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ML-For-Beginners/2-Regression/4-Logistic/notebook.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pumpkin Varieties and Color\n",
"\n",
"Load up required libraries and dataset. Convert the data to a dataframe containing a subset of the data: \n",
"\n",
"Let's look at the relationship between color and variety"
]
},
{
"cell_type": "code",
"execution_count": 1,
"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>City Name</th>\n",
" <th>Type</th>\n",
" <th>Package</th>\n",
" <th>Variety</th>\n",
" <th>Sub Variety</th>\n",
" <th>Grade</th>\n",
" <th>Date</th>\n",
" <th>Low Price</th>\n",
" <th>High Price</th>\n",
" <th>Mostly Low</th>\n",
" <th>...</th>\n",
" <th>Unit of Sale</th>\n",
" <th>Quality</th>\n",
" <th>Condition</th>\n",
" <th>Appearance</th>\n",
" <th>Storage</th>\n",
" <th>Crop</th>\n",
" <th>Repack</th>\n",
" <th>Trans Mode</th>\n",
" <th>Unnamed: 24</th>\n",
" <th>Unnamed: 25</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>BALTIMORE</td>\n",
" <td>NaN</td>\n",
" <td>24 inch bins</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>4/29/17</td>\n",
" <td>270.0</td>\n",
" <td>280.0</td>\n",
" <td>270.0</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>E</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>BALTIMORE</td>\n",
" <td>NaN</td>\n",
" <td>24 inch bins</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>5/6/17</td>\n",
" <td>270.0</td>\n",
" <td>280.0</td>\n",
" <td>270.0</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>E</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>BALTIMORE</td>\n",
" <td>NaN</td>\n",
" <td>24 inch bins</td>\n",
" <td>HOWDEN TYPE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>9/24/16</td>\n",
" <td>160.0</td>\n",
" <td>160.0</td>\n",
" <td>160.0</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>BALTIMORE</td>\n",
" <td>NaN</td>\n",
" <td>24 inch bins</td>\n",
" <td>HOWDEN TYPE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>9/24/16</td>\n",
" <td>160.0</td>\n",
" <td>160.0</td>\n",
" <td>160.0</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>BALTIMORE</td>\n",
" <td>NaN</td>\n",
" <td>24 inch bins</td>\n",
" <td>HOWDEN TYPE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>11/5/16</td>\n",
" <td>90.0</td>\n",
" <td>100.0</td>\n",
" <td>90.0</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>N</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 26 columns</p>\n",
"</div>"
],
"text/plain": [
" City Name Type Package Variety Sub Variety Grade Date \\\n",
"0 BALTIMORE NaN 24 inch bins NaN NaN NaN 4/29/17 \n",
"1 BALTIMORE NaN 24 inch bins NaN NaN NaN 5/6/17 \n",
"2 BALTIMORE NaN 24 inch bins HOWDEN TYPE NaN NaN 9/24/16 \n",
"3 BALTIMORE NaN 24 inch bins HOWDEN TYPE NaN NaN 9/24/16 \n",
"4 BALTIMORE NaN 24 inch bins HOWDEN TYPE NaN NaN 11/5/16 \n",
"\n",
" Low Price High Price Mostly Low ... Unit of Sale Quality Condition \\\n",
"0 270.0 280.0 270.0 ... NaN NaN NaN \n",
"1 270.0 280.0 270.0 ... NaN NaN NaN \n",
"2 160.0 160.0 160.0 ... NaN NaN NaN \n",
"3 160.0 160.0 160.0 ... NaN NaN NaN \n",
"4 90.0 100.0 90.0 ... NaN NaN NaN \n",
"\n",
" Appearance Storage Crop Repack Trans Mode Unnamed: 24 Unnamed: 25 \n",
"0 NaN NaN NaN E NaN NaN NaN \n",
"1 NaN NaN NaN E NaN NaN NaN \n",
"2 NaN NaN NaN N NaN NaN NaN \n",
"3 NaN NaN NaN N NaN NaN NaN \n",
"4 NaN NaN NaN N NaN NaN NaN \n",
"\n",
"[5 rows x 26 columns]"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"full_pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n",
"\n",
"full_pumpkins.head()\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"columns_to_select = [\"City Name\", \"Package\", \"Variety\",\"Origin\",\"Item Size\", \"Color\"]\n",
"pumpkins = full_pumpkins.loc[:,columns_to_select]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"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>City Name</th>\n",
" <th>Package</th>\n",
" <th>Variety</th>\n",
" <th>Origin</th>\n",
" <th>Item Size</th>\n",
" <th>Color</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>BALTIMORE</td>\n",
" <td>24 inch bins</td>\n",
" <td>HOWDEN TYPE</td>\n",
" <td>DELAWARE</td>\n",
" <td>med</td>\n",
" <td>ORANGE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>BALTIMORE</td>\n",
" <td>24 inch bins</td>\n",
" <td>HOWDEN TYPE</td>\n",
" <td>VIRGINIA</td>\n",
" <td>med</td>\n",
" <td>ORANGE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>BALTIMORE</td>\n",
" <td>24 inch bins</td>\n",
" <td>HOWDEN TYPE</td>\n",
" <td>MARYLAND</td>\n",
" <td>lge</td>\n",
" <td>ORANGE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>BALTIMORE</td>\n",
" <td>24 inch bins</td>\n",
" <td>HOWDEN TYPE</td>\n",
" <td>MARYLAND</td>\n",
" <td>lge</td>\n",
" <td>ORANGE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>BALTIMORE</td>\n",
" <td>36 inch bins</td>\n",
" <td>HOWDEN TYPE</td>\n",
" <td>MARYLAND</td>\n",
" <td>med</td>\n",
" <td>ORANGE</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" City Name Package Variety Origin Item Size Color\n",
"2 BALTIMORE 24 inch bins HOWDEN TYPE DELAWARE med ORANGE\n",
"3 BALTIMORE 24 inch bins HOWDEN TYPE VIRGINIA med ORANGE\n",
"4 BALTIMORE 24 inch bins HOWDEN TYPE MARYLAND lge ORANGE\n",
"5 BALTIMORE 24 inch bins HOWDEN TYPE MARYLAND lge ORANGE\n",
"6 BALTIMORE 36 inch bins HOWDEN TYPE MARYLAND med ORANGE"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pumpkins.dropna(inplace=True)\n",
"pumpkins.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x19c92ff9850>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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o0qUL7OzssG7dOpw/fx6bN29G+/btMW7cuCeu6ezsjJiYGFnbkSNHkJqaCnNzc4NjoqOjMXnyZERHR+udS05ORsuWLbF37158/vnnSExMRGxsLLRaLUaPHm1wvvz8fAwbNgyrVq1CXFwc/Pz89PocO3ZMem42btwIADh//rzUFhkZiYiICISEhCAlJUUaN3/+fPz1119YtmyZ1Fb4PF+8eBETJkzArFmz9MLZo3MXHo6OjoafRCIiemapqroAJVKr1dBoNAAAjUaDqVOnomPHjvj333/h4OCg1//atWsYO3Ysxo4diy+//FJ2rmnTpvjwww+fuOagQYOwYMECXL9+Hc7OzgAeBpZBgwZh1apVev337duHBw8eICwsDKtWrcLvv/+O9u3bS+dHjRoFQRAQHx8vC0Q+Pj4YOnSo3nzZ2dkICgrC8ePHceDAAXh5eRms89Hrt7W1BQA4OjrCxsZGag8JCcGWLVswYsQI7NixA+fOncOMGTOwbt062NvbS/0efZ7ff/99bN68Gdu2bUNoaKjU5/G5iYjo+cQdmnLKzMzEDz/8AA8PD9jZ2Rnss3HjRuTm5mLy5MkGzwuC8MR1ateujYCAAHz33XcAgP/++w/r1q0zGD4AICoqCkFBQahRowaCgoIQFRUlnbtz5w5iY2MxevRog7s7jweEzMxMdO/eHWfPnsWhQ4eKDDMlJQgCYmJicODAAaxYsQLBwcEYOHAgXn/99WLHmZmZIScnp8zrZmdnIz09XXYQEdGzgTs0ZbBjxw5YWFgAAO7fvw8nJyfs2LEDRkaG8+GFCxdgZWUl7TYAD0POkCFDpMeHDx9GkyZNil136NChmDBhAj766CNs2LABDRo0QPPmzfX6paenY8OGDTh8+DAAYPDgwejYsSMWLlwICwsLXLp0CaIowtvbu0TXO3v2bFhaWiIpKcngDlRZuLq64quvvsLw4cNRr149/Pbbb0X2FUURe/bswa+//oqQkBDZuXr16unNe+bMGYPzhIeH45NPPtFrt+ywCFaW+sHOtoF/SS6FiIiqAe7QlIFWq0VCQgISEhIQHx+PgIAAdOvWDVevXi1yzOO7MAEBAUhISMDPP/+M+/fvIz8//4nrdu/eHZmZmdi/fz+io6OL3J1Zs2YNGjRogGbNmgEAmjdvDldXV6xbtw7Aw4BQGq+88gru37+PuXPnlmrck7zzzjtwcnJCSEgIrKys9M4XBkdTU1N069YNAwYMwKxZs2R9Dhw4IP1dJCQk4JdffilyvdDQUKSlpUnH9evXK/R6iIio6nCHpgzMzc3h4eEhPf72229hbW2NFStW4NNPP9Xr37BhQ6SlpSE1NVXapbGwsICHhwdUqpL/FahUKrz11luYOXMmjh49is2bNxvsFxUVhTNnzsjmLigoQHR0NIYNG4aGDRtCEAScO3euROt26dIFISEheOONN1BQUICFCxeWuOYnUalURT4HWq0WkZGRMDExQZ06dQz2c3d3L/E9NGq1Gmq1ujzlEhFRNcUdmgogCAKMjIzw4MEDg+f79u2LGjVqYN68eeVea+jQodi3bx/eeOMN1KpVS+98YmIijh8/Dp1OJ9u50Ol0OHz4MM6dOwdbW1sEBARgyZIluH//vt4c9+7d02t75ZVXsH37dqxYsaJENzFXhMLg6OLiUqrgR0REzx/+liiD7Oxs6bNO7t69i8WLFyMzMxM9evQw2N/FxQXz58/HmDFjcOfOHQQHB8Pd3R137tzBDz/8AAAwNjYu0dqNGjXC//73P9SsWdPg+aioKLRu3RqdOnXSO9eqVStERUXh888/x5IlS/Dyyy+jdevWCAsLQ9OmTZGXl4ddu3YhMjISSUlJeuP9/f2xY8cO9OjRAwUFBVi8eHGJaq5Mt27dQlZWlqzNzs4ONWrUqKKKiIioKnCHpgxiY2Ph5OQEJycntGnTBseOHcP69esNfi5LoZCQEPz222/4999/0bdvXzRs2BCvvvoqrly5gtjY2CfeEPwoOzs7mJmZ6bXn5OTghx9+QJ8+fQyO69OnD1atWoXc3FzUr18fJ06cgFarxYQJE9C4cWN07doVe/bsQWRkZJFrd+7cGT///DNWrlyJ0aNHl/p+nIrm5eUl/V0UHn/88UeV1kRERE+fIFb1bySiKpKeng5ra2tcObmF73IiIlI47tAQERGR4jHQEBERkeIx0BAREZHiMdAQERGR4jHQEBERkeIx0BAREZHiMdAQERGR4jHQEBERkeIx0BAREZHiMdAQERGR4jHQEBERkeLx27bpuWdbXwsrK6uqLoOIiMqBOzRERESkeAw0REREpHgMNERERKR4DDRERESkeAw0REREpHgMNERERKR4DDRERESkeAw0REREpHgMNERERKR4/KRgeu5dXdUElmaVl+3dhl2ptLmJiOgh7tAQERGR4jHQEBERkeIx0BAREZHiMdAQERGR4jHQEBERkeIx0BAREZHiMdAQERGR4jHQEBERkeIx0BAREZHiMdAQERGR4jHQEBERkeIx0BAREZHiMdAQERGR4jHQEBERkeIx0ChMcHAwBEHAyJEj9c6NHj0agiAgODhY6tuzZ0+9sREREbJxW7ZsgSAI0mOdTgdBEHDv3j29Nby9vaFWq5GamirrW9yh0+kwa9YsNG/eXG++5ORkCIKAhIQEg/M5ODjg1VdfRWJiosHn4fEjMDCwBM8iERE9axhoFMjZ2Rlr167FgwcPpLasrCysXr0aLi4uxY41NTXFvHnzcPfu3VKve/DgQTx48AB9+/bFd999BwBo3749UlJSpKN///4IDAyUtbVv377Ua50/fx4pKSn49ddfkZ2dje7duyMnJ0fW5/F1UlJSsGbNmlKvRUREysdAo0AtWrSAs7MzNm3aJLVt2rQJLi4uePHFF4sd6+/vD41Gg/Dw8FKvGxUVhTfffBNvvfUWoqOjAQAmJibQaDTSYWZmBrVaLWszMTEp9VqOjo7QaDRo0aIFxo4di+vXr+PcuXOyPo+vo9FoUKtWrVKvRUREysdAo1BDhw5FTEyM9Dg6OhrvvPPOE8cZGxtj7ty5+Prrr/H333+XeL2MjAysX78egwcPRteuXZGWloYDBw6UqfbSSEtLw9q1awGgTMHoUdnZ2UhPT5cdRET0bFBVdQFUNoMHD0ZoaCiuXr0KADh06BDWrl0LnU73xLG9evVC8+bNMXPmTERFRZVovbVr16Jhw4bw8fEBAAwcOBBRUVHo2LFjma+hOPXq1QMA3L9/HwDw+uuvw9vbW9Znx44dsLCwkLVNmzYN06ZNMzhneHg4PvnkE712yw6LYGVpXhFlG3Tn8u4n9rFt4F9p6xMRPQ8YaBTKwcEB3bt3x8qVKyGKIrp37w57e/sSj583bx46d+6MiRMnlqh/dHQ0Bg8eLD0ePHgwfH198fXXX8PS0rLU9T/JgQMHULNmTRw5cgRz587FsmXL9PpotVpERkbK2mxtbYucMzQ0FOPHj5cep6enw9nZueKKJiKiKsNAo2BDhw7FBx98AABYsmRJqcZ26tQJAQEBCA0Nld4VVZSzZ8/iyJEjiI+Px5QpU6T2/Px8rF27FiNGjHjielZWVkhLS9NrL3wnlbW1tazd3d0dNjY28PLywq1btzBgwADs379f1sfc3BweHh5PXLuQWq2GWq0ucX8iIlIO3kOjYIGBgcjJyUFubi4CAgJKPT4iIgLbt2/H4cOHi+0XFRWFTp064c8//0RCQoJ0jB8/vsQvWXl5eeHvv//GzZs3Ze0nTpyAqalpse/OGj16NE6fPo3NmzeXaC0iInr+cIdGwYyNjZGUlCT9ubSaNGmCQYMGYdGiRUX2yc3Nxffff4+wsDA0btxYdm748OH48ssvcebMGenemqIEBATAy8sLQUFB+PTTT6HRaHDixAl8/PHHGDNmTLH116xZEyNGjMDMmTPRs2dP6TNzsrOzpc/DKaRSqUr10hsRET0buEOjcFZWVrCysirz+LCwMBQUFBR5ftu2bbh9+zZ69eqld65Ro0Zo1KhRiXZpVCoVfvvtN7i4uCAoKAiNGzfGzJkzMWbMGMyePfuJ4z/44AMkJSVh/fr1UltsbCycnJxkR4cOHZ44FxERPXsEURTFqi6CqCqkp6fD2toaV05uqdR3OZUE3+VERFQ+3KEhIiIixWOgISIiIsVjoCEiIiLFY6AhIiIixWOgISIiIsVjoCEiIiLFY6AhIiIixWOgISIiIsVjoCEiIiLFY6AhIiIixWOgISIiIsVjoCEiIiLFU1V1AURVzba+tlzfWE5ERFWPOzRERESkeAw0REREpHgMNERERKR4DDRERESkeAw0REREpHgMNERERKR4pQ40cXFxlVEHERERUZmVOtAEBgaiQYMG+PTTT3H9+vXKqImIiIioVEodaG7cuIEPPvgAGzZsQP369REQEICffvoJOTk5lVEfUaW7uqoJkqPcq7oMIiIqh1IHGnt7e4wbNw4JCQk4evQoPD09MWrUKNSpUwcffvgh/vzzz8qok4iIiKhI5bopuEWLFggNDcUHH3yAzMxMREdHo2XLlujYsSPOnDlTUTUSERERFatMgSY3NxcbNmzAq6++CldXV/z6669YvHgxbt68iUuXLsHV1RX9+vWr6FqJiIiIDCr1l1OGhIRgzZo1EEURb731Fj777DM0btxYOm9ubo4vvvgCderUqdBCiYiIiIpS6kBz9uxZfP311+jduzfUarXBPvb29nx7NxERET01pX7JaebMmejXr59emMnLy8P+/fsBACqVCr6+vhVTIREREdETlDrQaLVa3LlzR689LS0NWq22QooiIiIiKo1SBxpRFCEIgl777du3YW5uXiFFEREREZVGie+h6d27NwBAEAQEBwfLXnLKz8/HqVOn0L59+4qvkIiIiOgJShxorK2tATzcobG0tISZmZl0zsTEBG3btsWIESMqvkIiIiKiJyhxoImJiQEAuLm5YeLEiXx5iYiIiKqNMr3LSa1WY/fu3fjmm2+QkZEBAPjnn3+QmZlZ4QUSERERPUmpP4fm6tWrCAwMxLVr15CdnY2uXbvC0tIS8+bNQ3Z2NpYtW1YZdRIREREVqdQ7NGPGjMFLL72Eu3fvyu6j6dWrF/bs2VOhxREREVHpzZo1C82bN6/qMp6qUgeaAwcO4OOPP4aJiYms3c3NDTdu3KiwwoiIiJ5XqampCAkJQf369aFWq+Hs7IwePXpw46AYpQ40BQUFyM/P12v/+++/YWlpWSFFPY+e9MPr5uaGr776Surv5uYGQRBw5MgR2Txjx46Fn5+f9HjWrFkQBAGCIEClUsHe3h6dOnXCV199hezsbNlYPz8/qe+jx8iRI6U+j7ZbWVmhVatW2Lp1q2yelStXGpzH1NRU6hMcHIyePXsW+Xw8fr1FWbNmDYyNjTF69Ogn9iUiUoLk5GS0bNkSe/fuxeeff47ExETExsZCq9U+1X/rcnNzn9paFaHUgeaVV16R/aIRBAGZmZmYOXMmXn311Yqs7blR1h9eU1NTTJky5Ynz+/j4ICUlBdeuXUNcXBz69euH8PBwtG/fXrqpu9CIESOQkpIiOz777DNZn5iYGKSkpOD48eN4+eWX0bdvXyQmJsr6WFlZ6c1z9erVUjwrJRMVFYXJkydjzZo1yMrKqvD5iYietlGjRkEQBMTHx6NPnz7w9PSEj48Pxo8fL/1P7LVr1/DGG2/AwsICVlZW6N+/P27evFnknAUFBQgLC0O9evWgVqvRvHlzxMbGSueTk5MhCALWrVsHX19fmJqa4scff6z0a61IpQ408+fPx6FDh/DCCy8gKysLb775pvRy07x58yqjxmdeSX54DXn33Xdx5MgR/PLLL8XOr1KpoNFoUKdOHTRp0gQhISHYt28fTp8+rfd3VrNmTWg0GtlhZWUl62NjYwONRgNPT0/Mnj0beXl5el9GKgiC3jy1a9cu5TNTvCtXruD333/H1KlT4enpiU2bNhXbPzs7G+np6bKDiKg6uXPnDmJjYzF69GiDH49iY2ODgoICvPHGG7hz5w727duHXbt24a+//sKAAQOKnHfhwoWYP38+vvjiC5w6dQoBAQF4/fXXcfHiRVm/qVOnYsyYMUhKSkJAQECFX19lKnWgqVevHv78809MmzYN48aNw4svvoiIiAicPHkSjo6OlVHjM60kP7xFcXd3x8iRIxEaGoqCgoJSrevt7Y1u3bo9MQQUJy8vD1FRUQCgd0/V0xATE4Pu3bvD2toagwcPlmopSnh4OKytraXD2dkZAGDZYRGs/FY8jZKJiIp16dIliKIIb2/vIvvs2bMHiYmJWL16NVq2bIk2bdpg1apV2LdvH44dO2ZwzBdffIEpU6Zg4MCB8PLywrx589C8eXO9l/bHjh2L3r17w93dHU5OThV5aZWu1IEGePh//IMHD8Znn32GpUuXYvjw4bJ3PFHJleSHtzgff/wxrly5UqatQW9vbyQnJ8vali5dCgsLC9nx+NxBQUGwsLCAWq3GuHHj4Obmhv79+8v6pKWl6c3TrVu3UtdYlIKCAqxcuRKDBw8GAAwcOBAHDx7ElStXihwTGhqKtLQ06bh+/XqF1UNEVBFEUXxin6SkJDg7O0v/UwYAL7zwAmxsbJCUlKTXPz09Hf/88w9efvllWfvLL7+s1/+ll14qY+VVr0SfQ7Nt2zZ069YNNWrUwLZt24rt+/rrr1dIYc+LkvzwFsfBwQETJ07EjBkzit1uLGrtx79odNCgQfjoo49kbY+/VLRgwQL4+/vjr7/+wrhx47Bo0SLY2trK+lhaWuLEiROytooMvbt27cL9+/el+7bs7e3RtWtXREdHY/bs2QbHqNVq2XeQERFVNw0bNoQgCDh37lyVrK/kbwEoUaDp2bMnUlNT4ejoWOw7UwRBMPgOKCpaRfzwjh8/HkuXLsXSpUtLNS4pKQnu7u6yNmtra3h4eBQ7TqPRwMPDAx4eHoiJicGrr76Ks2fPyl5yNDIyeuI85REVFYU7d+7IQlJBQQFOnTqFTz75BEZGZdp8JCKqUra2tggICMCSJUvw4Ycf6gWMe/fuoVGjRrh+/TquX78u7dKcPXsW9+7dwwsvvKA3p5WVFerUqYNDhw7B19dXaj906BBat25duRf0FJXoX/2CggLpl1VBQUGRB8NM6T36w3v//n298/fu3XviHBYWFpg+fTrmzJmj966lopw7dw6xsbHo06dPaUuWad26NVq2bIk5c+aUa57SuH37NrZu3Yq1a9ciISFBOk6ePIm7d+/it99+e2q1EBFVtCVLliA/Px+tW7fGxo0bcfHiRSQlJWHRokVo164d/P390aRJEwwaNAgnTpxAfHw83n77bfj6+hb5ktGkSZMwb948rFu3DufPn8fUqVORkJCAMWPGPOWrqzyl+uqD3NxcBAYGYtmyZWjYsGFl1fTcWbJkCV5++WW0bt0aYWFhaNq0KfLy8rBr1y5ERkYafE30ce+++y4WLFiA1atXo02bNrJzeXl5SE1NRUFBAW7fvg2dTodPP/0UzZs3x6RJk2R9//vvP6Smpsra1Go1atWqVeTaY8eORa9evTB58mTUrVsXwMOXsx6fBwAcHR2l3ZO0tDQkJCTIztvZ2Un/x3Hjxg29866urvj+++9hZ2eH/v37671k9uqrryIqKgqBgYFF1ktEVJ3Vr18fJ06cwJw5czBhwgSkpKTAwcEBLVu2RGRkJARBwNatWxESEoJOnTrByMgIgYGB+Prrr4uc88MPP0RaWhomTJiAW7du4YUXXsC2bduerd/lYinZ29uLFy5cKO0weoJ//vlHHD16tOjq6iqamJiIdevWFV9//XUxLi5OFEVRdHV1FRcsWCD1f/yxKIri6tWrRQCir6+v1DZz5kwRgAhANDY2Fm1tbcUOHTqICxYsELOysmTjfX19pb6PHgEBAVIfAOLmzZtl4woKCkRvb2/x/fffF0VRFGNiYgzOA0BMSUkRRVEUhwwZYvD8sGHDpOszdP77778XmzRpIo4aNcrg87hu3TrRxMRE/Pfff5/4nKelpYkAxCsnt4i3L+16Yn8iIqq+BFEs3V2p48aNg1qtRkRERPmSFFEVS09Ph7W1Na6c3AIrS3PYNvCv6pKIiKiMSv1t23l5eYiOjsbu3bvRsmVLvRuWvvzyyworjoiIiKgkSh1oTp8+jRYtWgAALly4IDv3+P0MRERERE9DqQPN4x9xT0RERFTV+GEdREREpHil3qEBgOPHj+Onn37CtWvXkJOTIztXnu8GIiIiIiqLUu/QrF27Fu3bt0dSUhI2b96M3NxcnDlzBnv37oW1tXVl1EhERERUrFIHmrlz52LBggXYvn07TExMsHDhQpw7dw79+/eHi4tLZdRIREREVKxSB5rLly+je/fuAAATExPcv38fgiBg3LhxWL58eYUXSERERPQkpb6HplatWtL3BdWtWxenT59GkyZNcO/ePfz3338VXiAREdHTlBzl/uROFcRt2JWnttazrsQ7NKdPnwYAdOrUCbt27QIA9OvXD2PGjMGIESMQFBSELl26VE6VREREJLl+/TqGDh2KOnXqwMTEBK6urhgzZgxu374t9fHz84MgCBAEAaampvD09ER4eDgMfUHA4cOHYWxsLL0C86jk5GQIggBHR0e9L0Bu3rw5Zs2aJWu7dOkShg4dChcXF6jVatStWxddunTBjz/+iLy8PKlfYW2PH2vXri3Tc1LiQNO0aVO0adMGTZo0Qb9+/QAAH330EcaPH4+bN2+iT58+iIqKKlMRREREVDJ//fUXXnrpJVy8eBFr1qzBpUuXsGzZMuzZswft2rXDnTt3pL4jRoxASkoKzp8/j9DQUMyYMQPLli3TmzMqKgohISHYv38//vnnH4PrZmRk4Isvvii2tvj4eLRo0QJJSUlYsmQJTp8+DZ1Oh+HDhyMyMhJnzpyR9Y+JiUFKSors6NmzZ+mfFAAl/i6nAwcOICYmBhs2bEBBQQH69OmD4cOHo2PHjmVamKiqFX6XU1paGqysrKq6HCKqJqr7S07dunXD6dOnceHCBZiZmUntqampaNCgAd5++21ERkbCz88PzZs3x1dffSX1admyJVxdXWUfsZKZmQknJyccP34cM2fORNOmTTFt2jTpfHJyMtzd3TFp0iRERkbi8uXLcHR0BPBwh6Znz56YNWsWRFGEj48Patasifj4eBgZ6e+ZiKIofauAIAjYvHlzmQPM40q8Q9OxY0dER0cjJSUFX3/9NZKTk+Hr6wtPT0/MmzcPqampFVIQERERGXbnzh38+uuvGDVqlCzMAIBGo8GgQYOwbt06vZeVRFHEgQMHcO7cOZiYmMjO/fTTT/D29oaXlxcGDx6M6Ohogy9LBQUFwcPDA2FhYQZrS0hIQFJSEiZOnGgwzACV+xVJpX6Xk7m5Od555x3s27cPFy5cQL9+/bBkyRK4uLjg9ddfr4waiYiICMDFixchiiIaNWpk8HyjRo1w9+5d/PvvvwCApUuXwsLCAmq1Gp06dUJBQQE+/PBD2ZioqCgMHjwYABAYGIi0tDTs27dPb25BEBAREYHly5fj8uXLeucLv9/Ry8tLart16xYsLCykY+nSpbIxQUFBsvMWFha4du1aKZ6R/1eurz7w8PDAtGnT8PHHH8PS0hI///xzeaYjIiKiEijh3SIYNGgQEhIScOjQIXTr1g0fffQR2rdvL50/f/484uPjERQUBABQqVQYMGBAkffEBgQEoEOHDpg+fXqJ1rezs0NCQgISEhJgY2Oj9+0CCxYskM4XHnXq1CnR3I8r01cfAMD+/fsRHR2NjRs3wsjICP3798ewYcPKOh0RERE9gYeHBwRBQFJSEnr16qV3PikpCbVq1YKDgwMAwNraGh4eHgAevrTk4eGBtm3bwt/fH8DD3Zm8vDxZiBBFEWq1GosXLzb4DQARERFo164dJk2aJGtv2LAhgIch6cUXXwQAGBsbS+urVPqRQ6PRSOfLq1Q7NP/88w/mzp0LT09P+Pn54dKlS1i0aBH++ecfrFixAm3btq2QooiIiEifnZ0dunbtiqVLl+LBgweyc6mpqfjxxx8xYMAAg/eqWFhYYMyYMZg4cSJEUUReXh5WrVqF+fPny3ZI/vzzT9SpUwdr1qwxWEPr1q3Ru3dvTJ06Vdb+4osvwtvbG1988QUKCgoq7qJLqMQ7NN26dcPu3bthb2+Pt99+G0OHDpW9TkZERESVb/HixWjfvj0CAgLw6aefwt3dHWfOnMGkSZNQt25dzJkzp8ix7733HmbPno2NGzdCpVLh7t27GDZsmN5OTOFHsYwcOdLgPHPmzIGPj49s10UQBMTExKBr1654+eWXERoaikaNGiE3Nxf79+/Hv//+C2NjY9k89+7d03tTkaWlJczNzUv7tABiCfXo0UPcsmWLmJeXV9IhRNVaWlqaCEBMS0ur6lKIiEolOTlZHDJkiFi7dm2xRo0aorOzsxgSEiL+73//k/r4+vqKY8aM0Rv73nvviT4+PuJrr70mvvrqqwbnP3r0qAhA/PPPP8UrV66IAMSTJ0/K+rz77rsiAHHmzJmy9vPnz4tDhgwR69WrJ6pUKtHa2lrs1KmT+M0334i5ublSPwAGj/Dw8DI9JyX+HBqiZw0/h4aI6NlRrnc5EREREVUHZX6XE9Gz4uqqJrA008/2/NI4IiLl4A4NERERKR4DDRERESkeAw0REREpHgMNERERKR4DDRERESkeAw0REREpHgMNERERKR4/h4aIiOgRdy7vfmpr2Tbwf2prPeu4Q0NERKQQy5Ytg6WlJfLy8qS2zMxM1KhRA35+frK+Op0OgiDg8uXLcHNzw1dffaU336xZs9C8eXODj93c3CAIQpFHcHAwABR5fu3atRV89cXjDg0REZFCaLVaZGZm4vjx42jbti0A4MCBA9BoNDh69CiysrJgamoKAIiLi4OLiwsaNGhQprWOHTuG/Px8AMDvv/+OPn364Pz589J335mZmUl9Y2JiEBgYKBtvY2NTpnXLioGGiIhIIby8vODk5ASdTicFGp1OhzfeeAN79+7FkSNHpJ0anU4HrVZb5rUcHBykP9va2gIAHB0dDQYVGxsbaDSaMq9VEfiSExERkYJotVrExcVJj+Pi4uDn5wdfX1+p/cGDBzh69Gi5Ao3SMNAQEREpiFarxaFDh5CXl4eMjAycPHkSvr6+6NSpE3Q6HQDg8OHDyM7OlgWaKVOmwMLCQnbMnTu3QmoKCgrSm/vatWsVMndJ8SUnIiIiBfHz88P9+/dx7Ngx3L17F56ennBwcICvry/eeecdZGVlQafToX79+nBxcZHGTZo0SbqRt9CiRYuwf//+cte0YMEC+PvL37FVp06dcs9bGtyheQYEBwcbvMP80qVLAIDw8HAYGxvj888/1xu7cuVK2euhK1eulMYbGRnByckJAwYMwLVr1yCKIvz9/REQEKA3z9KlS2FjYwN/f/9i74p3c3OTxpSmrpJe8+M3pRERPWs8PDxQr149xMXFIS4uDr6+vgAeBghnZ2f8/vvviIuLQ+fOnWXj7O3t4eHhITsK740pL41Goze3SvV090wYaJ4RgYGBSElJkR3u7u4AgOjoaEyePBnR0dElmsvKygopKSm4ceMGNm7ciPPnz6Nfv34QBAExMTE4evQovvnmG6n/lStXMHnyZHz99dfYuHGjrAbg4d3vhY+PHTsmjSttXSW55jVr1pRpLiIiJdFqtdDpdNDpdLK3a3fq1Ak7d+5EfHz8c3X/DMBA88xQq9XQaDSyw9jYGPv27cODBw8QFhaG9PR0/P7770+cSxAEaDQaODk5oX379hg2bBji4+ORnp4OZ2dnLFy4EBMnTsSVK1cgiiKGDRuGV155BW+99Rasra1lNQD/f/e7RqOR7povS10lueZatWqVeh4iIqXRarU4ePAgEhISpB0aAPD19cU333yDnJycpxpo7t27h9TUVNlx//79p7Y+wHtonnlRUVEICgpCjRo1EBQUhKioKLRv377E42/duoXNmzfD2NgYxsbGAIAhQ4Zg8+bNGDp0KHr37o3Tp0/jzJkzT7WussjOzkZ2drb0OD09vVLXIyJlUsKn92q1Wjx48ADe3t6oXbu21O7r64uMjAzp7d1PyzvvvKPXFh4ejqlTpz61GiCS4g0ZMkQ0NjYWzc3NpaNv375iWlqaaGZmJiYkJIiiKIonT54ULSwsxIyMDGlsTEyMaG1tLXsMQDQ3Nxdr1qwpAhABiB9++KFszZs3b4r29vaikZGRuHnz5iJrA6B3vix1leSazc3NxTlz5hQ5ZubMmdL1PHpcOblFvH1pV5HjiIio+uMOzTNCq9UiMjJSemxubo41a9agQYMGaNasGQCgefPmcHV1xbp16zBs2LAi57K0tMSJEyeQm5uLnTt34scff8ScOXNkfRwdHfHee+9hy5Yt6NmzZ6lqLWtdj3v8mgEUe4NbaGgoxo8fLz0ufAmNiIiUj4HmGWFubg4PDw9ZW1RUFM6cOSO707ygoADR0dHFBgcjIyNprkaNGuHy5ct4//338f3338v6qVSqMt3FXta6HmfomoujVquhVqtLVSsRESkDA80zKjExEcePH4dOp5PtWty5cwd+fn44d+4cvL29SzTX1KlT0aBBA4wbNw4tWrSoNnUREREVYqB5RkVFRaF169bo1KmT3rlWrVohKirK4Oe/GOLs7IxevXphxowZ2LFjx1OrKz8/HwkJCbI+arUajRo1AvDwJt/U1FTZeZVKBXt7+3LVSEREysO3bT+DcnJy8MMPP6BPnz4Gz/fp0werVq1Cbm5uieccN24cfv75Z8THxz+1ujIzM/Hiiy/Kjh49ekj9Y2Nj4eTkJDs6dOhQ5vqIiEi5BFEUxaougqgqpKenw9raGldOboGVpbki3qpJRESGcYeGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUT1XVBRBVNdv6WlhZWVV1GUREVA7coSEiIiLFY6AhIiIixWOgISIiIsVjoCEiIiLFY6AhIiIixWOgISIiIsVjoCEiIiLFY6AhIiIixWOgoefe1VVNkBzlXtVlEBFROTDQEBERkeIx0BAREZHiMdAQERGR4jHQEBERkeIx0BAREZHiMdAQERGR4jHQEBERkeIx0BAREZHiMdAQERGR4jHQEBERkeIx0BAREZHiMdAQERGR4jHQEBERkeIx0BAREZHiMdAQERGR4jHQKFRwcDAEQZAOOzs7BAYG4tSpU7J+giBgy5YteuOTk5MhCAISEhL0zvn5+WHs2LHSYzc3N9lahUdERESR8xZ3zJ49G+bm5rh06ZJs7D///INatWph8eLFeuuam5ujRYsWWL9+vdR/1qxZBuf39vYuxTNJRETPAgYaBQsMDERKSgpSUlKwZ88eqFQqvPbaa5WyVlhYmLRW4RESEqLXz9nZWdZnwoQJ8PHxkbVNnDgRAQEBCA4ORkFBgTR2xIgRaNmyJUaPHq237smTJ9GqVSsMGDAAv//+u3T+8blTUlJw8ODBSnkOiIio+lJVdQFUdmq1GhqNBgCg0WgwdepUdOzYEf/++y8cHBwqdC1LS0tpreIYGxvL+llYWEClUumN/eabb+Dj44Mvv/wSEydOxMqVK3Ho0CEkJiZCEAS9dTUaDZYsWYIffvgB27dvR/v27QHA4NxFyc7ORnZ2tvQ4PT29ROOIiKj6Y6B5RmRmZuKHH36Ah4cH7OzsqrqcJ3JwcMDy5csRFBSEZs2aYdy4cVi4cCGcnZ2LHKNSqVCjRg3k5OSUac3w8HB88skneu2ubyfCysqqTHMSEVH1wJecFGzHjh2wsLCAhYUFLC0tsW3bNqxbtw5GRhX/1zplyhRprcLjwIED5ZqzZ8+e6N+/PwIDA+Hr64shQ4YU2TcnJwfh4eFIS0tD586dpfbExES9ukaOHGlwjtDQUKSlpUnH9evXy1U/ERFVH9yhUTCtVovIyEgAwN27d7F06VJ069YN8fHxcHV1rdC1Jk2ahODgYFlb3bp1yz3v9OnTsWrVKnz88ccGz0+ZMgUff/wxsrKyYGFhgYiICHTv3l067+XlhW3btsnGFLXbolaroVary10zERFVPww0CmZubg4PDw/p8bfffgtra2usWLECn376abFjC3/pp6Wl6Z27d+8erK2tZW329vaytSqKSqWS/fdxhUHKwsICtWvXlt1fAwAmJiaVUhcRESkLX3J6hgiCACMjIzx48OCJfW1tbWFvb48//vhD1p6eno5Lly7B09OzssoslcIgpdFo9MIMERFRIe7QKFh2djZSU1MBPHzJafHixcjMzESPHj1k/a5cuaL3eTMNGzbE+PHjMXfuXNSuXRtt27bF7du3MXv2bDg4OKB3796y/hkZGdJahWrWrFnlN9Pm5eXp1SUIAmrXrl1FFRERUVVgoFGw2NhYODk5AXj49mZvb2+sX78efn5+sn7jx4/XG3vgwAFMnjwZFhYWmDdvHi5fvgxbW1u8/PLLiIuLg5mZmaz/jBkzMGPGDFnbe++9h2XLllXsRZXSmTNnpOegkFqtRlZWVhVVREREVUEQRVGs6iKIqkJ6ejqsra2RlpZW5TtNRERUPryHhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+Bhp57V1c1QXKUe1WXQURE5cBAQ0RERIrHQENERESKx0BDREREisdAQ0RERIrHQENERESKx0BDREREisdAQ0RERIrHQENERESKx0BDREREisdAQ0RERIrHQENERESKx0BDREREisdAQ0RERIrHQENERESKx0BTCsHBwejZs6esbcOGDTA1NcX8+fOlPoIgICIiQtZvy5YtEARBerxy5UrY2NgYXEcQBGzZskXWtnHjRvj5+cHa2hoWFhZo2rQpwsLCcOfOnSLnS0pKgrOzM/r164ecnJxi13y09sePwMDAIsfMmjULzZs312tPTk6GIAhISEiQPTZ0HDlyRDb2wYMHsLW1hb29PbKzs/XmdnNzk8aamZnBzc0N/fv3x969e4usk4iInm0MNOXw7bffYtCgQYiMjMSECROkdlNTU8ybNw93796tkHU++ugjDBgwAK1atcLOnTtx+vRpzJ8/H3/++Se+//57g2OOHTuGjh07IjAwEOvWrYOJiUmJ1goMDERKSorsWLNmTYVcBwDs3r1bb/6WLVvK+mzcuBE+Pj7w9vbWC3aFwsLCkJKSgvPnz2PVqlWwsbGBv78/5syZU2G1EhGRcqiqugCl+uyzzzBz5kysXbsWvXr1kp3z9/fHpUuXEB4ejs8++6xc68THx2Pu3Ln46quvMGbMGKndzc0NXbt2xb179/TG7N27F2+88QZGjRqFefPmlWo9tVoNjUZTrpqLY2dn98T5o6KiMHjwYIiiiKioKAwYMECvj6WlpTSPi4sLOnXqBCcnJ8yYMQN9+/aFl5dXpdRPRETVE3doymDKlCmYPXs2duzYoRdmAMDY2Bhz587F119/jb///rtca/3444+wsLDAqFGjDJ5//CWkzZs3o3v37vj4449LHWaqg8uXL+Pw4cPo378/+vfvjwMHDuDq1aslGjtmzBiIooitW7caPJ+dnY309HTZQUREzwYGmlLauXMnPvvsM2zduhVdunQpsl+vXr3QvHlzzJw5s1zrXbx4EfXr10eNGjWe2DczMxP9+vXDpEmTMGXKlDKtt2PHDlhYWMiOuXPnFjsmMTFRb4yPj4/Bvu3bt9fr+6jo6Gh069YNtWrVgq2tLQICAhATE1Oi2m1tbeHo6Ijk5GSD58PDw2FtbS0dzs7OAADXtxPhNuxKidYgIqLqiS85lVLTpk3xv//9DzNnzkTr1q31fiE/at68eejcuTMmTpxY5vVEUSxxXzMzM3To0AErVqxAUFAQGjVqVOr1tFotIiMjZW22trbFjvHy8sK2bdtkbTdu3ICfn59e33Xr1hVZV35+Pr777jssXLhQahs8eDAmTpyIGTNmwMjoyflbFEXZzdePCg0Nxfjx46XH6enpUqghIiJlY6Appbp162LDhg3QarUIDAzEzp07YWlpabBvp06dEBAQgNDQUAQHB8vOWVlZ4f79+ygoKJD9oi68J8ba2hoA4OnpiYMHDyI3N/eJuzTGxsbYsmULevfuDa1Wi7i4uFKHGnNzc3h4eJRqjImJid4Ylcrwj5azs3OR8//666+4ceOG3j0z+fn52LNnD7p27VpsHbdv38a///4Ld3d3g+fVajXUanWxcxARkTLxJacycHV1xb59+5CamorAwEBkZGQU2TciIgLbt2/H4cOHZe1eXl7Iy8uT3tZc6MSJEwAeBhkAePPNN5GZmYmlS5canP/xm4LVajU2bdqEVq1aQavV4uzZs6W8uqoTFRWFgQMHIiEhQXYMHDgQUVFRTxy/cOFCGBkZ6b21noiInn3coSkjZ2dn6HQ6aLVaBAQEIDY2FlZWVnr9mjRpgkGDBmHRokWydh8fH7zyyisYOnQo5s+fj/r16+P8+fMYO3YsBgwYgLp16wIA2rRpg8mTJ2PChAm4ceMGevXqhTp16uDSpUtYtmwZOnToIHv3E/Aw1GzcuBH9+vWDVqvF3r17pXta8vPz9UKUWq2WdnKys7ORmpoqO69SqWBvb1+u56vQ7du39ea3sbFBRkYGtm/fjm3btqFx48ay82+//TZ69eqFO3fuSC9/ZWRkIDU1Fbm5ubhy5Qp++OEHfPvttwgPDy/1DhMRESkfA0051KtXTxZqfv31V4P9wsLCsG7dOr32devWYebMmXjvvffwzz//oF69eujVqxemT58u6zdv3jy0bNkSS5YswbJly1BQUIAGDRqgb9++GDJkiME1TUxMsGHDBvTv318KNcDDG4dffPFFWd8GDRrg0qVLAIDY2Fg4OTnJznt5eeHcuXMle1KewN/fX69tzZo1uHHjBszNzQ3eaN2lSxeYmZnhhx9+wIcffggAmDFjBmbMmAETExNoNBq0bdsWe/bsgVarrZA6iYhIWQSxNHedEj1D0tPTYW1tjbS0NIO7a0REpBy8h4aIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUr0oDTXBwMHr27KnXrtPpIAgC7t27J7Xl5+djwYIFaNKkCUxNTVGrVi1069YNhw4dkvqcO3cOgiDgyJEjsvnatm0LU1NTZGVlSW1ZWVkwNTVFVFSUVIsgCBAEATVq1EDt2rXRtWtXREdHo6CgQDafm5ub1PfRIyIiAgCQnJwMQRDg6OiIjIwM2djmzZtj1qxZBp+P8tZfkufy0cePXrOhw83NDQDg5+dn8PzIkSMNXkdxcwqCAK1Wixo1auDgwYOycffv30f9+vUxceJEvXVNTU3xwgsvYOnSpVL/lStXGpzf1NTUYF1ERPTsUsQOjSiKGDhwIMLCwjBmzBgkJSVBp9PB2dkZfn5+2LJlCwDA29sbGo0GOp1OGpuRkYETJ07AwcFBFhQOHz6M7OxsdO7cWWoLDAxESkoKkpOTsXPnTmi1WowZMwavvfYa8vLyZDWFhYUhJSVFdoSEhMj6ZGRk4IsvvijxdZa3/tJauHChrH4AiImJkR4fO3ZM6jtixAi96/3ss88Mzvton6+++gpWVlaytu3btyMkJATBwcG4f/++NG7y5MkwMzPDp59+qrfu2bNn0b9/f4wePRpr1qyRzj8+d0pKCq5evVrm54SIiJRJEYHmp59+woYNG7Bq1SoMHz4c7u7uaNasGZYvX47XX38dw4cPl34xarVaWSA4ePAgPD090aNHD1m7TqeDq6sr3N3dpTa1Wg2NRoO6deuiRYsWmDZtGrZu3YqdO3di5cqVsposLS2h0Whkh7m5uaxPSEgIvvzyS9y6davE11qe+kvL2tpaVj8A2NjYSI8dHBykvjVr1tS7XisrK4PzPtrH2toagiDI2iwsLDB37lyYmJhgypQpAIC4uDh8++23WLVqlWyHpXDd+vXrY9asWWjYsCG2bdsmnX98bo1Gg9q1axusKzs7G+np6bKDiIieDYoINKtXr5Z+qT9uwoQJuH37Nnbt2gXgYSA4ePCgtKMSFxcHPz8/+Pr6Ii4uThoXFxcHrVb7xLU7d+6MZs2aYdOmTaWuOygoCB4eHggLCyvxmIquv7oyNTXFqlWrsHz5cmzduhVDhw7FtGnT0LJly2LHmZmZIScnp0xrhoeHw9raWjqcnZ0BAHf+isOdy7sr9CAioqerygPNjh07YGFhITu6desm63PhwgU0atTI4PjC9gsXLgB4GAju378vvVyi0+ng6+uLTp064ejRo8jKysKDBw8QHx9f4kDg7e2N5ORkWduUKVP06j5w4ICsT+F9NcuXL8fly5dLtFZ56i/Jc1lWS5cu1Zv7xx9/LNecL730EkJDQ9G7d2/Y2dnho48+KrJvfn4+fvjhB5w6dUr2MltaWlqJrzk0NBRpaWnScf369XLVT0RE1YeqqgvQarWIjIyUtR09ehSDBw+WtYmiWKL5PDw8UK9ePeh0Ovj4+ODkyZPw9fWFo6MjXFxccPjwYYiiiOzs7BIHGlEUIQiCrG3SpEkIDg6WtdWtW1dvbEBAADp06IDp06dj9erVlVp/SZ/Lshg0aJBe4CjqpZ3SmD59OsLCwjB16lSoVPo/jkuXLsW3336LnJwcGBsbY9y4cXj//fel85aWljhx4oRsjJmZmcG11Go11Gp1uWsmIqLqp8oDjbm5OTw8PGRtf//9t+yxp6cnkpKSDI4vbPf09JTa/Pz8EBcXh6ZNm6Jhw4ZwdHQEAOllG1EU4eHhIb3k8CRJSUl696rY29vr1V2UiIgItGvXDpMmTSpR/7LWX5Lnsqysra1LfL2lURhiDIUZ4P+DlJmZGZycnGBkJN9UNDIyqpS6iIhIWar8JaeSGDhwIC5evIjt27frnZs/fz7s7OzQtWtXqU2r1eL333/Hrl274OfnJ7V36tQJOp0OOp2uxLsze/fuRWJiIvr06VPm+lu3bo3evXtj6tSpJepfkfUrXWGQqlu3rl6YISIiKlTlOzQlMXDgQKxfvx5DhgzB559/ji5duiA9PR1LlizBtm3bsH79etk7jArvQ4mOjsaKFSukdl9fXwwfPhwAMGrUKL11srOzkZqaivz8fNy8eROxsbEIDw/Ha6+9hrffflvWNyMjA6mpqbK2mjVrFvnOnzlz5sDHx6fInYhHlbX+yvTff//pXa9arUatWrWeah2PE0VRry4AcHR0ZAAiInqOKOJffEEQ8NNPP2HatGlYsGABvLy80LFjR1y9ehU6nU7vA+Xc3d3h6uqKjIwM+Pr6Su0uLi6oU6cOcnJyZDsfhWJjY+Hk5AQ3NzcEBgYiLi4OixYtwtatW2FsbCzrO2PGDDg5OcmOyZMnF3kNnp6eGDp0qOzD8YpS1vor04oVK/SuNygo6KnWYEh6erpeXU5OTqV6qzwRESmfIJb0bluiZ0x6ejqsra1x5eQWWFmaP3lAKdg28K/Q+YiIqHiK2KEhIiIiKg4DDRERESkeAw0REREpHgMNERERKR4DDRERESkeAw0REREpHgMNERERKR4DDRERESkeAw0REREpHgMNERERKR4DDRERESmeIr5tm6gy2dbXFvkt6UREpAzcoSEiIiLFY6AhIiIixWOgISIiIsXjPTT03BJFEQCQnp5exZUQ0dNmaWkJQRCqugyqQAw09Ny6ffs2AMDZ2bmKKyGipy0tLY1vBnjGMNDQc8vW1hYAcO3aNVhbW1dxNSWXnp4OZ2dnXL9+XTH/ICuxZkCZdSuxZuDp121paVnpa9DTxUBDzy0jo4e3kFlbWyvqH/5CVlZWiqtbiTUDyqxbiTUDyq2bqh5vCiYiIiLFY6AhIiIixWOgoeeWWq3GzJkzoVarq7qUUlFi3UqsGVBm3UqsGVBu3VR9CGLhe1eJiIiIFIo7NERERKR4DDRERESkeAw0REREpHgMNERERKR4DDT03FqyZAnc3NxgamqKNm3aID4+vspq2b9/P3r06IE6depAEARs2bJFdl4URcyYMQNOTk4wMzODv78/Ll68KOtz584dDBo0CFZWVrCxscGwYcOQmZlZaTWHh4ejVatWsLS0hKOjI3r27Inz58/L+mRlZWH06NGws7ODhYUF+vTpg5s3b8r6XLt2Dd27d0fNmjXh6OiISZMmIS8vr9LqjoyMRNOmTaUPcGvXrh127txZrWt+XEREBARBwNixY6t13bNmzYIgCLLD29u7WtdMCiYSPYfWrl0rmpiYiNHR