EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from data_dictionary import desc # descriptions for each column\n",
    "import seaborn as sns\n",
    "\n",
    "# maps\n",
    "import geoplot as gplt\n",
    "import geopandas as gpd\n",
    "import geoplot.crs as gcrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16260, 62)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"train1.csv\")\n",
    "df[\"issue_d\"] = pd.to_datetime(df[\"issue_d\"], format=\"%Y-%m-%d\")\n",
    "df[\"earliest_cr_line\"] = pd.to_datetime(df[\"earliest_cr_line\"], format=\"%Y-%m-%d\")\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>issue_d</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>purpose</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>addr_state</th>\n",
       "      <th>dti</th>\n",
       "      <th>...</th>\n",
       "      <th>num_tl_90g_dpd_24m</th>\n",
       "      <th>num_tl_op_past_12m</th>\n",
       "      <th>pct_tl_nvr_dlq</th>\n",
       "      <th>percent_bc_gt_75</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>tot_hi_cred_lim</th>\n",
       "      <th>total_bal_ex_mort</th>\n",
       "      <th>total_bc_limit</th>\n",
       "      <th>total_il_high_credit_limit</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5300.0</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>RENT</td>\n",
       "      <td>34750.0</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>959xx</td>\n",
       "      <td>CA</td>\n",
       "      <td>12.47</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15900.0</td>\n",
       "      <td>12283.0</td>\n",
       "      <td>12100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1575.849997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9000.0</td>\n",
       "      <td>2 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>2012-09-01</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>986xx</td>\n",
       "      <td>WA</td>\n",
       "      <td>22.44</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>94311.0</td>\n",
       "      <td>71192.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>72511.0</td>\n",
       "      <td>307.923808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12000.0</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>95000.0</td>\n",
       "      <td>2012-10-01</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>900xx</td>\n",
       "      <td>CA</td>\n",
       "      <td>16.12</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>33.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>79780.0</td>\n",
       "      <td>47431.0</td>\n",
       "      <td>29100.0</td>\n",
       "      <td>44180.0</td>\n",
       "      <td>262.697370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5500.0</td>\n",
       "      <td>8 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>2012-11-01</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>medical</td>\n",
       "      <td>734xx</td>\n",
       "      <td>OK</td>\n",
       "      <td>27.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20530.0</td>\n",
       "      <td>7069.0</td>\n",
       "      <td>7300.0</td>\n",
       "      <td>6430.0</td>\n",
       "      <td>1298.946425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6000.0</td>\n",
       "      <td>2 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>2012-11-01</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>346xx</td>\n",
       "      <td>FL</td>\n",
       "      <td>25.07</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100804.0</td>\n",
       "      <td>65137.0</td>\n",
       "      <td>28800.0</td>\n",
       "      <td>66504.0</td>\n",
       "      <td>-3519.260000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt emp_length home_ownership  annual_inc    issue_d  loan_status  \\\n",
       "0     5300.0   < 1 year           RENT     34750.0 2012-12-01   Fully Paid   \n",
       "1     9000.0    2 years           RENT     60000.0 2012-09-01   Fully Paid   \n",
       "2    12000.0  10+ years           RENT     95000.0 2012-10-01   Fully Paid   \n",
       "3     5500.0    8 years           RENT     20000.0 2012-11-01   Fully Paid   \n",
       "4     6000.0    2 years           RENT    110000.0 2012-11-01  Charged Off   \n",
       "\n",
       "              purpose zip_code addr_state    dti  ...  num_tl_90g_dpd_24m  \\\n",
       "0         credit_card    959xx         CA  12.47  ...                 0.0   \n",
       "1         credit_card    986xx         WA  22.44  ...                 0.0   \n",
       "2  debt_consolidation    900xx         CA  16.12  ...                 0.0   \n",
       "3             medical    734xx         OK  27.00  ...                 0.0   \n",
       "4         credit_card    346xx         FL  25.07  ...                 1.0   \n",
       "\n",
       "  num_tl_op_past_12m  pct_tl_nvr_dlq  percent_bc_gt_75  pub_rec_bankruptcies  \\\n",
       "0                0.0           100.0             100.0                   0.0   \n",
       "1                5.0            97.0              50.0                   0.0   \n",
       "2                1.0           100.0              33.3                   0.0   \n",
       "3                7.0           100.0              75.0                   0.0   \n",
       "4                0.0            88.0             100.0                   0.0   \n",
       "\n",
       "   tot_hi_cred_lim  total_bal_ex_mort  total_bc_limit  \\\n",
       "0          15900.0            12283.0         12100.0   \n",
       "1          94311.0            71192.0          8000.0   \n",
       "2          79780.0            47431.0         29100.0   \n",
       "3          20530.0             7069.0          7300.0   \n",
       "4         100804.0            65137.0         28800.0   \n",
       "\n",
       "   total_il_high_credit_limit       profit  \n",
       "0                         0.0  1575.849997  \n",
       "1                     72511.0   307.923808  \n",
       "2                     44180.0   262.697370  \n",
       "3                      6430.0  1298.946425  \n",
       "4                     66504.0 -3519.260000  \n",
       "\n",
       "[5 rows x 62 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In `Preprocessing 1.ipynb` we narrowed the number of columns from 151 down to 63. For our EDA it will help to group columns into categories and explore each category."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorizing columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['loan_amnt', 'int_rate', 'installment', 'emp_length',\n",
       "       'home_ownership', 'annual_inc', 'issue_d', 'loan_status',\n",
       "       'purpose', 'zip_code', 'addr_state', 'dti', 'delinq_2yrs',\n",
       "       'earliest_cr_line', 'fico_range_low', 'fico_range_high',\n",
