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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"%matplotlib inline\n",
"from sklearn.model_selection import train_test_split, cross_validate\n",
"from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler, Normalizer, OneHotEncoder\n",
"from sklearn.linear_model import LogisticRegression\n",
"from numpy import mean\n",
"from sklearn.metrics import f1_score, confusion_matrix, accuracy_score, precision_score, recall_score,roc_auc_score\n",
"oh = OneHotEncoder(drop='first', dtype= int)\n",
"from random import randint\n",
"from sklearn.model_selection import GridSearchCV, RandomizedSearchCV"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('/Users/youssef/Desktop/6006CEM/Final Assessment/adult.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import warnings\n",
"warnings.filterwarnings('ignore')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(48842, 15)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"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>age</th>\n",
" <th>fnlwgt</th>\n",
" <th>educational-num</th>\n",
" <th>capital-gain</th>\n",
" <th>capital-loss</th>\n",
" <th>hours-per-week</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>48842.000</td>\n",
" <td>48842.000</td>\n",
" <td>48842.000</td>\n",
" <td>48842.000</td>\n",
" <td>48842.000</td>\n",
" <td>48842.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>38.644</td>\n",
" <td>189664.135</td>\n",
" <td>10.078</td>\n",
" <td>1079.068</td>\n",
" <td>87.502</td>\n",
" <td>40.422</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>13.711</td>\n",
" <td>105604.025</td>\n",
" <td>2.571</td>\n",
" <td>7452.019</td>\n",
" <td>403.005</td>\n",
" <td>12.391</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>17.000</td>\n",
" <td>12285.000</td>\n",
" <td>1.000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>1.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>28.000</td>\n",
" <td>117550.500</td>\n",
" <td>9.000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>40.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>37.000</td>\n",
" <td>178144.500</td>\n",
" <td>10.000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>40.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>48.000</td>\n",
" <td>237642.000</td>\n",
" <td>12.000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>45.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>90.000</td>\n",
" <td>1490400.000</td>\n",
" <td>16.000</td>\n",
" <td>99999.000</td>\n",
" <td>4356.000</td>\n",
" <td>99.000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age fnlwgt educational-num capital-gain capital-loss \\\n",
"count 48842.000 48842.000 48842.000 48842.000 48842.000 \n",
"mean 38.644 189664.135 10.078 1079.068 87.502 \n",
"std 13.711 105604.025 2.571 7452.019 403.005 \n",
"min 17.000 12285.000 1.000 0.000 0.000 \n",
"25% 28.000 117550.500 9.000 0.000 0.000 \n",
"50% 37.000 178144.500 10.000 0.000 0.000 \n",
"75% 48.000 237642.000 12.000 0.000 0.000 \n",
"max 90.000 1490400.000 16.000 99999.000 4356.000 \n",
"\n",
" hours-per-week \n",
"count 48842.000 \n",
"mean 40.422 \n",
"std 12.391 \n",
"min 1.000 \n",
"25% 40.000 \n",
"50% 40.000 \n",
"75% 45.000 \n",
"max 99.000 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe().round(3)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 48842 entries, 0 to 48841\n",
"Data columns (total 15 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 age 48842 non-null int64 \n",
" 1 workclass 48842 non-null object\n",
" 2 fnlwgt 48842 non-null int64 \n",
" 3 education 48842 non-null object\n",
" 4 educational-num 48842 non-null int64 \n",
" 5 marital-status 48842 non-null object\n",
" 6 occupation 48842 non-null object\n",
" 7 relationship 48842 non-null object\n",
" 8 race 48842 non-null object\n",
" 9 gender 48842 non-null object\n",
" 10 capital-gain 48842 non-null int64 \n",
" 11 capital-loss 48842 non-null int64 \n",
" 12 hours-per-week 48842 non-null int64 \n",
" 13 native-country 48842 non-null object\n",
" 14 income 48842 non-null object\n",
"dtypes: int64(6), object(9)\n",
"memory usage: 5.6+ MB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"<Figure size 960x960 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"matrix = df.corr().round(2)\n",
"plt.figure(figsize=(12, 12), dpi=80)\n",
"sns.heatmap(data=matrix,annot=True)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"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>age</th>\n",
" <th>workclass</th>\n",
" <th>fnlwgt</th>\n",
" <th>education</th>\n",
" <th>educational-num</th>\n",
" <th>marital-status</th>\n",
" <th>occupation</th>\n",
" <th>relationship</th>\n",
" <th>race</th>\n",
" <th>gender</th>\n",
" <th>capital-gain</th>\n",
" <th>capital-loss</th>\n",
" <th>hours-per-week</th>\n",
" <th>native-country</th>\n",
" <th>income</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>25</td>\n",
" <td>Private</td>\n",
" <td>226802</td>\n",
" <td>11th</td>\n",
" <td>7</td>\n",
" <td>Never-married</td>\n",
" <td>Machine-op-inspct</td>\n",
" <td>Own-child</td>\n",
" <td>Black</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>38</td>\n",
" <td>Private</td>\n",
" <td>89814</td>\n",
" <td>HS-grad</td>\n",
" <td>9</td>\n",
" <td>Married-civ-spouse</td>\n",
" <td>Farming-fishing</td>\n",
" <td>Husband</td>\n",
" <td>White</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>50</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>28</td>\n",
" <td>Local-gov</td>\n",
" <td>336951</td>\n",
" <td>Assoc-acdm</td>\n",
" <td>12</td>\n",
" <td>Married-civ-spouse</td>\n",
" <td>Protective-serv</td>\n",
" <td>Husband</td>\n",
" <td>White</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>&gt;50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>44</td>\n",
" <td>Private</td>\n",
" <td>160323</td>\n",
" <td>Some-college</td>\n",
" <td>10</td>\n",
" <td>Married-civ-spouse</td>\n",
" <td>Machine-op-inspct</td>\n",
" <td>Husband</td>\n",
" <td>Black</td>\n",
" <td>Male</td>\n",
" <td>7688</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>&gt;50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>18</td>\n",
" <td>?</td>\n",
" <td>103497</td>\n",
" <td>Some-college</td>\n",
" <td>10</td>\n",
" <td>Never-married</td>\n",
" <td>?</td>\n",
" <td>Own-child</td>\n",
" <td>White</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>30</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age workclass fnlwgt education educational-num marital-status \\\n",
"0 25 Private 226802 11th 7 Never-married \n",
"1 38 Private 89814 HS-grad 9 Married-civ-spouse \n",
"2 28 Local-gov 336951 Assoc-acdm 12 Married-civ-spouse \n",
"3 44 Private 160323 Some-college 10 Married-civ-spouse \n",
"4 18 ? 103497 Some-college 10 Never-married \n",
"\n",
" occupation relationship race gender capital-gain capital-loss \\\n",
"0 Machine-op-inspct Own-child Black Male 0 0 \n",
"1 Farming-fishing Husband White Male 0 0 \n",
"2 Protective-serv Husband White Male 0 0 \n",
"3 Machine-op-inspct Husband Black Male 7688 0 \n",
"4 ? Own-child White Female 0 0 \n",
"\n",
" hours-per-week native-country income \n",
"0 40 United-States <=50K \n",
"1 50 United-States <=50K \n",
"2 40 United-States >50K \n",
"3 40 United-States >50K \n",
"4 30 United-States <=50K "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pre-Processing"
]
},
{
"cell_type": "code",
"execution_count": 9,
"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>age</th>\n",
" <th>workclass</th>\n",
" <th>fnlwgt</th>\n",
" <th>education</th>\n",
" <th>educationalNum</th>\n",
" <th>maritalStatus</th>\n",
" <th>occupation</th>\n",
" <th>relationship</th>\n",
" <th>race</th>\n",
" <th>gender</th>\n",
" <th>capitalGain</th>\n",
" <th>capitalLoss</th>\n",
" <th>hPw</th>\n",
" <th>nativeCountry</th>\n",
" <th>income</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>25</td>\n",
" <td>Private</td>\n",
" <td>226802</td>\n",
" <td>11th</td>\n",
" <td>7</td>\n",
" <td>Never-married</td>\n",
" <td>Machine-op-inspct</td>\n",
" <td>Own-child</td>\n",
" <td>Black</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>38</td>\n",
" <td>Private</td>\n",
" <td>89814</td>\n",
" <td>HS-grad</td>\n",
" <td>9</td>\n",
" <td>Married-civ-spouse</td>\n",
" <td>Farming-fishing</td>\n",
" <td>Husband</td>\n",
" <td>White</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>50</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>28</td>\n",
" <td>Local-gov</td>\n",
" <td>336951</td>\n",
" <td>Assoc-acdm</td>\n",
" <td>12</td>\n",
" <td>Married-civ-spouse</td>\n",
" <td>Protective-serv</td>\n",
" <td>Husband</td>\n",
" <td>White</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>&gt;50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>44</td>\n",
" <td>Private</td>\n",
" <td>160323</td>\n",
" <td>Some-college</td>\n",
" <td>10</td>\n",
" <td>Married-civ-spouse</td>\n",
" <td>Machine-op-inspct</td>\n",
" <td>Husband</td>\n",
" <td>Black</td>\n",
" <td>Male</td>\n",
" <td>7688</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>&gt;50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>18</td>\n",
