predicting_chargeoff.ipynb

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   "source": [
    "# Predicting LendingClub Loan Charge-offs from Loan Origination Data\n",
    "\n",
    "***By Joe Corliss***\n",
    "\n",
    "**June 14, 2018**"
   ]
  },
  {
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   },
   "source": [
    "## Contents"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "* [1. Introduction](#1)\n",
    " * [1.1 References](#1.1)\n",
    "* [2. Import the Data](#2)\n",
    "* [3. Response Variable](#3)\n",
    "* [4. Limit the Feature Space](#4)\n",
    " * [4.1 Drop features missing more than 30% data](#4.1)\n",
    " * [4.2 Only keep loan features known to potential investors](#4.2)\n",
    "* [5. Pre-processing and Exploratory Analysis](#5)\n",
    "* [6. More Pre-processing](#6)\n",
    " * [6.1 Convert loan status to 0/1 charge-off indicator](#6.1)\n",
    " * [6.2 Create dummy variables](#6.2)\n",
    " * [6.3 Train/test split](#6.3)\n",
    "* [7. Linear Dependence of Charge-off on the Predictors](#7)\n",
    "* [8. Model Training and Testing](#8)\n",
    " * [8.1 Logistic regression with SGD training](#8.1)\n",
    " * [8.2 Random forest classifier](#8.2)\n",
    " * [8.3 k-nearest neighbors](#8.3)\n",
    " * [8.4 Tune hyperparameters on the chosen model more finely](#8.4)\n",
    " * [8.5 Test set evaluation](#8.5)\n",
    "* [9. Conclusion](#9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Introduction\n",
    "<a id=\"1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[LendingClub](https://www.lendingclub.com/) is a US peer-to-peer lending company and the world's largest peer-to-peer lending platform. As explained by [Wikipedia](https://en.wikipedia.org/wiki/Lending_Club),\n",
    "\n",
    "> Lending Club enables borrowers to create unsecured personal loans between \\$1,000 and \\$40,000. The standard loan period is three years. Investors can search and browse the loan listings on Lending Club website and select loans that they want to invest in based on the information supplied about the borrower, amount of loan, loan grade, and loan purpose. Investors make money from interest. Lending Club makes money by charging borrowers an origination fee and investors a service fee.\n",
    "\n",
    "The goal of this project is to build a machine learning model to predict the probability that a loan will charge off. We will attempt to only use data available to investors via the LendingClub loan listing, including information about the borrower (income, employment length, FICO score, debt-to-income ratio, etc.) and the loan listing (the loan amount, loan purpose, loan grade, interest rate, installment, etc.). Such a predictive model could help LendingClub investors make better-informed investment decisions. We will only consider loans that LendingClub accepted under its credit underwriting policy."
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "## 1.1 References\n",
    "<a id=\"1.1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* LendingClub information\n",
    " * [LendingClub website](https://www.lendingclub.com/)\n",
    " * [LendingClub Wikipedia page](https://en.wikipedia.org/wiki/Lending_Club)\n",
    "* Datasets\n",
    " * [LendingClub statistics](https://www.lendingclub.com/info/download-data.action) - Original data source, aggregated on Kaggle\n",
    " * [All Lending Club loan data](https://www.kaggle.com/wordsforthewise/lending-club) - The dataset used in this project, hosted on Kaggle\n",
    " * [Lending Club Loan Data](https://www.kaggle.com/wendykan/lending-club-loan-data) - Another LendingClub dataset on Kaggle, not used in this project\n",
    "* This project\n",
    " * [Kaggle notebook](https://www.kaggle.com/pileatedperch/predicting-charge-off-from-initial-listing-data)\n",
    " * [GitHub repo](https://github.com/jgcorliss/lending-club)"
   ]
  },
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   "source": [
    "# 2. Import the Data\n",
    "<a id=\"2\"></a>"
   ]
  },
  {
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   "source": [
    "import numpy as np\n",
    "import scipy as sp\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Pandas options\n",
    "pd.set_option('display.max_colwidth', 1000, 'display.max_rows', None, 'display.max_columns', None)\n",
    "\n",
    "# Plotting options\n",
    "%matplotlib inline\n",
    "mpl.style.use('ggplot')\n",
    "sns.set(style='whitegrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "_uuid": "c4b355a5d50e067d1461d6504cb04d827f65c6cd"
   },
   "source": [
    "Read the data into a pandas dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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    "_uuid": "370f21d16cb87752040bb20a82a9115db2e36cb5"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Joe\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2728: DtypeWarning: Columns (0,18,48,58,117,128,129,130,133,134,135,138,144,145,146) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "loans = pd.read_csv('accepted_2007_to_2017Q3.csv.gz', compression='gzip', low_memory=True)"
   ]
  },
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    "_cell_guid": "b195b476-a58b-4a6b-9745-f26a86e9c61c",
    "_uuid": "ee053ebca5fbc378cf651939bde5e378e5d0cdae"
   },
   "source": [
    "Check basic dataframe info:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "f4a7c9a2-ff89-4219-8ff9-2324bef16bdf",
    "_uuid": "00c703a1943e72a68b83c4ddf46c00310656e22c"
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1646801 entries, 0 to 1646800\n",
      "Columns: 150 entries, id to settlement_term\n",
      "dtypes: float64(113), object(37)\n",
      "memory usage: 1.8+ GB\n"
     ]
    }
   ],
   "source": [
    "loans.info()"
   ]
  },
  {
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    "_cell_guid": "53dad5c0-e147-4a75-9143-263c25aca443",
    "_uuid": "9174c9f401807a208e48aae2553eb69fa807f1cf"
   },
   "source": [
    "There are 1.6 million rows and 150 variables. The size of the dataset is 1.8 GB.\n",
    "\n",
    "Let's peek at 5 randomly selected rows. Each row corresponds to a single loan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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    "scrolled": true
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       "      <td>27936.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237470</th>\n",
       "      <td>1044661</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19400.0</td>\n",
       "      <td>19400.0</td>\n",
       "      <td>19400.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>20.30</td>\n",
       "      <td>517.23</td>\n",
       "      <td>E</td>\n",
       "      <td>E5</td>\n",
       "      <td>Louis A. Roser Co.</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>65000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Dec-2011</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>Borrower added on 11/28/11 &gt; Importing toys and electronics for re-sale.&lt;br&gt;</td>\n",
       "      <td>small_business</td>\n",
       "      <td>Small Business Loan</td>\n",
       "      <td>840xx</td>\n",
       "      <td>UT</td>\n",
       "      <td>4.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Jul-1991</td>\n",
       "      <td>715.0</td>\n",
       "      <td>719.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10457.0</td>\n",
       "      <td>73.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14421.760000</td>\n",
       "      <td>14421.76</td>\n",
       "      <td>5526.85</td>\n",
       "      <td>6885.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2009.61</td>\n",
       "      <td>20.0824</td>\n",
       "      <td>Jan-2014</td>\n",
       "      <td>29.56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Oct-2016</td>\n",
       "      <td>499.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Individual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133849</th>\n",
       "      <td>12455743</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9950.0</td>\n",
       "      <td>9950.0</td>\n",
       "      <td>9950.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>10.99</td>\n",
       "      <td>325.71</td>\n",
       "      <td>B</td>\n",
       "      <td>B3</td>\n",
       "      <td>Operations</td>\n",
       "      <td>5 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Jun-2014</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>NaN</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>Debt consolidation</td>\n",
       "      <td>997xx</td>\n",
       "      <td>AK</td>\n",
       "      <td>13.27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Jun-1990</td>\n",
       "      <td>670.0</td>\n",
       "      <td>674.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12480.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>w</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4885.650000</td>\n",
       "      <td>4885.65</td>\n",
       "      <td>3753.55</td>\n",
       "      <td>1132.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Sep-2015</td>\n",
       "      <td>325.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Feb-2017</td>\n",
       "      <td>499.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Individual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7114.0</td>\n",
       "      <td>12480.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>4244.0</td>\n",
       "      <td>67.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>288.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>82.1</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29000.0</td>\n",
       "      <td>12480.0</td>\n",
       "      <td>12900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381992</th>\n",
       "      <td>61473495</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>12.29</td>\n",
       "      <td>500.30</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>Customer Service</td>\n",
       "      <td>9 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Oct-2015</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>n</td>\n",
       "      <td>NaN</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>Debt consolidation</td>\n",
       "      <td>920xx</td>\n",
       "      <td>CA</td>\n",
       "      <td>16.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Oct-2010</td>\n",
       "      <td>665.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11536.0</td>\n",
       "      <td>67.1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16973.282035</td>\n",
       "      <td>16973.28</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>1973.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Jan-2017</td>\n",
       "      <td>9974.56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Feb-2017</td>\n",
       "      <td>739.0</td>\n",
       "      <td>735.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Individual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18479.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2053.0</td>\n",
       "      <td>2819.0</td>\n",
       "      <td>78.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26923.0</td>\n",
       "      <td>18479.0</td>\n",
       "      <td>13300.0</td>\n",
       "      <td>9723.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327913</th>\n",
       "      <td>65403207</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18025.0</td>\n",
       "      <td>18025.0</td>\n",
       "      <td>18025.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>14.33</td>\n",
       "      <td>618.95</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>Student coordinator</td>\n",
       "      <td>8 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>41000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Nov-2015</td>\n",
       "      <td>Current</td>\n",
       "      <td>n</td>\n",
       "      <td>NaN</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>Debt consolidation</td>\n",
       "      <td>958xx</td>\n",
       "      <td>CA</td>\n",
       "      <td>25.53</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dec-2000</td>\n",
       "      <td>695.0</td>\n",
       "      <td>699.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5668.0</td>\n",
       "      <td>83.4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>w</td>\n",
       "      <td>6344.64</td>\n",
       "      <td>6344.64</td>\n",
       "      <td>15459.400000</td>\n",
       "      <td>15459.40</td>\n",
       "      <td>11680.36</td>\n",
       "      <td>3779.04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Dec-2017</td>\n",
       "      <td>618.95</td>\n",
       "      <td>Jan-2018</td>\n",
       "      <td>Dec-2017</td>\n",
       "      <td>744.0</td>\n",
       "      <td>740.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Individual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36447.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6800.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7289.0</td>\n",
       "      <td>848.0</td>\n",
       "      <td>84.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>85.7</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54380.0</td>\n",
       "      <td>36447.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>47580.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               id  member_id  loan_amnt  funded_amnt  funded_amnt_inv  \\\n",
       "1605390  70891233        NaN    18000.0      18000.0          18000.0   \n",
       "237470    1044661        NaN    19400.0      19400.0          19400.0   \n",
       "133849   12455743        NaN     9950.0       9950.0           9950.0   \n",
       "381992   61473495        NaN    15000.0      15000.0          15000.0   \n",
       "327913   65403207        NaN    18025.0      18025.0          18025.0   \n",
       "\n",
       "               term  int_rate  installment grade sub_grade  \\\n",
       "1605390   36 months     10.75       587.17     B        B4   \n",
       "237470    60 months     20.30       517.23     E        E5   \n",
       "133849    36 months     10.99       325.71     B        B3   \n",
       "381992    36 months     12.29       500.30     C        C1   \n",
       "327913    36 months     14.33       618.95     C        C5   \n",
       "\n",
       "                    emp_title emp_length home_ownership  annual_inc  \\\n",
       "1605390                 Owner    5 years           RENT    120000.0   \n",
       "237470     Louis A. Roser Co.  10+ years           RENT     65000.0   \n",
       "133849             Operations    5 years           RENT     25000.0   \n",
       "381992       Customer Service    9 years           RENT     45000.0   \n",
       "327913   Student coordinator     8 years           RENT     41000.0   \n",
       "\n",
       "        verification_status   issue_d  loan_status pymnt_plan  \\\n",
       "1605390        Not Verified  Feb-2016      Current          n   \n",
       "237470      Source Verified  Dec-2011  Charged Off          n   \n",
       "133849      Source Verified  Jun-2014  Charged Off          n   \n",
       "381992      Source Verified  Oct-2015   Fully Paid          n   \n",
       "327913      Source Verified  Nov-2015      Current          n   \n",
       "\n",
       "                                                                                   desc  \\\n",
       "1605390                                                                             NaN   \n",
       "237470     Borrower added on 11/28/11 > Importing toys and electronics for re-sale.<br>   \n",
       "133849                                                                              NaN   \n",
       "381992                                                                              NaN   \n",
       "327913                                                                              NaN   \n",
       "\n",
       "                    purpose                title zip_code addr_state    dti  \\\n",
       "1605390  debt_consolidation   Debt consolidation    923xx         CA  13.20   \n",
       "237470       small_business  Small Business Loan    840xx         UT   4.69   \n",
       "133849   debt_consolidation   Debt consolidation    997xx         AK  13.27   \n",
       "381992   debt_consolidation   Debt consolidation    920xx         CA  16.56   \n",
       "327913   debt_consolidation   Debt consolidation    958xx         CA  25.53   \n",
       "\n",
       "         delinq_2yrs earliest_cr_line  fico_range_low  fico_range_high  \\\n",
       "1605390          1.0         Apr-2002           715.0            719.0   \n",
       "237470           0.0         Jul-1991           715.0            719.0   \n",
       "133849           0.0         Jun-1990           670.0            674.0   \n",
       "381992           0.0         Oct-2010           665.0            669.0   \n",
       "327913           0.0         Dec-2000           695.0            699.0   \n",
       "\n",
       "         inq_last_6mths  mths_since_last_delinq  mths_since_last_record  \\\n",
       "1605390             1.0                     6.0                     NaN   \n",
       "237470              0.0                     NaN                     NaN   \n",
       "133849              3.0                    68.0                     NaN   \n",
       "381992              0.0                     NaN                     NaN   \n",
       "327913              0.0                    59.0                     NaN   \n",
       "\n",
       "         open_acc  pub_rec  revol_bal  revol_util  total_acc  \\\n",
       "1605390      22.0      0.0    15986.0        19.4       32.0   \n",
       "237470        3.0      0.0    10457.0        73.5        4.0   \n",
       "133849       12.0      0.0    12480.0        43.0       28.0   \n",
       "381992        9.0      0.0    11536.0        67.1       10.0   \n",
       "327913        6.0      0.0     5668.0        83.4        7.0   \n",
       "\n",
       "        initial_list_status  out_prncp  out_prncp_inv   total_pymnt  \\\n",
       "1605390                   w    7693.42        7693.42  12906.990000   \n",
       "237470                    f       0.00           0.00  14421.760000   \n",
       "133849                    w       0.00           0.00   4885.650000   \n",
       "381992                    f       0.00           0.00  16973.282035   \n",
       "327913                    w    6344.64        6344.64  15459.400000   \n",
       "\n",
       "         total_pymnt_inv  total_rec_prncp  total_rec_int  total_rec_late_fee  \\\n",
       "1605390         12906.99         10306.58        2600.41                 0.0   \n",
       "237470          14421.76          5526.85        6885.30                 0.0   \n",
       "133849           4885.65          3753.55        1132.10                 0.0   \n",
       "381992          16973.28         15000.00        1973.28                 0.0   \n",
       "327913          15459.40         11680.36        3779.04                 0.0   \n",
       "\n",
       "         recoveries  collection_recovery_fee last_pymnt_d  last_pymnt_amnt  \\\n",
       "1605390        0.00                   0.0000     Dec-2017           587.17   \n",
       "237470      2009.61                  20.0824     Jan-2014            29.56   \n",
       "133849         0.00                   0.0000     Sep-2015           325.71   \n",
       "381992         0.00                   0.0000     Jan-2017          9974.56   \n",
       "327913         0.00                   0.0000     Dec-2017           618.95   \n",
       "\n",
       "        next_pymnt_d last_credit_pull_d  last_fico_range_high  \\\n",
       "1605390     Jan-2018           Dec-2017                 764.0   \n",
       "237470           NaN           Oct-2016                 499.0   \n",
       "133849           NaN           Feb-2017                 499.0   \n",
       "381992           NaN           Feb-2017                 739.0   \n",
       "327913      Jan-2018           Dec-2017                 744.0   \n",
       "\n",
       "         last_fico_range_low  collections_12_mths_ex_med  \\\n",
       "1605390                760.0                         0.0   \n",
       "237470                   0.0                         0.0   \n",
       "133849                   0.0                         0.0   \n",
       "381992                 735.0                         0.0   \n",
       "327913                 740.0                         0.0   \n",
       "\n",
       "         mths_since_last_major_derog  policy_code application_type  \\\n",
       "1605390                          NaN          1.0       Individual   \n",
       "237470                           NaN          1.0       Individual   \n",
       "133849                          68.0          1.0       Individual   \n",
       "381992                           NaN          1.0       Individual   \n",
       "327913                           NaN          1.0       Individual   \n",
       "\n",
       "         annual_inc_joint  dti_joint verification_status_joint  \\\n",
       "1605390               NaN        NaN                       NaN   \n",
       "237470                NaN        NaN                       NaN   \n",
       "133849                NaN        NaN                       NaN   \n",
       "381992                NaN        NaN                       NaN   \n",
       "327913                NaN        NaN                       NaN   \n",
       "\n",
       "         acc_now_delinq  tot_coll_amt  tot_cur_bal  open_acc_6m  open_act_il  \\\n",
       "1605390             0.0           0.0      37249.0          1.0          2.0   \n",
       "237470              0.0           NaN          NaN          NaN          NaN   \n",
       "133849              0.0        7114.0      12480.0          NaN          NaN   \n",
       "381992              0.0           0.0      18479.0          NaN          NaN   \n",
       "327913              0.0           0.0      36447.0          NaN          NaN   \n",
       "\n",
       "         open_il_12m  open_il_24m  mths_since_rcnt_il  total_bal_il  il_util  \\\n",
       "1605390          2.0          2.0                 7.0       21263.0     76.0   \n",
       "237470           NaN          NaN                 NaN           NaN      NaN   \n",
       "133849           NaN          NaN                 NaN           NaN      NaN   \n",
       "381992           NaN          NaN                 NaN           NaN      NaN   \n",
       "327913           NaN          NaN                 NaN           NaN      NaN   \n",
       "\n",
       "         open_rv_12m  open_rv_24m  max_bal_bc  all_util  total_rev_hi_lim  \\\n",
       "1605390          2.0          6.0      5406.0      34.0           82600.0   \n",
       "237470           NaN          NaN         NaN       NaN               NaN   \n",
       "133849           NaN          NaN         NaN       NaN           29000.0   \n",
       "381992           NaN          NaN         NaN       NaN           17200.0   \n",
       "327913           NaN          NaN         NaN       NaN            6800.0   \n",
       "\n",
       "         inq_fi  total_cu_tl  inq_last_12m  acc_open_past_24mths  avg_cur_bal  \\\n",
       "1605390     1.0          0.0           1.0                   8.0       1774.0   \n",
       "237470      NaN          NaN           NaN                   NaN          NaN   \n",
       "133849      NaN          NaN           NaN                   7.0       1248.0   \n",
       "381992      NaN          NaN           NaN                   7.0       2053.0   \n",
       "327913      NaN          NaN           NaN                   2.0       7289.0   \n",
       "\n",
       "         bc_open_to_buy  bc_util  chargeoff_within_12_mths  delinq_amnt  \\\n",
       "1605390         45469.0     22.5                       0.0          0.0   \n",
       "237470              NaN      NaN                       0.0          0.0   \n",
       "133849           4244.0     67.1                       0.0          0.0   \n",
       "381992           2819.0     78.8                       0.0          0.0   \n",
       "327913            848.0     84.6                       0.0          0.0   \n",
       "\n",
       "         mo_sin_old_il_acct  mo_sin_old_rev_tl_op  mo_sin_rcnt_rev_tl_op  \\\n",
       "1605390                88.0                 166.0                    3.0   \n",
       "237470                  NaN                   NaN                    NaN   \n",
       "133849                106.0                 288.0                    4.0   \n",
       "381992                 43.0                  60.0                    4.0   \n",
       "327913                 94.0                 179.0                   12.0   \n",
       "\n",
       "         mo_sin_rcnt_tl  mort_acc  mths_since_recent_bc  \\\n",
       "1605390             3.0       0.0                   9.0   \n",
       "237470              NaN       NaN                   NaN   \n",
       "133849              4.0       0.0                   4.0   \n",
