd6C75IS/eNEbq6urBq1SrMnDkTiYlC9MmIiEhA9mYn9h9uRGVNPfYfaTTE7kOkLjPsUGVE3EGRzNAGdO91vPvuu6ipqUFZWZneRSEiIkFxtJ/84YyEPriDIpmhDeg+E3TBBRdAkiQMGjRI76IQEZGAONpPgZhhNNqImCeIzNAGdO8EERERBWOGfBSkDjOMRhsR8wSRGdqA7svhiMg4uAMT6YGj/RQIczrphzsoktFzRbETRESyMCaD9MLRfgrENRodKKcTf5Crizsoxjaj/y4QJllqJMJJiEREkbM3OzF3TaXfJUnjBuYwJwSpyt7sxE0vfIbT+lh9MpPvPejAA1eOYPuLca5Zas5IEGlD1N8FhkuWSkRi4w5MpCdbmgWLpg7BwrW78MjGKvfjpYXZWDKtiG2POCNBpDEz/C7gxghEFBJjMpTFfDfhsTc7ceu63T6J+cqr6vCXdbtZf0REGjPD7wLOBBFRSIzJUI7R11DrwQwjjkREZmKG3wWcCSKikMyQD0AEzHcTGTOMOHL2j4jMxAy/C9gJIqKQzJAPQATMdxMZo4841ja0YO6aSkx8cDOmPbYNE5dtxrw1lahtaNG7aEREETHD7wIuhyMiWbrnhLCmJiE9ORGNrR2orKmPOG9QLOUeMsOMRjBqncucDAsmnZ4bcHc4LUccAx1jsMeDzf5xZ0UiMiqj54piJ4iIZPPcgam2oQU3vfBZVLEtsRYfY/QZjWDUPJe2NAtumzoEW6u8OxIn21Jw1ZmnaPaF6+8YJ52ei0VTh+DWdbv9HntreyfjmYjItIy8MyOXwxGRbK64hk8OHMOBuiaMyMtEmiXB/Xw4sS2xGB9jhjXU/qh9Lu3NTnxnb8Xruw7i2qc/wpzVn6DsqR1Yv+sgvrO3atJWAh3jaX2sWLh2V8Bj7+gKnorP6LN/RBTbjBzvyJkgIpLF3yh4SWE2ls8oxg1rKtHs7AQgf3Q7Fnf8Mmt2e7XPZUNzO1Zs3OezRbbr30suVT9XUKBjLM7L9Mpd5GnLvqPoCtEJMvLsHxHFNqOv5mAniIhCCjQK7voRWlZa4PVDUM7ottnjYwIx+hpqf9Q+l03ODlTWNGDuhEKfmKCV5dVocnZE9f5yBDrGto6uoH/X7OzEuIE5Xp1eFyPP/hFRbDNDvCM7QUQUUrCR/oqqOpSVFHg9Jmd028zxMaEYeQ21P2qfy5b2TiyfUYxVFdVenW3XTGRre2dU7y9HoGNMTgy+qtyWmmTK2T8iim1mWM3BThARhRRqpN9zNFzu6LYrPoYj5Man9rnMTrfgwQ1fBlwOd/elw6J6fzkCHWPlNw0oLcxGebeyAT8euy3NYrrZPyKKbWZYzcGNEYgopFAj/a7R8HBGt82QY4BOUPtctndKPh0gl4qqOrR3Bo+7UUKgY9x70IEl04pCHrstzYIBuRkYmZ+FAbkZbN9EZGhmWM3BmSAik1Mid0uwkf6xA3OQ3zMN7y0YH/bothnjY2KVmueysS14zE9TiOeVEuwYIz32WMqTRT/ieSejM8NqDnaCiExMqZ1bQu1q1ieKXWDMFh8Ty9Q6lyKNOAY6xkiO3eg7K1FkeN7JDMyw22mcJEnqryNQicPhgM1mg91uh9Vq1bs4REKxNzsxd02l38DFcQNzZO/c4jlimZGcCEtCPOwtTqQnc9aGtGFvdmLemsqAI45G2IWoO6WuTyPh7EdsnncyN9d1LcpqjnD6BpwJIjIpJXZu4YilsvgjMDJmGHHszgw7K4WD95ITtD7vvOeQ2oy8moOdICKTinbnFjPkABAJfwRGx2zxY2bYWUku3kt+pOV55z2HKDjuDkdkUtHGUcgZsYw19mYn9h9uRGVNPfYfaYS9WV4dhPoRKPd9Yp2ZdlgLdn2mWRKQlWaJqK2JiPeSH2kV38Z7DlFonAkiMqlod26JpZFqOaIZVY21pU8UWqDrM82SgJWzzsJf1u3G1ipzjODzXvIjrXbU4j2HKDTOBBGZVLS5W0TakUtv0Y6q8kcgdRfo+lw0dQge3Vjl1QECjD2Cz3vJj7TKj8Z7DlFonAkiMrFo4ijMkANAKdGOqvJHIPnj7/rskiQsfHmX39cbdQSf9xJvWsS38Z5DFBpngohMLtI4Cq1GLI0g2lFV149Af2LxRyD9qPv1GSoxrBFH8Hkv8aV2fBvvOUShcSaIiAIy245ckYp2VNWMWzyTOsw6gs97ibZ4zyEKjZ0gIgoqnBwAZs1JocRyHv4IjJ7e7UuLzxd16ZgSx27kfCJGJNI9R+9rl8ifOEmSJL0LEalwssISkbrMnpOitqEl4KhqHxMcn+j0bl/Rfn44PwJFa2t61z0ZG9sPaSmcvgE7QUQUNXuzE3PXVPrdPGDcwBzTJEN0/ZDVe1Q11tibnfjDC59hcB8rivMy0dbRhZSkBHxSU4+9Bx144MoRqp6HaNt3JD8CRWlrsXJtkzrYfkhr4fQNuByOiKIWKzkpuJxHH3VNTvz87HysqqjGIxur3I+XFGZjdkkB6prUbV/RtO9Q26sH+hEoSluLlWub1MH2QyJjJ4iIosacFOYg6rr9ji4JqyqqUVFV5/W46993XDxU1c+Ppn0fbXTi4wP1mDuh0GcWa2V5tfA/AnltUzTYfkhk7AQRUdTMuqNVLBF53X5Xl+TTAXKpqKpDZ5e6q7qjad+Nbe1YPqPY7yzW8hnFaGoT+0cgr22KBtsPiYx5gogoasxJYWz2Zifufn0PZo05Fa/NK8GaX/8E6+eVYuaYU7Hk9T2wNzt1LV+zM3junGZnpyqfa292Yv/hRthbnFjz63Mwd0Ih0iwJXq8J1b4zUy0BZ7FWVVTDlir2tcFrm6LB9kMi40wQEUWNOSmM7ViTEzdMHIS71n/u9WO9tDAbi6YOxTGVY25CCdVRsKUqP5rsb2as9IfZmxvWVKLZ2SmrfTs7u4LOYjk7uxQvu5J4bVM02H5IZOwEEZEiRMhJIWpMi6hc9VXf7ISzswvF+VmorGlwz6yUV9Vh8frP8ddLh+laTq1z5wTazKC8qg5xcXF45foSxMfFyWrfjW3BZ7GaQjwvAhGubTIuth8SFTtBRKQYPXe0EjmmRUT+6quk20wHcOKHf0u7vrMVWo8mB9vRauu+o4iPi8OA3AxZ72WWmAhRdqsjY2L7IRGxE0REhhfpNsSxKlB9uZZtlZUWeAXxH2/Vf7ZCy9HkQDtapVkSUFZagLaOTlTW1MuabdR6FouIiORhJ4goClx+JQbmoghPsPqqqKpDWUmB12PWFDG+KrQaTfY3e5NmSfC7y1uo2UbGRBARiUmMbzYiA+LyK3EwF0V4QtVXW8ePy99KC7ORlR5bP9T9zd6UlRb43eVNzmxj38xU3H/lCNQ3O3G8tQM9khMRHwe0tHfC3swOOgXGgTYi9bATRBQBLr9SR6Rf+GaJu9BKqPpKTjyRPaG0MBtLphWhtzVFi2IJw9/sTXFeptcMkKdQs42B4q9mlxRg6Rtf4M5LhnHghHxwoE0s7JCaDztBRBHg8ivlRfOFz7iL8ASrr7EDc3ByZireunEsstItMdcBcukeg9QeIiFroNnGUPFXxflZHDghHxxoEws7pObEZKlEEeDyK2WF+sIPlazTNXLfPSkf4y78C1Zf910+HIP7WDG4jzVmO0AutjQLBuRmYGR+FnqGaEOBZhtDxV8V52W6B06IXOQMtJE2ov1+InFxJogoAlx+pSwlZtaYiyI8rK/wRDrbKDf+KtTASailOFyqoy6t65cDbeLgyg/z0r0T9N133+HPf/4z3nzzTTQ3N6OwsBCrVq3CqFGj9C4aUUBcfqUspb7wmYsiPKwv+SLd5U1u/FWwgZNQS3G4VEddetQvB9rEwQ6peem6HK6+vh4lJSVISkrCm2++iT179mDZsmXIysrSs1hEIXH5lbL4hU9G4Jo9e2/BeKybMwbvLRiPFTOK0SfID2HXgIk/JYXZqPymIejASailOIccrVyqoyK9lkIFazccaNMWv5/MS9eZoHvvvRd5eXlYtWqV+7GCgoIgf0EkDi4nUg5n1sgowp09CzSD5Nod7rkPa4IOnIRailPfxKU6atJrKRTzS4mD30/mpWsn6NVXX8WFF16IK6+8Eps3b8bJJ5+MOXPm4Ne//rXf17e1taGtrc39b4fDoVVRifwScTmREWMD+IVPeojkWpEbm2NvcSItORHxcXFIjI/D/VeOQFNbBxqa25FmSUBCfBwS4uPwwJUjgn5mqKU4jtaOoM93X6qjxP0h2HsY8f4TjJ5LoTjQJgZ+PwVn5Gte107QV199hccffxwLFizALbfcgh07duCGG26AxWLBzJkzfV6/dOlS3HnnnTqUlMgYjBwbwC980lIk10oksTmeMz53XjIMZ/TLCKucoZbiWFOCf417LtVR4v4Q6D3uvXw4JMCw959A9F4KJeJAWyzi95N/Rv7NAQBxkiQFT36gIovFglGjRmHbtm3ux2644Qbs2LED27dv93m9v5mgvLw82O12WK1WTcpMJCp7sxNz11T6XboxbmAO80oQ/SCSayXU39x/5Qjc9MJnfp8vKcxGcX4Wdn7TEPZ1aG92Yt6ayoBLce6/cgT++MJnAZ93fZ4S94dg77H0siK8sfMgtlaZ6/4Tqv6NelxE0RL1N4fD4YDNZpPVN9B1Y4Q+ffpgyJAhXo+dfvrpqKmp8fv65ORkWK1Wr/+I6ATmlSCSJ5JrJZrYnGjyAYXahKW3NUXWJi1K3B+CvUduj2S/HaBw3l9E3ASHyD8z/ObQdTlcSUkJ9u7d6/XYl19+iX79+ulUIiLj4jaeFC0jr+0ORyTXSrC/SbMkAACenDkKbR1dSElKwCc19VhZXo1mZycA+fmA/Am1FEfOUp1Ijrl7e+iUJKRZEtzH5Ml1fOG8v1FwKRSRL0frifjGstICFOdl+tz7jHDN69oJ+v3vf48xY8ZgyZIlmD59Oj788EP84x//wD/+8Q89i0VkSHqvXY8FZu4kGH1tdzgiuVYC/U2aJQHLZxTj7tf3YGtVnfvxksJsLJ9RjBvWVKLZ2SkrH1AwoWJDQj0f7jH7aw9jB+Z4HZMn1/HJfX+jYWwOkTdbahKWzyjGqopqPLKxyv24695nTRX/mtd1OdxZZ52FtWvXYs2aNRg2bBgWL16Mhx56CNdcc42exSIyJOaVUFdtQwvmrqnExAc3Y9pj2zBx2WbMW1OJ2oYWvYsWNb1yoeglkmsl0N+UlRZgVUW1VwcIOLEEblVFNcpKC2TlA1JbOMccqD1s3XcUT/1wTN0dPt7G+w9RDElPTsSqimpU+Ln3PVVRjfRkXedZZNG1EwQAU6dOxa5du9Da2oovvvgi4PbYRBQc166rx+ydBDOs7Q5HJNdKoL8Z0z/b50eAS0VVHUb3P7E73N6DDl2vw3COOVh7KK+qw5j+2T7vcd6gXrz/EMWQxtaOgPe+8qo6NIbYvl8E4nfTiEg2rl1Xx9FGJz4+UI+5Ewr9rn3WKiGlWsvxtI4nE2FZYSTXir+/sbcE7yBaEuPRx5YSMh+Q2uzNTrQ4OzH//IG4ZcrpSIg7kaco20/dh2oPKUkJeG/BeL/1xvsPUWwwQxwyO0FEJsO168prbGsPuva5qU39m72aMTtaxpOJFHsUybXS/W/2H24M+vqeaRb07xVebiClBatzf8cfqj3YUpMwINf/MfH+QxQbzBCHrPtyOCIi0WWmWgKufV5VUQ1bqro/+tRejqdVPFm0x2FvdmL/4UZU1tRj/5FGIZYhih6LF0mdi35MRKQ/M9wn2AkiIgrB2dkVNO7D2Rl8e+BoqR2zo1U8WTTHIerGFKLH4kVS56IfExHpzwz3CS6HI6KYFE5cSmNb8ADPphDPR/KZnpRaex3s87WIJ+uUpKC5dAIdR6jZDLUyk8s9XyLH4kXadkQ+JiISg9HvE+wEEVHMCTcuRYm1z9HEwmj1+WrGc9Q2tGDxa58HzaUT6DjkzGYoXe5wz5eosTDRtB1Rj4mIxGHk+wSXwxFRTNEjRiLaWBi9Pz9a7s8Pkksn2HHosXudWbZEN8O6fSIiNbATREQxRY8YiWhjevT+/GgF+/yKH/LOBDsOrXch0ru+lGSGdftERGrgcjgiiil6xEgoMZOh9+dHQ07emT5BlgS6ZjO2+OmYqDGboXd9Kc3o6/aJiNQQVSfI6XSiuroaAwYMQGIi+1NEJD49YiSUmsnQ+/MjJSfvTNDnf5jNuPmlnV4dIbVmM7SqLy2Txhp53T4RkRoi6rk0Nzdj3rx5ePrppwEAX375Jfr374958+bh5JNPxs0336xoIYmIlKL1rIJen2m2z9dyNkOL+hIpaSwRUSyKKCZo4cKF+Oyzz7Bp0yakpKS4Hz///PPx3HPPKVY4IiKl6REjoXdchlk+35ZmwYDcDIzMz8KA3AxVZ03UrC8zbbxARGRUcZIkSeH+Ub9+/fDcc8/hJz/5CXr06IHPPvsM/fv3R1VVFc444ww4