0eKZM2fEESNGiDY2NuLNmzerpJ5ffvlF/Oijj8RNmzaJAMTNmzfLzkdERIjW1tbili1bxD///FN8/fXXRXd3d/HBgwdSn8DAQLFZs2bikSNHxAMHDogeHh5iUFBQpdUcEBAgxsTEiKdPnxYTEhLEV199VXRxcREzMzOlPiNHjhSdnZ3FPXv2iMePHxfbtm0rtm/fXjqfl5cnNm7cWPT39xdPnjwp/vLLL6K9vb0YGhpaaXVv27ZN/Pnnn8ULFy6I58+fF6dNmybWqFFDPH36dLWt+VHx8fGim5ub2LRpU3HMmDFSe3Wse+bMmaKPj4+YkpIiHf/++2+1rpmUi4GGnkutW7cWR48eLT3Oz88X69SpI4aHh1dhVQ89HmgKCgpEjUYjfv7551LbvXv3RLVaLa5Zs0YURVE8e/asCEA8duyY1Gfnzp2iIAjijRs3nkrdt27dEgGI+/btk2qsUaOGuH79eqlPUlKSCEA8fPiwKIoPg5yRkZGYmpoq9YmMjBStrKzE7Ozsp1K3KIpirVq1xG+//bba15yRkSE2bNhQ3LVrl+jr6ysFmupa98yZM8VmzZoZPFddaybl4ktO9NzJycnBH3/8AX9/f6nNyMgI/v7+OHz4cBVWZtiVK1eQmpoqq9fa2hpt2rSR6j18+DBsbGzw0ksvSX38/f1hZGSEo0ePPpU609LSAPz/l37+8ccfyM3NldXt7e0NFxcXWd1NmjRB7dq1pT4BAQFIT0/HmTNnKr3m/Px8rF27Fvfv30e7du2qfc2jR49G9+7dZfUB1fu5vnjxIurUqYP69etj0KBBuHbtWrWvmZSJX05Jz53//e9/yM/Pl/0jCQC1a9fGuXPnqqiqoqWmpgKAwXoLz6WmpsLR0VF2XqVSwdbWVupTmQoKCjB27Fi8/PLLaNy4sVSTiYkJbGxsiq3b0HUVnqssiYmJaNeuHbKysmBhYYHNmzfjhRdeQEJCQrWtee3atThx4gSOHTumd666Ptdt2rTBypUr4eXlhZSUFHzyySfo2LEjTp8+XW1rJuVioCGichs9ejROnz6NgwcPVnUpJeLl5YWEhASkpaVhw4YNGDJkCPbt21fVZRXp+vXrGDNmDHbt2gVTU9OqLqfEunXrJv25adOmaNOmDVxdXfHTTz/BzMysCiujZxFfcqLnjr29PYyNjfXeTXHz5k1oNJoqqqpohTUVV69Go8GtW7dk5/Py8nDnzp1Kv6YPPvgAO3bsQFxcHOrVqyerOycnB/fu3Su2bkPXVXiuspiYmMDDwwMtW7ZEeHg4mjVrhoULF1bbmv/44w/cunULLVq0gEqlgkqlwr59+7Bo0SKoVCrUrl27Wtb9OBsbG3h6euLSpUvV9rkm5WKgoeeOiYkJWrZsiT179khtBQUF2LNnD9q1a1eFlRnm7u4OjUYjqzc9PR1Hjx6V6m3Xrh3u3buHP/74Q+qzd+9eFBQUoE2bNpVSlyiK+OCDD7B582bs3bsX7u7usvMtW7ZEjRo1ZHWfP38e165dk9WdmJgoC2O7du2ClZUVXnjhhUqp25CCggJkZ2dX25q7dOmCxMREJCQkSMdLL72EQYMGSX+ujnU/LjMzE5cvX4aTk1O1fa5Jwar6rmSiqrB27VpRrVaLK1euFM+ePSu+++67oo2NjezdFE9TRkaGePLkSfHkyZMiAPHLL78UT548KV69elUUxYdv27axsRG3bt0qnjp1SnzjjTcMvm37xRdfFI8ePSoePHhQbNiwYaW+bfv9998Xra2tRZ1OJ3tb7n///Sf1GTlypOji4iLu3btXPH78uNiuXTuxXbt20vnCt+W+8sorYkJCghgbGys6ODhU6ttyp06dKu7bt0+8cuWKeOrUKXHq1KmiIAjib7/9Vm1rNuTRdzlV17onTJgg6nQ68cqVK+KhQ4dEf39/0d7eXrx161a1rZmUi4GGnltff/216OLiIpqYmIitW7cWjxw5UmW1xMXFiQD0jiFDhoii+PCt29OnTxdr164tqtVqsUuXLuL58+dlc9y+fVsMCgoSLSwsRCsrK/Gdd94RMzIyKq1mQ/UCEGNiYqQ+Dx48EEeNGiXWqlVLrFmzptirVy8xJSVFNk9ycrLYrVs30czMTLS3txcnTJgg5ubmVlrdQ4cOFV1dXUUTExPRwcFB7NKlixRmqmvNhjweaKpj3QMGDBCdnJxEExMTsW7duuKAAQPES5cuVeuaSbkEURTFqtkbIiIiIqoYvIeGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiIiIFI+BhoiIiBSPgYaIiIgUj4GGiKgUkpOTIQgCEhISqroUInoEAw0REREpHgMNESlKQUEBPvvsM3h4eECtVsPFxQVz5swBACQmJqJz584wMzODnZ0d3n33XWRmZkpj/fz8MHbsWNl8PXv2RHBwsPTYzc0Nc+fOxdChQ2FpaQkXFxcsX75cOu/u7g4AePHFFyEIAvz8/CrtWomo5BhoiEhRQkNDERERgenTp+Ps2bNYvXo1ateujfv37yMgIAC1atXCsWPHsH79euzevRsffPBBqdeYP38+XnrpJZw8eRKjRo3C+++/j/PnzwMA4uPjAQC7d+9GSkoKNm3aVKHXR0Rlo6rqAoiISiojIwMLFy7E4sWLMWTIEABAgwYN0KFDB6xYsQJZWVlYtWoVzM3NAQCLFy9Gjx49MG/ePNSuXbvE67z66qsYNWoUAGDKlClYsGAB4uLi4OXlBQcHBwCAnZ0dNBpNBV8hEZUVd2iISDGSkpKQnZ2NLl26GDzXrFkzKcwAwMsvv4yCggJpd6WkmjZtKv1ZEARoNBrcunWr7IUTUaVjoCEixTAzMyvXeCMjI4iiKGvLzc3V61ejRg3ZY0EQUFBQUK61iahyMdAQkWI0bNgQZmZm2LNnj965Ro0a4c8//8T9+/eltkOHDsHIyAheXl4AAAcHB6SkpEjn8/Pzcfr06VLVYGJiIo0louqDgYaIFMPU1BRTpkzB5MmTsWrVKly+fBlHjhxBVFQUBg0aBFNTUwwZMgSnT59GXFwcQkJC8NZbb0n3z3Tu3Bk///wzfv75Z5w7dw7vv/8+7t27V6oaHB0dYWZmhtjYWNy8eRNpaWmVcKVEVFoMNESkKNOnT8eECRMwY8YMNGrUCAMGDMCtW7dQs2ZN/Prrr7hz5w5atWqFvn37okuXLli8eLE0dujQoRgyZAjefvtt+Pr6on79+tBqtaVaX6VSYdGiRfjmm29Qp04dvPHGGxV9iURUBoL4+AvKRERERArDHRoiIiJSPAYaIiIiUjwGGiIiIlI8BhoiIiJSPAYaIiIiUjwGGiIiIlI8BhoiIiJSPAYaIiIiUjwGGiIiIlI8BhoiIiJSPAYaIiIiUrz/A+sUfVTiRBWAAAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 609.375x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"\n",
"palette = {\n",
" \"ORANGE\": \"orange\",\n",
" \"WHITE\": \"wheat\"\n",
"}\n",
"\n",
"sns.catplot(data=pumpkins, y=\"Variety\", hue=\"Color\", kind=\"count\", palette=palette)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['med', 'lge', 'sml', 'xlge', 'med-lge', 'jbo', 'exjbo'],\n",
" dtype=object)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pumpkins[\"Item Size\"].unique()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import OrdinalEncoder\n",
"\n",
"item_size_categories = [['sml','med', 'med-lge', 'lge', 'xlge', 'jbo','exjbo']]\n",
"ordinal_features = ['Item Size']\n",
"ordinal_encoder = OrdinalEncoder(categories=item_size_categories)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import OneHotEncoder\n",
"\n",
"categorical_features=[\"City Name\", \"Package\", \"Variety\", \"Origin\"]\n",
"categorical_encoder = OneHotEncoder(sparse_output=False)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"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>ord__Item Size</th>\n",
" <th>cat__City Name_ATLANTA</th>\n",
" <th>cat__City Name_BALTIMORE</th>\n",
" <th>cat__City Name_BOSTON</th>\n",
" <th>cat__City Name_CHICAGO</th>\n",
" <th>cat__City Name_COLUMBIA</th>\n",
" <th>cat__City Name_DALLAS</th>\n",
" <th>cat__City Name_DETROIT</th>\n",
" <th>cat__City Name_LOS ANGELES</th>\n",
" <th>cat__City Name_MIAMI</th>\n",
" <th>...</th>\n",
" <th>cat__Origin_MICHIGAN</th>\n",
" <th>cat__Origin_NEW JERSEY</th>\n",
" <th>cat__Origin_NEW YORK</th>\n",
" <th>cat__Origin_NORTH CAROLINA</th>\n",
" <th>cat__Origin_OHIO</th>\n",
" <th>cat__Origin_PENNSYLVANIA</th>\n",
" <th>cat__Origin_TENNESSEE</th>\n",
" <th>cat__Origin_TEXAS</th>\n",
" <th>cat__Origin_VERMONT</th>\n",
" <th>cat__Origin_VIRGINIA</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 48 columns</p>\n",
"</div>"
],
"text/plain": [
" ord__Item Size cat__City Name_ATLANTA cat__City Name_BALTIMORE \\\n",
"2 1.0 0.0 1.0 \n",
"3 1.0 0.0 1.0 \n",
"4 3.0 0.0 1.0 \n",
"5 3.0 0.0 1.0 \n",
"6 1.0 0.0 1.0 \n",
"\n",
" cat__City Name_BOSTON cat__City Name_CHICAGO cat__City Name_COLUMBIA \\\n",
"2 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"5 0.0 0.0 0.0 \n",
"6 0.0 0.0 0.0 \n",
"\n",
" cat__City Name_DALLAS cat__City Name_DETROIT cat__City Name_LOS ANGELES \\\n",
"2 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"5 0.0 0.0 0.0 \n",
"6 0.0 0.0 0.0 \n",
"\n",
" cat__City Name_MIAMI ... cat__Origin_MICHIGAN cat__Origin_NEW JERSEY \\\n",
"2 0.0 ... 0.0 0.0 \n",
"3 0.0 ... 0.0 0.0 \n",
"4 0.0 ... 0.0 0.0 \n",
"5 0.0 ... 0.0 0.0 \n",
"6 0.0 ... 0.0 0.0 \n",
"\n",
" cat__Origin_NEW YORK cat__Origin_NORTH CAROLINA cat__Origin_OHIO \\\n",
"2 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"5 0.0 0.0 0.0 \n",
"6 0.0 0.0 0.0 \n",
"\n",
" cat__Origin_PENNSYLVANIA cat__Origin_TENNESSEE cat__Origin_TEXAS \\\n",
"2 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"5 0.0 0.0 0.0 \n",
"6 0.0 0.0 0.0 \n",
"\n",
" cat__Origin_VERMONT cat__Origin_VIRGINIA \n",
"2 0.0 0.0 \n",
"3 0.0 1.0 \n",
"4 0.0 0.0 \n",
"5 0.0 0.0 \n",
"6 0.0 0.0 \n",
"\n",
"[5 rows x 48 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.compose import ColumnTransformer\n",
"ct = ColumnTransformer(transformers=[(\"ord\", ordinal_encoder, ordinal_features),\n",
" (\"cat\", categorical_encoder, categorical_features)])\n",
"\n",
"ct.set_output(transform=\"pandas\")\n",
"encoded_features = ct.fit_transform(pumpkins)\n",
"encoded_features.head()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ord__Item Size</th>\n",
" <th>cat__City Name_ATLANTA</th>\n",
" <th>cat__City Name_BALTIMORE</th>\n",
" <th>cat__City Name_BOSTON</th>\n",
" <th>cat__City Name_CHICAGO</th>\n",
" <th>cat__City Name_COLUMBIA</th>\n",
" <th>cat__City Name_DALLAS</th>\n",
" <th>cat__City Name_DETROIT</th>\n",
" <th>cat__City Name_LOS ANGELES</th>\n",
" <th>cat__City Name_MIAMI</th>\n",
" <th>...</th>\n",
" <th>cat__Origin_NEW JERSEY</th>\n",
" <th>cat__Origin_NEW YORK</th>\n",
" <th>cat__Origin_NORTH CAROLINA</th>\n",
" <th>cat__Origin_OHIO</th>\n",
" <th>cat__Origin_PENNSYLVANIA</th>\n",
" <th>cat__Origin_TENNESSEE</th>\n",
" <th>cat__Origin_TEXAS</th>\n",
" <th>cat__Origin_VERMONT</th>\n",
" <th>cat__Origin_VIRGINIA</th>\n",
" <th>Color</th>\n",
" </tr>\n",
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" <td>0.0</td>\n",
" <td>1</td>\n",
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" <tr>\n",
" <th>1697</th>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>4.0</td>\n",
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" <td>0.0</td>\n",
" <td>1</td>\n",
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"</table>\n",
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"</div>"
],
"text/plain": [
" ord__Item Size cat__City Name_ATLANTA cat__City Name_BALTIMORE \\\n",
"1694 4.0 0.0 0.0 \n",
"1695 4.0 0.0 0.0 \n",
"1696 4.0 0.0 0.0 \n",
"1697 4.0 0.0 0.0 \n",
"1698 4.0 0.0 0.0 \n",
"\n",
" cat__City Name_BOSTON cat__City Name_CHICAGO cat__City Name_COLUMBIA \\\n",
"1694 0.0 0.0 0.0 \n",
"1695 0.0 0.0 0.0 \n",
"1696 0.0 0.0 0.0 \n",
"1697 0.0 0.0 0.0 \n",