       "       'inq_last_6mths', 'mths_since_last_delinq',\n",
       "       'mths_since_last_record', 'open_acc', 'pub_rec', 'revol_bal',\n",
       "       'revol_util', 'total_acc', 'initial_list_status',\n",
       "       'mths_since_last_major_derog', 'tot_coll_amt', 'tot_cur_bal',\n",
       "       'total_rev_hi_lim', 'acc_open_past_24mths', 'avg_cur_bal',\n",
       "       'bc_open_to_buy', 'bc_util', 'mo_sin_old_il_acct',\n",
       "       'mo_sin_old_rev_tl_op', 'mo_sin_rcnt_rev_tl_op', 'mo_sin_rcnt_tl',\n",
       "       'mort_acc', 'mths_since_recent_bc', 'mths_since_recent_bc_dlq',\n",
       "       'mths_since_recent_inq', 'mths_since_recent_revol_delinq',\n",
       "       'num_accts_ever_120_pd', 'num_actv_bc_tl', 'num_actv_rev_tl',\n",
       "       'num_bc_sats', 'num_bc_tl', 'num_il_tl', 'num_op_rev_tl',\n",
       "       'num_rev_accts', 'num_rev_tl_bal_gt_0', 'num_sats',\n",
       "       'num_tl_120dpd_2m', 'num_tl_30dpd', 'num_tl_90g_dpd_24m',\n",
       "       'num_tl_op_past_12m', 'pct_tl_nvr_dlq', 'percent_bc_gt_75',\n",
       "       'pub_rec_bankruptcies', 'tot_hi_cred_lim', 'total_bal_ex_mort',\n",
       "       'total_bc_limit', 'total_il_high_credit_limit'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['profit']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Target for prediction -- string\n",
    "target = ['loan_status']\n",
    "\n",
    "# Loan related -- float, string, & datetime\n",
    "loan_cols = ['loan_amnt', 'issue_d', 'purpose', 'initial_list_status']\n",
    "\n",
    "# Customer related -- float & string\n",
    "cust_cols = ['emp_length', 'home_ownership', 'annual_inc', 'zip_code', 'addr_state', 'dti']\n",
    "\n",
    "# Credit related (basic credit info) -- float & datetime\n",
    "cred_cols = ['earliest_cr_line', 'fico_range_low', 'fico_range_high']\n",
    "\n",
    "# Delinquency related -- float\n",
    "dlq_cols = ['delinq_2yrs', 'mths_since_last_delinq', 'pub_rec', 'mths_since_last_major_derog', 'tot_coll_amt', \n",
    "          'mths_since_recent_bc_dlq', 'mths_since_recent_revol_delinq', 'num_accts_ever_120_pd', 'num_tl_120dpd_2m', \n",
    "          'num_tl_30dpd', 'num_tl_90g_dpd_24m', 'pct_tl_nvr_dlq', 'pub_rec_bankruptcies']\n",
    "\n",
    "# Inquiries related -- float\n",
    "inq_cols = ['inq_last_6mths', 'mths_since_last_record', 'mths_since_recent_inq']\n",
    "\n",
    "# Accounts related -- float\n",
    "acc_cols = ['open_acc', 'total_acc', 'acc_open_past_24mths', 'mo_sin_old_il_acct', 'mo_sin_old_rev_tl_op', \n",
    "           'mo_sin_rcnt_rev_tl_op', 'mo_sin_rcnt_tl', 'mort_acc', 'mths_since_recent_bc', 'num_actv_bc_tl', \n",
    "           'num_actv_rev_tl', 'num_bc_sats', 'num_bc_tl', 'num_il_tl', 'num_op_rev_tl', 'num_rev_accts', \n",
    "           'num_rev_tl_bal_gt_0', 'num_sats', 'num_tl_op_past_12m']\n",
    "\n",
    "# Balance related -- float\n",
    "bal_cols = ['revol_bal', 'revol_util', 'tot_cur_bal', 'total_rev_hi_lim', 'avg_cur_bal', 'bc_open_to_buy', 'bc_util', \n",
    "           'percent_bc_gt_75', 'tot_hi_cred_lim', 'total_bal_ex_mort', 'total_bc_limit', 'total_il_high_credit_limit']\n",
    "\n",
    "# check to make sure we covered them all\n",
    "[x for x in df.columns if x not in target + loan_cols + cust_cols + cred_cols + dlq_cols + inq_cols + acc_cols + bal_cols]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since there are so many variables, we will explore `target`, `loan_cols`, `cust_cols`, and `cred_cols` in depth and spend less time on the others. We'll choose a few variables to focus on from the others."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Target variable\n",
    "We'll now explore `loan_status`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fully Paid     0.870234\n",
      "Charged Off    0.129766\n",
      "Name: loan_status, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAErCAYAAADOu3hxAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAQR0lEQVR4nO3de+zddX3H8eeLNggGhE3qrVBbTd3WTXD6W3GAF2QqyKRxY1nRzcyhDdlwJssymy1bMnGJzsucs9o1DI1G7dxAVrHaeAMyna4tYWAhuKYgVNwE79ONWn3vj3M6D8ff5RTOr9+ez3k+kub3+156fu8/2me//ZzLN1WFJGnyHdP1AJKk8TDoktQIgy5JjTDoktQIgy5JjTDoktSIpV394FNOOaVWrlzZ1Y+XpIm0e/fu+6tq2WzHOgv6ypUr2bVrV1c/XpImUpIvz3XMJRdJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGdPbGokmxcuNHux6hKXe94cKuR5Ca5RW6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSI0YKepLzk9yRZG+SjbMcPynJR5L8e5I9SV4x/lElSfNZMOhJlgCbgAuANcAlSdYMnfb7wG1VdQbwXOAtSY4d86ySpHmMcoW+FthbVfuq6gCwFVg3dE4BJyYJcALwDeDgWCeVJM1rlKAvB+4Z2N7f3zfoHcDPAfcCtwKvqaofjWVCSdJIRgl6ZtlXQ9svBG4GngA8DXhHkkf9xAMlG5LsSrLrvvvuO8xRJUnzGSXo+4HTBrZPpXclPugVwDXVsxe4E/jZ4Qeqqi1VNVNVM8uWLXuoM0uSZjFK0HcCq5Os6j/RuR7YNnTO3cB5AEkeC/wMsG+cg0qS5rfgPUWr6mCSy4EdwBLgqqrak+Sy/vHNwBXAe5LcSm+J5rVVdf8izi1JGjLSTaKrajuwfWjf5oHv7wVeMN7RJEmHw3eKSlIjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1IjRgp6kvOT3JFkb5KNc5zz3CQ3J9mT5IbxjilJWsjShU5IsgTYBDwf2A/sTLKtqm4bOOdk4J3A+VV1d5LHLNK8kqQ5jHKFvhbYW1X7quoAsBVYN3TOS4FrqupugKr62njHlCQtZJSgLwfuGdje39836CnATyW5PsnuJC8f14CSpNEsuOQCZJZ9NcvjPAM4Dzge+Nckn6+qLz3ogZINwAaAFStWHP60kqQ5jXKFvh84bWD7VODeWc75eFV9r6ruB24Ezhh+oKraUlUzVTWzbNmyhzqzJGkWowR9J7A6yaokxwLrgW1D5/wz8KwkS5M8EjgTuH28o0qS5rPgkktVHUxyObADWAJcVVV7klzWP765qm5P8nHgFuBHwJVV9cXFHFyS9GCjrKFTVduB7UP7Ng9tvwl40/hGkyQdDt8pKkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1IiRgp7k/CR3JNmbZOM85/1Skh8muXh8I0qSRrFg0JMsATYBFwBrgEuSrJnjvDcCO8Y9pCRpYaNcoa8F9lbVvqo6AGwF1s1y3quBq4GvjXE+SdKIRgn6cuCege39/X3/L8ly4CXA5vGNJkk6HKMEPbPsq6HttwGvraofzvtAyYYku5Lsuu+++0YcUZI0iqUjnLMfOG1g+1Tg3qFzZoCtSQBOAV6U5GBVXTt4UlVtAbYAzMzMDP+jIEl6GEYJ+k5gdZJVwFeA9cBLB0+oqlWHvk/yHuC64ZhLkhbXgkGvqoNJLqf36pUlwFVVtSfJZf3jrptL0lFglCt0qmo7sH1o36whr6rfefhjSZIOl+8UlaRGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGjBT0JOcnuSPJ3iQbZzn+siS39H99LskZ4x9VkjSfBYOeZAmwCbgAWANckmTN0Gl3As+pqtOBK4At4x5UkjS/Ua7Q1wJ7q2pfVR0AtgLrBk+oqs9V1Tf7m58HTh3vmJKkhYwS9OXAPQPb+/v75nIp8LGHM5Qk6fAtHeGczLKvZj0xOZde0M+Z4/gGYAPAihUrRhxRkjSKUa7Q9wOnDWyfCtw7fFKS04ErgXVV9fXZHqiqtlTVTFXNLFu27KHMK0mawyhB3wmsTrIqybHAemDb4AlJVgDXAL9dVV8a/5iSpIUsuORSVQeTXA7sAJYAV1XVniSX9Y9vBv4ceDTwziQAB6tqZvHGliQNG2UNnaraDmwf2rd54PtXAq8c72iSpMPhO0UlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaMdKHc0k6+qzc+NGuR2jKXW+4sOsRHjav0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhoxUtCTnJ/kjiR7k2yc5XiSvL1//JYkTx//qJKk+SwY9CRLgE3ABcAa4JIka4ZOuwBY3f+1AXjXmOeUJC1glCv0tcDeqtpXVQeArcC6oXPWAe+tns8DJyd5/JhnlSTNY+kI5ywH7hnY3g+cOcI5y4GvDp6UZAO9K3iA/05yx2FNq/mcAtzf9RALyRu7nkAd8M/meD1xrgOjBD2z7KuHcA5VtQXYMsLP1GFKsquqZrqeQxrmn80jZ5Qll/3AaQPbpwL3PoRzJEmLaJSg7wRWJ1mV5FhgPbBt6JxtwMv7r3Z5JvDtqvrq8ANJkhbPgksuVXUwyeXADmAJcFVV7UlyWf/4ZmA78CJgL/B94BWLN7Lm4FKWjlb+2TxCUvUTS92SpAnkO0UlqREGXZIaYdAljUWS1/S/nt31LNPKNfQJk+TX5jteVdccqVmkQUlurqqnJbmpqvw8pw6M8sYiHV1e3P/6GOAs4NP97XOB6wGDrq7cnuQuYFmSWwb2B6iqOr2bsaaHV+gTKsl1wKsOvd6//9k5m6pq3it4aTEleRy9lzhfNHysqr585CeaLl6hT66VQ2/e+i/gKV0NIyX5VFWdl2SH8e6GQZ9c1yfZAXyQ3ufmrAc+0+1ImnKPT/Ic4MVJPsjQZzxV1U3djDU9XHKZYP0nSJ/V37yxqj7c5TyabkkuBi4FzgF2DR2uqnrekZ9quhh0SWOV5M+q6oqu55hGBn3CJPmXqjonyXd58EcUH3olwaM6Gk2i/wF+v0Xv7mYF3AZ8oKoe6HSwKWHQJY1F/9aU24DPArvpXWQ8HTgbWFdVezocbyoY9AmX5DHAcYe2q+ruDsfRFEvyKeANVfWJof2/AvxpVZ3bzWTTw7f+T6gkFyX5D+BO4AbgLuBjnQ6labd8OOYAVfVJ4HEdzDN1DPrkugJ4JvClqloFnEfvv7pSV45J8ojhnUmOw5dIHxEGfXL9oKq+Tu8v0TFV9RngaR3PpOn2XuDqJCsP7eh//yHgfR3NNFX8V3NyfSvJCcCNwPuTfA042PFMmmJV9fr+3c1uTPLI/u7vAW+uqr/tcLSp4ZOiE6r/F+Z/6f0v62XAScD7+1ftUqeSnAhQVd/tepZpYtAnTJIz6d2j8cnArcClVXVbt1NJOhq4hj55NgF/BDwaeCvw192OI+loYdAnzzFV9YmqeqCq/hFY1vVAko4OPik6eU4eumvRg7a9Y5G64t20uuca+oRJ8u55DldV/e4RG0YaMPBnc9a7aXnzlcVn0CWNlXfT6o5r6JLGzbtpdcQ1dEnj5t20OuKSi6SxS/IS4Nn9Te+mdYQY9AmVZBfwbno3D/hm1/NIg5I8EVhdVZ/sv6t5ie8aXXyuoU+u9cATgJ1JtiZ5YZIs9JukxZbkVcA/AX/X37UcuLazgaaIV+gTLskxwK8C7wJ+BFwF/E1VfaPTwTS1ktwMrAW+UFW/2N93a1U9tdPBpoBX6BMsyenAW4A3AVcDFwPf4cev/5W68EBVHTi0kWQpD77/rRaJr3KZUEl2A98C/h7YOHAT3i8kObuzwSS4IcmfAMcneT7we8BHOp5pKrjkMqGSPKmq9nU9hzSsvwx4KfACejeK3gFcWcZm0Rn0CZPkD+c7XlVvPVKzSDq6uOQyeU7segBpPklu5SfXzL8N7AJe701YFo9X6JLGKslfAT8EPtDftb7/9TvAOVX14k4GmwIGfcIkeft8x6vqD47ULNJskny2qs6ebZ8vX1xcLrlMnt1dDyAt4IQkZ1bVFwCSrAVO6B/zRuaLyCt0SWOVZIbex1Icivh36b3q5Tbgwqr6UFeztc4r9AmV5DPM8maNqnpeB+NIACRZAjyrqp6a5CR6F43fGjjFmC8ir9AnVJJnDGweB/w6cLCq/rijkSQAklxfVc/teo5pZNAbkuSGqnpO13NouiX5S+Ak4B+A7x3aX1U3dTbUlHDJZUIl+emBzWOAZwCP62gcadBZ/a+vG9hXgMuBi8ygT67BV7scBO6k98ST1KmqOrfrGaaVSy4TJsmKqrq76zmk+SS5EPh5es/vAFBVr5v7d2gc/PjcyXPtoW+SXN3hHNKskmwGfhN4Nb0P5/oN4ImdDjUlDPrkGbwr0ZM6m0Ka21lV9XLgm1X1F8AvA6d1PNNUMOiTp+b4Xjpa/E//6/eTPAH4AbCqw3mmhk+KTp4zknyH3pX68f3v6W9XVT2qu9EkAK5LcjK9O2ndRO/C48pOJ5oSPikqadEkeQRwXFV9u+tZpoFBlzR2Sc4CVjKwClBV7+1soCnhkouksUryPuDJwM30PhcdessuBn2ReYUuaayS3A6s8R6iR56vcpE0bl/Ej6HohEsuksYiyUfoLa2cCNyW5N+ABw4dr6qLupptWhh0SePy5q4HmHYGXdK4fAV4bFV9dnBnkmf3j2mRuYYuaVzeRu92c8O+3z+mRWbQJY3Lyqq6ZXhnVe2i95p0LTKDLmlcjpvn2PFHbIopZtAljcvOJK8a3pnkUh58QxYtEt9YJGkskjwW+DBwgB8HfAY4FnhJVf1nV7NNC4MuaaySnAv8Qn9zT1V9ust5polBl6RGuIYuSY0w6JLUCIMuSY0w6JLUCIMuSY34Pwl3HDKRIXaHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts = df[\"loan_status\"].value_counts(normalize=True)\n",
    "print(counts)\n",
    "counts.plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.706161137440759"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts[\"Fully Paid\"]/counts[\"Charged Off\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see 87% of loans were fully paid while 13% were charged off (a ratio of 6.7:1). This is seems like a relatively large charge off rate, although it is hard to compare with other data because charge off rates are more often defined as a ratio of dollar amounts rather than raw number of loans (e.g. https://www.federalreserve.gov/releases/chargeoff/about.htm).\n",
    "\n",
    "We will need to consider class imbalance when modeling our data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loan columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['loan_amnt: 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.',\n",
       " 'issue_d: The month which the loan was funded',\n",
       " 'purpose: A category provided by the borrower for the loan request. ',\n",
       " 'initial_list_status: The initial listing status of the loan. Possible values are – W, F']"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[x + \": \" + desc[x] for x in loan_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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>issue_d</th>\n",
       "      <th>purpose</th>\n",
       "      <th>initial_list_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5300.0</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>w</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9000.0</td>\n",
       "      <td>2012-09-01</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12000.0</td>\n",
       "      <td>2012-10-01</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5500.0</td>\n",
       "      <td>2012-11-01</td>\n",
       "      <td>medical</td>\n",
       "      <td>w</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6000.0</td>\n",
       "      <td>2012-11-01</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt    issue_d             purpose initial_list_status\n",
       "0     5300.0 2012-12-01         credit_card                   w\n",
       "1     9000.0 2012-09-01         credit_card                   f\n",
       "2    12000.0 2012-10-01  debt_consolidation                   f\n",
       "3     5500.0 2012-11-01             medical                   w\n",
       "4     6000.0 2012-11-01         credit_card                   f"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[loan_cols].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"loan_amnt\"].plot(kind=\"hist\", bins=20, edgecolor=\"black\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    16260.000000\n",
       "mean     11883.673124\n",
       "std       7064.807027\n",
       "min       1000.000000\n",
       "25%       6800.000000\n",
       "50%      10000.000000\n",
       "75%      15000.000000\n",
       "max      35000.000000\n",
       "Name: loan_amnt, dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"loan_amnt\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loans range from \\\\$1,000 to \\\\$35,000 with a median of \\\\$10,000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[[\"loan_amnt\",\"loan_status\"]].boxplot(column=\"loan_amnt\", by=\"loan_status\")\n",
    "plt.title(\"\") # hide title"
   ]
  },
  {
   "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>loan_amnt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Charged Off</th>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fully Paid</th>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             loan_amnt\n",
       "loan_status           \n",
       "Charged Off    10000.0\n",
       "Fully Paid     10000.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"loan_amnt\",\"loan_status\"]].groupby(\"loan_status\").median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loan amount seems to be similarly distributed across outcomes, suggesting that it might not be useful in modeling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "issue_d\n",
       "2012-09-30    0.251169\n",
       "2012-10-31    0.256089\n",
       "2012-11-30    0.254059\n",
       "2012-12-31    0.238684\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index(\"issue_d\").groupby(pd.Grouper(freq=\"M\")).size()/df.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall that we limited our training data to reduce the missing value rates, so our origination window now only spans Sept. 2012 to Dec. 2012. The number of loans in each month is approximately even."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <th>Fully Paid</th>\n",
       "      <th>Charged Off</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>issue_d</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-09-30</th>\n",
       "      <td>0.861166</td>\n",
       "      <td>0.138834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-31</th>\n",
       "      <td>0.872238</td>\n",
       "      <td>0.127762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-30</th>\n",
       "      <td>0.871218</td>\n",
       "      <td>0.128782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31</th>\n",
       "      <td>0.876578</td>\n",
       "      <td>0.123422</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "loan_status Fully Paid Charged Off\n",
       "issue_d                           \n",
       "2012-09-30    0.861166    0.138834\n",
       "2012-10-31    0.872238    0.127762\n",
       "2012-11-30    0.871218    0.128782\n",
       "2012-12-31    0.876578    0.123422"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"issue_d\",\"loan_status\"]].set_index(\"issue_d\").groupby(pd.Grouper(freq=\"M\")).apply(lambda x: x.value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's hard to conclude much from only four time points, but in our sample it does not look like origination date is related to outcome."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"purpose\"].value_counts(normalize=True, ascending=True).plot(kind=\"barh\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The majority (approx. 60%) of loans are for debt consolidation and over 20% are for credit cards. The remainding categories represent less than 20% of the data, and it may be worth merging these into a single category called \"other\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pt = pd.pivot_table(df[[\"purpose\",\"loan_status\"]], columns=\"loan_status\", index=\"purpose\", aggfunc = len)\n",
    "(pt/pt.sum(axis=1).values.reshape(-1,1)).plot(kind=\"barh\")\n",
    "plt.xlabel(\"Fraction of loans in category\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"images/loan_purpose.png\", )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loans for debt consolidation have a higher charge off rate than loans for credit card purposes. Loans for renewable energy and small businesses have high charge off rates, presumably because the borrowers cause ends up failing, whereas loans for a major purchase or car half lower charge off rates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now investigate initial list status -- which can either be \"f\" for fractional (meaning multiple investors can get in on the loan) or \"w\" for whole (one lender must fund the whole loan)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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r8tcWJWlL+n8u5BXArcBXgTOs33rRNjLokrbD04C/AO6rqrVpD/PDwlsuktQIfykqSY0w6JLUCIMuSY0w6JLUiP8DhTe4F21iJB4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"initial_list_status\"].value_counts(normalize=True).plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Over 80% of loans are in the fractional market."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='initial_list_status'>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pt = pd.pivot_table(df[[\"initial_list_status\",\"loan_status\"]], columns=\"loan_status\", index=\"initial_list_status\", aggfunc = len)\n",
    "(pt/pt.sum(axis=1).values.reshape(-1,1)).plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loan outcome is fairly even across categories."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customer columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['emp_length: 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. ',\n",
       " 'home_ownership: The home ownership status provided by the borrower during registration\\xa0or obtained from the credit report.\\xa0Our values are: RENT, OWN, MORTGAGE, OTHER',\n",
       " 'annual_inc: The self-reported annual income provided by the borrower during registration.',\n",
       " 'zip_code: The first 3 numbers of the zip code provided by the borrower in the loan application.',\n",
       " 'addr_state: The state provided by the borrower in the loan application',\n",
       " 'dti: 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.']"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[x + \": \" + desc[x] for x in cust_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>addr_state</th>\n",
       "      <th>dti</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>RENT</td>\n",
       "      <td>34750.0</td>\n",
       "      <td>959xx</td>\n",
       "      <td>CA</td>\n",
       "      <td>12.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>986xx</td>\n",
       "      <td>WA</td>\n",
       "      <td>22.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>95000.0</td>\n",
       "      <td>900xx</td>\n",
       "      <td>CA</td>\n",
       "      <td>16.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>734xx</td>\n",
       "      <td>OK</td>\n",
       "      <td>27.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>346xx</td>\n",
       "      <td>FL</td>\n",
       "      <td>25.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  emp_length home_ownership  annual_inc zip_code addr_state    dti\n",
       "0   < 1 year           RENT     34750.0    959xx         CA  12.47\n",
       "1    2 years           RENT     60000.0    986xx         WA  22.44\n",
       "2  10+ years           RENT     95000.0    900xx         CA  16.12\n",
       "3    8 years           RENT     20000.0    734xx         OK  27.00\n",
       "4    2 years           RENT    110000.0    346xx         FL  25.07"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[cust_cols].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_order = ['< 1 year', '1 year', '2 years', '3 years', '4 years', '5 years', '6 years', '7 years', \n",
    "             '8 years', '9 years', '10+ years'][::-1] # reorder indices\n",
    "\n",
    "df[\"emp_length\"].value_counts(normalize=True)[bar_order].plot(kind=\"barh\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most borrowers have been employed for 10+ years. The remainding categories each represent only 5-10% of loans."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='emp_length'>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pt = pd.pivot_table(df[[\"emp_length\",\"loan_status\"]], columns=\"loan_status\", index=\"emp_length\", aggfunc = len)\n",
    "(pt/pt.sum(axis=1).values.reshape(-1,1)).loc[bar_order].plot(kind=\"barh\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interestingly, there aren't an patterns in loan outcome and employer length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"home_ownership\"].value_counts(normalize=True).plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only 10% of borrowers own a house. About 45% of borrowers have a mortgage, while another 45% rent their home."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='home_ownership'>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pt = pd.pivot_table(df[[\"home_ownership\",\"loan_status\"]], columns=\"loan_status\", index=\"home_ownership\", aggfunc = len)\n",
