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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": 19,
"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": 19,
"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": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<bound method DataFrame.info of 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\n",
"... ... ... ... ... ... ...\n",
"1694 ST. LOUIS 24 inch bins HOWDEN WHITE TYPE ILLINOIS xlge WHITE\n",
"1695 ST. LOUIS 24 inch bins HOWDEN WHITE TYPE ILLINOIS xlge WHITE\n",
"1696 ST. LOUIS 24 inch bins HOWDEN WHITE TYPE ILLINOIS xlge WHITE\n",
"1697 ST. LOUIS 24 inch bins HOWDEN WHITE TYPE ILLINOIS xlge WHITE\n",
"1698 ST. LOUIS 24 inch bins HOWDEN WHITE TYPE ILLINOIS xlge WHITE\n",
"\n",
"[991 rows x 6 columns]>"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"columns_to_select = ['City Name', 'Package', 'Variety','Origin','Item Size', 'Color']\n",
"pumpkins = full_pumpkins.loc[:,columns_to_select]\n",
"pumpkins.dropna(inplace=True)\n",
"pumpkins.info"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"d:\\DEV WORK\\Data Science Library\\ML-For-Beginners\\.venv\\lib\\site-packages\\seaborn\\axisgrid.py:123: UserWarning: The figure layout has changed to tight\n",
" self._figure.tight_layout(*args, **kwargs)\n"
]
},
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x139202c0850>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"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(\n",
"data=pumpkins, y=\"Variety\", hue=\"Color\", kind=\"count\",\n",
"palette=palette, \n",
")"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['med', 'lge', 'sml', 'xlge', 'med-lge', 'jbo', 'exjbo'],\n",
" dtype=object)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#select the unique item sizes\n",
"pumpkins['Item Size'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"OrdinalEncoder(categories=[['med', 'lge', 'sml', 'xlge', 'med-lge', 'jbo',\n",
" 'exjbo']])\n"
]
}
],
"source": [
"from sklearn.preprocessing import OrdinalEncoder\n",
"#encode the item size column\n",
"Item_size_categories =[['med', 'lge', 'sml', 'xlge', 'med-lge', 'jbo', 'exjbo']]\n",
"ordinal_features = ['Item Size']\n",
"ordinal_encoder = OrdinalEncoder(categories=Item_size_categories)\n",
"print(ordinal_encoder)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"OneHotEncoder(sparse_output=False)\n"
]
}
],
"source": [
"from sklearn.preprocessing import OneHotEncoder\n",
"#encode all other features useing one hot encoder\n",
"\n",
"categorical_features =['City Name', 'Package', 'Variety', 'Origin']\n",
"categorical_encoder = OneHotEncoder(sparse_output=False)\n",
"print(categorical_encoder)\n"
]
},
{
"cell_type": "code",
"execution_count": 32,
"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>0.0</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>0.0</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>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>5</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>6</th>\n",
" <td>0.0</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 0.0 0.0 1.0 \n",
"3 0.0 0.0 1.0 \n",
"4 1.0 0.0 1.0 \n",
"5 1.0 0.0 1.0 \n",
"6 0.0 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": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.compose import ColumnTransformer\n",
" \n",
"ct = ColumnTransformer(transformers=[\n",
" ('ord', ordinal_encoder, ordinal_features),\n",
" ('cat', categorical_encoder, categorical_features)\n",
" ])\n",
" \n",
"ct.set_output(transform='pandas')\n",
"encoded_features = ct.fit_transform(pumpkins)\n",
"encoded_features.head()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" 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_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",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
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" <td>0.0</td>\n",
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" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.0</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",
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" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</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",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0.0</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",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 49 columns</p>\n",
"</div>"
],
"text/plain": [
" ord__Item Size cat__City Name_ATLANTA cat__City Name_BALTIMORE \\\n",
"2 0.0 0.0 1.0 \n",
"3 0.0 0.0 1.0 \n",
"4 1.0 0.0 1.0 \n",
"5 1.0 0.0 1.0 \n",
"6 0.0 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_NEW JERSEY cat__Origin_NEW YORK \\\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_NORTH CAROLINA cat__Origin_OHIO cat__Origin_PENNSYLVANIA \\\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_TENNESSEE cat__Origin_TEXAS cat__Origin_VERMONT \\\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_VIRGINIA Color \n",
"2 0.0 0 \n",
"3 1.0 0 \n",
"4 0.0 0 \n",
"5 0.0 0 \n",
"6 0.0 0 \n",
"\n",
"[5 rows x 49 columns]"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# transform the label\n",
"from sklearn.preprocessing import LabelEncoder\n",
"# encode the 'Color' column using label encoding\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.head()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['ORANGE', 'WHITE']"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#verify the transformation is right\n",
"list(label_encoder.inverse_transform([0,1]))"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\jnopa\\AppData\\Local\\Temp\\ipykernel_10524\\666838959.py:13: 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(\n",
"d:\\DEV WORK\\Data Science Library\\ML-For-Beginners\\.venv\\lib\\site-packages\\seaborn\\axisgrid.py:123: UserWarning: The figure layout has changed to tight\n",
