eda_and_feature_selection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis / Data Wrangling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A variable dictionary can be found at https://s3.amazonaws.com/files.consumerfinance.gov/f/documents/cfpb_nfwbs-puf-codebook.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6394"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in dataframe and size\n",
    "df = pd.read_csv('NFWBS_PUF_2016_data.csv')\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output variable I am interested in is one's ability to handle an unexpected financial expense. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check how many null values our output variable has\n",
    "np.sum((df['FWB1_1'] == -1) | (df['FWB1_1'] == -4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YQtIywBbAmIh4OSJeAcYAW5ZVbzMza71eeWYjaTDwGeAuYOmImAYpIAEfzdmWAyYXJpuS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f52f433b048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a histogram of our target variable after removing null values\n",
    "df = df[(df['FWB1_1'] != -1) & (df['FWB1_1'] != -4)]\n",
    "sns.countplot(x='FWB1_1', data=df)\n",
    "plt.title('Distribution of Confidence in Handling Unexpected Expenses')\n",
    "plt.xlabel('Confidence in Handling Unexpected Expenses')\n",
    "x_labels = ['Not at all', 'Very little', 'Somewhat', 'Very well', 'Completely']\n",
    "plt.xticks(np.arange(5), x_labels)\n",
    "_ = plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because of the way this variable is distributed, I decided to turn it into a Boolean variable, with True corresponding to confidence in one's ability to handle an unexpected expense."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# True corresponds to confidence in ability to handle an unexpected financial expense\n",
    "df['target'] = df['FWB1_1'] > 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of True Responses: 37.035876547078175\n",
      "% of False Responses: 62.964123452921825\n"
     ]
    },
    {
     "data": {
      "image/png": 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KzVFvAJcDo3KxNmBoYbIhwMJO0s3MrAkaeVVVi6TN8/AGwMeAP+V+CyQJOAh4KE8yFTgi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\npnPYrp5CvSVwVPs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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f52d22ac4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Look at distribution of target variable\n",
    "sns.countplot(x='target', data=df)\n",
    "plt.title('Confidence in Handling Unexpected Expenses')\n",
    "_ = plt.xlabel('Confident in Handling Unxpected Expenses?')\n",
    "\n",
    "# Print out percent True and False\n",
    "percent_true = (np.sum(df['target'] == True)) / len(df)\n",
    "percent_false = (np.sum(df['target'] == False)) / len(df)\n",
    "print('% of True Responses: {}'.format(percent_true*100))\n",
    "print('% of False Responses: {}'.format(percent_false*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I then removed the variables that weren't relevant to my analysis, either because they were other measures of financial well-being or their association with my target variable wouldn't lead to actionable recommendations. I wanted to include only those variables that were about behavior relevant to absorbing financial shocks or demographic data to allow us to identify more vulnerable populations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of original columns: 218\n",
      "Number of columns in new df: 89\n"
     ]
    }
   ],
   "source": [
    "# Remove columns that won't be used for our analysis\n",
    "\n",
    "num_original_cols = len(df.columns)\n",
    "\n",
    "# Financial well being scores (includes output variable)\n",
    "fwb_cols = [col for col in df if col.startswith('FWB')]\n",
    "# Life survey questions\n",
    "swb_cols = [col for col in df if col.startswith('SWB')]\n",
    "# Specific financial knowledge questions\n",
    "kh_cols = [col for col in df if col.startswith('KHK')] + ['KHscore']\n",
    "# Specific questions answered correctly\n",
    "correct_cols = [col for col in df if col.endswith('correct')]\n",
    "# Financial knowledge questions (included in summary variable)\n",
    "finknowl_cols = [col for col in df if col.startswith('FINKNOWL')] + ['LMscore']\n",
    "# Questions about number of children (already included in more relevant variable)\n",
    "kid_cols = ['KIDS_1', 'KIDS_2', 'KIDS_3', 'KIDS_4']\n",
    "# Household demographic data (already included in more relevant variable)\n",
    "ppt_cols = [col for col in df if col.startswith('PPT')]\n",
    "# Product related questions\n",
    "prod_cols = [col for col in df if col.startswith('PROD')]\n",
    "# Interconnections questions\n",
    "connect_cols = [col for col in df if col.startswith('INTERCONNECTIONS')]\n",
    "# Questions about experiencing shocks\n",
    "shock_cols = [col for col in df if col.startswith('SHOCKS_')]\n",
    "# Housing-related questions\n",
    "housing_cols = ['HOUSING', 'LIVINGARRANGEMENT', 'HOUSERANGES', 'IMPUTATION_FLAG', \n",
    "                'VALUERANGES', 'MORTGAGE', 'HSLOC', 'HOUSESAT', 'VOLATILITY']\n",
    "# Social Security questions\n",
    "socsec_cols = ['SOCSEC1', 'SOCSEC2', 'SOCSEC3']\n",
    "# Health-related questions\n",
    "health_cols = ['CONNECT', 'HEALTH', 'DISCOUNT', 'MEMLOSS', 'DISTRESS']\n",
    "\n",
    "datakeeping_cols = ['PUF_ID', 'sample', 'fpl', 'finalwt']\n",
    "single_cols = ['EARNERS', 'SNAP', 'FRAUD2', 'PAIDHELP', 'EMPLOY', 'SUBNUMERACY1', 'SUBNUMERACY2', 'SUBKNOWL1', \n",
    "               'OBJNUMERACY1', 'MANAGE2', 'PAREDUC', 'LIFEEXPECT', 'MILITARY', 'RETIRE', 'PPREG4', 'PCTLT200FPL', \n",
    "               'OUTLOOK_1', 'OUTLOOK_2', 'GOALCONF', 'EMPLOY1_9']\n",
    "\n",
    "cols_to_remove = fwb_cols + swb_cols + kh_cols + correct_cols + finknowl_cols + kid_cols + ppt_cols + prod_cols \n",
    "cols_to_remove = cols_to_remove + connect_cols + shock_cols + housing_cols + socsec_cols + health_cols \n",
    "cols_to_remove = cols_to_remove + datakeeping_cols + single_cols\n",
    "\n",
    "df = df.drop(cols_to_remove, axis=1)\n",
    "\n",
    "num_new_cols = len(df.columns)\n",
    "\n",
    "# Create DataFrame for our independent variables\n",
    "dfx = df.drop('target', axis=1)\n",
    "\n",
    "print(\"Number of original columns:\", num_original_cols)\n",
    "print(\"Number of columns in new df:\", num_new_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FSscore</th>\n",
       "      <th>FS1_1</th>\n",
       "      <th>FS1_2</th>\n",
       "      <th>FS1_3</th>\n",
       "      <th>FS1_4</th>\n",
       "      <th>FS1_5</th>\n",
       "      <th>FS1_6</th>\n",
       "      <th>FS1_7</th>\n",
       "      <th>FS2_1</th>\n",
       "      <th>FS2_2</th>\n",
       "      <th>...</th>\n",
       "      <th>generation</th>\n",
       "      <th>PPEDUC</th>\n",
       "      <th>PPETHM</th>\n",
       "      <th>PPGENDER</th>\n",
       "      <th>PPHHSIZE</th>\n",
       "      <th>PPINCIMP</th>\n",
       "      <th>PPMARIT</th>\n",
       "      <th>PPMSACAT</th>\n",
       "      <th>PPREG9</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>57</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>54</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>58</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 89 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   FSscore  FS1_1  FS1_2  FS1_3  FS1_4  FS1_5  FS1_6  FS1_7  FS2_1  FS2_2  \\\n",
       "0       44      3      3      4      3      3      3      4      4      3   \n",
       "1       43      3      3      3      3      4      3      2      4      3   \n",
       "2       42      3      3      3      3      3      3      3      3      3   \n",
       "3       42      3      3      3      3      3      3      3      3      3   \n",
       "4       42      3      3      3      3      3      3      3      3      3   \n",
       "5       57      4      4      4      4      3      4      4      4      4   \n",
       "6       54      4      4      3      3      3      4      4      5      4   \n",
       "7       35      2      3      3      2      2      4      2      4      3   \n",
       "8       58      4      5      5      4      3      5      4      4      4   \n",
       "9       42      3      2      2      3      2      3      4      4      3   \n",
       "\n",
       "    ...    generation  PPEDUC  PPETHM  PPGENDER  PPHHSIZE  PPINCIMP  PPMARIT  \\\n",
       "0   ...             1       4       1         1         1         7        3   \n",
       "1   ...             3       2       1         1         2         6        3   \n",
       "2   ...             3       3       2         1         3         6        3   \n",
       "3   ...             3       2       1         1         1         8        3   \n",
       "4   ...             4       2       3         1         5         7        1   \n",
       "5   ...             4       4       1         1         2         7        1   \n",
       "6   ...             3       4       4         2         5         7        1   \n",
       "7   ...             4       1       1         2         3         4        4   \n",
