LC_MLtrain.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Supervised regression of LC loan data\n",
    "\n",
    "As my metric (buy rate) is a continuous function, I have turned a simple classification (good vs bad loans) into a regression of return. I find that this would be a better tool for picking loans to invest in, as it forecasts a predicted return, not just if the loan will be paid as agreed. My metric intereacts with the loan interest, prefering higher interest loans that are paid as agreed over lower interest loans, this is not done by simple classification. Successful forecasting of my metric can be the difference between having no defaulting loans, but a 4% return per year (low interest, safe loans) vs having no or few defaulting loans, and 10-15% return per year.\n",
    "\n",
    "\n",
    "### Getting started\n",
    "\n",
    "Data exploration and cleaning has been performed using the previous jupyter notebook named \"Data Cleaning.ipynb\". I exported a clean features and labels pickle using joblib in that notebook. I will import them and graph some preliminary things to start!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import joblib\n",
    "features=joblib.load('clean_LCfeatures.p')\n",
    "labels=joblib.load('clean_LClabels.p')\n",
    "clabels=joblib.load('clean_LCclassifierlabel.p')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from time import time\n",
    "from IPython.display import display # Allows the use of display() for DataFrames\n",
    "\n",
    "# Import supplementary visualization code visuals.py\n",
    "import visuals as vs\n",
    "\n",
    "# Pretty display for notebooks\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>emp_title</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>purpose</th>\n",
       "      <th>addr_state</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>earliest_cr_line</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B2</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>None</td>\n",
       "      <td>10.0</td>\n",
       "      <td>RENT</td>\n",
       "      <td>24000.00</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>AZ</td>\n",
       "      <td>27.65</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1985-01-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13648.0</td>\n",
       "      <td>83.7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B5</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>UCLA</td>\n",
       "      <td>10.0</td>\n",
       "      <td>OWN</td>\n",
       "      <td>75000.00</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>CA</td>\n",
       "      <td>10.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1989-10-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23336.0</td>\n",
       "      <td>67.1</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>C1</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>Va. Dept of Conservation/Recreation</td>\n",
       "      <td>0.5</td>\n",
       "      <td>RENT</td>\n",
       "      <td>30000.00</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>VA</td>\n",
       "      <td>10.08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004-04-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10452.0</td>\n",
       "      <td>91.7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B1</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>Target</td>\n",
       "      <td>3.0</td>\n",
       "      <td>RENT</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>IL</td>\n",
       "      <td>12.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2003-07-01</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7323.0</td>\n",
       "      <td>43.1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>D1</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Internal revenue Service</td>\n",
       "      <td>0.5</td>\n",
       "      <td>RENT</td>\n",
       "      <td>28000.00</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>MO</td>\n",
       "      <td>20.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2007-09-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6524.0</td>\n",
       "      <td>81.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>A1</td>\n",
       "      <td>9200.0</td>\n",
       "      <td>Network Interpreting Service</td>\n",
       "      <td>6.0</td>\n",
       "      <td>RENT</td>\n",
       "      <td>77385.19</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>CA</td>\n",
       "      <td>9.86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7314.0</td>\n",
       "      <td>23.1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>B4</td>\n",
       "      <td>21000.0</td>\n",
       "      <td>Osram Sylvania</td>\n",
       "      <td>10.0</td>\n",
       "      <td>RENT</td>\n",
       "      <td>105000.00</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>FL</td>\n",
       "      <td>13.22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1983-02-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32135.0</td>\n",
       "      <td>90.3</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>B3</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>Value Air</td>\n",
       "      <td>10.0</td>\n",
       "      <td>OWN</td>\n",
       "      <td>50000.00</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>TX</td>\n",
       "      <td>11.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1985-07-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10056.0</td>\n",
       "      <td>82.4</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>B3</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>Wells Fargo Bank</td>\n",
       "      <td>5.0</td>\n",
       "      <td>RENT</td>\n",
       "      <td>50000.00</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>CA</td>\n",
       "      <td>16.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2003-04-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17800.0</td>\n",
       "      <td>91.8</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>B1</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>Winfield Pathology Consultants</td>\n",
       "      <td>2.0</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>92000.00</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>IL</td>\n",
       "      <td>29.44</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2002-02-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13707.0</td>\n",
       "      <td>93.9</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sub_grade  loan_amnt                            emp_title  emp_length  \\\n",
       "0         B2     5000.0                                 None        10.0   \n",
       "5         B5    12000.0                                 UCLA        10.0   \n",
       "6         C1     9000.0  Va. Dept of Conservation/Recreation         0.5   \n",
       "7         B1     3000.0                               Target         3.0   \n",
       "9         D1     1000.0             Internal revenue Service         0.5   \n",
       "13        A1     9200.0         Network Interpreting Service         6.0   \n",
       "14        B4    21000.0                       Osram Sylvania        10.0   \n",
       "15        B3    10000.0                            Value Air        10.0   \n",
       "16        B3    10000.0                     Wells Fargo Bank         5.0   \n",
       "18        B1    15000.0       Winfield Pathology Consultants         2.0   \n",
       "\n",
       "   home_ownership  annual_inc             purpose addr_state    dti  \\\n",
       "0            RENT    24000.00         credit_card         AZ  27.65   \n",
       "5             OWN    75000.00  debt_consolidation         CA  10.78   \n",
       "6            RENT    30000.00  debt_consolidation         VA  10.08   \n",
       "7            RENT    15000.00         credit_card         IL  12.56   \n",
       "9            RENT    28000.00  debt_consolidation         MO  20.31   \n",
       "13           RENT    77385.19  debt_consolidation         CA   9.86   \n",
       "14           RENT   105000.00  debt_consolidation         FL  13.22   \n",
       "15            OWN    50000.00         credit_card         TX  11.18   \n",
       "16           RENT    50000.00  debt_consolidation         CA  16.01   \n",
       "18       MORTGAGE    92000.00         credit_card         IL  29.44   \n",
       "\n",
       "    delinq_2yrs earliest_cr_line  inq_last_6mths  open_acc  pub_rec  \\\n",
       "0           0.0       1985-01-01             1.0       3.0      0.0   \n",
       "5           0.0       1989-10-01             0.0      12.0      0.0   \n",
       "6           0.0       2004-04-01             1.0       4.0      0.0   \n",
       "7           0.0       2003-07-01             2.0      11.0      0.0   \n",
       "9           0.0       2007-09-01             1.0      11.0      0.0   \n",
       "13          0.0       2001-01-01             0.0       8.0      0.0   \n",
       "14          0.0       1983-02-01             0.0       7.0      0.0   \n",
       "15          0.0       1985-07-01             0.0       8.0      0.0   \n",
       "16          0.0       2003-04-01             0.0       6.0      0.0   \n",
       "18          0.0       2002-02-01             0.0       8.0      0.0   \n",
       "\n",
       "    revol_bal  revol_util  total_acc  collections_12_mths_ex_med  \n",
       "0     13648.0        83.7        9.0                         0.0  \n",
       "5     23336.0        67.1       34.0                         0.0  \n",
       "6     10452.0        91.7        9.0                         0.0  \n",
       "7      7323.0        43.1       11.0                         0.0  \n",
       "9      6524.0        81.5       23.0                         0.0  \n",
       "13     7314.0        23.1       28.0                         0.0  \n",
       "14    32135.0        90.3       38.0                         0.0  \n",
       "15    10056.0        82.4       21.0                         0.0  \n",
       "16    17800.0        91.8       17.0                         0.0  \n",
       "18    13707.0        93.9       31.0                         0.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.head(n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "features['earliest_cr_line']=features.earliest_cr_line.dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.earliest_cr_line.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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Q0gHATXgrzg75hIraZWb3pnsxJtUw34Bfm58D55jZxPw+9Sh4zarxc9y3/VL5EKEL4YXP\nl2ncglCLnfCa8Fsl/YZZE8mtio92NA8+Wllbr0HifmBLSVfgLSxP5grdrdDw3pvZO5IOwmvfz8Jd\nSZbGW2MeZvaJuxqmJ+mL+Ag3F+J5yoL4/ZzO7AXb2TCzF5Kb08XAHen6VyaSWw9/ji/A78/Z+Efm\n3+XzQ9yNtw6shE8CubU1MV+CpFMz9j2DDy7xTWb1saqc+xmSjsNHdFqT3Ad3crs7AW+5eAT/SB+N\nj6BUZCK3pVPeMwx3HRuFDwwh4Etm9nqKNwn/cD5M0jv4B3itCfaq6krSTcDekqbiBbdv0V7Xx4Pw\nfPo6SWPxVqFF8Y/tFc2sMqv0FfhIgBPxa7Yt1T/+a+VPl+PP/GmSDsYLRz8FCo8cVsTWNtzbIGg/\n1gM9uWMZmgv+Ur4Yfxm9gfcxuBPPgOfNxBtNGoUpE7Y43lfiIWDpFDYMH3r17pTey+n/0XjrQfbY\nN5MZaSmFVUZoGlfF1kJp4304LgNew0fWOAFv6Zg5klCd6zGOWSOKvIt/VEzCR+kYVSX+6Gy6eI3k\nlXht4Qzcr/t0Zh8ZZ8lk33Qyo+lUu8Y5uyZn1kekuD8Ajk3n+Rr+Qb1Cbt/3pGswNR3zPPyDbObI\nOQXtGpGJOy/ue/0Y/vHyWFqfp4qN387Zs0kK36ToNatzv7bAP/peT3q4CFglF6fQKEyZ+Avhw0je\niT8PM/Ba+hPwj4m2X4MUtiHeqvIGs4/sU+veF0mz0L1PcXfCn6cZuEvHH4ClmtUSXuA+Dy88vIFr\n8zJg/YLXf3l8VKRK595X8E7O+wIL52wZAzyQ4r2Q4o0hjcyVuSafbfDc7oK3tDyT0noUb2nMHm8Y\n/qH5GP6sXYkXWLL3anG8EuOhFOcF4B/A5gXOOzua0Vv4R/31+Mhei1WJv1ba/ho+cMIh+Fwa+We1\nlq5G4B/f09N5j8Wfxdk0VMPWMSne3A3iLYO3LE3BC+JT8ZGNdsrEGY4Xzl5My9l4f4xC+VPatlG6\n96+la78TBZ+borYO5N7GEktZS2UYuSAIgiAIgiAIgoZEH4ggCIIgCIIgCAoTBYggCIIgCIIgCAoT\nBYggCIIgCIIgCAoTBYggCIIgCIIgCAoTBYggCIIgCIIgCAoTBYggCIIgCIIgCAoTBYggCIIgCIIg\nCAoTBYggCIIgCIIgCAoTBYggCIIgCIIgCAoTBYiEpAmSxnbbjiDoNJLGSbq023ZUkDRZ0j7dtiPo\nPs3ky5GHB/WQdK+kMQ3ijJb0SodMCjpAkfuei7+JJJM0vNp6MIshXYBIH05jum1HL1LtZSxphCTr\nlk39SGisNgN5WadCxiZtNmlQMoQ0uC2wX7sSSx8Ok9uVXlAuzeo8fRRulws+D1gxE2e0pAntsTAo\ngw7kbzcASwHPl3iM2ZD0HUn/lPSipJckXStpow4ct6k8b0gXIIIgCILBgZm9YGbTu21H0L+Y2etm\n9ky37Qh6BzN708yeMrNOVp5ughdmPwOsDzwIXClp5VYTlDS3JLXHPCcKEDWQtKik8akE+LqkqyWt\nkdn+AUnnSHoibb9P0q65NCZIOknS4ZKek/SMpF9LKnTdJe0k6VZJ09O+f5K0dGZ7pWnt85JuT3b8\nU9Iykj4l6W5Jr0i6VNIHMvuNS2F7SpqSzvH3khaobAc+BeyR0jdJIwZ0QQMkzSvpqKSZ19K93Tyz\nfaD380BJT6c4v5c0f4t2StJPJf072TBR0k6Z7SOSnV+RdFU6l/slbZZLZ0tJD0p6Iz0LX69oKbUe\n/B5YMKOxMZnd3yPpFEnT0vX6f62cSzA7TeQpm0q6Od3b2yStk4kzOmlsU7l7wKvyGrIVMnHGSLo3\nd+zZWpwkrSTpIklPpTTukPTFAZzbbK2m8laqA+vpSNIikk6WNDXpdJKkr7dqQ9AbSFo8aet1SY9J\n+lZu++T0909J75NTeLgw9TGN7nuKs4ikU1P+N13SPySNrJNm3qWpYf6X4v005W2vSDoz5YmTi5yH\nme1oZmPN7E4zexD4PjAd2CKlvbOk5yXNlzvm2ZIuTv/HJPtGS/o3MAN/324s6aZk18uSbpH0kSJ2\n5YkCRG3G4SW/rYD1gNeAKzTro+w9wB3AF4E1gBOAUyRtmktnR+Bt4BPAD4GfAEVfUPMCBwNrpuMM\nB86pEu8XKd31gUXxkutBwO54SXYNYExun08CHwE+m+zZBtgzbdsTuBH/wFsqLY8XtDmoze/xgtk3\n8Gs/HrhE0pq5eK3cz0/hOtkU+ArwOeCoFu38JbAbsAewOnAEru0tc/EOA05Mx70VOFfSQgCSlgP+\nAvwtbR8LHJ3Z94Z0jq8xS2O/zmz/H2AisE46j6MlbdDi+QSzKJqnHAHsi1//54Gzpdlqr+bD3YW+\nBWwAvA/4bZO2LARcDmyW7Pkz8BdJqzaZTj1q6iidz2X4s7MrsBqe981o4/GD7jAO+BD+ftsa2BkY\nkdn+8fT7HTzv+TjBYGAcde57eub/BiyN539rA9cB10haqonj1M3/JH0Nf48ejOc9DwJ7tXZKgOfb\n7wFeTOt/wr/ft8occxH8O+70zH4r4N8bX8Xz2DeAi4Dr0/r6wPHAOy1ZZWaxeMvUBGBs+r8yYMDG\nme2LAC8D366TxrnA73Jp3piLc1U2TpM2rprsWiatb5LWN8/E+WEKWycTNga4N7M+Di8QzJUJOw24\nutr1iKUt+loJeBdYLhd+IXBSG+7nS8BCmbCdSDUOBWwbB1ya/i8IvA58MhfneOCy9H9Esum7me1L\np7CN0voRwKRcGvunOCPS+mjglSr2TAbOyYU9DBzY7fs42JaCecqGuTij0/oqmTg7Jr2pmkbr3e9c\nnJuy97mZfCgft5GO8ILLu8Bq3b4PsbRvAT6c9LlhJmx5/CNpTCbMgO1y+zbUaCy9uRS577hL0CvA\n/Ll97wJ+mv5X8sDhNdaL5H83AKfljnE1MLnFc/sV8ASwcCZsLHBFZv37wFPA3Gl9DPAWsEQmzvuT\n7Z9qxzWPFojqrIa/WG6sBJjZy3hN1uoAkuaSdICke1JT0it4J77lcmndk1t/Eli8iBGS1knNcY9J\nmg7cljbVO8bT6XdiLix/zPvNLFvqLGxX0BLrAALuT02HryTNbIkXLrK0cj/vMbNs0/uNeK1FPu1G\nrI7XdFyRs/P7Dex8Mv1W7FoVb5XIcnMTdrT83AS1aTFPyd9bgBnmTevZOPPiLWZFbVlQ0tFy97cX\nk85GVrFlINTT0drAVDOb1MbjBd2n8v6+pRJgZo8xS8fB4KTIfV8XWAB4Nvd++wjNvSsb5X+rkfl+\nTOTXCyFpT+C7wLZmNi2z6TRgM0nLpPVvAePN7O1MnCfMrPINgZm9gFcYXinpb5L2St4CLTF3qzsO\nYSodafYB9sabvCfipdrDmfMj560q+zcsuElaELgSL7V+E3gGdzf4Jy7UWsfwqhWzfFj+mC3ZFbTM\nMPwaf5w5r/3rufVW7me7qKT7JeC/deyabd3MLHm4tMuu0GebGWiewuzXP/uSqhbnXbzAnGWe3Pqv\ncZ/effCWgdeAM6vYMhBCR0OXGDFwaFLvvg/DK+A+WWXbtCphtWiU/7UFST8BDgU+b2a3ZLeZ2d2S\n7gBGS7oQr3zZKZfEq/k0zWxXScfjee+XgcMkbW1mVzZrXxQgqjMJF8IGuH8ckhYGPor7sQNsBFxi\nZn9I24U3ob3UJhtWxV/u+5vZo+kY27Yp7SK8CczVweMNdu7EP6iWNLNrS0j/o5IWNLNKhjEKv4f/\nbjKd+/Gm2OXN7JoB2PMAGf/MxHq59dBYZ+lknvIssIQkWWo7B9bKxdkIONPM/pxseQ9eC/hQSTbl\nuRNYStJq0QoxqHgAf3+vh7uSVPpkfTAX7y0i/xlMFLnvdwBLAO+a2X9KtGUS/g4+IxM2qpkEJO2F\n94fc0syurxHtNOCneL7+r1yrSE3M7G7gbuAoSZcDu+CVS00RNTFVMLOH8Y4mp0j6pKSPAmfhJdQ/\npmgPAZtK2ih1+huLd1hpF//FP+R+KGnF1IH10Dam34jJwHryEXOGq+DIUUF1zOwh4GxgnKTt0j0d\nKWmfNn3EzQ2cIWkN+WhIR+I+mHPUQDSwczpeM/xrSd+S9CFJa0n6nqTdm0jqt8BK8lHHVknn+N3K\nYdLvZHy0pc2SxhZoxtagaTqZp0zA/W33l4+2tBuQH3P/IWCb5FZVyWPfU5I91fg77lb3Z0mbS1oh\naXHrDtoQtJn0EXUF/v7eQNJauNtGvqV3Mv4OX1JSYde7oDcpeN+vBv4FXCQf7XCFFPcXkqq1SrTK\nCcAu8vkcVpa0H95huRDy0eKOxAczeShpdMnUUTrLOcCSuIvx6TQgne+Rkj4haXlJnwY+hlccNk18\nFNZmV9yX7uL0uwCwhZlVxPjLFH453krxKv6B2BbM7Fm8VLg1fnMPZmC9+Jvl13gN8f14bWI7/ZKH\nKrviLVhH47UllwIbA4+1Ie1/APcB1wJ/Ba7BayZa4ed4B6x9UppX4SM7PVo0geR7+hW8ifRufDSc\nQ9LmN1KcG/CCxjm4xlq1NyhAJ/OUVKP/fXzksHvwDsuH56LthbtR/RPPR29K/zuCmb0LfB7/oDgL\nrzU8gfa6UAXdYTSeX10DXIJX/E3Oxdkb+DQ+oMidHbQtKI/R1LnvqTX0C2n7afjoSOcDq9DGPjJm\ndh7+Dj0M19ZHgWObSGIP3OXzPGBqZjkhd5zpuP0z0m8jXsM9Zf6EV+CMx79bWxqxUbNal4Mg6Efk\n83YMN7OWx9DvBKkz2CHA+ywyniAIgmCIIGkf4IdmNqLN6V6Od5b+TjvTLUKRzrwbps53lUmIjpW0\nfPmmBUOZ0F3/I2kPSeulZtMd8JaNcd0uPIS2gl4i9BiURWhr8CKf7PjL+JxPJzSKXwZFXJhOBl6T\nT3a1N94p88xSrRoCpL4Vr9Raum1fD9Cy7uQz0E6UdJek21LY++WzJj+cfhfNxN9P0iPyWZOzM0Ov\nm9J5RNKJkg8zJGk+Seel8JvVw7N0S1quns40gCHcCvAh3J1qEu5r/1ugF2aUjjytzxjk+WXoMSiL\n0FYP0uCdXLQvxp246+X+ZnZviebWpKELk6Q7zGwdSQcBU8zs9EpYZ0wcnMhntF661nYze6SD5vQc\nA9GdfLr4kWb2XCbsaOAFMztS0r7Aomb2M0mr4z746+GjNVwNfNjM3pF0C/BjvKPlZcCJZna5pB8A\nHzOz70naHtjGzIrOLt5RJM3N7DOw5pmcGzd60BN5Wv8xmPPL0GNQFqGt3kTSh+psnpLpa9vTFBnG\ndbq8B/lOwMby0Xjy43kHTZIE0rcvvQ7Qbt1thc8oCd5xaALwsxR+rpnNAB6V9Ag++tRkfNbHmwAk\nnYl3Pr087TMmpXUBMFaabbjKniEVDkJnsxN5Wp8xyPPL0GNQFqGtHqSfKzyyFClAfB34BrCbmT2V\nXB5+Va5Z5TF8+HAbMWJEt80YNNx+++3PmdliJSQ9EN0ZcLWkd4BTzOxUfDr3qWn7U/hY0OC1mjdl\n9n0ihb2V/ufDK/s8Dv6BLull4APAc5n4yIc93R1gwQUXXHfVVVctaH7QiAHqblDlabWIvK699Ghe\n13OE7tpL5HWNCc21l6Kaa1iAMLOnyAw/ZWb/pY996EaMGMFtt93WbTMGDZLaMQTpHAxQdxuZ2RRJ\niwNXSXogl7ZJKr21IBVcTgUYOXKkhe7ax0B0N9jytFpEXtdeejSv6zlCd+0l8rrGhObaS1HN1SxA\nSJpOnSnBzWzhFuwKgrq0Q3dmNiX9PiPpr3j/hqclLWVmUyUthY8/DzAFWDaz+zIpbEr6nw/P7vNE\n6mOwCPB8gdMLukjkaUEvEXoMyiK0FXSCmgUIM3svgKRD8Qks/gAI2BFYqiPW9REaP37mf9tlly5a\n0t8MVHfyIeuGmdn09P9z+NwDF+OTaB2Zfi9Ku1wM/FHSsXgn6pWBW1In6mmSRuGdqHcGfpPZZxfg\nRnx23Wt6sf9Dv9PuZ2qo52mRR/UWQ12PZRAad4a6tkIHnaFIH4gvm9mamfWTJd0NHFSSTUEAretu\nCeCvacTVuYE/mtkVkm4Fzpe0Gz7z89cAzOw+SefjM/O+DexhZu+ktH4AjAPmxztPX57CTwf+kDpc\nvwBsP6AoH8T9AAAgAElEQVQzDTpN5GlBLxF6HADZj8Va4UP4IzK0FZRGkQLEq5J2BM7Fm8R2AF4t\n1aogaFF3ZvYfYM0q4c8Dm9bY5zB8yvl8+G3AR6qEvwF8tZEtQc8SeVrQS4Qeg7IIbQWlUWQiuW/g\ntbVPp+WrKSwIyiR0F5RFaCvoJUKPJaPx42cuQ4zQVlAadVsgJM2FT5K1VbMJS1oW7+2/BF7yPdXM\nTpD0fuA8fHKrycDXzOzFtM9+wG7AO8CPzezKFL4us1xJLgP2TCPpzJeOsS7eifXrZja5WVuD3mIg\nuguCeoS2gl4i9NgaQ7Ag0DShraBs6rZAJF/wHVpM+21gbzNbHRgF7JFm/d0X+LuZrQz8Pa2Ttm0P\nrAFsAZyUHgDw6di/g3dwXTltBy9svGhmHwKOA45q0daghxig7oKgJqGtoJcIPXaeodIaEdoKyqZI\nH4h/SRqLtxrM9J0zszvq7ZQm7Zqa/k+XNAmfgGtIzggcNE1LuguCAoS2gl4i9BiURWgrKI0iBYi1\n0u8hmTADPlP0IJJGAGvjw2F2fEbgoC8ZsO6CoAahraCXaEmPks4Avgg8Y2YfSWHhIhxkibwuKI0i\nM1F/eiAHkLQQ8GfgJ2Y2LQ2vWUm7IzMCS9od2B1gueWWK/twQRsYqO6CoBahraCXGIAexwFjmX1m\n4YqL8JGS9k3rP8u5CH8QuFrSh5ObS8VF+Ga8ALEF3sI/00VY0va4i/DXW7Q16AKR1wVl0nAUJkmL\nSDpW0m1pOUbSIkUSlzQPXng428z+koKfTjMB08YZgak3I7CZnWpmI81s5GKLLVbE9KDLDER3QVCP\n0FbQS7SqRzO7Dp+HJstWuGsw6XfrTPi5ZjbDzB4FKi7CS5FchJPr75m5fSppXQBsqmwNYIcZKn0X\n2knkdUGZFBnG9QxgOj4U2NeAacDvG+2UMprTgUlmdmxmU2UWX5hzRuDtJc0naQVmzQg8FZgmaVRK\nc+fcPpW0YkbgwUVLuguCAoS2gl6inXqs5yL8eCZexRV4aQq6CAMVF+FBwxAolEReF5RGkT4QK5nZ\nVzLrv5B0V4H9NgS+CUzMxN8fOJKYEThoTKu6C4JGhLaCXqIUPXbKRRjCTbiHibwuKI0iBYjXJW1k\nZtcDSNoQeL3RTil+rebOmBE4aERLuguCAoS2gl6inXp8WtJSZja1jS7CT9RzEQZ3EwZOBRg5cmR4\nAfQOkdcFpVGkAPF9YHzGb+5FYHRpFgWBE7oLyiK0FfQS7dRjxa33SOZ0Ef6jpGPxTtQVF+F3JE2T\nNArvRL0z8JtcWjcSLsL9SuR1QWkUGYXpLmBNSQun9WmlWxUMeUJ3QVmEtoJeolU9SjoHn1NpuKQn\ngIMJF+EgQ+R1QZk0LEBIOhw42sxeSuuL4jNMH1i2ccHQJXQXlEVoK+glWtWjmdWaZXjQuAh3snNz\n9li2yy51YvYPkdcFZVJkFKbPV8QHkCal+UJ5JvUeQ2Ckhl5kyOsuKI3QVtBLhB6DsmhJW5LOkPSM\npHszYe+XdJWkh9Pvoplt+0l6RNKDkjbPhK8raWLadmJlGOA02uZ5Kfxm+WTDlX12Scd4WNLgKMkN\nUooUIOaSz0gJgKT5gfnqxA+CdhC6C8oitBX0EqHHoCxa1dY4fELBLJVJClcG/p7W0eyTFG4BnCRp\nrrRPZZLCldNSSXPmJIXAcfgkhZWZ1A8G1gfWAw7OFlSC3qJIAeJs4O+Sdkt+lVcxa3KZICiL0F1Q\nFi1pK2rlgpKIvC4oi5a01cVJCjcHrjKzF1JryVXMWZAJeoQinaiPknQ38NkUdKiZXVmuWYOHwehX\n2QlCd0FZDEBb44Cx+IuwQqVW7khJ+6b1n+Vq5T4IXC3pw6njaqVW7mbgMvwFeTmZWjlJ2+O1cl/P\n1MqNBAy4XdLF6QUb9DmR1wVl0WZt1Zuk8KZMvMpkhG9RcJJCSZVJCmtNeBj0IEWGcQWYBLxtZldL\nWkDSe81sepmGBQGhu6A8mtaWmV2XbRVIbIWPhANeozYB+BmZWjng0TSSzXqSJpNq5QAkVWrlLk/7\njElpXQCMzdfKpX0qtXLntHTmQS8SeV1QFm3XVicnKayFYvLCrtPQhUnSd/CX2SkpaGngwjKNCoLQ\nXVAWbdZWvVq5ajVpS1OwVg5oulZO0u6SbpN027PPPtviKQWdJPK63mOwDJzSZm09ndySUPsmKUSz\nT1JYK605MLNTzWykmY1cbLHFWjylYCAU6QOxB7AhMA3AzB4GFi/TqCAgdBeURynaSn6+Xa2Vi5dq\nXxJ5XYbB8vHeI7RTW5WJBWHOSQq3T324VmDWJIVTgWmSRqWW1J1z+1TSyk5SeCXwOUmLpv5kn0th\nQQ9SpAAxw8zerKyk0mLMRklkdCXTtO4kLSvpWkn3S7pP0p4pfIykKZLuSssXMvu0raNr0De0M0/r\nqVq5oC+Jd2xQFi1pSz5J4Y3AKpKeSB2wjwQ2k/Qw3qfiSPBJCoHKJIVXMOckhb/DO1b/m9knKfxA\ncu3cizSiU3LTPBS4NS2HVFw3g96jSB+If0jaH5hf0ma4IC4p16wgaEl3b+OT5Nwh6b14Z9Or0rbj\nzOzX2cjt7OjahvMNOkc787RKTdqRzFkr90dJx+LaqtTKvSNpmqRRuLZ2Bn6TS+tGMrVykq4EDtes\nEZ4+B+zXor1B7xHv2KAsWtJWNycpNLMzgDMa2Rh0nyItEPsCzwITge/iH1Mxi2FQNk3rzsymmtkd\n6f90vPNYvREc2jn8XNA/tJSnRa1cUBLxjg3KIrQVlEaRYVzfBU5LCwCSNgT+VaJdPUu4K3WGgeou\nuRatjdfybgj8SNLOwG14K8WLtHf4ueeq2BCjRPQgrWorauWCMoh3bFAWoa2gTGq2QEiaS9IOkvaR\n9JEU9kVJN+BjoQdB22mH7iQtBPwZ+ImZTcPdkVYE1gKmAseUY/3sRIfW3iLytKCXCD0GZRHaCjpB\nvRaI0/GOe7cAJ0p6Ep/IaF8ziyHmgrIYkO4kzYMXHs42s78AmNnTme2nAZem1YF0dH0i19E16H0i\nTwt6idBjH9Cnk8GGtoLSqVeAGAl8zMzelfQefIzzlcwsPpbaQJ9mSp2gZd2lvginA5PM7NhM+FKZ\nsfq3Ae5N/9vW0XVgpxx0iMjTgl4i9BiURWgrKJ16BYg3k/8cZvaGpP+E+IIOMBDdbQh8E5go6a4U\ntj+wg6S18OHrJuOdyTCz+yRVOrq+zZwdXccB8+OdXLMdXf+QOrq+gI/iFPQHkacFvUToMRF9C9tO\naCsonXoFiFUl3ZP+C1gprQufM+ljpVsXDEVa1p2ZXZ/i5bmszj5t6+ga9DyRpwW9ROgxKIvQVlA6\n9QoQq3XMiqAQQ8TtKXQXlEVoK+glQo99Rh+9g0NbQenULECY2WOdNCQIIHQXlEdoK+glQo9BWYS2\ngk5QZCK5IAiCIAiCIAgCIAoQQRAEQRAEQRA0QcOZqIcqMSpEEARBEATV6KP+EEFQCi21QEga02Y7\ngqAhoTtH48fPXIL2ENoKeonQY1AWoa2gXbTaAnF7W60YZDT7YZePH7UZNQndBWUR2gp6iUGvx6gA\n6RqDXltBZ2ipAGFml7TbkCBoxGDUXRQee4PBqK2gfwk9BmUR2graRcMChKQVgB8BI7LxzezL5Zk1\nOIkal+KE7ponfHKLEdoKeonQY1AWoa2gTIq0QFwInA5cArxbrjlBMJPQXVAWoa2glwg9BmUR2gpK\no0gB4g0zO7F0S4KZRE0yMER11617P8Q0NyS1FfQsocc+p4ddUUNbQWkUKUCcIOlg4P+AGZVAM7uj\nNKuCIHRXCkOsoFCL0FbQS4Qeg7IIbQWlUaQA8VHgm8BnmNUEZml9UBF9FHqKIaO7fmEQFT5CW0Ev\nEXocZPRQXhnaCkqjSAHiq8CKZvZm2cYEQYbQXZ/QQy/LooS2gl4i9BiURWgrKI0iBYh7gfcBz5Rs\nS9AiZX/AdekDMXQ3iOgxH+HQVtBLDAk9Rgt/VxgS2gq6Q5ECxPuAByTdyuw+dDEMWA/Sh7XBtRi0\nuosXadcZtNoK+pLQ4yCmy+/k0FZQGkUKEAeXbkUQzEnobojQhRdsaCvoJUKPQVmEtoLSaFiAMLN/\ndMKQoDpDtbY6dBeURWgr6CVCj0FZhLaCMikyE/V0vNc+wLzAPMCrZrZwmYZ1iqHygV6rlrdXXZ4G\nu+6C7hHaCnqJ0GNQFqGtoEyKtEC8t/JfkoCtgFFlGtUMkrYATgDmAn5nZkd22aSOMNgLPqG7oCx6\nXVu1CM0NTnpdjwPR3WB/T/U6va6tWkRe1x8MayayORcCm5dkT1NImgv4X+DzwOrADpJW765VQbsJ\n3QVl0WvaqkVobmjQa3oM3Q0eek1btQjN9Q9FXJi2zawOA0YCb5RmUXOsBzxiZv8BkHQuXsK+v6tW\n9Qj9XPsTugvKose1VYvQ3CClx/UYumsjtd7JZbkP97i2atFWzfWqm/ZgoMgoTF/K/H8bmIzfzF5g\naeDxzPoTwPpdsqVvqJWJ9ViBI3QXlEUva6sWobnBSy/rMXTXAUr8yO1lbdWiNM1FYaK9yMwax+pR\nJG0HbGFm307r3wTWN7Mf5uLtDuyeVlcBHgSGA8910Nx20Is2L29mi3XbiE4yQN1Bb97HXid/zYaU\n7tqgOegv3fWirUNKcxB5Hb1h/5DS3RDM64rSyXMqpLmaLRCSDqqzn5nZoS2Z1V6mAMtm1pdJYbNh\nZqcCp2bDJN1mZiPLNa+99KPNzTLYdQdD4z62m3Zcsz7RVi0GpDnoL931k62t0id6HNJ5Xb/a3yfa\nqsWQyuuK0ovnVK8T9atVFoDdgJ+VbFdRbgVWlrSCpHmB7YGLu2xTMDBCd0FZ9IO2ahGaG3z0gx5D\nd/1JP2irFqG5PqFmC4SZHVP5L+m9wJ7ArsC5wDG19uskZva2pB8CV+LDfZ1hZvd12axgAITugrLo\nB23VIjQ3+OgHPYbu+pN+0FYtQnP9Q91O1JLeD+wF7AiMB9Yxsxc7YVhRzOwy4LIWdq3a9NXj9KPN\nTTPIdQdD5D62mbZcs37QVi0GqDnoL931k60t0w96HOJ5Xd/a3w/aqsUQy+uK0nPnVLMTtaRfAdvi\nRv+vmb3SScOCoUnoLiiL0FbQS4Qeg7IIbQWdoF4B4l1gBj70VzaS8E44MRV60HZCd0FZhLaCXiL0\nGJRFaCvoBDU7UZvZMDOb38zea2YLZ5b39rv4JG0h6UFJj0jat8PHXlbStZLul3SfpD1T+PslXSXp\n4fS7aGaf/ZKtD0raPBO+rqSJaduJaap6JM0n6bwUfrOkEZ08x4EwmHUH3dVePyDpDEnPSLo3E1bz\n2WiGwa6tCo00JufEtP0eSev0qJ2bSHpZ0l1pqTeyTN8x2PXYb3ldnXfzGElTMjr8QrdtbcRg11Yt\n+k1z1WjlG7FrmNmQWvBOOf8GVgTmBe4GVu/g8ZfCfREB3gs8hE/XfjSwbwrfFzgq/V892TgfsEKy\nfa607RZgFF6rcDnw+RT+A+C36f/2wHndvu6xdF97/bAAGwPrAPdmwqo+G7FUvX4NNQZ8IeUXSvnH\nzT1q5ybApd2+prGUc397banzbh4D7NNt+2JpeP/6TnM1zqOpb8RuLvWGcR2szJwm3czexEcl6NjM\njGY21czuSP+nA5PwmRe3wjs6kX63Tv+3As41sxlm9ijwCLCepKWAhc3sJnNFnZnbp5LWBcCmldaJ\noKt0VXv9gJldB7yQC671bARzUkRjWwFnmnMT8L6Un/SanUH/0nf3t867OegP+k5z1WjhG7FrDMUC\nRLVp0ruSSSTXorWBm4ElzGxq2vQUsET6X8vepdP/fPhs+5jZ28DLwAfafgJBs/SM9vqMWs9GMCdF\nNNYLOixqwyeSm9XlktbojGlBG+gFjbVM7t0M8KOkwzN6wnUkqEZfa64aBb8Ru8ZQLED0BJIWAv4M\n/MTMpmW3pRaF6r3bg2AIE8/GkOIOYDkz+xjwG+DCLtsTDAGqvJtPxt1i1gKm0uPzKASDg374RhyK\nBYhC06SXiaR5cGGcbWZ/ScFPV9wI0u8zKbyWvVPS/3z4bPtImhtYBHi+/WcSNEnXtden1Ho2gjkp\norFe0GFDG8xsmqXhJ83HhZ9H0vDOmRgMgF7QWNNUezeb2dNm9o6ZvQuchrvKBL1HX2quGk1+I3aN\noViA6Oo06akvwunAJDM7NrPpYmCX9H8X4KJM+PZpZKUVgJWBW1JT1jRJo1KaO+f2qaS1HXBNKrEG\n3aWr2utjaj0bwZwU0djFwM5pNKZRwMuZpvGesVPSkpmR5dbD31dREdIf9F1eV+vdnOsftA1wb37f\noCfoO81Vo4VvxK5RdybqwYh1f5r0DYFvAhMl3ZXC9geOBM6XtBvwGPC1ZO99ks4H7sfHdN7DzN5J\n+/0AGAfMj4+qcnkKPx34g6RH8A6p25d9UkFjekB7PY+kc/DRd4ZLegI4mBrPRjAntTQm6Xtp+2/x\nGV6/gA/I8Bqwa4/auR3wfUlvA68D20dFSH/Qp3ldrXfzDpLWwl1GJgPf7Y55QT36VHPVaOobsZvU\nnEguCIIgCIIgCIIgz1B0YQqCIAiCIAiCoEWiABEEQRAEQRAEQWGiABEEQRAEQRAEQWGiABEEQRAE\nQRAEQWGiABEEQRAMWtLswc9Iajj8pqTjJN2VlockvdQJG4MgCPqNKECUjKRrJW2eC/uJpJPr7PNK\n+ZYFg5XQXNANelh344AtikQ0s/8xs7XMbC189uu/NNon6B49rLlgEBO6c6IAUT7nMOc8DNun8CAo\ng9Bc0A16Undmdh0+H85MJK0k6QpJt0v6p6RVq+y6A/HM9Do9qblg0BO6IwoQneACYMs0MyKSRgAf\nBO6U9HdJd0iaKGmr/I6SNpF0aWZ9rKTR6f+6kv6RXoBX5mbLDIY2obmgG/ST7k4FfmRm6wL7ACfl\n7FkeWAG4pg3HCsqjnzQXDB5Cd0QBonTM7AXgFuDzKWh74Hx8ZtVtzGwd4NPAMZJUJE1J8+DN69ul\nF+AZwGHttj3oT0JzQTfoF91JWgj4BPAn+UyvpwD5F/X2wAVm9s5AjhWUS79oLhhchO6cubttwBCh\n0tx1UfrdDRBwuKSNgXeBpYElgKcKpLcK8BHgqqTNuYCp7Tc76GNCc0E36AfdDQNeSv0carE9sMcA\njxN0hn7QXDD4GPK6iwJEZ7gIOE7SOsACZnZ7arJaDFjXzN6SNBl4T26/t5m9laiyXcB9ZrZBuWYH\nfUxoLugGPa87M5sm6VFJXzWzP6Uawo+Z2d0AqT/EosCN7TpmUCo9r7lgUDLkdRcuTB3AzF4BrsWb\npCqdbBYBnkki+zSwfJVdHwNWlzSfpPcBm6bwB4HFJG0A3vQlaY1STyLoK0JzQTfoRd1JOgcvDKwi\n6QlJuwE7ArtJuhu4D8j6Km8PnGtm1sxxgu7Qi5oLBj+hu2iB6CTnAH9lVs/9s4FLJE0EbgMeyO9g\nZo9LOh+4F3gUuDOFvylpO+BESYvg9/F4/EUYBBVCc0E36CndmdkONTZVHdrVzMYUTTvoGXpKc8GQ\nYUjrTlHJEgRBEARBEARBUcKFKQiCIAiCIAiCwkQBIgiCIAiCIAiCwkQBIgiCIAiCIAiCwkQBIgiC\nIAiCIAiCwkQBIgiCIAiCIAiCwkQBIgiCIAiCIAiCwkQBIgiCIAiCIAiCwkQBIgiCIAiCIAiCwkQB\nIgiCIAiCIAiCwkQBIgiCIAiCIAiCwkQBIgiCIAiCIAiCwkQBIgiCIAiCIAiCwkQBogCSJkgaW2t9\nMCBpsqR9aq0Hc9INHUgySdt18pjdRNI4SZd2244gCPqDoZZHBv1DM++zdnxfSBqRnoeRA0mnFn1d\ngEg3Y0wXDr0tsF+7EuvRj/WPAye1M0FJoyVNaGeanSanubbqoNO0mrlIWljSiZKelDRD0iOSvtYN\nWwqku4mkye1MczDQxbyzZ0n6G9FtO4YSQ0mHoa/uMZR0Vo/Ke7Zd6c3droQGI5LmNbM38+Fm9kI3\n7GmVWudRDzN7tix7Bgv9poN2IGke4CrgBeBrwBPAMsCMbtoVBEFQBpLmMbO3um1HUA6tfB8FTl+3\nQGSRNK+koyQ9Iek1SbdK2jyzfS5Jp0t6VNLrkh6W9FNJwzJxxkm6VNLPJD2BfxxVO1bepanRsefJ\n1dg+LunISlrA8sCvUg1FodKhpFGSrpH0qqSX0/8PZuw7WdKvJT0L/Kupi0lVlyaTtLukP6Vj/kfS\nTrl9lpZ0rqQX0/I3SSs3e+x+oYoOJks6UNIpkqYlPfy/3D4fSvu9IelBSV+U9Iqk0S3acGRK5/V0\n/KMlvSezfVlJF0l6IWnzAUnbp82Ppt9b0/2dUOCQuwKLAVuZ2fVmNjn93pq7LidLOiYd91lJe0qa\nT9L/SnpJ0n8lfTOTbl1b0v5Tkq5+L2mBzLaNJd2UruPLkm6R9JHCF3GII2lRSePTtX1d0tWS1shs\nH52u7ZckPZS0e62kFXPpfEnS7Wn7o5IOkzRvZnvD56OBnXtJuiflP1Mk/U7S+3Jx6uWLkrS3PO+f\nkY5/ROtXLmgnSR9jJJ2V9PaUci3zquKelH9XJZZM75/XJD2Wf1fVsaHSErpD0s7rwHfTtk9I+kdK\nc0rK4xbO7Bv66gMyOjtD0kvA2Sm85veLpA8nXXw0l9bukp6TV6xV3kU3pzzwaUnHZfPAFphb0gkZ\nm36l2b9Zd5J/b06X9Iz8+2zpARyvKQZNAQL4PfAp4BvAR4DxwCWS1kzbhwFT8FrT1YADgP3xD6Is\nnwI+BmwBbNqmY/8Y2AbYHlgZ+DrwYNq2LV5QOQRYKi11SeleCzwCbAisD5zD7C1KOwECPgnsXPA8\nGnEQcBGwJnAecIak5ZJNCySb3sCvxQbAVODq7MfeEOB/gInAOsBRwNGSNgBID/5fcS1uAHwLGAPM\nN4DjvZrSWQ34Aa6xAzLbTwIWAD4NrAH8BHgpbVsv/W6B627bAsfbGi+Q/ia94O9PmfE8uXg7AtNx\nbR4JHA9cCDwEjMSfkd9Jqui9ni2fxJ+rz+LPzjbAngCS5sY1eT2uy/XTsd4pcC6BMw6/blvh9+E1\n4ApJ82fizAccjOeXGwBzAX+RJAB5hcnZwFhcZ98CtgMOzx2r5vNRgHdx/a6B57XrAb+pbCyQLx4O\n/Bw4Algd19h/Cx476Ax7AZNwfRwMHC6pSL6U5xfAxcBawKnAmWrOPfIIPO9cHbgwfTj+X0pzTVw7\nawFnZPYJffUPewEP4O+i/Rt9v5jZQ8Ct+Hsty47A+Wb2Vvpwvxy4E1gb2A3YAddDq+zIrO+F7wK7\n43lghXnx52RN4IvAcDzP6wxm1vcLsBL+clkuF34hcFKd/Y4Ers6sjwOeBebLxZsAjK22XuTYwInA\n3wHVsGMysE8T53s2cGOd7ROAe5q8hrPZUGXdgCMy63PjHxo7pfVvAQ9nzxH/yHge+Fq3NVKS7vK6\nmAyck4vzMHBg+v85/MN2ucz2jdK1HV3wmAZsV2f794BHMuv3AAfXiDsipTeyiXN+AM9kzwDWBb4C\nPAX8OnddbsysKz1XF2fC5gHerJxLLVvSM/k4MFcm7LTKcwu8P+33qW7roR8XvELDgI0zYYsALwPf\nTuujU5wNM3GWT1r+bFq/Dvh5Lu2tgVcqeUKj56MF27fAXeeGpfWa+SKwUNLt97p9zWOpeT8nA1fl\nwn4HXJ9ZnyP/o/q76rRcnKuBswrYUMmH9s6FnwmcngtbK8VdPPTVP0vSyyW5sIbfL3hF8GOZ/Gw5\n/NvvE2n9sJTGsEwao1MetUBaHwdcWtDOCXiFW9amA4En6uyzatLkMmm9oufC7/hmlsHSArEO/pFy\nf2r6fEXSK8CW+Ac+AJK+J+k2uUvFK3ht2HK5tO41s2b8uYscexye2Twkd+HYMtsM1QJrA9c0iHP7\nANKvxT2VP2b2Nv5RuHgKWhdYAZieuQYvA4uSuQdDgHty608y6xqtBkwxs2yt1M14JtQSkraTdH1q\nDXgFOI7ZNX0CcKCkGyX9UtK6rR4rMQx4BviOmd1uZn/GW6a+X6mNTmS1YmmfiZmwt4AXmXVt6nG/\nmWVbFGZeU/N+KOOAK1OT816VVrGgEKvh+ruxEmBmL+P3avVMvHeBWzJxHsPvQyXOusABuTzwj8CC\nwJKZdOo9H3WR9BlJVyXXkOnAX/AauEr69fLF1fFWlL8XOVbQNW6ssr56tYglp3Nbbn1dYKecviuu\nwSsR+uo3qt3fRt8v5wIfxFvEwVsXHjWzG9L6asBNZpZ9n1+P51EfatHOm9L7s8KNwNIV1zlJ68hd\nlB9LeWLlvDryDhwsnaiH4aWsjwP5zk6vA0j6Ou7asA9wAzAN2AN3h8jyaruPbWZ3yEdf2Bx3ixoP\n3C1ps5zY2kmz51GE/PkZs9zghgF34S40eYZSZ+N616itSBqFZ2q/wAvDLwFfBn498+Bmp0u6EvgC\n7gJ0g6QjzGxMi4edCryV+6CfhLtJDccLlVD9OrR6beruZ2a7Sjoer5H+MnCYpK3N7MoCaQe1yffH\nqtc/axiuwz9V2ZYdkKElDUhaHvgb3vp0EF4zuA7eXD8QH+OgvzC8wi5L3n2yHeTfn8Pw1pDjqsSd\nAny0SnjQu1S7v3W/X8zsGUlX4W5F16Xfswser20jH1WQtCBwJd669k28km448E86lCcOlgLEnXim\nsqSZXVsjzkbAzWaW7fTajprxIsfGzKYDFwAXSBoH3ISXSh/CXTnmavKYn2nV4JK4Ay+RP2dmLzWK\nPESZhNceLGtmj6ew9Wi9gLEh3qJxaCUgfWjNhpk9gfsBnyrpZ3j/gTG47qA57f0L+IakYZnC74dx\nd7bnmj6DWbRiy0zM7G7gbuAoSZcDu+CZa1CfSczysb0OfJhe/IPo95l4w3Ct3pDiLIfXxk1K2+8A\nVjWzR0qycyT+UvyfSuFV0hdzcerli5NwV4JNcTeDoDcZVWV9Umb9WTL9BCUtQfV+g6OYvX9CPp1m\nuURkSRoAACAASURBVANYo5a+JYW++pui3y9nAWMlnYrnkdkO/ZOAr+XejRvh77Z/t2jX+pKUaYUY\nBTxpZtOSN8FwYH8zexSgxf5CLTMoXJjMO7icDYxLLh0rShopaZ/MBX0IWEfS5yWtLOnneGeZ0o+d\n3Cp2kLSapA/hHQCnMWuUp8nAJ+WjAAwvcNhfAWtLOlXSmpJWkfTtLrtunA08DVwk6VOSVpCPSHCM\nBvFITE1yNd6H4ExJa6XOo8cBb7eY3kN4gWTHpLvv45ngTOQjOGyRtq+F19LfnzY/g7eSbS5pCUmL\nFDjmyXi/gxOS7jbHa55PyjW1NksrtpB0dqR8hJTlJX0aHwTh/kb7BmBmD+Od0E+R9El5Z9Gz8Pzp\nj5mobwPHS9og6Wg8cB+uafBBIL4h6RBJH5G0asoPj26TqQ/j76ufpHu+A7N3JoQ6+WKqwDkBOELS\nrpJWkrReemaC3mGUpP3SO/o7+AAg2Vr/a4A90jt2bdx98Y0q6Wwr6Tspnf3wD/vjB2DXUcB6kn4r\naW35aHpflHQKzKwgDH31L0W/Xy7EW7xOB25N338VTsIrVU5K33pb4v1sx5rZay3a9UE8311FPvrY\n/2PW8/BfvND6w/R+3xI4tEY6pTAoChCJXfEas6Pxj7RLgY3xTi8ApwDn4y/FW/HOJcd06NjT8Rt/\nC17SXQv4fEZUBwHL4qXUhvMvmNlduDvKqnhLxs1401vXxqpO57Ix8B/cjeEB/CNjUdzXfciTaiW2\nwZ+7m/GOeb+kxTkUzOwS/KPpeNy3fDNcS1mG4SPV3I/P3/A0Xjtf6cfyY+DbuC/6RQWO+TjeGXxd\nvMn3t3hN3wH19iuQbtO2JF7DW0D+hBeoxuMvg6MGYs8QY1c8b7o4/S4AbGFmr2fizMA7CZ6Ja3cY\nsG2l0JjcxbbER/u6JS370qZRaMzsHrzlbC9cy9/G3VGzcRrli/vhuvg5Xlv4Z3wOk6B3OBavALgT\nzxsPMrMLMtv3xt8xE/AW/d/hlQ95xuADPNwDfB/Y1TJDTTdL0t/G+HfDP/DWziPw/LRC6KtPKfr9\nkuL9FR/16KxcGlOAz+N9se7C34vn4KN9tsrZeKv8zbj75umkAoT5XF274INV3I+PxrTXAI7VNBpY\npWEQBANF3mHrh2Y2rtu2BEEe+RwlY81soW7bEgxe5DPGjzWzXzeKGwRB9ynSeW3D1FmjMmnFsdX8\nrIOgnYTugk4TmgvKIrQVdIPQXVAmRVyYTgZek0/SszfuZnNmqVYNcSRdrsxwcbml6eaw5CNfK737\nyjiHNjDkdCdp/zr36fIO2VDr+K9I+mTjFPqaIae5XqFP86hmCG31AL2Qx3aY0F0PImm5Bu/avhiK\nvKELk6Q7zGwdSQfhI76cXgnrjIlDD/mMhvPX2PxCGvu+mfTeCyxRY/NbaUz3nmIo6k7S+/EOytV4\nPflYlm1DvfGqp+T84gcVQ1FzvUI/5lHNENrqDXohj+0kobveRNLceH+aWkxO/QJ7miLDuE6Xj2Kw\nE7CxfAK0MsZdDhLtzsTSCBHT25lmBxhyuksFw67OmVHiMJz9wJDTXK/Qp3lUM4S2eoBeyGM7TOiu\nB0mFg75/1xZxYfo6PgLHbmb2FD6qwK9KtSoIQndB5wnNBWUR2gq6QeguKI0hNwrT8OHDbcSIEd02\nY9Bw++23P2dmi3Xbjl4ndNdeQneNCc21l9BcMUJ37SV015jQXHspqrmaLkySplNn+m0zW7hF27rK\niBEjuO2227ptxqBBUlt9k0N3QRHaqbvQXFCEVjQ3WLVVj9BdewndNSY0116Kaq5mAcLM3psSOhSY\nCvwBELAj1aeOD4IBE7oLOk1oLiiL0FbQDUJ3QSco0on6y2a2Zmb9ZEl3M+eMt0GPofHjZ/63XXbp\noiUtMWh11+f3ZTAzaDVXj9BjRwhthba6QegudFcaRTpRv5rG6J5L0jBJOwKvlm1YMOQJ3QWdJjQX\nlEVoK+gGobugNIq0QHwDOCEtBvwrhQV9RK0SeQ+X1EN3QacJzQVlEdoKukHoLiiNugUISXMB25jZ\nVh2yJwhCd0HHCc0FZRHaCrpB6C4om7ouTGb2DrBDh2wJAiB0F3Se0Jyj8eNnLkF7CG0F3SB0F5RN\nERemf0kaC5xHxnfOzO4ozaqgVPrk4yB0F3Sa0FxQFqGtoBuE7oLSKFKAWCv9HpIJM+Az7TcnCGYS\nugs6TWguKIvQVtANQndBaTQsQJjZpzthSBBkCd0FnSY0F5RFaCvoBqG7oEwaDuMqaRFJx0q6LS3H\nSFqkwH7LSrpW0v2S7pO0Zwp/v6SrJD2cfhfN7LOfpEckPShp80z4upImpm0nSlIKn0/SeSn8Zkkj\nWrkIQe/Rqu6CoFVCc0FZhLaCbhC6C8qkyDwQZwDTga+lZRrw+wL7vQ3sbWarA6OA/9/encdNUtX3\nHv98GRYF2UECIziASNhkmyBeEEFkM0bcwCEEB+VKEtHgNb6uaIyQAF40ARVxIzIy5rJIVDYvSwYE\njEZhBhxhhkUGGMKMyLDpAGFx4Hf/OKehpnm6n3q6u7q6n/6+X69+PV1VXdWnu79PVZ8+51QdJ2l7\n4ATg2ojYBrg2T5OXzQB2AA4Gvp7PIgDwDeDDwDb5dnCefwzweES8DvgS8IUS5bLh0GnuzDrlzFlV\nnC2rg3NnlSkzBmLriHhvYfofJM0fb6WIeJB0CXUi4glJdwBTgUOBffPDZgPXA5/K8y+MiGeB+yQt\nAvaQtBhYJyJ+ASDpu8C7gCvzOiflbX0fOEuSIiJKvC4bbB3lzqwLzpxVxdmyOjh3VpkyLRBPS9q7\nMSFpL+DpiTxJ7lq0K3AjsEmuXAD8Ftgk358KPFBYbUmeNzXfb56/0joRsQL4PbDhRMpmA6vr3JlN\nkDNnVXG2rA7OnVWmTAvEXwOzC/3mHgeOLvsEkl4F/AD4eEQsz8MXAIiIkFR5a4GkY4FjAbbYYouq\nn856o6vcmXXAmbOqdJQtSbOAdwDLImLHPG8D0mk5pwGLgcMj4vG87NOkrr3PA38TEVfn+bsD5wKv\nBK4Ajs/H3zWA7wK7A48C74+IxXmdmcBnc1FOiYihOP+3rcT7NKtMmbMwzQd2lrROnl5eduOSViNV\nHs6LiB/m2Q9J2jQiHpS0KbAsz18KbF5Y/TV53tJ8v3l+cZ0lklYF1iXtBJtfw9nA2QDTp09396Yh\n0E3uzDrhzFlVusjWucBZpC/5DY1xhKdJOiFPf6ppHOFmwDWSXp8vKNYYR3gjqQJxMKkb8IvjCCXN\nII0jfH+upJwITCed9vNmSZc1Kio2HLxPsyqVOQvT5yWtFxHLcwvC+pJOKbGegHOAOyLijMKiy4CZ\n+f5M4NLC/Bn5zEpbkgZL35S7Oy2XtGfe5gea1mls633Ajz3+YXLoNHdmnXLmrCqdZisifgI81jT7\nUNL4QfLfdxXmXxgRz0bEfUBjHOGm5HGE+fj43aZ1Gtv6PrB/Ps4eBMyJiMdypWEOL528xIaE92lW\npTJjIA6JiN81JvLO5O0l1tsLOAp4q6T5+fZ24DTgAEl3A2/L00TEQuAi4HbgKuC4/MsJwEeAb5N2\niPeQfjmBVEHZMA+4/gT5jE42KXSaO7NOOXNWlV5mqx/jCFtty4aL92lWmTJjIKZIWiOfHQlJrwTW\nGG+liPgpoBaL92+xzqnAqWPMnwfsOMb8Z4DDxiuLDaWOcmfWBWfOqlJJtvo1jnA8Hmc4sLxPs8qU\naYE4D7hW0jGSjiE1ZXowlVXNubN+c+asKr3M1kO5WxI9HEdI0zjCVtsaU0ScHRHTI2L6xhtv3OHL\nsgp0lDtJsyQtk7SgMK8vFwGWNDM/x915IL8NqHErEBHxBeAUYLt8Ozkivlh1wWy0dZM7SYvzTmu+\npHl5nq+Abm15X2dV6XG2+jGO8GrgwNxnfn3gwDzPhkgXuTuXl495qfwiwIXB+28E9gBOLB6rbbCU\n6cIEcAewIiKukbSmpLUj4okqC2ZGd7nbLyIeKUxXfuaS7l+uDQDv66wqE86WpAtIF17dSNIS0per\n04CL8i/K95OuMExELJTUGEe4gpePIzyXdBrXK1l5HOG/5nGEj5H2hUTEY5JOBubmx/1jRDQP5rbh\nMOHcRcRPxvhhrPKLAFMYvJ/XaQzev6CjV26VKnMWpg+TPuBv5VlTgUuqLJRZBbnrx5lLbIh1mjlJ\nm0u6TtLtkhZKOj7PP0nSUq18EonGOm71GiGdZisijoiITSNitYh4TUScExGPRsT+EbFNRLyt+MU+\nIk6NiK0jYtuIuLIwf15E7JiXfbRxtsKIeCYiDouI10XEHhFxb2GdWXn+6yLiO716L6x/enwcHajB\n+5KOlTRP0ryHH364w5dk3SgzBuI40hmVlgNExN3Aq6sslBnd5S5ILQk3Kw3ugxqugO4d3NDpNHMr\ngL+NiO2BPYHjcssWwJciYpd8uwJ62+RvQ8PHUatDJbnLFdBaB+973E39ylQgno2I5xoTSgOtaj/r\ng0163eRu74jYBTiE9GVun+LCfu38vIMbOh1lLiIejIhb8v0nSF0G2p3y0q1eo8fHUatDL3M3cIP3\nrV5lKhA3SPoM8EpJBwD/BlxebbHMOs9dRCzNf5cBF5MGY/Vj59dTmj37xZv1Rdf7uty1aFfSuBmA\nj0m6VemsJo3BgJW1evWS89dTPo5aHXqZOw/et5WUqUCcADwM3Ab8JWkw6WerLJQZHeZO0lqS1m7c\nJ+2AFuAroNv4utrXSXoV8APg4xGxnNQdaStgF+BB4PReF3iMMrjb3GDycdTq0Olx9ALg58C2kpbk\nAfuVXwQ4j+dpDN6fiwfvD7Rxz8IUES8A/5JvAEjaC/hZheWyEddF7jYBLs69O1YFzo+IqyTNpeIz\nl9hw62ZfJ2k1UuXhvIj4Yd7eQ4Xl/wL8KE920+q1pF2rV0ScDZwNMH36dFdqB4SPo1aHTnMXEUe0\nWFT5RYAjYhYwq135bDC0rEDkQX2Hk5rOr4qIBZLeAXyG9IVq1/4U0SZi2LsbdJu7fBaRnceY/yi+\nArqNodvM5Raqc4A7IuKMwvxNCwP3301qCYPUgnW+pDNIpw5utHo9L2m5pD1JXaA+AHy1sM5M0q+C\nXbd6Dft+Ylj4OGp1cO6sH9q1QJxD+sXrJuBMSb8BpgMnRIRP42pVce6s37rN3F7AUcBtkubneZ8B\njpC0C2nQ4mJSFwK3eo0W78+sDs6dVa5dBWI68IaIeEHSK0invtw6/5JrVhXnzvqtq8xFxE+Bsc6I\ndEWbddzqNRq8P7M6OHdWuXaDqJ/L/ecaB697HT7rA+fO+s2Zs6o4W1YH584q164F4o8l3ZrvC9g6\nT4t0Kv03VF46G0XOnfWbM2dVcbasDs6dVa5dBWK7vpXCBkJxYGXMnNnmkZVy7qzfnDmrirNldXDu\nrHItKxARcX8/C2IGzp31nzNnVXG2rA7OnfXDuNeBsME0IK0FZmZmZjZiylyJ2szMzMzMDHAFwszM\nzMzMJqCjCoSkk3pcDuuCZs9+8TaZOXfWb86cVcXZsjo4d9YrnY6BuLmnpTArx7mzfnPm8Jirijhb\nVgfnznqioxaIiLi81wUxG49zZ/3mzFlVnC2rg3NnvTJuC4SkLYGPAdOKj4+Id1ZXLBt1zp31mzNn\nVXG2rA7OnVWpTBemS4BzgMuBF6otjtmLnLsS3LWkp5w5q4qzZXVw7qwyZSoQz0TEmZWXxGxlzp31\nmzNnVXG2rA7OnVWmTAXiK5JOBP4deLYxMyJuqaxUZs6d9Z8zZ1VxtqwOzp1VpkwFYifgKOCtvNQE\nFnnarCrOnfWbM2dVcbasDs6dVaZMBeIwYKuIeK7qwlh7k/06D02cO+s3Z86q4mxZHZw7q0yZCsQC\nYD1gWcVlsQEyAINznTvrN2fOquJsWR2cO6tMmQrEesCdkuaych86nwbMquTcWb85cyUMwI8Lw8jZ\nsjo4d1aZMhWIEysvha1kxLoqteLcWb85c1aVkc9W83HNlc++GPncWXXGrUBExA39KIhZkXPXO/7F\nuBxnzqribFkdnDurUpkrUT9BGrUPsDqwGvBURKxTZcFstDl31m/OnFXF2bI6OHdWpTItEGs37ksS\ncCiwZ5WFMnPurN+cOauKs2V1cO6sSqtM5MGRXAIcVFF5JkzSwZLukrRI0gl1l2cy0uzZL97q4NxZ\nvzlz5dS9bxhGg5itdgYxdzZxw5Q7Z244lOnC9J7C5CrAdOCZyko0AZKmAF8DDgCWAHMlXRYRt9db\nsrH5IFuec2f9Ntkz5/1PfQY5W+14XzfchjF3ztzwKHMWpj8r3F8BLCY1gw2CPYBFEXEvgKQLSWUb\nmKD5oN0x566PPNAacOa64gy1NcjZamfgc2dtDWPunLkhoYgY/1EDStL7gIMj4n/m6aOAN0bER5se\ndyxwbJ7cFrirrwVd2UbAI5Po+V8bERv3cHsDrwe5qzsD3RqE8o9U7nq0rxuEz60X6nodI5U58L6u\niXPXByO6rxu08pbKXMsWCEmfa7NeRMTJHRWrBhFxNnB23eUAkDQvIqaP6vOPZxRyN+ifwXiGvfzN\nRiFzMHk+t2F6HZMpW+1M1n1d0TC9llHI3WTa1w1beRvadWF6aox5awHHABsCgxDApcDmhenX5Hk2\nvJw76zdnzqoyDNlqx7kbTsOcO2duSLSsQETE6Y37ktYGjgc+CFwInN5qvT6bC2wjaUtSwGYAf15v\nkawbzp31mzNnVRmSbLXj3A2hIc+dMzck2g6ilrQB8AngSGA2sFtEPN6PgpURESskfRS4GpgCzIqI\nhTUXazx1d6Wq+/nHNQK5G/jPYBzDXv6XGYHMweT53IbqdQx6ttrxvm4lQ/VahjV3I7qvG7byAm0G\nUUv6J+A9pBf2tYh4sp8Fs9Hk3Fm/OXNWFWfL6uDcWT+0q0C8ADxLOvVX8UEiDcLxpdCt55w76zdn\nzqribFkdnDvrh6E+jauZmZmZmfXXKnUXYNhJmiVpmaQFhXk7S/q5pNskXS5pnTz/SEnzC7cXJO2S\nl12fL93eWPbqks+/uaTrJN0uaaGk4/P8DSTNkXR3/rt+YZ1P50vE3yXpoML83XOZF0k6U5J69T5Z\nIung/L4vknRC3eWZKEmLc0bmS5pXd3lsfJMxc+32bzYYhjl3LY7rztyA60fmJpqNiX7fkrSGpO/l\n+TdKmlZYZ2Z+jrslzSzM3zI/dlFed/UqXvvLRIRvXdyAfYDdgAWFeXOBt+T7HwJOHmO9nYB7CtPX\nA9M7eP5NSYOjANYGfg1sD3wROCHPPwH4Qr6/PfArYA1gS+AeYEpedhOwJ6mZ80rgkLrf38l0Iw0I\nuwfYClg9fw7b112uCb6GxcBGdZfDt9Kf16TMXKv9m2+DcRv23LU4rjtzA3zrV+Ymko1Ovm8BHwG+\nme/PAL6X728A3Jv/rp/vr5+XXQTMyPe/Cfx1P95zt0B0KSJ+AjzWNPv1wE/y/TnAe8dY9QjSKdW6\nff4HI+KWfP8J4A5gKunS77Pzw2YD78r3DwUujIhnI+I+YBGwh6RNgXUi4heRUvjdwjrWG3sAiyLi\n3oh4jvT5H1pzmWxym6yZa7V/s8Ew1LlrcVx35gZbXzI3wWx08n2ruK3vA/vn1omDgDkR8Viks2nN\nAQ7Oy96aH9v8/JVyBaIaC3kpuIex8kVRGt4PXNA0b3Zupv/7TroP5aauXYEbgU0i4sG86LfAJvn+\nVOCBwmpL8ryp+X7zfOudVu/9MAngGkk3Szq27sLYuCZr5lrt32wwTIbcNXPmBludmevl960X14mI\nFcDvSRf/a7WtDYHf5cc2b6tSrkBU40PARyTdTOpW9FxxoaQ3Av8dEQsKs4+MiB2AN+fbURN5Qkmv\nAn4AfDwilheX5RquR8tbL+wdEbsAhwDHSdqn7gLZpNc2c96/Wb85c9bKKGXDFYgKRMSdEXFgROxO\namW4p+khM2hqfYiIpfnvE8D5pOa4UiStRqo8nBcRP8yzH8rNZOS/y/L8VpeJX5rvN8+33mn13g+N\nQk6XARczgZxaLSZr5lrt32wwDH3uxuDMDbY6M9fL71svriNpVWBd4NE223oUWC8/tnlblXIFogKN\nMyhJWgX4LGlQC4V5h1MY/yBpVUkb5furAe8Aiq0T7Z5LwDnAHRFxRmHRZUBjlP5M4NLC/Bl5pP+W\nwDbATbn5bbmkPfM2P1BYx3pjLrBNPmPC6qSK5GU1l6k0SWtJWrtxHziQkjm12kzWzLXav9lgGOrc\nteDMDbY6M9fL71vFbb0P+HFu1bgaOFDS+vksTwcCV+dl1+XHNj9/tfoxUnsy30gtCQ8CfyD1PTsG\nOJ50NqRfA6eRr7eRH78v8IumbawF3AzcSho/8RXySP0Sz783qbnsVmB+vr2d1C/uWuBu4Bpgg8I6\nf0dqFbmLwpmWgOmkg/M9wFnFcvvWs7y8PefiHuDv6i7PBMu+FemMEr/KOR2q8o/qbTJmrt3+zbfB\nuA157sY6rjtzA37rR+Ymmo2Jft8CXgH8G2nA9U3AVoV1PpTnLwI+WJi/VX7sorzuGv14v30hOTMz\nMzMzK81dmMzMzMzMrDRXIMzMzMzMrDRXIMzMzMzMrDRXIMzMzMzMrDRXIMzMzMzMrDRXICom6TpJ\nBzXN+7ikb7RZ58nqS2aTlTNndXDurN+cOauDc5e4AlG9C0gXNCl62ZWozXrImbM6OHfWb86c1cG5\nwxWIfvg+8Kf5yohImgZsBvxS0rWSbpF0m6RDm1eUtK+kHxWmz5J0dL6/u6QbJN0s6erGZdTNcOas\nHs6d9ZszZ3Vw7nAFonIR8RjpCoGH5FkzgIuAp4F3R8RuwH7A6fmS5uOStBrwVeB9EbE7MAs4tddl\nt+HkzFkdnDvrN2fO6uDcJavWXYAR0WjuujT/PQYQ8HlJ+wAvAFOBTYDfltjetsCOwJyczSmkS6ub\nNThzVgfnzvrNmbM6jHzuXIHoj0uBL0naDVgzIm7OTVYbA7tHxB8kLQZe0bTeClZuJWosF7AwIt5U\nbbFtiDlzVgfnzvrNmbM6jHzu3IWpDyLiSeA6UpNUY5DNusCyHLL9gNeOser9wPaS1pC0HrB/nn8X\nsLGkN0Fq+pK0Q6UvwoaKM2d1cO6s35w5q4Nz5xaIfroAuJiXRu6fB1wu6TZgHnBn8woR8YCki4AF\nwH3AL/P85yS9DzhT0rqkz/HLwMLKX4UNE2fO6uDcWb85c1aHkc6dIqLuMpiZmZmZ2ZBwFyYzMzMz\nMyvNFQgzMzMzMyvNFQgzMzMzMyvNFQgzMzMzMyvNFQgzMzMzMyvNFQgzMzMzMyvNFQgzMzMzMyvN\nFQgzMzMzMyvNFQgzMzMzMyvNFQgzMzMzMyvNFQgzMzMzMyvNFQgzMzMzMyvNFYgBJOlcST+q+Dmm\nSQpJ08eattHRj7xNFs3vVb/eO0mLJX2y6ueZjCQdLenJussxDCQ9KenousthZoPPFYiC/GXgpLrL\n0WuSrpd0VtPsB4BNgfkt1pkmKSovnAErZ6/TL4uT4UtmzurRPd7m0PxfSzpJ0oIxFv0J8PV+l6cV\nSV+RNE/SM5IWj7F8X0mXSnpQ0n9LulXSh/pQrqH/Hxgk+UelaXWXw8wGz6p1F2BYSFo9Ip6ruxy9\nEhHPA7+tuxxmNr6IeLjuMjRZBZgN7AQcOMby/wHcBnwReBA4CDhb0jMRcX7fSmlmZpVwC0QL+Zes\nkyTNkvQ74Lw8f6qkCyU9nm//T9I2ednr8y82OzVt61hJj0haLU/vI+nG/OvdQ5K+JGn1Dsv5staF\nYrcKSecCbwGOy2WL3LrgLksDSNL1wGuBf2p8XoVl75F0m6RnJT0g6e8kqd16kjaUdIGkJZKelrRQ\n0ge7KN/Bkv4jZ/8xSVdL2q7pMZtJOk/So/nX5/mS9issf3vO/9P5MZdLekWnZZpg+VeX9HlJ9+f3\n8V5Jf1NY3tX/ppL/Leme/Ppuk/QXTY8Z8/3JLS8nAjsU/lePzuus9Mu6pC0kXSzpiXz7oaTXFJaf\nJGmBpBm5LE9IukTSRoXH7CTpWknLlbqu/Kr4ObUTER+LiK8Cv26x/PMR8dmI+FlE3BsR3wB+CLy3\n5PvYKP/M/NqfkvSd/Pl9JOf/UUlnSFolr3M9Lf538vL98zafknSdpC0LyzZXajF5LH8md0qaUbKs\n7Y4JkjRH0jWF/9VXSbpb0tdKbHvf/FoOkXRzztR/SHqNpLfkz+xJST+StGHTuh+UdHvO8q8l/a/G\ne5WXv07p+PGMpLskvaPM6zUzA1cgxvMJ4E5gOvAZSWsC1wHPkL6Uv4n069o1ktaMiF8Dc4Ejm7Zz\nJHBRRPxB0lTgSuCXwK7AMcARwP+p6DUcD/wc+A6py9KmpO5LNpjeAywB/pGXPi8k7Q78G+lL2E7A\nCcCngY+2Ww94BXAL8A5gB+ArwLck7d9h+dYCvgzsAewL/B64vPElW9JawA3ANOBdwI6kL8Xk5QcD\nlwFzgN1J/0fX0b990WzgA6T/7e2AmcDjuWy9+N88Ja93HLB9Xvdbkv40P0e79+d7wOnAXbz0GX6v\n+Qnyl8BLgU2A/fJtM+CSxpfUbBrwfuDdpFaCXYFTC8vPJ+2/9gB2AU4i7duqsg75vS5pGnAoKbvv\nAQ4jZedPSK/nfwIfI70+aP0/ALAG6f/lQ6T99nrANwvLvw6sSXovdwA+DvxuvAKWOCYEKWO7AI0K\n4JnAc4XpMv4hl+mNwPqkXHwOOJb0f7gD6fNrlOvDwOfzY7YD/hb4FPCRvHwV4GLS/92bSO/LSaT3\nycxsfBHh2xg3YDFwedO8DwF3AyrMmwI8Chyep/8GuL/xGGAL4AXgf+TpU/M2Vils42jgWWDNPH0u\n8KOS5bweOKtp3krrt3jMNCCA6WNN+1Z79j7ZNO884MdN804ClrRbr8X2LwS+3SovEyzrWsDzwN55\n+sPAE8BGLR7/M+DCmt7XbXLGD26xfML/m8Xp/F48Dby5abtfBq4o+f6cBCxolwnggPyeTyss8Rb1\nLQAAF0xJREFU3yrvZ95W2M4zwLqFx/wdsKgwvRyY2eV7+klgcYnHvQP4A7BHye2elN/LYvm/DzwM\nrF6Yt9K+rcX/ztH5c9+2MO/I/Lk29tO3Aid28PrHPSbkee/Kz3dy/rtzye3vm8t+UGHeR/O83Vrl\nBvgv4KimbX0cuD3fPzBnaIvC8r3zdo/uJhO++ebbaNzcAtHevKbp3YEtgSdys/GTpF9g1we2zo+5\nkPRr4Jvz9BHAfRHxn3l6O+AXEfFCYbs/BVYHXtf7l2CTxHakL99FPwWmSlqn1UqSpih1dbo1d/l4\nkvRL7RadFELS1pLOz91ilgMPkX7FbGxvV+DWiHikxSZ2Ba7t5Ll7YFfSl+zrWizv9n9ze1KLz1WN\n/UN+v/+al/YP470/ZWwH/CYiFjdmRMS9wG9yGRruj4jfF6Z/A7y6MH0G8G1JP84Z+eMuytSSpL1I\nrR1/ExE3TWDV/2oq/0PAr2PlsWgPsfJrauXZiLirMP0b0ue6fp7+CvBZST+XdEpu8SujzDGBiLiE\n9B58FvhsRPyq5PYbbi3cfyj/va1p3qsBJG0MbE5q+Srm8LRCmbYDlkbEfxW2cSPp/8PMbFweRN3e\nU03Tq5DOWjRW39jHACJimaQ5pF+4fpL/nlfy+To569ELgJrmrdbBdmx4tcvNJ0ndF44nfeF4ktS1\nocyXrrH8iNRN5C+BpcAK4HbSl7HJrMz/ZuMHmT8j/QJc9IfeFqelYjmbnzModBWLiJMknQccQhrk\nfKKkv4qIWb0qjKS9gSuAz0UaBzERY5V/rHlTSmxrxRjrQX4/IuIcSVcDbwfeBvynpP8TESeNs91x\njwkASmN8/oT0q38nPxQVX3fkMjfPa3y2jb9/BfwnZmYVcAvExNxC2vk/EhGLmm6PFR73f4HD8q9Y\nO+XphjuAPYuD2UhNx88B93RQpodZua8vwM5N089R7iBrg2Gsz+sOYK+meXuTujA90Wa9vUld8f41\nIuaTMvb6TgqVB2n+MfD5iLgmIu4A1mblHyJ+CbxBhcG6TX4JdDr+olvzSfu8VgOFu/3fvJ3UPeW1\nY+wf7s+PGe/9KfO/egewmQqn15S0Fanl8/YS5XxRRNwdEWdGxJ8C55DGFfSEpH1IY0pOiogv92q7\n4+h4XxcRSyLi7Ig4nJfGF4yn7DHhn0jjCw4APijpnZ2UsYyIeIjUwrL1GGValB92B6n1cvPCqnvg\n7wRmVpJ3FhNzHqmp+NJ8Bowtlc7acnrjrBvZJaRWgHOAuZEGVzd8nXSg/7qk7fLgytNI/Xj/u4My\n/Rg4RNI7JW0r6QxS83XRYmAPpTMvbdT0BckGz2LgzfnsLo0vmqcDb1E6O83rJR1Jaln44jjr/RrY\nX9LeuYvKWaQuF514HHgE+HA+g8tbSANRi7/ung8sI/2PvFnSVjmbjS/tp5Iq16dI2l7SDvnsMGt2\nWKbS8v/hRaRuO+/N/79vlnRUfkhX/5u5IvfPwD9L+lB+j3aR9FeSGl9Gx3t/FgOvlbRb/l8da1Dr\nNaQuLedJmq50JrXzSF9mf1zmvZD0SklfUzrLzzRJbyRVlkpVQBqvjfR+rZ5f5y6FwfT7kioP3wTO\nl/RH+bZxme13YTEv/x8Yl9J1LQ7On8cuwMGUey/GPSZIOoTUYvcXEXEdabzCtyX90cRe2oScCPzv\n/L+1raQdJX1A0qfz8mtIJwj5bv7c3gR8iZe31JiZjclfJCcgf4nYB7iXdEacO0lndVmfwtlF8uMu\nJrUE/N+mbSwldRnYlfSL6CzgAuAzHRZrVuH2M9IAzYubHvPPpF/mbie1WHTU/9365nOkSuA9pM+L\niLiFdBaa9wILSF9sTyNVCFquRzor0E2kL3M/IXXLK9ulbiV5bMD7gTfkMnwN+HvSr+6NxzxFOhvN\nEuDy/Lh/4KVuF1eQzppzCOnX+BtILQL96nv9AdKX+DNJ/7/nAuvmsvXif/PvSV8QPwksJJ1t6r3A\nffk52r4/wA9IXX6uJX2GRzQ/QUQE6exED5PGc1xHuqbLu/KyMp4n7bfOJZ316WLS2do+UXL9b5M+\nv/9FagH9Zb5tlpcfTTqr0SdJZyVq3OaW3H6nxvofKGMV4KukfeQcUqVg5ngrjXdMyBWm7wCnRMSN\nebXTSC0A35HU3P20JyLi26QB3kcBvwL+g9Si0sjhC6T/w1VIYx++S9pXPDvW9szMmmm8400eADc/\nIp5SOp/5bsBXCk3yZj3n3FlVnC0zM7PulGmB+Abw35J2JnWZuIf0a4VZlZw7q4qzZWZm1oUyFYgV\nhSbzsyLia6SBk1YxpavNPtnmNpm7Ijl3fTZCeXO22pD0zTYZ+Ob4Wyj1HAvbPEfzhThrI+kzbcp5\nZQ+2X/l7bWZWhTJdmG4ArgI+SOrruQz4VUTsVH3xRpukVUkXeGtlcURMykFvzl3/jUrenK32JL2a\ndNXosSyPiGU9eI7X0vp00w8VzixWK0kbABu0WPx0HjfTzfYrf6/NzKpQpgLxR8Cfk84m9B/5V8h9\nI8JN/lYZ586q4myZmZl1Z9wKxGSz0UYbxbRp0+ouxqRx8803PxIRVZ+aceg5d73l3I3PmestZ87M\n7CUtr0Qt6QnaXH01Ilo1uw60adOmMW/evLqLMWlI6umZa5w7K6OT3E3WbLXizPVWr/d1ZmbDrGUF\nIiLWBpB0Mun83f8KCDiSl1/52KwnnDurirNlZmbWGy0rEAXvjIidC9PfkPQr0gV7JhXNnv3i/Zg5\n7jWErFojk7tWnMfKjGS2nCczM+uVMqdxfUrSkZKmSFoln2LvqaoLZiPPuSvQ7Nkv3qxrzpaZmVkX\nylQg/hw4HHgo3w7L88yq5NxZVZwtMzOzLrTtwiRpCvDuiDi0T+Uxc+6sMs6WmZlZ99q2QETE88AR\nfSqLGeDcWXWcLTMzs+6VGUT9M0lnAd+j0E84Im6prFRmzp1Vx9kyMzPrQpkKxC757z8W5gXw1t4X\nx+xFzp1VxdkyMzPrwrgViIjYrx8FMSty7qwqzpaZmVl3xj0Lk6R1JZ0haV6+nS5p3X4UzkaXc2dV\ncbbMzMy6U+Y0rrOAJ0inPTwcWA58p8pCmeHcWXWcLTMzsy6UGQOxdUS8tzD9D5LmV1Ugs8y5s6o4\nW2ZmZl0o0wLxtKS9GxOS9gKerq5IZoBzZ9VxtszMzLpQpgXir4HZhT7CjwNHV1Yis8S5s6o4W2Zm\nZl0ocxam+cDOktbJ08srL5WNPOfOquJsmZmZdafMWZg+L2m9iFgeEcslrS/plH4UzkZXJ7mTtLmk\n6yTdLmmhpOPz/A0kzZF0d/67fmGdT0taJOkuSQcV5u8u6ba87ExJyvPXkPS9PP9GSdOqeQesKt6n\nmZmZdafMGIhDIuJ3jYmIeBx4e3VFMgM6y90K4G8jYntgT+A4SdsDJwDXRsQ2wLV5mrxsBrADcDDw\ndUlT8ra+AXwY2CbfDs7zjwEej4jXAV8CvtDtC7W+8z7NzMysC2UqEFMkrdGYkPRKYI02jzfrhQnn\nLiIejIhb8v0ngDuAqcChwOz8sNnAu/L9Q4ELI+LZiLgPWATsIWlTYJ2I+EVEBPDdpnUa2/o+sH+j\ndcKGhvdpZmZmXShTgTgPuFbSMZKOAebw0hcos6p0lbvctWhX4EZgk4h4MC/6LbBJvj8VeKCw2pI8\nb2q+3zx/pXUiYgXwe2DDsuWygdBRtiTNkrRM0oLCvL50j5M0Mz/H3ZJm9uRdMDMz69C4FYiI+AJw\nCrBdvp0cEV8ss3FJi/OBcr6keXme+6PbuLrM3auAHwAfbx4gm1sUosfFbVWOY5Wvdvzwww/34ymt\nhC6ydS4vdWVrqLx7nKQNgBOBNwJ7ACcW95tmZmb9VuY0rpC6gqyIiGskrSlp7dxFpIz9IuKRwnTj\ngHuapBPy9KeaDribAddIen1EPM9LB9wbgStIB9wrKRxwJc0gHXDfX7JcNvgmnDtJq5EqD+dFxA/z\n7IckbRoRD+buScvy/KXA5oXVX5PnLc33m+cX11kiaVVgXeDRscoSEWcDZwNMnz69VKVFs8dvZCk+\nJmb6x+gOTThbEfGTMX6kOBTYN9+fDVwPfIpC9zjgPkmN7nGLyd3jACQ1usddmdc5KW/r+8BZ+ceS\ng4A5EfFYXmcOaR94QUev3MzMrEtlzsL0YdLB7Ft51lTgki6e0/3RbVyd5C5/9ucAd0TEGYVFlwGN\nb9ozgUsL82fklqwtSb8G35S7Oy2XtGfe5gea1mls633Aj3MubUj0eJ/Wj+5xrbZlZmZWizItEMeR\nms1vBIiIuyW9uuT2g9SS8DzwrfyLbLsD7i8K6zYOkn+g5AFXUuOAW2zxQNKxwLEAW2yxRcmiW806\nyd1ewFHAbZLm53mfAU4DLsr93e8HDs/bXCjpIuB20hmcjsstXgAfIXVZeSXp1+Er8/xzgH/Nvyg/\nRmo1q41bIzrSzT6tpYgISbVWJr2vMzOzfihTgXg2Ip5r/LCfu22UPUjuHRFL88F5jqQ7iwv7dcDt\npCuJ1W7CuYuInwKtWqD2b7HOqcCpY8yfB+w4xvxngMPaltwGXTf7tGb96B63lJe6STXWuX6swnhf\nZ2Zm/VDmLEw3SPoM8EpJBwD/BlxeZuMRsTT/XQZcTPrV76F8oKWHB1w0Tn/0idLs2S/erBYd585s\nHL3MVj+6x10NHKh0wbv1gQPzPDMzs1qUqUCcADwM3Ab8JWkQ82fHW0nSWpLWbtwnHfQW4P7oVk5H\nuTMrodN92gXAz4FtJS3JXeJOAw6QdDfwtjxNRCwEGt3jruLl3eO+TRrndQ8rd4/bMHeP+0QuJ3nw\n9MnA3Hz7x8aAajMzszqM24UpIl4A/iXfAJC0F/CzcVbdBLg4dxNYFTg/Iq6SNJdJ2h/deqeL3Jm1\n1Wm2IuKIFosq7x4XEbOAWe3KZ2Zm1i8tKxD5nOWHkwYqXxURCyS9gzQo9ZWki3S1FBH3AjuPMf9R\n3B/dWug2d2atOFtmZma90a4F4hzS+IKbgDMl/QaYDpwQEd2cxtWsHefOquJsmZmZ9UC7CsR04A0R\n8YKkV5BOubp1bkEwq8pI5c6D9PtqpLJlZmZWlXaDqJ/LfYUbXYXu9YHW+sC5s6o4W2ZmZj3QrgXi\njyXdmu8L2DpPi3QJhzdUXjobRc6dVcXZMjMz64F2FYjt+lYKs5c4d1YVZ8vMzKwHWlYgIuL+fhak\nLu6DPlhGJXdVKGY5Zs5s88jR5GyZmZn1RpkLyZmZmZmZmQGuQJiZmZmZ2QS4AmFmZmZmZqV1VIGQ\ndFKPy2E2LufOquJsmZmZlddpC8TNPS2FWTnOnVXF2TIzMyupowpERFze64KYjce5s6o4W2ZmZuW1\nuw4EAJK2BD4GTCs+PiLeWV2xbNQ5d1YVZ8vMzKw741YggEuAc4DLgReqLY7Zi5w7q4qzZWZm1oUy\nFYhnIuLMyktitjLnzqribJmZmXWhTAXiK5JOBP4deLYxMyJuqaxUZs6dVcfZMjMz60KZCsROwFHA\nW3mpuT/ytFlVnLsuaPbsF+/HzJk1lmQgOVtmZmZdKFOBOAzYKiKeq7owZgXOnVXF2TIzM+tCmQrE\nAmA9YFnFZTErcu56xK0RL+NsmZmZdaFMBWI94E5Jc1m5v7BPeWhVcu6sKs6WmZlZF8pUIE6svBRm\nL+fcWVWcLTMzsy6MW4GIiBv6URCzIufOquJsmZmZdafMlaifIJ2hBGB1YDXgqYhYp8qC2Whz7qrh\n8RDOlpmZWbfKtECs3bgvScChwJ5VFspsMueu+CXe+m8yZ8vMzKwfVpnIgyO5BDioovJMmKSDJd0l\naZGkE+ouj/Wec2dVGcRsteLMmZnZoCjThek9hclVgOnAM5WVaAIkTQG+BhwALAHmSrosIm6vt2TW\nLeeueqPanWmQs9XKZMmcmZlNDmXOwvRnhfsrgMWkJv9BsAewKCLuBZB0IalsPqgOP+euj0asMjHI\n2Wpl0mXOzMyGV5kxEB/sR0E6NBV4oDC9BHhjTWWxHnLu6jPZKxMDnq1W+p65yZ4DMzPrXMsKhKTP\ntVkvIuLkCspTCUnHAsfmyScl3VVYvBHwSNv1jz66opJN2LhlrcFre7mxEcjdIH6GLVWY/W7fhwnn\nbjJlq5Wy+7qJfq4DtA/sRt8zZ2Y2WbVrgXhqjHlrAccAGwKDcLBdCmxemH5NnreSiDgbOHusDUia\nFxHTqylebw1TWbswqXM3Ip/huGp6H4YhW62M1L6uCqP++s3MeqllBSIiTm/cl7Q2cDzwQeBC4PRW\n6/XZXGAbSVuSDqYzgD+vt0jWDefOqjIk2WrFmTMzs4HRdgyEpA2ATwBHArOB3SLi8X4UrIyIWCHp\no8DVwBRgVkQsrLlY1iXnzqoy6NlqxZkzM7NB0m4MxD8B7yE1h+8UEU/2rVQTEBFXAFd0sYkxm/sH\n1DCVtSMjkLtJ/xmW1Pf3YViy1cqI7euqMOqv38ysZxQRYy+QXgCeJZ3msPggkQYcrlN98WzUOHdW\nFWfLzMysN1pWIMzMzMzMzJqtUncB6iLpYEl3SVok6YS6ywMgabGk2yTNlzQvz9tA0hxJd+e/6xce\n/+lc/rskHVRfya2MQcxcFSTNkrRM0oLCPOe4JqOSu1bG2q+amVl3RrIFQtIU4NfAAaQLMs0FjoiI\nWq/qKmkxMD0iHinM+yLwWESclg/+60fEpyRtD1xAukLtZsA1wOsj4vkaim7jGNTMVUHSPsCTwHcj\nYsc8zzmuwSjlrpWx9qtmZtadUW2B2ANYFBH3RsRzpNM4HlpzmVo5lHS2GPLfdxXmXxgRz0bEfcAi\n0uuywTRMmetKRPwEeKxptnNcj5HJnZmZ9c+oViCmAg8UppfkeXUL4BpJN+crygJsEhEP5vu/BTbJ\n9wf1NdjYRv3zco7r4fd37P2qmZl1oe11IKzv9o6IpZJeDcyRdGdxYUSEpNHrc2aTinNsffay/Wpu\nJTMzsw6NagvEUmDzwvRr8rxaRcTS/HcZcDGp+8FDkjYFyH+X5YcP5Guwlkb983KO6zHy72+L/aqZ\nmXVhVCsQc4FtJG0paXVgBnBZnQWStJaktRv3gQOBBblcM/PDZgKX5vuXATMkrSFpS2Ab4Kb+ltom\nYOAy12fOcT1GOndt9qtmZtaFkezCFBErJH0UuBqYAsyKiIU1F2sT4GJJkD6X8yPiKklzgYskHQPc\nDxwOEBELJV0E3E66MNZxPnPN4BrQzFVC0gXAvsBGkpYAJwKn4Rz33SjlroUx96v1FsnMbPiN5Glc\nzczMzMysM6PahcnMzMzMzDrgCoSZmZmZmZXmCoSZmZmZmZXmCoSZmZmZmZXmCoSZmZmZmZXmCkTF\nJF0n6aCmeR+X9I026zxZfclssnLmrA7OnZnZ6HAFonoXkC7eVDQjzzergjNndXDuzMxGhCsQ1fs+\n8Kf5KrBImgZsBvxS0rWSbpF0m6RDm1eUtK+kHxWmz5J0dL6/u6QbJN0s6WpJm/bjxdhQcOasDs6d\nmdmIcAWiYhHxGHATcEieNQO4CHgaeHdE7AbsB5yufLnU8UhaDfgq8L6I2B2YBZza67LbcHLmrA7O\nnZnZ6Fi17gKMiEbT/qX57zGAgM9L2gd4AZgKbAL8tsT2tgV2BObk4/AU4MHeF9uGmDNndXDuzMxG\ngCsQ/XEp8CVJuwFrRsTNuXl+Y2D3iPiDpMXAK5rWW8HKrUSN5QIWRsSbqi22DTFnzurg3JmZjQB3\nYeqDiHgSuI7U/N4YULgusCwfUPcDXjvGqvcD20taQ9J6wP55/l3AxpLeBKmZX9IOlb4IGyrOnNXB\nuTMzGw1ugeifC4CLeeksJecBl0u6DZgH3Nm8QkQ8IOkiYAFwH/DLPP85Se8DzpS0Lulz/DKwsPJX\nYcPEmbM6OHdmZpOcIqLuMpiZmZmZ2ZBwFyYzMzMzMyvNFQgzMzMzMyvNFQgzMzMzMyvNFQgzMzMz\nMyvNFQgzMzMzMyvNFQgzMzMzMyvNFQgzMzMzMyvNFQgzMzMzMyvt/wMkylGi0yPvMQAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e3ed0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import imp\n",