" self._figure.tight_layout(*args, **kwargs)\n"
]
},
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x13920205ff0>"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 829.375x1620 with 9 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# use seaborn to visualize the new data\n",
"\n",
"palette ={\n",
" 'ORANGE': 'orange',\n",
" 'WHITE': 'white'\n",
"}\n",
"\n",
"## what is plotted on the x- axis??\n",
"# encoded Item Size\n",
"\n",
"pumpkins['Item Size'] = encoded_pumpkins['ord__Item Size']\n",
"\n",
"g = sns.catplot(\n",
" data=pumpkins,\n",
" x=\"Item Size\", y=\"Color\", row='Variety',\n",
" kind=\"box\", orient=\"h\",\n",
" sharex=False, margin_titles=True,\n",
" height=1.8, aspect=4, palette=palette,\n",
" )\n",
"\n",
"## define axis labels\n",
"g.set(xlabel=\"Item Size\", ylabel=\"\").set(xlim=(0,6))\n",
"g.set_titles(row_template=\"{row_name}\")"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"d:\\DEV WORK\\Data Science Library\\ML-For-Beginners\\.venv\\lib\\site-packages\\seaborn\\categorical.py:3370: 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",
"d:\\DEV WORK\\Data Science Library\\ML-For-Beginners\\.venv\\lib\\site-packages\\seaborn\\categorical.py:3370: 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": 40,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"d:\\DEV WORK\\Data Science Library\\ML-For-Beginners\\.venv\\lib\\site-packages\\seaborn\\categorical.py:3370: 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",
"d:\\DEV WORK\\Data Science Library\\ML-For-Beginners\\.venv\\lib\\site-packages\\seaborn\\categorical.py:3370: 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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31tHZ8AYQjR+j3Cp8RdOJhdsJ1a0nFo5/XixvHoHyBWB76Wx4nUhbwjXZcvCXzsXJKqKraRtd+951t2XEJLwFk4iGGs35jnbF2xIoxF86H4gRqttANHQw4Rj5CJQvxPbl0bl/E+HGLa5yfUXT8eSNJtLWQKjhNfNlr6ctORX4imdCJGTOd7g1Xl1PDv7yhdgJyXSmGcz4PeyJypNPPslbb711WNsPWaLy+5nQfIh1GncVVCw3A1dfWRVwzpuwaoz5NtrXso1mcE5MRHpMu80Mfn0Hb4DCubDwAXhqanLM9sL5W+G5T0LLR8nxBQ/Avo3w4crk2OgvwNir4IXlyTH/SFi+CVaNh3BzcvysdbDpbtj9VHJsyk0mEXrt+uRY/lRY/Hv4/QTXhxAwA/F5m03ClZhU9Ziz0iQSm+5OjlUsN/tdsyQ55smD87fAU1PcCUOPxU/B1gdgx2+SYxOvh5FzYcNVybHccbD0RVg11nWh6rX8PVh/FezfmBybebe5Q/XObcmxkiUm/szc5JgTgPO3w+qZ0L47OX76b8w5+eiB5NiR9N1ULhu2S8mQCrfu7h7w3CzHj79iER07n0/uu4C/4nS69m9yJyLdvCMmge0kDd4Ati8fX/EMOna9kKI2FoHKM7sH2bakqK/oVPOtvHl7UszJKceTW2kSnKSdeglULqGjZq37S0hPW8oW0tW4hWh7Q1LMkz8Oy5OdNHiDGfz9pXPNMUohUHkmofqNrqSqh3fkKcTCbYQbk69jdlYJ3vzxhOrWJxdqeQhUnkHHrnUpP4f+0rmEW3b23t1xtSVvDLZ/BJ17/5Ki2Gz8ZQu725Lc1wOjFtO55y2iCUlVb1sKp0A0kpR4AtiBkXgLpxDa/XKKttjmGO1+iVhCstvDVzKLSFsDkZadSTEntxJ/8YzkMjPEYMbvYX30A/Dhhx9SUVFBIBBg4cKF3HHHHYwePTrle0OhEKFQ/GQ1NTUNTaUONUkBcxs+1aAEZuD48N70F/rtj6ROUsB8W/fkpI7t3whb/yd1LNoFm3+aOknpKXdfigsVmDsMiY+VEoX2mbakSlLAPEaqfTr9PnPGpo41vgdbfpHyQk8sCpvvS52k9JTbmnxBBkxdcsenjoWbTVtSJSk95e5clSb2ROqEAMwx3/zT9P1h64OpkxQwj64S74Ykalhn+koqkQ7YfG/6OtU8kf68HEnfPYFEWutTvh6LhExCkKrvApGW3SmTFIBwW13vY4a+op2NhFMMPN17Jdy8PWWSAhBpqyMaSh4owbQj8TGDe6dd5s5EiiQFINxamzJJ6S034XGLq7ZdzYSbd6TeJxBu3pEySTHl1hFL85mItjcQTrNPYmHCzdvS9u1wW737bkniPtvqUiYEptg2wi07SJWkAIRbdqZMUqC7LWnuSkY79hFu2ZUyRixqjlGaOkVa64gk3sVKjKVp4/FoWCfTzp8/nwceeIDVq1dz7733snXrVhYtWkRzc+qOe8cdd5Cfn9/7U1VVdYxrnILjB9ufPu5Nk2yAucWe5mKF3V+5ltk2bblpPsA95Tppyu13nwOU6wTASnMRdALp9wn9t8UzwD7TlWt5B9jnAOXavsHHBiw3B0jzSLG/ci0H7H5u4aZLaAcq90j67onESn+ZtKx+vutZNunOt2XZ/Zab9row4D6d9OUOsM8B29JPudZhtoV+5lGYORb97LOf4StdEmhidj/l9nP8AIt+2tJfO/s9Rla/9e2/3AHO98fEsLbk3HPP5Qtf+ALTp09n2bJl/L//9/84ePAgv/71r1O+/5ZbbqGxsbH3p6amJuX7jtiYfzj091Zfbn5SCU42jwp8hckxy4axXzTzRgZbbvmnzLyEVBcWTy5M/AczkXWw5VZfZn5SyamGSTeYSbBJLFPm6ItSbzvmsvT7LF4EE651T+zsYftNrHhRcmygckdfBNVXkHKgCJSZtuRUp962up9y+zt+BbPMsffkJscsD4y/2sytGWy5lRfA+KtSX3h8BTDxOghOGXy5h9t3TzCe3FEpX7e8uXiC1fHJ4n23y6vEyS5NGXNyRqUt184qxpNXRcq+azl48sdg+fJTl5s7CidNuZ7cCpycNG3xZOEEq9Mm997cSpyc8pQxT+6otOXa/kK8wdGp+65l480bje1P3cecnIq0x8jJKceTV5kyhuPHCY7F8qT+AtTfse/v+Fm+fDz5Y9IkDhaevCrsrOKU2/Z3jJzs0u7znYLtxRusxvKmuKZ0l+tJU266149HGZVyjRgxgkmTJrF58+aUcb/fTzAYdP0MidPuPbT3lS2F6bdD2ZlmTkliB86uhNMeAU8WnP5r9yMV2w9z/9tM9pyz0kw4TVR9uRl8xl0N46/BdcHKn2omUGZXmvkmiXcjvEGzT1++mUSZ+LjFss2cjcrzYOo3oeLT7n0WL4IZd5kVPDPucl98A2WmDZ4ALHoM/AmTnW0fzL7HrGqZ/T0YOd9dbtXnYPKNZkXSpBXutuRNNHNtAsWmvol3Bjw55rVAsXlP3qSEQi2TaFRfYsqu+rx7nyPnm7oUTIfZ33ffUfAXmXkbnoD5b2LiZXlgxp3dx+DO5ASpYrk5dpXnmcnQiRffnGpTX1++mXicuFLHyYIF95tzNu+n5hwmtmX8NeZcT7yuO7lKUDDD9JG8CabPJN4B8RV0tyXbnPfElVmWA6fcavrm9NtNX010uH03nfI0SerHgJNVZOaUJPRdywngL56FZTv4S2a7Py+Wja9oOrY3F9/IU7B87uuUkzMKT7AaJ7cKJ9c9QFnePLOtJwtf8anuPmZ58JfMwrK9+ItnYvW52+jJH4cnuxRv/gTsrBJXzPYX4i2cghMowFsw2dUWHD++ktnYvW1JvANn4y2ciu0P4iuc6lrNBOBkl5n95laYpC2xLZ4cfMWnYjl+M+kzsY9ZDr7imd2xU7H63L3zBKvNIJw/DifbnSDZ/hGmLr4g3sKpuIYx24e/uy2+ktl9Ei8Lb8FkcwwKJyclSGbeywQ82aV48t2PjS1Pljnmthd/ySz3l0TLxld8qjlnRdOxvHmubXvOsydYnZSsWL4gvpGnYHtz8BVNd59v24u/ZHZ3H5uF5foyZ+EZMdH0zYJJ2AH3AhQ7YF7/uBjWybR9tbS0MHr0aG677Ta++tWvDvj+IV31A/DilbDzQfP/WWPhsx/B3leh6X0IngxF89zvb9sJdc+bJZ7ly9wXr3Ab7H7aPPOvOMe9LDcWg4YXzMTQwrnuZblglrru3WAGkNIz40uMwSx13b3aLGcuP9e9LDcahrpnzTLlksXuZbkA+9/oXp58kll5k6i91iy/9QZNuQnLDIl0mLaEW83dnYD7okjDy9Cy2dxhKJjujjVvgT0vm+WvZUvdH8yuJlMuQMW57sE+FjX1aa+F4tPdy3IBDrwdX55c0ifB6GiA2j+a5KfiXPfdm0inWYnU1Wjqk9XnW+Oe9fHlyYV97lK1bDPnLVBiln4nLJmkq8XMDYmGzflOHOxjMbOsua0Giha6l5QDNL4fX55csth9vkP7zPl2AqYtiYNVtAtqnzHLk8vOdCcucHT7buKquI/pJNq+ouF2ou37sBwvdlax61Z+LBoh0t4AsQhOVolrWa5Z6rqPWLgd2z/CtSwXzFLXaOgAlicLOzCydykrmKWukfY9YFmm3IQ+FotFibbvJRYJYQdGupblAkRDjUQ7m7C8ua4lxmCWukba94Ltwcku6actxa5luQCRjv3Eulqx/fnYfZKwaFerWWrtBFzLpU25XUTazHwKJ7vYNV/GLG3eSyzSgR0odC3LNceoiWioEcub7VpibI5RqPsYOd3HyEkoN0qkrQGiYZysIteSctOWA8S6lyfbfvddqmhXG9GOfViOv7sticco3H2MYt3HKN35LnAtKTdtae5enpzufDekbUu0fQ+xSBd21kjXknKASOgAsc4WLF8ujv8QvlwMs+Nm1c8///M/c9555zFmzBh2797NrbfeyltvvcX7779PcXHqW2iJhjxRERERkaPuuFn1s3PnTi699FL27dtHcXExp59+Ohs2bDikJEVEREQ+/oY1UXnkkTRLLkVERETIsMm0IiIiIomUqIiIiEjGUqIiIiIiGUuJioiIiGQsJSoiIiKSsZSoiIiISMZSoiIiIiIZS4mKiIiIZCwlKiIiIpKxlKiIiIhIxlKiIiIiIhlLiYqIiIhkLCUqIiIikrGUqIiIiEjGUqIiIiIiGUuJioiIiGQsJSoiIiKSsZSoiIiISMZSoiIiIiIZS4mKiIiIZCwlKiIiIpKxlKiIiIhIxlKiIiIiIhlLiYqIiIhkLCUqIiIikrGUqIiIiEjGUqIiIiIiGUuJioiIiGQsJSoiIiKSsZSoiIiISMZSoiIiIiIZS4mKiIiIZCwlKiIiIpKxlKiIiIhIxlKiIiIiIhlLiYqIiIhkLCUqIiIikrGUqIiIiEjGUqIiIiIiGUuJioiIiGQsJSoiIiKSsZSoiIiISMZSoiIiIiIZS4mKiIiIZCwlKiIiIpKxPMNdgUR33nknt9xyC1/72te45557hrs68LDl/vfF7bD919D4HuRPhTEXgROIx+vXQu2z4C+E6sshqzwea94C2x+BSAdUfQYKZ8djXU2w7WFo3QYj58Go88HuPjWxKOx6Cva+AlmVMPZy8BXEtz3wF6h5HCwHxlwMwZPisY4G2Pq/ENoDpWdA2dlgdbcpEoKa38LBtyFvEoy5BDzZ8W0bXoLa1eANmrZkV8ZjLdtg+68g3AqjPg1FCxLa0mJiLVugYBZUfRZsb7wtu1fDnpcgUAZjrwD/yPi2B9+DmsfM/1d9HkZMjcdC+2DbQ9BeC8WLoOIcsLrz7GgX1DwBB96A3PEw5lLw5sa33bsBdv0BPDkmllsdj7XtNOV2NUH5OVCyKB4Lt8H2R6H5bzBiOlR9Dhx/d1tiUPcc1D8P/mLTlkBJfNumv5ltYxGouhAKTo3HOg/A1oegfScUfcIcw962hGHXKtj3KuRUQ/Vl5hz02P861Dxp6jHmUsgbH4+115q2hPZD+dnmnPeIdBy9vvvG7dCwKh6vuBSWPIxkjlgsQqS1jmhnE7Y3BydnFJbt9MYjHfuItO3Bsj04uaOwPVm9sWhXG5HW3cSiYZzsUpxA/HoTi4aJtOwiGm7D9uXj5JRhdffdWCxGtL2BSMd+LMePJ7cSy/HFy+1sJtJaC4CTU47ty4uXG+kk3LKLWKQDJ1CInVWC1X2tisWi3W1pxPZk4+SOwrLjQ1ek4wCRtnrTlpwKbG/8OhYNtxNp2dXdlmKcQPx6E4tGiLTuJtrVgu0LdrfFibelYy+R9r3dbRmF1fPZl2POisViseGuBMDGjRu56KKLCAaDnHHGGYeUqDQ1NZGfn09jYyPBYHDA9w9K3yQllbyJcNY6yCqDP11hBugeThYsetwMqFvuh1evNYNWjyn/AjPvMoPz82dBR308NnIBnPlHsDyw7u+gYV085h8JZ/wRCmfBO7fDO7cmVMiCOT+ESddD/Tp44TwIt8TDlRfA6Y9B50FYs8QMWj1yqmHpOsgeDX++Bj76RTxm++C0R80gte0RWP/3EAvH4xOvh7k/gubN8NwSaN8VjxXMhLPWgJMDL15gkp8e3nxY8jQUL4RN34E3b3If3xn/CSffZBKNtedAV2M8Vr4MPrkKIm2w5iyTpPTIGgVnrYXgRHhtBXzwo4RD5IGFD0L1pbDzd/DyRRDtjMfHXQ3z74O2HaYtrdvisfypplxfAbz8Bdj5ZDzmyYHFf4DSJfDBj+G1G4CEj9a022DarbD/TVh7tkm8epQsNschFoHnz4Z9G+KxQAmc+bxJ2t66Gd6/K6EtNsz7GYz/Eux+Bl76LETa4/Exl8AnHjJ967nF0PxhPHYkfTclP1zWMcB75FiIRTrpqF1PrKu59zXLk4W/bCGWJ4vOvW8TaalJ2MLGVzILT04Z4ZZddO55i8S+68kbg69oGtGuVkK164lF4ufZ8gUJlC0A20OofiPR9j0JxXrwl87HCRTQ1biFrv2bXPX0FkzBO2I8kY4DhOr/bJL0nk2zivGXzoVohI66DcQ64599ywngL1+A7c2lc9+7hJu2JZRq4SuegSd3FOHWOjob3gCivVEntwpf0XRi4XZCdeuJheOfF8ubR6B8AdheOhteJ9KWcE22HPylc3GyigY6/HKIBjN+Z0Si0tLSwqxZs/jxj3/Mv/3bvzFjxozhTVR+UwFdtYf23nFXQcVyM3D1lVUB57wJq8aYb6N9LdtoBufERKTHtNvM4Nd38AYonAsLH4CnpibHbC+cvxWe+yS0fJQcX/AA7NsIH65Mjo3+Aoy9Cl5Ynhzzj4Tlm2DVeAg3J8fPWgeb7obdTyXHptxkEqHXrk+O5U+Fxb+H308wd1wSWTact9kkXIlJVY85K00isenu5FjFcrPfNUuSY548OH8LPDXFnTD0WPwUbH0AdvwmOTbxehg5FzZclRzLHQdLX4RVY81dnr6Wvwfrr4L9G5NjM+82d6jeuS05VrLExJ+ZmxxzAnD+dlg9E9p3J8dP/405Jx89kBw7kr6bymXDfikRoHPvO4Sbtye97uSU48mtJFSfov/ZXgKVS+ioWev+EtLNX7aQrsYtRNsbkmKe/HFYnmy69r2bFLO8efhL59Kx8/mUdQ1UnkmofqMrqerhHXkKsXAb4cbk65idVYI3fzyhuvXJhVoeAlVn0LFzXcrPob90LuGWnb13d1xtyRuD7R9B596/pCg2m0DlGb13euTIDGb8zohHP9dffz3Lly9n6dKl/Nu//Vva94VCIUKhUO+/m5qahqZCh5qkgLkNn2pQAjNwfHhv+gv99kdSJylgvq17clLH9m+Erf+TOhbtgs0/TZ2k9JS7L8WFCswdhsTHSolC+0xbUiUpYB4j1T6dfp85Y1PHGt+DLb9ITlLAvLb5vtRJSk+5rckXZMDUJXd86li42bQlVZLSU+7OVWliT6ROCMAc880/Td8ftj6YOkkB8+gq8W5IooZ1pq+kEukwbUlXp5on0p+XI+m7krFcdwISX2+tx+p5DNtXtMvcmUiRpACE22pTJim95SY8bkkU62om3LwjbV3DzTtSJimm3DpiaT4T0fYGwp7U+yQWJty0PW3fDrfVpz9GbXXEIqGUsVi4jVhnM5b/KN+9lwENe6LyyCOP8MYbb7BxY5oLeII77riDb3/728egVoPg+MHu59llumQDzC12y0l9W93ur1zLbJt2n2k+wD3lpnvW2u8+Aaefcp0AWF6IpfiQO4H0+4TDb0t/5VreAfY5QLm2D6Jp2mL7kl/v0W99cwAL1yOhxHLTPV6xnAGOUX99rJ/6DtR3vf2UK5nLSrNGwrLTxwDLSj8cWHRvm+oLhWX3zlNJKWFuzGBilu0QixxuW/op17Ix60hStcXpt9x+YzJkhvWo19TU8LWvfY2HHnqIQCAw4PtvueUWGhsbe39qamoG3OawFKd49JFO9eXmJ5XgZDNfxFeYHLNsGPtFM29ksOWWf8rMS0h1YfHkwoR/MBNZB1tu9WXmJ5Wcaph0g5kEm8QyZY6+KPW2Yy5Lv8/iRTDhWvfEzh6238SKFyXHBip39EVQfYWpW1+BMtOWnOrU21b3U25/x69gljn2ntzkmOWB8VebuTWDLbfyAhh3ZeqLpK8AJl0HwSmDL3egvjsxTd+VjObkjkr5uie3AicndczyZOEEq9Mm957cSpyc8jSxUWnLtf2FePNGp+67lo03bzS2P3Ufc3Iq8KRpi5NTjjevMmUMx48TrMbypE7unZxR6cvNHZX2+Fm+fGxfis+2DLlhTVRef/11GhoamDVrFh6PB4/HwwsvvMAPfvADPB4PkYj7G6bf7ycYDLp+hsTZfzi095Uthem3Q9mZZk5JYhafXQmnPQKeLDj91+5HKrYf5v63mew5Z6WZcJqo+nKYeJ2Z2Dn+GlyDbf5UM4Eyu9LMN0n8pu0Nmn368s0kysTHLZZt5mxUngdTvwkVn3bvs3gRzLjLrOCZcVd8pQ6Ygf30X4MnAIseA3/ChDLbB7PvMataZn8PRs53l1v1OZh8o1mRNGmFuy15E81cm0CxqW/inQFPjnktUGzekzcpoVDLJBrVl5iyqz7v3ufI+aYuBdNh9vfddxT8RWbehidg/puYeFkemHFn9zG4MzlBqlhujl3leWYydOLFN6fa1NeXbyYeJ67UcbJgwf3mnM37qTmHiW0Zf4051xOv606uEhTMMH0kb4LpM4l3QHwF3W3JNuc9cWWW5cApt5q+Of1201cTHW7fTafg9IHfI8eEN38CdlaJ6zXbX4i3cApOoABvwWRcn0PHj69kNrbt4C+Z3ecOnI23cCq2P4ivcCq2f4SrXCe7DE/+ODy5FXiC1a6Y5cnBV3wqluPHVzzT3ccsB1/xzO7YqVh97t55gtV4ckfhyR+Hk+1OkGz/CFMXXxBv4VRcw5jtw9/dFl/J7D6Jl4W3YLI5BoWTkxIkM+9lAp7sUjz57sfGlicLf3Gf67QcM8M6mba5uZnt291zDK6++momT57MN77xDU455ZR+tx/SVT8Aq8+E/WvN/1tBuLQR9r4KTe9D8GQomud+f9tOqHveLPEsX+Ye7MNtsPtp88y/4hz3stxYDBpeMBNDC+e6l+WCWeq6d4MZQErPjC8xBrPUdfdqs5y5/Fz3stxoGOqeNcuUSxa7l+UC7H+je3nySWblTaL2WrP81hs05SYsMyTSYdoSbjV3dwLuiyINL0PLZnOHoWC6O9a8Bfa8bJa/li11D/ZdTaZcgIpz3YN9LGrq014Lxae7l+UCHHg7vjy5pE+C0dEAtX80yU/Fue67N5FOsxKpq9HUJ6vPt8Y96+PLkwv73KVq2WbOW6DELP1OWDJJV4uZGxINm/OdONjHYmZZc1sNFC10LykHaHw/vjy5ZLH7fIf2mfPtBExbEh81Rbug9hmzPLnsTHfiAke37yauitMk2owUDTUS7WzC8ua6lhgDxMIdRNr3gu3ByS5xPbqJRSNE2hsgFsHJKk5alhvp2E+sqxXbn4/tc193o12tRDv2YzkB7Kwi18TTWLSLSJtZFeRkF7vmy5ilzXuJRTqwA4XYfRKXaGcT0VAjljfbtcQYIBYJEWnfA5aDk1XiWoYdi0WJtDVANIyTVYTlcd+5jXQcINa9PNn25/dpSxvRjn1Yjr+7LXrsczQdd6t+Ei1ZsmT4V/2IiIjIkBnM+K0UUURERDLWsK/66WvdunXDXQURERHJEId1R+XgwYP8/Oc/55ZbbmH//v0AvPHGG+zatWuALUVEREQO3aDvqLz99tssXbqU/Px8tm3bxrXXXkthYSGPP/44O3bs4MEHHxyKeoqIiMgJaNB3VG688UauuuoqPvzwQ9fvPvm7v/s7XnzxxaNaORERETmxDTpR2bhxI//3//7fpNdHjRpFXV3dUamUiIiICBxGouL3+1P+jZ0PPviA4uLio1IpERERETiMROX888/n9ttvp6vL/MEny7LYsWMH3/jGN/jc5z531CsoIiIiJ65BJyr/9V//RUtLCyUlJbS3t7N48WImTJhAXl4e//7v/z4UdRQREZET1KBX/eTn5/Pss8/y8ssv8/bbb9PS0sKsWbNYunTpwBuLiIiIDMKgE5UdO3ZQWlrK6aefzumnx/8QWSwWo6amhtGjRx/VCoqIiMiJa9CPfqqrq5k1axZbtmxxvd7Q0MDYsWPTbCUiIiIyeIf1m2mnTJnCvHnzWLNmjev1DPv7hiIiInKcG3SiYlkWP/7xj/n//r//j+XLl/ODH/zAFRMRERE5WgY9R6Xnrsk//dM/MXnyZC699FLeeecdvvWtbx31yomIiMiJ7Yj+evK5557Ln/70J84//3xeffXVo1UnEREREeAwHv0sXrwYn8/X+++TTz6ZP//5z4wYMUJzVEREROSosmLHcXbR1NREfn4+jY2NBIPB4a6OiIiIHILBjN+H9Oinqampt6BUf+cnkRIGEREROVoOKVEpKCigtraWkpISRowYkXJ1TywWw7IsIpHIUa+kiIiInJgOKVF5/vnnKSwsBGDt2rVDWiERERGRHpqjIiIiIsfUYMbvQ171s3fvXrZv3+567b333uPqq6/moosu4uGHHz682oqIiIikcciJyooVK1y/hbahoYFFixaxceNGQqEQV111Ff/zP/8zJJUUERGRE9MhJyobNmzg/PPP7/33gw8+SGFhIW+99Ra/+93v+I//+A9Wrlw5JJUUERGRE9MhJyp1dXVUV1f3/vv555/nwgsvxOMx83HPP/98Pvzww6NeQRERETlxHXKiEgwGOXjwYO+/X331VebPn9/7b8uyCIVCR7VyIiIicmI75ERlwYIF/OAHPyAajfLYY4/R3NzMmWee2Rv/4IMPqKqqGpJKioiIyInpkP8o4b/+679y1lln8b//+7+Ew2G++c1vUlBQ0Bt/5JFHWLx48ZBUUkRERE5Mh5yoTJ8+nU2bNvHKK69QVlbmeuwDcMkll3DyyScf9QqKiIjIiUu/8E1ERESOqSH5hW8iIiIix5oSFREREclYSlREREQkYylRERERkYx1yKt++mpoaKChoYFoNOp6ffr06UdcKRERERE4jETl9ddf58orr2TTpk30LBiyLItYLIZlWUQikaNeSRERETkxDTpR+dKXvsSkSZO47777KC0txbKsoaiXiIiIyOATlY8++ojf/va3TJgwYSjqIyIiItJr0JNpzzrrLP7yl78MRV1EREREXAZ9R+XnP/85V155Je+++y6nnHIKXq/XFT///POPWuVERETkxDboRGX9+vW88sorPP3000kxTaYVERGRo2nQj35WrFjBFVdcQW1tLdFo1PWjJEVERESOpkEnKvv27eOf/umfKC0tHYr6iIiIiPQadKJy4YUXsnbt2qGoi4iIiIjLoOeoTJo0iVtuuYWXX36ZadOmJU2m/epXv3rUKiciIiInNivW8+tlD9HYsWPTF2ZZfPTRR0dcqUPV1NREfn4+jY2NBIPBY7ZfEREROXyDGb8HfUdl69ath12xvu69917uvfdetm3bBsDUqVP51re+xbnnnnvU9nFEHu7zW3cvboftv4bG9yB/Koy5CJxAPF6/FmqfBX8hVF8OWeXxWPMW2P4IRDqg6jNQODse62qCbQ9D6zYYOQ9GnQ9296mJRWHXU7D3FciqhLGXg68gvu2Bv0DN42A5MOZiCJ4Uj3U0wNb/hdAeKD0Dys6Gnt8kHAlBzW/h4NuQNwnGXAKe7Pi2DS9B7WrwBk1bsivjsZZtsP1XEG6FUZ+GogUJbWkxsZYtUDALqj4Ltjfelt2rYc9LECiDsVeAf2R824PvQc1j5v+rPg8jpsZjoX2w7SFor4XiRVBxDljdTy6jXVDzBBx4A3LHw5hLwZsb33bvBtj1B/DkmFhudTzWttOU29UE5edAyaJ4LNwG2x+F5r/BiOlQ9Tlw/N1tiUHdc1D/PPiLTVsCJfFtm/5mto1FoOpCKDg1Hus8AFsfgvadUPQJcwx72xKGXatg36uQUw3Vl5lz0GP/61DzpKnHmEshb3w81l5r2hLaD+Vnm3PeI9Jx9Pru6mXAvniccrhsNzJ4sViMSHsD0Y79WJ4AnpxRWI6vNx4NNRFuq8XCwsmpwPbF+3UsEiLcsotYJIQTKMLOKur9TeGxWIRIax3RziZsbw5Ozigs2+ndNtKxj0jbHizbg5M7CtuTFd9nVxuR1t3EomGc7FKcQPx6E4uGibTsIhpuw/bl4+SUYXX33VgsRrS9gUjHfizHjye30t2WzmYirbUAODnl2L68hLZ0drelAydQiJ1VktCWaHdbGrE92Ti5o7Ds+NAV6ThApK3etCWnAtsbv45Fw+1EWnZ1t6UYJxC/3sSiESKtu4l2tWD7gt1tceJt6dhLpH1vd1tGYfV89oFoZ4s5RsTwZJdj++Of0Vikk3DrLmLhDmx/AU52qbstbfVEQwexPNl4ciuw7PhTiWjoIOG2eixsnNwKbG9Oyn5zohn0HZUenZ2dbN26lfHjx+PxHN7fNvz973+P4zhMnDiRWCzGL3/5S+6++27efPNNpk6dOuD2Q3pHpW+SkkreRDhrHWSVwZ+uMAN0DycLFj1uBtQt98Or15pBq8eUf4GZd5nB+fmzoKM+Hhu5AM78I1geWPd30LAuHvOPhDP+CIWz4J3b4Z1bEypkwZwfwqTroX4dvHAehFvi4coL4PTHoPMgrFliBq0eOdWwdB1kj4Y/XwMf/SIes31w2qNmkNr2CKz/e4iF4/GJ18PcH0HzZnhuCbTviscKZsJZa8DJgRcvMMlPD28+LHkaihfCpu/Amze5j++M/4STbzKJxtpzoKsxHitfBp9cBZE2WHOWSVJ6ZI2Cs9ZCcCK8tgI++FHCIfLAwgeh+lLY+Tt4+SKIdsbj466G+fdB2w7TltZt8Vj+VFOurwBe/gLsfDIe8+TA4j9A6RL44Mfw2g1Awkdr2m0w7VbY/yasPdskXj1KFpvjEIvA82fDvg3xWKAEznzeJG1v3Qzv35XQFhvm/QzGfwl2PwMvfRYi7fH4mEvgEw+ZvvXcYmj+MB47kr6bzmWHdSk5YcWiEUL1rxLtSOgLtpdA2QJsfz5dBz6g6+AHrm28I0/BG6wm0r6XUP1G13lxskvxlcyGaJiO2vXEupp7Y5YnC3/ZQixPFp173ybSUpNQqo2vZBaenDLCLbvo3PMWiX3XkzcGX9E0ol2thGrXE4t0xMv1BQmULQDbQ6h+I9H2PQnFevCXzscJFNDVuIWu/ZvcbSmYgnfEeCIdBwjV/9kk6T2bZhXjL50L0QgddRuIdcY/+5YTwF++ANubS+e+dwk3bUso1cJXPANP7ijCrXV0NrwBxP9wrpNbha9oOrFwO6G69cTC8c+L5c0jUL4AbC+dDa8TaUu4JlsO/tK5OFlFdDVto2vfu+62jJiEt2AS0VAjHXUbzJennrYECvGXzgdihOo2EA0dTDhGPgLlC7F9eXTu30S4cYurXF/RdDx5o/k4Gsz4PehEpa2tjRUrVvDLX/4SgA8++IBx48axYsUKRo0axc0333z4NQcKCwu5++67+fKXvzzge4csUTmUJKXHuKugYrkZuPrKqoBz3oRVY8y30b6WbTSDc2Ii0mPabWbw6zt4AxTOhYUPwFMpkjnbC+dvhec+CS0pHsMteAD2bYQPVybHRn8Bxl4FLyxPjvlHwvJNsGo8hJuT42etg013w+6nkmNTbjKJ0GvXJ8fyp8Li38PvJ5g7LoksG87bbBKuxKSqx5yVJpHYdHdyrGK52e+aJckxTx6cvwWemuJOGHosfgq2PgA7fpMcm3g9jJwLG65KjuWOg6UvwqqxrgtVr+XvwfqrYP/G5NjMu80dqnduS46VLDHxZ+Ymx5wAnL8dVs+E9hR3NU7/jTknHz2QHDuSvpuKEpVBSTV4A9i+fHzFM+jY9UKKrSwClWd2D7JtSVFf0anmW3nz9qSYk1OOJ7fSJDhJO/USqFxCR81a95eQbv6yhXQ1biHa3pAU8+SPw/JkJw3eYAZ/f+lcOnY+n6ItmLbUb3QlVT28I08hFm4j3Jh8HbOzSvDmjydUtz65UMtDoOoMOnauS/k59JfOJdyys/fujqsteWOw/SPo3Jv829ctTzb+soXdbUnu64FRi+nc8xbRhKSqty2FUyAaSUo8AezASLyFUwjtfjlFW2yyqpa67kx9XAzpo59bbrmFv/zlL6xbt45zzjmn9/WlS5dy2223HXaiEolE+M1vfkNraysLFy5M+Z5QKEQoFOr9d1NT02Ht66iqeTL1oARm4Pjw3vQX+u2/Sp2kgPm27klz22//Rtj6P6lj0S7Y/NPUSUpPuftSXKjA3GFIfKyUKLTPtCVVkgLmMVJt8i8B7N1nTpq5TY3vwZb7kpMUMK9tuS91ktJTbmvyBRkwdckdnzoWbjZtSZWk9JS7c1Wa2BOpEwIwx3zzT9P3h63/kzpJAfPoKvFuSKKGdebRSyqRDtOWdHWqeSL9eTmSvitHLNJal/L1aGcj4ZadabaKEW7enjJJAYi01RENJQ+UZn/1rscM7p12mTsTKZIUgHBbbcokpbfchMctrtp2NRNu3pF6n0C4eUfKJMWUW0cszWci2t5A2JN6n8TChJu2p+3b4bZ6992SxH221RGLhFLGYuE2wi07SJWkAIRbdqZMUqC7LWnuSkY79hFu2ZUyRixKpH0PntxRqeMniEEnKk8++SSPPvooCxYscP3l5KlTp7Jly5Z+tkztnXfeYeHChXR0dJCbm8sTTzzBySefnPK9d9xxB9/+9rcHvY8h5fjB9qeP9/eM0ck2c0tSdWC7v3Its21/5aZj++PzLAa1zwHKdQJgeSGW4kPuBNLvc8ByB9hnunIt7xHsM2Aed0XTtMXu59vNgG2xSHmhcwLpH69YDtiB1DFIn9D2lJuuvgP13f7KlSNn9fPbISwnfcjq57JtOenLtex+99lfuRbd26b6QmHZvfNU0tYpHbufdtoOscjhtqW/42djfjNHqrb0c/wAi37a0l87LRuLWJoUx+q3vv32kxPEoI/Anj17KCkpSXq9tbXVlbgcqpNOOom33nqLP//5z3zlK1/hyiuv5P3330/53ltuuYXGxsben5qampTvO3IjB35Lj+rLzU8qwcnmUYGvMDlm2TD2i2beyGDLLf8UjL/azLfoy5MLE//BTGQdbLnVl5mfVHKqYdINZhJsEsuUOfqi1NuOuSz9PosXwYRr3RM7e9h+EytelBwbqNzRF0H1FaZufQXKTFtyqlNvW91Puf0dv4JZ5th7cpNjlsecs/Jlgy+38gIYf1XqC5avACZdB8Epgy93oL47KU3flaMi3bdkO6sYT14VKfuu5eDJH4Ply0+5rZM7CidNuZ7cCpyc1DHLk4UTrE6b3HtyK3FyytPERqUt1/YX4g2OTt13LRtv3mhsf+o+5uRUpD1GTk453rzKlDEcP06wGithgrB721Hpy+3n+Fm+fDz5Y9IkJBaevCrsrOKU2/Z3jJzs0u7znYLtxclKHm9PNINOVObMmcNTT8XnIfQkJz//+c/TPrLpj8/nY8KECcyePZs77riDU089le9///sp3+v3+wkGg66fIXHZ3kN7X9lSmH47lJ1p5pQkduDsSjjtEfBkwem/dj9Ssf0w97/NZM85K82E00TVl8PE68zEzvHX4Lpg5U81EyizK818Eyfhw+gNmn368s0kysTHLZZt5mxUngdTvwkVn3bvs3gRzLjLrOCZcVd8pQ6Ygf30X4MnAIseA39RQlt8MPses6pl9vdg5Hx3uVWfg8k3mhVJk1a425I30cy1CRSb+iZ+g/fkmNcCxeY9eZMSCrVMolF9iSm76vPufY6cb+pSMB1mf999R8FfZOZteALmv4mJl+WBGXd2H4M7kxOkiuXm2FWeZyZDJ158c6pNfX35ZuJx4kodJwsW3G/O2byfmnOY2Jbx15hzPfG67uQqQcEM00fyJpg+k3gHxFfQ3ZZsc94TV2ZZDpxyq+mb0283fTXR4fbdtFIPCpKek1uFk+seoCxvHr6i6dieLHzFp7r7mOXBXzILy/biL56J1efRhyd/HJ7sUrz5E7D7DG62vxBv4RScQAHegsm4PoeOH1/JbGzbwV8yu88dOBtv4VRsfxBf4VRs/wh3G7LLzH5zK/AEq91t8eTgKz4Vy/HjK57p7mOWg694ZnfsVKw+d549wWo8uaPw5I/DyXYnSLZ/hKmLL4i3cCquYcz24e9ui69kdp/Ey8JbMNkcg8LJSQmSmfcyAU92KZ5892Njy5NljrntxV8yy/0l0bLxFZ9qzlnRdCxvnmvbnvPsCVYnJSuWL4hv5CnY3hx8RdPd59v24i+Z7VqtdaIa9GTal19+mXPPPZcrrriCBx54gP/7f/8v77//Pn/605944YUXmD179sCF9