" <td>?</td>\n",
" <td>103497</td>\n",
" <td>Some-college</td>\n",
" <td>10</td>\n",
" <td>Never-married</td>\n",
" <td>?</td>\n",
" <td>Own-child</td>\n",
" <td>White</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>30</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age workclass fnlwgt education educationalNum maritalStatus \\\n",
"0 25 Private 226802 11th 7 Never-married \n",
"1 38 Private 89814 HS-grad 9 Married-civ-spouse \n",
"2 28 Local-gov 336951 Assoc-acdm 12 Married-civ-spouse \n",
"3 44 Private 160323 Some-college 10 Married-civ-spouse \n",
"4 18 ? 103497 Some-college 10 Never-married \n",
"\n",
" occupation relationship race gender capitalGain capitalLoss \\\n",
"0 Machine-op-inspct Own-child Black Male 0 0 \n",
"1 Farming-fishing Husband White Male 0 0 \n",
"2 Protective-serv Husband White Male 0 0 \n",
"3 Machine-op-inspct Husband Black Male 7688 0 \n",
"4 ? Own-child White Female 0 0 \n",
"\n",
" hPw nativeCountry income \n",
"0 40 United-States <=50K \n",
"1 50 United-States <=50K \n",
"2 40 United-States >50K \n",
"3 40 United-States >50K \n",
"4 30 United-States <=50K "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.rename(columns={'capital-gain': 'capitalGain', 'capital-loss': 'capitalLoss', 'educational-num': 'educationalNum', 'marital-status': 'maritalStatus', 'hours-per-week': 'hPw', 'native-country': 'nativeCountry'}, inplace=True)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"36 1348\n",
"35 1337\n",
"33 1335\n",
"23 1329\n",
"31 1325\n",
" ... \n",
"88 6\n",
"85 5\n",
"87 3\n",
"89 2\n",
"86 1\n",
"Name: age, Length: 74, dtype: int64\n",
"Private 33906\n",
"Self-emp-not-inc 3862\n",
"Local-gov 3136\n",
"? 2799\n",
"State-gov 1981\n",
"Self-emp-inc 1695\n",
"Federal-gov 1432\n",
"Without-pay 21\n",
"Never-worked 10\n",
"Name: workclass, dtype: int64\n",
"203488 21\n",
"190290 19\n",
"120277 19\n",
"125892 18\n",
"126569 18\n",
" ..\n",
"188488 1\n",
"285290 1\n",
"293579 1\n",
"114874 1\n",
"257302 1\n",
"Name: fnlwgt, Length: 28523, dtype: int64\n",
"HS-grad 15784\n",
"Some-college 10878\n",
"Bachelors 8025\n",
"Masters 2657\n",
"Assoc-voc 2061\n",
"11th 1812\n",
"Assoc-acdm 1601\n",
"10th 1389\n",
"7th-8th 955\n",
"Prof-school 834\n",
"9th 756\n",
"12th 657\n",
"Doctorate 594\n",
"5th-6th 509\n",
"1st-4th 247\n",
"Preschool 83\n",
"Name: education, dtype: int64\n",
"9 15784\n",
"10 10878\n",
"13 8025\n",
"14 2657\n",
"11 2061\n",
"7 1812\n",
"12 1601\n",
"6 1389\n",
"4 955\n",
"15 834\n",
"5 756\n",
"8 657\n",
"16 594\n",
"3 509\n",
"2 247\n",
"1 83\n",
"Name: educationalNum, dtype: int64\n",
"Married-civ-spouse 22379\n",
"Never-married 16117\n",
"Divorced 6633\n",
"Separated 1530\n",
"Widowed 1518\n",
"Married-spouse-absent 628\n",
"Married-AF-spouse 37\n",
"Name: maritalStatus, dtype: int64\n",
"Prof-specialty 6172\n",
"Craft-repair 6112\n",
"Exec-managerial 6086\n",
"Adm-clerical 5611\n",
"Sales 5504\n",
"Other-service 4923\n",
"Machine-op-inspct 3022\n",
"? 2809\n",
"Transport-moving 2355\n",
"Handlers-cleaners 2072\n",
"Farming-fishing 1490\n",
"Tech-support 1446\n",
"Protective-serv 983\n",
"Priv-house-serv 242\n",
"Armed-Forces 15\n",
"Name: occupation, dtype: int64\n",
"Husband 19716\n",
"Not-in-family 12583\n",
"Own-child 7581\n",
"Unmarried 5125\n",
"Wife 2331\n",
"Other-relative 1506\n",
"Name: relationship, dtype: int64\n",
"White 41762\n",
"Black 4685\n",
"Asian-Pac-Islander 1519\n",
"Amer-Indian-Eskimo 470\n",
"Other 406\n",
"Name: race, dtype: int64\n",
"Male 32650\n",
"Female 16192\n",
"Name: gender, dtype: int64\n",
"0 44807\n",
"15024 513\n",
"7688 410\n",
"7298 364\n",
"99999 244\n",
" ... \n",
"1111 1\n",
"7262 1\n",
"22040 1\n",
"1639 1\n",
"2387 1\n",
"Name: capitalGain, Length: 123, dtype: int64\n",
"0 46560\n",
"1902 304\n",
"1977 253\n",
"1887 233\n",
"2415 72\n",
" ... \n",
"2465 1\n",
"2080 1\n",
"155 1\n",
"1911 1\n",
"2201 1\n",
"Name: capitalLoss, Length: 99, dtype: int64\n",
"40 22803\n",
"50 4246\n",
"45 2717\n",
"60 2177\n",
"35 1937\n",
" ... \n",
"69 1\n",
"87 1\n",
"94 1\n",
"82 1\n",
"79 1\n",
"Name: hPw, Length: 96, dtype: int64\n",
"United-States 43832\n",
"Mexico 951\n",
"? 857\n",
"Philippines 295\n",
"Germany 206\n",
"Puerto-Rico 184\n",
"Canada 182\n",
"El-Salvador 155\n",
"India 151\n",
"Cuba 138\n",
"England 127\n",
"China 122\n",
"South 115\n",
"Jamaica 106\n",
"Italy 105\n",
"Dominican-Republic 103\n",
"Japan 92\n",
"Guatemala 88\n",
"Poland 87\n",
"Vietnam 86\n",
"Columbia 85\n",
"Haiti 75\n",
"Portugal 67\n",
"Taiwan 65\n",
"Iran 59\n",
"Greece 49\n",
"Nicaragua 49\n",
"Peru 46\n",
"Ecuador 45\n",
"France 38\n",
"Ireland 37\n",
"Hong 30\n",
"Thailand 30\n",
"Cambodia 28\n",
"Trinadad&Tobago 27\n",
"Laos 23\n",
"Yugoslavia 23\n",
"Outlying-US(Guam-USVI-etc) 23\n",
"Scotland 21\n",
"Honduras 20\n",
"Hungary 19\n",
"Holand-Netherlands 1\n",
"Name: nativeCountry, dtype: int64\n",
"<=50K 37155\n",
">50K 11687\n",
"Name: income, dtype: int64\n"
]
}
],
"source": [
"#pd.set_option('display.max_rows', None)\n",
"for i in list(df.columns):\n",
" print(df[i].value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"df = df[df.occupation != '?']\n",
"df = df[df.nativeCountry != '?']\n",
"df = df[df.workclass != '?']"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(45222, 15)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 6 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 1600x1600 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.plot.box(subplots=True)\n",
"\n",
"plt.tight_layout()\n",
"plt.figure(figsize=(20, 20), dpi=80)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"df = df[df.age < 74]\n",
"df = df[df.educationalNum > 5]\n",
"df = df[df.capitalGain == 0]\n",
"df = df[df.capitalLoss == 0]\n",
"df = df[df.hPw < 55]\n",
"df = df[df.hPw > 30]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 6 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 1600x1600 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.plot.box(subplots=True)\n",
"\n",
"plt.tight_layout()\n",
"plt.figure(figsize=(20, 20), dpi=80)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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13X1vd9+X5Pok24Zlj+ruG7u7k7xpal8AwDoiUAIAYK677x6mP55kbpg+OsldU+vtHNoerH3nAu0AwDqzabULAADg0NHdXVU969epqnMzuYwuc3NzmZ+fX/I+d+/e/XX72X7iniXv91A1d/jq9285/t0O1kL/vuvZRurvRupror/r3Ubqr0AJAIBPVNXjuvvu4bK1e4b2XUmOnVrvmKFtV5Kt+7TPD+3HLLD+1+nui5JclCRbtmzprVu3LrTaKPPz89l3P2eff82S93uo2n7inrz25tU9nL/zzK0r9loL/fuuZxupvxupr4n+rncbqb8ueQMA4Ooke5/UdlaSq6baXzg87e3kJA8Ml8Zdl+TUqjpyuBn3qUmuG5Z9pqpOHp7u9sKpfQEA64gzlAAANpCqeksmZxcdVVU7M3la24VJrqiqc5J8LMlzh9WvTXJ6kh1JPp/kRUnS3fdW1SuTvGdY7xXdvfdG3z+WyZPkDk/yB8MXALDOCJQAADaQ7n7+fhadssC6neS8/eznkiSXLND+3iRPXEqNAMChzyVvAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMMqm1S4AWD6bz7/m69q2n7gnZy/Qfii488JnrnYJAAAALIIzlAAAAAAYxRlKwKpZ6IyqQ8m+Z3c5owoAAGDCGUoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAUQRKAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGCURQdKVfWNVfXuqvrzqrqlqn5+aD+uqm6qqh1V9daqeujQ/rBhfsewfPPUvl4+tH+kqp6x5F4BADBaVf274bjuw1X1luF4z7EdAPB1lnKG0heTPL27n5TkyUm2VdXJSV6T5HXd/fgk9yU5Z1j/nCT3De2vG9ZLVZ2Q5HlJvivJtiS/VlWHLaEuAABGqqqjk/x4ki3d/cQkh2VyjObYDgD4OosOlHpi9zD7DcNXJ3l6kiuH9suSPHuYPmOYz7D8lKqqof3y7v5id380yY4kJy22LgAAFm1TksOralOSb0pydxzbAQAL2LSUjYe/Nr0vyeOT/GqSv0pyf3fvGVbZmeToYfroJHclSXfvqaoHkjx2aL9xarfT2+z7eucmOTdJ5ubmMj8/v5TyV9zu3buXvebtJ+458ErrwNzhG6evy83YLd6+Y7fWfuaslln8rNsojB2rqbt3VdV/SfLXSf5Pkj/K5DhvJsd2sziuW+gztJ5/Bx4Kv+NX8mfWRvsZuZH6u5H6mujvereR+rukQKm7v5zkyVV1RJK3J/nO5SjqQV7voiQXJcmWLVt669ats3y5ZTc/P5/lrvns869Z1v0dqrafuCevvXlJb9cNy9gt3r5jd+eZW1evmDVkFj/rNgpjx2qqqiMzObvouCT3J/mdTC5Zm4lZHNct9Blaz8dKh8Lv+JX83bjRfkZupP5upL4m+rvebaT+LstT3rr7/iTvTPL9SY4YTpNOkmOS7BqmdyU5NkmG5Y9O8unp9gW2AQBgZfxwko929ye7+2+TvC3J0+LYDgBYwFKe8vbNw5lJqarDk/yTJLdlEiw9Z1jtrCRXDdNXD/MZlr+ju3tof97wpJDjkhyf5N2LrQsAgEX56yQnV9U3DfdCOiXJrXFsBwAsYCnnyD4uyWXDfZQekuSK7v79qro1yeVV9aokH0hy8bD+xUl+q6p2JLk3k6d/pLtvqaorMjlg2ZPkvOFSOgAAVkh331RVVyZ5fybHZB/I5JK0a+LYDgDYx6IDpe7+UJLvWaD9jizwJI/u/kKSf7Gffb06yasXWwsAAEvX3RckuWCfZsd2AMDXWZZ7KAEAAACwcQiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAUQRKAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwyqbVLgBgrdh8/jWrXcJod174zNUuAQAAWIecoQQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACACBJUlVHVNWVVfUXVXVbVX1/VT2mqq6vqtuH70cO61ZVvaGqdlTVh6rqKVP7OWtY//aqOmv1egQAzIpACQCAvV6f5A+7+zuTPCnJbUnOT3JDdx+f5IZhPklOS3L88HVukjcmSVU9JskFSZ6a5KQkF+wNoQCA9UOgBABAqurRSX4gycVJ0t1f6u77k5yR5LJhtcuSPHuYPiPJm3rixiRHVNXjkjwjyfXdfW9335fk+iTbVqwjAMCK2LTaBQAAcEg4Lsknk/xmVT0pyfuSvDTJXHffPazz8SRzw/TRSe6a2n7n0La/9q9RVedmcmZT5ubmMj8/v+QO7N69++v2s/3EPUve76Fq7vDV799y/LsdrIX+fdezjdTfjdTXRH/Xu43UX4ESAADJ5LjwKUle0t03VdXr83eXtyVJururqpfjxbr7oiQXJcmWLVt669atS97n/Px89t3P2edfs+T9Hqq2n7gnr715dQ/n7zxz64q91kL/vuvZRurvRupror/r3Ubqr0veAABIJmcS7ezum4b5KzMJmD4xXMqW4fs9w/JdSY6d2v6YoW1/7QDAOiJQAgAg3f3xJHdV1ROGplOS3Jrk6iR7n9R2VpKrhumrk7xweNrbyUkeGC6Nuy7JqVV15HAz7lOHNgBgHXHJGwAAe70kyZur6qFJ7kjyokz+AHlFVZ2T5GNJnjuse22S05PsSPL5Yd10971V9cok7xnWe0V337tyXQAAVoJACQCAJEl3fzDJlgUWnbLAup3kvP3s55IklyxrcQDAIcUlbwAAAACM4gylfWye4ZNAtp+4Z10/aQQAAADYGJyhBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAURYdKFXVsVX1zqq6tapuqaqXDu2Pqarrq+r24fuRQ3tV1RuqakdVfaiqnjK1r7OG9W+vqrOW3i0AAAAAZmUpZyjtSbK9u09IcnKS86rqhCTnJ7mhu49PcsMwnySnJTl++Do3yRuTSQCV5IIkT01yUpIL9oZQAAAAABx6Fh0odffd3f3+YfqzSW5LcnSSM5JcNqx2WZJnD9NnJHlTT9yY5IiqelySZyS5vrvv7e77klyfZNti6wIAAABgtjYtx06qanOS70lyU5K57r57WPTxJHPD9NFJ7prabOfQtr/2hV7n3EzObsrc3Fzm5+eXo/yvsf3EPcu+z73mDp/t/tczY7d4xm7x1sPYzeLn5IHs3r17VV53PTB2AACsFUsOlKrqEUl+N8lPdPdnquqry7q7q6qX+hpT+7soyUVJsmXLlt66dety7fqrzj7/mmXf517bT9yT1968LBnehmPsFs/YLd56GLs7z9y64q85Pz+fWfx83giMHQAAa8WSnvJWVd+QSZj05u5+29D8ieFStgzf7xnadyU5dmrzY4a2/bUDAAAAcAhaylPeKsnFSW7r7l+cWnR1kr1PajsryVVT7S8cnvZ2cpIHhkvjrktyalUdOdyM+9ShDQAAAIBD0FKu5XhakhckubmqPji0/UySC5NcUVXnJPlYkucOy65NcnqSHUk+n+RFSdLd91bVK5O8Z1jvFd197xLqAgAAAGCGFh0odfefJqn9LD5lgfU7yXn72dclSS5ZbC0AAAAArJwl3UMJAAAAgI1HoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAUQRKAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQCAr6qqw6rqA1X1+8P8cVV1U1XtqKq3VtVDh/aHDfM7huWbp/bx8qH9I1X1jFXqCgAwQwIlAACmvTTJbVPzr0nyuu5+fJL7kpwztJ+T5L6h/XXDeqmqE5I8L8l3JdmW5Neq6rAVqh0AWCECJQAAkiRVdUySZyb5jWG+kjw9yZXDKpclefYwfcYwn2H5KcP6ZyS5vLu/2N0fTbIjyUkr0gEAYMUIlAAA2OuXkvx0kq8M849Ncn937xnmdyY5epg+OsldSTIsf2BY/6vtC2wDAKwTm1a7AAAAVl9V/UiSe7r7fVW1dQVe79wk5ybJ3Nxc5ufnl7zP3bt3f91+tp+4Z+GV14G5w1e/f8vx73awFvr3Xc82Un83Ul8T/V3vNlJ/BUoAACTJ05I8q6pOT/KNSR6V5PVJjqiqTcNZSMck2TWsvyvJsUl2VtWmJI9O8ump9r2mt/mq7r4oyUVJsmXLlt66deuSOzA/P59993P2+dcseb+Hqu0n7slrb17dw/k7z9y6Yq+10L/veraR+ruR+pro73q3kfrrkjcAANLdL+/uY7p7cyY31X5Hd5+Z5J1JnjOsdlaSq4bpq4f5DMvf0d09tD9veArccUmOT/LuFeoGALBCnKEEAMCDeVmSy6vqVUk+kOTiof3iJL9VVTuS3JtJCJXuvqWqrkhya5I9Sc7r7i+vfNkAwCwJlAAA+BrdPZ9kfpi+Iws8pa27v5DkX+xn+1cnefXsKgQAVptL3gAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQAAAIBRPOUNYB3bfP41K/6a20/ck7MX+bp3XvjMZa4GAACYBWcoAQAAADCKQAkAAACAUQRKAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAo2xa7QIAAIC1afP516zYa20/cU/OXuLr3XnhM5epGgCcoQQAAADAKEsKlKrqkqq6p6o+PNX2mKq6vqpuH74fObRXVb2hqnZU1Yeq6ilT25w1rH97VZ21lJoAAAAAmK2lnqF0aZJt+7Sdn+SG7j4+yQ3DfJKcluT44evcJG9MJgFUkguSPDXJSUku2BtCAQAAAHDoWVKg1N3vSnLvPs1nJLlsmL4sybOn2t/UEzcmOaKqHpfkGUmu7+57u/u+JNfn60MqAAAAAA4Rs7gp91x33z1MfzzJ3DB9dJK7ptbbObTtr/3rVNW5mZzdlLm5uczPzy9f1YPtJ+5Z9n3uNXf4bPe/nhm7xTN2i2fsFmcp4zaLn+trye7duzf8GAAAsDbM9Clv3d1V1cu4v4uSXJQkW7Zs6a1bty7Xrr9qqU+OeDDbT9yT197swXqLYewWz9gtnrFbnKWM251nbl3eYtaY+fn5zOJ3GwAALLdZPOXtE8OlbBm+3zO070py7NR6xwxt+2sHAAAA4BA0i0Dp6iR7n9R2VpKrptpfODzt7eQkDwyXxl2X5NSqOnK4GfepQxsAAAAAh6AlXctRVW9JsjXJUVW1M5OntV2Y5IqqOifJx5I8d1j92iSnJ9mR5PNJXpQk3X1vVb0yyXuG9V7R3fve6BsAAACAQ8SSAqXufv5+Fp2ywLqd5Lz97OeSJJcspRYAAAAAVsYsLnkDAAAAYB0TKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAEhVHVtV76yqW6vqlqp66dD+mKq6vqpuH74fObRXVb2hqnZU1Yeq6ilT+zprWP/2qjprtfoEAMyOQAkAgCTZk2R7d5+Q5OQk51XVCUnOT3JDdx+f5IZhPklOS3L88HVukjcmkwAqyQVJnprkpCQX7A2hAID1Q6AEAEC6++7ufv8w/dkktyU5OskZSS4bVrssybOH6TOSvKknbkxyRFU9Lskzklzf3fd2931Jrk+ybeV6AgCshE2rXQAAAIeWqtqc5HuS3JRkrrvvHhZ9PMncMH10krumNts5tO2vfd/XODeTM5syNzeX+fn5Jde9e/fur9vP9hP3LHm/h6q5w9d3//a1HP1djvfZSlno/bxebaS+Jvq73m2k/gqUAAD4qqp6RJLfTfIT3f2Zqvrqsu7uqurleJ3uvijJRUmyZcuW3rp165L3OT8/n333c/b51yx5v4eq7SfuyWtv3jiH88vR3zvP3Lo8xayAhd7P69VG6muiv+vdRuqvS94AAEiSVNU3ZBImvbm73zY0f2K4lC3D93uG9l1Jjp3a/JihbX/tAMA6IlACACA1ORXp4iS3dfcvTi26OsneJ7WdleSqqfYXDk97OznJA8OlcdclObWqjhxuxn3q0AYArCMb5xxZAAAezNOSvCDJzVX1waHtZ5JcmOSKqjonyceSPHdYdm2S05PsSPL5JC9Kku6+t6pemeQ9w3qv6O57V6QHAMCKESgBAJDu/tMktZ/Fpyywfic5bz/7uiTJJctXHQBwqBEoAXDI2LzGbp5754XPXO0SAABgVbiHEgAAAACjCJQAAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAUQRKAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADDKptUuAADWqs3nX7Os+9t+4p6cvcz7nHbnhc+c2b4B1oLl/rk9S9tP3JOtq10EwINwhhIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEY5ZAKlqtpWVR+pqh1Vdf5q1wMAwOI5tgOA9e2QCJSq6rAkv5rktCQnJHl+VZ2wulUBALAYju0AYP3btNoFDE5KsqO770iSqro8yRlJbl3VqgAAWAzHdrAMNp9/zWqXMMqdFz5ztUsAVtChEigdneSuqfmdSZ66SrUAALA0ju1gA1psALb9xD05e42FZ0ux2P4K7DjUVHevdg2pquck2dbd/3qYf0GSp3b3i/dZ79wk5w6zT0jykRUtdOmOSvKp1S5ijTJ2i2fsFs/YLY5xW7y1Onbf1t3fvNpFcOg4mGO7GR3XrdXP0GLp7/q2kfq7kfqa6O96tx76e1DHdofKGUq7khw7NX/M0PY1uvuiJBetVFHLrare291bVruOtcjYLZ6xWzxjtzjGbfGMHevIAY/tZnFct9E+Q/q7vm2k/m6kvib6u95tpP4eEjflTvKeJMdX1XFV9dAkz0ty9SrXBADA4ji2A4B17pA4Q6m791TVi5Ncl+SwJJd09y2rXBYAAIvg2A4A1r9DIlBKku6+Nsm1q13HjK3Zy/UOAcZu8Yzd4hm7xTFui2fsWDdW6dhuo32G9Hd920j93Uh9TfR3vdsw/T0kbsoNAAAAwNpxqNxDCQAAAIA1QqA0I1V1bFW9s6purapbquqlQ/tjqur6qrp9+H7katd6qKmqb6yqd1fVnw9j9/ND+3FVdVNV7aiqtw43+WQfVXVYVX2gqn5/mDduB6Gq7qyqm6vqg1X13qHN5/UgVNURVXVlVf1FVd1WVd9v7A6sqp4wvN/2fn2mqn7C2MGBVdW2qvrI8Lvt/AWWP2z4nbdj+B24eRXKXDYH0d+fHI45P1RVN1TVt61GncvlQP2dWu+fV1VX1Zp9mtLB9LWqnjv1f4rfXukal9NBvJe/dfg/1AeG9/Ppq1HncqiqS6rqnqr68H6WV1W9YRiLD1XVU1a6xuV0EP09c+jnzVX1v6vqSStd43I6UH+n1vu+qtpTVc9ZqdpWkkBpdvYk2d7dJyQ5Ocl5VXVCkvOT3NDdxye5YZjna30xydO7+0lJnpxkW1WdnOQ1SV7X3Y9Pcl+Sc1avxEPaS5PcNjVv3A7eD3X3k6ce8+nzenBen+QPu/s7kzwpk/efsTuA7v7I8H57cpLvTfL5JG+PsYMHVVWHJfnVJKclOSHJ84djrGnnJLlv+N33ukx+F65JB9nfDyTZ0t3fneTKJP95ZatcPgfZ31TVIzM55rlpZStcPgfT16o6PsnLkzytu78ryU+sdJ3L5SD/bf9Dkiu6+3syeTrkr61slcvq0iTbHmT5aUmOH77OTfLGFahpli7Ng/f3o0l+sLtPTPLKrP37DF2aB+/v3vf8a5L80UoUtBoESjPS3Xd39/uH6c9m8h+so5OckeSyYbXLkjx7VQo8hPXE7mH2G4avTvL0TA6SEmO3oKo6Jskzk/zGMF8xbkvh83oAVfXoJD+Q5OIk6e4vdff9MXZjnZLkr7r7YzF2cCAnJdnR3Xd095eSXJ7J52ba9OfoyiSnDL8T16ID9re739ndnx9mb0xyzArXuJwO5t83mfyH9DVJvrCSxS2zg+nrjyb51e6+L0m6+54VrnE5HUx/O8mjhulHJ/mbFaxvWXX3u5Lc+yCrnJHkTcP/fW5MckRVPW5lqlt+B+pvd//vve/jrP2fUwfz75skL0nyu0nW8uf2QQmUVsBwmvX3ZPIXlLnuvntY9PEkc6tV16GsJpdtfTCTD9/1Sf4qyf3dvWdYZWcmAR1f65eS/HSSrwzzj41xO1id5I+q6n1Vde7Q5vN6YMcl+WSS3xxOT/+Nqnp4jN1Yz0vylmHa2MGDOzrJXVPzC/1u++o6w+/ABzL5nbgWHUx/p52T5A9mWtFsHbC/w6VBx3b3NStZ2AwczL/tdyT5jqr6X1V1Y1U96BkRh7iD6e/PJfmXVbUzk6dEvmRlSlsVYz/b68la/zl1QFV1dJJ/lrV/5tmDEijNWFU9IpNU8ie6+zPTy3ryiD2P2VtAd395uAzkmEz+mvGdq1vRoa+qfiTJPd39vtWuZY36R939lExOPz6vqn5geqHP635tSvKUJG8cTk//XPa5RMvYPbia3NfsWUl+Z99lxg4Yo6r+ZZItSX5htWuZlap6SJJfTLJ9tWtZIZsyuSRqa5LnJ/n1qjpiNQuasecnubS7j0lyepLfGv7NWSeq6ocyCZRettq1zNgvJXlZd3/lQCuuZT6cM1RV35BJmPTm7n7b0PyJvacyDt/X7elvy2G4dOadSb4/k9NANw2Ljkmya7XqOkQ9LcmzqurOTE4hfnom97Yxbgehu3cN3+/J5D42J8Xn9WDsTLKzu/few+LKTAImY3fwTkvy/u7+xDBv7ODB7Upy7NT8Qr/bvrrO8Dvw0Uk+vSLVLb+D6W+q6oeT/GySZ3X3F1eotlk4UH8fmeSJSeaHY56Tk1y9Rm/MfTD/tjuTXN3df9vdH03yl5kETGvRwfT3nCRXJEl3/1mSb0xy1IpUt/IO6rO9nlTVd2dya44zunut/kw+WFuSXD78nHpOkl+rqmevakUzIFCakeE6/YuT3Nbdvzi16OokZw3TZyW5aqVrO9RV1Tfv/ctLVR2e5J9kcg+qd2byYUyM3dfp7pd39zHdvTmTy2fe0d1nxrgdUFU9fLi5Z4bLtU5N8uH4vB5Qd388yV1V9YSh6ZQkt8bYjfH8/N3lbomxgwN5T5Lja/IU04dm8jvv6n3Wmf4cPSeT34lr9Wy/A/a3qr4nyX/LJExa6yH0g/a3ux/o7qO6e/NwzHNjJv1+7+qUuyQH817+vUzOTkpVHZXJJXB3rGCNy+lg+vvXmRxLpKr+YSaB0idXtMqVc3WSFw5Pezs5yQNTl7yvO1X1rUneluQF3f2Xq13PrHX3cVM/p65M8mPd/XurW9Xy23TgVVikpyV5QZKbh3sBJcnPJLkwyRVVdU6SjyV57uqUd0h7XJLLhrviPySTJz38flXdmknK+6pMnmZy8WoWuYa8LMbtQOaSvH24X+umJL/d3X9YVe+Jz+vBeEmSNw8Hh3ckeVGGz66xe3BDgPlPkvybqWa/J+BBdPeeqnpxkuuSHJbkku6+papekeS93X11Jr/rfquqdmRy09TnrV7FS3OQ/f2FJI9I8jvD77K/7u5nrVrRS3CQ/V0XDrKv1yU5dTgO/nKSf79Wz+w4yP5uz+Syvn+XySXfZ6/VMLiq3pJJGHjUcE+oCzJ52FC6+79mco+o05PsyORJry9anUqXx0H09z9mci+7Xxt+Tu3pv3uy8ppzEP3dEGqNfj4BAAAAWCUueQMAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAIA1rqouqap7qurDB7n+c6vq1qq6pap+e/TrecobAAAAwNpWVT+QZHeSN3X3Ew+w7vFJrkjy9O6+r6r+XnffM+b1nKEEAAAAsMZ197uS3DvdVlXfXlV/WFXvq6o/qarvHBb9aJJf7e77hm1HhUmJQAkAAABgvbooyUu6+3uT/FSSXxvavyPJd1TV/6qqG6tq29gdb1rGIgEAAAA4BFTVI5L8X0l+p6r2Nj9s+L4pyfFJtiY5Jsm7qurE7r7/YPcvUAIAAABYfx6S5P7ufvICy3Ymuam7/zbJR6vqLzMJmN4zZucAAAAArCPd/ZlMwqJ/kSQ18aRh8e9lcnZSquqoTC6Bu2PM/gVKAAAAAGtcVb0lyZ8leUJV7ayqc5KcmeScqvrzJLckOWNY/bokn66qW5O8M8m/7+5Pj3q97l6+6gEAAABY95yhBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAUQRKAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAUQRKAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEmwQVbW1qnauwuv+TFX9xgq8znxV/etZvw4AwEZRVf+4qj6yhO0vrapXLWM9K3JcCRwcgRKwbBYKrbr7P3X3igY9VXV2VXVV/fQ+7TurautK1gIAsFZ195909xP2zlfVnVX1w4vdX1U9sqp+cdjP56rqr6vqyqp66kHWs+LHlcD+CZSA9ereJD9dVY9c7UIAADa6qnpYknckOTHJjyR5VJJ/mOTyJKetYmnAIgmUYI2rqm+pqt+tqk9W1Uer6seH9sOH04zvq6pbk3zfPtt1VT1+av5rTkmuqjOq6oNV9Zmq+quq2ja0v6iqbquqz1bVHVX1b4b2hyf5gyTfUlW7h69vqaqfq6r/PrXfZ1XVLVV1/3CZ2j+cWnZnVf1UVX2oqh6oqrdW1TcOy46sqt8f+nnfMH3MgwzNbUn+LMlP7mfc9u3v15xdNdTy74daPldVF1fVXFX9wdD3/1lVRz7Yvw0AwEqqqmOr6m3D8dKnq+pXqurbq+odw/ynqurNVXXE1DZ3VtXLq+rW4RjrN6eOv756fFRVv5XkW5P8j+E476eH9t+pqo8Px27vqqrv2k95L0hyTJJnd/eHu/vL3f257r6yu39uqp7XV9VdwzHo+6rqH08t++pxZVVtHo5nzxrOdPpUVf3ssg4o8KAESrCGVdVDkvyPJH+e5OgkpyT5iap6RpILknz78PWMJGeN2O9JSd6U5N8nOSLJDyS5c1h8T/7ur0ovSvK6qnpKd38uk78u/U13P2L4+pt99vsdSd6S5CeSfHOSazM5KHno1GrPTbItyXFJvjvJ2UP7Q5L8ZpJvy+Rg5v8k+ZUDdOX/zWQ8HnOwfd/HP0/yT5J8R5J/mklg9jND7Q9J8uOL3C8AwLKqqsOS/H6SjyXZnMmx4eVJKsn/l+RbMjkj6NgkP7fP5mdmcrz47Zkc9/yHffff3S9I8tdJ/ulwnPefh0V/kOT4JH8vyfuTvHk/Jf5wkuuGY8YH854kT07ymCS/neR39gZc+/GPkjwhk+Pg/zj9x0pgtgRKsLZ9X5Jv7u5XdPeXuvuOJL+e5HmZBDOv7u57u/uuJG8Ysd9zklzS3dd391e6e1d3/0WSdPc13f1XPfHHSf4oyT9+0L39nf87yTXDfv82yX9JcniS/2tqnTd09990972ZhGVPHl730939u939+e7+bJJXJ/nBB3ux7v5gkuuTvOwg69vXL3f3J7p7V5I/SXJTd3+gu7+Q5O1JvmeR+wUAWG4nZRIa/fvhzJ8vdPefdveO4djri939ySS/mK8/hvqV7r5rOP56dZLnH+yLdvcl3f3Z7v5iJkHVk6rq0QuselSSj++dqaonD2esf6ambvzd3f99OO7b092vTfKwTAKj/fn57v4/3f3nmfyR9UkHWzuwNAIlWNu+LZNLzO7f+5XJGTRzmRxQ3DW17sdG7PfYJH+10IKqOq2qbqyqe4fXOz2TA4SD8S3TdXT3V4Yaj55a5+NT059P8ojhdb+pqv5bVX2sqj6T5F1Jjhj+Gvdg/mOSf1tVcwdZ47RPTE3/nwXmH7GIfQIAzMKxST7W3XumG4dL9i+vql3DMdR/z9cfu+17zPgtB/OCVXVYVV043B7hM/m7M9oXOjb8dJLH7Z3p7g929xFJ/n+ZhEZ79/lTw+0VHhiONR+9n/3tteCxIzB7AiVY2+5K8tHuPmLq65HdfXqSuzM5sNjrW/fZ9vNJvmlq/u/vs99v3/fFanIzxd/N5MyiueEg4NpMTqVOkj5AvX+TSQi2d3811LjrANslyfZM/jr11O5+VCaX4WXqtRc0nFn1tiT7XlP/uey//wAAa81dSb61qjbt0/6fMjlGO3E4hvqX+frjp32PGf8mC9v3WO//SXJGJpezPTqTS+2ywP6T5IYkpw733VzQcL+kn87kTPsjh2PNB/azP2CVCZRgbXt3ks9W1ctqchPuw6rqiVX1fUmuSPLy4WbWxyR5yT7bfjDJ/zNssy1fe+rzxUleVFWnVNVDquroqvrOJA/N5C9In0yyp6pOS3Lq1HafSPLY/ZzmnKGmZw77/YZMQqIvJvnfB9HXR2ZyVtD9wz2RLjiIbfb6+Uzu93TEVNsHk5xeVY+pqr+fyX2dAADWqndn8gfFC6vq4VX1jVX1tEyOoXYneaCqjs7kHpn7Oq+qjhmOsX42yVv38xqfSPIPpuYfmcmx3Kcz+UPdf3qQ+t401Pf24Xj1sOHeSFv22d+eTI41N1XVf8zkvp3AIUigBGtYd385kxtkPznJR5N8KslvZPIXop/P5JTlj2Zyn6Pf2mfzl2Zyo+n7M7kR4+9N7ffdGW64nclfhf44ybcN9y768UyCofsy+avU1VPb/UUmN92+Y7gE72tOl+7uj2TyV7FfHmr9p5nc2PFLB9HdX8rkfkufSnJjkj88iG32vu5HM+n/9F/EfiuT6+zvzGR89nfgBABwyBuOC/9pksdncvPsnZncv/Lnkzwlk2O6azI5c3tfv53J8dAdmdz24FULrJNMbu79H4bjvJ/KJCT6WCZnm9+ayTHa/ur7QpIfGta7Jslnknwkk3uCPndY7bpMjvH+ctjvF/K1l+MBh5DqPtAVKgAAAKxHVXVnkn/d3f9ztWsB1hZnKAEAAAAwikAJAAAAgFFc8gYAAADAKM5QAgAAAGCUTatdwGIdddRRvXnz5mXf7+c+97k8/OEPP/CKLIrxnS3jO1vGd/aM8WzNanzf9773faq7v3nZd8yGMavjurXIz8HZMr6zZXxny/jOnjGeONhjuzUbKG3evDnvfe97l32/8/Pz2bp167LvlwnjO1vGd7aM7+wZ49ma1fhW1ceWfadsKLM6rluL/BycLeM7W8Z3tozv7BnjiYM9tnPJGwAAAACjCJQAAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAUQRKAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMMqm1S4ADmWbz79mtUsY5dJtD1/tEgAA2KBu3vVAzl5Dx893XvjM1S4B1jRnKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAAAAGOWAgVJVXVJV91TVh6faHlNV11fV7cP3I4f2qqo3VNWOqvpQVT1lapuzhvVvr6qzptq/t6puHrZ5Q1XVcncSAAAAgOVzMGcoXZpk2z5t5ye5obuPT3LDMJ8kpyU5fvg6N8kbk0kAleSCJE9NclKSC/aGUMM6Pzq13b6vBQAAAMAh5ICBUne/K8m9+zSfkeSyYfqyJM+ean9TT9yY5IiqelySZyS5vrvv7e77klyfZNuw7FHdfWN3d5I3Te0LAAAAgEPQYu+hNNfddw/TH08yN0wfneSuqfV2Dm0P1r5zgXYAAAAADlGblrqD7u6q6uUo5kCq6txMLqXL3Nxc5ufnl/01du/ePZP9MrHWxnf7iXtWu4RR1tr4rjXGd/aM8WwZXwAAlstiA6VPVNXjuvvu4bK1e4b2XUmOnVrvmKFtV5Kt+7TPD+3HLLD+grr7oiQXJcmWLVt669at+1t10ebn5zOL/TKx1sb37POvWe0SRrl028PX1PiuNWvt/bsWGePZMr4AACyXxV7ydnWSvU9qOyvJVVPtLxye9nZykgeGS+OuS3JqVR053Iz71CTXDcs+U1UnD093e+HUvgAAAAA4BB3wDKWqeksmZxcdVVU7M3la24VJrqiqc5J8LMlzh9WvTXJ6kh1JPp/kRUnS3fdW1SuTvGdY7xXdvfdG3z+WyZPkDk/yB8MXAAAAAIeoAwZK3f38/Sw6ZYF1O8l5+9nPJUkuWaD9vUmeeKA6AAAAADg0LPaSNwAAAAA2KIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgDYQKrq2Kp6Z1XdWlW3VNVLh/afq6pdVfXB4ev0qW1eXlU7quojVfWMqfZtQ9uOqjp/qv24qrppaH9rVT10ZXsJAMyaQAkAYGPZk2R7d5+Q5OQk51XVCcOy13X3k4eva5NkWPa8JN+VZFuSX6uqw6rqsCS/muS0JCckef7Ufl4z7OvxSe5Lcs5KdQ4AWBkCJQCADaS77+7u9w/Tn01yW5KjH2STM5Jc3t1f7O6PJtmR5KTha0d339HdX0pyeZIzqqqSPD3JlcP2lyV59kw6AwCsmk2rXQAAAKujqjYn+Z4kNyV5WpIXV9ULk7w3k7OY7sskbLpxarOd+bsA6q592p+a5LFJ7u/uPQusP/3a5yY5N0nm5uYyPz+/PJ1a43bv3m0sZsj4ztbc4cn2E/cceMVDxFp7L3j/zp4xHkegBACwAVXVI5L8bpKf6O7PVNUbk7wySQ/fX5vkX83q9bv7oiQXJcmWLVt669ats3qpNWV+fj7GYnaM72z98puvymtvXjv/xbzzzK2rXcIo3r+zZ4zHWTufdgAAlkVVfUMmYdKbu/ttSdLdn5ha/utJfn+Y3ZXk2KnNjxnasp/2Tyc5oqo2DWcpTa8PAKwT7qEEALCBDPc4ujjJbd39i1Ptj5ta7Z8l+fAwfXWS51XVw6rquCTHJ3l3kvckOX54ottDM7lx99Xd3UnemeQ5w/ZnJblqln0CAFaeM5QAADaWpyV5QZKbq+qDQ9vPZPKUtidncsnbnUn+TZJ09y1VdUWSWzN5Qtx53f3lJKmqFye5LslhSS7p7luG/b0syeVV9aokH8gkwAIA1hGBEgDABtLdf5qkFlh07YNs8+okr16g/dqFtuvuOzJ5ChwAsE655A0AAACAUQRKAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAUQRKAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwChLCpSq6t9V1S1V9eGqektVfWNVHVdVN1XVjqp6a1U9dFj3YcP8jmH55qn9vHxo/0hVPWOJfQIAAABghhYdKFXV0Ul+PMmW7n5iksOSPC/Ja5K8rrsfn+S+JOcMm5yT5L6h/XXDeqmqE4btvivJtiS/VlWHLbYuAAAAAGZrqZe8bUpyeFVtSvJNSe5O8vQkVw7LL0vy7GH6jGE+w/JTqqqG9su7+4vd/dEkO5KctMS6AAAAAJiRTYvdsLt3VdV/SfLXSf5Pkj9K8r4k93f3nmG1nUmOHqaPTnLXsO2eqnogyWOH9hundj29zdeoqnOTnJskc3NzmZ+fX2z5+7V79+6Z7JeJtTa+20/cc+CVDiFrbXzXGuM7e8Z4towvAADLZdGBUlUdmcnZRccluT/J72RyydrMdPdFSS5Kki1btvTWrVuX/TXm5+czi/0ysdbG9+zzr1ntEka5dNvD19T4rjVr7f27Fhnj2TK+AAAsl6Vc8vbDST7a3Z/s7r9N8rYkT0tyxHAJXJIck2TXML0rybFJMix/dJJPT7cvsA0AAAAAh5ilBEp/neTkqvqm4V5IpyS5Nck7kzxnWOesJFcN01cP8xmWv6O7e2h/3vAUuOOSHJ/k3UuoCwAAAIAZWso9lG6qqiuTvD/JniQfyORytGuSXF5VrxraLh42uTjJb1XVjiT3ZvJkt3T3LVV1RSZh1J4k53X3lxdbFwAAAACztehAKUm6+4IkF+zTfEcWeEpbd38hyb/Yz35eneTVS6kFAAAAgJWxlEveAAAAANiABEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAA2kKo6tqreWVW3VtUtVfXSof0xVXV9Vd0+fD9yaK+qekNV7aiqD1XVU6b2ddaw/u1VddZU+/dW1c3DNm+oqlr5ngIAsyRQAgDYWPYk2d7dJyQ5Ocl5VXVCkvOT3NDdxye5YZhPktOSHD98nZvkjckkgMrkab9PzeQJvxfsDaGGdX50arttK9AvAGAFCZQAADaQ7r67u98/TH82yW1Jjk5yRpLLhtUuS/LsYfqMJG/qiRuTHFFVj0vyjCTXd/e93X1fkuuTbBuWPaq7b+zuTvKmqX0BAOvEptUuAACA1VFVm5N8T5Kbksx1993Doo8nmRumj05y19RmO4e2B2vfuUD7vq99biZnPGVubi7z8/NL68w6sXv3bmMxQ8Z3tuYOT7afuGe1yzhoa+294P07e8Z4HIESAMAGVFWPSPK7SX6iuz8zfZuj7u6q6lm+fndflOSiJNmyZUtv3bp1li+3ZszPz8dYzI7xna1ffvNVee3Na+e/mHeeuXW1SxjF+3f2jPE4LnkDANhgquobMgmT3tzdbxuaPzFcrpbh+z1D+64kx05tfszQ9mDtxyzQDgCsIwIlAIANZHji2sVJbuvuX5xadHWSvU9qOyvJVVPtLxye9nZykgeGS+OuS3JqVR053Iz71CTXDcs+U1UnD6/1wql9AQDrxNo5HxEAgOXwtCQvSHJzVX1waPuZJBcmuaKqzknysSTPHZZdm+T0JDuSfD7Ji5Kku++tqlcmec+w3iu6+95h+seSXJrk8CR/MHwBAOuIQAkAYAPp7j9NUvtZfMoC63eS8/azr0uSXLJA+3uTPHEJZQIAhziXvAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAUQRKAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAAAAGEWgBAAAAMAoAiUAAAAARtm02gUAy+fmXQ/k7POvWe0yDtqdFz5ztUsAAABgEZZ0hlJVHVFVV1bVX1TVbVX1/VX1mKq6vqpuH74fOaxbVfWGqtpRVR+qqqdM7eesYf3bq+qspXYKAAAAgNlZ6iVvr0/yh939nUmelOS2JOcnuaG7j09ywzCfJKclOX74OjfJG5Okqh6T5IIkT01yUpIL9oZQAAAAABx6Fh0oVdWjk/xAkouTpLu/1N33JzkjyWXDapclefYwfUaSN/XEjUmOqKrHJXlGkuu7+97uvi/J9Um2LbYuAAAAAGZrKfdQOi7JJ5P8ZlU9Kcn7krw0yVx33z2s8/Ekc8P00Unumtp+59C2v/avU1XnZnJ2U+bm5jI/P7+E8he2e/fumeyXibU2vttP3LPaJYwyd/jaqnktvReStff+XYuM8WwZXwAAlstSAqVNSZ6S5CXdfVNVvT5/d3lbkqS7u6p6KQXus7+LklyUJFu2bOmtW7cu166/an5+PrPYLxNrbXzX0g2uk0mY9Nqb18699u88c+tqlzDKWnv/rkXGeLaMLwAAy2Up91DamWRnd980zF+ZScD0ieFStgzf7xmW70py7NT2xwxt+2sHAAAA4BC06ECpuz+e5K6qesLQdEqSW5NcnWTvk9rOSnLVMH11khcOT3s7OckDw6Vx1yU5taqOHG7GferQBgAAAMAhaKnXxrwkyZur6qFJ7kjyokxCqiuq6pwkH0vy3GHda5OcnmRHks8P66a7762qVyZ5z7DeK7r73iXWBQAAAMCMLClQ6u4PJtmywKJTFli3k5y3n/1ckuSSpdQCAAAAwMpYyj2UAAAAANiABEoAABtIVV1SVfdU1Yen2n6uqnZV1QeHr9Onlr28qnZU1Ueq6hlT7duGth1Vdf5U+3FVddPQ/tbh1ggAwDojUAIA2FguTbJtgfbXdfeTh69rk6SqTkjyvCTfNWzza1V1WFUdluRXk5yW5IQkzx/WTZLXDPt6fJL7kpwz094AAKtCoAQAsIF097uSHOwDUM5Icnl3f7G7P5rJw1VOGr52dPcd3f2lJJcnOaOqKsnTk1w5bH9ZkmcvZ/0AwKFhqU95AwBgfXhxVb0wyXuTbO/u+5IcneTGqXV2Dm1Jctc+7U9N8tgk93f3ngXW/xpVdW6Sc5Nkbm4u8/Pzy9SNtW337t3GYoaM72zNHZ5sP3HPgVc8RKy194L37+wZ43EESgAAvDHJK5P08P21Sf7VLF+wuy9KclGSbNmypbdu3TrLl1sz5ufnYyxmx/jO1i+/+aq89ua181/MO8/cutoljOL9O3vGeJy182kHAGAmuvsTe6er6teT/P4wuyvJsVOrHjO0ZT/tn05yRFVtGs5Sml4fAFhH3EMJAGCDq6rHTc3+syR7nwB3dZLnVdXDquq4JMcneXeS9yQ5fnii20MzuXH31d3dSd6Z5DnD9mcluWol+gAArCxnKAEAbCBV9ZYkW5McVVU7k1yQZGtVPTmTS97uTPJvkqS7b6mqK5LcmmRPkvO6+8vDfl6c5LokhyW5pLtvGV7iZUkur6pXJflAkotXpmcAwEoSKAEAbCDd/fwFmvcb+nT3q5O8eoH2a5Ncu0D7HZk8BQ4AWMdc8gYAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAUQRKAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABG2bTaBQAwO5vPv2a1Sxjl0m0PX+0SAACAg+AMJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAAAAGEWgBAAAAMAoAiUAAAAARllyoFRVh1XVB6rq94f546rqpqraUVVvraqHDu0PG+Z3DMs3T+3j5UP7R6rqGUutCQAAAIDZWY4zlF6a5Lap+dckeV13Pz7JfUnOGdrPSXLf0P66Yb1U1QlJnpfku5JsS/JrVXXYMtQFAAAAwAwsKVCqqmOSPDPJbwzzleTpSa4cVrksybOH6TOG+QzLTxnWPyPJ5d39xe7+aJIdSU5aSl0AAAAAzM5Sz1D6pSQ/neQrw/xjk9zf3XuG+Z1Jjh6mj05yV5IMyx8Y1v9q+wLbAAAAAHCI2bTYDavqR5Lc093vq6qty1bRg7/muUnOTZK5ubnMz88v+2vs3r17JvtlYq2N7/YT9xx4pUPI3OFrq+a19F5I1t77N1lb74dkbY7xWmJ8AQBYLosOlJI8Lcmzqur0JN+Y5FFJXp/kiKraNJyFdEySXcP6u5Icm2RnVW1K8ugkn55q32t6m6/R3RcluShJtmzZ0lu3bl1C+Qubn5/PLPbLxFob37PPv2a1Sxhl+4l78tqbl/KxXll3nrl1tUsYZa29f5O19x6+dNvD19wYryVr8T0MAMChadGXvHX3y7v7mO7enMlNtd/R3WcmeWeS5wyrnZXkqmH66mE+w/J3dHcP7c8bngJ3XJLjk7x7sXUBAAAAMFvL8ZS3fb0syU9W1Y5M7pF08dB+cZLHDu0/meT8JOnuW5JckeTWJH+Y5Lzu/vIM6gIA2PCq6pKquqeqPjzV9piqur6qbh++Hzm0V1W9oap2VNWHquopU9ucNax/e1WdNdX+vVV187DNG4aHsAAA68yyBErdPd/dPzJM39HdJ3X347v7X3T3F4f2Lwzzjx+W3zG1/au7+9u7+wnd/QfLURMAAAu6NMm2fdrOT3JDdx+f5IZhPklOy+Ts8eMzuY/lG5NJAJXkgiRPzeTpvBfsDaGGdX50art9XwsAWAdmcYYSAACHqO5+V5J792k+I8llw/RlSZ491f6mnrgxk3tlPi7JM5Jc3933dvd9Sa5Psm1Y9qjuvnG4tcGbpvYFAKwja+fuvQAAzMpcd989TH88ydwwfXSSu6bW2zm0PVj7zgXav85KPL13LfI0xtkyvrPlicOz5f07e8Z4HIESAABf1d1dVb0CrzPzp/euRZ7GOFvGd7Z++c1XeeLwDHn/zp4xHsclbwAAfGK4XC3D93uG9l1Jjp1a75ih7cHaj1mgHQBYZwRKAABcnWTvk9rOSnLVVPsLh6e9nZzkgeHSuOuSnFpVRw434z41yXXDss9U1cnD091eOLUvAGAdWTvnIwIAsGRV9ZYkW5McVVU7M3la24VJrqiqc5J8LMlzh9WvTXJ6kh1JPp/kRUnS3fdW1SuTvGdY7xXdvfdG3z+WyZPkDk/yB8MXALDOCJQAADaQ7n7+fhadssC6neS8/eznkiSXLND+3iRPXEqNAMChzyVvAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAUQRKAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEWHShV1bFV9c6qurWqbqmqlw7tj6mq66vq9uH7kUN7VdUbqmpHVX2oqp4yta+zhvVvr6qzlt4tAAAAAGZlKWco7UmyvbtPSHJykvOq6oQk5ye5obuPT3LDMJ8kpyU5fvg6N8kbk0kAleSCJE9NclKSC/aGUAAAAAAcehYdKHX33d39/mH6s0luS3J0kjOSXDasdlmSZw/TZyR5U0/cmOSIqnpckmckub677+3u+5Jcn2TbYusCAAAAYLY2LcdOqmpzku9JclOSue6+e1j08SRzw/TRSe6a2mzn0La/9oVe59xMzm7K3Nxc5ufnl6P8r7F79+6Z7JeJtTa+20/cs9oljDJ3+NqqeS29F5K19/5N1tb7IVmbY7yWGF8OpKruTPLZJF9Osqe7twxnk781yeYkdyZ5bnffV1WV5PVJTk/y+SRn7/1j43ALg/8w7PZV3X1ZAIB1ZcmBUlU9IsnvJvmJ7v7M5Nhioru7qnqprzG1v4uSXJQkW7Zs6a1bty7Xrr9qfn4+s9gvE2ttfM8+/5rVLmGU7SfuyWtvXpaceEXceebW1S5hlLX2/k3W3nv40m0PX3NjvJasxfcwq+KHuvtTU/N7b2dwYVWdP8y/LF97O4OnZnI7g6dO3c5gS5JO8r6quno4Ex0AWCeW9JS3qvqGTMKkN3f324bmTwyXsmX4fs/QvivJsVObHzO07a8dAIDV53YGAMDXWfSpDMNpzhcnua27f3Fq0dVJzkpy4fD9qqn2F1fV5Zn8FeuB7r67qq5L8p+mbsR9apKXL7YuAAAWrZP80XCG+X8bzg6fye0MVuJWBmuRS1Nny/jOltsvzJb37+wZ43GWcm3M05K8IMnNVfXBoe1nMgmSrqiqc5J8LMlzh2XXZnKN/Y5MrrN/UZJ0971V9cok7xnWe0V337uEugAAWJx/1N27qurvJbm+qv5ieuFy3s5gJW5lsBa5NHW2jO9s/fKbr3L7hRny/p09YzzOoj/t3f2nSWo/i09ZYP1Oct5+9nVJkksWWwsAAEvX3buG7/dU1duTnJThdgbDmeUHezuDrfu0z8+4dABghS3pHkoAAKwPVfXwqnrk3ulMbkPw4fzd7QySr7+dwQtr4uQMtzNIcl2SU6vqyOGWBqcObQDAOrJ2zkcEAGCW5pK8fXhi76Ykv93df1hV74nbGQAA+xAoAQCQ7r4jyZMWaP903M4AANiHS94AAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAUQRKAAAAAIwiUAIAAABglE2rXQAA7HXzrgdy9vnXrHYZB+3OC5+52iUAAMCqECgBq2bzGgoOkuTSbQ9f7RIAAAAOCS55AwAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYJRNq10AwFpx864Hcvb516x2GQAAAKvOGUoAAAAAjCJQAgAAAGAUgRIAAAAAo7iH0hq3eY3dz+XSbQ9f7RIAAACAJXKGEgAAAACjCJQAAAAAGMUlb/vwWHAAAACAB+cMJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAo2xa7QIAYK3afP41q13CKJdue/hqlwAAwDrhDCUAAAAARhEoAQAAADCKQAkAAACAUQRKAAAAAIziptysqJt3PZCz19hNbAEAAICv5QwlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYJRDJlCqqm1V9ZGq2lFV5692PQAALJ5jOwBY3w6JQKmqDkvyq0lOS3JCkudX1QmrWxUAAIvh2A4A1r9DIlBKclKSHd19R3d/KcnlSc5Y5ZoAAFgcx3YAsM5tWu0CBkcnuWtqfmeSp+67UlWdm+TcYXZ3VX1kBrUcleRTM9gvSX7c+M6U8Z0t4zt7xni2fug1Mxvfb5vBPlnbDnhst0LHdWuRn4OzZXxna02Nb71mtSsYbU2N7xpljCcO6tjuUAmUDkp3X5Tkolm+RlW9t7u3zPI1NjLjO1vGd7aM7+wZ49kyvhxKVuK4bi3yOZ0t4ztbxne2jO/sGeNxDpVL3nYlOXZq/pihDQCAtcexHQCsc4dKoPSeJMdX1XFV9dAkz0ty9SrXBADA4ji2A4B17pC45K2791TVi5Ncl+SwJJd09y2rVI5Tr2fL+M6W8Z0t4zt7xni2jC8r4hA7tltrfE5ny/jOlvGdLeM7e8Z4hOru1a4BAAAAgDXkULnkDQAAAIA1QqAEAAAAwCgCpUFVHVFVV1bVX1TVbVX1/atd03pSVf+uqm6pqg9X1Vuq6htXu6a1rqouqap7qurDU22Pqarrq+r24fuRq1njWraf8f2F4WfEh6rq7VV1xCqWuKYtNL5Ty7ZXVVfVUatR23qxvzGuqpcM7+Nbquo/r1Z9wMSY391V9aiq2llVv7KSNa5lBzO+VfXkqvqz4efih6rq/16NWteSqtpWVR+pqh1Vdf4Cyx9WVW8dlt9UVZtXocw16yDG9yer6tbh/XpDVX3batS5Vh1ofKfW++fDMemWlaxvLREo/Z3XJ/nD7v7OJE9Kctsq17NuVNXRSX48yZbufmImN+d83upWtS5cmmTbPm3nJ7mhu49PcsMwz+Jcmq8f3+uTPLG7vzvJXyZ5+UoXtY5cmq8f31TVsUlOTfLXK13QOnRp9hnjqvqhJGckeVJ3f1eS/7IKdQFfa8zv7lcmedeKVLV+HMz4fj7JC4efi9uS/JI/Gu1fVR2W5FeTnJbkhCTPr6oT9lntnCT3dffjk7wuyWtWtsq16yDH9wOZ/N/qu5NcmcQfiA7SQY5vquqRSV6a5KaVrXBtESglqapHJ/mBJBcnSXd/qbvvX9Wi1p9NSQ6vqk1JvinJ36xyPWted78ryb37NJ+R5LJh+rIkz17JmtaThca3u/+ou/cMszcmOWbFC1sn9vP+TSYHnT+dxBMjlmg/Y/xvk1zY3V8c1rlnxQsD9nVQv7ur6nuTzCX5o5Upa9044Ph291929+3D9N8kuSfJN69UgWvQSUl2dPcd3f2lJJdnMs7Tpsf9yiSnVFWtYI1r2QHHt7vf2d2fH2Ydk45zMO/fZBLgvybJF1ayuLVGoDRxXJJPJvnNqvpAVf1GVT18tYtaL7p7VyZ/Bf/rJHcneaC7HQzNxlx33z1MfzyTA09m418l+YPVLmI9qaozkuzq7j9f7VrWse9I8o+Hyw/+uKq+b7ULAg78u7uqHpLktUl+aiULWydGHRtV1UlJHprkr2Zd2Bp2dJK7puZ3Dm0LrjP8Me6BJI9dkerWvoMZ32nnxDHpGAcc36p6SpJju/ualSxsLdq02gUcIjYleUqSl3T3TVX1+kxOh/1/V7es9WG4Vv2MTIK7+5P8TlX9y+7+76ta2DrX3V1VzvKYgar62SR7krx5tWtZL6rqm5L8TCaXuzE7m5I8JsnJSb4vyRVV9Q+6288KmKGq+p9J/v4Ci352euZBfnf/WJJru3unkzy+3jKM7979PC7JbyU5q7u/srxVwvKrqn+ZZEuSH1ztWtaLIcD/xSRnr3Ipa4JAaWJnkp3dvff6yCvj3jPL6YeTfLS7P5kkVfW2JP9XEoHS8vtEVT2uu+8eDopczrLMqursJD+S5BT/CV9W355J6Pznw3+Wjkny/qo6qbs/vqqVrS87k7xteO++u6q+kuSoTM7SBWaku394f8uq6mB+d39/JmcX/liSRyR5aFXt7m7Hq1mW8U1VPSrJNUl+trtvnFGp68WuJMdOzR8ztC20zs7hlhePTvLplSlvzTuY8U1V/XAmoekP7r2UnYNyoPF9ZJInJpkfjkn/fpKrq+pZ3f3eFatyjXDJW5LhPyt3VdUThqZTkty6iiWtN3+d5OSq+qbh2ulT4qbns3J1krOG6bOSXLWKtaw7VbUtk/v7PGvqunWWQXff3N1/r7s3d/fmTIKPpwiTlt3vJfmhJKmq78jkso5PrWZBwIF/d3f3md39rcPPx59K8iZh0kE74PhW1UOTvD2Tcb1yBWtbq96T5PiqOm4Yu+dlMs7Tpsf9OUne4Q9xB+2A41tV35Pkv2VyTOoPyOM86Ph29wPdfdTUMemNmYyzMGkBAqW/85Ikb66qDyV5cpL/tLrlrB/DmV9XJnl/kpszed9dtKpFrQNV9ZYkf5bkCcMjhM9JcmGSf1JVt2dyZtiFq1njWraf8f2VTP5qcX1VfbCq/uuqFrmG7Wd8WUb7GeNLkvyDqvpwJjehPMsBPqy6BX93V9WWqvqNVa1sfTiY8X1uJg/oOXv4/f7BqnryqlS7Bgz3RHpxkusy+SPxFd19S1W9oqqeNax2cZLHVtWOJD8ZV38ctIMc31/I5GzF3xner/sGeuzHQY4vB6kcRwIAAAAwhjOUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAUQRKAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAUQRKAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAAAAGEWgBAAAAMAoAiVg2VTVP66qjyxh+0ur6lXLWRMAAItXVXdW1Q+vdh3AoUegBCyb7v6T7n7C3vmlHIBU1daq2rl81QEAsFyGY7WvVNXuqvpsVX2kql602nUBK0egBAAAwGL8TXc/Ismjkrwsya9X1QmrXBOwQgRKsIFV1bFV9baq+mRVfbqqfqWqvr2q3jHMf6qq3lxVR0xtc2dVvbyqbq2q+6rqN6vqG4dlXz2rqKp+K8m3Jvkfw1+ufnpo/52q+nhVPVBV76qq71pE3f+wquar6v6quqWqnjW17PShts9W1a6q+qmh/aiq+v1hm3ur6k+qys9AAIADe3JVfWg4fnvr3mO/vXri95Lcl+SEqrqsqrYnSVUdXVVdVecN898+HIs5DoM1zocYNqiqOizJ7yf5WJLNSY5OcnmSSvL/JfmWJP8wybFJfm6fzc9M8owk357kO5L8h333390vSPLXSf5pdz+iu//zsOgPkhyf5O8leX+SN4+s+xuS/I8kfzTs4yVJ3lxVey+1uzjJv+nuRyZ5YpJ3DO3bk+xM8s1J5pL8TJIe89oAABvUc5NsS3Jcku9Ocvb0wqp6SFX9syRHJLk5yR8n2Tos/sEkdyT5gan5P+nur8y6aGC2BEqwcZ2USWj077v7c939he7+0+7e0d3Xd/cXu/uTSX4xk1/8036lu+/q7nuTvDrJ8w/2Rbv7ku7+bHd/MZOg6klV9egRdZ+c5BFJLuzuL3X3OzIJxvbW8LeZ/GXsUd19X3e/f6r9cUm+rbv/drjfk0AJAODA3tDdfzMc+/2PJE8e2r+lqu5P8qkkFyR5QXd/JJNA6R8NZyH9QJL/nORpwzY/OCwH1jiBEmxcx/7/27v/YEvr+k7w708gGNZoxB9zlwAuZO1kC2VCtBedyo+9iYmiZkR3UgaWhEaZtJZQm9T0TNImU4WjMYOTEDO6hhmMvULGiKzG0CUYJIw3bqqCgpEV8cfQYrN0B6FGiKRjlkybz/5xnsZD597ufrr73nP73ter6tR5zud5nnO+59PP7XvO+z4/ktzX3Xuni1U1V1XXDYeLPZrkPyV55n7r3j81fV8mwdRBVdVxVXVFVX1leO6dw6z9n/9AvjfJ/fv9Veu+TPawSpJ/luTlSe6rqj+tqn8y1H8zyY4kH6+qe6tq64jXBABYz742Nf3NTP64l0zOofS07n56d5/d3dclSXd/JcnfZBI8/Wgmf/z7y2GPcoESrBECJVi/7k/y7Ko6fr/6b2RyKNhZ3f3UJD+XyWFw006bmn52kr9c4jX23wPof0tyXpKfTPI9mRxql0We/0D+Mslp+x13/+wku5Oku2/v7vMyORzuj5JcP9T/uru3dPf3JXllkn9RVS8e8boAABy6P03yM0lO6O7dw+NNSU5KcucMxwUcJQIlWL8+neSBJFdU1ZOr6ruq6oeTPCXJniTfqKpTkvyrRda9tKpOraqnJ/m1JB9c4jUeTPJ9U4+fkuSxJF9P8t9lEl4d0DCux2/DuL+Z5Jer6juraj7JP01yXVWdUFUXVtX3dPd/S/Jokr8fnuenq+o5VVVJvpHkW/vmAQBw1P1pksuSfHJ4vDA8/rPu/tasBgUcPQIlWKeGX+T/NMlzMjl59q4kP5vk3yR5fiahy41J/nCR1f8gk5Ni35vkK0l+fYmX+bdJ/vVwZbV/meTaTA5P253kC0luO8gwT0nyt/vdThvG/bJMjtf/3SQXdfeXhnV+PsnO4ZC6N2RyAvFkciLwP8kkLPvzJL/b3Z84yOsDAHB4/jSTPybuC5T+LJM/KH5yyTWAY0o5Jy0wRlXtTPLPu/tPZj0WAAAAZsMeSgAAAACMIlACAAAAYBSHvAEAAAAwij2UAAAAABhFoAQAAADAKMfPegCH65nPfGaffvrpsx7GzP3N3/xNnvzkJ896GGuW/i4v/V1+ery89HfiM5/5zH/t7mfNehwcu6Y/1/m5mi39ny39ny39ny39n63p/h/qZ7tjNlA6/fTTc8cdd8x6GDO3sLCQ+fn5WQ9jzdLf5aW/y0+Pl5f+TlTVfbMeA8e26c91fq5mS/9nS/9nS/9nS/9na7r/h/rZziFvAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABjl+FkPAFi/7tr9jVy89cZZD+OQ7bziFbMeAgBwBE5f5Z87tpy19wmfjXz2AFYzeygBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAGAdqaptVfVQVX1+qvbBqrpzuO2sqjuH+ulV9bdT8/7D1DovqKq7qmpHVb2zqmqoP72qbqmqe4b7k1b8TQIAy06gBACwvrwvybnThe7+2e4+u7vPTvLhJH84Nfsr++Z19xum6lcl+YUkG4bbvufcmuTW7t6Q5NbhMQCwxgiUAADWke7+ZJKHF5s37GX0miQfONBzVNXJSZ7a3bd1dye5NsmrhtnnJblmmL5mqg4ArCHHz3oAAACsGj+a5MHuvmeqdkZVfTbJo0n+dXf/30lOSbJrapldQy1J5rr7gWH6a0nmFnuhqtqcZHOSzM3NZWFhIUmyZ8+ex6dZeWu9/1vO2jvrIRzQ3IlPHONa/rdYjdb69r/a6f9sHU7/BUoAAOxzQZ64d9IDSZ7d3V+vqhck+aOqeu6hPll3d1X1EvOuTnJ1kmzcuLHn5+eTTL5A75tm5a31/l+89cZZD+GAtpy1N1fe9e2vaDsvnJ/dYNahtb79r3b6P1uH03+BEgAAqarjk/yvSV6wr9bdjyV5bJj+TFV9Jcn3J9md5NSp1U8daknyYFWd3N0PDIfGPbQS4wcAVpZzKAEAkCQ/meRL3f34oWxV9ayqOm6Y/r5MTr5973BI26NV9aLhvEsXJblhWG17kk3D9KapOgCwhgiUAADWkar6QJI/T/IDVbWrqi4ZZp2ff3gy7h9L8rmqujPJh5K8obv3ndD7jUl+L8mOJF9J8rGhfkWSn6qqezIJqa5YrvcCAMyOQ94AANaR7r5gifrFi9Q+nOTDSyx/R5LnLVL/epIXH9koAYDVzh5KAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgHDZSq6rSq+kRVfaGq7q6qXxzqb66q3VV153B7+dQ6b6qqHVX15ap66VT93KG2o6q2TtXPqKpPDfUPVtUJR/uNAgAAAHB0HMoeSnuTbOnuM5O8KMmlVXXmMO8d3X32cLspSYZ55yd5bpJzk/xuVR1XVccleXeSlyU5M8kFU8/z9uG5npPkkSSXHKX3BwAAAMBRdtBAqbsf6O6/GKb/OskXk5xygFXOS3Jddz/W3V9NsiPJOcNtR3ff291/l+S6JOdVVSX5iSQfGta/JsmrDvP9AAAAALDMRp1DqapOT/JDST41lC6rqs9V1baqOmmonZLk/qnVdg21perPSPJX3b13vzoAAAAAq9Dxh7pgVX13kg8n+aXufrSqrkry1iQ93F+Z5HXLMspvj2Fzks1JMjc3l4WFheV8uWPCnj179GEZ6e/ymjsx2XLW3oMvuEoci9uCbXh56S8AAOvVIQVKVfWdmYRJ7+/uP0yS7n5wav57knx0eLg7yWlTq5861LJE/etJnlZVxw97KU0v/wTdfXWSq5Nk48aNPT8/fyjDX9MWFhaiD8tHf5fXu95/Q66865Bz7ZnbeeH8rIcwmm14eekvAADr1aFc5a2SvDfJF7v7t6fqJ08t9uoknx+mtyc5v6qeVFVnJNmQ5NNJbk+yYbii2wmZnLh7e3d3kk8k+Zlh/U1JbjiytwUAAADAcjmUXQN+OMnPJ7mrqu4car+ayVXazs7kkLedSV6fJN19d1Vdn+QLmVwh7tLu/laSVNVlSW5OclySbd199/B8v5Lkuqr69SSfzSTAAgAAAGAVOmig1N1/lqQWmXXTAdZ5W5K3LVK/abH1uvveTK4CBwAAAMAqN+oqbwAAAAAgUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAA60hVbauqh6rq81O1N1fV7qq6c7i9fGrem6pqR1V9uapeOlU/d6jtqKqtU/UzqupTQ/2DVXXCyr07AGClCJQAANaX9yU5d5H6O7r77OF2U5JU1ZlJzk/y3GGd362q46rquCTvTvKyJGcmuWBYNknePjzXc5I8kuSSZX03AMBMCJQAANaR7v5kkocPcfHzklzX3Y9191eT7EhyznDb0d33dvffJbkuyXlVVUl+IsmHhvWvSfKqozl+AGB1OH7WAwAAYFW4rKouSnJHki3d/UiSU5LcNrXMrqGWJPfvV39hkmck+avu3rvI8k9QVZuTbE6Subm5LCwsJEn27Nnz+DQrb633f8tZew++0AzNnfjEMa7lf4vVaK1v/6ud/s/W4fRfoAQAwFVJ3pqkh/srk7xuOV+wu69OcnWSbNy4sefn55NMvkDvm2blrfX+X7z1xlkP4YC2nLU3V9717a9oOy+cn91g1qG1vv2vdvo/W4fTf4ESAMA6190P7puuqvck+ejwcHeS06YWPXWoZYn615M8raqOH/ZSml4eAFhDnEMJAGCdq6qTpx6+Osm+K8BtT3J+VT2pqs5IsiHJp5PcnmTDcEW3EzI5cff27u4kn0jyM8P6m5LcsBLvAQBYWfZQAgBYR6rqA0nmkzyzqnYluTzJfFWdnckhbzuTvD5Juvvuqro+yReS7E1yaXd/a3iey5LcnOS4JNu6++7hJX4lyXVV9etJPpvkvSvzzgCAlSRQAgBYR7r7gkXKS4Y+3f22JG9bpH5TkpsWqd+byVXgAIA1zCFvAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYJSDBkpVdVpVfaKqvlBVd1fVLw71p1fVLVV1z3B/0lCvqnpnVe2oqs9V1fOnnmvTsPw9VbVpqv6CqrprWOedVVXL8WYBAAAAOHKHsofS3iRbuvvMJC9KcmlVnZlka5Jbu3tDkluHx0nysiQbhtvmJFclkwAqyeVJXpjknCSX7wuhhmV+YWq9c4/8rQEAAACwHA4aKHX3A939F8P0Xyf5YpJTkpyX5JphsWuSvGqYPi/JtT1xW5KnVdXJSV6a5Jbufri7H0lyS5Jzh3lP7e7buruTXDv1XAAAAACsMqPOoVRVpyf5oSSfSjLX3Q8Ms76WZG6YPiXJ/VOr7RpqB6rvWqQOAAAAwCp0/KEuWFXfneTDSX6pux+dPs1Rd3dV9TKMb/8xbM7kMLrMzc1lYWFhuV9y1duzZ48+LCP9XV5zJyZbzto762EcsmNxW7ANLy/9BQBgvTqkQKmqvjOTMOn93f2HQ/nBqjq5ux8YDlt7aKjvTnLa1OqnDrXdSeb3qy8M9VMXWf4f6O6rk1ydJBs3buz5+fnFFltXFhYWog/LR3+X17vef0OuvOuQc+2Z23nh/KyHMJpteHnpLwAA69WhXOWtkrw3yRe7+7enZm1Psu9KbZuS3DBVv2i42tuLknxjODTu5iQvqaqThpNxvyTJzcO8R6vqRcNrXTT1XAAAAACsMoeya8APJ/n5JHdV1Z1D7VeTXJHk+qq6JMl9SV4zzLspycuT7EjyzSSvTZLufriq3prk9mG5t3T3w8P0G5O8L8mJST423AAAAABYhQ4aKHX3nyWpJWa/eJHlO8mlSzzXtiTbFqnfkeR5BxsLAAAAALM36ipvAAAAACBQAgAAAGAUgRIAAAAAowiUAADWkaraVlUPVdXnp2q/WVVfqqrPVdVHquppQ/30qvrbqrpzuP2HqXVeUFV3VdWOqnrncLXeVNXTq+qWqrpnuD9pxd8kALDsBEoAAOvL+5Kcu1/tliTP6+5/nOS/JHnT1LyvdPfZw+0NU/WrkvxCkg3Dbd9zbk1ya3dvSHLr8BgAWGMESgAA60h3fzLJw/vVPt7de4eHtyU59UDPUVUnJ3lqd982XOH32iSvGmafl+SaYfqaqToAsIYIlAAAmPa6JB+benxGVX22qv60qn50qJ2SZNfUMruGWpLMdfcDw/TXkswt62gBgJk4ftYDAABgdaiqX0uyN8n7h9IDSZ7d3V+vqhck+aOqeu6hPl93d1X1Eq+1OcnmJJmbm8vCwkKSZM+ePY9Ps/LWev+3nLX34AvN0NyJTxzjWv63WI3W+va/2un/bB1O/wVKAACkqi5O8tNJXjwcxpbufizJY8P0Z6rqK0m+P8nuPPGwuFOHWpI8WFUnd/cDw6FxDy32et19dZKrk2Tjxo09Pz+fZPIFet80K2+t9//irTfOeggHtOWsvbnyrm9/Rdt54fzsBrMOrfXtf7XT/9k6nP475A0AYJ2rqnOT/HKSV3b3N6fqz6qq44bp78vk5Nv3Doe0PVpVLxqu7nZRkhuG1bYn2TRMb5qqAwBriD2UAADWkar6QJL5JM+sql1JLs/kqm5PSnLLJB/KbcMV3X4syVuq6r8l+fskb+jufSf0fmMmV4w7MZNzLu0779IVSa6vqkuS3JfkNSvwtgCAFSZQAgBYR7r7gkXK711i2Q8n+fAS8+5I8rxF6l9P8uIjGSMAsPo55A0AAACAUQRKAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAUQRKAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQCAdaSqtlXVQ1X1+ana06vqlqq6Z7g/aahXVb2zqnZU1eeq6vlT62walr+nqjZN1V9QVXcN67yzqmpl3yEAsBIOGigt8aHjzVW1u6ruHG4vn5r3puEDxJer6qVT9XOH2o6q2jpVP6OqPjXUP1hVJxzNNwgAwBO8L8m5+9W2Jrm1uzckuXV4nCQvS7JhuG1OclUyCaCSXJ7khUnOSXL5vhBqWOYXptbb/7UAgDXgUPZQel8W/yDwju4+e7jdlCRVdWaS85M8d1jnd6vquKo6Lsm7M/lQcmaSC4Zlk+Ttw3M9J8kjSS45kjcEAMDSuvuTSR7er3xekmuG6WuSvGqqfm1P3JbkaVV1cpKXJrmlux/u7keS3JLk3GHeU7v7tu7uJNdOPRcAsIYcNFBa4kPHUs5Lcl13P9bdX02yI5O/Wp2TZEd339vdf5fkuiTnDbtA/0SSDw3rT3+AAQBgZcx19wPD9NeSzA3TpyS5f2q5XUPtQPVdi9QBgDXm+CNY97KquijJHUm2DH+dOiXJbVPLTH+I2P9DxwuTPCPJX3X33kWWBwBghXV3V1Uv9+tU1eZMDqPL3NxcFhYWkiR79ux5fJqVt9b7v+WsvQdfaIbmTnziGNfyv8VqtNa3/9VO/2frcPp/uIHSVUnemqSH+yuTvO4wn+uQLfXBYz3zQ7e89Hd57f+habU7FrcF2/Dy0l/WkAer6uTufmA4bO2hob47yWlTy5061HYnmd+vvjDUT11k+X+gu69OcnWSbNy4sefnJ0+3sLCQfdOsvLXe/4u33jjrIRzQlrP25sq7vv0VbeeF87MbzDq01rf/1U7/Z+tw+n9YgVJ3P7hvuqrek+Sjw8OlPnRkifrXMzkW//hhL6UlP3QMr7voB4/1zA/d8tLf5fWu99/whA9Nq92x+KHONry89Jc1ZHuSTUmuGO5vmKpfVlXXZbJ3+TeG0OnmJL8xdSLulyR5U3c/XFWPVtWLknwqyUVJ3rWSbwQAWBmHclLuf2D4y9U+r06y7wpw25OcX1VPqqozMrmyx6eT3J5kw3BFtxMyOXH39uFkjZ9I8jPD+tMfYAAAOMqq6gNJ/jzJD1TVrqq6JJMg6aeq6p4kPzk8TpKbktybyXkx35PkjUnS3Q9nspf67cPtLUMtwzK/N6zzlSQfW4n3BQCsrIPuGjB86JhP8syq2pXJJWLnq+rsTA5525nk9UnS3XdX1fVJvpBkb5JLu/tbw/NcluTmJMcl2dbddw8v8StJrquqX0/y2STvPVpvDgCAJ+ruC5aY9eJFlu0kly7xPNuSbFukfkeS5x3JGAGA1e+ggdISHzqWDH26+21J3rZI/aZM/sq1f/3eTK4CBwAAAMAx4LAOeQMAAABg/RIoAQAAADCKQAkAAACAUQRKAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAUQRKAAAAAIwiUAIAAABgFIESAAAAAKMIlAAASFX9QFXdOXV7tKp+qareXFW7p+ovn1rnTVW1o6q+XFUvnaqfO9R2VNXW2bwjAGA5HT/rAQAAMHvd/eUkZydJVR2XZHeSjyR5bZJ3dPdvTS9fVWcmOT/Jc5N8b5I/qarvH2a/O8lPJdmV5Paq2t7dX1iJ9wEArAyBEgAA+3txkq90931VtdQy5yW5rrsfS/LVqtqR5Jxh3o7uvjdJquq6YVmBEgCsIQIlAAD2d36SD0w9vqyqLkpyR5It3f1IklOS3Da1zK6hliT371d/4f4vUFWbk2xOkrm5uSwsLCRJ9uzZ8/g0K2+t93/LWXtnPYQDmjvxiWNcy/8Wq9Fa3/5XO/2frcPpv0AJAIDHVdUJSV6Z5E1D6aokb03Sw/2VSV53pK/T3VcnuTpJNm7c2PPz80kmX6D3TbPy1nr/L95646yHcEBbztqbK+/69le0nRfOz24w69Ba3/5XO/2frcPpv0AJAIBpL0vyF939YJLsu0+SqnpPko8OD3cnOW1qvVOHWg5QBwDWCFd5AwBg2gWZOtytqk6emvfqJJ8fprcnOb+qnlRVZyTZkOTTSW5PsqGqzhj2djp/WBYAWEPsoQQAQJKkqp6cydXZXj9V/ndVdXYmh7zt3Devu++uquszOdn23iSXdve3hue5LMnNSY5Lsq27716p9wAArAyBEgAASZLu/pskz9iv9vMHWP5tSd62SP2mJDcd9QECAKuGQ94AAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADDKQQOlqtpWVQ9V1eenak+vqluq6p7h/qShXlX1zqraUVWfq6rnT62zaVj+nqraNFV/QVXdNazzzqqqo/0mAQAAADh6DmUPpfclOXe/2tYkt3b3hiS3Do+T5GVJNgy3zUmuSiYBVJLLk7wwyTlJLt8XQg3L/MLUevu/FgAAAACryEEDpe7+ZJKH9yufl+SaYfqaJK+aql/bE7cleVpVnZzkpUlu6e6Hu/uRJLckOXeY99Tuvq27O8m1U88FAAAAwCp0uOdQmuvuB4bpryWZG6ZPSXL/1HK7htqB6rsWqQMAAACwSh1/pE/Q3V1VfTQGczBVtTmTQ+kyNzeXhYWFlXjZVW3Pnj36sIz0d3nNnZhsOWvvrIdxyI7FbcE2vLz0FwCA9epwA6UHq+rk7n5gOGztoaG+O8lpU8udOtR2J5nfr74w1E9dZPlFdffVSa5Oko0bN/b8/PxSi64bCwsL0Yflo7/L613vvyFX3nXEufaK2Xnh/KyHMJpteHnpLwAA69XhHvK2Pcm+K7VtSnLDVP2i4WpvL0ryjeHQuJuTvKSqThpOxv2SJDcP8x6tqhcNV3e7aOq5AAAAAFiFDrprQFV9IJO9i55ZVbsyuVrbFUmur6pLktyX5DXD4jcleXmSHUm+meS1SdLdD1fVW5PcPiz3lu7ed6LvN2ZyJbkTk3xsuAEAAACwSh00UOruC5aY9eJFlu0kly7xPNuSbFukfkeS5x1sHAAAAACsDod7yBsAAAAA65RACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAgCRJVe2sqruq6s6qumOoPb2qbqmqe4b7k4Z6VdU7q2pHVX2uqp4/9TybhuXvqapNs3o/AMDyESgBADDtx7v77O7eODzemuTW7t6Q5NbhcZK8LMmG4bY5yVXJJIBKcnmSFyY5J8nl+0IoAGDtECgBAHAg5yW5Zpi+JsmrpurX9sRtSZ5WVScneWmSW7r74e5+JMktSc5d4TEDAMvs+FkPAACAVaOTfLyqOsl/7O6rk8x19wPD/K8lmRumT0ly/9S6u4baUvUnqKrNmezZlLm5uSwsLCRJ9uzZ8/g0K2+t93/LWXtnPYQDmjvxiWNcy/8Wq9Fa3/5XO/2frcPpv0AJAIB9fqS7d1fVP0pyS1V9aXpmd/cQNh2xIay6Okk2btzY8/PzSSZfoPdNs/LWev8v3nrjrIdwQFvO2psr7/r2V7SdF87PbjDr0Frf/lc7/Z+tw+m/Q94AAEiSdPfu4f6hJB/J5BxIDw6HsmW4f2hYfHeS06ZWP3WoLVUHANYQgRIAAKmqJ1fVU/ZNJ3lJks8n2Z5k35XaNiW5YZjenuSi4WpvL0ryjeHQuJuTvKSqThpOxv2SoQYArCEOeQMAIJmcG+kjVZVMPiP+QXf/cVXdnuT6qrokyX1JXjMsf1OSlyfZkeSbSV6bJN39cFW9Ncntw3Jv6e6HV+5tAAArQaAEAEC6+94kP7hI/etJXrxIvZNcusRzbUuy7WiPEQBYPRzyBgAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAUQRKAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKEcUKFXVzqq6q6rurKo7htrTq+qWqrpnuD9pqFdVvbOqdlTV56rq+VPPs2lY/p6q2nRkbwkAAACA5XQ09lD68e4+u7s3Do+3Jrm1uzckuXV4nCQvS7JhuG1OclUyCaCSXJ7khUnOSXL5vhAKAAAAgNVnOQ55Oy/JNcP0NUleNVW/tiduS/K0qjo5yUuT3NLdD3f3I0luSXLuMowLAAAAgKPgSAOlTvLxqvpMVW0eanPd/cAw/bUkc8P0KUnun1p311Bbqg4AAADAKnT8Ea7/I929u6r+UZJbqupL0zO7u6uqj/A1HjeEVpuTZG5uLgsLC0frqY9Ze/bs0YdlpL/La+7EZMtZe2c9jEN2LG4LtuHlpb8AAKxXRxQodffu4f6hqvpIJudAerCqTu7uB4ZD2h4aFt+d5LSp1U8daruTzO9XX1ji9a5OcnWSbNy4sefn5xdbbF1ZWFiIPiwf/V1e73r/DbnyriPNtVfOzgvnZz2E0WzDy0t/AQBYrw77m1xVPTnJd3T3Xw/TL0nyliTbk2xKcsVwf8OwyvYkl1XVdZmcgPsbQ+h0c5LfmDoR90uSvOlwxwUAAADM1ulbbxy1/Jaz9ubikescbTuveMVMX/9YcyS7Bswl+UhV7XueP+juP66q25NcX1WXJLkvyWuG5W9K8vIkO5J8M8lrk6S7H66qtya5fVjuLd398BGMCwAAAIBldNiBUnffm+QHF6l/PcmLF6l3kkuXeK5tSbYd7lgAAAAAWDlHepU3AAAAANYZgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAABSVadV1Seq6gtVdXdV/eJQf3NV7a6qO4fby6fWeVNV7aiqL1fVS6fq5w61HVW1dRbvBwBYXod9lTcAANaUvUm2dPdfVNVTknymqm4Z5r2ju39reuGqOjPJ+Umem+R7k/xJVX3/MPvdSX4qya4kt1fV9u7+woq8CwBgRQiUAABIdz+Q5IFh+q+r6otJTjnAKuclua67H0vy1arakeScYd6O7r43SarqumFZgRIArCEOeQMA4Amq6vQkP5TkU0Ppsqr6XFVtq6qThtopSe6fWm3XUFuqDgCsIfZQAgDgcVX13Uk+nOSXuvvRqroqyVuT9HB/ZZLXHYXX2Zxkc5LMzc1lYWEhSbJnz57Hp1l5a73/W87aO+shHNDciU8c41r+t1iN1vr2v9LG/rztv/3Pwnr+9z+c7V+gBABAkqSqvjOTMOn93f2HSdLdD07Nf0+Sjw4Pdyc5bWr1U4daDlB/XHdfneTqJNm4cWPPz88nmXyY3zfNylvr/b94642zHsIBbTlrb66869tf0XZeOD+7waxDa337X2ljf9723/5nYT3/zB3O9u+QNwAAUlWV5L1Jvtjdvz1VP3lqsVcn+fwwvT3J+VX1pKo6I8mGJJ9OcnuSDVV1RlWdkMmJu7evxHsAAFaOPZQAAEiSH07y80nuqqo7h9qvJrmgqs7O5JC3nUlenyTdfXdVXZ/Jybb3Jrm0u7+VJFV1WZKbkxyXZFt3371ybwMAWAkCJQAA0t1/lqQWmXXTAdZ5W5K3LVK/6UDrAawGp+93SNaWs/au6sMid17xilkPAZ7AIW8AAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYJTjZz0AAABg4vStN8709bectTcXjxjDzitesYyjAWA1s4cSAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwyvGzHgAAAABwYKdvvXHWQ4AnsIcSAAAAAKMIlAAAAAAYxSFvAADAYXEIDtNsD7C+CJQAAFizfMEFgOUhUAIAAFiFBKLAarZqAqWqOjfJv09yXJLf6+4rZjwkAAAOk892ABxrjrUQd+cVr5jp66+Kk3JX1XFJ3p3kZUnOTHJBVZ0521EBAHA4fLYDgLVvVQRKSc5JsqO77+3uv0tyXZLzZjwmAAAOj892ALDGrZZA6ZQk90893jXUAAA49vhsBwBr3Ko5h9KhqKrNSTYPD/dU1ZdnOZ5V4plJ/uusB7GG6e/yOqb6W2+f9QgOyzHV42OQ/k78D7MeAMeeA3yu83M1Q/+7/s+U/s+W/s+W/o93lL+fTPf/kD7brZZAaXeS06YenzrUnqC7r05y9UoN6lhQVXd098ZZj2Ot0t/lpb/LT4+Xl/7Ckg762W6pz3V+rmZL/2dL/2dL/2dL/2frcPq/Wg55uz3Jhqo6o6pOSHJ+ku0zHhMAAIfHZzsAWONWxR5K3b23qi5LcnMml5bd1t13z3hYAAAcBp/tAGDtWxWBUpJ0901Jbpr1OI5BDgFcXvq7vPR3+enx8tJfWMIRfLbzczVb+j9b+j9b+j9b+j9bo/tf3b0cAwEAAABgjVot51ACAAAA4BghUDrGVNXTq+qWqrpnuD/pAMs+tap2VdX/sZJjPJYdSn+r6uyq+vOquruqPldVPzuLsR5LqurcqvpyVe2oqq2LzH9SVX1wmP+pqjp9BsM8Zh1Cf/9FVX1h2F5vrSqXeB/pYD2eWu6fVVVXlSuUwCGoqu+qqk9X1f8z/F79N0P9jOH3wY7h98MJsx7rWnSA/r+vqr5aVXcOt7NnPNQ1q6qOq6rPVtVHh8e2/RW0SP9t+yukqnZW1V1Dn+8Yaof8XZcjs0T/31xVu6e2/5cf7HkESseerUlu7e4NSW4dHi/lrUk+uSKjWjsOpb/fTHJRdz83yblJfqeqnrZyQzy2VNVxSd6d5GVJzkxyQVWdud9ilyR5pLufk+QdSd6+sqM8dh1ifz+bZGN3/+MkH0ry71Z2lMe2Q+xxquopSX4xyadWdoRwTHssyU909w8mOTvJuVX1okx+D7xj+L3wSCa/Jzj6lup/kvyr7j57uN05qwGuA7+Y5ItTj237K2v//ie2/ZX040Of9/0hbsx3XY7c/v1PJv//7Nv+D3oeRIHSsee8JNcM09ckedViC1XVC5LMJfn4ygxrzThof7v7v3T3PcP0XyZ5KMmzVmqAx6Bzkuzo7nu7+++SXJdJn6dN9/1DSV5cVbWCYzyWHbS/3f2J7v7m8PC2JKeu8BiPdYeyDSeTEP/tSf6/lRwcHMt6Ys/w8DuHWyf5iUx+HyQH+LzDkTlA/1kBVXVqklck+b3hccW2v2L27z+rwiF912X1ECgde+a6+4Fh+muZhEZPUFXfkeTKJP9yJQe2Rhy0v9Oq