       "381992              4.0       0.0                   4.0   \n",
       "327913             12.0       0.0                  12.0   \n",
       "\n",
       "         mths_since_recent_bc_dlq  mths_since_recent_inq  \\\n",
       "1605390                       NaN                    3.0   \n",
       "237470                        NaN                    NaN   \n",
       "133849                       68.0                    5.0   \n",
       "381992                        NaN                    9.0   \n",
       "327913                        NaN                   12.0   \n",
       "\n",
       "         mths_since_recent_revol_delinq  num_accts_ever_120_pd  \\\n",
       "1605390                             NaN                    0.0   \n",
       "237470                              NaN                    NaN   \n",
       "133849                             68.0                    5.0   \n",
       "381992                              NaN                    0.0   \n",
       "327913                              NaN                    0.0   \n",
       "\n",
       "         num_actv_bc_tl  num_actv_rev_tl  num_bc_sats  num_bc_tl  num_il_tl  \\\n",
       "1605390             8.0             11.0         12.0       15.0        5.0   \n",
       "237470              NaN              NaN          NaN        NaN        NaN   \n",
       "133849              3.0              6.0          5.0       13.0        2.0   \n",
       "381992              5.0              7.0          5.0        5.0        2.0   \n",
       "327913              2.0              3.0          2.0        2.0        3.0   \n",
       "\n",
       "         num_op_rev_tl  num_rev_accts  num_rev_tl_bal_gt_0  num_sats  \\\n",
       "1605390           20.0           27.0                 11.0      22.0   \n",
       "237470             NaN            NaN                  NaN       NaN   \n",
       "133849            12.0           26.0                  6.0      12.0   \n",
       "381992             8.0            8.0                  7.0       9.0   \n",
       "327913             4.0            4.0                  3.0       6.0   \n",
       "\n",
       "         num_tl_120dpd_2m  num_tl_30dpd  num_tl_90g_dpd_24m  \\\n",
       "1605390               0.0           0.0                 0.0   \n",
       "237470                NaN           NaN                 NaN   \n",
       "133849                0.0           0.0                 0.0   \n",
       "381992                0.0           0.0                 0.0   \n",
       "327913                0.0           0.0                 0.0   \n",
       "\n",
       "         num_tl_op_past_12m  pct_tl_nvr_dlq  percent_bc_gt_75  \\\n",
       "1605390                 4.0            96.9               0.0   \n",
       "237470                  NaN             NaN               NaN   \n",
       "133849                  2.0            82.1              40.0   \n",
       "381992                  3.0           100.0              60.0   \n",
       "327913                  1.0            85.7             100.0   \n",
       "\n",
       "         pub_rec_bankruptcies  tax_liens  tot_hi_cred_lim  total_bal_ex_mort  \\\n",
       "1605390                   0.0        0.0         110536.0            37249.0   \n",
       "237470                    0.0        0.0              NaN                NaN   \n",
       "133849                    0.0        0.0          29000.0            12480.0   \n",
       "381992                    0.0        0.0          26923.0            18479.0   \n",
       "327913                    0.0        0.0          54380.0            36447.0   \n",
       "\n",
       "         total_bc_limit  total_il_high_credit_limit  revol_bal_joint  \\\n",
       "1605390         58700.0                     27936.0              NaN   \n",
       "237470              NaN                         NaN              NaN   \n",
       "133849          12900.0                         0.0              NaN   \n",
       "381992          13300.0                      9723.0              NaN   \n",
       "327913           5500.0                     47580.0              NaN   \n",
       "\n",
       "         sec_app_fico_range_low  sec_app_fico_range_high  \\\n",
       "1605390                     NaN                      NaN   \n",
       "237470                      NaN                      NaN   \n",
       "133849                      NaN                      NaN   \n",
       "381992                      NaN                      NaN   \n",
       "327913                      NaN                      NaN   \n",
       "\n",
       "        sec_app_earliest_cr_line  sec_app_inq_last_6mths  sec_app_mort_acc  \\\n",
       "1605390                      NaN                     NaN               NaN   \n",
       "237470                       NaN                     NaN               NaN   \n",
       "133849                       NaN                     NaN               NaN   \n",
       "381992                       NaN                     NaN               NaN   \n",
       "327913                       NaN                     NaN               NaN   \n",
       "\n",
       "         sec_app_open_acc  sec_app_revol_util  sec_app_open_act_il  \\\n",
       "1605390               NaN                 NaN                  NaN   \n",
       "237470                NaN                 NaN                  NaN   \n",
       "133849                NaN                 NaN                  NaN   \n",
       "381992                NaN                 NaN                  NaN   \n",
       "327913                NaN                 NaN                  NaN   \n",
       "\n",
       "         sec_app_num_rev_accts  sec_app_chargeoff_within_12_mths  \\\n",
       "1605390                    NaN                               NaN   \n",
       "237470                     NaN                               NaN   \n",
       "133849                     NaN                               NaN   \n",
       "381992                     NaN                               NaN   \n",
       "327913                     NaN                               NaN   \n",
       "\n",
       "         sec_app_collections_12_mths_ex_med  \\\n",
       "1605390                                 NaN   \n",
       "237470                                  NaN   \n",
       "133849                                  NaN   \n",
       "381992                                  NaN   \n",
       "327913                                  NaN   \n",
       "\n",
       "         sec_app_mths_since_last_major_derog hardship_flag hardship_type  \\\n",
       "1605390                                  NaN             N           NaN   \n",
       "237470                                   NaN             N           NaN   \n",
       "133849                                   NaN             N           NaN   \n",
       "381992                                   NaN             N           NaN   \n",
       "327913                                   NaN             N           NaN   \n",
       "\n",
       "        hardship_reason hardship_status  deferral_term  hardship_amount  \\\n",
       "1605390             NaN             NaN            NaN              NaN   \n",
       "237470              NaN             NaN            NaN              NaN   \n",
       "133849              NaN             NaN            NaN              NaN   \n",
       "381992              NaN             NaN            NaN              NaN   \n",
       "327913              NaN             NaN            NaN              NaN   \n",
       "\n",
       "        hardship_start_date hardship_end_date payment_plan_start_date  \\\n",
       "1605390                 NaN               NaN                     NaN   \n",
       "237470                  NaN               NaN                     NaN   \n",
       "133849                  NaN               NaN                     NaN   \n",
       "381992                  NaN               NaN                     NaN   \n",
       "327913                  NaN               NaN                     NaN   \n",
       "\n",
       "         hardship_length  hardship_dpd hardship_loan_status  \\\n",
       "1605390              NaN           NaN                  NaN   \n",
       "237470               NaN           NaN                  NaN   \n",
       "133849               NaN           NaN                  NaN   \n",
       "381992               NaN           NaN                  NaN   \n",
       "327913               NaN           NaN                  NaN   \n",
       "\n",
       "         orig_projected_additional_accrued_interest  \\\n",
       "1605390                                         NaN   \n",
       "237470                                          NaN   \n",
       "133849                                          NaN   \n",
       "381992                                          NaN   \n",
       "327913                                          NaN   \n",
       "\n",
       "         hardship_payoff_balance_amount  hardship_last_payment_amount  \\\n",
       "1605390                             NaN                           NaN   \n",
       "237470                              NaN                           NaN   \n",
       "133849                              NaN                           NaN   \n",
       "381992                              NaN                           NaN   \n",
       "327913                              NaN                           NaN   \n",
       "\n",
       "        disbursement_method debt_settlement_flag debt_settlement_flag_date  \\\n",
       "1605390                Cash                    N                       NaN   \n",
       "237470                 Cash                    N                       NaN   \n",
       "133849                 Cash                    N                       NaN   \n",
       "381992                 Cash                    N                       NaN   \n",
       "327913                 Cash                    N                       NaN   \n",
       "\n",
       "        settlement_status settlement_date  settlement_amount  \\\n",
       "1605390               NaN             NaN                NaN   \n",
       "237470                NaN             NaN                NaN   \n",
       "133849                NaN             NaN                NaN   \n",
       "381992                NaN             NaN                NaN   \n",
       "327913                NaN             NaN                NaN   \n",
       "\n",
       "         settlement_percentage  settlement_term  \n",
       "1605390                    NaN              NaN  \n",
       "237470                     NaN              NaN  \n",
       "133849                     NaN              NaN  \n",
       "381992                     NaN              NaN  \n",
       "327913                     NaN              NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "6e847348-5344-4ebb-b820-03a772ad5059",
    "_uuid": "841ca8b2103b35b98730139c2e64b790f2edeb89"
   },
   "source": [
    "# 3. Response Variable\n",
    "<a id=\"3\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b9e79844-28d9-4b5e-a6d3-c9dbfd17820e",
    "_uuid": "138869442509b04e45df20b0356e7ed91f77f7c2"
   },
   "source": [
    "We're going to try to predict the `loan_status` variable. What are the value counts for this variable?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_cell_guid": "d5b467af-c5a1-4ae0-a044-3a421252eb78",
    "_uuid": "84e9c41ee08246a83bc6f8ad04a90c83a1bf408e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Current                                                788950\n",
       "Fully Paid                                             646902\n",
       "Charged Off                                            168084\n",
       "Late (31-120 days)                                      23763\n",
       "In Grace Period                                         10474\n",
       "Late (16-30 days)                                        5786\n",
       "Does not meet the credit policy. Status:Fully Paid       1988\n",
       "Does not meet the credit policy. Status:Charged Off       761\n",
       "Default                                                    70\n",
       "NaN                                                        23\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['loan_status'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "527520b1-32a4-4b95-ab0a-b24ac47b3cf6",
    "_uuid": "49a92fd5f4aab3a3561edf9929df506c9fd34a9d"
   },
   "source": [
    "We're going to try to learn differences in the features between completed loans that have been fully paid or charged off. We won't consider loans that are current, don't meet the credit policy, defaulted, or have a missing status. So we only keep the loans with status \"Fully Paid\" or \"Charged Off.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "f4f361b5-32f2-4828-8e37-3e43a32c32c0",
    "_uuid": "5c7dc5453d31b32b849bec55fe252c449d8e9557"
   },
   "outputs": [],
   "source": [
    "loans = loans.loc[loans['loan_status'].isin(['Fully Paid', 'Charged Off'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many loans remain in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "be5c5ad3-f002-48f3-9695-3a80a1539c1b",
    "_uuid": "c71ee5ec2f8dc3d8297a7eea7eef441f27c51317"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(814986, 150)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 814,986 loans remaining. Check that the statuses are as expected:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "590a5146-c659-43ed-bfb4-7bea32672932",
    "_uuid": "d70396f915861568607ee204c4ff4574f80d5bdc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fully Paid     646902\n",
       "Charged Off    168084\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['loan_status'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Status counts as percentages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_cell_guid": "590a5146-c659-43ed-bfb4-7bea32672932",
    "_uuid": "d70396f915861568607ee204c4ff4574f80d5bdc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fully Paid     0.793758\n",
       "Charged Off    0.206242\n",
       "Name: loan_status, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['loan_status'].value_counts(normalize=True, dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "c44aa6b1-041a-490e-8f64-81d010b1bf4a",
    "_uuid": "715d0aed4051332c5f32f35142e130e7da555150"
   },
   "source": [
    "About 79% of the remaining loans have been fully paid and 21% have charged off, so we have a somewhat unbalanced classification problem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "a6220e50-5df9-427a-a92b-5e6138234fb9",
    "_uuid": "34206dc6425cbf3c744c23006e6b10f522777a9d"
   },
   "source": [
    "# 4. Limit the Feature Space\n",
    "<a id=\"4\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "89f9038c-fb12-46e9-9136-5f5e0a81d2bb",
    "_uuid": "a0a000e470b72a6128fc3ec216e181ab4ccc3809"
   },
   "source": [
    "The full dataset has 150 features for each loan. We'll select features in two steps:\n",
    "\n",
    "1. Drop features with more than 30% of their data missing.\n",
    "2. Of the remaining features, choose only those that would be available to an investor before deciding to fund the loan.\n",
    "\n",
    "Definitions of the features are given in the LendingClub Data Dictionary [available here](https://www.lendingclub.com/info/download-data.action)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "033676d1-c042-4af2-a41c-1f49d678d03f",
    "_uuid": "6d374ac8147274a76355bbeca958bbcf6ebb1de4"
   },
   "source": [
    "## 4.1 Drop features missing more than 30% data\n",
    "<a id=\"4.1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First calculate the percentage of missing data for each feature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_fractions = loans.isnull().mean().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 10 features missing the most data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "member_id                                     1.000000\n",
       "next_pymnt_d                                  1.000000\n",
       "orig_projected_additional_accrued_interest    0.999876\n",
       "sec_app_mths_since_last_major_derog           0.999628\n",
       "hardship_length                               0.999275\n",
       "hardship_type                                 0.999275\n",
       "hardship_reason                               0.999275\n",
       "hardship_status                               0.999275\n",
       "deferral_term                                 0.999275\n",
       "hardship_amount                               0.999275\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_fractions.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the distribution of missing data percentages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Feature count')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pa2uLc+fO4c8//0RCQgKWLl2Kffv2aW2rtbU1bt68qfn+s4uLC7Zt24Y///wTV65cwcyZ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\n",
      "text/plain": [
       "<Figure size 540x270 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,3), dpi=90)\n",
    "missing_fractions.plot.hist(bins=20)\n",
    "plt.title('Histogram of Feature Incompleteness')\n",
    "plt.xlabel('Fraction of data missing')\n",
    "plt.ylabel('Feature count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above histogram, we see there's a large gap between features missing \"some\" data (&lt;20%) and those missing \"lots\" of data (&gt;40%). Because it's generally very difficult to accurately impute data with more than 30% missing values, we drop such columns. First store all variables missing more than 30% data in an alphabetical list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_cell_guid": "b2bccd07-6308-4d83-b45a-de8ade091106",
    "_uuid": "dd01b28cdf204ed38fa2d7123254fe9dd5f62c87"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['all_util', 'annual_inc_joint', 'debt_settlement_flag_date', 'deferral_term', 'desc', 'dti_joint', 'hardship_amount', 'hardship_dpd', 'hardship_end_date', 'hardship_last_payment_amount', 'hardship_length', 'hardship_loan_status', 'hardship_payoff_balance_amount', 'hardship_reason', 'hardship_start_date', 'hardship_status', 'hardship_type', 'il_util', 'inq_fi', 'inq_last_12m', 'max_bal_bc', 'member_id', 'mths_since_last_delinq', 'mths_since_last_major_derog', 'mths_since_last_record', 'mths_since_rcnt_il', 'mths_since_recent_bc_dlq', 'mths_since_recent_revol_delinq', 'next_pymnt_d', 'open_acc_6m', 'open_act_il', 'open_il_12m', 'open_il_24m', 'open_rv_12m', 'open_rv_24m', 'orig_projected_additional_accrued_interest', 'payment_plan_start_date', 'revol_bal_joint', 'sec_app_chargeoff_within_12_mths', 'sec_app_collections_12_mths_ex_med', 'sec_app_earliest_cr_line', 'sec_app_fico_range_high', 'sec_app_fico_range_low', 'sec_app_inq_last_6mths', 'sec_app_mort_acc', 'sec_app_mths_since_last_major_derog', 'sec_app_num_rev_accts', 'sec_app_open_acc', 'sec_app_open_act_il', 'sec_app_revol_util', 'settlement_amount', 'settlement_date', 'settlement_percentage', 'settlement_status', 'settlement_term', 'total_bal_il', 'total_cu_tl', 'verification_status_joint']\n"
     ]
    }
   ],
   "source": [
    "drop_list = sorted(list(missing_fractions[missing_fractions > 0.3].index))\n",
    "print(drop_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many features will be dropped?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(drop_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "7fcfae32-fdf5-42af-a14a-0b797c17b034",
    "_uuid": "4477765ea99a243a25421550ff4087b68fc6fa1b"
   },
   "source": [
    "Drop these features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "loans.drop(labels=drop_list, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(814986, 92)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "33b90b6b-0fcd-4657-ae11-0ff2f6a95823",
    "_uuid": "4066be79385b7b0c6b7d4f96533106093304b746"
   },
   "source": [
    "## 4.2 Only keep loan features known to potential investors\n",
    "<a id=\"4.2\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We examine the LendingClub website and Data Dictionary to determine which features would have been available to potential investors. Here's the list of features we currently have, in alphabetical order:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['acc_now_delinq', 'acc_open_past_24mths', 'addr_state', 'annual_inc', 'application_type', 'avg_cur_bal', 'bc_open_to_buy', 'bc_util', 'chargeoff_within_12_mths', 'collection_recovery_fee', 'collections_12_mths_ex_med', 'debt_settlement_flag', 'delinq_2yrs', 'delinq_amnt', 'disbursement_method', 'dti', 'earliest_cr_line', 'emp_length', 'emp_title', 'fico_range_high', 'fico_range_low', 'funded_amnt', 'funded_amnt_inv', 'grade', 'hardship_flag', 'home_ownership', 'id', 'initial_list_status', 'inq_last_6mths', 'installment', 'int_rate', 'issue_d', 'last_credit_pull_d', 'last_fico_range_high', 'last_fico_range_low', 'last_pymnt_amnt', 'last_pymnt_d', 'loan_amnt', 'loan_status', 'mo_sin_old_il_acct', 'mo_sin_old_rev_tl_op', 'mo_sin_rcnt_rev_tl_op', 'mo_sin_rcnt_tl', 'mort_acc', 'mths_since_recent_bc', 'mths_since_recent_inq', 'num_accts_ever_120_pd', 'num_actv_bc_tl', 'num_actv_rev_tl', 'num_bc_sats', 'num_bc_tl', 'num_il_tl', 'num_op_rev_tl', 'num_rev_accts', 'num_rev_tl_bal_gt_0', 'num_sats', 'num_tl_120dpd_2m', 'num_tl_30dpd', 'num_tl_90g_dpd_24m', 'num_tl_op_past_12m', 'open_acc', 'out_prncp', 'out_prncp_inv', 'pct_tl_nvr_dlq', 'percent_bc_gt_75', 'policy_code', 'pub_rec', 'pub_rec_bankruptcies', 'purpose', 'pymnt_plan', 'recoveries', 'revol_bal', 'revol_util', 'sub_grade', 'tax_liens', 'term', 'title', 'tot_coll_amt', 'tot_cur_bal', 'tot_hi_cred_lim', 'total_acc', 'total_bal_ex_mort', 'total_bc_limit', 'total_il_high_credit_limit', 'total_pymnt', 'total_pymnt_inv', 'total_rec_int', 'total_rec_late_fee', 'total_rec_prncp', 'total_rev_hi_lim', 'verification_status', 'zip_code']\n"
     ]
    }
   ],
   "source": [
    "print(sorted(loans.columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each of these features, we check the description in the Data Dictionary and only keep the features that would have been available to investors considering an investment in the loan. These include features in the loan application, and any features added by LendingClub when the loan listing was accepted, such as the loan grade and interest rate.\n",
    "\n",
    "I'm using my best available knowledge to determine which loan features are known to potential investors. I am not an investor on LendingClub, so my knowledge of the LendingClub investment process is not exact. When in doubt, I err on the side of dropping the feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "keep_list = ['addr_state', 'annual_inc', 'application_type', 'dti', 'earliest_cr_line', 'emp_length', 'emp_title', 'fico_range_high', 'fico_range_low', 'grade', 'home_ownership', 'id', 'initial_list_status', 'installment', 'int_rate', 'issue_d', 'loan_amnt', 'loan_status', 'mort_acc', 'open_acc', 'pub_rec', 'pub_rec_bankruptcies', 'purpose', 'revol_bal', 'revol_util', 'sub_grade', 'term', 'title', 'total_acc', 'verification_status', 'zip_code']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(keep_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The list of features to drop is any feature not in `keep_list`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['funded_amnt', 'funded_amnt_inv', 'pymnt_plan', 'delinq_2yrs', 'inq_last_6mths', 'out_prncp', 'out_prncp_inv', 'total_pymnt', 'total_pymnt_inv', 'total_rec_prncp', 'total_rec_int', 'total_rec_late_fee', 'recoveries', 'collection_recovery_fee', 'last_pymnt_d', 'last_pymnt_amnt', 'last_credit_pull_d', 'last_fico_range_high', 'last_fico_range_low', 'collections_12_mths_ex_med', 'policy_code', 'acc_now_delinq', 'tot_coll_amt', 'tot_cur_bal', 'total_rev_hi_lim', 'acc_open_past_24mths', 'avg_cur_bal', 'bc_open_to_buy', 'bc_util', 'chargeoff_within_12_mths', 'delinq_amnt', 'mo_sin_old_il_acct', 'mo_sin_old_rev_tl_op', 'mo_sin_rcnt_rev_tl_op', 'mo_sin_rcnt_tl', 'mths_since_recent_bc', 'mths_since_recent_inq', 'num_accts_ever_120_pd', 'num_actv_bc_tl', 'num_actv_rev_tl', 'num_bc_sats', 'num_bc_tl', 'num_il_tl', 'num_op_rev_tl', 'num_rev_accts', 'num_rev_tl_bal_gt_0', 'num_sats', 'num_tl_120dpd_2m', 'num_tl_30dpd', 'num_tl_90g_dpd_24m', 'num_tl_op_past_12m', 'pct_tl_nvr_dlq', 'percent_bc_gt_75', 'tax_liens', 'tot_hi_cred_lim', 'total_bal_ex_mort', 'total_bc_limit', 'total_il_high_credit_limit', 'hardship_flag', 'disbursement_method', 'debt_settlement_flag']\n"
     ]
    }
   ],
   "source": [
    "drop_list = [col for col in loans.columns if col not in keep_list]\n",
    "print(drop_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(drop_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop these features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "loans.drop(labels=drop_list, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(814986, 31)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Pre-processing and Exploratory Analysis\n",