OPMM89k9OjRPPDAAwO+d8h/j8rDQSDh+ellMdj7KjS9D8GToWie+/1tO6HuebPEs3yZe7APt8Hup80z/4pz3MtyYzFoeMFMDC2c616WC2ap694NZgApPTO+xBjMUtfdq81y5vJz3ctyo2Goe9YsUy5Z7F6WC7D/je7lySeZlTeJ2mvN8ltv0JSbOJkr0mHaEm41d3cCfTL+hpehZbO5w1Aw3R1r3gJ7XjbLX8uWuj+YXU2mXICKc92DfSxq6tNeC8Wnu5flAhx4O748uaRPgtHRALV/NMlPxbnuuzeRTrMSqavR1Cerz7fGPevjy5ML+9ylatlmzlugxCz9TlgySVeLmRsSDZvznTjYx2JmWXNbDRQtdC8pB2h8P748uWSx+3yH9pnz7QRMWxIHq2gX1D5jlieXnelOXODo9t3ECeeaRHtEop0tREMHsDxZ2IGRrjvTsUgnkfY9YFk4WSWuZbmxWJRo+15ikRB2YKRrWS5ANNRItLMJy5vrWmIMEAt3EGnfC7YHJ7vE9egmFo0QaW+AWAQnq9i1LBcg0rGfWFcrtj8f2+e+7ka7Ws1SayfgWi5tyu0i0mZWBTnZxa75MmZp815ikQ7sQGHSstxoZxPRUCOWN9u1xNgco1D3MXK6j5GTUG6USFsDRMM4WUVYHved20jHAWLdy5Ntv/suVbSrjWjHPizH392WxGMU7j5Gse5jFL8+mqXN+4iF27H9Ba4l5aYtzd3Lk9Od74aUbfm4GdJVPwBbtmzhzjvv5C9/+QstLS3MmjWLb3zjG0ybNm1Q5dxyyy2ce+65jB49mubmZh5++GHuuusunnnmGc4+++wBt9cvfBMRETn+DOmqH4Dx48fzs5/97LAql6ihoYEvfvGL1NbWkp+fz/Tp0w85SREREZGPv0EnKo7jUFtbmzShdt++fZSUlBCJHMIvhup23333DXb3IiIicgIZ9GTadE+KQqEQPt/H75fSiIiIyPA55DsqP/jBDwCzyufnP/85ubnxCUKRSIQXX3yRyZMnH/0aioiIyAnrkBOV733ve4C5o/KTn/wEx4nPRvb5fFRXV/OTn/zk6NdQRERETliHnKj0/NXkM844g8cff5yCgkP53QoiIiIih2/Qk2nXrl07FPUQERERSXLIicqNN954SO/77ne/e9iVEREREUl0yInKm2++OeB7Dudv/YiIiIikc8iJih75iIiIyLE2ZH8/OhgM8tFHHw1V8SIiInICGLJE5TD+hJCIiIiIy5AlKiIiIiJHSomKiIiIZCwlKiIiIpKxlKiIiIhIxlKiIiIiIhlryBKV9vZ22tvbh6p4EREROQEMWaKSlZVFVlbWUBUvIiIiJwA9+hEREZGMpURFREREMpYSFREREclYQ5ao6C8pi4iIyJHS3/oRERGRjDVkicrTTz/NqFGjhqp4EREROQF4DuVNN9544yEX+N3vfheA008//fBqJCIiItLtkBKVN9980/XvN954g3A4zEknnQTABx98gOM4zJ49++jXUERERE5Yh5SorF27tvf/v/vd75KXl8cvf/lLCgoKADhw4ABXX301ixYtGppaioiIyAnJig1y1uuoUaP44x//yNSpU12vv/vuu3zqU59i9+7dR7WC/WlqaiI/P5/GxkaCweAx26+IiIgcvsGM34OeTNvU1MSePXuSXt+zZw/Nzc2DLU5EREQkrUEnKp/97Ge5+uqrefzxx9m5cyc7d+7kt7/9LV/+8pe58MILh6KOIiIicoI6pDkqiX7yk5/wz//8z1x22WV0dXWZQjwevvzlL3P33Xcf9QqKiIjIiWtQc1QikQivvPIK06ZNw+fzsWXLFgDGjx9PTk7OkFUyHc1REREROf4MZvwe1B0Vx3H41Kc+xaZNmxg7dizTp08/ooqKiIiI9GfQc1ROOeUUPvroo6Goi4iIiIjLoBOVf/u3f+Of//mf+cMf/kBtbS1NTU2uHxEREZGjZdC/R8W247lN4l9IjsViWJZFJBI5erUbgOaoiIiIHH+GbI4KuH9LrYiIiMhQGnSisnjx4qGoh4iIiEiSQScqAAcPHuS+++5j06ZNAEydOpUvfelL5OfnH9XKiYiIyIlt0JNpX3vtNcaPH8/3vvc99u/fz/79+/nud7/L+PHjeeONN4aijiIiInKCGvRk2kWLFjFhwgR+9rOf4fGYGzLhcJhrrrmGjz76iBdffHFIKpqKJtOKiIgcfwYzfg86UcnKyuLNN99k8uTJrtfff/995syZQ1tb2+BrfJiUqIiIiBx/hvSvJweDQXbs2JH0ek1NDXl5eYMtTkRERCStQScqF198MV/+8pd59NFHqampoaamhkceeYRrrrmGSy+9dCjqKCIiIieoQa/6+c53voNlWXzxi18kHA4D4PV6+cpXvsKdd9551CsoIiIiJ65Bz1Hp0dbW5vrrydnZ2Ue1YodCc1RERESOP0P6m2l7ZGdnM23atMPdHIA77riDxx9/nL/+9a9kZWXxiU98grvuuouTTjrpiMo9ah623P++uB22/xoa34P8qTDmInAC8Xj9Wqh9FvyFUH05ZJXHY81bYPsjEOmAqs9A4ex4rKsJtj0Mrdtg5DwYdT7Y3acmFoVdT8HeVyCrEsZeDr6C+LYH/gI1j4PlwJiLIZhw7DoaYOv/QmgPlJ4BZWdDz589iISg5rdw8G3ImwRjLgFPQrLZ8BLUrgZv0LQluzIea9kG238F4VYY9WkoWpDQlhYTa9kCBbOg6rNge+Nt2b0a9rwEgTIYewX4R8a3Pfge1Dxm/r/q8zBiajwW2gfbHoL2WiheBBXngNX95DLaBTVPwIE3IHc8jLkUvLnxbfdugF1/AE+OieVWx2NtO025XU1Qfg6ULIrHwm2w/VFo/huMmA5VnwPH392WGNQ9B/XPg7/YtCVQEt+26W9m21gEqi6EglPjsc4DsPUhaN8JRZ8wx7C3LWHYtQr2vQo51VB9mTkHPfa/DjVPmnqMuRTyxsdj7bWmLaH9UH62Oec9Ih1Hr++unoObA5eFOd60bf2D699ZY84l0lpLtKsZ25uHk1OOZTu98Uj7XiLte7EcL56cUVie+PGLdrUSad1NLBbFk12K7R/RG4tFuwi37CYWbsP2j8DJLsXqPt+xWIxIewPRjv1YnoAp1/HFyw01EW6rxcLCyanA9sX7dSwSItyyi1gkhBMows4q6v2zJrFYhEhrHdHOJmxvDk7OKHdbOvYRaduDZXtwckdhe7IS2tJm2hIN42SX4gTi15tYNEykZRfRcBu2Lx8np8zVlmh7A5GO/ViOH09upbstnc1EWmsBcHLKsX3xOY2xSGd3WzpwAoXYWSUJbYl2t6UR25ONkzsKy44PXZGOA0Ta6k1bciqwvfHrWDTcTqRlV3dbinEC8etNLBoh0rqbaFcLti/Y3RYn3paOnvPtx5M7Cqvnsw9EO1vMMSKGJ7sc2x//jMYinYRbdxELd2D7C7rPd0Jb2uqJhg5iebLx5FZg9VwfgWjoIOG2eixsnNwKbG9OvNxwhyk30oWTVYSTVdSnLUen73bU/RWie3vjeErJrprLcDrsOypHwznnnMMll1zC3LlzCYfDfPOb3+Tdd9/l/fffJycnZ8Dth/SOSt8kJZW8iXDWOsgqgz9dYQboHk4WLHrcDKhb7odXrzWDVo8p/wIz7zKD8/NnQUd9PDZyAZz5R7A8sO7voGFdPOYfCWf8EQpnwTu3wzu3JlTIgjk/hEnXQ/06eOE8CLfEw5UXwOmPQedBWLPEDFo9cqph6TrIHg1/vgY++kU8ZvvgtEfNILXtEVj/9xBLGJgmXg9zfwTNm+G5JdC+Kx4rmAlnrQEnB168wCQ/Pbz5sORpKF4Im74Db97kPr4z/hNOvskkGmvPga7GeKx8GXxyFUTaYM1ZJknpkTUKzloLwYnw2gr44EcJh8gDCx+E6kth5+/g5Ysg2hmPj7sa5t8HbTtMW1q3xWP5U025vgJ4+Quw88l4zJMDi/8ApUvggx/DazcACR+tabfBtFth/5uw9myTePUoWWyOQywCz58N+zbEY4ESOPN5k7S9dTO8f1dCW2yY9zMY/yXY/Qy89FmItMfjYy6BTzxk+tZzi6H5w3jsSPpuOpcN26Vk0PomKalYnhz85QuxHD+de94k0ro7IWjjL5mDk11CuLmGzr1vk3i+Pfnj8RVOIdrZTEfdBvPFoJvtH4G/bAFgEap/lWhHQl+wvQTKFmD78+k68AFdBz9w1ck78hS8wWoi7XsJ1W90nRcnuxRfyWyIhumoXU+sqzmhLVn4yxZiebLo3Ps2kZaahFJtfCWz8OSUEW7ZReeet9xtyRuDr2ga0a5WQrXriUU64uX6ggTKFoDtIVS/kWj7noRiPfhL5+MECuhq3ELX/k3uthRMwTtiPJGOA4Tq/2yS9J5Ns4rxl86FaISOug3EOuOffcsJ4C9fgO3NpXPfu4SbtiWeNXzFM/DkjiLcWkdnwxtANH6McqvwFU0nFm4nVLeeWDj+ebG8eQTKF4DtpbPhdSJtCddky8FfOhcnq4iupm107XvX3ZYRk/AWTCIaajTnO9oVb0ugEH/pfCBGqG4D0dDBhGPkI1C+ENuXR+f+TYQbt7jK9RVNx5M3mkhbA6GG18yXvZ625FTgK54JkZA53+HWeHWPoO+mkz320wO+ZzCGdHnyUNqzZw8lJSW88MILfPKTnxzw/UOWqBxKktJj3FVQsdwMXH1lVcA5b8KqMebbaF/LNprBOTER6THtNjP49R28AQrnwsIH4KmpyTHbC+dvhec+CS0fJccXPAD7NsKHK5Njo78AY6+CF5Ynx/wjYfkmWDUews3J8bPWwaa7YfdTybEpN5lE6LXrk2P5U2Hx7+H3E1wfQsAMxOdtNglXYlLVY85Kk0hsujs5VrHc7HfNkuSYJw/O3wJPTXEnDD0WPwVbH4Adv0mOTbweRs6FDVclx3LHwdIXYdVY14Wq1/L3YP1VsH9jcmzm3eYO1Tu3JcdKlpj4Mym+1TgBOH87rJ4J7buT46f/xpyTjx5Ijh1J303lOElU2rb+Eegc8H0ATm4lTnZJ94DnZjl+/BWL6Nj5fHLfBfwVp9O1f5M7EenmHTEJbCdp8Aawffn4imfQseuFFDWyCFSe2T3IJv8qCF/RqeZbefP25LbklOPJrTQJTtJOvQRGLaFj51r3l5CetpQtpKtxC9H2hqSYJ38clic7afAGM/j7S+eaY5RCoPJMQvUbXUlVD+/IU4iF2wg3Jl/H7KwSvPnjCdWtTy7U8hCoPIOOXetSfg79pXMJt+zsvbvjakveGGz/CDr3/iVFsdn4yxZ2tyW5rwdGLaZzz1tEE5Kq3rYUToFoJCnxBLADI/EWTiG0++UUbbHNMdr9ErGEZLeHr2QWkbYGIi07k2JH0ndTGc5E5bAf/QyFxkZzggsLC1PGQ6EQoVD8ZDU1NR2TevWr5snUgxKYgePDe9Nf6Lf/KnWSAubbuifNXaX9G2Hr/6SORbtg809TJyk95e5LcaECc4ch8bFSotA+05ZUSQqYx0i1T6ffZ87Y1LHG92DLfak/LLEobL4vdZLSU25r8gUZMHXJHZ86Fm42bUmVpPSUu3NVmtgTqRMCMMd880/T94etD6ZOUsA8ukq8G5KoYZ159JJKpMO0JV2dap5If16OpO8e1w4tSQHMt+o03+VikZBJCNJc6MMtu1ImKQDhtrrexwx9RTsbCacYeLr3SrhlR8okxdS3jmgoeaAEiLTWux4zuHfa1d2W1I/wwq21KZOU3nK9qecoxrqaCTcn/zqL3nKbd6RMUky5dcTSfCai7Q2EPWnmRcbChJu3pe3b4bZ6992SxH221aVMCEyxbYRbdpDu7kO4ZWfKJAW625LmrmS0Yx/hll0pY8Si5hilqVOktY5I4l2sxNgR9N1MM+jlyUMlGo3yj//4j5x22mmccsopKd9zxx13kJ+f3/tTVVV1jGuZguMH258+7k2TbAA42WZuSSp2f+VaZtv+yk3H9sfnWQxqnwOU6wTASnMRdALp9zlQuekuRgOVa3kPf59OwDzuGmxswHJzgDR36/or13LM45h00iW0A5V7JH33RGHZ8flDqcJW+u96JhFJfb6tAcpNe13oLbef7dKVO2Bb+qtPP9ta9gDb9lNfu5922g5ph6gB29Lf8bP7Kbef4wdYDHDs08b6O0bWAPUdqvOdUfcp+pUxicr111/Pu+++yyOPpPnmCNxyyy00Njb2/tTU1KR97zFTfbn5SSU42Twq8KW4Q2TZMPaLZt7IYMst/xSMv9rMt+jLkwsT/8FMZB1sudWXmZ9Ucqph0g1mEmwSy5Q5+qLU2465LP0+ixfBhGvdEzt72H4TK16UHBuo3NEXQfUVpBwoAmWmLTnVqbet7qfc/o5fwSxz7D25yTHLY85Z+bLBl1t5AYy7MvVFx1cAk66D4JTBl3u4ffd4ZxcN/J5unpxReHJHpYxZ3lw8wer4ZPG+2+ZV4mSXpow5/ZRrZxXjyasiZd+1HDzBMVi+1H8A1skdhZOmXE9uBU5OmrZ4snCCY9Mm997cSpyc8pQxT+6otOXa/kK8wdGp+65l480bje1P3cecnIq0x8jJKcebV5kyhuPHCY7F8qRO7vs79v0dP8uXjyd/TJqExMKTV4WdVZxy2/6OkZNd2n2+U7C9eILVWN4U15Tucj1pyj2SvptpMiJRueGGG/jDH/7A2rVrqaxM0/kAv99PMBh0/QyJQ33WXrYUpt8OZWeaOSWJHTi7Ek57BDxZcPqv3Y9UbD/M/W8z2XPOSjPhNFH15TDxOjOxc/w1uC5Y+VPNBMrsSjPfJPGbtjdo9unLN5MoEx+3WLaZs1F5Hkz9JlT0ed5YvAhm3GVW8My4y92BA2WmDZ4ALHoM/AkXetsHs+8xq1pmfw9GzneXW/U5mHyjWZE0aYW7LXkTzVybQLGpb+KdAU+OeS1QbN6TNymhUMskGtWXmLKrPu/e58j5pi4F02H29913FPxFZt6GJ2D+m5h4WR6YcWf3MbgzOUGqWG6OXeV5ZjJ04sU3p9rU15dvJh4nrtRxsmDB/eaczfupOYeJbRl/jTnXE6/rTq4SFMwwfSRvgukziXdAfAXdbck25z1xZZblwCm3mr45/XbTVxMdbt/9GMges2DgNwF2oAhvwSScrCIzpySh71pOAH/xLCzbwV8y2/15sWx8RdOxvbn4Rp6C5XNfp5ycUXiC1Ti5VTi57gHK8uaZbT1Z+IpPdfcxy4O/ZBaW7cVfPBOrz91GT/44PNmlePMnYGeVuGK2vxBv4RScQAHegsmutuD48ZXMxu5tS+IdOBtv4VRsfxBf4VTXaiYAJ7vM7De3wgx8iW3x5OArPhXL8ZtJn4l9zHLwFc/sjp2K1efunSdYbQbh/HE42e4EyfaPMHXxBfEWTsU1jNk+/N1t8ZXM7pN4WXgLJptjUDg5KUEy814m4MkuxZPvfmxsebLMMbe9+Etmub8kWja+4lPNOSuajuV1/4b2nvPsCVYnJSuWL4hv5CnY3hx8RdPd59v24i+Z3d3HZmG5vsxZeEZMNH2zYBJ2wJ18H3bfTSt1YnysDOtk2lgsxooVK3jiiSdYt24dEydOHNT2Q/57VPpOqr0sBntfhab3IXgyFM1zx9t2Qt3zZoln+TJ3Bwi3we6nzTP/inPcy3JjMWh4wUwMLZzrXpYLZqnr3g1mACk9M77EGMxS192rzXLm8nPdy3KjYah71ixTLlnsXpYLsP+N7uXJJ5mVN4naa83yW2/QlJuwzJBIh2lLuNXc3Qm4L4o0vAwtm80dhoLp7ljzFtjzsln+WrbU/cHsajLlAlSc6x7sY1FTn/ZaKD7dvSwX4MDb8eXJJX0SjI4GqP2jSX4qznXfvYl0mpVIXY2mPll9vjXuWR9fnlzY5y5VyzZz3gIlZul3wpJJulrM3JBo2JzvxME+FjPLmttqoGihe0k5QOP78eXJJYvd5zu0z5xvJ2DakjhYRbug9hmzPLnsTHfiAke37yZ+No6TSbR9tW37E8T2d//LJnvs3xEJHSDW2YLly8XxuxO0aLidaPs+LMeLnVXsupUfi0aItDdALIKTVeJalmuWuu4jFm7H9o9wLcsFs9Q1GjqA5cnCDozsXcoKZqlrpH0PWJYpN6GPxWJRou17iUVC2IGRrmW5ANFQI9HOJixvrmuJMZilrpH2vWB7cLJL+mlLsWtZLkCkYz+xrlZsfz52nyQs2tVqllo7AddyaVNuF5E2M5/CyS52zZcxS5v3Eot0YAcKXctyzTFqIhpqxPJmu5YYm2MU6j5GTvcxchLKjRJpa4BoGCeryLUs17TlALHu5cm23z0YR7vaiHbsw3L83W1JPEbh7mMU6z5G6c53gWtJuWlLc/fy5HTnuyFtW6Lte4hFurCzRrqWlANHte8mroo72pNoexw3q36uu+46Hn74YX73u9+5fndKfn4+WVn9PJPvpl/4JiIicvw5bhKVxEwy0f33389VV1014PZKVERERI4/x83y5Az6FS4iIiKSgTJiMq2IiIhIKkpUREREJGMpUREREZGMpURFREREMpYSFREREclYSlREREQkYylRERERkYylREVEREQylhIVERERyVhKVERERCRjKVERERGRjKVERURERDKWEhURERHJWEpUREREJGMpUREREZGMpURFREREMpYSFREREclYSlREREQkYylRERERkYylREVEREQylhIVERERyVhKVERERCRjKVERERGRjKVERURERDKWEhURERHJWEpUREREJGMpUREREZGMpURFREREMpYSFREREclYSlREREQkYylRERERkYylREVEREQylhIVERERyVhKVERERCRjKVERERGRjKVERURERDKWEhURERHJWEpUREREJGMpUREREZGMpURFREREMpYSFREREclYSlREREQkYylRERERkYylREVEREQylhIVERERyVie4dz5iy++yN13383rr79ObW0tTzzxBJ/5zGeGs0puD1vuf1/cDtt/DY3vQf5UGHMROIF4vH4t1D4L/kKovhyyyuOx5i2w/RGIdEDVZ6BwdjzW1QTbHobWbTByHow6H+zuUxOLwq6nYO8rkFUJYy8HX0F82wN/gZrHwXJgzMUQPCke62iArf8LoT1QegaUnQ1Wd5siIaj5LRx8G/ImwZhLwJMd37bhJahdDd6gaUt2ZTzWsg22/wrCrTDq01C0IKEtLSbWsgUKZkHVZ8H2xtuyezXseQkCZTD2CvCPjG978D2oecz8f9XnYcTUeCy0D7Y9BO21ULwIKs4BqzvPjnZBzRNw4A3IHQ9jLgVvbnzbvRtg1x/Ak2NiudXxWNtOU25XE5SfAyWL4rFwG2x/FJr/BiOmQ9XnwPF3tyUGdc9B/fPgLzZtCZTEt236m9k2FoGqC6Hg1His8wBsfQjad0LRJ8wx7G1LGHatgn2vQk41VF9mzkGP/a9DzZOmHmMuhbzx8Vh7rWlLaD+Un23OeY9Ix9Hru6vn4OaFyzqR40ekYx+Rtj1YtgcndxS2J6s3Fu1qI9K6m1g0jJNdihOIX29i0TCRll1Ew23YvnycnDKs7r4bi8WItjcQ6diP5fjx5FZiOb54uZ3NRFprAXByyrF9efFyI52EW3YRi3TgBAqxs0qwuq9VsViUSGsd0c5GbE82Tu4oLDs+dEU6DhBpqzdtyanA9savY9FwO5GWXd1tKcYJxK83sWiESOtuol0t2L5gd1uceFs69hJp39vdllFYPZ99OeasWCwWG66dP/3007zyyivMnj2bCy+8cNCJSlNTE/n5+TQ2NhIMBgfeYDD6Jimp5E2Es9ZBVhn86QozQPdwsmDR42ZA3XI/vHqtGbR6TPkXmHmXGZyfPws66uOxkQvgzD+C5YF1fwcN6+Ix/0g4449QOAveuR3euTWhQhbM+SFMuh7q18EL50G4JR6uvABOfww6D8KaJWbQ6pFTDUvXQfZo+PM18NEv4jHbB6c9agapbY/A+r+HWDgen3g9zP0RNG+G55ZA+654rGAmnLUGnBx48QKT/PTw5sOSp6F4IWz6Drx5k/v4zvhPOPkmk2isPQe6GuOx8mXwyVUQaYM1Z5kkpUfWKDhrLQQnwmsr4IMfJRwiDyx8EKovhZ2/g5cvgmjCIDvuaph/H7TtMG1p3RaP5U815foK4OUvwM4n4zFPDiz+A5QugQ9+DK/dACR8tKbdBtNuhf1vwtqzTeLVo2SxOQ6xCDx/NuzbEI8FSuDM503S9tbN8P5dCW2xYd7PYPyXYPcz8NJnIdIej4+5BD7xkOlbzy2G5g/jsSPpu+lcNmyXEjlEsViMzr1vE2mpSXjVxlcyC09OGeGWXXTueYvEvuvJG4OvaBrRrlZCteuJRTp6Y5YvSKBsAdgeQvUbibbvSSjWg790Pk6ggK7GLXTt3+Sqi7dgCt4R44l0HCBU/2eTpPdsmlWMv3QuRCN01G0g1hn/7FtOAH/5AmxvLp373iXctC2hVAtf8Qw8uaMIt9bR2fAGEO2NOrlV+IqmEwu3E6pbTywc/7xY3jwC5QvA9tLZ8DqRtoRrsuXgL52Lk1V0iEdaBjKY8XtYE5VElmVlTqJyKElKj3FXQcVyM3D1lVUB57wJq8aYb6N9LdtoBufERKTHtNvM4Nd38AYonAsLH4CnpibHbC+cvxWe+yS0fJQcX/AA7NsIH65Mjo3+Aoy9Cl5Ynhzzj4Tlm2DVeAg3J8fPWgeb7obdTyXHptxkEqHXrk+O5U+Fxb+H308wd1wSWTact9kkXIlJVY85K00isenu5FjFcrPfNUuSY548OH8LPDXFnTD0WPwUbH0AdvwmOTbxehg5FzZclRzLHQdLX4RVY81dnr6Wvwfrr4L9G5NjM+82d6jeuS05VrLExJ+ZmxxzAnD+dlg9E9p3J8dP/405Jx89kBw7kr6bihKVjBdpqydUn6L/2V4Co5bQsXOt+0tIN3/ZQroatxBtb0iKefLHYXmy6dr3blLM8ubhL51Lx87nU9YnUHkmofqNxLqSrynekacQC7cRbky+jtlZJXjzxxOqW59cqOUhUHUGHTvXpfwc+kvnEm7Z2Xt3x9WWvDHY/hF07v1LimKzCVSe0Xun52iIRCJ0daW4VnxM+Hw+bDv1DJPBjN/D+uhnsEKhEKFQqPffTU1Nw1ibbjVPph6UwAwcH96b/kK//VepkxQw39Y9Oalj+zfC1v9JHYt2weafpk5Sesrdl+JCBeYOQ+JjpUShfaYtqZIUMI+Rap9Ov8+csaljje/Bll8kJylgXttyX+okpafc1u2pY7VPQ8641LFws2lLqiSlp9ydq9LEnkidEIA55pt/mr4/bH0wdZIC5tFV4t2QRA3rzKOXVCIdpi3p6lTzRPrzciR9V45LrrsEiaJdhJu3p0xSAMJttSmTFIBIaz1WwuOWRLGuZsLNO9LWJ9y8I2WSYsqtI5bmMxFtbyCcZp/EwoSbtqft2+G2+rTHIdJWRywSShmLhduIdTZj+Y/8S3EsFqOuro6DBw8ecVmZzLZtxo4di8/nG/jN/TiuEpU77riDb3/728NdDTfHD3Y/zy7TJRsATraZW5LqtrrdX7mWuT3fX7np2P74PItB7XOAcp0AWF6IpfiQO4H0+4TDb0t/5Vpe8ARSxw6lXNsH0TRtsfv50Hn6KzcHsHA9EkosN93jFcvp/xj128f6qe9AfdfbT7lyfLLSr5+w+othm21TfaGw7H63pXvex2Bjlu0Qi6Qp17JNndJt21+5lo1ZR5KqLU6