extensive-feature-engineering-and-modeling.ipynb

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    {
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     "output_type": "stream",
     "text": [
      "/kaggle/input/credit-risk-dataset/LCDataDictionary.xlsx\n",
      "/kaggle/input/credit-risk-dataset/loan/loan.csv\n",
      "/kaggle/input/processed-dataset-for-cra/processed_df.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "#importing helping hands\n",
    "import os\n",
    "import time\n",
    "from collections import Counter\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "pd.set_option('display.float_format', lambda x: '%.2f' % x)\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import category_encoders as ce\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.metrics import confusion_matrix, f1_score\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ae88d73a",
   "metadata": {
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   "outputs": [],
   "source": [
    "df_loan = pd.read_csv(\"/kaggle/input/processed-dataset-for-cra/processed_df.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68d1eafc",
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    "tags": []
   },
   "source": [
    "# Initial Analysis"
   ]
  },
  {
   "cell_type": "code",
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   "id": "89ce56ab",
   "metadata": {
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    {
     "data": {
      "text/plain": [
       "['loan_amnt',\n",
       " 'funded_amnt',\n",
       " 'funded_amnt_inv',\n",
       " 'int_rate',\n",
       " 'dti',\n",
       " 'delinq_2yrs',\n",
       " 'inq_last_6mths',\n",
       " 'mths_since_last_delinq',\n",
       " 'open_acc',\n",
       " 'revol_util',\n",
       " 'total_acc',\n",
       " 'total_rev_hi_lim',\n",
       " 'term',\n",
       " 'grade',\n",
       " 'sub_grade',\n",
       " 'emp_length',\n",
       " 'home_ownership',\n",
       " 'verification_status',\n",
       " 'issue_d',\n",
       " 'loan_status',\n",
       " 'pymnt_plan',\n",
       " 'purpose',\n",
       " 'zip_code',\n",
       " 'addr_state',\n",
       " 'earliest_cr_line',\n",
       " 'initial_list_status',\n",
       " 'last_pymnt_d',\n",
       " 'next_pymnt_d',\n",
       " 'last_credit_pull_d',\n",
       " 'application_type']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_loan.columns.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "901d6916",
   "metadata": {
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt                 0\n",
       "funded_amnt               0\n",
       "funded_amnt_inv           0\n",
       "int_rate                  0\n",
       "dti                       0\n",
       "delinq_2yrs               0\n",
       "inq_last_6mths            0\n",
       "mths_since_last_delinq    0\n",
       "open_acc                  0\n",
       "revol_util                0\n",
       "total_acc                 0\n",
       "total_rev_hi_lim          0\n",
       "term                      0\n",
       "grade                     0\n",
       "sub_grade                 0\n",
       "emp_length                0\n",
       "home_ownership            0\n",
       "verification_status       0\n",
       "issue_d                   0\n",
       "loan_status               0\n",
       "pymnt_plan                0\n",
       "purpose                   0\n",
       "zip_code                  0\n",
       "addr_state                0\n",
       "earliest_cr_line          0\n",
       "initial_list_status       0\n",
       "last_pymnt_d              0\n",
       "next_pymnt_d              0\n",
       "last_credit_pull_d        0\n",
       "application_type          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_loan.isnull().sum()"
   ]
  },
  {
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   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "      <th>291309</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>loan_amnt</th>\n",
       "      <td>12000.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>11100.00</td>\n",
       "      <td>4500.00</td>\n",
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       "      <td>14000.00</td>\n",
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       "      <td>13297.17</td>\n",
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       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
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       "    <tr>\n",
       "      <th>funded_amnt</th>\n",
       "      <td>12000.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>11100.00</td>\n",
       "      <td>4500.00</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>12000.00</td>\n",
       "      <td>10400.00</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>14000.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>...</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
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       "      <td>13297.17</td>\n",
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       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
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       "    <tr>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <td>12000.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>11100.00</td>\n",
       "      <td>4500.00</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>12000.00</td>\n",
       "      <td>10400.00</td>\n",
       "      <td>3950.00</td>\n",
       "      <td>14000.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>...</td>\n",
       "      <td>13291.86</td>\n",
       "      <td>13291.86</td>\n",
       "      <td>13291.86</td>\n",
       "      <td>13291.86</td>\n",
       "      <td>13291.86</td>\n",
       "      <td>13291.86</td>\n",
       "      <td>13291.86</td>\n",
       "      <td>13291.86</td>\n",
       "      <td>13291.86</td>\n",
       "      <td>13291.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>int_rate</th>\n",
       "      <td>6.62</td>\n",
       "      <td>9.67</td>\n",
       "      <td>14.98</td>\n",
       "      <td>19.22</td>\n",
       "      <td>13.53</td>\n",
       "      <td>16.24</td>\n",
       "      <td>17.57</td>\n",
       "      <td>7.90</td>\n",
       "      <td>14.98</td>\n",
       "      <td>11.99</td>\n",
       "      <td>...</td>\n",
       "      <td>12.62</td>\n",
       "      <td>12.62</td>\n",
       "      <td>12.62</td>\n",
       "      <td>12.62</td>\n",
       "      <td>12.62</td>\n",
       "      <td>12.62</td>\n",
       "      <td>12.62</td>\n",
       "      <td>12.62</td>\n",
       "      <td>12.62</td>\n",
       "      <td>12.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dti</th>\n",
       "      <td>14.05</td>\n",
       "      <td>15.55</td>\n",
       "      <td>3.73</td>\n",
       "      <td>16.23</td>\n",
       "      <td>22.56</td>\n",
       "      <td>10.68</td>\n",
       "      <td>18.51</td>\n",
       "      <td>15.47</td>\n",
       "      <td>14.59</td>\n",
       "      <td>10.47</td>\n",
       "      <td>...</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
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       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mths_since_last_delinq</th>\n",
       "      <td>43.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>64.00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>41.00</td>\n",
       "      <td>34.00</td>\n",
       "      <td>...</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open_acc</th>\n",
       "      <td>12.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>17.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>...</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revol_util</th>\n",
       "      <td>21.60</td>\n",
       "      <td>44.40</td>\n",
       "      <td>66.20</td>\n",
       "      <td>93.10</td>\n",
       "      <td>39.60</td>\n",
       "      <td>51.60</td>\n",
       "      <td>88.20</td>\n",
       "      <td>61.20</td>\n",
       "      <td>81.60</td>\n",
       "      <td>65.30</td>\n",
       "      <td>...</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_acc</th>\n",
       "      <td>22.00</td>\n",
       "      <td>22.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>35.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>41.00</td>\n",
       "      <td>23.00</td>\n",
       "      <td>24.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>...</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <td>61100.00</td>\n",
       "      <td>22300.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>20500.00</td>\n",
       "      <td>24700.00</td>\n",
       "      <td>21600.00</td>\n",
       "      <td>20600.00</td>\n",
       "      <td>4600.00</td>\n",
       "      <td>16800.00</td>\n",
       "      <td>10200.00</td>\n",
       "      <td>...</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>term</th>\n",
       "      <td>36 months</td>\n",
       "      <td>36 months</td>\n",
       "      <td>36 months</td>\n",
       "      <td>36 months</td>\n",
       "      <td>36 months</td>\n",
       "      <td>60 months</td>\n",
       "      <td>60 months</td>\n",
       "      <td>36 months</td>\n",
       "      <td>36 months</td>\n",
       "      <td>36 months</td>\n",
       "      <td>...</td>\n",
       "      <td>60 months</td>\n",
       "      <td>60 months</td>\n",
       "      <td>36 months</td>\n",
       "      <td>36 months</td>\n",
       "      <td>60 months</td>\n",
       "      <td>60 months</td>\n",
       "      <td>36 months</td>\n",
       "      <td>36 months</td>\n",
       "      <td>36 months</td>\n",
       "      <td>60 months</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade</th>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>E</td>\n",
       "      <td>D</td>\n",
       "      <td>E</td>\n",
       "      <td>D</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub_grade</th>\n",
       "      <td>A2</td>\n",
       "      <td>B1</td>\n",
       "      <td>C3</td>\n",
       "      <td>D4</td>\n",
       "      <td>B5</td>\n",
       "      <td>C5</td>\n",
       "      <td>D2</td>\n",
       "      <td>A4</td>\n",
       "      <td>C3</td>\n",
       "      <td>B3</td>\n",
       "      <td>...</td>\n",
       "      <td>E5</td>\n",
       "      <td>D4</td>\n",
       "      <td>E4</td>\n",
       "      <td>D1</td>\n",
       "      <td>C3</td>\n",
       "      <td>B1</td>\n",
       "      <td>C4</td>\n",
       "      <td>B2</td>\n",
       "      <td>B5</td>\n",
       "      <td>E3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emp_length</th>\n",
       "      <td>10+ years</td>\n",
       "      <td>7 years</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>2 years</td>\n",
       "      <td>2 years</td>\n",
       "      <td>5 years</td>\n",
       "      <td>5 years</td>\n",
       "      <td>2 years</td>\n",
       "      <td>...</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>4 years</td>\n",
       "      <td>3 years</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>8 years</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>1 year</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home_ownership</th>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>RENT</td>\n",
       "      <td>OWN</td>\n",
       "      <td>RENT</td>\n",
       "      <td>RENT</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>RENT</td>\n",
       "      <td>RENT</td>\n",
       "      <td>...</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>RENT</td>\n",
       "      <td>RENT</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>RENT</td>\n",
       "      <td>RENT</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>RENT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>verification_status</th>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>...</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Source Verified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>issue_d</th>\n",
       "      <td>Dec-2013</td>\n",
       "      <td>Dec-2013</td>\n",
       "      <td>Dec-2013</td>\n",
       "      <td>Dec-2013</td>\n",
       "      <td>Dec-2013</td>\n",
       "      <td>Dec-2013</td>\n",
       "      <td>Dec-2013</td>\n",
       "      <td>Dec-2013</td>\n",
       "      <td>Dec-2013</td>\n",
       "      <td>Dec-2013</td>\n",
       "      <td>...</td>\n",
       "      <td>Jan-2015</td>\n",
       "      <td>Jan-2015</td>\n",
       "      <td>Jan-2015</td>\n",
       "      <td>Jan-2015</td>\n",
       "      <td>Jan-2015</td>\n",
       "      <td>Jan-2015</td>\n",
       "      <td>Jan-2015</td>\n",
       "      <td>Jan-2015</td>\n",
       "      <td>Jan-2015</td>\n",
       "      <td>Jan-2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <td>Current</td>\n",
       "      <td>Current</td>\n",
       "      <td>Current</td>\n",
       "      <td>Current</td>\n",
       "      <td>Current</td>\n",
       "      <td>Current</td>\n",
       "      <td>Current</td>\n",
       "      <td>Current</td>\n",
       "      <td>Current</td>\n",
       "      <td>Current</td>\n",
       "      <td>...</td>\n",
       "      <td>Current</td>\n",
       "      <td>Current</td>\n",
       "      <td>Current</td>\n",
       "      <td>Late (31-120 days)</td>\n",
       "      <td>Current</td>\n",
       "      <td>Current</td>\n",
       "      <td>Current</td>\n",
       "      <td>Current</td>\n",
       "      <td>Current</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pymnt_plan</th>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purpose</th>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>...</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>debt_consolidation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zip_code</th>\n",
       "      <td>060xx</td>\n",
       "      <td>027xx</td>\n",
       "      <td>103xx</td>\n",
       "      <td>750xx</td>\n",
       "      <td>750xx</td>\n",
       "      <td>852xx</td>\n",
       "      <td>785xx</td>\n",
       "      <td>532xx</td>\n",
       "      <td>342xx</td>\n",
       "      <td>209xx</td>\n",
       "      <td>...</td>\n",
       "      <td>380xx</td>\n",
       "      <td>967xx</td>\n",
       "      <td>882xx</td>\n",
       "      <td>115xx</td>\n",
       "      <td>925xx</td>\n",
       "      <td>138xx</td>\n",
       "      <td>471xx</td>\n",
       "      <td>531xx</td>\n",
       "      <td>432xx</td>\n",
       "      <td>010xx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>addr_state</th>\n",
       "      <td>CT</td>\n",
       "      <td>MA</td>\n",
       "      <td>NY</td>\n",
       "      <td>TX</td>\n",
       "      <td>TX</td>\n",
       "      <td>AZ</td>\n",
       "      <td>TX</td>\n",
       "      <td>WI</td>\n",
       "      <td>FL</td>\n",
       "      <td>MD</td>\n",
       "      <td>...</td>\n",
       "      <td>TN</td>\n",
       "      <td>HI</td>\n",
       "      <td>NM</td>\n",
       "      <td>NY</td>\n",
       "      <td>CA</td>\n",
       "      <td>NY</td>\n",
       "      <td>IN</td>\n",
       "      <td>WI</td>\n",
       "      <td>OH</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>earliest_cr_line</th>\n",
       "      <td>Mar-1994</td>\n",
       "      <td>Oct-1989</td>\n",
       "      <td>Jun-2001</td>\n",
       "      <td>Mar-1990</td>\n",
       "      <td>Dec-1993</td>\n",
       "      <td>May-2003</td>\n",
       "      <td>Oct-1988</td>\n",
       "      <td>Apr-2004</td>\n",
       "      <td>Jul-1991</td>\n",
       "      <td>Jun-1993</td>\n",
       "      <td>...</td>\n",
       "      <td>Apr-2003</td>\n",
       "      <td>Sep-2001</td>\n",
       "      <td>Apr-1994</td>\n",
       "      <td>Oct-2003</td>\n",
       "      <td>Aug-2002</td>\n",
       "      <td>Aug-1983</td>\n",
       "      <td>Nov-2006</td>\n",
       "      <td>Aug-1994</td>\n",
       "      <td>Jun-1995</td>\n",
       "      <td>Oct-2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>initial_list_status</th>\n",
       "      <td>w</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>w</td>\n",
       "      <td>f</td>\n",
       "      <td>w</td>\n",
       "      <td>f</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>...</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>w</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_pymnt_d</th>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>...</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Oct-2015</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Jan-2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>next_pymnt_d</th>\n",
       "      <td>Feb-2016</td>\n",
       "      <td>Feb-2016</td>\n",
       "      <td>Feb-2016</td>\n",
       "      <td>Feb-2016</td>\n",
       "      <td>Feb-2016</td>\n",
       "      <td>Feb-2016</td>\n",
       "      <td>Feb-2016</td>\n",
       "      <td>Feb-2016</td>\n",
       "      <td>Feb-2016</td>\n",
       "      <td>Feb-2016</td>\n",
       "      <td>...</td>\n",
       "      <td>Feb-2016</td>\n",
       "      <td>Feb-2016</td>\n",
       "      <td>Feb-2016</td>\n",
       "      <td>Feb-2016</td>\n",
       "      <td>Feb-2016</td>\n",
       "      <td>Feb-2016</td>\n",
       "      <td>Feb-2016</td>\n",
       "      <td>Feb-2016</td>\n",
       "      <td>Feb-2016</td>\n",
       "      <td>Feb-2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_credit_pull_d</th>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>...</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Jan-2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>application_type</th>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>...</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 291310 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    0                   1             2       \\\n",
       "loan_amnt                         12000.00            10000.00      11100.00   \n",
       "funded_amnt                       12000.00            10000.00      11100.00   \n",
       "funded_amnt_inv                   12000.00            10000.00      11100.00   \n",
       "int_rate                              6.62                9.67         14.98   \n",
       "dti                                  14.05               15.55          3.73   \n",
       "delinq_2yrs                           0.00                2.00          1.00   \n",
       "inq_last_6mths                        1.00                0.00          0.00   \n",
       "mths_since_last_delinq               43.00               11.00         16.00   \n",
       "open_acc                             12.00                9.00          9.00   \n",
       "revol_util                           21.60               44.40         66.20   \n",
       "total_acc                            22.00               22.00         12.00   \n",
       "total_rev_hi_lim                  61100.00            22300.00      10000.00   \n",
       "term                             36 months           36 months     36 months   \n",
       "grade                                    A                   B             C   \n",
       "sub_grade                               A2                  B1            C3   \n",
       "emp_length                       10+ years             7 years     10+ years   \n",
       "home_ownership                    MORTGAGE            MORTGAGE      MORTGAGE   \n",
       "verification_status           Not Verified        Not Verified  Not Verified   \n",
       "issue_d                           Dec-2013            Dec-2013      Dec-2013   \n",
       "loan_status                        Current             Current       Current   \n",
       "pymnt_plan                               n                   n             n   \n",
       "purpose                 debt_consolidation  debt_consolidation         other   \n",
       "zip_code                             060xx               027xx         103xx   \n",
       "addr_state                              CT                  MA            NY   \n",
       "earliest_cr_line                  Mar-1994            Oct-1989      Jun-2001   \n",
       "initial_list_status                      w                   f             f   \n",
       "last_pymnt_d                      Jan-2016            Jan-2016      Jan-2016   \n",
       "next_pymnt_d                      Feb-2016            Feb-2016      Feb-2016   \n",
       "last_credit_pull_d                Jan-2016            Jan-2016      Jan-2016   \n",
       "application_type                INDIVIDUAL          INDIVIDUAL    INDIVIDUAL   \n",
       "\n",
       "                              3             4                   5       \\\n",
       "loan_amnt                    4500.00       5000.00            12000.00   \n",
       "funded_amnt                  4500.00       5000.00            12000.00   \n",
       "funded_amnt_inv              4500.00       5000.00            12000.00   \n",
       "int_rate                       19.22         13.53               16.24   \n",
       "dti                            16.23         22.56               10.68   \n",
       "delinq_2yrs                     1.00          0.00                1.00   \n",
       "inq_last_6mths                  0.00          1.00                0.00   \n",
       "mths_since_last_delinq         10.00         45.00               10.00   \n",
       "open_acc                        5.00         12.00                7.00   \n",
       "revol_util                     93.10         39.60               51.60   \n",
       "total_acc                      10.00         35.00               13.00   \n",
       "total_rev_hi_lim            20500.00      24700.00            21600.00   \n",
       "term                       36 months     36 months           60 months   \n",
       "grade                              D             B                   C   \n",
       "sub_grade                         D4            B5                  C5   \n",
       "emp_length                 10+ years     10+ years             2 years   \n",
       "home_ownership                  RENT           OWN                RENT   \n",
       "verification_status     Not Verified  Not Verified            Verified   \n",
       "issue_d                     Dec-2013      Dec-2013            Dec-2013   \n",
       "loan_status                  Current       Current             Current   \n",
       "pymnt_plan                         n             n                   n   \n",
       "purpose                        other         other  debt_consolidation   \n",
       "zip_code                       750xx         750xx               852xx   \n",
       "addr_state                        TX            TX                  AZ   \n",