HA41yurD4XDAZrPBbrfDarVq8plEZA6upUieswoAgi5Pinb50iFHK+qbnHC0dsCamoisNAt6W1P8vlaNpVL+jlnJ44v280UTzvkKxbNuUy0JWL/zoFeOJE/vLRiPAbkZ0RafiEh1St4nlRBO3yCi5XBHjhxBbm6uz+NNTU2Ii4uL5C2JiDTVPUYi1PKkaJcvhfP3ai2VChYXosXyLCPFpShZH/7eq3uOJE9G23iBiGJTTV0TFq7dhQqP9AelhdlYMq0I+dnpOpZMnoiWw40aNQqvv/66+9+ujs+//vUvjB49WpmSERF1Y292Yv/hRlTW1GP/kUbFlg2FWp50yNEa1fKlcJY/qblUKlD9cXmWNyXrI9B7eeZI6k7tjSqIiKJ1yNHq0wECgPKqOtyydhcOOVp1Kpl8Ec0ELVmyBJMnT8aePXvQ0dGBhx9+GHv27MG2bduwefNmpctIRKTqTEWovDD1TaHzxgSb4ZCTd8b19+G8NhzB6q+1vVOVzzQqJc9BqBxJZSXenSAmMCUiI6hvcvp0gFzKq+pQ3+TUdVmcHBHNBJWWluLTTz9FR0cHioqK8M477yA3Nxfbt2/HmWeeqXQZiQxFrdkKUT5PD2rPVITKC+No7Qj6fKjlS+HknVEjR02o+uvoCh4aapTlWUpdC0qeg1Dv1dbR5f7/0Wy8EAv3ASISR6jvxVDPiyDi5D4DBgzAP//5TyXLQmR4Wm97Gyvb7Ko1O+ISKi+MNSX4rTLU8qVw8s6okaMmVP11hegEGWF5lpLXgpLnINR79c9Jx7o5Y6LaKCJW7gNEJI5Q34uhnhdBRDNBb7zxBt5++22fx99++228+eabUReKKBQRRz21jquIpTgONWZHPLnywvgzbmAOstKDPx9q+VKo9/f8+3BeK1eo+mt2dir+mVpS+lpQ8hyEeq8+tpSotvyOpfuAHkT8riESQVa6BaWF2X6fKy3MRla62N8bQISdoJtvvhmdnb7bekqSxESppLrahhbMXVOJiQ9uxrTHtmHiss2Yt6YStQ0tupZLzmyFkT9PT2rMjngKlRemtzUlqrwx4eSdUSNHTaj6s6Um6ZpHKFpKXwtKngO1cw7F0n1Aa6J+1xCJoLc1BUumFfl0hFy7w4keDwREuBxu3759GDJkiM/jgwcPRlVVVdSFIgok1KjnihnFiv9gk5s7Re3ZCr0/T0+u0fQtfn7sKTVT0TczFStmFAfMYxPq+WjfP9LXyiGn/mxpFkU/U0uO1nakWRJQVlqA4rxMtHV0ISUpAZ/U1GNlebX7WggnD5KS56BvZiruv3KEKrk0Yuk+oCU9vmuIjCY/Ox3Lpo/88d6WkoisdH3zBIUjok6QzWbDV199hVNPPdXr8aqqKqSni78vOBmX2rEh3YWz1l7t2YrutP48PblG029+aafXD3mlZypC5bGJNs9NOH+vZE4dufVnpDw+nmypSVg+oxirKqrxyMYfB+JcuXhsqUkRxc0oVR9qxuzE0n1AS1p/1xAZVW9rimE6Pd1F1Am65JJLMH/+fKxduxYDBgwAcKID9Ic//AE/+9nPFC0gkSctRz3DHQnUYrbCk9afpzelZ0dijZnrLz05Easqqn22a62oqkMcgPuuGKHbqL7SMwrdZ7MyUhIx6fRcbPjisM9rzXgf0Apn2IjML6JO0H333YeLLroIgwcPximnnAIA+PbbbzF27Fg88MADihaQyJOWo57hjgRqNVuh1+eJwKgzFaIwa/01tnYEzVfhaGnXbVRfyRmFQDNKf710GAB4dYTMfB/QAmfYiMwv4uVw27Ztw4YNG/DZZ58hNTUVw4cPx7hx45QuH5EXLWc/IhkJ1Hq03cyj+0RyqZ3nKRA5MUbh3EeCvV+wGaW/rNuN+68cgZsnd/A+oJBYm2knikURb+IdFxeHCy64ABdccIGS5SEKSsvZj0hHArUebTfr6D6RXGrnefJHbpyP3PtIqPcLNaPU2NqBAbkZYR8H+ReLM+1EsUZ2J2j58uX4zW9+g5SUFCxfvjzoa2+44YaoC0YUiFazHxwJJDKGUNeqK8+TUtdyOHE+cu4jct6PMSra40w7kbnFSZIUPFX4DwoKCvDRRx8hOzsbBQUFgd8wLg5fffWVYgUMxuFwwGazwW63w2q1avKZFFtqG1oCjgT2YSZ2ImGEulaVvJb3H27ExAc3B3z+vQXjvWZlQn22nPcDENZnEhHFonD6BrJngqqrq/3+fyIz40ggkTGonefJU7izMqE+W877FeSkc2Y6CuHkiCKi2BB2TFB7ezsGDx6M9evX4/TTT1ejTERCYcwNkTGonefJJZJ4wWCfLef9GKMSOTXzNBGRcYXdCUpKSkJra6saZSEiUhVHg0kJSscLyn0/zkyHT+k8TURkHvGR/NH111+Pe++9Fx0dwbcdJSISRW1DC+auqcTEBzdj2mPbMHHZZsxbU4nahha9i0YG45qVGTcwx+vxSGdlwnk/W5oFA3IzMDI/CwNyM/gDPgQ5eZqIKDZFtEX2jh078N577+Gdd95BUVER0tPTvZ5/+eWXFSkcESlPtNkQLcoTK6PB0dalaG0jFD3LG2pWJtyycZZHHWbZVU9OezLa9RsrRD0vopZLSxF1gjIzM3H55ZcrXRYiUploa+O1Ko+c0WCj3/yjrUvR2kYoIpQ3UJxPpGXTK/7QzD+GIs33JhI57UmE64F8iXpeRC2X1mRvkQ0AXV1duP/++/Hqq6/C6XRiwoQJuOOOO5Caqk+FcYts0pLRfyjYm52Yu6bSb2dg3MAczWdD1ChPoHNUWVOPaY9tC/h36+aMwcj8rLCPQRTR1qVobSMUkcsrctn8kftjyKj3P3uzE/PWVAaMtxLtfHQnpz0BMFSbixWi3gtELZdSVNkiGwDuvvtu3HHHHTj//PORmpqK5cuX48iRI1i5cmVUBSYSnRlGTUSbDVG6PMHOkS3V+KPBwURbl6K1jVBELq/IZetO7jJRI9//jL6rntyYJqO0uVgi6r1A1HLpIaxO0L///W889thjuO666wAA7777LqZMmYJ//etfiI+PaI8FIuGZJZ5EtLXxSpYn1Dm6/8oRps6xEm1ditY2QhG5vCKXrTu5P7CNfv8zcryVnPYUajmPSG0uloh6LxC1XHoIqxNUU1ODn/70p+5/n3/++YiLi0NtbS1OOeUUxQtHJAKlR030WlYi2tp4JcsT6hw1tnYYejQ4lGjrUrS2EYrI5RW5bN3J/TFkhlHjSOOt9F4GqER7EqnNxRJR7wWilksPYXWCOjo6kJKS4vVYUlIS2ttjp9dIsUfJURM9l5UondtEpPLIOUcDcjMMOxocSrR1KVrbCEXk8opctu7k/BiK5VFjEZYBym1PRmlzsUTUe4Go5dJDWGvYJEnCrFmzcNlll7n/a21txW9/+1uvx4hEdsjRiv8ddODD6mP43/cOHHIET/6r1KhJqCVb9mZ181UondtEpPLIPUdmzbESbV2K1jZC8VfeNEsCll5WhFunnI6vjjZh/5FG1a8puWUDxKxL148hf1w/hmJ11Fjv+7WLnPZkpDYXS0Q9L0qXy97sxP7DjaisqdftvhupsHaHmz17tqzXrVq1KuIChYO7w1G4auqasHDtLlRU1bkfKy3MxpJpRcjPTvf7N0rtLrT/cCMmPrg54PPvLRiPAbkZMo4iOq7lHaLMhihRHqPvAKWUaOtStLYRiqu8TW3tsKZasGjdbmytEiN43yh1WdvQEnCZaJ/M1Ji9tkS5X7vIaU9GaXOxRtTzokS5RJgt7S6cvkFYnSDRsBNE4TjkaMWC5z/16gC5lBZmY9n0kehtTfHzl6F/KMhh9m2a9abEOaLo6RFDYfYtX9UW6sdQLF5bvF8TBSfqfVe1LbKJjKy+yem3AwQA5VV1qG9yBuwEKbG7UKwuK9GKkXeAMgu9RgW55Wt0Qm0aEIvXFu/XRMGZ4b7LThDFDEdrR1TPR5vNPZJgRL13JjKaaM8RRU7PreSNELxv9GtZ72tL6/pj8DhRcI7WdqRZElBWWoDivEy0dXQhJSkBn9TUY2V5tRD33VDYCaKYYU0J3txDPR+tcJP2ibjWNtYZ/YesmvQcFRR91J7XcnT0qD+jJ1klUpstNQnLZxRjVUU1HtlY5X68pDAby2cUwxoiSbkI2AmimJGVbkFpYTbKA8QEZaWr/6Umd1mJWRK0mgl/yAan52yMyKP2vJajo2f9RbMMkAMmZHbpyYlYVVHtE2ZQUVWHOADLpo/UpVzhCGuLbKXdcccdiIuL8/pv8ODBehaJTKy3NQVLphWhtDDb63HX7nCB4oGUJmebZrmZ3EkbomyXKzI9Z2NE3YoW4LUcLb3rL5Jt9WsbWjB3TSUmPrgZ0x7bhonLNmPemkrUNrSoWlYiLTW2dgSNs24MEWIgAt1ngoYOHYp3333X/e/ERN2LRCaWn52OZdNHor7JCUdrB6wpichKt2jWAZLLCDEOetFjhNUMAaBq03s2RtTgfV7L0QlVf01t7ULNunDmj2KFGe5tuvc4EhMTcdJJJ+ldDDKQaL/weltThOv0dCd6jINe9FqSZoabvdpEiKHQO3jfH17L0QlWf2mWBFhTLT7b9Oq5TJUDJhQrzHBv070TtG/fPvTt2xcpKSkYPXo0li5divz8fL+vbWtrQ1tbm/vfDodDq2KSIGIlLkPvUXUR6TnCaoabvRaUno0RaYQ/UryWoxOs/hZNHeKTIBfQd9aFAyYUK8xwb9M1Juicc87BU089hbfeeguPP/44qqurMXbsWBw/ftzv65cuXQqbzeb+Ly8vT+MSk55iKS5D5BgHvegZG+C62ftjlJu9ViKJofDHLHEVvJajE6z+zsjP9OkAuegVb8UBE4oVZri3xUmSJOldCJeGhgb069cPDz74IK699lqf5/3NBOXl5cnKCkvGt/9wIyY+uDng8+8tGI8BuRl+n/McUc5IToQlIR4NLU5kpIg9uhwqk7to7M1O1DU50dEloUuS0NzWAVuaxV3H0Yzsh5PBXe7nhPO67x2t+La+BXFxce48CKP6ZeHey4ejj0FmIfWeWQmnvvXIRK5m/ci9lvU+R6LyV39fHW2SfU/Qir3ZiXlrKgOOjjMmiMzmkKP1xzjr1ERkpekbZ+1wOGCz2WT1DXRfDucpMzMTgwYNQlVVld/nk5OTkZycrHGpSBSRLjPwt4SupDAbs0sKMOOfH2BUvyzNl9PJ/aEjYoxDILUNLbjtld34+dn5PttmjhuYg79eOgx3rd+Dd7847PW43LqXO8Iqd8lkNK8bOzAHb9wwFllpSWGdHz1/4Oq9lDScz9cjrkLt+pFzLet9jkTmr/6sKcFnevSYdREhNo5IK0a/Z+m6HK67xsZG7N+/H3369NG7KCSgSJYZBFpCV1FVh1UV1SgrLdB8OZ1Zlvl4ctXz4D5Wv3kDtuw7ilvW7sLgPlafx4PVvb3Zif2HG1FZU48uScLSy4qQZknweZ1rSZrcJZPRvm7rvqO47ZXdMmrmR3qed72Xkob7+VrHVehdP6KUwWhEXabqio17b8F4rJszBu8tGI8VM4oNM2NMJIcZ7lm6doJuuukmbN68GV9//TW2bduGadOmISEhATNmzNCzWCSoSL7wgo0oV1TVoTgvE4B268fNcNPwx1XPxXmZQfMGuOrbU6C6795pmPS3LXhj10GsnHWWV0fIc4RVbtyQ0q8LRe/zrneulXA/X+u4Cr3rR5QyGI3IMQlKxcYRicoM9yxdl8N9++23mDFjBurq6tCrVy+Ulpbiv//9L3r16qVnsUhQkSwzCDWi3NbR5f7/WuzaY9btU1317FmfwIktbMtKC1Ccl4m2ji7k9kjG3AmFWFlejWZnp/t13es+2AxMHIA3bxiL+manT2yF3BmESF/X/XicHZ2wN4c+Z3qfd713rAr38+XsOqTk0kK96yfSMjB+SNz8UERmJ8J9M1q6doKeffZZPT+eDCjcL7xQI8rJiT9OhmqxftwMNw1/XPXsWZ9plgQsn1GMVRXVeGTjj3F+JYXZWD6jGDesqXR3hLrXfahOQ0eX5DfgWe4MQiSvC3Q8ctY/633e9d6xKtzPDzbgcd/lw9Hk7FR0Hbre9RNJGYy+Fl9JRoqdJDILEe6b0RIqJohIjnCWGQRbQldSmI3KbxoAaLd+3Aw3DX9c9Vz5TQNKCrMBAGWlBX7jgzzjsQD/dR9pp0HukslIXhfoeOQsadP7vOsdOxHJ5weKq0i1JCi+tFDv+gm3DJEsr/SMr9t/pNGwS29FxfqlWCPCfTNa7ASRqQVaM+7aHW5lebWm68fNcNPwx1XPew86MLukACWF2UHjg1zxWIHqPtJOg9wYgUheF+x4Qq1/1vu86x07Eenn+xvwUGMdut71E24Zwq0DM27GIhLWL8UiEe6b0RIqT1C4wtkLnGKbZ46J9B/yBNlbnEhP1n79+MGGFmz68ghyeySjraMLKUkJOORoxXmDeuEkgy9jceUJ6uyS0NDcjiuf2B7wtS/+djQGBpjJizbXRrg5WeS87svDjbjy74GPJ1ROktqGloDxbFrtGqV33iklPj+cfFGRlK+huR1Nzg40OTuRmZqE3B7JwtVRuDmz9Mi5FCtYv+phzJvy1KhTvb9XujNsniAitfhfM56uS1kkAG/sPOiV6XzcwByMH2T8DUE863n/4cagr80Kso4/2lwbcmMEwnldzxCvC7WkTYQAbr1jJ5T4fDWXFjY5O/GXV3bLirNR6weanDoKpw703pTD7Fi/6mDMm/LUqlO9v1eiwU4QkYbca/mr/K/lN9OooZwdvoIRodPgKdrjAYz9ZSEKJc6DP6HibDyvTb1/oIVTB3pvymF2rF/lhXMtkjysU/8YE0SkITPsqy+XEuuFRcq1YYb1z2ag1nmQe23qnfMJCK8O9N6Uw+xYv8qLpe9JrbBO/eNMEJGGlBw1NMJ66Uhnc0Q9Nr1mp5SqD1HrVS7P8i+aOkTR2D6516Yoy5/ktkW1Zs7oBNav8owyu2ak+6lR6lRr7AQRaUipUUO9l+OEI9wlYKIfm9ZL2pSqD9HrNRS1yy/32hTpx4ScthhtfB0Fx/pVnhFm14x2PzVCneqBy+GINKTEVsnhLMcxWu4KEZYaiUSp+jB6vWpRfrnXphF/TATKuaTVroRmx/pVlt4pBUIx4v1U9DrVCztBRBpSIp5B7tpeI+au4Lplb0rVh9HrVYvyy702jfpjQqT4OjNi/SpH9PhLI95PRa9TvXA5HJmGUdbnRhtXImc5TiQ7wahVf+G8rxJLjURvB1rXh5LvE4jada7VEjQ51yaXPxGpT7TdQT2JtCQ2HCLXqV7YCSJTMNr63GjiSuQsxwk3eFut+gv3faNdaiR6O9C6PpR+H3+0qHMtl6DJuTb5Y4JIfaKmFDDiklgXUetUL1wOR4ZnxPW50ZCzHEfuSJW92Yl9h47ji4MOzC4pwNwJhUizJACIPsYokvMSzVIj0duB1vWhxvt0p1Wdi7gEjcufiGKTiPcjigw7QWR4RlyfGw05a3tDjVRZU5PcMUOT/rYF1z79Ecqe2oHKmnosn1Hs1RGKNMYokvMSzbpl0duB1vWhxvt0p1Wdcz07EYmC9yPz4HI4Up1Z4gW0FKrOXMtxGprb0eTsQJOzE5mpSe7Oi2uk6qMD9SgrLUBxXibaOrqQkpSAQ45WZCQn4g8vfObzA7aiqg4AUFZagEc2VgEIL8bIs9zJifGYO6EQK8ur0ezs9DnGQOcl0qVGoreDTknCkzNHuc/DJzX1XnWjZH24zkNjWzsy0yxwdnShsa1Dt9w6SuASNIplosc6xhrej8yBnSBSldniBbQgt86anJ34yyu7A77u3suH48CxZqzYuM/doQGAsQNzMLp/Nj4+UO/38yuq6lBWUuD+t5wYo7omJ5qcnT7lLinMxvIZxbhhTaVPRyjYeYlk3bLI7aC2oQWLX/scW3/oZAK+daNUfbjaz8cHTszq3ff2XnfnFtAnt45SuJ6dYpHosY6xivcj4+NyOFJNLMcLhMMzzmbfoeP4tr4Zf75wMF6dW4Jnf/MTvPS7MTijXxZuf2W3u87k1G2aJQGPbqzy+gEMAFv3HcWiV3ajrLQAgbR1dAGQH2PU2SX5LU9FVR1WVVT7fJYa50XUduA+V93Og2fdKFU+z3ZRVlqAVRXVPudfj9w6RBQZ0WMdKTJGy+FnVpwJItWEu0NZpIy8ZW33Eb40SwKenDkK9771hdeP5rGFOZhdeirqmk7UmdxYjK1V/l+zdd9RzBpzasByJSfGd4sxCn6D7uySApan+8ySWudF1HYQ7FxVVNXh+nMLcc3Z+YqUz/OzivMyvWYAPSl1/Yla50RmodX3KGmHM3viYCeIVGOmeAE11mP7G+G7bnx/VB9twqySAsw4p59X7AjKJdx28VAA8upWirBcYwfmoLBXhlcuoZwMCyadnovT+ljd8UXplkR0dHUhzZIAR2s7Vs46yyfOxcWWmoR1c8aovm66b2Yq7r9yBOqbnHC0dsCamoisNAt6W1NU+Tw5Qp2rlKQExTLLe36WazYvEC1z68ghYsyDiGUic5DbtkSPdaTwRJLDj9TDThCpxizxAmqN2nQf4UuzJGDy0D64c/3nXkuYPGNHOrtOdG2UqNtTslIxbmCO3xH87j/KbWkWLJo6BAvX7sIjG6uQZknA8hnFPsutAsUAZf2wnbDaRBxhC3WubKnKXQeen5WcGHy1s9a5dYIR8byJWCYyh3DalsixjhQ+zuyJhZ0gUo0rXmCLnwveKPECao7adB/hKystwOJuHSDAe8c2V8dCbt12n71xzSztPejASdYU2SP49mYnbl2326ss/uJN/O0up9W5FnWETcvrwPOzKr9pQElhts85UuNzoyHieROxTGQO4bYtM3yP0o84sycWboxAqjHDXvpq5kHpPsJXnJfpEzzvUlFVh+K8TPesgZy6dc3eVNbU49qnP8Kc1Z+g7Kkd+LSmHoumDnG/Rk7Cx+71UJyX6ffHtWdZu5dHbaLmCdLyOvD8rJXl1ZhdUoCSwmzVPzcaIp43EctE5hBu2zLD9yj9iDN7YuFMEKlKy7301Vi/r+aoTfcRvlAxHK6/cQlVt91nb1zKq+rwl3W7wxrN7p7jJivtRE4if/l/gBM38vcWjNc0b4LeI2z2ZicOH2+Do7UdWakWOLu60NzWAVuaBTnpFs2uA8920dTWjiWXFsHZ2YWmtg4hc1nofd78EbFMZA6RtC3mpDEPzuyJhZ0gUp0We+mrtX5fzVGb7jtrhYrhOCUr1aceg9WtUmuP/eW4GVuYEzD/DwBkp2sTA+RJzxG22oYW/PnFnfi45kRungc3fInKmgZ3otqvjzYhLysNva3JmtSLkfJXiDgyKmKZyBwibVtGuqYpMO6oKRZ2gsjw1Fy/r/aojecIX5ckYezAHL8dl3EDc3BSmDucRTqa7TmjlpGciI8O1OPjmgav12ytOgoJklfsj2dZ9RjN0muEzd7sxJ9f3ImtVUcxd0IhVlVUo7Kmwb1xhGf9MLDel4gjoyKWicyBbYs4sycOxgSR4am5fl+L9diuuJyBvXvgXgU/K5IRx9qGFsxdU4mJD27GtMe2YdLftmD9zlosn1GMNEuC12vLq+owur848SZ6rZ0/2uh052NyxUppkajULESMeRCxTGQObFsE/Pi9Hyoel9TFmSBBMT+FfGqv39dy1EbJPDfhjjgGmlHzt+ObS0pSPDb+YTw6uyR0dklodnagub0T9mZ9tvlMtyRg0dQhaGhpR4YlAWmWRGSmJalaFn+5ebRIVGomIo6MyikT79MUCRHbO1EsYidIQMxPER4t1u9rtR5byXMf7trjYDNqFVV1KCspcP87zZKAstICpCcn4liTE+2dXajYX+dOlKpHew1Wd7Y09T7XX24erRKVmomIMQ/BysT7NEVDxPZOFGu4HE4woeJbuIzGl2vGwx8jrbFW49y7RhzfWzAe6+aMwXsLxmPFjGKfZKhA6Bk11w97V6LUypp6XPTQVlzx9+2Y8c8PUPnDpgBplgTV2qu92Yn9hxtRWVOP/Uca3e+v53Xj2f5cuXm0TFRK2uN9mojMLNB3rdlwJkgwzCYcPqPtthJoCY1a517uiGOoGTXXD3u5iVKVbq/BRt5b2zt1u25c7e/PL+3EyvJqLJ9RjEOOVkMkKqXI8D5NRGYVS7Pc7AQJRs34FjOvXzfKGutgN5fGNn1zk4SKISrslYF1c8YgJSkhYLxL92VzSpTZlX+n5lgzZpcUYERepnvZnWvk/S9Th7iX6BXnZbrzGX1SU4+V5dWq113fzFQ8MqMYh4+34XhrOwbkpGP0gGzc9srnPudaxI45+XfI0eo3Po95hIzDzN97REpTc7ddEbETJBi14ltioWcv+hrrUDeXxZcMC/r3ai+hCjWj1iczFf2Qjsqa+qDv4xkPE22Z/bXbksJsrxxFW/YdhSRJfrekdr3Wmqr+8jN/7e8RA3TMyb+auiYsXLvLazavtDAbS6YVITNEe+JyRzHEwvcekZJibZabnSDBqJFDwEw9eyOP6oW6uTg7u3TPHyFnRk3usrloyxzObnVxiAu4RC8OwLLpIyMuRzRE75iTf4ccrT4dIODEtvC3rN2F+64YEfW1auR7mRGY6XuPSCuxNsvNTpBg1IhvMUvP3uijeqFuLk1tHULENoX64R6so15SmI3KbxoUKXM4u9V1SZLf+BvgxA/XxtYO9LZGXBSKMfVNzqDtydHSHtW1avR7mRGY5XuPSEta7LYrEnaCBKR0fEunJOHJmaN84iSanZ0AAvfs1RypDOe95cSEBBvVE2XE1d/NxTOOpb1LQkt7J+6/cgTanJ1odHagydmJzNQkn0SlLlofm+vzbpg4EL87d4B7W2wAWDR1CIrzM3G8tQOXjOgbsMxy+es0etZXmiURK2edhUOOVrS2d8iKCRKlLYhWFq3K1P39M5IT0dTWAXuLOHUAAI7WjpDPD+5jjeg+rdcMhYjtTU2ij2jH2vkgY1BjNZLI2AkSlFLLaGobWrD4tc+x1WNUs3tMhb+evZojleG8t9yYkECjeiKNuHa/ubi2mu4exzJ2YA6uP68QZU/tcHdU/ZVZ62Pz93ljB+Zg/bxSxMfFYdG63Vj48i7FytK90xisvsb0H4ZHri7Gk+X+Y4JsqUlCtQWRyqJVmfy9f2lhNmaVFLivZb3rwMWaEvyr0fV8JPdpPWYoRGxvahN5RDsWzwcZg9F2240W8wSZmHvE0U+cxKqKapSVFvjt2auZAyOc9w4WE+Iqv4u/UT3Rcnm4bi6unDKBtpreuu8oVmzc53V83cus9bH5+7w0SwJG5GXC0dqBr440Yv6kQXh1bgme+L8zsXLWWRiel4nbX9kdcVm6538KVl/bvqrDqnL/MUFPVVQjOSlB8fqKNI+CaO1SizIFev/ybteyKHl2stItKC3M9vtcaWE2stIj/yGg9QyFiO1NC6Lmj4vV80HGEU5+QaNjJ8jEQsVUjOmf7bdnL2ekUo0ydX/vUOUvzst0/9vfqJ6axxEpz5vLT4edFDDuoPvxAd5l1vrYun+eZ8LUSx+tQNnTH+Hyx7fh3rf+h4T4OMx95hNU1tTjqrPzUdcUWVm6dxqL8zID1lduj2Sfzr6LK4ZDyfqqbWjB3DWVmPjgZkx7bBsmLtuMeWsqUdvQEvJvRWyXapcpnGtZrzrw1NuagiXTinw6Qq7d4XpbUyJ+b61nKERsb1rofv9w0XtEO1bPBxmLLc2CAbkZGJmfhQG5GaabAXLhcjgTCzXimJKU4Ldnr+ZIZTjvHeq1rq2YA43qibom3LWEJpytpl1cZdb62Lp/ntyEqQBwx8VDI/5cz/i4YJ0pf3XlqXuMR/f4IWdHJ+zN8pYhRRvTIWK7VLtMcq/lQJ8XKH5CzbiK/Ox0LJs+8sc8QSmJyEq3RNUBArRfcy9ie9OKiPnjYvl8EImGnSATCzXiaAuQ60LNkcpw3lvOVszBRvVEXhMOyN9q2utvUpNgb3YiNSkBj11zht+NLgDlj617WYvzMmUlTK2oqkNnlxTVZ7vjLg43BnyNv7ry5BnjESi2SO6a/GhjOkRsl2qXKdy27vl5/uInJp2ei0VTh+DWdbtVjavobU2JutPTndZr7kVsb1oSbZv6WD8fRCJhJ8jEIh1xVHOkMpz3DvbasQNzUNgrI+iou+i7nMjZatrTpNNzYUmIx9w1lT4bRTx69Rn47NsGFJ1sA3Biy2i5MxuRlLWtoyvobmyeI/uenTMly+Dp8PG2oOc6K/3Hvw00i6XVTI6I7VJumSKdeQmnrXf/PH+zbqf1sfrN42OUHDBazlCI2N5iGc8HkTgYE2Rika6JVnMtdTjvHey1910+HP1y0oOWRdQ14S6Byjd2YA7mTRjo3noaOFHmO342FAvX7vL5QVhZ04CUpHh89PUxXPv0R7j26Y8w6W9bZMeoRFLWtKQfY4KuffojzFn9Ccqe2oHKmnosn1GMtKQft8cONOMYbRlcxg3MwXmDegU9172tKe7ng8UWyVmTH+1IrojtUk6ZoomDCvT+pYXZmF1S4G7r3esg0KxbtOdQBFqtuRexvcUyng8iccRJkhTdWhUdORwO2Gw22O12WK3MhBiIa/Q23BHHSP9O6TJFWw7Pv7emJiE9ORGNrR3C5Gfwd3wAfB472ujExAc3+/z93AmFqKyp9/ujcNzAHEVHxV1lTYgHbl232+9nun7YXvv0RxF/vutz7C1OpCUnIj4uDonxcchO9183rvcP1VbszU58ebgRV/59e8DPXjdnDEbmZwUt27w1lQFHcgMdb6AcOY4WMWIVPMvYvf7szU6fGUiXcM5x9/fPSPmxDvxdm51dEi59tMJnNvGxa87AnNWfBPycUOcwFql5P6fw8XwQqSOcvgGXw8WASNdEq7mWOpz3jrYcnn9f29CCm174TKj8DIGOr/tjXx1t8vv3weJzlM474irr/sONAUfiy6vqUFbaP+KRzUC5oWaXFGDpG1/gzkuGYUBuRtDyBSt/zxDlkTuTE05MR7C8IP17+T8WPQSqP6Vy2wQ7P/6uzbEDc7xygrmEigFjXIUv0WJjYh3PB5H+2AmimKFXpnY55ZITZ+G5DMszHifNkoiVs87yu0ECoM5uQ6HiYqwpiRHPAAXKDQUAxflZUZ8rJdbkhxPTIVK7izSmR+0drQLV0dZ9RyFJktdugwBQ+U0DSguzUR5g9pNxFUREFIowMUH33HMP4uLiMH/+fL2LQiYlYn6Ggw0teGP39/i6rgkH7a04UNeMN3Z/j4N+4ixcP949c/Rc+/RHmPHP/3rH41gSvP5OjVHxUHExWRGOcsrJJxPsXMlJYKrUmny5MR2itLtoYnrU3tEqWB2V/5DTzNPegw4smVbEuAoiIoqYEDNBO3bswBNPPIHhw4frXRQyMdHyM9ibnThwrBnrd9Z6LS0rKcxGQU460iwJfjeK2PzlEdk5epQYFfc3e5CTYcGk03NxWh+re3e41KQEdEkS0i0JsLc4sf9IY9jxVnLzyfg7VwcbWrDpyyPI7ZGMto4u1De348PqYzh3UC+ffFha7s4lQruLdjZK7R2t5OQ0e2/BeJ9zJUoOGDXzFRERkTp07wQ1NjbimmuuwT//+U/89a9/1bs4ZGKi5WdoaG7Hio37AnZmllxa5PNDqm9mKkb1y8LCl3f5fU/PHD1KjIoHimW59/LhWDR1CBau3eW1TKm0MBuzSgpww9MfodnZGXa8ldx8Mt3PVbgdSkC7NfkitLtoY3rUzm0jJ6eZvzgwEeIqgsV76RVnSEREoem+HO7666/HlClTcP7554d8bVtbGxwOh9d/RHK5RrP90SOOoMnZEXBzgYqqOjQ5O/w+19jm/3GXHilJeG/BeKyYUewzAxKOYLMHm748glvX+u4OV15Vh1UV1SgrLXC/9uaXdvpdluZPsHPkyifj71wF61Cu2LgPDc36ZWEXod0pMRvlmj17b8F4rJszRpE25iJCHUUi1Ayb3HZPRETa03Um6Nlnn8Unn3yCHTt2yHr90qVLceedd6pcKvPhUo0TbGkW3Hv5cK8lUylJCTjkaMV5g3opUifh1HVTiCSigZKMhho1z063BNw9LZxyBps9yO2RjK1VgWN3XLNRQPi7h/mbcXDtDvfchzV+Zx4i7VBqQe1ZFDnkzkaFahdqzbyIUEeRUGrXPKPidwvrgMjIdOsEffPNN7jxxhuxYcMGpKSkyPqbhQsXYsGCBe5/OxwO5OXlqVVEU+BSDW8SgDd2HvT6AT9uYA7GD+oV9XuHW9eZIZKIBkoyGm18htxyBps9cMXmyH0+nLgXz3gde0s70iwJSIiPQ0J8HB64coTfHxiRdii1omUMkj9y2oze9wq96ygSIsR76UXv9iIC1gGRsem2HO7jjz/G4cOHccYZZyAxMRGJiYnYvHkzli9fjsTERHR2+v5oSU5OhtVq9fqPAuNSDW/u+qiKrD6C7TwWSV3n9kjG2ABLgMYOzEFuj2S/z0W6u5m92Yl9h47ji4MOzC4pwNwJhe6d5PyVM9jsQag8Ld2fDzfuxbXz2hn9sjC4jxUDe/dA/16Bd2CLtEOpJbm7yan12cHaDAAh7hV61lEkRIj30gO/W1gHRGag20zQxIkTsWuXd3D37NmzMXjwYPz5z39GQkJCgL8kuWJ9qUZ30dRHqBG/SN7btTwvkiVA4Y6aB0pA6pmIsns5g80eHD7eFvA5V+yO5/GoHdPh6lD6OwfBOpSxJFib2X+4kfeKCKi9a56o+N3COiAyA906QT169MCwYcO8HktPT0d2drbP4+RLzjpkz6Uansk1XbEwXZKkdbF1FenSFXuzE7e9shsj8jIxa8yp7vr7pKYet7+yGw9cOSLi945mCZDc+IxgCUiTE+OxYkYxgBNL2JwdnbA3O93vHShO47xBvTB+UC/3c672Nbp/NhLi4tDS3om5Ewqx96ADd10yTPUfA9F0KM0k0pieWF7WFQ2jxjLJEawtsb2wDojMQPctsil8ctchu5ZquJJrrqqo9trOeOwPX9SxsnY50qUrdU1O/PzsfJ/6cwXr1zU5A763q3OQkpSAypp6TYPNXQKNWKZZEnD1Of3wVEU1tnpsKuDZlkJ10lbMKEZdkxMSgDte2e3TvpZOK1Jk9zA5jBhToqRo4hNidVmXEszY7kK1JbYXXjNEZqD7FtmeNm3ahIceekjvYggtnHXIrqUaZaUFfpNrbo2xtcuRbsMbFwekJiXgmnP6YeWss9yxNBU/bAfd2SX5fW9X57Oyph4XPbwV0x7bhonLNmPemkrUNrQofnyBBBqxdLWLrd3aRfe2FCxOw5ZmQXa6BXe8+rnP+2zddxS3rN2lafsyWkyJUqKNTzDqFtWiMFO7k9OW2F54zRCZgVCdIApNzjpkF9dSjTH9swNuH9z9b8wskg0FahtacNu63bjmXx9gzupPUPbUDlTW1GP5jGJ3R6izS/L73oE6n1oHzgYasSzOy1SkXYTTJkkd0Z6DSDfbIPORG+sS6+2FdUBkfFwOZzDhrkPum5mK7+3BZx2MvnY5nDwN4Sxd+XE3Od8EnMCJTs4jG6vc2y93f++UpASv5WEuaZYEDM/LxEF7K7462hR2bolw81IECt4Otc213HYRi2vjRcsNomQyVC2WdYlWf/QjuW3JjMsAw8VrhsjY2AkymEjWIdtSg98o1Vq7rMVNO5I4CLkxOMFGRD0Tgnpuv+z53pU19T5/Fyg+S27sRqTH6y94O9S20nLbRaytjRcxN4hS50Dt+DRAzPozm2juveG0JS3ai+girYNwzhGvGSJ1sBNkMJFsyarHNq5a3LRDrV1fMaM4qi/oUCOibR1dQevP34+JQEvkPjpQj81fHsGofllobOvw+6UYzfH6G7HMSElUpF3E0jbBare5SBnlHIhaf2YS7b3XKG3JyMI5R7xmiNTDmCCDiWQdstZrl7VKIqd2LEqoEdHM1KSg9ecvcNZfHI5rdmj9zlpM+tuWgBsoKBH34Rm83duaoki7iKW18aLGPxnlHIhaf2ahxL3XKG3JqMI9R7xmiNTDmSADimQdspZrl7VKIqd2LEqwEdGxA3PcHYlA/C1D8xeHE2oDBddIX6ckYeWss5AYH4ee6Ra0d3bB0dKBHQeOYWV5dUTHG227OORoRX2TE47WDtwy5XTcnZQAe4sTqRZzxgeIHP9khBgNJeqPsRGBKXXvNUJbMqpwz5HI9xwio2MnyKAiWYes1fptrW7aasaiuH5o3TBxIH537gBU7K/DyvJqNDs73SOiwTpALv42S+iuOC/T7wYKwIkvxbomJ5qcnVj8mvc21K48RXtq7Vg+oxjWEDE+gUTaLmrqmrBw7S6vzltpYTaWTCtCfnZ6RGURnejxT+GeS607FNHWH2MjglPy3st4H3WEe45Ev+cQGRk7QaQ4rW7aaq1d9/dDa+zAHLw2rxRxALLD/KHo+WPC3uz0KXOoXdo6uyT3LnWu5Kuj+mUhJTEBtrQk/OnCwZAgITlRu9WthxytPh0gACivqsMta3dh2fSRsjqJRmOmeIloOxSRdKCiqT/GRoTGH8ziC/ccmemeQyQaxgSR4rRKIqfG2vVAP7S27juKO1/9POwOkJwyh+q8dHZJ2LrvqFfy1VmrduDn//wvJj+8FYtf34PvGlpx2yu7UVPXFHHZwlHf5AyYY6i8qg71TeZcp26WeIloY0dqG1owd00lJj64OawkwNHUH2MjQmMCT/GFe47Mcs8hElGcJEmS3oWIlMPhgM1mg91uh9Vq1bs4hqfk0pjahhafLZldN+0+Ci9b8YxLsaYmIivNEnQWIthx7j/ciIkPbvb5G9cMzNSiPmhp74y6flxlON7ajqw0Cxa9sttdV67PKs7LBAD0taWg1t6KrLQk1De3Iy4uDp/U1LuX5wEnlsYV52fh05p6TWZhPqw+hulPbA/4/Iu/HY2BuRlhtScjxXp4nj8jxksEaucu7y0YjwG5GX6fszc7MXdNpd8OybiBObJmZOTWn2ebSE6Mxxu7v/dq957WzRmDkflZQT83Fmh576XIRHKOjH7PIdJKOH0DLocjAMqvtdcqsDbccod6vb/12tHm9vGn+3p71wYKHx2ox6NXn4GDPyS4bevowkFHGw47WhEfF4d5ayrR7OxESWE2ls8oxg0//NuVt+iRjVWob3Kq3gmypgS+daRZEpCdbvH5oRzNeRGN0eMlookdUSL4Xk79+WsT3du9Jy71OoGbGogvknNk9HsOkYi4HI5U29K6+5bMem/FLef14eT2UXLLb9eX4ls3jEVqUjxe33UQ1z79Eeas/gRlT+3A+l0HkZIUj+vG9wdwIlnrqopqlJUWuN/DFVvkaO2IujyB2Jud2H+4EU3ODjzz63Mwd0Ih0izemz0smnI6bnvlc0XPS7jlq6ypx/4jjYptx2420cSOaLHxSaA24a/dA1zq1Z3a916KHs8Rkf7YCSLDrrUPt9xyXi83t0+wz4mULc0CCcCK96t8Pq+iqg6PvF+F807L9XrMtWQO+DG2KNgsTTQ840Auf3w7rv7nB/i0ph7LZxS7O0Klhdk4o18WtlYpe17CLV84cSqxKJrYES2C74O1ie7tnrERREQUCS6HI8PmIQi33HJePyA3wyu3T5olAT3TLXhy5ii0dXQhJSnBJx5HyfppcnYE7HD5S7LqKhsAZKZZsHTaMPRMV/7HYKCR+fKqOsTFxeHl340BAGSlW0J2OiI5L3LKd/frezBrzKn400WnobG1Ez1SEnHI0Yolr+/B3dOK+CPZg78cVoC8DoUWu1WFahO21CSsmzOGS72IiChi7ASRYbdVDbfccl/vWppW1+SEBOCOV3b75OfxjEtQsn6a/AR8e3J1vFxxSg++s9erbGMH5mC8x2yRUoKNzG/ddxRJCfHuQPrGEMvxIj0vwRxrcuKGiYNw1/rPffIWLZo6FMealEnQayaRxo5E04GSK1SbyPphKREREVGk2Akiw+YhCLfc4bze9UNu7ppKr04G8OOMTFlpAXZ+06Bo/WSGSHiaGB/n/mx/cUpbI8iZImdXtnBma9Q8LwHFwacDBJyYqVq8/nP89dJhod8jBkUabK128L1R70lERGQcjAkiw+YhCLfc4b4+VFzCmP7ZitdPbo9kjA0QqzG2MAflP8TajOmfrUicktw4mnBma9Q+L/60tncFzVvU0h48IS2FT83AbqPek4iIyDiYJ8jkwsm9YtQ8BOGW2/X6prZ22FItcHZ2obGtw6t+7M1OfNfQgq/rmv3GAQHAy78bgzP6KZeXxFUue4sTqUkJaHJ24khjG5ITE3DI0Yox/bNxvNWJHikWNDk78E19C/raUoE4CU1tnUi3JACIw8a9h3DeoF4Ynhe8bOHke7E3O3HTC5/htD5WFOdlesVH7T3owANXjvCpcznnxbN9ZiQnwpIQD3uLE+nJ4bW/D6vrMP2J/wZ8/vnrRuPsgp6GykUUSzzbflpyIuLj4pAYH4f05EQ0tXXA0WKsexIF/u7hNUiiYZuMjmj1xzxBBCD83CtGzUMQbrldr/dXP5NOz8WiqUNw67rdIfOT2EIsXQuHqywfH6j/ISfRl14zG+MG5mD8oF7IzkjBnzxed89bX3i9rqQwG3PPK4QtNXR9hJPvxZZmwaKpQ7Bw7S6vXEmlhdlYEmDTgVDnRcncQKFmqmypiZrkIhLty8AIAuUDml1SgOc+rMGdlwxD/16M/zGSQNfaXy8dhrvW78G7Xxz2elzUfGBkfkbLUScao9cfZ4JMyjXK//GBepSVFniN3h9ytOKnw06K6R9nnrMgaZYEdx0lJcQjMSEO2/bX+cz8lBRmozg/C49srPKZKVGqLHMnFKKypt7v0q5xA3MwuagPFr68K+jrSgqzMXV435DnuLKmHtMe2xbw+XVzxmBkfpZPGf2VK9y6UPr9Djla8YfnP0W5n/ooLczGfVeM8Lu7XaSf549IXwZG6YwFaweu623nNw2KXWukvmDntLQwGyN/uId6UvJ+SiSX0t9DsUbU+gunb8CYIJM62uh0zxZU1tR7J97cWYv6ZjG3vdaKaxbEtcuaq45+ufJDXP3PD1DZLf8N8GN+EqXjEjxnZELlJMrtkRzydRVVdcjtkRwyLiicOB+lc0kp/X69rSlYMq0IpYXZXo+7ZqpanJ2q5sJSK+FwJIyUL0lOPiCRc5WRr2DntLxbjicXnmPSg1FzJIrCDPXH5XAGEMmorqO1HWWlBXjmgwMozs9CWUmBVxzHX1/fg2V+4ji0ovdItWu3s0C7rHnuAOcatUyzJOCUrFQsmjoE3zta0dzeqUi5PXdea+sIHsDvel7O60Ll18nJsGDS6bkB43w8d+BytLZ7zZi1dXQh3ZKIjq4uxMXFoa7JCRxplF0fauSmys9Ox7LpI1Hf5ISjtQPWlERkpVvQ25qCypp6xT/PUzhLC9UUqjMm2shmqHbgauei5iojX3LPaXc8x6Q1o+ZIFIUZ6o+dIMFFusTGmpKEUflZGJmXiVUV1V7LD1zr7et0yp0iwrIh1yxIcV6mz9IMl4qqOpSVFAD4MS/PX9fv8doyW4lye87IJCcGn5x1PS/ndaHy64QT52NLTfohVulEW3LVR/cOpNz6UCs3VW9rCnpbUzT7PBdRvgxE6YzJFeq8uNq5qLnKyJfcc9odzzFpzag5EkVhhvrjcjiBRbPEJifDgp4ZloCzHKsqqtHZpX04mCjLhlx5SOTOvLhmjLrnDFKi3BkpiXjmV+fgsWvOQGaaBUumDfNahucybmAODh9vAwBUftOACYN7Ye6EQjw5cxQeu+YMrJx1FuZOKMSEwb1w+HhbyFwq9mYnbl23229unb+s2+11TOnJiV5tKdAMmtz6cNW/P2rkgVHi8+zNTuw/3IjKmnrsP9LodYyifBmI0hmTK9h5KSnMRuU3DcwLZDDBzmnpD+e0O55j0oPW30NmY4b6YydIYNGst7SlWZCUEB80bkSPTpAoa0hdeUhCJSd1jVoqlZenu9qGFtz0wme4+l8fYM7qT3D549vw5q7v8eTMUV4dIVcc0rmDemHcwBw8+2EN/nzR6T7xXp/W1OOWnw7BhEG9Qo74h3MuGls7vI4/VOxSqPrQOg9MtJ8XKs5GlC8DUTpjcgU6L67Z6r0HHcwLZDDBrrUl04qw96DD53GeY9ID85FFxwz1x+VwAot2VLfF2RH0ec+dz7QS6pia2to1ixfqm5mKhPg4jB2Y4+4MeMa9AMDJWanYfNO5qG9u84mJ8cwfFMkIu79ZsTRLAkbkZ0KSgH+XnY10SwLSLInITEty18GKGcVoaG7HX9bt8juLc++bX+Cv04qw/3AjGtvakZlmgbPDNxdSOO2r+2uDzaClWRLQJUnYf7gx6Dnsm5mKFTOKNctNFcnn2ZudOHy8DTXHmjG7pAAj8jLduwZ2j7O55/LhuPmlndjSbcv1O342FEcbnfjqaJPq8W+uztgWP51bUUfmPM+LveVE7FlCfBwS4uP85p8i8QW71h64coQh89GROWn9PWQ2Rq8/doIEFu2obqhcMUrmuZEr2DGlWRJgTbX4bLmoZrxQb2sK7v3hx+tH7hw93jFU4wbmYPElw/DI1cV4stw3vmr5jGJYI6jL7jMxnnE23T//nsuHw5Z24t+2NMuJv/UzE5NmScBVZ+fjpuc/xcc1DVg+oxj3vb3Xb9xOqPPv2b66n7dA6/pdx3DXa5/Lip3SOjdVOJ8XKH+NZ74ozzib7l8G1tQkWBLicfPLuzRrz4E6Y6KPzBk1RxkFFuic8lyTaNgmo2Pk+mMnSGDRjuqGs/uXVoId06KpQ7Bo3W5srdJ2ZyvXj9dAsytb9h3Ftq/q8MbOWr/xVXEAlk0fGfbndp9dCRVn43n8gWZxPN9j7oTCoO93/5UjZLev7ufNFZM0pK/Nq21JkoTVHxwIGDsl2u5kgXjO0nWfAUyzJGDFjGLM+6Ej5Dlj5vllECiHglbt2agjc0RERFpgTJDAol1v6dr9y1/cyKKpQ3T5URTsmM7Iz/TpALmoHS9kS7Ogo0vyO7sCALk9kgM+V15Vh8bW4EsP/ek+uxJOnE2gGTXP9wj1fo2tHbLbV/fzFigm6amKr3H1Of38buxglLwBQOA8UnNWf4Kr//kBnqr42p1HKtCMrJ7xb7Y0CwbkZmBkfhYG5GawA0RERNQNZ4IEF82orr3ZibvW7/GbJ2jx+j2qrbcPFdMT6Ji+OtoU9H3V3tkqWIxMR5eEuRMK/cYDdZ8NkMtzdiXNkoCe6RY8OXOU3/cHvI8/0IyaZ6xO97id7jMazo5O5GRYZLcvz/PWJUm4980v/LatZz444JVfyZNou5MFEiqP1Naqo+iChEVThwScUTXaTm16ijQOUO98Y0REZFzsBBlApOst65qc+PnZ+ZrmCZKbA8jfMVlTgo+Mq72zVaDZlTRLAgqy0/Hv7V/7jQe6YU1lRGVzza7c/spuXHV2Ph58Z6/XbFP3+BPPzwgU++G5251n3E6oeKMBuRmyy2xLs+CrI434+Tn9AratxLg4v38v2u5kgcjNI3VbkBlVo+3UppdI84aJkG+MiIiMi8vhTKyjS9I0T1C0OYD03mY40OeXlRZg8frPA9ZjsNmAUPpmpuKv04rwtJ8cRK73Lyst8Hv8rpmZ9xaMx7o5Y/DegvEYkJvhPobKbxpQUpjtPoZw8/oEy4sTqm3Z0nx/4GtxDoOVORxy80i1BNlhUe/2bASR3jNEyTdGRETGxU6QiXV1SZrmCYo2BkLvPecDff6Y/tkB44EqqupwRn5mVGVrbO0I+v5j+meHPH4JAOKAlMR43PvDMawsr8bskgKUFGaHndcnVF6cUG3LkuB9a9HiHIYqczjk5pEKNpujd3s2gkjvGaLkGyMiIuPicjgTa9Y4T5ASMRDplgQsmjoEDS3tyPCTI0dtnjvFNTk70OTsRLolAXMnFHrF53gKNhsgR6h6S0lKQB8/y3uCLQdyxe40tbVjyaVFONzYFvQzPM9NqFH2FTOKQ7at1o5OvLdgfMS7k3WP9chITkRTWwfsLf5jP+SUOdw25C+PlCc5szncqS24SO8ZjLciIqJosRNkYlrnCYo2BiLYj3pXjhwtNDk78ZdXdgfND+Mp2tiOUPXm7zzJ+dHvGefTEWLWz/MY5Iyyh2pbmakW2XFG3flrB6WF2ZhVUuCu/+6xH7LKHEHHwzOPVKR5d4ycQ0Ftkd4zGG9FRETR4nI4E9M6JiGazxNljX+gcnjG53hSoh4jqbdwlwOF8xlyRtnValuB6r+8W/13bxdqzgz4i71aMaPY7+wchSfSdsR4KyIiihY7QSamdUxCNJ8nyhr/YOWoqKpDcV6m+99K1WMk9Rbuj/5wPkPOKLtabSuc+vdsF2rPDDDvjjoibUeMtyIiomhxOZwCRM5VoXVMQqSfp/caf9c5rGtyYuWss3xy9LjYUpOwbs4YRevxkKMVjpZ2zJ1QiFumnI60pAS0dHSiqbUDze2dsDf7LuWK5Ee/3HPjLweRK8fQmP7ZsLc4sf9II3LSvXMMWVOTkJ6ciMbWDlTW1Ed0LYRqB913a3O1i5wMCyadnovT+lh9cjntPejgzIDAIr1nmD3eSuTvFaJwsT2TiNgJipIRclVoHZMQyefpucbf3zkMFAOUlRZ5rIs/NXVNWLh2l9dOa6HiX4DAyVKB4MuB5Jyb7jmI5OYYqm1owU0vfBbVtRCqHXjmPgJ+bBe2NAsWTR2ChWt3eZWxtDAbS6YV8ctWBj1/pER6jzJrvJURvleI5GJ7JlFxOVwURIlj0ZpSuVg8abnG37P8+w4dx+Yvj+DjA/Ver/EXA6R0OQ45Wn06QGmWBIzMz0JqUgL+/oszsXLWWRiel4nbX9ntVc9KLgfydz4942Beub4ET4fIMaTUtRCsHZQUZqPymwavY3WdD3uzE7eu2+1TxvKqOvxl3W7TXotKUXJ7cYpOrH6vkDmxPZPIOBMUBbV2pBKZWiM63WcfPN9byTX+4cz6VFTVoaykQJVyAEB9k9OnA+RvxqWkMBuzSwpQ1+TdnpRYDhTqfNrSLNh/uDFgHiPPuBwlroVA7cBzdsxVRs/zEYvXolLU2F6cIse2bExc7uUf2zOJjJ2gKOgdxxIOJW7Qav9YUnuNf7Cd3wDguvH90d4pecWU9LWlYOMfxiNbhS80R6t3rp2y0gKs8jPj4vr3HRcP9XmPaJYDyT2fctp5qLS74VwL/tpBRsqJPEHP/Oocv+3C0drujlvqHhO0srxa12tR9B9H/JEiFiN9r9AJXO4VGNsziYydoCgYJVeFUjdoLX4sqbnGP1j5K2sacPvUobhz/edeszBjVdxtypriffkV52V6fbaniqo6dIbI9RMuuedTiXYe7rUQbjuwpSYFnEVbPqMYVoVzYsllhB9H/JEiFqN8r9AJnEkNju2ZRMaYoCgYIVeFkutxjf5jKVj5y0oLsHj95z6zMFtVXLeclW5BaWG2+9/ddz7rrvtOdZFyxQC5dsKbO6EQaZYEn9d57rwWqp3rfS2kJycGnEV7qqIa6cnaj/cYZS08f6SIRe9ricIjSnoHUbE9k8jYCYqCEXJVKHmDNvqPpWDlL87LlBX3oqTe1hQsmVbk7gh13/msO5sCsxmeAfDTn9iOsqd2oLKmHstnFPt0hDx3XgvVzvW+FhpbO3w6QC7lVXVo7Lb0UAtG+XHEHyli0ftaovAYfXBQbWzPJDJdl8M9/vjjePzxx/H1118DAIYOHYrbbrsNkydP1rNYYRE9V4WSN+hIt2VWUzjxFsHKDyCimJJo4z3ys9OxbPpI1Ded+EE8dmCO3x/O/upX7me7XmdvcaKtowsj8jLx8YF698ySq/NQVlrgXkrW/fPktHM9rwW57VzL+Jxwrj29t6fWYlMSkk/07xX68Zrt6JKC5pYTfXBQC2zPJCpdO0GnnHIK7rnnHgwcOBCSJOHpp5/GJZdcgsrKSgwd6hsELiqRc1UoOXsj2o+lcOMtgpU/r2dq2DElSsV79LamoLc1BQBwr8z6lfvZcnfDk7MTntwcQ3pcC6HauTU1SfP4HLnXnghxQ/yRIh6Rv1dindz7KmdSf8T2TCKKkyRJ2WjrKPXs2RP3338/rr322pCvdTgcsNlssNvtsFqtGpTOeOzNTsxbUxlw9iaSoE3XCJieP5bszU7MXVMZcNYk2HEdcrSivskJR2sHrKmJyEqzIA7A75//1O+SqtLCbCybPtLdUYn28+UcW7D6lfvZwV5XUpiN4vwsrw7f89eNRna6Ragfv+HMdgVr5/dfOcInkavn82oEL8u59gCo1o6ISHly76uuwaQ+gmyAQhQrwukbCLM7XGdnJ1544QU0NTVh9OjRfl/T1taGtrY2978dDodWxTMsNWZvRBjRiXSnukCj7nddMgyVNQ1+388VU9Lb41pSc6e8UPUr97ODvc5z5sclO92CAbkZEZVZDeHMkIRq542tHZpvAy3n2tt/uJHbUxMZSKj76q0/PR3TRp4s1GASEfmneydo165dGD16NFpbW5GRkYG1a9diyJAhfl+7dOlS3HnnnRqX0PiMttRFzuh/JLFOwXbrWvTKbq+YGBdXnFBbRycqa+rd5WlsUz4YVu6sR6ck4cmZo9DW0YV0SyI6uroQFxeH1vZOpCQloOuHyd1QdeS5G51oyzYi2XY2WDuvrKkP+nlqBS/3zUzF/VeO8Jl5dM0qxnpQteg5lNQQi8dsJqGuWWdHF4b0tWlUGiKKhu6doNNOOw2ffvop7HY7XnzxRcycORObN2/22xFauHAhFixY4P63w+FAXl6elsU1LBFmb+SQO/ofSaxTsBG8rfuOYtaYU70eS7Mk+I0Tcs0cpVkSAm5bHW4wbDgxPotf+xxbq+q8yue5jM+d2yjEbnKu3ehEDICPdKYtUDvXa2fDUOfV6DsuRkOEWCitxeIxm00sX7NEZqP7FtkWiwWFhYU488wzsXTpUowYMQIPP/yw39cmJyfDarV6/UfmEU5elUi29Q01gtddWWmB39wzW/YdxW2v7Maiqf5nLMOdVZF73O7Xeezm5q98rtxG6cmJAeto7MAc5PdMw3sLxmPFjGLh1q0rPUOixzbQcs5rrG5PbZQcSkqKxWM2o1i9ZonMSPdOUHddXV1ecT8UO8LJqxJJ7oFQI3inZKV6vV9xXmbA3DNb9h3FGfmZiuQ+kHvc3V8XqnyNrR0B6+i+y4djYO8eGJCbIdQMkIvSo6165KqQO5sVizk0jJJDSUmxeMxmFKvXLJEZ6bocbuHChZg8eTLy8/Nx/PhxPPPMM9i0aRPefvttPYtFOgl39D/cWKfueYI88wIBQHxcHO6/cgSa2jrgaGlHe1fwjRNbnJ2KxFrJPW7P16VZEtAz3eKODfLMaeRaone8tR0DcjMMFQ/mokZOKq1j4+SeVy3LJUo8SqQzfaKUPxKxHv9lJkaLsyUi/3TtBB0+fBi//OUvcfDgQdhsNgwfPhxvv/02Jk2apGexSCeRjP6HE+vkuVvXRwfqA8b73HP5cPTvlYH9hxtDlkeJWCu5x+16nSsW6MF39rqXxgG+eSpcf2eUeDBPauWk0rIuwmnPcssVTSdApHiUSK51kcofCcaSmIsR76tE5E3XTtCTTz6p58eTADx/1GUkJ2LpZUVYvH6Pz4YDSq21do3gNTS34y/rdvmN93HtPhbpbES4P1Tlfo7rdcPzMv3GAlV4xArt/KbB8GvTQ+2sJjqlZ7Oi6QREstuemsKtG9HKHwk1ZjeJiChywiVLDQeTpRqbvx91Ywfm4PrzClH21A6vrNtKJ53bf7gREx/cHPD59xaMx4DcDNQ2tAScjfBXnkh/qMr9nNqGFnx9tAlX/+uDgO/1zK/OQUFOunCbHYTL6CP/gPzzGkq0yXnltncthVM3IpY/Ekq1ByIi8s+QyVIptrhGdj8+UI+5EwpRnJfpjm2pPtqEt24Yi2PNTtXWWqsRrxHNaLXcz+mbmYrv7S1By56SlKDoDyo94jDMMPIPKBc7EG1yXhHjUcKpGxHLHwnGkhARiYOdINLF0UYnPg4Ql1NSmI3R/bMxMj9Ltc9XI14j2h+qcj/Hlhr8NaHyA4VDr9mYaOtSJErEDkTbCRA1HkVu3Yha/kgwloSISAzCbZFNscHR2h4wz01FVR0WvbJb1bwZauR60Gq0Wqs8FXrmNTHLyL9Sou0EGD23idHLT0RE4mEniHRhTUkKmudmq8p5M9TI9aDVaLVWeSr0zGtippF/JUTbCTB6bhOjl5+IiMTD5XCki5wMC76uawr6GrVH+5Ven6/l7k9axBZ0n43xzKvU1tEFZ0cn7M3qLEvjTlrelNgy3OjxKEYvf7SMnCOJiEhE7ASRLmxpFpySFTymRIvRfiXX56uV2ybY56n5I8hzNibNkoBHrz4DBz02ZTjoaMOn39px7qBeiu9spXVdGoESnQCjx6MYvfyRMsNOiUREomEniHRzkjXFdKP9Zhqt9pyNuW58f6QkxeP1XQe9ljCWFGajICcdaZYExY/RTHWplFjtBMQys+yUSEQkGuYJMiBRlkUoUY5I8maIcvxKC/e41K4He7MT3zta8W19C/pmpmLXtw2otbei6GSbezvzT2rqsafWjtunDkW/nHTFPpuITjBLjiQiIi0wT5CJibIsQqlyhDvar/fxq9XxCPe41K6H7u+fZknAqlmj0NnlPWbS15aCM/Oz0NzeEfVnEpEv7pRIRKQOzgQZSLRZ441eDr2PX62OR7jHpXY9+Hv/308aiLNP7YlH3q/yWQ4397xCZGdYMKi3+a9BIq1xJoiISL5w+gbcIttA9NyyWIRy6Hn8aubMCfe41K4Hf+9/3mm5Ph0g4EROp0fer0JSPG8lRGpgjiQiInVwOZyBiLIsQq9yhPO5Si9bk9PxiPT9w63P+HjgtXklaGztRI+URBxytOLPL+10d36irf9A5QmU06miqg4t7Z1RfaZcZo0HIwqEOyUSEamDnSADESWBpF7lkPu5aixbU7PjF0591tQ14S/rdnt1SEoLs7H6Vz/BNf/6L442OqOuf3/laW4L3slpdqrfCdI7HoxIL9wpkYhIeVzDYiCiLIvQqxxyPletZWtqdvzk1uchRysWrt3lMyNTXlWHxes/x72XD1ek/v2VJzEhLujf2FLV7YCruRyRyAhsaRYMyM3AyPwsDMjNYAeIiChK7AQZiGtZRPcfqFovi9CrHHI+V614GTU7fnLrs77JGXBJWnlVHU6ypihS//7KU151FGML/R//2IE5yO2RHNVnhiJKPBwRERGZA3eHMyBXXITeyyL0Kkegz7U3O/FdQwu+rmt257BZWV7ttVTr1evHoF92ekRxJd/VN+NAXTMaWtrd77/3oAN3XTIsYE4jJY7L5cPqY5j+xPaAf//8daNxdkHPqMvhrzzW1CRYEuJxy9pdYeV0UkplTT2mPbYt4PPr5ozByPwsVctAREREYmOeIJMTJWu8XuXw97n+4kVKCrOxfEYxblhTiWZnJ9IsCbCmWny2f5YTV1Lb0IKbX97l9XdjB+Zg6bQixToAoerTmhL8cg31vBLl0SsuQZR4OCIiIjIHdoLI8ALFi7iWjpWVFuCRjVVYNHUI/rp+D0bkZWLWmFPR1tHlntG5/ZXdeODKEX5/0Ad6/637juKWtbs0y8+UlW5BaWE2yv0siSstzEZWuvpl0Kvjm5NhwaTTc3FaHyuK8zK9zt3egw5uE0xERERhYSeIDC9YvEhFVR3KSgowbmAOzuyXiV49krGqohqPbKxyv6akMBuzSwpQ1+R/m2s1t8cOR29rCpZMK8Ita3d5dYRKC7OxZFoReltTVC+DXmxpFiyaOgQL1+7yOneuYxdhZpSIiIiMg50gMrxQ21fbUpOwYkYxDh1vwzMfHEBxfhbKSgq8ZhOe+eAAFk4+Xdb7p1kSUFZagFH5WbClJcHZ2YVPDhyDLc2iSN6aYLlw8rPTsWz6SNQ3OeFo7YA1JRFZ6RZTd4CAE3Vya7etwYETG0L8Zd1uzWbjupeJOYuIxMDrkYjCxU4QGV6oeJGsH5Zwfe9oxdXn9As4E9QVYI8Qz/dPsyRg+YxiPPPBAYzMy8QD7+z1+mEebd4aOblweltTTN/p6U6U2TgX5iwiEgevRyKKBLfIJsOTu311HIBVFdU+swkVVXVYVVGNOPjPheP5/mWlBVhVUY0hfW1+3yuavDXMhROYmslqw8XzRCQOXo9EFCl2gsjw5ObZ6ZIQMM9ORVVdwJkgz/cvzstERVWd+3/9iTRvTbi5cOzNTuw/3IjKmnrsP9Jo6i97kXaHY84iInHweiSiSHE5HClGizXZgT6jb2ZqyO2bm50dQd/bM59Qd673//JwIwCgraPL/ZwrRshz17JAHapgwpntiLXlH67ZuC1+fuxEm6w2XCLNShHFOl6PRBQpdoJIEVr8KA/1GaG2b7alBv+hbEsNPptgS7Og5w/vn5x4YhLVFSPUPc5o7A+zUOEcu9zZjlDLP/TYJEBtrtm4m1/a6TdZq5bHK9KsFFGs4/VIRJFiJ4iipsWPciU+Q4nZBNd7VH7TgJLCbBTnZ/mNDdoawbHLLZ9r+Ye/GahPauoDbvVtdHJm+7Qg0qwUUazj9UhEkWJMEEVNizXZSnyG3NghOe+x96ADs0sKMLp/tmKxQXLL52htd89AVdbU49qnP8Kc1Z+g7KkdqKypR/gL8YzDlmbBgNwMjMzPwoDcDF06e0q0IyJSBq9HIooUZ4IoalqsyVbqM6KZTfCMR/rTRYNhSYzH0cY2RcoVTvmsKUnuXer87XR3x6uf4xETLokTiSizUkTE65GIIsNOEEVNizXZSn5GqNghfwLFI911yTCkWRICbqoQybGHKl9OhgVj+md7xSB52qpD3pxYFEk7IiJ18HokonBxOZzOzLDNsdw8PaJ/RiDB4pFue2U3Fk0domm5bGkWWBKDX7pK74h0yNGK/x104MPqY/jf9w4ccrQq+v5EREREWuJMkI7Mss2xFjt36bk7WKh4pFunnO4TmKt2ubJCvK+SOyLV1DVh4dpdXkvvSguzsWRaEfKz0xX7HCIiIiKtsBOkk3B2O9Mi/060tFiTrde671DxSC3OTs3LpdWOSIccrT4dIAAor6rDLWt3Ydn0kehtTVHks4iIiIi0wk6QTuTsdmZLsxhqtkiLNdl6rPuWE4+kdbm0mhmrb3IG3P2uvKoO9U1OdoIEYoQBEyIiIhGwE6QTObudxWJSTBGJmodCi5kxR2tHVM+Tdow0YEJERKQ3boygEzmzC1rk36HQRM5DoXbeHGtK8HGSUM+TNkINmBhxwxUiIiI18ReMTuTMLnx1tCnoeyi9AxgFFqt5KLLSLSgtzEa5nyVxpYXZyEo39/EbhdzltURERHQCZ4J0Imd2QYv8OySf2rMuIuptTcGSaUUoLcz2ety1OxzjgcSgRcJiIiIiM+FMkI5CzS6IGotidAweD09+djqWTR+J+iYnHK0dsKYkIivdwg6QQDhgQkREFB52gnQWbFcxPXPjmBWDxyPT25rCTo/AOGBCREQUnjhJkiS9CxEph8MBm80Gu90Oq9Wqd3FU45q5ECUWxagzKfZmJ/7wwmcY3MeK4rxMtHV0ISUpAZ/U1GPvQQceuHKEasdh1Doj46htaAk4YNKHHXwiIooB4fQN2AmisBh5JuWrI4346mgTVlVUe+W+KSnMxuySAvTPSUf/XhmKf66R64yMRbQBEyIiIi2F0zfgxggkm9G34e3oknw6QABQUVWHVRXV6OxSfjzA6HVGxhKLm3cQERFFgp0gks3oeYu6uiSfDpBLRVWdKp0go9cZERERkRmxE0SyGX0b3mZnR4jnOxX/TKPXGREREZEZsRNEshl9G15bavClQbZU5ctv9DojIiIiMiNdO0FLly7FWWedhR49eiA3NxeXXnop9u7dq2eRKAjXNrz+GGEbXj3Kb/Q6IyIiIjIjXTtBmzdvxvXXX4///ve/2LBhA9rb23HBBRegqalJz2JRAK68Rd1/1Bslb5Ee5Td6nRERERGZkVBbZB85cgS5ubnYvHkzxo0bF/L13CJbH0bfhleP8nt+pjU1CenJiWhs7WDeICIiIiKFhNM3SNSoTLLY7XYAQM+ePf0+39bWhra2Nve/HQ6HJuUib7Y0Y/9g16P8np9Z29CCm174jHmDiIiIiHQizMYIXV1dmD9/PkpKSjBs2DC/r1m6dClsNpv7v7y8PI1LSRQd5g0iIiIi0p8wnaDrr78eu3fvxrPPPhvwNQsXLoTdbnf/980332hYQqLoMW8QERERkf6EWA43d+5crF+/Hlu2bMEpp5wS8HXJyclITk7WsGSRccV/MN6DulMjbxDbGxEREVF4dO0ESZKEefPmYe3atdi0aRMKCgr0LI4iahtafJY7Md6DXJTOG8T2RkRERBQ+XZfDXX/99fjPf/6DZ555Bj169MD333+P77//Hi0tLXoWK2KM9xCfvdmJ/YcbUVlTj/1HGjU/J0rmDWJ7IyIiIoqMrjNBjz/+OADg3HPP9Xp81apVmDVrlvYFipKceA8uU9KPCLMmrrxBN7+0E1u6lSPcvEFsb0RERESR0X05nJmoEe9BynDNmnx8oB5zJxSiOC8TbR1dSElKwOYvj+Cnw07SrMPQNzMVK2YUR52riO2NiIiIKDJCbIxgFkrHe5ByjjY68fGBeiyfUYxVFdV4ZGOV+7mSwmyM7p+t6ayJErmK2N6IiIiIIiPMFtlmoGS8BynL0dqOstICrKqoRkVVnddzFVV1WPTKbsPF0LC9EREREUWGnSAFueI9uv8wjSTeQ2R6by4QCWtKEorzMn06QC5bDZijJ1baGxEREZHSuBxOYUrFe4hKhM0F5OieOycjJREJcXFB/8ZIMTSex7do6hBYEuJhb3EiPTn89sY8Q0RERBRr2AlSgRLxHiIKtSXzihnFQhx3oI7anZcMRZolAc3OTr9/Z5QYGiU7okbp1BIREREpicvhSDY5WzLrLVhH7fZXPseiqUP8/p1RYmiUzA3EPENEREQUq9gJItmMsCVzqI7aGfmZho6hUbIjaoROLREREZEauByOZDPClsyhOmotzk5Dx2wp2RE1QqeWiIiISA3sBJFsri2Zt/iZPRBlOZmcjpqRY7aU7IgaoVNLREREpAYuhyPZjLAls9lz5yh5fGavKyIiIqJA4iRJkvQuRKQcDgdsNhvsdjusVqvexYkZri2VRV1OVtvQgptf2uk1Y+XqqPUxwY5nSh6f2euKiIiIYkc4fQN2gsiURO+oRUvJ4zN7XREREVFsCKdvwJggMiUjx/3IoeTxmb2uiIiIiLpjTBAREREREcUUdoKIiIiIiCimsBNEREREREQxhZ0gIiIiIiKKKewEERERERFRTGEniIiIiIiIYgo7QUREREREFFPYCSIiIiIiopjCThAREREREcUUdoKIiIiIiCimsBNEREREREQxhZ0gIiIiIiKKKewEERERERFRTGEniIiIiIiIYkqi3gWIhiRJAACHw6FzSYiIiIiISE+uPoGrjxCMoTtBx48fBwDk5eXpXBIiIiIiIhLB8ePHYbPZgr4mTpLTVRJUV1cXamtr0aNHD8TFxeldHE05HA7k5eXhm2++gdVq1bs4FEPY9khPbH+kF7Y90gvbnnySJOH48ePo27cv4uODR/0YeiYoPj4ep5xyit7F0JXVauUFQbpg2yM9sf2RXtj2SC9se/KEmgFy4cYIREREREQUU9gJIiIiIiKimMJOkEElJyfj9ttvR3Jyst5FoRjDtkd6YvsjvbDtkV7Y9tRh6I0RiIiIiIiIwsWZICIiIiIiiinsBBERERERUUxhJ4iIiIiIiGIKO0FERERERBRT2AnS0dKlS3HWWWehR48eyM3NxaWXXoq9e/d6vaa1tRXXX389srOzkZGRgcsvvxyHDh3yek1NTQ2mTJmCtLQ05Obm4o9//CM6Ojq8XrNp0yacccYZSE5ORmFhIZ566im1D48M5J577kFcXBzmz5/vfoxtj9Ty3Xff4Re/+AWys7ORmpqKoqIifPTRR+7nJUnCbbfdhj59+iA1NRXnn38+9u3b5/Uex44dwzXXXAOr1YrMzExce+21aGxs9HrNzp07MXbsWKSkpCAvLw/33XefJsdHYurs7MSiRYtQUFCA1NRUDBgwAIsXL4bn/lBse6SULVu24OKLL0bfvn0RFxeHdevWeT2vZVt74YUXMHjwYKSkpKCoqAhvvPGG4sdrSBLp5sILL5RWrVol7d69W/r000+ln/70p1J+fr7U2Njofs1vf/tbKS8vT3rvvfekjz76SPrJT34ijRkzxv18R0eHNGzYMOn888+XKisrpTfeeEPKycmRFi5c6H7NV199JaWlpUkLFiyQ9uzZI61YsUJKSEiQ3nrrLU2Pl8T04YcfSqeeeqo0fPhw6cYbb3Q/zrZHajh27JjUr18/adasWdIHH3wgffXVV9Lbb78tVVVVuV9zzz33SDabTVq3bp302WefST/72c+kgoICqaWlxf2aiy66SBoxYoT03//+V9q6datUWFgozZgxw/283W6XevfuLV1zzTXS7t27pTVr1kipqanSE088oenxkjjuvvtuKTs7W1q/fr1UXV0tvfDCC1JGRob08MMPu1/DtkdKeeONN6Rbb71VevnllyUA0tq1a72e16qtVVRUSAkJCdJ9990n7dmzR/rLX/4iJSUlSbt27VK9DkTHTpBADh8+LAGQNm/eLEmSJDU0NEhJSUnSCy+84H7NF198IQGQtm/fLknSiYssPj5e+v77792vefzxxyWr1Sq1tbVJkiRJf/rTn6ShQ4d6fdZVV10lXXjhhWofEgnu+PHj0sCBA6UNGzZI48ePd3eC2PZILX/+85+l0tLSgM93dXVJJ510knT//fe7H2toaJCSk5OlNWvWSJIkSXv27JEASDt27HC/5s0335Ti4uKk7777TpIkSXrsscekrKwsd1t0ffZpp52m9CGRQUyZMkUqKyvzeuyyyy6TrrnmGkmS2PZIPd07QVq2tenTp0tTpkzxKs8555wjXXfddYoeoxFxOZxA7HY7AKBnz54AgI8//hjt7e04//zz3a8ZPHgw8vPzsX37dgDA9u3bUVRUhN69e7tfc+GFF8LhcODzzz93v8bzPVyvcb0Hxa7rr78eU6ZM8WkfbHuklldffRWjRo3ClVdeidzcXBQXF+Of//yn+/nq6mp8//33Xu3GZrPhnHPO8Wp7mZmZGDVqlPs1559/PuLj4/HBBx+4XzNu3DhYLBb3ay688ELs3bsX9fX1ah8mCWjMmDF477338OWXXwIAPvvsM5SXl2Py5MkA2PZIO1q2NX4PB8ZOkCC6urowf/58lJSUYNiwYQCA77//HhaLBZmZmV6v7d27N77//nv3azx/hLqedz0X7DUOhwMtLS1qHA4ZwLPPPotPPvkES5cu9XmObY/U8tVXX+Hxxx/HwIED8fbbb+N3v/sdbrjhBjz99NMAfmw7/tqNZ7vKzc31ej4xMRE9e/YMq31SbLn55pvx85//HIMHD0ZSUhKKi4sxf/58XHPNNQDY9kg7Wra1QK9hWwQS9S4AnXD99ddj9+7dKC8v17soFAO++eYb3HjjjdiwYQNSUlL0Lg7FkK6uLowaNQpLliwBABQXF2P37t34+9//jpkzZ+pcOjKz559/HqtXr8YzzzyDoUOH4tNPP8X8+fPRt29ftj2iGMSZIAHMnTsX69evx/vvv49TTjnF/fhJJ50Ep9OJhoYGr9cfOnQIJ510kvs13Xfscv071GusVitSU1OVPhwygI8//hiHDx/GGWecgcTERCQmJmLz5s1Yvnw5EhMT0bt3b7Y9UkWfPn0wZMgQr8dOP/101NTUAPix7fhrN57t6vDhw17Pd3R04NixY2G1T4otf/zjH92zQUVFRfi///s//P73v3fPhrPtkVa0bGuBXsO2yE6QriRJwty5c7F27Vps3LgRBQUFXs+feeaZSEpKwnvvved+bO/evaipqcHo0aMBAKNHj8auXbu8LpQNGzbAarW6f2iMHj3a6z1cr3G9B8WeiRMnYteuXfj000/d/40aNQrXXHON+/+z7ZEaSkpKfFIBfPnll+jXrx8AoKCgACeddJJXu3E4HPjggw+82l5DQwM+/vhj92s2btyIrq4unHPOOe7XbNmyBe3t7e7XbNiwAaeddhqysrJUOz4SV3NzM+LjvX/2JCQkoKurCwDbHmlHy7bG7+Eg9N6ZIZb97ne/k2w2m7Rp0ybp4MGD7v+am5vdr/ntb38r5efnSxs3bpQ++ugjafTo0dLo0aPdz7u2Kb7gggukTz/9VHrrrbekXr16+d2m+I9//KP0xRdfSI8++ii3KSYfnrvDSRLbHqnjww8/lBITE6W7775b2rdvn7R69WopLS1N+s9//uN+zT333CNlZmZKr7zyirRz507pkksu8bt1bHFxsfTBBx9I5eXl0sCBA722jm1oaJB69+4t/d///Z+0e/du6dlnn5XS0tK4TXEMmzlzpnTyySe7t8h++eWXpZycHOlPf/qT+zVse6SU48ePS5WVlVJlZaUEQHrwwQelyspK6cCBA5IkadfWKioqpMTEROmBBx6QvvjiC+n222/nFtk/YCdIRwD8/rdq1Sr3a1paWqQ5c+ZIWVlZUlpamjRt2jTp4MGDXu/z9ddfS5MnT5ZSU1OlnJwc6Q9/+IPU3t7u9Zr3339fGjlypGSxWKT+/ft7fQaRJPl2gtj2SC2vvfaaNGzYMCk5OVkaPHiw9I9//MPr+a6uLmnRokVS7969peTkZGnixInS3r17vV5TV1cnzZgxQ8rIyJCsVqs0e/Zs6fjx416v+eyzz6TS0lIpOTlZOvnkk6V77rlH9WMjcTkcDunGG2+U8vPzpZSUFKl///7Srbfe6rW9MNseKeX999/3+xtv5syZkiRp29aef/55adCgQZLFYpGGDh0qvf7666odt5HESZJHqmQiIiIiIiKTY0wQERERERHFFHaCiIiIiIgoprATREREREREMYWdICIiIiIiiinsBBERERERUUxhJ4iIiIiIiGIKO0FERERERBRT2AkiIiIiIqKYwk4QERGZwqJFi/Cb3/xG1c9Yt24dCgsLkZCQgPnz5/t9zdGjR5Gbm4tvv/1W1bIQEVHk4iRJkvQuBBERGc/27dtRWlqKiy66CK+//rquZfn+++8xaNAg7Nq1C/369VPtc3r37o3Zs2fjhhtuQI8ePTBv3jw0NDRg3bp1Xq+76aabUF9fjyeffFK1shARUeQ4E0RERBF58sknMW/ePGzZsgW1tbVBXytJEjo6OlQry7/+9S+MGTNG1Q5QY2MjDh8+jAsvvBB9+/ZFjx49Ar529uzZWL16NY4dO6ZaeYiIKHLsBBERUdgaGxvx3HPP4Xe/+x2mTJmCp556yuv5TZs2IS4uDm+++SbOPPNMJCcno7y8HF1dXVi6dCkKCgqQmpqKESNG4MUXX3T/XWdnJ6699lr386eddhoefvjhkOV59tlncfHFF3s99uKLL6KoqAipqanIzs7G+eefj6amJvfnLFiwAJmZmcjOzsaf/vQnzJw5E5deeqnf99+0aZO70zNhwgTExcXh3HPPxdNPP41XXnkFcXFxiIuLw6ZNmwAAQ4cORd++fbF27VqZNUpERFpiJ4iIiML2/PPPY/DgwTjttNPwi1/8AitXroS/1dU333wz7rnnHnzxxRcYPnw4li5din//+9/4+9//js8//xy///3v8Ytf/AKbN28GAHR1deGUU07BCy+8gD179uC2227DLbfcgueffz5gWY4dO4Y9e/Zg1KhR7scOHjyIGTNmoKysDF988QU2bdqEyy67zF3GZcuW4amnnsLKlStRXl6OY8eOBe2wjBkzBnv37gUAvPTSSzh48CBeffVVTJ8+HRdddBEOHjyIgwcPYsyYMe6/Ofvss7F169bwKpaIiDSRqHcBiIjIeJ588kn84he/AABcdNFFsNvt2Lx5M84991yv1911112YNGkSAKCtrQ1LlizBu+++i9GjRwMA+vfvj/LycjzxxBMYP348kpKScOedd7r/vqCgANu3b8fzzz+P6dOn+y1LTU0NJElC37593Y8dPHgQHR0duOyyy9xL5IqKitzPP/TQQ1i4cCEuu+wyAMDf//53vP322wGP12KxIDc3FwDQs2dPnHTSSQCA1NRUtLW1uf/tqW/fvqisrAz4nkREpB92goiIKCx79+7Fhx9+6J45SUxMxFVXXYUnn3zSpxPkOTtTVVWF5uZmd6fIxel0ori42P3vRx99FCtXrkRNTQ1aWlrgdDoxcuTIgOVpaWkBAKSkpLgfGzFiBCZOnIiioiJceOGFuOCCC3DFFVcgKysLdvv/b+9eQqHtwziO/+ZxmKSZKadCRMZxp9Q47qTYYSFKoxBZkNhhIYXS2LBSs2HBDsViyEZGTcghKQsbGwvnktVt5l08vVPTy/sahx5v8/3UXdM987+6+u9+Xfd/7kddXV3J4XAEfx8dHa2SkpJXp1kfFRcXp+fn5y+rBwD4OoQgAEBY3G63DMMImbwEAgGZzWbNzs7KZrMF78fHxwc/Pz09SZLW19eVnp4eUtNsNkv6fbZncHBQLpdLZWVlslgsmpqaks/ne7OfpKQkSdL9/b2Sk5MlSVFRUdrc3NTu7q42NjY0MzOjoaEh+Xw+JSQkfHIH3ufu7i7YDwDgZ+FMEADg3QzD0Pz8vFwul46OjoLX8fGx0tLStLi4+ObaoqIimc1mXV5eym63h1wZGRmSJK/Xq/LycvX09Ki4uFh2u10XFxf/2lNOTo6sVqvOzs5C7ptMJlVUVGh0dFSHh4eKjY3V8vKybDabUlNTQ4KVYRg6ODgIez9iY2P18vLy6nenp6chEy4AwM/BJAgA8G5ra2u6v79Xe3t7yMRHkhobG+V2u9Xd3f3qWovFosHBQfX398vv96uyslKPj4/yer2yWq1yOp3Kzc3V/Py8PB6PsrOztbCwoL29PWVnZ7/Z069fv1RdXa2dnZ3gv7v5fD5tbW2ppqZGKSkp8vl8ur6+VmFhoSSpr69Pk5OTys3NVUFBgaanp/Xw8BD2fmRlZcnj8ej8/FyJiYmy2WyKiYnR8/OzDg4OND4+HnZNAMD3YxIEAHg3t9ut6urqfwQg6XcI2t/f18nJyZvrx8bGNDIyoomJCRUWFgZftPp3yOnq6lJDQ4OamprkcDh0e3urnp6e/+yro6NDS0tL8vv9kiSr1art7W3V1dUpLy9Pw8PDcrlcqq2tlSQNDAyotbVVTqcz+NhdfX192PvR2dmp/Px8lZSUKDk5WV6vV5K0urqqzMxMVVVVhV0TAPD9TIGvPAUKAMAfEAgE5HA41N/fr+bm5g/VaGtr08PDg1ZWVj7dT2lpqXp7e9XS0vLpWgCAr8ckCADwv2cymTQ3NyfDMP50K7q5uVFDQ8OHwxgA4PsxCQIAQF87CQIA/GyEIAAAAAARhcfhAAAAAEQUQhAAAACAiEIIAgAAABBRCEEAAAAAIgohCAAAAEBEIQQBAAAAiCiEIAAAAAARhRAEAAAAIKL8BasWWKZG07dlAAAAAElFTkSuQmCC",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Scatter plot to visualize the relationship between 'area' and 'price'\n",
"plt.figure(figsize=(10, 6))\n",
"sns.scatterplot(x='area', y='price', data=df)\n",
"plt.title('Scatter Plot of Area vs Price')\n",
"plt.xlabel('Area (sq ft)')\n",
"plt.ylabel('Price')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "e1295156-01e6-4a09-8131-f2b4f6273c89",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Count Plots for categorical features to understand distribution\n",
"categorical_columns = ['mainroad', 'guestroom', 'basement', 'hotwaterheating', 'airconditioning', 'prefarea', 'furnishingstatus']\n",
"\n",
"plt.figure(figsize=(18, 12))\n",
"for i, column in enumerate(categorical_columns, 1):\n",
" plt.subplot(3, 3, i)\n",
" sns.countplot(x=column, data=df)\n",
" plt.title(f'Distribution of {column}')\n",
" plt.xlabel(column.capitalize())\n",
" plt.ylabel('Count')\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "440b716a-ab1a-4d64-b347-a685e1cdf970",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(12, 6))\n",
"for i, col in enumerate(df.columns[:3]): # Only plotting the first 3 features\n",
" plt.subplot(1, 3, i+1) # Create subplots for each feature\n",