    "<a id=\"5\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll inspect each feature individually, and do the following:\n",
    "\n",
    "1. Drop the feature if it is not useful for predicting the loan status.\n",
    "2. View summary statistics and visualize the data, plotting against the loan status.\n",
    "3. Modify the feature to make it useful for modeling, if necessary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define a function for plotting a variable and comparing with the loan status:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_var(col_name, full_name, continuous):\n",
    "    \"\"\"\n",
    "    Visualize a variable with and without faceting on the loan status.\n",
    "    - col_name is the variable name in the dataframe\n",
    "    - full_name is the full variable name\n",
    "    - continuous is True if the variable is continuous, False otherwise\n",
    "    \"\"\"\n",
    "    f, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12,3), dpi=90)\n",
    "    \n",
    "    # Plot without loan status\n",
    "    if continuous:\n",
    "        sns.distplot(loans.loc[loans[col_name].notnull(), col_name], kde=False, ax=ax1)\n",
    "    else:\n",
    "        sns.countplot(loans[col_name], order=sorted(loans[col_name].unique()), color='#5975A4', saturation=1, ax=ax1)\n",
    "    ax1.set_xlabel(full_name)\n",
    "    ax1.set_ylabel('Count')\n",
    "    ax1.set_title(full_name)\n",
    "\n",
    "    # Plot with loan status\n",
    "    if continuous:\n",
    "        sns.boxplot(x=col_name, y='loan_status', data=loans, ax=ax2)\n",
    "        ax2.set_ylabel('')\n",
    "        ax2.set_title(full_name + ' by Loan Status')\n",
    "    else:\n",
    "        charge_off_rates = loans.groupby(col_name)['loan_status'].value_counts(normalize=True).loc[:,'Charged Off']\n",
    "        sns.barplot(x=charge_off_rates.index, y=charge_off_rates.values, color='#5975A4', saturation=1, ax=ax2)\n",
    "        ax2.set_ylabel('Fraction of Loans Charged-off')\n",
    "        ax2.set_title('Charge-off Rate by ' + full_name)\n",
    "    ax2.set_xlabel(full_name)\n",
    "    \n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the remaining features for future reference:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['id', 'loan_amnt', 'term', 'int_rate', 'installment', 'grade', 'sub_grade', 'emp_title', 'emp_length', 'home_ownership', 'annual_inc', 'verification_status', 'issue_d', 'loan_status', 'purpose', 'title', 'zip_code', 'addr_state', 'dti', 'earliest_cr_line', 'fico_range_low', 'fico_range_high', 'open_acc', 'pub_rec', 'revol_bal', 'revol_util', 'total_acc', 'initial_list_status', 'application_type', 'mort_acc', 'pub_rec_bankruptcies']\n"
     ]
    }
   ],
   "source": [
    "print(list(loans.columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1 id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Dictionary: \"A unique [LendingClub] assigned ID for the loan listing.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "327317     65604280\n",
       "1644795    68586579\n",
       "227773     10636780\n",
       "838555      7956003\n",
       "46010      31547421\n",
       "Name: id, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['id'].sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Are all the IDs unique?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       814986\n",
       "unique      814986\n",
       "top       14680062\n",
       "freq             1\n",
       "Name: id, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['id'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yes, they are all unique. The ID is not useful for modeling, either as a categorical variable (there are too many distinct values) or as a numerical variable (the IDs vary wildly in magnitude, likely without any significance), so we drop this variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "loans.drop('id', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2 loan_amnt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Dictionary: \"The listed amount of the loan applied for by the borrower. If at some point in time, the credit department reduces the loan amount, then it will be reflected in this value.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    814986.000000\n",
       "mean      14315.458210\n",
       "std        8499.799241\n",
       "min         500.000000\n",
       "25%        8000.000000\n",
       "50%       12000.000000\n",
       "75%       20000.000000\n",
       "max       40000.000000\n",
       "Name: loan_amnt, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['loan_amnt'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loan amounts range from \\$500 to \\$40,000, with a median of \\$12,000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Joe\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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HDtW3336rPn36aNiwYbp27Vq2sSclJeWrrbBUqFDhjmn5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\nAKSCSAEAAACkgkgBAAAApMJvQQ8jKm0YpAEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_var('loan_amnt', 'Loan Amount', continuous=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Charged-off loans tend to have higher loan amounts. Let's compare the summary statistics by loan status:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Charged Off</th>\n",
       "      <td>168084.0</td>\n",
       "      <td>15384.975964</td>\n",
       "      <td>8606.476332</td>\n",
       "      <td>900.0</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>40000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fully Paid</th>\n",
       "      <td>646902.0</td>\n",
       "      <td>14037.566316</td>\n",
       "      <td>8449.740674</td>\n",
       "      <td>500.0</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>40000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count          mean          std    min     25%      50%  \\\n",
       "loan_status                                                                \n",
       "Charged Off  168084.0  15384.975964  8606.476332  900.0  9000.0  14000.0   \n",
       "Fully Paid   646902.0  14037.566316  8449.740674  500.0  7500.0  12000.0   \n",
       "\n",
       "                 75%      max  \n",
       "loan_status                    \n",
       "Charged Off  20000.0  40000.0  \n",
       "Fully Paid   20000.0  40000.0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.groupby('loan_status')['loan_amnt'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "8e98e8d9-9c8f-49b4-a123-4a11534454e3",
    "_uuid": "77ae6e4830589e4ee9dd0c815043300017c7fd75"
   },
   "source": [
    "## 5.3 term"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Dictionary: \"The number of payments on the loan. Values are in months and can be either 36 or 60.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_cell_guid": "e3559ee4-0c7c-4e29-9952-ef06f302937a",
    "_uuid": "b8fa81ef40fc0928639d94608d126039f73e3f3d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 36 months    618460\n",
       " 60 months    196526\n",
       "Name: term, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['term'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "a0a18d84-ed5f-4d4f-a377-c877a6d1be54",
    "_uuid": "7abe9deb95eb215cc2c8fa6425cc4d81bbf79bef"
   },
   "source": [
    "Convert `term` to integers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "_cell_guid": "dd9462d5-673b-4dce-ae3b-61f427b1abcb",
    "_uuid": "09eed29a2d6f5e30beec07ae5d29088234c99b1c"
   },
   "outputs": [],
   "source": [
    "loans['term'] = loans['term'].apply(lambda s: np.int8(s.split()[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "_cell_guid": "dd9462d5-673b-4dce-ae3b-61f427b1abcb",
    "_uuid": "09eed29a2d6f5e30beec07ae5d29088234c99b1c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36    0.75886\n",
       "60    0.24114\n",
       "Name: term, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['term'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare the charge-off rate by loan period:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "term\n",
       "36    0.165710\n",
       "60    0.333793\n",
       "Name: loan_status, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.groupby('term')['loan_status'].value_counts(normalize=True).loc[:,'Charged Off']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "About 76% of the completed loans have three-year periods, and the rest have five-year periods. Loans with five-year periods are more than twice as likely to charge-off as loans with three-year periods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.4 int_rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Dictionary: \"Interest Rate on the loan.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    814986.000000\n",
       "mean         13.490993\n",
       "std           4.618486\n",
       "min           5.320000\n",
       "25%           9.990000\n",
       "50%          13.110000\n",
       "75%          16.290000\n",
       "max          30.990000\n",
       "Name: int_rate, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['int_rate'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interest rates range from 5.32% to 30.99% (!) with a median of 13.1%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Joe\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_var('int_rate', 'Interest Rate', continuous=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "1fd2a00f-0f95-481c-b9ff-d26526eb5f04",
    "_uuid": "321c4fb18ebd1bcf6689d7a5d3f23f80752bcfb3"
   },
   "source": [
    "Charged-off loans tend to have much higher interest rates. Let's compare the summary statistics by loan status:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Charged Off</th>\n",
       "      <td>168084.0</td>\n",
       "      <td>15.736335</td>\n",
       "      <td>4.625755</td>\n",
       "      <td>5.32</td>\n",
       "      <td>12.59</td>\n",
       "      <td>15.31</td>\n",
       "      <td>18.55</td>\n",
       "      <td>30.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fully Paid</th>\n",
       "      <td>646902.0</td>\n",
       "      <td>12.907587</td>\n",
       "      <td>4.434262</td>\n",
       "      <td>5.32</td>\n",
       "      <td>9.49</td>\n",
       "      <td>12.68</td>\n",
       "      <td>15.61</td>\n",
       "      <td>30.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count       mean       std   min    25%    50%    75%    max\n",
       "loan_status                                                                 \n",
       "Charged Off  168084.0  15.736335  4.625755  5.32  12.59  15.31  18.55  30.99\n",
       "Fully Paid   646902.0  12.907587  4.434262  5.32   9.49  12.68  15.61  30.99"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.groupby('loan_status')['int_rate'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.5 installment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Dictionary: \"The monthly payment owed by the borrower if the loan originates.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    814986.000000\n",
       "mean        436.749624\n",
       "std         255.732093\n",
       "min           4.930000\n",
       "25%         251.400000\n",
       "50%         377.040000\n",
       "75%         576.290000\n",
       "max        1714.540000\n",
       "Name: installment, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['installment'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Installments range from \\$4.93 to \\$1,714, with a median of \\$377."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Joe\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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1fv16PfPMM5JuvQ/S0tLKVK533nnnTdNuF39R2xxBfrnm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\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_var('installment', 'Installment', continuous=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "1fd2a00f-0f95-481c-b9ff-d26526eb5f04",
    "_uuid": "321c4fb18ebd1bcf6689d7a5d3f23f80752bcfb3"
   },
   "source": [
    "Charged-off loans tend to have higher installments. Let's compare the summary statistics by loan status:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Charged Off</th>\n",
       "      <td>168084.0</td>\n",
       "      <td>459.973673</td>\n",
       "      <td>255.309267</td>\n",
       "      <td>21.62</td>\n",
       "      <td>276.14</td>\n",
       "      <td>402.39</td>\n",
       "      <td>595.87</td>\n",
       "      <td>1569.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fully Paid</th>\n",
       "      <td>646902.0</td>\n",
       "      <td>430.715339</td>\n",
       "      <td>255.496761</td>\n",
       "      <td>4.93</td>\n",
       "      <td>244.76</td>\n",
       "      <td>370.92</td>\n",
       "      <td>569.72</td>\n",
       "      <td>1714.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count        mean         std    min     25%     50%     75%  \\\n",
       "loan_status                                                                    \n",
       "Charged Off  168084.0  459.973673  255.309267  21.62  276.14  402.39  595.87   \n",
       "Fully Paid   646902.0  430.715339  255.496761   4.93  244.76  370.92  569.72   \n",
       "\n",
       "                 max  \n",
       "loan_status           \n",
       "Charged Off  1569.11  \n",
       "Fully Paid   1714.54  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.groupby('loan_status')['installment'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loans that charge off have \\$30 higher installments on average."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "1b745ce5-987f-45b9-8e3f-6d5f760e3a7c",
    "_uuid": "687c301f0bd5f40efdeca5307e9d0cb6a1fb6161"
   },
   "source": [
    "## 5.6 grade, sub_grade"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Dictionary for `grade`: \"LendingClub assigned loan grade.\"\n",
    "\n",
    "Data Dictionary for `sub_grade`: \"LendingClub assigned loan subgrade.\"\n",
    "\n",
    "What are the possible values of `grade` and `sub_grade`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['A', 'B', 'C', 'D', 'E', 'F', 'G']\n"
     ]
    }
   ],
   "source": [
    "print(sorted(loans['grade'].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['A1', 'A2', 'A3', 'A4', 'A5', 'B1', 'B2', 'B3', 'B4', 'B5', 'C1', 'C2', 'C3', 'C4', 'C5', 'D1', 'D2', 'D3', 'D4', 'D5', 'E1', 'E2', 'E3', 'E4', 'E5', 'F1', 'F2', 'F3', 'F4', 'F5', 'G1', 'G2', 'G3', 'G4', 'G5']\n"
     ]
    }
   ],
   "source": [
    "print(sorted(loans['sub_grade'].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b1d34f86-4ffb-4024-ad0f-714bd5cb3f21",
    "_uuid": "efb24fbebc6a4b092a37b79e7fb6f29133d3315a"
   },
   "source": [
    "The grade is implied by the subgrade, so let's drop the grade column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "_cell_guid": "9c00600b-0a58-4648-9448-4e960b705eb6",
    "_uuid": "a46021ef96a484732a847ed2f3cece9d423f73ec"
   },
   "outputs": [],
   "source": [
    "loans.drop('grade', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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k/PPP0ttr+/ZnV1dXqYLWW+9ufEokL+5BQUQyq169Onbu3FnqDuS1a9eGhoYG6tWrB21t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\n9/+mg+sNFteapgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_var('sub_grade', 'Subgrade', continuous=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "e53dbddd-aff4-4d44-bc8e-a7637e953ab0",
    "_uuid": "cf7685607c32b9857f4fc0cce38c6feea215e2fe"
   },
   "source": [
    "There's a clear trend of higher probability of charge-off as the subgrade worsens."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.7 emp_title"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Dictionary: \"The job title supplied by the Borrower when applying for the loan.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      766415\n",
       "unique     280473\n",
       "top       Teacher\n",
       "freq        11351\n",
       "Name: emp_title, dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['emp_title'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are too many different job titles for this feature to be useful, so we drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "loans.drop(labels='emp_title', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "98efbb15-f5d7-42ef-992a-92bb0860a803",
    "_uuid": "e84911043e002622f7c181e2be71d09ac277dbc8"
   },
   "source": [
    "## 5.8 emp_length"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Dictionary: \"Employment length in years. Possible values are between 0 and 10 where 0 means less than one year and 10 means ten or more years.\" The actual data does not match this description:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "_cell_guid": "a60d507b-dc0d-4e44-912e-0d214b1b85a9",
    "_uuid": "372fd175e1e4458f8a9bc860fd91798d696892be"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1 year        53411\n",
       "10+ years    264873\n",
       "2 years       73493\n",
       "3 years       64999\n",
       "4 years       48752\n",
       "5 years       52149\n",
       "6 years       40290\n",
       "7 years       39407\n",
       "8 years       38887\n",
       "9 years       31900\n",
       "< 1 year      64572\n",
       "NaN           42253\n",
       "Name: emp_length, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['emp_length'].value_counts(dropna=False).sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note there are 42,253 loans without data on the length of employment.\n",
    "\n",
    "Convert `emp_length` to integers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "loans['emp_length'].replace(to_replace='10+ years', value='10 years', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "loans['emp_length'].replace('< 1 year', '0 years', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "_cell_guid": "f5ce8306-8ca3-4a78-8332-7a12fd233f98",
    "_uuid": "fc61f2f288d38767f5fa1e3f7df5b58c7bc52a35"
   },
   "outputs": [],
   "source": [
    "def emp_length_to_int(s):\n",
    "    if pd.isnull(s):\n",
    "        return s\n",
    "    else:\n",
    "        return np.int8(s.split()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "_cell_guid": "f5ce8306-8ca3-4a78-8332-7a12fd233f98",
    "_uuid": "fc61f2f288d38767f5fa1e3f7df5b58c7bc52a35"
   },
   "outputs": [],
   "source": [
    "loans['emp_length'] = loans['emp_length'].apply(emp_length_to_int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "_cell_guid": "f5ce8306-8ca3-4a78-8332-7a12fd233f98",
    "_uuid": "fc61f2f288d38767f5fa1e3f7df5b58c7bc52a35"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0.0      64572\n",
       " 1.0      53411\n",
       " 2.0      73493\n",
       " 3.0      64999\n",
       " 4.0      48752\n",
       " 5.0      52149\n",
       " 6.0      40290\n",
       " 7.0      39407\n",
       " 8.0      38887\n",
       " 9.0      31900\n",
       " 10.0    264873\n",
       "NaN       42253\n",
       "Name: emp_length, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['emp_length'].value_counts(dropna=False).sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+IiIi0j9HR0f8+uuvGD58OLy8vDBmzBj88ssvUCgUGi/j5MmT8Pf3h5ubG4KD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\nRK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_var('emp_length', 'Employment Length', continuous=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loan status does not appear to vary much with employment length on average, except for a small drop in charge-offs for borrowers with over 10 years of employment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "e994c5db-880e-4ec0-8cbd-4aacad373993",
    "_uuid": "332158cff90d2e051f5a80bee5983019ada32a30"
   },
   "source": [
    "## 5.9 home_ownership"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Dictionary: \"The home ownership status provided by the borrower during registration or obtained from the credit report. Our values are: RENT, OWN, MORTGAGE, OTHER.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "_cell_guid": "d574efb7-1a6b-4ec2-a446-827d75d0d9cf",
    "_uuid": "017c6be384f35a700cebfa761dbaee4ec6b73256"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MORTGAGE    406866\n",
       "RENT        325071\n",
       "OWN          82765\n",
       "OTHER          144\n",
       "ANY             94\n",
       "NONE            46\n",
       "Name: home_ownership, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['home_ownership'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replace the values `ANY` and `NONE` with `OTHER`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "loans['home_ownership'].replace(['NONE', 'ANY'], 'OTHER', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MORTGAGE    406866\n",
       "RENT        325071\n",
       "OWN          82765\n",
       "OTHER          284\n",
       "Name: home_ownership, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['home_ownership'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FQumXqurVq8PIyEj8PHz4cEyaNAnHjh1D06ZN8d///hdeXl4oV+7/D1L74osvxPefffYZ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\nXcHqKzIDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_var('home_ownership', 'Home Ownership', continuous=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There appear to be large differences in charge-off rates by home ownership status. Renters and homeowners have a higher probability of charge-off. Let's compare the charge-off rates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "home_ownership\n",
       "MORTGAGE    0.177808\n",
       "OTHER       0.176056\n",
       "OWN         0.215804\n",
       "RENT        0.239422\n",
       "Name: loan_status, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.groupby('home_ownership')['loan_status'].value_counts(normalize=True).loc[:,'Charged Off']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.10 annual_inc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Dictionary: \"The self-reported annual income provided by the borrower during registration.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    8.149860e+05\n",
       "mean     7.523039e+04\n",
       "std      6.524373e+04\n",
       "min      0.000000e+00\n",
       "25%      4.500000e+04\n",
       "50%      6.500000e+04\n",
       "75%      9.000000e+04\n",
       "max      9.550000e+06\n",
       "Name: annual_inc, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['annual_inc'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Annual income ranges from \\$0 to \\$9,550,000, with a median of \\$65,000. Because of the large range of incomes, we should take a log transform of the annual income variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "loans['log_annual_inc'] = loans['annual_inc'].apply(lambda x: np.log10(x+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "loans.drop('annual_inc', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    814986.000000\n",
       "mean          4.810836\n",
       "std           0.231893\n",
       "min           0.000000\n",
       "25%           4.653222\n",
       "50%           4.812920\n",
       "75%           4.954247\n",
       "max           6.980003\n",
       "Name: log_annual_inc, dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['log_annual_inc'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Joe\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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D0+l8gJmBpKQkRo4ciclkIjMzk7a2Ni5cuIDD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\nEREREREREb/wD7tUTevTnl10AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_var('log_annual_inc', 'Log Annual Income', continuous=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears that individuals with higher income are more likely to pay off their loans. Let's compare the summary statistics by loan status:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Charged Off</th>\n",
       "      <td>168084.0</td>\n",
       "      <td>4.777072</td>\n",
       "      <td>0.227015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.632467</td>\n",
       "      <td>4.778158</td>\n",
       "      <td>4.915096</td>\n",
       "      <td>6.949393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fully Paid</th>\n",
       "      <td>646902.0</td>\n",
       "      <td>4.819608</td>\n",
       "      <td>0.232342</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.672107</td>\n",
       "      <td>4.812920</td>\n",
       "      <td>4.963793</td>\n",
       "      <td>6.980003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count      mean       std  min       25%       50%       75%  \\\n",
       "loan_status                                                                    \n",
       "Charged Off  168084.0  4.777072  0.227015  0.0  4.632467  4.778158  4.915096   \n",
       "Fully Paid   646902.0  4.819608  0.232342  0.0  4.672107  4.812920  4.963793   \n",
       "\n",
       "                  max  \n",
       "loan_status            \n",
       "Charged Off  6.949393  \n",