    "imp.reload(vs)\n",
    "vs.distribution(features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some weird scaling going on, just to check my sanity, check the max values of a few of the features. these may need to be log scaled just to deal with the large values. Income almost always is going to need log scaling anyway."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'annual_inc': 8900060.0,\n",
       " 'collectmax': 6.0,\n",
       " 'delinqmax': 29.0,\n",
       " 'openmax': 76.0,\n",
       " 'pubmax': 15.0,\n",
       " 'revolbalmax': 1743266.0,\n",
       " 'revolutilmax': 892.29999999999995}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valuespread={'annual_inc':max(features.annual_inc),'delinqmax':max(features.delinq_2yrs),\n",
    "             'openmax':max(features.open_acc), 'pubmax':max(features.pub_rec),\n",
    "             'revolbalmax':max(features.revol_bal),'revolutilmax':max(features.revol_util),\n",
    "             'collectmax':max(features.collections_12_mths_ex_med)}\n",
    "valuespread"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are some crazy revolving balances and utilizations. I need to check those quickly. Everything else looks extreme, but within ranges I'd assume could exist."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>emp_title</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>purpose</th>\n",
       "      <th>addr_state</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>earliest_cr_line</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>131832</th>\n",
       "      <td>B2</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>plastic surgery</td>\n",
       "      <td>10.0</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>FL</td>\n",
       "      <td>33.48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1984</td>\n",
       "      <td>4.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1743266.0</td>\n",
       "      <td>29.5</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sub_grade  loan_amnt        emp_title  emp_length home_ownership  \\\n",
       "131832        B2    35000.0  plastic surgery        10.0       MORTGAGE   \n",
       "\n",
       "        annual_inc             purpose addr_state    dti  delinq_2yrs  \\\n",
       "131832    400000.0  debt_consolidation         FL  33.48          0.0   \n",
       "\n",
       "        earliest_cr_line  inq_last_6mths  open_acc  pub_rec  revol_bal  \\\n",
       "131832              1984             4.0      28.0      0.0  1743266.0   \n",
       "\n",
       "        revol_util  total_acc  collections_12_mths_ex_med  \n",
       "131832        29.5       59.0                         0.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.query('revol_bal>1000000.0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I guess these are pretty understandable, high paying jobs and income.\n",
    "Lets check the utilizations - having over 100% seems... not possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>emp_title</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>purpose</th>\n",
       "      <th>addr_state</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>earliest_cr_line</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>68911</th>\n",
       "      <td>B4</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>united states Navy</td>\n",
       "      <td>8.0</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>CT</td>\n",
       "      <td>30.54</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11533.0</td>\n",
       "      <td>128.1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72075</th>\n",
       "      <td>D4</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>Blachford, Inc.</td>\n",
       "      <td>0.5</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>294000.0</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>GA</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1982</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33066.0</td>\n",
       "      <td>127.6</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74550</th>\n",
       "      <td>D4</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>Goldman Sachs</td>\n",
       "      <td>3.0</td>\n",
       "      <td>RENT</td>\n",
       "      <td>113000.0</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>UT</td>\n",
       "      <td>12.76</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1990</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7357.0</td>\n",
       "      <td>120.2</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210575</th>\n",
       "      <td>A5</td>\n",
       "      <td>12600.0</td>\n",
       "      <td>AVP, Senior Leasing Assistant</td>\n",
       "      <td>8.0</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>96011.0</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>MD</td>\n",
       "      <td>11.56</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1981</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20033.0</td>\n",
       "      <td>146.1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238136</th>\n",
       "      <td>C3</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>Systems Analyst</td>\n",
       "      <td>10.0</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>CA</td>\n",
       "      <td>7.85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19616.0</td>\n",
       "      <td>127.4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242534</th>\n",
       "      <td>C5</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>Doorline</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RENT</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>TN</td>\n",
       "      <td>18.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2956.0</td>\n",
       "      <td>123.2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294319</th>\n",
       "      <td>C2</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>Superintendent</td>\n",
       "      <td>10.0</td>\n",
       "      <td>OWN</td>\n",
       "      <td>91000.0</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>CA</td>\n",
       "      <td>20.94</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1997</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9344.0</td>\n",
       "      <td>150.7</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294407</th>\n",
       "      <td>B4</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>Budget Analyst</td>\n",
       "      <td>10.0</td>\n",
       "      <td>RENT</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>CA</td>\n",
       "      <td>14.67</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2677.0</td>\n",
       "      <td>892.3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306159</th>\n",
       "      <td>G2</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>HR Director</td>\n",
       "      <td>10.0</td>\n",
       "      <td>RENT</td>\n",
       "      <td>165800.0</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>MA</td>\n",
       "      <td>7.42</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16521.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524608</th>\n",
       "      <td>C4</td>\n",
       "      <td>18275.0</td>\n",
       "      <td>Consultant</td>\n",
       "      <td>1.0</td>\n",
       "      <td>OWN</td>\n",
       "      <td>155000.0</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>MA</td>\n",
       "      <td>18.39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15237.0</td>\n",
       "      <td>141.1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528603</th>\n",
       "      <td>B5</td>\n",
       "      <td>9600.0</td>\n",
       "      <td>phlebotomist</td>\n",
       "      <td>0.5</td>\n",
       "      <td>RENT</td>\n",
       "      <td>30912.0</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>AK</td>\n",
       "      <td>17.16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2006</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7315.0</td>\n",
       "      <td>126.1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547364</th>\n",
       "      <td>D4</td>\n",
       "      <td>9175.0</td>\n",
       "      <td>Deckhand</td>\n",
       "      <td>6.0</td>\n",
       "      <td>RENT</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>TN</td>\n",
       "      <td>28.67</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2007</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18632.0</td>\n",
       "      <td>132.1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603535</th>\n",
       "      <td>C5</td>\n",
       "      <td>7350.0</td>\n",
       "      <td>sales manager</td>\n",
       "      <td>3.0</td>\n",
       "      <td>RENT</td>\n",
       "      <td>33000.0</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>MT</td>\n",
       "      <td>26.11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10990.0</td>\n",
       "      <td>126.3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sub_grade  loan_amnt                      emp_title  emp_length  \\\n",
       "68911         B4     7200.0             united states Navy         8.0   \n",
       "72075         D4    35000.0                Blachford, Inc.         0.5   \n",
       "74550         D4    12000.0                  Goldman Sachs         3.0   \n",
       "210575        A5    12600.0  AVP, Senior Leasing Assistant         8.0   \n",
       "238136        C3    20000.0                Systems Analyst        10.0   \n",
       "242534        C5     4000.0                      Doorline          2.0   \n",
       "294319        C2    10000.0                 Superintendent        10.0   \n",
       "294407        B4     3500.0                 Budget Analyst        10.0   \n",
       "306159        G2    35000.0                    HR Director        10.0   \n",
       "524608        C4    18275.0                     Consultant         1.0   \n",
       "528603        B5     9600.0                   phlebotomist         0.5   \n",
       "547364        D4     9175.0                       Deckhand         6.0   \n",
       "603535        C5     7350.0                  sales manager         3.0   \n",
       "\n",
       "       home_ownership  annual_inc             purpose addr_state    dti  \\\n",
       "68911        MORTGAGE     40000.0         credit_card         CT  30.54   \n",
       "72075        MORTGAGE    294000.0  debt_consolidation         GA   7.80   \n",
       "74550            RENT    113000.0  debt_consolidation         UT  12.76   \n",
       "210575       MORTGAGE     96011.0  debt_consolidation         MD  11.56   \n",
       "238136       MORTGAGE     90000.0  debt_consolidation         CA   7.85   \n",
       "242534           RENT     35000.0  debt_consolidation         TN  18.18   \n",
       "294319            OWN     91000.0  debt_consolidation         CA  20.94   \n",
       "294407           RENT     45000.0  debt_consolidation         CA  14.67   \n",
       "306159           RENT    165800.0  debt_consolidation         MA   7.42   \n",
       "524608            OWN    155000.0  debt_consolidation         MA  18.39   \n",
       "528603           RENT     30912.0  debt_consolidation         AK  17.16   \n",
       "547364           RENT     45000.0  debt_consolidation         TN  28.67   \n",
       "603535           RENT     33000.0  debt_consolidation         MT  26.11   \n",
       "\n",
       "        delinq_2yrs  earliest_cr_line  inq_last_6mths  open_acc  pub_rec  \\\n",
       "68911           0.0              2001             0.0       4.0      0.0   \n",
       "72075           0.0              1982             2.0       6.0      0.0   \n",
       "74550           4.0              1990             0.0       5.0      0.0   \n",
       "210575          2.0              1981             1.0       9.0      0.0   \n",
       "238136          0.0              2000             1.0       5.0      0.0   \n",
       "242534          0.0              2008             4.0      14.0      0.0   \n",
       "294319          0.0              1997             0.0       9.0      0.0   \n",
       "294407          0.0              1998             0.0       2.0      0.0   \n",
       "306159          0.0              2005             0.0       5.0      0.0   \n",
       "524608          0.0              2002             0.0      14.0      1.0   \n",
       "528603          0.0              2006             1.0       5.0      0.0   \n",
       "547364          0.0              2007             1.0       4.0      0.0   \n",
       "603535          0.0              2003             0.0       9.0      0.0   \n",
       "\n",
       "        revol_bal  revol_util  total_acc  collections_12_mths_ex_med  \n",
       "68911     11533.0       128.1       20.0                         0.0  \n",
       "72075     33066.0       127.6       19.0                         0.0  \n",
       "74550      7357.0       120.2       19.0                         0.0  \n",
       "210575    20033.0       146.1       22.0                         0.0  \n",
       "238136    19616.0       127.4        9.0                         0.0  \n",
       "242534     2956.0       123.2       18.0                         0.0  \n",
       "294319     9344.0       150.7       37.0                         0.0  \n",
       "294407     2677.0       892.3        9.0                         0.0  \n",
       "306159    16521.0       153.0        5.0                         0.0  \n",
       "524608    15237.0       141.1       26.0                         0.0  \n",
       "528603     7315.0       126.1       21.0                         0.0  \n",
       "547364    18632.0       132.1       10.0                         0.0  \n",
       "603535    10990.0       126.3       18.0                         0.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.query('revol_util>120')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having a utilization over 100% is odd, but having a utilization of almost 900% is crazy! I am going to just remove this one loan - not sure what is going on with this outlier. The others are clearly weird, but there doesn't seem to be an obvious cutoff beyond removing the huge outlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "features.drop([294407],inplace=True)\n",
    "labels.drop([294407],inplace=True)\n",
    "clabels.drop([294407],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Q0gHATXgrzg75hIraZWb3pnsxJtUw34Bfm58D55jZxPw+9Sh4zarxc9y3/VL5EKEL4YXP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\nMzMzM7PS3IAwMzMzM7PS/j9cMinVsXmjQgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a65ccc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imp.reload(vs)\n",
    "vs.distribution(features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utilization looks much more normal!\n",
    "Now to log transform the skewed distributions.\n",
    "\n",
    "## Log transform Skewed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BYEtJ/8A/qE+Z2VN1/N+Iz5rxa0mH4h+0g/EpCxte0Eo+k9FG+EwdTwAL4zUqT+EDxKpi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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118639b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Log-transform the skewed features\n",
    "\n",
    "skewed = ['annual_inc','delinq_2yrs','open_acc', 'pub_rec','revol_bal','total_acc', 'collections_12_mths_ex_med']\n",
    "features_raw=features.copy()\n",
    "features_raw[skewed] = features[skewed].apply(lambda x: np.log(x + 1))\n",
    "\n",
    "# Visualize the new log distributions\n",
    "imp.reload(vs)\n",
    "vs.distribution(features_raw, transformed = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see a clear improvement in income, open accounts, and revolving balance. The others are improved but still are dominated by low numbers. That's okay for now. There are extreme outliers but they are important. I am tempted to winsorize, but the values are so heavily weighted toward 0, that the winsorization would affect all values greater than zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# import scipy.stats\n",
    "# winsors=['delinq_2yrs','pub_rec','collections_12_mths_ex_med']\n",
    "# for feature in winsors:\n",
    "#     winsor[feature]=scipy.stats.mstats.winsorize(features[feature], limits=0.01,axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalization\n",
    "\n",
    "Now we normalize, by scaling by the max and min values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>emp_title</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>purpose</th>\n",
       "      <th>addr_state</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>earliest_cr_line</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B2</td>\n",
       "      <td>0.125364</td>\n",
       "      <td>None</td>\n",
       "      <td>1.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.230473</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>AZ</td>\n",
       "      <td>0.691423</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.534483</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.319143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.662532</td>\n",
       "      <td>0.547059</td>\n",
       "      <td>0.327121</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B5</td>\n",
       "      <td>0.329446</td>\n",
       "      <td>UCLA</td>\n",
       "      <td>1.00</td>\n",
       "      <td>OWN</td>\n",
       "      <td>0.378689</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>CA</td>\n",
       "      <td>0.269567</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.603448</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.590484</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.699854</td>\n",
       "      <td>0.438562</td>\n",
       "      <td>0.667499</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>C1</td>\n",
       "      <td>0.241983</td>\n",
       "      <td>Va. Dept of Conservation/Recreation</td>\n",
       "      <td>0.05</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.259499</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>VA</td>\n",
       "      <td>0.252063</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.862069</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.370513</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.643968</td>\n",
       "      <td>0.599346</td>\n",
       "      <td>0.327121</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B1</td>\n",
       "      <td>0.067055</td>\n",
       "      <td>Target</td>\n",
       "      <td>0.30</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.169337</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>IL</td>\n",
       "      <td>0.314079</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.844828</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.572058</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.619215</td>\n",
       "      <td>0.281699</td>\n",
       "      <td>0.376658</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>D1</td>\n",
       "      <td>0.008746</td>\n",
       "      <td>Internal revenue Service</td>\n",
       "      <td>0.05</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.250524</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>MO</td>\n",
       "      <td>0.507877</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.913793</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.572058</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.611177</td>\n",
       "      <td>0.532680</td>\n",
       "      <td>0.564987</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>A1</td>\n",
       "      <td>0.247813</td>\n",
       "      <td>Network Interpreting Service</td>\n",
       "      <td>0.60</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.382761</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>CA</td>\n",
       "      <td>0.246562</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.810345</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.505829</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.619130</td>\n",
       "      <td>0.150980</td>\n",
       "      <td>0.616404</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>B4</td>\n",
       "      <td>0.591837</td>\n",
       "      <td>Osram Sylvania</td>\n",
       "      <td>1.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.422457</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>FL</td>\n",
       "      <td>0.330583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.478714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.722116</td>\n",
       "      <td>0.590196</td>\n",
       "      <td>0.696900</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>B3</td>\n",
       "      <td>0.271137</td>\n",
       "      <td>Value Air</td>\n",
       "      <td>1.00</td>\n",
       "      <td>OWN</td>\n",
       "      <td>0.325946</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>TX</td>\n",
       "      <td>0.279570</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.534483</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.505829</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.641281</td>\n",
       "      <td>0.538562</td>\n",
       "      <td>0.541346</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>B3</td>\n",
       "      <td>0.271137</td>\n",
       "      <td>Wells Fargo Bank</td>\n",