6pwkJyT5ynIP7Bh2SpL7px7vGmqLLtPde5N8I8kzVmR0x75D6e+0S5J8bFlHtPYctMdV9fwkp3X3jSs5MFgLhkNO7szkDzS3ZPI79a+G3wfJwf9f4wjs3//u3reX5duGQ6XfUVVPmt0I17TfSfLLSf5+ePyM2PZX0u/kif3fx7a/MjrJx6vqM1W1eaiN+i7GEVms/0ly2bD9bzuUQw4FSqtQVf1JVX1+kdv+ex10Fv8r0huT3NTdu1ZkwMeYo9Dffc9zcpLfT/La7t7/FxGsOlX1c0k2JvnNWY9lLRlC/N9OsmXWY4FjUXd/q7vPzmTvyXOS/E+zHdH6sn//q+p5Sd6Uyb/D/5zk6Ul+ZXYjXJuq6qeTPNTdn5n1WNajA/Tftr9yfqS7n5/JKQUuraofm555sO9iHLHF+n9Vkv8xk0OgH8hkJ5UDOn45R8jh6e6fXGpeVT1YVSd39wNDoPHQIov9kyQ/WlVvTPLdSU6oqj3d7RjUHJX+pqqemuTGJL/W3bct01DXit1JTpt6fOpQW2yZXVV1fJLvSfL1lRneMe9Q+puq+skkv5bkf+nux1ZobGvFwXr8lCTPS7IwHKn53yfZXlWv7O47VmyUcIzr7r+qqk9k8jnmaVV1/LCnxqL/r3F0TfX/3O7+raH8WFX9n7HX+3L44SSvHE56+11Jnprk38e2v1L+Qf+r6j91988N8237y6y7dw/3D1XVRzL5g8IhfRfjyC3W/+5+/PzLVfWeJB892PPYQ+nYsz3JpmF6U5Ib9l+guy/s7md39+mZ/Cd4rTDpkB20v8PVNj6SSV8/tP98/oHbk2wYrlpyQpLzM+nztOm+/0yS/zz8VYKDO2h/q+qHkvzHJK/sbr+Yxztgj7v7G939zO4+ffh/97ZMei1MgoOoqmftu7BFVZ2Y5KcyOUHuJzL5fZAs8fuYI7dE/780fJHbd06fVyX5/KzGuFZ195u6+9Th98b5mXz2uTC2/RWxRP9/zra/MqrqycPFTFJVT07ykkx6fdDvYhy5pfq/b/sfvDqHsP3bQ+nYc0WS66vqkiT3JXlNktTkEtVv6O5/PsvBrQGH0t/XJPmxJM+oqouH9S52FYjFdffeqrosyc1Jjkuyrbvvrqq3JLmju7cneW+S36+qHUkezuQXO4fgEPv7m5nsrfh/DXvQ/L/d/cqZDfoYc4g9Bg7PyUmuGa6m+B1Jru/uj1bVF5JcV1W/nsmVKt87y0GuYUv1/z9X1bOSVJI7k7xhhmNcb34ltv1Zer9tf0XMJfnI8Ln0+CR/0N1/XFW3Z5HvYhx1S/X/96vq7EwONdyZ5PUHe6KyEwAAAAAAYzjkDQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwyv8PYqqdnA6xiVwAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 1440x1440 with 6 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df.hist(figsize=(20,20), column = list(df.columns))\n",
"plt.show() # takes the fineprint out"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"df.drop(['fnlwgt','education','capitalGain','capitalLoss'],axis=1,inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"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>age</th>\n",
" <th>educationalNum</th>\n",
" <th>hPw</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>27650.000</td>\n",
" <td>27650.000</td>\n",
" <td>27650.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>37.937</td>\n",
" <td>10.346</td>\n",
" <td>41.445</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>11.696</td>\n",
" <td>2.044</td>\n",
" <td>4.129</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>17.000</td>\n",
" <td>6.000</td>\n",
" <td>31.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>28.000</td>\n",
" <td>9.000</td>\n",
" <td>40.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>37.000</td>\n",
" <td>10.000</td>\n",
" <td>40.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>46.000</td>\n",
" <td>12.000</td>\n",
" <td>42.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>73.000</td>\n",
" <td>16.000</td>\n",
" <td>54.000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age educationalNum hPw\n",
"count 27650.000 27650.000 27650.000\n",
"mean 37.937 10.346 41.445\n",
"std 11.696 2.044 4.129\n",
"min 17.000 6.000 31.000\n",
"25% 28.000 9.000 40.000\n",
"50% 37.000 10.000 40.000\n",
"75% 46.000 12.000 42.000\n",
"max 73.000 16.000 54.000"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe().round(3)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"def barCheck(columnName):\n",
" colour=['black', 'red', 'green', 'blue', 'cyan']\n",
" perPercent = df[columnName].value_counts(normalize = True)\n",
" print(perPercent*100)\n",
" barPlot = df[columnName].value_counts().plot(kind = 'bar', color = colour[randint(0,4)])\n",
" plt.figure(figsize=(20, 20), dpi=80)\n",
" plt.show()\n",
" return(print(barPlot))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"def twoUniq(columnName, dataFrame):\n",
" dfni = dataFrame[[columnName]]\n",
" dfni = oh.fit_transform(dfni).toarray()\n",
" dfni = pd.DataFrame(dfni)\n",
" dataFrame = pd.concat([dataFrame.reset_index(drop=True),dfni.reset_index(drop=True)], axis=1)\n",
" dataFrame.drop([columnName],axis = 1, inplace=True)\n",
" dataFrame.rename(columns={0: columnName}, inplace=True)\n",
" return(dataFrame)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<=50K 78.350814\n",
">50K 21.649186\n",
"Name: income, dtype: float64\n"
]
},
{
"data": {
"image/png": 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Sd3XdzLHOMJA06yS5CHiy/fbxncA13XZ07DMMJM1GvwXc3qbvBi5vN1jUNPEfd7T9U3t+sMsmpGFJzgB+GhgfHf8isBW4tMO2jnmOM5AkuWcwipKcmuR9E2rvSLLwYOtIOrYZBqPpZeDLSYZ/RvALDH76UtIIMgxGULv5170MbltNkncA86uq32ljkjpjGIyuLwBXt+nVwBc77EVSx7xr6Yiqqicz8OMMbkfxc133JKk77hmMttsZ7CE8VlXPd92MpO54aekIa78puwf41ar6atf9SOqOYSBJ8jCRJMkwkCRhGEiSMAwkSRgGkiTg/wFGYr5PdMzYYgAAAABJRU5ErkJggg==",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 1600x1600 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"AxesSubplot(0.125,0.125;0.775x0.755)\n"
]
}
],
"source": [
"barCheck('income')"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"df = twoUniq('income', df)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Private 75.645570\n",
"Local-gov 7.385172\n",
"Self-emp-not-inc 6.072333\n",
"State-gov 4.669078\n",
"Federal-gov 3.634720\n",
"Self-emp-inc 2.575045\n",
"Without-pay 0.018083\n",
"Name: workclass, dtype: float64\n"
]
},
{
"data": {
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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 1600x1600 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"AxesSubplot(0.125,0.125;0.775x0.755)\n"
]
}
],
"source": [
"barCheck('workclass')"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"df = df[df.workclass != 'Without-pay']"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Private 75.659251\n",
"Local-gov 7.386508\n",
"Self-emp-not-inc 6.073431\n",
"State-gov 4.669922\n",
"Federal-gov 3.635377\n",
"Self-emp-inc 2.575511\n",
"Name: workclass, dtype: float64\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 1600x1600 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"AxesSubplot(0.125,0.125;0.775x0.755)\n"
]
}
],
"source": [
"barCheck('workclass')"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Married-civ-spouse 45.302948\n",
"Never-married 31.915355\n",
"Divorced 15.872671\n",
"Separated 3.450895\n",
"Widowed 2.213782\n",
"Married-spouse-absent 1.179237\n",
"Married-AF-spouse 0.065111\n",
"Name: maritalStatus, dtype: float64\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 1600x1600 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"AxesSubplot(0.125,0.125;0.775x0.755)\n"
]
}
],
"source": [
"barCheck('maritalStatus')"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"df = df[df.maritalStatus != 'Married-AF-spouse']"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Married-civ-spouse 45.332465\n",
"Never-married 31.936149\n",
"Divorced 15.883013\n",
"Separated 3.453144\n",
"Widowed 2.215224\n",
"Married-spouse-absent 1.180005\n",
"Name: maritalStatus, dtype: float64\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 1600x1600 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"AxesSubplot(0.125,0.125;0.775x0.755)\n"
]
}
],
"source": [
"barCheck('maritalStatus')"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Craft-repair 15.242335\n",
"Adm-clerical 14.199877\n",
"Exec-managerial 13.034350\n",
"Prof-specialty 12.762877\n",
"Sales 10.507837\n",
"Other-service 8.806602\n",
"Machine-op-inspct 7.572302\n",
"Transport-moving 4.843088\n",
"Handlers-cleaners 4.575234\n",
"Tech-support 3.568972\n",
"Protective-serv 2.374489\n",
"Farming-fishing 2.236942\n",
"Priv-house-serv 0.238897\n",
"Armed-Forces 0.036196\n",
"Name: occupation, dtype: float64\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 1600x1600 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"AxesSubplot(0.125,0.125;0.775x0.755)\n"
]
}
],
"source": [
"barCheck('occupation')"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"df = df[df.occupation != 'Armed-Forces']"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Craft-repair 15.247855\n",
"Adm-clerical 14.205019\n",
"Exec-managerial 13.039070\n",
"Prof-specialty 12.767498\n",
"Sales 10.511641\n",
"Other-service 8.809791\n",
"Machine-op-inspct 7.575044\n",
"Transport-moving 4.844842\n",
"Handlers-cleaners 4.576891\n",
"Tech-support 3.570265\n",
"Protective-serv 2.375349\n",
"Farming-fishing 2.237752\n",
"Priv-house-serv 0.238983\n",
"Name: occupation, dtype: float64\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 1600x1600 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"AxesSubplot(0.125,0.125;0.775x0.755)\n"
]
}
],
"source": [
"barCheck('occupation')"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Husband 40.355578\n",
"Not-in-family 27.725676\n",
"Own-child 12.890611\n",
"Unmarried 11.880364\n",
"Wife 4.279972\n",
"Other-relative 2.867799\n",
"Name: relationship, dtype: float64\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 1600x1600 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"AxesSubplot(0.125,0.125;0.775x0.755)\n"
]
}
],
"source": [
"barCheck('relationship')"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"White 84.839048\n",
"Black 10.464569\n",
"Asian-Pac-Islander 2.922113\n",
"Amer-Indian-Eskimo 1.060941\n",
"Other 0.713329\n",
