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9735833-YE-s1/Adult-Income.ipynb

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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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",
"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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7YWA6C15IgiAIgoZZVJv+2rbvz8//DKydn68H3FM5797cNlR7EARB0ENqL+Q6FdktltFJ0lRJ/ZL6586dW0psEARBwKIr/Qey2Yb8+GBuvw+YVDlv/dw2VPsC2D7Jdp/tvokTJy7i8IIgWGSk0W3BuGJRlf40oOOBcwDwk0r7/tmLZzvg0WwGugTYWdLqeQF359wWBEEQ9JBh8+lLOht4LbCmpHtJXjifB86TdBBwN/D2fPpFwO7AHOBJ4EAA2w9JOgaYkc872nb34nAQBEHQMPJiXGChr6/P/f39Cx5ouohEyA/5Ib85+UHjSJppu2+wYxGRGwRB0CJC6QdBELSIUPpBEAQtIpR+EARBiwilHwRB0CJC6QdBELSIUPpBEAQtIpR+EARBiwilHwRB0CJC6QdBELSIUPpBEAQtIpR+EARBixg2y2YQBEFRIqHbmBIz/SAIghYRM/0gCJYc4i5iWGKmHwRB0CJqKX1Jf5B0s6RZkvpz2xqSpku6Iz+untsl6ThJcyTdJGmrEm8gCIIgGDklZvqvs71FpUrLJ4FLbU8GLs37ALsBk/M2FTihQN9BEATBKGjCvDMFOCM/PwPYs9J+phO/BVaTtE4D/QdBEARDUFfpG/iFpJmSpua2tW3fn5//GVg7P18PuKfy2ntz2wAkTZXUL6l/7ty5NYcXBEEQVKnrvbOD7fskrQVMl/S76kHbljSq5XHbJwEnQSqMXnN8QRAEQYVaM33b9+XHB4EfAdsAD3TMNvnxwXz6fcCkysvXz21BEARBj1hkpS9pJUnP6TwHdgZuAaYBB+TTDgB+kp9PA/bPXjzbAY9WzEBBEARBD6hj3lkb+JFSMMTSwPdtXyxpBnCepIOAu4G35/MvAnYH5gBPAgfW6DsIgiBYBBZZ6du+E9h8kPa/AjsN0m7g0EXtLwiCIKhPROQGQRC0iFD6QRAELSKUfhAEQYsIpR8EQdAiQukHQRC0iFD6QRAELSKUfhAEQYsIpR8EQdAiQukHQRC0iFD6QRAELSKUfhAEQYsIpR8EQdAiQukHQRC0iFD6QRAELSKUfhAEQYsIpR8EQdAieq70Je0q6XZJcyR9stf9B0EQLDLS6LbFkJ4qfUkTgG8BuwGbAvtK2rSXYwiCIGgzvZ7pbwPMsX2n7X8C5wBTejyGIAiC1lKnMPqisB5wT2X/XmDb6gmSpgJT8+4Tkm4fhfw1gb8s0FruNivkh/yQPz7lj+exDy1/aF4w1IFeK/1hsX0ScNKivFZSv+2+wkMK+SE/5I9z+eN57KXl99q8cx8wqbK/fm4LgiAIekCvlf4MYLKkDSUtC+wDTOvxGIIgCFpLT807tp+W9AHgEmACcKrt2QW7WCSzUMgP+SF/iZc/nsdeVL5sl5IVBEEQLOZERG4QBEGLCKUfBEHQIkLpB0EQtIhxrfQlTZD0n2M9jiAhaSlJq4z1OEaDpMNG0raIsj8yyHaQpC1KyG8aSd8dSVvQLJJWl7RZMXnjfSFX0nW2tyks8yMLO277qyX7axJJM4FTge/bfrgB+d8H3gfMI7nkrgJ83faXCsgWsB/wQttHS3o+8Dzb19WVXenjettbdbXdYHvLArK/D/QBP81NbwRuAjYAzrf9xQJ9rAT83fYzkjYGXgz83Pa/Csge8Nnk3Fk32y6WL0vSccA5tn9dSmZF9k+BbgX3KNAPnGj7HzXl30RKJXOu7d/XkTWI7CuAPUgeljOBB4FrbC9UN42EcT3Tz1wj6ZuSXiVpq85WU+ZzhtlqI+lxSY8Nsj0u6bESfWT2BtYFZkg6R9IuWZmWYlPbjwF7Aj8HNgTeVUj28cD2wL55/3FSwr7aSNo3K4UNJU2rbJcDD5XogxR8uJXtj9r+KPBvwFrAq4F3F+rjKmB5SesBvyB99qfXESjpcEmPA5tVf5ckxfOTugPuYibwaUm/l/RlSSWjWu8EngC+k7fHSL+hjfN+Xd4EPA2cJ2mGpI/liUkJVs3/q7cAZ9reFvj3IpJtj+sNuHyQ7bKxHtfitpEu8HuQIqD/CBwFrFFA7mxgGeB84DW57cZCY74+P95QaSsl+wXAa4HfAK+pbFsBSxfq43fAMpX95YDfdb+nQp/RB4GP5+ezCv1eTi0xxhH2twZwMHApcEchmTOGagNmFx7/ZOBMYF4heTcD65Au5FvntptKyF7scu+MFtuva0q2pOWBg4CXAstX+nxPA32t1dXHHwvK3gw4ENgduAA4C9gBuAzYoqb4E4E/ADcCV0l6AWlGVYJ/ZZOCASRNBJ4pIdj23cDdkvYD/uR8qy9pBdIM/Q8FujkLuFZSZ3b8JuD72SRzawH5kKxg25PMYAfltgl1hTqZi7auK2cUvIhkmnoBcFshmStLen7nv5Rn4SvnY/8s0UH+ve+dt3nAx0vIBY4mBbFeY3uGpBcCd5QQPG5t+pLeaft7Q9nfXcDuLul80mztHaQvYT/gNttFFvpyH3sAXyGZYB4k/+htv7SQ/JnAI8ApwAW2n6oc+6Htt5Top6vPpW0/XUDOfqQ/01bAGcDbgE/bPr+u7Eof/cArnFJ9k9ODXGO7iMLLivMVefca2/0l5Fbkvxr4WJb9hawcPmz7QwVknwF80/aMurIW0scXgTcDvyfZx39s+5FCsncHvp1li2R6fD9wBXCw7a/VlH8t6S73POA823fWkdcrxvNMf6X8WMTGPgQvsr2XpCm2z8gLc1cX7uMYYDvgl7a3lPQ64J0F5e/V/WOUtKHtu0oofElrA/8DrGt7N6WiONuTLjK1sH1WvmjtRPrT7mm71Cyww9IdhZ/7/GdW/KW4nmRSWxrSbLPUXVy+C9rD9h6dtvxd11b4mW2B/STdDfyN9B3YdhFPkry29Diwve3RpA0eEbYvkjSZdAcBcLvnL95+rY5sSUsBP7T9hTpyFiJ/Y+AEYG3bL8t363vY/mxt4SXtWkvaBlyXH68CXkbKaX1n4T768+ONwFKd5wXlXz9I28yC8n8OvL0zZpJyu7mQ7O2A51T2VwG2Lfz5T89/ps7+FODSQrI/SMqBPpvktXMzheyylT5+W1Jel+wXDLYV7qPIb2Uh8l9BulPfv7MVlN3f4LivJBWduqHSdksJ2eN5pg80bnc/SdLqwKdJ2UBXBv67gNwqj0hamXRhOUvSg6RZVS0kvZj0mawqqTqjX4XK51SANW2fJ+lweDap3rxCsk8gmXY6PDFIW13eR/rcO15B95CUQwkOAzax/ddC8gbjBknTSAvpz/5ubP+wrmDbd0vaHHhVbrra9o115XZxvaSt3YAJKccUbATMItnbIa0PnVmoi19K+hhwLgM/+xLeXyvavq7L0a62yRTGt3mnw3dJdvddqNjdSwi2fXJ+ehXwwhIyB2EK8A/gP0ljX5X0PuqyCckvfDXSAmKHx0leEqX4m6TnMn+xdTuSL3QJ5DzFgWcXF4v+Zp38q7fLF15sP1FQ/D2U+yyGYnngr8COlTYDtZW+UpDawRVZ35N0ku1v1JVdoUkTUh/Jpbiphcu98+OhlTZTRlf8RdJGzP9fvQ24v4Dc8buQ26ETSCPpJtubSVqGNCPZroDs/wG+6LywlGf9H7X96bqye4Wk7W3/pkH5WwHfIJm/bgEmAm+zfVMB2T8kLbqdkJveD7zO9p51ZVf6eC5wBMmbycCvgKNLzM4lnUK6+P4MeHYB3eMkuC8HH21v+295fyXgN4UUcqePQcv6OXlX1ZV9PvAh20WUZS/JC/InkcxTDwN3Ae+0/Ye6speEmX4n8vARSS8D/kwKgCnBbrY/1dmx/XD2CKit9CX9yvYOOeileuXtzHRqpTOQ9A3mzxL27T7uAt4dWc71kl5DUm4iLZbVjgbNvA84jvR5m+TDPXWhrxg955Du5N6a9/cj3a6XCIT5Y96WzVtxGl3wS99n1VQ3L7cVI5uQdgAm2z4tu+WuPNzrRsiawK2SrmPgRXePoV8yciStCHwEeL7tqXnReBPbF9aV7bQg/+/5QruU7cfryuywJMz030vyPd8MOI30g/mM7RMLyL6JFBjxVN5fgbR4U8SdskkkHbCw47bPqCl/oZ4/JWzKvUDSLbZf1tV2s+2Xj9WYRoOkK4H/IqUV2DK3LfCeFlH2R4ADgB+RlP0U4HTXdHXs6uMIkhlmE9sbS1qXlKLilQVkv2awdttX1pWd5Z9LiijeP19wVwR+bXuLArIbszKMe6XfJJI+QbKHn5abDgSmuUDOlCx/Aiky8MXDnryYIem0hRx2nYV0SR+3/cXq3UqX8FIuiUj6KnAdydcaUizANrY/VkPm12x/WIPnfik208x9zbC9tSr5giTNKqF4sqytqJi+bN9QQm5F/ixgS5KXWWf8N5U0ITWFcrHyrs/+RtubF5D9rMxK2wJ5ohaFcW/eyTbZI4FXkn6YVwPHlLDJOgW73ETyEyfLvaSu3Ir8eZJuL+m73U2+Xf4EsCkDvZt2HPJFI8D2gTWHtjA6C/FFA5mG4GDgwySHAJHSD/xN0iEsupmtk4nyy0VGuHAaW/CroCy/qGkn80/bltQZ/0rDvWA4mjadVvhnvvvvjH0jKmakmkyQtFyXlWG5EoLHvdKnWZsstn9O8kVvitWB2dnuWHX7KjUbPIv0ebyBZCM/AJhbSDYAkt7Agi6zi+yBZLuTlfJm29fXHN5wfRUP7rM9Mz99LvAzV6KgG+BQ0oLfiyXdR17wKyFY0n8De5HMpwJOk3R+ofWCDudJOhFYTdLBwHuomQzN9g75scnATUgOABcDkySdRZp4vruQ7LOASyt31AeSotJrM+7NO03YZHs4U+iF3XGm7X+r3jJ3TAKF5H8bWBF4HXAyyTxyne2DFvrCkcm+HHge8ANS+tpb6socpI9XkhKU/U3SO0kxAF8rceeV/7A7kiYl5wIXu0B6iiH6Kr/gJ90ObO6BeYlm2d6kVB9Z7uuBnUn/r0tsTy8oewKwNpUJbsm76mxp2I409t+6QGSxknP++qSJVGfyOr2UlWFJUPrFbbK9JrutTbb9y7wYNKHUn1fSb21vJ+kSkifMn4Af2N6okPyOq2zncWVSPvdXDfvikcl/Hinid29SYNm5JWea2Xy3OckR4HTShevttge9GC+C/GWA3Ujj34H0531vCdlZ/nKku9wNGKjYasd65IvumyuLiauRUg/UMg129bES8I9s6tyE5AVWqh7AB0mz8QeYn6jPpdYLhpgwfN1l3E0bcyZYEpT+46Q8PM+QZuUTmG8mWeRZea8WWfMt7VRSmuONstvXt23vNMxLRyr/jaR1jkkkf/pVgKNsTysk/1rb20r6LSn390OkcPEXlZBf6eflpAyGe9su5v7YWRzLpoz7bJ9SasGs0scywK6kW/RX216zoOyLSQFgM6m4V9r+SgHZPwa2JqWqMPB60gTr3txHiaRuM0kRv6uTYiT6SXb+/QrInkNK29FIRHTXhOE0Ur6pIhMGNZjsbtzb9Juy2/VikTVzKCnHxrW53zuU0iwXwfN9hh8lmWBKc2GeAX6RpHggzZZrI+klpBnyW0lRp+cCHy0hu8LjSikk3gm8WimR1jIlBEvqzPBfSwoyO5l011KS9W3vWlhmhx/lrcMVDfQh209KOgg4IXttzSoku+mI6KfzIvQU4Ft5wlDbrJlpLFJ53Cv9bP/aD9jQ9jGSJgHruExJvaYXWQGecsrsCIBSmoHat19Nuz0qpQy+x/YxeX9lUkKx3wHH1pFd4VTSQv0utv9USGY3e5MSch1k+89KOddrl3rM7E+6UB3S4GLuryW93PbNpQU7ZZZdlpSl0qTAuyJ56CtIhesBaH669TuBKyQ1FRHd2ISBlFamEca90ieV1HuGtGB2DCkp17dIt6V1+UwBGcNxpaRPASvkBa33M7+mah2adns8kbzIpJTT/fOkrJJbkLxJ3lZHeDav3WX76/WGuXBs/xn4amX/jxRKyGV7X6XU06/PF/XrbD9YQnaFHYB3S7qLpNiKzQiVos9PpJKPXtIh2aOtFIcBhwM/sj1bKf3A5TVldu7+m46IbmzC4AaT3S0JNv2OTfYGFw6QyLIaW2TN8pcizXCe9V4ATvZi/sVUP2OlDJVzbR+Z94sEB0m6GtipgdlltY+3AF8gpe4QBT20JO1F8tW/Ist9FfBftn9QV3aljyZz1/wOeKPtOXl/I5ILaiPrXJKely/CTcheCljZqe5sE/Lf6ALpFyryupPdvRkok