       "      <td>0.50</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.325946</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>CA</td>\n",
       "      <td>0.400350</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.844828</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.447974</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.681012</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.486824</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>B1</td>\n",
       "      <td>0.416910</td>\n",
       "      <td>Winfield Pathology Consultants</td>\n",
       "      <td>0.20</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>0.405264</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>IL</td>\n",
       "      <td>0.736184</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.827586</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.505829</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.662832</td>\n",
       "      <td>0.613725</td>\n",
       "      <td>0.643151</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sub_grade  loan_amnt                            emp_title  emp_length  \\\n",
       "0         B2   0.125364                                 None        1.00   \n",
       "5         B5   0.329446                                 UCLA        1.00   \n",
       "6         C1   0.241983  Va. Dept of Conservation/Recreation        0.05   \n",
       "7         B1   0.067055                               Target        0.30   \n",
       "9         D1   0.008746             Internal revenue Service        0.05   \n",
       "13        A1   0.247813         Network Interpreting Service        0.60   \n",
       "14        B4   0.591837                       Osram Sylvania        1.00   \n",
       "15        B3   0.271137                            Value Air        1.00   \n",
       "16        B3   0.271137                     Wells Fargo Bank        0.50   \n",
       "18        B1   0.416910       Winfield Pathology Consultants        0.20   \n",
       "\n",
       "   home_ownership  annual_inc             purpose addr_state       dti  \\\n",
       "0            RENT    0.230473         credit_card         AZ  0.691423   \n",
       "5             OWN    0.378689  debt_consolidation         CA  0.269567   \n",
       "6            RENT    0.259499  debt_consolidation         VA  0.252063   \n",
       "7            RENT    0.169337         credit_card         IL  0.314079   \n",
       "9            RENT    0.250524  debt_consolidation         MO  0.507877   \n",
       "13           RENT    0.382761  debt_consolidation         CA  0.246562   \n",
       "14           RENT    0.422457  debt_consolidation         FL  0.330583   \n",
       "15            OWN    0.325946         credit_card         TX  0.279570   \n",
       "16           RENT    0.325946  debt_consolidation         CA  0.400350   \n",
       "18       MORTGAGE    0.405264         credit_card         IL  0.736184   \n",
       "\n",
       "    delinq_2yrs  earliest_cr_line  inq_last_6mths  open_acc  pub_rec  \\\n",
       "0           0.0          0.534483           0.125  0.319143      0.0   \n",
       "5           0.0          0.603448           0.000  0.590484      0.0   \n",
       "6           0.0          0.862069           0.125  0.370513      0.0   \n",
       "7           0.0          0.844828           0.250  0.572058      0.0   \n",
       "9           0.0          0.913793           0.125  0.572058      0.0   \n",
       "13          0.0          0.810345           0.000  0.505829      0.0   \n",
       "14          0.0          0.500000           0.000  0.478714      0.0   \n",
       "15          0.0          0.534483           0.000  0.505829      0.0   \n",
       "16          0.0          0.844828           0.000  0.447974      0.0   \n",
       "18          0.0          0.827586           0.000  0.505829      0.0   \n",
       "\n",
       "    revol_bal  revol_util  total_acc  collections_12_mths_ex_med  \n",
       "0    0.662532    0.547059   0.327121                         0.0  \n",
       "5    0.699854    0.438562   0.667499                         0.0  \n",
       "6    0.643968    0.599346   0.327121                         0.0  \n",
       "7    0.619215    0.281699   0.376658                         0.0  \n",
       "9    0.611177    0.532680   0.564987                         0.0  \n",
       "13   0.619130    0.150980   0.616404                         0.0  \n",
       "14   0.722116    0.590196   0.696900                         0.0  \n",
       "15   0.641281    0.538562   0.541346                         0.0  \n",
       "16   0.681012    0.600000   0.486824                         0.0  \n",
       "18   0.662832    0.613725   0.643151                         0.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Initialize a scaler, then apply it to the features\n",
    "scaler = MinMaxScaler()\n",
    "numerical = ['loan_amnt','emp_length', 'annual_inc','dti','delinq_2yrs','earliest_cr_line','inq_last_6mths','open_acc', 'pub_rec','revol_bal','revol_util','total_acc', 'collections_12_mths_ex_med']\n",
    "features_raw[numerical] = scaler.fit_transform(features_raw[numerical])\n",
    "\n",
    "# Show an example of a record with scaling applied\n",
    "display(features_raw.head(n = 10))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One Hot Encoding\n",
    "\n",
    "Up to this point, I have only dealt with the \"continuous\" numerical features, or features where the values have some relation. for example, higher income can be directly compared to lower income. Now I want to deal with discrete features, where values are independent. One example is the address state. Although we all might have a specific way to rank the states, there is no \"correct\" relationship between any two possible states - they are discrete and independent. These will be one-hot encoded!\n",
    "\n",
    "Of note is the employment title - this is put in by the person applying to the loan and can be pretty much anything. This will certainly cause an explosion in feature space. I think, at this point, I will just remove that from the feature space instead of encode it.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>emp_title</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>purpose</th>\n",
       "      <th>addr_state</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>earliest_cr_line</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B2</td>\n",
       "      <td>0.125364</td>\n",
       "      <td>None</td>\n",
       "      <td>1.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.230473</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>AZ</td>\n",
       "      <td>0.691423</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.534483</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.319143</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.662532</td>\n",
       "      <td>0.547059</td>\n",
       "      <td>0.327121</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B5</td>\n",
       "      <td>0.329446</td>\n",
       "      <td>UCLA</td>\n",
       "      <td>1.00</td>\n",
       "      <td>OWN</td>\n",
       "      <td>0.378689</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>CA</td>\n",
       "      <td>0.269567</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.603448</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.590484</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.699854</td>\n",
       "      <td>0.438562</td>\n",
       "      <td>0.667499</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>C1</td>\n",
       "      <td>0.241983</td>\n",
       "      <td>Va. Dept of Conservation/Recreation</td>\n",
       "      <td>0.05</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.259499</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>VA</td>\n",
       "      <td>0.252063</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.862069</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.370513</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.643968</td>\n",
       "      <td>0.599346</td>\n",
       "      <td>0.327121</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B1</td>\n",
       "      <td>0.067055</td>\n",
       "      <td>Target</td>\n",
       "      <td>0.30</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.169337</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>IL</td>\n",
       "      <td>0.314079</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.844828</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.572058</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.619215</td>\n",
       "      <td>0.281699</td>\n",
       "      <td>0.376658</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>D1</td>\n",
       "      <td>0.008746</td>\n",
       "      <td>Internal revenue Service</td>\n",
       "      <td>0.05</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.250524</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>MO</td>\n",
       "      <td>0.507877</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.913793</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.572058</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.611177</td>\n",
       "      <td>0.532680</td>\n",
       "      <td>0.564987</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>A1</td>\n",
       "      <td>0.247813</td>\n",
       "      <td>Network Interpreting Service</td>\n",
       "      <td>0.60</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.382761</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>CA</td>\n",
       "      <td>0.246562</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.810345</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.505829</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.619130</td>\n",
       "      <td>0.150980</td>\n",
       "      <td>0.616404</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>B4</td>\n",
       "      <td>0.591837</td>\n",
       "      <td>Osram Sylvania</td>\n",
       "      <td>1.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.422457</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>FL</td>\n",
       "      <td>0.330583</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.478714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.722116</td>\n",
       "      <td>0.590196</td>\n",
       "      <td>0.696900</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>B3</td>\n",
       "      <td>0.271137</td>\n",
       "      <td>Value Air</td>\n",
       "      <td>1.00</td>\n",
       "      <td>OWN</td>\n",
       "      <td>0.325946</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>TX</td>\n",
       "      <td>0.279570</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.534483</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.505829</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.641281</td>\n",
       "      <td>0.538562</td>\n",
       "      <td>0.541346</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>B3</td>\n",
       "      <td>0.271137</td>\n",
       "      <td>Wells Fargo Bank</td>\n",
       "      <td>0.50</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.325946</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>CA</td>\n",
       "      <td>0.400350</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.844828</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.447974</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.681012</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.486824</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>B1</td>\n",
       "      <td>0.416910</td>\n",
       "      <td>Winfield Pathology Consultants</td>\n",
       "      <td>0.20</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>0.405264</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>IL</td>\n",
       "      <td>0.736184</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.827586</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.505829</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.662832</td>\n",
       "      <td>0.613725</td>\n",
       "      <td>0.643151</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>C2</td>\n",
       "      <td>0.416910</td>\n",
       "      <td>nyc transit</td>\n",
       "      <td>0.90</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.349662</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>NY</td>\n",
       "      <td>0.380595</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.844828</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.478714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.603852</td>\n",
       "      <td>0.376471</td>\n",
       "      <td>0.376658</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>B3</td>\n",
       "      <td>0.096210</td>\n",
       "      <td>Shands Hospital at the University of Fl</td>\n",
       "      <td>1.00</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>0.423690</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>FL</td>\n",
       "      <td>0.140785</td>\n",
       "      <td>0.203795</td>\n",
       "      <td>0.517241</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.590484</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.606616</td>\n",
       "      <td>0.246405</td>\n",
       "      <td>0.735781</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>B3</td>\n",
       "      <td>0.227405</td>\n",
       "      <td>Oakridge homes</td>\n",
       "      <td>0.05</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.235783</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>MN</td>\n",
       "      <td>0.304826</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.896552</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.505829</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.610287</td>\n",
       "      <td>0.386275</td>\n",
       "      <td>0.398406</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>A3</td>\n",
       "      <td>0.107143</td>\n",
       "      <td>None</td>\n",
       "      <td>0.70</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>0.186441</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>NY</td>\n",
       "      <td>0.508627</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.568966</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.447974</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.648840</td>\n",
       "      <td>0.567974</td>\n",
       "      <td>0.398406</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>A4</td>\n",
       "      <td>0.907434</td>\n",
       "      <td>Audubon Mutual Housing Corporation</td>\n",
       "      <td>0.50</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>0.378689</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>NJ</td>\n",
       "      <td>0.350838</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.465517</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.590484</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.699300</td>\n",
       "      <td>0.179085</td>\n",
       "      <td>0.596989</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>A5</td>\n",
       "      <td>0.125364</td>\n",
       "      <td>Good Samaritan Society</td>\n",
       "      <td>0.20</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.230711</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>OR</td>\n",
       "      <td>0.298325</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.879310</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.505829</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.536314</td>\n",
       "      <td>0.191503</td>\n",
       "      <td>0.471294</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>C5</td>\n",
       "      <td>0.183673</td>\n",
       "      <td>GREG BARRETT DRYWALL</td>\n",
       "      <td>0.70</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.275780</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>CA</td>\n",
       "      <td>0.158790</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.913793</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.447974</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.606650</td>\n",
       "      <td>0.395425</td>\n",
       "      <td>0.230212</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>B2</td>\n",
       "      <td>0.341108</td>\n",
       "      <td>Sharp Lawn Inc.</td>\n",
       "      <td>1.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.300132</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>KY</td>\n",
       "      <td>0.295074</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.879310</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.530085</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.648122</td>\n",
       "      <td>0.373856</td>\n",
       "      <td>0.398406</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>A4</td>\n",
       "      <td>0.416910</td>\n",
       "      <td>Gateway Hospice</td>\n",
       "      <td>0.10</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.312241</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>OH</td>\n",
       "      <td>0.212053</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.862069</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.478714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.613071</td>\n",
       "      <td>0.329412</td>\n",
       "      <td>0.606870</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>B4</td>\n",
       "      <td>0.154519</td>\n",
       "      <td>Cox Communications</td>\n",
       "      <td>0.10</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.286257</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>AZ</td>\n",
       "      <td>0.265566</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.913793</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.478714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.619092</td>\n",
       "      <td>0.434641</td>\n",
       "      <td>0.398406</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>A4</td>\n",
       "      <td>0.329446</td>\n",
       "      <td>John Wiley Jr.</td>\n",
       "      <td>1.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.354556</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>NJ</td>\n",
       "      <td>0.417604</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.689655</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.638286</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.630407</td>\n",
       "      <td>0.137255</td>\n",
       "      <td>0.586735</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>D2</td>\n",
       "      <td>0.107872</td>\n",
       "      <td>citizens bank</td>\n",
       "      <td>1.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.338344</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>RI</td>\n",
       "      <td>0.500375</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.862069</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.478714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.705303</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>0.376658</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>A1</td>\n",
       "      <td>0.154519</td>\n",
       "      <td>Stewart Enterprises, Inc.</td>\n",
       "      <td>1.00</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>0.313964</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>LA</td>\n",
       "      <td>0.133533</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.706897</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.447974</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.565387</td>\n",
       "      <td>0.212418</td>\n",
       "      <td>0.616404</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>A5</td>\n",
       "      <td>0.125364</td>\n",
       "      <td>STERIS Corporation</td>\n",
       "      <td>1.00</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>0.416111</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>OH</td>\n",
       "      <td>0.408352</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.706897</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.665401</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.780485</td>\n",
       "      <td>0.405882</td>\n",
       "      <td>0.675153</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>A1</td>\n",
       "      <td>0.271137</td>\n",
       "      <td>Helicoil</td>\n",
       "      <td>1.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.349662</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>CT</td>\n",
       "      <td>0.318580</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.655172</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.572058</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.664398</td>\n",
       "      <td>0.127451</td>\n",
       "      <td>0.501514</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>A2</td>\n",
       "      <td>0.300292</td>\n",
       "      <td>cognizant technology solutions</td>\n",
       "      <td>0.50</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.369714</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>CT</td>\n",
       "      <td>0.271318</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.827586</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.412486</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.651447</td>\n",
       "      <td>0.237908</td>\n",
       "      <td>0.266493</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>B1</td>\n",
       "      <td>0.416910</td>\n",
       "      <td>Caterpillar Inc.</td>\n",
       "      <td>0.80</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>0.387084</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>IL</td>\n",
       "      <td>0.228057</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.655172</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.530085</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.652673</td>\n",
       "      <td>0.416340</td>\n",
       "      <td>0.616404</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>B1</td>\n",
       "      <td>0.725948</td>\n",
       "      <td>City of Santa Monica</td>\n",
       "      <td>0.90</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.428509</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>CA</td>\n",
       "      <td>0.392848</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.775862</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.572058</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.707611</td>\n",
       "      <td>0.405229</td>\n",
       "      <td>0.606870</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>B2</td>\n",
       "      <td>0.183673</td>\n",
       "      <td>Aerotek Scientific</td>\n",
       "      <td>0.05</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.296920</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>CA</td>\n",