       "8   ...             3       1       3         2         4         7        1   \n",
       "9   ...             3       2       1         1         3         8        1   \n",
       "\n",
       "   PPMSACAT  PPREG9  target  \n",
       "0         1       8   False  \n",
       "1         1       3   False  \n",
       "2         1       9   False  \n",
       "3         1       7   False  \n",
       "4         1       4   False  \n",
       "5         1       3    True  \n",
       "6         1       4   False  \n",
       "7         1       4   False  \n",
       "8         1       7    True  \n",
       "9         1       2   False  \n",
       "\n",
       "[10 rows x 89 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to make the data more manageable, I looked for variables that I could remove because they were very closely and obviously related to our target. For example, the amount of money in savings. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Get info for plotting percentage of target being True for each category in a chosen column\n",
    "def percentages_of_true_across_categories(column_name, data=df):\n",
    "    \n",
    "    # Calculate number of categories\n",
    "    num_categories = len(data[column_name].unique())\n",
    "    \n",
    "    # Create table counting occurences of True and False for each category\n",
    "    frequency_table = pd.crosstab(data[column_name], data['target'])\n",
    "    \n",
    "    # Generate x values\n",
    "    x_vals = np.arange(num_categories) + 1\n",
    "    \n",
    "    # Initialize y values\n",
    "    y_vals = np.empty(num_categories)\n",
    "    # Loop through categories\n",
    "    for i in np.arange(num_categories):\n",
    "        # If variable is boolean\n",
    "        if num_categories == 2:\n",
    "            # Calculate percentage of respondents who responded True for each category\n",
    "            y_vals[i] = frequency_table.loc[i, True] / np.sum(frequency_table.iloc[i,0:2])\n",
    "        else:\n",
    "            y_vals[i] = frequency_table.loc[i+1, True] / np.sum(frequency_table.iloc[i,0:2])\n",
    "            \n",
    "    return x_vals, y_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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m7wh1cgoPAtdLuhJ4tbGyRo9mMzMbYuokCg+Xx5zlYWZms6g6TVKPbkcgZmbW\neXVaH40EvgKsBMzdWB8RHrzOzGwWU6ei+QLgXmAccDTwEDmEhZmZzWLqJAoLR8Q5wOsR8fuI2AtY\np8VxmZlZB9SpaG7Mx/y4pC3JkU5Hty4kMzPrlDqJwnGSFgAOBk4D5ge+0NKozMysI+q0PrqiPH0e\n+FBrwzEzs07qbTrO0+hlzuSIOKglEZmZWcf0llOYXHl+NDkonpmZzcJ6m47zh43nkj5fXTYzs1lT\nnSap0EsxkpmZzTrqJgoDImlzSfdJmiLp0G62f1HS3ZLukHSNpKVaGY+ZmfWux0RB0ouSXpD0ArBq\n43ljfV8HljQ7cDqwBbAisIukFZt2uxUYHxGrAj8DvjngMzEzsxnWW53CiBk89lrAlIh4EEDSRcA2\nwN2V97iusv+NwG4z+J5mZjYD6s7RPBCjyAl6GqYCa/ey/97AVS2MxwyAsYde2ekQAHjohC07HYLZ\n27QyUVA367qtsJa0GzAe2KCH7fsC+wKMGTNmZsVnZmZNWlnRPBVYsrI8mhw3aTqSNgG+CmwdEa82\nbweIiAkRMT4ixo8cObIlwZqZWWsThUnAcpLGSZoT2Bm4rLqDpPcB3ycThCdbGIuZmdXQZ6IgaTtJ\n90t6vj+tjyLiDeAA4GrgHuCSiLhL0jGSti67nQjMB/xU0m2SLuvhcGZm1gZ16hS+CWwVEff09+AR\nMRGY2LTuiMrzTfp7TDMza506xUdPDCRBMDOzoadOTmGypIuBXwJdFcERcWnLojIzs46okyjMD/wH\n+HBlXQBOFMzMZjF1JtnZsx2BmJlZ5/WZKEiam+xtvBIwd2N9ROzVwrjMzKwD6lQ0nw+8G9gM+D3Z\nCe3FVgZlZmadUSdRWDYiDgdeLhPtbAms0tqwzMysE+okCq+Xv89JWhlYABjbsojMzKxj6rQ+miBp\nIeBr5DAV8wFH9P4SMzMbiuq0Pjq7PP0DsHRrwzEzs07qbea1L0rau5v1B0r6fGvDMjOzTuitTmEv\nsuVRswllm5mZzWJ6SxQiIl7rZuWrdD+BjpmZDXG9tj6StFiddWZmNmvoLVE4EbhS0gaSRpTHhsDl\nwEltic7MzNqqx9ZHEfEjSU8BxwArk4Pg3QUcGRFXtSk+MzNro16bpJaLvxMAM7N3iFbO0WxmZkNM\nnR7NAyZpc+DbwOzA2RFxQtP29YFTgVWBnSPiZ62Mx1pj7KFXdjqELg+dsGWnQzAb0lqWU5A0O3A6\nsAWwIrCLpBWbdnsY2AP4SaviMDOz+monCpLWkXStpD9J2rbGS9YCpkTEg6W/w0XANtUdIuKhiLgD\neKtfUZuZWUv0NszFu5tWfRGbYundAAAfCUlEQVTYGtgcOLbGsUcBj1SWp5Z1/SZpX0mTJU1+6qmn\nBnIIMzOrobecwpmSDi8zrwE8B+wK7AS8UOPY3fV6jn7Gly+KmBAR4yNi/MiRIwdyCDMzq6HHRCEi\ntgVuA66Q9Ang82QxzzxAneKjqcCSleXRwGMDD9XMzFqtr34Kl0uaCHwWuBQ4PiL+WPPYk4DlJI0D\nHgV2JnMaHTNYWsm4hYyZDVa91SlsLekG4FrgTvKi/lFJF0papq8DR8QbwAHA1cA9wCURcZekYyRt\nXd5jTUlTgR2A70u6a8ZPyczMBqq3nMJxwLrAcGBiRKwFfFHScsDxZCLRq4iYCExsWndE5fkksljJ\nzMwGgd4ShefJC/9w4MnGyoi4nxoJgpmZDT29JQofBXYBXqfDdQHvRIOl/gNcB2L2TtLbKKlPA6e1\nMRYzM+swD4hnZmZdnCiYmVkXJwpmZtbFiYKZmXVxomBmZl2cKJiZWRcnCmZm1sWJgpmZdXGiYGZm\nXZwomJlZFycKZmbWxYmCmZl1caJgZmZdnCiYmVmXliYKkjaXdJ+kKZIO7Wb7XJIuLttvkjS2lfGY\nmVnvWpYoSJodOB3YAlgR2EXSik277Q08GxHLAqcA/9eqeMzMrG+tzCmsBUyJiAcj4jXgImCbpn22\nAX5Ynv8M2FiSWhiTmZn1opWJwijgkcry1LKu230i4g1yXuiFWxiTmZn1QhHRmgNLOwCbRcQ+ZfkT\nwFoRcWBln7vKPlPL8gNln2eajrUvsG9ZXB64ryVB17MI8HQH378/hlKsMLTidaytMZRihaEV7/IR\nMaKvnXqco3kmmAosWVkeDTzWwz5TJQ0DFgD+3XygiJgATGhRnP0iaXJEjO90HHUMpVhhaMXrWFtj\nKMUKQyteSZPr7NfK4qNJwHKSxkmaE9gZuKxpn8uAT5bn2wPXRquyLmZm1qeW5RQi4g1JBwBXA7MD\n50bEXZKOASZHxGXAOcD5kqaQOYSdWxWPmZn1rZXFR0TERGBi07ojKs//C+zQyhhaYFAUY9U0lGKF\noRWvY22NoRQrDK14a8XasopmMzMbejzMhZmZdXGiYGZmXZwo9JOkeSXtKmlkp2OZEZKWlHSwpE07\nHcvMImmuTscwM0haVtL/SFqg07EM1FAamUDSgZI+KqnPNvzvBE4U+kHSFsBkYBfgFElfLOuH1Oco\naUvgKmAE8CVJn5e0VIfDGjBJW0u6AzhZ0mc7Hc9AKR0F/Bz4MDBB0vqNbZ2MrS+SFpT0VUl7SJpr\nKDQtl7S0pN8B6wDrA6dJWq3DYXXckLqYdULTBX814IcRsRXwTeAwSStExFudiW7A1iWbCB8FfJ68\nAG07FO+0JY0HvgzsBXwd2EnSDqVvzFAzD/Ae4GMRcRCZcP9E0uyD+SIraSXgWvIm4wPAkZI27GhQ\n9SwLPBcRH4+ILwD/BT4jaUw73lzSYuXvoPquOlHoQblrmx1YSNI8koYDcwFPSJozIu4Avg8c3di/\ng+H2StL7JG0saaGy6iVgvnKxuQd4DXgfecc06JX/S8O8wAPA7RHxKHAdcASweidi6y9JyzcuDsBy\nwLuBx8vyb8nzO6DsO1h/r2sCf42IQ8kEel5gN0mLdjas6UmaX9JOkt5VVr0BPCfpvWX5XjKh2KgN\nsawHXFp+g6+VdXO0+n3rGKxfso4rd2YrABcDNwKLA68D6zX+icDhwNqS1o+IGGwJg6Q5JJ0KnEsO\nYX5uuQv6A/Besgjs5LL7f4CR5XWD6jyqJO0P/FLSdpJGkXG/BmxddnkB+BeDPFGQtJCkX5EdOE+U\ntGVE3EYOCnmcpM2ALwI/IYv45hwsOdKSkO1cmf/kMWB+Se+OiOeZ9l3arkMhvo2k3YHfA1sC/ydp\nT+Bm8vM+UdLBwFbAncDY8ppW/g5uBe4BPl7e69jy/h3nRKEHkuYjL/qvAqdHxIPAqcAaysmDZisJ\nx5mUntiDMIu/MHnnuXFEfAm4HfhpRPyZvJv+J5l93pYcZHALGJTnAYCkb5A/6u+SY2lNiIhJwE3A\nxyX9kUzszgN261ScNW0N3B8R6wEXAltK2hn4FHA/OdfISxGxP3kxW69jkVZI+jLwU2BV8iZjY+Bu\n4MmyfAKZc/gLsHBTrq6T1gS+FhG7k7/j7wHvAo4ELgUWBfYATgY+BjPndyDpM5I+IWmJyrrZIuJl\n4DTgq5JuA4YDv57R95spIsKPbh5kzuB2YBngQ8CmwNzA/mTu4YNlv0OBPTsdbw/nsBlwOdM6Kb6f\nvIvbuSzPUdn3YGDXTsfczTk0Yp8f+DGweFmeh0zUvlCWFwPeX3ndpeQIlur0OVRimg+YrTz/HvCd\n8nw2MmG4Hli2cX6V1/0EWGQQxC/gR8A6ZXkn4O/AuHJuOwFfBeYgK25/28FYl6h8louQxYrLV7bf\nR9arNb9uHeCUmfD+w4ELgGvICcSuJ0eAbmzfjKyHuRs4r/oZd/r/7JxCDyLicXJI3G+TX/QzgLPJ\nL9vjwO6SfkNWcP69U3H2JiKuJu+GjpC0OXkndCF5d0REvC5pTUnXkQMS/rVTsTYrRV+HAPtIWiAi\nXiBH1N2n7DI38EfgEEmLRMQTEfFX5RSvpwFPRsTTUX5pnVTqdP4MfAc4vqw+H3i/pHXIG4//AH9j\n2vhfb0haT9INZLHlq50o1pO0mqRxZXE0ebF7uSz/lKxn+0REvARcEhHHR8TrZPHRXzoQ7+zl/38F\n8A1JuwHPAH8CjpW0haQjgCuBzSWtXl63iKQfkb/xW2ZCKIsBYyJi48hK7KuB7SStKuk9wFeAk4C1\nyRzVuo1TmAnvPUM8zEUvJM1PtqZYkbwrXZzMZv4a+AewfkRc3LkIu9rmLxgRTzStHxY5KOEa5N3P\npsD1EXGqpEuBYyPi1nJR+kBEnPz2o3dGKav+MZlTe50cUPEHZH3BL8i7rg3IxG0z4M6I+G65eF1O\nFicdHBHPtTv2ZqUS+QLg7Ii4SNJVZKuis8gbig+SLY5OJi+wC0XEyaW4YT/gsYg4swNxL0DWRY0i\ny9nvIIvtvkkOcf8HYI3yfDywQUT8u1zwTgSWBg6IiN+3Oe4PAvtHxK7lu78dWQR8HLAn+XuOiNhH\nOTjnYxFxZmlIcgTwzYh4dgDvOyc54vMNkY03KDeNJ0bEbyUtTd7QPA58t3qzUorkVo2IT8zAqc88\nnc6qDJUHmQX9Ljkk+OimbbN1KKZPAa8ARwHvqqwXTdlQYO7ydyR5lzpXN8fryHl0E8f/AD8vz2cn\n764vBhYsj48Aa5btJwBbV857ycF0PmTRykWNuMjKxKeALcrycEoxHplInNjDcdp6LuUzPq08X56s\nOzuEHERzBzJRO7Js/xGwW3k+ilKk16HPew/y5qexfDyZA960LFeL5c4Fxs/oZ00mjreR/Ut+BnwL\nmJMsYfhy5f+7K5k7WLjx3S5/5wbm7fR3tfFw8VEfSnHEhuQXfy3ggigzxTVEG1uFNJolljvJUWSR\nxCJkH4pGPBERIekjko5srJb0EfIu9d/lGNNlVdt5Hn34FzCbpOUj4k0y3hHAPhHxXERMjIhJyh6o\nqwDPQtd5P6JikJzPcOBFYJOyHOSd91qShkfEK5HFeDsAnyPLmbs0/kcdOJf/R5kaNyLuI3Np+wIr\nRsRPgQMj4uiy73+BP5d9H42IU0rsLb2+VH4L1e/xtcB/JO0tad5yDteQRTezAa9Jeo+kXwBj6GbW\ntAF81isDN0XEx8jmwxuRn98UshipMWrAb8h56YeV93mz/P1vRLzc6s+rrkERxGAWEa8Cb5GVfVsD\nN0uau91xlOtco9/EXOSF8HsRcQjZ72ADSaPLvsOUva0PIyuyIBOQjYBvRcTnIuLVKLcpg9DTZHO9\ns0qitg1ZXLFASaTnU07Reidwc0T8sfriRqLYzoAlrSDpM+V510UqIu4mL1QfUvae/SxZBLM1WTmL\npL3IooVDIuKq6nHbeR6NxLQsfg9Yp9xYrEImbr8FGtPpviVp/VLnsTDZ3r/lNxmN95B0HLCvSse+\nSuL5MJmj36zE+0/yYvxKiWeesu32iNgkIh4aQAyLS9pM0/oVvEKe/0IR8S+y/nEXssjtEeATyhED\nXiC/1/N0d9xBchPj4qP+PMhEdHgH338l4HdkWfv7KEVAZf35wEcb8QGr9HGsjrZyAD4BnE7eRc3b\n+Hwr2+cgK7+/CixElrvfVNm+HrDYYDgfsqjgPPLmYZnq96XyfC5g7cryT4A1yvO5OxT3omRrujW6\n+26QxVlnkP10PkG2KPoGWaQ3H3AQsF+HYr+PLP7ZoIfY5wIWKM/fC/ySacU1w7r7H/Xxfo3jHkQ2\nLLmc7Ly6Llln90Ng6cr+vyN7pkN2cP012Z/jiE59T2t/tp0OwI+a/6j8EV5Etpr4VFmnyvYvke2v\nRza9bljTcsebvJFDa9xGVqReChxDU5lq84WSbBZ8ajfn0/F6gxLH0WRrmwtq7Ptlshhvjqb1bTsX\n8gbidrJS+KryPxneHAdZT9O4mG5DVpJ2d7yWxk7lZozsXPZ7si7tMEpz3ebvRln3/8hWajOlnoNs\nnbRBeb47cFd5fn75Pi9UlncDrqu8bgyDoFlxnYeLj4aOEeQdz0HAlFLPMU5SIyt6GnkHd6ikOyV9\nCHJa1OpBonxDO6VkuVcgE7YzgWPJnEBjKIeRkr4L7C9pbkmjJF1I5iqu6+Z8OprlLiUu85CtcLYB\nVi8tYKr7LFv+zi/pSnKsqS9ENt3s0uZzWRM4KyK+DHyBzAlsXVqtdcVRvi8qdR7Hki273qZVsUta\nWNLZZE6s4QWySOgOskXgyiWGN5SjGK9SXrshmVifF6WeYwZjWYzsLzO1vN+PgMeVTaePBzYEti27\n/4dsJUfZ9+GIeHqw1Bv0qtOpkh/1H2SF2RVk1vQ+ss/BycCosv1BsnJr707H2hT3QWR3/sXK8jnA\nqeX5HOSP6QqylcvxZHn2sLJ9BNnqZY5OxN6PczyfbBm1HZkLOp9s6bUY2XJqy7LfuMpr2pkz6OrI\nR5Zp/x/ZRLPxOf+2/F+WL8uLA6uV51uRfRK2aPNn+iWy5dDvyNZPjbvw1YArKvv8iSxKmqd8/rdW\njjF75XntXDLTimZna1r/S+CoyvI44NHyPf1Q+U1eTdYl/E+nv5cDebR0jmab6T7K9P0mFiXbgy+p\n7ITzs4j4SmPn0gKnYzkD5VAhPyYv/P8EPlkqwE8BviNpmYh4QNK95FgwawOHR7nrLHetL5IXsMbw\nAG3PGZTczY5kD90nyzpFdFVwzk92kHqdbLO/DPBMRDxVWkjdSFbUEhH/qLy+5edSGiWcSTbz3Ywc\nuO4/kqaQfT3GlPN7jkzAliJvOHYkcxO7lfO+vNWxNsX9VTJn/GEysb2AHKcI8rP+g6YNCzIn8MuI\n+A85yNz+kjaKiGsj4s3G/6rub6F8RzeUtGtkp7zqd+8w4M+SvhMR/46Ifyj7nmwREZdImkT2Pbk2\nmnKCQ8Xgz8pYl4h4ISJ+HdnR7PdkBfOHgH9GxJWNBKG0UqKTCUKxBNl/YsuI+CxwA9ljdwEy+/8V\ngMgWGwsBb0XEW8peqYpBUFRUiiD+Sl4cT5R0eNk0rHKxeZ4swniO7EG+C7CipAVLojYb8HD1uG38\n32xEtlT7DfBRZYdMyJzM6WSR4xwRsQPZSmr7sn0C8F5Jy0XEf6E9AyVW3uPUiNgtslf6FPIzbAwY\nN4psxXVQ+XskeWO0atl+LJVRBvqRGHxY0t/I4s1DGglCOcZb5SblHuAScu6FRtHtXGQOnYh4KSKu\njmxmPCSvr84pDDHlzm9d8oK6CPDjyCE5ukRp/9yh2Kq9qx8kh2f4YET8iSyC2J0cTO1i4BJJe0bE\nD8jiiunabw8Sq5J3oYeXuoE/S5oYEbdAV6KxOjmiKZFDqiPp+2Ti9xw5xlGn7hpvIS/27yIbKvxe\n0jXlQj9J0i2VxHZOYCJARLwi6SuUPi1lXcsTssZ7RA4Y17jBmYMsQlqwbLtJ0i4R8Zeyz1LAA43P\nPiKu7897ShpREu8VyNZjjTqJ+SOHV2nk7Bo3KQeSieZ3lD2V5yJzL83nMjiamPbTkEzJ3slikPSb\naCbpU+QF8DOaNl79wmR575rlR3UPeQc3thSjfA34gKR7yB/VhR0IfTqSFtO0cWggi30eV84mNoWs\nwzmq7HsU2URzakTcERF3NNquR8RREfHP8rxjxQgR8WRkn5THyWaUnwBGVu7Ih0laWTkkw1Zk0VHj\ntddExNsudjOTcn6DccphJrpyuY34IuLNkoAtRhbLNYpyGgnCsIj4Z7np6O97z6McJ+mW8l7fAe6X\n9KXyvz1b0pmSlmgUFUrahOyB/GkyYfhBRHyw8b+eFXjsoyGsZE/niohXOvX+JVu9BNnbdTg5wcrP\nI+K6ss+uZDn7lRFxjXI+h2vJtvHPlx//yOay+g6dz2HkMAn/JMfaP5ccWXZHcmTZRl1HY2jrB4DH\ne7ojbNe51KjzoDwfRjZpPj8iflx5/ebkCLNfb3Wslfdclvx8ZycHApwrIvYs8b6tvqVcjI8lRyee\n4TtwSfuR9REPkDnUb0T2kv8Q2aDjW+QNwElkpfFJZM7veODkiLi56Xgdqe9qBecUhrCIeKsTCUK5\nY+qrd/VSZfdryNYZ+0hahBzy4S5Kb95SJv9kOWYnE4Q5yDqabck6gWfIsX9+Ro4O+rHK7qcCe0UO\n6fCWepgzoE0Jwob0XefRKJJ5g8zZbCfpaElXSJovsp7q6+V47bomjAMeiYgPknUDH5C0Rwn3LUnL\nlrv1hlfIi/O4bo5VSyOBVFZQr0v+n3cjr4MvAZSbmdUi4ssld/UlMgc1f0Q8QOaGP9h87FklQQAn\nCjYA5SLTmJXu5vK8MSLp+WQ2//3KsX2eILPZ/yDHj7oH+EPk1JnTHbPdCYKk0SWhguwQtRQwpRSZ\nnEEO5fBJsknsvsopFCHvbrvuFDtcB9Ko89iCvJM+UNIaEfF6yR2sr2xN0/AfMuHbEDg6KpWp0NL+\nBnOUu/CGBYFHJc0bOZPhQcBByiFMdiK/Wy9W9p8KXFguzP1978ZNzCIlYbg8Ij4ZEX8v7z0vlVni\nIuJv1ZeTkx41/sfH0YEhwdsqBkG7WD+G1oOB964eS1ZEdzr+4WSl9ySyVU5jwqSrgP+t7LcueQGY\nkywe+1HZ5wHKqJsdiH0xYN3K8iFkC5xGu/pDyYseZKucvwDbl+V3k+Mu7VV5fct7uJOjqt5LTnRz\nCtnL+L3keFbV0X0nlu/OPJSRRGdiDI0hYm4jizOHMa2Pxp6UkU3L8jCyNdwpZK/vz3X6O9vW71in\nA/Bj6D14+6x0G5L9JeYp2+cie1h/ixy0buOm13d0aAqyff6PyvPPkAOo7VguHHcyrZPUImS591pl\neSTZpLYjHenINvL3lYTsuPKZb082kawOTXE/OU7RqN4+63b9H8ihNHYozz9ONj4YUT7boyv7rU32\nAm4MtzH7zEi06Psm5gByateu9eSIyCcyRIammJkPFx9Zv8XbZ6X7PlkBd5ykUZEtpLYkh304JSKu\naXp9p8tfV6U0bySHT/gD2UnqNfKCeyZARDxNJgxPl+WnIuKi6EAb9JlZ51GpfG5VUdFCmjZi73zk\n5z2lbP4JmXs5nmxWvbmk/ynb5gUmRakni2x5NDOKFPsaIuYq4CPKUU4b7zcpsl5haAxNMRO9o07W\nZqqPknfYE4H/JS+sb5KdiLYke1cvGxHnQHs6PvWlEsNVwJuSVikXoMlkJeYm5IVqcUknSbqWzPX8\np/lY7UjYWlXnMZMutD3FfAJ50T9P2ZfgJXK8pK9V3vsksrf0/GQ9yFaSriCH2fjHzI6pj5uYMcBD\nZGX9WpXXROV5p29i2spNUm2GlQvXUeQQwltFpTOdcrz7tlfEqodpSsu2d5N1BLNFxFFl3WfJGfX+\nt9zlrkAOhTyhjWE34htO1l+MJVt2HR0Rf1IOp/DHmNZSaF2y2eQGZFPa9cgirveQQ1r/ts1xjyXL\n4XckixW3JetfziXL878QEX9QTn5zODmnwYXlfLciz+3x7o49E2Jrnlp3UbL48wIyF/PBiPh1K957\nqHFOwQZMb5+VblD0rlb3HemqMf2LLNceJ6lR7HIvmRAQEVMj4neNBKEDxQdrkZPCrEnOSb2LpB3J\nSthdJS1U9rufbM21eon1YHJc/xXanSAUSwDviWz59Bty8Lj3kAPYnUrWMxHZW3kxSm/pyNnnLomI\nx1v1WUf3Q8RsADwZES82EoTBkKPtNCcKNmAxiHpXq8Y0pWV7o3z9JuAy4OTSkelbwF+6uyh1oPhg\nyNV5FLcCd0hqDB99C9kxbbOIOJ8szz9dORXmasCTzQdo5WfdzU3MBdFN0+hWvf9Q4bGPbIZExB+g\n66L8fJTB09ql3NnNBiwo6SWmdaR7opRvbyBpSkQ8UuJ9U9LiwJsR8XNJr5AX4ZMi4oJ2xl7i7ype\nq3TeuwrYqNR5/E3SZLKitFHnca2kk8je1q/T5joP5fShtwD3R4642ujNOxuZgG0s6bcR8Yykf5Y4\nIYvsxpKt1XaMNg//ERGvSmrcxPwOWFrS3O3+zg52rlOwIU/SSmQl4kiybP3ucgFYiWy3fylwVUwb\n7fNwYJOI2KCbY7VraIp5yOKe2yPisqZtg7LOo9QZnEcmRP8k62y2b9rnfeQ0ng9HxInKocOvJoeW\nfr5p344NDaEODxEzmLn4yIa00uTxcOBVcqrIW8liFiLiLrI/xQaUYTWKk4AXy0WucZyuMYLaEHNj\nGPGR5B3rdAZxncc44LmI2DQi9iFbae0tac7KPneRxTOflfR5sk7kTjIhmU4nW/VEh4aIGQqcKNhQ\nV3ea0sM0bZrS4eTQG083DtLqxKBS57Ey2c/gsog4KHJimOp+jQtsx+s8JA3X9CPGvgE8IWlUWT6a\nrEsa29ifnLltEtlJ7VXg1xGxb/N52uDl4iMb8iRdQw6YNjewJNnm/HHgWxHxqKQHyQrxEyLi7DbH\n1qjzWIhsETUv2dFsLDkF5vZkUcxDEXFpeU2jzuNJSR8h6zweaWedh6QDyVFEnyTv9M8jL/JHAsdH\nGR9I0vnAvRFxfIn17IhYopvjzTKjiM7qnFOwWUHdjnRnQ3ubHZYcyArkMAt/JVtG3UoOUfFz4Aly\n4LfTJDXqOPYjx2YiIiZGxAmNBKHVsUtaWNI+TJu+cyey8n7jyPkwngO2kTSyvOQ0ssnsHBExEfhj\nya1NxwnC0OGcgs1SBltHulLncTZZzPWriJhQmsWuQ1bGPlL2OxjYMCK2KkVfFwMHRsRDZXtLK8Al\njSOLqP4FnEUOFjepbDsMGBMRn5G0Ctk7+bqIOFM5N/g+ZPHdcGBT4MZSL2JDkJuk2ixBg3ea0kad\nx3bAGEkbkT1ob4/ph61uNEWFnMzlLNpU56Ece+ibZMe304GXIyIqieizZJNflSayE4CPSbqa7AR2\nXMkJvEx2WLMhzDkFm2VIWh8YQ2mDDvx1MLRB76XO41SypdTxwCrkEM1tH6tf0jHAExFxellWNVGQ\ndB7w52rz11LhvQkwOSL+3e2BbUhyTsFmGZ3uSNeLj/L2cXfGkhXIW5GVyHt3LLoccXU9SXeRxT+z\nlZzXpWTT2WHATyWtTVaST4icr/o34ErkWY1zCmZt1FTn8WFyjKNXyraOXFxLf43DgM2By8ke0vMA\ny5F9QL5DVjAvSrY8+nm7Y7T2cU7BrA16qPP4d9nWmEu5I3fbEfGQpP3JFltzRcR/laOKnkIWa60F\nfDEiTu1EfNZezimYtclgrfPojqRdyX4KG5Mz0T1T1ruoaBbnRMGszQbruDtlzKW1gM+RfZgOj4gb\nyjYnBu8QThTMDOgaYmNHcg7qH3Q6HusMJwpm1i3nDt6ZnCiYmVkXj31kZmZdnCiYmVkXJwpmZtbF\niYKZmXVxomAGSPqqpLsk3SHptjLOT3+PcbakFVsRn1m7uPWRveOVKSdPJuczeLWMTzRnRDzW4dDM\n2s45BTNYHHg6Il4FiIinI+IxSUdImlTmdp6g9F5JNzdeKGmspDvK8+sljS/PX5J0vKTbJd0oabGy\nfpmyPEnSMZJeKusXl/SHkku5U9L/a/unYIYTBTPIIaCXlPR3SWdUpsX8bkSsGRErk7OK/U+ZknJO\nSUuXfXYCLunmmPOSM5CtRk4P+qmy/tvAtyNiTaCaE9kVuDoiVgdWA26bmSdoVpcTBXvHKzOgrQHs\nCzwFXCxpD+BDkm6S9DdgI3KWMchEYMfyfCdy6sxmrwFXlOe3kPMnQI6U+tPy/CeV/ScBe0o6Clgl\nIl6csbMyGxgnCmbkVJ0RcX1EHAkcAHwcOAPYPiJWIafHnLvsfjGwo6T35Evj/m4O+XplCs036WOY\n+jJB0PrAo8D5knaf4ZMyGwAnCvaOJ2l5SctVVq0O3FeePy1pPmD7xsaIeIC80B9O97mE3txIzl4G\nsHMlhqWAJyPiLOAc4P39PK7ZTOFJdsxgPuA0SQsCbwBTyKKk54C/AQ+RxTtVFwMnAuP6+V6fB34s\n6WDgSuD5sn5D4MuSXgdeApxTsI5wk1SzNpI0DzkFZ0jaGdglIrbpdFxmDc4pmLXXGsB3JYnMiezV\n4XjMpuOcgpmZdXFFs5mZdXGiYGZmXZwomJlZFycKZmbWxYmCmZl1caJgZmZd/j9Hg5ObSJTRWgAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f52b56c0748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create bar graph of percentage of True responses for each category of savings\n",
    "\n",
    "# Remove respondents who refused to answer\n",
    "savings_df = df[(df['SAVINGSRANGES'] != -1) & (df['SAVINGSRANGES'] < 98)]\n",
    "\n",
    "# Calculate x and y values\n",
    "x_vals, y_vals = percentages_of_true_across_categories('SAVINGSRANGES', savings_df)\n",
    "\n",
    "# Create bar graph \n",
    "plt.bar(x_vals, y_vals)\n",
    "plt.title('Relating Savings and Handling Unexpected Expenses')\n",
    "plt.xlabel('Savings')\n",
    "plt.ylabel('% Can Handle Unexpected Expense')\n",
    "x_labels= ['$0', '$1-99', '$100-999', '$1,000-4,999', '$5,000-19,999', 'S20,000-74,999', '$75,000+']\n",
    "plt.xticks(np.arange(8)+1, x_labels, rotation=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see from both of these graphs that there is a clear effect of the amount one has in savings on one's confidence in being able to handle unexpected expenses, as we would of course intuitively expect. This is thus a variable that we don't want to include in our model. Closely related is the SAVEHABIT variable, or whether putting money into savings is a habit. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZhBIJgiAIyiaUSBAEQVA2oUSCIAiCsgklEgRBEJRNKJEgCIKgbEKJBEEQBGUTSiQIgiAo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\nICIiIrkpiIiISG4KIiIikpuCiIiI5PYbqMxhlVEnT6wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f52b5676780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Remove respondents who refused to answer\n",
    "save_habit_df = df[df['SAVEHABIT'] != -1]\n",
    "\n",
    "# Get x and y values\n",
    "x_vals, y_vals = percentages_of_true_across_categories('SAVEHABIT', save_habit_df)\n",
    "\n",
    "# Create bar graph\n",
    "plt.bar(x_vals, y_vals)\n",
    "plt.title('Relating Savings Habits and Handling Unexpected Expenses')\n",
    "plt.xlabel('Is Saving Money A Habit?')\n",
    "plt.ylabel('% Can Handle Unexpected Expense')\n",
    "x_labels = ['Strongly disagree', 'Disagree', 'Disagree slightly', 'Agree slightly', 'Agree', 'Strongly agree']\n",
    "plt.xticks(x_vals, x_labels, rotation=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another obviously correlated variable could be ABSORBSHOCK, or one's confidence in the ability to raise $2,000 in 30 days. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AfyJip1nYHjMz6wPK1JwOA1aNiNc7u/KIuBm4uWrc8MLnrTq7TjMz6/vK9NZ7\nHr8iw8zMulGZmtME4C5JNwHTOiyUeUKEmZnZzCgTnP6T/+bMf2ZmZk1Vpiv5Cd2REDMzs4oyvfUG\nAEcCqwNzV8ZHxBZNTJeZmbWxMh0irgD+ASwPnAA8R7qHyczMrCnKBKdFI+K3wIcRcXdE7Ads2OR0\nmZlZGyvTIeLD/P8lSTuQHkE0sHlJMjOzdlcmOJ0saUHgCOBsoD/w/aamyszM2lqZ3no35o9vAZ9v\nbnLMzMwav6b9bOq84gIgIg5tSorMzKztNao5jSl8PoH08FczM7Oma/Sa9ksrnyV9rzhsZmbWTGW6\nkkOD5j0zM7OuVjY4mZmZdZtGHSKmML3GNK+ktyuTgIiI/s1OnJmZtadG15wW6M6EmJmZVbhZz8zM\nWo6Dk5mZtRwHJzMzazkOTmZm1nI6DE6SdpX0jKS3JL0taUqh556ZmVmXK/NU8p8CO0bEU81OjJmZ\nGZRr1nvFgcnMzLpTmZrTGElXA9cD71dGRsQfm5YqMzNra2WCU3/gf8AXCuMCcHAyM7OmKPOywX27\nIyFmZmYVHQYnSXMD+wOrA3NXxkfEfk1Ml1mfNPiom3o6CT3quVN36OkkWC9RpkPE5cCSwDbA3cBA\nYEozE2VmZu2tTHBaKSJ+DPw3v3BwB2DNMiuXtK2kpyWNl3RUjemfk/SwpI8k7da5pJuZWV9VJjh9\nmP+/KWkNYEFgcEcLSeoHnAtsBwwBhkkaUjXbf4B9gCtLptfMzNpAmd56F0paGDgWGAnMDwwvsdz6\nwPiImAAg6SpgZ+DJygwR8VyeNrVzyTYzs76sTG+93+SPfwNW6MS6lwGeLwxPBDboxPLTSDoAOABg\n0KBBM7MKMzPrReo260k6XNL+NcYfIul7JdatGuOixrgORcSFETE0IoYOGDBgZlZhZma9SKNrTvuR\neupVuzBP68hEYNnC8EDgxfJJMzOzdtUoOEVEfFBj5PvUrhVVGw2sLGl5SXMCe5CuWZmZmTXUsLee\npCXKjKslIj4CDgZuA54CromIcZJOlLRTXtdnJE0EvgJcIGlcZzfAzMz6nkYdIk4HbpJ0BPBwHrce\n6RUaZ5RZeUTcDNxcNW544fNoUnOfmZnZNHWDU0RcJmkScCKwBqkzwzjguIi4pZvSZ2ZmbahhV/Ic\nhByIzMysW5W5CddsGj+41A8uNesOZR5fZGZm1q0cnMzMrOWUDk6SNpR0h6R7Je3SzESZmVl7q3vN\nSdKSEfFyYdThwE6kG3D/Dlzf5LSZmVmbatQh4nxJDwGnR8R7wJvAnsBU4O3uSJyZmbWnus16EbEL\nMBa4UdI3gO+RAtO8gJv1zMzqQDw5AAAW1UlEQVSsaTq6z+lPkm4GvgP8ETglIu7plpQ1ibtCuyu0\nmbW+Rq/M2EnSKOAO4AnSg1u/JOn3klbsrgSamVn7aVRzOhnYCJgHuDki1gcOl7QycAopWJmZmXW5\nRsHpLVIAmgd4tTIyIp7BgcnMzJqo0X1OXyJ1fviI1EvPzMysWzR6KvlrwNndmBYzMzPAjy8yM7MW\n5OBkZmYtx8HJzMxajoOTmZm1HAcnMzNrOQ5OZmbWchyczMys5Tg4mZlZy3FwMjOzluPgZGZmLcfB\nyczMWo6Dk5mZtRwHJzMzazkOTmZm1nKaGpwkbSvpaUnjJR1VY/pckq7O0x+QNLiZ6TEzs96hacFJ\nUj/gXGA7YAgwTNKQqtn2ByZHxErAmcBpzUqPmZn1Hs2sOa0PjI+ICRHxAXAVsHPVPDsDl+bPI4At\nJamJaTIzs16g7ptwu8AywPOF4YnABvXmiYiPJL0FLAq8VpxJ0gHAAXnwHUlPNyXF3WMxqravO6n3\n102df7PG+TdrenP+LddFyegWzQxOtWpAMRPzEBEXAhd2RaJ6mqQxETG0p9PRWzn/Zo3zb9Y4/7pP\nM5v1JgLLFoYHAi/Wm0fS7MCCwBtNTJOZmfUCzQxOo4GVJS0vaU5gD2Bk1Twjgb3z592AOyLiEzUn\nMzNrL01r1svXkA4GbgP6ARdFxDhJJwJjImIk8FvgcknjSTWmPZqVnhbSJ5one5Dzb9Y4/2aN86+b\nyBUVMzNrNX5ChJmZtRwHJzMzazkOTm1E0nyS5uvpdPQmkhbP/5eTtH1Pp6c3kDSbJJ9bukDOy349\nnY6e4B2oD1OyhqTt8qjdmbF7vzWQT7DjJM0FvA/sJ2k9SRtLmr+Hk9cyqgNRREyNiKl5/1tV0rx5\nPj/9paRKnua8/Lin09MTHJz6mGJJK3fL3wDYOk/eCDhR0p8KAcv4ZGlf0mwRMRV4ipRvmwDbAtfm\n4bl6JKE9TNIihWAz7QRamD6PpM0k/Rh4FLgYOLkyubvT2xtU144K+x65cHmmpG9KmqPnUtn9HJz6\niAYlrQnAx5I2AeYA1gT+Avy1+1PZOjoo7a8CLJAn3QAsQrpv7zfAZRFxRkS83r0p7jn5BPkrSQ8D\n1wM7QMozSctI2lvSQXn2eYD9gK2ATwNbAHtLWqIYxNqVpKVzfn25UvsuHrOS5sz5unXO7z1IT83Z\nEBjecynvfg5OvVDJklblWYRjgd9HxCjgLOB24KaI+LDbE95DZqK0fwlwfJ58ZkT8EfgP6Z69NXMA\na4vrAJIWBA4hPc3lq6SHNU/K03YjPdB5TWApSScAbwIPAs8C/SPivTz8+e5PfevIQelaUsFwW9IT\nc4bmaV/Krw66F/h+XuRxYClgdEQcTioYbdRO14yb+Ww96wKSliY1y70D3BYR71SdWOeMiA8kfQE4\nFbg5T9pA0sCIGA5MzuP+AfQH1pX074j4qPu2pHtJWgP4LqlZ8x3gbODaSmmfVLKfNyJ+xfTS/mBS\naX8u4AVJp0bEK5L6RcTHkl4g1T4Xj4hXun+rmquynVWjVweGRsS3C+Puyv/vJdWkVgF+CKwNXAH8\nE1iRlJ+P5vk2IQWydvVF4PWIWL0yQul9dnOS8u884H7gdkn/iogReX97O88+AZhCqkHd3r1J7xmu\nObWomShpPcb0ktb3gV8Dn62UtPKJ513gJWCpPh6Y+tO4tH81nSjtF07Y/wFeBY6QNEzSmt22UV2o\n0pxUfQ2jzoX3XYDfFjsz5JqjIuIl4FjSe9vGkpo+d8ifZweWz4vcCWwraZ6u3pbeIOfzt0j5hKTZ\nc/69n18ndDapEPAnYGVg87zoKNKxDylIjSMVqtqCg1PrmlbSiohhEXEWcF9VSWsLYEdJu0XEy0C9\nkhZM/60rTVMTJP2k+tpLb1OneW0NUmn/lIgYHxGTI+KuPO1e0sF/EekVArsAK5FK+2+QSvuV+TYp\nrjQi3iQF/TXyMpO6clu6g6T1gOsl9a9u2pV0rKRd8+dK4PoAWKn4zMvIJK0DfBbYIiLOBOYE1ouI\nSaTgtGqe/17gSKBPNCXn2LyhpNXrTB+Yj9NKofBDYG6g0sPz45x/lWPv86Rj+evkHrW5dn8j05tD\nPwCeIBVA20KvPjH1VU0qaQVARNwB/ILUjPWj3nKRuhtL+4PzIjVL+xHxQERsHxEn5QJBS5K0rKS1\nlbrBF71BCqpDcg38oMK0AaSCT9EdwFqSlsrrnS3/X4XUk3FJYE9JhwMfA8vnPLuQ/CLRfE30j725\ntp53n8r1ygAOAz6fr//OW2ihGEQ6vtbJ836cA9XdTA80cxSvEwNLAItGxKukPNyYFPRHAfPmgsRU\nYERE7NMd29sKHJy6QRNLWnvSuKT1OOnkQfHEEBHjIuKu3vIE+Jko7X9I49L+RsxY2l+3s6V9Sf2K\nwa8VaMbu8PsAexWmDcofJ5Fe8LkWqaa9iaTKG6o/Bh4CKOTzPcAzwMmS5s3X7JYC/o+UX/sCXybV\nQv8f8LmIeDcixlaCd6UXZDO2uVmKxyNM23+KBbkHSL041wLOIDUjA3yKdI33wco25wLl3eQHW0fE\nB4UOTLuTgtBsku4AjgZ+BTwXEe9FxBoR8XZertcG95nh4NQknShpLcvMl7ReY3pJa2M+WdL6Q0Ts\nWy99TdjsWdKFpf3baVzaX4oZS/srFEr7l1WWaVTaj4iPWyW4F06CUwv7yChSd/gjJT1Oevr/URHx\nDqnQsllEPEiqhR+bA/sWpKajaXKQOjwPXiJpLPBnUmDrFxEPRcSuEXFYRPwz77+f2LdaJa/KkHQO\nuTUipnfxXl3S0ZK+mGd7lBSQ3wYuB7aUNJAU+NevFIYq64yIa4GJkn6jdCP3vpL+ACwWEf8kBfsT\ngZ0i4scRMbqQnrY8T7flRjfDLJS0htC5ktYepNJssaR1HvBsZ0parXKycGm/Y5puhTxc2dcq+0tI\nWlTSbpL2zIs9CixE6q24IalTyHeVejE+CSyo1JvzflIvzu8A7wKLVn9/pI40BwA/BXaLiDUj4oSI\neKuQxn7F9DQhG5qiTg34EOBvefqWkh4Evk2qVR8s6XOk43luYNWIuI90s/GZeZ6blG9dyOuorH8v\nYAxwOrAl6YbuSwAiYnRuzfioWLDN03pF03tXc3DqAt1c0lo0Ip5hxpLW8N5W0nJpv5xcgwtgR1LQ\nJdKtA7PloNRP0n6k649bkgL3tyLiDVKAfgmYLVJnjhtINexXSL0O189fM5zUE3Qe4Lla6cg1xTER\nMT6na4aTeivVJOuR1L8S4Csq6Za0oKTKjdcrAM/l7ZsMrAecFul6zz3A1hHxP9L+tHIuAF1JOrbP\nA16KiP9VFR6UWzouiIgtIuLrEXFVRPy3kL5p87drQCpq+ZNYq3FJqxyX9mde1Um/8lveBswjaRul\nWwxGSdoCmJqnfZ70mKDFgD1ywecJ0sl1ibyOZ0kBfiKpW/wW+TueJd2gPU9EvNOoxljIr5YPRvCJ\nG9aXBE7L4+fI/4+RdBepA8wJSk3i/wJeB9aJiIdJ+Tgwr+MJYGGl+w8fIj2rcvE87RekWxAqNf7i\n7xjF/3nfq35KScvnZ3dycGrAJa2Z49J+59RIV+UEtpxSR5o5I+J9Ut4cTgokPyUF5y9HxAukWvSV\npFsIJpACzyjS/VyVN0wvQgrykG74fLaQjMWBvyp1yKmbJ62QX43kYDRDcI/pPToD+LKkW4BrJc0O\njAf2i4h1SQG+co32EWCb/Pn+wudnSZ1k1iL18PwUuXt3RDxOut/wkcp310tn3vda5phtRQ5OVVzS\nmjku7Zej9ETzkyX9UlKlJ2WlwNNf0iBJ80u6BLiV9BilY/LitwELRcSoiLie1FS5s9JrPbaLiM0i\n4ihSJ5E1I+I/pLweIulOYF3gt/k7b42In+U0bUcKdjdFL3oCttJjp2Z4yk0ORlGYZztJR0i6G/ge\nKRg9EhG7RLom+1fg20rPsZuX6YWfm4DN8uc7gc/lz8+TesIuT7o37hTyNU9JXwZ2JRUKbBa1/eOL\n8gk/CsGgWJoplrTeV3q6wHjgqoiYIOkR4N+kk2SlpPUw00taD/DJktY3SCWt5yLicUmPkXqQdVjS\n6rqtnnU5gE8t5Nu00j5p+x6OiPclVUr7J5GC0HeARSI9nuU0UvPdfcBbpMBzFymP9iAFrurSfrE7\nfrG0Xzd/ejogVUgaDSwM/JyU9pMknU16pNTepELKCFJeLBIRn1LqYXiMUpfja0lNlZX99gnSyXAS\nMEjSlqTHBgWwoqS5gZ8B43Ogqk7P7PkEfUdErNHETe8yklYj1W42JAXekyTdm/e1+Un3rC0E3BCp\nc8shpOuYu0fEi5KOJnVCqlgPWC0i1s2FhdslDSY9meWsvM7RpOugc0XEZEk/KTQP31dY11KkR4i9\n2Jytby9tFZyUugt/GDPe8zO1ap7tSDvvTqSDv1LSOiZP/yvwQ0lb88mS1t6k3l93At/M44slrduo\nXdJq6ddXSNqYlMb+wE8i4uWY3vGjP+lk8AZwDul60b9JgeR40jZvE+nBs+RS/s6S/kYq7a+Vx19P\nKu1fJqlY2n+PdMMwEXErqTZRLO0f2WqBu4HRwHsRcV5uLj6YdAtBkO5XWyEiXpP0FdLTPSDtPyNJ\ntcerc61q+4i4WdKmwKhc69oXOIL0yKbjgKdzU+AdlS+XZnzobeU4yPO1tJx2kYLNh8ChpNaKRUhP\n3V+JVJh5k9QEfJSkE0lNx3tFRCVg3EbK94rFmd6CtBqpiX7jiLhC0u3AErllZN2cDhUCE4VxERHn\ndPV2t7M+H5xc0po1Lu13qWtJ91IdTmqiXJkUbP9Nejht5QG9HwJTJC0eEa9KWpn0skNI+XecpB1J\nzU7fAoiI64Dran1p4eTZa69xROra/21gUETsWJg0EUDSWqRmtjNIQfpLpELi3cBBhfU8nJtPFyNd\n830E2EbS06RWjx8Bf8/zDiumoZKPNdLWEjXzvqbPBieXtLqMS/tdJCLulLSEpANJnTneJxVW3iF1\nYtiY1OtzFKm35/6SLiMViCpPm/8ZqTB1c0QcBDOeNHNz6wyBqA+dPGcjHatImiPyfW35utMapObg\nTUh5umtEPKR0Q/cbkjaIiAfyes4itXTMSdqH/w+YvXDMT1NsMu5D+dgr9Nng5JJWl3Fpv2uNBvYn\nFYzui4gpAJLuJz0H8W852P+QdCvBjaSa6Y0w7f63aTTjk0Na7tpkF5sKvClpnki3CgCpwCLpXeB3\nwEmRbmJH6cbrV0j77W6ka8CQWisurFMzry4M9eX8bGl9NjhlLmnNIpf2u9zfgMERcUPV+IdJj7ga\nDhARj0n6Ya2TY9U+1teCdyNPkq7xrgg8oenvMpubdI1zGHBOboreldQZ6bukAmjxBvd3SPtvreDe\nTvnZ0tR3zwGQa04rAz8ulrTytB+QrpvUKmldArwSET/I4+cnXU/psKTVF+Xmyv5ML+2/msefAfwv\n0gsNK7XRvUj3Lo0g3aP1Wo31zdaX86sRpXdA3R8R8xWDjNJN2AtGemp6cX6RCllT+3DALkXpeZSn\nAAtExP553PLA9qTm+HdJBc7VSQWjv0R+lJf1Pn09OG1Kel7bmRFRXdL6DKmkNTupqaVY0lqTVHh/\nvMY62+7EKuk4Uml/36rxewKHRcQGhXE1u3XXG9+OJN1K6nDzVocz2wxy56NTSc9UXJn01uJbgROj\ncIN6jeVqNrFb6+rrwcklrS7g0r61GqVbCV6NiIdqTOvzrRntoE8HJ3BJq6u4tN+1XJOcedXHpqpu\nCLe+oc8HpwqXtMz6Fhcg+7a2CE4uaXUNl/bNrLu0RXCqcEnLzKx3aKvgZGZmvYNfmWFmZi3HwcnM\nzFqOg5OZmbUcByfrkKQvSQql149Uxm0u6cY6898saaH8ufIMs6Uljcif15a0fSfTsLmkz3ZymX0k\nTZI0VtI/JH2/xDI7STqqM9/TYF2DJe1TNe5wSU9KekzS7UovZ2yYn4Vlh0r6ZSfTcKukRyWNk3R+\n7qmKpEUk/UXSM/n/wjWW3VzSW5IekfS0pL9J+mJnvt9sZjk4WRnDSA923aPMzBGxfUS8WTXuxYjY\nLQ+uTXpKR2dsDnQqOGVXR8TapAfU/kjSso1mjoiREXHqTHzPDCQdRHrdykmS7lJ+JTvpqfZDI71k\ncQTphYmlRMSYiDi0k0n5akR8mvSg4wHAV/L4o4DbI2Jl4PY8XMs9EbFORKxKeu3MOUrv4DJrKgcn\nayg/9HZj0mseqoNTf0nX5ZrA+ZWbmSU9p/SKkeJ6Bkt6QtKcwInA7rlGs3suvQ/I880maXxx+fyU\njwOB7+dlNpW0XK55VGoggxptR0S8Tnqr8VJ5nTtKeiDXCv4qaYk8fh9J5+TPX8lpflTpzb1I6ifp\ndEmj83d/u0aeLQCcQHoI7o9Jz3f8b07HnRHxvzzr/cDAGst/Jqdrharx02pXko6XdFEOfBMk1Qxa\nhcdxzU56qn6le+7OwKX586XALo3yL69rLOm3Ozin4RN5mH+/mr9nrfw0q8fByTqyC3BrRPyT9CqR\ndQvT1ie9C2tN0msMdu1oZfkJ8MPJNZqIuJr0Hp6v5Vm2Ah4tPs08Ip4Dzic9wHftiLiH9Er4y3IN\n5AqgYXNXDl5zA4/lUaOADSNiHeAq4Mgaiw0nvWL+08BOedz+wFsR8RnSw4O/pfS8xqKppEDQv5L+\nyO9tqrI/cEtVOj+bt3XniJjQaJtIL7vchvQ7HCdpjlozSboNeJX0MsgRefQSlWci5v+Ld/BdFQ/n\n74UaeZifslLv96yVn2Y1OThZR4aRTjzk/8UXKj4YERPyUyN+T3o31sy4iFTLANgPuLjEMhsBV+bP\nlzf47t0ljSO9e+qsiHgvjx8I3CbpceAHpIf/VrsXuETSt4B+edwXgL0kjSW9vG5R0jMbp8nPbNwL\n+AmpWe8MpYfkTiPp66T3Y51eGP0p0osdd6z1epYaboqI9/OJ/1VgiVozRcQ2pBrjXMAWJdbbiAqf\n6+Vhvd+zVn6a1eTgZHVJWpR0MvuNpOdIJ6DdJVVOUNV3cM/UHd0R8TzwiqQtgA2oqk2UXU2d8VdH\nxOrApsDPCtd+zgbOiYg1SS9JnLtGug4EjgWWBcbm/BBwSK7BrR0Ry0fEn2ssO5J0feenpGs9R1Sm\nSdqK9AblnWLG182/BLwHrFNym4vLfkyDl4fmoDyS1JwHKb8rTZxLkYJbGesAT+XPNfOw3u9ZJz/N\nanJwskZ2IzWdLRcRgyNiWdI7ryq1lPUlLZ+vNe1OauYpYwqwQNW435Cag66p8/y+6mX+zvRrYF/r\n6Lsj4j5SDeuwPGpB4IX8ee9ay0haMSIeiPQyxddIJ9XbgIMqTWiSVlF6NUtxufkrvfByup+qpF3S\nOsAFpMBUHRDeBHYAfiJp80bbU0ZORyUAzU7qhPKPPHkk07d7b6D6zby11rcW6RrauXlUozz8xO9Z\nJz/NanJwskaGAddVjfsDsGf+fB/pdSRPkIJW9bz13AkMqXSIyONGAvNTv0nvT8CXKh0iSD3H9pX0\nGOmdXIfVWa7otLzMAsDxwLWS7iGdKGs5XdLjkp4gvV79UdJJ90ng4Tz+Aj5ZY5kjjx8BHAdsB5xV\nWWfezmvztowsLhgRrwA7AudK2oBZMx8wMufRo6Ta0fl52qnA1pKeAbbOw7Vsmjs8PE0KSodGxO15\n2vHUz8Nav2et/DSryc/Ws5YgaSipw8OmPZ2WrpJ7GW4eEZf0bEq6X1/8Pa171W2jNusuSje9HsT0\nHl59xZvA2J5ORHfrw7+ndSPXnMzMrOX4mpOZmbUcByczM2s5Dk5mZtZyHJzMzKzlODiZmVnL+f/x\n5Q+wFGzE3wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f52b57c58d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Remove rows where response was 8 (corresponding to 'I don't know') or -1 (no response)\n",
    "absorb_df = df[(df['ABSORBSHOCK'] != 8) & (df['ABSORBSHOCK'] != -1)]\n",
    "\n",
    "# Calculate x and y values\n",
    "x_vals, y_vals = percentages_of_true_across_categories('ABSORBSHOCK', absorb_df)\n",
    "\n",
    "# Create bar graph \n",
    "plt.bar(x_vals, y_vals)\n",
    "plt.title('Relating Ability to Raise Money and Handling Unexpected Expenses')\n",
    "plt.xlabel('Ability to Raise $2k in 30 Days')\n",
    "plt.ylabel('% Can Handle Unexpected Expense')\n",
    "x_labels = ['Certainly Can\\'t', 'Probably Can\\'t', 'Probably Can', 'Certainly Can']\n",
    "plt.xticks(x_vals, x_labels, rotation=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally I wanted to look at ENDSMEET, or the difficulty of covering monthly expenses and bills."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oTjMzM+tO3WBjZmbWa101EJjEjBzN/JImNhYBERELtTpxZmbWHrqqsxk6OxNi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f52b55e7e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Remove respondents who refused to answer\n",
    "endsmeet_df = df[df['ENDSMEET'] != -1]\n",
    "\n",
    "# Get x and y values\n",
    "x_vals, y_vals = percentages_of_true_across_categories('ENDSMEET', endsmeet_df)\n",
    "\n",
    "# Create bar graph \n",