+zODeay7sVGsoVPYH6t0ImUqz96MDAD+H3en0yhXOs9/HymA6tV9lcnucXVlXsLuZYsyaTz6uqxQmM/M3/+nyHlOPkI8JHCn88c4CUNffY3AmtV9idSsFZCV19rAc/vbIVkzujaV3db4fewQO2HmvK+T3Jc6JSnvJd00R0PY78JWKmyvxJRI/dZjiMtNq0l6XPkknqFZDe6yArJNYsFbe6Pkswyn3V9z4OJrpSfc0oaV+I9nE0yTf0F+Du5opikF1Fu8ez3eVuK5iqkPeDy1bg6LOWB5py/kt5LMTREuU2Sj3dd+iVdRHKHNilQa0a+O8Ll8yuVjvjd1PZjSmU3fw58kuRsUGrNpkrpsTeW7G5cK/18y3YXyZWviZJ6jSyydvFz0hf6/by/DynY6c8kv/E3Df6yETNPA4tDv4AC78H25yRdCqwD/MJ5OkJSah+sKz/3cRSApBVtP1lC5iD0KyXO+jEDF/tKKLSLc3zE2Xl/b8pHdzdZbnN5ko97xwVxLrAC6TdZJGd/F7UicQdhmewuuyfJ/fFfyukeGuCQwvJOA66V1PGe2pMCJUhhybDp3+CuxEQFZX+RVFR8f5Iiez9wq+3/V7CPBXzCK+sUtQM0JO1KWli9kvl25akumEOoKbJXxykkW+zz88LWIbbfX7CPwRLH2WUqryHpraTwfEiLcT9a2PmLIL+T9OtGYEunQjPF1rR6gQZJrWz7rgJyP0TKO3UjKQ3J84HvuVzg4IokF+Ln2z5YBVMrZ/mdZHeQfjtFkt0tCUr/y8BvSJGCRd9MLxZZ85/1YGcX0+wKebLtzUtd0CStSZoNQqFQ8V4g6VqSuW6aC6cN7iWSVmFgtGyJcnod2b8kzQI/T8r18yApHfgrCsg+jcHdfYtcEHMfjaVWHqK/pV0oFYYaSK0s6XTb787PD3DNFOiD9rEEKP1ORO7TpLKDxfPjNElW8qeS6gAIeAx4L6mY9htsn7eQl49EfieO4YW2j85uZc9zmTiGRtH8aN8bXD51bePpm5UydR5F+l0+w/zfZrHSm0ppDP5OMqt1ym2eVWAtqHOX0mF5kgfJn0p8NpU+ZlE4tbKkd9r+XsVffwAu5KevBlIrd8kqGhneYVzb9KGZiNwhFlerfRbL9e0UZv1ySavm/eoiaC2Fn6nGMRxNqpF7AWXiGJrmHkmvAJxts4dRqP4xvUnf/DHgZU3eWTnlfem4FZ/RcSsuJPuC6r6ks0mpEkpSPLUyaU0Mmlv879BEauXGZ+HjXulXkXSks694Td5YQMaIUSU1cWfR2AUSZmW2zesDN2S5DytFWY4H3gd8HVgPuA/4BQOLUC8yzumbq7fPDfiJ/x5oagEaGJi7CdiI9Fl9m/k1IEoymXKlSDsUT61M+hwgrb+dX1PWwmgitfL6ko4j3RV2nj9LkTvQ8W7eqVL6dkjShsD9Hphadm0XKE5c6aOx1MRZ/rWk4sozsvKfSPK2aWTxezzTwO9nS7IXBgM9g0qbR7YBrq2YBYpkaNSCqcX/DBzefQdQoJ+iqZXznfpmwMwmzCNdfRVNrayGy5zCEjbTp7yv7PkkhdlhXm4raRp5heenJj5K0lco69bXiWNYu4E4hkbJ3lOfJdmsLyb9kf/T9vea6rKwvBOBy0g5iZ4Z5txFpTG34iZMp91ku/u5dRV9FxcDDwMrS6pG4BZd79P81Mo/U0qt/ClJtVIrD6bUS9+BFg0UGQvyB9/h3wZpq8PSrqQAyM9Lm0b+nh+fzJ4L/yL5vhfB9lmkOIb/IeXS37PhW96S7OwUNv9G4A/Ai0hFwJuiuJ+47Y/YPs32GZ2tcB9XamDupvMpk7sJSa/s2NglvVPSVzVE2ocaPAf4haSrJX1AKVdRLWz/l+3VSCkjVqlszyns4HEC6X+7OSla/PcUytvUxUUlhY17pU/KEQ+A7We622oyVyniEQClFKqlF+U6qYm/BFxPUm5nL+wFi0BncW8pUnDNeKFzJ/oGkhtfI2lyJW2VfbonZN/oUvxc0lRJ60hao7MVlA8pynQu6W7iEJKCKHUnV1VqH6UBpWb7KNsvJa3VrEO6iP2ykOwpnedKdSVK83R23+6kVv4WzSweF70DHbc2faXAnVeQilpXU/muQqr2U8KtbyNSrcp1SR/8PcC7bP++ruwh+lsOWL6kctOCdU73JCnQknVOG0HS50nj/TvJbr0acKHtbQv20fl8OtGle1Lo81HKfNlNUZfNJlEPCsxU+noe6XvYB3hOSQ+5LL/4uCVdSTIlHQi8mhQjcWOJ9ZSuft5v+/hi8sax0n81afHzfSRvhQ6PAz+1fUfBvlYGsP1EKZld8l/BguXuisyo1KM6p02RZ8aPOhW1WYmkEIrZN8fr59MLt+JeKDVJ7ycVlplIMk2dZ/vWUvIr/Tzr/15Q5vNIqZVn2L46x8C8ts5/d7g7QRcI7BvPC7lH2N5J0kudc7Q0he0nJF1IA66ckr5LcjGbxfwES6bcbfSfSIE1/8j7y5HcH8cFnR+5pJNsT6VSzKYQPfl8KuMvRS/cipssMNNhEvBh27MKy+2mdG4c3Ewthpmk/79IaSMezs9XI9UG2LCm/HE907+VFLl6CumHOcDuZfv6wv0VnylkubeRsgE28kWoB3VOe0GDZoUf04PPp8HxN+5WnOWWzhe/ilMGzEFntiVmtMrZQLt4FLjZBYrZdLm0LkuqmvWE7VULyP4OqbDMRXl/N5ITRu2L13ie6f83Kc/6+lSuthmTIlBLUiTZ0SDcAjwPuL8h+b2oc1ocpbxH29n+dW4qXXGqQ68+n6bG3wu3YkjR3MWUPimr7BsZOLPtYKDEusdBwPbMr8T12tzfhpKOtv3dOsKrLq1KPrNTmJ/jqi7b2T640tfPswtzbcbtTL+DpM8412kdj0i6nFRi8DoGBvCUrMPb6Wt1YJLtm0rLboKm7q4G6WdZYOO8e7vtfxWSu1e3e+xgbTX7mOWuBF9qIMtmr76Lkiiltd7f9gN5f22S+WVf4Co3kLiv1OeUx3410IlJ2Y9UqKh27dzxPNMHwKkY+uqkEPHlK+1X1ZWd3byOIRWmWJrCwR2ZIwvKWgBJVwB7kMY/E3hQ0jW2B01GtZhxqVLSr+IZVDtIei1wBslVVqSQ+gNK/H5ItV+7FfxgbXWYK2kP29OgMbdiaMAmDiDpUts7Dde2iEzqKPzMg7ntIUm1L+xd5qOlSNlC/zHE6aNlX1Kahx+R7nyuym21WRJm+u8lJeJan7QYuh3wG9u1zTuS5gBvIdkAx+UH1Zl55M9pku0jVDOLYa/Q/Ayq80hum8UvupJmAu+wfXve3xg42/a/1ZC5G7A7ySvl3MqhVUjrN9vUGHJ3X426FTflWSZpeVL8yOUks0vHvLMKcLEL1OGVdDxpMbRzkX0ruWQiyfX3dTXlV2sxPE2aOHynxHpBpY+VbBd1Xhj3M32Swt+alPfidZJeTIo+LcE9pHqvjSl8SduRgsleQloMmgD8raBiW1rSOiQFVKz4Sy9wD9IAkKJmb6/0+X9KGT3r8CdS9s49SHdXHR4H/rOm7AFk5b5dE27FDXuWHUKKsVmX9Bl1lP5jwDcLyIcU8FUtYnMmcEH+P9dS+AC2D6wrYyjyxfZkUsr1sgWE3FCR415t5ELNpB/mcvn57EKytyb5KR9Oc4W5+0npBW4gKfwDgf8tKH8vUpHl4/P+C0k//DH/7kYwdpFK/30m708Ctincx6n5z/XavH0HOLWA3AnA93v8eV1YWN5tZGtAg2P+YC8/o8JjX59kfnkwbxcA6xeSfW3+vd9QabulhOwlIQ3DvTmNwY+B6ZJ+AixywqMuPkdKjbs8Kby6sxXF9hxggu15tk8Ddi0o+3zbmznPEGzfafutw71uMeF4kvfFO/L+E8C3CvfxH8CtwIfydmtuq4XteaT1gV6msV6vsLyOZ1mTPJP/v0ByNsgBW7WR9BZJd0h6VNJjkh7XwARsdTkNmEa6W1mXlPNosPKbi4Tte7qa5g164igZ9+Yd22/OT4/MnjCrkmbnJVjXzZfmezIrhlnZJet+GsqJ1JSveIM0XgvA9lMkl9+vSlrHdknX2buAayRNoxJU5kKVmwahTA1V6ackM85zgFslNelZdrBTzpqO7IeV8uqXSDvwReBNtksV3ulmYp6kdThd0ocLyW6sgNC4VvqSJpBMOS8GsH1l4S4ukrSz7V8UllvlXSRTwAdI9t5JJDtkE5ROHdw0/8rfcacy0USaS1EM8DOg5EXx93lbiuarOAF8VNJmru+S++UioxkZEyTJ2X6Rv+9SF/YHGlT4AH9VSqncSZC4L1C7TGWmsQJCS4L3zk9IdsE/NiC74z3yFCnl8bipvyvpMNtfl/RK29fkts/aHhe59AEk7UdKBbAVya3ybcCn3VBq6AajrhvL3TSYSy5QxCW3F9G+kr5Ecok+MTcdAtxj+6MFZH+dZJ76MQPvVH441GtGKf8FJCeM7UkTk18DH2pCF5VkSVD6V5EKK1/HwFvo4sFNTdBULEAnaGccmnQGkL2xdiJ9Lpc2OXNT6WyG0suA75JKGULyn9/f9uyCfdzghlxyJfWTivz8M+8vS7qgFIv2zZHXhzC/vON04OS8JlJX9mD2ddt+T13ZTZMvuB9kQXfZ2npt3Jp3JL0IWJuUiqHKq6iZ0kDD5FR32bw+X6OZWIDbJN0BrCupervfuags9n76mTtIbnxLA0h6fumZlKQdSIXFj88mpJVtD5YWebScRPL2ujz381qSd9ArFvKa0dKkS+4CRYQaWFN5RtLpwGWuuM4Wkt2YSyU8a248mAUVc4mLyo9JecV+SmGT5rhV+iRlebjtm6uNkh4i+emfUkP2V/Lj8qQouxtJynIzkovl9jVkd9NILIDtfZVSv15Cuv0fd0j6ICkq8QGS54JIt9HFLliSjiB9x5uQPC+WIYW+l6i+tlJH4QPYvkK5ElVBjiJ9x7+yPUPSC0kXyhI0Hu2rVKToSyQ7/oaStgCOrjOjlfRx21+U9A0GST/tckkGf0JKlfBLCnnWVPiH7eOGP230jGelv3a3wgewfbOkDeoIdo7Uk/RDYKtOP/l2/cg6sgfh46QF4ysZaHes7eHhlPp1czWUW6YHHAZsYrvU4thgvJlkHrwewPafJJVadL1T0mdIJh5IMQd3FpLdWfScVL1rs30n5RwB3gecJelbJOV5L7B/IdkdjiAVyLkCwPasbNqoQ8cE2F9TznCsaPsTDcn+ep6Q/IKBeqG2lWE8K/3VFnKsVEnATaoXFtu3SHpJIdkdPkfyP1+e8vV3kfQaUiTiHyifW6Zp7iGlwm2Sf9q2pI73SMmZ+HtIM/HOwuHVua0IToVl9mVg5bhiuMFo3wr/sv2oNDAzeh2Btn+aH0vXI+7mQkm7O6c/LszLSZ59OzLfvFMke/B4Vvr9kg62PaCYdV7QmjnEa0bLTZJOZmCmuxsLye7QdCzAV0kFxgfkliEXkV8ckdTxPLkTuELSzyh8F1ThPEknAqtl//D3UKhAuu2HgQ9JWhV4xvbjJeR2cY2kb5Jy/FQdGWrPCJWyUv4P6Te6m6RNge1t1zGddjNb0jtIrpuTSQFyvx7mNSNCUh9pnaPjJAHUryqm+Xn0BXxK0lOk3DtZfBHvvr2AF1bXVEoxbr138g/yR8A/ma/k+0iz5Te7QEk9paRQ/0FaHFbuZ0PbB9WVXenji8Avm4oFGMyTo5R3R1Pk29qhsO2jC/f3emBn0nd8ie3pheRuTUrz0DEXPQq8x3apSUknNXc3dpmEgz8nrXP8P9ubS1qalBagZLnEFUmKeefcdAlwjFPQXF3Zt5OSq91MZTHUdqmI/cZQKu4z1QWTtz0re7wq/Q6SXgd0ZsqzbV9WWP6WpDQAbyfNPC+wXSohVHcsQOnZQsdtbR4D71YmjBO3tcbz0TdJ9po61PbVeX8HUg6kxfaCCyBpadtPS5phe+tq/IIGyd9fs6/GvmNJv7K9Q105I+zrSNtHFpR3BclhYQaFo6HHvdJvgmwC2TdvfyHdOn/M9gvGdGCLgKTlSJF8nR//1STFU3sm1TSDxRiUjjtQyon+BWAt0ky/WADeYMFeDYy/uAmmM8aseN4KTM/72wFfsP2aIoOn2e9Y0k6k//ClNBCc1dVX6e910M/YBbIOjGebfpP8jqQc3+iUDA1JRVPiDkYDs4UJwI1OaSqayvdSHM3PR7+epKrb2irMvxsqRZP5Wa7M6wVnk2zAe5PWKLaCYvEep5NNMHn//0iTlDp2986q6kdICcU2knQNMJEUFV2bHn3HBwIvJrnhVhdDiyt9CqY4yf/bE12gpsBghNIfnLcA+wCXS7oYOIfe5K3Zg4Iuodm74/YmApoappOPfi+SEoOkCB6gcD56ms3P0ilZ2L1GsSXl6jivafs8SYcDZLNMXZ/xiZXF9B8BF5F+/08B/05K1V2XXtQc2Nr2JoVkLUC+S/xZvmsu5hjR9P82lP4g2P4x8OPsvjeFVOxhLUknkCrUN5WArYkLy+okD4nxlKbiVtLaw7LMd3F8PmlGW7I4NyQvsHNpID+La1ZmGiF/k/Rc5