    "(pt/pt.sum(axis=1).values.reshape(-1,1)).plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Renters appear slightly more likely to have a charge off status than mortgage holders or home owners."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.626000e+04\n",
       "mean     6.802495e+04\n",
       "std      7.159384e+04\n",
       "min      7.200000e+03\n",
       "25%      4.160079e+04\n",
       "50%      5.800000e+04\n",
       "75%      8.100000e+04\n",
       "max      7.141778e+06\n",
       "Name: annual_inc, dtype: float64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"annual_inc\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Annual incomes range from \\\\$7,200 to \\\\$7.1m."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": true
   },
   "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>annual_inc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Charged Off</th>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fully Paid</th>\n",
       "      <td>60000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             annual_inc\n",
       "loan_status            \n",
       "Charged Off     50000.0\n",
       "Fully Paid      60000.0"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"annual_inc\",\"loan_status\"]].groupby(\"loan_status\").median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The median annual income for charged off borrowers is about \\\\$10,000 lower than of fully paid borrowers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = gplt.datasets.get_path(\"contiguous_usa\")\n",
    "contiguous_usa = gpd.read_file(path)\n",
    "contiguous_usa = contiguous_usa.sort_values(\"state\").set_index(\"state\")\n",
    "\n",
    "# map state code --> state name\n",
    "state_abbrevs = pd.read_csv(\"state_abbrevs.csv\")\n",
    "abbrev_map = {y:x for x,y in zip(state_abbrevs[\"State\"], state_abbrevs[\"Code\"])}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# group loans by PropertyState\n",
    "data = df.groupby(\"addr_state\").count()[\"loan_status\"]\n",
    "data = data.reindex(abbrev_map.keys(), fill_value=0)\n",
    "data.name = \"NumLoans\"\n",
    "\n",
    "# filter by contiguous states only for visualization\n",
    "data_contiguous = data[data.index.isin(state_abbrevs[\"Code\"])]\n",
    "data_contiguous.index = data_contiguous.index.map(lambda x: abbrev_map[x])\n",
    "contiguous_usa_join = contiguous_usa.join(data_contiguous, how=\"inner\")\n",
    "\n",
    "# plot\n",
    "ax = gplt.polyplot(contiguous_usa_join, projection=gcrs.AlbersEqualArea())\n",
    "gplt.choropleth(\n",
    "  contiguous_usa_join,\n",
    "  hue=\"NumLoans\",\n",
    "  edgecolor=\"black\",\n",
    "  linewidth=1,\n",
    "  cmap=\"viridis\",\n",
    "  legend=True,\n",
    "  legend_kwargs={\"frameon\":True, \"loc\":\"lower right\"},\n",
    "  scheme=\"FisherJenks\",\n",
    "  projection=gcrs.AlbersEqualArea(),\n",
    "  ax=ax\n",
    ")\n",
    "plt.title(\"Number of loans per state\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "California has the most loans by far; however, this is not surprising considering their population. Let's tree to normalize by population to see if we can identify states which are borrowing \"disproportionately\" often."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "contiguous_usa_join[\"NormalizedNumLoans\"] = contiguous_usa_join[\"NumLoans\"].div(contiguous_usa_join[\"population\"])\n",
    "\n",
    "# plot\n",
    "ax = gplt.polyplot(contiguous_usa_join, projection=gcrs.AlbersEqualArea())\n",
    "gplt.choropleth(\n",
    "  contiguous_usa_join,\n",
    "  hue=\"NormalizedNumLoans\",\n",
    "  edgecolor=\"black\",\n",
    "  linewidth=1,\n",
    "  cmap=\"viridis\",\n",
    "  legend=True,\n",
    "  legend_kwargs={\"frameon\":True, \"loc\":\"lower right\"},\n",
    "  scheme=\"FisherJenks\",\n",
    "  projection=gcrs.AlbersEqualArea(),\n",
    "  ax=ax\n",
    ")\n",
    "\n",
    "# legend formatting\n",
    "leg = ax.get_legend()\n",
    "labels = leg.get_texts()\n",
    "legend_scale = 5 # 10**2\n",
    "for i in range(len(labels)):\n",
    "    low, hi = map(float, labels[i].get_text().split(\" - \"))\n",
    "    low, hi = np.round([low*10**legend_scale, hi*10**legend_scale], 3)\n",
    "    labels[i].set_text( \"%0.3f - %0.3f\" % (low,hi))\n",
    "leg.set_title(r\"Loans/pop $\\times 10^{%i}$\" % legend_scale)\n",
    "plt.title(\"Number of loans per state normalized by population\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we see that Nevada has a disproportionaly large number of borrowers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tysonp\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_enc = df[[\"addr_state\",\"loan_status\"]]\n",
    "df_enc[\"loan_status\"] = df_enc[\"loan_status\"].replace({\"Charged Off\":1, \"Fully Paid\":0})\n",
    "data = df_enc.groupby(\"addr_state\").mean()[\"loan_status\"]\n",
    "data = data.reindex(abbrev_map.keys(), fill_value=0)\n",
    "data\n",
    "data.name = \"ChargeOff\"\n",
    "\n",
    "# filter by contiguous states only for visualization\n",
    "data_contiguous = data[data.index.isin(state_abbrevs[\"Code\"])]\n",
    "data_contiguous.index = data_contiguous.index.map(lambda x: abbrev_map[x])\n",
    "contiguous_usa_join = contiguous_usa.join(data_contiguous, how=\"inner\")\n",
    "\n",
    "# plot\n",
    "ax = gplt.polyplot(contiguous_usa_join, projection=gcrs.AlbersEqualArea())\n",
    "gplt.choropleth(\n",
    "  contiguous_usa_join,\n",
    "  hue=\"ChargeOff\",\n",
    "  edgecolor=\"black\",\n",
    "  linewidth=1,\n",
    "  cmap=\"viridis\",\n",
    "  legend=True,\n",
    "  legend_kwargs={\"frameon\":True, \"loc\":\"lower right\"},\n",
    "  scheme=\"FisherJenks\",\n",
    "  projection=gcrs.AlbersEqualArea(),\n",
    "  ax=ax\n",
    ")\n",
    "plt.title(\"Loan charge off rate per state\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "States in the western U.S. have high charge off rates -- which is bad considering they also have the highest number of loans. Indiana has an extremely high charge off rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"dti\"].plot(kind=\"hist\", bins=20, edgecolor=\"black\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DTI has a symmetric, unimodal distributed peaked around 15%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "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>dti</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Charged Off</th>\n",
       "      <td>18.465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fully Paid</th>\n",