       "earliest_cr_line            Mar-1990      Dec-1993            May-2003   \n",
       "initial_list_status                f             w                   f   \n",
       "last_pymnt_d                Jan-2016      Jan-2016            Jan-2016   \n",
       "next_pymnt_d                Feb-2016      Feb-2016            Feb-2016   \n",
       "last_credit_pull_d          Jan-2016      Jan-2016            Jan-2016   \n",
       "application_type          INDIVIDUAL    INDIVIDUAL          INDIVIDUAL   \n",
       "\n",
       "                                 6             7                   8       \\\n",
       "loan_amnt                      10400.00       4000.00            14000.00   \n",
       "funded_amnt                    10400.00       4000.00            14000.00   \n",
       "funded_amnt_inv                10400.00       3950.00            14000.00   \n",
       "int_rate                          17.57          7.90               14.98   \n",
       "dti                               18.51         15.47               14.59   \n",
       "delinq_2yrs                        0.00          0.00                0.00   \n",
       "inq_last_6mths                     0.00          1.00                0.00   \n",
       "mths_since_last_delinq            64.00         50.00               41.00   \n",
       "open_acc                          17.00         10.00               15.00   \n",
       "revol_util                        88.20         61.20               81.60   \n",
       "total_acc                         41.00         23.00               24.00   \n",
       "total_rev_hi_lim               20600.00       4600.00            16800.00   \n",
       "term                          60 months     36 months           36 months   \n",
       "grade                                 D             A                   C   \n",
       "sub_grade                            D2            A4                  C3   \n",
       "emp_length                      2 years       5 years             5 years   \n",
       "home_ownership                     RENT      MORTGAGE                RENT   \n",
       "verification_status     Source Verified  Not Verified        Not Verified   \n",
       "issue_d                        Dec-2013      Dec-2013            Dec-2013   \n",
       "loan_status                     Current       Current             Current   \n",
       "pymnt_plan                            n             n                   n   \n",
       "purpose                     credit_card   credit_card  debt_consolidation   \n",
       "zip_code                          785xx         532xx               342xx   \n",
       "addr_state                           TX            WI                  FL   \n",
       "earliest_cr_line               Oct-1988      Apr-2004            Jul-1991   \n",
       "initial_list_status                   w             f                   w   \n",
       "last_pymnt_d                   Jan-2016      Jan-2016            Jan-2016   \n",
       "next_pymnt_d                   Feb-2016      Feb-2016            Feb-2016   \n",
       "last_credit_pull_d             Jan-2016      Jan-2016            Jan-2016   \n",
       "application_type             INDIVIDUAL    INDIVIDUAL          INDIVIDUAL   \n",
       "\n",
       "                                    9       ...           291300       291301  \\\n",
       "loan_amnt                         10000.00  ...         13297.17     13297.17   \n",
       "funded_amnt                       10000.00  ...         13297.17     13297.17   \n",
       "funded_amnt_inv                   10000.00  ...         13291.86     13291.86   \n",
       "int_rate                             11.99  ...            12.62        12.62   \n",
       "dti                                  10.47  ...            18.74        18.74   \n",
       "delinq_2yrs                           0.00  ...             0.46         0.46   \n",
       "inq_last_6mths                        1.00  ...             0.45         0.45   \n",
       "mths_since_last_delinq               34.00  ...            34.14        34.14   \n",
       "open_acc                             11.00  ...            11.05        11.05   \n",
       "revol_util                           65.30  ...            56.93        56.93   \n",
       "total_acc                            31.00  ...            24.72        24.72   \n",
       "total_rev_hi_lim                  10200.00  ...         23762.28     23762.28   \n",
       "term                             36 months  ...        60 months    60 months   \n",
       "grade                                    B  ...                E            D   \n",
       "sub_grade                               B3  ...               E5           D4   \n",
       "emp_length                         2 years  ...         < 1 year    10+ years   \n",
       "home_ownership                        RENT  ...         MORTGAGE     MORTGAGE   \n",
       "verification_status           Not Verified  ...  Source Verified     Verified   \n",
       "issue_d                           Dec-2013  ...         Jan-2015     Jan-2015   \n",
       "loan_status                        Current  ...          Current      Current   \n",
       "pymnt_plan                               n  ...                n            n   \n",
       "purpose                 debt_consolidation  ...      credit_card  credit_card   \n",
       "zip_code                             209xx  ...            380xx        967xx   \n",
       "addr_state                              MD  ...               TN           HI   \n",
       "earliest_cr_line                  Jun-1993  ...         Apr-2003     Sep-2001   \n",
       "initial_list_status                      w  ...                w            w   \n",
       "last_pymnt_d                      Jan-2016  ...         Jan-2016     Jan-2016   \n",
       "next_pymnt_d                      Feb-2016  ...         Feb-2016     Feb-2016   \n",
       "last_credit_pull_d                Jan-2016  ...         Jan-2016     Jan-2016   \n",
       "application_type                INDIVIDUAL  ...       INDIVIDUAL   INDIVIDUAL   \n",
       "\n",
       "                                    291302              291303  \\\n",
       "loan_amnt                         13297.17            13297.17   \n",
       "funded_amnt                       13297.17            13297.17   \n",
       "funded_amnt_inv                   13291.86            13291.86   \n",
       "int_rate                             12.62               12.62   \n",
       "dti                                  18.74               18.74   \n",
       "delinq_2yrs                           0.46                0.46   \n",
       "inq_last_6mths                        0.45                0.45   \n",
       "mths_since_last_delinq               34.14               34.14   \n",
       "open_acc                             11.05               11.05   \n",
       "revol_util                           56.93               56.93   \n",
       "total_acc                            24.72               24.72   \n",
       "total_rev_hi_lim                  23762.28            23762.28   \n",
       "term                             36 months           36 months   \n",
       "grade                                    E                   D   \n",
       "sub_grade                               E4                  D1   \n",
       "emp_length                         4 years             3 years   \n",
       "home_ownership                    MORTGAGE                RENT   \n",
       "verification_status               Verified            Verified   \n",
       "issue_d                           Jan-2015            Jan-2015   \n",
       "loan_status                        Current  Late (31-120 days)   \n",
       "pymnt_plan                               n                   n   \n",
       "purpose                 debt_consolidation  debt_consolidation   \n",
       "zip_code                             882xx               115xx   \n",
       "addr_state                              NM                  NY   \n",
       "earliest_cr_line                  Apr-1994            Oct-2003   \n",
       "initial_list_status                      f                   f   \n",
       "last_pymnt_d                      Jan-2016            Oct-2015   \n",
       "next_pymnt_d                      Feb-2016            Feb-2016   \n",
       "last_credit_pull_d                Jan-2016            Jan-2016   \n",
       "application_type                INDIVIDUAL          INDIVIDUAL   \n",
       "\n",
       "                                    291304              291305  \\\n",
       "loan_amnt                         13297.17            13297.17   \n",
       "funded_amnt                       13297.17            13297.17   \n",
       "funded_amnt_inv                   13291.86            13291.86   \n",
       "int_rate                             12.62               12.62   \n",
       "dti                                  18.74               18.74   \n",
       "delinq_2yrs                           0.46                0.46   \n",
       "inq_last_6mths                        0.45                0.45   \n",
       "mths_since_last_delinq               34.14               34.14   \n",
       "open_acc                             11.05               11.05   \n",
       "revol_util                           56.93               56.93   \n",
       "total_acc                            24.72               24.72   \n",
       "total_rev_hi_lim                  23762.28            23762.28   \n",
       "term                             60 months           60 months   \n",
       "grade                                    C                   B   \n",
       "sub_grade                               C3                  B1   \n",
       "emp_length                       10+ years           10+ years   \n",
       "home_ownership                        RENT            MORTGAGE   \n",
       "verification_status        Source Verified            Verified   \n",
       "issue_d                           Jan-2015            Jan-2015   \n",
       "loan_status                        Current             Current   \n",
       "pymnt_plan                               n                   n   \n",
       "purpose                 debt_consolidation  debt_consolidation   \n",
       "zip_code                             925xx               138xx   \n",
       "addr_state                              CA                  NY   \n",
       "earliest_cr_line                  Aug-2002            Aug-1983   \n",
       "initial_list_status                      f                   w   \n",
       "last_pymnt_d                      Jan-2016            Jan-2016   \n",
       "next_pymnt_d                      Feb-2016            Feb-2016   \n",
       "last_credit_pull_d                Jan-2016            Jan-2016   \n",
       "application_type                INDIVIDUAL          INDIVIDUAL   \n",
       "\n",
       "                                    291306              291307       291308  \\\n",
       "loan_amnt                         13297.17            13297.17     13297.17   \n",
       "funded_amnt                       13297.17            13297.17     13297.17   \n",
       "funded_amnt_inv                   13291.86            13291.86     13291.86   \n",
       "int_rate                             12.62               12.62        12.62   \n",
       "dti                                  18.74               18.74        18.74   \n",
       "delinq_2yrs                           0.46                0.46         0.46   \n",
       "inq_last_6mths                        0.45                0.45         0.45   \n",
       "mths_since_last_delinq               34.14               34.14        34.14   \n",
       "open_acc                             11.05               11.05        11.05   \n",
       "revol_util                           56.93               56.93        56.93   \n",
       "total_acc                            24.72               24.72        24.72   \n",
       "total_rev_hi_lim                  23762.28            23762.28     23762.28   \n",
       "term                             36 months           36 months    36 months   \n",
       "grade                                    C                   B            B   \n",
       "sub_grade                               C4                  B2           B5   \n",
       "emp_length                       10+ years             8 years     < 1 year   \n",
       "home_ownership                        RENT                RENT     MORTGAGE   \n",
       "verification_status               Verified            Verified     Verified   \n",
       "issue_d                           Jan-2015            Jan-2015     Jan-2015   \n",
       "loan_status                        Current             Current      Current   \n",
       "pymnt_plan                               n                   n            n   \n",
       "purpose                 debt_consolidation  debt_consolidation  credit_card   \n",
       "zip_code                             471xx               531xx        432xx   \n",
       "addr_state                              IN                  WI           OH   \n",
       "earliest_cr_line                  Nov-2006            Aug-1994     Jun-1995   \n",
       "initial_list_status                      w                   f            f   \n",
       "last_pymnt_d                      Jan-2016            Jan-2016     Jan-2016   \n",
       "next_pymnt_d                      Feb-2016            Feb-2016     Feb-2016   \n",
       "last_credit_pull_d                Jan-2016            Jan-2016     Jan-2016   \n",
       "application_type                INDIVIDUAL          INDIVIDUAL   INDIVIDUAL   \n",
       "\n",
       "                                    291309  \n",
       "loan_amnt                         13297.17  \n",
       "funded_amnt                       13297.17  \n",
       "funded_amnt_inv                   13291.86  \n",
       "int_rate                             12.62  \n",
       "dti                                  18.74  \n",
       "delinq_2yrs                           0.46  \n",
       "inq_last_6mths                        0.45  \n",
       "mths_since_last_delinq               34.14  \n",
       "open_acc                             11.05  \n",
       "revol_util                           56.93  \n",
       "total_acc                            24.72  \n",
       "total_rev_hi_lim                  23762.28  \n",
       "term                             60 months  \n",
       "grade                                    E  \n",
       "sub_grade                               E3  \n",
       "emp_length                          1 year  \n",
       "home_ownership                        RENT  \n",
       "verification_status        Source Verified  \n",
       "issue_d                           Jan-2015  \n",
       "loan_status                        Current  \n",
       "pymnt_plan                               n  \n",
       "purpose                 debt_consolidation  \n",
       "zip_code                             010xx  \n",
       "addr_state                              MA  \n",
       "earliest_cr_line                  Oct-2003  \n",
       "initial_list_status                      w  \n",
       "last_pymnt_d                      Jan-2016  \n",
       "next_pymnt_d                      Feb-2016  \n",
       "last_credit_pull_d                Jan-2016  \n",
       "application_type                INDIVIDUAL  \n",
       "\n",
       "[30 rows x 291310 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_loan.T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77d1ce1e",
   "metadata": {
    "papermill": {
     "duration": 0.019477,
     "end_time": "2023-12-18T18:12:19.622779",
     "exception": false,
     "start_time": "2023-12-18T18:12:19.603302",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8470bba3",
   "metadata": {
    "papermill": {
     "duration": 0.018727,
     "end_time": "2023-12-18T18:12:19.660243",
     "exception": false,
     "start_time": "2023-12-18T18:12:19.641516",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Transforming some variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "07511093",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T18:12:19.701962Z",
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     "shell.execute_reply": "2023-12-18T18:12:20.565273Z"
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     "start_time": "2023-12-18T18:12:19.679309",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_loan['grade'] = df_loan['grade'].replace(['A', 'B', 'C', 'D', 'E', 'F', 'G'], [0, 1, 2, 3, 4, 5, 6])\n",
    "df_loan['sub_grade'] = df_loan['sub_grade'].str.strip('ABCDEFG').astype('float64')\n",
    "df_loan['emp_length'] = df_loan['emp_length'].str.strip(' <years')\n",
    "df_loan['emp_length'] = df_loan['emp_length'].replace('10+',11).astype('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d9e592d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T18:12:20.611850Z",
     "iopub.status.busy": "2023-12-18T18:12:20.611356Z",
     "iopub.status.idle": "2023-12-18T18:12:23.284427Z",
     "shell.execute_reply": "2023-12-18T18:12:23.283123Z"
    },
    "papermill": {
     "duration": 2.697221,
     "end_time": "2023-12-18T18:12:23.287598",
     "exception": false,
     "start_time": "2023-12-18T18:12:20.590377",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_loan[['Issue Month','Issue Year']] = df_loan.issue_d.str.split(\"-\",expand=True)\n",
    "df_loan[['Earliest Credit Month', 'Earliest Credit Year']] = df_loan.earliest_cr_line.str.split(\"-\",expand=True)\n",
    "\n",
    "df_loan['Issue Year'] = df_loan['Issue Year'].astype('int32')\n",
    "df_loan['Earliest Credit Year'] = df_loan['Earliest Credit Year'].astype('int32')\n",
    "   \n",
    "df_loan = df_loan.drop(labels=['issue_d', 'earliest_cr_line'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "156b7bd6",
   "metadata": {
    "papermill": {
     "duration": 0.018148,
     "end_time": "2023-12-18T18:12:23.388346",
     "exception": false,
     "start_time": "2023-12-18T18:12:23.370198",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Transforming dates further"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4f3ca621",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T18:12:23.427245Z",
     "iopub.status.busy": "2023-12-18T18:12:23.426815Z",
     "iopub.status.idle": "2023-12-18T18:12:28.230946Z",
     "shell.execute_reply": "2023-12-18T18:12:28.229619Z"
    },
    "papermill": {
     "duration": 4.826802,
     "end_time": "2023-12-18T18:12:28.233652",
     "exception": false,
     "start_time": "2023-12-18T18:12:23.406850",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>last_pymnt_d</th>\n",
       "      <th>last_credit_pull_d</th>\n",
       "      <th>next_pymnt_d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "          last_pymnt_d   last_credit_pull_d         next_pymnt_d\n",
       "0  2016-01-01 00:00:00  2016-01-01 00:00:00  2016-02-01 00:00:00\n",
       "1  2016-01-01 00:00:00  2016-01-01 00:00:00  2016-02-01 00:00:00\n",
       "2  2016-01-01 00:00:00  2016-01-01 00:00:00  2016-02-01 00:00:00\n",
       "3  2016-01-01 00:00:00  2016-01-01 00:00:00  2016-02-01 00:00:00\n",
       "4  2016-01-01 00:00:00  2016-01-01 00:00:00  2016-02-01 00:00:00"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select date columns\n",
    "date_columns = [ \n",
    "    'last_pymnt_d', \n",
    "    'last_credit_pull_d',\n",
    "    'next_pymnt_d'\n",
    "]\n",
    "\n",
    "# drop rows whose date is NaN\n",
    "df_loan = df_loan.dropna(subset=date_columns)\n",
    "df_loan = df_loan.reset_index(drop=True)\n",
    "\n",
    "# format dates\n",
    "for col in date_columns:\n",
    "    df_loan.loc[:, col] = pd.to_datetime(df_loan[col])\n",
    "\n",
    "df_loan[date_columns].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "752d043b",
   "metadata": {
    "execution": {
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     "start_time": "2023-12-18T18:12:28.253218",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "    .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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>291300</th>\n",
       "      <th>291301</th>\n",
       "      <th>291302</th>\n",
       "      <th>291303</th>\n",
       "      <th>291304</th>\n",
       "      <th>291305</th>\n",
       "      <th>291306</th>\n",
       "      <th>291307</th>\n",
       "      <th>291308</th>\n",
       "      <th>291309</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>loan_amnt</th>\n",
       "      <td>12000.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>11100.00</td>\n",
       "      <td>4500.00</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>12000.00</td>\n",
       "      <td>10400.00</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>14000.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>...</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>funded_amnt</th>\n",
       "      <td>12000.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>11100.00</td>\n",
       "      <td>4500.00</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>12000.00</td>\n",
       "      <td>10400.00</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>14000.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>...</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <td>12000.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>11100.00</td>\n",
       "      <td>4500.00</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>12000.00</td>\n",
       "      <td>10400.00</td>\n",
       "      <td>3950.00</td>\n",
       "      <td>14000.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>...</td>\n",
       "      <td>13291.86</td>\n",
       "      <td>13291.86</td>\n",
       "      <td>13291.86</td>\n",
       "      <td>13291.86</td>\n",
       "      <td>13291.86</td>\n",
       "      <td>13291.86</td>\n",
       "      <td>13291.86</td>\n",
       "      <td>13291.86</td>\n",
       "      <td>13291.86</td>\n",
       "      <td>13291.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>int_rate</th>\n",
       "      <td>6.62</td>\n",
       "      <td>9.67</td>\n",
       "      <td>14.98</td>\n",
       "      <td>19.22</td>\n",
       "      <td>13.53</td>\n",
       "      <td>16.24</td>\n",
       "      <td>17.57</td>\n",
       "      <td>7.90</td>\n",
       "      <td>14.98</td>\n",
       "      <td>11.99</td>\n",
       "      <td>...</td>\n",
       "      <td>12.62</td>\n",
       "      <td>12.62</td>\n",
       "      <td>12.62</td>\n",
       "      <td>12.62</td>\n",