iel2476bq4TSMU7un+PK9aU+yy2bwRulPT93E8TcSS/lrSp7VsLyevmTcCxSqlgzpV0se1SdymD/W9te0pdwWHTHyFKdXj3AvZ2mfqdg/WxlO2iIddN2gabQtKxJKXzkY6bo6RVSNkf/277sIJ9NZafRdJhpAvV4yQ30K2AT5acNGRT0TdIzgy3kE0wrpFps7R9epi+FogjAYrEkUi6jVT56y7SBb14ChKltMe7kUx3O5DWP95bQG71fyuSB+E+tl9aW3Yo/bFF0hnAYbYfyfurA1+pq3Syu+n7SFW5bgZOKTgLaRSlnEEbu+vHmW2dv7M9eWxGNjok3ZhdHXchfRefBr5bWqFmV8pNSMqh9kx5sAXoplADNYqznI6iXMA9s7TLZlb8u5LWEF5te81Ccjueg3uRLlw/tP2NunLDvDP2bNZR+PBsEYkSf7gzSOmgrybNRDYlpTUYD7hb4efGecqRs3VRb/KzdMwjuwNn2p6dlVF9wcmePBgbS6prnmrkTnYImqhRjG1L+lbJmIJu8mL03qQym1eQym6+vabMwTwHVdJUGEp/7FlK0upO0ZtIWoMy38umnR+8pFNIqafHC7dK2t/2gALcSgUrfleoj17kZ5kp6RfAhsDhSjl9Spnv3rSQY7U8VGw/tKivXQRmasGo91LfyfWStrY9o5C8bvYnKeVDCrpAN+45GOadMUbS/sCngE4wyl7A52x/d+hXjUjuALtsL+20dZG0Hklp/Z2B0dYrkKKt7xursY0GpVzxWwB32n4kL7iuV8fevqTRZByJpN+RzJt3kxZDF/u04pL2JHkOvpJU9vUcUn2Bukno5vcRSn/syQE1Hfe9y0p4G2S3vc6qv0gK80kKBh81jaQdgc7C1a22L22gj42Bj7FgTvQS7pSdC1h37pcSi5QLrYzl5urwFkNd5U4bkD9oMGVdm34n0lfzc/A8e4hygX0dz8F9SbrhTAp5DobSXwzQ/CIep6lsEY9gGCTdCHybdEfxrH+7C+THkfQFks331opsl3CX1fz8RJsAW5OCqCCZfa6z/c66ffQCNVjutNLHWqQstgCMs5iV4p6DofTHGFWKeNjeWNK6wPm2SxTxCIZB0sy6niILkX07aaG+sZQX2Uf8DRXX1ueQAoZe3VSfJVGD5U6VCrR8BViXVDv4BcBtRdweG75LaZJYyB17miziEQxBXjAH+Kmk95MiT6vBWSUWM+8kpQBoMs/R2qRMsx3+mdvGC93lTktyDLAdKYvtlkrJGYvcAY3jaPdQ+osBTRbxCIZmJvNzokNKwdvBwAsL9PEkMEtSd8KvUuX6INl6r5P0o7y/J8ldd7GmR3Ek/7L9V0lL5cDHyyV9raD88RjtHkp/MWCwIh4nj/GYlnhKekMshGnMt7U3gu3P5fxQHe+XA23f0GSfhehFHMkjSlW/riKVfXyQinIuQJN3KY0RNv3FADVUxCMYHkmHAmd1RUTva/v4MR3YKMj25bUZ6CG0WJscJN1ciSNZmrT4XMSluGNyyXfNfweWIvn/r0r6rmvVXB7P0e4QSn/MkXSQK7nP8x/407aPGsNhtQYNUhSkVBoCpfJ//0uaxVa9R0qYjjp9fJCUxfMBkofQYu+LDs3GkVRlSbrAdqlC8R355zLwLuVuF8wH1TRh3hl7dpL0VuAgYA1SfvTFNhnaEsgESeqkfcgX3VIF6k8jKeRjgdeRcrMsVUh2h8NInl+1Zq9jwOaSHsvPBayQ90v4uldTXRS7wFYYz9HuofTHGtvvkLQ36Tbxb6TkU9eM8bDaxMWktLgn5v1DclsJVrB9ab6o3A0cmROM/Xch+QD3UD+Vcs+xPaFJ8UM8L8WzCe2c6hc00EVzhNIfY7IJ4DDgAuAlwLuyeeHJsR1Za/gESdH/R96fTrmF9KdyKoY7JH0AuI+UMrokd5Kqcf2MgR5Ci31EboNsXrlrWKHrjqJExGyTdymNEzb9MSbnBzm0MyMklah7T4kAkmBskbQ1KbHbaiSf8VWAL9n+bcE+uqtyARBrQsFQhNIfYyStYvuxrraNbf/fUK8JytHUYmteG/iC7Y/VG2EQlKX0olIwQiR9HMD2Y5L26jr87t6PqLWcBpxAqsH7OlKw0/cW+ophkLS07XnM951vDEkTJX1J0kWSLutsTfcbjF9C6Y8d+1SeH951bNdeDqTlrJCzd8r23baPBN5QU2bHm+MGSdMkvUvSWzpbTdndnEXKwb4hcBSp7GBT+eODJYBYyB07NMTzwfaD5mhysXV54K+k1LidlA+1CpwMwnNtnyLpMKe6x1dKCqUfDEko/bFjYW5lsdDSOw4DVgQ+RFps3RE4oKbMtXK++1sYmN8Hyn+3HffB+yW9AfgTKd4jCAYllP7YsTC3suWHfllQkkopvSdIwVMlmEC6Wxjsjq200v+spFWBjwLfIHkIFS2vFyxZhPdO0EokLTQRWp1MiSVTCgRBaWKmH7SV7UnRrGcD11J2HaXxNRlJ32Ahdw2F0zcHSxCh9IO28jzg9aQapO8AfgacbXt2Adm1S9qNgP7K86NIOX6CYFjCvBO0HknLkZT/l4CjbH9zjIc0KkplBQ3aQcz0g9aSlf0bSAp/A+A4UtnE8UbM3IIRE0o/aCWSzgReBlxEmt3fMsZDCoKeEOadoJVIeob5pfOqf4LxkSlRepz5416RVI8Xxsn4g7EjlH4QBEGLiNw7QRAELSKUfhAEQYsIpR8EQdAiQukHQRC0iFD6QRAELeL/A1XQQVcbCSjVAAAAAElFTkSuQmCC",
"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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fWX8z8M1R1jFvPNsHUcdMOcd0aafvhe/FTD7HRLWzukwVldGtwLqV9XVKmTHGmAliqgiEi4ANJK0vaVlgV+DkSW6TMcYsUUyJOYSIeETSu4FTgVnAoRFx5SirOXic2wdRx0w5xyDqmCnnGEQdM+Ucg6jD55jYOtqcYxEqeiZjjDFLOFNFZWSMMWaSsUAwxhgDTJE5BGOMmQkUo5hnlNVrI+Kfk9me0eI5hBmApI3IkB/Ld8oi4ojJa1EzJUTJeyLiK122bwUcQDrOLA0IiIh4amWf50bE5X3OsxLw94h4rKwvBSwfEX8byIUMiHI/1qTSMYuIP05eiwZPucYjIuJNi6HuTXttj4iLB33OXkh6OXA4cCP57K4L7BkRZ7U8fgPgs4z8Lz+1tt+OwC86z/cgmdYCobxAFkTEXyXtDmwKfC0ibqrssyaweVm9MCLubFuHpH2BoyLi3h5teDz5EntJKToT+GRE3F/ZZzbwIUb+0NvU6npibfsf+x0vaS7w8rLtFDIe1DkR8bpy7OVA1x85Ip7X4hzv63Z82efLvbbXrvHCiNiiy7ZrgPcC84FHK/XfXdnnbGA54AfA0dX7XNnnfOCVEfGXsr4y8OuIeJGkOcDHGCl0ntdQz4jfQ9I2EfFbSa9tuoaIOKFfHaV8X2AucAfw2NDmeF7b+y1pjy7bF3UGyu/6NmA9hguet1T2WQ3YoNbOsyT9jN7Pzk7l+BWB9wNPjoi3lRfbMyPi52X7OcA2kWFpRiDpGcBBwJoRsZGk5wE7RcSnK/uM6PQAe5XP5YE5wKXk7/k80iHrhZXj55D/0X8B/g5cAZxW/W93+U3vB7YC/tLjPnR+j/nAbhFxbeW6jomIzSrnWNRZKdufBfwyIv5Z7tNc4CvAjuX6loqI/67dr6OAFwLHkxaZ19S2930vdmO6q4wOAp4v6fnkA/l94AjgZQCS/gP4AvA78kH5hqQPRMRPWtaxJnCRpIuBQ4FTY6QEPZR8uP6jrL8ZOAyoPlxHAz8C/g14B7AncFdno6SdgC+RD+ud5MvqauA5LY5/HfB84JKI2KsIwKMq596hfL6rfB5ZPus9tl7neFz5fCYpXDs+IjsCF1au47XA54Ankve787JdpXKecyV9s5zrr53C0pu7PyJ+SQ8i4iXlhfMWYL6kC4HDIuK0ym7Ld4RBOeYv5aXVuc4PAJcz9CIeRp/f42XAb8u1j2gecEKLOgD2I1+adzfU87iGsiY2r3xfHngFcDH5/Hb4KXA28BsqQraDpLeWtqwDLAC2BH4PbAN8sez2WuBJDD1XbyQFWYfDSCHeeQHfChwH/LysX0/+7icz/DfvdCS+R/4m3y3ll0n6IfDp0sZunZ6ty/YTgE07I8ciPA4o3/cC9gVuKG28ttyrFwMfknQF8IkiqPcu13BGadfLyzGbke+Qh+jx/APLdIRBuY7/k7QMwzkLeEkRwr8mfbDeQP4fV4iI0yWpvLwPKEJmmECIiN0lrUL+Dj+QFOU3OCYiHqTPe7Eno3FrnmoLcHH5/G9g72pZ+X4p8MTK+mzg0lHWIeDVZMC9hcBngKdVti9oaNeC2vr88nlZpeyiWjufQL7UAbYGDmlzPDnqgXxwVyntvaahTZd0u39t2ljWzwIeV1l/HHBWZX0h8Ow+v9kZDctvy7b/JQX4C8lezabkH72pnlnAv5Mvn6uBa4DXlm3nVo8j/9C/L9/PafFc9fw9Stn6Dcet37aOct1LD/j/sCrwq17PYsMxl5MvyAVl/VnACbV9RoQ/qJZ1vlefMSr/M7LXO2JpeJYvaWp3aeNSnTrJjtpple1XNrTvyvL5LvJF2+36NwZeUb6fSo5SqJznVGB1stPX7/k/lHz5vrws3yN78E3vm32BD1avFTivXOcJwLuB15DzEN3a/gRgf1JF9UvgulJvz3dar2W6jxAelPQRslf+kqIrrkrkpWK4iuhuRlpW9awjIkLS7cDtwCPAasBPJJ0WER8E/i7pxRFxDiwarv29do7OxNJtkv4N+BP5kC3aHhF3S1pK0lIRcYakr7Y8fp6kVcmHbz45tP19w72SpK0i4tyy8qLavejXRsg/SHXY/3Ap63BHRFzdcO5FROnVdaET0HBO9RCyt9q5iOeRQ+l/A04DdoyIiyX9C3ndJ5B/kuMk/YkUkE8ie2EAcyV9Hzid7PF12lVV9fT7PSCH63Ud9k9I4dOmjuuB30n6Ra0di9RvkpYne63PYbg65y0081dg/VrZzyVtHxGndDnmHxHxD0lIWi4irpH0zNo+K0l6akRcX9q1PrBSZfvDklagqJckPa12TQeW8pXLel398udyTOf41wG3VbZ3VCyPlJ7xnQwPdXNZ+U07I5g3AZeVc32ry3V32ragsrpuRFRHPneWsnsk/ZP+z///IwXQe8r62cC3a6eUpBeWNu5dymaVz/2AFcvxnyKf+z3rbZa0M/CfwNPJnv8WEXFnGQVfBdxU3mm7Ay9teC92ZboLhDcAuwFviYjbJT2Z7GF2+JWkU4FjKvvXVRJd65C0H7AHGS3w+8AHInV9S5HS+IPkQ3C4ci4B4F7yx6ry6bL9/cA3yJ78eyvb7yt/lrOAoyXdSWVo3ev4iHhn2ec7kn4FrBIRlzXcq72BQ0s9Ku2svlj6tRHy4btQ0ollfZdy7R312DxJPwJOosvLtpxjLvDSUrRozqWPsOjwDeAQ4KMRsUjwRsSfJH28fL9I0rNIFRcMt/bYi+wFL0NFd09R9RS6/h6l3ucAj6/pnFdhuH6732/6x7IsW5YmjiRHPq8GPkm+RBYJ3JqOfylSpfLjWh37AR+V9BAp9OtqvFtKh+Ik4DRJ9wJ1XfN7SeF1fTn+KcDbK9vnAr8C1pV0NKlz/89KOzcq17J6Wf8zsEcMRSN4F+lR+yxJt5Lqnd0r9ffr9OxF/g/3K+tnkWqTRUg6HNgvIu4r66sBX6oJ199J+jmp7oIcgf5Oqfe/jxT4I57/zsER8RDw5bJ0Y3/gI8CJEXGlpKdSVFQRcVHZ5y8MzY808RrgK1GbrI6Iv0naG7iSfKft3eW92JVpPakMIOkpwAYR8ZsiIWdF6tE6219L6gsBzo6IE9vWIelAcsg3YjJG0rOrveHScyEiHhjDNawE/IP8s70JeDw5YdqkX64f+xpS5XJ/WV8VeHlEnNRl/8eXdo6YjG3Z1s0Yup9nRcQlkg7rcUjE8AnM48nhd+eP9Gbg+RHx2rL93xjZI/5ky7b1nfCVdG1E1HvA9Xq6/h6ld7YLsBPD4209CBwbEef1q6PNtZQ6LomITSRdFjnZvAz5DG9Ztld1wo8AN0XELW3rbzjfy0o7fxW1CWBJy5GCFFIl+VBt+xPI+QcB50fEnyvbzgM+FhFnlPWXA5+JiBfV6liJHNU/SBckrUf3Tk+va7skIjbpVSZJpBDYqhSdCxwflZdk0/Nf2dZkwHE/MA/4dAw3jlg0WpL01YjYX10m8aNM3pfjZgG/adl5GjXTWiBIehuZH2H1iHiacrLxOxHxirJ9feC2iPhHWV+B1BHeOIo6epoGSvoM8Plaz+P9EfFxSR+MiM9L+gbNP/R76mVdrvOpwNdI3fpjZO/ovRFxvaQFEbFxbf+mh3858mFfr3Ytnyzb+1qj9LsfqqikKvsPK+vS3gURsbGk75BD5q3JEdnryDmSvbv82aBiJSTpwIiY20VARUS8pWz7QkRc1bBPayS9MCKaVHNtj59NjjDrwq+qHrswIraQdBbwTlJteWFEPHU0LwZ1sSIq257cdEzUzF+VKsb1GP67H1G29eyUSLo0Ip5fq29RWbf/EGnhd426mJdGMSvVSHPlzvaqufKlpU33lvXVgTMj4rlNdXejz/P/eXLi/odl067k83w78OKI2FHSc8mR9urks3sX8L8RcVxNwFev88xaG04n58saO3WStiRH0s8mR5+zgL9ExOOb9q8y3VVG7yKzrV0AEBHXKc38OhwHVHshj5ayqnVG1zqUAfcOoGYaSJq1ddguIj7aWYmIeyVtD3ycoeH9vKbGS3qQ3mZ9nWH9D8kUo68p67uSarAX0Oxt3vS7/pTsrcynos6pbe9qjVLaWzWVfJTyMmbofnyDkXr1elmvOZcXlRf7ZRFxoKQvMaTi24E+RMTc8tlruL0lsEDSDeR9qAqUcyLixZXfRdXPiFilI+SB3SS9saH+vegtuDq/aceqawcaLM8KB5eX4yfI0cjKFIuTiHhU0mOSHt9rtKfeVkQAv6hc4/LkHMS1DFlDIelI4Gnl+M6zEQxZM82tjrwj4j6lZdBJpeh6SZ9gyMJtd3IOpUO3/9CaZCflSw2XVp1bOoQGc+UaXwJ+L6mjDno9aSCyCPWxkmvx/L8yIqrP+uWSLo6ITZXmn5CWVO+rj5bI99ITSP+Cpv9nlb+Uuk9juNVWp4P5TfIdcRw5H7cHQ85yPZnuAuGhiHhYJYOZpKUZ/mdcujr0LfvW9bW96tif7qaBHWYpJ+MeKsevQNrJExE/K5+Hdzn28HLMp8hJtCMZUjGsVdlvxYg4srJ+lKQPlO/zJH2ZFBiQAm5+w7nWiYhte1zHihHxoR7boYuppHKS7EXAbA23oV+FoQmzDtU5FwH3MKRv7giGvyknie+m3IdoYUOtdvb7Xe9BRLy4fPYy++wp5NuO+oAnRMQhkvYrPcAzJV1U3SEivl++ngk8dUQN/V8MkL/Z5qQaZ2vlHMhnKvsO6yGX3vg7Gc4cYMOq6qRGv07JW4ADGZqnOZvh81eN/6GIeFtpY79RUBtz5SMkzWNIiLy2YZT4edJIoZthROPzX7uOLSLiwnIdmzP0/D9SPlfqCIPSrs4cBaQZ61fKiPBHpOquc1yVExg+5zWCiFgoaVZEPAocJukScu6iJ9NdIJwp6aPACpL+lXyQf1bZfpeknSLiZFg0O19PJ9erjpvJXnUvjgZOr6gp9mLoRd/KsYd0wqkOqQ+SdKmGrFJ+KenDpOlrkBPhHauRfcke5I/K+mkM+RxUOU+9vXz7WaNA9/uxLNl7XZrhNvQPkGqfRURadTxfzXMuPy/qhi+Q9vRBTiQuostw+K+lF9fXfr8jWFRzGKtTXowvLm04J4quuIWQr9fT6JhGC6uuLgLuftJEeAEtXgy0syJaRKTFVj197RWkpdZtDYdAn05JUdP0EpSN/yF1mQuq1Nu59jMkfYG8F1VjhkWeypKOjIg3k1Y49bIO/azk+r0P3koabqxMdnYeAN5aXvifLft0HS1F+hEtQ/pZvBH4ltKa8a216+737P2tdHwXFDXWbbSMWzfd5xCWIq1nXkX+AKdGxPcq259GPmz/UrbfTFo3LOxVB5nOMyQdQlqqdDUNLHVsRzoFQdpHn1rKOzrBRseeiHhv2e888s/UeeG/kfxTrcXQcL5ORM2lvReSriLN1EaoSsr2B0lTwm7WKPS7H5Ke0q0n37L3Xt1/OdLB7P5a+TwahsMR0bf3U45vdBiLiKqK5L9JlULnhbMLcFwM95zt6X3e7zySdiB7yusyZNV1YKfzUvb5YbnGTgdlB9Kccr3Sns+X3vSTo+IQVbveE8kX7P5k7/he0oFq+7K9+rssRar3nhARr67UcQZpr38hw3/3jqfySmSn5JVl02mkU1nneW4khk+Wbls9PiJOrQiIJ5Ij0N+W9a2B8yJih0r7GqofNh9zcVWdo5wLuDwiNqyUfY38n55Eg5XcKN4HXQ03igrwQCqGLsABMdxjehlyJLsX8NKIWKNWR88QF0ojmTvIDtN7SUOBb1ffe92Y7gJhv4j4WouybvbP/eqf21Qexa56FPXMi4g53cqUlhNfI60bgrRu2D8qk98Ndba2TCj7P6XLtfRVxVTq6Hk/yh+zqS3bVI5t9HaO9L5cnhyhLeqZAwdFMQoo55gXEXNULG9K2SUx3FqkMwm/ZamnOgl/Kfli/E2kBc/WwO4RsXfl+GtJy6eqMcKCqFgnSfo1OSr7LypzAB21W7/zSFq+el1NFNXB9jE8BMcvyJfFfFIgfRFYNiLWl7QxacK7U5f6RlgR1X7TR0gnp+Nr97zVZGeX80GfDlHZt6u1YLnXe0bEbWV9LeAHVaHVow0fAT4KrAD8jaHO1cPAwdWOhHoYI5Ttfd8HGp+V3Hbk6P/lpGf0j8mQK4/U9usa4kLjjB013QXCMKlfyi4h7YuP6tYrjeHOP30DqnU5d30CctEmRvasrwb+LYY79pwSEc9ueZ1NMWvWi4hPjvbP2qAqWTFaWHK0bOdmldXlSaumRyId+Dr7nEXei86f/XHkRNpLJf2YNN/svDh2A1aNiNfXjn8laYV0Ozkc/s+oqNyUsYy+xZD/ya7AvhHxgopAuRTYJNLh6dLa8WcAr4khq5dVSe/dao9zfkRsVhNMF0XE5uV7z/NIWkj24s4uyzkNo6FrgOdG8aEoo6ZLI+JZ5Tl/jBQ6v+sIRElXRMRG5fss0mP3WSxGlDF5/ouRVkid0VK/DlE/S7+rq/+VMqq/slbW80Us6bNtR5FjRT2s5Cr7dLUuk3QM2cn4ZfSYWK48e5dHmQPqlJXvPWNH9WJaziEorTt2A9ZXxkfp8DhyknKlyno/uloo9Prxot0EZIeejj3q75HaLWZN315a5RzdYuucS/4Z+1ly9Lwf5bM+mX2uMtZQlV7enhtVh/Ckbrg+8fdmUrXxbvK+rsvwuFHQexL+PnV3OuuYB98PXKmcrA3gXxkeswb6zwF0PQ9ARDxdafL5EtLr+luS7ovhJrlHAxdI+mlZ3xH4oVJFcxUZQuV+aZhGcVF8pkhLpGslPTm6RFHtMsLs2M5/N3L+ode8DaT67jvkS7DJyqefp3M/a8HTNdLB9DeVa2h8Edfa8DGlpc/6EfEpSesCa0WZAC71rFOus+OHcDbpzHZL2d7PVLiXlVyHrtZlEdFktdbEQ0UoXqe0hLyVnMPr0C92VHdigLFUJmohX2YvJ1UBL6ssm9InPgw5vK6uX9Bj31+TL+qrS/2HAp/rU/+qpBNOvXw5Mgjd80kLiuq240hX9T+UB+TXZHTCXuf4Vfm+AelBeVV5EK4Hrm845lL6xOdpcd973g/yhdhZ1iA9bK+t1fGx0pYDyrKA9DqGHBlsWdn3BeTwt3r8fg3t2q92/s8BHyZ7rE8h/8SfLfusRL7Qli73+j2kzpyy3nWpnXMHUv2yEelpOp80DqDfecr2dUjVyXfI5/gXwEcarm0Oad2yHzCntu0QsmN0WXkOvkH2rKv7nEWOuk4n1XQnAydXtn+NNGvesSxHkeEWvgUcWfaZR84/XVKuaa/O/Szb5/d5brYlvbJ/R1pM3Qi8uv4fZOjZXJpKTK1S9hpSRfIVcvRW3XZZ7XNl0oGvus9B5ZquLuurMTJW12nl2pYuy38yPGZSv+e/cx3nkx2v5YCFtXP0iku2JRns7i9kR+lR4IGG+7l5ucZ1yKB2JzD8fzO3aWn1Hx/NC2G6LeUBXK92I+vB7boGVOvz461Lutv/nLQuWInsZd9Fw8ucnBTbjZwE3YOc3O5su6R6HjKswvk9rmsZ4P/K93PIEcNl5MvvAFKPXD+mE4DsUlLfSMO96NrGfvejfL+BFEg3kKE9fk065NTbshlDL7lNKuVXkz3cG8vyWCm7vHJvRgTpqty/6vnrywghOUHP4CpUBGWl/DGyR7xzj2Of3LRUtq8I/A/5ErmInMhdvlbHy5qWpt+v4RnvBIjrPDvV3/2SyvcDyLmftZqutezTq0P0eVLPfw05GjsR+J/aPmuSAmsHKgEry7Y2L+KLG9pdf/4XNNyLBZXv/Z7/T5CdtX9nSJ35yVp955fPU8mR4SbAHzr3mR6CdyKWaaky6tBiKPtZMp7R14G1SXOuutNSr4BqvdQCR5C9nePJHtA8srf73Ii4vdbOfo49nfPcp4z7cjtpWdE5vlfMmlYhc+mjwmjRxmo7G9UkEbE+LYiI+ZJupgy7KyqNXn4SO5X70E1N2Or8ahGiW2kB9ClGziutoi5e55Vre0+p4+2kNck/yJd/x4mpMze1CTl5vpvSpPg60nP2kEp1HacxyEnR9YFry1zNO8iXx+XAC6PZXp3or1JcuapSKmqsjvqho9rrZ8a4Z/n8QKWseq2QI5hnkr/58yURQ3kbPkz2vC8nVamnkKofSpv6hbFvMldedHzhn2VOJUqdsxkZ/vzuolbqqKbeSPrCLKqjfHZ7/j9Vvh6vjIk0wkqO5phh+1fq6Os/0E/NR6qpRjyjUcu/0shESp9BL7SQqKRq6Z/kQ/ykUdbfVS3AyN7FLZSed0M9V1Mm8Ltsfys5hH0Z2cO9k6E//FYM791tRb5InlaObRUyl/4qjJ5t7Hc/yvZlSr0/Kcu7SRPHah07kS+/v5I990ephS8mX9bDesSMQk1IZYRDbbRDuxDdC0nv0xH3g+FqpBvpolYq17hGn/OsTArB/yEDyt3UZ/9NyRfdj0jVzttJE8mvNuz7IGkHX18epKKGALYn1TlnkC/cm8ie60qkpVvn3q9AvrzmksHbnj6K/9HcUv8dpIrjduAntX1WIJ2+mo6/lD5h7CvblgMe31D+JlJddku539cCr6/t85Syz13kf/Akho/I+j3/ywPvI/+Lx5NzXMs3tbN23s59Povs2B5Bjpre23Sd9FHzkSPwzrJV+b0+3+q3avujTsWF/kPZT1B6UOXPcw1p4QJpAkj5AUcsLc59KfkS7wyRh63X9j2OnMAa7fX9nBxx1MufC/ysfO+pTxzFucbUxlod3yed8rYpy2GkT0f9vjXOZTBSWDxGQ6z7Fu34RmX5Hilkf1K2ndvi+DPoItxr+13SY9uvyMntrs9ueTa/SzonPaXltV1O2s931pemZaz7HnVW1Tl9X2Bd6tiITBLVpBK9nN75DHYiX9A3lPWNGT7PcXntXEuVOl/ba2lo47PICex306dTMMZ78GNyXmfrsnyP9Bfpd9wfy2crwUsLNV/D9gvbXMO0VhnRfyj7BDJW+N/JOCa/Il9av6CFJVKxhtiXkeZ0O5E9hfkwzGmsY6JZHy6vAVxVLG6aHHsaA8+RgfhGeBZHxOVK3wWiT8hcdY+X1FFhnFk+H9etjW3VJMDmMdzj+rfF7LJKrzwBnyIn1obZ7re5jqiofCJi39o9WJV0koIWIbrJSehTJJ1JDwekLu3p8BHSO/yCWh2de7VdRNRjFw2/sGansT+RPeROfY/UrIzGQqM6R+3Tr86lIaMZQ+rGfvkM5pJWRr8r9S4o/70OTWHsT6E5a92i5lHz4o5MNXmNpH1ieKTins83Q/+V5hMN/aZtrOS61U8M+QT9nVQ3dqOnmk8ZuK/DUuRI4fEt2jHtBUJPE8RIx60VJD0zIq4tN/xfy7bvls9eN/4kUuL/jJq+MSLWG0U7D+iz/ac0B55btccxKwBoZI7gTvueVz57msUqbcDXJE3sqryEoVAFjXF7GnhU0tMi4g+l7qcy0gyx11xGV2HR7zr6UE0cswrpoPSqyvb6y+N/SAG7PN1zFfTju6Rn7eVUnh1Ju0fEUcCbm17kNaFTveZHyI7M8cBfJT3QqZIMu/IADcKxH31e5n0DChb6pXHtl8/gnzHSfHbRCzgiPqDhYewPjoYw9qPgHaRByKL2Vb4fSAqosXCxpC0j4nwAZQiQNv+dNSVd1m1jjMz3/X7gHEl/IH/z9YF3FnPkw8l7HGXbI+Roe2/aMOhh00Qu9DBBLN93pMdQtJQ9lXzhd/SGPwWeWrZ1NUnt0p4DxngdV3QpPwZ4W0P5W4Efle/XkkPu9Umh8BQa1A8MNwntLMvQQi3VsG1lYOWG8lcwZF74O1LHvnVtn5VIId5k9vmbUvc3y7V/jQxRMNr7+TOGTCx/QaqM/rds26ph/61q642/R9lW1c0/Qnfd/CVdjn97+ZzbtNT2fX3D8SPKxrPQR51T2e8pZDRPyM5INZVkqzSuZZ/1gOfVytqYzz4J2Jn8Tz+plI1J7dvtt2mxrfH3KPfwMpqt5K5qeG7qczqPUPnv1pcubRm3mq9pmZGeyjHktTmfHp6cZb2XV+tu5AP6a7oEzerVnrZqDkkHA9+Imnqo9LROJK09Ok5fc8he62sisyGdE8VJrheSbiRHUPeW869KTu6tSsZLGREhVRVPyLJezXwlUojuQZo/3lzasxw5X7MLOTn74Yi4p1LH+0hhdmvD+VZkKKnM7uTL5ejq8W1Qj8QxXZ6Z+u/2eVJt9evRnLdW52fIl8LPGP7stL6WNm0dLxrKuTCf1Hs/