       "      <td>16.700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                dti\n",
       "loan_status        \n",
       "Charged Off  18.465\n",
       "Fully Paid   16.700"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"dti\",\"loan_status\"]].groupby(\"loan_status\").median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Borrowers that fully pay their loans have a lower DTI than borrowers that end up charged off."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Credit columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "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>earliest_cr_line</th>\n",
       "      <th>fico_range_low</th>\n",
       "      <th>fico_range_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2004-05-01</td>\n",
       "      <td>665.0</td>\n",
       "      <td>669.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1999-09-01</td>\n",
       "      <td>660.0</td>\n",
       "      <td>664.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1996-12-01</td>\n",
       "      <td>715.0</td>\n",
       "      <td>719.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>675.0</td>\n",
       "      <td>679.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1998-05-01</td>\n",
       "      <td>675.0</td>\n",
       "      <td>679.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  earliest_cr_line  fico_range_low  fico_range_high\n",
       "0       2004-05-01           665.0            669.0\n",
       "1       1999-09-01           660.0            664.0\n",
       "2       1996-12-01           715.0            719.0\n",
       "3       2005-01-01           675.0            679.0\n",
       "4       1998-05-01           675.0            679.0"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[cred_cols].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2e0a68b72c8>]"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = df[cred_cols].set_index(\"earliest_cr_line\").groupby(pd.Grouper(freq=\"Y\")).size() # earliest credit line\n",
    "\n",
    "dates = mdates.date2num(data.index.values)\n",
    "plt.plot_date(dates, data.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most people had a credit line as early as 2000 (12 years before loan origination)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tysonp\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2e0a59d7408>]"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_enc = df[[\"earliest_cr_line\",\"loan_status\"]]\n",
    "df_enc[\"loan_status\"] = df_enc[\"loan_status\"].replace({\"Charged Off\":1, \"Fully Paid\":0})\n",
    "data = df_enc[[\"earliest_cr_line\",\"loan_status\"]].set_index(\"earliest_cr_line\").groupby(pd.Grouper(freq=\"Y\")).mean()\n",
    "\n",
    "dates = mdates.date2num(data.index.values)\n",
    "plt.plot_date(dates, data.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generally borrowers with shorter credit histories have higher charge off rates. We "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 fico_range_high  fico_range_low\n",
      "fico_range_high              1.0             1.0\n",
      "fico_range_low               1.0             1.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    16260.000000\n",
       "mean         4.000123\n",
       "std          0.011090\n",
       "min          4.000000\n",
       "25%          4.000000\n",
       "50%          4.000000\n",
       "75%          4.000000\n",
       "max          5.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df[[\"fico_range_high\",\"fico_range_low\"]].corr())\n",
    "(df[\"fico_range_high\"] - df[\"fico_range_low\"]).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`fico_range_high` is essentially always 4 points higher than `fico_range_low`. Because of this, we should drop one column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"fico_range_high\"].plot(kind=\"hist\", bins=20, edgecolor=\"black\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '')"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[[\"fico_range_high\",\"loan_status\"]].boxplot(column=\"fico_range_high\", by=\"loan_status\")\n",
    "plt.title(\"\") # hide title"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we should expect, better credit score is associated with better loan outcome."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Delinquency columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"delinq_2yrs: The number of 30+ days past-due incidences of delinquency in the borrower's credit file for the past 2 years\",\n",
       " \"mths_since_last_delinq: The number of months since the borrower's last delinquency.\",\n",
       " 'pub_rec: Number of derogatory public records',\n",
       " 'mths_since_last_major_derog: Months since most recent 90-day or worse rating',\n",
       " 'tot_coll_amt: Total collection amounts ever owed',\n",
       " 'mths_since_recent_bc_dlq: Months since most recent bankcard delinquency',\n",
       " 'mths_since_recent_revol_delinq: Months since most recent revolving delinquency.',\n",
       " 'num_accts_ever_120_pd: Number of accounts ever 120 or more days past due',\n",
       " 'num_tl_120dpd_2m: Number of accounts currently 120 days past due (updated in past 2 months)',\n",
       " 'num_tl_30dpd: Number of accounts currently 30 days past due (updated in past 2 months)',\n",
       " 'num_tl_90g_dpd_24m: Number of accounts 90 or more days past due in last 24 months',\n",
       " 'pct_tl_nvr_dlq: Percent of trades never delinquent',\n",
       " 'pub_rec_bankruptcies: Number of public record bankruptcies']"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[x + \": \" + desc[x] for x in dlq_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df[dlq_cols].corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interestingly, these columns are not as correlated as I thought they would be. Perhaps people learn from their financial mistakes/try to avoid being delinquent on multiple loans. We do see some high correlations like `pub_rec` and `pub_rec_bankruptcies`, we'll need to deal with these when modeling.\n",
    "\n",
    "Let's look at one delinquency-related column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     0.831919\n",
       "1.0     0.120172\n",
       "2.0     0.032288\n",
       "3.0     0.008426\n",
       "4.0     0.003444\n",
       "5.0     0.001722\n",
       "7.0     0.000615\n",
       "6.0     0.000615\n",
       "9.0     0.000246\n",
       "8.0     0.000185\n",
       "11.0    0.000062\n",
       "18.0    0.000062\n",
       "13.0    0.000062\n",
       "12.0    0.000062\n",
       "14.0    0.000062\n",
       "10.0    0.000062\n",
       "Name: delinq_2yrs, dtype: float64"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = df[\"delinq_2yrs\"].value_counts(normalize=True)\n",
    "counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One borrower has 18 delinquencies in the last 2 years!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts[:4].plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "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>delinq_2yrs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Charged Off</th>\n",
       "      <td>0.241706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fully Paid</th>\n",
       "      <td>0.249965</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             delinq_2yrs\n",
       "loan_status             \n",
       "Charged Off     0.241706\n",
       "Fully Paid      0.249965"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"delinq_2yrs\",\"loan_status\"]].groupby(\"loan_status\").mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There does not seem to be much of a relationship between `delinq_2yrs` and `loan_status`, and in fact fully paid loans have a slightly higher 2-year delinquency rate here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inquiries columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['inq_last_6mths: The number of inquiries in past 6 months (excluding auto and mortgage inquiries)',\n",
       " 'mths_since_last_record: The number of months since the last public record.',\n",
       " 'mths_since_recent_inq: Months since most recent inquiry.']"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[x + \": \" + desc[x] for x in inq_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"mths_since_recent_inq\"].plot(kind=\"hist\", bins=20, edgecolor=\"black\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "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>mths_since_recent_inq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Charged Off</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fully Paid</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             mths_since_recent_inq\n",
       "loan_status                       \n",
       "Charged Off                    4.0\n",
       "Fully Paid                     5.0"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"mths_since_recent_inq\",\"loan_status\"]].groupby(\"loan_status\").median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Charged off loans are associated with a slightly more recent inquiry."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Account columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"open_acc: The number of open credit lines in the borrower's credit file.\",\n",
       " \"total_acc: The total number of credit lines currently in the borrower's credit file\",\n",
       " 'acc_open_past_24mths: Number of trades opened in past 24 months.',\n",
       " 'mo_sin_old_il_acct: Months since oldest bank installment account opened',\n",
       " 'mo_sin_old_rev_tl_op: Months since oldest revolving account opened',\n",
       " 'mo_sin_rcnt_rev_tl_op: Months since most recent revolving account opened',\n",
       " 'mo_sin_rcnt_tl: Months since most recent account opened',\n",
       " 'mort_acc: Number of mortgage accounts.',\n",
       " 'mths_since_recent_bc: Months since most recent bankcard account opened.',\n",
       " 'num_actv_bc_tl: Number of currently active bankcard accounts',\n",
       " 'num_actv_rev_tl: Number of currently active revolving trades',\n",
       " 'num_bc_sats: Number of satisfactory bankcard accounts',\n",
       " 'num_bc_tl: Number of bankcard accounts',\n",
       " 'num_il_tl: Number of installment accounts',\n",
       " 'num_op_rev_tl: Number of open revolving accounts',\n",
       " 'num_rev_accts: Number of revolving accounts',\n",
       " 'num_rev_tl_bal_gt_0: Number of revolving trades with balance >0',\n",
       " 'num_sats: Number of satisfactory accounts',\n",
       " 'num_tl_op_past_12m: Number of accounts opened in past 12 months']"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[x + \": \" + desc[x] for x in acc_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df[acc_cols].corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Several of these are highly correlated (positively or negatively). Let's look at `mo_sin_rcnt_tl`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    16211.000000\n",
       "mean         8.859725\n",
       "std          9.832141\n",
       "min          0.000000\n",
       "25%          3.000000\n",
       "50%          6.000000\n",
       "75%         11.000000\n",
       "max        137.000000\n",
       "Name: mo_sin_rcnt_tl, dtype: float64"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"mo_sin_rcnt_tl\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution is highly skewed right, with some borrowers having their most recent account opened 137 months ago (11 years). The median value is 6 months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "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>mo_sin_rcnt_tl</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Charged Off</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fully Paid</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             mo_sin_rcnt_tl\n",
       "loan_status                \n",
       "Charged Off             5.0\n",
       "Fully Paid              6.0"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"mo_sin_rcnt_tl\",\"loan_status\"]].groupby(\"loan_status\").median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Balance columns\n",
    "We'll just look at the correlations here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['revol_bal: Total credit revolving balance',\n",
       " 'revol_util: Revolving line utilization rate, or the amount of credit the borrower is using relative to all available revolving credit.',\n",
       " 'tot_cur_bal: Total current balance of all accounts',\n",
       " 'total_rev_hi_lim: Total revolving high credit/credit limit',\n",
       " 'avg_cur_bal: Average current balance of all accounts',\n",
       " 'bc_open_to_buy: Total open to buy on revolving bankcards.',\n",
       " 'bc_util: Ratio of total current balance to high credit/credit limit for all bankcard accounts.',\n",
       " 'percent_bc_gt_75: Percentage of all bankcard accounts > 75% of limit.',\n",
       " 'tot_hi_cred_lim: Total high credit/credit limit',\n",
       " 'total_bal_ex_mort: Total credit balance excluding mortgage',\n",
       " 'total_bc_limit: Total bankcard high credit/credit limit',\n",
       " 'total_il_high_credit_limit: Total installment high credit/credit limit']"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[x + \": \" + desc[x] for x in bal_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df[bal_cols].corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again there are several highly correlated columns that we'll need to deal with."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float64           54\n",