       "      <td>0.184546</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.862069</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.478714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.654545</td>\n",
       "      <td>0.607190</td>\n",
       "      <td>0.398406</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>B1</td>\n",
       "      <td>0.329446</td>\n",
       "      <td>Scott &amp; White</td>\n",
       "      <td>0.10</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.315100</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>TX</td>\n",
       "      <td>0.202801</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.793103</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.572058</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.654402</td>\n",
       "      <td>0.340523</td>\n",
       "      <td>0.398406</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619822</th>\n",
       "      <td>A3</td>\n",
       "      <td>0.096210</td>\n",
       "      <td>TELESERVICE REP</td>\n",
       "      <td>1.00</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>0.345252</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>WI</td>\n",
       "      <td>0.025756</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.724138</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.370513</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.325151</td>\n",
       "      <td>0.003268</td>\n",
       "      <td>0.501514</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619826</th>\n",
       "      <td>E1</td>\n",
       "      <td>0.212828</td>\n",
       "      <td>heat treat operator</td>\n",
       "      <td>1.00</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>0.309318</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>TN</td>\n",
       "      <td>0.149287</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.896552</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.412486</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.376658</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619830</th>\n",
       "      <td>C3</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Sales</td>\n",
       "      <td>0.50</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>0.535303</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>NC</td>\n",
       "      <td>0.307327</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.344828</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.607545</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.725668</td>\n",
       "      <td>0.465359</td>\n",
       "      <td>0.643151</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619841</th>\n",
       "      <td>B1</td>\n",
       "      <td>0.854227</td>\n",
       "      <td>Accounts Payable Coordinator and Leas</td>\n",
       "      <td>0.80</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>0.349662</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>OR</td>\n",
       "      <td>0.231558</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.706897</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.623428</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.691853</td>\n",
       "      <td>0.341830</td>\n",
       "      <td>0.596989</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619843</th>\n",
       "      <td>B3</td>\n",
       "      <td>0.125364</td>\n",
       "      <td>Qc inspector</td>\n",
       "      <td>0.50</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>0.315100</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>CA</td>\n",
       "      <td>0.202551</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.913793</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.572058</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.805274</td>\n",
       "      <td>0.143791</td>\n",
       "      <td>0.553424</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619844</th>\n",
       "      <td>B4</td>\n",
       "      <td>0.224490</td>\n",
       "      <td>Operator</td>\n",
       "      <td>0.10</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>0.416111</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>TX</td>\n",
       "      <td>0.930233</td>\n",
       "      <td>0.323008</td>\n",
       "      <td>0.913793</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.572058</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.605433</td>\n",
       "      <td>0.327451</td>\n",
       "      <td>0.667499</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619845</th>\n",
       "      <td>C3</td>\n",
       "      <td>0.154519</td>\n",
       "      <td>Account Executive</td>\n",
       "      <td>0.40</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.349662</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>NY</td>\n",
       "      <td>0.527632</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.896552</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.689656</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.602946</td>\n",
       "      <td>0.201307</td>\n",
       "      <td>0.703779</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619858</th>\n",
       "      <td>A5</td>\n",
       "      <td>0.037901</td>\n",
       "      <td>medical biller</td>\n",
       "      <td>0.80</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>0.263764</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>CA</td>\n",
       "      <td>0.211303</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.862069</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.607545</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.577888</td>\n",
       "      <td>0.110458</td>\n",
       "      <td>0.758918</td>\n",
       "      <td>0.356207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619865</th>\n",
       "      <td>B1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Manager</td>\n",
       "      <td>0.10</td>\n",
       "      <td>OWN</td>\n",
       "      <td>0.496134</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>CA</td>\n",
       "      <td>0.218055</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.706897</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.552026</td>\n",
       "      <td>0.646241</td>\n",
       "      <td>0.609198</td>\n",
       "      <td>0.505229</td>\n",
       "      <td>0.616404</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619871</th>\n",
       "      <td>C3</td>\n",
       "      <td>0.183673</td>\n",
       "      <td>Hotel Clerk</td>\n",
       "      <td>1.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.322468</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>NY</td>\n",
       "      <td>0.271318</td>\n",
       "      <td>0.203795</td>\n",
       "      <td>0.706897</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.607545</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.652738</td>\n",
       "      <td>0.379739</td>\n",
       "      <td>0.717036</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619875</th>\n",
       "      <td>A2</td>\n",
       "      <td>0.679300</td>\n",
       "      <td>owner</td>\n",
       "      <td>0.05</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>0.378689</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>TX</td>\n",
       "      <td>0.532883</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.810345</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.552026</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.669644</td>\n",
       "      <td>0.426144</td>\n",
       "      <td>0.486824</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619878</th>\n",
       "      <td>E4</td>\n",
       "      <td>0.187318</td>\n",
       "      <td>None</td>\n",
       "      <td>0.00</td>\n",
       "      <td>OWN</td>\n",
       "      <td>0.185618</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>FL</td>\n",
       "      <td>0.726182</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.810345</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.530085</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.579353</td>\n",
       "      <td>0.252288</td>\n",
       "      <td>0.515450</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619898</th>\n",
       "      <td>C5</td>\n",
       "      <td>0.854227</td>\n",
       "      <td>Teacher</td>\n",
       "      <td>1.00</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>0.393431</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>CA</td>\n",
       "      <td>0.413603</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.844828</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.638286</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.717901</td>\n",
       "      <td>0.458824</td>\n",
       "      <td>0.741753</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619902</th>\n",
       "      <td>B5</td>\n",
       "      <td>0.329446</td>\n",
       "      <td>utility Pre-Craft Trainee</td>\n",
       "      <td>0.10</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.360074</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>CA</td>\n",
       "      <td>0.212303</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.672414</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.623428</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.659077</td>\n",
       "      <td>0.291503</td>\n",
       "      <td>0.586735</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619910</th>\n",
       "      <td>D4</td>\n",
       "      <td>0.353499</td>\n",
       "      <td>Sales Associate</td>\n",
       "      <td>0.60</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>0.290248</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>TX</td>\n",
       "      <td>0.225806</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.896552</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.505829</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.652867</td>\n",
       "      <td>0.462092</td>\n",
       "      <td>0.576079</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619920</th>\n",
       "      <td>D1</td>\n",
       "      <td>0.203353</td>\n",
       "      <td>Sub teacher / Coach</td>\n",
       "      <td>0.20</td>\n",
       "      <td>OWN</td>\n",
       "      <td>0.245794</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>CA</td>\n",
       "      <td>0.465616</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.793103</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.782999</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.667802</td>\n",
       "      <td>0.307190</td>\n",
       "      <td>0.723429</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619932</th>\n",
       "      <td>C1</td>\n",
       "      <td>0.329446</td>\n",
       "      <td>Groomer</td>\n",
       "      <td>0.40</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>0.338344</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>CO</td>\n",
       "      <td>0.664666</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.896552</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.731629</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.632275</td>\n",
       "      <td>0.194118</td>\n",
       "      <td>0.741753</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619936</th>\n",
       "      <td>C5</td>\n",
       "      <td>0.189504</td>\n",
       "      <td>Asst. General Superintendent</td>\n",
       "      <td>0.50</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>0.376943</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>TX</td>\n",
       "      <td>0.556389</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.879310</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.505829</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.598433</td>\n",
       "      <td>0.298039</td>\n",
       "      <td>0.398406</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619943</th>\n",
       "      <td>C2</td>\n",
       "      <td>0.562682</td>\n",
       "      <td>Letter Carrier</td>\n",
       "      <td>1.00</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>0.381407</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>CA</td>\n",
       "      <td>0.320830</td>\n",
       "      <td>0.203795</td>\n",
       "      <td>0.465517</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.552026</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.646684</td>\n",
       "      <td>0.390196</td>\n",
       "      <td>0.682597</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619950</th>\n",
       "      <td>E4</td>\n",
       "      <td>0.708455</td>\n",
       "      <td>shop foreman</td>\n",
       "      <td>1.00</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>0.338344</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>TN</td>\n",
       "      <td>0.780695</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.775862</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.607545</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.697124</td>\n",
       "      <td>0.413072</td>\n",
       "      <td>0.501514</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619952</th>\n",
       "      <td>D3</td>\n",
       "      <td>0.150875</td>\n",
       "      <td>Sales</td>\n",
       "      <td>0.50</td>\n",
       "      <td>OWN</td>\n",
       "      <td>0.279550</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>MS</td>\n",
       "      <td>0.473368</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.896552</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.552026</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.552234</td>\n",
       "      <td>0.177124</td>\n",
       "      <td>0.553424</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619953</th>\n",
       "      <td>D1</td>\n",
       "      <td>0.150875</td>\n",
       "      <td>Elementary School Teacher</td>\n",
       "      <td>0.30</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>0.300132</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>SC</td>\n",
       "      <td>0.667667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.896552</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.552026</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.621744</td>\n",
       "      <td>0.403268</td>\n",
       "      <td>0.616404</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619968</th>\n",
       "      <td>D5</td>\n",
       "      <td>0.329446</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>0.90</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.303266</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>NC</td>\n",
       "      <td>0.668667</td>\n",
       "      <td>0.203795</td>\n",
       "      <td>0.810345</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.478714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.498652</td>\n",
       "      <td>0.222876</td>\n",
       "      <td>0.541346</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619971</th>\n",
       "      <td>D5</td>\n",
       "      <td>0.059038</td>\n",
       "      <td>None</td>\n",
       "      <td>0.00</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>0.320049</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>FL</td>\n",
       "      <td>0.367342</td>\n",
       "      <td>0.203795</td>\n",
       "      <td>0.810345</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.552026</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.527632</td>\n",
       "      <td>0.220915</td>\n",
       "      <td>0.501514</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619985</th>\n",
       "      <td>C1</td>\n",
       "      <td>0.416910</td>\n",
       "      <td>Merchandiser</td>\n",
       "      <td>0.60</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>0.378689</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>GA</td>\n",
       "      <td>0.583896</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.775862</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.607545</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.721374</td>\n",
       "      <td>0.532680</td>\n",
       "      <td>0.586735</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619992</th>\n",
       "      <td>D1</td>\n",
       "      <td>0.795918</td>\n",
       "      <td>sales manager</td>\n",
       "      <td>0.30</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.439827</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>NY</td>\n",
       "      <td>0.665166</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.844828</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.665401</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.736005</td>\n",
       "      <td>0.591503</td>\n",
       "      <td>0.689843</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619993</th>\n",
       "      <td>B3</td>\n",
       "      <td>0.154519</td>\n",
       "      <td>Office Administrator</td>\n",
       "      <td>0.40</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.303266</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>MD</td>\n",
       "      <td>0.266567</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.551724</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.677848</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.584075</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.769788</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620003</th>\n",
       "      <td>A1</td>\n",
       "      <td>0.293732</td>\n",
       "      <td>Coordinator of RSVP</td>\n",
       "      <td>0.05</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.335957</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>FL</td>\n",
       "      <td>0.330583</td>\n",
       "      <td>0.203795</td>\n",
       "      <td>0.362069</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.530085</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.646092</td>\n",
       "      <td>0.168627</td>\n",
       "      <td>0.541346</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620005</th>\n",
       "      <td>D3</td>\n",
       "      <td>0.161079</td>\n",
       "      <td>Painter</td>\n",
       "      <td>0.20</td>\n",
       "      <td>RENT</td>\n",
       "      <td>0.245794</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>FL</td>\n",
       "      <td>0.464616</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.982759</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.319143</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.519882</td>\n",
       "      <td>0.637908</td>\n",
       "      <td>0.138792</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620009</th>\n",
       "      <td>E2</td>\n",
       "      <td>0.295918</td>\n",
       "      <td>None</td>\n",
       "      <td>0.00</td>\n",
       "      <td>OWN</td>\n",
       "      <td>0.267894</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>OH</td>\n",
       "      <td>0.736184</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.827586</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.530085</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.615948</td>\n",
       "      <td>0.271895</td>\n",
       "      <td>0.528706</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>162959 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       sub_grade  loan_amnt                                emp_title  \\\n",
       "0             B2   0.125364                                     None   \n",
       "5             B5   0.329446                                     UCLA   \n",
       "6             C1   0.241983      Va. Dept of Conservation/Recreation   \n",
       "7             B1   0.067055                                   Target   \n",
       "9             D1   0.008746                 Internal revenue Service   \n",
       "13            A1   0.247813             Network Interpreting Service   \n",
       "14            B4   0.591837                           Osram Sylvania   \n",
       "15            B3   0.271137                                Value Air   \n",
       "16            B3   0.271137                         Wells Fargo Bank   \n",
       "18            B1   0.416910           Winfield Pathology Consultants   \n",
       "19            C2   0.416910                              nyc transit   \n",
       "20            B3   0.096210  Shands Hospital at the University of Fl   \n",
       "21            B3   0.227405                           Oakridge homes   \n",
       "22            A3   0.107143                                     None   \n",
       "23            A4   0.907434       Audubon Mutual Housing Corporation   \n",
       "24            A5   0.125364                   Good Samaritan Society   \n",
       "25            C5   0.183673                     GREG BARRETT DRYWALL   \n",
       "26            B2   0.341108                          Sharp Lawn Inc.   \n",
       "28            A4   0.416910                          Gateway Hospice   \n",
       "29            B4   0.154519                       Cox Communications   \n",
       "31            A4   0.329446                           John Wiley Jr.   \n",
       "33            D2   0.107872                            citizens bank   \n",
       "34            A1   0.154519                Stewart Enterprises, Inc.   \n",
       "36            A5   0.125364                       STERIS Corporation   \n",
       "38            A1   0.271137                                 Helicoil   \n",
       "39            A2   0.300292           cognizant technology solutions   \n",
       "40            B1   0.416910                         Caterpillar Inc.   \n",
       "41            B1   0.725948                    City of Santa Monica    \n",
       "42            B2   0.183673                       Aerotek Scientific   \n",
       "44            B1   0.329446                            Scott & White   \n",
       "...          ...        ...                                      ...   \n",
       "619822        A3   0.096210                          TELESERVICE REP   \n",
       "619826        E1   0.212828                      heat treat operator   \n",
       "619830        C3   1.000000                                    Sales   \n",
       "619841        B1   0.854227    Accounts Payable Coordinator and Leas   \n",
       "619843        B3   0.125364                             Qc inspector   \n",
       "619844        B4   0.224490                                 Operator   \n",
       "619845        C3   0.154519                        Account Executive   \n",
       "619858        A5   0.037901                           medical biller   \n",
       "619865        B1   1.000000                                  Manager   \n",
       "619871        C3   0.183673                              Hotel Clerk   \n",
       "619875        A2   0.679300                                    owner   \n",
       "619878        E4   0.187318                                     None   \n",
       "619898        C5   0.854227                                  Teacher   \n",
