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US Census Income Dataset Analysis
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adult.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# US Census Income Dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Dataset: http://archive.ics.uci.edu/ml/datasets/Census+Income"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Exploration"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Since CSV files don't have headers, manually specify them\n",
"\n",
"cols = ['age', 'workclass', 'fnlwgt', 'education', 'education_num', 'marital_status',\n",
" 'occupation', 'relationship', 'race', 'sex', 'capital_gain', 'capital_loss',\n",
" 'hours_per_week', 'country']\n",
"target = 'income'"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"# Read data\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"data = pd.read_csv('data.csv', skipinitialspace=True, header=None,\n",
" names=cols + ['income'], na_values=['?'])"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(48842, 15)\n"
]
}
],
"source": [
"print(data.shape)"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\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>education_num</th>\n",
" <th>marital_status</th>\n",
" <th>occupation</th>\n",
" <th>relationship</th>\n",
" <th>race</th>\n",
" <th>sex</th>\n",
" <th>capital_gain</th>\n",
" <th>capital_loss</th>\n",
" <th>hours_per_week</th>\n",
" <th>country</th>\n",
" <th>income</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>39</td>\n",
" <td>State-gov</td>\n",
" <td>77516</td>\n",
" <td>Bachelors</td>\n",
" <td>13</td>\n",
" <td>Never-married</td>\n",
" <td>Adm-clerical</td>\n",
" <td>Not-in-family</td>\n",
" <td>White</td>\n",
" <td>Male</td>\n",
" <td>2174</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>50</td>\n",
" <td>Self-emp-not-inc</td>\n",
" <td>83311</td>\n",
" <td>Bachelors</td>\n",
" <td>13</td>\n",
" <td>Married-civ-spouse</td>\n",
" <td>Exec-managerial</td>\n",
" <td>Husband</td>\n",
" <td>White</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>38</td>\n",
" <td>Private</td>\n",
" <td>215646</td>\n",
" <td>HS-grad</td>\n",
" <td>9</td>\n",
" <td>Divorced</td>\n",
" <td>Handlers-cleaners</td>\n",
" <td>Not-in-family</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>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>53</td>\n",
" <td>Private</td>\n",
" <td>234721</td>\n",
" <td>11th</td>\n",
" <td>7</td>\n",
" <td>Married-civ-spouse</td>\n",
" <td>Handlers-cleaners</td>\n",
" <td>Husband</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>4</th>\n",
" <td>28</td>\n",
" <td>Private</td>\n",
" <td>338409</td>\n",
" <td>Bachelors</td>\n",
" <td>13</td>\n",
" <td>Married-civ-spouse</td>\n",
" <td>Prof-specialty</td>\n",
" <td>Wife</td>\n",
" <td>Black</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>Cuba</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age workclass fnlwgt education education_num \\\n",
"0 39 State-gov 77516 Bachelors 13 \n",
"1 50 Self-emp-not-inc 83311 Bachelors 13 \n",
"2 38 Private 215646 HS-grad 9 \n",
"3 53 Private 234721 11th 7 \n",
"4 28 Private 338409 Bachelors 13 \n",
"\n",
" marital_status occupation relationship race sex \\\n",
"0 Never-married Adm-clerical Not-in-family White Male \n",
"1 Married-civ-spouse Exec-managerial Husband White Male \n",
"2 Divorced Handlers-cleaners Not-in-family White Male \n",
"3 Married-civ-spouse Handlers-cleaners Husband Black Male \n",
"4 Married-civ-spouse Prof-specialty Wife Black Female \n",
"\n",
" capital_gain capital_loss hours_per_week country income \n",
"0 2174 0 40 United-States <=50K \n",
"1 0 0 13 United-States <=50K \n",
"2 0 0 40 United-States <=50K \n",
"3 0 0 40 United-States <=50K \n",
"4 0 0 40 Cuba <=50K "
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\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>education_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>48842.000000</td>\n",
" <td>4.884200e+04</td>\n",
" <td>48842.000000</td>\n",
" <td>48842.000000</td>\n",
" <td>48842.000000</td>\n",
" <td>48842.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>38.643585</td>\n",
" <td>1.896641e+05</td>\n",
" <td>10.078089</td>\n",
" <td>1079.067626</td>\n",
" <td>87.502314</td>\n",
" <td>40.422382</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>13.710510</td>\n",
" <td>1.056040e+05</td>\n",
" <td>2.570973</td>\n",
" <td>7452.019058</td>\n",
" <td>403.004552</td>\n",
" <td>12.391444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>17.000000</td>\n",
" <td>1.228500e+04</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>28.000000</td>\n",
" <td>1.175505e+05</td>\n",
" <td>9.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>40.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>37.000000</td>\n",
" <td>1.781445e+05</td>\n",
" <td>10.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>40.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>48.000000</td>\n",
" <td>2.376420e+05</td>\n",
" <td>12.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>45.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>90.000000</td>\n",
" <td>1.490400e+06</td>\n",
" <td>16.000000</td>\n",
" <td>99999.000000</td>\n",
" <td>4356.000000</td>\n",
" <td>99.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age fnlwgt education_num capital_gain capital_loss \\\n",
"count 48842.000000 4.884200e+04 48842.000000 48842.000000 48842.000000 \n",
"mean 38.643585 1.896641e+05 10.078089 1079.067626 87.502314 \n",
"std 13.710510 1.056040e+05 2.570973 7452.019058 403.004552 \n",
"min 17.000000 1.228500e+04 1.000000 0.000000 0.000000 \n",
"25% 28.000000 1.175505e+05 9.000000 0.000000 0.000000 \n",
"50% 37.000000 1.781445e+05 10.000000 0.000000 0.000000 \n",
"75% 48.000000 2.376420e+05 12.000000 0.000000 0.000000 \n",
"max 90.000000 1.490400e+06 16.000000 99999.000000 4356.000000 \n",
"\n",
" hours_per_week \n",
"count 48842.000000 \n",
"mean 40.422382 \n",
"std 12.391444 \n",
"min 1.000000 \n",
"25% 40.000000 \n",
"50% 40.000000 \n",
"75% 45.000000 \n",
"max 99.000000 "
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.describe()"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"age 52\n",
"workclass 48\n",
"fnlwgt 52\n",
"education 52\n",
"education_num 52\n",
"marital_status 52\n",
"occupation 48\n",
"relationship 52\n",
"race 52\n",
"sex 52\n",
"capital_gain 52\n",
"capital_loss 52\n",
"hours_per_week 52\n",
"country 51\n",
"income 52\n",
"dtype: int64"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check for duplicate values\n",
"data[data.duplicated()].count()"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Remove duplicate values\n",
"data.drop_duplicates(inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<=50K 0.760586\n",
">50K 0.239414\n",
"Name: income, dtype: float64"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Class distribution\n",
"data['income'].value_counts() / data['income'].count()"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"age 0\n",
"workclass 2795\n",
"fnlwgt 0\n",
"education 0\n",
"education_num 0\n",
"marital_status 0\n",
"occupation 2805\n",
"relationship 0\n",
"race 0\n",
"sex 0\n",
"capital_gain 0\n",
"capital_loss 0\n",
"hours_per_week 0\n",
"country 856\n",
"income 0\n",
"dtype: int64\n"
]
}
],
"source": [
"# Find missing value count\n",
"print(data.shape[0] - data.count())"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of people with:\n",
"* no occupation or workclass: 2795\n",
"* an occupation but no workclass: 0\n",
"* no occupation but a workclass: 10\n"
]
}
],
"source": [
"print(\"Number of people with:\")\n",
"print(\"* no occupation or workclass:\",\n",
" data[data.occupation.isnull() & data.workclass.isnull()].shape[0])\n",
"print(\"* an occupation but no workclass:\",\n",
" data[~data.occupation.isnull() & data.workclass.isnull()].shape[0])\n",
"print(\"* no occupation but a workclass:\",\n",
" data[data.occupation.isnull() & ~data.workclass.isnull()].shape[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can infer that missing values in occupation and workclass are related.\n",
"A null workclass implies a null occupation.\n",
"The converse is mostly true but not always.\n",
"\n",
"In particular, there are 10 cases where occupation is null but workclass is not.\n",
"Let's see what those cases are."
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" occupation workclass\n",
"5361 NaN Never-worked\n",
"10845 NaN Never-worked\n",
"14772 NaN Never-worked\n",
"20337 NaN Never-worked\n",
"23232 NaN Never-worked\n",
"32304 NaN Never-worked\n",
"32314 NaN Never-worked\n",
"41346 NaN Never-worked\n",
"44168 NaN Never-worked\n",
"46459 NaN Never-worked\n"
]
}
],
"source": [
"print(data[data.occupation.isnull() & ~data.workclass.isnull()][['occupation', 'workclass']])"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" occupation workclass\n",
"5361 NaN Never-worked\n",
"10845 NaN Never-worked\n",
"14772 NaN Never-worked\n",
"20337 NaN Never-worked\n",
"23232 NaN Never-worked\n",
"32304 NaN Never-worked\n",
"32314 NaN Never-worked\n",
"41346 NaN Never-worked\n",
"44168 NaN Never-worked\n",
"46459 NaN Never-worked\n"
]
}
],
"source": [
"print(data[data['workclass'] == 'Never-worked'][['occupation', 'workclass']])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see that occupation is null and workclass is not null if and only if\n",
"workclass is `Never-worked`.\n",
"\n",
"Since we now know the occupation for these people,\n",
"we can replace these null values by 'none'."
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data.loc[data['workclass'] == 'Never-worked', 'occupation'] = 'none'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Data cleaning"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\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_num</th>\n",
" <th>marital_status</th>\n",
" <th>occupation</th>\n",
" <th>relationship</th>\n",
" <th>race</th>\n",
" <th>hours_per_week</th>\n",
" <th>country</th>\n",
" <th>capital_profit</th>\n",
" <th>is_male</th>\n",
" <th>is_rich</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>39</td>\n",
" <td>State-gov</td>\n",
" <td>77516</td>\n",
" <td>13</td>\n",
" <td>Never-married</td>\n",
" <td>Adm-clerical</td>\n",
" <td>Not-in-family</td>\n",
" <td>White</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>2174</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>50</td>\n",
" <td>Self-emp-not-inc</td>\n",
" <td>83311</td>\n",
" <td>13</td>\n",
" <td>Married-civ-spouse</td>\n",
" <td>Exec-managerial</td>\n",
" <td>Spouse</td>\n",
" <td>White</td>\n",
" <td>13</td>\n",
" <td>United-States</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>38</td>\n",
" <td>Private</td>\n",
" <td>215646</td>\n",
" <td>9</td>\n",
" <td>Divorced</td>\n",
" <td>Handlers-cleaners</td>\n",
" <td>Not-in-family</td>\n",
" <td>White</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>53</td>\n",
" <td>Private</td>\n",
" <td>234721</td>\n",
" <td>7</td>\n",
" <td>Married-civ-spouse</td>\n",
" <td>Handlers-cleaners</td>\n",
" <td>Spouse</td>\n",
" <td>Black</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>28</td>\n",
" <td>Private</td>\n",
" <td>338409</td>\n",
" <td>13</td>\n",
" <td>Married-civ-spouse</td>\n",
" <td>Prof-specialty</td>\n",
" <td>Spouse</td>\n",
" <td>Black</td>\n",
" <td>40</td>\n",
" <td>Cuba</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age workclass fnlwgt education_num marital_status \\\n",
"0 39 State-gov 77516 13 Never-married \n",
"1 50 Self-emp-not-inc 83311 13 Married-civ-spouse \n",
"2 38 Private 215646 9 Divorced \n",
"3 53 Private 234721 7 Married-civ-spouse \n",
"4 28 Private 338409 13 Married-civ-spouse \n",
"\n",
" occupation relationship race hours_per_week country \\\n",
"0 Adm-clerical Not-in-family White 40 United-States \n",
"1 Exec-managerial Spouse White 13 United-States \n",
"2 Handlers-cleaners Not-in-family White 40 United-States \n",
"3 Handlers-cleaners Spouse Black 40 United-States \n",
"4 Prof-specialty Spouse Black 40 Cuba \n",
"\n",
" capital_profit is_male is_rich \n",
"0 2174 1 0 \n",
"1 0 1 0 \n",
"2 0 1 0 \n",
"3 0 1 0 \n",
"4 0 0 0 "
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Remove missing values\n",
"\n",
"data.dropna(inplace=True)\n",
"\n",
"# Remove redundant attributes\n",
"\n",
"del data['education']\n",
"# use education_num instead\n",
"\n",
"# Merge capital_loss and capital_gain\n",
"\n",
"data['capital_profit'] = data['capital_gain'] - data['capital_loss']\n",
"del data['capital_gain']\n",
"del data['capital_loss']\n",
"\n",
"# Transform income, sex and relationship\n",
"\n",
"data['is_male'] = data['sex'].map({'Male': 1, 'Female': 0})\n",
"def to_spouse(s):\n",
" return 'Spouse' if s in ('Husband', 'Wife') else s\n",
"data['relationship'] = data['relationship'].map(to_spouse)\n",
"del data['sex']\n",
"data['is_rich'] = data['income'].map({'<=50K': 0, '>50K': 1})\n",
"del data['income']\n",
"\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\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_num</th>\n",
" <th>marital_status</th>\n",
" <th>occupation</th>\n",
" <th>relationship</th>\n",
" <th>race</th>\n",
" <th>hours_per_week</th>\n",
" <th>country</th>\n",
" <th>capital_profit</th>\n",
" <th>is_male</th>\n",
" <th>is_rich</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>39</td>\n",
" <td>state_gov</td>\n",
" <td>77516</td>\n",
" <td>13</td>\n",
" <td>never_married</td>\n",
" <td>adm_clerical</td>\n",
" <td>not_in_family</td>\n",
" <td>white</td>\n",
" <td>40</td>\n",
" <td>united_states</td>\n",
" <td>2174</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>50</td>\n",
" <td>self_emp_not_inc</td>\n",
" <td>83311</td>\n",
" <td>13</td>\n",
" <td>married_civ_spouse</td>\n",
" <td>exec_managerial</td>\n",
" <td>spouse</td>\n",
" <td>white</td>\n",
" <td>13</td>\n",
" <td>united_states</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>38</td>\n",
" <td>private</td>\n",
" <td>215646</td>\n",
" <td>9</td>\n",
" <td>divorced</td>\n",
" <td>handlers_cleaners</td>\n",
" <td>not_in_family</td>\n",
" <td>white</td>\n",
" <td>40</td>\n",
" <td>united_states</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>53</td>\n",
" <td>private</td>\n",
" <td>234721</td>\n",
" <td>7</td>\n",
" <td>married_civ_spouse</td>\n",
" <td>handlers_cleaners</td>\n",
" <td>spouse</td>\n",
" <td>black</td>\n",
" <td>40</td>\n",
" <td>united_states</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>28</td>\n",
" <td>private</td>\n",
" <td>338409</td>\n",
" <td>13</td>\n",
" <td>married_civ_spouse</td>\n",
" <td>prof_specialty</td>\n",
" <td>spouse</td>\n",
" <td>black</td>\n",
" <td>40</td>\n",
" <td>cuba</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age workclass fnlwgt education_num marital_status \\\n",
"0 39 state_gov 77516 13 never_married \n",
"1 50 self_emp_not_inc 83311 13 married_civ_spouse \n",
"2 38 private 215646 9 divorced \n",
"3 53 private 234721 7 married_civ_spouse \n",
"4 28 private 338409 13 married_civ_spouse \n",
"\n",
" occupation relationship race hours_per_week country \\\n",
"0 adm_clerical not_in_family white 40 united_states \n",
"1 exec_managerial spouse white 13 united_states \n",
"2 handlers_cleaners not_in_family white 40 united_states \n",
"3 handlers_cleaners spouse black 40 united_states \n",
"4 prof_specialty spouse black 40 cuba \n",
"\n",
" capital_profit is_male is_rich \n",
"0 2174 1 0 \n",
"1 0 1 0 \n",
"2 0 1 0 \n",
"3 0 1 0 \n",
"4 0 0 0 "
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Standardize categorical variables\n",
"\n",
"import re\n",
"def stdize_name(s):\n",
" return re.sub('[^0-9a-zA-Z]+', '_', s).lower()\n",
"\n",
"multi_cat_cols = ['workclass', 'marital_status', 'occupation',\n",
" 'relationship', 'race', 'country']\n",
"for col in multi_cat_cols:\n",
" data[col] = data[col].map(stdize_name)\n",
"\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\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>education_num</th>\n",
" <th>hours_per_week</th>\n",
" <th>capital_profit</th>\n",
" <th>is_male</th>\n",
" <th>workclass_state_gov</th>\n",
" <th>workclass_self_emp_not_inc</th>\n",
" <th>workclass_private</th>\n",
" <th>workclass_federal_gov</th>\n",
" <th>...</th>\n",
" <th>country_scotland</th>\n",
" <th>country_trinadad_tobago</th>\n",
" <th>country_greece</th>\n",
" <th>country_nicaragua</th>\n",
" <th>country_vietnam</th>\n",
" <th>country_hong</th>\n",
" <th>country_ireland</th>\n",
" <th>country_hungary</th>\n",
" <th>country_holand_netherlands</th>\n",
" <th>is_rich</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>39</td>\n",
" <td>77516</td>\n",
" <td>13</td>\n",
" <td>40</td>\n",
" <td>2174</td>\n",
" <td>1</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",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>50</td>\n",
" <td>83311</td>\n",
" <td>13</td>\n",
" <td>13</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>...</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>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>38</td>\n",
" <td>215646</td>\n",
" <td>9</td>\n",
" <td>40</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</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>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>53</td>\n",
" <td>234721</td>\n",
" <td>7</td>\n",
" <td>40</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</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>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>28</td>\n",
" <td>338409</td>\n",
" <td>13</td>\n",
" <td>40</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>...</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>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 88 columns</p>\n",
"</div>"
],
"text/plain": [
" age fnlwgt education_num hours_per_week capital_profit is_male \\\n",
"0 39 77516 13 40 2174 1 \n",
"1 50 83311 13 13 0 1 \n",
"2 38 215646 9 40 0 1 \n",
"3 53 234721 7 40 0 1 \n",
"4 28 338409 13 40 0 0 \n",
"\n",
" workclass_state_gov workclass_self_emp_not_inc workclass_private \\\n",
"0 1 0 0 \n",
"1 0 1 0 \n",
"2 0 0 1 \n",
"3 0 0 1 \n",
"4 0 0 1 \n",
"\n",
" workclass_federal_gov ... country_scotland country_trinadad_tobago \\\n",
"0 0 ... 0 0 \n",
"1 0 ... 0 0 \n",
"2 0 ... 0 0 \n",
"3 0 ... 0 0 \n",
"4 0 ... 0 0 \n",
"\n",
" country_greece country_nicaragua country_vietnam country_hong \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" country_ireland country_hungary country_holand_netherlands is_rich \n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
"[5 rows x 88 columns]"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# One-hot encode\n",
"\n",
"ohdata = pd.DataFrame(data[['age', 'fnlwgt', 'education_num', 'hours_per_week',\n",
" 'capital_profit', 'is_male']])\n",
"\n",
"for col in multi_cat_cols:\n",
" values = data[col].unique()\n",
" for value in values:\n",
" ohdata[col + '_' + value] = data[col].map(lambda x: 1 if x == value else 0)\n",
"\n",
"ohdata['is_rich'] = data['is_rich']\n",
"ohdata.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Classification"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Decision Tree"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split, cross_val_score, StratifiedKFold\n",
"from sklearn import metrics\n",
"\n",
"def my_cross_val_score(clf, X, y):\n",
" fold_generator = StratifiedKFold(n_splits=5, random_state=3, shuffle=True)\n",
" return cross_val_score(clf, X, y, cv=fold_generator, n_jobs=-1)\n",
"\n",
"def print_cv_accuracy(clf, X, y):\n",
" accs = my_cross_val_score(clf, X, y)\n",
" print(\"{}-fold CV accuracy: {} % ± {} %\".format(5,\n",
" 100 * accs.mean(), 100 * accs.std()))\n",
"\n",
"def my_tts_score(clf, X, y):\n",
" X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
" test_size=0.2, random_state=3)\n",
" clf.fit(X_train, y_train)\n",
" y_pred = clf.predict(X_test)\n",
" return metrics.accuracy_score(y_test, y_pred)\n",
"\n",
"def print_tts_accuracy(clf, X, y):\n",
" acc = my_tts_score(clf, X, y)\n",
" print(\"TTS accuracy = {} %\".format(acc))"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5-fold CV accuracy: 81.14640971213582 % ± 0.22269343742340633 %\n"
]
}
],
"source": [
"y = ohdata['is_rich']\n",
"X = ohdata[ohdata.columns[:-1]]\n",
"\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"\n",
"print_cv_accuracy(DecisionTreeClassifier(), X, y)"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(56, 13453)"
]
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dtree = DecisionTreeClassifier()\n",
"dtree.fit(X, y)\n",
"dtree.tree_.max_depth, dtree.tree_.node_count"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By default, scikit-learn makes completely unpruned trees.\n",
"Let's try pre-pruning. (Scikit doesn't yet support post-pruning).\n",
"\n",
"We will try making a decision tree for various gini thresholds\n",
"and plot a graph showing the variation.\n",
"This will help us find out the optimally pruned tree.\n",
"This tree will be pruned enough to make it general and avoid overfitting,\n",
"and it will not be too pruned to not have sufficient complexity.\n",
"\n",
"We will also see if certain features can be eliminated.\n",
"By reading descriptions of features, we guess that `fnlwgt` and `relationship`\n",
"might not be appropriate attributes for classification.\n",
"\n",
"We'll be pre-pruning and selecting features at the same time."
]
},
{
"cell_type": "code",
"execution_count": 208,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 171,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Get different features\n",
"\n",
"cols = [list(ohdata.columns[:-1]) for i in range(4)]\n",
"cols[1].remove('fnlwgt')\n",
"cols[3].remove('fnlwgt')\n",
"for value in data.relationship.unique():\n",
" cols[2].remove('relationship_' + value)\n",
" cols[3].remove('relationship_' + value)\n",
"Xs = [X[cols[i]] for i in range(4)]"
]
},
{
"cell_type": "code",
"execution_count": 176,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.reset_orig()"
]
},
{
"cell_type": "code",
"execution_count": 173,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"primary_colors = ['blue', 'red', 'green', 'darkmagenta']\n",
"secondary_colors = ['cyan', 'yellow', 'lightgreen', 'magenta']\n",
"\n",
"def plot_acc_vs_impurity(Xs, minimp, maximp, intervals=10, stddev_factor=1):\n",
" # Plots accuracy vs impurities for all impurities from minimp to maximp\n",
" # and their (intervals - 1) arithmetic means in between.\n",
" impurities = [minimp + (maximp - minimp) * x / intervals\n",
" for x in range(intervals + 1)]\n",
" for i, X in enumerate(Xs):\n",
" maccs = []\n",
" maccsa = []\n",
" maccsb = []\n",
" for impurity in impurities:\n",
" dtree = DecisionTreeClassifier(min_impurity_split=impurity)\n",
" accs = my_cross_val_score(dtree, X, y)\n",
" maccs.append(accs.mean())\n",
" maccsa.append(accs.mean() + accs.std() * stddev_factor)\n",
" maccsb.append(accs.mean() - accs.std() * stddev_factor)\n",
" print(i)\n",
" plt.plot(impurities, maccs, primary_colors[i])\n",
" line, = plt.plot(impurities, maccsa, secondary_colors[i])\n",
" plt.setp(line, linestyle='dashed')\n",
" line, = plt.plot(impurities, maccsb, secondary_colors[i])\n",
" plt.setp(line, linestyle='dashed')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we'll plot a graph of impurity vs accuracy for all of them and see which gives higher accuracy. We'll also plot the standard deviation envelopes of the accuracies in a lighter shade using dashed lines.\n",
"\n",
"Here is the legend:\n",
"\n",
"* blue - don't remove any features.\n",
"* red - remove `fnlwgt`.\n",
"* green - remove `relationship_*`.\n",
"* magenta - remove both."
]
},
{
"cell_type": "code",
"execution_count": 177,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"1\n",
"2\n",
"3\n"
]
},
{
"data": {
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UHdHP1V5KKCrqZXXYoJI3rFb1M50+He65RwmH9HSV0HEoDRzNAiWlZF/tPpvl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\nM6RUXSAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f8e77cd3828>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_acc_vs_impurity(Xs, 0, 0.4, 15)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We observe that removing `fnlwgt` always leads to better results.\n",
"Removing both is almost always best.\n",
"However, the difference in accuracy is not much compared to standard deviation,\n",
"hence removing the attributes is neither very useful nor futile.\n",
"\n",
"So we'll just remove `fnlwgt` and work with the rest of the dataset from now on."
]
},
{
"cell_type": "code",
"execution_count": 188,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"X = Xs[1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With some trial-and-error, we reach this graph:"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"1\n",
"2\n",
"3\n"
]
},
{
"data": {
"image/png": 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Ydg9+yDuwcwoRga4vyuZ8vISqbqW29kvKyobvZH5oH11XKSsKE69PYDsd+yL8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58gQiZNglmzAghRe86N3/0ujg8+sAJXFy4MMmBA6ceWE83mT0vv5OPtHwJWcPwDQ2Yyo8/vKpVz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\nmVEhIiIiIpn5f9fz15FH7lnOAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f8e6a436668>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_acc_vs_impurity(Xs, 0.33, 0.36405, 15, 0.5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It can be observed that best value of gini threshold is around 0.36."
]
},
{
"cell_type": "code",
"execution_count": 189,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"min_impurity = 0.362"
]
},
{
"cell_type": "code",
"execution_count": 194,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5-fold CV accuracy: 82.06043696207871 % ± 0.4544900108918053 %\n"
]
}
],
"source": [
"print_cv_accuracy(DecisionTreeClassifier(), X, y)"
]
},
{
"cell_type": "code",
"execution_count": 193,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5-fold CV accuracy: 84.28462961115588 % ± 0.26754219466852136 %\n"
]
}
],
"source": [
"print_cv_accuracy(DecisionTreeClassifier(min_impurity_split=min_impurity), X, y)"
]
},
{
"cell_type": "code",
"execution_count": 146,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(50, 3977)"
]
},
"execution_count": 146,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dtree = DecisionTreeClassifier(min_impurity_split=min_impurity)\n",
"dtree.fit(X, y)\n",
"dtree.tree_.max_depth, dtree.tree_.node_count"
]
},
{
"cell_type": "code",
"execution_count": 197,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1. relationship_spouse :\t0.3923705247185067\n",
"2. education_num :\t0.1954857338380243\n",
"3. capital_profit :\t0.14421800888283753\n",
"4. age :\t0.09488034673168079\n",
"5. hours_per_week :\t0.0568920613684147\n",
"6. occupation_exec_managerial:\t0.009780342719594739\n",
"7. is_male :\t0.00973332071558035\n",
"8. workclass_self_emp_not_inc:\t0.008733986317293108\n",
"9. occupation_prof_specialty:\t0.007061736902897098\n",
"10. workclass_private :\t0.006292473084223405\n",
"\n",
"Contribution of 10 most important features: 0.925448535279\n",
"\n",
"Contribution of workclass: 0.030517863897657312\n",
"Contribution of marital_status: 0.0\n",
"Contribution of occupation: 0.05348753968646164\n",
"Contribution of relationship: 0.3923705247185067\n",
"Contribution of race: 0.011036308367738718\n",
"Contribution of country: 0.011378291793097876\n"
]
}
],
"source": [
"# Important features\n",
"\n",
"def print_importance_data(col_names, clf, multi_cat_cols):\n",
" fi = sorted(zip(col_names, clf.feature_importances_),\n",
" key=(lambda x: x[1]), reverse=True)\n",
" for i, feature_importance in enumerate(fi[:10]):\n",
" feature, importance = feature_importance\n",
" print('{}. {}:\\t{}'.format(i + 1, feature.ljust(20), importance))\n",
" #print('\"{}\",'.format(feature))\n",
" print()\n",
" print(\"Contribution of 10 most important features:\",\n",
" sum([x[1] for x in fi[:10]]))\n",
" print()\n",
" for col in multi_cat_cols:\n",
" print(\"Contribution of {}: {}\".format(col,\n",
" sum([x[1] for x in fi if x[0].startswith(col)])))\n",
"\n",
"dtree = DecisionTreeClassifier(min_impurity_split=min_impurity)\n",
"dtree.fit(X, y)\n",
"print_importance_data(X.columns, dtree, multi_cat_cols)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Ensemble Classifiers"
]
},
{
"cell_type": "code",
"execution_count": 200,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AdaBoost</th>\n",
" <th>ExtraTrees</th>\n",
" <th>RandomForest</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>84.835671</td>\n",
" <td>82.399027</td>\n",
" <td>83.799936</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>85.585917</td>\n",
" <td>82.582726</td>\n",
" <td>84.191658</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td>86.086087</td>\n",
" <td>82.662379</td>\n",
" <td>84.289037</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" AdaBoost ExtraTrees RandomForest\n",
"10 84.835671 82.399027 83.799936\n",
"40 85.585917 82.582726 84.191658\n",
"100 86.086087 82.662379 84.289037"
]
},
"execution_count": 200,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.ensemble import ExtraTreesClassifier\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.ensemble import AdaBoostClassifier\n",
"\n",
"clf_list = [ExtraTreesClassifier, RandomForestClassifier, AdaBoostClassifier]\n",
"clf_to_name = {clf_type: clf_type.__name__[:-len('Classifier')]\n",
" for clf_type in clf_list}\n",
"name_to_accs = {clf_name: [] for clf_name in clf_to_name.values()}\n",
"ns = [10, 40, 100]\n",
"\n",
"for n in ns:\n",
" for clf_type, clf_name in clf_to_name.items():\n",
" clf = clf_type(n_estimators=n)\n",
" accs = my_cross_val_score(clf, X, y)\n",
" name_to_accs[clf_name].append(100 * accs.mean())\n",
"\n",
"pd.DataFrame(name_to_accs, index=ns)"
]
},
{
"cell_type": "code",
"execution_count": 201,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1. age :\t0.23305355529735855\n",
"2. capital_profit :\t0.14523852278454322\n",
"3. education_num :\t0.13031006001210027\n",
"4. hours_per_week :\t0.11357968849076419\n",
"5. marital_status_married_civ_spouse:\t0.06838558228936456\n",
"6. relationship_spouse :\t0.06110261758208114\n",
"7. marital_status_never_married:\t0.022509801267175354\n",
"8. occupation_exec_managerial:\t0.02015727195879177\n",
"9. is_male :\t0.01757340850957281\n",
"10. occupation_prof_specialty:\t0.01569921585932996\n",
"\n",
"Contribution of 10 most important features: 0.827609724051\n",
"\n",
"Contribution of workclass: 0.03644562231521494\n",
"Contribution of marital_status: 0.10218251322325284\n",
"Contribution of occupation: 0.08715169852717543\n",
"Contribution of relationship: 0.0950343530796143\n",
"Contribution of race: 0.014603455499365158\n",
"Contribution of country: 0.024827122261038316\n"
]
}
],
"source": [
"# Find feature importances\n",
"\n",
"clf = RandomForestClassifier(n_estimators=100)\n",
"clf.fit(X, y)\n",
"print_importance_data(X.columns, clf, multi_cat_cols)"
]
},
{
"cell_type": "code",
"execution_count": 202,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Average depth: 54.11\n",
"Average nodes: 15528.66\n"
]
}
],
"source": [
"# Stats about random forest\n",
"\n",
"depth_sum = 0\n",
"node_count_sum = 0\n",
"for est in clf.estimators_:\n",
" depth_sum += est.tree_.max_depth\n",
" node_count_sum += est.tree_.node_count\n",
"print(\"Average depth:\", depth_sum / 100)\n",
"print(\"Average nodes:\", node_count_sum / 100)"
]
},
{
"cell_type": "code",
"execution_count": 203,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1. capital_profit :\t0.54\n",
"2. education_num :\t0.09\n",
"3. age :\t0.08\n",
"4. hours_per_week :\t0.03\n",
"5. occupation_other_service:\t0.02\n",
"6. relationship_not_in_family:\t0.02\n",
"7. is_male :\t0.01\n",
"8. workclass_self_emp_not_inc:\t0.01\n",
"9. workclass_federal_gov:\t0.01\n",
"10. workclass_self_emp_inc:\t0.01\n",
"\n",
"Contribution of 10 most important features: 0.82\n",
"\n",
"Contribution of workclass: 0.03\n",
"Contribution of marital_status: 0.03\n",
"Contribution of occupation: 0.10999999999999999\n",
"Contribution of relationship: 0.03\n",
"Contribution of race: 0.01\n",
"Contribution of country: 0.04\n"
]
}
],
"source": [
"clf = AdaBoostClassifier(n_estimators=100)\n",
"clf.fit(X, y)\n",
"print_importance_data(X.columns, clf, multi_cat_cols)"
]
},
{
"cell_type": "code",
"execution_count": 204,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Average depth: 1.0\n",
"Average nodes: 3.0\n"
]
}
],
"source": [
"# Stats about AdaBoost\n",
"\n",
"depth_sum = 0\n",
"node_count_sum = 0\n",
"for est in clf.estimators_:\n",
" depth_sum += est.tree_.max_depth\n",
" node_count_sum += est.tree_.node_count\n",
"print(\"Average depth:\", depth_sum / 100)\n",
"print(\"Average nodes:\", node_count_sum / 100)"
]
},
{
"cell_type": "code",
"execution_count": 205,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
" 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
" 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
" 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
" 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
" 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
"[ 0.24791413 0.31866208 0.40381495 0.40799864 0.45967988 0.41367252\n",
" 0.46037128 0.46599978 0.45934324 0.4838581 0.45498294 0.47854952\n",
" 0.45846815 0.47023524 0.49454372 0.48934183 0.491371 0.4912983\n",
" 0.49415499 0.48888573 0.4815515 0.48720579 0.49140986 0.48781078\n",
" 0.47444548 0.466832 0.49155993 0.49084052 0.47422785 0.48462521\n",
" 0.49122316 0.49443611 0.49120826 0.4910786 0.48164671 0.48266937\n",
" 0.48448382 0.49111735 0.48036037 0.48889572 0.49453881 0.48937479\n",
" 0.49624664 0.48010991 0.48347391 0.49632824 0.49484594 0.49252391\n",
" 0.49256823 0.49043583 0.49584646 0.48507159 0.49377603 0.49866603\n",
" 0.49572367 0.48867822 0.49604731 0.49448118 0.49208686 0.4927865\n",
" 0.49678316 0.49384536 0.49487045 0.49447138 0.49506091 0.49627152\n",
" 0.4903995 0.48619787 0.49832104 0.49647675 0.4985203 0.49889239\n",
" 0.49901818 0.49598562 0.49651015 0.49462048 0.49392358 0.49309441\n",
" 0.49273712 0.49369469 0.49416888 0.49482084 0.49484625 0.49420075\n",
" 0.49372265 0.49447959 0.4950211 0.4993276 0.49850616 0.49780793\n",
" 0.49790584 0.49478888 0.49743099 0.4955966 0.49778702 0.49478478\n",
" 0.49907324 0.49462968 0.49742011 0.49456702]\n"
]
}
],
"source": [
"print(clf.estimator_weights_)\n",
"print(clf.estimator_errors_)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We find that some of the most important features are always the same (although not in the same order), indpendent of the classifier used to find them."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Binning"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We need to bin data to apply association rule mining and naive-bayes.\n",
"Let's analyze distribution of continuous variables so that we can bin them appropriately."