/x6jf2CD0JCklJSVkZ2cf1bs0mSIajbJ7925qa2sZPXr0EbXxuFr1c8stt9DY2Nj7U1NTM/BGQ636cvOTSnCymS/iK0yOWTaM/aKZNzLYcss/ZeYlWCnyTE8uTPwHM5F1sOVWX2Z+Usmphkk3mEmwSSxT5uiLUm875rL0+yxeBBOudU/s7GH7Tax4UXJsoHJHXwTVV5i69RUoM23JqU69bXU/5fZ3/ApmwYR/MOegL8sD4682c2sGW27lBTDuytQXSV8BTLoOglMGX+5AfXdimr4rxy0nZ1TK1y1PFk5wbNrE35NbiZNTniY2Km25tr8Qb3B06r5r2XiDo7H9qfuYk1OBJzd1uU5OOZ68ypQxHD9OsBrLkzq5d3JGpS83dxROmpjly8f2pfhsD1IkEulNUkaOHElWVhaBQOBj95OdnU1xcTFtbW2Ew6nv1B2q4ypR8fv9BINB18+QONRn7WVLYfrtUHammVOSmMVnV8Jpj4AnC07/tfuRiu2Huf9tJnvOWWkmnCaqvhwmXmcmdo6/Btdgmz/VTKDMrjTzTRK/aXuDZp++fDOJMvFxi2WbORuV58HUb0LFp937LF4EM+4yK3hm3BVfqQNmYD/91+YOxaLHwJ8wocz2wex7zKqW2d+DkfPd5VZ9DibfaFYkTVrhbkveRDPXJlBs6pt4Z8CTY14LFJv35E1KKNQyiUb1Jabsqs+79zlyvqlLwXSY/X33HQV/kZm34QmY/yYmXpYHZtzZfQzuTE6QKpabY1d5npkMnXjxzak29fXlm4nHiSt1nCxYcL85Z/N+as5hYlvGX2PO9cTrupOrBAUzTB/Jm2D6TOIdEF9Bd1uyzXlPXJllOXDKraZvTr/d9NVEh9t35bjmBArwFkzG9Tl0/PhKZmPbDv6S2X3uwNl4C6di+4P4Cqdi+0e4y8suw5M/Dk9uBZ5gtStmeXLwFZ+K5fjxFc909zHLwVc8szt2Klafu3eeYDWe3FF48sfhZLsTJNs/wtTFF8RbOBXXMGb78He3xVcyu0/iZeEtmGyOQeHkpATJzHuZgCe7FE/+eFfM8mThL+5znT5MPXNSsrP7ufv6MdHzyCcSOYTJ+P3QZNr+9J1Ue1kM9r4KTe9D8GQomueOt+2EuufNEs/yZe7BPtwGu582z/wrznEvy43FoOEFMzG0cK57WS6Ypa57N5gBpPTM+BJjMEtdd682y5nLz3Uvy42Goe5Zs0y5ZLF7WS7A/je6lyefZFbeJGqvNctvvUFTbsIyQyIdpi3hVnN3J3FZLkDDy9Cy2dxhKJjujjVvgT0vm+WvZUvdg31XkykXoOJc92Afi5r6tNdC8enuZbkAB96OL08u6ZNgdDRA7R9N8lNxrvvuTaTTrETqajT1yerzrXHP+vjy5MI+d6latpnzFigxS78TlkzS1WLmhkTD5nwnDvaxmFnW3FYDRQvdS8oBGt+PL08uWew+36F95nw7AdOWxEdN0S6ofcYsTy470524wNHtu4mfDU2iPe7Ewh1E2veC7cHJLnE9uolFI0TaGyAWwckqTlqWG+nYT6yrFdufj+1zX3ejXa1EO/ZjOQHsrCLX7f5YtItIm1kV5GQXYyX0MbO0eS+xSAd2oBC7T+IS7WwiGmrE8ma7lhgDxCIhIu17wHJwskqwbCeh3CiRtgaIhnGyirD6PA6OdBwg1r082fbn92lLG9GOfViOv7stR+d7fUdHB1u3bmXs2LEEAv08nv4Y6K+tx82qn5aWFjZv3gzAzJkz+e53v8sZZ5xBYWEho0ePHnD7IU9UREREjiIlKsZgxu9hffTz2muvMXPmTGbONLfUbrzxRmbOnMm3vvWt4ayWiIjIx85tt93GjBkzhrsagzasicqSJUuIxWJJPw888MBwVktERCTj1NXVsWLFCsaNG4ff76eqqorzzjuPNWvWDHfVhtRxtTxZRETkRLRt2zZOO+00RowYwd133820adPo6urimWee4frrr+evf/3rMalHV1cXXq934DceRcfVqh8REZET0XXXXYdlWbz66qt87nOfY9KkSUydOpUbb7yRDRvMn93YsWMHF1xwAbm5uQSDQS666CLq69P8kj/M7zq5/fbbqaysxO/3M2PGDFavjv+Zk23btmFZFo8++iiLFy8mEAjw0EMPDXlb+1KiIiIiksH279/P6tWruf7668nJSf5FjCNGjCAajXLBBRewf/9+XnjhBZ599lk++ugjLr744rTlfv/73+e//uu/+M53vsPbb7/NsmXLOP/88/nwww9d77v55pv52te+xqZNm1i2LM3vghpCevQjIiKSwTZv3kwsFmPy5Mlp37NmzRreeecdtm7dSlVVFQAPPvggU6dOZePGjcydm/y3wr7zne/wjW98g0suuQSAu+66i7Vr13LPPfewcmX878H94z/+IxdeeOFRbtWh0x0VERGRDHYov0Vk06ZNVFVV9SYpACeffDIjRoxg06ZNSe9vampi9+7dnHbaaa7XTzvttKT3z5kz5zBrfnQoUREREclgEydOxLKsYzZhtq9Uj5uOJSUqIiIiGaywsJBly5axcuVKWltbk+IHDx5kypQp1NTUuP4G3vvvv8/Bgwc5+eSTk7YJBoNUVFTwyiuvuF5/5ZVXUr5/OGmOioiISIZbuXIlp512GvPmzeP2229n+vTphMNhnn32We69917ef/99pk2bxuWXX84999xDOBzmuuuuY/HixWkf3dx0003ceuutjB8/nhkzZnD//ffz1ltvDcvKnv4oUREREclw48aN44033uDf//3f+frXv05tbS3FxcXMnj2be++9F8uy+N3vfseKFSv45Cc/iW3bnHPOOfzwhz9MW+ZXv/pVGhsb+frXv05DQwMnn3wyq1atYuLEicewZQPLmD9KeDj0t35EROR4or/1Yxw3f+tHREREpD9KVERERCRjKVERERGRjKVERURERDKWEhURERHJWEpUREREJGMpUREREZGMpURFREREMpYSFREREclYSlREREQkYylRERERkUO2cuVKqqurCQQCzJ8/n1dffXVI96dERURE5HhU8ySsngeP5pj/1jw55Lt89NFHufHGG7n11lt54403OPXUU1m2bBkNDQ1Dtk8lKiIiIsebmifhpc/C/o0QaTP/fenCIU9Wvvvd73Lttddy9dVXc/LJJ/OTn/yE7OxsfvGLXwzZPpWoiIiIHG/e+48UL8bg/TuGbJednZ28/vrrLF26tPc127ZZunQp69evH7L9KlERERE53jS+N7jXj4K9e/cSiUQoLS11vV5aWkpdXd2Q7VeJioiIyPEmf+rgXj+OKVERERE53kz9JmD1edHqfn1oFBUV4TgO9fX1rtfr6+spKysbsv0qURERETneVH0GFj0OI+eBJ8f895NPQOUFQ7ZLn8/H7NmzWbNmTe9r0WiUNWvWsHDhwiHbr2fIShYREZGhU/UZ83MM3XjjjVx55ZXMmTOHefPmcc8999Da2srVV189ZPtUoiIiIiKH5OKLL2bPnj1861vfoq6ujhkzZrB69eqkCbZHkxIVEREROWQ33HADN9xwwzHbn+aoiIiISMZSoiIiIiIZS4mKiIiIZCwlKiIiIpKxlKiIiIhIxlKiIiIiIhlLiYqIiIhkLCUqIiIikrGUqIiIiEjGUqIiIiIiGUuJioiIiAzoxRdf5LzzzqOiogLLsnjyySePyX6VqIiIiByHwq11dOx6ibZtT9Ox6yXCrXVDur/W1lZOPfVUVq5cOaT76Ut/lFBEROQ4E26to7Phtd5/Rzsbzb9L5uDJKRuSfZ577rmce+65Q1J2f3RHRURE5DgTPvhh6tcbNx/jmgy9jLijsnLlSu6++27q6uo49dRT+eEPf8i8efOGu1rwsOX+98XtsP3X0Pge5E+FMReBE4jH69dC7bPgL4TqyyGrPB5r3gLbH4FIB1R9Bgpnx2NdTbDtYWjdBiPnwajzwe4+NbEo7HoK9r4CWZUw9nLwFcS3PfAXqHkcLAfGXAzBk+KxjgbY+r8Q2gOlZ0DZ2WB1tykSgprfwsG3IW8SjLkEPNnxbRtegtrV4A2atmRXxmMt22D7ryDcCqM+DUULEtrSYmItW6BgFlR9FmxvvC27V8OelyBQBmOvAP/I+LYH34Oax8z/V30eRkyNx0L7YNtD0F4LxYug4hywuvPsaBfUPAEH3oDc8TDmUvDmxrfduwF2/QE8OSaWWx2Pte005XY1Qfk5ULIoHgu3wfZHoflvMGI6VH0OHH93W2JQ9xzUPw/+YtOWQEl826a/mW1jEai6EApOjcc6D8DWh6B9JxR9whzD3raEYdcq2Pcq5FRD9WXmHPTY/zrUPGnqMeZSyBsfj7XXmraE9kP52eac94h0HL2+u3o5UB+PUwWX7UA+/mLRMJGWXUTDbdi+fJycMqzuvhuLxYi2NxDp2I/l+PHkVmI5vt5to53NRFprAXByyrF9efFyI52EW3YRi3TgBAqxs0qwuq9VsViUSGsd0c5GbE82Tu4oLDs+dEU6DhBpq8eyPTg5Fdje+HUsGm4n0rKLWDSMk12ME0i43hznol0tqV/vbD7GNRl6ViwWiw1nBR599FG++MUv8pOf/IT58+dzzz338Jvf/Ia//e1vlJSU9LttU1MT+fn5NDY2EgwG+33voPVNUlLJmwhnrYOsMvjTFWaA7uFkwaLHzYC65X549VozaPWY8i8w8y4zOD9/FnQkXPhHLoAz/wiWB9b9HTSsi8f8I+GMP0LhLHjndnjn1oQKWTDnhzDpeqhfBy+cB+GEzlx5AZz+GHQehDVLzKDVI6calq6D7NHw52vgo1/EY7YPTnvUDFLbHoH1fw+xcDw+8XqY+yNo3gzPLYH2XfFYwUw4aw04OfDiBSb56eHNhyVPQ/FC2PQdePMm9/Gd8Z9w8k0m0Vh7DnQ1xmPly+CTqyDSBmvOMklKj6xRcNZaCE6E11bABz9KOEQeWPggVF8KO38HL18E0c54fNzVMP8+aNth2tK6LR7Ln2rK9RXAy1+AnU/GY54cWPwHKF0CH/wYXrsBSPhoTbsNpt0K+9+EtWebxKtHyWJzHGIReP5s2LchHguUwJnPm6TtrZvh/bsS2mLDvJ/B+C/B7mfgpc9CpD0eH3MJfOIh07eeWwzNCd/AjqTvpnPZsF5KZIhFu1oJ1a4nFunofc3yBQmULQDbQ6h+I9H2PfENbA/+0vk4gQK6GrfQtX+TqzxvwRS8I8YT6ThAqP7PJknv2TSrGH/pXIhG6KjbQKwz/tm3nAD+8gXY3lw6971LuGlbQqkWvuIZeHJHdT8aeQOI9kad3Cp8RdN7k6Dh0NHRwdatWxk7diyBQGDgDdKVs+slognHpYftH0Gg4vQjqeIhsSyLJ554gs985jNp39NfWwczfg97ojJ//nzmzp3Lj35kBpNoNEpVVRUrVqzg5ptv7nfbIUtUDiVJ6THuKqhYbgauvrIq4Jw3YdUY8220r2UbzeCcmIj0mHabGfz6Dt4AhXNh4QPw1NTkmO2F87fCc5+Elo+S4wsegH0b4cMUk6FGfwHGXgUvLE+O+UfC8k2wajyEU2TsZ62DTXfD7qeSY1NuMonQa9cnx/KnwuLfw+8nmDsuiSwbzttsEq7EpKrHnJUmkdh0d3KsYrnZ75olyTFPHpy/BZ6a4k4Yeix+CrY+ADt+kxybeD2MnAsbrkqO5Y6DpS/CqrHmLk9fy9+D9VfB/o3JsZl3mztU79yWHCtZYuLPzE2OOQE4fzusngntu5Pjp//GnJOPHkiOHUnfTUWJysdaR92rRNsbkl735I/D8mTTte/dpJjlzcNfOpeOnc+nLDNQeSah+o3EupKvKd6RpxALtxFuTL6O2VklePPHE6pbn1yo5SFQdQYdO9el/Bz