       "619902        B5   0.329446                utility Pre-Craft Trainee   \n",
       "619910        D4   0.353499                          Sales Associate   \n",
       "619920        D1   0.203353                      Sub teacher / Coach   \n",
       "619932        C1   0.329446                                  Groomer   \n",
       "619936        C5   0.189504            Asst. General Superintendent    \n",
       "619943        C2   0.562682                           Letter Carrier   \n",
       "619950        E4   0.708455                             shop foreman   \n",
       "619952        D3   0.150875                                    Sales   \n",
       "619953        D1   0.150875                Elementary School Teacher   \n",
       "619968        D5   0.329446                               Accounting   \n",
       "619971        D5   0.059038                                     None   \n",
       "619985        C1   0.416910                             Merchandiser   \n",
       "619992        D1   0.795918                            sales manager   \n",
       "619993        B3   0.154519                     Office Administrator   \n",
       "620003        A1   0.293732                      Coordinator of RSVP   \n",
       "620005        D3   0.161079                                  Painter   \n",
       "620009        E2   0.295918                                     None   \n",
       "\n",
       "        emp_length home_ownership  annual_inc             purpose addr_state  \\\n",
       "0             1.00           RENT    0.230473         credit_card         AZ   \n",
       "5             1.00            OWN    0.378689  debt_consolidation         CA   \n",
       "6             0.05           RENT    0.259499  debt_consolidation         VA   \n",
       "7             0.30           RENT    0.169337         credit_card         IL   \n",
       "9             0.05           RENT    0.250524  debt_consolidation         MO   \n",
       "13            0.60           RENT    0.382761  debt_consolidation         CA   \n",
       "14            1.00           RENT    0.422457  debt_consolidation         FL   \n",
       "15            1.00            OWN    0.325946         credit_card         TX   \n",
       "16            0.50           RENT    0.325946  debt_consolidation         CA   \n",
       "18            0.20       MORTGAGE    0.405264         credit_card         IL   \n",
       "19            0.90           RENT    0.349662  debt_consolidation         NY   \n",
       "20            1.00       MORTGAGE    0.423690  debt_consolidation         FL   \n",
       "21            0.05           RENT    0.235783         credit_card         MN   \n",
       "22            0.70       MORTGAGE    0.186441  debt_consolidation         NY   \n",
       "23            0.50       MORTGAGE    0.378689  debt_consolidation         NJ   \n",
       "24            0.20           RENT    0.230711  debt_consolidation         OR   \n",
       "25            0.70           RENT    0.275780         credit_card         CA   \n",
       "26            1.00           RENT    0.300132         credit_card         KY   \n",
       "28            0.10           RENT    0.312241  debt_consolidation         OH   \n",
       "29            0.10           RENT    0.286257  debt_consolidation         AZ   \n",
       "31            1.00           RENT    0.354556  debt_consolidation         NJ   \n",
       "33            1.00           RENT    0.338344  debt_consolidation         RI   \n",
       "34            1.00       MORTGAGE    0.313964  debt_consolidation         LA   \n",
       "36            1.00       MORTGAGE    0.416111  debt_consolidation         OH   \n",
       "38            1.00           RENT    0.349662         credit_card         CT   \n",
       "39            0.50           RENT    0.369714  debt_consolidation         CT   \n",
       "40            0.80       MORTGAGE    0.387084  debt_consolidation         IL   \n",
       "41            0.90           RENT    0.428509         credit_card         CA   \n",
       "42            0.05           RENT    0.296920  debt_consolidation         CA   \n",
       "44            0.10           RENT    0.315100         credit_card         TX   \n",
       "...            ...            ...         ...                 ...        ...   \n",
       "619822        1.00       MORTGAGE    0.345252  debt_consolidation         WI   \n",
       "619826        1.00       MORTGAGE    0.309318  debt_consolidation         TN   \n",
       "619830        0.50       MORTGAGE    0.535303  debt_consolidation         NC   \n",
       "619841        0.80       MORTGAGE    0.349662  debt_consolidation         OR   \n",
       "619843        0.50       MORTGAGE    0.315100         credit_card         CA   \n",
       "619844        0.10       MORTGAGE    0.416111         credit_card         TX   \n",
       "619845        0.40           RENT    0.349662  debt_consolidation         NY   \n",
       "619858        0.80       MORTGAGE    0.263764         credit_card         CA   \n",
       "619865        0.10            OWN    0.496134         credit_card         CA   \n",
       "619871        1.00           RENT    0.322468  debt_consolidation         NY   \n",
       "619875        0.05       MORTGAGE    0.378689         credit_card         TX   \n",
       "619878        0.00            OWN    0.185618         credit_card         FL   \n",
       "619898        1.00       MORTGAGE    0.393431  debt_consolidation         CA   \n",
       "619902        0.10           RENT    0.360074  debt_consolidation         CA   \n",
       "619910        0.60       MORTGAGE    0.290248  debt_consolidation         TX   \n",
       "619920        0.20            OWN    0.245794  debt_consolidation         CA   \n",
       "619932        0.40       MORTGAGE    0.338344  debt_consolidation         CO   \n",
       "619936        0.50       MORTGAGE    0.376943         credit_card         TX   \n",
       "619943        1.00       MORTGAGE    0.381407         credit_card         CA   \n",
       "619950        1.00       MORTGAGE    0.338344  debt_consolidation         TN   \n",
       "619952        0.50            OWN    0.279550  debt_consolidation         MS   \n",
       "619953        0.30       MORTGAGE    0.300132  debt_consolidation         SC   \n",
       "619968        0.90           RENT    0.303266  debt_consolidation         NC   \n",
       "619971        0.00       MORTGAGE    0.320049  debt_consolidation         FL   \n",
       "619985        0.60       MORTGAGE    0.378689  debt_consolidation         GA   \n",
       "619992        0.30           RENT    0.439827  debt_consolidation         NY   \n",
       "619993        0.40           RENT    0.303266  debt_consolidation         MD   \n",
       "620003        0.05           RENT    0.335957  debt_consolidation         FL   \n",
       "620005        0.20           RENT    0.245794  debt_consolidation         FL   \n",
       "620009        0.00            OWN    0.267894  debt_consolidation         OH   \n",
       "\n",
       "             dti  delinq_2yrs  earliest_cr_line  inq_last_6mths  open_acc  \\\n",
       "0       0.691423     0.000000          0.534483           0.125  0.319143   \n",
       "5       0.269567     0.000000          0.603448           0.000  0.590484   \n",
       "6       0.252063     0.000000          0.862069           0.125  0.370513   \n",
       "7       0.314079     0.000000          0.844828           0.250  0.572058   \n",
       "9       0.507877     0.000000          0.913793           0.125  0.572058   \n",
       "13      0.246562     0.000000          0.810345           0.000  0.505829   \n",
       "14      0.330583     0.000000          0.500000           0.000  0.478714   \n",
       "15      0.279570     0.000000          0.534483           0.000  0.505829   \n",
       "16      0.400350     0.000000          0.844828           0.000  0.447974   \n",
       "18      0.736184     0.000000          0.827586           0.000  0.505829   \n",
       "19      0.380595     0.000000          0.844828           0.125  0.478714   \n",
       "20      0.140785     0.203795          0.517241           0.000  0.590484   \n",
       "21      0.304826     0.000000          0.896552           0.000  0.505829   \n",
       "22      0.508627     0.000000          0.568966           0.000  0.447974   \n",
       "23      0.350838     0.000000          0.465517           0.000  0.590484   \n",
       "24      0.298325     0.000000          0.879310           0.000  0.505829   \n",
       "25      0.158790     0.000000          0.913793           0.125  0.447974   \n",
       "26      0.295074     0.000000          0.879310           0.250  0.530085   \n",
       "28      0.212053     0.000000          0.862069           0.000  0.478714   \n",
       "29      0.265566     0.000000          0.913793           0.125  0.478714   \n",
       "31      0.417604     0.000000          0.689655           0.000  0.638286   \n",
       "33      0.500375     0.000000          0.862069           0.000  0.478714   \n",
       "34      0.133533     0.000000          0.706897           0.125  0.447974   \n",
       "36      0.408352     0.000000          0.706897           0.000  0.665401   \n",
       "38      0.318580     0.000000          0.655172           0.125  0.572058   \n",
       "39      0.271318     0.000000          0.827586           0.000  0.412486   \n",
       "40      0.228057     0.000000          0.655172           0.250  0.530085   \n",
       "41      0.392848     0.000000          0.775862           0.000  0.572058   \n",
       "42      0.184546     0.000000          0.862069           0.000  0.478714   \n",
       "44      0.202801     0.000000          0.793103           0.125  0.572058   \n",
       "...          ...          ...               ...             ...       ...   \n",
       "619822  0.025756     0.000000          0.724138           0.000  0.370513   \n",
       "619826  0.149287     0.000000          0.896552           0.000  0.412486   \n",
       "619830  0.307327     0.000000          0.344828           0.000  0.607545   \n",
       "619841  0.231558     0.000000          0.706897           0.000  0.623428   \n",
       "619843  0.202551     0.000000          0.913793           0.000  0.572058   \n",
       "619844  0.930233     0.323008          0.913793           0.000  0.572058   \n",
       "619845  0.527632     0.000000          0.896552           0.125  0.689656   \n",
       "619858  0.211303     0.000000          0.862069           0.125  0.607545   \n",
       "619865  0.218055     0.000000          0.706897           0.000  0.552026   \n",
       "619871  0.271318     0.203795          0.706897           0.000  0.607545   \n",
       "619875  0.532883     0.000000          0.810345           0.125  0.552026   \n",
       "619878  0.726182     0.000000          0.810345           0.375  0.530085   \n",
       "619898  0.413603     0.000000          0.844828           0.125  0.638286   \n",
       "619902  0.212303     0.000000          0.672414           0.125  0.623428   \n",
       "619910  0.225806     0.000000          0.896552           0.000  0.505829   \n",
       "619920  0.465616     0.000000          0.793103           0.000  0.782999   \n",
       "619932  0.664666     0.000000          0.896552           0.250  0.731629   \n",
       "619936  0.556389     0.000000          0.879310           0.250  0.505829   \n",
       "619943  0.320830     0.203795          0.465517           0.250  0.552026   \n",
       "619950  0.780695     0.000000          0.775862           0.125  0.607545   \n",
       "619952  0.473368     0.000000          0.896552           0.125  0.552026   \n",
       "619953  0.667667     0.000000          0.896552           0.000  0.552026   \n",
       "619968  0.668667     0.203795          0.810345           0.000  0.478714   \n",
       "619971  0.367342     0.203795          0.810345           0.125  0.552026   \n",
       "619985  0.583896     0.000000          0.775862           0.000  0.607545   \n",
       "619992  0.665166     0.000000          0.844828           0.000  0.665401   \n",
       "619993  0.266567     0.000000          0.551724           0.125  0.677848   \n",
       "620003  0.330583     0.203795          0.362069           0.000  0.530085   \n",
       "620005  0.464616     0.000000          0.982759           0.125  0.319143   \n",
       "620009  0.736184     0.000000          0.827586           0.125  0.530085   \n",
       "\n",
       "         pub_rec  revol_bal  revol_util  total_acc  collections_12_mths_ex_med  \n",
       "0       0.000000   0.662532    0.547059   0.327121                    0.000000  \n",
       "5       0.000000   0.699854    0.438562   0.667499                    0.000000  \n",
       "6       0.000000   0.643968    0.599346   0.327121                    0.000000  \n",
       "7       0.000000   0.619215    0.281699   0.376658                    0.000000  \n",
       "9       0.000000   0.611177    0.532680   0.564987                    0.000000  \n",
       "13      0.000000   0.619130    0.150980   0.616404                    0.000000  \n",
       "14      0.000000   0.722116    0.590196   0.696900                    0.000000  \n",
       "15      0.000000   0.641281    0.538562   0.541346                    0.000000  \n",
       "16      0.000000   0.681012    0.600000   0.486824                    0.000000  \n",
       "18      0.000000   0.662832    0.613725   0.643151                    0.000000  \n",
       "19      0.000000   0.603852    0.376471   0.376658                    0.000000  \n",
       "20      0.000000   0.606616    0.246405   0.735781                    0.000000  \n",
       "21      0.000000   0.610287    0.386275   0.398406                    0.000000  \n",
       "22      0.000000   0.648840    0.567974   0.398406                    0.000000  \n",
       "23      0.000000   0.699300    0.179085   0.596989                    0.000000  \n",
       "24      0.000000   0.536314    0.191503   0.471294                    0.000000  \n",
       "25      0.000000   0.606650    0.395425   0.230212                    0.000000  \n",
       "26      0.000000   0.648122    0.373856   0.398406                    0.000000  \n",
       "28      0.000000   0.613071    0.329412   0.606870                    0.000000  \n",
       "29      0.000000   0.619092    0.434641   0.398406                    0.000000  \n",
       "31      0.000000   0.630407    0.137255   0.586735                    0.000000  \n",
       "33      0.000000   0.705303    0.647059   0.376658                    0.000000  \n",
       "34      0.000000   0.565387    0.212418   0.616404                    0.000000  \n",
       "36      0.000000   0.780485    0.405882   0.675153                    0.000000  \n",
       "38      0.000000   0.664398    0.127451   0.501514                    0.000000  \n",
       "39      0.000000   0.651447    0.237908   0.266493                    0.000000  \n",
       "40      0.000000   0.652673    0.416340   0.616404                    0.000000  \n",
       "41      0.000000   0.707611    0.405229   0.606870                    0.000000  \n",
       "42      0.000000   0.654545    0.607190   0.398406                    0.000000  \n",
       "44      0.000000   0.654402    0.340523   0.398406                    0.000000  \n",
       "...          ...        ...         ...        ...                         ...  \n",
       "619822  0.000000   0.325151    0.003268   0.501514                    0.000000  \n",
       "619826  0.000000   0.000000    0.000000   0.376658                    0.000000  \n",
       "619830  0.000000   0.725668    0.465359   0.643151                    0.000000  \n",
       "619841  0.000000   0.691853    0.341830   0.596989                    0.000000  \n",
       "619843  0.000000   0.805274    0.143791   0.553424                    0.000000  \n",
       "619844  0.000000   0.605433    0.327451   0.667499                    0.000000  \n",
       "619845  0.000000   0.602946    0.201307   0.703779                    0.000000  \n",
       "619858  0.000000   0.577888    0.110458   0.758918                    0.356207  \n",
       "619865  0.646241   0.609198    0.505229   0.616404                    0.000000  \n",
       "619871  0.000000   0.652738    0.379739   0.717036                    0.000000  \n",
       "619875  0.000000   0.669644    0.426144   0.486824                    0.000000  \n",
       "619878  0.250000   0.579353    0.252288   0.515450                    0.000000  \n",
       "619898  0.000000   0.717901    0.458824   0.741753                    0.000000  \n",
       "619902  0.000000   0.659077    0.291503   0.586735                    0.000000  \n",
       "619910  0.000000   0.652867    0.462092   0.576079                    0.000000  \n",
       "619920  0.000000   0.667802    0.307190   0.723429                    0.000000  \n",
       "619932  0.000000   0.632275    0.194118   0.741753                    0.000000  \n",
       "619936  0.000000   0.598433    0.298039   0.398406                    0.000000  \n",
       "619943  0.000000   0.646684    0.390196   0.682597                    0.000000  \n",
       "619950  0.000000   0.697124    0.413072   0.501514                    0.000000  \n",
       "619952  0.250000   0.552234    0.177124   0.553424                    0.000000  \n",
       "619953  0.000000   0.621744    0.403268   0.616404                    0.000000  \n",
       "619968  0.000000   0.498652    0.222876   0.541346                    0.000000  \n",
       "619971  0.250000   0.527632    0.220915   0.501514                    0.000000  \n",
       "619985  0.000000   0.721374    0.532680   0.586735                    0.000000  \n",
       "619992  0.000000   0.736005    0.591503   0.689843                    0.000000  \n",
       "619993  0.000000   0.584075    0.066667   0.769788                    0.000000  \n",
       "620003  0.000000   0.646092    0.168627   0.541346                    0.000000  \n",
       "620005  0.000000   0.519882    0.637908   0.138792                    0.000000  \n",
       "620009  0.000000   0.615948    0.271895   0.528706                    0.000000  \n",
       "\n",
       "[162959 rows x 18 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "features_raw=features_raw.drop(['emp_title','addr_state'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sub_grade                      object\n",
       "loan_amnt                     float64\n",
       "emp_length                    float64\n",
       "home_ownership                 object\n",
       "annual_inc                    float64\n",
       "purpose                        object\n",
       "dti                           float64\n",
       "delinq_2yrs                   float64\n",
       "earliest_cr_line              float64\n",
       "inq_last_6mths                float64\n",
       "open_acc                      float64\n",
       "pub_rec                       float64\n",
       "revol_bal                     float64\n",
       "revol_util                    float64\n",
       "total_acc                     float64\n",
       "collections_12_mths_ex_med    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_raw.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "53 total features after one-hot encoding.\n",
      "['loan_amnt', 'emp_length', 'annual_inc', 'dti', 'delinq_2yrs', 'earliest_cr_line', 'inq_last_6mths', 'open_acc', 'pub_rec', 'revol_bal', 'revol_util', 'total_acc', 'collections_12_mths_ex_med', 'sub_grade_A1', 'sub_grade_A2', 'sub_grade_A3', 'sub_grade_A4', 'sub_grade_A5', 'sub_grade_B1', 'sub_grade_B2', 'sub_grade_B3', 'sub_grade_B4', 'sub_grade_B5', 'sub_grade_C1', 'sub_grade_C2', 'sub_grade_C3', 'sub_grade_C4', 'sub_grade_C5', 'sub_grade_D1', 'sub_grade_D2', 'sub_grade_D3', 'sub_grade_D4', 'sub_grade_D5', 'sub_grade_E1', 'sub_grade_E2', 'sub_grade_E3', 'sub_grade_E4', 'sub_grade_E5', 'sub_grade_F1', 'sub_grade_F2', 'sub_grade_F3', 'sub_grade_F4', 'sub_grade_F5', 'sub_grade_G1', 'sub_grade_G2', 'sub_grade_G3', 'sub_grade_G4', 'sub_grade_G5', 'home_ownership_MORTGAGE', 'home_ownership_OWN', 'home_ownership_RENT', 'purpose_credit_card', 'purpose_debt_consolidation']\n"
     ]
    }
   ],
   "source": [
    "# One-hot encode the 'features_raw' data using pandas.get_dummies()\n",
    "feat = pd.get_dummies(features_raw)\n",
    "\n",
    "\n",
    "#print(income.head(n=10))\n",
    "# Print the number of features after one-hot encoding\n",
    "encoded = list(feat.columns)\n",
    "print (\"{} total features after one-hot encoding.\".format(len(encoded)))\n",
    "\n",
    "# Uncomment the following line to see the encoded feature names\n",
    "print (encoded)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! There are the original continuous features, and now a feature for all types of home ownership, purposes for the loan, and different address states!\n",
    "\n",
    "## Shuffle and split data\n",
    "\n",
    "Specifically, I believe that this data is ordered by the origination date, so it is important to shuffle the data. Luckily this happens anyway with train_test_split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set has 130367 samples.\n",
      "Testing set has 32592 samples.\n"
     ]
    }
   ],
   "source": [
    "# Import train_test_split\n",
    "from sklearn.model_selection import train_test_split #sklearn 0.18.1 and up\n",
    "# Split the 'features' and 'income' data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(feat, labels, test_size = 0.2, random_state = 1)\n",
    "\n",
    "# Show the results of the split\n",
    "print (\"Training set has {} samples.\".format(X_train.shape[0]))\n",
    "print (\"Testing set has {} samples.\".format(X_test.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing different models\n",
    "\n",
    "For almost all supervised learning, a version of Decision trees is necessary to test, if only for it's extreme intuitiveness. At the very least, it may be used to shrink the feature space and improve speed for other algorithms. I will use Gradient Boosting regression for this. Secondly, I want to test a kernel based non-linear regression, and I will use KNN for this. Finally I want to test a high order polynomial regression.\n",
    "\n",
    "### defining model tester"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "def train_predict(learner, sample_size, X_train, y_train, X_test, y_test): \n",
    "    '''\n",
    "    inputs:\n",
    "       - learner: the learning algorithm to be trained and predicted on\n",
    "       - sample_size: the size of samples (number) to be drawn from training set\n",
    "       - X_train: features training set\n",
    "       - y_train: income training set\n",
    "       - X_test: features testing set\n",