    "plt.bar(x_vals, y_vals)\n",
    "plt.title('Relating Making Ends Meet and Handling Unexpected Expenses')\n",
    "plt.xlabel('Difficulty of Covering Monthly Expenses/Bills')\n",
    "plt.ylabel('% Can Handle Unexpected Expense')\n",
    "x_labels = ['Not at all difficult', 'Somewhat difficult', 'Very difficult']\n",
    "plt.xticks(x_vals, x_labels, rotation=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of these graphs suggest we should remove their corresponding variables from our dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Drop obviously related variables\n",
    "dfx.drop(['SAVINGSRANGES', 'SAVEHABIT', 'ABSORBSHOCK', 'ENDSMEET'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A more interesting question would be whether income has this clear of an effect, or if financial management might be more important than how much you make. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hQ4cO+Pv7W6QwIYQQj+e+AX/48GH0ej3NmzfHaDTyySefkJ6eTt26dRk2bBgu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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f52b566c278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Look at relationship between household income and confidence in ability to handle unexpected expenses\n",
    "sns.set(rc={'figure.figsize':(5,5)})\n",
    "sns.boxplot(x='target', y='PPINCIMP', data=df)\n",
    "plt.xlabel('Confident in Handling Unexpected Expenses?')\n",
    "plt.ylabel('Household Income ($)')\n",
    "plt.title('Income vs. Confidence in Handling Unexpected Expenses')\n",
    "y_labels= ['<20k', '20k-30k', '30k-40k', '40k-50k', '50k-60k', '60k-75k', '75k-100k', '100k-150k', '150k+']\n",
    "_ = plt.yticks(np.arange(9)+1, y_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, as one would expect, lower income is associated with a lack of confidence in absorbing financial shocks while higher income is associated with the opposite. However, there is an interesting subset of the population where income doesn't seem to have a determinstic effect on our output variable, and other factors come into play. Let's look more closely at what incomes this happens at."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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s9+/fz4YNG8xbkm3atMHT0/OR87Rv3542bdoAEBkZSalSpdi7dy9NmjQBwN3d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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f52b5517748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create plot comparing confidence across household income\n",
    "sns.countplot(x='PPINCIMP', hue='target', data=df)\n",
    "plt.xlabel('Household Income ($)')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Differences in Target Variable across Income')\n",
    "x_labels= ['<20k', '20k-30k', '30k-40k', '40k-50k', '50k-60k', '60k-75k', '75k-100k', '100k-150k', '150k+']\n",
    "_ = plt.xticks(np.arange(9), x_labels, rotation=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We again see stark differences in how confident people are in handling unexpected expenses for most incomes. However, for people with incomes from $100k-150k, there seem to be other important factors determining this confidence, since the number of people who say they are confident and the number who say they aren't at this range are almost equal. Perhaps the most obvious possible explanation is household size. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bODk5vbRXnJSUJrnNgiox8YncJeQ7til/lS1b3Ore0zelTXL/RyAZwkFBQRgy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\nHQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f52b57a2240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Only include respondents with relevant income\n",
    "subset_by_income = df[df['PPINCIMP'] == 8]\n",
    "\n",
    "# Calculate x and y values\n",
    "x_vals, y_vals = percentages_of_true_across_categories('PPHHSIZE', subset_by_income)\n",
    "\n",
    "#Create bar graph \n",
    "plt.bar(x_vals, y_vals)\n",
    "plt.title('Relating Household Size and Handling Unexpected Expenses')\n",
    "plt.xlabel('Household Size')\n",
    "plt.ylabel('% Can Handle Unexpected Expense')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that increasing household size has a negative effect on one's confidence in handling unexpected expenses, but the effect is not too drastic. It could thus be useful to look for other behaviors that would be better predictors of our output variable than income our household size, particularly for the subset of respondents with an income between $100,000 and S149,999. \n",
    "\n",
    "Let's also look at some groupings of variables that are suggested by our data dictionary. For example, there are two groups of financial skill variables of size 7 and size 3 that are summarized by a financial skill score. However, some of these variables might not be very well correlated and worth distinguishing. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bbyhpBhH5ALfCzZv1qHDW1dUhMzPTs7W0tKC0tBRNTU0wGo3Q6/WoqKgAAMTG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\nsgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f52b55e7cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract financial skill columns and use them to create a DataFrame\n",
    "fs1_cols = [col for col in df if col.startswith('FS1')]\n",
    "fs2_cols = [col for col in df if col.startswith('FS2')]\n",
    "fs_cols = fs1_cols + fs2_cols\n",
    "\n",
    "financial_skills_df = df[fs_cols]\n",
    "\n",
    "# Create correlation matrix using Spearman method (ordinal variables) and heatmap\n",
    "financial_skills_corr_matrix = financial_skills_df.corr(method='spearman')\n",
    "_ = sns.heatmap(financial_skills_corr_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that while the FS1 scores are moderately correlated with one another, the FS2 scores are less correlated with all of the scores (particularly FS2_3). Looking at the variable dictionary, we can see that the FS2 scores do not necessarily lead to actionable recommendations, so we can dispose of these. Checking the dictionary, we see that the FS1 variables make meaningful distinctions in behavior that would be useful in making recommendations. They are also only moderately correlated so we'd like to keep them instead of using a summary variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Drop individual FS2 scores\n",
    "dfx = dfx.drop(fs2_cols + ['FSscore'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When looking at the effect of these financial skills on confidence in handling unexpected expenses, the overall score will be more manageable and easier to use than 7 individual scores. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NhQsXsmfPHsDce7b0y3y90tponrzqfAQEmxIv39DJXMTnyHnbljpi/NuhNeoo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\nilJExMMQRSki4mGIohQR8TD+P6tFChfWPgtGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f52b56fc908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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i0jhemmPAwvkdukK9Q1AqlNLDk7iFFDyJSxTpjYBz8Ts7bUJ8UKsUlyUDTdU5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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f52b52347b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create density plot of financial skill score for entire dataset, colored by target \n",
    "g = sns.FacetGrid(subset_by_income, hue=\"target\")\n",
    "g = g.map(sns.distplot, \"FSscore\", hist=False)\n",
    "plt.legend()\n",
    "plt.title('Density Plot of FSscore (Entire Dataset)')\n",
    "\n",
    "# Create density plot of financial skill score for $100k-150k income bracket, colored by target \n",
    "g = sns.FacetGrid(subset_by_income, hue=\"target\")\n",
    "g = g.map(sns.distplot, \"FSscore\", hist=False)\n",
    "plt.legend()\n",
    "_ = plt.title('Density Plot of FSscore ($100k-150K household income)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that moderate values are by far the most common. A higher score does seem to correspond to more confidence in handling unexpected expenses, but the effect is not drastic and there is considerable overlap between groups. This effect is consistent across the entire dataset and the $100k-150k income bracket.\n",
    "\n",
    "Let's also look at some other possible groupings. There is a group of variables associated with financial planning, for example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PROPPLAN_1</th>\n",
       "      <th>PROPPLAN_2</th>\n",
       "      <th>PROPPLAN_3</th>\n",
       "      <th>PROPPLAN_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PROPPLAN_1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.605300</td>\n",
       "      <td>0.474601</td>\n",
       "      <td>0.488235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PROPPLAN_2</th>\n",
       "      <td>0.605300</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.590555</td>\n",
       "      <td>0.626030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PROPPLAN_3</th>\n",
       "      <td>0.474601</td>\n",
       "      <td>0.590555</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.623693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PROPPLAN_4</th>\n",
       "      <td>0.488235</td>\n",
       "      <td>0.626030</td>\n",