       "object             7\n",
       "datetime64[ns]     2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>purpose</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>addr_state</th>\n",
       "      <th>initial_list_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>RENT</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>959xx</td>\n",
       "      <td>CA</td>\n",
       "      <td>w</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>986xx</td>\n",
       "      <td>WA</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>900xx</td>\n",
       "      <td>CA</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>medical</td>\n",
       "      <td>734xx</td>\n",
       "      <td>OK</td>\n",
       "      <td>w</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>346xx</td>\n",
       "      <td>FL</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16255</th>\n",
       "      <td>5 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>981xx</td>\n",
       "      <td>WA</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16256</th>\n",
       "      <td>10+ years</td>\n",
       "      <td>OWN</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>011xx</td>\n",
       "      <td>MA</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16257</th>\n",
       "      <td>6 years</td>\n",
       "      <td>OWN</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>home_improvement</td>\n",
       "      <td>797xx</td>\n",
       "      <td>TX</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16258</th>\n",
       "      <td>1 year</td>\n",
       "      <td>RENT</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>112xx</td>\n",
       "      <td>NY</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16259</th>\n",
       "      <td>8 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>908xx</td>\n",
       "      <td>CA</td>\n",
       "      <td>w</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16260 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      emp_length home_ownership  loan_status             purpose zip_code  \\\n",
       "0       < 1 year           RENT   Fully Paid         credit_card    959xx   \n",
       "1        2 years           RENT   Fully Paid         credit_card    986xx   \n",
       "2      10+ years           RENT   Fully Paid  debt_consolidation    900xx   \n",
       "3        8 years           RENT   Fully Paid             medical    734xx   \n",
       "4        2 years           RENT  Charged Off         credit_card    346xx   \n",
       "...          ...            ...          ...                 ...      ...   \n",
       "16255    5 years           RENT   Fully Paid  debt_consolidation    981xx   \n",
       "16256  10+ years            OWN   Fully Paid  debt_consolidation    011xx   \n",
       "16257    6 years            OWN   Fully Paid    home_improvement    797xx   \n",
       "16258     1 year           RENT   Fully Paid         credit_card    112xx   \n",
       "16259    8 years           RENT   Fully Paid  debt_consolidation    908xx   \n",
       "\n",
       "      addr_state initial_list_status  \n",
       "0             CA                   w  \n",
       "1             WA                   f  \n",
       "2             CA                   f  \n",
       "3             OK                   w  \n",
       "4             FL                   f  \n",
       "...          ...                 ...  \n",
       "16255         WA                   f  \n",
       "16256         MA                   f  \n",
       "16257         TX                   f  \n",
       "16258         NY                   f  \n",
       "16259         CA                   w  \n",
       "\n",
       "[16260 rows x 7 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.columns[df.dtypes==\"object\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mths_since_last_record            0.974600\n",
       "mths_since_last_major_derog       0.788007\n",
       "mths_since_recent_bc_dlq          0.736470\n",
       "mths_since_recent_revol_delinq    0.652829\n",
       "mths_since_last_delinq            0.509041\n",
       "mths_since_recent_inq             0.129951\n",
       "mo_sin_old_il_acct                0.054428\n",
       "emp_length                        0.043419\n",
       "bc_util                           0.010394\n",
       "bc_open_to_buy                    0.009779\n",
       "percent_bc_gt_75                  0.009779\n",
       "mths_since_recent_bc              0.008241\n",
       "mo_sin_old_rev_tl_op              0.003075\n",
       "mo_sin_rcnt_rev_tl_op             0.003075\n",
       "num_accts_ever_120_pd             0.003014\n",
       "avg_cur_bal                       0.003014\n",
       "tot_cur_bal                       0.003014\n",
       "mo_sin_rcnt_tl                    0.003014\n",
       "tot_coll_amt                      0.003014\n",
       "total_il_high_credit_limit        0.003014\n",
       "total_rev_hi_lim                  0.003014\n",
       "num_actv_bc_tl                    0.003014\n",
       "num_tl_90g_dpd_24m                0.003014\n",
       "tot_hi_cred_lim                   0.003014\n",
       "num_bc_tl                         0.003014\n",
       "num_actv_rev_tl                   0.003014\n",
       "num_il_tl                         0.003014\n",
       "num_op_rev_tl                     0.003014\n",
       "num_rev_accts                     0.003014\n",
       "num_rev_tl_bal_gt_0               0.003014\n",
       "pct_tl_nvr_dlq                    0.003014\n",
       "num_tl_120dpd_2m                  0.003014\n",
       "num_tl_30dpd                      0.003014\n",
       "num_tl_op_past_12m                0.003014\n",
       "revol_util                        0.000677\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing = df.isna().mean().sort_values(ascending=False)\n",
    "missing[missing > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Future work\n",
    "A bivariate analysis (looking at pairs of variables) would be a good addition to our analysis. We did look at how several variables related to loan outcome, and we looked at correlations between some numeric variables, but there is still a lot to be done here. Does loan purpose relate to previous account delinquencies? By studying `earliest_cr_line`, `delinq_2yrs`, and `loan_status` can we determine an acceptable ratio for the number of delinquencies to credit history length? That is, how does someone with a 10 year credit history and 5 delinquencies in 2 years compare to someone with a 5 year credit history and 3 delinquencies? Do geographical delinquency/charge-off rates remain relatively constant or do they change rapidly in time?"
   ]
  }
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