    "       - y_test: income testing set\n",
    "    '''\n",
    "    \n",
    "    results = {}\n",
    "    \n",
    "    # Fit the learner to the training data using slicing with 'sample_size'\n",
    "    start = time() # Get start time\n",
    "    learner = clf.fit(X_train.sample(n=sample_size,random_state=1),y_train.sample(n=sample_size,random_state=1)) #df.sample(frac=percent) maybe too?\n",
    "    end = time() # Get end time\n",
    "    \n",
    "    # Calculate the training time\n",
    "    results['train_time'] = end-start\n",
    "        \n",
    "    # Get the predictions on the test set,\n",
    "    #       then get predictions on the first 300 training samples\n",
    "    start = time() # Get start time\n",
    "    predictions_test = clf.predict(X_test)\n",
    "    predictions_train = clf.predict(X_train[:300])\n",
    "    end = time() # Get end time\n",
    "    \n",
    "    # Calculate the total prediction time\n",
    "    results['pred_time'] = end-start\n",
    "            \n",
    "    # Compute mean square error on the first 300 training samples\n",
    "    results['mse_train'] = mean_squared_error(y_train[:300],predictions_train)\n",
    "        \n",
    "    # Compute mean square error on test set\n",
    "    results['mse_test'] = mean_squared_error(y_test,predictions_test)\n",
    "    \n",
    "    # Compute R^2 on the the first 300 training samples\n",
    "    results['R2_train'] = r2_score(y_train[:300],predictions_train)\n",
    "        \n",
    "    # Compute R^2 on the test set\n",
    "    results['R2_test'] = r2_score(y_test,predictions_test)\n",
    "       \n",
    "    # Success\n",
    "    print (\"{} trained on {} samples.\".format(learner.__class__.__name__, sample_size))\n",
    "        \n",
    "    # Return the results\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline trained on 1304 samples.\n",
      "Pipeline trained on 13037 samples.\n",
      "Pipeline trained on 130367 samples.\n",
      "GradientBoostingRegressor trained on 1304 samples.\n",
      "GradientBoostingRegressor trained on 13037 samples.\n",
      "GradientBoostingRegressor trained on 130367 samples.\n",
      "Ridge trained on 1304 samples.\n",
      "Ridge trained on 13037 samples.\n",
      "Ridge trained on 130367 samples.\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZK/qh/dmAYmw3V0T+CdNCOA+mQFAb5gvgD5gvjtK2FEA/EfkNpru6G4B2F7G9l2HeOrJG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\nhCRJkqSBOYCQJEmSNDAHEJIkSZIG9v/BVKUMMQv4wQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1197acdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.linear_model import LinearRegression, Ridge\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.pipeline import make_pipeline\n",
    "# Initialize models\n",
    "\n",
    "clf_A = make_pipeline(PolynomialFeatures(2,interaction_only=True),LinearRegression())\n",
    "clf_B = GradientBoostingRegressor(random_state=2)\n",
    "clf_C = Ridge()\n",
    "\n",
    "# Calculate the number of samples for 1%, 10%, and 100% of the training data\n",
    "samples_1 = len(y_train.sample(frac=.01))\n",
    "samples_10 = len(y_train.sample(frac=.1))\n",
    "samples_100 = len(y_train.sample(frac=1))\n",
    "\n",
    "# Collect results on the learners\n",
    "results = {}\n",
    "for clf in [clf_A, clf_B, clf_C]: #clf_A, \n",
    "    clf_name = clf.__class__.__name__\n",
    "    results[clf_name] = {}\n",
    "    for i, samples in enumerate([samples_1, samples_10, samples_100]):\n",
    "        results[clf_name][i] = \\\n",
    "        train_predict(clf, samples, X_train, y_train, X_test, y_test)\n",
    "#         print(results[clf])\n",
    "\n",
    "# Run metrics visualization for the three supervised learning models chosen\n",
    "vs.evaluate(results,.5,.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking the polynomial regression\n",
    "\n",
    "These values look huge, I need to check why they are breaking the graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'GradientBoostingRegressor': {0: {'R2_test': -0.027685254500754652,\n",
       "   'R2_train': 0.01810268176249441,\n",
       "   'mse_test': 0.090920842956927345,\n",
       "   'mse_train': 0.091524044036148666,\n",
       "   'pred_time': 0.0909121036529541,\n",
       "   'train_time': 0.24426484107971191},\n",
       "  1: {'R2_test': 0.03819812536723366,\n",
       "   'R2_train': 0.052787195008189847,\n",
       "   'mse_test': 0.085092042350696048,\n",
       "   'mse_train': 0.088291051279462518,\n",
       "   'pred_time': 0.07419967651367188,\n",
       "   'train_time': 1.8192949295043945},\n",
       "  2: {'R2_test': 0.049926645164842354,\n",
       "   'R2_train': 0.080675475466687852,\n",
       "   'mse_test': 0.084054402760203303,\n",
       "   'mse_train': 0.085691545036429229,\n",
       "   'pred_time': 0.08397531509399414,\n",
       "   'train_time': 25.46335196495056}},\n",
       " 'Pipeline': {0: {'R2_test': -2.5892925015822432e+22,\n",
       "   'R2_train': -1.8912946891109295e+22,\n",
       "   'mse_test': 2.2907855870742744e+21,\n",
       "   'mse_train': 1.7629026497621302e+21,\n",
       "   'pred_time': 1.3424639701843262,\n",
       "   'train_time': 1.1775388717651367},\n",
       "  1: {'R2_test': -9.4839930127228027e+18,\n",
       "   'R2_train': -2.1152683435113517e+19,\n",
       "   'mse_test': 8.3906296751651302e+17,\n",
       "   'mse_train': 1.9716716750719946e+18,\n",
       "   'pred_time': 1.4754998683929443,\n",
       "   'train_time': 4.059266090393066},\n",
       "  2: {'R2_test': -1.4727784572763279e+19,\n",
       "   'R2_train': 0.076118319149359404,\n",
       "   'mse_test': 1.3029890060008484e+18,\n",
       "   'mse_train': 0.086116324051220161,\n",
       "   'pred_time': 1.4393022060394287,\n",
       "   'train_time': 50.93333673477173}},\n",
       " 'Ridge': {0: {'R2_test': -0.0048736434966243358,\n",
       "   'R2_train': -0.017429963309490315,\n",
       "   'mse_test': 0.088902665803350656,\n",
       "   'mse_train': 0.094836092365322866,\n",
       "   'pred_time': 0.009238958358764648,\n",
       "   'train_time': 0.04173588752746582},\n",
       "  1: {'R2_test': 0.029448528206297397,\n",
       "   'R2_train': 0.035674368925567634,\n",
       "   'mse_test': 0.085866132224927366,\n",
       "   'mse_train': 0.089886162111194129,\n",
       "   'pred_time': 0.007704973220825195,\n",
       "   'train_time': 0.02990102767944336},\n",
       "  2: {'R2_test': 0.033955390217294434,\n",
       "   'R2_train': 0.056455815485514149,\n",
       "   'mse_test': 0.085467403439692946,\n",
       "   'mse_train': 0.087949093952680885,\n",
       "   'pred_time': 0.00740361213684082,\n",
       "   'train_time': 0.21401071548461914}}}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression Postmortem\n",
    "\n",
    "Woof, terrible bias, almost no structure is described by these models. I'm actually not sure what to make of such extreme values, but negative R^2 are bad, and large MSE is bad. Likely, they are too simple. I'd like to try and overfit, before I move towards a more modest approach, such as classification. If I can tune a decision tree from overfitting towards something useful, maybe the metric and this regression still have hope.\n",
    "\n",
    "### Attempting to overfit.\n",
    "\n",
    "For this I will use a standard decision tree, and attempt to overfit with hopes for Post-pruning "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'R2_test': -0.96710235974236092,\n",
       " 'R2_train': 0.99999998067169482,\n",
       " 'mse_test': 0.17403247146639395,\n",
       " 'mse_train': 1.6953117790367594e-09}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "results2={}\n",
    "clf_D = DecisionTreeRegressor(random_state=1)\n",
    "clf_D.fit(X_train,y_train)\n",
    "predictions_test = clf_D.predict(X_test)\n",
    "predictions_train = clf_D.predict(X_train[:3000])\n",
    "end = time() # Get end time\n",
    "\n",
    "# Calculate the total prediction time\n",
    "\n",
    "# Compute mean square error on the first 300 training samples\n",
    "results2['mse_train'] = mean_squared_error(y_train[:3000],predictions_train)\n",
    "\n",
    "# Compute mean square error on test set\n",
    "results2['mse_test'] = mean_squared_error(y_test,predictions_test)\n",
    "\n",
    "# Compute R^2 on the the first 300 training samples\n",
    "results2['R2_train'] = r2_score(y_train[:3000],predictions_train)\n",
    "\n",
    "# Compute R^2 on the test set\n",
    "results2['R2_test'] = r2_score(y_test,predictions_test)\n",
    "\n",
    "results2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overfitting with Decision Trees - Success?\n",
    "\n",
    "Looks like we have classic high variance now (great training scores, terrible test scores)! This makes me more confident that the metric is tenable. \n",
    "\n",
    "## Gridsearch Decision Tree model tuning\n",
    "\n",
    "Now I'll try to tune the decision tree using gridsearch CV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unoptimized model\n",
      "------\n",
      "MSE score on testing data: 0.1725\n",
      "R2-score on testing data: -0.9501\n",
      "\n",
      "Optimized Model\n",
      "------\n",
      "Final MSE score on the testing data: 0.0858\n",
      "Final R2-score on the testing data: 0.0305\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import make_scorer\n",
    "# Initialize the classifier\n",
    "clf = DecisionTreeRegressor(random_state=2)\n",
    "\n",
    "# Create the parameters list you wish to tune\n",
    "parameters = {'max_depth':(4,6,20,None),'min_samples_split':(2,50),'min_samples_leaf':(1,101)} #',,\n",
    "\n",
    "# Make an R2_score scoring object\n",
    "scorer = make_scorer(r2_score)\n",
    "\n",
    "# TODO: Perform grid search on the classifier using 'scorer' as the scoring method\n",
    "grid_obj = GridSearchCV(clf,parameters,scoring=scorer,n_jobs=-1)\n",
    "\n",
    "# TODO: Fit the grid search object to the training data and find the optimal parameters\n",
    "grid_fit = grid_obj.fit(X_train,y_train)\n",
    "\n",
    "# Get the estimator\n",
    "best_clf = grid_fit.best_estimator_\n",
    "\n",
    "# Make predictions using the unoptimized and model\n",
    "predictions = (clf.fit(X_train, y_train)).predict(X_test)\n",
    "best_predictions = best_clf.predict(X_test)\n",
    "# Report the before-and-afterscores\n",
    "print (\"Unoptimized model\\n------\")\n",
    "print (\"MSE score on testing data: {:.4f}\".format(mean_squared_error(y_test, predictions)))\n",
    "print (\"R2-score on testing data: {:.4f}\".format(r2_score(y_test, predictions)))\n",
    "print (\"\\nOptimized Model\\n------\")\n",
    "print (\"Final MSE score on the testing data: {:.4f}\".format(mean_squared_error(y_test, best_predictions)))\n",
    "print (\"Final R2-score on the testing data: {:.4f}\".format(r2_score(y_test, best_predictions)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interestingly, the best boosted regressor is the \"stock\" one. It is pretty poor. I think it is time to try classification instead of regression\n",
    "\n",
    "# Classification of Loan Data\n",
    "\n",
    "In this case, we take the classification data, which takes \"current\" and \"fully paid\" loans and groups, them, and groups all other delinquent loans together. First, we must get a new test split, and then we will get a baseline naive predictor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set has 130367 samples.\n",
      "Testing set has 32592 samples.\n"
     ]
    }
   ],
   "source": [
    "# Import train_test_split\n",
    "from sklearn.model_selection import train_test_split #sklearn 0.18.1 and up\n",
    "# Split the 'features' and 'income' data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(feat, clabels, test_size = 0.2, random_state = 1)\n",
    "\n",
    "# Show the results of the split\n",
    "print (\"Training set has {} samples.\".format(X_train.shape[0]))\n",
    "print (\"Testing set has {} samples.\".format(X_test.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Predictor\n",
    "\n",
    "Here we make a naive predictor, that simply guesses every loan is \"good\" (or bad, if more loans are bad than good). We need to beat these scores in order to really say that our model is doing better than this in order to say it is meaningful. For classification I will test accuracy, $f_{0.5}$ score, ROC AUC, and cohen's kappa to determine the quality of the model with this highly imbalanced dataset. I use $f_{0.5}$ specifically instead of other beta values because I care most about being precise! I'd rather misclassify a few good loans as bad than have bad loans slip through. Missing a good loan carries much less risk than funding a bad loan. I could look at tuning the classification by implementing a cost of misclassification...\n",
    "ROC AUC takes into consideration true positive rates and false positive rates, and can deal with unbalanced data.\n",
    "Cohen's kappa is specifically for unbalanced data, and describes the agreement "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Predictor: [Accuracy score: 0.7983, F-score: 0.8318, ROC AUC: 0.5000, Cohen's k: 0.0000, precision: 0.7983]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.metrics import accuracy_score,fbeta_score, roc_auc_score, cohen_kappa_score, precision_score\n",
    "allones=np.ones(len(clabels))\n",
    "acc=accuracy_score(clabels,allones)\n",
    "fbeta=fbeta_score(clabels,allones,beta=.5)\n",
    "auc=roc_auc_score(clabels,allones)\n",
    "cohen=cohen_kappa_score(clabels,allones)\n",
    "prec=precision_score(clabels,allones)\n",
    "\n",
    "# Print the results \n",
    "print (\"Naive Predictor: [Accuracy score: {:.4f}, F-score: {:.4f}, ROC AUC: {:.4f}, Cohen's k: {:.4f}, precision: {:.4f}]\".format(acc, fbeta,auc,cohen,prec))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow! accuracy and F-score are pretty high. likely, this is because there is an order of magnitude more fully paid loans than others. This can be seen in the data cleaning notebook! Interestingly, ROC AUC and Cohen's k show how poor a naive classifier should be. I'll use those to judge my models.\n",
    "\n",
    "## Testing Classifiers\n",
    "\n",
    "I am choosing three classifiers: Decision trees, as it is obligatory to try them on any classification - they get 80% of the way there 80% of the time. SVM, as the margin is a desirable trait when \"current\" loans are ambiguous and we'd like a large berth for the classification. and finally, KNN, as it is one of the simpliest models (a lazy learner) and I love to see simplicity win out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import fbeta_score, roc_auc_score, cohen_kappa_score\n",
    "def train_predictclass(learner, sample_size, X_train, y_train, X_test, y_test): \n",
    "    '''\n",
    "    inputs:\n",
    "       - learner: the learning algorithm to be trained and predicted on\n",
    "       - sample_size: the size of samples (number) to be drawn from training set\n",
    "       - X_train: features training set\n",
    "       - y_train: income training set\n",
    "       - X_test: features testing set\n",
    "       - y_test: income testing set\n",
    "    '''\n",
    "    \n",
    "    resultsclass = {}\n",
    "    \n",
    "    # Fit the learner to the training data using slicing with 'sample_size'\n",
    "    start = time() # Get start time\n",
    "    learner = clf.fit(X_train.sample(n=sample_size,random_state=1),y_train.sample(n=sample_size,random_state=1)) #df.sample(frac=percent) maybe too?\n",
    "    end = time() # Get end time\n",
    "    \n",
    "    # Calculate the training time\n",
    "    resultsclass['train_time'] = end-start\n",
    "        \n",
    "    # Get the predictions on the test set,\n",
    "    #       then get predictions on the first 300 training samples\n",
    "    start = time() # Get start time\n",
    "    predictions_test = clf.predict(X_test)\n",
    "    predictions_train = clf.predict(X_train[:300])\n",
    "    end = time() # Get end time\n",
    "    \n",
    "    # Calculate the total prediction time\n",
    "    resultsclass['pred_time'] = end-start\n",
    "            \n",
    "    # Compute accuracy on the first 300 training samples\n",
    "    resultsclass['ROC_AUC_train'] = roc_auc_score(y_train[:300],predictions_train)\n",
    "        \n",
    "    # Compute accuracy on test set\n",
    "    resultsclass['ROC_AUC_test'] = roc_auc_score(y_test,predictions_test)\n",
    "    \n",
    "    # Compute F-score on the the first 300 training samples\n",
    "    resultsclass['precision_train'] = precision_score(y_train[:300],predictions_train)\n",
    "        \n",
    "    # Compute F-score on the test set\n",
    "    resultsclass['precision_test'] = precision_score(y_test,predictions_test)\n",
    "       \n",
    "    # Success\n",
    "    print (\"{} trained on {} samples.\".format(learner.__class__.__name__, sample_size))\n",
    "        \n",
    "    # Return the results\n",
    "    return resultsclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GaussianNB trained on 1304 samples.\n",
      "GaussianNB trained on 13037 samples.\n",
      "GaussianNB trained on 130367 samples.\n",
      "DecisionTreeClassifier trained on 1304 samples.\n",
      "DecisionTreeClassifier trained on 13037 samples.\n",
      "DecisionTreeClassifier trained on 130367 samples.\n",
      "RandomForestClassifier trained on 1304 samples.\n",
      "RandomForestClassifier trained on 13037 samples.\n",
      "RandomForestClassifier trained on 130367 samples.\n"
     ]
    }
   ],
   "source": [
    "# Initialize models\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "# TODO: Initialize the three models\n",
    "clf_A = GaussianNB()\n",
    "clf_B = DecisionTreeClassifier(random_state=5)\n",
    "clf_C = RandomForestClassifier(n_estimators=50)\n",
    "\n",
    "\n",
    "# Calculate the number of samples for 1%, 10%, and 100% of the training data\n",
    "samples_1 = len(y_train.sample(frac=.01))\n",
    "samples_10 = len(y_train.sample(frac=.1))\n",
    "samples_100 = len(y_train.sample(frac=1))\n",
    "\n",
    "# Collect results on the learners\n",
    "resultsclass = {}\n",
    "for clf in [clf_A, clf_B, clf_C]: \n",
    "    clf_name = clf.__class__.__name__\n",
    "    resultsclass[clf_name] = {}\n",
    "    for i, samples in enumerate([samples_1, samples_10, samples_100]):\n",
    "        resultsclass[clf_name][i] = \\\n",
    "        train_predictclass(clf, samples, X_train, y_train, X_test, y_test)\n",
    "#         print(results[clf])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZoUOH29u2bTt79OjR2EGDBl0fO3Zsze3bt9/XYNi5c6fLjRs3dHv27DkdERFx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aAAAg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\nQEiSJEnq2v8HX9wjh7AY0FMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11aa3ac18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run metrics visualization for the three supervised learning models chosen\n",
    "imp.reload(vs)\n",
    "vs.classevaluate(resultsclass,auc,prec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results\n",
    "\n",
    "This is promising! The decision tree unsurprisingly shows the best scores on the testing set. I'll try to optimize decision trees next! I will attempt to prune the tree a bit with grid search."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "# Initialize the classifier\n",
    "clf = DecisionTreeClassifier(random_state=3)\n",
    "\n",
    "# Create the parameters list you wish to tune\n",
    "parameters = {'criterion':('gini','entropy'),'max_depth':(20,50, None),'max_leaf_nodes':(100,300,None)}\n",
    "\n",
    "# Make an R2_score scoring object\n",
    "scorer = make_scorer(precision_score)\n",
    "\n",
    "# TODO: Perform grid search on the classifier using 'scorer' as the scoring method\n",
    "grid_obj = GridSearchCV(clf,parameters,scoring=scorer,n_jobs=-1)\n",
    "\n",
    "# TODO: Fit the grid search object to the training data and find the optimal parameters\n",
    "grid_fit = grid_obj.fit(X_train,y_train)\n",
    "\n",
    "# Get the estimator\n",
    "best_clf = grid_fit.best_estimator_\n",
    "\n",
    "# Make predictions using the unoptimized and model\n",
    "predictions = (clf.fit(X_train, y_train)).predict(X_test)\n",
    "best_predictions = best_clf.predict(X_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unoptimized model\n",
      "------\n",
      "ROC AUC score on testing data: 0.5355\n",
      "Precision on testing data: 0.8103\n",
      "\n",
      "Optimized Model\n",
      "------\n",
      "Final ROC AUC score on the testing data: 0.5391\n",
      "Final Precision on the testing data: 0.8116\n"
     ]
    }
   ],
   "source": [
    "# Report the before-and-afterscores\n",
    "print (\"Unoptimized model\\n------\")\n",
    "print (\"ROC AUC score on testing data: {:.4f}\".format(roc_auc_score(y_test, predictions)))\n",
    "print (\"Precision on testing data: {:.4f}\".format(precision_score(y_test, predictions)))\n",
    "print (\"\\nOptimized Model\\n------\")\n",
    "print (\"Final ROC AUC score on the testing data: {:.4f}\".format(roc_auc_score(y_test, best_predictions)))\n",
    "print (\"Final Precision on the testing data: {:.4f}\".format(precision_score(y_test, best_predictions)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Interestingly, this appears to be a case where using entropy instead of gini impurity makes a small difference in the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            presort=False, random_state=3, splitter='best')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.81237759,  0.81331516,  0.81444099,  0.81647287,  0.81157708,\n",
       "        0.81099923,  0.81566641,  0.81495759,  0.81451927,  0.81093349,\n",
       "        0.81212473,  0.81436421,  0.81066925,  0.81110464,  0.81621937,\n",
       "        0.81067961,  0.81239007,  0.81476432,  0.81167057,  0.81475728])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(best_clf,X_train, y_train, cv=20,scoring=scorer, n_jobs=-1)\n",
    "scores                                              \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.80975538483 2.88657986403e-15\n"
     ]
    }
   ],
   "source": [
    "difference=scores.mean()-prec\n",
    "import math\n",
    "from scipy import stats,special\n",
    "zscore=difference/(scores.std())\n",
    "p_values = 1-special.ndtr(zscore)\n",
    "print(zscore,p_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00060461367689483472"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(scores.std()/math.sqrt(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19119563234767986"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.std()*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['decisiontree.p']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "joblib.dump(clf, 'decisiontree.p')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sub_grade', 'loan_amnt', 'emp_title', 'emp_length', 'home_ownership',\n",