       "Fully Paid   6.980003  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.groupby('loan_status')['log_annual_inc'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "119da036-552d-4a95-a61f-7fdd464bcf5b",
    "_uuid": "840799ab7f984830bf29f0b677f4f14b672d4253"
   },
   "source": [
    "## 5.11 verification_status"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Dictionary: \"Indicates if income was verified by [Lending Club], not verified, or if the income source was verified.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LAIiMjIRMJkNoaCi0tbUBAEuXLoWDgwOOHTsGNze3IjHm5OTgxx9/xMyZM9GzZ08p1uvX\nryM8PBzOzs7SdE1TU1MYGxvD2NgYwKuv44UiIiKUppHm5uaiQYMGCAoKwpAhQwC8WFR6+PDh8Pb2\nlq7BPj4+2LVrF1JTU9G6dWvo6elBR0dHun6uW7cOX375pRSvubk5MjMzsW7dOnh6er4yHn9/f+m9\nnT9/Pj799FNERkaib9++cHNzw4EDB6RFvvfv349PP/30lefXo0cPzJkzB8eOHUPfvn2lfZycnGBi\nYlKiz6Nw1Oi/6erqKt0LmJiYKC3ILYTA1q1bMWLECHh4eAAAxo0bh7y8PDx79gy6uroIDg7GgAED\nAAD169dH3759MX36dAAv7l+0tbVhYGBQ7HmGh4ejV69eGDNmDIAX9y3Z2dmYN28e/P39pRgmTZqE\n1q1bA3jRJ/38/PDw4cNi22zQoAEiIiLw/fff4+TJk1i7di3Wrl2LmjVrIjg4GN27d4ehoWGRz7x5\n8+ZwdXVFq1atpHPZsWMHEhISoK2trdRHDQ0Ni/3MXnbnzh08f/4cH374IerVq4d69ephw4YNap3q\nQurHJAXRe8rLywu7du3CpEmTkJeXh0OHDuHLL7+U6q9du4b8/HzppquQXC5XGrpXOGe1NBISEuDu\n7q5U9vKTQTw8PLBlyxbEx8fj5s2biIuLAwCVVgq/ceOGNI2iUOGF9caNG1ISwsLCQqovvBl73R/7\n1apVw5QpUzBlyhRkZmbi999/x44dOzBr1izUrVu3yDGBF0Mqo6KisGzZMqSkpCA5ORlJSUlSPCVl\nYmKCESNGYM6cOVi1ahU6dOiALl26oFevXqVqj4iI/k+1atUAoERT6D766CPp98LkRm5uLgBg4MCB\nOHnypPTHeHx8PO7cuSNN9wBeJPwLExQAcPXqVbi4uCgdo2XLllKS4tq1a3jw4EGR64hMJkNSUhIu\nX74s/cEJvPhyYsKECcjLy0PLli2V9imcevA2unXrhsmTJ6OgoACXL1/GwoUL0a1bN3h7e0vbVK9e\nHYMGDUJERATi4uKQmpoqreVU3HX9wYMHuHv3LhYuXIhvv/1WKs/Ly0N+fj4UCgV0dXWLjadFixbS\n70ZGRrC0tJS+GPDy8sIPP/yApKQk1K9fH0ePHsWCBQteeW56enrSFIW+ffsiOzsbZ86cUZoq8abP\no1Bp75kePnyI+/fvF/kCZfz48dLvxsbGCAsLQ2JionTP9HIfe50bN24USfq0adMGeXl5StN8S3rP\nVLduXcyYMQMzZsxASkoKzp8/jx9++AH+/v7Yu3evNF33ZZ999hnOnTuHRYsWITU1FYmJiUhLSyv1\ne2djY4NevXrBx8cHH374ITp06IDu3bujS5cupWqPygcmKYjeUx4eHlixYgWio6ORnZ2NnJwcpW9e\ndHR0YGJigp9//rnIvi9nn1++qSqpl+ep/tuNGzcwaNAgNG/eHO3atYOLiwvy8vJUfjxqcYtziv+/\n2NLLxy1uQSrxikWZwsPD0aBBAzg7OwN4cfH9/PPP4e7ujp49e+L06dPFJinWrl2L8PBweHl54ZNP\nPoGPjw+2bt2KzMxMlc4FKHoDFxAQgMGDB+P06dM4d+4cgoKCcODAAYSHh6vcJhERFWVubo7q1asj\nOjq6SKIAeDHffdOmTZgzZ45UVqlS0WXchBAQQuCLL75ASkoKevfujc8++wx2dnbSCMJC/76WVq5c\n+bULBOro6KBRo0b47rvvitQZGxvDwMBAWuuhsP2HDx8W21Z+fv5rr8eqMDIyQoMGDQC8+EPW2NgY\nEydORNWqVfHFF18AAO7du4f+/fujdu3a6NKlCzp37oxatWoprevw73MEgBkzZqBt27ZF6l8Xc+Fo\nhkIFBQVSQqNJkyZo3LgxfvnlF1hbW0NbW/uNC4Z7eXlh4MCBuHfvHo4fP47q1aujY8eOSrG+7vMo\nVNp7pjctnnnhwgWMGTMGTk5OaNmyJfr06YPU1FQEBwer1H5xcRXed7z8PheXFHpVP124cCE6d+4M\nBwcHAC/6hYWFBdzc3NClSxecO3eu2CTF9OnTceLECXh6eqJHjx6YNGmS0r81Vbx8z6SlpYXly5fj\nyy+/lO6Z/Pz80K9fP8yaNatE7VL5wSQF0Xuqdu3aaN++PY4cOYKsrCx0795d6UL68ccfS98iFd54\n5OfnY+rUqejevXuxN24l1bBhQ+lboUJff/01qlatCh0dHdSpUwcbNmyQ6n766ScA/3dBfHnRsn9r\n1KgRoqKilMoKF7xs2LBhqeKNiYnB4cOH0a1bN6UbIF1dXejr66N69erFxrVlyxZMmDABI0aMkMpu\n3rwpXfj/vX3hzcjTp0+lstTUVOn3tLQ0fP/99/jmm28wePBgDB48GIcOHcKkSZOQlZUlxUFERCVX\nqVIleHp6YufOnRg5cqTS4plCCISHhyMjIwM1a9bErVu3XttWYmIizp07h71796Jp06YAXky7uH//\n/muTENbW1oiOjpYWCQQgLYIIvLhG79q1CyYmJtLw95ycHEydOhXDhw+Ho6OjdO0u9MEHH0BHRwd/\n/fWXNPUEeHFtbNSokQrvjOp69uwJNzc3rFy5Ep06dYK1tTWOHz+Op0+fYvv27dI19OzZswCKv64b\nGxujdu3ayMjIkKZZAMCPP/6IuLi41/7heu3aNekcs7OzkZKSojQ11dPTE7t370Z6ejp69+79xiSA\nvb09LCwscPz4cRw6dAgeHh5KiSlVPo+3YWxsjJo1a+LKlStKX4Z4e3ujU6dOiImJQYcOHbB8+XKp\n7vz58wBevLdaWlqvvWdq2LAh/vrrL6X+9tdff0FHRwfm5ua4du1aiWO+cOECUlJSpCRFIQMDA1Su\nXLnYe6acnBzs2bMHK1asQI8ePQC8GDmTnp6OunXrFtke+L97ppenx7x8z3TlyhUcPHgQQUFBaNSo\nEUaNGoX169fju+++Y5LiP4xP9yB6j3l5eeH48eM4depUkW8y2rVrB3t7e/j7++Py5ctISUnB9OnT\n8dtvv0nrWLyt0aNH45dffsGPP/6ItLQ0/Pzzz4iMjETXrl1hamqKW7du4fz587h16xb2798vrXiu\nUCgAAIaGhsjIyCj2JnHMmDG4cuUKFi5ciOTkZJw9exazZ89Gp06dSp2k8PPzQ2pqKr744gv88ccf\nuHXrFi5fvoxp06bh0aNH6N+/vxTXo0ePkJycDLlcDlNTU5w7dw5JSUm4ceMG5syZg6ioKKXzAID4\n+Hg8fPgQtWrVQr169bB582YkJyfj8uXLWL58uXRhrlatGg4fPoxZs2YhKSkJSUlJOHz4MMzNzaVh\nykREVHq+vr6oX78+Bg0ahIMHDyI9PR1RUVGYMGECLl26hHnz5r32j75CVatWReXKlXH48GFkZGQg\nKioKvr6+UCgU0jWgOKNHj0ZkZCS2b9+O1NRUbNmyRelRnr1790a1atXg7++PK1euICEhAVOmTEF0\ndLRSAuJlenp6GDFiBJYvX44jR44gNTUV4eHhOHbsmFIS/V2ZNm0aDA0NMWPGDBQUFKBatWrIycnB\n0aNHcevWLZw8eVL6pv/l6+Hdu3eRnp6OvLw8jBs3Dps3b8bOnTuRlpaGX375BaGhocWuRfWyxYsX\n4/Tp04iPj8fUqVNRo0YNpS9X3N3dkZqaKn1jrwpPT0/s2rULUVFRRe6ZSvN5lNTo0aOxefNmREZG\nIi0tDWvWrEF0dDQ6deoEU1NTxMfH4++//0Z6ejp++OEH6WklL7+3qampxT4BZty4cTh8+DDWr1+P\n1NRUHD58GCtXrkTfvn2VvsAqiUmTJuHMmTOYOnUq/ve//yEjIwN//PEHJk6ciJo1a0prc7z8mVep\nUgUGBgY4efIk0tLScO3aNUyZMgW3b98ucs8UFxeHJ0+ewMrKCgYGBli3bh3S0tJw5swZbNq0SYrD\n2NgY27dvx9KlS5GWloa4uDj89ttvxa49Rv8dTFIQvce6deuGJ0+ewMjIqEiWX0tLC6tXr0ajRo3g\n6+sLT09PpKamYsOGDe/sG5du3bph5syZ2Lx5M1xdXbFlyxYsWrQI7du3x9ChQ9G9e3dMmjQJ7u7u\n2L59O2bPng0DAwNcuXIFwIvFx1JSUuDi4qL0ODEAsLKywrp16/Dnn3/C3d0dQUFB6N69O1asWFHq\neG1sbLBz505UrVoVX331FZydnTFhwgQoFAr89NNPqFGjBgDA2dkZ9erVg7u7O06dOoWFCxfi8ePH\n8PT0xIgRI5CdnY0pU6YgMTERMpkMRkZG8Pb2xrfffovp06dDS0sLixYtwqNHj+Du7o7g4GBMmTJF\n+tbG2NgY69evR3p6Ovr164fPP/8ccrkc4eHhxQ45JiKikjE0NMS2bdvg5uaG7777Dm5ubhg/fjwK\nCgqwc+dOldcUql27NubPn48jR46gV69e+Oqrr9C8eXO4u7tL17LidO7cGdOnT8f3338PNzc3/Pbb\nb/D09JS+NdbT08OmTZugp6eHYcOGYeDAgcjLy8OWLVteO5pu4sSJ6N+/P+bPny89fnTp0qVqWdPI\n1NQUQUFBiI6OxrZt29CrVy8MGzYMISEhcHV1xYoVK+Dr64sGDRpI74WXlxfy8/Ph4uKCa9euYeDA\ngZg8eTK+//57uLi4YPny5fD19VVaQ6s4vr6+mDdvHj7//HMUFBRgw4YNSlMVTE1N0bFjR5ibm0sj\nXN7ks88+Q0JCAlq1agVzc3OlutJ+HiUxdOhQjB49GosXL4abmxtOnjyJdevW4eOPP8aECRNgY2OD\nUaNGoU+fPjh27BhCQ0MBQHpvhw8fjjNnzsDd3b3IWhWffPIJFi5ciIiICLi5uWHx4sUYOnSo0mPd\nS+rTTz/FDz/8gJycHPj5+aFnz54ICAjAhx9+iG3btklTTF7+zOPi4rB8+XJcvXoVbm5u8PX1xQcf\nfICRI0dKI28bNWoEZ2dnTJo0CStXroSRkREWL16M2NhYuLi4YOXKldKi6cCL9WJWr16N8+fPw93d\nHUOHDsWHH36IpUuXlvrcSPO0xOvGohERERER0Tt16dIl1KpVS2nKxsyZM3Hz5k3pG3J6O15eXujd\nu7daRpEQkXpxTQoiIiIiojJ05swZHD16FPPnz0edOnXwv//9D7/88ov0SEkqvRMnTiA6Ohqpqakq\nT/UgovKFIymIiIiIiMqQXC7HwoULcezYMWRnZ8PMzAxDhgxRWtiQSqdPnz7IzMzErFmzpKd1EdF/\nC5MURERERERERFQucAU2IiIiIiIiIioXmKQgIiKi91J0dDQ8PDxgb2+PQYMGIS0trcg2T548wVdf\nfQVHR0d06NABISEh0qPwEhISYGNjgxYtWkg/R48eLevTICIiqlA43aOECgoK8PTpU+jo6Kj0/Gwi\nIqKKQAiB58+fw9DQsFw8Klcul8PJyQkBAQFwdnZGeHg4fv/9d+zYsUNpuxkzZuDRo0dYsGAB5HI5\nxo0bhy5dumDs2LE4cOAADh8+jLVr15bo2LxXICIiKp4q9wt8ukcJPX36FAkJCZoOg4iIqFyysrKC\nsbFxqfYdOXIkNm7ciIiICHh4eLxVHBcuXICJiQl69+4NABg3bhy2bNmCpKQkNGzYUNpOCAFfX18Y\nGhrC0NAQbm5uOH/+PADg+vXraNy4cYmPzXsFIiKi13vd/UKZJymOHj2KZcuW4e7du7C0tMT06dPR\nokULREdHIzg4GKmpqWjSpAlCQ0Nhbm4OAGVe9zo6OjoAXrypurq66niLiIiI/nMUCgUSEhKk62Rp\nREVF4cGDB5gzZ85bJylSUlJgaWkpvdbW1oaZmVmRJEVISIjSfqdPn0aTJk0AAPHx8VAoFOjSpQu0\ntLTQv39/+Pj4vPHYhe+BhYXFW70fRERE75vnz58jJSXltdfHMk1SZGRkIDAwEJs3b0bz5s2xZ88e\n+Pv749ixY/Dz81MakhkYGIgdO3ZALpeXad2bFA7b1NXVRZUqVdT9lhEREf2nvM30hpYtW6JDhw4A\nABsbm2K3iYuLU6mtZ8+eQU9PT6lMX18fMpnslfssXrwYycnJWLx4MQDAxMQETZs2xYABA5CZmQkf\nHx/UrFkTXl5erz124XuQkpKiUqxEREQVzevuF8o0SVG/fn2cO3cOhoaGePLkCR4+fAgTE5PXDsnM\nyMgo07qXv10hIiKisrN69WpcvXoVI0eOxPr169+qLX19feTm5iqVyWQyGBoaFtk2Ly8PwcHBuHjx\nIjZv3oxq1aoBAJYsWSJt06iM2hGKAAAgAElEQVRRIwwePBi//vrrG5MUhTjqkoiISFnhyMvXKfPp\nHoaGhoiPj4eHhwe0tbURFhaGGzduvHJIZmZmZpnWqZqkKFz5m4iIiN7NddHDwwNHjhyBj48P2rZt\n+1ZtWVpaYt++fdLr/Px8pKWlwcLCQmk7hUIBPz8/ZGdnY+fOnahevToAIDc3FytWrICvr680Z1ah\nUJRoFCVHXRIREZWcRhbOtLS0RExMDA4cOIDx48dj1KhRrxyS+brhmuqoUxUXxCIiInq37t69i5iY\nGKxfvx6enp4o7gFkdevWVaktBwcHZGVlISIiAi4uLggPD4e5uXmRLyPmzp2Lx48fY+vWrdDX15fK\n9fT0cO7cOQghMGXKFKSkpGD79u1F1rAgIiKid0sjSYrCRTL69OmDzZs3v3ZIZlnXqYpDOImIiP6P\nKsM336Rr167o168ftLS00LVrV6U6IQS0tLRUXpNCT08PYWFhCA4OxuzZs2FjY4Ply5cDAFxdXeHj\n44MuXbpg9+7dqFy5Mtq3by/t26pVK2zYsAGrVq3C7Nmz4ejoCCMjI4wdOxadOnV6q3MkIiKi1yvT\nJMXvv/+O9evXY9OmTVKZQqGApaUlIiIipLKXh2Tq6Oi8crimOupUxSGcRERE79aSJUvw9ddfo2fP\nnjh48OBbt2dra4s9e/YUKY+MjJR+f13S46OPPlK6ZyEiIiL1q1SWB2vcuDGuXr2KyMhI5OXlYdu2\nbcjPz4ejo6M0JFOhUGDt2rXSkMyXh2uWRR0RERFpTu3atbFz507Uq1cP9erVg56eHmrXri29JiIi\noveblihuwqcaXb58GSEhIcjIyECTJk0wa9YsWFpaIjY2FsHBwUhOToaNjQ1CQ0Nhbm4OAGVe9zpy\nuRyxsbGwtbXlSAoqdwZ9vV3TIVAZ2LFosKZDICriXV4fFQoFFi5ciD179kAul0NHRweurq6YNWvW\nf+Lay3sFIqrIeD/6/nube1FVrpFlviZF69atlaZ2FHrVkExN1BEREZHmrFixAlFRUVizZg3q16+P\nmzdvYtmyZVi+fDkCAgI0HR4RERGpkUYWziQiIiJ6lcOHD2P79u2oU6cOAMDc3ByWlpbo378/kxRE\nRETvuTJdk4KIiIjoTXJyclC9enWlspo1a+L58+caioiIiIjKCpMUREREVK40a9YM69atUypbu3Yt\nmjZtqqGIiIiIqKxwugcRERGVK19//TWGDh2K3bt3o27dusjMzERBQQE2btyo6dCIiIhIzZikICIi\nonLF2toax44dw4kTJ5CVlYW6deuic+fOMDIy0nRoREREpGac7kFERETlzgcffIA+ffogMzMTbm5u\nTFAQERFVEExSEBERUbl14MABTYdAREREZYhJCiIiIiIiIiIqF5ikICIionJLCKHpEIiIiKgMMUlB\nRERE5dahQ4c0HQIRERGVIT7dg4iIiMqF77777o3bfPnll2UQCREREWkKkxRERERULly8eBEAkJub\niytXrsDGxgZmZma4e/cuoqOj0aFDBw1HSEREROrGJAUREalk0NfbNR0ClYEdiwZr7Ng//PADACAw\nMBBeXl4YOHCgVLdv3z4cO3ZMU6ERERFRGWGSgoiIiMqVY8eOYf78+Upl7u7umDNnjoYiInp/MOFc\nMWgy4Uz0trhwJhEREZUrpqam+Ouvv5TKzp8/j1q1amkoIiIiIiorHElBRERE5YqPjw9Gjx4NZ2dn\n1KlTBxkZGThx4gRCQ0M1HRoRERGpGZMUREREVK707dsX9evXx4EDBxAbG4vatWtj06ZNaNmypaZD\nIyIiIjVjkoKIiIjKnXbt2qF169bIzMyEmZkZtLS0NB0SERERlQGuSUFERETlilwux6xZs2Bvb4/P\nPvsMN2/ehIuLC27duqXp0IiIiEjNyjxJceLECbi6uqJly5bw8vKSFsaaMGEC7Ozs0KJFC7Ro0QJe\nXl7SPnv37kXnzp3RokULfP3118jNzVVrHREREWnOkiVLkJ6ejp9++gk6OjqoW7cu7OzsMHfuXE2H\nRkRERGpWpkmK9PR0BAQEIDg4GJcvX8bw4cPh6+uLnJwcXL9+HT/99BOioqIQFRWFvXv3AgDi4uKw\naNEihIWF4ezZs8jOzsa6devUVkdERESadezYMSxevBjNmjWDlpYWqlSpgpkzZyIqKkrToREREZGa\nlWmS4vbt2+jXrx/atm2LSpUqwd3dHQAQHx+PzMxMNGrUqMg+Bw8ehIuLC6ytrWFkZIQJEyZg9+7d\naqsjIiIizXr+/Dn09fUBAEIIAIC2tjYqV+ZSWkRERO+7Mr3at23bFm3btpVe//3335DJZMjPz4eB\ngQHGjBmD+Ph4NG7cGDNmzEDDhg2RnJyMDh06SPtYWFjg/v37yM7OVkudiYmJSueiUCje5q0gIio1\nuVyu6RDoPVba/vUur4uOjo6YM2cOZs6cKS2YuWLFCrRq1eqdHYOIiIjKJ419JXHz5k2MHz8eEydO\nRF5eHpo3b46AgACYmZkhLCwMvr6+OHjwIGQyGfT09KT9Cr9Zyc3NVUudqhISEkpx1sCSnddLtR/9\nt0zp31jTIdB7LDY2VtMh0HusPPSvoKAgjB07Fm3atEF+fj5atmyJevXqITw8XOU2oqOjERwcjNTU\nVDRp0gShoaEwNzdX2ubJkyeYM2cOzp49C21tbfTq1Qtff/01dHV1kZubi2nTpuH06dMwNDSEv78/\nPD093/WpEhER0b9oJEkRExMDHx8fDBo0CKNGjQIAtG/fXqofP348tmzZgqSkJOjp6Sl9qyOTyQAA\nBgYGaqlTlZWVFXR1dVXeXsIkRYVga2urmQOzf1UI7F+kTqXtXwqFotQJ/H+rUaMGdu3ahStXruDW\nrVuoXbs27OzsVJ7uIZfL4efnh4CAADg7OyM8PByBgYHYsWOH0naLFi2CXC7HyZMnIZfLMW7cOGzc\nuBFjx47F0qVLIZPJcPbsWSQmJmL06NGwt7eHhYXFOzlHIiIiKl6ZJynOnj0Lf39/BAYGom/fvgCA\n3377Dc+ePYOrqysAID8/H/n5+ahSpQosLS2RkpIi7Z+SkoIaNWqgatWqaqlTla6uLqpUqVLq94He\nb+wbpE7sX6RO5aF/Xbp0Sfq9Ro0ayM/PR1RUFHR0dGBqalpkRMS/XbhwASYmJujduzcAYNy4cdKX\nHw0bNpS2E0LA19cXhoaGMDQ0hJubG86fPw8AiIyMxLp166Cvr49mzZrBzc0Ne/bswdSpU9VwxkRE\nRFSoTJMUqampmDBhAkJDQ+Hs7CyVP3/+HPPmzUPjxo1hZmaGZcuWwcrKChYWFnBxccHo0aPh6emJ\nBg0aYOXKlXBzcwMAtdQRERGRZgUGBiIzMxOVKlVCtWrV8PDhQxQUFEBbWxt5eXmwsLBAWFjYK5MV\nKSkpsLS0lF5ra2vDzMysSJIiJCREab/Tp0+jSZMmePToEf755x+lNiwsLHDu3LkSnQfXryIiTeH6\nVaROb9O/VLk2lmmSYufOnZDJZAgMDERgYKBUvn79eowcORIjR47E48eP0apVK6xYsQLAi2GngYGB\nmDhxIh4+fIiuXbti0qRJaqsjIiIizXJzc8P9+/cxffp0GBgYQCaTYeHChahRowZGjhyJ7777DiEh\nIa9co+LZs2dKa08BL9afKpzeWZzFixcjOTkZixcvlrYrXLMKAPT09Eq0dhVQ+vWriIjeVnlYX4je\nX+ruX2WapAgICEBAQECxda1bt8bo0aOLrfPw8ICHh0eZ1REREZHmRERE4OjRo1KiQV9fH4GBgejR\nowe+/PJL+Pv7Kz2l69/09fWLJBRkMhkMDQ2LbJuXl4fg4GBcvHgRmzdvRrVq1ZCdnQ3gxYLahetV\nvfy7qkq9fhWROnF9oQpBY+tXAexjFcDb9C9V1rDiA8eJiIioXFEoFPjnn39Qv359qez+/fvS8NK8\nvDxoa2u/cn9LS0vs27dPep2fn4+0tLQii14qFAr4+fkhOzsbO3fuRPXq1QEAJiYmMDU1lZ4MAryY\nQlLSRTO5fhURaQr/7yF1Unf/qqTW1omIiIhKyNPTE6NHj8aePXtw/vx57Nq1C2PGjIGHhwcePHiA\nSZMmoWPHjq/c38HBAVlZWYiIiIBCocDatWthbm6utB4FAMydOxePHz/G1q1bpQRFIVdXV6xcuRI5\nOTmIjY3FwYMHuX4VERFRGeBICiIiIipXpkyZAiMjI4SFheHOnTuoU6cO+vbtixEjRuDq1aswMzOD\nv7//K/fX09NDWFgYgoODMXv2bNjY2GD58uUAXiQffHx80KVLF+zevRuVK1dWegx6q1atsGHDBkye\nPBlz586Fk5MT9PX1ERQUBBsbG7WfOxERUUXHJAURERGVKz/++CNGjBgBX1/fInV2dnaws7N7Yxu2\ntrbYs2dPkfLIyEjp97i4uFfub2BggAULFqgYMREREb0rnO5BRERE5crKlSu54CQREVEFxSQFERER\nlSsdO3bE5s2b8c8//2g6FCIiIipjnO5BRERE5cr//vc/HDp0CN9++22RutdN0SAiIqL/PiYpiIiI\nqFxZtGiRpkMgIiIiDWGSgoiIiMqVtm3bFluenZ1dxpEQERFRWWOSgoiIiMqVa9euYcGCBbhz5w4K\nCgoAAHl5ecjKykJsbKyGoyMiIiJ14sKZREREVK7MmTMHNWrUQMeOHWFhYYEhQ4agUqVKmDRpkqZD\nIyIiIjVjkoKIiIjKlevXr2PBggUYMmQI8vLyMGLECCxbtgyHDh3SdGhERESkZkxSEBERUblibGwM\nPT09mJmZISkpCQBgb2+PjIwMDUdGRERE6sYkBREREZUrlpaW2LVrF3R1dWFoaIi4uDgkJSVBS0tL\n06ERERGRmnHhTCIiIipX/P39MW7cODg4OGDkyJHo27cvKleujIEDB2o6NCIiIlIzJimIiIioXGnR\nogXOnDmDypUro1+/fmjcuDEeP36Mjh07ajo0IiIiUjMmKYiIiKjcefLkCdLS0pCXlwcAqFKlCi5d\nuoQ2bdpoODIiIiJSJyYpiIiIqFzZtWsXgoODUVBQoFSupaWFuLg4DUVFREREZYFJCiIiIipX1qxZ\ng5CQEPTu3Rs6OjqaDoeIiIjKEJMUREREVK48e/YMXl5emg6DiIiINKBMH0F64sQJuLq6omXLlvDy\n8sJff/0FADh9+jScnZ1hb2+PsWPH4sGDB9I+ZV1HREREmuXg4IA///xT02EQERGRBpRZkiI9PR0B\nAQEIDg7G5cuXMXz4cPj6+uL27duYPHky5syZg4sXL6JGjRpYsGABAOD+/ftlWkdERESaExQUhKCg\nIMhkMvj4+MDPz08qK/whIiKi91uZJSlu376Nfv36oW3btqhUqRLc3d0BABEREWjdujUcHBxQpUoV\nTJkyBYcPH0ZOTg6OHz9epnVERESkeTVq1EDPnj1RtWpVTYdCREREZazM1qRo27Yt2rZtK73++++/\nIZPJkJaWBktLS6m8WrVqMDQ0xM2bN5GcnFymdU2bNlX5fBQKRYnfA6o45HK5pkOg9xj7F6lTafvX\nu7gucmQjERERaWThzJs3b2L8+PGYOHEikpOToaenp1Svr68PmUwGmUwGY2PjMqsriYSEhBJtTxVL\nbGyspkOg9xj7F6mTpvvXpUuXcOPGDQwaNAjAi+THsGHDMGnSJKUvO4iIiOj9VOZJipiYGPj4+GDQ\noEEYNWoUQkJCkJubq7SNTCaDoaEh9PX1y7SuJKysrKCrq1uifQAAO6+XfB/6z7G1tdXMgdm/KgT2\nL1Kn0vYvhULx1gn8qKgojBkzBqNHj1Zq18LCAmPGjMHWrVvRvHnztzoGERERlW9lmqQ4e/Ys/P39\nERgYiL59+wIALCwscO7cOWmbBw8e4MmTJzA3Ny/zupLQ1dVFlSpVSvweUMXAvkHqxP5F6qTJ/rV2\n7VpMnjwZQ4cOlcqMjIwwf/58mJmZYfXq1QgPD9dYfERERKR+ZbZwZmpqKiZMmID58+dLCQoA6Nat\nGy5duoTz589DLpdj2bJl6Nq1KwwNDcu8joiIiDQnNjYWAwYMKLbO29sbV65cKeOIiIiIqKyV2UiK\nnTt3QiaTITAwEIGBgVL5+vXrsWzZMsybNw937txB69atERoaCgCoXbt2mdYRERGR5igUCujo6BRb\nZ2hoiLy8vDKOiIiIiMpamSUpAgICEBAQ8Mr6Tz755JXlZVlHREREmtGgQQPExMQUu+5ETEwMatWq\npYGoiIiIqCyV2XQPIiIiotfx8vLCzJkzcffuXaXyu3fvYtasWXB1dS1Re9HR0fDw8IC9vT0GDRqE\ntLS0V26bnZ2Nrl27IiMjQypLSEiAjY0NWrRoIf0cPXq0ZCdFREREJaKRR5ASERER/dvAgQNx9uxZ\n9OjRAy1btkSNGjVw//59REVFoW3btvjiiy9Ubksul8PPzw8BAQFwdnZGeHg4AgMDsWPHjiLbxsTE\nYNq0abh165ZS+fXr19G5c2esXbv2rc+NiIiIVMMkBREREZULlSpVwtq1a3Ho0CGcOnUKDx48QJ06\ndTB48GB07969RG1duHABJiYm6N27NwBg3Lhx2LJlC5KSktCwYUNpu8TERIwdOxZfffWV0ppZwIsk\nRePGjd/+xEpo0Nfby/yYVPZ2LBqs6RCIiMolJimIiIio3NDS0oKrq2uJp3b8W0pKCiwtLaXX2tra\nMDMzK5Kk+PDDD3H8+HEYGhoWSVLEx8dDoVCgS5cu0NLSQv/+/eHj46NyDAqF4q3Ogd5vcrlc0yHQ\ne4z9i9TpbfqXKtdGJimIiIjovfPs2TPo6ekplenr60MmkymVGRkZvbINExMTNG3aFAMGDEBmZiZ8\nfHxQs2ZNeHl5qRRDQkJCyQOnCiM2NlbTIdB7jP2L1End/atESYpt27ZhyJAhRcpXr14NPz+/dxYU\nERER0dvQ19dHbm6uUplMJoOhoaHKbSxZskT6vVGjRhg8eDB+/fVXlZMUVlZW0NXVVfl4kp3XS74P\n/efY2tpq5sDsXxWCxvoXwD5WAbxN/1IoFG9M4r8xSfHw4UMkJiYCAL799ls0btwYQgip/smTJ9iw\nYQOTFERERFRuWFpaYt++fdLr/Px8pKWlwcLCQqX9c3NzsWLFCvj6+sLY2BjAixurKlWqqByDrq5u\nibanioV9g9SJ/YvUSd39641JCh0dHfj7+yMrKwsAioyk0NHRQb9+/dQTHREREVV4SUlJMDIyQu3a\ntVXex8HBAVlZWYiIiICLiwvCw8Nhbm6utB7F6+jp6eHcuXMQQmDKlClISUnB9u3bERISUtrTICIi\nIhVUetMGRkZGOH/+PK5fv46PPvoI169fV/q5cuUKZsyYURaxEhERUQUQExMDb29vAMBPP/0EV1dX\nODk54eTJkyq3oaenh7CwMPzwww9wcHDA77//juXLlwMAXF1dceDAgTe2sWrVKsTHx8PR0RFjxozB\n2LFj0alTp9KdFBEREamkRGtSHDlyRF1xEBEREQEAFi9ejFatWkEIgfDwcMyfPx+mpqZYsmQJnJyc\nVG7H1tYWe/bsKVIeGRlZ7Pbx8fFKrz/66CNs2rSpZMETERHRWylRkiIpKQmLFy9GcnIy8vPzlepK\n8u0GERER0askJSVh69atSE1Nxb1799CrVy/o6+tj0qRJmg6NiIiI1KxESYrg4GBUqVIFY8aMQeXK\nfHopERERvXtaWlp4+vQpTp8+jebNm0NfXx+3bt167eNCiYiI6P1QokzD9evXcebMGRgYGKgrHiIi\nIqrgnJ2d0b9/f9y9exfTpk1DYmIi/Pz84ObmpunQiIiISM1KlKSoXr06ZDIZkxRERESkNtOmTUNE\nRAT09PTg6uqKmzdvYuDAgdJimkRERPT+KlGSYsiQIfjyyy8xbNgwVK9eXamuTZs27zQwIiIiqpi0\ntbXRp08f6XWDBg0wfPhwzQVEREREZaZESYp58+YBAKKiopTKtbS0EBcX9+6iIiIiogqLC3UTERFV\nXCVek4KIiIhInbhQNxERUcVVoit/ZmbmK+vq1q371sEQERERcaFuIiKiiqtESYquXbtCS0sLQggA\nL6Z5AC/mjsbGxr776IiIiKjC4ULdREREFVeJkhSnT59Wev3gwQOsX78eHTt2LPGBN23ahPj4eISG\nhgIAJkyYgFOnTkFbWxsAYGFhgb179wIA9u7di5UrV+LRo0fo3r075syZAz09PbXVERERkeZwoW4i\nIqKKq1JJNq5du7bSj42NDebOnYvvvvtO5TYUCgWWL1+ORYsWKZVfv34dP/30E6KiohAVFSUlKOLi\n4rBo0SKEhYXh7NmzyM7Oxrp169RWR0RERJo1b948REVFwd/fH97e3tLP0KFDNR0aERERqVmJkhTF\nefToEXJyclTePjAwEHFxcejbt69U9uzZM2RmZqJRo0ZFtj948CBcXFxgbW0NIyMjTJgwAbt371Zb\nHREREWnW9evXi/3hk8SIiIjefyWa7hEUFKT0Oj8/H3/88QfatWunchuBgYGoVasWVq1ahVu3bgEA\n4uPjYWBggDFjxiA+Ph6NGzfGjBkz0LBhQyQnJ6NDhw7S/hYWFrh//z6ys7PVUmdiYqLSeSgUCpXP\nmSoeuVyu6RDoPcb+RepU2v71rq+LMpkMjx49QkFBAQAgLy8PN27cgJOT0zs9DhEREZUvb/VcLx0d\nHQwbNgwDBgxQeZ9atWoVKZPJZGjevDkCAgJgZmaGsLAw+Pr64uDBg5DJZEprRejr6wMAcnNz1VKn\nqoSEBJW3pYqHC8mSOrF/kTqVh/514MABBAcHF7kum5qaMklBRET0nitRkmLBggVqCaJ9+/Zo3769\n9Hr8+PHYsmULkpKSoKenp/StjkwmAwAYGBiopU5VVlZW0NXVLclpvrDzesn3of8cW1tbzRyY/atC\nYP8idSpt/1IoFO8sgb969WpMnDgRhoaG+PPPPzF8+HAsXry4VAt1ExER0X9LiZIUQghs2rQJP//8\nM27fvo0aNWrA09MTfn5+0uNIS+O3337Ds2fP4OrqCuDFNJL8/HxUqVIFlpaWSElJkbZNSUlBjRo1\nULVqVbXUqUpXVxdVqlQp9TnT+419g9SJ/YvUqTz0r7t372LYsGG4ffs2du3ahaZNm2L+/PkYMWIE\nRo8erenwiIiISI1KtHDm999/j+3bt2PUqFFYt24dxowZg3379iEsLOytgnj+/DnmzZuHpKQkKBQK\nLFmyBFZWVrCwsICLiwsOHjyIq1evIicnBytXroSbmxsAqKWOiIiINKt69ep4/vw56tSpg7S0NABA\n3bp18c8//2g4MiIiIlK3Eo2k2LVrF9auXQsrKysAQLt27dCiRQuMGzcOY8eOLXUQPXr0QFpaGkaO\nHInHjx+jVatWWLFiBYAXw04DAwMxceJEPHz4EF27dsWkSZPUVkdERESaZWdnh9mzZ2PatGn46KOP\nsGPHDujr66u8uDURERH9d5UoSZGVlYWGDRsqlTVs2BCPHz8u8YHHjx+v9Hr06NGvHMLp4eEBDw+P\nMqsjIiIizfnmm28wffp0PHnyBFOnTsXYsWORm5urtrWxiIiIqPwoUZLCysoKe/bsQb9+/aSyPXv2\noFGjRu88MCIiIqqYatasKU0lrV27Ni5cuACFQlGiBa6JiIjov6lESYrJkydj5MiR2Lt3L+rVq4f0\n9HTcuHED69evV1d8REREVAEdPXpUaaFuLy8vjoAkIiKqAEq0cKadnR3279+P9u3bw9DQEN26dcMv\nv/yC1q1bqys+IiIiqmD27t2LmTNnwtbWFiNGjEDz5s0xf/58/PTTT5oOjYiIiNRMpZEUQgjMmjUL\nOTk5WLJkCSZMmIB//vkHHTt2REZGBubMmaPuOImIiKiC2LhxI1avXq30JUinTp0wffp0DBgwQIOR\nERERkbqpNJJi69at+P333+Ht7S2V1ahRA9u2bcPZs2fxww8/qC1AIiIiqlju3LmDVq1aKZW1bNkS\n9+7d01BEREREVFZUSlLs3r0bS5Ysgb29vVJ569atMX/+fOzatUstwREREVHFY2ZmhlOnTimVnTp1\nCubm5poJiIiIiMqMStM9bt++DTs7u2Lr2rZti8zMzHcaFBEREVVcfn5+mDBhAnr06IF69eohIyMD\nJ06cwNKlSzUdGhEREamZSiMpdHV18ezZs2Lr5HI5Klcu0UNCiIiIiF6pW7duWL9+PSpXroyrV6/C\nyMgIW7duRbdu3VRuIzo6Gh4eHrC3t8egQYOQlpb2ym2zs7PRtWtXZGRkSGW5ubmYMmUKWrdujU6d\nOmHfvn1vdU5ERESkGpWyC61bt8b+/fsxcODAInW//PILbGxs3nlgREREVHE5OjrC0dFRev38+XPE\nxMS8cmTny+RyOfz8/BAQEABnZ2eEh4cjMDAQO3bsKLJtTEwMpk2bhlu3bimVL126FDKZDGfPnkVi\nYiJGjx4Ne3t7WFhYvP3JERER0SupNJJi5MiRWLRoETZu3Ii7d+8iPz8fd+7cwaZNmxAaGopRo0ap\nO04iIiKqwB48eID+/furtO2FCxdgYmKC3r17Q1dXF+PGjcONGzeQlJSktF1iYiLGjh2LkSNHFmkj\nMjIS48aNg76+Ppo1awY3Nzfs2bPnnZwLERERvZpKIyns7e0REhKCOXPmYPHixVK5iYkJ5syZg44d\nO6otQCIiIiLgxSPRVZGSkgJLS0vptba2NszMzJCUlISGDRtK5R9++CGOHz8OQ0NDBAYGSuWPHj3C\nP//8o9SGhYUFzp07V6J4FQpFibanikUul2s6BHqPsX+ROr1N/1Ll2qjyYhKurq7o3r07/vrrLzx4\n8AC1atWCvb09dHR0Sh0gERERkaq0tLRU2u7Zs2fQ09NTKtPX14dMJlMqMzIyKnb/wu309fWlMj09\nPeTm5pYkXCQkJJRoe6pYYmNjNR0CvcfYv0id1N2/SrTipa6uLtq1a6euWIiIiIjemr6+fpGEgkwm\ng6GhoUr7FyY4cnNzYby7d0gAACAASURBVGBgUOR3VVlZWUFXV7dE+wAAdl4v+T70n2Nra6uZA7N/\nVQga618A+1gF8Db9S6FQvDGJz8dyEBERUbkQERHxyrpHjx6p3I6lpaXS0zjy8/ORlpam8qKXJiYm\nMDU1RWpqKpo0aQLgxRSSki6aqauriypVqpRoH6o42DdIndi/SJ3U3b+YpCAiIqJyYeXKla+tr1On\njkrtODg4ICsrCxEREXBxcUF4eDjMzc2V1qN4E1dXV6xcuRLffvstUlNTcfDgQWzevFnl/YmIiKh0\nmKQgIiKicuHXX399J+3o6ekhLCwMwcHBmD17NmxsbLB8+XIAL5IPPj4+cHd3f20bkydPxty5c+Hk\n5AR9fX0EBQXxketERERlgEkKIiIieu/Y2toW+8jQyMjIYrePj49Xem1gYIAFCxaoJTYiIiJ6tUqa\nDoCIiIiIiIiICGCSgoiIiIiIiIjKCSYpiIiIqFz45ptvgP/X3r2HRVnn/x9/IWdRI8UoLUoszTRW\nBUFMNBEPiaJ5igXdtBI0dUs7oJnlmlqpSSvrqmR5qtW19UzbspqbpxTEXMtasgUL8RyIIYIDeP/+\n8Ov8mkVlQIcZ4/m4Lq6L+/O5D++Z3nm/rzf3fEbSvn377BwJAACwF7s0KZYuXapJkyaZt7dv365e\nvXqpbdu2Gj16tPLz8+02BwAA7OPTTz+VyWRSfHy8vUMBAAB2UqNNCpPJpHfffVezZ882j505c0YT\nJ07U9OnTlZaWJh8fH/NCVTU9BwAA7Kd58+YKCwtTSUmJunfvftUfAADw61aj3+4xadIkFRUVaciQ\nITKZTJKkLVu2KCgoSCEhIZKkF154QWFhYXr99ddrfK5evXpWv5Yr8QNXc/HiRXuHgF8x8gu2VN38\nuhn3xYULF2rPnj169dVXNW7cuBs+HwAAuPXUeJPijjvuUFJSko4dOyZJys7Olr+/v3mf22+/XV5e\nXvrxxx9rfK5169ZWv5bDhw9X6z1A7XDo0CF7h4BfMfILtmTP/GrcuLGioqJ0/vx5Pf7443aLAwAA\n2E+NNinuuOOOCmPFxcWqX7++xZinp6eKi4trfK4qWrRoITc3tyodI0n6a2bVj8Etp02bNva5MPlV\nK5BfsKXq5pfJZLppDfyYmBilpqZqzZo1OnHihHx8fDRw4EANGDDgppwfAAA4rhptUlyNp6enSkpK\nLMaKi4vl5eVV43NV4ebmJnd39yodg9qD3IAtkV+wJUfIr3Xr1untt99WdHS0evfurZycHM2aNUsl\nJSWKjo62d3gAAMCG7N6kaNasmXbt2mXezs/PV2Fhofz8/Gp8DgAA2N8HH3ygBQsWKCgoyDzWtWtX\nvfrqqzQpAAD4lbPLV5D+UkREhPbt26fdu3fr4sWLSkxMVHh4uLy8vGp8DgAA2N/JkycVGBhoMda+\nfXudPn3aThEBAICaYvcmha+vrxITEzVz5kyFhobq1KlTmj59ul3mAACA/d1zzz36/PPPLcY+//xz\nnnoEAKAWsMvHPcaPH2+xHRYWprCwsKvuW9NzAADAvsaOHavf//736tmzp5o2barc3Fxt3bpV8+bN\ns3doAADAxuz+JAUAAMAvRURE6L333pOLi4u++eYb1atXTytWrFBERIS9QwMAADZm94UzAQAA/lfH\njh3VsWNHe4cBAABqGE9SAAAAAAAAh0CTAgAAAAAAOASaFAAAAAAAwCHQpAAAAAAAAA6BhTMBAIBD\nycrK0pw5c5Sdna3y8nKLuc8++8xOUQEAgJpAkwIAADiU119/Xe7u7ho1apRcXChVAACoTbjzAwAA\nh5KZmakdO3aobt269g4FAADUMNakAAAADqVRo0YqLi62dxgAAMAOeJICAAA4lGHDhmncuHF68skn\n1ahRI4u5Dh062CkqAABQE2hSAAAAhzJz5kxJ0oEDByzGnZyc9J///MceIQEAgBpCkwIAADiUzMxM\ne4cAAADshDUpAACAwzl+/LiSk5M1bdo0LViwQD/88EOVjj948KAGDBigtm3bKiYmRjk5ORX2uXTp\nkmbOnKmQkBCFhoYqOTnZPHf+/Hm1atVK7dq1M/8sXbr0Rl8WAACoBE0KAADgUA4cOKDIyEj961//\nUlFRkXbu3Kn+/fsrIyPDquMvXryosWPH6umnn1Z6ero6deqkSZMmVdhv5cqVOnjwoFJTU7V69Wqt\nXr1ae/bskSR99913euCBB3TgwAHzz8iRI2/q6wQAABXxcQ8AAOBQ5s6dq4SEBEVHR5vHVq1apblz\n52r16tWVHr937155e3urX79+kqQxY8Zo+fLlysrKUvPmzc37paSk6Omnn5a3t7e8vb01bNgwrVmz\nRqGhocrMzNSDDz54Q6/DZDLd0PH4dbt48aK9Q8CvGPkFW7qR/LLm3kiTAgAAOJTvv/9eQ4YMsRgb\nMmSI5syZY9XxR44ckb+/v3nb2dlZ99xzT4UmRXZ2tsV+zZo10/r16yVdfpLihx9+UK9evXThwgVF\nRkZq4sSJcnNzs/p1HD582Op9UfscOnTI3iHgV4z8gi3ZOr9oUgAAAIfSoEEDHTlyRPfff795LDs7\nWw0bNrTq+AsXLsjDw8NizNPTU8XFxRZjxcXF8vT0NG97eHiopKREklS3bl0FBwcrPj5ehYWFGj9+\nvBYvXqzx48db/TpatGhRpaaG2V9ZOLQ2aNOmjX0uTH7VCnbLL4kcqwVuJL9MJlOlTXyaFAAAwKEM\nGTJEo0ePVlxcnJo0aaLc3FwtWbKkwtMV1+Lp6WluNlxRXFwsLy8vi7FfNiUkqaSkRHXr1pUkizUs\n6tevr7i4OC1atKhKTQo3Nze5u7tbvT9qF3IDtkR+wZZsnV8OtXDm22+/rYcffti8inZISIgkafv2\n7erVq5fatm2r0aNHKz8/33yMLeYAAID9xMXFqX///lq4cKHGjBmjpUuXaujQoXrmmWesOt7f39/i\n20DKy8uVk5OjZs2aXXe/I0eOmPeZP3++jh49ap4zmUwU/QAA1ACHalJkZmZq3rx55lW009LSdObM\nGU2cOFHTp09XWlqafHx89Oabb0qSTeYAAIB9OTk5afz48frXv/6lr7/+WqmpqYqLi5Ozs7NVx4eE\nhCgvL08bNmyQyWTSwoUL5efnZ7EehSRFRkYqOTlZeXl5ysnJ0YcffmhebPPbb7/VvHnzVFxcrGPH\njik5OVlRUVE3/bUCAABLDtek+N+VtLds2aKgoCCFhITI3d1dL7zwgj799FOdP3/eJnMAAMA+pk2b\nJkmaPHnyNX+s4eHhocWLF2vlypUKCQnRF198oXfffVfS5cbEpk2bJEnDhw9XUFCQoqKiFB0drejo\naHXv3l2SNGPGDJWVlalr164aPHiwIiIi9Nvf/vbmv2gAAGDBYdakOH36tAoKCjRr1iwdOHBAfn5+\neuWVVyqsvH377bfLy8tLP/74o03mWrdubVW8fK0YroevfYItkV+wperm1824LxqGccPnuKJNmzZa\nu3ZthfFPPvnE/LuLi4sSEhKUkJBQYT8fHx8lJSXdtHgAAIB1HKZJcfbsWQUHBysuLk6tW7fWxo0b\nFR8fr/DwcN15550W+15Zobu4uFj169e/qXPW4mvFcD187RNsifyCLdkzv/7whz9Ikp588skKT1ZK\n0t69e2s6JAAAUMMcpknRsmVLLV++3Lw9ZMgQrVixQunp6erZs6fFvldW6L7e6t3VnbMWXyuG6+Fr\nxWBL5Bdsqbr5Zc1XilkrJiZGX375pcXY+fPnNWbMGB04cOCmXAMAADgmh2lS7N+/X999951iYmLM\nYyaTSU8++aT27NljHsvPz1dhYaH8/PzUrFkz7dq166bOWYuvFcP1kBuwJfILtmSv/Dp69Kj69Omj\nsrIyGYahVq1aVdinbdu2dogMAADUJIdZONPNzU1z5sxRRkaGysrKtGLFCplMJvXq1Uv79u3T7t27\ndfHiRSUmJio8PFxeXl6KiIi46XMAAKDm3XPPPfr444+1bNkyubu7a/ny5RY/q1at0gcffGDvMAEA\ngI05zJMUDz/8sF5//XVNnjxZZ86c0YMPPqhFixbJ19dXiYmJmjlzpk6ePKmgoCC99dZbkmSTOQAA\nYB9X1qFISUmRq6urbrvtNnl6eiozM1MNGjSQp6ennSMEAAC25jBNCkkaMGCABgwYUGE8LCxMYWFh\nVz3GFnMAAMB+vv/+ez3//PP66KOP9PDDD2vHjh1atGiRFixYoNDQUHuHBwAAbMihmhQAAACJiYma\nN2+eHn74YUlSXFyc/P39NXv2bK1fv97O0QEAAFtymDUpAAAAJCk3N1cREREWY927d9fRo0ftFBEA\nAKgpNCkAAIBDufvuu7Vz506LsbS0NN111112iggAANQUPu4BAAAcSnx8vMaOHauePXuqadOmOnHi\nhLZs2aI333zT3qEBAAAbo0kBAAAcSt++feXj46ONGzfqm2++0V133aUlS5YoMDDQ3qEBAAAbo0kB\nAAAcTseOHdWxY0eLsfLycjk7O9spIgAAUBNoUgAAAIdy7NgxJSUl6eTJk7p06ZIkqaysTD/++KN2\n795t5+gAAIAtsXAmAABwKFOnTlVubq7q16+v0tJStWjRQocPH9YTTzxh79AAAICN8SQFAABwKAcO\nHNDnn3+uEydO6J133tGrr76qLl26KDk52d6hAQAAG+NJCgAA4FA8PT1122236b777tN///tfSVKX\nLl2UlZVl58gAAICt0aQAAAAOxc/PTzt37pSHh4ecnJyUm5urU6dOqayszN6hAQAAG+PjHgAAwKHE\nx8dr/Pjx2rx5s4YMGaKhQ4fKxcVF3bt3t3doAADAxmhSAAAAh/LII48oNTVVjRo10pgxY3Tvvfeq\nsLBQjz/+uL1DAwAANkaTAgAAOJTIyEitX79eLi6Xy5Q+ffrYOSIAAFBTWJMCAAA4lNLSUplMJnuH\nAQAA7IAnKQAAgEPp2LGjBg8erLCwMDVu3Nhibty4cXaKCgAA1ASaFAAAwKEcO3ZMTZs2VXZ2trKz\ns83jTk5OdowKAADUBJoUAADAoaxcudLeIQAAADthTQoAAOAQnn/+eXuHAAAA7KxWNykOHjyoAQMG\nqG3btoqJiVFOTo69QwIAoNbasWOHxXZoaGi1z2XNPf7SpUuaOXOmQkJCFBoaquTkZKvmAACA7dTa\nJsXFixc1duxYPf3000pPT1enTp00adIke4cFAAD+T1lZWbWOs/Yev3LlSh08eFCpqalavXq1Vq9e\nrT179lQ6BwAAbKfWNin27t0rb29v9evXT25ubhozZoy+//57ZWVl2Ts0AACg6i+Uae09PiUlRU89\n9ZS8vb117733atiwYVqzZk2lcwAAwHZq7cKZR44ckb+/v3nb2dlZ99xzj7KystS8efNrHmcYhiSp\nqKioWt/h3qCua9WDxS2nsLDQLtclv2oH8gu2VN38Ki0tlfT/75P2ZO09Pjs722K/Zs2aaf369ZXO\nVYZaAdbg33LYkr3ySyLHaoMbyS9r6oVa26S4cOGCPDw8LMY8PT1VXFx83eOuvKlHjhyp1nVH9bt2\nAwS/HocPH7bLdcmv2oH8gi3daH6VlpZWuL9aq6ysTBs2bLA41y+3JWnAgAGVnsfae3xxcbE8PT3N\n2x4eHiopKal0rjLUCrAG/5bDluyVXxI5VhvcjPy6Xr1Qa5sUnp6eFYqN4uJieXl5Xfc4Ly8vtWjR\nQq6urnxfOwAA/8cwDJWWllZ6H70eHx8fzZ8/37x9++23W2w7OTlZ1aSw9h7/v42HkpIS1a1bt9K5\nylArAABwddbUC7W2SeHv72/x2GZ5eblycnLUrFmz6x5Xp04d1a9f39bhAQBwy6nuExRXbNu27abE\nYe093t/fXz/88IMeeOABSZeffLiyz/XmKkOtAADAtVVWL9TahTNDQkKUl5enDRs2yGQyaeHChfLz\n87vuehQAAMDxWXuPj4yMVHJysvLy8pSTk6MPP/xQ/fr1q3QOAADYjpPhCCtc2cmhQ4f0+uuvKzs7\nW61atdJbb70lPz8/e4cFAABu0LXu8ZGRkYqPj1dUVJTKysr0zjvvaNOmTTIMQyNGjFBcXJwkXXcO\nAADYTq1uUgAAAAAAAMdRaz/uAQAAAAAAHAtNCgAAAAAA4BBoUgAAAAAAAIdAkwKVys/P14ULF+wd\nBgAAcFDUCgCAm4UmRS3QsmVLzZ0712IsNzdXLVu2tOr4xx57TPn5+RXGhw8frsTExArj2dnZCggI\n0Llz56oU52uvvaZFixZJkr777juFh4crMDBQ6enpioyMrNK5JGndunUaPnx4lY/DZf/973/1zDPP\nKDAwUO3bt9ewYcOUkZFh77AqIA9xxc3MBfIAtQ21AqqLeoFcvJVQK9waaFLUEkuXLtVXX31VrWML\nCgquOj5o0CClpKRUGN+wYYN69Oih2267rUrXmT59ukaPHi1J2rZtm+6//37t379fwcHB+uSTT6oe\nOKqtvLxccXFx6tKli/bu3av09HT1799fo0aN0pkzZ+wdngXyEFfczFwgD1AbUSugqqgXyMVbDbXC\nrYEmRS0RFRWlV155RSaT6arz27ZtU79+/RQYGKjo6Gh9/fXXkqTY2FhJUt++fXXgwAGLY3r37q2C\nggJ9+eWX5rFLly5p48aNGjx4sCSpqKhIU6dO1SOPPKJu3bppyZIl5n3Dw8M1ZcoUBQcHa8mSJZo0\naZKSkpK0dOlSLViwQLt371avXr2Ulpam8PBw83GbN29W7969FRwcrHHjxikvL0/S5e+0nzFjhoKC\nghQREaGDBw/ehHeudiooKNCxY8cUGRkpV1dXubi4aMiQIYqOjja/31lZWRo5cqSCgoLUu3dvbdy4\n0Xx8y5YtlZuba94ODw9XWlqacnNz1blzZ40fP17BwcHav3+/jh49qqeeekrt2rVTjx499M9//lOS\nVFpaqnnz5qlr167q3LmzZs+erdLS0gqxkoe4orJcIA+A66NWQFVRL5CLtxpqhVsDTYpaIj4+Xs7O\nzlqwYEGFuczMTE2cOFEvv/yy0tLSNGTIED3zzDMqKCjQRx99JElKSUlRu3btLI7z8PBQZGSkRTdy\n7969cnV1VceOHSVJb731lvLz85WamqqVK1dq7dq1+vvf/27ev7CwULt27VJ0dLR5bOTIkYqPj1e/\nfv2Umppqcc0vv/xSs2bN0rx587Rz5041adJECQkJkqQPP/xQ6enp+sc//qHly5friy++uMF3rfZq\n1KiRAgMDFRsbq8WLF+vf//63SktLlZCQoAcffFAmk0lPP/20goOD9cUXX2j27NmaNWuW0tPTKz33\nmTNnFBgYqO3bt+s3v/mNnnvuObVs2VJpaWl66623lJCQoJ9++knvv/++0tPTtXbtWm3atEkHDx7U\n0qVLK5yPPMQVleUCeQBcH7UCqop6gVy81VAr3BpoUtQSLi4uevPNN7Vs2TJ98803FnOffvqpunfv\nrrCwMLm4uGjQoEG67777tH379krPO3jwYH366acqKyuTJK1fv16DBg2Sk5OTDMPQxo0b9dJLL6le\nvXq6++67NWLECK1bt858fM+ePeXm5qZ69epZ9To2bNigoUOH6qGHHpK7u7smTJigL774Qj/99JP+\n+c9/6sknn5SPj4+aNm3KZ7pu0Pvvv6+BAwdqy5YtiomJUWhoqBITE2UYhjIyMnTp0iWNHj1abm5u\nCggI0NChQy3+OnI9ffr0kaenp44fP67Dhw9rwoQJcnNzU2BgoFauXCkvLy+tX79e48ePl4+Pjxo2\nbKgxY8Zo/fr1Vz0feYgrrpULksgDoBLUCqgO6oVrIxcdE7WC43OxdwCoOQ899JBGjhypyZMna/78\n+ebxs2fPqkmTJhb7NmnSRKdOnar0nAEBAfLx8dHu3bsVFBSkbdu26cUXX5R0eaXvixcvmh+hky4/\nTnX33Xebt318fKr0Gk6cOKGNGzea/2ojXS6qjh8/rry8PPn6+lq8BlSfp6en4uLiFBcXp/Pnz2v7\n9u2aPn267rzzTtWvX1933XWXnJyczPs3adJE3377rVXnbty4sSQpLy9P3t7ecnNzM8+1adNGknTy\n5EmNHz9edepc7qUahmFxvV8iD3HFtXKBPACsQ62AqqJeuDZy0TFRKzg+mhS1zLPPPqutW7eaV6KV\nJF9fX2VnZ1vsl5ubq0ceecSqcw4cOFCbN29Wfn6+goKCzP+zeXt7y9XVVampqWrUqJGky0XOLz/r\neq2byLX4+Pho3LhxGjVqlHksKytL9957r3x8fHTixAnzuKMt2HQr+eSTT7Ro0SJt3rxZklSvXj1F\nRkZq3759Onz4sCIjI3X8+HGLQiA3N9f839nJyUnl5eWSLhcL/7tS8pVjfH19VVBQIJPJZC48li5d\nqi5dusjHx0eJiYkKCAiQJJ0/f15nz569ZszkIa64Wi6Ul5eTB4CVqBVgLeqF6yMXHRe1gmPj4x61\njJubm2bNmqVNmzaZx/r06aNt27Zp586dKisr09q1a5WVlaWuXbtKklxdXXX+/PlrnrN///7atWuX\nUlJSLDqPzs7OeuyxxzR37lxduHBB586d0+9//3uLoqeqoqKitGrVKmVlZenSpUtavny5YmNjVVpa\nqr59++qDDz7QyZMnderUKa1cubLa16ntQkNDdfr0aSUlJamwsFDl5eX6+uuvtX37dnXt2lUBAQHy\n9PTUokWLVFpaqq+++koff/yx+vTpI0m69957lZKSorKyMr3//vsqKiq66nWaNGmiNm3aKCkpSaWl\npdq/f78WLlyoBg0aqF+/fkpKStLZs2dVXFys1157TW+88cY1YyYPccXVcoE8AKxHrQBrUS9cH7no\nuKgVHBtNilooICBATz31lHm7WbNmmjdvnmbPnq0OHTroL3/5i9577z3zI3aPP/64oqOjr/m504YN\nG6pDhw7KzMxUt27dLOZee+01SVKPHj3Us2dPNW3aVC+//HK1Yw8NDdXYsWP17LPPKigoSCkpKVq8\neLE8PT31xBNPKCIiQlFRUXriiSfUqVOnal+ntmvYsKE+/PBDffPNN4qIiFBgYKBeeeUVPffcc3r0\n0Ufl5uamhQsXau/everYsaMmTJigF198UY8++qgkadq0aUpJSVFISIhycnLUvn37a15r3rx5yszM\nVKdOnTRlyhTNnTtXjRs31rPPPqv7779f/fv3V1hYmEpKSvTmm29eN2byENK1c4E8AKxHrQBrUC9c\nH7nouKgVHJuTYRiGvYMAAAAAAADgSQoAAAAAAOAQaFIAAAAAAACHQJMCAAAAAAA4BJoUAAAAAADA\nIdCkAAAAAAAADoEmBQAAAAAAcAg0KQA4rPPnzys/P9/eYZgdPXrU6n0dLXYAAH6tHO2eS70A3Bia\nFMCv3FNPPaXnn3/+qnPJycl67LHHZBhGlc/7zDPPaNGiRZKk06dPa/DgwWrbtq0mT56syMhIbdq0\n6YbilqSePXvq+++/lyRt2rRJkZGRN3zOqzl37pymTZumLl26qG3bturcubMSEhJ08uRJ8z5z5szR\nggULrD7nL2MHAMDRUS9UjnoBqBku9g4AgG3FxsbqueeeU35+vho2bGgeNwxDa9as0ciRI+Xk5FTl\n8y5ZssT8+969e5WTk6O0tDS5u7vflLglKS8vz/x7VFSUoqKibtq5f2nChAmqV6+e1q1bJx8fH505\nc0azZs3SyJEjtXnzZrm4uFjEYo2q7g8AgD1RL1SOegGoGTxJAfzKdevWTb6+vtq4caPF+M6dO3X2\n7FkNGDBAknTmzBlNnDhRnTp1UpcuXTRz5kyVlJRIktatW6eBAwdqxIgRCgoK0pYtWzR8+HAlJSXp\no48+0pQpU/Tzzz+rY8eOSktLU3h4uNatWydJys/P10svvaTg4GCFhITopZdeUlFRkSTpwIED+t3v\nfqfOnTsrICBATzzxhA4dOiRJ6tWrlyRp1KhRWrFihdatW6fw8HBz/Fu3btWAAQPUvn179enTR2vX\nrjXPDR8+XHPnzlVMTIzatWunvn37KjU19Zrv0f79+9WrVy/5+PhIkho3bqxXXnlFv/nNb/Tzzz9r\nwYIF2rx5szZv3qzBgwffcOyS1KNHD/N7lJGRoYEDByowMFA9evTQ22+/rUuXLln3HxgAgJuAeoF6\nAXAYBoBfvffee8/o06ePxdiYMWOMGTNmGIZhGOXl5cagQYOMl19+2SgsLDR++uknY8SIEcbUqVMN\nwzCMtWvXGi1atDBSUlKMkpISo6SkxBg2bJgxf/5883y3bt3M5+7WrZuxdu1awzAMIzY21oiPjzfO\nnj1rnDt3zhg2bJgxdepUo6SkxAgODjZWrVpllJeXG0VFRcZzzz1nxMTEmM/TokULY+/evRWusWfP\nHqN169bGli1bjLKyMmPfvn1Ghw4djL///e+GYRjGsGHDjODgYOPQoUOGyWQy/vjHPxrBwcHGxYsX\nr/r+TJ482Wjfvr0xdepUY/PmzUZOTk6FfRISEoyEhATDMIwbiv2KiIgI83v06KOPGqtWrTIMwzCO\nHj1qdO7c2di1a9dVYwUAwFaoF6gXAEfAkxRALTB48GDl5ubqyy+/lCSdOnVKO3bsUGxsrCTp0KFD\n+s9//qPXXntN9erVU6NGjTRx4kT97W9/k8lkkiS5u7urT58+cnd3t/oRzWPHjmnfvn1KSEiQt7e3\nGjRooLlz52rEiBFycXHRX//6V0VHR6usrEwnTpyQt7e3xec6r2XdunXq0aOHIiIi5OzsrKCgIP32\nt7/Vxx9/bN6nd+/eat26tVxdXRUVFaWCgoJrPlI5Y8YMTZ8+XT/99JPeeOMNRUREqEePHtf8nOyN\nxH41bm5u+uyzz7R9+3Y1bNhQ27dv1yOPPFKtcwEAUF3UC9QLgCNgTQqgFvD29lbfvn318ccfq337\n9lqzZo1CQ0N13333SZJyc3NVXl6url27Whzn7OysY8eOSZIaNWpU5c+injlzRpLUtGlT85ivr6/5\n9/T0dMXHx6uosXVHRQAAA6VJREFUqEj+/v5ydXW1alGuvLw8tWrVymLs7rvvtnhE88qjmNLlIkGS\nysvLr3q+OnXqKDIyUpGRkTIMQ9nZ2dq0aZNefvllNW7cWKGhoRb7Ozs7Vzv2q1m2bJnmz5+vV199\nVWfPnlXnzp01bdo03XnnndU6HwAA1UG9QL0AOAKepABqieHDh+sf//iHCgsL9be//U3Dhw83z/n6\n+srT01NpaWnKyMhQRkaGdu7cqQ0bNsjPz0+SqrVY1l133SVJOnHihHns22+/VXJysr766itNnz5d\n77zzjnbt2qUVK1ZY/deAJk2aKDc312IsJydHjRs3rnKMO3fuVFBQkC5cuCDp8uts3ry5JkyYoIce\nekjffvtthWOqGnudOnVUVlZm3jYMQwUFBZIkk8mkI0eO6I033jC/5z///LNmzZpV5dcCAMCNol64\nOuoFoObQpABqiQcffFCtWrXSjBkz5OHhobCwMPNcQECAmjZtqjlz5qi4uFhFRUWaNm2ann322Ru6\npq+vrzp16qS5c+eqsLBQ586d09tvv61jx47p3LlzqlOnjjw8PCRJ//73v7VixQqVlpaaj3d1dVVh\nYWGF8w4cOFBbt27V1q1bVV5eroyMDK1evdq8qFdVdOjQQd7e3po4caKysrIkSYWFhVq/fr2OHDmi\nRx99VNLlRyyvxFLV2P39/XX69Gnt2LFD5eXlWrZsmcXrmjhxoj744AOVlZXpzjvvlKurq26//fYq\nvxYAAG4U9cLVUS8ANYcmBVCLDBs2TBs2bFBsbKzFXzpcXV21ePFiHT9+XN27d1e3bt1UUFCgJUuW\nyNnZ+Yau+c4778jDw0O9e/dWr1695Ofnp8mTJ6tz586KiYlRbGysOnTooOnTpys2Nlb5+fnKz8+X\nJA0dOlQvvPCC/vSnP1mcs127dkpMTFRSUpKCgoI0ZcoUPf/88xo0aFCV4/Pw8NBf/vIX3XHHHRo1\napTatWunbt266ZNPPtGyZcvUvHlzSdJjjz2mjIwM9ejRo8qxBwQEKC4uTlOmTFGnTp10/PhxdejQ\nQdLlYubPf/6zPvvsM4WEhCg8PFx33HGHXnzxxRt52wEAqDbqhYqoF4Ca42RU90NRAAAAAAAANxFP\nUgAAAAAAAIdAkwIAAAAAADgEmhQAAAAAAMAh0KQAAAAAAAAOgSYFAAAAAABwCDQpAAAAAACAQ6BJ\nAQAAAAAAHAJNCgAAAAAA4BBoUgAAAAAAAIdAkwIAAAAAADiE/weQ+Y2ZLvd6dgAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_var('verification_status', 'Verification Status', continuous=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.12 issue_d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Dictionary: \"The month which the loan was funded.\"\n",
    "\n",
    "Because we're only using variables available to investors before the loan was funded, `issue_d` will not be included in the final model. We're keeping it for now just to perform the train/test split later, then we'll drop it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "3a91aec4-9bc4-42d6-8c62-e3892a9a2754",
    "_uuid": "87124cd85754a1e6ef201217421ff2f50108cb2a"
   },
   "source": [
    "## 5.13 purpose"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Dictionary: \"A category provided by the borrower for the loan request.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "_cell_guid": "9de7d1b4-212b-4478-88a2-a3176a03e982",
    "_uuid": "5d4c3c93e59ee2cde87f4fc5487b43c555c33faf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "debt_consolidation    481652\n",
       "credit_card           175123\n",
       "home_improvement       50793\n",
       "other                  43900\n",
       "major_purchase         17463\n",
       "small_business         10214\n",
       "car                     8936\n",
       "medical                 8772\n",
       "moving                  5725\n",
       "vacation                5116\n",
       "house                   4095\n",
       "wedding                 2272\n",
       "renewable_energy         599\n",
       "educational              326\n",
       "Name: purpose, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['purpose'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the charge-off rates by purpose:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "purpose\n",
       "wedding               0.121919\n",
       "car                   0.148053\n",
       "educational           0.171779\n",
       "credit_card           0.178412\n",
       "home_improvement      0.181383\n",
       "major_purchase        0.183245\n",
       "vacation              0.198788\n",
       "house                 0.214652\n",
       "other                 0.216970\n",
       "debt_consolidation    0.217398\n",
       "medical               0.229366\n",
       "moving                0.242969\n",
       "renewable_energy      0.247078\n",
       "small_business        0.302428\n",
       "Name: loan_status, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.groupby('purpose')['loan_status'].value_counts(normalize=True).loc[:,'Charged Off'].sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "240c20e9-6336-47c6-9d92-69cc73433966",
    "_uuid": "fab8e8d284e011c9da9fedfa1a586a409f77ea0b"
   },
   "source": [
    "Notice that only 12% of completed loans for weddings have charged-off, but 30% of completed small business loans have charged-off."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.14 title"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Dictionary: \"The loan title provided by the borrower.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                 807068\n",
       "unique                 60298\n",
       "top       Debt consolidation\n",
       "freq                  371874\n",
       "Name: title, dtype: object"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['title'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "View the top 10 loan titles, and their frequencies:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Debt consolidation         371874\n",
       "Credit card refinancing    133334\n",
       "Home improvement            39171\n",
       "Other                       33265\n",
       "Debt Consolidation          15059\n",
       "Major purchase              12311\n",
       "Medical expenses             6908\n",
       "Business                     6666\n",
       "Car financing                5667\n",
       "Consolidation                5090\n",
       "Name: title, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['title'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 60,298 different titles in the dataset, and based on the top 10 titles, the `purpose` variable appears to already contain this information. So we drop the `title` variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "loans.drop('title', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b6908ad9-66be-4a38-8e45-bacde206e987",
    "_uuid": "f68d4795cca9861dd340a2c155c0b956ae9f53fa"
   },
   "source": [
    "## 5.15 zip_code, addr_state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Dictionary for `zip_code`: \"The first 3 numbers of the zip code provided by the borrower in the loan application.\"\n",
    "\n",
    "Data Dictionary for `addr_state`: \"The state provided by the borrower in the loan application.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1578897    325xx\n",
       "449772     629xx\n",
       "908626     934xx\n",
       "845461     286xx\n",
       "980965     334xx\n",
       "Name: zip_code, dtype: object"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['zip_code'].sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "925"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['zip_code'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "117981    AL\n",
       "341171    PA\n",
       "224304    NY\n",
       "973786    MD\n",
       "428015    MD\n",
       "Name: addr_state, dtype: object"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['addr_state'].sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['addr_state'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "a2485d09-8385-427d-9bcb-7c47b8034e53",
    "_uuid": "69aa28976c0826de8e461d39c5e3b5bfdf49e61c"
   },
   "source": [
    "There are a lot of different zip codes, so let's just keep the state column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "_cell_guid": "aef7a94f-613c-4137-86f2-d0591ea3a4bc",
    "_uuid": "d6d4c635eb86b895acda91d0cc1fa38327a96e7d"
   },
   "outputs": [],
   "source": [
    "loans.drop(labels='zip_code', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the charge-off rates by address state:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "addr_state\n",
       "DC    0.130378\n",
       "ME    0.136564\n",
       "NH    0.142554\n",
       "IA    0.142857\n",
       "VT    0.149225\n",
       "OR    0.152827\n",
       "CO    0.158083\n",
       "WA    0.166538\n",
       "KS    0.171004\n",
       "SC    0.172705\n",
       "WI    0.175315\n",
       "MT    0.177759\n",
       "WY    0.178142\n",
       "UT    0.179049\n",
       "WV    0.182457\n",
       "CT    0.183810\n",
       "GA    0.191224\n",
       "MA    0.192974\n",
       "IL    0.193302\n",
       "ID    0.193487\n",
       "RI    0.194009\n",
       "CA    0.198303\n",
       "AZ    0.199321\n",
       "TX    0.202286\n",
       "AK    0.204141\n",
       "HI    0.204535\n",
       "DE    0.206575\n",
       "VA    0.207942\n",
       "MI    0.208880\n",
       "MN    0.210363\n",
       "PA    0.214776\n",
       "MD    0.214845\n",
       "NC    0.215282\n",
       "NJ    0.216208\n",
       "KY    0.216353\n",
       "MO    0.219400\n",
       "FL    0.222007\n",
       "OH    0.225400\n",
       "NM    0.225694\n",
       "SD    0.225787\n",
       "NY    0.227045\n",
       "IN    0.228179\n",
       "TN    0.230479\n",
       "NV    0.234202\n",
       "ND    0.237232\n",
       "LA    0.237900\n",
       "AR    0.243154\n",
       "AL    0.247650\n",
       "OK    0.248252\n",
       "NE    0.267946\n",
       "MS    0.275619\n",
       "Name: loan_status, dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.groupby('addr_state')['loan_status'].value_counts(normalize=True).loc[:,'Charged Off'].sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The charge-off rate ranges from 13.0% in Washington, DC to 27.6% in Mississippi."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.16 dti"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Dictionary: \"A ratio calculated using the borrower’s total monthly debt payments on the total debt obligations, excluding mortgage and the requested LC loan, divided by the borrower’s self-reported monthly income.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    814950.000000\n",
       "mean         17.867719\n",
       "std           8.856477\n",
       "min          -1.000000\n",
       "25%          11.640000\n",
       "50%          17.360000\n",
       "75%          23.630000\n",
       "max         999.000000\n",
       "Name: dti, dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['dti'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note sure if the values of -1 and 999 make sense...\n",
    "\n",
    "There are several outliers that mess up our default plots. Plot a histogram for `dti` less than 60:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Joe\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Debt-to-income Ratio')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+vgoKChQUFOSSMUeYTCb5+PjcxKu7ea6qoar841LZvLy8auxrdzZ66TxVqZfu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\nAGBw/w/tVwrRgMCHbAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x270 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,3), dpi=90)\n",
    "sns.distplot(loans.loc[loans['dti'].notnull() & (loans['dti']<60), 'dti'], kde=False)\n",
    "plt.xlabel('Debt-to-income Ratio')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Debt-to-income Ratio')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many of the `dti` values are \"outliers\" (above 60)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "179"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(loans['dti']>=60).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Very few. Compare the summary statistics by loan status:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Charged Off</th>\n",
       "      <td>168078.0</td>\n",
       "      <td>20.005334</td>\n",
       "      <td>8.991035</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.65</td>\n",
       "      <td>19.77</td>\n",
       "      <td>26.21</td>\n",
       "      <td>999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fully Paid</th>\n",
       "      <td>646872.0</td>\n",
       "      <td>17.312298</td>\n",
       "      <td>8.735991</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>11.22</td>\n",
       "      <td>16.77</td>\n",
       "      <td>22.90</td>\n",
       "      <td>999.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count       mean       std  min    25%    50%    75%    max\n",
       "loan_status                                                                \n",
       "Charged Off  168078.0  20.005334  8.991035  0.0  13.65  19.77  26.21  999.0\n",
       "Fully Paid   646872.0  17.312298  8.735991 -1.0  11.22  16.77  22.90  999.0"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.groupby('loan_status')['dti'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Completed loans that are charged off tend to have higher debt-to-income ratios."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.17 earliest_cr_line"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Dictionary: \"The month the borrower's earliest reported credit line was opened.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "872604    Aug-2003\n",
       "975699    Oct-1999\n",
       "758818    Mar-1999\n",
       "274952    Aug-1971\n",
       "30876     Mar-2002\n",
       "Name: earliest_cr_line, dtype: object"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['earliest_cr_line'].sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['earliest_cr_line'].isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's just retain the year for simplicity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "loans['earliest_cr_line'] = loans['earliest_cr_line'].apply(lambda s: int(s[-4:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    814986.000000\n",
       "mean       1998.170962\n",
       "std           7.385629\n",
       "min        1944.000000\n",
       "25%        1994.000000\n",
       "50%        2000.000000\n",
       "75%        2003.000000\n",
       "max        2014.000000\n",
       "Name: earliest_cr_line, dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['earliest_cr_line'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Joe\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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X3CQkJOjmzZu6du2aXF1dLTZuSXl8TLHV3Eric0mRAgAAlFhPP/20IiIijCJF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\nrkJggg==\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_var('earliest_cr_line', 'Year of Earliest Credit Line', continuous=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Borrowers who are charged-off tend to have shorter lines of credit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.18 fico_range_low, fico_range_high"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Dictionary for `fico_range_low`: \"The lower boundary range the borrower’s FICO at loan origination belongs to.\"\n",
    "\n",
    "Data Dictionary for `fico_range_high`: \"The upper boundary range the borrower’s FICO at loan origination belongs to.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fico_range_low</th>\n",
       "      <th>fico_range_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>814986.000000</td>\n",
       "      <td>814986.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>695.603151</td>\n",
       "      <td>699.603264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>31.352251</td>\n",
       "      <td>31.352791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>625.000000</td>\n",
       "      <td>629.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>670.000000</td>\n",
       "      <td>674.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>690.000000</td>\n",
       "      <td>694.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>710.000000</td>\n",
       "      <td>714.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>845.000000</td>\n",
       "      <td>850.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fico_range_low  fico_range_high\n",
       "count   814986.000000    814986.000000\n",
       "mean       695.603151       699.603264\n",
       "std         31.352251        31.352791\n",
       "min        625.000000       629.000000\n",
       "25%        670.000000       674.000000\n",
       "50%        690.000000       694.000000\n",
       "75%        710.000000       714.000000\n",
       "max        845.000000       850.000000"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans[['fico_range_low', 'fico_range_high']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the Pearson correlation between these values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fico_range_low</th>\n",
       "      <th>fico_range_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fico_range_low</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fico_range_high</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 fico_range_low  fico_range_high\n",
       "fico_range_low              1.0              1.0\n",
       "fico_range_high             1.0              1.0"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans[['fico_range_low','fico_range_high']].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We only need to keep one of the FICO scores. We'll take the average of the two and call it `fico_score`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "loans['fico_score'] = 0.5*loans['fico_range_low'] + 0.5*loans['fico_range_high']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "loans.drop(['fico_range_high', 'fico_range_low'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Joe\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
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ASJfV3hGdPXtWAQEBmjFjhtq1a2de3qZNG82ZM0f79u2Tr6+v5syZ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\nAABEECkAAACACCIFAAAAEOEG6jZwJYDtcmoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_var('fico_score', 'FICO Score', continuous=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a noticeable difference in FICO scores between fully paid and charged-off loans. Compare the summary statistics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Charged Off</th>\n",
       "      <td>168084.0</td>\n",
       "      <td>689.027278</td>\n",
       "      <td>25.083105</td>\n",
       "      <td>662.0</td>\n",
       "      <td>672.0</td>\n",
       "      <td>682.0</td>\n",
       "      <td>702.0</td>\n",
       "      <td>847.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fully Paid</th>\n",
       "      <td>646902.0</td>\n",
       "      <td>699.831485</td>\n",
       "      <td>32.416650</td>\n",
       "      <td>627.0</td>\n",
       "      <td>677.0</td>\n",
       "      <td>692.0</td>\n",
       "      <td>717.0</td>\n",
       "      <td>847.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count        mean        std    min    25%    50%    75%  \\\n",
       "loan_status                                                                \n",
       "Charged Off  168084.0  689.027278  25.083105  662.0  672.0  682.0  702.0   \n",
       "Fully Paid   646902.0  699.831485  32.416650  627.0  677.0  692.0  717.0   \n",
       "\n",
       "               max  \n",
       "loan_status         \n",
       "Charged Off  847.5  \n",
       "Fully Paid   847.5  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.groupby('loan_status')['fico_score'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loans that charge off have a FICO score 10 points lower on average."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.19 open_acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Dictionary: \"The number of open credit lines in the borrower's credit file.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Number of Open Credit Lines')"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aNmyYevXqpWXLlqlq1ao6fvy4UlJSNHv2bHf2BwAAAMAkDIeHyMhI9evXT6dO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\nABhCeAAAAABgyP8DA8FCElfF/WoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 900x270 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,3), dpi=90)\n",
    "sns.countplot(loans['open_acc'], order=sorted(loans['open_acc'].unique()), color='#5975A4', saturation=1)\n",
    "_, _ = plt.xticks(np.arange(0, 90, 5), np.arange(0, 90, 5))\n",
    "plt.title('Number of Open Credit Lines')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is there a difference in number of credit lines between fully paid loans and charged-off loans?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Charged Off</th>\n",
       "      <td>168084.0</td>\n",
       "      <td>11.883094</td>\n",
       "      <td>5.515590</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fully Paid</th>\n",
       "      <td>646902.0</td>\n",
       "      <td>11.427041</td>\n",
       "      <td>5.270369</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count       mean       std  min  25%   50%   75%   max\n",
       "loan_status                                                           \n",
       "Charged Off  168084.0  11.883094  5.515590  0.0  8.0  11.0  15.0  76.0\n",
       "Fully Paid   646902.0  11.427041  5.270369  0.0  8.0  11.0  14.0  90.0"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.groupby('loan_status')['open_acc'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's does not appear to be a significant difference."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.20 pub_rec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Dictionary: \"Number of derogatory public records.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     681509\n",