       "      <td>12.62</td>\n",
       "      <td>12.62</td>\n",
       "      <td>12.62</td>\n",
       "      <td>12.62</td>\n",
       "      <td>12.62</td>\n",
       "      <td>12.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dti</th>\n",
       "      <td>14.05</td>\n",
       "      <td>15.55</td>\n",
       "      <td>3.73</td>\n",
       "      <td>16.23</td>\n",
       "      <td>22.56</td>\n",
       "      <td>10.68</td>\n",
       "      <td>18.51</td>\n",
       "      <td>15.47</td>\n",
       "      <td>14.59</td>\n",
       "      <td>10.47</td>\n",
       "      <td>...</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mths_since_last_delinq</th>\n",
       "      <td>43.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>64.00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>41.00</td>\n",
       "      <td>34.00</td>\n",
       "      <td>...</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open_acc</th>\n",
       "      <td>12.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>17.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>...</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revol_util</th>\n",
       "      <td>21.60</td>\n",
       "      <td>44.40</td>\n",
       "      <td>66.20</td>\n",
       "      <td>93.10</td>\n",
       "      <td>39.60</td>\n",
       "      <td>51.60</td>\n",
       "      <td>88.20</td>\n",
       "      <td>61.20</td>\n",
       "      <td>81.60</td>\n",
       "      <td>65.30</td>\n",
       "      <td>...</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_acc</th>\n",
       "      <td>22.00</td>\n",
       "      <td>22.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>35.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>41.00</td>\n",
       "      <td>23.00</td>\n",
       "      <td>24.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>...</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <td>61100.00</td>\n",
       "      <td>22300.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>20500.00</td>\n",
       "      <td>24700.00</td>\n",
       "      <td>21600.00</td>\n",
       "      <td>20600.00</td>\n",
       "      <td>4600.00</td>\n",
       "      <td>16800.00</td>\n",
       "      <td>10200.00</td>\n",
       "      <td>...</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>term</th>\n",
       "      <td>36 months</td>\n",
       "      <td>36 months</td>\n",
       "      <td>36 months</td>\n",
       "      <td>36 months</td>\n",
       "      <td>36 months</td>\n",
       "      <td>60 months</td>\n",
       "      <td>60 months</td>\n",
       "      <td>36 months</td>\n",
       "      <td>36 months</td>\n",
       "      <td>36 months</td>\n",
       "      <td>...</td>\n",
       "      <td>60 months</td>\n",
       "      <td>60 months</td>\n",
       "      <td>36 months</td>\n",
       "      <td>36 months</td>\n",
       "      <td>60 months</td>\n",
       "      <td>60 months</td>\n",
       "      <td>36 months</td>\n",
       "      <td>36 months</td>\n",
       "      <td>36 months</td>\n",
       "      <td>60 months</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub_grade</th>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>...</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emp_length</th>\n",
       "      <td>11.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home_ownership</th>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>RENT</td>\n",
       "      <td>OWN</td>\n",
       "      <td>RENT</td>\n",
       "      <td>RENT</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>RENT</td>\n",
       "      <td>RENT</td>\n",
       "      <td>...</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>RENT</td>\n",
       "      <td>RENT</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>RENT</td>\n",
       "      <td>RENT</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>RENT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>verification_status</th>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>...</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Source Verified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <td>Current</td>\n",
       "      <td>Current</td>\n",
       "      <td>Current</td>\n",
       "      <td>Current</td>\n",
       "      <td>Current</td>\n",
       "      <td>Current</td>\n",
       "      <td>Current</td>\n",
       "      <td>Current</td>\n",
       "      <td>Current</td>\n",
       "      <td>Current</td>\n",
       "      <td>...</td>\n",
       "      <td>Current</td>\n",
       "      <td>Current</td>\n",
       "      <td>Current</td>\n",
       "      <td>Late (31-120 days)</td>\n",
       "      <td>Current</td>\n",
       "      <td>Current</td>\n",
       "      <td>Current</td>\n",
       "      <td>Current</td>\n",
       "      <td>Current</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pymnt_plan</th>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purpose</th>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>...</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>debt_consolidation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zip_code</th>\n",
       "      <td>060xx</td>\n",
       "      <td>027xx</td>\n",
       "      <td>103xx</td>\n",
       "      <td>750xx</td>\n",
       "      <td>750xx</td>\n",
       "      <td>852xx</td>\n",
       "      <td>785xx</td>\n",
       "      <td>532xx</td>\n",
       "      <td>342xx</td>\n",
       "      <td>209xx</td>\n",
       "      <td>...</td>\n",
       "      <td>380xx</td>\n",
       "      <td>967xx</td>\n",
       "      <td>882xx</td>\n",
       "      <td>115xx</td>\n",
       "      <td>925xx</td>\n",
       "      <td>138xx</td>\n",
       "      <td>471xx</td>\n",
       "      <td>531xx</td>\n",
       "      <td>432xx</td>\n",
       "      <td>010xx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>addr_state</th>\n",
       "      <td>CT</td>\n",
       "      <td>MA</td>\n",
       "      <td>NY</td>\n",
       "      <td>TX</td>\n",
       "      <td>TX</td>\n",
       "      <td>AZ</td>\n",
       "      <td>TX</td>\n",
       "      <td>WI</td>\n",
       "      <td>FL</td>\n",
       "      <td>MD</td>\n",
       "      <td>...</td>\n",
       "      <td>TN</td>\n",
       "      <td>HI</td>\n",
       "      <td>NM</td>\n",
       "      <td>NY</td>\n",
       "      <td>CA</td>\n",
       "      <td>NY</td>\n",
       "      <td>IN</td>\n",
       "      <td>WI</td>\n",
       "      <td>OH</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>initial_list_status</th>\n",
       "      <td>w</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>w</td>\n",
       "      <td>f</td>\n",
       "      <td>w</td>\n",
       "      <td>f</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>...</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>w</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_pymnt_d</th>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2015-10-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>next_pymnt_d</th>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_credit_pull_d</th>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>application_type</th>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>...</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Issue Month</th>\n",
       "      <td>Dec</td>\n",
       "      <td>Dec</td>\n",
       "      <td>Dec</td>\n",
       "      <td>Dec</td>\n",
       "      <td>Dec</td>\n",
       "      <td>Dec</td>\n",
       "      <td>Dec</td>\n",
       "      <td>Dec</td>\n",
       "      <td>Dec</td>\n",
       "      <td>Dec</td>\n",
       "      <td>...</td>\n",
       "      <td>Jan</td>\n",
       "      <td>Jan</td>\n",
       "      <td>Jan</td>\n",
       "      <td>Jan</td>\n",
       "      <td>Jan</td>\n",
       "      <td>Jan</td>\n",
       "      <td>Jan</td>\n",
       "      <td>Jan</td>\n",
       "      <td>Jan</td>\n",
       "      <td>Jan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Issue Year</th>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "      <td>...</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earliest Credit Month</th>\n",
       "      <td>Mar</td>\n",
       "      <td>Oct</td>\n",
       "      <td>Jun</td>\n",
       "      <td>Mar</td>\n",
       "      <td>Dec</td>\n",
       "      <td>May</td>\n",
       "      <td>Oct</td>\n",
       "      <td>Apr</td>\n",
       "      <td>Jul</td>\n",
       "      <td>Jun</td>\n",
       "      <td>...</td>\n",
       "      <td>Apr</td>\n",
       "      <td>Sep</td>\n",
       "      <td>Apr</td>\n",
       "      <td>Oct</td>\n",
       "      <td>Aug</td>\n",
       "      <td>Aug</td>\n",
       "      <td>Nov</td>\n",
       "      <td>Aug</td>\n",
       "      <td>Jun</td>\n",
       "      <td>Oct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earliest Credit Year</th>\n",
       "      <td>1994</td>\n",
       "      <td>1989</td>\n",
       "      <td>2001</td>\n",
       "      <td>1990</td>\n",
       "      <td>1993</td>\n",
       "      <td>2003</td>\n",
       "      <td>1988</td>\n",
       "      <td>2004</td>\n",
       "      <td>1991</td>\n",
       "      <td>1993</td>\n",
       "      <td>...</td>\n",
       "      <td>2003</td>\n",
       "      <td>2001</td>\n",
       "      <td>1994</td>\n",
       "      <td>2003</td>\n",
       "      <td>2002</td>\n",
       "      <td>1983</td>\n",
       "      <td>2006</td>\n",
       "      <td>1994</td>\n",
       "      <td>1995</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32 rows × 291310 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     0                    1       \\\n",
       "loan_amnt                          12000.00             10000.00   \n",
       "funded_amnt                        12000.00             10000.00   \n",
       "funded_amnt_inv                    12000.00             10000.00   \n",
       "int_rate                               6.62                 9.67   \n",
       "dti                                   14.05                15.55   \n",
       "delinq_2yrs                            0.00                 2.00   \n",
       "inq_last_6mths                         1.00                 0.00   \n",
       "mths_since_last_delinq                43.00                11.00   \n",
       "open_acc                              12.00                 9.00   \n",
       "revol_util                            21.60                44.40   \n",
       "total_acc                             22.00                22.00   \n",
       "total_rev_hi_lim                   61100.00             22300.00   \n",
       "term                              36 months            36 months   \n",
       "grade                                     0                    1   \n",
       "sub_grade                              2.00                 1.00   \n",
       "emp_length                            11.00                 7.00   \n",
       "home_ownership                     MORTGAGE             MORTGAGE   \n",
       "verification_status            Not Verified         Not Verified   \n",
       "loan_status                         Current              Current   \n",
       "pymnt_plan                                n                    n   \n",
       "purpose                  debt_consolidation   debt_consolidation   \n",
       "zip_code                              060xx                027xx   \n",
       "addr_state                               CT                   MA   \n",
       "initial_list_status                       w                    f   \n",
       "last_pymnt_d            2016-01-01 00:00:00  2016-01-01 00:00:00   \n",
       "next_pymnt_d            2016-02-01 00:00:00  2016-02-01 00:00:00   \n",
       "last_credit_pull_d      2016-01-01 00:00:00  2016-01-01 00:00:00   \n",
       "application_type                 INDIVIDUAL           INDIVIDUAL   \n",
       "Issue Month                             Dec                  Dec   \n",
       "Issue Year                             2013                 2013   \n",
       "Earliest Credit Month                   Mar                  Oct   \n",
       "Earliest Credit Year                   1994                 1989   \n",
       "\n",
       "                                     2                    3       \\\n",
       "loan_amnt                          11100.00              4500.00   \n",
       "funded_amnt                        11100.00              4500.00   \n",
       "funded_amnt_inv                    11100.00              4500.00   \n",
       "int_rate                              14.98                19.22   \n",
       "dti                                    3.73                16.23   \n",
       "delinq_2yrs                            1.00                 1.00   \n",
       "inq_last_6mths                         0.00                 0.00   \n",
       "mths_since_last_delinq                16.00                10.00   \n",
       "open_acc                               9.00                 5.00   \n",
       "revol_util                            66.20                93.10   \n",
       "total_acc                             12.00                10.00   \n",
       "total_rev_hi_lim                   10000.00             20500.00   \n",
       "term                              36 months            36 months   \n",
       "grade                                     2                    3   \n",
       "sub_grade                              3.00                 4.00   \n",
       "emp_length                            11.00                11.00   \n",
       "home_ownership                     MORTGAGE                 RENT   \n",
       "verification_status            Not Verified         Not Verified   \n",
       "loan_status                         Current              Current   \n",
       "pymnt_plan                                n                    n   \n",
       "purpose                               other                other   \n",
       "zip_code                              103xx                750xx   \n",
       "addr_state                               NY                   TX   \n",
       "initial_list_status                       f                    f   \n",
       "last_pymnt_d            2016-01-01 00:00:00  2016-01-01 00:00:00   \n",
       "next_pymnt_d            2016-02-01 00:00:00  2016-02-01 00:00:00   \n",
       "last_credit_pull_d      2016-01-01 00:00:00  2016-01-01 00:00:00   \n",
       "application_type                 INDIVIDUAL           INDIVIDUAL   \n",
       "Issue Month                             Dec                  Dec   \n",
       "Issue Year                             2013                 2013   \n",
       "Earliest Credit Month                   Jun                  Mar   \n",
       "Earliest Credit Year                   2001                 1990   \n",
       "\n",
       "                                     4                    5       \\\n",
       "loan_amnt                           5000.00             12000.00   \n",
       "funded_amnt                         5000.00             12000.00   \n",
       "funded_amnt_inv                     5000.00             12000.00   \n",
       "int_rate                              13.53                16.24   \n",
       "dti                                   22.56                10.68   \n",
       "delinq_2yrs                            0.00                 1.00   \n",
       "inq_last_6mths                         1.00                 0.00   \n",
       "mths_since_last_delinq                45.00                10.00   \n",
       "open_acc                              12.00                 7.00   \n",
       "revol_util                            39.60                51.60   \n",
       "total_acc                             35.00                13.00   \n",
       "total_rev_hi_lim                   24700.00             21600.00   \n",
       "term                              36 months            60 months   \n",
       "grade                                     1                    2   \n",
       "sub_grade                              5.00                 5.00   \n",
       "emp_length                            11.00                 2.00   \n",
       "home_ownership                          OWN                 RENT   \n",
       "verification_status            Not Verified             Verified   \n",
       "loan_status                         Current              Current   \n",
       "pymnt_plan                                n                    n   \n",
       "purpose                               other   debt_consolidation   \n",
       "zip_code                              750xx                852xx   \n",
       "addr_state                               TX                   AZ   \n",
       "initial_list_status                       w                    f   \n",
       "last_pymnt_d            2016-01-01 00:00:00  2016-01-01 00:00:00   \n",
       "next_pymnt_d            2016-02-01 00:00:00  2016-02-01 00:00:00   \n",
       "last_credit_pull_d      2016-01-01 00:00:00  2016-01-01 00:00:00   \n",
       "application_type                 INDIVIDUAL           INDIVIDUAL   \n",
       "Issue Month                             Dec                  Dec   \n",
       "Issue Year                             2013                 2013   \n",
       "Earliest Credit Month                   Dec                  May   \n",
       "Earliest Credit Year                   1993                 2003   \n",
       "\n",
       "                                     6                    7       \\\n",
       "loan_amnt                          10400.00              4000.00   \n",
       "funded_amnt                        10400.00              4000.00   \n",
       "funded_amnt_inv                    10400.00              3950.00   \n",
       "int_rate                              17.57                 7.90   \n",
       "dti                                   18.51                15.47   \n",
       "delinq_2yrs                            0.00                 0.00   \n",
       "inq_last_6mths                         0.00                 1.00   \n",
       "mths_since_last_delinq                64.00                50.00   \n",
       "open_acc                              17.00                10.00   \n",
       "revol_util                            88.20                61.20   \n",
       "total_acc                             41.00                23.00   \n",
       "total_rev_hi_lim                   20600.00              4600.00   \n",
       "term                              60 months            36 months   \n",
       "grade                                     3                    0   \n",
       "sub_grade                              2.00                 4.00   \n",
       "emp_length                             2.00                 5.00   \n",
       "home_ownership                         RENT             MORTGAGE   \n",
       "verification_status         Source Verified         Not Verified   \n",
       "loan_status                         Current              Current   \n",
       "pymnt_plan                                n                    n   \n",
       "purpose                         credit_card          credit_card   \n",
       "zip_code                              785xx                532xx   \n",
       "addr_state                               TX                   WI   \n",
       "initial_list_status                       w                    f   \n",
       "last_pymnt_d            2016-01-01 00:00:00  2016-01-01 00:00:00   \n",
       "next_pymnt_d            2016-02-01 00:00:00  2016-02-01 00:00:00   \n",
       "last_credit_pull_d      2016-01-01 00:00:00  2016-01-01 00:00:00   \n",
       "application_type                 INDIVIDUAL           INDIVIDUAL   \n",
       "Issue Month                             Dec                  Dec   \n",
       "Issue Year                             2013                 2013   \n",
       "Earliest Credit Month                   Oct                  Apr   \n",
       "Earliest Credit Year                   1988                 2004   \n",
       "\n",
       "                                     8                    9       ...  \\\n",
       "loan_amnt                          14000.00             10000.00  ...   \n",
       "funded_amnt                        14000.00             10000.00  ...   \n",
       "funded_amnt_inv                    14000.00             10000.00  ...   \n",
       "int_rate                              14.98                11.99  ...   \n",
       "dti                                   14.59                10.47  ...   \n",
       "delinq_2yrs                            0.00                 0.00  ...   \n",
       "inq_last_6mths                         0.00                 1.00  ...   \n",
       "mths_since_last_delinq                41.00                34.00  ...   \n",
       "open_acc                              15.00                11.00  ...   \n",
       "revol_util                            81.60                65.30  ...   \n",
       "total_acc                             24.00                31.00  ...   \n",
       "total_rev_hi_lim                   16800.00             10200.00  ...   \n",
       "term                              36 months            36 months  ...   \n",
       "grade                                     2                    1  ...   \n",
       "sub_grade                              3.00                 3.00  ...   \n",
       "emp_length                             5.00                 2.00  ...   \n",
       "home_ownership                         RENT                 RENT  ...   \n",
       "verification_status            Not Verified         Not Verified  ...   \n",
       "loan_status                         Current              Current  ...   \n",
       "pymnt_plan                                n                    n  ...   \n",
       "purpose                  debt_consolidation   debt_consolidation  ...   \n",
       "zip_code                              342xx                209xx  ...   \n",
       "addr_state                               FL                   MD  ...   \n",
       "initial_list_status                       w                    w  ...   \n",
       "last_pymnt_d            2016-01-01 00:00:00  2016-01-01 00:00:00  ...   \n",
       "next_pymnt_d            2016-02-01 00:00:00  2016-02-01 00:00:00  ...   \n",
       "last_credit_pull_d      2016-01-01 00:00:00  2016-01-01 00:00:00  ...   \n",
       "application_type                 INDIVIDUAL           INDIVIDUAL  ...   \n",
       "Issue Month                             Dec                  Dec  ...   \n",
       "Issue Year                             2013                 2013  ...   \n",