6S+fiZJemrM+xcLQSlb5zVHr4hnCOSqJjmagM66Ofzs5OXn/9dW655Zbe12zbZunSpaxfn9wBQ6EQoVCo999NTU3HpJ79qnky9aAEZuD48N70F/rtj6ROUsB8W/fkpI7t3whb/yd1LNoFm3+aOknpKXdfioESzB2GxMdKiUL7TFtSJSlgHiPVPp1+nzljU8ca34Mt9yUnKWBe23Jf6iSlp9zW7aljtU+bx0CphJtNW1IlKT3l7lyVJvZE6oQAzDHf/NP0/WHrg6mTFDCPrhLvhiRqWGf6SiqRDth8b/o61TyR/rwcSd+VE0osFk2ZpABEWuuxEh63uLbraibcnP6xYLh5R8okxZRbRyzNZyLa3kA4zT6JhQk3bU/bt8Nt9cOaqBwtnpwyKJlDuHEz0c5mbF8envwJxyRJOdaGNVHZu3cvkUiE0lJ3pyktLeWvf/1r0vvvuOMOvv3tbx+r6h0axw+2P308XbIB5ha75aS+rW73V65ltk1bbpoPcE+5Tppy+93nAOU6AbC8EAuljqXb54DlDrDPdOVa3iPYZ8A87oqmaYvtS379kMrNASxcj4QSy033eMVy+j/f/faxfuo7UN/19lOunHgsO/UXCsvunaeSku30U2b6mGU7xCJpyrVsrH7Wglj9ldtfXY8znpyyY5qYtLS0sHlzfLLu1q1beeuttygsLGT06NFDtt/j6ozdcsstNDY29v7U1NQM0Z4GkW1XX25+UglONvNFfIXJMcuGsV8080YGW275p8y8BCtFnunJhYn/YCayDrbc6svMTyo51TDpBjMJNollyhx9Ueptx1yWfp/Fi2DCte6JnT1sv4kVL0qODVTu6Iug+gpTt74CZaYtOdWpt63up9z+jl/BLHPsPbnJMcsD4682c2sGW27lBTDuyviE20S+Aph4HQSnDL7cgfruxDR9V044lmXj5JSnjHlyR+HkjEoZs/2FePNGp+67lo03OBrbn7qPOTkVeHJTl+vklOPJq0wZw/HjBKuxPKmT+3R1lYG99tprzJw5k5kzZwJw4403MnPmTL71rW8N6X6HNVEpKirCcRzq6+tdr9fX11NWljwg+v1+gsGg62dIXHaIvzSnbClMvx3KzjRzShKz+OxKOO0R8GTB6b92P1Kx/TD3v81kzzkrzYTTRNWXm8Fn3NUw/hpcg23+VDOBMrvSzDdJ/KbtDZp9+vLNJMrExy2WbeZsVJ4HU78JFZ9277N4Ecy4y6zgmXFXfKUOmIH99F+DJwCLHgN/UUJbfDD7HrOqZfb3YOR8d7lVn4PJN5oVSZNWuNuSN9HMtQkUm/om3hnw5JjXAsXmPXmTEgq1TKJRfYkpu+rz7n2OnG/qUjAdZn/ffUfBX2TmbXgC5r+JiZflgRl3dh+DO5MTpIrl5thVnmcmQydefHOqTX19+WbiceJKHScLFtxvztm8n5pzmNiW8deYcz3xuu7kKkHBDNNH8iaYPpN4B8RX0N2WbHPeE1dmWQ6ccqvpm9NvN3010eH23bTyBn6LHNd8hVOx/SNcrznZZXjyx+HJrcATrHbFLE8OvuJTsRw/vuKZ7j5mOfiKZ3bHTsXqc/fOE6zGkzsKT/44nGx3gmT7R5i6+IJ4C6fiGsZsH/6S2di2g69kdp+7qhbegsk4gUPpz5LKkiVLiMViST8PPPDAkO43IybTzps3jx/+8IeAmUw7evRobrjhhuGbTNvj4ULgQPzfl8Vg76vQ9D4ET4aiPkuo23ZC3fNmiWf5MvdgH26D3U+bZ/4V57iX5cZi0PCCmRhaONe9LBfMUte9G8wAUnpmfIkxmKWuu1eb5czl57qX5UbDUPesWaZcsti9LBdg/xvdy5NPMitvErXXmuW33qApN2GZIZEO05Zwq7m7E+izOqvhZWjZbO4wFEx3x5q3wJ6XzfLXsqXuwb6ryZQLUHGue7CPRU192muh+HT3slyAA2/HlyeX9EkwOhqg9o8m+ak41333JtJpViJ1NZr6ZPX51rhnfXx5cmGfu1Qt28x5C5SYpd8JSybpajFzQ6Jhc74TB/tYzCxrbquBooXuJeUAje/HlyeXLHaf79A+c76dgGlL4pLyaBfUPmOWJ5ed6U5c4Oj23cQJ55pEe0KJdOwn1tWK7c/H9rmvu9GuVqId+7GcAHZWkWt1TSzaRaTNrApysouxEvqYWdq8l1ikAztQiN0ncYl2NhENNWJ5s5OWGMciISLte8BycLJKsBIeNcViUSJtDRAN42QVYXkOf/Lq0XK0JtMeDz42q34effRRrrzySv77v/+befPmcc899/DrX/+av/71r0lzV/oa8kRFRETkKFKiYhw3q34ALr74Yvbs2cO3vvUt6urqmDFjBqtXrx4wSREREZGPv2FPVABuuOEGbrjhhuGuhoiIiGSY42rVj4iIyMfBMM+6OCaOVhuVqIiIiBwjXq+ZRNzW1jbMNRl6nZ3mz5M4Tj+/S+cQZMSjHxERkROB4ziMGDGChgbzm36zs7OH9W8PDZVoNMqePXvIzs7G4zmyVEOJioiIyDHU83vCepKVjyvbthk9evQRJ2JKVERERI4hy7IoLy+npKSErq40f2/rY8Dn82HbRz7DRImKiIjIMHAc54jnb5wINJlWREREMpYSFREREclYSlREREQkYx3Xc1R6fplMU1PTMNdEREREDlXPuH0ovxTuuE5UmpubAaiqqhrmmoiIiMhgNTc3k5+f3+97hv2vJx+JaDTK7t27ycvL+1j+whxxa2pqoqqqipqaGv21bJGPGX2+TyyxWIzm5mYqKioGXMJ8XN9RsW2bysrK4a6GHGPBYFAXMpGPKX2+TxwD3Unpocm0IiIikrGUqIiIiEjGUqIixw2/38+tt96K3+8f7qqIyFGmz7ekc1xPphUREZGPN91RERERkYylREVEREQylhIVERERyVhKVERERCRjKVGR48bKlSuprq4mEAgwf/58Xn311eGukogcoRdffJHzzjuPiooKLMviySefHO4qSYZRoiLHhUcffZQbb7yRW2+9lTfeeINTTz2VZcuW0dDQMNxVE5Ej0NrayqmnnsrKlSuHuyqSobQ8WY4L8+fPZ+7cufzoRz8CzN95qqqqYsWKFdx8883DXDsRORosy+KJJ57gM5/5zHBXRTKI7qhIxuvs7OT1119n6dKlva/Zts3SpUtZv379MNZMRESGmhIVyXh79+4lEolQWlrqer20tJS6urphqpWIiBwLSlREREQkYylRkYxXVFSE4zjU19e7Xq+vr6esrGyYaiUiIseCEhXJeD6fj9mzZ7NmzZre16LRKGvWrGHhwoXDWDMRERlqnuGugMihuPHGG7nyyiuZM2cO8+bN45577qG1tZWrr756uKsmIkegpaWFzZs39/5769atvPXWWxQWFjJ69OhhrJlkCi1PluPGj370I+6++27q6uqYMWMGP/jBD5g/f/5wV0tEjsC6des444wzkl6/8soreeCBB459hSTjKFERERGRjKU5KiIiIpKxlKiIiIhIxlKiIiIiIhlLiYqIiIhkLCUqIiIikrGUqIiIiEjGUqIiIiIiGUuJioiIiGQsJSoiktFuu+02ZsyYMdzVEJFhokRFRIZUXV0dK1asYNy4cfj9fqqqqjjvvPNcf2RSRCQd/VFCERky27Zt47TTTmPEiBHcfffdTJs2ja6uLp555hmuv/56/vrXvx6TenR1deH1eo/JvkTk6NIdFREZMtdddx2WZfHqq6/yuc99jkmTJjF16lRuvPFGNmzYAMCOHTu44IILyM3NJRgMctFFF1FfX5+2zGg0yu23305lZSV+v58ZM2awevXq3vi2bduwLItHH32UxYsXEwgEeOihh4a8rSIyNJSoiMiQ2L9/P6tXr+b6668nJycnKT5ixAii0SgXXHAB+/fv54UXXuDZZ5/lo48+4uKLL05b7ve//33+67/+i+985zu8/fbbLFu2jPPPP58PP/zQ9b6bb76Zr33ta2zatIlly5Yd9faJyLGhRz8iMiQ2b95MLBZj8uTJad+zZs0a3nnnHbZu3UpVVRUADz74IFOnTmXjxo3MnTs3aZvvfOc7fOMb3+CSSy4B4K677mLt2rXcc889rFy5svd9//iP/8iFF154lFslIsea7qiIyJCIxWIDvmfTpk1UVVX1JikAJ598MiNGjGDTpk1J729qamL37t2cdtpprtdPO+20pPfPmTPnMGsuIplEiYqIDImJEydiWdYxmzDbV6rHTSJy/FGiIiJDorCwkGXLlrFy5UpaW1uT4gcPHmTKlCnU1NRQU1PT+/r777/PwYMHOfnkk5O2CQaDVFRU8Morr7hef+WVV1K+X0SOf5qjIiJDZuXKlZx22mnMmzeP22+/nenTpxMOh3n22We59957ef/995k2bRqXX34599xzD+FwmOuuu47FixenfXRz0003ceuttzJ+/HhmzJjB/fffz1tvvaWVPSIfU0pURGTIjBs3jjfeeIN///d/5+tf/zq1tbUUFxcze/Zs7r33XizL4ne/+x0rVqzgk5/8JLZtc8455/DDH/4wbZlf/epXaWxs5Otf/zoNDQ2cfPLJrFq1iokTJx7DlonIsWLFDmXGm4iIiMgw0BwVERERyVhKVERERCRjKVERERGRjKVERURERDKWEhURERHJWEpUREREJGMpUREREZGMpURFREREMpYSFREREclYSlREREQkYylRERERkYz1/wPgOOXtcFflHwAAAABJRU5ErkJggg==",
"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\", hue='Color' , data=encoded_pumpkins, palette=palette)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
" from sklearn.model_selection import train_test_split\n",
" \n",
" X = encoded_pumpkins[encoded_pumpkins.columns.difference(['Color'])]\n",
" y = encoded_pumpkins['Color']\n",
"\n",
" X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"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": [
" from sklearn.metrics import f1_score, classification_report \n",
" from sklearn.linear_model import LogisticRegression\n",
"\n",
" model = LogisticRegression()\n",
" model.fit(X_train, y_train)\n",
" predictions = model.predict(X_test)\n",
"\n",
" print(classification_report(y_test, predictions))\n",
" print('Predicted labels: ', predictions)\n",
" print('F1-score: ', f1_score(y_test, predictions))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.11"
},
"metadata": {
"interpreter": {
"hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
}
},
"orig_nbformat": 2
},
"nbformat": 4,
"nbformat_minor": 2
}
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