       "1.0     112483\n",
       "2.0      14115\n",
       "3.0       4107\n",
       "4.0       1400\n",
       "5.0        651\n",
       "6.0        338\n",
       "7.0        146\n",
       "8.0         90\n",
       "9.0         42\n",
       "10.0        28\n",
       "11.0        24\n",
       "12.0        15\n",
       "13.0        11\n",
       "14.0         2\n",
       "15.0         5\n",
       "16.0         2\n",
       "17.0         1\n",
       "18.0         3\n",
       "19.0         2\n",
       "21.0         1\n",
       "24.0         1\n",
       "28.0         2\n",
       "34.0         1\n",
       "40.0         1\n",
       "46.0         1\n",
       "49.0         2\n",
       "54.0         1\n",
       "63.0         1\n",
       "86.0         1\n",
       "Name: pub_rec, dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['pub_rec'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is there a difference in average public records between fully paid loans and charged-off loans?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Charged Off</th>\n",
       "      <td>168084.0</td>\n",
       "      <td>0.232247</td>\n",
       "      <td>0.640855</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fully Paid</th>\n",
       "      <td>646902.0</td>\n",
       "      <td>0.198845</td>\n",
       "      <td>0.569304</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count      mean       std  min  25%  50%  75%   max\n",
       "loan_status                                                        \n",
       "Charged Off  168084.0  0.232247  0.640855  0.0  0.0  0.0  0.0  86.0\n",
       "Fully Paid   646902.0  0.198845  0.569304  0.0  0.0  0.0  0.0  63.0"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.groupby('loan_status')['pub_rec'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.21 revol_bal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Dictionary: \"Total credit revolving balance.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    8.149860e+05\n",
       "mean     1.606864e+04\n",
       "std      2.160500e+04\n",
       "min      0.000000e+00\n",
       "25%      6.014000e+03\n",
       "50%      1.118500e+04\n",
       "75%      1.972300e+04\n",
       "max      2.568995e+06\n",
       "Name: revol_bal, dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['revol_bal'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do a log transform:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "loans['log_revol_bal'] = loans['revol_bal'].apply(lambda x: np.log10(x+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "loans.drop('revol_bal', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Joe\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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TpmHGjBmIjo5G79698cknn2DixIkICgpS29+jR4/wxRdfwNnZGe3atcPHH38MJycnfPbZ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\nYII=\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_var('log_revol_bal', 'Log Revolving Credit Balance', continuous=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Charged Off</th>\n",
       "      <td>168084.0</td>\n",
       "      <td>3.997810</td>\n",
       "      <td>0.497362</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.787602</td>\n",
       "      <td>4.051307</td>\n",
       "      <td>4.288585</td>\n",
       "      <td>6.242223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fully Paid</th>\n",
       "      <td>646902.0</td>\n",
       "      <td>3.994933</td>\n",
       "      <td>0.527335</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.777137</td>\n",
       "      <td>4.047975</td>\n",
       "      <td>4.296665</td>\n",
       "      <td>6.409763</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count      mean       std  min       25%       50%       75%  \\\n",
       "loan_status                                                                    \n",
       "Charged Off  168084.0  3.997810  0.497362  0.0  3.787602  4.051307  4.288585   \n",
       "Fully Paid   646902.0  3.994933  0.527335  0.0  3.777137  4.047975  4.296665   \n",
       "\n",
       "                  max  \n",
       "loan_status            \n",
       "Charged Off  6.242223  \n",
       "Fully Paid   6.409763  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.groupby('loan_status')['log_revol_bal'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There isn't a large difference in the means."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.22 revol_util"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Dictionary: \"Revolving line utilization rate, or the amount of credit the borrower is using relative to all available revolving credit.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    814496.000000\n",
       "mean         53.031137\n",
       "std          24.320981\n",
       "min           0.000000\n",
       "25%          35.000000\n",
       "50%          53.700000\n",
       "75%          71.900000\n",
       "max         892.300000\n",
       "Name: revol_util, dtype: float64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['revol_util'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Joe\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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h4aSkpLBo0aJ61d6hQwc2bNjAunXrSEpKwt/f\nn8GDB//jVY4zZ85w5syZGm1Lly79bsipjb+/P2vXriU5OZni4mLc3d2ZNGkScXFxtS7ftWtX0tLS\n2LJlC5s2beLTp0+0a9eOsLCwGj/LFRwczKJFiwgKCuLcuXM/rOPw4cOUlJRw5MgR9u3bZzzJvH//\n/qSmpnLjxg1CQ0MxmUx069aNadOmYbFYsNvttG3bltGjRzNlyhQSExNrbDc0NJQ3b96watUqbDYb\nnTt3JiUlhcGDB9f5GImIiChjKGMoY8if5D/Vf/9dIBGReigrK6OiooKePXsabQkJCfj4+LB48eIG\nrExERET+ZMoYIo2PbvcQkV/24sULzGYz9+/fB+D69evk5uYyfPjwBq5MRERE/mTKGCKNj2ZSiMhv\nceDAAdLT03nx4gXt27cnNjaWiRMnNnRZIiIi8odTxhBpXDRIISIiIiIiIiJOQbd7iIiIiIiIiIhT\n0CCFiIiIiIiIiDgFDVKIiIiIiIiIiFPQIIWIiIiIiIiIOAUNUoiIiIiIiIiIU9AghYiIiIiIiIg4\nBQ1SiIiIiIiIiIhT+C/2cXP+YBWAHwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_var('revol_util', 'Revolving Line Utilization', continuous=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Charged Off</th>\n",
       "      <td>167974.0</td>\n",
       "      <td>56.475417</td>\n",
       "      <td>23.566253</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.5</td>\n",
       "      <td>57.5</td>\n",
       "      <td>74.7</td>\n",
       "      <td>366.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fully Paid</th>\n",
       "      <td>646522.0</td>\n",
       "      <td>52.136273</td>\n",
       "      <td>24.433954</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.9</td>\n",
       "      <td>52.7</td>\n",
       "      <td>71.0</td>\n",
       "      <td>892.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count       mean        std  min   25%   50%   75%    max\n",
       "loan_status                                                              \n",
       "Charged Off  167974.0  56.475417  23.566253  0.0  39.5  57.5  74.7  366.6\n",
       "Fully Paid   646522.0  52.136273  24.433954  0.0  33.9  52.7  71.0  892.3"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.groupby('loan_status')['revol_util'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.23 total_acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Dictionary: \"The total number of credit lines currently in the borrower's credit file.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Total Number of Credit Lines')"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AYBubG8/Lly/Lx8fHKubj46MbN27ka8ejRo2yWh40aJAGDRqU67qhoaEKDQ0t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\nNJ4AAAAAALv6/wBYYgU10GoNiAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x270 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,3), dpi=90)\n",
    "sns.countplot(loans['total_acc'], order=sorted(loans['total_acc'].unique()), color='#5975A4', saturation=1)\n",
    "_, _ = plt.xticks(np.arange(0, 176, 10), np.arange(0, 176, 10))\n",
    "plt.title('Total Number of Credit Lines')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Charged Off</th>\n",
       "      <td>168084.0</td>\n",
       "      <td>25.158653</td>\n",
       "      <td>12.121106</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>176.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fully Paid</th>\n",
       "      <td>646902.0</td>\n",
       "      <td>25.489618</td>\n",
       "      <td>11.930122</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>156.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count       mean        std  min   25%   50%   75%    max\n",
       "loan_status                                                              \n",
       "Charged Off  168084.0  25.158653  12.121106  2.0  16.0  23.0  32.0  176.0\n",
       "Fully Paid   646902.0  25.489618  11.930122  2.0  17.0  24.0  32.0  156.0"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.groupby('loan_status')['total_acc'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No large differences here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "57408b52-70fd-4a45-b5bb-12f0c2612190",
    "_uuid": "2a671f42515e7c4727430a48e6163c6f77801275"
   },
   "source": [
    "## 5.24 initial_list_status"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Dictionary: \"The initial listing status of the loan. Possible values are – W, F.\" I'm not sure what this means."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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T+3u/yPWSOZ+NlNKhQwd169ZNFSpUUKlSpYzXS+nSpTN7HZkzZ1a/fv3Ur18/nT9/Xj/9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\nAAACAk0KAAAAAAAQEP4HsiZQNYb4qtEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_var('initial_list_status', 'Initial List Status', continuous=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b34ab6cc-0a3f-4780-a234-743b9d406dca",
    "_uuid": "77b5eba4a13bd157c68f522ab8002094bb8b456d"
   },
   "source": [
    "## 5.25 application_type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Dictionary: \"Indicates whether the loan is an individual application or a joint application with two co-borrowers.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "_cell_guid": "0fc4f079-695a-49e9-abac-05bf841bd9e5",
    "_uuid": "afc3918d6aa0db8f5ddeab49d17359155db1376d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Individual    811207\n",
       "Joint App       3779\n",
       "Name: application_type, dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['application_type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's just compare the charge-off rates by application type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "application_type\n",
       "Individual    0.206316\n",
       "Joint App     0.190262\n",
       "Name: loan_status, dtype: float64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.groupby('application_type')['loan_status'].value_counts(normalize=True).loc[:,'Charged Off']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Joint loans are slightly less likely to be charged-off."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.26 mort_acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Dictionary: \"Number of mortgage accounts.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    767705.000000\n",
       "mean          1.758707\n",
       "std           2.081730\n",
       "min           0.000000\n",
       "25%           0.000000\n",
       "50%           1.000000\n",
       "75%           3.000000\n",
       "max          51.000000\n",
       "Name: mort_acc, dtype: float64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['mort_acc'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not sure how someone can have 51 mortgage accounts...but apparently they do. Check the top 10 values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    301446\n",
       "1.0    131811\n",
       "2.0    109869\n",
       "3.0     82723\n",
       "4.0     58698\n",
       "5.0     37090\n",
       "6.0     21817\n",
       "7.0     11568\n",
       "8.0      5906\n",
       "9.0      3056\n",
       "Name: mort_acc, dtype: int64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['mort_acc'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare the summary statistics by loan status:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Charged Off</th>\n",
       "      <td>161198.0</td>\n",
       "      <td>1.447437</td>\n",
       "      <td>1.901888</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fully Paid</th>\n",
       "      <td>606507.0</td>\n",
       "      <td>1.841436</td>\n",
       "      <td>2.119298</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count      mean       std  min  25%  50%  75%   max\n",
       "loan_status                                                        \n",
       "Charged Off  161198.0  1.447437  1.901888  0.0  0.0  1.0  2.0  29.0\n",
       "Fully Paid   606507.0  1.841436  2.119298  0.0  0.0  1.0  3.0  51.0"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.groupby('loan_status')['mort_acc'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Individuals who pay off their loans are more likely to have several mortgage accounts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.27 pub_rec_bankruptcies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Dictionary: \"Number of public record bankruptcies.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     714642\n",
       "1.0      93680\n",
       "2.0       4673\n",
       "3.0        926\n",
       "4.0        234\n",
       "5.0         85\n",
       "6.0         28\n",
       "7.0         14\n",
       "8.0          5\n",
       "9.0          1\n",
       "12.0         1\n",
       "Name: pub_rec_bankruptcies, dtype: int64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['pub_rec_bankruptcies'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oAQDez6X18vJCeHg4Fi9eDENDQyxZsgTdu3cHAISHhyMgIABjxoyBiooKunXr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\niIiIiIhk4f8AQpkL2A14RpcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_var('pub_rec_bankruptcies', 'Public Record Bankruptcies', continuous=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. More Pre-processing\n",
    "<a id=\"6\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.1 Convert loan status to 0/1 charge-off indicator\n",
    "<a id=\"6.1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change the response variable `loan_status` to a 0/1 variable, where 0 indicates fully paid and 1 indicates charge-off:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "loans['charged_off'] = (loans['loan_status'] == 'Charged Off').apply(np.uint8)\n",
    "loans.drop('loan_status', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.2 Create dummy variables\n",
    "<a id=\"6.2\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many variables do we currently have?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(814986, 25)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If any categorical variables have missing values, we'll need to create NaN dummy variables for those. So first check which variables have missing data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_fractions = loans.isnull().mean().sort_values(ascending=False) # Fraction of data missing for each variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mort_acc                0.058014\n",
      "emp_length              0.051845\n",
      "pub_rec_bankruptcies    0.000855\n",
      "revol_util              0.000601\n",
      "dti                     0.000044\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(missing_fractions[missing_fractions > 0]) # Print variables that are missing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no categorical variables with missing values, and therefore we don't need any `NaN` dummy variables.\n",
    "\n",
    "Create dummy variables for the categorical variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['loan_amnt', 'term', 'int_rate', 'installment', 'sub_grade',\n",
      "       'emp_length', 'home_ownership', 'verification_status', 'issue_d',\n",
      "       'purpose', 'addr_state', 'dti', 'earliest_cr_line', 'open_acc',\n",
      "       'pub_rec', 'revol_util', 'total_acc', 'initial_list_status',\n",
      "       'application_type', 'mort_acc', 'pub_rec_bankruptcies',\n",
      "       'log_annual_inc', 'fico_score', 'log_revol_bal', 'charged_off'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(loans.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "loans = pd.get_dummies(loans, columns=['sub_grade', 'home_ownership', 'verification_status', 'purpose', 'addr_state', 'initial_list_status', 'application_type'], drop_first=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many variables are there now?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(814986, 122)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check our data with the new dummy variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>issue_d</th>\n",
       "      <th>dti</th>\n",
       "      <th>earliest_cr_line</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>mort_acc</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>log_annual_inc</th>\n",
       "      <th>fico_score</th>\n",
       "      <th>log_revol_bal</th>\n",
       "      <th>charged_off</th>\n",
       "      <th>sub_grade_A2</th>\n",
       "      <th>sub_grade_A3</th>\n",
       "      <th>sub_grade_A4</th>\n",
       "      <th>sub_grade_A5</th>\n",
       "      <th>sub_grade_B1</th>\n",
       "      <th>sub_grade_B2</th>\n",
       "      <th>sub_grade_B3</th>\n",
       "      <th>sub_grade_B4</th>\n",
       "      <th>sub_grade_B5</th>\n",
       "      <th>sub_grade_C1</th>\n",
       "      <th>sub_grade_C2</th>\n",
       "      <th>sub_grade_C3</th>\n",
       "      <th>sub_grade_C4</th>\n",
       "      <th>sub_grade_C5</th>\n",
       "      <th>sub_grade_D1</th>\n",
       "      <th>sub_grade_D2</th>\n",
       "      <th>sub_grade_D3</th>\n",
       "      <th>sub_grade_D4</th>\n",
       "      <th>sub_grade_D5</th>\n",
       "      <th>sub_grade_E1</th>\n",
       "      <th>sub_grade_E2</th>\n",
       "      <th>sub_grade_E3</th>\n",
       "      <th>sub_grade_E4</th>\n",
       "      <th>sub_grade_E5</th>\n",
       "      <th>sub_grade_F1</th>\n",
       "      <th>sub_grade_F2</th>\n",
       "      <th>sub_grade_F3</th>\n",
       "      <th>sub_grade_F4</th>\n",
       "      <th>sub_grade_F5</th>\n",
       "      <th>sub_grade_G1</th>\n",
       "      <th>sub_grade_G2</th>\n",
       "      <th>sub_grade_G3</th>\n",
       "      <th>sub_grade_G4</th>\n",
       "      <th>sub_grade_G5</th>\n",
       "      <th>home_ownership_OTHER</th>\n",
       "      <th>home_ownership_OWN</th>\n",
       "      <th>home_ownership_RENT</th>\n",
       "      <th>verification_status_Source Verified</th>\n",
       "      <th>verification_status_Verified</th>\n",
       "      <th>purpose_credit_card</th>\n",
       "      <th>purpose_debt_consolidation</th>\n",
       "      <th>purpose_educational</th>\n",
       "      <th>purpose_home_improvement</th>\n",
       "      <th>purpose_house</th>\n",
       "      <th>purpose_major_purchase</th>\n",
       "      <th>purpose_medical</th>\n",
       "      <th>purpose_moving</th>\n",
       "      <th>purpose_other</th>\n",
       "      <th>purpose_renewable_energy</th>\n",
       "      <th>purpose_small_business</th>\n",
       "      <th>purpose_vacation</th>\n",
       "      <th>purpose_wedding</th>\n",
       "      <th>addr_state_AL</th>\n",
       "      <th>addr_state_AR</th>\n",
       "      <th>addr_state_AZ</th>\n",
       "      <th>addr_state_CA</th>\n",
       "      <th>addr_state_CO</th>\n",
       "      <th>addr_state_CT</th>\n",
       "      <th>addr_state_DC</th>\n",
       "      <th>addr_state_DE</th>\n",
       "      <th>addr_state_FL</th>\n",
       "      <th>addr_state_GA</th>\n",
       "      <th>addr_state_HI</th>\n",
       "      <th>addr_state_IA</th>\n",
       "      <th>addr_state_ID</th>\n",
       "      <th>addr_state_IL</th>\n",
       "      <th>addr_state_IN</th>\n",
       "      <th>addr_state_KS</th>\n",
       "      <th>addr_state_KY</th>\n",
       "      <th>addr_state_LA</th>\n",
       "      <th>addr_state_MA</th>\n",
       "      <th>addr_state_MD</th>\n",
       "      <th>addr_state_ME</th>\n",
       "      <th>addr_state_MI</th>\n",
       "      <th>addr_state_MN</th>\n",
       "      <th>addr_state_MO</th>\n",
       "      <th>addr_state_MS</th>\n",
       "      <th>addr_state_MT</th>\n",
       "      <th>addr_state_NC</th>\n",
       "      <th>addr_state_ND</th>\n",
       "      <th>addr_state_NE</th>\n",
       "      <th>addr_state_NH</th>\n",
       "      <th>addr_state_NJ</th>\n",
       "      <th>addr_state_NM</th>\n",
       "      <th>addr_state_NV</th>\n",
       "      <th>addr_state_NY</th>\n",
       "      <th>addr_state_OH</th>\n",
       "      <th>addr_state_OK</th>\n",
       "      <th>addr_state_OR</th>\n",
       "      <th>addr_state_PA</th>\n",
       "      <th>addr_state_RI</th>\n",
       "      <th>addr_state_SC</th>\n",
       "      <th>addr_state_SD</th>\n",
       "      <th>addr_state_TN</th>\n",
       "      <th>addr_state_TX</th>\n",
       "      <th>addr_state_UT</th>\n",
       "      <th>addr_state_VA</th>\n",
       "      <th>addr_state_VT</th>\n",
       "      <th>addr_state_WA</th>\n",
       "      <th>addr_state_WI</th>\n",
       "      <th>addr_state_WV</th>\n",
       "      <th>addr_state_WY</th>\n",
       "      <th>initial_list_status_w</th>\n",
       "      <th>application_type_Joint App</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1550084</th>\n",
       "      <td>18000.0</td>\n",
       "      <td>36</td>\n",
       "      <td>7.89</td>\n",
       "      <td>563.15</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Mar-2016</td>\n",
       "      <td>10.70</td>\n",
       "      <td>1992</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.903095</td>\n",
       "      <td>707.0</td>\n",
       "      <td>3.772835</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>956540</th>\n",
       "      <td>15500.0</td>\n",
       "      <td>36</td>\n",
       "      <td>15.31</td>\n",
       "      <td>539.67</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Oct-2012</td>\n",
       "      <td>25.68</td>\n",
       "      <td>1999</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.3</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.653222</td>\n",
       "      <td>697.0</td>\n",
       "      <td>4.050534</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99139</th>\n",
       "      <td>15000.0</td>\n",
       "      <td>36</td>\n",
       "      <td>16.99</td>\n",
       "      <td>534.72</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Aug-2014</td>\n",
       "      <td>23.56</td>\n",
       "      <td>1995</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.913819</td>\n",
       "      <td>677.0</td>\n",
       "      <td>4.306275</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>832384</th>\n",
       "      <td>2750.0</td>\n",
       "      <td>36</td>\n",
       "      <td>19.20</td>\n",
       "      <td>101.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nov-2013</td>\n",
       "      <td>10.50</td>\n",
       "      <td>2009</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.079217</td>\n",
       "      <td>667.0</td>\n",
       "      <td>3.596927</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179977</th>\n",
       "      <td>14000.0</td>\n",
       "      <td>36</td>\n",
       "      <td>10.99</td>\n",
       "      <td>458.28</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Apr-2014</td>\n",
       "      <td>15.60</td>\n",
       "      <td>2003</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>94.1</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.819551</td>\n",
       "      <td>672.0</td>\n",
       "      <td>4.214128</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         loan_amnt  term  int_rate  installment  emp_length   issue_d    dti  \\\n",
       "1550084    18000.0    36      7.89       563.15        10.0  Mar-2016  10.70   \n",
       "956540     15500.0    36     15.31       539.67         4.0  Oct-2012  25.68   \n",
       "99139      15000.0    36     16.99       534.72         4.0  Aug-2014  23.56   \n",
       "832384      2750.0    36     19.20       101.09         NaN  Nov-2013  10.50   \n",
       "179977     14000.0    36     10.99       458.28         7.0  Apr-2014  15.60   \n",
       "\n",
       "         earliest_cr_line  open_acc  pub_rec  revol_util  total_acc  mort_acc  \\\n",
       "1550084              1992      25.0      0.0        12.0       46.0       0.0   \n",
       "956540               1999      12.0      0.0        38.3       29.0       1.0   \n",
       "99139                1995      16.0      0.0        48.2       26.0       4.0   \n",
       "832384               2009       4.0      0.0        48.2        5.0       0.0   \n",
       "179977               2003       6.0      0.0        94.1       12.0       3.0   \n",