       "      <td>0.623693</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            PROPPLAN_1  PROPPLAN_2  PROPPLAN_3  PROPPLAN_4\n",
       "PROPPLAN_1    1.000000    0.605300    0.474601    0.488235\n",
       "PROPPLAN_2    0.605300    1.000000    0.590555    0.626030\n",
       "PROPPLAN_3    0.474601    0.590555    1.000000    0.623693\n",
       "PROPPLAN_4    0.488235    0.626030    0.623693    1.000000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract relevant columns and use them to create DataFrame\n",
    "planning_cols = [col for col in df if col.startswith('PROPPLAN')]\n",
    "\n",
    "planning_df = df[planning_cols]\n",
    "\n",
    "# Create correlation matrix using Spearman method (ordinal variables) \n",
    "planning_corr_matrix = planning_df.corr(method='spearman')\n",
    "planning_corr_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see variables 1 and 2 are moderately correlated, as well as 3 and 4. Looking at the variable dictionary, we see that 1 and 2 have to do with keeping track of your budget, and 3 and 4 have to do with financial goals. However, I would say that 4 (having a plan of action for achieving financial goals) includes 3 (having financial goals) and 2 (considering how to stick to your budget) includes 1 (consulting your budget), so let's remove 1 and 3. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Remove redundant planning columns\n",
    "dfx = dfx.drop(['PROPPLAN_1', 'PROPPLAN_3'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is also a grouping related to money management we can inspect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MANAGE1_1</th>\n",
       "      <th>MANAGE1_2</th>\n",
       "      <th>MANAGE1_3</th>\n",
       "      <th>MANAGE1_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MANAGE1_1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.449185</td>\n",
       "      <td>0.465718</td>\n",
       "      <td>0.427331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MANAGE1_2</th>\n",
       "      <td>0.449185</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.453673</td>\n",
       "      <td>0.419643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MANAGE1_3</th>\n",
       "      <td>0.465718</td>\n",
       "      <td>0.453673</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.322690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MANAGE1_4</th>\n",
       "      <td>0.427331</td>\n",
       "      <td>0.419643</td>\n",
       "      <td>0.322690</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           MANAGE1_1  MANAGE1_2  MANAGE1_3  MANAGE1_4\n",
       "MANAGE1_1   1.000000   0.449185   0.465718   0.427331\n",
       "MANAGE1_2   0.449185   1.000000   0.453673   0.419643\n",
       "MANAGE1_3   0.465718   0.453673   1.000000   0.322690\n",
       "MANAGE1_4   0.427331   0.419643   0.322690   1.000000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract relevant columns and use them to create DataFrame\n",
    "manage_cols = [col for col in df if col.startswith('MANAGE1')]\n",
    "\n",
    "manage_df = df[manage_cols]\n",
    "\n",
    "# Create correlation matrix using Spearman method (ordinal variables) \n",
    "manage_corr_matrix = manage_df.corr(method='spearman')\n",
    "manage_corr_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since these variables are only moderately correlated, we'd like to keep them. However, MANAGE1_4 won't provide a very useful recommendation, so let's remove it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Remove MANAGE1_4 variable from our data set\n",
    "dfx = dfx.drop('MANAGE1_4', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can look at variables related to materialism and self-control. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MATERIALISM_1</th>\n",
       "      <th>MATERIALISM_2</th>\n",
       "      <th>MATERIALISM_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MATERIALISM_1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.484030</td>\n",
       "      <td>0.622616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MATERIALISM_2</th>\n",
       "      <td>0.484030</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.495582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MATERIALISM_3</th>\n",
       "      <td>0.622616</td>\n",
       "      <td>0.495582</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               MATERIALISM_1  MATERIALISM_2  MATERIALISM_3\n",
       "MATERIALISM_1       1.000000       0.484030       0.622616\n",
       "MATERIALISM_2       0.484030       1.000000       0.495582\n",
       "MATERIALISM_3       0.622616       0.495582       1.000000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract relevant columns and use them to create DataFrame\n",
    "materialism_cols = [col for col in df if col.startswith('MATERIALISM')]\n",
    "\n",
    "materialism_df = df[materialism_cols]\n",
    "\n",
    "# Create correlation matrix using Spearman method (ordinal variables) \n",
    "materialism_corr_matrix = materialism_df.corr(method='spearman')\n",
    "materialism_corr_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1 and 3 are moderately correlated, while 2 has low correlation with both. Let's remove 1 because it is slightly redundant given 3 and more easily leads to recommendations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SELFCONTROL_1</th>\n",
       "      <th>SELFCONTROL_2</th>\n",
       "      <th>SELFCONTROL_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SELFCONTROL_1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.223181</td>\n",
       "      <td>-0.174319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SELFCONTROL_2</th>\n",
       "      <td>-0.223181</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.443601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SELFCONTROL_3</th>\n",
       "      <td>-0.174319</td>\n",
       "      <td>0.443601</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               SELFCONTROL_1  SELFCONTROL_2  SELFCONTROL_3\n",
       "SELFCONTROL_1       1.000000      -0.223181      -0.174319\n",
       "SELFCONTROL_2      -0.223181       1.000000       0.443601\n",
       "SELFCONTROL_3      -0.174319       0.443601       1.000000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove redundant materialism column\n",
    "dfx = dfx.drop('MATERIALISM_1', axis=1)\n",
    "\n",
    "# Extract relevant columns and create DataFrame containing them\n",
    "self_control_cols = [col for col in df if col.startswith('SELFCONTROL')]\n",
    "\n",
    "self_control_df = df[self_control_cols]\n",
    "\n",
    "# Create a correlation matrix using Spearman method (ordinal variables)\n",
    "self_control_corr_matrix = self_control_df.corr(method='spearman')\n",
    "self_control_corr_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see all of these variables are weakly correlated, so let's include all of them. \n",
    "\n",
    "There are also two variable dealing with retirement accounts whose values we would like to change. The default values are 0 (not automated), 1 (automated), and 2 (I do not have this type of account) for AUTOMATED_1 (a retirement savings account) and AUTOMATED_2 (a non-retirement savings account). We are more interested in whether a respondent has this type of account or not. We deal with null values by turning them into True values, since these are more common than False are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create variables for having a savings account\n",
    "dfx['SAVINGS_RETIREMENT'] = (dfx['AUTOMATED_1'] != 7)\n",
    "dfx['SAVINGS_NON_RETIREMENT '] = (dfx['AUTOMATED_2'] != 7)\n",
    "# Remove original columns\n",
    "dfx.drop(['AUTOMATED_1', 'AUTOMATED_2'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can deal with null values. All of our null values are now encoded as -1. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows with null values: 1397\n",
      "Number of total rows: 6383\n"
     ]
    }
   ],
   "source": [
    "# Create Boolean showing whether row contains any null values\n",
    "row_contains_null = (dfx == -1).any(axis=1)\n",
    "# See how many rows contain null values\n",
    "num_nulls = np.sum(row_contains_null)\n",
    "\n",
    "print('Number of rows with null values: {}'.format(num_nulls))\n",
    "print('Number of total rows: {}'.format(len(dfx)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that there a significant number of rows with at least one null value. Fortunately, when we look at the variable dictionary, we see that null values almost always account for less than 0.5% of responses, with an extremely rare maximum of 3%. In light of this rarity, I decided to impute the null values with the most common value of that variable instead of removing those respondents completely. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace -1 with NaNs\n",
    "dfx.replace(-1, np.nan, inplace=True)\n",
    "# Fill NA's with most common value for that variable\n",
    "dfx = dfx.fillna(dfx.mode().iloc[0])\n",
    "# Check how many null values we have left\n",
    "dfx.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Selection\n",
    "\n",