"1698 0.0 0.0 0.0 \n",
"\n",
" cat__City Name_DALLAS cat__City Name_DETROIT \\\n",
"1694 0.0 0.0 \n",
"1695 0.0 0.0 \n",
"1696 0.0 0.0 \n",
"1697 0.0 0.0 \n",
"1698 0.0 0.0 \n",
"\n",
" cat__City Name_LOS ANGELES cat__City Name_MIAMI ... \\\n",
"1694 0.0 0.0 ... \n",
"1695 0.0 0.0 ... \n",
"1696 0.0 0.0 ... \n",
"1697 0.0 0.0 ... \n",
"1698 0.0 0.0 ... \n",
"\n",
" cat__Origin_NEW JERSEY cat__Origin_NEW YORK \\\n",
"1694 0.0 0.0 \n",
"1695 0.0 0.0 \n",
"1696 0.0 0.0 \n",
"1697 0.0 0.0 \n",
"1698 0.0 0.0 \n",
"\n",
" cat__Origin_NORTH CAROLINA cat__Origin_OHIO cat__Origin_PENNSYLVANIA \\\n",
"1694 0.0 0.0 0.0 \n",
"1695 0.0 0.0 0.0 \n",
"1696 0.0 0.0 0.0 \n",
"1697 0.0 0.0 0.0 \n",
"1698 0.0 0.0 0.0 \n",
"\n",
" cat__Origin_TENNESSEE cat__Origin_TEXAS cat__Origin_VERMONT \\\n",
"1694 0.0 0.0 0.0 \n",
"1695 0.0 0.0 0.0 \n",
"1696 0.0 0.0 0.0 \n",
"1697 0.0 0.0 0.0 \n",
"1698 0.0 0.0 0.0 \n",
"\n",
" cat__Origin_VIRGINIA Color \n",
"1694 0.0 1 \n",
"1695 0.0 1 \n",
"1696 0.0 1 \n",
"1697 0.0 1 \n",
"1698 0.0 1 \n",
"\n",
"[5 rows x 49 columns]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.preprocessing import LabelEncoder\n",
"\n",
"label_encoder = LabelEncoder()\n",
"encoded_label = label_encoder.fit_transform(pumpkins[\"Color\"])\n",
"encoded_pumpkins = encoded_features.assign(Color=encoded_label)\n",
"encoded_pumpkins.tail()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['ORANGE', 'WHITE']"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(label_encoder.inverse_transform([0,1]))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\billg\\AppData\\Local\\Temp\\ipykernel_16772\\2446460816.py:3: FutureWarning: \n",
"\n",
"Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
"\n",
" g = sns.catplot(data=pumpkins, x=\"Item Size\", y=\"Color\", row=\"Variety\",\n",
"c:\\Users\\billg\\Desktop\\alvinsstuff\\Learning\\ML-For-Beginners\\.venv\\Lib\\site-packages\\seaborn\\axisgrid.py:123: UserWarning: Tight layout not applied. tight_layout cannot make Axes height small enough to accommodate all Axes decorations.\n",
" self._figure.tight_layout(*args, **kwargs)\n"
]
},
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x19c94840f20>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 600x1350 with 9 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pumpkins['Item Size'] = encoded_pumpkins['ord__Item Size']\n",
"\n",
"g = sns.catplot(data=pumpkins, x=\"Item Size\", y=\"Color\", row=\"Variety\",\n",
" kind=\"box\", orient=\"h\",\n",
" sharex=False, margin_titles=True,\n",
" height=1.5,aspect=4,palette=palette)\n",
"\n",
"g.set(xlabel=\"Item Size\", ylabel=\"\").set(xlim=(0,6))\n",
"g.set_titles(row_template=\"{row_name}\")"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\billg\\AppData\\Local\\Temp\\ipykernel_16772\\1781995252.py:5: FutureWarning: \n",
"\n",
"Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
"\n",
" sns.swarmplot(x=\"Color\", y=\"ord__Item Size\", data=encoded_pumpkins, palette=palette)\n",
"c:\\Users\\billg\\Desktop\\alvinsstuff\\Learning\\ML-For-Beginners\\.venv\\Lib\\site-packages\\seaborn\\categorical.py:3399: UserWarning: 63.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
" warnings.warn(msg, UserWarning)\n",
"c:\\Users\\billg\\Desktop\\alvinsstuff\\Learning\\ML-For-Beginners\\.venv\\Lib\\site-packages\\seaborn\\categorical.py:3399: UserWarning: 21.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
" warnings.warn(msg, UserWarning)\n"
]
},
{
"data": {
"text/plain": [
"<Axes: xlabel='Color', ylabel='ord__Item Size'>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\billg\\Desktop\\alvinsstuff\\Learning\\ML-For-Beginners\\.venv\\Lib\\site-packages\\seaborn\\categorical.py:3399: UserWarning: 79.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
" warnings.warn(msg, UserWarning)\n",
"c:\\Users\\billg\\Desktop\\alvinsstuff\\Learning\\ML-For-Beginners\\.venv\\Lib\\site-packages\\seaborn\\categorical.py:3399: UserWarning: 35.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
" warnings.warn(msg, UserWarning)\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"palette = {\n",
" \"0\": 'orange',\n",
" \"1\": 'wheat'\n",
" }\n",
"sns.swarmplot(x=\"Color\", y=\"ord__Item Size\", data=encoded_pumpkins, palette=palette)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"X = encoded_pumpkins[encoded_pumpkins.columns.difference([\"Color\"])]\n",
"\n",
"y = encoded_pumpkins[\"Color\"]\n",
"\n",
"XTrain, XTest, yTrain, yTest = train_test_split(X, y, test_size=0.2, random_state=0)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import f1_score, classification_report\n",
"from sklearn.linear_model import LogisticRegression\n",
"\n",
"model = LogisticRegression()\n",
"model.fit(XTrain, yTrain)\n",
"preds = model.predict(XTest)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.94 0.98 0.96 166\n",
" 1 0.85 0.67 0.75 33\n",
"\n",
" accuracy 0.92 199\n",
" macro avg 0.89 0.82 0.85 199\n",
"weighted avg 0.92 0.92 0.92 199\n",
"\n",
"Predicted labels: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0\n",
" 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0\n",
" 0 0 0 0 1 0 0 0 0 0 1 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\n",
" 0 0 0 1 0 0 0 0 0 0 0 0 1 1]\n",
"F1-score: 0.7457627118644068\n"
]
}
],
"source": [
"print(classification_report(yTest, preds))\n",
"print(\"Predicted labels: \", preds)\n",
"print(\"F1-score: \", f1_score(yTest, preds))"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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t79698Pb2Rnp6Orp27YqQkBCjKBcAC4ZJ4CXCiYj0040bN5CRkYHu3bsbVbkAeLt2k8BTVImI9NOoUaNQoUIFeHp6qm/Bbiw4gmECeIoqEZH+OHjwIJ49e6Z+7OXlZXTlAmDBMAn3/k0GwFNUiYik9vvvv8PT0xPt2rVDYmKi1HF0igXDBPAQCRGR9Hbs2IHPPvsMmZmZqFy5MmxsjPtnMguGkUtMy8SzlEwAvAYGEZFUtm/fjp49eyIrKwt9+vTB+vXrYWFh3NMgWTCMXPYZJB/YymFnZdz/mImI9NFvv/0GHx8fZGVloW/fviZRLgAWDKPHS4QTEUln586d6nLRr18/rFu3zuCv0Jlfxl+hTBznXxARSad69eooUaIE2rZti7Vr15pMuQBYMIweCwYRkXSqVq2KU6dOoXTp0iZVLgAeIjF6d/9lwSAiKky//vor/vzzT/XjcuXKmVy5ADiCYfQ4B4OIqPBs2rQJvr6+kMvlOHXqFGrVqiV1JMlwBMOIvbpN+/+u4slTVImIdGrjxo3w9fWFSqXC559/jo8//ljqSJJiwTBisS9SkaUSkJub8TbtREQ6tGHDBnW5GDhwIH7++WeYmZn2r1geIjEiT5LSkZKRpX4cc/85AKBcUWvepp2ISEfWrVsHf39/CCEwaNAgLF++3OTLBcCCYTR2n3+EEZvP5vo1zr8gItKNqKgodbkYMmQIgoKCWC7+hwXDSFx+9OqmOZbmMlhZ/P9sZbmFGbrVLytVLCIio9a8eXP4+PigaNGiCAoKgkzG0eJsLBhG5vNPP0Sgl2lPLCIiKiwWFhbYsGEDzM3NWS7+g+M4REREGli9ejUGDBgAlUoF4FXJYLl4E0cwiIiI8mnlypUYPHgwAMDd3R29evWSOJH+4ggGERFRPvz888/qcjF69Gj4+PhInEi/sWAQERG9w/LlyzFkyBAAwJgxY7B48WIeFnkHFgwiIqK3CAoKwrBhwwAAY8eOxcKFC1ku8oEFg4iIKA93795FQEAAAGDcuHGYP38+y0U+cZInERFRHj788EOEhobi1KlTmD17NsuFBlgwiIiI/iMpKQn29vYAgC5duqBLly4SJzI8PERCRET0msWLF6NmzZq4ffu21FEMGgsGERHR/yxatAgBAQG4d+8efvvtN6njGDQWDCIiIgALFy7E2LFjAQBTp05Vf04Fw4JBREQmb/78+Rg3bhwAYNq0aZgxYwYndL4nFgwiIjJp33//PcaPHw8AmD59OsuFlvAsEiIiMllpaWnYtGkTAGDGjBmYNm2axImMBwsGERGZLIVCgfDwcGzfvl19nxHSDh4iISIik3P27Fn158WLF2e50AEWDCIiMimzZs1C/fr1sXLlSqmjGDUWDCIiMhmvz7N4+vSpxGmMG+dgEBGRScg+QwQA5s2bhwkTJkicyLixYBARkVETQmD69OmYOXMmgFenpX799dcSpzJ+LBhERGS0hBCYNm0aZs+eDQBYsGABr9BZSFgwiIjIJCxatAhjxoyROobJYMEgIiKjJZPJMHPmTLRv3x5NmzaVOo5J4VkkRERkVIQQWLVqFVJSUgC8KhksF4WPBYOIiIyGEAITJkzAoEGD0LlzZyiVSqkjmSweIiEiIqMghMD48eOxYMECAEDXrl1hbm4ucSrTxYJBREQGTwiBcePGYdGiRQCAoKAgDBs2TOJUpo0Fg4iIDJoQAgEBAViyZAkAYPny5RgyZIi0oYgFg4iIDNvUqVPV5WLFihW8cZme4CRPIiIyaN26dUOxYsXw888/s1zoEY5gEBGRQatfvz7++ecfFCtWTOoo9BqOYBARkUHJPlvk5MmT6mUsF/qHBYOIiAyGSqXC8OHDMX/+fLRv3563XNdjPERCREQGQaVSYdiwYfj5558hk8mwZMkSjlzoMRYMIiLSeyqVCkOHDsXKlSshk8kQHBwMX19fqWPRW7BgGAkhdQAiIh1RqVQYPHgwVq9eDTMzM6xbtw6ff/651LHoHVgwjMSp26+OQ5Z2VEichIhIu4KCgtTlYv369ejbt6/UkSgfWDCMwLW4JJy5+wwWZjJ41ysrdRwiIq0aOHAgwsLC0KdPH/Tp00fqOJRPLBhG4NdT9wAA7tVLooQ9RzCIyPCpVCrIZDLIZDIoFAr88ccfkMlkUsciDfA0VQOXmqHEb9EPAAC9G5eXOA0R0ftTKpXw9/fH119/DSFezTBjuTA8kheMoKAguLi4QKFQoHHjxjh16tRb11+yZAmqVq0Ka2trODs7Y8yYMUhLSyuktPpnz4VYJKVloVxRa7T4yEnqOERE7yW7XKxfvx5LlizB+fPnpY5EBSRpwQgJCUFAQAACAwMRHR2NOnXqwMPDA48fP851/c2bN2PixIkIDAzElStXsGbNGoSEhGDy5MmFnFx/ZB8e6d2oPMzM2PCJyHAplUr4+flhw4YNMDc3x5YtW1CnTh2pY1EBSVowFi1ahIEDB8Lf3x81atTAihUrYGNjg7Vr1+a6/vHjx9GsWTP06dMHLi4uaNeuHXr37v3OUQ9j9frkzh4Ny0kdh4iowLKysuDr64tNmzbBwsICISEh+Oyzz6SORe9BsoKRkZGBM2fOwN3d/f/DmJnB3d0dJ06cyHWbpk2b4syZM+pCcevWLezduxcdOnTI83XS09ORmJiY48NYcHInERmD7HKxefNmWFhYIDQ0FN27d5c6Fr0nyc4iSUhIgFKpRMmSJXMsL1myJK5evZrrNn369EFCQgKaN28OIQSysrIwZMiQtx4imTt3LmbMmKHV7Prg9cmdfTi5k4gM2LFjx7BlyxZYWFhg69at8Pb2ljoSaYHkkzw1ERUVhTlz5mDZsmWIjo7G9u3bsWfPHsyaNSvPbSZNmoQXL16oP+7fv1+IiXUne3KnczFrNOfkTiIyYK6urggODsa2bdtYLoyIZCMYTk5OMDc3R3x8fI7l8fHxKFWqVK7bTJ06Ff369cOAAQMAALVq1UJycjIGDRqEb775BmZmb/YlKysrWFlZaf8NSCz78EivTzi5k4gMT2ZmJp4/f47ixYsDAO8rYoQkG8GQy+Vo0KABIiIi1MtUKhUiIiLQpEmTXLdJSUl5o0SYm5sDgPpcaVPAyZ1EZMgyMzPRu3dvtGzZEnFxcVLHIR2R9EqeAQEB8PPzQ8OGDdGoUSMsWbIEycnJ8Pf3B/Cq0ZYtWxZz584FAHh5eWHRokWoV68eGjdujBs3bmDq1Knw8vJSFw1TsO3Mq8M8bWtwcicRGZbMzEz06tUL27dvh1wux8WLF/MctSbDJmnB8PHxwZMnTzBt2jTExcWhbt262L9/v3ri571793KMWEyZMgUymQxTpkzBw4cPUbx4cXh5eeHbb7+V6i1I4u+7zwAAHh/zm5KIDEdGRgZ69eqFHTt2QC6XY8eOHTnOJCTjIhOmdGwBQGJiIhwdHfHixQs4ODhIHUdjWUoVak4PQ1qmChFjXVGpuJ3UkYiI3ikjIwM9e/bE77//DisrK+zcuROenp5SxyINafI7lDc7MzA3nrxEWqYKdlYWqPCBrdRxiIjeKSMjAz169MCuXbtgZWWF33//HR4eHlLHIh0zqNNUCbjw4AUA4OMyDjx7hIgMwtOnT3Hp0iUoFArs2rWL5cJEcATDwFx8+Kpg1CrrKHESIqL8KVWqFCIjI3Hjxg20atVK6jhUSDiCYWDOZxeMciwYRKS/0tPTERUVpX7s7OzMcmFiWDAMSJZShSuxr+6lUpMjGESkp9LS0tCtWze4u7tj69atUschibBgGBBO8CQifZeWloauXbti7969kMvl+OCDD6SORBLhHAwDwgmeRKTPUlNT4e3tjT///BM2NjbYs2cP3NzcpI5FEmHBMCAXOMGTiPRUamoqunTpggMHDsDGxgZ79+6Fq6ur1LFIQiwYBuQCJ3gSkR5KT09H586dER4eDltbW+zduxctW7aUOhZJjHMwDAQneBKRvpLL5ahcuTJsbW2xb98+lgsCwIJhMDjBk4j0lUwmw08//YTo6Gi0aNFC6jikJ1gwDAQneBKRPklOTsbMmTORmZkJADAzM0OVKlUkTkX6hHMwDAQneBKRvkhOTkbHjh1x6NAh3Lp1C8HBwVJHIj3EEQwDwQmeRKQPXr58iQ4dOuDQoUNwcHDAkCFDpI5EeoojGAbg9QmeHMEgIqlkl4sjR47AwcEBf/75Jxo3bix1LNJTHMEwAK9P8HThBE8ikkBSUhLat2+PI0eOwNHREQcOHGC5oLfiCIYBOM8JnkQkISEEevTogaNHj6JIkSI4cOAAGjZsKHUs0nMcwTAAvEU7EUlJJpNh0qRJKFu2LMLDw1kuKF84gmEAOMGTiKTm6uqKGzduQKFQSB2FDARHMPQcJ3gSkRRevHgBLy8vXLx4Ub2M5YI0wREMPccJnkRU2J4/fw4PDw+cOnUKN27cwMWLF2Fubi51LDIwLBh6jhM8iagwPX/+HO3atcPp06fxwQcfYMuWLSwXVCA8RKLnOMGTiArLs2fP0LZtW5w+fRpOTk44ePAg6tSpI3UsMlAcwdBjSpVAxJXHAIB65YtKnIaIjNnTp0/Rtm1bREdHq8tFrVq1pI5FBowjGHrs8D9P8PB5KhytLdGmegmp4xCREZs8eTKio6NRvHhxREZGslzQe2PB0GO//nUPANCtflkoLHkMlIh0Z/78+fD29sbBgwdRs2ZNqeOQEeAhEj0Vn5iGiKuvDo/0aVRe4jREZIxSU1NhbW0NALC3t8eOHTskTkTGhCMYeir09H0oVQKfuBRF5ZL2UschIiOTkJCATz/9FHPnzpU6ChkpFgw9pFQJbDl9HwDQpzFHL4hIu548eYLWrVvj/Pnz+OGHH/D06VOpI5ERYsHQQ69P7mxfs7TUcYjIiDx+/BitW7fGhQsXUKpUKURFRaFYsWJSxyIjxDkYeoiTO4lIF7LLxaVLl1C6dGlERkaiatWqUsciI8URDD3DyZ1EpAvx8fFo1aoVLl26hDJlyiAqKorlgnSKIxh6hpM7iUgXwsLCcPnyZXW5qFy5stSRyMixYOgRTu4kIl3x9fVFWloaWrVqxXJBhYIFQ49wcicRaVNcXBysrKxQtOirWw0MGjRI4kRkSjgHQ49s/t/kzu71y3FyJxG9l9jYWLi5uaFdu3Z4/vy51HHIBLFg6Im4F2k4mD25s7GzxGmIyJA9evQIbm5uuHbtGuLj4/Hs2TOpI5EJYsHQE6F/v5rc2cilGD4qwcmdRFQwDx8+hJubG65fv44PP/wQhw4dQoUKFaSORSaIczD0gFIlEPK/yZ29OXpBRAX04MEDtGrVCjdu3MCHH36IqKgouLi4SB2LTBRHMPTA4euc3ElE7+f+/ftwc3PDjRs34OLiwnJBkmPB0AObT3FyJxG9n9TUVKSkpKBChQosF6QXeIhEYpzcSUTaUKVKFURGRsLa2hrly/M6OiQ9jmBIjJM7iaig7t69i4iICPXjqlWrslyQ3mDBkNDrkzt55U4i0sSdO3fg5uaGjh074uDBg1LHIXoDC4aEXp/c6VmzlNRxiMhAZJeLO3fuwNnZmTctI73EgiEhTu4kIk3dvn0brq6uuHv3LipXroyoqCiULVtW6lhEb2DBkAgndxKRpm7dugU3Nzfcu3cPVapUYbkgvcazSAqRSiVw/Oa/eJmeicirTzi5k4jyLfvy3/fv30fVqlURGRmJ0qV53RzSXywYhWjTX3cx9fdLOZZxcicR5UeJEiXQrFkzxMTE4ODBgywXpPdYMAqJEALrTtwFAFQpaQcHhSVcnGzRoRZ/SBDRu1lYWGDDhg14/vw5nJycpI5D9E4sGIXk77vPcOPxS1hbmuO3oU1hr7CUOhIR6bnr169j1apV+O6772BmZgYLCwuWCzIYLBiFZPNfr84Y6VynDMsFEb3TtWvX0KpVK8TGxsLOzg6BgYFSRyLSCM8iKQTPUzKw50IsAKA351wQ0TtcvXpVXS5q1qyJoUOHSh2JSGMcwSgEv0U/REaWCjVKO6BOOUep4xCRHssuF3FxcahVqxYiIiJQvHhxqWMRaYwjGDomhMCv/7ugVu/G5SGTySRORET66sqVK3Bzc0NcXBxq166NgwcPslyQwWLB0LHTd/5/cqd33TJSxyEiPZWWlgYPDw/Ex8ejbt26OHjwICd0kkFjwdCx7NELTu4kordRKBQICgpC48aNER4ejg8++EDqSETvhQVDh54lc3InEb2dEEL9uZeXF44fP85yQUbhvQpGWlqatnIYpe1nObmTiPJ2/vx5NGzYELdu3VIvMzPj331kHDT+l6xSqTBr1iyULVsWdnZ26m+MqVOnYs2aNVoPaKiEENj816srd3JyJxH917lz59C6dWtER0dj3LhxUsch0jqNC8bs2bMRHByM77//HnK5XL28Zs2aWL16tVbDGbLTd57h5pNkTu4kojfExMSgTZs2+Pfff9GwYUP+cUZGSeOCsX79eqxcuRJ9+/aFubm5enmdOnVw9epVrYYzZNmjF5zcSUSvO3v2rLpcfPLJJzhw4ACKFi0qdSwirdO4YDx8+BAfffTRG8tVKhUyMzO1EsrQPUvOwN6LcQB4t1Qi+n/R0dFo06YNnj59ikaNGuHAgQMoUqSI1LGIdELjglGjRg0cOXLkjeXbtm1DvXr1tBLK0L0+ubM2J3cSEV7Nyxo7diyePXuGxo0b488//4SjI38+kPHS+FLh06ZNg5+fHx4+fAiVSoXt27fj2rVrWL9+PXbv3q2LjAaFkzuJKDcymQxbt27FhAkTsHjxYjg4OEgdiUinNB7B6NKlC/744w+Eh4fD1tYW06ZNw5UrV/DHH3+gbdu2ushoUDi5k4he9++//6o/d3Jywpo1a1guyCQU6GZnLVq0wIEDB7SdxShwcicRZTt9+jQ8PDwwb948DBo0SOo4RIVK4xGMihUr5mjk2Z4/f46KFStqJZSh4uROIsp26tQpuLu749mzZ9i0aROUSqXUkYgKlcYF486dO7l+o6Snp+Phw4daCWWofot+wMmdRIS//voLbdu2RWJiIlq0aIE9e/bkOK2fyBTk+xDJrl271J+HhYXlmP2sVCoREREBFxcXrYYzJK/flr0PJ3cSmawTJ07Aw8MDSUlJaNmyJfbs2QM7OzupYxEVunwXDG9vbwCvZkL7+fnl+JqlpSVcXFywcOFCrYYzJKduP1VP7uzCyZ1EJun48ePw9PREUlIS3NzcsHv3btja2kodi0gS+S4YKpUKAFChQgWcPn0aTk5OOgtliHhbdiKKjIxEUlISWrVqhT/++IPlgkyaxmeR3L59Wxc5DBondxIRAEyePBllypSBj48PbGxspI5DJKkC3Rc4OTkZe/fuxYoVK/Djjz/m+NBUUFAQXFxcoFAo0LhxY5w6deqt6z9//hzDhw9H6dKlYWVlhSpVqmDv3r0FeRtawyt3Epmu6OhoJCcnA3h1CNnf35/lgggFGME4e/YsOnTogJSUFCQnJ6NYsWJISEiAjY0NSpQogVGjRuX7uUJCQhAQEIAVK1agcePGWLJkCTw8PHDt2jWUKFHijfUzMjLQtm1blChRAtu2bUPZsmVx9+5dya/lf+nRCwCAZ81SnNxJZEIOHz6MDh06oFGjRti9ezeLBdFrNB7BGDNmDLy8vPDs2TNYW1vj5MmTuHv3Lho0aIAFCxZo9FyLFi3CwIED4e/vjxo1amDFihWwsbHB2rVrc11/7dq1ePr0KXbu3IlmzZrBxcUFrq6uqFOnjqZvQyesLAo0IEREBujQoUNo3749kpOTYWlpyT8uiP5D49+IMTExGDt2LMzMzGBubo709HQ4Ozvj+++/x+TJk/P9PBkZGThz5gzc3d3/P4yZGdzd3XHixIlct9m1axeaNGmC4cOHo2TJkqhZsybmzJnz1gvYpKenIzExMccHEdH7iIqKUo/kenh4YOfOnbC2tpY6FpFe0bhgWFpawszs1WYlSpTAvXuvzp5wdHTE/fv38/08CQkJUCqVKFmyZI7lJUuWRFxcXK7b3Lp1C9u2bYNSqcTevXsxdepULFy4ELNnz87zdebOnQtHR0f1h7Ozc74zEhH918GDB9XlwtPTk+WCKA8az8GoV68eTp8+jcqVK8PV1RXTpk1DQkICNmzYgJo1a+oio5pKpUKJEiWwcuVKmJubo0GDBnj48CHmz5+PwMDAXLeZNGkSAgIC1I8TExNZMoioQA4ePIhOnTohNTUV7du3x/bt26FQKKSORaSXNC4Yc+bMQVJSEgDg22+/ha+vL4YOHYrKlStjzZo1+X4eJycnmJubIz4+Psfy+Ph4lCpVKtdtSpcuDUtLyxyX3K1evTri4uKQkZEBuVz+xjZWVlawsrLKdy4iorwUKVIECoUCrVu3xm+//cafLURvoXHBaNiwofrzEiVKYP/+/QV6YblcjgYNGiAiIkJ9lVCVSoWIiAiMGDEi122aNWuGzZs3Q6VSqQ/TXL9+HaVLl861XBARaVP9+vVx/PhxVKhQgeWC6B20dtpDdHQ0OnXqpNE2AQEBWLVqFdatW4crV65g6NChSE5Ohr+/PwDA19cXkyZNUq8/dOhQPH36FKNHj8b169exZ88ezJkzB8OHD9fW2yAiyuHPP//E8ePH1Y+rVavGckGUDxqNYISFheHAgQOQy+UYMGAAKlasiKtXr2LixIn4448/4OHhodGL+/j44MmTJ5g2bRri4uJQt25d7N+/Xz3x8969e+qRCgBwdnZGWFgYxowZg9q1a6Ns2bIYPXo0JkyYoNHrEhHlx/79++Ht7Q25XI4TJ07g448/ljoSkcHId8FYs2YNBg4ciGLFiuHZs2dYvXo1Fi1ahJEjR8LHxwcXL15E9erVNQ4wYsSIPA+JREVFvbGsSZMmOHnypMavQ0SkiX379qFr165IT09H+/btUblyZakjERmUfB8i+eGHH/Ddd98hISEBoaGhSEhIwLJly3DhwgWsWLGiQOWCiEgf7d27F97e3khPT0fXrl0RGhrKeV5EGsp3wbh58yZ69OgBAOjWrRssLCwwf/58lCtXTmfhiIgK2+7du9G1a1dkZGSge/fuCAkJgaUl75BMpKl8F4zU1FT1dfZlMhmsrKxQunRpnQUjIipsx48fR7du3ZCRkYHPPvsMv/76K8sFUQFpNMlz9erVsLOzAwBkZWUhODgYTk5OOdbR5GZnRET6pH79+nB3d4ednR02bdrEckH0HvJdMMqXL49Vq1apH5cqVQobNmzIsY5MJmPBICKDpVAosH37dlhYWMDCQuPLBBHRa/L9HXTnzh0dxiAiksaOHTtw8uRJzJs3DzKZjJf+JtISVnQiMlnbt2+Hj48PsrKyULduXfTu3VvqSERGQ2tX8iQiMiTbtm1Dz549kZWVhb59+6rPkiMi7WDBICKTs3XrVvTq1QtKpRL9+vXDunXrOOeCSMtYMIjIpISEhKB3795QKpXw9fXFL7/8kuMOzUSkHSwYRGQy7t+/j379+kGpVMLPzw9r165luSDSkQIVjJs3b2LKlCno3bs3Hj9+DODVdfsvXbqk1XBERNrk7OyM1atXo3///lizZg3LBZEOaVwwDh06hFq1auGvv/7C9u3b8fLlSwDAuXPnEBgYqPWARETvKzMzU/25r68vVq9ezXJBpGMaF4yJEydi9uzZ6tu2Z2vdujXvckpEemfjxo2oV68e4uLipI5CZFI0LhgXLlxA165d31heokQJJCQkaCUUEZE2bNiwAX5+frh06RJWrlwpdRwik6JxwShSpAhiY2PfWH727FmULVtWK6GIiN7XunXr4OfnB5VKhcGDB2PKlClSRyIyKRoXjF69emHChAmIi4uDTCaDSqXCsWPHMG7cOPj6+uoiIxGRRoKDg+Hv7w8hBIYMGYJly5bBzIwnzREVJo2/4+bMmYNq1arB2dkZL1++RI0aNdCyZUs0bdqUfyEQkeR++eUXfPnllxBCYNiwYSwXRBLR+NJ1crkcq1atwtSpU3Hx4kW8fPkS9erVQ+XKlXWRj4go39LS0jB37lwIITB8+HAsXboUMplM6lhEJknjgnH06FE0b94c5cuXR/ny5XWRiYioQBQKBSIiIrBu3Tp88803LBdEEtJ43LB169aoUKECJk+ejMuXL+siExGRRm7fvq3+3NnZGVOmTGG5IJKYxgXj0aNHGDt2LA4dOoSaNWuibt26mD9/Ph48eKCLfEREb/Xzzz+jSpUqCA0NlToKEb1G44Lh5OSEESNG4NixY7h58yZ69OiBdevWwcXFBa1bt9ZFRiKiXC1fvhxDhgxBVlYWTp8+LXUcInrNe02trlChAiZOnIh58+ahVq1aOHTokLZyERG91bJlyzBs2DAAwNixY/H9999LnIiIXlfggnHs2DEMGzYMpUuXRp8+fVCzZk3s2bNHm9mIiHL1008/Yfjw4QCAr7/+GvPnz+ecCyI9o/FZJJMmTcKWLVvw6NEjtG3bFj/88AO6dOkCGxsbXeQjIsph6dKlGDVqFABg/PjxmDdvHssFkR7SuGAcPnwYX3/9NXr27AknJyddZCIiytO1a9cAvLrx4pw5c1guiPSUxgXj2LFjushBRJQvS5cuRbt27eDl5cVyQaTH8lUwdu3ahfbt28PS0hK7du1667qdO3fWSjAiomy///472rdvD7lcDplMxp8zRAYgXwXD29sbcXFxKFGiBLy9vfNcTyaTQalUaisbEREWLlyIcePGwdvbG9u2bYO5ubnUkYgoH/JVMFQqVa6fExHp0vz58zF+/HgAQO3atXnTMiIDovF36/r165Genv7G8oyMDKxfv14roYiIvvvuO3W5CAwMxIwZMzjngsiAaFww/P398eLFizeWJyUlwd/fXyuhiMi0zZs3DxMnTgQATJ8+HdOnT5c2EBFpTOOzSIQQuf4V8eDBAzg6OmolFBGZrvnz52PSpEkAgJkzZ2Lq1KkSJyKigsh3wahXrx5kMhlkMhnatGkDC4v/31SpVOL27dvw9PTUSUgiMh2NGjWCjY0NJk2ahClTpkgdh4gKKN8FI/vskZiYGHh4eMDOzk79NblcDhcXF3Tv3l3rAYnItLi6uuLKlSsoX7681FGI6D3ku2AEBgYCAFxcXODj4wOFQqGzUERkWhYsWABPT0/UrFkTAFguiIyAxpM8/fz8WC6ISGumT5+Or7/+Gq1bt8a///4rdRwi0pJ8jWAUK1YM169fh5OTE4oWLfrWU8WePn2qtXBEZLyEEJg+fTpmzpwJ4NWNyz744AOJUxGRtuSrYCxevBj29vbqz3kuOhG9DyEEpk2bhtmzZwN4dYhk7NixEqciIm3KV8Hw8/NTf/7FF1/oKgsRmQAhBKZOnYpvv/0WALBo0SKMGTNG4lREpG0az8GIjo7GhQsX1I9///13eHt7Y/LkycjIyNBqOCIyPqtXr1aXi8WLF7NcEBkpjQvG4MGDcf36dQDArVu34OPjAxsbG2zdulV9WV8iorz06tULzZo1w5IlS/DVV19JHYeIdETjK3lev34ddevWBQBs3boVrq6u2Lx5M44dO4ZevXphyZIlWo5IRIbu9SsA29vbIyoqKsfF+ojI+Gg8giGEUN9RNTw8HB06dAAAODs7IyEhQbvpiMjgCSHw9ddfY+7cueplLBdExk/j7/KGDRti9uzZcHd3x6FDh7B8+XIAwO3bt1GyZEmtByQiwyWEwLhx47Bo0SIAgKenJ+rVqydxKiIqDBqPYCxZsgTR0dEYMWIEvvnmG3z00UcAgG3btqFp06ZaD0hEhkkIgYCAAHW5WL58OcsFkQnReASjdu3aOc4iyTZ//nyYm5trJRQRGTYhBMaMGYMffvgBAPDzzz9j0KBBEqciosJU4AOhZ86cwZUrVwAANWrUQP369bUWiogMlxACo0ePxtKlSwEAK1euxMCBAyVORUSFTeOC8fjxY/j4+ODQoUMoUqQIAOD58+do1aoVtmzZguLFi2s7IxEZkEOHDmHp0qWQyWRYtWoV+vfvL3UkIpKAxnMwRo4ciZcvX+LSpUt4+vQpnj59iosXLyIxMRGjRo3SRUYiMiBubm5YsmQJVq9ezXJBZMI0HsHYv38/wsPDUb16dfWyGjVqICgoCO3atdNqOCIyDCqVCsnJyep7Fo0ePVriREQkNY1HMFQqFSwtLd9Ybmlpqb4+BhGZDpVKhWHDhqFVq1Z4/vy51HGISE9oXDBat26N0aNH49GjR+plDx8+xJgxY9CmTRuthiMi/aZSqTBkyBD8/PPPiI6OxuHDh6WORER6QuOC8dNPPyExMREuLi6oVKkSKlWqhAoVKiAxMVE9a5yIjJ9KpcLgwYOxatUqmJmZYf369ejcubPUsYhIT2g8B8PZ2RnR0dGIiIhQn6ZavXp1uLu7az0cEeknlUqFgQMHYu3atepy0bdvX6ljEZEe0ahghISEYNeuXcjIyECbNm0wcuRIXeUiIj2lUqkwYMAA/PLLLzAzM8OGDRvQp08fqWMRkZ7Jd8FYvnw5hg8fjsqVK8Pa2hrbt2/HzZs3MX/+fF3mIyI9Exsbi/3798PMzAybNm1Cr169pI5ERHoo33MwfvrpJwQGBuLatWuIiYnBunXrsGzZMl1mIyI9VLZsWURGRmLr1q0sF0SUp3wXjFu3bsHPz0/9uE+fPsjKykJsbKxOghGR/lAqlYiJiVE/rlq1Krp16yZdICLSe/kuGOnp6bC1tf3/Dc3MIJfLkZqaqpNgRKQflEolvvjiC3z66acICwuTOg4RGQiNJnlOnToVNjY26scZGRn49ttv4ejoqF6WfWtmIjJ8WVlZ8PPzw+bNm2FhYYGXL19KHYmIDES+C0bLli1x7dq1HMuaNm2KW7duqR/LZDLtJSMiSWVlZcHX1xe//vorLCwsEBISwsMiRJRv+S4YUVFROoxBRPokKysLn3/+OUJCQmBhYYHQ0FB07dpV6lhEZEA0vtAWERm3rKws9O3bF6GhobC0tMTWrVvRpUsXqWMRkYFhwSCiN5ibm8PS0hLbtm3j5b+JqEA0vhcJERk3CwsLrF+/HseOHWO5IKICY8EgImRmZmLZsmVQKpUAXpWMTz75ROJURGTIWDCITFxGRgZ8fHwwfPhwDB8+XOo4RGQkClQwjhw5gs8//xxNmjTBw4cPAQAbNmzA0aNHtRqOiHQru1zs2LEDVlZWnMxJRFqjccH47bff4OHhAWtra5w9exbp6ekAgBcvXmDOnDlaD0hEupGRkYEePXpg586dsLKyws6dO9G+fXupYxGRkdC4YMyePRsrVqzAqlWrYGlpqV7erFkzREdHazUcEelGeno6PvvsM+zatQsKhQK7du2Cp6en1LGIyIhofJrqtWvX0LJlyzeWOzo64vnz59rIREQ61rdvX/zxxx/qctG2bVupIxGRkdF4BKNUqVK4cePGG8uPHj2KihUrFihEUFAQXFxcoFAo0LhxY5w6dSpf223ZsgUymQze3t4Fel0iU+Xn5wdHR0f88ccfLBdEpBMaF4yBAwdi9OjR+OuvvyCTyfDo0SNs2rQJ48aNw9ChQzUOEBISgoCAAAQGBiI6Ohp16tSBh4cHHj9+/Nbt7ty5g3HjxqFFixYavyaRqfPy8sKdO3fg7u4udRQiMlIaF4yJEyeiT58+aNOmDV6+fImWLVtiwIABGDx4MEaOHKlxgEWLFmHgwIHw9/dHjRo1sGLFCtjY2GDt2rV5bqNUKtG3b1/MmDGjwKMmRKYkLS0N/fv3z3FzwiJFikgXiIiMnsYFQyaT4ZtvvsHTp09x8eJFnDx5Ek+ePMGsWbM0fvGMjAycOXMmx19RZmZmcHd3x4kTJ/LcbubMmShRogT69+//ztdIT09HYmJijg8iU5KamoouXbpg7dq16NSpk/piWkREulTge5HI5XLUqFHjvV48ISEBSqUSJUuWzLG8ZMmSuHr1aq7bHD16FGvWrEFMTEy+XmPu3LmYMWPGe+UkMlTZ5eLAgQOwtbXFihUrYG5uLnUsIjIBGheMVq1aQSaT5fn1gwcPvlegt0lKSkK/fv2watUqODk55WubSZMmISAgQP04MTERzs7OuopIpDdSUlLQpUsXhIeHw9bWFvv27eOcJSIqNBoXjLp16+Z4nJmZiZiYGFy8eBF+fn4aPZeTkxPMzc0RHx+fY3l8fDxKlSr1xvo3b97EnTt34OXlpV6mUqkAvLp3wrVr11CpUqUc21hZWcHKykqjXESGLiUlBZ07d0ZERATs7Oywb98+NG/eXOpYRGRCNC4YixcvznX59OnT8fLlS42eSy6Xo0GDBoiIiFCfaqpSqRAREYERI0a8sX61atVw4cKFHMumTJmCpKQk/PDDDxyZIPqf8ePHq8vF/v370axZM6kjEZGJKfAcjP/6/PPP0ahRIyxYsECj7QICAuDn54eGDRuiUaNGWLJkCZKTk+Hv7w8A8PX1RdmyZTF37lwoFArUrFkzx/bZM+H/u5zIlE2fPh3nzp3Dd999h6ZNm0odh4hMkNYKxokTJ6BQKDTezsfHB0+ePMG0adMQFxeHunXrYv/+/eqJn/fu3YOZGW/6SvQuSqVSPYHTyckJhw8ffut8KSIiXdK4YHTr1i3HYyEEYmNj8ffff2Pq1KkFCjFixIhcD4kAQFRU1Fu3DQ4OLtBrEhmTly9folOnTujduzcGDx4MACwXRCQpjQuGo6NjjsdmZmaoWrUqZs6ciXbt2mktGBHlT1JSEjp06ICjR4/i3Llz6N69e77PsiIi0hWNCoZSqYS/vz9q1aqFokWL6ioTEeVTUlIS2rdvj2PHjsHR0RFhYWEsF0SkFzSa3GBubo527drxrqlEeiAxMRGenp7qcnHgwAE0atRI6lhERAAKcKnwmjVr5rifAREVvuxycfz4cRQpUgTh4eH45JNPpI5FRKSmccGYPXs2xo0bh927dyM2Npb3+SCSQGhoKE6cOIGiRYsiPDwcDRs2lDoSEVEO+Z6DMXPmTIwdOxYdOnQAAHTu3DnHLHUhBGQyGW+kRFQI+vfvjydPnsDDwwP169eXOg4R0RvyXTBmzJiBIUOGIDIyUpd5iCgPL168gIWFBWxtbSGTyTBp0iSpIxER5SnfBUMIAQBwdXXVWRgiyt3z58/Rrl072NnZYffu3bCxsZE6EhHRW2k0B4MX7iEqfM+ePUPbtm1x+vRpnD9/Hvfu3ZM6EhHRO2l0HYwqVaq8s2Q8ffr0vQIR0f97+vQp2rZti+joaDg5OSEiIgLVqlWTOhYR0TtpVDBmzJjxxpU8iUg3nj59Cnd3d5w9exZOTk44ePAgatWqJXUsIqJ80ahg9OrVCyVKlNBVFiL6n3///Rfu7u6IiYlB8eLFcfDgQd4xmIgMSr7nYHD+BVHhefToEe7evYsSJUogMjKS5YKIDI7GZ5EQke7VqlUL4eHhUCgUqFGjhtRxiIg0lu+CoVKpdJmDyOQlJCTg9u3b6kt+8wJaRGTINL5UOBFp35MnT9C6dWu0adMGJ0+elDoOEdF7Y8Egktjjx4/RunVrXLhwAXZ2dihatKjUkYiI3ptGZ5EQkXZll4tLly6hTJkyiIyMRJUqVaSORUT03jiCQSSR+Ph4tGrVCpcuXULZsmURFRXFckFERoMjGEQSePLkCVq1aoUrV66oy8VHH30kdSwiIq1hwSCSgL29PVxcXJCUlITIyEiWCyIyOiwYRBJQKBTYvn07Hj9+jPLly0sdh4hI6zgHg6iQPHr0CN999536onUKhYLlgoiMFkcwiArBw4cP0apVK/zzzz9QqVSYNGmS1JGIiHSKIxhEOvbgwQO4ubnhn3/+wYcffojevXtLHYmISOdYMIh06P79+3Bzc8ONGzfg4uKCQ4cOwcXFRepYREQ6x4JBpCPZ5eLmzZuoUKECoqKi8OGHH0odi4ioULBgEOlAeno62rRpg1u3bqFixYosF0RkclgwiHTAysoK06ZNQ5UqVRAVFcWzRYjI5LBgEOnI559/jvPnz8PZ2VnqKEREhY4Fg0hLbt++DU9PT8TGxqqXWVlZSZiIiEg6LBhEWnDr1i24ubkhLCwMQ4YMkToOEZHkWDCI3tPNmzfh5uaGe/fuoUqVKli+fLnUkYiIJMcreRK9h+xy8eDBA1StWhWRkZEoXbq01LGIiCTHEQyiArpx4wZcXV3x4MEDVKtWDVFRUSwXRET/w4JBVEADBgzAw4cPUb16dURGRqJUqVJSRyIi0hssGEQFtGHDBnh5ebFcEBHlgnMwiDSQmpoKa2trAICzszN27dolcSIiIv3EEQyifLp27RqqVq2K0NBQqaMQEek9FgyifLh69Src3Nxw//59zJs3D1lZWVJHIiLSaywYRO9w5coVuLm5IS4uDrVr18aff/4JCwseXSQiehsWDKK3uHz5Mtzc3BAfH486deogIiICTk5OUsciItJ7LBhEebh06RJatWqFx48fo27duiwXREQaYMEgysPmzZvx+PFj1KtXDxEREfjggw+kjkREZDB4IJkoD7Nnz0aRIkXQv39/FCtWTOo4REQGhSMYRK+5ceMGMjIyAAAymQxff/01ywURUQGwYBD9z7lz5/Dpp5+iZ8+e6pJBREQFw4JBBCAmJgatW7fGv//+i0ePHiE1NVXqSEREBo0Fg0ze2bNn0aZNGzx9+hSNGzfGgQMH4OjoKHUsIiKDxoJBJi06OlpdLj799FOEhYWxXBARaQELBpmsM2fOoE2bNnj27BmaNGnCckFEpEUsGGSykpOTkZGRgaZNm2L//v1wcHCQOhIRkdHgdTDIZLVs2RKRkZGoXr067O3tpY5DRGRUWDDIpJw6dQoKhQK1a9cGADRq1EjiRERExomHSMhknDx5Em3btkWbNm1w9epVqeMQERk1FgwyCSdOnEC7du2QmJiIGjVqoFy5clJHIiIyaiwYZPSOHz8ODw8PJCUlwdXVFXv37oWdnZ3UsYiIjBoLBhm1Y8eOqcuFm5sb9uzZA1tbW6ljEREZPRYMMlpnzpyBp6cnXr58idatW7NcEBEVIp5FQkarSpUqqFOnDhQKBXbt2gUbGxupIxERmQwWDDJa9vb22LdvH8zNzVkuiIgKGQ+RkFE5dOgQ5s+fr35sb2/PckFEJAGOYJDRiIyMRKdOnZCSkoLy5cvDx8dH6khERCaLIxhkFA4ePIiOHTsiJSUFnp6e6NKli9SRiIhMGgsGGbyIiAh06tQJqamp6NChA3bs2AGFQiF1LCIik8ZDJGTQwsPD4eXlhbS0NHTo0AHbt2+HlZWV1LGIiEweRzDIYD18+BCdO3dGWloaOnbsyHJBRKRHOIJBBqts2bKYN28ewsPDsXXrVpYLIiI9whEMMjhCCPXno0aNws6dO1kuiIj0DAsGGZR9+/ahRYsWePbsmXqZmRn/GRMR6Rv+ZCaDsXfvXnh7e+PYsWM5LqZFRET6hwWDDMLu3bvRtWtXZGRkoHv37pgxY4bUkYiI6C1YMEjv/fHHH+jWrRsyMjLw2Wef4ddff4WlpaXUsYiI6C30omAEBQXBxcUFCoUCjRs3xqlTp/Jcd9WqVWjRogWKFi2KokWLwt3d/a3rk2HbtWsXunfvjszMTPTo0QObN29muSAiMgCSF4yQkBAEBAQgMDAQ0dHRqFOnDjw8PPD48eNc14+KikLv3r0RGRmJEydOwNnZGe3atcPDhw8LOTnpWnp6OkaPHo3MzEz4+PiwXBARGRDJC8aiRYswcOBA+Pv7o0aNGlixYgVsbGywdu3aXNfftGkThg0bhrp166JatWpYvXo1VCoVIiIiCjk56ZqVlRXCwsIwcuRIbNy4ERYWvGwLEZGhkLRgZGRk4MyZM3B3d1cvMzMzg7u7O06cOJGv50hJSUFmZiaKFSuW69fT09ORmJiY44P0W0JCgvrzKlWq4Mcff2S5ICIyMJIWjISEBCiVSpQsWTLH8pIlSyIuLi5fzzFhwgSUKVMmR0l53dy5c+Ho6Kj+cHZ2fu/cpDvbtm1DhQoVEBYWJnUUIiJ6D5IfInkf8+bNw5YtW95698xJkybhxYsX6o/79+8XckrKr61bt6JXr154+fIltm3bJnUcIiJ6D5KOOzs5OcHc3Bzx8fE5lsfHx6NUqVJv3XbBggXq+1DUrl07z/WsrKx4GWkDEBoaij59+kCpVMLX1xcrVqyQOhIREb0HSUcw5HI5GjRokGOCZvaEzSZNmuS53ffff49Zs2Zh//79aNiwYWFEJR3asmWLulz4+flh7dq1MDc3lzoWERG9B8lnzgUEBMDPzw8NGzZEo0aNsGTJEiQnJ8Pf3x8A4Ovri7Jly2Lu3LkAgO+++w7Tpk3D5s2b4eLiop6rYWdnBzs7O8neBxXMr7/+is8//xwqlQr+/v5YtWoVywURkRGQvGD4+PjgyZMnmDZtGuLi4lC3bl3s379fPfHz3r17OW5mtXz5cvUVHV8XGBiI6dOnF2Z00oJ9+/ZBpVLhyy+/xKpVq3jjMiIiIyF5wQCAESNGYMSIEbl+LSoqKsfjO3fu6D4QFZq1a9fC1dUV/v7+LBdEREaEP9Gp0B09ehRKpRIAYGFhgf79+7NcEBEZGf5Up0K1bt06tGzZEv3791eXDCIiMj4sGFRogoOD4e/vDyEErK2tIZPJpI5EREQ6woJBhWLt2rX48ssvIYTA0KFDERQUxMMiRERGjD/hSefWrFmDAQMGQAiBYcOGsVwQEZkA/pQnnXq9XIwYMQI//fQTD40QEZkAvThNlYxXiRIlYGlpiaFDh2LJkiUsF0REJoIFg3TKy8sLZ86cQc2aNVkuiIhMCA+RkNatW7cON2/eVD+uVasWywURkYlhwSCtWrZsGb744gu0atUKCQkJUschIiKJsGCQ1gQFBWH48OEAXt1j5oMPPpA4ERERSYUFg7Ri6dKl6vvJjB8/Ht9//z0PixARmTAWDHpvP/74I0aNGgUAmDBhAubNm8dyQURk4lgw6L1s3LgRo0ePBgBMmjQJc+fOZbkgIiKepkrvx9PTE7Vr14aXlxdmzZrFckFERABYMOg9OTk54fjx47CxsWG5ICIiNR4iIY3Nnz8fK1asUD+2tbVluSAiohw4gkEa+e677zBx4kQAwCeffIIGDRpInIiIiPQRRzAo3+bNm6cuFzNmzGC5ICKiPLFgUL7MmTMHkyZNAgDMmjUL06ZNkzgRERHpMx4ioXf69ttvMWXKFPXnkydPljgRERHpOxYMeqvDhw+ry8XroxhERERvw4JBb9WyZUtMmzYNNjY2mDBhgtRxiIjIQLBg0BuEEMjMzIRcLgfwakInERGRJjjJk3IQQiAwMBAeHh5ISUmROg4RERkoFgxSE0Jg2rRpmDVrFqKiorB7926pIxERkYHiIRIC8KpcTJkyBXPmzAEALFq0CD179pQ4FRERGSoWDIIQApMnT8a8efMAAIsXL8ZXX30lbSgiIjJoLBgmTgiBSZMm4bvvvgMA/PDDDxg1apTEqYiIyNCxYJi4R48eYeXKlQCApUuXYsSIERInIiIiY8CCYeLKli2LiIgI/P333xg4cKDUcYiIyEiwYJggIQTu3LmDChUqAADq1auHevXqSZyKiIiMCU9TNTFCCIwdOxZ16tTBiRMnpI5DRERGigXDhAghMGbMGCxevBhJSUm4dOmS1JGIiMhI8RCJiRBCYPTo0Vi6dCkAYOXKlRgwYIDEqYiIyFixYJgAIQRGjhyJoKAgAMCqVatYLoiISKdYMIycEAIjRozAsmXLIJPJsHr1anz55ZdSxyIiIiPHgmHkMjMzcefOHchkMqxZswb+/v5SRyIiIhPAgmHk5HI5fvvtNxw6dAgeHh5SxyEiIhPBs0iMkEqlwtatWyGEAAAoFAqWCyIiKlQsGEZGpVJhyJAh6NmzJ8aPHy91HCIiMlE8RGJEVCoVBg0ahDVr1sDMzAx169aVOhIREZkoFgwjoVKpMHDgQKxduxZmZmbYsGED+vTpI3UsIiIyUSwYRkCpVGLAgAEIDg6GmZkZNm3ahF69ekkdi4iITBjnYBiBQYMGITg4GObm5ti8eTPLBRERSY4Fwwi0atUKcrkcmzdvho+Pj9RxiIiIeIjEGHz++edwdXWFs7Oz1FGIiIgAcATDIGVlZWHixImIjY1VL2O5ICIifcKCYWCysrLg6+uL7777Dh4eHsjKypI6EhER0Rt4iMSAZGVloV+/ftiyZQssLCwwc+ZMWFjwfyEREekf/nYyEFlZWejbty9CQ0NhaWmJrVu3okuXLlLHIiIiyhULhgHIzMxE3759sXXrVlhaWuK3336Dl5eX1LGIiIjyxDkYBmDChAnYunUr5HI5tm/fznJBRER6jwXDAAQEBODjjz/G9u3b0alTJ6njEBERvRMPkegpIQRkMhkAoFy5coiJieGETiIiMhgcwdBDGRkZ6NGjB0JCQtTLWC6IiMiQsGDomfT0dHz