       "Earliest Credit Month                   Jul                  Jun  ...   \n",
       "Earliest Credit Year                   1991                 1993  ...   \n",
       "\n",
       "                                     291300               291301  \\\n",
       "loan_amnt                          13297.17             13297.17   \n",
       "funded_amnt                        13297.17             13297.17   \n",
       "funded_amnt_inv                    13291.86             13291.86   \n",
       "int_rate                              12.62                12.62   \n",
       "dti                                   18.74                18.74   \n",
       "delinq_2yrs                            0.46                 0.46   \n",
       "inq_last_6mths                         0.45                 0.45   \n",
       "mths_since_last_delinq                34.14                34.14   \n",
       "open_acc                              11.05                11.05   \n",
       "revol_util                            56.93                56.93   \n",
       "total_acc                             24.72                24.72   \n",
       "total_rev_hi_lim                   23762.28             23762.28   \n",
       "term                              60 months            60 months   \n",
       "grade                                     4                    3   \n",
       "sub_grade                              5.00                 4.00   \n",
       "emp_length                             1.00                11.00   \n",
       "home_ownership                     MORTGAGE             MORTGAGE   \n",
       "verification_status         Source Verified             Verified   \n",
       "loan_status                         Current              Current   \n",
       "pymnt_plan                                n                    n   \n",
       "purpose                         credit_card          credit_card   \n",
       "zip_code                              380xx                967xx   \n",
       "addr_state                               TN                   HI   \n",
       "initial_list_status                       w                    w   \n",
       "last_pymnt_d            2016-01-01 00:00:00  2016-01-01 00:00:00   \n",
       "next_pymnt_d            2016-02-01 00:00:00  2016-02-01 00:00:00   \n",
       "last_credit_pull_d      2016-01-01 00:00:00  2016-01-01 00:00:00   \n",
       "application_type                 INDIVIDUAL           INDIVIDUAL   \n",
       "Issue Month                             Jan                  Jan   \n",
       "Issue Year                             2015                 2015   \n",
       "Earliest Credit Month                   Apr                  Sep   \n",
       "Earliest Credit Year                   2003                 2001   \n",
       "\n",
       "                                     291302               291303  \\\n",
       "loan_amnt                          13297.17             13297.17   \n",
       "funded_amnt                        13297.17             13297.17   \n",
       "funded_amnt_inv                    13291.86             13291.86   \n",
       "int_rate                              12.62                12.62   \n",
       "dti                                   18.74                18.74   \n",
       "delinq_2yrs                            0.46                 0.46   \n",
       "inq_last_6mths                         0.45                 0.45   \n",
       "mths_since_last_delinq                34.14                34.14   \n",
       "open_acc                              11.05                11.05   \n",
       "revol_util                            56.93                56.93   \n",
       "total_acc                             24.72                24.72   \n",
       "total_rev_hi_lim                   23762.28             23762.28   \n",
       "term                              36 months            36 months   \n",
       "grade                                     4                    3   \n",
       "sub_grade                              4.00                 1.00   \n",
       "emp_length                             4.00                 3.00   \n",
       "home_ownership                     MORTGAGE                 RENT   \n",
       "verification_status                Verified             Verified   \n",
       "loan_status                         Current   Late (31-120 days)   \n",
       "pymnt_plan                                n                    n   \n",
       "purpose                  debt_consolidation   debt_consolidation   \n",
       "zip_code                              882xx                115xx   \n",
       "addr_state                               NM                   NY   \n",
       "initial_list_status                       f                    f   \n",
       "last_pymnt_d            2016-01-01 00:00:00  2015-10-01 00:00:00   \n",
       "next_pymnt_d            2016-02-01 00:00:00  2016-02-01 00:00:00   \n",
       "last_credit_pull_d      2016-01-01 00:00:00  2016-01-01 00:00:00   \n",
       "application_type                 INDIVIDUAL           INDIVIDUAL   \n",
       "Issue Month                             Jan                  Jan   \n",
       "Issue Year                             2015                 2015   \n",
       "Earliest Credit Month                   Apr                  Oct   \n",
       "Earliest Credit Year                   1994                 2003   \n",
       "\n",
       "                                     291304               291305  \\\n",
       "loan_amnt                          13297.17             13297.17   \n",
       "funded_amnt                        13297.17             13297.17   \n",
       "funded_amnt_inv                    13291.86             13291.86   \n",
       "int_rate                              12.62                12.62   \n",
       "dti                                   18.74                18.74   \n",
       "delinq_2yrs                            0.46                 0.46   \n",
       "inq_last_6mths                         0.45                 0.45   \n",
       "mths_since_last_delinq                34.14                34.14   \n",
       "open_acc                              11.05                11.05   \n",
       "revol_util                            56.93                56.93   \n",
       "total_acc                             24.72                24.72   \n",
       "total_rev_hi_lim                   23762.28             23762.28   \n",
       "term                              60 months            60 months   \n",
       "grade                                     2                    1   \n",
       "sub_grade                              3.00                 1.00   \n",
       "emp_length                            11.00                11.00   \n",
       "home_ownership                         RENT             MORTGAGE   \n",
       "verification_status         Source Verified             Verified   \n",
       "loan_status                         Current              Current   \n",
       "pymnt_plan                                n                    n   \n",
       "purpose                  debt_consolidation   debt_consolidation   \n",
       "zip_code                              925xx                138xx   \n",
       "addr_state                               CA                   NY   \n",
       "initial_list_status                       f                    w   \n",
       "last_pymnt_d            2016-01-01 00:00:00  2016-01-01 00:00:00   \n",
       "next_pymnt_d            2016-02-01 00:00:00  2016-02-01 00:00:00   \n",
       "last_credit_pull_d      2016-01-01 00:00:00  2016-01-01 00:00:00   \n",
       "application_type                 INDIVIDUAL           INDIVIDUAL   \n",
       "Issue Month                             Jan                  Jan   \n",
       "Issue Year                             2015                 2015   \n",
       "Earliest Credit Month                   Aug                  Aug   \n",
       "Earliest Credit Year                   2002                 1983   \n",
       "\n",
       "                                     291306               291307  \\\n",
       "loan_amnt                          13297.17             13297.17   \n",
       "funded_amnt                        13297.17             13297.17   \n",
       "funded_amnt_inv                    13291.86             13291.86   \n",
       "int_rate                              12.62                12.62   \n",
       "dti                                   18.74                18.74   \n",
       "delinq_2yrs                            0.46                 0.46   \n",
       "inq_last_6mths                         0.45                 0.45   \n",
       "mths_since_last_delinq                34.14                34.14   \n",
       "open_acc                              11.05                11.05   \n",
       "revol_util                            56.93                56.93   \n",
       "total_acc                             24.72                24.72   \n",
       "total_rev_hi_lim                   23762.28             23762.28   \n",
       "term                              36 months            36 months   \n",
       "grade                                     2                    1   \n",
       "sub_grade                              4.00                 2.00   \n",
       "emp_length                            11.00                 8.00   \n",
       "home_ownership                         RENT                 RENT   \n",
       "verification_status                Verified             Verified   \n",
       "loan_status                         Current              Current   \n",
       "pymnt_plan                                n                    n   \n",
       "purpose                  debt_consolidation   debt_consolidation   \n",
       "zip_code                              471xx                531xx   \n",
       "addr_state                               IN                   WI   \n",
       "initial_list_status                       w                    f   \n",
       "last_pymnt_d            2016-01-01 00:00:00  2016-01-01 00:00:00   \n",
       "next_pymnt_d            2016-02-01 00:00:00  2016-02-01 00:00:00   \n",
       "last_credit_pull_d      2016-01-01 00:00:00  2016-01-01 00:00:00   \n",
       "application_type                 INDIVIDUAL           INDIVIDUAL   \n",
       "Issue Month                             Jan                  Jan   \n",
       "Issue Year                             2015                 2015   \n",
       "Earliest Credit Month                   Nov                  Aug   \n",
       "Earliest Credit Year                   2006                 1994   \n",
       "\n",
       "                                     291308               291309  \n",
       "loan_amnt                          13297.17             13297.17  \n",
       "funded_amnt                        13297.17             13297.17  \n",
       "funded_amnt_inv                    13291.86             13291.86  \n",
       "int_rate                              12.62                12.62  \n",
       "dti                                   18.74                18.74  \n",
       "delinq_2yrs                            0.46                 0.46  \n",
       "inq_last_6mths                         0.45                 0.45  \n",
       "mths_since_last_delinq                34.14                34.14  \n",
       "open_acc                              11.05                11.05  \n",
       "revol_util                            56.93                56.93  \n",
       "total_acc                             24.72                24.72  \n",
       "total_rev_hi_lim                   23762.28             23762.28  \n",
       "term                              36 months            60 months  \n",
       "grade                                     1                    4  \n",
       "sub_grade                              5.00                 3.00  \n",
       "emp_length                             1.00                 1.00  \n",
       "home_ownership                     MORTGAGE                 RENT  \n",
       "verification_status                Verified      Source Verified  \n",
       "loan_status                         Current              Current  \n",
       "pymnt_plan                                n                    n  \n",
       "purpose                         credit_card   debt_consolidation  \n",
       "zip_code                              432xx                010xx  \n",
       "addr_state                               OH                   MA  \n",
       "initial_list_status                       f                    w  \n",
       "last_pymnt_d            2016-01-01 00:00:00  2016-01-01 00:00:00  \n",
       "next_pymnt_d            2016-02-01 00:00:00  2016-02-01 00:00:00  \n",
       "last_credit_pull_d      2016-01-01 00:00:00  2016-01-01 00:00:00  \n",
       "application_type                 INDIVIDUAL           INDIVIDUAL  \n",
       "Issue Month                             Jan                  Jan  \n",
       "Issue Year                             2015                 2015  \n",
       "Earliest Credit Month                   Jun                  Oct  \n",
       "Earliest Credit Year                   1995                 2003  \n",
       "\n",
       "[32 rows x 291310 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_loan.T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bb260ab",
   "metadata": {
    "papermill": {
     "duration": 0.020535,
     "end_time": "2023-12-18T18:12:32.171573",
     "exception": false,
     "start_time": "2023-12-18T18:12:32.151038",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Formatting target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dadbf664",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T18:12:32.217796Z",
     "iopub.status.busy": "2023-12-18T18:12:32.216399Z",
     "iopub.status.idle": "2023-12-18T18:12:32.248528Z",
     "shell.execute_reply": "2023-12-18T18:12:32.247660Z"
    },
    "papermill": {
     "duration": 0.057914,
     "end_time": "2023-12-18T18:12:32.250839",
     "exception": false,
     "start_time": "2023-12-18T18:12:32.192925",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Current', 'Late (31-120 days)', 'Late (16-30 days)',\n",
       "       'In Grace Period', 'Default', 'Issued'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_loan['loan_status'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6a695f1d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T18:12:32.295311Z",
     "iopub.status.busy": "2023-12-18T18:12:32.294055Z",
     "iopub.status.idle": "2023-12-18T18:12:32.726336Z",
     "shell.execute_reply": "2023-12-18T18:12:32.725212Z"
    },
    "papermill": {
     "duration": 0.457416,
     "end_time": "2023-12-18T18:12:32.729054",
     "exception": false,
     "start_time": "2023-12-18T18:12:32.271638",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "\n",
    "ax = df_loan['loan_status'].value_counts().sort_values().plot.barh(edgecolor='black', grid=False, fontsize=12)\n",
    "\n",
    "for p in ax.patches:\n",
    "    width = p.get_width()\n",
    "    ax.text(width+5000, p.get_y(), '{:.2%}'.format(width/len(df_loan)), ha='left', va='bottom', fontsize=14)\n",
    "\n",
    "sns.despine() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "afb0ad1c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T18:12:32.776320Z",
     "iopub.status.busy": "2023-12-18T18:12:32.775744Z",
     "iopub.status.idle": "2023-12-18T18:12:33.623867Z",
     "shell.execute_reply": "2023-12-18T18:12:33.621693Z"
    },
    "papermill": {
     "duration": 0.878079,
     "end_time": "2023-12-18T18:12:33.629998",
     "exception": false,
     "start_time": "2023-12-18T18:12:32.751919",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Examples:\n",
      "    Total: 291293\n",
      "    Positive: 10528 (3.61% of total)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# removed the 'Issued' class since these loans just got approved and have not begun any repayment processes yet\n",
    "issue = df_loan['loan_status'] == 'Issued'\n",
    "df_loan_issue = df_loan[~issue]\n",
    "\n",
    "# binary classification\n",
    "label_categories = [\n",
    "    (0, ['Current']),\n",
    "    (1, ['Late (31-120 days)', 'Late (16-30 days)', 'In Grace Period','Default'])\n",
    "]\n",
    "\n",
    "# function to apply the transformation\n",
    "def classify_label(text):\n",
    "    for category, matches in label_categories:\n",
    "        if any(match in text for match in matches):\n",
    "            return category\n",
    "    return None\n",
    "\n",
    "df_loan_issue.loc[:, 'label'] = df_loan_issue['loan_status'].apply(classify_label)\n",
    "df_loan_issue = df_loan_issue.drop('loan_status', axis=1)\n",
    "\n",
    "# check label classes imbalance\n",
    "neg, pos = np.bincount(df_loan_issue['label'])\n",
    "total = neg + pos\n",
    "print('Examples:\\n    Total: {}\\n    Positive: {} ({:.2f}% of total)\\n'.format(\n",
    "    total, pos, 100 * pos / total))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dd38ac9",
   "metadata": {
    "papermill": {
     "duration": 0.021803,
     "end_time": "2023-12-18T18:12:33.676669",
     "exception": false,
     "start_time": "2023-12-18T18:12:33.654866",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Formatting other columns\n",
    "##### - Home ownership"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9ac667f9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T18:12:33.721829Z",
     "iopub.status.busy": "2023-12-18T18:12:33.721199Z",
     "iopub.status.idle": "2023-12-18T18:12:34.365452Z",
     "shell.execute_reply": "2023-12-18T18:12:34.364248Z"
    },
    "papermill": {
     "duration": 0.669996,
     "end_time": "2023-12-18T18:12:34.368254",
     "exception": false,
     "start_time": "2023-12-18T18:12:33.698258",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "ax = sns.countplot(x='home_ownership', data=df_loan_issue, edgecolor='black')\n",
    "\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()\n",
    "    ax.text(p.get_x()+.4, height, '{:.2%}'.format(height/len(df_loan)), ha='center', va='bottom', fontsize=14)\n",
    "\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7ea3f5d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T18:12:34.414402Z",
     "iopub.status.busy": "2023-12-18T18:12:34.413966Z",
     "iopub.status.idle": "2023-12-18T18:12:34.495311Z",
     "shell.execute_reply": "2023-12-18T18:12:34.494228Z"
    },
    "papermill": {
     "duration": 0.107838,
     "end_time": "2023-12-18T18:12:34.498265",
     "exception": false,
     "start_time": "2023-12-18T18:12:34.390427",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_loan_issue['home_ownership'] = df_loan_issue['home_ownership'].replace(['NONE', 'ANY'], 'OTHER')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43628e4d",
   "metadata": {
    "papermill": {
     "duration": 0.022148,
     "end_time": "2023-12-18T18:12:34.542868",
     "exception": false,
     "start_time": "2023-12-18T18:12:34.520720",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Ordinal Coding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7bcaecef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T18:12:34.589379Z",
     "iopub.status.busy": "2023-12-18T18:12:34.588928Z",
     "iopub.status.idle": "2023-12-18T18:12:34.627714Z",
     "shell.execute_reply": "2023-12-18T18:12:34.626549Z"
    },
    "papermill": {
     "duration": 0.065786,
     "end_time": "2023-12-18T18:12:34.630723",
     "exception": false,
     "start_time": "2023-12-18T18:12:34.564937",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_loan_issue['term'] = pd.factorize(df_loan_issue['term'])[0]\n",
    "df_loan_issue['sub_grade'] = pd.factorize(df_loan_issue['sub_grade'])[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "423f0866",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T18:12:34.679186Z",
     "iopub.status.busy": "2023-12-18T18:12:34.678082Z",
     "iopub.status.idle": "2023-12-18T18:12:34.780193Z",
     "shell.execute_reply": "2023-12-18T18:12:34.779111Z"
    },
    "papermill": {
     "duration": 0.12961,
     "end_time": "2023-12-18T18:12:34.783073",
     "exception": false,
     "start_time": "2023-12-18T18:12:34.653463",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_loan_issue['emp_length'] = pd.factorize(df_loan_issue['emp_length'])[0]\n",
    "df_loan_issue['home_ownership'] = pd.factorize(df_loan_issue['home_ownership'])[0]\n",
    "df_loan_issue['purpose'] = pd.factorize(df_loan_issue['purpose'])[0]\n",
    "df_loan_issue['verification_status'] = pd.factorize(df_loan_issue['verification_status'])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "93bac683",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T18:12:34.830486Z",
     "iopub.status.busy": "2023-12-18T18:12:34.829569Z",
     "iopub.status.idle": "2023-12-18T18:12:34.863039Z",
     "shell.execute_reply": "2023-12-18T18:12:34.861907Z"
    },
    "papermill": {
     "duration": 0.060531,
     "end_time": "2023-12-18T18:12:34.866255",
     "exception": false,
     "start_time": "2023-12-18T18:12:34.805724",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_loan_issue['addr_state'] = pd.factorize(df_loan_issue['addr_state'])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b0f05da4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T18:12:34.913134Z",
     "iopub.status.busy": "2023-12-18T18:12:34.912747Z",
     "iopub.status.idle": "2023-12-18T18:12:34.943726Z",
     "shell.execute_reply": "2023-12-18T18:12:34.942389Z"
    },
    "papermill": {
     "duration": 0.057547,
     "end_time": "2023-12-18T18:12:34.946259",
     "exception": false,
     "start_time": "2023-12-18T18:12:34.888712",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_loan_issue['initial_list_status'] = pd.factorize(df_loan_issue['initial_list_status'])[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88610eef",
   "metadata": {
    "papermill": {
     "duration": 0.022377,
     "end_time": "2023-12-18T18:12:34.991519",
     "exception": false,
     "start_time": "2023-12-18T18:12:34.969142",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "##### - Label encding payment plan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "aa6f715e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T18:12:35.038329Z",
     "iopub.status.busy": "2023-12-18T18:12:35.037858Z",
     "iopub.status.idle": "2023-12-18T18:12:35.123387Z",
     "shell.execute_reply": "2023-12-18T18:12:35.122219Z"
    },
    "papermill": {
     "duration": 0.112326,
     "end_time": "2023-12-18T18:12:35.126206",
     "exception": false,