       "       'annual_inc', 'purpose', 'addr_state', 'dti', 'delinq_2yrs',\n",
       "       'earliest_cr_line', 'inq_last_6mths', 'open_acc', 'pub_rec',\n",
       "       'revol_bal', 'revol_util', 'total_acc', 'collections_12_mths_ex_med'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# from sklearn import tree\n",
    "# import pydotplus \n",
    "# dot_data = tree.export_graphviz(best_clf, out_file=None,feature_names=feat.columns,class_names=['Bad Loan','Fully Paid']) \n",
    "# graph = pydotplus.graph_from_dot_data(dot_data)\n",
    "# graph.write_pdf(\"LC_decisiontree.pdf\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  8.78611385e-02,   4.80302100e-02,   9.80980980e-02,\n",
       "         1.33278706e-01,   1.52620611e-02,   7.11749595e-02,\n",
       "         2.59255646e-02,   5.89322975e-02,   1.11276439e-02,\n",
       "         1.18001190e-01,   1.24071531e-01,   8.32322664e-02,\n",
       "         1.21932882e-03,   2.96520987e-03,   2.84892836e-03,\n",
       "         2.82460236e-03,   3.86121836e-03,   3.49306832e-03,\n",
       "         3.92906339e-03,   4.13379581e-03,   5.00577961e-03,\n",
       "         4.34072019e-03,   4.64841240e-03,   5.44956869e-03,\n",
       "         5.16780815e-03,   5.11037132e-03,   5.04825422e-03,\n",
       "         4.21738236e-03,   3.58476310e-03,   4.71351934e-03,\n",
       "         3.82616843e-03,   2.99350719e-03,   2.30868354e-03,\n",
       "         1.95518848e-03,   1.81667206e-03,   1.88203090e-03,\n",
       "         7.31526859e-04,   1.07781292e-03,   3.65938167e-04,\n",
       "         3.58518247e-05,   2.77103333e-04,   2.12901525e-04,\n",
       "         2.17930550e-04,   3.73002075e-05,   0.00000000e+00,\n",
       "         0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "         8.50745413e-03,   5.83953644e-03,   7.79237147e-03,\n",
       "         5.97631100e-03,   6.58824903e-03])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_clf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cmPPHSzLKy8srTYarMnLkSJYsWVLt+UVk+1aShNjMyoB7gFOAw4ALzOywrGJX\nAnPc/UjgWOBXZrZzKeITkfSYMGEC9evXZ8CAARXjjjzySI4++mgmTpzIaaedVjF+4MCBjBw5Egit\nrDfccAMdO3akvLyc1157jZNPPplvfOMbFb8MV9n8SZdffjnl5eW0b9+eG2+8EQi/OLdkyRKOO+44\njjvuuIp1fvLJJwwePJh77rmnYv6bbrqJIUOGAHDHHXfQtWtXOnToULGsQuWb96yzzqJLly60b9+e\n+++/H4DBgwezZs0aOnbsyEUXXcSiRYs4/PDDK+YZMmQIN910ExBawwcNGkR5eTl33nkny5cv5+yz\nz6Zr16507dqVl19+eYtYevbsWZEAT5o0iQEDBlT8DPWrr75Kly5dKCsrY/Xq1Xzve9+jW7dudOrU\nqeIHRpJ1v3z5ck488UTat2/PZZddxgEHHMAnn3wCwFdffcX3v/992rdvz0knncSaNWsYM2YM06ZN\n46KLLqJjx46sWbOmqHoUke1fqVqIuwEL3P0dd18PjALOzCrjQBMzM6Ax8ClQWNOCiEiBZs2aRZcu\nXao1b+vWrZkxYwZHH300/fr1Y8yYMUyZMqXoRPTWW29l2rRpzJw5k+eff56ZM2dy1VVXVfxMcvZP\nJZ9//vk89thjFcOPPfYY559/Ps888wzz58/n1VdfZcaMGUyfPp0XXngh5zqPO+44OnbsyLe+9S2A\nSucdMWIE06dPZ9q0aQwbNowVK1Zw22230bBhQ2bMmMEjjzxS5TauX7+eadOm8aMf/Yirr76aa665\nhqlTp/L4449v9tPTGckW4kmTJtG7d2922WUXvvjiCyZNmlTRqn3rrbdy/PHH8+qrrzJhwgSuu+46\nVq9evdmybr75Zo4//nhmz57NOeecw/vvv18xbf78+Vx55ZXMnj2bZs2a8fjjj3POOedQXl7OI488\nwowZM2jYsGGV2yciO5ZS9SHeH/ggMbwY+FZWmbuBccASoAlwvrtvKk14IiJVO+OMMwA44ogjWLVq\nFU2aNKFJkybssssurFy5suDlPPbYY9x///1s3LiRjz76iDlz5tChQ4e85Tt16sSyZctYsmQJy5cv\nZ/fdd6dVq1bceeedPPPMM3Tq1AmAVatWMX/+fHr37r3FMiZMmMCee+5ZMfzMM8/knXfYsGGMHTsW\ngA8++ID58+fTvHnzgrcPQhKf8eyzzzJnzpyK4c8//5xVq1bRuHHjinEHHHAA69ev5+OPP2bevHkc\nfPDBdO2lhwKCAAAgAElEQVTalVdeeYVJkybxwx/+sCLucePGVbSQr127drOEF+Cll16qiL9Pnz7s\nvvvuFdPatm1Lx44dgdAvedGiRUVtl4jsmLalm+pOBmYAxwPfAP5hZi+6++fJQmbWH+gPobVGRKQY\n7du3Z8yYMTmn1atXj02bvv4evnbt2s2m77LLLgDstNNOFa8zwxs3bqxyfoB3332XIUOGMHXqVHbf\nfXf69euXs1y2c889lzFjxvDxxx9XJJvuzg033MAPfvCDKufPlm/eiRMn8uyzzzJ58mQaNWrEscce\nmzO+qrZ11113rXi9adMmpkyZQoMGDSqNqWfPnowePZp9990XM6N79+68/PLLvPrqq/To0aMi7scf\nf5yDDz54s3kL7QOe3G9lZWXqHiEiQOm6THwItEoMt4zjkr4L/NmDBcC7wCHZC3L3+9293N3LW7Ro\nUWsBi8iO6fjjj2fdunUVfWMBZs6cyYsvvsgBBxzAnDlzWLduHStXruS5554ratmFzP/555+z6667\n0rRpU5YuXcrf//73imlNmjThiy++yLns888/n1GjRjFmzBjOPfdcAE4++WRGjBjBqlWrAPjwww9Z\ntmxZQbHmm/ezzz5j9913p1GjRsybN48pU6ZUzFO/fn02bNgAwN57782yZctYsWIF69at48knn8y7\nrpNOOom77rqrYjjTNzhbz549GTp0aEXy26NHDx566CH22WcfmjZtWhH3XXfdhbsD8Prrr2+xnF69\nelV0MXnmmWf417/+VWV9VFb3IrLjK1UL8VSgnZm1JSTCfYELs8q8D5wAvGhmewMHA++UKD4RqSOl\n/hUoM2Ps2LEMGjSI22+/nQYNGtCmTRuGDh1Kq1atOO+88zj88MNp27ZtRXeCQhUy/5FHHkmnTp04\n5JBDaNWqFb169aqY1r9/f/r06VPRlzipffv2fPHFF+y///7su+++QEg0586dW5FANm7cmIcffpi9\n9tqryljzzdunTx+GDx/OoYceysEHH0z37t03i69Dhw507tyZRx55hJ/97Gd069aN/fffn0MO2aL9\nosKwYcO48sor6dChAxs3bqR3794VNyIm9erVi2uuuaYipn333Zevvvpqs6di/Pd//zeDBg2iQ4cO\nbNq0ibZt226RjN94441ccMEF/OEPf6BHjx7ss88+NGnSpCL5z6Vfv34MGDCAhg0bMnnyZPUjFkkZ\ny3zLrvUVmZ0KDAXKgBHufquZDQBw9+Fmth8wEtgXMOA2d3+4smWWl5f7tGnTajdwEalRc+fO5dBD\nD63rMGQHtm7dOsrKyqhXrx6TJ0/m8ssvz9sqXQgdszuOtP90c5q238ymu3v+B79nKVkfYncfD4zP\nGjc88XoJcFKp4hERkR3T+++/z3nnncemTZvYeeedeeCBB+o6JBHZxm1LN9WJiIhstXbt2uXsWywi\nkk/JfqlORCSjVF21RLaWjlWRdFBCLCIl1aBBA1asWKFEQ7Z57s6KFSuqfFyciGz/1GVCREqqZcuW\nLF68mOXLl9d1KCJVatCgAS1btqzrMGpEmm6oEimWEmIRKan69evTtm3bug5DRESkgrpMiIiIiEiq\nKSEWERERkVRTQiwiIiIiqaaEWERERERSTQmxiIiIiKSaEmIRERERSTUlxCIiIiKSakqIRURERCTV\nlBCLiIiISKopIRYRERGRVFNCLCIiIiKppoRYRERERFJNCbGIiIiIpJoSYhERERFJNSXEIiIiIpJq\nSohFREREJNWUEIuIiIhIqtWr6wBERERqmz34YNHz+KWX1kIkIrItKlkLsZn1MbO3zGyBmQ3OMf06\nM5sR/2aZ2Vdmtkep4hMRERGRdCpJQmxmZcA9wCnAYcAFZnZYsoy73+HuHd29I3AD8Ly7f1qK+ERE\nREQkvUrVQtwNWODu77j7emAUcGYl5S8A/liSyEREREQk1UqVEO8PfJAYXhzHbcHMGgF9gMdLEJeI\niIiIpNy2+JSJ04GX83WXMLP+ZjbNzKYtX768xKGJiIiIyI6mVAnxh0CrxHDLOC6XvlTSXcLd73f3\ncncvb9GiRQ2GKCIiIiJpVKqEeCrQzszamtnOhKR3XHYhM2sKHAP8pURxiYiIiEjKleQ5xO6+0cwG\nAk8DZcAId59tZgPi9OGx6LeBZ9x9dSniEhEREREp2Q9zuPt4YHzWuOFZwyOBkaWKSURERERkW7yp\nTkRERESkZJQQi4iIiEiqKSEWERERkVRTQiwiIiIiqaaEWERERERSTQmxiIiIiKSaEmIRERERSTUl\nxCIiIiKSakqIRURERCTVlBCLiIiISKopIRYRERGRVFNCLCIiIiKpVq+uAxARkdplDz5Y9Dx+6aW1\nEImIyLZJLcQiIiIikmpKiEVEREQk1ZQQi4iIiEiqKSEWERERkVRTQiwiIiIiqaaEWERERERSTQmx\niIiIiKSaEmIRERERSTUlxCIiIiKSakqIRURERCTVSpYQm1kfM3vLzBaY2eA8ZY41sxlmNtvMni9V\nbCIiIiKSXvVKsRIzKwPuAU4EFgNTzWycu89JlGkG3Av0cff3zWyvUsQmIiIiIulWqhbibsACd3/H\n3dcDo4Azs8pcCPzZ3d8HcPdlJYpNRERERFKsVAnx/sAHieHFcVzSQcDuZjbRzKab2SUlik1ERERE\nUqwkXSYKVA/oApwANAQmm9kUd387WcjM+gP9AVq3bl3yIEVERERkx1KqFuIPgVaJ4ZZxXNJi4Gl3\nX+3unwAvAEdmL8jd73f3cncvb9GiRa0FLCIiIiLpUKqEeCrQzszamtnOQF9gXFaZvwBHmVk9M2sE\nfAuYW6L4RERERCSlStJlwt03mtlA4GmgDBjh7rPNbECcPtzd55rZU8BMYBPwW3efVYr4RERERCS9\nStaH2N3HA+Ozxg3PGr4DuKNUMYmIiIiI6JfqRERERCTVlBCLiIiISKopIRYRERGRVFNCLCIiIiKp\npoRYRERERFJNCbGIiIiIpJoSYhERERFJtZI9h1hEpC7Ygw8WPY9femktRCIiItsqtRCLiIiISKop\nIRYRERGRVFNCLCIiIiKppoRYRERERFJNCbGIiIiIpJoSYhERERFJNSXEIiIiIpJqSohFREREJNWU\nEIuIiIhIqikhFhEREZFUU0IsIiIiIqmmhFhEREREUk0JsYiIiIikmhJiEREREUk1JcQiIiIikmol\nS4jNrI+ZvWVmC8xscI7px5rZZ2Y2I/79rFSxiYiIiEh61SvFSsysDLgHOBFYDEw1s3HuPier6Ivu\nflopYhIRERERgSJaiM3s3Dzjzylg9m7AAnd/x93XA6OAMwtdt4iIiIhIbSmmy8Tv8oy/v4B59wc+\nSAwvjuOy9TSzmWb2dzNrX0RsIiIiIiLVUmWXCTM7ML7cyczaApaYfCCwtoZieQ1o7e6rzOxU4Amg\nXY54+gP9AVq3bl1DqxYRERGRtCqkD/ECwAmJ8MKsaR8DNxWwjA+BVonhlnFcBXf/PPF6vJnda2Z7\nuvsnWeXuJ7ZKl5eXewHrFhERERHJq8qE2N13AjCz5939mGquZyrQLrYwfwj0BS5MFjCzfYCl7u5m\n1o3QnWNFNdcnIiIiIlKQgp8ysRXJMO6+0cwGAk8DZcAId59tZgPi9OHAOcDlZrYRWAP0dXe1AIuI\niIhIrSo4IY6tu7cCHYHGyWnuXmVnXncfD4zPGjc88fpu4O5C4xERERERqQnFPIf4UUIf4h8BX9ZO\nOCIiIiIipVVMQtwe6OXum2orGBERERGRUivmOcQvAJ1qKxARERERkbpQaQuxmf08MbgIeMrMxhIe\nt1bB3X9W86GJyNayBx8seh6/9NJaiERERGTbVVWXiVZZw08C9XOMFxERERHZLlWaELv7d0sViIiI\niIhIXSjmsWsH5pm0DvhIN9uJiIiIyPaomKdMZH7CGcLPOCd/NGOTmY0DrnD3pTUVnIiIiIhIbSvm\nKRPfJzyL+CCgAXAw8AfgCuAIQnJ9T00HKCIiIiJSm4ppIb4Z+Ka7r43DC8zsCuBtd/+NmfUD5td0\ngCIiIiIitamYFuKdgDZZ41oDZfH1aopLsEVERERE6lwxCexQ4J9m9nvgA6Al8N04HuBUYHLNhici\nIiIiUrsKTojd/X/NbCZwLtAZ+Aj4T3d/Kk5/AniiVqIUEREREaklRXVxiMnvU7UUi4iIiIhIyVX1\n080/cfdb4+uf5yunn24WERERke1VVS3ELROv9XPNIiIiIrLDqeqnmy9PvNbPOIuIiIjIDqeoPsRm\ndgjhprq93X2gmR0M7OLuM2slOhERERGRWlbwc4jN7FzgRWB/4JI4ugnw61qIS0RERESkJIr5YY6f\nA//m7gOAr+K4N4AjazwqEREREZESKSYh3gvIdI3wxH/PXVxEREREZNtXTEI8HfhO1ri+wKs1F46I\niIiISGkVc1PdVcAzZvafwK5m9jRwEHBSrUQmIiIiIlICVSbEZnYe8IK7z4tPmTgNeBL4AHjS3VfV\ncowiIiIiIrWmkC4TtwAfmtnbwDBgF+Axdx9VTDJsZn3M7C0zW2Bmgysp19XMNprZOYUuW0RERESk\nuqpMiN39IMKj1n4CrAF+BCw0s/fM7A9mdllVyzCzMuAe4BTgMOACMzssT7nbgWeK2goRERERkWoq\n6KY6d//Y3Ue7+w/dvSPQgpDgngj8poBFdAMWuPs77r4eGAWcmaPcD4HHgWUFRS8iIiIispUKuqnO\nzAzoCPSOfz2BJcBjhB/rqMr+hD7HGYuBb2WtY3/g28BxQNdKYukP9Ado3bp1IeGLiIiIiORVyE11\nfwM6AW8BLwH3A/3c/YsajmUocL27bwr5d27ufn+MgfLycj0DWURERES2SiEtxAcB64B3gYWErg/F\nJsMfAq0Swy3juKRyYFRMhvcETjWzje7+RJHrEhEREREpWJUJsbu3M7N9gKMJ3SUGmdmewMuE7hIv\nufuMKhYzFWhnZm0JiXBf4MKs9bTNvDazkYRHuikZFhEREZFaVVAfYnf/GBgd/zCz3YHvAz8l3GBX\nVsX8G81sIPB0LDvC3Web2YA4fXi1t0BEREREZCtU96a6o4BmwDRgRCHLcPfxwPiscTkTYXfvV8gy\nRURERES2ViE31Y0HegA7A68AzwN3A5PdfW3thiciIiIiUrsKaSF+gfBrdVPdfUMtxyMiIiIiUlKF\n3FR3WykCEalp9uCDRc/jl15aC5GIiIjItqygX6oTEREREdlRKSEWERERkVRTQiwiIiIiqaaEWERE\nRERSTQmxiIiIiKSaEmIRERERSTUlxCIiIiKSakqIRURERCTVlBCLiIiISKopIRYRERGRVFNCLCIi\nIiKppoRYRERERFJNCbGIiIiIpJoSYhERERFJNSXEIiIiIpJqSohFREREJNWUEIuIiIhIqikhFhER\nEZFUU0IsIiIiIqmmhFhEREREUq1kCbGZ9TGzt8xsgZkNzjH9TDObaWYzzGyamR1VqthEREREJL3q\nlWIlZlYG3AOcCCwGpprZOHefkyj2HDDO3d3MOgCPAYeUIj4RERERSa9StRB3Axa4+zvuvh4YBZyZ\nLODuq9zd4+CugCMiIiIiUstKlRDvD3yQGF4cx23GzL5tZvOAvwHfy7UgM+sfu1RMW758ea0EKyIi\nIiLpsU3dVOfuY939EOAs4H/ylLnf3cvdvbxFixalDVBEREREdjilSog/BFolhlvGcTm5+wvAgWa2\nZ20HJiIiIiLpVqqEeCrQzszamtnOQF9gXLKAmX3TzCy+7gzsAqwoUXwiIiIiklIlecqEu280s4HA\n00AZMMLdZ5vZgDh9OHA2cImZbQDWAOcnbrITEREREakVJUmIAdx9PDA+a9zwxOvbgdtLFY+IiIiI\nCGxjN9WJiIiIiJSaEmIRERERSTUlxCIiIiKSakqIRURERCTVlBCLiIiISKopIRYRERGRVFNCLCIi\nIiKpVrLnEEvp2YMPFj2PX3ppLUQiIiIisu1SC7GIiIiIpJoSYhERERFJNSXEIiIiIpJqSohFRERE\nJNWUEIuIiIhIqikhFhEREZFUU0IsIiIiIqmmhFhEREREUk0JsYiIiIikmhJiEREREUk1JcQiIiIi\nkmpKiEVEREQk1ZQQi4iIiEiqKSEWERERkVRTQiwiIiIiqVayhNjM+pjZW2a2wMwG55h+kZnNNLM3\nzWySmR1ZqthEREREJL1KkhCbWRlwD3AKcBhwgZkdllXsXeAYdz8C+B/g/lLEJiIiIiLpVqoW4m7A\nAnd/x93XA6OAM5MF3H2Su/8rDk4BWpYoNhERERFJsVIlxPsDHySGF8dx+fwn8PdcE8ysv5lNM7Np\ny5cvr8EQRURERCSNtrmb6szsOEJCfH2u6e5+v7uXu3t5ixYtShuciIiIiOxw6pVoPR8CrRLDLeO4\nzZhZB+C3wCnuvqJEsYmIiIhIipWqhXgq0M7M2prZzkBfYFyygJm1Bv4MfMfd3y5RXCIiIiKSciVp\nIXb3jWY2EHgaKANGuPtsMxsQpw8HfgY0B+41M4CN7l5eivhEREREJL1K1WUCdx8PjM8aNzzx+jLg\nslLFIyIiIiIC2+BNdSIiIiIipaSEWERERERSTQmxiIiIiKSaEmIRERERSTUlxCIiIiKSakqIRURE\nRCTVlBCLiIiISKopIRYRERGRVFNCLCIiIiKppoRYRERERFJNCbGIiIiIpJoSYhERERFJNSXEIiIi\nIpJqSohFREREJNWUEIuIiIhIqtWr6wBqiz34YDXm6lfTYRTFL/U6Xb+IiIhIGqmFWERERERSTQmx\niIiIiKTaDttlQqrHHrQ6Xb+6jYiIiEipqYVYRERERFJNLcQiCXXdQg5qJRcRESk1tRCLiIiISKop\nIRYRERGRVCtZQmxmfczsLTNbYGaDc0w/xMwmm9k6M7u2VHGJiIiISLqVpA+xmZUB9wAnAouBqWY2\nzt3nJIp9ClwFnFWKmEREREREoHQtxN2ABe7+jruvB0YBZyYLuPsyd58KbChRTCIiIiIiJXvKxP7A\nB4nhxcC3qrMgM+sP9Ado3br11kcmIpvRkzZERCRttrub6tz9fncvd/fyFi1a1HU4IiIiIrKdK1VC\n/CHQKjHcMo4TEREREalTpUqIpwLtzKytme0M9AXGlWjdIiIiIiJ5laQPsbtvNLOBwNNAGTDC3Web\n2YA4fbiZ7QNMA3YDNpnZIOAwd/+8FDGKiGSoH7WISLqU7Keb3X08MD5r3PDE648JXSlERKSO1fWX\nAn0hEJFS2u5uqhMRERERqUlKiEVEREQk1ZQQi4iIiEiqlawPsYiIyPZE/ahF0kMtxCIiIiKSakqI\nRURERCTVlBCLiIiISKqpD7GIiIjkpH7UkhZqIRYRERGRVFNCLCIiIiKppoRYRERERFJNfYhFRERE\n8lA/6nRQC7GIiIiIpJoSYhERERFJNSXEIiIiIpJqSohFREREJNWUEIuIiIhIqikhFhEREZFU02PX\nRERERCSnun7sHJTm0XNqIRYRERGRVFNCLCIiIiKppoRYRERERFJNCbGIiIiIpFrJEmIz62Nmb5nZ\nAjMbnGO6mdmwOH2mmXUuVWwiIiIikl4lSYjNrAy4BzgFOAy4wMwOyyp2CtAu/vUH7itFbCIiIiKS\nbqVqIe4GLHD3d9x9PTAKODOrzJnAQx5MAZqZ2b4lik9EREREUsrca//ZbmZ2DtDH3S+Lw98BvuXu\nAxNlngRuc/eX4vBzwPXuPi1rWf0JLcgABwNv1foGVG5P4JM6jqGupb0O0r79oDoA1QGoDkB1AKoD\nUB1sC9t/gLu3KLTwdvfDHO5+P3B/XceRYWbT3L28ruOoS2mvg7RvP6gOQHUAqgNQHYDqAFQH2+P2\nl6rLxIdAq8Rwyziu2DIiIiIiIjWqVAnxVKCdmbU1s52BvsC4rDLjgEvi0ya6A5+5+0clik9ERERE\nUqokXSbcfaOZDQSeBsqAEe4+28wGxOnDgfHAqcAC4Evgu6WIrQZsM9036lDa6yDt2w+qA1AdgOoA\nVAegOgDVwXa3/SW5qU5EREREZFulX6oTERERkVRTQiwiIiIiqbbdPXZtW2FmNwGrCM/Ze8bdl8Tx\nvwV+7e5z6jA8ERERESmQWoi3Xj9gv8yAu1+WpmTYzBaZ2Z7VmK+fme2XGP5t5ue8q7vMulRZzGbW\nxsxmFbm8kfEHbbZbVW1Dcp+XIJZVpVhPbTGzs2qyrsysmZldUUWZNmZ2YQHLKvr43lYkj9HqHo81\nvW9yLH9V/L+fmY2prfVIzaiNc42ZdTSzUxPDN5nZtVllJtXwOvuZ2d3VmK/Q80YHM5tsZrPN7E0z\na1DEOqqsj+pQQlwEM/uJmb1tZi8RfiUPoBx4xMxmmFlDM5toZtv0w6jjo+3qet/3o46/SGwj9ZBa\nafvyuJXOAmoy6WoGVJoQA22AKj/YtldmVpYc3orjcav3jZlVebXW3Ze4e41+STaz/2dms+LfoJjM\nzDOzR8xsrpmNMbNGsWwXM3vezKab2dNmtm8cP9HMbjezV+Pn49GVrK+Nmb1oZq/Fv56JadfHxOgN\nM7stjvummT0bx71mZt+oye3fjnQkPIUrL3fvWdn0EmpDFeeNeLw/DAxw9/bAscCGItZRZX1Ui7vr\nr4A/oAvwJtAI2I3weLhrgYlAeaLcZsPbyh/hIH0LeAiYDVwKTAZeA0YDjYE+wOjEPMcCT8bXF8Tt\nnwXcniizCNizknXOSgxfC9wEnEPobvIWMANomKy3ypa5HdfDPOARYC4wBmgUp/2M8JzuWYTH1GSe\n/DISOKeGtvkJYHrc3v5x3CrgVuANYAqwd2K9w4BJwDuZGJJ1EIfvBvptzTZk7fN88ewNjI3j3wB6\nVrMOVsX/BtwRY30TOD+Obww8F4+DN4EzE/tuLvBArL9ngIaVrOf7sS7eAB5P7OeRwH1x296J9Tki\nLntkMs7segB6Ap8C7xLeL9+ogWNiFLAmLu+OPHUyBfgslrkm1sWLsY5ey+wLst7neY7/LeaL066P\n63wDuC2O+ybwbBz3WlXbC1wMvBrj/A3h0Z73AdPiPrs56316e1xu3+QxmnU8nkTWeSGOvw2YA8wE\nhhS6b3JtUzwGXiQ8g//tAo7dinomNCj8GXgKmA/8b6J8zthzLDfzmbYr4fifDXQCHOgVy4wgnLfr\nE84JLeL48wmPT83U26/i61OBZyvZlkZAg/i6HTAtvj4lLj/zftkj/n8F+HZ83SAzvZrHfK7jZBXh\n2J8d90+3uD3vAGck6vovcfx84MZCzjXx9XWE88FM4nFIJecUoGssm3lfzgJ2Bt4Hlsfx5xM+R0ck\nYr0qcZwcG8eP4evPncw5uU8c9xrhPP9kJdvRD7g7vj497ovXYz1lzs/HxJhmxGlNyDpv5Fn2qcDD\n+eqvsn1SaH3EZe0K/I3wvptFPLfl3eatOamm6Q8YBPw8Mfxrtr+EeBPQnfAb4y8Au8Zp1xOSmnrx\nQMuMv49wEtkvjm8Ry/wTOCuWWUSRCXGueqK0CXFd1MMWHzLx9R6Jcn8ATo+vR1JzCXHmw6VhPCk0\nj/Fk1vW/wE8T6x1NuHp0GLAgjj+W/AlxtbYha5/ni+dPwKD4ugxoWs06yHxYnA38Iy5r77g/9437\nc7dYZk/CF16L+24j0DFOewy4uJL1NE+8vgX4YaIuRsVlngl8DhwR63l6YvmV7ZcaOR6y35uV1En2\nPs+XzFQsK8+6ai0JAg4F/grUj8P3ApckllUWj7MOiffpfyXmr6jXzPFI/vNCc8KX6Uxy0azQfZNr\nm2L9rgbaFnjsJvdZP8IHf9O4vPcIv/SaM/Y8y72azT/T/oeQWL2fGHc84Qv14YRjNpP8vEm4dyZT\nb5lz297Ec0aedTYlnCPejMv5Mo7/FfD9rLJNgMU1dLznO04cOCWOG0tITusDRwIzEnX9Udz/mXNo\n3s/4xP46idhAQHifPwn0ppJzSlx2j/j6tqz9fXdiHTcR3ju7xH2+gs0T4s8Iv/a7E+HL0VHxOPmA\n8B60uN5CE+Ld+fq4v4yvvwD9NbHvGxPOo8dWttxYdlA8Dp4mJOfJ92Sh+6Sq+qhPOLc9kDz+KotL\nN9Wly3vuPsXMTiMkOy+bGYRvXJM9/IDKU8Dpsa/avwP/RTgpTnT35QBm9gjhjf1EXWxEDaiLevjA\n3V+Orx8mfPAMAY4zs/8ifEDuQfhW/Nca2s6Mq8zs2/F1K8IJcT3hBA0hITsxUf4Jd98EzDGzvQtY\nfk1sQ754jid8cOHuXxFO9FvjKOCPcVlLzex5QqvM34FfmFlvwhem/Qkf7gDvuvuMRGxtKln+4WZ2\nC6FLQmPCCT/jr+7uZvYmsNTd3wQws9lxmTOofL/Ulnx18nlWufrA3WbWEfgKOKjA5eeb79+A37v7\nlwDu/qmZNQH2d/excdzaKpZ9AqGlc2p8DzcElgHnmVl/wgf0voT3+cw4z5+qWGZ3cpwXCMfeWuB3\nZvYkX++nSuXbprjsV9393UKWk8Nz7v5ZXNYc4ADCcZcr9mJ4jmEDZrt7jzzzrIv/v6Lym/WvAZYS\nkpudCPVZCvmOk/WEVnYISfo6d98Q36NtEvP/w91XAJjZnwnvmWlVrPOk+Pd6HG5MOPe+T45zipk1\nA5q4e2Z/PQqcVsny/+bu64B1ZrYMaJ2Y9qq7L47xzojbsiqud34c/zDQv4ptyGgJ/Cl2k9mZcEUE\n4GXg1/Gz8M/uvjjWb1XqEeqwK+GH2J4zs+nu/hyF75Ns2fWxd5z/V2Z2OyFJf7GyoNR/snAvAGfF\nfsJNCJcQAL4gfJPdHqyO/43wBu8Y/w5z9/+M00YB5xESkWnu/sVWrG8jmx9jBXear2WlrgfI8SET\nbyK4l9C6dAThElqN1pGZHUtIPHq4+5GEk3MDYIPHr8xs+SG2LrmI+D/nvqzBbagsnlK4iNDy38Xd\nO/7/9u4/9qq6juP485USIdTXQTgCV2tZUYk1psvNtShWDI2lWy5x/iLnzOnUtQpyVuZo6tp01dJK\nK3o9CAgAAAftSURBVJxulsWUDETKMSOqEXMSW1KbgPuiqOCgIdD49e6P9+fg4XK/93u/v794X4/t\nOy7nx+d87rnnx/vz6xzypl19j/r+6C1vS4Abyr74HsfuiyqdIw1pHqmlOdL7oZV6MHM2eWMcyvXa\nIeDB2jn8YeBBsjVqdkScRTaZ1n+HvU3SaUzzuOtCRBwim29/RwYqK1um0p7e8tJKs+Oy1TWt0Rry\nnnaKpPHARWXaeyVVge+lwF/ImvHJ1XRJYyR9rB957gK2lwL35WQNPmQLxYJaf+WJ5Zq7TdKFZdrY\nan4/HHecRMRtHHu+HT0vS/7q516zQkI727yjts0zIuIXZV5frik9aUyj1byBXkd+TNbIzgCupZxP\nEXEnWWM8jiyETW8zvW3AnyNiZykQrwBmlnnt/iaNjvvOEfGfku5GYLGk77TKlAPiNkXEs2TNwgay\nNukfZdYS4KfVoLoRyl5f/R04T9IZAJLGS6pqbZ4hD6BryKAQst/VpyW9uwxEmV+W682rwGmSJkka\ny7Gl3dFQkBiu/QDNbzLVTXqnpAlk3+rB1gXsioh95WJ1bj/TeRH4aLkpnUrWuMDQf4engesgB0FJ\n6hpgemuAL5e0JpM1/OvI/fRaqYn4DFnb1h/vBLZLGkMG2YNlsM+Xeno97ZPGbfYUzPRmKIOgp4Ev\nSTqtSoOsKdsL/Le0cMxtM5+VpteFcnx3RcQKMsj/eFm+5W8zyIFdv/LeQ76eJe9f68guHQ8Au8jg\n93pJz5NN5fdFxAHy3L5L0gayNaM/g7juBa4saUynFAgiYiXZl3p9qdGsnhhwOdnC9U+ySXxKP7YJ\nTY4TSX05xz9X1hlHDqJc29sKZOvQV8pxg6Rp1fabiYjdwB5JnyyTLqnNHozzfxNZE10NTJzfh3W7\ngJfK5yuriZI+EBEbI+IuMiaa3mZenwJmlMLYyWRf5L4MaG1rfyifZLUvIh4m+yXPbLX8aKp9GPUi\n4vvkgJdGS2ufZw1PbvovInZIugp4pASqALeSAzsOl+bAqygHfkRsl7QIWE2WepdHxLI2tnNQ0u3k\nBfcl8oSsLCELEvuBnprhhtRw7Yeiusn8kjzx7ytB6v1kv7FXeLOQNZhWAl8tN7d/kzfMPouIbkmP\nknndQmkGjIjdQ/wdbgJ+LulqstR/HX1vAq57jDzeNpC1PN+MiFdKk98TpVluPcceq33xbTK42FH+\nHawg9tfA/ZJuJGvjXxhIYhHxuqS1yselPUl2J2jcJ68Dh0vwsoQMZpZKuoI8rtqt3Wy6XkSsVHaj\nWC/pAFlLdAsZBP2sXDsOAheT/WWbfY9/SboVWKV8YsxB4Hry+NxE9plsJ3ipp9nTdWEPsKy0igj4\nWpnXzm/T7DsNulbXtB6Wv5scDwPkUyCAQxFxWZNlnyMLS43TZ9U+76RFs3Zprj+rNmlhbd6dZL/Z\nxuU/21N67WpxnLRrHXmfP50cDNZbdwkiYpWkjwB/U3YjeIMci9JYm1t3NXksHSErW6ouYquBRaWw\ncEcf8l3Pz/+U3YiWS9pHFoTbvT7dBvxW0i5y7Mz7y/SbSwXCEbKr3JPl89HrRkTc0yQvuyTdTd4v\nAlgREcv78HXa3R8zgB+U/XmQUrnSk6qTtJmZmXWwEhD/ISLOHOGsjBqlgHF2RNwwDNuaEBHVc6cX\nAe+JiJuGaFuzyMHdrfopdxTXEJuZmRkRsZV8osSASJpDPt6ubktEXNRseTvqAknfImOzF8kWShsm\nriG2AZM0ieyj1Wh2lJG5ncD7oTlJj/FmE1tlYUQ81Wz5E4GknwDnNUz+YUT8aiTyMxo4COr/ceFr\nx4nlrfR7SVpAdk2rWxsRfelS0lPaJ9Q1wQGxmZmZmXU0P2XCzMzMzDqaA2IzMzMz62gOiM3MzMys\nozkgNjMbRpK2Stov6Y3a39QBpDdL0rbBzKOZWadxQGxmNvzmRcSE2t/LI5WR8qYoM7OO5oDYzGwU\nkHSupL9K2i1pQ3lwfjVvgaTnJe2RtFnStWX6ePLtUFPrtc2SlkhaXFv/mFrkUku9sLwSd6+kk8t6\nSyXtkLSlvHnNzKwjOCA2MxthkqYBy4HFwETg6+TrjieXRV4DvgC8C1gA3CNpZkTsBeYCL/ejtnk+\ncAFwKvm61SfI1zdPA2aTr2WdMyhf0MxslHNAbGY2/B4vNcG7JT0OXAasiIgVEXEkIv4IrAfOB4iI\n5RHxQqRngFXApwaYhx9FRHdE7AfOASZHxO0RcSAiNgP3A5cMcBtmZicE9x0zMxt+F0bEn6r/SLoX\nuFjSvNoyY4DVZf5c4LvAh8iKjFOAjQPMQ3ft8/vIbhe7a9NOAtYMcBtmZicEB8RmZiOvG3goIq5p\nnCFpLLAUuAJYFhEHS62yyiLNXje6lwyaK1OaLFNfr5t8peoH+5N5M7MTnbtMmJmNvIeBeZLmSDpJ\n0jvKQLjTgbcDY4EdwKFSW/z52rqvApMkddWmPQecL2mipCnAzb1sfx2wpwy0G1fycKakcwbtG5qZ\njWIOiM3MRlhEdANfBG4hA99u4BvA2yJiD3Aj8CiwC7gU+H1t3U3AI8Dm0id5KvAQOUBuK9nf+De9\nbP8wOWjvE8AWYCfwANDVaj0zs7cKRTRrbTMzMzMz6wyuITYzMzOzjuaA2MzMzMw6mgNiMzMzM+to\nDojNzMzMrKM5IDYzMzOzjuaA2MzMzMw6mgNiMzMzM+toDojNzMzMrKP9H0yWvOkQ7opvAAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11aa3aa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances = best_clf.feature_importances_\n",
    "\n",
    "# Plot\n",
    "import importlib\n",
    "importlib.reload(vs)\n",
    "vs.feature_plot(importances, X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40813"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_clf.tree_.node_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['dti', 'revol_util', 'revol_bal', 'annual_inc', 'loan_amnt',\n",
       "       'total_acc', 'earliest_cr_line', 'open_acc', 'emp_length',\n",
       "       'inq_last_6mths'], dtype=object)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indices = np.argsort(importances)[::-1]\n",
    "columns = X_train.columns.values[indices[:10]]\n",
    "values = importances[indices][:10]\n",
    "np.cumsum(values)\n",
    "values\n",
    "columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Leave one out training of robustness\n",
    "Here, as a test of robustness, I do some cross validation and model fitting, using the inputs of the optimal tree, and then determine the feature importance variations to determine model stability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KFold(n_splits=10, random_state=1, shuffle=True)\n",
      "TRAIN: [     0      1      2 ..., 130364 130365 130366] TEST: [    10     12     38 ..., 130325 130333 130355]\n",
      "TRAIN: [     0      2      3 ..., 130364 130365 130366] TEST: [     1      6      8 ..., 130338 130352 130361]\n",
      "TRAIN: [     0      1      2 ..., 130364 130365 130366] TEST: [     4     13     24 ..., 130337 130341 130346]\n",
      "TRAIN: [     0      1      2 ..., 130364 130365 130366] TEST: [    17     32     65 ..., 130331 130332 130354]\n",
      "TRAIN: [     0      1      2 ..., 130364 130365 130366] TEST: [    19     23     25 ..., 130347 130350 130351]\n",
      "TRAIN: [     0      1      2 ..., 130364 130365 130366] TEST: [     3     14     22 ..., 130349 130358 130359]\n",
      "TRAIN: [     0      1      3 ..., 130364 130365 130366] TEST: [     2     11     33 ..., 130348 130360 130363]\n",
      "TRAIN: [     1      2      3 ..., 130363 130364 130365] TEST: [     0      5     16 ..., 130342 130362 130366]\n",
      "TRAIN: [     0      1      2 ..., 130364 130365 130366] TEST: [     7     41     60 ..., 130353 130356 130357]\n",
      "TRAIN: [     0      1      2 ..., 130362 130363 130366] TEST: [     9     15     18 ..., 130340 130364 130365]\n",
      "[0.81111433827088775, 0.80891348674628605, 0.8103498684338758, 0.81320074095739492, 0.81292085488435639, 0.82515576323987538, 0.81929466627805303, 0.80872121509064188, 0.81057140075927192, 0.81248182963465454]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "splits=10\n",
    "kf=KFold(n_splits=splits,shuffle=True,random_state=1)\n",
    "kf_clf=DecisionTreeClassifier(random_state=5,criterion='entropy')\n",
    "kfprec=[]\n",
    "kfimportance=[]\n",
    "print(kf)\n",
    "# print(X_train.iloc[130366,:])\n",
    "for kftrain, kftest in kf.split(X_train,y_train):\n",
    "    print(\"TRAIN:\", kftrain, \"TEST:\", kftest)\n",
    "    kfX_train = X_train.iloc[kftrain]\n",
    "    kfy_train = y_train.iloc[kftrain]\n",
    "#     print(kfy_train)\n",
    "    kf_clf.fit(kfX_train,kfy_train)\n",
    "    kfprec.append(precision_score(y_train.iloc[kftest],kf_clf.predict(X_train.iloc[kftest])))\n",
    "    kfimportance.append(kf_clf.feature_importances_)\n",
    "#     try:\n",
    "#         pd.concat(kfimportance,kf_clf.feature_importances_)\n",
    "#         print('okay')\n",
    "#         print(kfimportance)\n",
    "#     except:\n",
    "#         kfimportance=pd.DataFrame(kf_clf.feature_importances_)\n",
    "#         print('first')\n",
    "print(kfprec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "weightstds=[]\n",
    "weightmeans=[]\n",
    "for i in range(53):\n",
    "    weightarray=np.array([item[i] for item in kfimportance])\n",
    "    weightstds.append(weightarray.std())\n",
    "    weightmeans.append(weightarray.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "weightstds\n",
    "# np.argsort(weightmeans)\n",
    "indices = np.argsort(best_clf.feature_importances_)[::-1]\n",
    "topstd=np.array(weightstds)[indices][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "topmean=np.array(weightmeans)[indices][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature: 1 Mean: 0.132332757659 Std: 0.00322494618423 mean/std 41.0340979663\n",
      "Feature: 2 Mean: 0.120051319321 Std: 0.0017739669224 mean/std 67.673933378\n",
      "Feature: 3 Mean: 0.120476757666 Std: 0.00283476295869 mean/std 42.4997643266\n",
      "Feature: 4 Mean: 0.0969561800158 Std: 0.00233112335199 mean/std 41.592041851\n",
      "Feature: 5 Mean: 0.090501457895 Std: 0.00232717815401 mean/std 38.8889255165\n",
      "Feature: 6 Mean: 0.0807944310143 Std: 0.00190680572823 mean/std 42.3716112334\n",
      "Feature: 7 Mean: 0.0737207828414 Std: 0.00197425161094 mean/std 37.3411283714\n",
      "Feature: 8 Mean: 0.0608968909431 Std: 0.0024971978452 mean/std 24.3860898167\n",
      "Feature: 9 Mean: 0.0504994193403 Std: 0.00142080499157 mean/std 35.5428223015\n",
      "Feature: 10 Mean: 0.0265346148799 Std: 0.00190447397897 mean/std 13.9327789053\n"
     ]
    }
   ],
   "source": [
    "count=1\n",
    "for m,s in zip(topmean,topstd):\n",
    "    print('Feature:',count,'Mean:',m,'Std:',s,'mean/std',m/s)\n",
    "    count+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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