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def get_rich_and_poor(data, condition):\n",
" if condition is None:\n",
" rich_people = data[data['is_rich'] == 1]\n",
" poor_people = data[data['is_rich'] == 0]\n",
" else:\n",
" rich_people = data[condition & (data['is_rich'] == 1)]\n",
" poor_people = data[condition & (data['is_rich'] == 0)]\n",
" return (rich_people, poor_people)\n",
"\n",
"def draw_stacked_hist(data, bins, attribute, condition=None):\n",
" rich_people, poor_people = get_rich_and_poor(data, condition)\n",
" n, bins, patches = plt.hist([poor_people[attribute], rich_people[attribute]],\n",
" bins=bins, stacked=True)\n",
" _ = plt.setp(patches[0], color='blue')\n",
" _ = plt.setp(patches[1], color='gold')"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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SJBXBUCJJkopgKJEkSUUwlEiSpCIYSiRJUhEMJZIkqQiGEkmSVARDiSRJKoKhRJIkFcFQ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\nkqQiGEokSVIR/j+45vpCAZFdgQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f8e697f2400>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"draw_stacked_hist(data, 50, 'capital_profit', data['capital_profit'] != 0)"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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X9xj6Lk9f+boZz/HwyMdmLA8Mtk00HNj2RMPBwYEdWp7rMTP6MTizH3ff3eA9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\nscv/AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f8e695333c8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"draw_stacked_hist(data, 100, 'capital_profit',\n",
" (data['capital_profit'] != 0) & (data['capital_profit'] < 20000))"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\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_num</th>\n",
" <th>marital_status</th>\n",
" <th>occupation</th>\n",
" <th>relationship</th>\n",
" <th>race</th>\n",
" <th>hours_per_week</th>\n",
" <th>country</th>\n",
" <th>capital_profit</th>\n",
" <th>is_male</th>\n",
" <th>is_rich</th>\n",
" <th>capital_profit_binned</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>39</td>\n",
" <td>state_gov</td>\n",
" <td>77516</td>\n",
" <td>13</td>\n",
" <td>never_married</td>\n",
" <td>adm_clerical</td>\n",
" <td>not_in_family</td>\n",
" <td>white</td>\n",
" <td>40</td>\n",
" <td>united_states</td>\n",
" <td>2174</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>50</td>\n",
" <td>self_emp_not_inc</td>\n",
" <td>83311</td>\n",
" <td>13</td>\n",
" <td>married_civ_spouse</td>\n",
" <td>exec_managerial</td>\n",
" <td>spouse</td>\n",
" <td>white</td>\n",
" <td>13</td>\n",
" <td>united_states</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>38</td>\n",
" <td>private</td>\n",
" <td>215646</td>\n",
" <td>9</td>\n",
" <td>divorced</td>\n",
" <td>handlers_cleaners</td>\n",
" <td>not_in_family</td>\n",
" <td>white</td>\n",
" <td>40</td>\n",
" <td>united_states</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>53</td>\n",
" <td>private</td>\n",
" <td>234721</td>\n",
" <td>7</td>\n",
" <td>married_civ_spouse</td>\n",
" <td>handlers_cleaners</td>\n",
" <td>spouse</td>\n",
" <td>black</td>\n",
" <td>40</td>\n",
" <td>united_states</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>28</td>\n",
" <td>private</td>\n",
" <td>338409</td>\n",
" <td>13</td>\n",
" <td>married_civ_spouse</td>\n",
" <td>prof_specialty</td>\n",
" <td>spouse</td>\n",
" <td>black</td>\n",
" <td>40</td>\n",
" <td>cuba</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age workclass fnlwgt education_num marital_status \\\n",
"0 39 state_gov 77516 13 never_married \n",
"1 50 self_emp_not_inc 83311 13 married_civ_spouse \n",
"2 38 private 215646 9 divorced \n",
"3 53 private 234721 7 married_civ_spouse \n",
"4 28 private 338409 13 married_civ_spouse \n",
"\n",
" occupation relationship race hours_per_week country \\\n",
"0 adm_clerical not_in_family white 40 united_states \n",
"1 exec_managerial spouse white 13 united_states \n",
"2 handlers_cleaners not_in_family white 40 united_states \n",
"3 handlers_cleaners spouse black 40 united_states \n",
"4 prof_specialty spouse black 40 cuba \n",
"\n",
" capital_profit is_male is_rich capital_profit_binned \n",
"0 2174 1 0 2 \n",
"1 0 1 0 1 \n",
"2 0 1 0 1 \n",
"3 0 1 0 1 \n",
"4 0 0 0 1 "
]
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Manually bin capital_profit\n",
"\n",
"data['capital_profit_binned'] = 0\n",
"data.loc[data['capital_profit'] == 0, 'capital_profit_binned'] = 1\n",
"data.loc[(data['capital_profit'] > 0) & (data['capital_profit'] < 6000),\n",
" 'capital_profit_binned'] = 2\n",
"data.loc[(data['capital_profit'] >= 6000) & (data['capital_profit'] < 12000),\n",
" 'capital_profit_binned'] = 3\n",
"data.loc[(data['capital_profit'] >= 12000) & (data['capital_profit'] < 18000),\n",
" 'capital_profit_binned'] = 4\n",
"data.loc[data['capital_profit'] >= 18000, 'capital_profit_binned'] = 5\n",
"\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def draw_bar_chart(data, attribute, condition=None):\n",
" rich_people, poor_people = get_rich_and_poor(data, condition)\n",
" vc_rich = rich_people[attribute].value_counts()\n",
" vc_poor = poor_people[attribute].value_counts()\n",
" min_val, max_val = data[attribute].min(), data[attribute].max()\n",
" size = max_val - min_val + 1\n",
" rich_arr = np.array([0] * size)\n",
" poor_arr = np.array([0] * size)\n",
"\n",
" for age, count in vc_rich.to_dict().items():\n",
" rich_arr[age - min_val] = count\n",
" for age, count in vc_poor.to_dict().items():\n",
" poor_arr[age - min_val] = count\n",
" y_pos = np.arange(min_val, max_val + 1)\n",
"\n",
" df = pd.DataFrame(np.array(np.transpose([poor_arr, rich_arr])),\n",
" columns=['poor', 'rich'], index=y_pos)\n",
" df.plot(kind='bar', stacked=True, color=['blue', 'gold'])"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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n3CP4o6OCCBERkTqT9OmM3cL/HwIOBaYDuwNXA9sDG3Ke3wrsEP7uL19ERETq\nSNKnM1Lh/2+Z2T8AnHMXAouAuws8v6uf8orl95JOp0inUzQ0+Ngo+j/37+jxqFHpktPT6VTe9CTL\nitLVXrV3uLY3nleoLYW2TyK9XstSe9XeempvOZIOIl4I/78cS1uDDwYa6TuqMAlYH/5eXyC/pdTK\nJ00aTyqV6n7c1DQu79/R44kTx5ecPmHC2LzpSZYVpau9am8i7W0tUEcrJW1Tbll92punrNx6CymU\nl1R6vZal9g5uWWpveWWVIukg4lmgDdgXeCSkvRHIAncBH8t5/mzgL+Hvh/DzIq4DcM6lgZnAT0qt\nvLV1U/dIRFPTONra2uno6ASgra0d6NlRbW3tbNiwqeT0jRs3501PsqwoXe1Ve5Nob1OsTfE64unF\ntim3rNz25isLyPv5jBTKSyq9XstSe9XeemhvbqBfikSDCDPrcM79FPiKc+4B4BXgfHxg8AvgfOfc\nKcD1wGHAXOBtYfMrgRudczfi14g4F9iMv9SzJJ2dXXR29pz96OjoZOvWzu6/46K8UtM7O7vypidZ\nVpSu9qq9w7W92WyWJ57IdB+4pk1rZvTo0QXbNFjp9VqW2qv21lN7S5H4OhHAecBvgWXA3wADPm9m\n64F3A58D/gV8BzjRzDIAZrY4bHsT8BI+yDjGzF4bhDaKSA1kMi2sXXYwrJnD2mUHk8mUfLZSROpQ\n4utEmFkWHyh8Lk/eH4AZRbZdACxIuk0iUj+ap8Lst/q/o8uxstksmUxL9/DqlClTSae1Kr9IvRuM\nkQgRkbJkMi2sW3EITa0Hk1kyh8ce0wiFyFCgUF9E6kJ8hKKttk0RkRJpJEJEREQqoiBCREREKqIg\nQkRERCqiIEJEREQqoiBCREREKqIgQkRERCqiSzxFpG7lLkJVbKlsEak+BREiUreiRaiapwKtsHY1\ndHTcx4wZs2rdNBFBQYSI1Ln4IlTQs1S2iNSe5kSIiIhIRTQSISLDSjabZdWq/m83LiIDpyBCRIak\nQsFCfB6F5lCIDC4FESIyJBULFvLdblxEkqcgQmQE2bJlC5nVPY8zq+H107fUrkEDpGBBpLYURIiM\nMCfPPx94c3j0JAsXFn9+PPDIrIbJM4Zu0CEiyRpWQUTuwjRTpkwlnR5WXRQZkMbGRuBYYHZIWU5j\n46Z+t+sJPJ5k0aLBa5+IDC3D6hs2k2nhqKPWAM1AhiVL2pk+fUZtGyVSA0mOHvQOPJbT2NieRBOr\nTj8yRJKh1X7sAAAgAElEQVQ3DD9BzfT8yhqaBzuRJGj0oLf4RMzMMmibs1Q/MkQGaBgGESIyXEYP\nkhafiNlW26aIDAtasVJEREQqoiBCREREKqLTGSIC9J2MOZTXjxCR6lAQISLd4pMx+1s/QkREQYSI\nAPkmY/a/foSIjGyaEyEiIiIVURAhIiIiFVEQISIiIhVRECEiIiIV0cRKEamI7u4pIgoiRKRiuj+H\nyMimIEJEKqL7c4jIoAURzrkrgM+bWTo8PhS4FJgGPANcamY3xJ5/JvBZYCdgFXC2ma0crPaJiIjI\nwAzKxErn3L7AR4Gu8Hhn4DfAj4DXA2cBVzvnZob8Y4ELgZOAycCdwELn3LjBaJ+IiIgMXOJBhHMu\nBVwJfCeWfCJgZnatmWXN7B7gduDUkH8acI2ZPWRmrwGX4QOQY5Nun4iIiCRjMEYiPg20AzfE0mYC\nuacmVuJPpgLMiuebWRfwSCxfRIaQ6MqN5S3+yo0tW3TlhshwlOicCOfcTsDXgINysrYH1uaktQI7\nxPI3FMkXkSFGN/MSGf6Snlj5HeCnZmbOuTf089wUYc5Ehfl9N0ilej1Op1OMGuUHWxoaeg+6NDSk\nGTUqXXJ6VFZuepJlqb1q73Bp79ixY4hfuTF2bHvN2ttfHdlslsceayGdTjFhwlg2btzM3nu/hdGj\nR3dvH/8/t+x8eUml12tZau/wbm85EgsinHOHAQcAnwxJ8W/09fQdVZgU0ovlt5TThm237T0Pc8KE\nsUycOB6ApqbeeU1N45g4cXzJ6VFZuelJlqX2qr0jor2t/af3qqOVAZXVXx3Llz/O2mUH0zwVeBGe\nXg0TJixj9uzZZLNZHn300e5t9tlnn+7gIrdN+SSVXq9lqb2DW1at21uKJEciTgR2BJ5xzoGfb5Fy\nzv0TP0IxL+f5s4G/hL8fws+LuA7AOZfGz6P4STkNePnldmCb7scbN25mwwZ/O+O2tnagZ0e1tbWz\nYcOmktOjsnLTkyxL7VV7R0J7mwrUHU+P1xFPr6SsUupongqz39p3m5UrV3QHGJnV0Na2lJkzZ3U/\nr6EhTVPTONra2uno6Ew8vV7LUnuHZ3ujHwHlSDKIOBv4auzx7sCDwD6hnvOcc6cA1wOHAXOBt4Xn\nXgnc6Jy7Eb9GxLnAZvylniXr6up99qOzs4utW/1Oy915HR2dbN3aWXJ6VFZuepJlqb1qr9qbfHsH\nWkc8wNgQ0gs9f7DS67UstXd4t7cUiQURZvYy8HL02DnXCHSZ2fPh8buB7wM/BNYAJ5pZJmy72Dl3\nHnATfh2J5cAx4XJPERnmdB8OkaFp0FasNLOngYbY4z8AM4o8fwGwYLDaIyL1TffhEBl6dO8MEak5\n3YdDZGgalGWvRUREZPhTECEiIiIV0ekMEalb8QmX4Cddvn66Jl2K1AsFESJS13omXIKW0BapLwoi\nRKRqci/l7G9UofeES/CTLjcNZhNFpAwKIkSkqnRjLpHhQ0GEiFRN30s5NaogMpTp6gwRERGpiEYi\nRGRYKXfehYhUTkGEiAw7gz3vIpvNksm0dN8FccqUqaTTOpzKyKN3vYgMSYVGHKox7yKTaWHdikP8\nLcKXQducpUyfXvDWQCLDloIIERmyanmlR/wW4W3VrVqkbiiIEJEhSVd6iNSers4QERGRimgkQkRG\nhNw5FJNn6KoNkYFSECEiI0Z8DsWiRbVujcjQpyBCREaEvnMo2mvcIpGhT3MiREREpCIaiRARqYJs\nNsuqVRmamsbR1tbOtGnNjB49utbNEhkQBREiIlUQX6Bq7Wro6LiPGTNm1bpZIgOiIEJERrT4VRsw\nsPtt9LccdnyBqg0DabRInVAQISIjXs9VGzCQ1S+1HLaMNAoiRGRE633VBsRXv6zkjqBaDltGEgUR\nIiJF1PL+HCL1TkGEiEgBuj+HSHFaJ0JEREQqoiBCREREKqIgQkRERCqiOREiImXSHUFFPAURIiIV\n0B1BRRREiIiUTXcEFfE0J0JEREQqoiBCREREKpLo6Qzn3BTge8BBQBZYDHzezNqcc/uGvH2BfwAL\nzOy7sW0/DMwH3ggYMN/M7k6yfSIi9Ua3CJehLOmRiDuAVmB3YD+gGbjcOTc25C0BdgZOAM5zzr0P\nIAQYPwe+COwAXAHc6pzbJeH2iYjUlUymhbXLDoY1c1i77GAymZZaN0mkZIkFEc65bYHlwHlm1m5m\n64Br8aMS7wIagYtD3sPAT4DTwub/AdxpZovNLGtmNwAtwElJtU9EpJaiy0KXt/jLQrds6bksNLpp\nV/PUGjZQpAKJnc4ws5eBU3OSdweeA2YBq8ysK5a3Mvb8WUDurW1W0nNbPRGRIU8385LhZtAu8XTO\n7QecAbwH+DCwIecprcCk8Pf2BfL3LqfOVCrV63E6nWLUKD/Y0tDQe9CloSHNqFHpktOjsnLTkyxL\n7VV71d7k21vNfdLZ2dFrEapd9+vo3idjx44hflno2LHtReuIP47/X2l6kmVVow61t3btLcegBBHO\nuXcAtwNfMrN7w6TJXCmgK096qfl9bLvtuF6PJ0wYy8SJ4wFoauqd19Q0jokTx5ecHpWVm55kWWqv\n2qv2Jt/eau+TubHRhj/+cUxp+6S1b3q+NuVTbnqSZVWjjiTLUnvLK6sUiQcRzrl3A78ETjez60Py\neiD3bN8k4KVY/g558teXU/fLL7cD23Q/3rhxMxs2+Fv3trW1Az07qq2tnQ0bNpWcHpWVm55kWWqv\n2qv2Jt/eau6T9vatxEcbXnuttH3SlKfuSENDuvvKjY6OzorTkyyrGnWovdVvb77gtT9JX+J5AH4y\n5QfN7J5Y1kPAp51zaTOLejEH+Essf1ZOcbOBG8upv6ur98BFZ2cXW7f66nJ3XkdHJ1u3dpacHpWV\nm55kWWqv2qv2Jt/eobpPCrVpoOlDrQ61t3btLUViQYRzrgG4Gn8K456c7LuANuCrzrnLgOnAKcC8\nkH81sMw5Nxe4FzgR2BM/oiEiIiJ1KMmRiLcD04D/cc59Hz+fIZrX4IB3AwuA84AXgC+b2W8BzCzj\nnDsRvxjVFOBx4F1m9s8E2yciMmRks1kymZbuIecpU6aSTo/qk64FqqSWkrzE8w9AQz9PO7DI9rcB\ntyXVHhGRoSyTaWHdikNongqZZdA2ZynTp8/olU4rrF0NHR33MWNG7hlhkcGnu3iKiNSpaBEq8OeD\n86VD3+vjRapFQYSISA1FK1mCX1vi9dO3FN9ApI4oiBARqTGtZClDlYIIEZEaamxsJL62RGPjpn62\nEKkfSd/FU0REREYIBREiIiJSEQURIiIiUhHNiRARGSay2SyrVmW0CJVUjYIIEZE6lHvp5+QZW/qk\nR3nRZaHxhai0CJVUg4IIEZE6Fb/0c9GifOmQe1lofCEqLUIlg01BhIhIHep76Wd7nnTQZaFSS5pY\nKSIiIhXRSISIyDBX6I6gIgOlkQgRkWEumnDZ1HowmSVzeOyxllo3SYYJhaIiIsNEsZt5FbojqMhA\nKIgQERlGdDMvqSYFESIiw0S5N/PSXAkZKM2JEBEZ5qLTHMtb/GmOLVt6L06luRJSKYWcIiIjQKGF\nqwrNldAS2lIKBREiIsNcoYWriim0hHah4EKnRkYmvcIiIpJXviW0CwUX8fTMMmibs5Tp02fUrO1S\nHQoiRERGqEI3+epPoftz6DLSkUdBhIjICFZorkShNSeKrUUhI4+CCBGREaq/uRKF1pzIl17pqIYM\nbQoiRESkj0JrThRbiyLfqEbuhEtd6TG8KIgQEZEBKzSqEZ9wSWvvyZgy9CmIEBGRQRWfcAk9kzG1\nFsXQpyBCRERqothaFFpzYmjQqyIiIoMmPuES+l7pkftcgEceWcnSW45kj11hzXNw6HFLmDVrTtXa\nPNwlGaQpiBARkUHVM+ESSrnSA+Cyn/WkH3qcT9MIRTKSXBhMe19ERAZN7wmXUMqVHoUmaWqEIhmF\nRoAqoSBCRESGjHwjFJUY6aMahRYZK9fI2WMiIjKkVXIjsUIKjWqMhHUtktyPdRVEOOfeAPwQ2B94\nBfiVmX25tq0SEZF6V+zuooUuI803qhEPLsAHGJs/8DvmzNm/Jv2qd3UVRAC/BpYDJwA7AXc5514w\ns+/VtlkiIlLPckcWoi/+QunFfo33BBcAT3LwB6rfn6GiboII59x+wHTgUDPbCGx0zn0X+DygIEJE\nRIqKjyzEv/gLpedTbCKoFsfqq26CCGAmsMbM4neQXQk459yEEFiIiIj0Ucm9PsqVybRw1FFrgGbg\n7yxe3Nm9ONYtt/w/0ukUEyaM5eij30M6PapgOiQXkNR6Dkc9BRHb0/vW9ACtsTwFESIiUmPN9IxS\n+IAkk2nhzDPXEY12LFnSwvTpMwqmQ+HTL8XmduS7mqTYHI5qjJzUUxCRTyr831XSk1Mp4A7gceBJ\n0ukjGDUqDUBDQ7pXXkPD4YwalQ7pmVBChoaGN+VNT6ffnCe9+DblldWT3tPe/stSe9Vetbf2dQy1\nfaL2DqS9hb5H3gTsDXSRTqeKpkftvexnJwG7AOs49Lg0o0alWbUqw+GHX92dvmTJJ5k5cxarVmU4\n6qifdqffe+9p7LvvzJxyKKmsYvuxXKmurpK+nwedc+5U4Dwze3MsbQ7wJ6DJzF6tWeNERESkj8pC\nj8HxEPAG59ykWNoc4HEFECIiIvWnbkYiAJxzfwIeA84BdgXuBC4zs6tq2jARERHpo55GIgA+hA8e\nXgDuBX6uAEJERKQ+1dVIhIiIiAwd9TYSISIiIkOEgggRERGpiIIIERERqYiCCBEREamIgggRERGp\niIIIERERqYiCiDrinHu7c66x1u2oNufcrs65VP/PHH7Ud/V9pFHfh1ff6/0GXP1yzv0UuMnMFufJ\nmwvMAH5rZiudc+8BTgEagGeANmASsBV4Dh9UHYC/Rdsk/I2/XgL+AvzIzH6bU/4o4IvAQcD+wP5m\n9oRzbjzwdeAtwL+b2Rjn3MXAGaGu24EzzeyVWFn/CXwZ2Mc590/gfOBkYDLwMnAxcDXwTeB4/KJc\n/wL+G/iGmXXEyhoLXBT6foeZfd859x3gP0Ifnwt93y7W96eAMfhb1O0Q+r4+9H2BmcXvSBPVMw/4\nr9Cuq8xsvXMuDZwe+j7XzKY45z4JfA7YEvp+iZltiRX1ODANeD6U+4nQ913xt8m7AH/XmzNC32cC\nTwPfNLPrc9r0mdDvhWZ2u3PuDODUkL0E/3pOp/fr/hz+DjUzS+l76PdBwGHAAWa2PqRHff86cLyZ\n3V2s7865DwJXAvsAz5fQ78mh3bcAnzSzXou8FOl7Y+hjKzAx1u8/AQZ8FP+eV99r2/dd8J/DFjM7\nJ5Q7kPf8UOp7tV73sj7rhfpeyXGugr4XfM0r7PvjwDZhm9y+/9LM/plbRymG/GJTzrktwP/hd/a5\nZvZESD8D/wXXgv+C+mr4dw/wfmA0sBr/Jv01PgjYG79a5teAJ/F3EZ2E/6D9B/BlM/txrO4rgPeG\n7c8GNgMnAXOBdwK/DekXAscBlwD/AzQBr+G/0CJ7AJ34F7sJeCU8fx4wAdgJeBb/prgc+AHwZ+Dt\noU/vMbNsaNcPQv2LgGPwS4nvEZ7/yVD2M6Gea/Bv6o+E+n8MPJzT97nAiWb2m1jf5wPnAq/D3/fk\nTcB7gPcBnwD+CHwg1Hdh6C/4D8w/gL/H+n4I/sD2Gv5DdCFwFT6ge0Po8+/x91L5CT5wexqYAlxj\nZqeFNn0dHyQ+EPr/y9CGHwJvDXlPhPxjgWvxH9QjgXbgRyG/YN9j/V4Syn4p7Ps/O+f+K/T99cCL\nwFf66fvBQAewHNgxbFes32vwB6GtofxTouC5SN+XhPStwF/x7/dr8e+pDwI7498n9+MPOup77fq+\nBv/FtBf+y8DhjzuVvOeHWt+r8bqX9Vkv1ncKH+d+GF67fMe5cvue9zWvsO974b+rtuC/r3K/3/YE\n3mVmf6RMwyGIeBX/RXY2MB//ofgesAD4uJktc869PaQfAlyBf4EeAa4L2xwBHAp8PPzbzcyOjdVx\nCT56fR/+zRc5HbgR/yH8An7Z7uvwH6D9zOwZ51w7/sWabWYtzrlbQ13g34yR3+Hf3Kfiv8iPNLOH\nnHP/wn9BvwkfMe5tZuace9XMtnHOvRe4Gfhn2O524Df4UZF1zrk98G+YqcDdoX9P47+0j8UHWrsB\nZ+Hf+J83s1mh39Gb9i34IOjCWHu/iR+B+EIYafkcfvTjVeAoM8s45zaHfXEMfsTkzWF/HRf2F2Hf\nXBNevw2h3FPCL5qX8F/+zcBdwDvC6xn1/Quh/Y+G8r8e9ttfnXPNwCpg37DfW/Af8HPN7M3OuYOA\nz4ayz8IHY0eY2cH99D3q99PA90M/LgL+Hbgt7NPl4f9b+un71fjA8wL8yNMJxfod2vUq/qBzH/6A\n9nKxvgM34N/jfw1lnQx81sxOCPtkOfDmqN/qe+36Hup/Cf++vAT/i/JDlbznh1rfq/G6U+CzHut7\nseNcbt/zHufM7I/OuYfwX8qdob5IWX0v8prfGfb30WX0/c3477435fY99P8U4PTo2F+OIX86AyAM\n5V/unLsaP5z0K/wv93Odc0/hv2BH4X8xNwM/x0eEu+J/+f445D+K/zLLHdY5A//L/XX4N2BkG/wo\nRxeQDkNKJ+Mjve3CNl1Ag5m1hLa+3zn3PvyH7RP4L7UXnXMd+CjxT/g32sOhjq34UZNH8F+4z+e0\n7c/4N/Tx+DfsF4FxwG3OuRfwHwrwIyw7AstCOU34EZy3h8cPhLqujJX9fXyAtDnsz/NieTvgR0lG\nhX593znXhR9peTk8pzPsowfx0e4Z+F8q2wI3xkZOrsGfklrnnLscf7AglP0q/j4qDfgRlbir8aNG\nVwCfx0fz18dec/AfJoA3Aj8Dvh0eP4gPttKh7/fjTyf11/eo3wCjYv2+C9guHFQIfSjad+fclfiD\nz00hr9R+P4X/gvm30JZifX8j/hdnF/6XT9Rv8L9wZuN/4cWp77Xpe9T/u/Hv1X/l6X+p7/mh1vdq\nvO6FPuvxvhc6zuX2vdBxjtD3z+G/8PcALh1g33Nf85+GvnwzHOPPKaHvDfgfmx15+g5wPf5HVtmG\n1cRKM3vZzL6JDw6eBbL4oaHP4XfeASF9Nv7L87mQ/wr+S/ocfDS6MafoM/AfhD+Z2SHRP/yb8ffA\n4aEuzOwW/BDWA865I8L2h+WUtxs+SPgn0BL7BRC5G3hX+Ps6fFD0BXxQ8iPn3BsAnHP/jv8lsNDM\n/mhmx+Hf9H/DD6W9gB8teQk/xPV4+P9k/FDeyfgI+wn86YxPhToiXwFWAN8AHjWzN0b/8MHLJfQM\nW2JmPyCcY3PO7R2STzazTjPrMrPv46PkduAx59yR9PUA/tQSoW8L8CNA64GLnZ+HQhhh+Tlwv5n9\n0sxmh36swH8g3xnqifbjk/jo/6nweC5+/0d9PzrUUbTvUb/D36/F+v1zoNE5d2pI6yyz7yX1Gx/w\nRf3uLKHvT+J/GR4T+h71m7DNJTn9Vt9r1/eo/7cA64DvUvl7fqj1vRqve6HPetT3f1LgOJen73mP\ncyG/C/898zf8aYSK+17gNf83/LF9Yvj7/SX0/Sn8SHy+voMfHX8mT3q/hsNIRJ+ZrmbW6Zz7FvAt\n/JyI7fBDOv8PH61FEeBy/NDQ5cBC/Jf314G/Oed+FZ6zA37OQCP+DRV3Dn7ewy457TgVHw3eDIwx\nsweiDOfcffgg5mgz+4Nz7hf4X//jwr+1oaw34U9NnIuPUC/FjxQch/8gpPFzIW4LfYv63h4m3PwG\nHx2vw7+hbqf3SMNz+Ej5eHzQdGfo4+Kw76K+RxOKooAocl7YZ7lXk5yBPz/3EDDazH4Z6/t1of0n\n4D/4P3TOrQzZD4bRmCb863VoKOv+sB//AZwW+joaH6itwH/wIheGul8K+/LDwC9DkJbCz4lZ5Zxb\njP9AnY7/0N2Jn1R6fwl9Pw9Y6Jzbi9hrbmZfdc7tjv/VEB1MivY9tHEM/tfC+P76HU6NjcUHye/P\n2e95+44/MNwWntMSyjw9PP5d2CdPOed+jD+dVO99343er/lw6jvASuBE/C/JT+Ff67Lf8wPo+4E1\n6ns13vPFPuv74efE/Synjrx9p8TjnJndFgKIsvtOgdfczJ5yzp0d6/uPS+h7G37UfRRwc/iOiPf9\ncPz7rmzDYU7Ejy024SQn7xj8ZJrFZrbcOXcI8Gn8kE8X/lf4QjP7RXh+M35yyjhg+1DMevwb5S5g\nJzNbm1PHePxpgg4zeyaWPhk/ejHHzK6LpX8UH4V2xMtyzn0RP/Hls6H+NWb2Qsg7FD97d2f8GyuF\nP4f2P2b2T+fc7nnaFR0Mf2tmrzrntscHDDuFvm8BfmVmT4bn/xt+wtXbQv3gR1eWANeb2YuxsrcP\n22+LPyf505y8vfHBxQFhZChKPxVYamZ/Dmlj8EN0Ub//FYppM7OHwzb/HurZjP8CSeEnKF1gZg/l\n9Hl7/NyPPUM9a51z0/CneDrwp6umhKcvNLPfh212x38I30Dv170FuCHPvv0M/hxkOidAPD7Uc6CZ\nnZWzzZdDmx4Mj8fif30cjj+ItEb9Dvkn4Q847bF+v4CfuPteM3uVHM65c0I5S0Lf98KPNn0EHwA/\nFfU7PH8ePsicFf5tjz/IdOLf77+0nln4jfj3eRr/HhpnZg/E0qNg9Yio77G8j+F/SUV9bwL+Fx+c\nR697G34Yd0f8iOHr6HnNx4b9uquZdQfMOXVMxJ8Tjl736fjTe9vg3+9r8UHvg7F+7IL/BRv1vQv/\nvr0L+EWs76Nj2+wY63uUvg7/iy/e9yjv48C9sb6PAW7N0/dMeP7b8J+tR0Lbotf+cOC8+Hs+VsfE\nUF78PX8u/nOcCn2/Az+nasfw+u6e03foOdZdH/U9Vtfu8dc9J+/dwOElvOfH4L/wjgA+E/U99p7/\nEP6znvuen2Nmp5OHc+4t9H7d98KfJo4+52vp/Z7fF38c3CPW763449x18eNceP52+ABmLLBN1PeQ\nPh3/eYkf57YLfbsvOs7F+n4DfvSh+3UPx7ntwj6ZgB/tiPp+EHBhnuPcduHP3XL6Pg1/On5X/Hvq\nWXqOc9vhf7yehD9G5r7mP7E8V6aUYsgHEUkJB7bL8S/cc8DFZnZvyNsWuAz/JXgf/pKb+3K22RP/\nQSqYnlNPqWVFdZeUXmEdO+JHKN6CHwEZgz+N8hH8mzmazXwuPtr9CD2Xv7aFtItieYW2yU2/jp7z\njS8OsKz+0uN1XI8/xxjvy++BeWbWPefE+Utt94mnVZKeZFkJ1/EqfnLd8yEY/hE981za8UHO1/Aj\nVlH65jzpDfgvph/gA7bR+F9exbZpDOlX9VPH/8Tq6Cihjgtj2zQW6EcDPkD+P/yX9rb4X9Pvwb9/\nOvGTfb8etonSuwqkb8EPXb8NH7T8uJ9t0kXqyC2rnHZdFPreXx1p/K/yo81sNUEYCfwVfp7Wc3nS\nv2hmzxJTKK+EskpKD3md+KCzpHYVSd8e/x6fgQ8o5+OPBe8P+/BF/KjsbSHvBnpGPzbjr3I4q4T0\nYmVFx6dSyxpIHb/Az6GIb/Mv/Ocofnn/H4ErzOxRKjAigggXZrkWS3d+cl8z/sX6Hj4i/JKZ/TCW\nty/+l/MZ+APZfjnbZIulJ1TWa8CX8L8g3pJQHQfif2Xcgj/YXIW/vKwT/ytnHH4U5H9D+lfwB6ZZ\n+OG2NP5XTu42k/Ef5iTKim/Tgb80qVgd8fR4HU/hP2RRHYcQJsaG/6MPRO58oc5+0nPzCqWXUlY1\n6ojyoj5fjf+V9GX8L+UT8ftsK35/falI+hr8KM/f8L/itw1lFdumlnWswb+XdsMPX++B/2V+IX7m\n/Hr8pLRd8b+Ei6WvwV+BMBl/arMplFvrsorV8Rh+9GQn/HHgu+bXPtiMD0TOxk/euyKMdOZNByh3\nmzLSvxsbCUqqjuvwIzG34K+kS+MnR34Nf/XDLPzo9Mshrw0foO6AP6bshJ9XViz9wiJlNYTXo9Sy\nCj2/WB2FynoS/4NpPT7w6MQHGa34y15PxF/eejtlGilBRLuZjSuW7pxbh78sc104D3cgsBg4E/+L\nfz9gtflLbvYLeWmgOWzzKv4XfrH0JMtKuo6pZvZS6Hsj/gv2D/hAJY1/M04EnJk96Zx7Hn9a4ffh\n/0yxbapRVoV1nIFfdOcq/LnI2eHtcQH+VMBV+BGL5/pJT+F/2b6G/+UbvxS43LKqUUcKH1Dtj/+C\neQR/WbBFn4twiusJYK9i6eA/S/gh9T/gD2Rvr6SsatQRynoF/xn/Hf79Mj18Ll7BB6zbhP2yW7H0\nWFlvxQc5W+qhrH7qeBUfOK3Gf6G+P/w/Dz/MPQkfePWXfjv+1Mc2zrkpJW5TyzreG/bns2H09Xn8\nKbIXwvH/neG9tW+Uh5+j0hzeIxl6grK86UmWlXAdy4Gj8HMuWvDv/V+Y2TvCe+JI4L/NbC/KNOSv\nznDO3VDg35roHzCmv3T8G+6K8PeocB7qPfgh0/H4yUcpgFjetviDGkCqhPQky0qyju3wUW2kw/xc\niXH4KHZj+DtKJzx+HpgQDsz9bVONssquw8zOxp9vPgL/HhhrZk+b2X/gh/7m4K8V7y99Taj3OHxw\nds0AyqpGHWvCfllrZk+H/RYtjBP9soie0196lLcGf+74lQGUVY06CNu/Fsr6B350K0qfjJ+T0VlC\nepT3JvxoR72UVawOQvoWM/sE/pjwIv7Hw8v4c+QzS0j/LjAunC5bOMCyqlHHtsDToax7wn6IrsQb\nh7+KYQx+/lmUNy7sy1fpmUReLD3JspKs43X49/+rYZt1+GAjcg9+NK5sQz6IwM9u3Q1/QIj/2xF/\ngIiWHe0v/QX8ubJu5lfvOik8/3f0HJSivD8Dtzrnzory+klPsqwk63gUv67EJ0P6nmGzh/DzCi7E\nR7JRepS3MKRTwjbVKKuSOjCzR/BzR54Efu+cu8Q5Nw5/4H0P/qqeUtLBR/8HlLFNLesA2CX8wvsT\n/hWwDPkAAALoSURBVL0DkHJ+It3X8PNe+ksHH7BeiL/6aSBlVaMO8MH2ffiRme8A1zjnDsav8bKU\nnvUEiqY7P1HvT/irsR6sl7L6qaMBf7rkBgAzW2Nm5+LPvc/En+K8ob9085c9bo7SB1JWleq4H7/Q\n4HfxP7LW4OcSgP8Ffxf+dNnXY3kr8e+57+C/rPtLT7KsJOtYgb8iZz7+eP8V/OgczrkJ+B8YT1CB\n4XCJ58fx5/XeZb3vRfG/IX0O8A8zO7mf9Cn4Gcyv0hNgYGYLnXMn4M9Vjs6p+wR8BHchPrormp5k\nWQnX8XH8ZaRfw1+SGl1lcg7+zfcK8M5YOvgV0KaEsihhm2qUVUkdOOeewJ9/PgL/i+U7+C/qceG5\nVzrnbikjvQsod5ta1AF+bkh06doU/OjGGPy51mjS3qn9pIMPXOeH9JkDKKsadYA/1bUtPsD6SUg7\nOvy/GT9f6NAS09vww8p71VlZhdJT+NME8UWVwI9OPor/kiklHQAz+3me5HLLqkYd/4n/4t0Zv07P\n4cAdzrkz8YFVdOVbY5SHHxU6hJ5Tie8qlp5kWYNQR/Q+2Iw/ZRZdNvp+/BUr0cKEZRnyQYSZLXbO\n/Qz/RfiDAumpEtKfwd/8altyrpc1s1udn71+eE76M8CezrmJxIaGCqUnWVbCdTzqnNsZf4nR3jnp\nc4EnLOcyR/yiVavIGSYttE01yqqkjuAzwAozawuPT3LOHYZ/H2wKZf+jnPRKtqlRHWebWbTyXlRm\nOvxi/aeF8+jF0oMZ+dLLLasadQS/xE/eW+ec24XelxQ+a2Yd5aYD1GNZedL3MLOl9JVv8bdi6ZVs\nU7M6wvFhd/ylvc+ZWZdzbiZ+XYwG/OjPuDx5o/DzhzIlpidZVpJ1NOGD6Y34hROjS+pvwl/OXdEE\nyRExsVJERESSNxzmRIiIiEgNKIgQERGRiiiIEBERkYooiBAREZGKKIgQERGRiiiIEBERkYooiBAR\nEZGKKIgQERGRivx/MH/Jaq1M1D4AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f8e6932ee48>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"draw_bar_chart(data, 'age')"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data['age_binned'] = np.sqrt(data['age']).astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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W7u4uZsxoVo+O2cQ4buPnmHXe+u9B078XTbM1jFvpULH+t/YHMvMPABHxbuDb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\nDF0JRaWYcgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f8e68fcef98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"draw_bar_chart(data, 'age_binned')"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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tKo+9ktt91yJifxw/2C8r+0wtx94T+8zuwHfMbDm8JXBbFj6+/4iu\nx/YO/KLuUDMbhR+f6vDj3wN4K/BNsSx5bP8Lfizewcz2xZOLLLAqfkzfBr/wO4Sux/ZL8AvIk81s\njRjjDPzY+nU8IXsxLqNky3SLRgjhQ+B1/CQPviPeTkweosPxE+/XzOyxEMLnZtY/hJABjsW7Wgbg\nzWjP4yfDe/CM8xD85DcW3+kG4CfdkfgO0x/4PZ7B/hTfGRvwL8NZeNP+YXgGvRx+kv1pXNYWMb5M\n3v/JDyyDt5zsA3yVzpaGa4AxcVlXA8SWlo/p7CZY3cw+i2XrArPNLBO32xD8SqQBb2F4At+Zfwws\nb/HXcuPg13l4S87cEEL/GANx/k/wL8J2wPi82HfAd+QdzGxyCCFrZpkQwj74l/dp4NuJmNrxK4SX\ngU3M7MMQwlt44paJX+LcmJu38MSvFc/O3wW2Agaa2adxvuXxVo5+ZtYaQqjD94sP8QNOzrp48pIz\nDP+yf4J/1tNjvXr8APRTvKn854nYO2LsrwJfMLOZIYRp+EEtGfuguL0n4vvQP/GuvLa4jdtCCPVx\nnhUTsdfHecqN/SR8P851icyNn8XH8fNbA08kkrGvlVi/Orzl6v9iHC/H9z2Q2OJUZbFXcrvPwr9P\nMxLx58eeK5vGsrPP1HLsPb3PtNHZFZ+ToevxMRvnexNPDubEdbkUv2A+A0802hOxN+AXmc10Pb6v\nmvhsVsKTmrPwfTZ3bP8R/l0dbGbz43d6Fp5crRrr9gc+MrOBlGiZbtHAk4jVgBPN7Ag8E2zFr3SP\niGXz8J317hBCLpPEfEDQSXhGmsGbwlbGWzqONrPz8JP4jXi/f66pfxLeKnAv3sVyJN70NYLOLosz\n8B3+zFjvbbw5a398DMIx+A71ezOrw1tcXsd35q+bWV0sz+KJ00S8C+LCGPuP8abEH9PZj57zCvAC\ncG4IoQFvKTknLosQwnLAz/Ck6o0Y67/xq1XwFoacI2O9H8fEbBP85H08/sXYDb+am4ln8LuY2Y5m\ntmPczvcAj4UQdo2fA2Z2X9xWa8V4jonvNR/YBf9y/SmEsDnwcKyXCSEMCiHsgF8l3INn36fi/aUz\nYpzJbPy6GMO34uvv4UnSi2a2npmtF7fLHOD8RNk8vPlxPN7cOhxoNbMOM7sSTxB3ibHvloh9yxjb\ndSGE1fCun3m5YEII68bP4lP8IPAlvFmzPf7bK866F34Q/DwR+x5xHcuN/YoY90Z4i1PygPc8fpDO\nj524/4Af0PeKsX8XT6xXi9v3kyqMvZLb/WP8ZPviomKP5dkCsdfyPlPLsXf3PjMPP77nYm8hHtsT\nx/d5dD22z8OTpn/hLTSD8daFH+HH9mPw7vhk7K/EdXqMzuP7A3Q1H2/dmEvXY3smbtPGON/W8fWn\nibq543vJlukWDYAQwil4xvqbOHbir8AEMzs6Tp+Ln+x3wE/uw82sPk7bEd+ZW0IIO+MtFyeb2azE\n8kfiH1IfPLN9GPi3mbXH6XX4SXOamWVjK8DX8Q9tCt6lsDve8vEgnjQ04wnCafiB+kt0NuvfDJxn\nZueFEFrxrPNgPKkZi1+19wkhXICfZP+KdyO8F+N4H995V8D7+D4ENo2r80+8q6UZ/0LsiCcvk/G+\nyAfie02N6xzwbqT14vJWxK9iDM/CVwb+g2fLVwMPJLb7PDxjH4e3DK2S2O5P4jv8cnRNkl7Am/Qu\nwAeA9o3LaKBz8O7deAI0Ck84lsOvZC7Ax7fk7lrpgx8sDorbeG28j3cUnhj1wa/Kzox1x5nZj+L+\n8u34ed0W6y9nZg0x9ptiWb8Y27wYw7P4AeK38bOqj7H0jZ/hQDw5uh4fUPxfvIVsLPCnuIxmOvvH\nL6MzOVo3fjYbVSD2g2P9OfH9Z+EtFPvmxZ5rgp6Lf+4v4k3AtRB7pbf7FPwk2CfW7RJ7jC+3v1fr\nPtMQy7prf+9N+8wNMY5/4i2rN+OtvK/g36Nd4nJOpPPYPhe/UDwbb93dnnhsj7FfgF8ID6Drsf01\nvOvlrrh9+se4343bagM8gbgR7/bMHdt/HLdxH/z8sH6M/TwzOzcOSr0Nb7m6mBIt88/RMLOLEn+/\nB2wem49yZQPin4+EEDbDM7nctCcTfz+OX53nL//lxMsXCkxPDprCzFrxgTh3JGb7UwjB8ObnffC+\nT/AdZzLeT9cEEELYHj+Bg59QPwVmmdmJIYQ/0dniMBAf93F7COF5/EA4FB//8IaZPRCX9UP8y7MK\nvnNOwe/S+ByYEkJ4G786OdHMJsTXh+JfkHFm9r3YpHY0fiUwJ77/jXjS9ADeFLkh3qyXsyF+oqrH\nrwr2TUz7bVyPIfgYl4Pj9KPxAbinhBAei9OXw78od+Cjxz8KIQw1sydCCOvhVzGT4vq9QGcrz3V0\nttgchY/YHoJ3u7TiVwW3mNknIYTx+EEDYDczezqE8EncbufE7ZF79sqreELTD//SnoAnTWeZ2ach\nhGPjsjfED5o74Vcs481sYlxObuDaeDN7LYSwFd4vvQ6eyF5rZk+FEH6Ld5GNjO/3Fn6FPRm41cze\nDyHcA4wNIQxIxP4WfqD7NT4QN6cJP6CtENdplxj/O+ZdTgeFEE7C96NNgCfxRHUY8OvYVPssfrV1\nuZm9GUJoxFvI9sD7j6+MsV+Md0tOpfNA2IHvLzfjB8XnErHtFl8fEOc9Bzg0dD7PZkqMfRB+R8Hm\n+Oj89/Cr4R/izcRfjNt9txj/I2Y2IW735/EuyLvjttsGP3GtFrftNfhA5JtjHLntvjm+Lz0A/MHM\nPg4hPAAMDyHUxWPAHnHeZOy55+5MxZP6vnGf+VFc5ln4ifvkAvvMM/jdArl9Jhf7PWb2XtxnLo7L\nfwtP9J/Hxybsh1/RJ/eZifj+/nHcZ9bIbffEPvMZfqfe2MTn0hSXnYv9MLzr96zEPnMsfoLLxf5H\nYEpiu/89bsvxcbs34knDhrntnthncnd55GJvi9vmpvhdfRK/Yk/G/p8Ye/K7OijuM9vh+/v38ePX\nccB78bv63bjPrB+30U54y/Ezed/VbYC/m9/1shV+cbYeXb+rN+HH6z7xfebjx5t/AKNj3b/QeZz5\nrpndHLtt/o1/V5OtyQPxuyRXx48BQ/FE4Q0z+wD4StxeIcY/Hv+uvkY8vsfv6ijgB+Z3rryJj9db\nG28tucrMborvNxUfzzaeMizzLRqSrrDwLcPn4dn+RYmybfHBVrnX5+K3+t2cN8/qecsqtt4aBd6v\n2GU9SOcttqfgrRtD6WzqnkMcLIw3ZW6DX30cmFf2GzpP2u/grVb3x2WBN90WWlZ+va/hJ6wlxXAx\nXZ/1ckg8wORuL9x8Ua+LLUurXgjhcjM7KXQ+l+Y+/KRSjycFb+MJIviJ9gb8wHpoXtnO+AmsPpbl\n6mXi6+sXUW8l/GSZie+3pGXl5hkYt3ny+Tlrxs8s19x9O34C2SV+NvPxZHlonKc1zrMS/pkmy3L1\niPWmF6g3KFFvSe9Xn5hnJXyfTD7n50j8CvwUM5sWP6f2WHaqmf27iLJTzGxa/utC88SyDuJ3La/e\nkt5vFTyh2QLfX36NJ565u9XGx/XKdd+8jyfrV+EXb7myr8X6pdYrVLYP/h1f0rIupeuzk3LjMdri\n3w3xc5sVy/vi+1pbgbIs3j1TTL0OvEVjxRhPKe/XL9aryDOelGjUiBDCZxYHIpVS1g312vAWgxvx\ng9tP8ANAPZ23EZ+KX8ldROdtxf3xhCQ5T7n1/kHnbV7FLGsoftXbHz9J556hsgV+Jf07vH91OTqv\nlp/Exx/8B/9STltCWQb/kpa6rGLrbcjCz3qpJbnBcLmusT3xJOpyPBnsgyeAG+MJQxu+rUYmygaX\nWW9plvU+Cz8/50O8FemPeIvBivgJ6LRYbyR+grkvMU87fqXanfUG0/U5P2vhLQcn44nUpXgL0Nll\nlJ1cxDyXxO1Xar1L8cRidTqfSbQJnS2Tu+NX7cvh3R5PxXXsiNvh/UTZivgYiVLrLc2yPqbrs5OG\n44nqnXgL2w74MWxDvGVsK7y1JtfikivLdXEVqvc63nKarJcrW1y9Qu+XXNZleFfLbnhyPtbM7qVE\ntXiA6q0yZZalXa8ev7f6KvPbZefhg15vjGU/ifMNBGbG19vj+15ynqWp95USl3VaYlm3mQ9qy936\ntqv5YNr98CbEvngT7P/gB5MNY70lla1Y5rKKrfc7vB/4TLxra258/Sv8wLIfflXYjDcb74e36tyQ\nV/anbq63bfx8cmOBPsUH6D1kZlPxA9x+QL2ZTTWzO+M2qsdvqU2WUWa9cpZVH/99CTg+lrXjV35f\nAk4ws4fjPtQXv2PqdTxJ2RbvukvOM6AH6rXF/Woz/LkSdXFZP8WT7zfwE9JU/ISzZgll/YpY1ptl\n1nsDb0G8CU+axuKtONuZ2Vl4K8XK+IXDPua3tH8Vb+nsm1fWv8x6S7OslfHv72l4F9pywPtm9j0z\n2wVPSLYFdjKzr+LdxP3j/8myVRdTb7sC9bYrol6h90su6wgzu9PMjo2fwYWUYZkfo1ELgj/jI982\nea/7hRDeySvrX0RZ2vUALo3NneAH4xbgvBDCVPOxLeA76V0hhA/N7PEQAnnzZCtYr5RlXRhCeDPx\n+hXwZ6rEdeoblwU+mIsSyspd1hLrmdmRwQciX4MnH1m8D/ka/MFCM8xsTJxnAt4tdJSZHR1C+E2y\nDO+X7c56HWb2TmxGb6FzzBF44vE0Xc3CrwizeWUrJV6XUq+cZc2M9XLPz3kTb8kYmleWe/hb7vV/\n8JN8Nm+eVfGr2u6s1wA+Vi2E8FN87NMn+DilYXjiV4+3PAyL9abjXUpLKsuWuaxi6+XuLLkxlhG3\nP3SOC8vQeZfEJ/H/fgXKyq1X7rKWp+uzkzJ460fOHfiA1dw8j+OfXd+8soZurjeEzvEuubKhlEEt\nGtVhJ7yfb17i3+p4RjmfzjslCpUlXxcq6456uWd+5G4BewIfePbn4LcMt+NXIccBtyfKkvNkK1iv\n2GXlbrFNvj4l+J1C0Hk1niv7Od7kOLGIslllLqvYermByNvE7d4HvzV6p/j6zhDC+fgdQDPxZvSn\nCpX1QL0+IYQN8OPPRKAhhDA0+F1hz+IHwY5EWTN+As3klWXLrLc0y7oKuCeEsC/ehdKGd1s8HEK4\nHE9YZgGPhhB+iHfhPYk3SyfnaemBenUhhJEhhO/ggxrbzOwU81tBt8Qf0Dc/8fpU/MReTFm5yyq2\n3t/xrof98a7aNuD+4FcHv4jbYB7w8xDC8LivzY3lybKOMustzbJmAmfG7+8ZcZ7ZACGEFfA7D9vj\nPCvg48da4r9kWWs312vBW5ZIlE2lDBqjUQVCCLvjfWGjrfOBUl3KQgif4V+yZFlu4FyyXpeybqg3\nF+/TmxCvXj/D+yD/io+J+GJ8/Sp+Vftb/JbWXFlynvUqWK/YZX2Mn8z3S7w+Az/ADcTHhVyNJy9Z\nfER9Az6ifHFluXqlLquoetZ5F9LUOM8Y/G6PbfAEcZtYP/f6S/jB5ddLKOuOermrQOL6ZOg6ZqOd\nzt9myCVck/F++WQZZdYre1lmtlkI4Xh80PNKLPyQpbpEWSZvWqF5urNebhBiC77PH2edd90B/n0u\np6wb6m2OD5JeE78j4kz8WUUNcZ1m4K0F69A5zmk/fLzN+omyk/ExH6XWq9SywFs8ct1udfhFwrn4\nnS8rxdez8KTrgkTZ7G6uNwvvBvp7COEw/BHlB5nZJEqkrpMqYGYPB79V8XD8LoRCZZkCZVn8pLmg\nXoGytOthZslbhjPW9Tbi2fjjzp8JIaxkZrNCvI04V5aYp5L1iloWfsvrcvithMnXb9P5DJX78MFQ\nT5lZC0C8yl1sWaxX8rKKrRd9PzcP3oKQe97Lu2Z2aOL1HPPnvyyprDvqZcLin0vzkcXfU8h/XWxZ\nufWKWZaZXRX8tsbc83MyePL0b7xPfp1EWTs+6HJx83RXvS7P+Qkh3M3CdiuzLNV6ZvbPEMJQuj6T\naAW8Va0Zb4Wajd99sxp+C+eMEMKjBcrGlVlvaZaV/+wk8AS8Af9OzwwhXIqPS2nDf8RsZvBbXheU\ndXc9M5sZ696O//hbWS0TatEQERGR1GiMhoiIiKRGiYaIiIikRomGiIiIpEaJhoiIiKRGiYaIiIik\nRomGiIiIpEaJhoiIiKRGiYaIiIik5v8D9K1pg4iBqJUAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f8e69146c18>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"draw_bar_chart(data, 'hours_per_week')"
]
},
{
"cell_type": "code",
"execution_count": 169,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4661"
]
},
"execution_count": 169,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[(data['hours_per_week'] == 40) & (data['is_rich'] == 1)].shape[0]"
]
},
{
"cell_type": "code",
"execution_count": 167,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"16674"
]
},
"execution_count": 167,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[(data['hours_per_week'] == 40) & (data['is_rich'] == 0)].shape[0]"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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IaLE4yMyywBeAs919obu/BZwOHGhmo0uMUURERAZISYmEu7/l7r929y4AMzPg88D1cZQR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\nI5GRkZGRkZFRG9mRyMjIyMjIyKiN/wCf4c6pTU2t2gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f8e69141898>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"draw_bar_chart(data[data['hours_per_week'] != 40], 'hours_per_week')"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Manually bin hours_worked\n",
"\n",
"data['hours_per_week_binned'] = 0 # Part-time\n",
"data.loc[(data['hours_per_week'] >= 25) & (data['hours_per_week'] < 40),\n",
" 'hours_per_week_binned'] = 1 # Full-time\n",
"data.loc[data['hours_per_week'] == 40, 'hours_per_week_binned'] = 2 # Mode\n",
"data.loc[(data['hours_per_week'] > 40) & (data['hours_per_week'] < 60),\n",
" 'hours_per_week_binned'] = 3 # Over-time\n",
"data.loc[data['hours_per_week'] >= 60, 'hours_per_week_binned'] = 4 # Too-much"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age_binned</th>\n",
" <th>workclass</th>\n",
" <th>education_num</th>\n",
" <th>marital_status</th>\n",
" <th>hours_per_week_binned</th>\n",
" <th>occupation</th>\n",
" <th>relationship</th>\n",
" <th>race</th>\n",
" <th>is_male</th>\n",
" <th>country</th>\n",
" <th>capital_profit_binned</th>\n",
" <th>is_rich</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>6</td>\n",
" <td>state_gov</td>\n",
" <td>13</td>\n",
" <td>never_married</td>\n",
" <td>2</td>\n",
" <td>adm_clerical</td>\n",
" <td>not_in_family</td>\n",
" <td>white</td>\n",
" <td>1</td>\n",
" <td>united_states</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>7</td>\n",
" <td>self_emp_not_inc</td>\n",
" <td>13</td>\n",
" <td>married_civ_spouse</td>\n",
" <td>0</td>\n",
" <td>exec_managerial</td>\n",
" <td>spouse</td>\n",
" <td>white</td>\n",
" <td>1</td>\n",
" <td>united_states</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>6</td>\n",
" <td>private</td>\n",
" <td>9</td>\n",
" <td>divorced</td>\n",
" <td>2</td>\n",
" <td>handlers_cleaners</td>\n",
" <td>not_in_family</td>\n",
" <td>white</td>\n",
" <td>1</td>\n",
" <td>united_states</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>7</td>\n",
" <td>private</td>\n",
" <td>7</td>\n",
" <td>married_civ_spouse</td>\n",
" <td>2</td>\n",
" <td>handlers_cleaners</td>\n",
" <td>spouse</td>\n",
" <td>black</td>\n",
" <td>1</td>\n",
" <td>united_states</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>private</td>\n",
" <td>13</td>\n",
" <td>married_civ_spouse</td>\n",
" <td>2</td>\n",
" <td>prof_specialty</td>\n",
" <td>spouse</td>\n",
" <td>black</td>\n",
" <td>0</td>\n",
" <td>cuba</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age_binned workclass education_num marital_status \\\n",
"0 6 state_gov 13 never_married \n",
"1 7 self_emp_not_inc 13 married_civ_spouse \n",
"2 6 private 9 divorced \n",
"3 7 private 7 married_civ_spouse \n",
"4 5 private 13 married_civ_spouse \n",
"\n",
" hours_per_week_binned occupation relationship race is_male \\\n",
"0 2 adm_clerical not_in_family white 1 \n",
"1 0 exec_managerial spouse white 1 \n",
"2 2 handlers_cleaners not_in_family white 1 \n",
"3 2 handlers_cleaners spouse black 1 \n",
"4 2 prof_specialty spouse black 0 \n",
"\n",
" country capital_profit_binned is_rich \n",
"0 united_states 2 0 \n",
"1 united_states 1 0 \n",
"2 united_states 1 0 \n",
"3 united_states 1 0 \n",
"4 cuba 1 0 "
]
},
"execution_count": 116,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Make Binned dataframe\n",
"\n",
"cat_cols = ['age_binned', 'workclass', 'education_num', 'marital_status',\n",
" 'hours_per_week_binned', 'occupation', 'relationship', 'race',\n",
" 'is_male', 'country', 'capital_profit_binned', 'is_rich']\n",
"bindata = pd.DataFrame(data[cat_cols], index=data.index)\n",
"bindata.head()"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Write binned data to a CSV file\n",
"\n",
"# bindata.to_csv('binned.csv', index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Normalize data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll normalize using `(x-mean)/std`."
]
},
{
"cell_type": "code",
"execution_count": 152,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['age', 'workclass', 'fnlwgt', 'education_num', 'marital_status',\n",
" 'occupation', 'relationship', 'race', 'hours_per_week', 'country',\n",
" 'capital_profit', 'is_male', 'is_rich', 'capital_profit_binned',\n",
" 'age_binned', 'hours_per_week_binned'],\n",
" dtype='object')"
]
},
"execution_count": 152,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.columns"
]
},
{
"cell_type": "code",
"execution_count": 153,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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"<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>education_num</th>\n",
" <th>hours_per_week</th>\n",
" <th>capital_profit</th>\n",
" <th>is_male</th>\n",
" <th>workclass_state_gov</th>\n",
" <th>workclass_self_emp_not_inc</th>\n",
" <th>workclass_private</th>\n",
" <th>workclass_federal_gov</th>\n",
" <th>...</th>\n",
" <th>country_scotland</th>\n",
" <th>country_trinadad_tobago</th>\n",
" <th>country_greece</th>\n",
" <th>country_nicaragua</th>\n",
" <th>country_vietnam</th>\n",
" <th>country_hong</th>\n",
" <th>country_ireland</th>\n",
" <th>country_hungary</th>\n",
" <th>country_holand_netherlands</th>\n",
" <th>is_rich</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>39</td>\n",
" <td>77516</td>\n",
" <td>13</td>\n",
" <td>40</td>\n",
" <td>2174</td>\n",
" <td>1</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",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>50</td>\n",