     "start_time": "2023-12-18T18:12:35.013880",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "label_encoder = LabelEncoder()\n",
    "df_loan_issue['pymnt_plan'] = label_encoder.fit_transform(df_loan_issue['pymnt_plan'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da3759e5",
   "metadata": {
    "papermill": {
     "duration": 0.022325,
     "end_time": "2023-12-18T18:12:35.170711",
     "exception": false,
     "start_time": "2023-12-18T18:12:35.148386",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "##### - Dropping zipcode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "eb3c54f6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T18:12:35.218018Z",
     "iopub.status.busy": "2023-12-18T18:12:35.217621Z",
     "iopub.status.idle": "2023-12-18T18:12:35.379692Z",
     "shell.execute_reply": "2023-12-18T18:12:35.378539Z"
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     "exception": false,
     "start_time": "2023-12-18T18:12:35.192922",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# pymnt_plan has only zeros. application_type contains mostly one value\n",
    "df_loan_issue = df_loan_issue.drop(columns=['zip_code'])\n",
    "df_loan_issue = df_loan_issue.drop(columns=['application_type'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d98b79ac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T18:12:35.429889Z",
     "iopub.status.busy": "2023-12-18T18:12:35.429473Z",
     "iopub.status.idle": "2023-12-18T18:12:35.640003Z",
     "shell.execute_reply": "2023-12-18T18:12:35.638363Z"
    },
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     "start_time": "2023-12-18T18:12:35.405316",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 291293 entries, 0 to 291309\n",
      "Data columns (total 30 columns):\n",
      " #   Column                  Non-Null Count   Dtype  \n",
      "---  ------                  --------------   -----  \n",
      " 0   loan_amnt               291293 non-null  float64\n",
      " 1   funded_amnt             291293 non-null  float64\n",
      " 2   funded_amnt_inv         291293 non-null  float64\n",
      " 3   int_rate                291293 non-null  float64\n",
      " 4   dti                     291293 non-null  float64\n",
      " 5   delinq_2yrs             291293 non-null  float64\n",
      " 6   inq_last_6mths          291293 non-null  float64\n",
      " 7   mths_since_last_delinq  291293 non-null  float64\n",
      " 8   open_acc                291293 non-null  float64\n",
      " 9   revol_util              291293 non-null  float64\n",
      " 10  total_acc               291293 non-null  float64\n",
      " 11  total_rev_hi_lim        291293 non-null  float64\n",
      " 12  term                    291293 non-null  int64  \n",
      " 13  grade                   291293 non-null  int64  \n",
      " 14  sub_grade               291293 non-null  int64  \n",
      " 15  emp_length              291293 non-null  int64  \n",
      " 16  home_ownership          291293 non-null  int64  \n",
      " 17  verification_status     291293 non-null  int64  \n",
      " 18  pymnt_plan              291293 non-null  int64  \n",
      " 19  purpose                 291293 non-null  int64  \n",
      " 20  addr_state              291293 non-null  int64  \n",
      " 21  initial_list_status     291293 non-null  int64  \n",
      " 22  last_pymnt_d            291293 non-null  object \n",
      " 23  next_pymnt_d            291293 non-null  object \n",
      " 24  last_credit_pull_d      291293 non-null  object \n",
      " 25  Issue Month             291293 non-null  object \n",
      " 26  Issue Year              291293 non-null  int32  \n",
      " 27  Earliest Credit Month   291293 non-null  object \n",
      " 28  Earliest Credit Year    291293 non-null  int32  \n",
      " 29  label                   291293 non-null  int64  \n",
      "dtypes: float64(12), int32(2), int64(11), object(5)\n",
      "memory usage: 66.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df_loan_issue.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fb9e4000",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T18:12:35.690476Z",
     "iopub.status.busy": "2023-12-18T18:12:35.689215Z",
     "iopub.status.idle": "2023-12-18T18:12:39.629359Z",
     "shell.execute_reply": "2023-12-18T18:12:39.627940Z"
    },
    "papermill": {
     "duration": 3.967266,
     "end_time": "2023-12-18T18:12:39.632681",
     "exception": false,
     "start_time": "2023-12-18T18:12:35.665415",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>291300</th>\n",
       "      <th>291301</th>\n",
       "      <th>291302</th>\n",
       "      <th>291303</th>\n",
       "      <th>291304</th>\n",
       "      <th>291305</th>\n",
       "      <th>291306</th>\n",
       "      <th>291307</th>\n",
       "      <th>291308</th>\n",
       "      <th>291309</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>loan_amnt</th>\n",
       "      <td>12000.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>11100.00</td>\n",
       "      <td>4500.00</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>12000.00</td>\n",
       "      <td>10400.00</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>14000.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>...</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>funded_amnt</th>\n",
       "      <td>12000.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>11100.00</td>\n",
       "      <td>4500.00</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>12000.00</td>\n",
       "      <td>10400.00</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>14000.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>...</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "      <td>13297.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <td>12000.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>11100.00</td>\n",
       "      <td>4500.00</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>12000.00</td>\n",
       "      <td>10400.00</td>\n",
       "      <td>3950.00</td>\n",
       "      <td>14000.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>...</td>\n",
       "      <td>13291.86</td>\n",
       "      <td>13291.86</td>\n",
       "      <td>13291.86</td>\n",
       "      <td>13291.86</td>\n",
       "      <td>13291.86</td>\n",
       "      <td>13291.86</td>\n",
       "      <td>13291.86</td>\n",
       "      <td>13291.86</td>\n",
       "      <td>13291.86</td>\n",
       "      <td>13291.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>int_rate</th>\n",
       "      <td>6.62</td>\n",
       "      <td>9.67</td>\n",
       "      <td>14.98</td>\n",
       "      <td>19.22</td>\n",
       "      <td>13.53</td>\n",
       "      <td>16.24</td>\n",
       "      <td>17.57</td>\n",
       "      <td>7.90</td>\n",
       "      <td>14.98</td>\n",
       "      <td>11.99</td>\n",
       "      <td>...</td>\n",
       "      <td>12.62</td>\n",
       "      <td>12.62</td>\n",
       "      <td>12.62</td>\n",
       "      <td>12.62</td>\n",
       "      <td>12.62</td>\n",
       "      <td>12.62</td>\n",
       "      <td>12.62</td>\n",
       "      <td>12.62</td>\n",
       "      <td>12.62</td>\n",
       "      <td>12.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dti</th>\n",
       "      <td>14.05</td>\n",
       "      <td>15.55</td>\n",
       "      <td>3.73</td>\n",
       "      <td>16.23</td>\n",
       "      <td>22.56</td>\n",
       "      <td>10.68</td>\n",
       "      <td>18.51</td>\n",
       "      <td>15.47</td>\n",
       "      <td>14.59</td>\n",
       "      <td>10.47</td>\n",
       "      <td>...</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mths_since_last_delinq</th>\n",
       "      <td>43.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>64.00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>41.00</td>\n",
       "      <td>34.00</td>\n",
       "      <td>...</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open_acc</th>\n",
       "      <td>12.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>17.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>...</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revol_util</th>\n",
       "      <td>21.60</td>\n",
       "      <td>44.40</td>\n",
       "      <td>66.20</td>\n",
       "      <td>93.10</td>\n",
       "      <td>39.60</td>\n",
       "      <td>51.60</td>\n",
       "      <td>88.20</td>\n",
       "      <td>61.20</td>\n",
       "      <td>81.60</td>\n",
       "      <td>65.30</td>\n",
       "      <td>...</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_acc</th>\n",
       "      <td>22.00</td>\n",
       "      <td>22.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>35.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>41.00</td>\n",
       "      <td>23.00</td>\n",
       "      <td>24.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>...</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <td>61100.00</td>\n",
       "      <td>22300.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>20500.00</td>\n",
       "      <td>24700.00</td>\n",
       "      <td>21600.00</td>\n",
       "      <td>20600.00</td>\n",
       "      <td>4600.00</td>\n",
       "      <td>16800.00</td>\n",
       "      <td>10200.00</td>\n",
       "      <td>...</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>term</th>\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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub_grade</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emp_length</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home_ownership</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>verification_status</th>\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>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pymnt_plan</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purpose</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>addr_state</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>initial_list_status</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_pymnt_d</th>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2015-10-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>next_pymnt_d</th>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_credit_pull_d</th>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Issue Month</th>\n",
       "      <td>Dec</td>\n",
       "      <td>Dec</td>\n",
       "      <td>Dec</td>\n",
       "      <td>Dec</td>\n",
       "      <td>Dec</td>\n",
       "      <td>Dec</td>\n",
       "      <td>Dec</td>\n",
       "      <td>Dec</td>\n",
       "      <td>Dec</td>\n",
       "      <td>Dec</td>\n",
       "      <td>...</td>\n",
       "      <td>Jan</td>\n",
       "      <td>Jan</td>\n",
       "      <td>Jan</td>\n",
       "      <td>Jan</td>\n",
       "      <td>Jan</td>\n",
       "      <td>Jan</td>\n",
       "      <td>Jan</td>\n",
       "      <td>Jan</td>\n",
       "      <td>Jan</td>\n",
       "      <td>Jan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Issue Year</th>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013</td>\n",
       "      <td>...</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earliest Credit Month</th>\n",
       "      <td>Mar</td>\n",
       "      <td>Oct</td>\n",
       "      <td>Jun</td>\n",
       "      <td>Mar</td>\n",
       "      <td>Dec</td>\n",
       "      <td>May</td>\n",
       "      <td>Oct</td>\n",
       "      <td>Apr</td>\n",
       "      <td>Jul</td>\n",
       "      <td>Jun</td>\n",
       "      <td>...</td>\n",
       "      <td>Apr</td>\n",
       "      <td>Sep</td>\n",
       "      <td>Apr</td>\n",
       "      <td>Oct</td>\n",
       "      <td>Aug</td>\n",
       "      <td>Aug</td>\n",
       "      <td>Nov</td>\n",
       "      <td>Aug</td>\n",
       "      <td>Jun</td>\n",
       "      <td>Oct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earliest Credit Year</th>\n",
       "      <td>1994</td>\n",
       "      <td>1989</td>\n",
       "      <td>2001</td>\n",
       "      <td>1990</td>\n",
       "      <td>1993</td>\n",
       "      <td>2003</td>\n",
       "      <td>1988</td>\n",
       "      <td>2004</td>\n",
       "      <td>1991</td>\n",
       "      <td>1993</td>\n",
       "      <td>...</td>\n",
       "      <td>2003</td>\n",
       "      <td>2001</td>\n",
       "      <td>1994</td>\n",
       "      <td>2003</td>\n",
       "      <td>2002</td>\n",
       "      <td>1983</td>\n",
       "      <td>2006</td>\n",
       "      <td>1994</td>\n",
       "      <td>1995</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 291293 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     0                    1       \\\n",
       "loan_amnt                          12000.00             10000.00   \n",
       "funded_amnt                        12000.00             10000.00   \n",
       "funded_amnt_inv                    12000.00             10000.00   \n",
       "int_rate                               6.62                 9.67   \n",
       "dti                                   14.05                15.55   \n",
       "delinq_2yrs                            0.00                 2.00   \n",
       "inq_last_6mths                         1.00                 0.00   \n",
       "mths_since_last_delinq                43.00                11.00   \n",
       "open_acc                              12.00                 9.00   \n",
       "revol_util                            21.60                44.40   \n",
       "total_acc                             22.00                22.00   \n",
       "total_rev_hi_lim                   61100.00             22300.00   \n",
       "term                                      0                    0   \n",
       "grade                                     0                    1   \n",
       "sub_grade                                 0                    1   \n",
       "emp_length                                0                    1   \n",
       "home_ownership                            0                    0   \n",
       "verification_status                       0                    0   \n",
       "pymnt_plan                                0                    0   \n",
       "purpose                                   0                    0   \n",
       "addr_state                                0                    1   \n",
       "initial_list_status                       0                    1   \n",
       "last_pymnt_d            2016-01-01 00:00:00  2016-01-01 00:00:00   \n",
       "next_pymnt_d            2016-02-01 00:00:00  2016-02-01 00:00:00   \n",
       "last_credit_pull_d      2016-01-01 00:00:00  2016-01-01 00:00:00   \n",
       "Issue Month                             Dec                  Dec   \n",
       "Issue Year                             2013                 2013   \n",
       "Earliest Credit Month                   Mar                  Oct   \n",
       "Earliest Credit Year                   1994                 1989   \n",
       "label                                     0                    0   \n",
       "\n",
       "                                     2                    3       \\\n",
       "loan_amnt                          11100.00              4500.00   \n",
       "funded_amnt                        11100.00              4500.00   \n",
       "funded_amnt_inv                    11100.00              4500.00   \n",
       "int_rate                              14.98                19.22   \n",
       "dti                                    3.73                16.23   \n",
       "delinq_2yrs                            1.00                 1.00   \n",
       "inq_last_6mths                         0.00                 0.00   \n",
       "mths_since_last_delinq                16.00                10.00   \n",
       "open_acc                               9.00                 5.00   \n",
       "revol_util                            66.20                93.10   \n",
       "total_acc                             12.00                10.00   \n",
       "total_rev_hi_lim                   10000.00             20500.00   \n",
       "term                                      0                    0   \n",
       "grade                                     2                    3   \n",
       "sub_grade                                 2                    3   \n",
       "emp_length                                0                    0   \n",
       "home_ownership                            0                    1   \n",
       "verification_status                       0                    0   \n",
       "pymnt_plan                                0                    0   \n",
       "purpose                                   1                    1   \n",
       "addr_state                                2                    3   \n",
       "initial_list_status                       1                    1   \n",
       "last_pymnt_d            2016-01-01 00:00:00  2016-01-01 00:00:00   \n",
       "next_pymnt_d            2016-02-01 00:00:00  2016-02-01 00:00:00   \n",
       "last_credit_pull_d      2016-01-01 00:00:00  2016-01-01 00:00:00   \n",
       "Issue Month                             Dec                  Dec   \n",
       "Issue Year                             2013                 2013   \n",
       "Earliest Credit Month                   Jun                  Mar   \n",
       "Earliest Credit Year                   2001                 1990   \n",
       "label                                     0                    0   \n",
       "\n",
       "                                     4                    5       \\\n",
       "loan_amnt                           5000.00             12000.00   \n",
       "funded_amnt                         5000.00             12000.00   \n",
       "funded_amnt_inv                     5000.00             12000.00   \n",
       "int_rate                              13.53                16.24   \n",
       "dti                                   22.56                10.68   \n",
       "delinq_2yrs                            0.00                 1.00   \n",
       "inq_last_6mths                         1.00                 0.00   \n",
       "mths_since_last_delinq                45.00                10.00   \n",
       "open_acc                              12.00                 7.00   \n",
       "revol_util                            39.60                51.60   \n",
       "total_acc                             35.00                13.00   \n",
       "total_rev_hi_lim                   24700.00             21600.00   \n",
       "term                                      0                    1   \n",
       "grade                                     1                    2   \n",
       "sub_grade                                 4                    4   \n",
       "emp_length                                0                    2   \n",
       "home_ownership                            2                    1   \n",
       "verification_status                       0                    1   \n",
       "pymnt_plan                                0                    0   \n",
       "purpose                                   1                    0   \n",
       "addr_state                                3                    4   \n",
       "initial_list_status                       0                    1   \n",
       "last_pymnt_d            2016-01-01 00:00:00  2016-01-01 00:00:00   \n",
       "next_pymnt_d            2016-02-01 00:00:00  2016-02-01 00:00:00   \n",
       "last_credit_pull_d      2016-01-01 00:00:00  2016-01-01 00:00:00   \n",
       "Issue Month                             Dec                  Dec   \n",
       "Issue Year                             2013                 2013   \n",
       "Earliest Credit Month                   Dec                  May   \n",
       "Earliest Credit Year                   1993                 2003   \n",
       "label                                     0                    0   \n",
       "\n",
       "                                     6                    7       \\\n",
       "loan_amnt                          10400.00              4000.00   \n",
       "funded_amnt                        10400.00              4000.00   \n",
       "funded_amnt_inv                    10400.00              3950.00   \n",
       "int_rate                              17.57                 7.90   \n",
       "dti                                   18.51                15.47   \n",
       "delinq_2yrs                            0.00                 0.00   \n",
       "inq_last_6mths                         0.00                 1.00   \n",
       "mths_since_last_delinq                64.00                50.00   \n",
       "open_acc                              17.00                10.00   \n",
       "revol_util                            88.20                61.20   \n",
       "total_acc                             41.00                23.00   \n",
       "total_rev_hi_lim                   20600.00              4600.00   \n",
       "term                                      1                    0   \n",
       "grade                                     3                    0   \n",
       "sub_grade                                 0                    3   \n",
       "emp_length                                2                    3   \n",
       "home_ownership                            1                    0   \n",
       "verification_status                       2                    0   \n",
       "pymnt_plan                                0                    0   \n",
       "purpose                                   2                    2   \n",
       "addr_state                                3                    5   \n",
       "initial_list_status                       0                    1   \n",
       "last_pymnt_d            2016-01-01 00:00:00  2016-01-01 00:00:00   \n",
       "next_pymnt_d            2016-02-01 00:00:00  2016-02-01 00:00:00   \n",
       "last_credit_pull_d      2016-01-01 00:00:00  2016-01-01 00:00:00   \n",
       "Issue Month                             Dec                  Dec   \n",
       "Issue Year                             2013                 2013   \n",
       "Earliest Credit Month                   Oct                  Apr   \n",
       "Earliest Credit Year                   1988                 2004   \n",
       "label                                     0                    0   \n",
       "\n",
       "                                     8                    9       ...  \\\n",
       "loan_amnt                          14000.00             10000.00  ...   \n",
       "funded_amnt                        14000.00             10000.00  ...   \n",