22Wf47bff0L9/fzx58kTqSERERBrjn8V6JLtc7N69GwqFAtu3b0fx4sWljkVERKQxFgw9kZ6eju7du2PPnj1QKBTYtWsX2rZtK3UsIiKiAmHB0ANpaWno3r079u7dC4VCgT/++APu7u5SxyIiIiowzsHQA+vWrcPevXthbW2N3bt3s1wQEZHB4wiGHhg0aBCuX7+Ojh07onXr1lLHISIiem8sGBJJTU2Fubk55HI5ZDIZFi5cKHUkIiIireEhEgmkpqaiS5cu6NmzJzIyMqSOQ0REpHUcwShkKSkp6NKlC8LDw2Fra4urV6+idu3aUsciIiLSKhaMQpSSkgIvLy8cPHgQtra22LdvH8sFEREZJR4iKSTJycno1KkTDh48CDs7O+zfvx8tWrSQOhYREZFOcASjEGSXi6ioKNjb22P//v1o2rSp1LGIiIh0hgWjEFy9ehWnT5+Gvb09wsLC0KRJE6kjERER6RQLRiFo0KAB9uzZA7lcznJBREQmgQVDR16+fIkHDx6gWrVqAABXV1eJExERERUeTvLUgaSkJLRv3x4tWrTAhQsXpI5DRERU6FgwtCwxMRGenp44evQoMjMzkZaWJnUkIiKiQqcXBSMoKAguLi5QKBRo3LgxTp069db1t27dimrVqkGhUKBWrVrYu3dvISV9u7T0dHh6euL48eMoUqQIwsPD8cknn0gdi4iIqNBJXjBCQkIQEBCAwMBAREdHo06dOvDw8MDjx49zXf/48ePo3bs3+vfvj7Nnz8Lb2xve3t64ePFiISd/0+pVq3DixAkULVoU4eHhaNiwodSRiIiIJCETQggpAzRu3BiffPIJfvrpJwCASqWCs7MzRo4ciYkTJ76xvo+PD5KTk7F79271sk8//RR169bFihUr3vl6iYmJcHR0xIsXL+Dg4KCV9zBy42n8cfExnkWuhfk/kQgPD0f9+vW18txERET6QpPfoZKOYGRkZODMmTNwd3dXLzMzM4O7uztOnDiR6zYnTpzIsT4AeHh45Ll+eno6EhMTc3xom0z26r/WNjaIiIhguSAiIpMnacFISEiAUqlEyZIlcywvWbIk4uLict0mLi5Oo/Xnzp0LR0dH9Yezs7N2wr+mcqkiqFfOAVPHjkS9evW0/vxERESGxuivgzFp0iQEBASoHycmJmq9ZIxsUxkj21TW6nMSEREZMkkLhpOTE8zNzREfH59jeXx8PEqVKpXrNqVKldJofSsrK1hZWWknMBEREeWLpIdI5HI5GjRogIiICPUylUqFiIiIPC+p3aRJkxzrA8CBAwd4CW4iIiI9IvkhkoCAAPj5+aFhw4Zo1KgRlixZguTkZPj7+wMAfH19UbZsWcydOxcAMHr0aLi6umLhwoXo2LEjtmzZgr///hsrV66U8m0QERHRayQvGD4+Pnjy5AmmTZuGuLg41K1bF/v371dP5Lx37x7MzP5/oKVp06bYvHkzpkyZgsmTJ6Ny5crYuXMnatasKdVbICIiov+Q/DoYhU0X18EgIiIyBQZzHQwiIiIyTiwYREREpHUsGERERKR1LBhERESkdSwYREREpHUsGERERKR1LBhERESkdSwYREREpHUsGERERKR1LBhERESkdSwYREREpHUsGERERKR1LBhERESkdZLfrr2wZd88NjExUeIkREREhiX7d2d+bsRucgUjKSkJAODs7CxxEiIiIsOUlJQER0fHt64jE/mpIUZEpVLh0aNHsLe3h0wm08pzJiYmwtnZGffv34eDg4NWntPUcZ9qH/epdnF/ah/3qXbpYn8KIZCUlIQyZcrAzOztsyxMbgTDzMwM5cqV08lzOzg48JtCy7hPtY/7VLu4P7WP+1S7tL0/3zVykY2TPImIiEjrWDCIiIhI61gwtMDKygqBgYGwsrKSOorR4D7VPu5T7eL+1D7uU+2Sen+a3CRPIiIi0j2OYBAREZHWsWAQERGR1rFgEBERkdaxYBAREZHWsWDkU1BQEFxcXKBQKNC4cWOcOnXqretv3boV1apVg0KhQK1atbB3795CSmo4NNmnq1atQosWLVC0aFEULVoU7u7u7/x/YGo0/TeabcuWLZDJZPD29tZtQAOk6T59/vw5hg8fjtKlS8PKygpVqlTh9/5rNN2fS5YsQdWqVWFtbQ1nZ2eMGTMGaWlphZRW/x0+fBheXl4oU6YMZDIZdu7c+c5toqKiUL9+fVhZWeGjjz5CcHCw7gIKeqctW7YIuVwu1q5dKy5duiQGDhwoihQpIuLj43Nd/9ixY8Lc3Fx8//334vLly2LKlCnC0tJSXLhwoZCT6y9N92mfPn1EUFCQOHv2rLhy5Yr44osvhKOjo3jw4EEhJ9dPmu7PbLdv3xZly5YVLVq0EF26dCmcsAZC032anp4uGjZsKDp06CCOHj0qbt++LaKiokRMTEwhJ9dPmu7PTZs2CSsrK7Fp0yZx+/ZtERYWJkqXLi3GjBlTyMn11969e8U333wjtm/fLgCIHTt2vHX9W7duCRsbGxEQECAuX74sli5dKszNzcX+/ft1ko8FIx8aNWokhg8frn6sVCpFmTJlxNy5c3Ndv2fPnqJjx445ljVu3FgMHjxYpzkNiab79L+ysrKEvb29WLduna4iGpSC7M+srCzRtGlTsXr1auHn58eC8R+a7tPly5eLihUrioyMjMKKaFA03Z/Dhw8XrVu3zrEsICBANGvWTKc5DVV+Csb48ePFxx9/nGOZj4+P8PDw0EkmHiJ5h4yMDJw5cwbu7u7qZWZmZnB3d8eJEydy3ebEiRM51gcADw+PPNc3NQXZp/+VkpKCzMxMFCtWTFcxDUZB9+fMmTNRokQJ9O/fvzBiGpSC7NNdu3ahSZMmGD58OEqWLImaNWtizpw5UCqVhRVbbxVkfzZt2hRnzpxRH0a5desW9u7diw4dOhRKZmNU2L+bTO5mZ5pKSEiAUqlEyZIlcywvWbIkrl69mus2cXFxua4fFxens5yGpCD79L8mTJiAMmXKvPHNYooKsj+PHj2KNWvWICYmphASGp6C7NNbt27h4MGD6Nu3L/bu3YsbN25g2LBhyMzMRGBgYGHE1lsF2Z99+vRBQkICmjdvDiEEsrKyMGTIEEyePLkwIhulvH43JSYmIjU1FdbW1lp9PY5gkMGZN28etmzZgh07dkChUEgdx+AkJSWhX79+WLVqFZycnKSOYzRUKhVKlCiBlStXokGDBvDx8cE333yDFStWSB3NIEVFRWHOnDlYtmwZoqOjsX37duzZswezZs2SOhrlE0cw3sHJyQnm5uaIj4/PsTw+Ph6lSpXKdZtSpUpptL6pKcg+zbZgwQLMmzcP4eHhqF27ti5jGgxN9+fNmzdx584deHl5qZepVCoAgIWFBa5du4ZKlSrpNrSeK8i/0dKlS8PS0hLm5ubqZdWrV0dcXBwyMjIgl8t1mlmfFWR/Tp06Ff369cOAAQMAALVq1UJycjIGDRqEb775BmZm/PtYU3n9bnJwcND66AXAEYx3ksvlaNCgASIiItTLVCoVIiIi0KRJk1y3adKkSY71AeDAgQN5rm9qCrJPAeD777/HrFmzsH//fjRs2LAwohoETfdntWrVcOHCBcTExKg/OnfujFatWiEmJgbOzs6FGV8vFeTfaLNmzXDjxg11WQOA69evo3Tp0iZdLoCC7c+UlJQ3SkR2eRO8hVaBFPrvJp1MHTUyW7ZsEVZWViI4OFhcvnxZDBo0SBQpUkTExcUJIYTo16+fmDhxonr9Y8eOCQsLC7FgwQJx5coVERgYyNNU/0PTfTpv3jwhl8vFtm3bRGxsrPojKSlJqregVzTdn//Fs0jepOk+vXfvnrC3txcjRowQ165dE7t37xYlSpQQs2fPluot6BVN92dgYKCwt7cXv/76q7h165b4888/RaVKlUTPnj2legt6JykpSZw9e1acPXtWABCLFi0SZ8+eFXfv3hVCCDFx4kTRr18/9frZp6l+/fXX4sqVKyIoKIinqeqDpUuXivLlywu5XC4aNWokTp48qf6aq6ur8PPzy7F+aGioqFKlipDL5eLjjz8We/bsKeTE+k+Tffrhhx8KAG98BAYGFn5wPaXpv9HXsWDkTtN9evz4cdG4cWNhZWUlKlasKL799luRlZVVyKn1lyb7MzMzU0yfPl1UqlRJKBQK4ezsLIYNGyaePXtW+MH1VGRkZK4/F7P3o5+fn3B1dX1jm7p16wq5XC4qVqwofvnlF53l4+3aiYiISOs4B4OIiIi0jgWDiIiItI4Fg4iIiLSOBYOIiIi0jgWDiIiItI4Fg4iIiLSOBYOIiIi0jgWDiIiItI4Fg8jIBAcHo0iRIlLHKDCZTIadO3e+dZ0vvvgC3t7ehZKHiAqGBYNID33xxReQyWRvfNy4cUPqaAgODlbnMTMzQ7ly5eDv74/Hjx9r5fljY2PRvn17AMCdO3cgk8kQExOTY50ffvgBwcHBWnm9vEyfPl39Ps3NzeHs7IxBgwbh6dOnGj0PyxCZKt6unUhPeXp64pdffsmxrHjx4hKlycnBwQHXrl2DSqXCuXPn4O/vj0ePHiEsLOy9nzuv23e/ztHR8b1fJz8+/vhjhIeHQ6lU4sqVK/jyyy/x4sULhISEFMrrExkyjmAQ6SkrKyuUKlUqx4e5uTkWLVqEWrVqwdbWFs7Ozhg2bBhevnyZ5/OcO3cOrVq1gr29PRwcHNCgQQP8/fff6q8fPXoULVq0gLW1NZydnTFq1CgkJye/NZtMJkOpUqVQpkwZtG/fHqNGjUJ4eDhSU1OhUqkwc+ZMlCtXDlZWVqhbty7279+v3jYjIwMjRoxA6dKloVAo8OGHH2Lu3Lk5njv7EEmFChUAAPXq1YNMJoObmxuAnKMCK1euRJkyZXLcJh0AunTpgi+//FL9+Pfff0f9+vWhUChQsWJFzJgxA1lZWW99nxYWFihVqhTKli0Ld3d39OjRAwcOHFB/XalUon///qhQoQKsra1RtWpV/PDDD+qvT58+HevWrcPvv/+uHg2JiooCANy/fx89e/ZEkSJFUKxYMXTp0gV37tx5ax4iQ8KCQWRgzMzM8OOPP+LSpUtYt24dDh48iPHjx+e5ft++fVGuXDmcPn0aZ86cwcSJE2FpaQkAuHnzJjw9PdG9e3ecP38eISEhOHr0KEaMGKFRJmtra6hUKmRlZeGHH37AwoULsWDBApw/fx4eHh7o3Lkz/vnnHwDAjz/+iF27diE0NBTXrl3Dpk2b4OLikuvznjp1CgAQHh6O2NhYbN++/Y11evTogX///ReRkZHqZU+fPsX+/fvRt29fAMCRI0fg6+uL0aNH4/Lly/j5558RHByMb7/9Nt/v8c6dOwgLC4NcLlcvU6lUKFeuHLZu3YrLly9j2rRpmDx5MkJDQwEA48aNQ8+ePeHp6YnY2FjExsaiadOmyMzMhIeHB+zt7XHkyBEcO3YMdnZ28PT0REZGRr4zEek1nd2nlYgKzM/PT5ibmwtbW1v1x2effZbrulu3bhUffPCB+vEvv/wiHB0d1Y/t7e1FcHBwrtv2799fDBo0KMeyI0eOCDMzM5GamprrNv99/uvXr4sqVaqIhg0bCiGEKFOmjPj2229zbPPJJ5+IYcOGCSGEGDlypGjdurVQqVS5Pj8AsWPHDiGEELdv3xYAxNmzZ3Os89/by3fp0kV8+eWX6sc///yzKFOmjFAqlUIIIdq0aSPmzJmT4zk2bNggSpcunWsGIYQIDAwUZmZmwtbWVigUCvWtsBctWpTnNkIIMXz4cNG9e/c8s2a/dtWqVXPsg/T0dGFtbS3CwsLe+vxEhoJzMIj0VKtWrbB8+XL1Y1tbWwCv/pqfO3curl69isTERGRlZSEtLQ0pKSmwsbF543kCAgIwYMAAbNiwQT3MX6lSJQCvDp+cP38emzZtUq8vhIBKpcLt27dRvXr1XLO9ePECdnZ2UKlUSEtLQ/PmzbF69WokJibi0aNHaNasWY71mzVrhnPnzgF4dXijbdu2qFq1Kjw9PdGpUye0a9fuvfZV3759MXDgQCxbtgxWVlbYtGkTevXqBTMzM/X7PHbsWI4RC6VS+db9BgBVq1bFrl27kJaWho0bNyImJgYjR47MsU5QUBDWrl2Le/fuITU1FRkZGahbt+5b8547dw43btyAvb19juVpaWm4efNmAfYAkf5hwSDSU7a2tvjoo49yLLtz5w46deqEoUOH4ttvv0WxYsVw9OhR9O/fHxkZGbn+opw+fTr69OmDPXv2YN++fQgMDMSWLVvQtWtXvHz5EoMHD8aoUaPe2K58+fJ5ZrO3t0d0dDTMzMxQunRpWFtbAwASExPf+b7q16+P27dvY9++fQgPD0fPnj3h7u6Obdu2vXPbvHh5eUEIgT179uCTTz7BkSNHsHjxYvXXX758iRkzZqBbt25vbKtQKPJ8Xrlcrv5/MG/ePHTs2BEzZszArFmzAABbtmzBuHHjsHDhQjRp0gT29vaYP38+/vrrr7fmffnyJRo0aJCj2GXTl4m8RO+LBYPIgJw5cwYqlQoLFy5U/3Wefbz/bapUqYIqVapgzJgx6N27N3755Rd07doV9evXx+XLl98oMu9iZmaW6zYODg4oU6YMjh07BldXV/XyY8eOoVGjRjnW8/HxgY+PDz777DN4enri6dOnKFasWI7ny57voFQq35pHoVCgW7du2LRpE27cuIGqVauifv366q/Xr18f165d0/h9/teUKVPQunVrDB06VP0+mzZtimHDhqnX+e8IhFwufyN//fr1ERISghIlSsDBweG9MhHpK07yJDIgH330ETIzM7F06VLcunULGzZswIoVK/JcPzU1FSNGjEBUVBTu3r2LY8eO4fTp0+pDHxMmTMDx48cxYsQIxMTE4J9//sHvv/+u8STP13399df47rvvEBISgmvXrmHixImIiYnB6NGjAQCLFi3Cr7/+iqtXr+L69evYunUrSpUqlevFwUqUKAFra2vs378f8fHxePHiRZ6v27dvX+zZswdr165VT+7MNm3aNKxfvx4zZszApUuXcOXKFWzZsgVTpkzR6L01adIEtWvXxpw5cwAAlStXxt9//42wsDBcv34dU6dOxenTp3Ns4+LigvPnz+PatWtISEhAZmYm+vbtCycnJ3Tp0gVHjhzB7du3ERUVhVGjRuHBgwcaZSLSW1JPAiGiN+U2MTDbokWLROnSpYW1tbXw8PAQ69evFwDEs2fPhBA5J2Gmp6eLXr16CWdnZyGXy0WZMmXEiBEjckzgPHXqlGjbtq2ws7MTtra2onbt2m9M0nzdfyd5/pdSqRTTp08XZcuWFZaWlqJOnTpi37596q+vXLlS1K1bV9ja2goHBwfRpk0bER0drf46XpvkKYQQq1atEs7OzsLMzEy4urrmuX+USqUoXbq0ACBu3rz5Rq79+/eLpk2bCmtra+Hg4CAaNWokVq5cmef7CAwMFHXq1Hlj+a+//iqsrKzEvXv3RFpamvjiiy+Eo6OjKFKkiBg6dKiYOHFiju0eP36s3r8ARGRkpBBCiNjYWOHr6yucnJyElZWVqFixohg4cKB48eJFnpmIDIlMCCGkrThERERkbHiIhIiIiLSOBYOIiIi0jgWDiIiItI4Fg4iIiLSOBYOIiIi0jgWDiIiItI4Fg4iIiLSOBYOIiIi0jgWDiIiItI4Fg4iIiLSOBYOIiIi07v8A1k+hWenkwRsAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 600x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.metrics import roc_curve, roc_auc_score\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"y_scores = model.predict_proba(XTest)\n",
"fpr, tpr, thresholds = roc_curve(yTest, y_scores[:,1])\n",
"\n",
"fig = plt.figure(figsize=(6, 6))\n",
"plt.plot([0, 1], [0, 1], 'k--')\n",
"plt.plot(fpr, tpr)\n",
"plt.xlabel('False Positive Rate')\n",
"plt.ylabel('True Positive Rate')\n",
"plt.title('ROC Curve')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.9749908725812341\n"
]
}
],
"source": [
"auc = roc_auc_score(yTest,y_scores[:,1])\n",
"print(auc)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.5"
},
"metadata": {
"interpreter": {
"hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
}
},
"orig_nbformat": 2
},
"nbformat": 4,
"nbformat_minor": 2
}
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