" <td>83311</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
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" <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",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>38</td>\n",
" <td>215646</td>\n",
" <td>9</td>\n",
" <td>40</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</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>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>53</td>\n",
" <td>234721</td>\n",
" <td>7</td>\n",
" <td>40</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</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>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>28</td>\n",
" <td>338409</td>\n",
" <td>13</td>\n",
" <td>40</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>...</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>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 88 columns</p>\n",
"</div>"
],
"text/plain": [
" age fnlwgt education_num hours_per_week capital_profit is_male \\\n",
"0 39 77516 13 40 2174 1 \n",
"1 50 83311 13 13 0 1 \n",
"2 38 215646 9 40 0 1 \n",
"3 53 234721 7 40 0 1 \n",
"4 28 338409 13 40 0 0 \n",
"\n",
" workclass_state_gov workclass_self_emp_not_inc workclass_private \\\n",
"0 1 0 0 \n",
"1 0 1 0 \n",
"2 0 0 1 \n",
"3 0 0 1 \n",
"4 0 0 1 \n",
"\n",
" workclass_federal_gov ... country_scotland country_trinadad_tobago \\\n",
"0 0 ... 0 0 \n",
"1 0 ... 0 0 \n",
"2 0 ... 0 0 \n",
"3 0 ... 0 0 \n",
"4 0 ... 0 0 \n",
"\n",
" country_greece country_nicaragua country_vietnam country_hong \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" country_ireland country_hungary country_holand_netherlands is_rich \n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
"[5 rows x 88 columns]"
]
},
"execution_count": 153,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"normdata = pd.DataFrame(ohdata, copy=True)\n",
"normdata.head()"
]
},
{
"cell_type": "code",
"execution_count": 154,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\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>education_num</th>\n",
" <th>hours_per_week</th>\n",
" <th>capital_profit</th>\n",
" <th>is_male</th>\n",
" <th>workclass_state_gov</th>\n",
" <th>workclass_self_emp_not_inc</th>\n",
" <th>workclass_private</th>\n",
" <th>workclass_federal_gov</th>\n",
" <th>...</th>\n",
" <th>country_scotland</th>\n",
" <th>country_trinadad_tobago</th>\n",
" <th>country_greece</th>\n",
" <th>country_nicaragua</th>\n",
" <th>country_vietnam</th>\n",
" <th>country_hong</th>\n",
" <th>country_ireland</th>\n",
" <th>country_hungary</th>\n",
" <th>country_holand_netherlands</th>\n",
" <th>is_rich</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.033893</td>\n",
" <td>-1.062264</td>\n",
" <td>1.129065</td>\n",
" <td>-0.078261</td>\n",
" <td>0.154027</td>\n",
" <td>1</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>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.866159</td>\n",
" <td>-1.007413</td>\n",
" <td>1.129065</td>\n",
" <td>-2.326545</td>\n",
" <td>-0.134557</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</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>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.041768</td>\n",
" <td>0.245164</td>\n",
" <td>-0.438392</td>\n",
" <td>-0.078261</td>\n",
" <td>-0.134557</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</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>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.093141</td>\n",
" <td>0.425712</td>\n",
" <td>-1.222120</td>\n",
" <td>-0.078261</td>\n",
" <td>-0.134557</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</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>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.798374</td>\n",
" <td>1.407139</td>\n",
" <td>1.129065</td>\n",
" <td>-0.078261</td>\n",
" <td>-0.134557</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</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>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 88 columns</p>\n",
"</div>"
],
"text/plain": [
" age fnlwgt education_num hours_per_week capital_profit is_male \\\n",
"0 0.033893 -1.062264 1.129065 -0.078261 0.154027 1 \n",
"1 0.866159 -1.007413 1.129065 -2.326545 -0.134557 1 \n",
"2 -0.041768 0.245164 -0.438392 -0.078261 -0.134557 1 \n",
"3 1.093141 0.425712 -1.222120 -0.078261 -0.134557 1 \n",
"4 -0.798374 1.407139 1.129065 -0.078261 -0.134557 0 \n",
"\n",
" workclass_state_gov workclass_self_emp_not_inc workclass_private \\\n",
"0 1 0 0 \n",
"1 0 1 0 \n",
"2 0 0 1 \n",
"3 0 0 1 \n",
"4 0 0 1 \n",
"\n",
" workclass_federal_gov ... country_scotland country_trinadad_tobago \\\n",
"0 0 ... 0 0 \n",
"1 0 ... 0 0 \n",
"2 0 ... 0 0 \n",
"3 0 ... 0 0 \n",
"4 0 ... 0 0 \n",
"\n",
" country_greece country_nicaragua country_vietnam country_hong \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" country_ireland country_hungary country_holand_netherlands is_rich \n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
"[5 rows x 88 columns]"
]
},
"execution_count": 154,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"normslice = normdata.loc[:, normdata.columns[:5]]\n",
"normdata.loc[:, normdata.columns[:5]] = (normslice - normslice.mean()) / normslice.std()\n",
"normdata.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### K Nearest Neighbors"
]
},
{
"cell_type": "code",
"execution_count": 155,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"Xs = []\n",
"Xs.append(normdata[normdata.columns[:-1]])\n",
"Xs.append(normdata[list(normdata.columns[:1]) + list(normdata.columns[2:-1])])\n",
"X = Xs[0]\n",
"\n",
"y = normdata[normdata.columns[-1]]"
]
},
{
"cell_type": "code",
"execution_count": 156,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Plot k vs accuracy\n",
"\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"\n",
"def plot_knn(X, y, k_range, std_factor=1):\n",
" maccs = []\n",
" maccsa = []\n",
" maccsb = []\n",
" for k in k_range:\n",
" knn = KNeighborsClassifier(n_neighbors=k, n_jobs=-1)\n",
" knn.fit(X, y)\n",
" accs = my_cross_val_score(knn, X, y)\n",
" maccs.append(accs.mean())\n",
" maccsa.append(accs.mean() - std_factor * accs.std())\n",
" maccsb.append(accs.mean() + std_factor * accs.std())\n",
" plt.plot(k_range, maccs)\n",
" plt.plot(k_range, maccsa)\n",
" plt.plot(k_range, maccsb)"
]
},
{
"cell_type": "code",
"execution_count": 157,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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CchKyB1R/MTQkRAghRixd0/DX1+GtrMRXWYG3sgJfZSW+2pqYizT1ZXY6owJC9yMIs8s1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\nERGRiCiJEBERkYj8f6eYOk46lHOpAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f8e69482d68>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_knn(X, y, range(1, 11))"
]
},
{
"cell_type": "code",
"execution_count": 206,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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Lo7dzu0gYklQIIWLm4UPTWyJHDujVy7TWPn7cdGLMlMnq6ISdmzABKlQw9bv+/lCqVMwe\nnzUr/P672aokQwb44AOzYmTPnviJV8SMJBVCiOh59AjGjjVLQ7t1M2X5R4+acessWayOTti54GAz\nItG2LbRubXZRf/312F/Px8dcY/FiU8Dp42MatJ47Z7uYRcxJUiGEeLm9eyFPHtOoqkIFOHzY7NGR\nPbvVkQkHcPGi+bH57Tez0uOXX0ytRFwpZUYqDhwwXd5XroTcuc0AWkBA3K8vYk7adIuXCw42rZVP\nnXr2dueO2UGySRNZKujM/vnHTGJnywZLlsgW2CJGNm82fybc3U2nzPjYTuSVV8wISKNGplHrsGGm\nAPSbb8xxd3fbP6eImtJOUOGilPIG/P39/fGWHfViTmvTqChy0vDkduHC00qoV16BN980n1Dd3EzL\nuyRJTGLRrp1ZUC6cx4kTpoFA+vSwfj2kSWN1RMJBaA1jxkDXrlC6NMyebX6MEsLFi2bp6ZQpZqXI\nkCFQu7YZ2RD/tWfPHnx8fAB8tNZxqk6RkQpX8fChaUD0vMTh/v2n56ZLZ5KG7NmhTJmnX2fPDm+8\n8ex21BcvwsSJ5jZ+PJQsafZ3aNDAJBvCcZ09C++9Z8ruV6+WhEJE28OHpv/ElCmmQ/vQoQk7WpA5\nM/z6K3z2GfToYfpdlC1rRjBKlEi4OFyRjFQ4C61N4/znJQ0XLz4918Pj6WhD5Fu2bLGbyggJMUPj\nY8c+fQP69FMzepEzp82+TZFALl82GzCEhpox6ycdiYR4ifPnzYKggwfNSo+mTa2OyNRafPmlqb34\n+OOnbVWEYcuRCkkqHNnJkyYNP3rUJA4PHjy9L336qJOG7NnNsr+Iow22dvy4GbWYMsW0bH7/fTN6\nUbOmmT4R9u3GDVNVFxBgEgr56yuiacMGM0iZNCnMn282BbMXoaFmC5q+fc2PeOfOppv8q69aHZn1\nJKmIxCWTiosXzdTEk/LnyKMNnp5WR2iSnNmzzejF9u3m027r1tCqlXzytVd37kDFiubna9Mms+JD\niJfQ2rQu+eILk4/+8Qe89prVUUXt/n3Tn23IEDNo27evmaqJqpunq5CkIhKXSypu3DBD04GBprTa\nEXoE7NtnkosZM8yEa61aZvTivfcgkaxstguBgVC5Mhw5Yj5yFipkdURO7/Fjk8fdvh31LSTE1MmW\nLWubJZjxISjIrLCYPt0kFYMHO8aA5JUrZquaiRPNbPDgwWaViisWc0pSEYlLJRV375o34rNnTUKR\nO7fVEcXM3bvmr8/YsWbSNWdOU3fRvLnsE2GlBw+gRg3TpnDt2vhZ9+ekQkJenBi86Pa8jbHc3Myw\nvNZw86YZeKxUCapXh2rVTL20PThzxtRPHDliCiN9fa2OKOYOHYKePU1JWMmSppizTBmro0pYklRE\n4jJJxYMH5q/K3r1med/bb1sdUexpDVu2mORizhzz8aBhQzN6UaKEa35csMqjR6Y8fv16U9FWrpzV\nEdmNO3fMJ9krV56fGAQGRv3YV14xiUFsbp6e5ldAa9i/H5Ytg+XLYetWCAuDggWfJhilS1vTh2Ht\nWvMrmzKlqZ8oXDjhY7Cldeuge3fz5/XgQddqxyJJRSQukVSEhEC9erBmjVld4Uyp9PXrps3e+PFm\nh6EiRczoRePG9lEb4swePzbl8IsXm1vlylZHZDfOnzdv2idPmhnGJ2/4adJELzFIntz2ufHt26Y1\nzPLl5nbtmnlTr1zZxFqtWtxaX0eH1qYmoUcPM2jq5+c8g4xhYWbmr2JFqyNJWLZMKtBaO/wN8Aa0\nv7+/dkqhoVo3aqS1u7vWy5dbHU38CQ0131+tWlonSqR1ihRad+ig9YEDVkfmnEJDtW7aVGs3N60X\nLLA6Gruyf7/WmTJpnTWr1ocOWR1N1EJDtd69W+uBA7UuWVJrpbQGrd9+W+vevbXevFnrkBDbPmdg\noNYff2yep2dPrR8/tu31hTX8/f01oAFvHdf347hewB5uTp1UhIWZN1altP7zT6ujSThnz2rdp4/W\nGTOaH9OyZbWeMUPrhw+tjsw5hIVp3a6d+bny87M6Gruydq3WKVNqXaSI1pcuWR1N9F2/bn5FGjfW\nOl0682vz6qtaN2yo9bRpWl+9GrfrnzypdaFCWidP7lp/ilyBLZMKKbu3d337ml6348ebBeCuIksW\n+PZbs+XgrFlm0rhxY/Dygq++MtMkIna0NpPH48aZDRI+/tjqiOzGzJlm89USJWDjxvifSrCldOnM\n3hfTp5sakO3bTS+GU6fgk0/MNuHFipn9MLZvN30bomvVKiha1CzH3L7dtf4UiRiKa1ZiDzecdaRi\n2DDzcWPoUKsjsQ+HD2vdpYvWqVOb6ZHPPtP6zh2ro3I8/fqZn6v//c/qSOxGWJjWgwebl+WTT7R+\n9MjqiGzr6lWtf/vNTF28+qr5PtOmNaMa06ebUY6oPHldEiXSulo1rW/dSti4RcKQ6Q9XSComTTL/\ne3r3tjoS+3P/vtY//mjGYTNmNH8Vw8KsjsoxDBlifq4GD7Y6Ervx+LHWHTual6VPH+f/UQoJ0XrL\nFq2//trUX4CZBStRQusBA7TetcvUa9y7p/VHH5n7v/5a6iecmSQVzp5UzJplPhp06OD8f+Hi4vz5\np3/13nlH63/+sToi+zZ69NN3TqG11jooSOs6dcyv27hxVkdjjUuXtJ482fwqpUplfkTSp9c6Wzat\nPT21njfP6ghFfJOaCme2YoWpHfj4Y/jf/6Rfw4u88YZpA75ypWkrXbiwWef2vMYBrmzKFOjUCbp1\ng4EDrY7GLty4YZZErlwJCxaYrpCu6PXXzd5/s2eb1d0bN0KLFmbfjh07TAsTIaLLAZqpupAtW0x7\nuipVYOpUaV8dXZUrm+0Hhw2DQYPMwvkRI0xfD0nK4M8/zX4rbdrATz/Ja4IpXqxWzfR92LABihe3\nOiL74O5udgAoX97qSISjkncte7Fvn2mTXLz409UOIvoSJzZbDh46ZDqN1q9vyviPH7c6MmstXgxN\nmphlAWPHSkIB+PtDqVKm0dHWrZJQCGFLklTYg2PHzOhErlywaJHZN1jETrZs5jVctMi8rgUKQL9+\nz24L7yrWrDE7JNWqZaY/ZOSL5cvNLppvvmkSipw5rY5ICOcif2Wsdv48vP++6XO7fLnpuSvirmZN\n+OcfU2MxZAjkz28+tbuKzZuhdu2nfZQdYdvIePbrr+bH4t13zT4P9ro1txCOTJIKK12/bhIKpUx3\nmXTprI7IuSRLZhpoHTxodnOtVcvcnL1x1u7dZrep4sVh7lz73TM7gWhttrhu1crc5s83+3IIIWxP\nkgqrBASYKY87d8wwtb3sZeyMcuUyq2pmz4Y9e8yoxaBBEBxsdWS2d+CA+bl66y2ZSsPsw9e6NQwY\nAN99Z8pKZNBGiPgjSYUVgoLMOOzp02aEQiZ2459Spr7gyBHTu3jAALN/9KpVVkdmO8eOQaVKpsX5\n8uWQIoXVEVkqMNDMAE2bZm69e0udqhDxTZKKhPbokVmZ4O8Py5ZBoUJWR+RaPD3hxx/NaptMmcyn\n+vr14cIFqyOLmzNnTP1EunQmUUqd2uqILHX1KrzzDvz1FyxdCs2aWR2REK5BkoqEFBpqdvZZs8Z0\n2ylVyuqIXNdbb8H69Wb3pb/+grx5YehQM17uaC5ehIoVzbLa1atdvgLx2DHzq3XxovlfW7my1REJ\n4TokqUgoWkPHjqYHhZ+fKdAU1lLKdC89ehRatjS7nxYpYloKOopr18yUx+PHsHatGX2JB3//DV27\nwh9/mHIge7VtG5QubfKr7dvN/07hWC7evciUvVO4/eC21aGIWJCkIqH07m22L580yXTNFPYjVSr4\n+WczJZUqlRk3b9LE7B9tz27dMh/Db982