       "funded_amnt_inv                    14000.00             10000.00  ...   \n",
       "int_rate                              14.98                11.99  ...   \n",
       "dti                                   14.59                10.47  ...   \n",
       "delinq_2yrs                            0.00                 0.00  ...   \n",
       "inq_last_6mths                         0.00                 1.00  ...   \n",
       "mths_since_last_delinq                41.00                34.00  ...   \n",
       "open_acc                              15.00                11.00  ...   \n",
       "revol_util                            81.60                65.30  ...   \n",
       "total_acc                             24.00                31.00  ...   \n",
       "total_rev_hi_lim                   16800.00             10200.00  ...   \n",
       "term                                      0                    0  ...   \n",
       "grade                                     2                    1  ...   \n",
       "sub_grade                                 2                    2  ...   \n",
       "emp_length                                3                    2  ...   \n",
       "home_ownership                            1                    1  ...   \n",
       "verification_status                       0                    0  ...   \n",
       "pymnt_plan                                0                    0  ...   \n",
       "purpose                                   0                    0  ...   \n",
       "addr_state                                6                    7  ...   \n",
       "initial_list_status                       0                    0  ...   \n",
       "last_pymnt_d            2016-01-01 00:00:00  2016-01-01 00:00:00  ...   \n",
       "next_pymnt_d            2016-02-01 00:00:00  2016-02-01 00:00:00  ...   \n",
       "last_credit_pull_d      2016-01-01 00:00:00  2016-01-01 00:00:00  ...   \n",
       "Issue Month                             Dec                  Dec  ...   \n",
       "Issue Year                             2013                 2013  ...   \n",
       "Earliest Credit Month                   Jul                  Jun  ...   \n",
       "Earliest Credit Year                   1991                 1993  ...   \n",
       "label                                     0                    0  ...   \n",
       "\n",
       "                                     291300               291301  \\\n",
       "loan_amnt                          13297.17             13297.17   \n",
       "funded_amnt                        13297.17             13297.17   \n",
       "funded_amnt_inv                    13291.86             13291.86   \n",
       "int_rate                              12.62                12.62   \n",
       "dti                                   18.74                18.74   \n",
       "delinq_2yrs                            0.46                 0.46   \n",
       "inq_last_6mths                         0.45                 0.45   \n",
       "mths_since_last_delinq                34.14                34.14   \n",
       "open_acc                              11.05                11.05   \n",
       "revol_util                            56.93                56.93   \n",
       "total_acc                             24.72                24.72   \n",
       "total_rev_hi_lim                   23762.28             23762.28   \n",
       "term                                      1                    1   \n",
       "grade                                     4                    3   \n",
       "sub_grade                                 4                    3   \n",
       "emp_length                                7                    0   \n",
       "home_ownership                            0                    0   \n",
       "verification_status                       2                    1   \n",
       "pymnt_plan                                0                    0   \n",
       "purpose                                   2                    2   \n",
       "addr_state                               12                   20   \n",
       "initial_list_status                       0                    0   \n",
       "last_pymnt_d            2016-01-01 00:00:00  2016-01-01 00:00:00   \n",
       "next_pymnt_d            2016-02-01 00:00:00  2016-02-01 00:00:00   \n",
       "last_credit_pull_d      2016-01-01 00:00:00  2016-01-01 00:00:00   \n",
       "Issue Month                             Jan                  Jan   \n",
       "Issue Year                             2015                 2015   \n",
       "Earliest Credit Month                   Apr                  Sep   \n",
       "Earliest Credit Year                   2003                 2001   \n",
       "label                                     0                    0   \n",
       "\n",
       "                                     291302               291303  \\\n",
       "loan_amnt                          13297.17             13297.17   \n",
       "funded_amnt                        13297.17             13297.17   \n",
       "funded_amnt_inv                    13291.86             13291.86   \n",
       "int_rate                              12.62                12.62   \n",
       "dti                                   18.74                18.74   \n",
       "delinq_2yrs                            0.46                 0.46   \n",
       "inq_last_6mths                         0.45                 0.45   \n",
       "mths_since_last_delinq                34.14                34.14   \n",
       "open_acc                              11.05                11.05   \n",
       "revol_util                            56.93                56.93   \n",
       "total_acc                             24.72                24.72   \n",
       "total_rev_hi_lim                   23762.28             23762.28   \n",
       "term                                      0                    0   \n",
       "grade                                     4                    3   \n",
       "sub_grade                                 3                    1   \n",
       "emp_length                                4                    6   \n",
       "home_ownership                            0                    1   \n",
       "verification_status                       1                    1   \n",
       "pymnt_plan                                0                    0   \n",
       "purpose                                   0                    0   \n",
       "addr_state                               37                    2   \n",
       "initial_list_status                       1                    1   \n",
       "last_pymnt_d            2016-01-01 00:00:00  2015-10-01 00:00:00   \n",
       "next_pymnt_d            2016-02-01 00:00:00  2016-02-01 00:00:00   \n",
       "last_credit_pull_d      2016-01-01 00:00:00  2016-01-01 00:00:00   \n",
       "Issue Month                             Jan                  Jan   \n",
       "Issue Year                             2015                 2015   \n",
       "Earliest Credit Month                   Apr                  Oct   \n",
       "Earliest Credit Year                   1994                 2003   \n",
       "label                                     0                    1   \n",
       "\n",
       "                                     291304               291305  \\\n",
       "loan_amnt                          13297.17             13297.17   \n",
       "funded_amnt                        13297.17             13297.17   \n",
       "funded_amnt_inv                    13291.86             13291.86   \n",
       "int_rate                              12.62                12.62   \n",
       "dti                                   18.74                18.74   \n",
       "delinq_2yrs                            0.46                 0.46   \n",
       "inq_last_6mths                         0.45                 0.45   \n",
       "mths_since_last_delinq                34.14                34.14   \n",
       "open_acc                              11.05                11.05   \n",
       "revol_util                            56.93                56.93   \n",
       "total_acc                             24.72                24.72   \n",
       "total_rev_hi_lim                   23762.28             23762.28   \n",
       "term                                      1                    1   \n",
       "grade                                     2                    1   \n",
       "sub_grade                                 2                    1   \n",
       "emp_length                                0                    0   \n",
       "home_ownership                            1                    0   \n",
       "verification_status                       2                    1   \n",
       "pymnt_plan                                0                    0   \n",
       "purpose                                   0                    0   \n",
       "addr_state                               13                    2   \n",
       "initial_list_status                       1                    0   \n",
       "last_pymnt_d            2016-01-01 00:00:00  2016-01-01 00:00:00   \n",
       "next_pymnt_d            2016-02-01 00:00:00  2016-02-01 00:00:00   \n",
       "last_credit_pull_d      2016-01-01 00:00:00  2016-01-01 00:00:00   \n",
       "Issue Month                             Jan                  Jan   \n",
       "Issue Year                             2015                 2015   \n",
       "Earliest Credit Month                   Aug                  Aug   \n",
       "Earliest Credit Year                   2002                 1983   \n",
       "label                                     0                    0   \n",
       "\n",
       "                                     291306               291307  \\\n",
       "loan_amnt                          13297.17             13297.17   \n",
       "funded_amnt                        13297.17             13297.17   \n",
       "funded_amnt_inv                    13291.86             13291.86   \n",
       "int_rate                              12.62                12.62   \n",
       "dti                                   18.74                18.74   \n",
       "delinq_2yrs                            0.46                 0.46   \n",
       "inq_last_6mths                         0.45                 0.45   \n",
       "mths_since_last_delinq                34.14                34.14   \n",
       "open_acc                              11.05                11.05   \n",
       "revol_util                            56.93                56.93   \n",
       "total_acc                             24.72                24.72   \n",
       "total_rev_hi_lim                   23762.28             23762.28   \n",
       "term                                      0                    0   \n",
       "grade                                     2                    1   \n",
       "sub_grade                                 3                    0   \n",
       "emp_length                                0                    5   \n",
       "home_ownership                            1                    1   \n",
       "verification_status                       1                    1   \n",
       "pymnt_plan                                0                    0   \n",
       "purpose                                   0                    0   \n",
       "addr_state                               16                    5   \n",
       "initial_list_status                       0                    1   \n",
       "last_pymnt_d            2016-01-01 00:00:00  2016-01-01 00:00:00   \n",
       "next_pymnt_d            2016-02-01 00:00:00  2016-02-01 00:00:00   \n",
       "last_credit_pull_d      2016-01-01 00:00:00  2016-01-01 00:00:00   \n",
       "Issue Month                             Jan                  Jan   \n",
       "Issue Year                             2015                 2015   \n",
       "Earliest Credit Month                   Nov                  Aug   \n",
       "Earliest Credit Year                   2006                 1994   \n",
       "label                                     0                    0   \n",
       "\n",
       "                                     291308               291309  \n",
       "loan_amnt                          13297.17             13297.17  \n",
       "funded_amnt                        13297.17             13297.17  \n",
       "funded_amnt_inv                    13291.86             13291.86  \n",
       "int_rate                              12.62                12.62  \n",
       "dti                                   18.74                18.74  \n",
       "delinq_2yrs                            0.46                 0.46  \n",
       "inq_last_6mths                         0.45                 0.45  \n",
       "mths_since_last_delinq                34.14                34.14  \n",
       "open_acc                              11.05                11.05  \n",
       "revol_util                            56.93                56.93  \n",
       "total_acc                             24.72                24.72  \n",
       "total_rev_hi_lim                   23762.28             23762.28  \n",
       "term                                      0                    1  \n",
       "grade                                     1                    4  \n",
       "sub_grade                                 4                    2  \n",
       "emp_length                                7                    7  \n",
       "home_ownership                            0                    1  \n",
       "verification_status                       1                    2  \n",
       "pymnt_plan                                0                    0  \n",
       "purpose                                   2                    0  \n",
       "addr_state                                8                    1  \n",
       "initial_list_status                       1                    0  \n",
       "last_pymnt_d            2016-01-01 00:00:00  2016-01-01 00:00:00  \n",
       "next_pymnt_d            2016-02-01 00:00:00  2016-02-01 00:00:00  \n",
       "last_credit_pull_d      2016-01-01 00:00:00  2016-01-01 00:00:00  \n",
       "Issue Month                             Jan                  Jan  \n",
       "Issue Year                             2015                 2015  \n",
       "Earliest Credit Month                   Jun                  Oct  \n",
       "Earliest Credit Year                   1995                 2003  \n",
       "label                                     0                    0  \n",
       "\n",
       "[30 rows x 291293 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_loan_issue.T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5daaf19d",
   "metadata": {
    "papermill": {
     "duration": 0.024759,
     "end_time": "2023-12-18T18:12:39.681575",
     "exception": false,
     "start_time": "2023-12-18T18:12:39.656816",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "##### Converting timestamp columns to numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "041a9de5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T18:12:39.731647Z",
     "iopub.status.busy": "2023-12-18T18:12:39.731124Z",
     "iopub.status.idle": "2023-12-18T18:12:42.291078Z",
     "shell.execute_reply": "2023-12-18T18:12:42.289699Z"
    },
    "papermill": {
     "duration": 2.588942,
     "end_time": "2023-12-18T18:12:42.294194",
     "exception": false,
     "start_time": "2023-12-18T18:12:39.705252",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_loan_issue['last_pymnt_d_timestamp'] = pd.to_datetime(df_loan_issue['last_pymnt_d'])\n",
    "df_loan_issue['next_pymnt_d_timestamp'] = pd.to_datetime(df_loan_issue['next_pymnt_d'])\n",
    "df_loan_issue['last_credit_pull_d_timestamp'] = pd.to_datetime(df_loan_issue['last_credit_pull_d'])\n",
    "\n",
    "df_loan_issue['last_pymnt_d_numeric'] = df_loan_issue['last_pymnt_d_timestamp'].astype(int)\n",
    "df_loan_issue['next_pymnt_d_numeric'] = df_loan_issue['next_pymnt_d_timestamp'].astype(int)\n",
    "df_loan_issue['last_credit_pull_d_numeric'] = df_loan_issue['last_credit_pull_d_timestamp'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2571e300",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T18:12:42.344751Z",
     "iopub.status.busy": "2023-12-18T18:12:42.344256Z",
     "iopub.status.idle": "2023-12-18T18:12:42.462250Z",
     "shell.execute_reply": "2023-12-18T18:12:42.460697Z"
    },
    "papermill": {
     "duration": 0.147479,
     "end_time": "2023-12-18T18:12:42.465513",
     "exception": false,
     "start_time": "2023-12-18T18:12:42.318034",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "columns_to_drop = ['last_pymnt_d_timestamp', 'next_pymnt_d_timestamp', 'last_credit_pull_d_timestamp', 'last_pymnt_d', 'next_pymnt_d', 'last_credit_pull_d']\n",
    "\n",
    "df_loan_issue = df_loan_issue.drop(columns=columns_to_drop)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e731f4d2",
   "metadata": {
    "papermill": {
     "duration": 0.024001,
     "end_time": "2023-12-18T18:12:42.513367",
     "exception": false,
     "start_time": "2023-12-18T18:12:42.489366",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Month encoding to use features - Issue Month, Earliest Credit Month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "da01a657",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T18:12:42.564708Z",
     "iopub.status.busy": "2023-12-18T18:12:42.563540Z",
     "iopub.status.idle": "2023-12-18T18:12:42.724507Z",
     "shell.execute_reply": "2023-12-18T18:12:42.722783Z"
    },
    "papermill": {
     "duration": 0.19043,
     "end_time": "2023-12-18T18:12:42.727750",
     "exception": false,
     "start_time": "2023-12-18T18:12:42.537320",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "month_encoding = {\n",
    "    'Jan': 1,\n",
    "    'Feb': 2,\n",
    "    'Mar': 3,\n",
    "    'Apr': 4,\n",
    "    'May': 5,\n",
    "    'Jun': 6,\n",
    "    'Jul': 7,\n",
    "    'Aug': 8,\n",
    "    'Sep': 9,\n",
    "    'Oct': 10,\n",
    "    'Nov': 11,\n",
    "    'Dec': 12\n",
    "}\n",
    "\n",
    "df_loan_issue['issue_month_encoded'] = df_loan_issue['Issue Month'].map(month_encoding)\n",
    "df_loan_issue['earliest_credit_month_encoded'] = df_loan_issue['Earliest Credit Month'].map(month_encoding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b8c097ac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T18:12:42.779302Z",
     "iopub.status.busy": "2023-12-18T18:12:42.778010Z",
     "iopub.status.idle": "2023-12-18T18:12:42.825342Z",
     "shell.execute_reply": "2023-12-18T18:12:42.823813Z"
    },
    "papermill": {
     "duration": 0.07679,
     "end_time": "2023-12-18T18:12:42.828623",
     "exception": false,
     "start_time": "2023-12-18T18:12:42.751833",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "columns_to_drop = ['Issue Month', 'Earliest Credit Month']\n",
    "\n",
    "df_loan_issue = df_loan_issue.drop(columns=columns_to_drop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0bc8b7a5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T18:12:42.879225Z",
     "iopub.status.busy": "2023-12-18T18:12:42.878644Z",
     "iopub.status.idle": "2023-12-18T18:12:42.978500Z",
     "shell.execute_reply": "2023-12-18T18:12:42.977027Z"
    },
    "papermill": {
     "duration": 0.129824,
     "end_time": "2023-12-18T18:12:42.982731",
     "exception": false,
     "start_time": "2023-12-18T18:12:42.852907",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "      <th>loan_amnt</th>\n",
       "      <td>12000.00</td>\n",
       "      <td>10000.00</td>\n",
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       "      <td>10000.00</td>\n",
       "      <td>11100.00</td>\n",
       "      <td>4500.00</td>\n",
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       "      <td>4500.00</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>12000.00</td>\n",
       "      <td>10400.00</td>\n",
       "      <td>3950.00</td>\n",
       "      <td>14000.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>...</td>\n",
       "      <td>13291.86</td>\n",
       "      <td>13291.86</td>\n",
       "      <td>13291.86</td>\n",
       "      <td>13291.86</td>\n",
       "      <td>13291.86</td>\n",
       "      <td>13291.86</td>\n",
       "      <td>13291.86</td>\n",
       "      <td>13291.86</td>\n",
       "      <td>13291.86</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>int_rate</th>\n",
       "      <td>6.62</td>\n",
       "      <td>9.67</td>\n",
       "      <td>14.98</td>\n",
       "      <td>19.22</td>\n",
       "      <td>13.53</td>\n",
       "      <td>16.24</td>\n",
       "      <td>17.57</td>\n",
       "      <td>7.90</td>\n",
       "      <td>14.98</td>\n",
       "      <td>11.99</td>\n",
       "      <td>...</td>\n",
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       "      <td>12.62</td>\n",
       "      <td>12.62</td>\n",
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       "      <td>12.62</td>\n",
       "      <td>12.62</td>\n",
       "      <td>12.62</td>\n",
       "      <td>12.62</td>\n",
       "      <td>12.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dti</th>\n",
       "      <td>14.05</td>\n",
       "      <td>15.55</td>\n",
       "      <td>3.73</td>\n",
       "      <td>16.23</td>\n",
       "      <td>22.56</td>\n",
       "      <td>10.68</td>\n",
       "      <td>18.51</td>\n",
       "      <td>15.47</td>\n",
       "      <td>14.59</td>\n",
       "      <td>10.47</td>\n",
       "      <td>...</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "      <td>18.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mths_since_last_delinq</th>\n",
       "      <td>43.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>64.00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>41.00</td>\n",
       "      <td>34.00</td>\n",
       "      <td>...</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "      <td>34.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open_acc</th>\n",
       "      <td>12.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>17.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>...</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revol_util</th>\n",
       "      <td>21.60</td>\n",
       "      <td>44.40</td>\n",
       "      <td>66.20</td>\n",
       "      <td>93.10</td>\n",
       "      <td>39.60</td>\n",
       "      <td>51.60</td>\n",
       "      <td>88.20</td>\n",
       "      <td>61.20</td>\n",
       "      <td>81.60</td>\n",
       "      <td>65.30</td>\n",