       "\n",
       "         pub_rec_bankruptcies  log_annual_inc  fico_score  log_revol_bal  \\\n",
       "1550084                   0.0        4.903095       707.0       3.772835   \n",
       "956540                    0.0        4.653222       697.0       4.050534   \n",
       "99139                     0.0        4.913819       677.0       4.306275   \n",
       "832384                    0.0        4.079217       667.0       3.596927   \n",
       "179977                    0.0        4.819551       672.0       4.214128   \n",
       "\n",
       "         charged_off  sub_grade_A2  sub_grade_A3  sub_grade_A4  sub_grade_A5  \\\n",
       "1550084            0             0             0             0             1   \n",
       "956540             0             0             0             0             0   \n",
       "99139              0             0             0             0             0   \n",
       "832384             1             0             0             0             0   \n",
       "179977             1             0             0             0             0   \n",
       "\n",
       "         sub_grade_B1  sub_grade_B2  sub_grade_B3  sub_grade_B4  sub_grade_B5  \\\n",
       "1550084             0             0             0             0             0   \n",
       "956540              0             0             0             0             0   \n",
       "99139               0             0             0             0             0   \n",
       "832384              0             0             0             0             0   \n",
       "179977              0             1             0             0             0   \n",
       "\n",
       "         sub_grade_C1  sub_grade_C2  sub_grade_C3  sub_grade_C4  sub_grade_C5  \\\n",
       "1550084             0             0             0             0             0   \n",
       "956540              0             1             0             0             0   \n",
       "99139               0             0             0             0             0   \n",
       "832384              0             0             0             0             0   \n",
       "179977              0             0             0             0             0   \n",
       "\n",
       "         sub_grade_D1  sub_grade_D2  sub_grade_D3  sub_grade_D4  sub_grade_D5  \\\n",
       "1550084             0             0             0             0             0   \n",
       "956540              0             0             0             0             0   \n",
       "99139               0             0             1             0             0   \n",
       "832384              0             0             1             0             0   \n",
       "179977              0             0             0             0             0   \n",
       "\n",
       "         sub_grade_E1  sub_grade_E2  sub_grade_E3  sub_grade_E4  sub_grade_E5  \\\n",
       "1550084             0             0             0             0             0   \n",
       "956540              0             0             0             0             0   \n",
       "99139               0             0             0             0             0   \n",
       "832384              0             0             0             0             0   \n",
       "179977              0             0             0             0             0   \n",
       "\n",
       "         sub_grade_F1  sub_grade_F2  sub_grade_F3  sub_grade_F4  sub_grade_F5  \\\n",
       "1550084             0             0             0             0             0   \n",
       "956540              0             0             0             0             0   \n",
       "99139               0             0             0             0             0   \n",
       "832384              0             0             0             0             0   \n",
       "179977              0             0             0             0             0   \n",
       "\n",
       "         sub_grade_G1  sub_grade_G2  sub_grade_G3  sub_grade_G4  sub_grade_G5  \\\n",
       "1550084             0             0             0             0             0   \n",
       "956540              0             0             0             0             0   \n",
       "99139               0             0             0             0             0   \n",
       "832384              0             0             0             0             0   \n",
       "179977              0             0             0             0             0   \n",
       "\n",
       "         home_ownership_OTHER  home_ownership_OWN  home_ownership_RENT  \\\n",
       "1550084                     0                   0                    1   \n",
       "956540                      0                   0                    0   \n",
       "99139                       0                   0                    0   \n",
       "832384                      0                   0                    1   \n",
       "179977                      0                   0                    0   \n",
       "\n",
       "         verification_status_Source Verified  verification_status_Verified  \\\n",
       "1550084                                    0                             0   \n",
       "956540                                     1                             0   \n",
       "99139                                      0                             0   \n",
       "832384                                     0                             1   \n",
       "179977                                     1                             0   \n",
       "\n",
       "         purpose_credit_card  purpose_debt_consolidation  purpose_educational  \\\n",
       "1550084                    0                           1                    0   \n",
       "956540                     0                           1                    0   \n",
       "99139                      0                           1                    0   \n",
       "832384                     0                           1                    0   \n",
       "179977                     0                           1                    0   \n",
       "\n",
       "         purpose_home_improvement  purpose_house  purpose_major_purchase  \\\n",
       "1550084                         0              0                       0   \n",
       "956540                          0              0                       0   \n",
       "99139                           0              0                       0   \n",
       "832384                          0              0                       0   \n",
       "179977                          0              0                       0   \n",
       "\n",
       "         purpose_medical  purpose_moving  purpose_other  \\\n",
       "1550084                0               0              0   \n",
       "956540                 0               0              0   \n",
       "99139                  0               0              0   \n",
       "832384                 0               0              0   \n",
       "179977                 0               0              0   \n",
       "\n",
       "         purpose_renewable_energy  purpose_small_business  purpose_vacation  \\\n",
       "1550084                         0                       0                 0   \n",
       "956540                          0                       0                 0   \n",
       "99139                           0                       0                 0   \n",
       "832384                          0                       0                 0   \n",
       "179977                          0                       0                 0   \n",
       "\n",
       "         purpose_wedding  addr_state_AL  addr_state_AR  addr_state_AZ  \\\n",
       "1550084                0              0              0              0   \n",
       "956540                 0              0              0              0   \n",
       "99139                  0              0              1              0   \n",
       "832384                 0              0              0              1   \n",
       "179977                 0              0              0              0   \n",
       "\n",
       "         addr_state_CA  addr_state_CO  addr_state_CT  addr_state_DC  \\\n",
       "1550084              0              0              0              0   \n",
       "956540               0              0              0              0   \n",
       "99139                0              0              0              0   \n",
       "832384               0              0              0              0   \n",
       "179977               0              0              0              0   \n",
       "\n",
       "         addr_state_DE  addr_state_FL  addr_state_GA  addr_state_HI  \\\n",
       "1550084              0              0              0              0   \n",
       "956540               0              0              0              0   \n",
       "99139                0              0              0              0   \n",
       "832384               0              0              0              0   \n",
       "179977               0              0              0              0   \n",
       "\n",
       "         addr_state_IA  addr_state_ID  addr_state_IL  addr_state_IN  \\\n",
       "1550084              0              0              0              0   \n",
       "956540               0              0              0              0   \n",
       "99139                0              0              0              0   \n",
       "832384               0              0              0              0   \n",
       "179977               0              0              0              0   \n",
       "\n",
       "         addr_state_KS  addr_state_KY  addr_state_LA  addr_state_MA  \\\n",
       "1550084              0              0              0              0   \n",
       "956540               0              0              0              0   \n",
       "99139                0              0              0              0   \n",
       "832384               0              0              0              0   \n",
       "179977               0              1              0              0   \n",
       "\n",
       "         addr_state_MD  addr_state_ME  addr_state_MI  addr_state_MN  \\\n",
       "1550084              0              0              0              0   \n",
       "956540               0              0              0              0   \n",
       "99139                0              0              0              0   \n",
       "832384               0              0              0              0   \n",
       "179977               0              0              0              0   \n",
       "\n",
       "         addr_state_MO  addr_state_MS  addr_state_MT  addr_state_NC  \\\n",
       "1550084              0              0              0              0   \n",
       "956540               0              0              0              0   \n",
       "99139                0              0              0              0   \n",
       "832384               0              0              0              0   \n",
       "179977               0              0              0              0   \n",
       "\n",
       "         addr_state_ND  addr_state_NE  addr_state_NH  addr_state_NJ  \\\n",
       "1550084              0              0              0              0   \n",
       "956540               0              0              0              0   \n",
       "99139                0              0              0              0   \n",
       "832384               0              0              0              0   \n",
       "179977               0              0              0              0   \n",
       "\n",
       "         addr_state_NM  addr_state_NV  addr_state_NY  addr_state_OH  \\\n",
       "1550084              0              0              1              0   \n",
       "956540               1              0              0              0   \n",
       "99139                0              0              0              0   \n",
       "832384               0              0              0              0   \n",
       "179977               0              0              0              0   \n",
       "\n",
       "         addr_state_OK  addr_state_OR  addr_state_PA  addr_state_RI  \\\n",
       "1550084              0              0              0              0   \n",
       "956540               0              0              0              0   \n",
       "99139                0              0              0              0   \n",
       "832384               0              0              0              0   \n",
       "179977               0              0              0              0   \n",
       "\n",
       "         addr_state_SC  addr_state_SD  addr_state_TN  addr_state_TX  \\\n",
       "1550084              0              0              0              0   \n",
       "956540               0              0              0              0   \n",
       "99139                0              0              0              0   \n",
       "832384               0              0              0              0   \n",
       "179977               0              0              0              0   \n",
       "\n",
       "         addr_state_UT  addr_state_VA  addr_state_VT  addr_state_WA  \\\n",
       "1550084              0              0              0              0   \n",
       "956540               0              0              0              0   \n",
       "99139                0              0              0              0   \n",
       "832384               0              0              0              0   \n",
       "179977               0              0              0              0   \n",
       "\n",
       "         addr_state_WI  addr_state_WV  addr_state_WY  initial_list_status_w  \\\n",
       "1550084              0              0              0                      1   \n",
       "956540               0              0              0                      0   \n",
       "99139                0              0              0                      0   \n",
       "832384               0              0              0                      1   \n",
       "179977               0              0              0                      0   \n",
       "\n",
       "         application_type_Joint App  \n",
       "1550084                           0  \n",
       "956540                            0  \n",
       "99139                             0  \n",
       "832384                            0  \n",
       "179977                            0  "
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.3 Train/test split\n",
    "<a id=\"6.3\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll make our modeling problem more realistic by performing the train/test split based on the month that the loan was funded. That is, we'll use loans funded on earlier dates to predict whether future loans will charge-off. The variable `issue_d` includes the month and year that the loan was funded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1559071    Mar-2016\n",
       "1577670    Feb-2016\n",
       "629412     Mar-2015\n",
       "133052     Jul-2014\n",
       "693233     Jan-2015\n",
       "Name: issue_d, dtype: object"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['issue_d'].sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Are there any missing values?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['issue_d'].isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No. Let's convert the issue dates to datetime objects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "loans['issue_d'] = pd.to_datetime(loans['issue_d'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "888937   2013-06-01\n",
       "27556    2014-11-01\n",
       "698033   2015-01-01\n",
       "891384   2013-06-01\n",
       "669716   2015-01-01\n",
       "Name: issue_d, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['issue_d'].sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The new datetime values are all on the first day of the month. Check the summary statistics of the issue dates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                  814986\n",
       "unique                    124\n",
       "top       2014-10-01 00:00:00\n",
       "freq                    33699\n",
       "first     2007-06-01 00:00:00\n",
       "last      2017-09-01 00:00:00\n",
       "Name: issue_d, dtype: object"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans['issue_d'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are only 124 unique issue dates over the 10-year period because we only have month/year information. In this particular dataset, the first loans were issued in June 2007, and the most recent loans were issued in September 2017. The busiest month was October 2014 with 33,699 loans funded in that month. What is the distribution of loans funded in each year?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Loans Funded per Year')"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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txaVLl9CpUyfY2dkhNzcXf/zxB3r27Kn2Sm/evInp06cjNDQUGRkZMDc3V+q3sLCATCaD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\niMgIscATEREZof8H+OH1+s+w+GUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 540x270 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,3), dpi=90)\n",
    "loans['issue_d'].dt.year.value_counts().sort_index().plot.bar(color='darkblue')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Loans Funded')\n",
    "plt.title('Loans Funded per Year')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll form the test set from the most recent 10% of the loans."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "loans_train = loans.loc[loans['issue_d'] <  loans['issue_d'].quantile(0.9)]\n",
    "loans_test =  loans.loc[loans['issue_d'] >= loans['issue_d'].quantile(0.9)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check that we properly partitioned the loans:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of loans in the partition:    814986\n",
      "Number of loans in the full dataset: 814986\n"
     ]
    }
   ],
   "source": [
    "print('Number of loans in the partition:   ', loans_train.shape[0] + loans_test.shape[0])\n",
    "print('Number of loans in the full dataset:', loans.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the test size?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10291955935439381"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans_test.shape[0] / loans.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "About 10.2%. The partition looks good, so we can delete the original `loans` dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "del loans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the summary statistics of the issue dates in the train and test sets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                  731108\n",
       "unique                    109\n",
       "top       2014-10-01 00:00:00\n",
       "freq                    33699\n",
       "first     2007-06-01 00:00:00\n",
       "last      2016-06-01 00:00:00\n",
       "Name: issue_d, dtype: object"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans_train['issue_d'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                   83878\n",
       "unique                     15\n",
       "top       2016-07-01 00:00:00\n",
       "freq                    10825\n",
       "first     2016-07-01 00:00:00\n",
       "last      2017-09-01 00:00:00\n",
       "Name: issue_d, dtype: object"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans_test['issue_d'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training set includes loans from June 2007 to June 2016. The test set includes loans from July 2016 to September 2017.\n",
    "\n",
    "Now we need to delete the `issue_d` variable, because it was not available before the loan was funded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "loans_train.drop('issue_d', axis=1, inplace=True)\n",
    "loans_test.drop('issue_d', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now separate the predictor variables from the response variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = loans_train['charged_off']\n",
    "y_test = loans_test['charged_off']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = loans_train.drop('charged_off', axis=1)\n",
    "X_test = loans_test.drop('charged_off', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "del loans_train, loans_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Linear Dependence of Charge-off on the Predictors\n",
    "<a id=\"7\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the training set, we compute the Pearson correlation, $F$-statistic, and $p$ value of each predictor with the response variable `charged_off`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_dep = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pearson correlations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in X_train.columns:\n",
    "    linear_dep.loc[col, 'pearson_corr'] = X_train[col].corr(y_train)\n",
    "linear_dep['abs_pearson_corr'] = abs(linear_dep['pearson_corr'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$F$-statistics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import f_classif\n",
    "for col in X_train.columns:\n",
    "    mask = X_train[col].notnull()\n",
    "    (linear_dep.loc[col, 'F'], linear_dep.loc[col, 'p_value']) = f_classif(pd.DataFrame(X_train.loc[mask, col]), y_train.loc[mask])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sort the results by the absolute value of the Pearson correlation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_dep.sort_values('abs_pearson_corr', ascending=False, inplace=True)\n",
    "linear_dep.drop('abs_pearson_corr', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reset the index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_dep.reset_index(inplace=True)\n",
    "linear_dep.rename(columns={'index':'variable'}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "View the results for the top 20 predictors most correlated with `charged_off`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>pearson_corr</th>\n",
       "      <th>F</th>\n",
       "      <th>p_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>int_rate</td>\n",
       "      <td>0.256691</td>\n",
       "      <td>51570.855220</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>term</td>\n",
       "      <td>0.188604</td>\n",
       "      <td>26965.820737</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fico_score</td>\n",
       "      <td>-0.139928</td>\n",
       "      <td>14600.790875</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dti</td>\n",
       "      <td>0.131005</td>\n",
       "      <td>12766.403302</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mort_acc</td>\n",
       "      <td>-0.076676</td>\n",
       "      <td>4044.121007</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>log_annual_inc</td>\n",
       "      <td>-0.074252</td>\n",
       "      <td>4053.225270</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>sub_grade_A4</td>\n",
       "      <td>-0.069089</td>\n",
       "      <td>3506.566794</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>revol_util</td>\n",
       "      <td>0.066569</td>\n",
       "      <td>3252.333239</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sub_grade_A5</td>\n",
       "      <td>-0.066226</td>\n",
       "      <td>3220.711982</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>loan_amnt</td>\n",
       "      <td>0.063598</td>\n",
       "      <td>2969.118688</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>home_ownership_RENT</td>\n",
       "      <td>0.063576</td>\n",
       "      <td>2967.107649</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>sub_grade_A2</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>2891.694037</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>sub_grade_A3</td>\n",
       "      <td>-0.062051</td>\n",
       "      <td>2825.878474</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>sub_grade_E2</td>\n",
       "      <td>0.060711</td>\n",
       "      <td>2704.674296</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>sub_grade_E3</td>\n",
       "      <td>0.060043</td>\n",
       "      <td>2645.273355</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>sub_grade_E4</td>\n",
       "      <td>0.058985</td>\n",
       "      <td>2552.541431</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>sub_grade_E5</td>\n",
       "      <td>0.057718</td>\n",
       "      <td>2443.729458</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>sub_grade_B1</td>\n",
       "      <td>-0.057400</td>\n",
       "      <td>2416.802596</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>verification_status_Verified</td>\n",
       "      <td>0.056540</td>\n",