    "We'd like to do some scaling in prepraration for using shrinkage methods. Since our data is not normally distributed and doesn't contain outliers (the data are categorical or ordinal), we'll use min-max scaling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FRUGALITY [ 1.   0.8  0.6  0.4  0.   0.2]\n",
      "ASK1_1 [ 0.75  0.5   1.    0.    0.25]\n",
      "ASK1_2 [ 0.5   0.25  0.75  0.    1.  ]\n",
      "CHANGEABLE [ 0.5         0.16666667  0.83333333  0.          0.66666667  0.33333333\n",
      "  1.        ]\n",
      "CONSPROTECT1 [ 0.66666667  0.33333333  0.          1.        ]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Use a min-max scaler on our data\n",
    "scaler=MinMaxScaler()\n",
    "scaled_dfx = scaler.fit_transform(dfx)\n",
    "scaled_dfx = pd.DataFrame(scaled_dfx, columns=dfx.columns)\n",
    "\n",
    "# Inspect subset of scaled data\n",
    "for col in scaled_dfx.columns[15:20]:\n",
    "    print(col, scaled_dfx[col].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I suspected that some of our coefficients were highly correlated, so I didn't want to default to using Lasso as our shrinkage method, which is sensitive to such a condition. I instead used Elastic Net with cross-validation to find a good L1 ratio. The L1 ratio ended up being low, significantly favoring Ridge regression as I anticipated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import ElasticNetCV\n",
    "\n",
    "# Create arrays for X and y values\n",
    "X = scaled_dfx.values\n",
    "y = df['target'].values\n",
    "\n",
    "# Use ElasticNet with 5 cross-validation folds \n",
    "elastic_net = ElasticNetCV([.025, .05, .1, .25, .5, .75, .9], cv=10)\n",
    "elastic_net.fit(X, y)\n",
    "# Look at L1 ratio\n",
    "elastic_net.l1_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('ACT1_2', 0.30234376880906744),\n",
       " ('MANAGE1_3', 0.18446364832344025),\n",
       " ('FS1_7', 0.17735456762921867),\n",
       " ('PPINCIMP', 0.17453589546351023),\n",
       " ('FS1_2', 0.13320024834619423),\n",
       " ('MATHARDSHIP_6', 0.10823321068162108),\n",
       " ('PROPPLAN_2', 0.099392056940950727),\n",
       " ('FS1_5', 0.094542035485121245),\n",
       " ('SCFHORIZON', 0.088641958630620341),\n",
       " ('FS1_6', 0.082958620836839636)]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a list of tuples containing variables and their coefficients\n",
    "\n",
    "# Initialize an empty list\n",
    "coefficients = []\n",
    "\n",
    "# Populate this list with variable names and absolute values of coefficients (for sorting)\n",
    "for i, variable in enumerate(dfx.columns):\n",
    "    coefficients.append((variable, abs(elastic_net.coef_[i])))\n",
    "    \n",
    "# Sort the variables from highest to lowest coefficient values\n",
    "coefficients.sort(key=lambda x: x[1], reverse=True)\n",
    "\n",
    "# Look at top 10 variables\n",
    "coefficients[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interestingly, the only demographic variable to appear is income. Let's split our variables into two groups: those related to financial behaviors/attitudes and demographic variables. Then we can see how often they appear in the top variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Demographic variables in top 10 variables: 1\n",
      "Demographic variables in top 20 variables: 4\n",
      "Demographic variables in top 30 variables: 9\n"
     ]
    }
   ],
   "source": [
    "# Extract financial and demographic columns\n",
    "financial_cols = list(dfx.loc[:,'FS1_1':'PEM'].columns) + ['SAVINGS_RETIREMENT', 'SAVINGS_NON_RETIREMENT']\n",
    "demographic_cols = list(dfx.loc[:,'HHEDUC':'PPREG9'].columns)\n",
    "\n",
    "# Look at top 10, 20, and 30 variables\n",
    "for n in [10, 20, 30]:\n",
    "    # Initialize count to 0\n",
    "    demographic_count = 0\n",
    "    \n",
    "    for pair in coefficients[:n]:\n",
    "        # If variable name is demographic\n",
    "        if pair[0] in demographic_cols:\n",
    "            # Add 1 to our count\n",
    "            demographic_count += 1\n",
    "\n",
    "    print(\"Demographic variables in top {} variables: {}\".format(n, demographic_count))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['PPINCIMP', 'EMPLOY1_5', 'PPHHSIZE', 'Military_Status']\n"
     ]
    }
   ],
   "source": [
    "# Get top demographic variables\n",
    "\n",
    "# Initialize empty list\n",
    "demographic_x = []\n",
    "\n",
    "# Collect top demographic variables from top 20\n",
    "for pair in coefficients[:20]:\n",
    "    if pair[0] in demographic_cols:\n",
    "        demographic_x.append(pair[0])\n",
    "\n",
    "# Look at top demographic variables\n",
    "print(demographic_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ACT1_2', 'MANAGE1_3', 'FS1_7', 'FS1_2']\n"
     ]
    }
   ],
   "source": [
    "# Get top financial variables\n",
    "\n",
    "# Initialize empty list\n",
    "financial_x = []\n",
    "\n",
    "# Collect top financial variables from top 5\n",
    "for pair in coefficients[:5]:\n",
    "    if pair[0] in financial_cols:\n",
    "        financial_x.append(pair[0])\n",
    "\n",
    "# Look at top demographic variables\n",
    "print(financial_x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that income, household size, whether the respondent is a student, and age are the 3 variables most associated with our target variable (according to elastic net). Looking at financial behaviors, we see that the top variables are following through on financial goals, the frequency of paying off one's monthly credit balance, income, knowing how to save, and knowing where to go for financial advice. Knowing to how to save is a bit too obvious, so we can dispense with this variable and add another variable. I decided skip the next highest variable, about utilities being shut off because of non-payment, and add the one after it related to budgeting because its relationship to our other variables was more interesting. Let's examine the multicollinearity of these variables using their VIFs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Prepare data for calculating VIF factors\n",
    "\n",
    "# Create DataFrame of financial variables\n",
    "financial_df = dfx[financial_x]\n",
    "# Turn into array\n",
    "X_financial = financial_df.values\n",
    "\n",
    "# Create DataFrame of demographic variables\n",
    "demographic_df = dfx[demographic_x]\n",
    "# Turn into array\n",
    "X_demographic = demographic_df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VIF</th>\n",
       "      <th>features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30.703462</td>\n",
       "      <td>ACT1_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.314138</td>\n",
       "      <td>MANAGE1_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28.282349</td>\n",
       "      <td>FS1_7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.769152</td>\n",
       "      <td>FS1_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.985020</td>\n",
       "      <td>PPINCIMP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.058268</td>\n",
       "      <td>EMPLOY1_5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.154544</td>\n",
       "      <td>PPHHSIZE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11.635814</td>\n",
       "      <td>Military_Status</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         VIF         features\n",
       "0  30.703462           ACT1_2\n",
       "1   8.314138        MANAGE1_3\n",
       "2  28.282349            FS1_7\n",
       "3  17.769152            FS1_2\n",
       "4   5.985020         PPINCIMP\n",
       "5   1.058268        EMPLOY1_5\n",
       "6   5.154544         PPHHSIZE\n",
       "7  11.635814  Military_Status"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "# Aggregate financial and demographic data\n",
    "x_list = financial_x + demographic_x\n",
    "# Create DataFrame of both groups of variables \n",
    "subset_df = dfx[x_list]\n",
    "# Turn into array\n",
    "X = subset_df.values\n",
    "\n",
    "# Look at VIFs for aggregated data\n",
    "vif = pd.DataFrame()\n",
    "vif[\"VIF\"] = [variance_inflation_factor(X, i) for i in range(X.shape[1])]\n",
    "vif[\"features\"] = subset_df.columns\n",
    "vif"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a lot of multicollinearity in the aggregated data, so I removed variables with the highest VIFs, sometimes removing the less interesting variable if scores were close, until all of them were sufficiently low (<10)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VIF</th>\n",
       "      <th>features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.833288</td>\n",
       "      <td>MANAGE1_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.815990</td>\n",
       "      <td>FS1_7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16.347400</td>\n",
       "      <td>FS1_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.970706</td>\n",
       "      <td>PPINCIMP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.058211</td>\n",
       "      <td>EMPLOY1_5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.136591</td>\n",
       "      <td>PPHHSIZE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11.125703</td>\n",