       "      <td>...</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "      <td>56.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_acc</th>\n",
       "      <td>22.00</td>\n",
       "      <td>22.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>35.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>41.00</td>\n",
       "      <td>23.00</td>\n",
       "      <td>24.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>...</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "      <td>24.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <td>61100.00</td>\n",
       "      <td>22300.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>20500.00</td>\n",
       "      <td>24700.00</td>\n",
       "      <td>21600.00</td>\n",
       "      <td>20600.00</td>\n",
       "      <td>4600.00</td>\n",
       "      <td>16800.00</td>\n",
       "      <td>10200.00</td>\n",
       "      <td>...</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "      <td>23762.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>term</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade</th>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>...</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub_grade</th>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>...</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emp_length</th>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>...</td>\n",
       "      <td>7.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home_ownership</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>verification_status</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pymnt_plan</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purpose</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>addr_state</th>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>...</td>\n",
       "      <td>12.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>37.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>initial_list_status</th>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Issue Year</th>\n",
       "      <td>2013.00</td>\n",
       "      <td>2013.00</td>\n",
       "      <td>2013.00</td>\n",
       "      <td>2013.00</td>\n",
       "      <td>2013.00</td>\n",
       "      <td>2013.00</td>\n",
       "      <td>2013.00</td>\n",
       "      <td>2013.00</td>\n",
       "      <td>2013.00</td>\n",
       "      <td>2013.00</td>\n",
       "      <td>...</td>\n",
       "      <td>2015.00</td>\n",
       "      <td>2015.00</td>\n",
       "      <td>2015.00</td>\n",
       "      <td>2015.00</td>\n",
       "      <td>2015.00</td>\n",
       "      <td>2015.00</td>\n",
       "      <td>2015.00</td>\n",
       "      <td>2015.00</td>\n",
       "      <td>2015.00</td>\n",
       "      <td>2015.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earliest Credit Year</th>\n",
       "      <td>1994.00</td>\n",
       "      <td>1989.00</td>\n",
       "      <td>2001.00</td>\n",
       "      <td>1990.00</td>\n",
       "      <td>1993.00</td>\n",
       "      <td>2003.00</td>\n",
       "      <td>1988.00</td>\n",
       "      <td>2004.00</td>\n",
       "      <td>1991.00</td>\n",
       "      <td>1993.00</td>\n",
       "      <td>...</td>\n",
       "      <td>2003.00</td>\n",
       "      <td>2001.00</td>\n",
       "      <td>1994.00</td>\n",
       "      <td>2003.00</td>\n",
       "      <td>2002.00</td>\n",
       "      <td>1983.00</td>\n",
       "      <td>2006.00</td>\n",
       "      <td>1994.00</td>\n",
       "      <td>1995.00</td>\n",
       "      <td>2003.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_pymnt_d_numeric</th>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>...</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>1443657600000000000.00</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>next_pymnt_d_numeric</th>\n",
       "      <td>1454284800000000000.00</td>\n",
       "      <td>1454284800000000000.00</td>\n",
       "      <td>1454284800000000000.00</td>\n",
       "      <td>1454284800000000000.00</td>\n",
       "      <td>1454284800000000000.00</td>\n",
       "      <td>1454284800000000000.00</td>\n",
       "      <td>1454284800000000000.00</td>\n",
       "      <td>1454284800000000000.00</td>\n",
       "      <td>1454284800000000000.00</td>\n",
       "      <td>1454284800000000000.00</td>\n",
       "      <td>...</td>\n",
       "      <td>1454284800000000000.00</td>\n",
       "      <td>1454284800000000000.00</td>\n",
       "      <td>1454284800000000000.00</td>\n",
       "      <td>1454284800000000000.00</td>\n",
       "      <td>1454284800000000000.00</td>\n",
       "      <td>1454284800000000000.00</td>\n",
       "      <td>1454284800000000000.00</td>\n",
       "      <td>1454284800000000000.00</td>\n",
       "      <td>1454284800000000000.00</td>\n",
       "      <td>1454284800000000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_credit_pull_d_numeric</th>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>...</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "      <td>1451606400000000000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>issue_month_encoded</th>\n",
       "      <td>12.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>earliest_credit_month_encoded</th>\n",
       "      <td>3.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>...</td>\n",
       "      <td>4.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 291293 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              0                      1       \\\n",
       "loan_amnt                                   12000.00               10000.00   \n",
       "funded_amnt                                 12000.00               10000.00   \n",
       "funded_amnt_inv                             12000.00               10000.00   \n",
       "int_rate                                        6.62                   9.67   \n",
       "dti                                            14.05                  15.55   \n",
       "delinq_2yrs                                     0.00                   2.00   \n",
       "inq_last_6mths                                  1.00                   0.00   \n",
       "mths_since_last_delinq                         43.00                  11.00   \n",
       "open_acc                                       12.00                   9.00   \n",
       "revol_util                                     21.60                  44.40   \n",
       "total_acc                                      22.00                  22.00   \n",
       "total_rev_hi_lim                            61100.00               22300.00   \n",
       "term                                            0.00                   0.00   \n",
       "grade                                           0.00                   1.00   \n",
       "sub_grade                                       0.00                   1.00   \n",
       "emp_length                                      0.00                   1.00   \n",
       "home_ownership                                  0.00                   0.00   \n",
       "verification_status                             0.00                   0.00   \n",
       "pymnt_plan                                      0.00                   0.00   \n",
       "purpose                                         0.00                   0.00   \n",
       "addr_state                                      0.00                   1.00   \n",
       "initial_list_status                             0.00                   1.00   \n",
       "Issue Year                                   2013.00                2013.00   \n",
       "Earliest Credit Year                         1994.00                1989.00   \n",
       "label                                           0.00                   0.00   \n",
       "last_pymnt_d_numeric          1451606400000000000.00 1451606400000000000.00   \n",
       "next_pymnt_d_numeric          1454284800000000000.00 1454284800000000000.00   \n",
       "last_credit_pull_d_numeric    1451606400000000000.00 1451606400000000000.00   \n",
       "issue_month_encoded                            12.00                  12.00   \n",
       "earliest_credit_month_encoded                   3.00                  10.00   \n",
       "\n",
       "                                              2                      3       \\\n",
       "loan_amnt                                   11100.00                4500.00   \n",
       "funded_amnt                                 11100.00                4500.00   \n",
       "funded_amnt_inv                             11100.00                4500.00   \n",
       "int_rate                                       14.98                  19.22   \n",
       "dti                                             3.73                  16.23   \n",
       "delinq_2yrs                                     1.00                   1.00   \n",
       "inq_last_6mths                                  0.00                   0.00   \n",
       "mths_since_last_delinq                         16.00                  10.00   \n",
       "open_acc                                        9.00                   5.00   \n",
       "revol_util                                     66.20                  93.10   \n",
       "total_acc                                      12.00                  10.00   \n",
       "total_rev_hi_lim                            10000.00               20500.00   \n",
       "term                                            0.00                   0.00   \n",
       "grade                                           2.00                   3.00   \n",
       "sub_grade                                       2.00                   3.00   \n",
       "emp_length                                      0.00                   0.00   \n",
       "home_ownership                                  0.00                   1.00   \n",
       "verification_status                             0.00                   0.00   \n",
       "pymnt_plan                                      0.00                   0.00   \n",
       "purpose                                         1.00                   1.00   \n",
       "addr_state                                      2.00                   3.00   \n",
       "initial_list_status                             1.00                   1.00   \n",
       "Issue Year                                   2013.00                2013.00   \n",
       "Earliest Credit Year                         2001.00                1990.00   \n",
       "label                                           0.00                   0.00   \n",
       "last_pymnt_d_numeric          1451606400000000000.00 1451606400000000000.00   \n",
       "next_pymnt_d_numeric          1454284800000000000.00 1454284800000000000.00   \n",
       "last_credit_pull_d_numeric    1451606400000000000.00 1451606400000000000.00   \n",
       "issue_month_encoded                            12.00                  12.00   \n",
       "earliest_credit_month_encoded                   6.00                   3.00   \n",
       "\n",
       "                                              4                      5       \\\n",
       "loan_amnt                                    5000.00               12000.00   \n",
       "funded_amnt                                  5000.00               12000.00   \n",
       "funded_amnt_inv                              5000.00               12000.00   \n",
       "int_rate                                       13.53                  16.24   \n",
       "dti                                            22.56                  10.68   \n",
       "delinq_2yrs                                     0.00                   1.00   \n",
       "inq_last_6mths                                  1.00                   0.00   \n",
       "mths_since_last_delinq                         45.00                  10.00   \n",
       "open_acc                                       12.00                   7.00   \n",
       "revol_util                                     39.60                  51.60   \n",
       "total_acc                                      35.00                  13.00   \n",
       "total_rev_hi_lim                            24700.00               21600.00   \n",
       "term                                            0.00                   1.00   \n",
       "grade                                           1.00                   2.00   \n",
       "sub_grade                                       4.00                   4.00   \n",
       "emp_length                                      0.00                   2.00   \n",
       "home_ownership                                  2.00                   1.00   \n",
       "verification_status                             0.00                   1.00   \n",
       "pymnt_plan                                      0.00                   0.00   \n",
       "purpose                                         1.00                   0.00   \n",
       "addr_state                                      3.00                   4.00   \n",
       "initial_list_status                             0.00                   1.00   \n",
       "Issue Year                                   2013.00                2013.00   \n",
       "Earliest Credit Year                         1993.00                2003.00   \n",
       "label                                           0.00                   0.00   \n",
       "last_pymnt_d_numeric          1451606400000000000.00 1451606400000000000.00   \n",
       "next_pymnt_d_numeric          1454284800000000000.00 1454284800000000000.00   \n",
       "last_credit_pull_d_numeric    1451606400000000000.00 1451606400000000000.00   \n",
       "issue_month_encoded                            12.00                  12.00   \n",
       "earliest_credit_month_encoded                  12.00                   5.00   \n",
       "\n",
       "                                              6                      7       \\\n",
       "loan_amnt                                   10400.00                4000.00   \n",
       "funded_amnt                                 10400.00                4000.00   \n",
       "funded_amnt_inv                             10400.00                3950.00   \n",
       "int_rate                                       17.57                   7.90   \n",
       "dti                                            18.51                  15.47   \n",
       "delinq_2yrs                                     0.00                   0.00   \n",
       "inq_last_6mths                                  0.00                   1.00   \n",
       "mths_since_last_delinq                         64.00                  50.00   \n",
       "open_acc                                       17.00                  10.00   \n",
       "revol_util                                     88.20                  61.20   \n",
       "total_acc                                      41.00                  23.00   \n",
       "total_rev_hi_lim                            20600.00                4600.00   \n",
       "term                                            1.00                   0.00   \n",
       "grade                                           3.00                   0.00   \n",
       "sub_grade                                       0.00                   3.00   \n",
       "emp_length                                      2.00                   3.00   \n",
       "home_ownership                                  1.00                   0.00   \n",
       "verification_status                             2.00                   0.00   \n",
       "pymnt_plan                                      0.00                   0.00   \n",
       "purpose                                         2.00                   2.00   \n",
       "addr_state                                      3.00                   5.00   \n",
       "initial_list_status                             0.00                   1.00   \n",
       "Issue Year                                   2013.00                2013.00   \n",
       "Earliest Credit Year                         1988.00                2004.00   \n",
       "label                                           0.00                   0.00   \n",
       "last_pymnt_d_numeric          1451606400000000000.00 1451606400000000000.00   \n",
       "next_pymnt_d_numeric          1454284800000000000.00 1454284800000000000.00   \n",
       "last_credit_pull_d_numeric    1451606400000000000.00 1451606400000000000.00   \n",
       "issue_month_encoded                            12.00                  12.00   \n",
       "earliest_credit_month_encoded                  10.00                   4.00   \n",
       "\n",
       "                                              8                      9       \\\n",
       "loan_amnt                                   14000.00               10000.00   \n",
       "funded_amnt                                 14000.00               10000.00   \n",
       "funded_amnt_inv                             14000.00               10000.00   \n",
       "int_rate                                       14.98                  11.99   \n",
       "dti                                            14.59                  10.47   \n",
       "delinq_2yrs                                     0.00                   0.00   \n",
       "inq_last_6mths                                  0.00                   1.00   \n",
       "mths_since_last_delinq                         41.00                  34.00   \n",
       "open_acc                                       15.00                  11.00   \n",
       "revol_util                                     81.60                  65.30   \n",
       "total_acc                                      24.00                  31.00   \n",
       "total_rev_hi_lim                            16800.00               10200.00   \n",
       "term                                            0.00                   0.00   \n",
       "grade                                           2.00                   1.00   \n",
       "sub_grade                                       2.00                   2.00   \n",
       "emp_length                                      3.00                   2.00   \n",
       "home_ownership                                  1.00                   1.00   \n",
       "verification_status                             0.00                   0.00   \n",
       "pymnt_plan                                      0.00                   0.00   \n",
       "purpose                                         0.00                   0.00   \n",
       "addr_state                                      6.00                   7.00   \n",
       "initial_list_status                             0.00                   0.00   \n",
       "Issue Year                                   2013.00                2013.00   \n",
       "Earliest Credit Year                         1991.00                1993.00   \n",
       "label                                           0.00                   0.00   \n",
       "last_pymnt_d_numeric          1451606400000000000.00 1451606400000000000.00   \n",
       "next_pymnt_d_numeric          1454284800000000000.00 1454284800000000000.00   \n",
       "last_credit_pull_d_numeric    1451606400000000000.00 1451606400000000000.00   \n",
       "issue_month_encoded                            12.00                  12.00   \n",
       "earliest_credit_month_encoded                   7.00                   6.00   \n",
       "\n",
       "                               ...                 291300  \\\n",
       "loan_amnt                      ...               13297.17   \n",
       "funded_amnt                    ...               13297.17   \n",
       "funded_amnt_inv                ...               13291.86   \n",
       "int_rate                       ...                  12.62   \n",
       "dti                            ...                  18.74   \n",
       "delinq_2yrs                    ...                   0.46   \n",
       "inq_last_6mths                 ...                   0.45   \n",
       "mths_since_last_delinq         ...                  34.14   \n",
       "open_acc                       ...                  11.05   \n",
       "revol_util                     ...                  56.93   \n",
       "total_acc                      ...                  24.72   \n",
       "total_rev_hi_lim               ...               23762.28   \n",
       "term                           ...                   1.00   \n",
       "grade                          ...                   4.00   \n",
       "sub_grade                      ...                   4.00   \n",
       "emp_length                     ...                   7.00   \n",
       "home_ownership                 ...                   0.00   \n",
       "verification_status            ...                   2.00   \n",
       "pymnt_plan                     ...                   0.00   \n",
       "purpose                        ...                   2.00   \n",
       "addr_state                     ...                  12.00   \n",
       "initial_list_status            ...                   0.00   \n",
       "Issue Year                     ...                2015.00   \n",
       "Earliest Credit Year           ...                2003.00   \n",
       "label                          ...                   0.00   \n",
       "last_pymnt_d_numeric           ... 1451606400000000000.00   \n",
       "next_pymnt_d_numeric           ... 1454284800000000000.00   \n",
       "last_credit_pull_d_numeric     ... 1451606400000000000.00   \n",
       "issue_month_encoded            ...                   1.00   \n",
       "earliest_credit_month_encoded  ...                   4.00   \n",
       "\n",
       "                                              291301                 291302  \\\n",
       "loan_amnt                                   13297.17               13297.17   \n",
       "funded_amnt                                 13297.17               13297.17   \n",
       "funded_amnt_inv                             13291.86               13291.86   \n",
       "int_rate                                       12.62                  12.62   \n",
       "dti                                            18.74                  18.74   \n",
       "delinq_2yrs                                     0.46                   0.46   \n",
       "inq_last_6mths                                  0.45                   0.45   \n",
       "mths_since_last_delinq                         34.14                  34.14   \n",
       "open_acc                                       11.05                  11.05   \n",