SNrqP6uyTz9P4m+TZqO7kr3Xv5DmmnuV7S9tOncM5WRYkRzldkZtp5IJZTqhQ95KWs39lnw2XkZmkFsmIr6rUYaYkbROdEkk1Ov+dvs9GApL30i0DA9Tevgd9dozyDmPX8ZQtr/qviPyU5D3vVc7Tui1HZieAkFDnsovZriqYxXg0cqDen5EbFkTEotCDTStl7JLI+L5kj5LqqX+QCUfQnQx31JDYpqW19Mv8NzW5KQd5KTRbyvHvoI0jzud7jpxJH2PnFjtBN57FTlvsT3wp4ioR7hE0sKIeHplvTHzFalaeWVk7tmXkvr6fckR2bMj4nWVOuaSk4/3kNYyx5GCo/4gdvQH/yDv/8ci4vTGG9iApCeReukgbfTXJ/0s9iedmzqsQgrX51eO7Rvor8X5b2gojhgKQNbVO10Z02Z78j79qHL8KmQY6i3atqNFO3u+zMs+CyiexJX/0eUR8VylnmediLi5lK9HLaOZ0kyyw/KlrvmR4Rpm0SfRjzK21IuihJ1WZmc7L/pkviv7vq/X9hgZmK7ppd/695D0fFLdCukcV59D69XW+eXY1cgIAhcBD0ctLpG6m4h3VIw9gwH2YrrOIZxH6rfXYHjIhQcpybULV5Ze/qzSq3lPObZKY2jpMjHzBlJ91DYmyFh7btv12lhewGd02bwX/XMEQ1oeva1S568lfZHUF29V27fTK6uPGrrGcq/0fN9A6niPJ+2xF9Su5UDgQEnPK/ueSb5oXkkD5YWxEenyv1HTPl3aXu1RfoP8ff9OuxDd45mv6NTRzyfiSLrnS/4TqXveieG/wYOMzHM9ZsrL/LORMZt65eTumjMkIkLSKaSKkWgIyBgRwyZ/lWEjvlq2tUn0czd57R0epOIf0Eu40iJ8TW0kv2JtfibIl3Tf30OZg/1tDP33jpJ0cER8o18bOlXEUF7kb0dGs13QsF/P/BTKYIAbRi0YYJsGTEuBUIZgN5HmpJ0ew0vJNHFVB519yaHoQ6RK6FTSkqXKf5TPt9fKdyUl7ark3EIjSieTAyi9AqVlyid7PNzdrgf1idHfhc3b9JRIZ5oPMWRt8wbSLvx9ZEyU39GglqrV0S2W+waSli73/hWkeqFDt2fsTlJldTcVJ7w6kQ46lyotQdryAdIDekSPUtIPIuImSStGxN+aDu6n4mhDUYO8j7Rj36d0SJ7JUNKTp0fE6yXtHBGHK0Ndn13Oc6mkK8jwDoeP4rpHRZuXeeFM9c47crGkzWPI4q0ft5DOpB0aE/2QKjfIEWQnplOQcwlVodVLuJ7Sr10tOwCXlt9IDGUfu7amztkbeEFEdGJjfY6cPG8tEJTJpt7E0CTwrIb9+uWnWLcjDAp3kD49fZmWAkHpBfjhiLiiSL+LSQn+tCKRvwpQ/vAfK0sjvXpy5SV5jTKT1Qhz0cKh5A/UESxvJu3vX0tL1D3w3HN6HVc4T9KG0T9H8G7kxOVJZf3cUnY/qdpZl6Ee+C+qaqkKb2Eo81UwlPnqneRL489kL/zscl1Pp5ZQRNI7yXs1m1QXva1F24liFdaSXj3Kf5H0S3Ly+slliP/2iKhmCat63C5ScVAJ9NeCw8oxnRSut5LX+0lyJNnTO730nNeVtOwoRqhjoc3L/EOkIUOjJzHp7b+7cp7qr4xUeVbNOpcin7fqPNwJDPWqO/uJ1JFDqgz/UNn/p7X2dRWuZBrSlcmO0DFtnrUevIi0vrqxtG9dSXtWOgpiuFXdozDMLL0f+5PmyidGxJVKK70mzUBPM3b6BAPsxXSdQ7gyhhKNfBR4VkTsoQylfC5DPYtGYnhijnoM/rPJCbN/qEVoafVIGj+K67mUPjH6exx7NalPbJx/GATlHlVDJRxa6xmhDCOyFhm/vdNDegZpjXRxZb/PkpPKCwbVvob2HkH2eus9ysvI+EkvJq3NGg0NGupbl/QG/vdRtKET/vqSynkuJee4Ni1qreNLO39ACqhPVAVfuY5nk9ZSo4ta2b6d15C/6000v8xn0SWEtootvPrk2pC0Z6X4EeDGiDhXmcFwnYj4VtnvQrKjEMCHIuK4ltfQmRg/i/wv305aPnXma55JjvjfQAriY4Bje4yIup1nPrBblGRE5fk+hnSc+0/lfMWepCEIpGHFDzod1FGcp2f+lpbvpdcyNJdxVrQ0052WIwSGeleQKorvAUTEg5IeI1VJN5M/1gX0ltJHkD3IzrBuN+BISbuS4X/7xZLvlTS+9fVEd2etfvSK/7MI9YlZ34fDyXt+Njnf8Wwq8VdKPefXD4qI/6ucv+Ms84XaemffUVkS9aFbj/JxZOKVmzXc5r0pZHOVuoqjDQ8rE+sEgDJ730PkCKUz0dmZuO2knlxpeBWLrmMp2oVyHws9k8xE7xDaJ5GBIG+SdHwPgfkTMpXno5BCpqjUPki+qDssSzpRrUyOsI4r+/f0tSFHAauRkQlOLsf/d2W/a8mR7YFlRLgr2Yu+PSJGzJ/1YJmoZKaLiP9TZjfr+Px8uWgVOlZ/e0VJvdoGSc8l30er56ruIj2+r6zuFy1C3kcalfS1KqozXQXCzZL2Jf+om5JhAih/wGVI/dq/ktY3u5G26MfUb2yh0buwzx+hyjuAI1TS5pFmnXv22L+J+9Qj8FwvRjH/0C9mfS82jKFEHIcwMtZ8GzrOMjBSQAfNSeTHRAxlcBsxTyDpuUqTvSh/5v0Y0jd39umn4mjDAeRzua6ko8mJ+73IGDQr09xJGTZcr1zHmDL+taHl87MaaaBxIcOfy+o19Pr9TieTGnXavwJpyr1MFOukwjmlY3CPhhLPQxoUfICak1/lGjrqqzN7tUOZQ+CJpK/FSvSYG+zCPEnfZ8jp7k2kqvpFkjZh6H6cM3RKbRrtk0x9l/SfqFrxfY8htWOn0uok+LLkO29RUE+1CODYjekqEPYmdbGvBN4QJbMVGfbgsNIT+RXp8r4cKRh+J+nAiPhmra5e3oWNf4TaHMIDkSaqq5RtD2i4231Xio59TVKl8XfSYuFNZE9o3x6HVutoO//wSEQc1KbOBhaNyGKMoRJ6zdUMGuXE3CE0zxO8g3R4W5vU6/+ajG9Tpepd+gjZmTh3NG2ItOKaTz6TIh0m/yzptmifUrHq94FyjmZEj3E8tHx+PtHl8Kr5bi/d8/JVYRYRfykjhGpSFyLi3ZXV2ZXvd0Ul13TDNTSGfuncZ0kvId8Bu5BC5VgypWprw4/C/yOflU6oirPJoHJ/Ju9hNyHfdu6pqxXfsAork+DKP+PO5HPW4fPAjlEJz9GaGKDX42Qu1CKZkp58ryV7xheRD/XaDcf1isH/B3rEki/HN8Xn75kwpLLfqL2EG/a9lBaJb2gRs77HOR5lpGflCO/cUfxWq5ETtS/tLAN+Fi4gJ8kvqZR19T5eTM/j6U1l9PCEbdj/PBjy9CZDTIzac3sQz0+f56L6TDR5bZ9LyTFS1jcjrW+OptkT/+2kEO6sv4Ic2b6RhuB1ZOfvR6QK6v2dpWy7meyxv5taHoUx3KuVSJVjZ30W6ZTZ+jftU/+J5HtqvbJ8nJxgbnPsJZXvfQM4dlum6wihiVMofgBlMm6jUnZgRFzR47g2Ovhhyb/LOZ5F9qIeX4ZoHVahvelo3+B1LWg7/9BRY/WKWd9IRDSZvo2JMpm6HxmddQHZs/k9o7Pg6UuMnCfo9LIbA5lFxHvUPZhb64n6MgG/IrBG0Wt3GrEKOSp5UbdjG2jVYxwnXZ8f9fe0b/tc7A8cJ+lP5dgnkXr8PwInKX2FOmqVzcjO3C6V4/v52qwTEd3+xy+Omqdw+V3ui/L2HAXdVF+DomrFB0NWfMOovW+WIs3E/1EpaxPAsZGZJBCq//7dSRXPfsB7Ki+GRbo0SatERKc3M4JIr9tFLvukJc/apB7+FaRN+Q6kn0LV8eZB0jmlDav22LZCyzo68w9n02P+ISZQZdOH/ciQ3edHxNZFsH5mwOe4uWGeYBVJL6Z3sLG2wdx68XbyBfgvDI+G+wDwzRjd5Hk3v49B0nX+KgbgoFfquaj8zh1/mar9/oskbcOQiqrJ5Lmfr815kp7b1LkC9pT044i4pqiWfkXG/3lE0m4R0cocs9BN9fXBbgdIekIUf5h+RMS9DKmjelF93zxCajZ2rpS1CeDYtREzYgHeOcr9f14+b2Bk2sXry7YF5KRNdThWjUU/i5IPeIxt7hu8rkUdK5K9hFnkKGBfhqdq/GDlez0hyGcm4XfqxG1fQEmlyBhyHvQ5xxqkOuIOUi9+FBme4fflz/N5Kqk7u9SxJikgRqRsbNmGfQdwHasBXyd7z/NJ797VBnyvugYbHEDdA3n2SIujDRvKryBNia8i57muLeuXM5Ry9UqGzOv3Ie36Z5FWY61yBFTOV1d9zQF+37DfH8rvtgUluF3L+qtBGTvLkWSHZmAB7Hot09UPYfVe22NAJoySLogMcndJpH9AJxlJNRbShTHG2DJqEbyux7FNw/kR8X+AL0SJzaKRQdwGGiitDZJOJFUA+5NqontJa5PtF+M5VyM7DP+jtJnftSwrkFY/x0TEdZX96ykbXwJUUza2Pe9GZFjpajiFI1oc19fvY1AUA4jbYiiQ3AqkKvPGAdR98SCePXXxtSHniTbudlykOewlMeQHcjzpJ/Pd0bah7L85OSH9p1K0FmnU0hQY8r3AF0nT076/eTnma+RketWh7AHyWlcB7qPH5H0MpW9dhzSj75jUnk0aNTQG9KtXMu0WhvfqHyVn+e8u329oWcemvZayT5vk318hwzW/pH78KK5na7Jnvy+wzQDuzyxyWHwFw0c3l9T2u2S85xpnO19GxodZdkD1rUvGuf85aYm2EvmnvBP4WsP+m5DpVx+tlV9Ky5SNPdoylz5pI3sc2zdF5gB/g3nV+092RkZk5Bpj3QN59ugeGrpvpjjgfHI+cTYZUHH9yrbGEN0NdWzOUMjtZcgJ6t+W//3q5DzCUyr7b0mOVt4K/HgU19kzExrt07eeRna6li7Lf9IQ0rxpmZZzCFH04coInidGxCllfTuGT0b14ks9tgXZe60m/96H1G/Wk3dvXD6rpoSd41sRvYPXjZoYHv+nGqOp3ruYsOFh0bX+M0pPV+k9uikZmnpQoRmOIG3RjyeNBeaRqqnnRRltlVHeduQI4RXkKOCAWj1LRUTVRv1uhmfia0O/pDG9GITfR1uWrt7/yCB2Y00KVCe6fG9a715Jd1+JJ6pHNNNIj+79Sce42cBXIuKGUtf2ZGegDd8lJ5MhnV4/ylA034PJzkOnjf9Gji53jHRcq8dI60XPTGhRiWslaf/oHudqdkQcVln/gaT92zRgWgqECvUInr9UxrLvS/QOt7uzpHdFutR/r0wuzwY2k3RfVFQHveqZbCJjxX9bQ9m0Opm1KOujDaQ3Hn5FCtfriv9Fx+xwB0lbRMSHB3CO1SPigPL9VEmvB94UGV++46i4PfmCPRbYJ0qYjXpb1ZyycTT0SxvZi3H7fYyCuyTtFMXOXxlO4s8Dqvv5g3j2evhKzKK7kx+wyIN+RLSB0ols+5vOit7RfB9ShudYlxQUm0TEn8rvPhqrsPfTOxPasEvoUc/dknZn6Pl9I5XosL2Y7gLhT5I+znDPwT/12H8RkraJiN/WTLg6/A/DZ+jrLvWLBELp+X0G+JeI2E7ShsALI+KQUV/NYiAGaDI6TlaLIT39nqTeft/SG51PjsbGTc3U827SLFikffcRpH36vV2OfTqpP6+nbOwIr9HQL21kL56vAaXIbME7SOuib5b6byaTHo2bAT57n6I51/Ym0cfJr2EEEaTAO6czWmjBLPWO5vs68vl9mFQzH6rMHbIzJaxOGyLiFGVU3I4AuzbK3A4lXHhL3kLOIXyFvN7zaMi33sS0nFTuUCaX55LOTUGazn0yWkwqK72W50o6rGHzDhExu7LvN6N4Uaok3als+yUpJD4W6bG8NKkmeO64Lm6GoUoiIknnkpPdJ5X1S6OSoGYc57iRtFNv9BiNEuysx/E/Bz4SIzPXPZe0itmx+ci+7VqP5jwDUwYtxvAY40VDgQIvJYXAY+X7Y9EnIZWas6mtTsZwOiAijm3YXq/jY+TI8s9kGOlNIyJKB+LwqMVDkrQpKTguiVGYtUpqFMRRJqVrhiQrkqalMMCOwrQWCB0krdRl6D/W+oZlCqtt+0NEPK2yflFEbF6zZlgQo4h2uiQg6ShyYvVWsje1fmQykFWBMwchEMZL57fssm1YOtEWdY07p8LiRNLuEXFUNx18DDCi6niR9BtybvCzpEnxneRE7w5tOn9d6lydHHG0tXRqFc13PGh4zo/lSaFycVQyDrY4vq8VUi+mtcpI6Xz0fXrHtu9XR1MclL9KeltEfK+279sZOcH3V2UClij7bEktB4AB0llvP/I+vyqGgs5tSFoCLRYkHVCZV+jHqj22tXUU7DCInAqLk45ue3FFUR036hPra6zCABY5nraenIke0XzV3aO7s1+rnntEDItfVjpLfUcwFapOlweS2pNRMa1HCJIuIPV3rWPbN9TxK/IFPp+hKKArk0PKh2hwqY+IOyrHb0rq6zYizTxnA6+byuqBqYJGFwlyrOcYjb37McBvGzoCbwX+NSLeMI52jDqnwpLO4lLhlTq2JvNPDExAS/oUmcXsSFKN8yZgrYj4754Hdq9vGTIGV5uMiPVjF2ksRsO0HiFAY8ya0YZ27hYH5UD1d6knIi5WJqx4JvkQ1NPqme58n7HnoW7LaDNWnSjpTfRPJzpaxpJTYbEhqddLKiKinmp2Mhh3rC81x6danTQ+GcjkeYWdaqrPg8pcR0+BoExj+i7Su7nT1o43daskQQ2Mqac/3QVCU8ya0YZ87RoHpQiAplSSi2iYCNpU0qKJINOTxWpPWdis7Y5l5Pei0nvsl060JxqZU2ETRp9TYXHSNOe2Emka/ARG5h6fDFbtsa2tCq8enyqAuwc551jhr6UzcWw5zxtpl9fkMDLf++GkSvphMkbRTdHGu3iATHeV0RpkbPtXki+XXwPvaWll1Ok5LA1sQHo+jzoF5XgngpZkJO3SsTQacL2zyTmL9RgeH39E5MjFhYbSRgZDaSPPm6jzjwZl6tn9SGHwY+BLMdwxb1JYXCo8SftExMGDaGOt3vXI99FW5O9+LrB/tAgDUqy8PkE6VB5JJRFQ2wn+QVghTXeBsFXUEpc0lXU59im9tkctZO4o2rQqma+1VWrLJYUy19KVQc4lFBvwsxk+L0SkM9FiRb3zBH8wRhkPaXFSLG3eR+q6DyfDezT6aEwGGkesrz71TngMr34o/XE+TGZ4/BHDBcKBE9WO6a4y+gYjddBNZU3cwfAAYocUx5Px8lfSw9AMp02okEGxYkR8aID1jYZ+eYKnhECQ9AUy0czBZJKmKed/MEgVXo3FoqpUBiXcm5x3rAY07DkylbQt8GUyuummUUv7OpFMS4GgTJH4ImB2zY56FUoCmxbUE8dvSA6bR9uWnzFcV7whOew2FWJiQ3z8XNL2UWJcTTDLRrs8wZPN+0kV6ceBj6khZ8hkNaxODDjWF8PzCQySI8lAmK8mY5u9iXZzmh8jw4MPLDXqWJmWKqNi1fNysof/ncqmB8nUk9c1HVerY5GjUfEuvnAsw8jSlg6TMhE0HZD0wYj4fPn++og4rrLtMxHx0QGe60FygvQhUuhP2EtuNE6NZuIoqtw9GDmv1CYhTdtzXBIZWuOyiHheMXQ5OyqRDaY603KEEBFnAmdK+sFYdf2MM4CYRsasH5TKaaayKxnnBeAjDDen25aMIDkQYkCZvsbIBaNwajQTxylkKOzLqejnB0znnXKfMhfG7cATF9O5FgvTUiBI+mpE7A98U9KIIU5E7NSimvEGEBuIymkJQl2+N62P/2QZ5G4DhutyJyJsxHtplyfYTCzLR0TXUNkD4uDy3H2CnA9YmT4+CFON6aoy2iwi5tfUNYsoI4jF3YaBqJyWFDSg7Fktz/VWUjivQ+ZD2JJMdThhYSNqTo1XDmAi1IwDZQazv5DJk6qJ5weSXXGmMC0FwlRgcb/UZhqSHiUtsEQ6FVVtpJePiGUGeK7LyeBn50fExsoE75+JiKZQ52YJQNK7yLD29zFkBBLRJwJuy7p7jjza+hFMBaalyqiDpK3IbFdPIa+lo+4Z94/cgomMWT/tiYnNy/CPiPiHJCQtFxHXKDO0mSWX9wNPj4hBJf+p0pmzeibZETm5rO/INJs3mtYCATiE1NkOc0CaCCb4BWdGxy3FquQk4DRJ9wJjNT4wM4OFDI1KB0rHcUzSWaQfwYNl/QDgF4vjnIuL6S4Q7o+IX052I8zUIiI6gegOkHQG8HgyhadZcvkrsKA8D9U5hIGZnZKhuqv5wR8uZdOG6S4Qzigelycw/EeeSkHEzASjzElxZUQ8GBFnKnPbbgJcMMlNM5PHSWVZnBwBXCjpxLK+CyNzIU9ppvWkcpH2MDRJ1NHfT5UkJGYSkHQJJc1hWV8KmOdJf7O4kbQZQ7m4z4qISyazPaNlWo4QKrP6Py+fAdzF6BJnm5mLotLTiczBOy2fdTMYlMnrP0v6C1V9UwZqgFLM4W/unEPSkyPij4M8x+JkqcluwBh5XFlWLsvjyCiIv5S0a68DzRLB9ZLeI2mZsuxHhjc3Sy6HAQeR4WW2JtU7Rw3yBJJ2knQdcANwZvmcVnOc01plVEejTJxtZiaSnkhmn9qGHD2eTsaln/QY/2ZykDQ/IjarOZTOj4jWCZRanONS8pn7TYlptDWwe0TsPahzLG5m1DA6Rpk428xMyovfI0VT5aEyl3SdpHcDt5LahUHyz4i4W9JSkpaKiDMkfXXA51iszCiBUCTylEnwYSaWTkRVDU9fuYgBmxia6cV+ZBax95DpQbcB9ux5xOi5r2Q+Ows4WtKdtEuhOWWYlioj9UmcHRHXTHyrzGQjaceI+JmG0lcOIyKmlQmgmV6UfBf/IK0d30T6vxwdEXdPasNGwXQVCPX0l4szcbYxZppTTNSbRo02Ua8wLVVG48iBYJYAJD0D+C9GJkPxn3/J5b8q35cH/p20OBoYkl4LfI7MgSCmYVyzaTlCMKYXxdrjO9RiXEXE/K4HmSUOSRdGxBYDrG8hsGNEtEmbOSWZliMEY/rwSEQcNNmNMFOHYpLeYSkyadHjB3yaO6azMAALBDMz+ZmkdwIn4mQoJplPziGIVBXdAAzaP2CepB+RMZOqz90JAz7PYsMqIzPjkNQUvmSi8mSYJRRJhzUUR0S8ZcIbM0YsEIwxM5YS6XbNiLiurL+ezNgHcGpE3DFpjZuCWCCYGYOkbSLit8XaYwTTaehuBoOkg4HzIuIHZX0hGV9oBXKu6R0DOMeMcYj0HIKZSbwM+C2ZurBOkHkzzJLF5sDbK+sPRsS+AJLOGdA5OhPJ8wZU36ThEYIxZsZSDWZX1jeKiCvK9ysiYqMBnWcW8LmI+K++O09hPEIwM46ST3kPRjqmTZuhuxkYj0l6UkTcDlARBmsDjw3iBJKWjohHJG01iPomEwsEMxM5BTgfuJwB/enNtOULpBny+4FO9rJNgS+WbYPgwlLnAkknA8dRCWo3neauLBDMTGT5iHhf/93MTCcijpL0Z+DTwHPIuaQrgf+OiEEnr1keuJuhPBxims1deQ7BzDgkvRf4C5li1Y5pZrEi6RbgywwJgGpOloiIL09Kw8aARwhmJvIwqQ74GENmgAHYMW0JQ9LHgW936wxI2gZYMSJ+3rS9JbPIZDtNybmmVY/bIwQz45B0PbBFRPx5sttiJhdJOwMfJPMUXAzcRap2NgA2Bn4DfCYi7hrHOS6eKWl7LRDMjEPSr4FdIuJvk90WMzWQtAGwFbAW8HfSd+CsiPj7AOq+JCI2GW89UwELBDPjkHQiOYF4BsPnEGx2apC0GnBfDOjlJ2n1mTI/5TkEMxM5qSxmCUfSfwM/johrJC1Hhq3YGHhE0m4R8ZvxnmOmCAPwCMHMUCQtCzyjrF4bEf+czPaYyUHSlcBGERGS9gF2A15BPhuHDzJBzkzAIwQz45D0cuBw4EbS8mNdSXtGxFmT2CwzOTxcUQ29GjgmIh4Frpbk918N3xAzE/kS8KqIuBYW5Vg+hsySZZYsHpK0EXAHsDXDcyuvODlNmrpYIJiZyDIdYQAQEf8naZnJbJCZNPYHfgLMBr4SETcASNqeoVAWpuA5BDPjkHQoGcPoqFL0JmDWdMpcZcxkYIFgZhzFmuRdwItL0dmkt+pD3Y8yMxFJ9ZhWAfwZOKczWjBDWCCYGYmk2QDj8UA10x9JcxuKVycnmA+IiGMnuElTGgsEM2OQJGAu8G5gqVL8KPCNiPjkpDXMTDkkrQ78ZqaEnBgUS/XfxZhpw3vJ8ASbR8TqEbE68AJgqxIB1RhgkTNZUzC6JRoLBDOTeDPwxqpuOCKuB3YnM6gZA4CkrYF7J7sdUw2bnZqZxDJNEU4j4i6bnS6ZSLqckSGoVwf+hDsJI7BAMDOJh8e4zcxcdqitB3B3RPy1aeclHU8qmxmDpEep5LKtbiLTanqUYJC0T0QcPNntmIpYIBhjlihmUkKbQeNJZWPMkoati7rgEYIxZolC0joRcctkt2MqYoFgjJnxNISwALgfmB8RCya4OVMWCwRjzIxH0g+BOcDPStEOwGXAesBxEfH5SWralMICwRgz45F0FrB9RPylrK8M/ALYlhwlbDiZ7ZsqeFLZGLMk8ESgGu32n8CaEfH3WvkSjR3TjDFLAkcDF0j6aVnfEfihpJWAqyavWVMLq4yMMUsEkjYHXlRWz42IeZPZnqmIBYIxZolA0ixgTSqakYj44+S1aOphlZExZsYjaV8yV8YdZI4MkXGNnjeZ7ZpqeIRgjJnxSFoIvCAi7p7stkxlbGVkjFkSuJl0RDM9sMrIGLMkcD3wO0m/oGJmGhFfnrwmTT0sEIwxSwJ/LMuyZTENeA7BGGMM4BGCMWYGI+mrEbG/pJ8xMpUmEbHTJDRrymKBYIyZyRxZPr84qa2YJlggGGNmLBExv3xdBfhFRDw2me2Z6tjs1BizJPAG4DpJn5f0rMluzFTFk8rGmCUCSasAbwT2IucTDgOOiYgHJ7VhUwiPEIwxSwQR8QDwE+BYYC3gNcDFJayFwSMEY8wSgKSdyJHB04EjgMMj4k5JKwJXRcR6k9m+qYInlY0xSwL/DnwlIs6qFkbE3yTtPUltmnJ4hGCMMQbwCMEYM4OR9CANDmmU8NcRscoEN2lK4xGCMcYYwFZGxpglAElHtilb0rFAMMYsCTynuiJpaWCzSWrLlMUCwRgzY5H0kTKP8DxJD5TlQTKV5k8nuXlTDs8hGGNmPJI+GxEfmex2THUsEIwxMx5JL20qr/slLOlYIBhjZjwlH0KH5YEtgPkRsc0kNWlKYj8EY8yMJyJ2rK5LWhf46uS0ZuriSWVjzJLILcCzJ7sRUw2PEIwxMx5J32DIY3kpYBPg4slr0dTEAsEYsyRwFTCrfL+PzINw7uQ1Z2pigWCMmbEUB7TPAG8B/liKnwwcKunCiPjnpDVuCuI5BGPMTOYLwOrA+hGxaURsCjwVWBX44mQ2bCpis1NjzIxF0nXAM6L2opM0C7gmIjaYnJZNTTxCMMbMZKIuDErhozSHxV6isUAwxsxkrpK0R71Q0u7ANZPQnimNVUbGmBmLpLWBE4C/A/NL8RxgBeA1EXHrZLVtKmKBYIyZ8UjahqEQ2FdFxOmT2Z6pigWCMcYYwHMIxhhjChYIxhhjAAsEY4wxBQsEY4wxgAWCMcaYwv8H1x3+LdoAfGUAAAAASUVORK5CYII=",
"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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",
"text/plain": [
"<Figure size 960x960 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"matrix = df.corr().round(1)\n",
"plt.figure(figsize=(12, 12), dpi=80)\n",
"sns.heatmap(data=matrix,annot=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Normal Model"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"X = df.drop(\"income\", axis=1)\n",
"Y = df[\"income\"]"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"scaler = MinMaxScaler()\n",
"X = scaler.fit_transform(X)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"X_train, X_test, Y_train, Y_test = train_test_split(X,Y,train_size=0.35,random_state=1)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"def modeling(algorithim, xTrn, xTst):\n",
" model = algorithim\n",
" model.fit(X_train,Y_train)\n",
" score = model.score(xTrn, Y_train)\n",
" print(\"Training Score: {:.3f}\".format(score))\n",
" score = model.score(xTst, Y_test)\n",
" print(\"Testing Score : {:.3f}\".format(score))\n",
" # Y_test_pred = model.predict(X_test)\n",
" # print('Predicted Values: ', Y_test_pred)\n",
" # print('Actual Values: ', Y_test)\n",
" # from sklearn.metrics import classification_report, confusion_matrix \n",
" # print(confusion_matrix(Y_test, Y_test_pred))\n",