       "      <td>2344.706287</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>sub_grade_E1</td>\n",
       "      <td>0.056529</td>\n",
       "      <td>2343.731745</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        variable  pearson_corr             F  p_value\n",
       "0                       int_rate      0.256691  51570.855220      0.0\n",
       "1                           term      0.188604  26965.820737      0.0\n",
       "2                     fico_score     -0.139928  14600.790875      0.0\n",
       "3                            dti      0.131005  12766.403302      0.0\n",
       "4                       mort_acc     -0.076676   4044.121007      0.0\n",
       "5                 log_annual_inc     -0.074252   4053.225270      0.0\n",
       "6                   sub_grade_A4     -0.069089   3506.566794      0.0\n",
       "7                     revol_util      0.066569   3252.333239      0.0\n",
       "8                   sub_grade_A5     -0.066226   3220.711982      0.0\n",
       "9                      loan_amnt      0.063598   2969.118688      0.0\n",
       "10           home_ownership_RENT      0.063576   2967.107649      0.0\n",
       "11                  sub_grade_A2     -0.062767   2891.694037      0.0\n",
       "12                  sub_grade_A3     -0.062051   2825.878474      0.0\n",
       "13                  sub_grade_E2      0.060711   2704.674296      0.0\n",
       "14                  sub_grade_E3      0.060043   2645.273355      0.0\n",
       "15                  sub_grade_E4      0.058985   2552.541431      0.0\n",
       "16                  sub_grade_E5      0.057718   2443.729458      0.0\n",
       "17                  sub_grade_B1     -0.057400   2416.802596      0.0\n",
       "18  verification_status_Verified      0.056540   2344.706287      0.0\n",
       "19                  sub_grade_E1      0.056529   2343.731745      0.0"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_dep.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The variables most linearly correlated with `charged_off` are the interest rate, loan period (term), FICO score, debt-to-income ratio, number of mortgages, income, the loan grade, and the loan amount.\n",
    "\n",
    "Now view the results for the 20 least correlated predictors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>pearson_corr</th>\n",
       "      <th>F</th>\n",
       "      <th>p_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>addr_state_ND</td>\n",
       "      <td>0.002876</td>\n",
       "      <td>6.048675</td>\n",
       "      <td>0.013917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>addr_state_KY</td>\n",
       "      <td>0.002838</td>\n",
       "      <td>5.890675</td>\n",
       "      <td>0.015222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>sub_grade_C2</td>\n",
       "      <td>0.002769</td>\n",
       "      <td>5.607547</td>\n",
       "      <td>0.017884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>addr_state_MD</td>\n",
       "      <td>0.002663</td>\n",
       "      <td>5.184521</td>\n",
       "      <td>0.022789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>purpose_renewable_energy</td>\n",
       "      <td>0.002634</td>\n",
       "      <td>5.070839</td>\n",
       "      <td>0.024332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>addr_state_SD</td>\n",
       "      <td>0.002580</td>\n",
       "      <td>4.866787</td>\n",
       "      <td>0.027379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>addr_state_ME</td>\n",
       "      <td>-0.002400</td>\n",
       "      <td>4.212869</td>\n",
       "      <td>0.040119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>addr_state_ID</td>\n",
       "      <td>0.002174</td>\n",
       "      <td>3.455750</td>\n",
       "      <td>0.063033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>addr_state_AZ</td>\n",
       "      <td>-0.002092</td>\n",
       "      <td>3.201519</td>\n",
       "      <td>0.073570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>purpose_educational</td>\n",
       "      <td>-0.001986</td>\n",
       "      <td>2.884806</td>\n",
       "      <td>0.089419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>addr_state_RI</td>\n",
       "      <td>-0.001603</td>\n",
       "      <td>1.879509</td>\n",
       "      <td>0.170390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>addr_state_MI</td>\n",
       "      <td>0.001580</td>\n",
       "      <td>1.823772</td>\n",
       "      <td>0.176865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>home_ownership_OTHER</td>\n",
       "      <td>-0.001233</td>\n",
       "      <td>1.110832</td>\n",
       "      <td>0.291902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>addr_state_MN</td>\n",
       "      <td>0.001140</td>\n",
       "      <td>0.950030</td>\n",
       "      <td>0.329712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>addr_state_VA</td>\n",
       "      <td>0.001014</td>\n",
       "      <td>0.750969</td>\n",
       "      <td>0.386170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>log_revol_bal</td>\n",
       "      <td>-0.000803</td>\n",
       "      <td>0.471145</td>\n",
       "      <td>0.492461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>addr_state_IA</td>\n",
       "      <td>-0.000511</td>\n",
       "      <td>0.190680</td>\n",
       "      <td>0.662351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>addr_state_DE</td>\n",
       "      <td>0.000506</td>\n",
       "      <td>0.187470</td>\n",
       "      <td>0.665031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>addr_state_HI</td>\n",
       "      <td>0.000170</td>\n",
       "      <td>0.021066</td>\n",
       "      <td>0.884599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>purpose_vacation</td>\n",
       "      <td>-0.000074</td>\n",
       "      <td>0.003921</td>\n",
       "      <td>0.950072</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     variable  pearson_corr         F   p_value\n",
       "100             addr_state_ND      0.002876  6.048675  0.013917\n",
       "101             addr_state_KY      0.002838  5.890675  0.015222\n",
       "102              sub_grade_C2      0.002769  5.607547  0.017884\n",
       "103             addr_state_MD      0.002663  5.184521  0.022789\n",
       "104  purpose_renewable_energy      0.002634  5.070839  0.024332\n",
       "105             addr_state_SD      0.002580  4.866787  0.027379\n",
       "106             addr_state_ME     -0.002400  4.212869  0.040119\n",
       "107             addr_state_ID      0.002174  3.455750  0.063033\n",
       "108             addr_state_AZ     -0.002092  3.201519  0.073570\n",
       "109       purpose_educational     -0.001986  2.884806  0.089419\n",
       "110             addr_state_RI     -0.001603  1.879509  0.170390\n",
       "111             addr_state_MI      0.001580  1.823772  0.176865\n",
       "112      home_ownership_OTHER     -0.001233  1.110832  0.291902\n",
       "113             addr_state_MN      0.001140  0.950030  0.329712\n",
       "114             addr_state_VA      0.001014  0.750969  0.386170\n",
       "115             log_revol_bal     -0.000803  0.471145  0.492461\n",
       "116             addr_state_IA     -0.000511  0.190680  0.662351\n",
       "117             addr_state_DE      0.000506  0.187470  0.665031\n",
       "118             addr_state_HI      0.000170  0.021066  0.884599\n",
       "119          purpose_vacation     -0.000074  0.003921  0.950072"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_dep.tail(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like the borrower's state of residence, the revolving balance, and several of the loan purposes are irrelevant for predicting charge-off."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "52309b77-d97d-412a-b20f-da81f44a486f",
    "_uuid": "bbabe5a3810756b27f10e862f3d7ea34a21a20f8"
   },
   "source": [
    "# 8. Model Training and Testing\n",
    "<a id=\"8\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We implement machine learning pipelines consisting of one or more of the following steps, depending on the particular model:\n",
    "1. Mean imputation of missing values\n",
    "2. Dimension reduction using linear discriminant analysis (LDA)\n",
    "3. Data standardization: rescaling to zero mean and unit variance\n",
    "4. The chosen model\n",
    "\n",
    "We will evaluate and compare the following models using a cross-validated AUROC score on the training set:\n",
    "1. Logistic regression with SGD training\n",
    "2. Random forest\n",
    "3. k-nearest neighbors\n",
    "\n",
    "We'll perform some hyperparameter tuning for each model to choose the most promising model, then more carefully tune the hyperparameters of the best-performing model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import Imputer, StandardScaler\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.1 Logistic regression with SGD training\n",
    "<a id=\"8.1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "fc774c42-47e6-427a-ac9c-a4bdbf7b5837",
    "_uuid": "39ac0b9f118cbb5b11d6749f2ec3f8aa3f97ac6d"
   },
   "source": [
    "The `SGDClassifier` estimator in scikit-learn implements linear classifiers (SVM, logistic regression, and others) with stochastic gradient descent (SGD) training. A particular linear classifier is chosen through the `loss` hyperparameter. Because we want to predict the probability of charge-off, we choose logistic regression (a probabilistic classifier) by setting `loss = 'log'`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import SGDClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The machine learning pipeline:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline_sgdlogreg = Pipeline([\n",
    "    ('imputer', Imputer(copy=False)), # Mean imputation by default\n",
    "    ('scaler', StandardScaler(copy=False)),\n",
    "    ('model', SGDClassifier(loss='log', max_iter=1000, tol=1e-3, random_state=1, warm_start=True))\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A small grid of hyperparameters to search over:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid_sgdlogreg = {\n",
    "    'model__alpha': [10**-5, 10**-2, 10**1],\n",
    "    'model__penalty': ['l1', 'l2']\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the search grid object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "_cell_guid": "c016e122-86dd-4c96-88c2-a901fe48cf70",
    "_uuid": "8144ec0a936a93a1fbf5e2dd00cd0082dbc40782"
   },
   "outputs": [],
   "source": [
    "grid_sgdlogreg = GridSearchCV(estimator=pipeline_sgdlogreg, param_grid=param_grid_sgdlogreg, scoring='roc_auc', n_jobs=-1, pre_dispatch='2*n_jobs', cv=5, verbose=1, return_train_score=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conduct the grid search and train the final model on the whole dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "_cell_guid": "b907b40c-402d-4d4a-a919-e4b63e0a832d",
    "_uuid": "76e6c2e828e670a1494d4d4fbec01f232f067512"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  30 out of  30 | elapsed:  3.3min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=Pipeline(memory=None,\n",
       "     steps=[('imputer', Imputer(axis=0, copy=False, missing_values='NaN', strategy='mean', verbose=0)), ('scaler', StandardScaler(copy=False, with_mean=True, with_std=True)), ('model', SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,\n",
       "       e...ty='l2', power_t=0.5, random_state=1, shuffle=True,\n",
       "       tol=0.001, verbose=0, warm_start=True))]),\n",
       "       fit_params=None, iid=True, n_jobs=-1,\n",
       "       param_grid={'model__alpha': [1e-05, 0.01, 10], 'model__penalty': ['l1', 'l2']},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "       scoring='roc_auc', verbose=1)"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_sgdlogreg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mean cross-validated AUROC score of the best model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7138651282339733"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_sgdlogreg.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "67901f97-33dc-4ff0-8968-127eda9da452",
    "_uuid": "a6829cf727f13a804180637da8cfc8477754b8c6"
   },
   "source": [
    "Best hyperparameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "_cell_guid": "14983fb4-d45a-431d-9c03-20d737862cc7",
    "_uuid": "fffc4de31000514e733ccd7c8f0aac9306226644"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'model__alpha': 0.01, 'model__penalty': 'l2'}"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_sgdlogreg.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.2 Random forest classifier\n",
    "<a id=\"8.2\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "fc774c42-47e6-427a-ac9c-a4bdbf7b5837",
    "_uuid": "39ac0b9f118cbb5b11d6749f2ec3f8aa3f97ac6d"
   },
   "source": [
    "Next we train a random forest model. Note that data standardization is not necessary for a random forest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline_rfc = Pipeline([\n",
    "    ('imputer', Imputer(copy=False)),\n",
    "    ('model', RandomForestClassifier(n_jobs=-1, random_state=1))\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The random forest takes very long to train, so we don't test different hyperparameter choices. We'll still use `GridSearchCV` for the sake of consistency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid_rfc = {\n",
    "    'model__n_estimators': [50] # The number of randomized trees to build\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The AUROC will always improve (with decreasing gains) as the number of estimators increases, but it's not necessarily worth the extra training time and model complexity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "_cell_guid": "c016e122-86dd-4c96-88c2-a901fe48cf70",
    "_uuid": "8144ec0a936a93a1fbf5e2dd00cd0082dbc40782"
   },
   "outputs": [],
   "source": [
    "grid_rfc = GridSearchCV(estimator=pipeline_rfc, param_grid=param_grid_rfc, scoring='roc_auc', n_jobs=-1, pre_dispatch='2*n_jobs', cv=5, verbose=1, return_train_score=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "_cell_guid": "b907b40c-402d-4d4a-a919-e4b63e0a832d",
    "_uuid": "76e6c2e828e670a1494d4d4fbec01f232f067512"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed:  6.3min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=Pipeline(memory=None,\n",
       "     steps=[('imputer', Imputer(axis=0, copy=False, missing_values='NaN', strategy='mean', verbose=0)), ('model', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_im...stimators=10, n_jobs=-1,\n",
       "            oob_score=False, random_state=1, verbose=0, warm_start=False))]),\n",
       "       fit_params=None, iid=True, n_jobs=-1,\n",
       "       param_grid={'model__n_estimators': [50]}, pre_dispatch='2*n_jobs',\n",
       "       refit=True, return_train_score=False, scoring='roc_auc', verbose=1)"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_rfc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mean cross-validated AUROC score of the random forest:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6972644945776202"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_rfc.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not quite as good as logistic regression, at least according to this metric."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.3 k-nearest neighbors\n",
    "<a id=\"8.3\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "fc774c42-47e6-427a-ac9c-a4bdbf7b5837",
    "_uuid": "39ac0b9f118cbb5b11d6749f2ec3f8aa3f97ac6d"
   },
   "source": [
    "Next we try k-nearest neighbors. We need to reduce the number of variables to 10 or fewer ([reference](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm#Dimension_reduction)) for kNN to perform well. We'll use LDA for dimension reduction. The number of component variables to keep is a hyperparameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline_knn = Pipeline([\n",
    "    ('imputer', Imputer(copy=False)),\n",
    "    ('scaler', StandardScaler(copy=False)),\n",
    "    ('lda', LinearDiscriminantAnalysis()),\n",
    "    ('model', KNeighborsClassifier(n_jobs=-1))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid_knn = {\n",
    "    'lda__n_components': [3, 9], # Number of LDA components to keep\n",
    "    'model__n_neighbors': [5, 25, 125] # The 'k' in k-nearest neighbors\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "_cell_guid": "c016e122-86dd-4c96-88c2-a901fe48cf70",
    "_uuid": "8144ec0a936a93a1fbf5e2dd00cd0082dbc40782"
   },
   "outputs": [],
   "source": [
    "grid_knn = GridSearchCV(estimator=pipeline_knn, param_grid=param_grid_knn, scoring='roc_auc', n_jobs=-1, pre_dispatch='2*n_jobs', cv=5, verbose=1, return_train_score=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "_cell_guid": "b907b40c-402d-4d4a-a919-e4b63e0a832d",
    "_uuid": "76e6c2e828e670a1494d4d4fbec01f232f067512"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  30 out of  30 | elapsed:  7.9min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=Pipeline(memory=None,\n",
       "     steps=[('imputer', Imputer(axis=0, copy=False, missing_values='NaN', strategy='mean', verbose=0)), ('scaler', StandardScaler(copy=False, with_mean=True, with_std=True)), ('lda', LinearDiscriminantAnalysis(n_components=None, priors=None, shrinkage=None,\n",
       "          ...ski',\n",
       "           metric_params=None, n_jobs=-1, n_neighbors=5, p=2,\n",
       "           weights='uniform'))]),\n",
       "       fit_params=None, iid=True, n_jobs=-1,\n",
       "       param_grid={'lda__n_components': [3, 9], 'model__n_neighbors': [5, 25, 125]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "       scoring='roc_auc', verbose=1)"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mean cross-validated AUROC score of the best model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7098796124703738"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_knn.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "67901f97-33dc-4ff0-8968-127eda9da452",
    "_uuid": "a6829cf727f13a804180637da8cfc8477754b8c6"
   },
   "source": [
    "Best hyperparameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "_cell_guid": "14983fb4-d45a-431d-9c03-20d737862cc7",
    "_uuid": "fffc4de31000514e733ccd7c8f0aac9306226644"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'lda__n_components': 3, 'model__n_neighbors': 125}"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_knn.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only 3 LDA components are necessary for kNN to perform almost as well as logistic regression."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "04560103-0efe-4ace-831b-be73ece4fe7b",
    "_uuid": "4476709021bd10a8e39d8f7e99e8fdf4adcb6970"
   },
   "source": [
    "## 8.4 Tune hyperparameters on the chosen model more finely\n",
    "<a id=\"8.4\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b7460548-ee72-4f66-b921-2ea68bd5db24",
    "_uuid": "edd6b1de60cf7b9da054d0259ea83e8b4e4e8719"
   },
   "source": [
    "The three models performed quite similarly according to the AUROC:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validated AUROC scores\n",
      "0.7138651282339733 - Logistic regression\n",
      "0.6972644945776202 - Random forest\n",
      "0.7098796124703738 - k-nearest neighbors\n"
     ]
    }
   ],
   "source": [
    "print('Cross-validated AUROC scores')\n",
    "print(grid_sgdlogreg.best_score_, '- Logistic regression')\n",
    "print(grid_rfc.best_score_, '- Random forest')\n",
    "print(grid_knn.best_score_, '- k-nearest neighbors')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logistic regression squeaked out ahead, and coupled with the fact that `SGDClassifier` trains much faster than the other two models, we'll select logistic regression as our final model. Now we'll tune the hyperparameters more finely."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'model__alpha': array([3.16227766e-05, 1.00000000e-04, 3.16227766e-04, 1.00000000e-03,\n",
      "       3.16227766e-03, 1.00000000e-02, 3.16227766e-02, 1.00000000e-01,\n",
      "       3.16227766e-01, 1.00000000e+00, 3.16227766e+00]), 'model__penalty': ['l1', 'l2']}\n"
     ]
    }
   ],
   "source": [
    "param_grid_sgdlogreg = {\n",
    "    'model__alpha': np.logspace(-4.5, 0.5, 11), # Fills in the gaps between 10^-5 and 10^1\n",
    "    'model__penalty': ['l1', 'l2']\n",
    "}\n",
    "\n",
    "print(param_grid_sgdlogreg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "_cell_guid": "c016e122-86dd-4c96-88c2-a901fe48cf70",
    "_uuid": "8144ec0a936a93a1fbf5e2dd00cd0082dbc40782"
   },
   "outputs": [],
   "source": [
    "grid_sgdlogreg = GridSearchCV(estimator=pipeline_sgdlogreg, param_grid=param_grid_sgdlogreg, scoring='roc_auc', n_jobs=-1, pre_dispatch='2*n_jobs', cv=5, verbose=1, return_train_score=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "_cell_guid": "b907b40c-402d-4d4a-a919-e4b63e0a832d",
    "_uuid": "76e6c2e828e670a1494d4d4fbec01f232f067512",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 22 candidates, totalling 110 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:  4.0min\n",
      "[Parallel(n_jobs=-1)]: Done 110 out of 110 | elapsed:  9.1min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=Pipeline(memory=None,\n",
       "     steps=[('imputer', Imputer(axis=0, copy=False, missing_values='NaN', strategy='mean', verbose=0)), ('scaler', StandardScaler(copy=False, with_mean=True, with_std=True)), ('model', SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,\n",
       "       e...ty='l2', power_t=0.5, random_state=1, shuffle=True,\n",
       "       tol=0.001, verbose=0, warm_start=True))]),\n",
       "       fit_params=None, iid=True, n_jobs=-1,\n",
       "       param_grid={'model__alpha': array([3.16228e-05, 1.00000e-04, 3.16228e-04, 1.00000e-03, 3.16228e-03,\n",
       "       1.00000e-02, 3.16228e-02, 1.00000e-01, 3.16228e-01, 1.00000e+00,\n",
       "       3.16228e+00]), 'model__penalty': ['l1', 'l2']},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "       scoring='roc_auc', verbose=1)"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_sgdlogreg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mean cross-validated AUROC score of the best model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7138651282339733"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_sgdlogreg.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "67901f97-33dc-4ff0-8968-127eda9da452",
    "_uuid": "a6829cf727f13a804180637da8cfc8477754b8c6"
   },
   "source": [
    "Best hyperparameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "_cell_guid": "14983fb4-d45a-431d-9c03-20d737862cc7",
    "_uuid": "fffc4de31000514e733ccd7c8f0aac9306226644"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'model__alpha': 0.01, 'model__penalty': 'l2'}"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_sgdlogreg.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By some coincidence, the optimal hyperparameters here are the same as those from our first grid search for logistic regression!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "61ffd538-9579-4638-8156-bcb3f29b39ab",
    "_uuid": "f3b87036a9ae078b8a9cc36ce5b47f734059358c"
   },
   "source": [
    "## 8.5 Test set evaluation\n",
    "<a id=\"8.5\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can finally see how our chosen model performs on the test data (the most recent 10% of the loans)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6893909107572427"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_score = grid_sgdlogreg.predict_proba(X_test)[:,1]\n",
    "roc_auc_score(y_test, y_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The test set AUROC score is somewhat lower than the cross-validated score (0.713)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. Conclusion\n",
    "<a id=\"9\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We applied machine learning methods to predict the probability that a requested loan on LendingClub will charge off. After training and evaluating three different models (logistic regression, random forest, and k-nearest neighbors), we found that all three performed similarly according to a cross-validated AUROC score on the training data. We selected logistic regression (with ridge penalty) because it was the fastest model to train, and this model obtained an AUROC score of 0.689 on a test set consisting of the most recent 10% of the loans.\n",
    "\n",
    "This model, while far from perfect, can provide a somewhat informed prediction of the likelihood that a loan will charge off, using only data available to potential investors before the loan is fully funded.\n",
    "\n",
    "We also found that, according to the Pearson correlations between the predictors and the response, the most important variables for predicting charge-off are the loan interest rate and term, and the borrower's FICO score and debt-to-income ratio."
   ]
  }
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