CUXWrDZ/itBQsyK3aFH4/Xfw9TUDIZUrw5gx9jVrtGCB\nGbDJl880p42Hl0PEo6CQIAZuHEju0blpsagFuf6Xi9E7RxMS6oCjhy5MkoqEMGQIDB5sWkd/+qnV\n0YjnKVLE9Hf49VezIUSePDBqlBkFsDf37pk+0xcumOm0XLls/hQnTph2zb16weefw6VLcPYsDB9u\nBt66dAEvL5NwDBpkFp6Y/f0S3pgxpiv7Bx+YGaA0aayJQ8Sc1pqZB2aSZ3QeBm0aRMdiHTnS8Qh1\n8tbhs+WfUXBsQZYcW/Jk80hh7+K6I5k93LDnXUrHjTPb/vXrZ3UkIiZu3tS6bVuzJ3SRIlpv3Wp1\nRE/dv691+fJap0yp9e7dNr98WJjWY8ZonSyZ1tmza/3XX1Gfd/u21jNmaN2ggdnNEsz53bppvWGD\n2WI7voWGat2zp3nuLl3MfwvHsf38dl1yUklNf3TdP+rq4zePP3P/vsv7dMVpFTX90ZV+q6T3X9lv\nUaTOzbKtz4GswBLgBnAaGPyc8xQwIPycu8A+oMFzzq0FhAHlIxzLAswLf55LwBQg5Qviss+kws/P\nvCl17ixbmDuqnTu19vExvyotWmh97Zq18Tx8qHWVKuYdf/Nmm1/+wgVzedC6XTut792LfljLl5vH\nvP66eXzatFp/8onZOjsw0Oah6uBgrRs3Ns/100+2v76IP+cDzusm85po+qMLjy2s151a99xzw8LC\n9KIji3Tu/+XWiQYk0q0WttKX711OwGidny2TCqVjMKSklNoN7AK+BDIAy4CxWuuRkc7rAPQC3gVO\nAtWB+eEBH4xwXjLgAPAa8IHWelP48f3hz9MZeBVYAOzTWrd5TlzegL+/vz/e3t7R/n7i1bJlZpF8\no0ay74KjCw2FCROeNjro08e6CfvffjNTM0uXmiWkNqK1qR/u2NE0Iv31V7MfW2yEhZmmngsXmjqH\nQ4cgSRJTm1y7tmnRkj593OINCDDTHX/9ZV6Shg3jdj2RMIJCghi6ZSg/bv0RTw9Pvqv4HZ8W+RS3\nRG4vfWxIaAjjdo+j/8b+PAp9RK+yvehWshtJ3V27wZst7NmzBx8fHwAfrfWeOF0sutkHUBR4RIQR\nA6AtcCiKcycBfpGOXQYaRTr2IzAeOEX4SAWQKvzxr0U4ryNw5AWx2ddIxaZNWidJonWdOgkzBiwS\nxtWrWjdvbj4aW3VLkkTrxYtt+m1dv651/frm8r6+ZubHlo4f13rYMK3LljUDd0ppXaaM1j/+qPWx\nYzG/3oULWhcqpHXq1GaaRdi/sLAwPePvGfqN4W9oj289dI9VPXTAw4BYXetW0C3dbUU37T7QXXsN\n99Iz/p6hw2QkOE4sGalQSrUBumutc0c4VgzYDqTSWgdGOF4XGANUBQ4B1YDfgLe01hfDzykIrAQK\nAP7AJzp8pCKK5x4CFNVaR/nRzK5GKvbsMTsWFS1qPk0mSWJtPML27t+PfT+LGIwMRilJEpu23l66\n1OyH8eiRaWHdoIHNLh2l69dhyRIzgrF6tdk8Nl8+qFPHjGIUK/biQb1//jEjKEqZpqFvvRW/8Yq4\n23FhB11XdmX7he18mO9Dfqz0IznS5IjzdY/fPE7PNT2Zf2Q+JTKXYHiV4ZT2Km2DiF2PVSMVvYAd\nkY7lAEKBrFGcPwBTKxEK3APqRrp/M9Ai/OvTRKipiHReUeA+8O4LYrOPkYrDh7VOl07r4sW1vnvX\n2liEeIG7d7Vu1cqMTlSvrvWlSwkfw/37Wi9YYAZ/0qUzsbz+uqmPXbbM1GlEtGGD1qlSmVGKCxcS\nPl4RM+funNON5zb+t25i/en18fI8G05v0N7jvTX90Q1mN9Cnb5+Ol+dxZrYcqYjr1jpPOuk/8/FL\nKdUUaBaeEBwEKgEzlVLntNb+SqnWgNJaT37hxZUqAywCemit178smG7dupEqVapnjvn6+uLr6xvd\n7yf2zp41k8YZMsi+C8KubdpkGrveuGFKRVq1smZPjGTJzOhE7dqmbGXrVjOCsXChaeni6WlWzdau\nbVb1tmkD5cqZjVcj/ZoLO/KkbmLIliGkSJyCiTUnRrtuIjYqvFmBXa138fv+3+m9rjd5R+ela8mu\n9C7Xm5SJU8bLczoyPz8//Pz8njkWYMuOdtHNPoBWwMlIx4oDj4FkkY7vAL6KdGweMBJIi6mvKBTh\nvv+MVAAfAHeAxtGIzdqRiitXtM6Vy6ynu3jRmhiEeIkHD7T+4gtT01CunNYnT1odUdTCwrQ+eFDr\nQYO0LlYh3t/VAAAgAElEQVTsaTlJ48ZmxYewT6FhoXr6/un/1k30XN0z1nUTsRUYHKi/Wf+NTjoo\nqX7tx9f0uF3jdEio1LW9jC1HKmKyJGE3kFUpFbGtTHFMoWZQpHPdwm8RJQ7/twaQBlijlLqulLoO\neAELlVI/AyilSgPTgHpa6xkxiDHhXb9uJnkDA00TonhqkyxEXPj7g48P/O9/Zj+19eshe3aro4qa\nUqZW4uuvYedO099r/XqzysPDw+roRFS2X9hO6V9L02R+E0pkLsHhjocZXGlwgo8UJPdITv93+nO8\n83Gq56pOu6XtKDKuCCtPrEzQOFxZtJMKrfU+YCcwWCmVQimVF+iGKchEKXUkPBkAM2XRSilVUCnl\nppSqDFTELCudBWQDigCFw2+XgJZAP6WUGzAR6Km1XmuLbzLebN4Mb79tWg2uXg3ZslkdkRDPCAmB\ngQOhZEmzH4a/P3TvDm7xMxIdLzJnNp3TZVW2/TkfcJ4m85pQ6tdSBIcGs+GTDcxpMIfsr1qbsWZO\nmZmpdaayu/Vu0iZLS9UZVak2oxqHrh+yNC5XENNf04+AzMAVYB0wVWs9Lvy+XIBn+NffY1Z7LABu\nA8OAVlrrjVrrh1rrSxFvmCmUG1rrAKAUkBcYpZR6oJQKivCvV1y+WZsJCzMf9955B3LkgL17pQxd\n2J0jR6BMGZNUfPWV2WCrQAGroxLO4P6j+/Tf0J88o/Ow5tQaJtWcxO7Wu6nwZgWrQ3uGTyYfNnyy\ngXkN5nH85nEKjS1Eh6UduH7/utWhOa0YNb+yVwm6pPTWLVPltmSJ2RRh4EB4Ja71rkLYTlgYjB4N\nPXtClixmI7Dixa2OSjiDMB2G3wE/eq7pyfWg63xe8nN6levlEAWRj0IfMXrnaAZuHIhG06dcHz4r\n8RmJX0n88gc7OVsuKZUBxZjYscNMd2zdahb4f/+9JBTCrpw7Z3ZC79LFrJbYu1cSCmEbEesmSnmV\n4nDHw/xQ6QeHSCgAPNw8+LzU55z47ASfFP6EXmt7ke+XfMz+ZzbO8OHaXkhSER1am62xy5UzhZh7\n90L16lZHJcS/tIZp06BgQbO76Jo15kc2WTKrIxOO7nzAeRrPa0ypX0vxKPQRG5tvZHb92ZbXTcRW\numTpGFVtFAc7HKRA+gI0mNOAclPKsfPiTqtDcwqSVLxMQAB89BF07QqdO8PGjWZMWQg7ce0a1K0L\nzZubfw8csOm2IMJF3X90n2/Wf0Oe0XlYe2otv9b6lV2td1E+a3mrQ7OJvOnyssh3Eaubrubeo3uU\nmFSCJvOacC7gnNWhOTQZu3+RPXugfn24eRPmzze9hIWwI/PnQ9u25ut580xSIURsaK05F3COzec2\ns+X8FhYeXcjNoJt8XupzepXtRYrEztnQr1L2Suxps4ep+6by9bqvmXt4Lv5t/Mn/Wn6rQ3NIklRE\nRWvT0q9LFzOevHq1/S7qFy4pIAA++8z0bqhd23TGjOvOn8K1hIaFcuDaATaf2/zv7eK9i4D5FF8r\ndy16lOlBtledf6m8WyI3Wnq3pMFbDZhxYAb50uWzOiSHJUlFZPfumY9+T/aB/ukns8BfCDuxdi18\n+qlJLKZOhWbNrGmzLRzL/Uf32XFxx78jEdvOb+Peo3u4J3KnWOZiNCrYiLJZylLaqzTpkqWzOlxL\npEicgnZF21kdhkOTpCKiAwdM/cTly/Dnn/G/ZaMQMXD2LAwbZpaLVqwIU6ZIeY94viuBV9hybosZ\nhTi/mb2X9xKqQ0mdJDVlvMrQu1xvyniVoWimoiR1t93Ot8K1SVLxxJQp0KED5M4Nu3ebf4WwkNaw\nb5/ZYGvhQvN10qRmVUenTtJhUjyltebIjSNsOb/l36mMk7dPApAtdTbKZClDq7dbUTZLWfK9lo9E\nSn54RPyQpOL+fTPNMW2a2a5x1Cjzl1sIC4SEmF1EnyQS586ZHTlr1DC91qpWhZSO0RZAxKPgx8H4\nX/b/dypjy7kt3Hxwk0QqEUUyFqFGrhqUyVKGMl5lyJwys9XhChfi2knF4cNmdcfp06birWlTqyMS\nLujePVixwiQRS5fCnTvg5fV0W/AKFcDd3eoohZVuP7jN1vNb/53K2HVxF8GhwSR3T07JN0rSqXgn\nymYpS4nMJZx2lYZwDK6bVMyYYQoys2SBXbsgvywfEgnn8mVYtMgkEmvXwqNHULiwWdFRu7Zp3CrF\nl84nJDSE+yH3CXwUyP1H5t/AR4H/Hot8/Nr9a2y7sI1/rv8DQEbPjJTNUpYhlYZQNktZCmcszCuJ\nXPfPuLA/rvfT+OCBaWQ1YYIZmRg7FpIntzoq4eS0NgNjT6Y1duwwO4WWL2/2pqtVSza5tVfnAs5x\nI+hGtJKAwJAXJwvBocEvfb4kryQhuXtyPD08eTXpq5TxKkOPMj0om6Us2VJnQ0m2KeyYayUVx4+b\n6Y6jR2HiRGjZUj4OingTGgrbtj1NJI4fN/lr1aqmjKdGDUiTxuooXy40LJTN5zbz99W/aVu0LR5u\nHlaHlGBGbBvB56s+j/K+xG6JSe5h3vw9PTz/TQQ8PTzJ4JmBHO45zPEI50Q+L/Ljk3skl5EH4dBc\n56d39myTRGTMaPaALlzY6oiEE3rwwPRKW7gQFi+G69chQwYzEjFihGmfnSSJ1VG+XEhoCBvObGDu\n4bnMPzKfa/evAbD78m6m1p7qEp+W5x6ayxervqBria40KdTkmSQguXty3N2k0EWIyJw/qQgOhu7d\nzeL+Bg3MCIWUzwsbunEDliwxicTKlSaxyJMHWrQw9RElSjjG8s/gx8GsObWGuYfnsvDoQm49uEW2\n1NloVqgZ9fLX49TtUzSe1xivlF4MqjjI6nDj1bbz22gyvwkNCzTkpyo/yRJMIaLJuZOK06dNIvH3\n3/DLL9C+vUx3CJs4efLptMbmzaZmomRJ6N/fJBJ58lgdYfQEhQSx8sRK5h6ey+Jji7kbfJc8afPQ\nzqcd9fLX4+2Mb/87KlHyjZJcvHuRHmt64JXSi7ZF21ocffw4eesktf6oRdFMRZlSe4okFELEgPMm\nFQsXmm0bX30Vtm4FHx+rIxI2EhxsWlQ/emRuISHR/zom5z7v66tX4cgR0729UiWzTUzNmmaawxHc\nC77HsuPLmHN4DsuOLyMoJIiC6QvyecnP+Sj/R+R/Lf9zpze6l+7O+bvn6bCsA5lSZKJmnpoJHH38\nuhl0k+ozq5MmaRoWNFxAklccYK5KCDvifElFSIjpEvTTT2ZX0SlTIHVqq6MSkTx8CLdvx+724EHc\nntvD4+nN3f2/X0d1zMPDFFl6eEC+fPDdd1C5Mnh62ub1iG93Ht5h0dFFzD08l5UnVhIcGozP6z70\nKdeHevnrkTtt9DrIKqUYUWUEF+5eoOGchmxovoHimYvHc/QJ4+Hjh9T5sw63Htxie8vtpE2W1uqQ\nhHA4zpVUXLli+hfv2gXDh5ulozLdEW8ePoRbt2KXGDx8GPU1kyQxg0sRb9mygbf3s8dSpTIjBS9K\nAqL62s3NdX4kbgTdYMGRBcw9PJe1p9YSEhZCaa/SfP/e93yY70PeTP1mrK7rlsiNGR/OoNLvlfhg\n5gdsbbmVnGly2jb4BBamw/h04afsvrSbdc3WkSNNDqtDEsIhOVdS0aiRKcLctAlKlbI6Gqf1+LGp\nHRg82CybjCxp0v8mBjly/PdYVDdHWBlhzy7fu8z8I/OZe3guG85sAKB81vIMrzKcunnr2qxlc1L3\npCz6eBGlJ5em2oxqbG2xldeSv2aTa1uhz7o+/HnwT2bXn00pL/nbIURsOVdSUaCAqaVIK8OW8eXs\nWZO77dhhZplKlvxvYiA7xSescwHnmHd4HnMOzWHr+a24JXKjYraKjK0xljp565A+efp4ed60ydKy\novEKSv1aig/8PmD9J+tJ5p4sXp4rPk3wn8APm3/gp8o/US9/PavDEcKhOVdSMXKkJBTxaO5cs+da\nqlTw118yGGSlE7dOMPfQXOYensuuS7vwcPOgco7KTKk9hZp5apImacJ01cr2ajaWNlpKhakV+HjO\nx8xrOM+hmjetOLGCDks70LFYR7qV7GZ1OEI4PMf57Y8OR2gG4IAePIBu3cwqh/r1TYdzqX1NeLcf\n3OaXXb8w59Ac9l/dT9JXklItVzW6lexGjdw1SJnYmv4rPpl8mF1/NjX9atJpWSfG1hjrEM2x9l/Z\nT/3Z9amWqxojq450iJiFsHfOlVQImzt4ED7+GE6dMslEq1auU+hoL7TWzDwwk89XfU7go0Bq56lN\n3/J9qZqzKsk97GPfmmq5qjGh5gRaLmpJ1lRZ6VWul9UhvdCFuxeoMbMGedLm4Y96fzjU6IoQ9kx+\nk0SUtDZJRNeukDMn7N4tG7la4fjN43RY1oE1p9bQ4K0GjKgygkwpMlkdVpRavN2C8wHn6b2uN2+k\nfIOmhZtaHVKU7gbfpcbMGrglcmOx72K7ScyEcAaSVIj/uH0bWrc2NRTt25uWH0mTWh2Vawl+HMzg\nzYP5YfMPZEqRieWNl1M1Z1Wrw3qpfhX6cS7gHC0WtSCjZ0bez/G+1SE9IyQ0hAazG3D2zlm2tNjC\n6yletzokIZyKJBXiGVu2mNUdd++apOLDD62OyPWsO72O9kvbc/r2ab4s/SVfl//aYVZVKKUY98E4\nLgVeot6sevz16V8Uzmgfm/dpremwtANrT69lZZOVvJX+LatDEsLpSGWjAEy/iUGDoEIF8PKC/fsl\noUho1+5fo+n8prz323tkSJ6Bfe328d173zlMQvGEu5s7s+vPJlfaXFSfWZ1zAeesDgmAwZsHM2nv\nJCbVnETFbBWtDkcIpyRJheDiRXj/fejXD3r3hg0bIEsWq6NyHWE6jIn+E8k7Oi/Ljy9ncq3JbGy+\nkfyvOW4Ri6eHJ0sbLcXDzYNqM6px+8FtS+PxO+BH73W9+abCN3xS5BNLYxHCmUlS4eKWLIHCheHo\nUVi3DgYOhFdkUizBHLh6gLKTy9JmSRtq563NkU5H+PTtT51ieWNGz4ysaLyCK4FXqPNnHYIfB1sS\nx6azm2i+sDnNCjfjmwrfWBKDEK5CkgoXFRxsVnbUrGmaWO3fD++8Y3VUruP+o/v0WN2Dt8e/zZ2H\nd9jwyQam1J5CumTprA7NpvKky8Ni38XsvLiTZguaEabDEvT5j944Sp0/6lDGqwwTa050imRNCHsm\nn0ld0LFjpvfEP//Azz9D587SeyIhLTm2hE7LOnH1/lUGvDOAL8t8iYebh9VhxZvSXqWZ8eEMPpr1\nEV4pvRhWeViCPO+1+9eoPrM6r6d4nXkN5zn1ayyEvZCkwoVoDb/9Bh07QubMsH07vP221VG5jgt3\nL/DZ8s+Yf2Q+VXJUYW2ztS6zG+aH+T7k56o/89mKz/BK6UWXkl3i9fmCQoKo5VeLoJAg1jVbR+ok\n0gJWiIQgSYWLuHfP9JyYMQOaN4f//Q88Pa2OyjU8DnvM6J2j6bu+L54envxR7w8avNXA5YbiO5fo\nzPm75+m2shtvpHwj3jbvCg0Lpcm8Jhy4doCNzTeSNXXWeHkeIcR/SVLhAnbvNtMd166ZpKJRI6sj\nch07L+6k3ZJ27Luyjw7FOvBdxe9IlSSV1WFZZnClwZy/e57G8xqTwTMDZbOUtflz9Fjdg4VHF7Kg\n4QKKZipq8+sLIZ5PCjWdWFiY6YZZurTZknzvXkkoEkrAwwA6LetEyUkl0Wi2t9rO6OqjXTqhAEik\nEjG19lRKeZWill8tjtw4YtPrj945muHbhzOq6ihq5qlp02sLIV5Okgonde0a1KgB3btDly6mU2YO\n15i+t5TWmj8P/kneX/Iybf80hlcZzq7WuyieubjVodmNxK8kZn7D+WRKkYmq06ty+d5lm1x30dFF\ndFnRhc9Lfk7H4h1tck0hRMxIUuGE1qwxvSf27IHly2HoUPCQwvd4d/LWSarNqMbHcz+mtFdpDnc8\nTNeSXWUHzCikTpKa5Y2XExIWQo2ZNbgXfC9O19t9aTe+c32pk7cOQysPtVGUQoiYkqTCiYSEQK9e\nULkyFChgek9Utf89qBxe8ONgBm0aRIGxBTh84zCLfRczt8Fc3kj5htWh2TWvVF4sb7yck7dPUn92\nfUJCQ2J1nbN3zvLBzA8olKEQ0+tOJ5GSP2tCWEV++5zE6dNQrhwMGwY//AArV0LGjFZH5fw2ntlI\nkfFF6L+hP58V/4xDHQ7xQe4PrA7LYRTKUIj5Deez7vQ62ixpg9Y6Ro+/8/AO1WdWJ7lHchZ+vJCk\n7rKdrhBWknFZJ/Dnn9CmDaRJA5s3Q4kSVkfk/G4E3aD7qu5M2z+NUm+UYm/bvRTMUNDqsBxSxWwV\nmVJ7Ck3mNyFLyiwMeHdAtB73KPQRH/75IVcCr7C1xVbSJ08fz5EKIV5GkgoHFhRkijAnTYKGDWH8\neEjl2osL4p3Wmin7pvDl6i8J02FM+GACLb1bypB7HDUu1Jjzd8/Ta20v3kj5Bq19Wr/wfK01rRe3\nZsv5LaxpuoY86fIkUKRCiBeRpMJBnTgB9erB8eMmqWjRQlptx7eTt07SenFr1p9ZT5NCTfip8k/y\n6diGepbpyfmA87Rf2p5MKTJRI3eN5547YOMAftv/GzM/nEm5rOUSMEohxIvIxysHtHAh+PjAgwew\nYwe0bCkJRXwKDQtl+LbhFBxbkNN3TrOqySp+r/u7JBQ2ppRiVLVRfJD7AxrMacDuS7ujPG/avmkM\n2DiA7yt+j29B3wSOUgjxIpJUOJDHj+Grr6BOHXjvPdi1CwrKNH68OnjtIKUnl6b7qu608WnDgfYH\neD/H+1aH5bTcErkxs95MCmUoRI2ZNTh1+9Qz9689tZZWi1vR2rs1X5X9yqIohRDPI0mFg7h61SwV\nHTbM9J2YO1fqJ+LTo9BHDNgwAO/x3twLvseWFlsYWXUknh6yYUp8S+aejMW+i0mVOBVVp1flRtAN\nAP659g/1ZtXjvWzv8Uv1X1xu7xQhHIEkFQ5gyxbw9oZDh2DtWtMlU/6exp+dF3fiM8GHQX8NomeZ\nnuxtu5dSXqWsDsulpEuWjuWNl3Pn4R1q+tXk1O1TVJ9ZnaypszKr/izc3dytDlEIEQVJKuyY1vDz\nz/DOO5A9u+mQWaGC1VE5r6CQILqv6k6pX0uR2C0xu1vv5tuK35L4lcRWh+aScqTJwdJGS/n76t/k\n/yU/oWGhLG20lJSJU1odmhDiOSSpsFOBgeDrC127wmefwbp1kCmT1VE5r/Wn11NwbEF+2fULP7z3\nA9tbbadwxsJWh+XyimUuxqyPZpEzTU6WNloqXUqFsHOypNQOHT5slouePw+zZkH9+lZH5LwCHgbQ\nY3UPJuyZQLks5VjeeDm50+a2OiwRQY3cNV64vFQIYT8kqbAzs2aZnhNZs5rVHXnzWh2R81p8dDHt\nlrbjbvBdxlQfQ9uibaWJlRBCxEGM/4IqpbIqpZYopW4opU4rpQY/5zyllBoQfs5dpdQ+pVSD55xb\nSykVppQqH9PncRYhIWaqo2FDqFnT9J+QhCJ+XL9/Hd+5vtT6oxaFMxTmnw7/0L5Ye0kohBAijmIz\nUjEX2AV8DGQAlimlrmitR0Y6rz3QAngXOAlUB+YrpQ5prQ8+OUkplQwYAQTG8nkc3sWLJpnYsQNG\njYJOnWR1R3zQWuN30I/Pln8GwO91f6dxwcayNFEIIWwkRh/NlFJFgUJAT611oNb6JDAcaBPF6d7A\nZq31CW0sBW6GPz6i/sAa4EYsn8ehrV9vloueOQObNkHnzpJQxIfzAeep6VeTxvMaUyl7JQ51PEST\nQk0koRBCCBuK6XivN3BGa303wrE9QB6lVOSuQEuBd5RShZVS7kqpWkBSYOOTE5RSBYEmQC8g4l/3\nmDyPQ9IafvwRKlWCAgXMctFS0grB5sJ0GON3j+etMW+x5/IeFjRcwB8f/SEttoUQIh7EdPojLXA7\n0rFbEe77dwpDaz1fKVUE2AtoIAhoprW+GOGxY4E+WutbkT4xRvt5HFFAADRvDgsWQK9e8O234OZm\ndVTO5/jN47Re3JqNZzfS6u1WDK08lNRJUlsdlhBCOC1brP54kg3oZw4q1RRoBhQFDgKVgJlKqXNa\na3+lVGtAaa0nx+V5IurWrRupIvWu9vX1xdfXfjYd+vtvs1z0+nWzMVitWlZH5Hwehz1m5PaR9F3f\nl9c9X2dN0zW8l/09q8MSQgjL+fn54efn98yxgIAAm10/pknFdSBdpGNpMG/0NyId7wSM11rvCf/v\nZUqpdUBTpdQZYCBQxQbP868RI0bg7e39su/BMr//Dm3bQu7csGIF5MhhdUTO5++rf9NyUUv8L/nT\ntWRXvn33W5J7JLc6LCGEsAtRfdDes2cPPj4+Nrl+TGsqdgNZlVJpIhwrDhzSWgdFOtct/BbRk37H\nNTBJwhql1HWl1HXAC1iolPo5/HnejObz2L3gYGjfHpo1gwYNYOtWSShsLfhxMN+s/wafCT4EhQSx\nteVWhlcZLgmFEEIkoBiNVGit9ymldgKDlVJfAJmBbsBQAKXUEaCF1norsAhopZRaBBwC3gMqAj8C\nOzArPiLaDnQF1mqtA5RSO573PI7k3Dn46CPYvx8mTIBWrWR1h61tv7CdlotacuzmMXqX7U3vcr1l\nvw4hhLBAbGoqPgImAleAAGCs1npc+H25gCerM77HjFQsAF4DzgCttNZPVn9cinhRpdRj4IbW+snk\nzouexyGsXAmNG4Onp9lptGhRqyNyLvcf3afv+r6M3D4Sn0w++Lfxp1CGyCuWhRBCJJQYJxVa60uY\n6Yuo7nOL8PVj4JvwW3Sumz26z2PvwsJg0CDo3x+qVIHp0yFtWqujci5rT62l9eLWXA68zI/v/0jX\nkl15JZF0nRdCCCvJX2Ebu3ULmjQxhZj9+0OfPpBIuj/bzPGbx+m9rjdzDs2hQtYKrGq6ipxpclod\nlhBCCCSpsCl/f7Nc9N49WL7cjFII27gaeJUBGwcwcc9EXvd8nam1p9K0cFPZr0MIIeyIJBU2oDVM\nmmT27ChUCDZuNLuMiri7F3yPn7b9xLCtw3B3c+f7it/TqXgnkrontTo0IYQQkUhSEUdPlotOmQLt\n2sHIkZBYFh7EWUhoCBP3TGTAxgEEPAygc/HO9CrXizRJ07z8wUIIISwhSUUcaA0dOsCMGTBtmulD\nIeJGa83cw3PpvbY3J26doFnhZgx8dyBZUmWxOjQhhBAvIUlFHIwZA5Mnw9SpklDYwsYzG+mxpgc7\nL+6kWs5qzGkwR5aICiGEA5GkIpY2boSuXaFLF/jkE6ujcWwHrx3kqzVfsfT4UopmKsq6Zut4N9u7\nVoclhBAihiSpiIVz56B+fShfHoYNszoax3Xh7gX6re/HtP3TeDP1m/xR7w/qv1VfVnQIIYSDkqQi\nhoKCoE4dSJ4c/vwTXpFXMMbuPLzD4M2D+XnHz3h6eDKyykjaFm2Lh5uH1aEJIYSIA3lLjAGtoXVr\nOHIEtm2DdJH3URUvFPw4mF92/cJ3f33Hw8cP6V6qO1+W+ZKUiVNaHZoQQggbkKQiBn76CWbOhD/+\ngMKFrY7GcYTpMGb8PYO+6/ty4e4FWnm34psK3/B6itetDk0IIYQNSVIRTatWQc+e8NVX0LCh1dE4\nBq01q06uoueanuy/up+6eeuyoskK8qbLa3VoQggh4oEkFdFw8iR8/LFpuz1okNXROAb/S/70XNOT\ntafXUsarDFtabKG0V2mrwxJCCBGPJKl4icBAqF3b1E/MnAlubi9/jCs7dfsUfdb1we+gH3nT5WVB\nwwXUylMLpZTVoQkhhIhnklS8QFiY6UFx9izs2AGpU1sdkf26fv86gzYNYuzusbyW/DUm1pxI8yLN\nZTtyIYRwIfIX/wW++w7mzYMFCyB/fqujsU/3H91n5PaRDNkyBKUU/d/pT9eSXUnmnszq0IQQQiQw\nSSqeY9Ei6NcPBgww0x/iWWE6jMl7J9NvfT9uBN2gQ7EO9Cnfh3TJZJ2tEEK4KkkqonD4MDRpYppc\n9eljdTT2adCmQXyz4Rt8C/gyqOIgsr+a3eqQhBBCWEySikju3DEjE15e8NtvkEg6Rv/HutPr6L+h\nP99U+Ib+7/S3OhwhhBB2QpKKCEJDoXFjuH4ddu2CFCmsjsj+XAm8QqO5jaiYrSJ9y/e1OhwhhBB2\nRJKKCPr2hRUrYNkyyJnT6mjsT2hYKI3mNkIpxYwPZ+CWSNbXCiGEeEqSinCzZsEPP8CQIabJlfiv\ngRsHsvHsRtY0XUMGzwxWhyOEEMLOSFIB7N8Pn34Kvr7w5ZdWR2OfVp9czbebvmXAOwN4N9u7Vocj\nhBDCDrl8GeLNm2aVR+7cMGkSSOPH/7p07xKN5zWmUvZK9C7X2+pwhBBC2CmXHql4/BgaNDCtuDds\ngGTSr+k/Hoc9ptHcRri7uTP9w+lSRyGEEOK5XDqp6NEDNm6ENWsga1aro7FP/Tf0569zf7H+k/Wk\nT57e6nCEEELYMZdNKn77DUaMgFGj4J13rI7GPq08sZLv//qeQRUHUT5reavDEUIIYedcsqZi1y5o\n08YUZ3bqZHU09unC3Qs0md+EKjmr8FXZr6wORwghhANwuaTi6lWoWxcKF4YxY6QwMyqPwx7jO9eX\nxG6J+b3u7yRSLvdjIoQQIhZcavrj0SOoV890zpw/H5IksToi+9R3XV+2nd/GxuYbZYMwIYQQ0eZS\nSUWXLrBzpynOzJTJ6mjs07Ljyxi8ZTBDKg2hTJYyVocjhBDCgbhMUjFhAowbBxMnQqlSVkdjn84H\nnKfp/KZUz1Wd7qW7Wx2OEEIIB+MSk+VbtpiCzA4doFUrq6OxTyGhITSc05Dk7sn5rc5vUkchhBAi\nxpx+pOLCBVNHUbKkWUIqovb1uq/ZdWkXm5pvIm2ytFaHI4QQwgE5dVLx8CF8+CG4u8OcOeDhYXVE\n9mnJsSUM3TqUYe8Po5SXzA0JIYSIHadNKrSGdu3gwAHYvBnSSzPIKJ29c5Zm85tRM3dNPi/1udXh\nCDb0rLEAABZlSURBVCGEcGBOm1SMGgXTpsHvv4OPj9XR2KdHoY9oOKchKROnZGqdqShp2iGEECIO\nnDKpWLcOvvgCPv8cmjSxOhr71WtNL/Zc3sNfn/5FmqRprA5HCCGEg3O6pOL0abPzaMWKMGSI1dHY\nr4VHFjJ8+3BGVBlBiTdKWB2OEEIIJ+BU6wYfPDAtuFOlgj/+gFecLmWyjdO3T9N8YXPq5q1LlxJd\nrA5HCCGEk3Cqt90BA+DECdi2DdLIaH6UntRRpE6Smsm1J0sdhRBCCJtxqqRi9WqzdLRgQasjsV9f\nrvqSfVf2saXFFlInSW11OEIIIZyIUyUVLVqYRlciavMOz2PUzlGMqjqKYpmLWR2OEEIIJ+NUNRXt\n21sdgf06dfsULRa24KP8H9GpeCerwxFCCOGEnCqpSORU343tBD8OpsHsBqRLlo5JNSdJHYUQQoh4\n4VTTHyJqX6z6ggPXDrCt5TZSJUlldThCCCGclCQVTm7WP7P4Zdcv/FL9F7xf97Y6HCGEEE5MJgyc\n2IlbJ2i1qBUN32pI+6JScCKEECJ+SVLhpB4+fkj92fXJ6JmRCTUnSB2FEEKIeCfTH06q24puHL5+\nmO2ttpMycUqrwxFCCOECJKlwQn4H/BjnP47xH4ynSMYiVocjhBDCRcR4+kMplVUptUQpdUMpdVop\nNfg55yml1IDwc+4qpfYppRpEuP9VpdRvSqlrSqlbSqkNSqliEe4vrJRaq5S6rZS6pJT6XSmVLnbf\npus4euMobZa0wbeAL629W1sdjhBCCBcSm5qKucB54E2gElBXKdU1ivPaAy2A94FUwNfAdKVUgfD7\nJwMpgNxARsAfWKKUclNKJQKWAVuB14C3gPTAL7GI12U8CHlAgzkNyJQiE+M/GC91FEIIIRJUjJIK\npVRRoBDQU2sdqLU+CQwH2kRxujewWWt9QhtLgZvhjweYBXTWWt/RWj8CpgLpMMlDJuB1YLrW+rHW\n+jYwD3g7xt+hC+myogvHbh5jdv3ZpEicwupwhBBCuJiYjlR4A2e01ncjHNsD5FFKeUY6dynwTvg0\nhrtSqhaQFNgIoLX201pfAFBKvQZ8DmzSWl8GLgJ7gTZKqeRKqfTAR8DiGMbrMmb8PYOJeyYyutpo\nCmUo9PIHCCGEEDYW06QiLXA70rFbEe77l9Z6PjABkxw8BGYAn2qtL0Y8Tyl1BLiCmU5pGP5YjUki\n6gB3gcvhsfaOYbwu4ciNI7Rd0pamhZrS4u0WVocjhBDCRdli9ceTiXv9zEGlmgLNgKLAQUz9xUyl\n1Dmttf+T87TWeZVSaYE+wGalVCEgDDMq8SfwPeAJjAVmAs/dh7Rbt26kSvVsG2pfX198fX3j9A3a\ns6CQIOrPro9XKi/G1BgjdRRCCCGey8/PDz8/v2eOBQQE2Oz6ygwKRPNkpVoBvbTWOSIcK44pqEyp\ntQ6KcHwHMF9rPTjCsXnAOa31fwo7lVJuwB3gE+ABMFtr7Rnh/kLAPiCN1vpOpMd6A/7+/v54e7tW\nK+qWC1vid9CPna13UiB9gZc/QAghhIhgz549+Pj4APhorffE5Voxnf7YDWRVSqWJcKw4cChiQhHO\nLfwWUWIApZSnUuqUUqpwhPs0ZtQjJPxxicJXgTyRhEijIa5Ka82GMxuo/HtlJu+bzJgaYyShEEII\nYbkYJRVa633ATmCwUiqFUiov0A0YA6Y+QilVOvz0RUArpVTB8GWilYGKmNGLQOAwMFQplVEplQQY\ngKm92IIZ+QgEBiilkoZPj/QGNkYepXAlWmuWHltK2SlleXfau1y7f4059efQvEhzq0MTQgghYlVT\n8REwEVNcGQCM1VqPC78vF6b+AUwthBuwANNr4gzQSmu9Mfz+JsAITHIBsB+oprW+BaCUqgL8BFwA\ngoENQLtYxOvw/t/e3cdbOeZ7HP/82pXqIEokEjUITTq7RGlGhEkMQ+OMNI6ak6c5Z3qSYUsnBlue\nYno5ajgy0YOHk3AqMqUxRQftQtjSmDpF6EltVHrYv/njvjtn2fbT2vvWtdZe3/frdb92+77udfdb\nv9e17/Vb13Xda+0u3c304ukULijk7c/fptvh3ZjZbyZ9ju6jNRQiIpIx0i4q3H0tcG4FbXkp/94F\njI638o79AhhQyf+zlGhkI2ft3L2TKcumMGbhGJZvXM6Zbc9k/uXzOa3NaSomREQk4+i7PzLQtp3b\nmLh0Ine9dhert6zmgmMv4LELH6PrYV1DhyYiIlIhFRUZpOSbEiYsnsDYRWNZv3U9l3S4hIIeBVqE\nKSIiWUFFRQbYuHUj414fx7g3xvH1jq8Z0GkA1596Pe2atav6wSIiIhlCRUVAn375KfcuupcJiydQ\n6qVc1fkqru1+LYfvf3jo0ERERNKmoiKAlV+s5K5X72LiWxNpVL8RQ08ZypCTh9DiH1qEDk1ERKTG\nVFTsRcXri7lj4R1MXTaVZo2bcfNpN/Prk35N00ZNq36wiIhIhlNRsRcUrS2icGEhM4pn0Gq/Vtx7\n9r1c0fkKmjRoEjo0ERGRxKio+B4t+N8F3L7gduZ8NId2B7bjoZ8+xGUdL2Of+vuEDk1ERCRxKioS\n5u7M+WgOty+4nYWrF9Lh4A5MvWgqF59wMfXrKd0iIlJ36VUuIaVeyoziGRQuLGTJp0voelhXnrvk\nOc475jzqWbrf2yYiIpJ9VFTUkrszZdkUChcUUryhmNOPPJ25l83ljKPO0Edpi4hITlFRUUt/KPoD\n18y6hvOOOY9Hzn+Ebq27hQ5JREQkCBUVtbBp2yZGvjySgZ0GMvGCiaHDERERCUqT/bUwev5odu7e\nSWGvwtChiIiIBKeRihp6d927jF88njt63UHLfVuGDkdERCQ4jVTUgLsz9MWhtD2wLUNOGRI6HBER\nkYygkYoaeG75c8xbOY+Z/WbSMK9h6HBEREQygkYq0rR913aGzxlO7x/0ps/RfUKHIyIikjE0UpGm\nsYvGsqZkDbP7z9bnUIiIiKTQSEUaPin5hMIFhQzuOpj2B7UPHY6IiEhGUVGRhhvm3UCTBk0Yddqo\n0KGIiIhkHE1/VNOiNYuY/M5kHv7pwxzQ6IDQ4YiIiGQcjVRUQ6mXMvjFweQfms/ATgNDhyMiIpKR\nNFJRDZPemsTitYtZMHABefXyQocjIiKSkTRSUYWSb0oomFdAvw796HFEj9DhiIiIZCwVFVW47S+3\nUfJNCXeeeWfoUERERDKaiopKfLjxQ+7/n/sp6FFA66atQ4cjIiKS0VRUVGL4nOG02q8VI7qPCB2K\niIhIxtNCzQq8sOIFZq2YxdMXP03jBo1DhyMiIpLxNFJRjh27dzBszjB6HtmTvsf1DR2OiIhIVtBI\nRTkeeOMBVmxawVMXP6Xv9xAREakmjVSUse7rddzyyi1c1fkqOh7SMXQ4IiIiWUNFRRkj540kz/K4\n9fRbQ4ciIiKSVTT9kaJobRGPLH2EceeMo3mT5qHDERERySoaqYi5O0NeHMLxLY7n6i5Xhw5HREQk\n62ikIvbEu0/w6ppXmXvZXOrXU1pERETSpZEK4OsdX3Pdn67jwvYX0qttr9DhiIiIZCUVFcCdr97J\nhq0buOfse0KHIiIikrVyvqhYtXkVd792N9d2u5a2B7YNHY6IiEjWyvmiYsRLI2jWuBkFPyoIHYqI\niEhWy+kVifNXzmd68XQev/Bx9m24b+hwREREslrOjlTsKt3FkBeH0O3wbvT/Yf/Q4YiIiGS9nB2p\neLjoYZatW8abV7yp7/cQERFJQE6OVGzatomb5t/EwE4D6dKqS+hwRERE6oScLCpGzx/Nzt07KexV\nGDoUERGROiPnpj/eXfcu4xeP545ed9By35ahwxEREakzcmqkwt0Z+uJQ2h7YliGnDAkdjoiISJ2S\nUyMVz37wLPNWzmNmv5k0zGsYOhwREZE6JWdGKrbv2s61L11L7x/0ps/RfUKHIyIiUufkzEjF2EVj\nWVOyhtn9Z+sWUhERke9BToxUfFLyCYULChncdTDtD2ofOhwREZE6Ke2iwszamNlMM9tgZivNbEwF\nx5mZ3RIfU2Jmb5nZP6W0H2hmj5nZOjPbZGZ/NrOTypxjpJmtNbMvzewlM2uT/lOEG+bdQJMGTRh1\n2qiaPFxERESqoSYjFdOBNcCRwJnAhWY2tJzjrgF+BZwFNAVGApPNrEPcPhHYDzgGaAkUATPNLA/A\nzP4VuBT4MXAo8D4wLN1gF61ZxOR3JlPYq5ADGh2Q7sNFRESkmtIqKsysC9ARuN7dv3L3j4CxwJXl\nHJ4PLHT3v3pkFrAxfjzAU8Bv3H2zu+8A/ggcBBwctw8Hbowf/5W7D3X38oqXCpV6KYNfHEz+ofkM\n7DQwnYeKiIhImtIdqcgHVrl7Scq+JcCxZlb2az5nAT3N7EQza2Bm5wONgVcA3H2au38MYGYtiIqI\nv7j7p2bWCjgKaG5m78VTLU+b2UHpBDvprUksXruY3/f+PXn18tJ8qiIiIpKOdIuK5sAXZfZtSmn7\nP+4+A3gIWApsB6YAA939k9TjzOwD4DOi6ZRfxLsPj3/+HDiDaHTj8Ph81VLyTQkF8wro16EfPY7o\nUd2HiYiISA0lcUvpnvsz/Vs7zS4D/hnoArxLtP5iqpmtdveiPce5e3szaw7cBCw0s44p57zT3T+P\nzzcamG1mDePpku8YNmwYTZs2BeD99e+zYfMGul/XPYGnKCIikv2mTZvGtGnTvrVvy5YtiZ3f3L3q\no/YcbDYIKHD3din7ugKvAfu7+9aU/a8DM9x9TMq+Z4DV5a2NiBdobgYuJ1q0uRLId/e34vZjgGKg\nzZ5pk5TH5gNFRUVF5Ofn8+HGD+nwYAdG/XiU7vgQERGpxJIlS+jcuTNAZ3dfUptzpTv9sRhoY2bN\nUvZ1Bd5PLShiefGWah8AM9vXzP5mZiemtDnRCMUO4GOgBOiU0n4UsBNYW1WQw+cM57D9D2NE9xHV\neEoiIiKShLSKinjU4A1gjJntZ2btiW7zfBCi9RFmtme+4XlgkJn90MzyzOxsovURM9z9K6JRh7vN\nrKWZNQJuIVp78Zq77wYeAUaaWTszOxgYBTzu7qWVxfjCiheYtWIW95x1D40bNE7n6YmIiEgt1GRN\nxc+Bh4kWV24Bxrv7hLjtaGDPXSCFRCMVzwItgFXAIHd/JW7/JXAfUXEB8DZwjrvvWfhZADQkKmLq\nA/8FVPrVojt372TYnGH0PLInFx13UQ2emoiIiNRU2kWFu68Fzq2gLS/l37uA0fFW3rFfAAMq+X92\nAL+Jt2p58r0nWbFpBU9d/JS+30NERGQvq1Pf/fFQ0UNc3flqOh7SseqDRUREJFF1qqioZ/X43em/\nCx2GiIhITqpTRcU1Xa6heZPmVR8oIiIiiatTRUXf4/uGDkFERCRn1amion69JD4gVERERGqiThUV\nIiIiEo6KChEREUmEigoRERFJhIoKERERSYSKChEREUmEigoRERFJhIoKERERSYSKChEREUmEigoR\nERFJhIoKERERSYSKChEREUmEigoRERFJhIoKERERSYSKChEREUmEioocN23atNAhZB3lrGaUt/Qp\nZzWjvIWjoiLH6Y8vfcpZzShv6VPOakZ5C0dFhYiIiCRCRYWIiIgkQkWFiIiIJKJ+6AAS0giguLg4\ndBxZZ8uWLSxZsiR0GFlFOasZ5S19ylnNKG/pSXntbFTbc5m71/YcwZnZpcCU0HGIiIhksf7uPrU2\nJ6grRUVz4CfAKmB72GhERESySiPgSGCOu2+szYnqRFEhIiIi4WmhpoiIiCRCRYWIiIgkQkWFiIiI\nJEJFhYiIiCRCRYWIiIgkIiuLCjP7iZl9ZmbfuZ/WzH5hZm+bWYmZvWlmZ4WIMdNUlDMzu9zMdpvZ\n1njbFv/sEirWTGFmR5jZM2a2wczWmtmjZrZ/3NbJzP5sZpvNbLmZDQ8db6aoKG9m1sbMSsvpazmf\nOzM70czmxv3pUzN7wswOjtvOMLPXzWyLmS2LP5dHqDBvh5jZaRX0tb6hY84kZnafmZWm/F7rvpZ1\nRYWZXQfcD3xYTlsn4I/Ab4GDgPuAGWbWam/GmGkqy1nsFXdvEm+N45+L92KImeq/gU1Aa6ALcAJw\nj5k1itvmAocClwAFZvazUIFmmHLzFrd5OX1tbKhAM4GZNQTmAC8DLYAOwCHAeDNrCTwHPBi3DQUe\nNrP8QOFmjEry9mB8yKpy+tr0QOFmnPj18jLA498PJYG+lnVFBbAN6Ap8VE7bvwCz3H2Ou++IPxls\nGfDLvRlgBqosZ1IOM2sKvAkUuPs2d18LTAJ+DJwLNABuj9uWAv8JXBks4AxRRd6kfE2AG4Ex7r4z\n/vChZ4heJPsDy919UnxNmwc8DwwKF27GqCxvUgkzM2A8cG/K7kT6WtYVFe7+gLt/WUFzZ6DsB74v\nAU76fqPKbFXkDKC1mb1kZpvM7K9m1n+vBZeh3H2Luw9y9/Upu1sDnxD1s3f8258cl/P9DCrM2xFE\neYPoejYpnhb53MwKzSwvQKgZw903u/tEdy8FMLNjgQHAE+iaVqFK8jYtPmT/eBpuvZmtMbNhoWLN\nQFcTvdlMnQ7PJ4G+lnVFRRWaA1+U2beJaCpEyreeaFpkBNHQ4UjgUTPrGTKoTBOvMfk34HYq7mfN\n9nZcmS4lb7cB3wCvAtOJCrRziUYRRwULMIPEa1G+Ad4DXgduQde0KlWQtxLgHWAs0RTlr4DRZjYg\nVJyZwswOAW4GrinTlEhfq2tFRXmMeM5IvsvdZ7v7ue7+TjyE+CTREOLA0LFlCjM7lWju9np3f7mi\nw1A/+5aUvP3W3ee7+2fu/iN3f97dd8frdgpRXwPA3Ve7+z7AsfH2eAWHqq+lKCdvk919qbuf4e4L\n3X2Xu/8JmID6GkRTHo+4+/JqHJt2X6trRcV6vltVNYv3S/WtAnJ6ceseZnYeMAsY7O7/Ee+uqJ/V\n6ot46pIK8laeVUDLvRJUlnD3j4hGDPsBO9A1rVpS8xZ/yWRZq8jx65qZ9QK6A7fu2ZXSnMjrZ10r\nKhYTzUGmOoloSEzKYWZXmdnFZXYfB/wtRDyZxMy6Ey0y7OvuU1KaFgMnmlnq34/6WayivMW3q91Y\n5vDjiS72OcvMTjezD8rs9nibS3QHTSr1NarMW08zu7pM2/HoutYfOBhYbWbrgSKidU7riG5qqH1f\nc/es3IBHgall9p0AfA2cA+xDNI+2GTg4dLyZsFWQs8HAp0TFWH3+/91Rp9DxBs5VHtEc7aBy2hoS\nXZz+HWgMnEw099g7dNyhtyry9o/AduDSuK91IVrAOSR03IFztj+wFhgT96cWwGxgPtE7x83xtWwf\noA/wFXBC6LhDb1Xk7fz4teDMuE+eRbTO4oLQcQfOWVOi0Zo928lAKdG6k9ZJ9LXgT7IGSdkGbAV2\nxts2YGtK+8+A5fH+IuDU0DGH3qqRsxvjF8mt8QvCOaFjDr0BPYDdcU62lfnZmuhdz4J430rgytAx\nZ8JWjbxdACyNL/gfE623CB536I3oDdH8+CL+GdGq/ENTcro0zmNxrr8wppG3QcAHcV/7CBgQOt5M\n24A2wO6U32vd1yw+kYiIiEit1LU1FSIiIhKIigoRERFJhIoKERERSYSKChEREUmEigoRERFJhIoK\nERERSYSKChEREUmEigoRERFJhIoKERERSYSKChEREUmEigoRERFJxN8Bs/xMlNryf4YAAAAASUVO\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f8e69482898>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_knn(X, y, range(10, 37, 2))"
]
},
{
"cell_type": "code",
"execution_count": 207,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5-fold CV accuracy: 84.34214927948078 % ± 0.11558247726631235 %\n"
]
}
],
"source": [
"print_cv_accuracy(KNeighborsClassifier(n_neighbors=34), X, y)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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