" # print(classification_report(Y_test, Y_test_pred))\n",
" return"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training Score: 0.888\n",
"Testing Score : 0.810\n"
]
}
],
"source": [
"modeling(KNeighborsClassifier(n_neighbors=2), X_train, X_test)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training Score: 0.841\n",
"Testing Score : 0.834\n"
]
}
],
"source": [
"modeling(LogisticRegression(), X_train, X_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Scaling"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"def allScalling(algorithim, X_train, X_test):\n",
" methods = [MinMaxScaler(),StandardScaler(),RobustScaler(),Normalizer()]\n",
" for method in methods:\n",
" sc= method\n",
" sc.fit(X_train)\n",
" X_train_scaled =sc.fit_transform(X_train) \n",
" X_test_scaled =sc.transform(X_test)\n",
" print('The following are the results after using ', method, ' scaling method:')\n",
" modeling(algorithim(), X_train_scaled, X_test_scaled)\n",
" return"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The following are the results after using MinMaxScaler() scaling method:\n",
"Training Score: 0.870\n",
"Testing Score : 0.820\n",
"The following are the results after using StandardScaler() scaling method:\n",
"Training Score: 0.848\n",
"Testing Score : 0.824\n",
"The following are the results after using RobustScaler() scaling method:\n",
"Training Score: 0.838\n",
"Testing Score : 0.820\n",
"The following are the results after using Normalizer() scaling method:\n",
"Training Score: 0.735\n",
"Testing Score : 0.732\n"
]
}
],
"source": [
"allScalling(KNeighborsClassifier, X_train, X_test)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The following are the results after using MinMaxScaler() scaling method:\n",
"Training Score: 0.841\n",
"Testing Score : 0.834\n",
"The following are the results after using StandardScaler() scaling method:\n",
"Training Score: 0.832\n",
"Testing Score : 0.826\n",
"The following are the results after using RobustScaler() scaling method:\n",
"Training Score: 0.821\n",
"Testing Score : 0.820\n",
"The following are the results after using Normalizer() scaling method:\n",
"Training Score: 0.779\n",
"Testing Score : 0.784\n"
]
}
],
"source": [
"allScalling(LogisticRegression, X_train, X_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Grid Search and Random Search Implementation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Search Implementation Code is partialy inspired from https://towardsdatascience.com/grid-search-for-model-tuning-3319b259367e"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"def WSearch(searchAlgo, model):\n",
" if model == LogisticRegression:\n",
" para_grid = {'penalty': ['l1', 'l2'],'C':[0.001,.009,0.01,.09,1,5,10,25]}\n",
" gridModelAcc = searchAlgo(LogisticRegression(), para_grid,scoring = 'recall')\n",
" gridModelAcc.fit(X_train, Y_train)\n",
" YPredAcc = gridModelAcc.predict(X_test)\n",
" elif model == KNeighborsClassifier:\n",
" k_list =list(range(1,30)) #values we wanna search through from neighbours\n",
" weight_list =['uniform','distance'] \n",
" para_grid =dict(n_neighbors=k_list,weights=weight_list) #dictionary storing hyperparameters\n",
" gridModelAcc=searchAlgo(KNeighborsClassifier(),para_grid,cv=5,scoring=\"accuracy\",return_train_score=False) \n",
" gridModelAcc.fit(X_train,Y_train)\n",
" YPredAcc = gridModelAcc.predict(X_test)\n",
" else:\n",
" return(print(\"Model Used is an invalid parameter\"))\n",
" print('Optimal hyperparameter values : ', gridModelAcc.best_params_)\n",
" cfm = confusion_matrix(Y_test,YPredAcc)\n",
" labels = [\"True Negative\", \"False Positive\", \"False Negative\", \"True Positive\"] \n",
" labels_counts = ['{0:0.0f}'.format(value) for value in cfm.flatten()] \n",
" labels_percentages = ['{:.2%}'.format(value) for value in cfm.flatten()/np.sum(cfm)] \n",
" labels = [f'{v1}\\n{v2}\\n{v3}' for v1, v2, v3 in zip(labels,labels_counts,labels_percentages)]\n",
" labels = np.asarray(labels).reshape(2,2)\n",
" sns.heatmap(cfm, annot=labels , fmt='', cmap='Blues')\n",
" return pd.DataFrame({'Accuracy Score' : str(accuracy_score(Y_test,YPredAcc)),\n",
" 'Precision Score ' : str(precision_score(Y_test,YPredAcc)), \n",
" 'Recall Score' : str(recall_score(Y_test,YPredAcc)),\n",
" 'F1 Score' : str(f1_score(Y_test,YPredAcc)),\n",
" 'ROC AUC Score' : str(mean(roc_auc_score(Y_test,YPredAcc))),\n",
" 'Best Score ' : gridModelAcc.best_score_}, index = [searchAlgo])\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimal hyperparameter values : {'n_neighbors': 10, 'weights': 'uniform'}\n"
]
},
{
"data": {
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"<style scoped>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Accuracy Score</th>\n",
" <th>Precision Score</th>\n",
" <th>Recall Score</th>\n",
" <th>F1 Score</th>\n",
" <th>ROC AUC Score</th>\n",
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" <td>0.8242135711703025</td>\n",
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" <td>0.5357313385325481</td>\n",
" <td>0.6958370716560307</td>\n",
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"text/plain": [
" Accuracy Score \\\n",
"<class 'sklearn.model_selection._search.GridSea... 0.8242135711703025 \n",
"\n",
" Precision Score \\\n",
"<class 'sklearn.model_selection._search.GridSea... 0.6228719467061435 \n",
"\n",
" Recall Score \\\n",
"<class 'sklearn.model_selection._search.GridSea... 0.4699804523876012 \n",
"\n",
" F1 Score \\\n",
"<class 'sklearn.model_selection._search.GridSea... 0.5357313385325481 \n",
"\n",
" ROC AUC Score \\\n",
"<class 'sklearn.model_selection._search.GridSea... 0.6958370716560307 \n",
"\n",
" Best Score \n",
"<class 'sklearn.model_selection._search.GridSea... 0.832792 "
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"WSearch(GridSearchCV, KNeighborsClassifier)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimal hyperparameter values : {'weights': 'uniform', 'n_neighbors': 14}\n"
]
},
{
"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>Accuracy Score</th>\n",
" <th>Precision Score</th>\n",
" <th>Recall Score</th>\n",
" <th>F1 Score</th>\n",
" <th>ROC AUC Score</th>\n",
" <th>Best Score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>&lt;class 'sklearn.model_selection._search.RandomizedSearchCV'&gt;</th>\n",
" <td>0.8294564300349524</td>\n",
" <td>0.6364958197019266</td>\n",
" <td>0.48896956157497906</td>\n",
" <td>0.5530638029058749</td>\n",
" <td>0.7060616654412972</td>\n",
" <td>0.831897</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Accuracy Score \\\n",