" sns.boxplot(x=df[col])\n",
" plt.title(f\"Boxplot for {col}\")\n",
"plt.tight_layout()\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "e8f4d72f-0d03-4089-88e1-be059027198f",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" price | \n",
" area | \n",
" bedrooms | \n",
" bathrooms | \n",
" stories | \n",
" mainroad | \n",
" guestroom | \n",
" basement | \n",
" hotwaterheating | \n",
" airconditioning | \n",
" parking | \n",
" prefarea | \n",
" furnishingstatus | \n",
" price_Outlier | \n",
" area_Outlier | \n",
" bedrooms_Outlier | \n",
" bathrooms_Outlier | \n",
" stories_Outlier | \n",
" parking_Outlier | \n",
"
\n",
" \n",
" \n",
" \n",
" | 1 | \n",
" 9205000.0 | \n",
" 8960.0 | \n",
" 4 | \n",
" 4 | \n",
" 4 | \n",
" yes | \n",
" no | \n",
" no | \n",
" no | \n",
" yes | \n",
" 2.5 | \n",
" no | \n",
" furnished | \n",
" False | \n",
" False | \n",
" False | \n",
" True | \n",
" False | \n",
" False | \n",
"
\n",
" \n",
" | 5 | \n",
" 9205000.0 | \n",
" 7500.0 | \n",
" 3 | \n",
" 3 | \n",
" 1 | \n",
" yes | \n",
" no | \n",
" yes | \n",
" no | \n",
" yes | \n",
" 2.0 | \n",
" yes | \n",
" semi-furnished | \n",
" False | \n",
" False | \n",
" False | \n",
" True | \n",
" False | \n",
" False | \n",
"
\n",
" \n",
" | 6 | \n",
" 9205000.0 | \n",
" 8580.0 | \n",
" 4 | \n",
" 3 | \n",
" 4 | \n",
" yes | \n",
" no | \n",
" no | \n",
" no | \n",
" yes | \n",
" 2.0 | \n",
" yes | \n",
" semi-furnished | \n",
" False | \n",
" False | \n",
" False | \n",
" True | \n",
" False | \n",
" False | \n",
"
\n",
" \n",
" | 7 | \n",
" 9205000.0 | \n",
" 10500.0 | \n",
" 5 | \n",
" 3 | \n",
" 2 | \n",
" yes | \n",
" no | \n",
" no | \n",
" no | \n",
" no | \n",
" 0.0 | \n",
" no | \n",
" unfurnished | \n",
" False | \n",
" False | \n",
" False | \n",
" True | \n",
" False | \n",
" False | \n",
"
\n",
" \n",
" | 11 | \n",
" 9205000.0 | \n",
" 6000.0 | \n",
" 4 | \n",
" 3 | \n",
" 2 | \n",
" yes | \n",
" yes | \n",
" yes | \n",
" yes | \n",
" no | \n",
" 2.0 | \n",
" no | \n",
" semi-furnished | \n",
" False | \n",
" False | \n",
" False | \n",
" True | \n",
" False | \n",
" False | \n",
"
\n",
" \n",
" | 33 | \n",
" 8190000.0 | \n",
" 5960.0 | \n",
" 3 | \n",
" 3 | \n",
" 2 | \n",
" yes | \n",
" yes | \n",
" yes | \n",
" no | \n",
" no | \n",
" 1.0 | \n",
" no | \n",
" unfurnished | \n",
" False | \n",
" False | \n",
" False | \n",
" True | \n",
" False | \n",
" False | \n",
"
\n",
" \n",
" | 89 | \n",
" 6440000.0 | \n",
" 8580.0 | \n",
" 5 | \n",
" 3 | \n",
" 2 | \n",
" yes | \n",
" no | \n",
" no | \n",
" no | \n",
" no | \n",
" 2.0 | \n",
" no | \n",
" furnished | \n",
" False | \n",
" False | \n",
" False | \n",
" True | \n",
" False | \n",
" False | \n",
"
\n",
" \n",
" | 112 | \n",
" 6083000.0 | \n",
" 4300.0 | \n",
" 6 | \n",
" 2 | \n",
" 2 | \n",
" yes | \n",
" no | \n",
" no | \n",
" no | \n",
" no | \n",
" 0.0 | \n",
" no | \n",
" furnished | \n",
" False | \n",
" False | \n",
" True | \n",
" False | \n",
" False | \n",
" False | \n",
"
\n",
" \n",
" | 153 | \n",
" 5530000.0 | \n",
" 3300.0 | \n",
" 3 | \n",
" 3 | \n",
" 2 | \n",
" yes | \n",
" no | \n",
" yes | \n",
" no | \n",
" no | \n",
" 0.0 | \n",
" no | \n",
" semi-furnished | \n",
" False | \n",
" False | \n",
" False | \n",
" True | \n",
" False | \n",
" False | \n",
"
\n",
" \n",
" | 195 | \n",
" 4970000.0 | \n",
" 4410.0 | \n",
" 4 | \n",
" 3 | \n",
" 2 | \n",
" yes | \n",
" no | \n",
" yes | \n",
" no | \n",
" no | \n",
" 2.0 | \n",
" no | \n",
" semi-furnished | \n",
" False | \n",
" False | \n",
" False | \n",
" True | \n",
" False | \n",
" False | \n",
"
\n",
" \n",
" | 290 | \n",
" 4200000.0 | \n",
" 2610.0 | \n",
" 4 | \n",
" 3 | \n",
" 2 | \n",
" no | \n",
" no | \n",
" no | \n",
" no | \n",
" no | \n",
" 0.0 | \n",
" no | \n",
" semi-furnished | \n",
" False | \n",
" False | \n",
" False | \n",
" True | \n",
" False | \n",
" False | \n",
"
\n",
" \n",
" | 395 | \n",
" 3500000.0 | \n",
" 3600.0 | \n",
" 6 | \n",
" 1 | \n",
" 2 | \n",
" yes | \n",
" no | \n",
" no | \n",
" no | \n",
" no | \n",
" 1.0 | \n",
" no | \n",
" unfurnished | \n",
" False | \n",
" False | \n",
" True | \n",
" False | \n",
" False | \n",
" False | \n",
"
\n",
" \n",
" | 499 | \n",
" 2660000.0 | \n",
" 3630.0 | \n",
" 3 | \n",
" 3 | \n",
" 2 | \n",
" no | \n",
" yes | \n",
" no | \n",
" no | \n",
" no | \n",
" 0.0 | \n",
" no | \n",
" unfurnished | \n",
" False | \n",
" False | \n",
" False | \n",
" True | \n",
" False | \n",
" False | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" price | \n",
" area | \n",
" bedrooms | \n",
" bathrooms | \n",
" stories | \n",
" mainroad | \n",
" guestroom | \n",
" basement | \n",
" hotwaterheating | \n",
" airconditioning | \n",
" parking | \n",
" prefarea | \n",
" furnishingstatus | \n",
" price_Outlier | \n",
" area_Outlier | \n",
" bedrooms_Outlier | \n",
" bathrooms_Outlier | \n",
" stories_Outlier | \n",
" parking_Outlier | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 9205000.0 | \n",
" 7420.0 | \n",
" 4.0 | \n",
" 2.0 | \n",
" 3.0 | \n",
" yes | \n",
" no | \n",
" no | \n",
" no | \n",
" yes | \n",
" 2.0 | \n",
" yes | \n",
" furnished | \n",
" False | \n",
" False | \n",
" False | \n",
" False | \n",
" False | \n",
" False | \n",
"
\n",
" \n",
" | 1 | \n",
" 9205000.0 | \n",
" 8960.0 | \n",
" 4.0 | \n",
" NaN | \n",
" NaN | \n",
" yes | \n",
" no | \n",
" no | \n",
" no | \n",
" yes | \n",
" 2.5 | \n",
" no | \n",
" furnished | \n",
" False | \n",
" False | \n",
" False | \n",
" True | \n",
" False | \n",
" False | \n",
"
\n",
" \n",
" | 2 | \n",
" 9205000.0 | \n",
" 9960.0 | \n",
" 3.0 | \n",
" 2.0 | \n",
" 2.0 | \n",
" yes | \n",
" no | \n",
" yes | \n",
" no | \n",
" no | \n",
" 2.0 | \n",
" yes | \n",
" semi-furnished | \n",
" False | \n",
" False | \n",
" False | \n",
" False | \n",
" False | \n",
" False | \n",
"
\n",
" \n",
" | 3 | \n",
" 9205000.0 | \n",
" 7500.0 | \n",
" 4.0 | \n",
" 2.0 | \n",
" 2.0 | \n",
" yes | \n",
" no | \n",
" yes | \n",
" no | \n",
" yes | \n",
" 2.5 | \n",
" yes | \n",
" furnished | \n",
" False | \n",
" False | \n",
" False | \n",
" False | \n",
" False | \n",
" False | \n",
"
\n",
" \n",
" | 4 | \n",
" 9205000.0 | \n",
" 7420.0 | \n",
" 4.0 | \n",
" 1.0 | \n",
" 2.0 | \n",
" yes | \n",
" yes | \n",
" yes | \n",
" no | \n",
" yes | \n",
" 2.0 | \n",
" no | \n",
" furnished | \n",
" False | \n",
" False | \n",
" False | \n",
" False | \n",
" False | \n",
" False | \n",
"
\n",
" \n",
"
\n",
"