"Name: race, dtype: float64\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 1600x1600 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"AxesSubplot(0.125,0.125;0.775x0.755)\n"
]
}
],
"source": [
"barCheck('race')"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Male 67.110838\n",
"Female 32.889162\n",
"Name: gender, dtype: float64\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 1600x1600 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"AxesSubplot(0.125,0.125;0.775x0.755)\n"
]
}
],
"source": [
"barCheck('gender')"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"df = twoUniq('gender', df)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"United-States 92.439439\n",
"Mexico 1.194916\n",
"Philippines 0.684361\n",
"Germany 0.441757\n",
"Puerto-Rico 0.369338\n",
"India 0.322265\n",
"Canada 0.322265\n",
"Cuba 0.282435\n",
"Jamaica 0.267951\n",
"England 0.238983\n",
"China 0.231741\n",
"Poland 0.210016\n",
"South 0.199153\n",
"El-Salvador 0.195532\n",
"Japan 0.195532\n",
"Italy 0.191911\n",
"Dominican-Republic 0.184669\n",
"Vietnam 0.184669\n",
"Columbia 0.181048\n",
"Haiti 0.141217\n",
"Guatemala 0.130354\n",
"Taiwan 0.123113\n",
"Portugal 0.115871\n",
"Peru 0.112250\n",
"Iran 0.112250\n",
"Nicaragua 0.097766\n",
"Ireland 0.083282\n",
"France 0.079661\n",
"Ecuador 0.076040\n",
"Greece 0.072419\n",
"Cambodia 0.068798\n",
"Thailand 0.061556\n",
"Outlying-US(Guam-USVI-etc) 0.057935\n",
"Hong 0.057935\n",
"Scotland 0.054314\n",
"Trinadad&Tobago 0.050693\n",
"Laos 0.050693\n",
"Yugoslavia 0.047072\n",
"Hungary 0.036210\n",
"Honduras 0.032589\n",
"Name: nativeCountry, dtype: float64\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 1600x1600 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"AxesSubplot(0.125,0.125;0.775x0.755)\n"
]
}
],
"source": [
"barCheck('nativeCountry')"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"df = df[df.nativeCountry == 'United-States']\n",
"df.drop(['nativeCountry'],axis = 1, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"dfni = df[['workclass','maritalStatus','occupation','relationship','race']]\n",
"dfni = oh.fit_transform(dfni).toarray()\n",
"dfni = pd.DataFrame(dfni)\n",
"df = pd.concat([df.reset_index(drop=True),dfni.reset_index(drop=True)], axis=1)\n",
"df.drop(['workclass','maritalStatus','occupation','relationship','race'],axis = 1, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"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>age</th>\n",
" <th>educationalNum</th>\n",
" <th>hPw</th>\n",
" <th>income</th>\n",
" <th>gender</th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>...</th>\n",
" <th>21</th>\n",
" <th>22</th>\n",
" <th>23</th>\n",
" <th>24</th>\n",
" <th>25</th>\n",
" <th>26</th>\n",
" <th>27</th>\n",
" <th>28</th>\n",
" <th>29</th>\n",
" <th>30</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>25</td>\n",
" <td>7</td>\n",
" <td>40</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>38</td>\n",
" <td>9</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>28</td>\n",
" <td>12</td>\n",
" <td>40</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>24</td>\n",
" <td>10</td>\n",
" <td>40</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>36</td>\n",
" <td>13</td>\n",
" <td>40</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 36 columns</p>\n",
"</div>"
],
"text/plain": [
" age educationalNum hPw income gender 0 1 2 3 4 ... 21 22 23 \\\n",
"0 25 7 40 0 1 0 1 0 0 0 ... 0 0 0 \n",
"1 38 9 50 0 1 0 1 0 0 0 ... 0 0 0 \n",
"2 28 12 40 1 1 1 0 0 0 0 ... 0 0 0 \n",
"3 24 10 40 0 0 0 1 0 0 0 ... 0 0 0 \n",
"4 36 13 40 0 1 0 0 0 0 0 ... 0 0 0 \n",
"\n",
" 24 25 26 27 28 29 30 \n",
"0 1 0 0 0 1 0 0 \n",
"1 0 0 0 0 0 0 1 \n",
"2 0 0 0 0 0 0 1 \n",
"3 0 1 0 0 0 0 1 \n",
"4 0 0 0 0 0 0 1 \n",
"\n",
"[5 rows x 36 columns]"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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tzPnXz4zny3U7+Wrdzjb5tyx0Otle2ozxDJYYPVV/ZP5S1UF1v6rL+yagnpCH2c8JuD7yhix0fEgpFaO1rmj6/0TgWq31dUZiGr2i09VCp6f06+35W0dO1tkTwu5qXugY0XxFx1tXWksN52BUZ09qPXHuxV28Ra2bOlvodFfzQseIyXURhuoHy7w2Oiean5Qakdb2wvZpmYMwjxnjadS6cOPz4XbjpylhElnonFqwLHTkrWu+9YxS6nIar9jk8cPb2oQQQgghhGgURDudBRJZ6PiQ1lq+qUoIIYQQQgg/kO/REUIIIYQQQgQduaIjhBBCCCGEP8muaz4hV3SEEEIIIYQQQUeu6AghhBBCCOFPTV+4LcwlC53TjNHtoS/dPd1wDnuH32+ovi3dhIM5x3iIYGB0K2AztiM2ujW0GYxuDQ0mbGFrcGtogK+s4Ybq35xifIvr747EGY5hlNF+SKv3/y6tF9xaaTjG529Hm5CJMWacI76rNjanzo4s83sOV9ca3+K6wOBmvYGw5XkgbN1uxpwUPx7y1jUhhBBCCCFE0JErOkIIIYQQQviTbEbgE3JFRwghhBBCCBF0ZKEjhBBCCCGECDqmvnVNKRUNVGitDX7krtP4k4A3tdb5TbfHAzFa65k+ur9XgC+01nOUUncCLwO/0Vovavr7WGCK1vpKX9y/JyL6pjNo3h+wJsbgrnCyd+ICnHuPdbv+tOdeYNPmbeTlF7Ls5fmcNbB/l+XD+tjpNXMylsRY3BVV5D40h9r9R9qUsWam0mvmZCLO6Ufd0QIOjP1hE4OQjEyiJk0lJCYO7ayiau7TuI8eblM/dNA52MZPbroRSv23X+P8+zyod5nWF0bbARDXJ42rn8siMjGG2opq1j+wkJJ9ue3uK/Oyn3LZo7/EaotAa03Dps8omPEKYb0zDOdgpA/M6gcz8vBFDE/rR/dNY9jc8YQnxuCqcLJj4kLKOxhPW69khs3NIuHcPlQdKeKrq6eYloO3cyps+2aq3vuQhMceISQ+Fl1ZRcmTM6g/dLhNvfChFxA34R6ULQI01Hy6jbIFfwf9w4f4ve2HtWOmmlLfSD8UrfuCr//6JtF9Ug3n0BFPxlOl2Im840FUVCy6xknNa8/ScPxIh2UBIic+jeWMn1A5ZZxpORitb9b5wds5ceymCX7Pwcx5HQjnGKP1zegHo3n46nHLbxrkrWu+cLpd0ZkEpDff0Fq/6KtFTidygCeVMmHbEZMNnJlF/uJ17Lx8Ikfnv8uguRM8qn/NqBG89sIs7Ond20HL/pcJlLy5mv1XZeFYuJzMmZPalWmodFIwexFHJ7Ufoqh7p1D70fuU3ftfVL/9OlETH21Xpv7Q95RPyaJ88t2U3/9bQuITCL/+5lPm5klfGG0HwKhnfsc3r29k8RUPsev597lqdlaH5WrLqvhownxev+oRltzwGLahZxF/62hTcjDSB2BOP5iRhy9ieFp/6Iy7OLh4I6tHTGHP/JUMm9vxeLoqq9k9fSnb7l1geg7ezqmw884hacZTVK1YScG4O6hY9CaJjz/crl5DRQXFf3qKgv/4HQV3ZBF23jnYrr/G1H4wox+97YfkYQPoPe5nPhlL8Gw8I/7zPuo2r6LqiXuoW7OUiN882GlZ6+hbaCg6bnoORuubdX4wMh6BkIMZ9c2KcbJgOE96moevHrdEcOn2QkcpNUwptUEptVMp9blSalzT77OUUvuVUp8Dk0+qo5VS8a1uO5RSfZr+f7ZS6iOl1FdNP+Obfv+AUmqHUuqLpn8vbfr944AdeKvpb+crpf6slJrT9HeLUmqmUmp308/fmhckSqlXlFILlVLrlVL7lFJvt/rbVUqprU1t+kYpdVcX3fAFsAtod+QppfoopUpb3Y5WSulWt7VS6v8ppT5TSh1WSt2slHq0qT/3K6Wu7NZAdMCaHEv0kH4ULMsGwLFyG+H2ZCL6pJ+i5g8uOn8w6akp3SprSYojcvAASldsBKB81adYM1II653Rppy7rBLnzm/Rzto2v1dx8YT+ZBB1m9YC4NryMSHJKYSkZ7a9o7pacDdtRR1qhbCwNq84d8STvjDaDoDIpFhSz+vH3rc/BeDAhzuIzkgkrk9au7KOb3IoP1LUGLPWRc23hwgfcKbhHIz0gVn9YEYevojhaf3wpFgShvTjyPLNAOR+sB2bPYmoDsbTVVpF8fZ9uE/RH57mYGRO1eccJTQzA+fqxmOrekM2lrRULL3sbXPf9z3uvKYn1HUuXPsOEJrxQz5G+8GMfjTSD6W7c4gdlGn6WIJn46mi47CcOZD67RsAqP98Myo+GZWS0a5sSMaZhA65lLo1S03NwWh9s84PRuZEIORgRn2zYpwsGM6Tnubhq8ctEXy6tdBpWqy8BNyutb4IGAM8q5S6AngCGKm1vgDo1pd6KKVCgXeBV7TW52mtzwOWNf15kdZ6mNb6fOA+Gt8uhtb6SSAP+KXW+nyt9Rcnhf1vYBgwFDgf6E/bhdf5wI3A2UAacFvT73cBI5ry/xnwuFKqVxfpTwUeUUrFdqetJ6nUWl8M3AUsBo439edUwOuXG8LtydQVlIL7h8ueNbkOIjKTvQ3ZJWtGMvVFJW3uz5VXhNXevYVSSHIqDSeK23w5VkNRISEp7a8mhaSmEzvn/0hY9C7aWUXtqhVdxvakL4y2AyDankhVYSm6VYzKvGKi7V1/p4otJY7Y6y6nZs8hwzmczNP5YEY/mJGHL2J4Wj8yM5GaghNtxtOZW4wt0/vvyPE0ByNzKmLk5biL246lO7+Q0LT2T0CahSQmEDl6JNWbt7b8zmg/mNGPRvqh19jhlH17xPSxBM/GUyWkoMtL2rwlRZ8oIiThpHNdiIWI2ydS+8bfuvWlgT15XJh1fjAyJwIhBzPqmxXjZMFwnvQ0D189bvmT1g0B9RMsuntF5zKgH7BKKfUFsK7p90OAVVrr5mvtL3Qz3iAgQmv9RvMvtNaOpv9eoJT6WCm1G3gRGKSU6s4C6moaF061Wut64O80LsiavaO1dmqt3cB2GhdCAEnA0qb729B0+9zO7kRrvRd4D/Dmmzvfavp3JxAFvNl0ezswwIt4Qa+hMJ/ySXdx4s5bUaFWwi4d6e+UDLNGR3LDyw/ieGk5td8f9Xc6Igg0zynnh2vQdV1/hq01FWUj+dm/UrHoTVx79vkww57R3A97nl9J+f72nxcIVGE33I7riy005Mv5QAghzNTdzQgU8I3W+rI2v1Tq5E91nfy+IjdgaXW7y68vb3o72dvAKK31jqarJmVAOODpV+GenEvNSXk1t/1F4EPgNq21VkrtOlWewJ+BL4HDrX5Xz6nb2pyDG0Br3fp2h2OhlHoAeKD59r3R5/HLqIFtytTmOQhLiwdLSMurGxGZydTkOvAF13EHoSmJbe7Pak/BlVfUrfoNjkJCEpIgxNLy6mVISioNRYWdV6qppm7zBsJGjqHukw1t/pQ6biS9sm4EoHDF5m73hbftiL9lNEl33UyatrD/3a1EpcajLCEtr25F25OozOv4G+qtURHctOghDq35F9H/twJLUpyhvuyIp/PB6HialUczb8fT2xya768exZEVW4hIS2gznrbMJJy5HY+nWW1oLn+2Uobm1Jnr3yDq365vc1+W9FTqCwra1VO2SJLnTKc6ewuVbyzDdt0Yov9zHGPqrF71Q9qVg4npn8GYtdO87kdrfBRj1k7DpfCqH3754ZNYoyLp84ufERJqMWUswfs5qU8UoWITISSk5aqOSkih4UTbc51lwGBCElIIu+LGxvNihI2op17B+vFUXMXlhnLoiCfHppHzQ/O5sleDxdCx5c8czJzXgGnnmNaMPgfw13mydVzomcdv8ePT3YXOFqCvUupqrfU6AKXU+cAm4FGlVHrTTmjjT6r3PXAx8KFS6lYar2IA7AWcSqlfNV/VUUolA3VAGNC8bcZ9J8UrB+I6yXEd8Bul1OtAA3A3sKYbbUsAcpoWOSNpvErVJa11nlLqf2l8y1lO06/zG5uhfqq1/hb4TTfu+5S01rOB2c23s9PHtfuQistRTuXXh0j7xUgK3tpE8thLqD1eTM3hfDNSaMddXEbNNweIv3kUpcvXE3vd5dTnO6jL6d6HaHVZKfUH9hF25RjqNqzGetkVNBQX0ZDf9hXYkPRMGoryGz+nExqK9ZKf4c450C5e4dJsCpdmt9xOHH1Bt/rC23aUvrOB0nc2sMkVD0DvUUMYdOvl7Fn6Cf2vH0ZVfgllh9s/sbTawrlp0cMc2fQVO+e9y5VW433ZEU/ngy9y8CaPZt6Op7c5NN9fQdMeI+mjh3DmbSPIWZJN5g3DcR4voaqD8TSzDc3lv7KGA97PqV72Ulx79mO7dgzODz4icvRI3IVFuI/ltamnIiNInjOdmm3bqXh5MQDOVWtxrlrL1iMZXvVDwaavyRh9fsuuSt70o6u0irVjplIQqrzqh73LP2XH3BWk1Wuvc+iIt3NSV5bhPvo9ocNHU79tHaEXjECXOtAnbThQPfuhlv+rxFSipi6g6rE7cRVHG86hI54cm0bOD83nyu+qGx+2vR0Pf+Zg5rxuZta8bIlv8DmAv86TreM28/Xjd0CTXdd8QulTfLi7paBSFwKzaHxrl5XGxcjNwB3AFKCSxqsxTzZvL62Uug6YB1QAHwC/By7SWh9WSg0C/gZk0LgweV5rvVAp9TCNH/Z30PjWrhlAgta6VCl1N/Aw4ATubLr/eK31JKWUBZgOXNuU8ibgAa11XettopvymkXj52X+rJQaAzwPVNG42cBAYIbWekUH20vfrLW+uSlGHHAQ+Lp5e2ml1G+Bx4BiGj9z9EyrvtCt2tFmG+6mzwTt0Vr/8KjWiY4WOgCR/e0MmjuB0IRo3JXV7J34PM497bcwvXT39A7jPjFjHtlbtuMoOUFcbCxRtkhWLflHh2X3Dr+fsL6Zjds6JsTQUOnk2MNzqN2bg/3p+6hY9xkV67ejIsIZuH4hKsxKSIwNd3EZpSs2Ylk9n5DMM4i+/1FUTCy6uoqqedNx5xzE9oeHcG3/FNf2LYRfcyPhY29tPPgtFuq/2oXzlRfBVcfuzzrfHa47fZFoa7xAaKQdb017F4D4fhlcPfu/iUiIpq6ymvUPvkTxnsbtMEfNuJtDa3dxeO0uht53E8Mn39qyPW60clP+4WbKVn3qdQ7fPbHE6z4wqx86y8GTPLpiNEZ36