"<class 'sklearn.model_selection._search.Randomi... 0.8294564300349524 \n",
"\n",
" Precision Score \\\n",
"<class 'sklearn.model_selection._search.Randomi... 0.6364958197019266 \n",
"\n",
" Recall Score \\\n",
"<class 'sklearn.model_selection._search.Randomi... 0.48896956157497906 \n",
"\n",
" F1 Score \\\n",
"<class 'sklearn.model_selection._search.Randomi... 0.5530638029058749 \n",
"\n",
" ROC AUC Score \\\n",
"<class 'sklearn.model_selection._search.Randomi... 0.7060616654412972 \n",
"\n",
" Best Score \n",
"<class 'sklearn.model_selection._search.Randomi... 0.831897 "
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"WSearch(RandomizedSearchCV, KNeighborsClassifier)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimal hyperparameter values : {'C': 5, 'penalty': 'l2'}\n"
]
},
{
"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>Accuracy Score</th>\n",
" <th>Precision Score</th>\n",
" <th>Recall Score</th>\n",
" <th>F1 Score</th>\n",
" <th>ROC AUC Score</th>\n",
" <th>Best Score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>&lt;class 'sklearn.model_selection._search.GridSearchCV'&gt;</th>\n",
" <td>0.8337953477160419</td>\n",
" <td>0.653716847217034</td>\n",
" <td>0.4886903099692823</td>\n",
" <td>0.5592841163310962</td>\n",
" <td>0.7087269270587209</td>\n",
" <td>0.52247</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Accuracy Score \\\n",
"<class 'sklearn.model_selection._search.GridSea... 0.8337953477160419 \n",
"\n",
" Precision Score \\\n",
"<class 'sklearn.model_selection._search.GridSea... 0.653716847217034 \n",
"\n",
" Recall Score \\\n",
"<class 'sklearn.model_selection._search.GridSea... 0.4886903099692823 \n",
"\n",
" F1 Score \\\n",
"<class 'sklearn.model_selection._search.GridSea... 0.5592841163310962 \n",
"\n",
" ROC AUC Score \\\n",
"<class 'sklearn.model_selection._search.GridSea... 0.7087269270587209 \n",
"\n",
" Best Score \n",
"<class 'sklearn.model_selection._search.GridSea... 0.52247 "
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"WSearch(GridSearchCV, LogisticRegression)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimal hyperparameter values : {'penalty': 'l2', 'C': 25}\n"
]
},
{
"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>Accuracy Score</th>\n",
" <th>Precision Score</th>\n",
" <th>Recall Score</th>\n",
" <th>F1 Score</th>\n",
" <th>ROC AUC Score</th>\n",
" <th>Best Score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>&lt;class 'sklearn.model_selection._search.RandomizedSearchCV'&gt;</th>\n",
" <td>0.8342171869350368</td>\n",
" <td>0.6543898809523809</td>\n",
" <td>0.4912035744205529</td>\n",
" <td>0.5611740309459243</td>\n",
" <td>0.7099067130536638</td>\n",
" <td>0.522469</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Accuracy Score \\\n",
"<class 'sklearn.model_selection._search.Randomi... 0.8342171869350368 \n",
"\n",
" Precision Score \\\n",
"<class 'sklearn.model_selection._search.Randomi... 0.6543898809523809 \n",
"\n",
" Recall Score \\\n",
"<class 'sklearn.model_selection._search.Randomi... 0.4912035744205529 \n",
"\n",
" F1 Score \\\n",
"<class 'sklearn.model_selection._search.Randomi... 0.5611740309459243 \n",
"\n",
" ROC AUC Score \\\n",
"<class 'sklearn.model_selection._search.Randomi... 0.7099067130536638 \n",
"\n",
" Best Score \n",
"<class 'sklearn.model_selection._search.Randomi... 0.522469 "
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"WSearch(RandomizedSearchCV,LogisticRegression)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Cross Validation Implementation"
]
},
{
"cell_type": "code",
"execution_count": 55,
"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>fit_time</th>\n",
" <th>score_time</th>\n",
" <th>test_f1</th>\n",
" <th>train_f1</th>\n",
" <th>test_recall</th>\n",
" <th>train_recall</th>\n",
" <th>test_roc_auc</th>\n",
" <th>train_roc_auc</th>\n",
" <th>test_precision</th>\n",
" <th>train_precision</th>\n",
" <th>test_accuracy</th>\n",
" <th>train_accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.381601</td>\n",
" <td>0.019278</td>\n",
" <td>0.572133</td>\n",
" <td>0.572330</td>\n",
" <td>0.500719</td>\n",
" <td>0.500480</td>\n",
" <td>0.880366</td>\n",
" <td>0.881743</td>\n",
" <td>0.667306</td>\n",
" <td>0.668268</td>\n",
" <td>0.836911</td>\n",
" <td>0.837094</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.370437</td>\n",
" <td>0.019334</td>\n",
" <td>0.566694</td>\n",
" <td>0.571153</td>\n",
" <td>0.492086</td>\n",
" <td>0.497602</td>\n",
" <td>0.882604</td>\n",
" <td>0.881003</td>\n",
" <td>0.667969</td>\n",
" <td>0.670220</td>\n",
" <td>0.836102</td>\n",
" <td>0.837259</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.326220</td>\n",
" <td>0.022144</td>\n",
" <td>0.561332</td>\n",
" <td>0.578695</td>\n",
" <td>0.497122</td>\n",
" <td>0.507434</td>\n",
" <td>0.873639</td>\n",
" <td>0.884019</td>\n",
" <td>0.644590</td>\n",
" <td>0.673242</td>\n",
" <td>0.830774</td>\n",
" <td>0.839087</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.327807</td>\n",
" <td>0.020737</td>\n",
" <td>0.575114</td>\n",
" <td>0.566313</td>\n",
" <td>0.497122</td>\n",
" <td>0.493046</td>\n",
" <td>0.884731</td>\n",
" <td>0.880127</td>\n",
" <td>0.682132</td>\n",
" <td>0.665157</td>\n",
" <td>0.840019</td>\n",
" <td>0.835536</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" fit_time score_time test_f1 train_f1 test_recall train_recall \\\n",
"0 0.381601 0.019278 0.572133 0.572330 0.500719 0.500480 \n",
"1 0.370437 0.019334 0.566694 0.571153 0.492086 0.497602 \n",
"2 0.326220 0.022144 0.561332 0.578695 0.497122 0.507434 \n",
"3 0.327807 0.020737 0.575114 0.566313 0.497122 0.493046 \n",
"\n",
" test_roc_auc train_roc_auc test_precision train_precision \\\n",
"0 0.880366 0.881743 0.667306 0.668268 \n",
"1 0.882604 0.881003 0.667969 0.670220 \n",
"2 0.873639 0.884019 0.644590 0.673242 \n",
"3 0.884731 0.880127 0.682132 0.665157 \n",
"\n",
" test_accuracy train_accuracy \n",
"0 0.836911 0.837094 \n",
"1 0.836102 0.837259 \n",
"2 0.830774 0.839087 \n",
"3 0.840019 0.835536 "
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame(cross_validate(LogisticRegression(), X,Y,cv=4, scoring= {'accuracy', 'precision', 'recall', 'f1', 'roc_auc'} , return_train_score = True, n_jobs=-1))"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"pd.DataFrame(cross_validate(KNeighborsClassifier(), X,Y,cv=4, scoring= {'accuracy', 'precision', 'recall', 'f1', 'roc_auc'} , return_train_score = True, n_jobs=-1))"
]
}
],
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