       "      <td>Military_Status</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         VIF         features\n",
       "0   7.833288        MANAGE1_3\n",
       "1  20.815990            FS1_7\n",
       "2  16.347400            FS1_2\n",
       "3   5.970706         PPINCIMP\n",
       "4   1.058211        EMPLOY1_5\n",
       "5   5.136591         PPHHSIZE\n",
       "6  11.125703  Military_Status"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove variables with high VIFs\n",
    "x_list.remove('ACT1_2')\n",
    "\n",
    "# Create DataFrame of both groups of variables \n",
    "subset_df = dfx[x_list]\n",
    "# Turn into array\n",
    "X = subset_df.values\n",
    "\n",
    "# Look at VIFs for aggregated data\n",
    "vif = pd.DataFrame()\n",
    "vif[\"VIF\"] = [variance_inflation_factor(X, i) for i in range(X.shape[1])]\n",
    "vif[\"features\"] = subset_df.columns\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VIF</th>\n",
       "      <th>features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.880089</td>\n",
       "      <td>MANAGE1_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.238580</td>\n",
       "      <td>FS1_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.912018</td>\n",
       "      <td>PPINCIMP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.058115</td>\n",
       "      <td>EMPLOY1_5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.103822</td>\n",
       "      <td>PPHHSIZE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10.237828</td>\n",
       "      <td>Military_Status</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         VIF         features\n",
       "0   6.880089        MANAGE1_3\n",
       "1  11.238580            FS1_2\n",
       "2   5.912018         PPINCIMP\n",
       "3   1.058115        EMPLOY1_5\n",
       "4   5.103822         PPHHSIZE\n",
       "5  10.237828  Military_Status"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove variables with high VIFs\n",
    "x_list.remove('FS1_7')\n",
    "\n",
    "# Create DataFrame of both groups of variables \n",
    "subset_df = dfx[x_list]\n",
    "# Turn into array\n",
    "X = subset_df.values\n",
    "\n",
    "# Look at VIFs for aggregated data\n",
    "vif = pd.DataFrame()\n",
    "vif[\"VIF\"] = [variance_inflation_factor(X, i) for i in range(X.shape[1])]\n",
    "vif[\"features\"] = subset_df.columns\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VIF</th>\n",
       "      <th>features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.657379</td>\n",
       "      <td>MANAGE1_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.935435</td>\n",
       "      <td>FS1_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.876181</td>\n",
       "      <td>PPINCIMP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.053330</td>\n",
       "      <td>EMPLOY1_5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.122263</td>\n",
       "      <td>PPHHSIZE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        VIF   features\n",
       "0  6.657379  MANAGE1_3\n",
       "1  8.935435      FS1_2\n",
       "2  5.876181   PPINCIMP\n",
       "3  1.053330  EMPLOY1_5\n",
       "4  4.122263   PPHHSIZE"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove variables with high VIFs\n",
    "x_list.remove('Military_Status')\n",
    "\n",
    "# Create DataFrame of both groups of variables \n",
    "subset_df = dfx[x_list]\n",
    "# Turn into array\n",
    "X = subset_df.values\n",
    "\n",
    "# Look at VIFs for aggregated data\n",
    "vif = pd.DataFrame()\n",
    "vif[\"VIF\"] = [variance_inflation_factor(X, i) for i in range(X.shape[1])]\n",
    "vif[\"features\"] = subset_df.columns\n",
    "vif"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because I was still interested in the ACT1_2, I found another set of variables which contained ACT1_2 that had low enough VIFs (<10) that the issue of multicollinearity would not be very problematic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VIF</th>\n",
       "      <th>features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.601904</td>\n",
       "      <td>ACT1_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.902008</td>\n",
       "      <td>MANAGE1_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.374284</td>\n",
       "      <td>PPINCIMP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.040406</td>\n",
       "      <td>EMPLOY1_5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        VIF   features\n",
       "0  9.601904     ACT1_2\n",
       "1  7.902008  MANAGE1_3\n",
       "2  5.374284   PPINCIMP\n",
       "3  1.040406  EMPLOY1_5"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_list = ['ACT1_2', 'MANAGE1_3', 'PPINCIMP', 'EMPLOY1_5'] \n",
    "\n",
    "# Create DataFrame of financial variables\n",
    "subset_df = dfx[x_list]\n",
    "# Turn into array\n",
    "X_subset = subset_df.values\n",
    "\n",
    "# Recalculate VIFs\n",
    "vif = pd.DataFrame()\n",
    "vif[\"VIF\"] = [variance_inflation_factor(X_subset, i) for i in range(X_subset.shape[1])]\n",
    "vif[\"features\"] = subset_df.columns\n",
    "vif"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have a list of variables we might want to include in our model, we can look at their relationship with out output variable (keeping in mind these single-variable analyses are done without considering the effect of any other variables)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GjbCxscHdu3dx9+5dueMREVEeUtQ5oP379wMAtm/fnmv8gAEDoFKpcOjQITli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\ngiAIbUIUQIIgCEKbEAWQIAiC0Cb+C0ra5LJDRfcKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f52b54ba550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Remove respondents who refused to answer\n",
    "act_df = df[df['ACT1_2'] != -1]\n",
    "\n",
    "# Get x and y values\n",
    "x_vals, y_vals = percentages_of_true_across_categories('ACT1_2', act_df)\n",
    "\n",
    "# Create bar graph\n",
    "plt.bar(x_vals, y_vals)\n",
    "plt.title('Relating Following Through Financially and Handling Unexpected Expenses')\n",
    "plt.xlabel('Do You Follow Through on Your Financial Goals?')\n",
    "plt.ylabel('% Can Handle Unexpected Expense')\n",
    "x_labels = ['Not at all', 'Very little', 'Somewhat', 'Often', 'Always']\n",
    "plt.xticks(x_vals, x_labels, rotation=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HDmxsTA8X+Pn5YWdnR8+ePZk6dSqjR49m4cKFcgNTIYR4DJMXl588eTIjR47k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\nAJmMJUmSmoD/AxKPlPBtLng5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f52b54d87f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Remove respondents who refused to answer\n",
    "manage_df = df[df['MANAGE1_3'] != -1]\n",
    "\n",
    "# Get x and y values\n",
    "x_vals, y_vals = percentages_of_true_across_categories('MANAGE1_3', manage_df)\n",
    "\n",
    "# Create bar graph\n",
    "plt.bar(x_vals, y_vals)\n",
    "plt.title('Relating Paying off Credit and Handling Unexpected Expenses')\n",
    "plt.xlabel('Frequency of Paying off Monthly Credit Card Balance')\n",
    "plt.ylabel('% Can Handle Unexpected Expense')\n",
    "x_labels = ['Not applicable or never', 'Seldom', 'Sometimes', 'Often', 'Always']\n",
    "plt.xticks(x_vals, x_labels, rotation=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UlATgYSh+9913aNGiBcqUKfPM7Tzru3Xw4EEcP34cu3fvRkxMDCIjI3Wv2717N7RaLdLT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f52b51cb160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Remove respondents who refused to answer\n",
    "hh_df = df[df['PPHHSIZE'] != -1]\n",
    "\n",
    "# Get x and y values\n",
    "x_vals, y_vals = percentages_of_true_across_categories('PPHHSIZE', hh_df)\n",
    "\n",
    "# Create bar graph\n",
    "plt.bar(x_vals, y_vals)\n",
    "plt.title('Relating Household Size and Handling Unexpected Expenses')\n",
    "plt.xlabel('Household Size')\n",
    "plt.ylabel('% Can Handle Unexpected Expense')\n",
    "x_labels = ['1', '2', '3', '4', '5+']\n",
    "plt.xticks(x_vals, x_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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i8uTJsLOzw/Tp0wEAjRo1klrTzZs3R0ZGBoQQZV7S5+BQESqVfB84vTicnOzL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\nUjzuGZu3b/wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f52b52f27b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get x and y values\n",
    "x_vals, y_vals = percentages_of_true_across_categories('EMPLOY1_5', df)\n",
    "\n",
    "# Create bar graph\n",
    "plt.bar(x_vals, y_vals)\n",
    "plt.title('Relating Being a Student and Handling Unexpected Expenses')\n",
    "plt.xlabel('Full-Time Student')\n",
    "plt.ylabel('% Can Handle Unexpected Expense')\n",
    "x_labels = ['No', 'Yes']\n",
    "plt.xticks(x_vals, x_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that most of these variables are very clearly associated with our target variable. The only slight exception is household size. However, his might interact with the other variables in a way that is predictive of confidence in handling unexpected expenses. For example, we see a clear pattern between household size and our output variable for people with incomes from $100k-150k."
   ]
  },
  {
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}