       "revol_util                                     56.93                  56.93   \n",
       "total_acc                                      24.72                  24.72   \n",
       "total_rev_hi_lim                            23762.28               23762.28   \n",
       "term                                            1.00                   0.00   \n",
       "grade                                           3.00                   4.00   \n",
       "sub_grade                                       3.00                   3.00   \n",
       "emp_length                                      0.00                   4.00   \n",
       "home_ownership                                  0.00                   0.00   \n",
       "verification_status                             1.00                   1.00   \n",
       "pymnt_plan                                      0.00                   0.00   \n",
       "purpose                                         2.00                   0.00   \n",
       "addr_state                                     20.00                  37.00   \n",
       "initial_list_status                             0.00                   1.00   \n",
       "Issue Year                                   2015.00                2015.00   \n",
       "Earliest Credit Year                         2001.00                1994.00   \n",
       "label                                           0.00                   0.00   \n",
       "last_pymnt_d_numeric          1451606400000000000.00 1451606400000000000.00   \n",
       "next_pymnt_d_numeric          1454284800000000000.00 1454284800000000000.00   \n",
       "last_credit_pull_d_numeric    1451606400000000000.00 1451606400000000000.00   \n",
       "issue_month_encoded                             1.00                   1.00   \n",
       "earliest_credit_month_encoded                   9.00                   4.00   \n",
       "\n",
       "                                              291303                 291304  \\\n",
       "loan_amnt                                   13297.17               13297.17   \n",
       "funded_amnt                                 13297.17               13297.17   \n",
       "funded_amnt_inv                             13291.86               13291.86   \n",
       "int_rate                                       12.62                  12.62   \n",
       "dti                                            18.74                  18.74   \n",
       "delinq_2yrs                                     0.46                   0.46   \n",
       "inq_last_6mths                                  0.45                   0.45   \n",
       "mths_since_last_delinq                         34.14                  34.14   \n",
       "open_acc                                       11.05                  11.05   \n",
       "revol_util                                     56.93                  56.93   \n",
       "total_acc                                      24.72                  24.72   \n",
       "total_rev_hi_lim                            23762.28               23762.28   \n",
       "term                                            0.00                   1.00   \n",
       "grade                                           3.00                   2.00   \n",
       "sub_grade                                       1.00                   2.00   \n",
       "emp_length                                      6.00                   0.00   \n",
       "home_ownership                                  1.00                   1.00   \n",
       "verification_status                             1.00                   2.00   \n",
       "pymnt_plan                                      0.00                   0.00   \n",
       "purpose                                         0.00                   0.00   \n",
       "addr_state                                      2.00                  13.00   \n",
       "initial_list_status                             1.00                   1.00   \n",
       "Issue Year                                   2015.00                2015.00   \n",
       "Earliest Credit Year                         2003.00                2002.00   \n",
       "label                                           1.00                   0.00   \n",
       "last_pymnt_d_numeric          1443657600000000000.00 1451606400000000000.00   \n",
       "next_pymnt_d_numeric          1454284800000000000.00 1454284800000000000.00   \n",
       "last_credit_pull_d_numeric    1451606400000000000.00 1451606400000000000.00   \n",
       "issue_month_encoded                             1.00                   1.00   \n",
       "earliest_credit_month_encoded                  10.00                   8.00   \n",
       "\n",
       "                                              291305                 291306  \\\n",
       "loan_amnt                                   13297.17               13297.17   \n",
       "funded_amnt                                 13297.17               13297.17   \n",
       "funded_amnt_inv                             13291.86               13291.86   \n",
       "int_rate                                       12.62                  12.62   \n",
       "dti                                            18.74                  18.74   \n",
       "delinq_2yrs                                     0.46                   0.46   \n",
       "inq_last_6mths                                  0.45                   0.45   \n",
       "mths_since_last_delinq                         34.14                  34.14   \n",
       "open_acc                                       11.05                  11.05   \n",
       "revol_util                                     56.93                  56.93   \n",
       "total_acc                                      24.72                  24.72   \n",
       "total_rev_hi_lim                            23762.28               23762.28   \n",
       "term                                            1.00                   0.00   \n",
       "grade                                           1.00                   2.00   \n",
       "sub_grade                                       1.00                   3.00   \n",
       "emp_length                                      0.00                   0.00   \n",
       "home_ownership                                  0.00                   1.00   \n",
       "verification_status                             1.00                   1.00   \n",
       "pymnt_plan                                      0.00                   0.00   \n",
       "purpose                                         0.00                   0.00   \n",
       "addr_state                                      2.00                  16.00   \n",
       "initial_list_status                             0.00                   0.00   \n",
       "Issue Year                                   2015.00                2015.00   \n",
       "Earliest Credit Year                         1983.00                2006.00   \n",
       "label                                           0.00                   0.00   \n",
       "last_pymnt_d_numeric          1451606400000000000.00 1451606400000000000.00   \n",
       "next_pymnt_d_numeric          1454284800000000000.00 1454284800000000000.00   \n",
       "last_credit_pull_d_numeric    1451606400000000000.00 1451606400000000000.00   \n",
       "issue_month_encoded                             1.00                   1.00   \n",
       "earliest_credit_month_encoded                   8.00                  11.00   \n",
       "\n",
       "                                              291307                 291308  \\\n",
       "loan_amnt                                   13297.17               13297.17   \n",
       "funded_amnt                                 13297.17               13297.17   \n",
       "funded_amnt_inv                             13291.86               13291.86   \n",
       "int_rate                                       12.62                  12.62   \n",
       "dti                                            18.74                  18.74   \n",
       "delinq_2yrs                                     0.46                   0.46   \n",
       "inq_last_6mths                                  0.45                   0.45   \n",
       "mths_since_last_delinq                         34.14                  34.14   \n",
       "open_acc                                       11.05                  11.05   \n",
       "revol_util                                     56.93                  56.93   \n",
       "total_acc                                      24.72                  24.72   \n",
       "total_rev_hi_lim                            23762.28               23762.28   \n",
       "term                                            0.00                   0.00   \n",
       "grade                                           1.00                   1.00   \n",
       "sub_grade                                       0.00                   4.00   \n",
       "emp_length                                      5.00                   7.00   \n",
       "home_ownership                                  1.00                   0.00   \n",
       "verification_status                             1.00                   1.00   \n",
       "pymnt_plan                                      0.00                   0.00   \n",
       "purpose                                         0.00                   2.00   \n",
       "addr_state                                      5.00                   8.00   \n",
       "initial_list_status                             1.00                   1.00   \n",
       "Issue Year                                   2015.00                2015.00   \n",
       "Earliest Credit Year                         1994.00                1995.00   \n",
       "label                                           0.00                   0.00   \n",
       "last_pymnt_d_numeric          1451606400000000000.00 1451606400000000000.00   \n",
       "next_pymnt_d_numeric          1454284800000000000.00 1454284800000000000.00   \n",
       "last_credit_pull_d_numeric    1451606400000000000.00 1451606400000000000.00   \n",
       "issue_month_encoded                             1.00                   1.00   \n",
       "earliest_credit_month_encoded                   8.00                   6.00   \n",
       "\n",
       "                                              291309  \n",
       "loan_amnt                                   13297.17  \n",
       "funded_amnt                                 13297.17  \n",
       "funded_amnt_inv                             13291.86  \n",
       "int_rate                                       12.62  \n",
       "dti                                            18.74  \n",
       "delinq_2yrs                                     0.46  \n",
       "inq_last_6mths                                  0.45  \n",
       "mths_since_last_delinq                         34.14  \n",
       "open_acc                                       11.05  \n",
       "revol_util                                     56.93  \n",
       "total_acc                                      24.72  \n",
       "total_rev_hi_lim                            23762.28  \n",
       "term                                            1.00  \n",
       "grade                                           4.00  \n",
       "sub_grade                                       2.00  \n",
       "emp_length                                      7.00  \n",
       "home_ownership                                  1.00  \n",
       "verification_status                             2.00  \n",
       "pymnt_plan                                      0.00  \n",
       "purpose                                         0.00  \n",
       "addr_state                                      1.00  \n",
       "initial_list_status                             0.00  \n",
       "Issue Year                                   2015.00  \n",
       "Earliest Credit Year                         2003.00  \n",
       "label                                           0.00  \n",
       "last_pymnt_d_numeric          1451606400000000000.00  \n",
       "next_pymnt_d_numeric          1454284800000000000.00  \n",
       "last_credit_pull_d_numeric    1451606400000000000.00  \n",
       "issue_month_encoded                             1.00  \n",
       "earliest_credit_month_encoded                  10.00  \n",
       "\n",
       "[30 rows x 291293 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_loan_issue.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ff3ce1eb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T18:12:43.037978Z",
     "iopub.status.busy": "2023-12-18T18:12:43.037527Z",
     "iopub.status.idle": "2023-12-18T18:12:43.122553Z",
     "shell.execute_reply": "2023-12-18T18:12:43.121174Z"
    },
    "papermill": {
     "duration": 0.117043,
     "end_time": "2023-12-18T18:12:43.126363",
     "exception": false,
     "start_time": "2023-12-18T18:12:43.009320",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_final = df_loan_issue.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5291a128",
   "metadata": {
    "papermill": {
     "duration": 0.026715,
     "end_time": "2023-12-18T18:12:43.181309",
     "exception": false,
     "start_time": "2023-12-18T18:12:43.154594",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "aed5c2eb",
   "metadata": {
    "papermill": {
     "duration": 0.02739,
     "end_time": "2023-12-18T18:12:43.236032",
     "exception": false,
     "start_time": "2023-12-18T18:12:43.208642",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "98565a95",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T18:12:43.292595Z",
     "iopub.status.busy": "2023-12-18T18:12:43.291761Z",
     "iopub.status.idle": "2023-12-18T18:13:02.113039Z",
     "shell.execute_reply": "2023-12-18T18:13:02.111604Z"
    },
    "papermill": {
     "duration": 18.852833,
     "end_time": "2023-12-18T18:13:02.116110",
     "exception": false,
     "start_time": "2023-12-18T18:12:43.263277",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31mERROR: Could not find a version that satisfies the requirement tensorflow==2.7.0 (from versions: 2.8.0rc0, 2.8.0rc1, 2.8.0, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.0rc0, 2.9.0rc1, 2.9.0rc2, 2.9.0, 2.9.1, 2.9.2, 2.9.3, 2.10.0rc0, 2.10.0rc1, 2.10.0rc2, 2.10.0rc3, 2.10.0, 2.10.1, 2.11.0rc0, 2.11.0rc1, 2.11.0rc2, 2.11.0, 2.11.1, 2.12.0rc0, 2.12.0rc1, 2.12.0, 2.12.1, 2.13.0rc0, 2.13.0rc1, 2.13.0rc2, 2.13.0, 2.13.1, 2.14.0rc0, 2.14.0rc1, 2.14.0, 2.14.1, 2.15.0rc0, 2.15.0rc1, 2.15.0, 2.15.0.post1)\u001b[0m\u001b[31m\r\n",
      "\u001b[0m\u001b[31mERROR: No matching distribution found for tensorflow==2.7.0\u001b[0m\u001b[31m\r\n",
      "\u001b[0mCollecting scikeras\r\n",
      "  Obtaining dependency information for scikeras from https://files.pythonhosted.org/packages/5d/fa/9c1967952e7889d698f10ba8b6af79dfaa2e05178d97a79fbd9d1b44e589/scikeras-0.12.0-py3-none-any.whl.metadata\r\n",
      "  Downloading scikeras-0.12.0-py3-none-any.whl.metadata (4.0 kB)\r\n",
      "Requirement already satisfied: packaging>=0.21 in /opt/conda/lib/python3.10/site-packages (from scikeras) (21.3)\r\n",
      "Requirement already satisfied: scikit-learn>=1.0.0 in /opt/conda/lib/python3.10/site-packages (from scikeras) (1.2.2)\r\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=0.21->scikeras) (3.0.9)\r\n",
      "Requirement already satisfied: numpy>=1.17.3 in /opt/conda/lib/python3.10/site-packages (from scikit-learn>=1.0.0->scikeras) (1.24.3)\r\n",
      "Requirement already satisfied: scipy>=1.3.2 in /opt/conda/lib/python3.10/site-packages (from scikit-learn>=1.0.0->scikeras) (1.11.4)\r\n",
      "Requirement already satisfied: joblib>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from scikit-learn>=1.0.0->scikeras) (1.3.2)\r\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.10/site-packages (from scikit-learn>=1.0.0->scikeras) (3.2.0)\r\n",
      "Downloading scikeras-0.12.0-py3-none-any.whl (27 kB)\r\n",
      "Installing collected packages: scikeras\r\n",
      "Successfully installed scikeras-0.12.0\r\n"
     ]
    }
   ],
   "source": [
    "# scikeras library requires tensorflow >= 2.7.0\n",
    "!pip install -U tensorflow==2.7.0\n",
    "!pip install -U scikeras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "dee95862",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T18:13:02.172903Z",
     "iopub.status.busy": "2023-12-18T18:13:02.172147Z",
     "iopub.status.idle": "2023-12-18T18:13:18.350485Z",
     "shell.execute_reply": "2023-12-18T18:13:18.349260Z"
    },
    "papermill": {
     "duration": 16.209884,
     "end_time": "2023-12-18T18:13:18.353137",
     "exception": false,
     "start_time": "2023-12-18T18:13:02.143253",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score\n",
    "from tensorflow import keras\n",
    "from scikeras.wrappers import KerasClassifier\n",
    "\n",
    "import eli5\n",
    "from eli5.sklearn import PermutationImportance\n",
    "from sklearn.metrics import confusion_matrix, roc_curve, precision_recall_curve"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11819eca",
   "metadata": {
    "papermill": {
     "duration": 0.025859,
     "end_time": "2023-12-18T18:13:18.405582",
     "exception": false,
     "start_time": "2023-12-18T18:13:18.379723",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Splitting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f2d25a92",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T18:13:18.460121Z",
     "iopub.status.busy": "2023-12-18T18:13:18.459638Z",
     "iopub.status.idle": "2023-12-18T18:13:18.741371Z",
     "shell.execute_reply": "2023-12-18T18:13:18.740213Z"
    },
    "papermill": {
     "duration": 0.312188,
     "end_time": "2023-12-18T18:13:18.744046",
     "exception": false,
     "start_time": "2023-12-18T18:13:18.431858",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(((233034, 29), (233034,)), ((58259, 29), (58259,)))"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# split train test set\n",
    "train, test = train_test_split(df_loan_issue, test_size=0.2, stratify=df_loan_issue['label'], random_state=42)\n",
    "\n",
    "y_train = train.pop('label')\n",
    "y_test = test.pop('label')\n",
    "X_train = train\n",
    "X_test = test\n",
    "\n",
    "(X_train.shape, y_train.shape), (X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9871fe81",
   "metadata": {
    "papermill": {
     "duration": 0.026486,
     "end_time": "2023-12-18T18:13:18.797754",
     "exception": false,
     "start_time": "2023-12-18T18:13:18.771268",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Normalizing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "859e754c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T18:13:18.853892Z",
     "iopub.status.busy": "2023-12-18T18:13:18.853463Z",
     "iopub.status.idle": "2023-12-18T18:13:18.986765Z",
     "shell.execute_reply": "2023-12-18T18:13:18.985685Z"
    },
    "papermill": {
     "duration": 0.164677,
     "end_time": "2023-12-18T18:13:18.989664",
     "exception": false,
     "start_time": "2023-12-18T18:13:18.824987",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# normalize data\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4e74a47e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T18:13:19.045037Z",
     "iopub.status.busy": "2023-12-18T18:13:19.044342Z",
     "iopub.status.idle": "2023-12-18T18:13:19.186889Z",
     "shell.execute_reply": "2023-12-18T18:13:19.185917Z"
    },
    "papermill": {
     "duration": 0.173243,
     "end_time": "2023-12-18T18:13:19.189576",
     "exception": false,
     "start_time": "2023-12-18T18:13:19.016333",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# set metrics to track\n",
    "METRICS = [\n",
    "      keras.metrics.BinaryAccuracy(name='accuracy'),\n",
    "      keras.metrics.Precision(name='precision'),\n",
    "      keras.metrics.Recall(name='recall'),\n",
    "      keras.metrics.AUC(name='auc'),\n",
    "      keras.metrics.AUC(name='prc', curve='PR'), \n",
    "]\n",
    "\n",
    "# function to build simple dnn\n",
    "def make_model(metrics, size):\n",
    "    model = keras.Sequential([\n",
    "        keras.layers.Dense(\n",
    "            16, \n",
    "            activation='relu', \n",
    "            input_shape=(size,)),\n",
    "        keras.layers.Dropout(0.5),\n",
    "        keras.layers.Dense(\n",
    "            8, \n",
    "            activation='relu'),\n",
    "        keras.layers.Dropout(0.5),        \n",
    "        keras.layers.Dense(\n",
    "            1, \n",
    "            activation='sigmoid'),\n",
    "  ])\n",
    "\n",
    "    model.compile(\n",
    "      optimizer=keras.optimizers.Adam(learning_rate=1e-3),\n",
    "      loss=keras.losses.BinaryCrossentropy(),\n",
    "      metrics=metrics)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "dc782b53",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T18:13:19.246333Z",
     "iopub.status.busy": "2023-12-18T18:13:19.245875Z",
     "iopub.status.idle": "2023-12-18T18:13:19.252753Z",
     "shell.execute_reply": "2023-12-18T18:13:19.251611Z"
    },
    "papermill": {
     "duration": 0.037771,
     "end_time": "2023-12-18T18:13:19.254950",
     "exception": false,
     "start_time": "2023-12-18T18:13:19.217179",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weight for class 0: 0.52\n",
      "Weight for class 1: 13.83\n"
     ]
    }
   ],
   "source": [
    "# find the class weight to handle unbalanced label classes\n",
    "weight_for_0 = (1 / neg) * (total / 2.0)\n",
    "weight_for_1 = (1 / pos) * (total / 2.0)\n",
    "\n",
    "class_weight = {0: weight_for_0, 1: weight_for_1}\n",
    "\n",
    "print('Weight for class 0: {:.2f}'.format(weight_for_0))\n",
    "print('Weight for class 1: {:.2f}'.format(weight_for_1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8438aa82",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T18:13:19.312070Z",
     "iopub.status.busy": "2023-12-18T18:13:19.310881Z",
     "iopub.status.idle": "2023-12-18T18:13:19.316607Z",
     "shell.execute_reply": "2023-12-18T18:13:19.315660Z"
    },
    "papermill": {
     "duration": 0.037109,
     "end_time": "2023-12-18T18:13:19.318948",
     "exception": false,
     "start_time": "2023-12-18T18:13:19.281839",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "EPOCHS = 100\n",
    "BATCH_SIZE = 2048\n",
    "\n",
    "early_stopping = keras.callbacks.EarlyStopping(\n",
    "    monitor='val_prc', \n",
    "    verbose=1,\n",
    "    patience=10,\n",
    "    mode='max',\n",
    "    restore_best_weights=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5092f576",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T18:13:19.374750Z",
     "iopub.status.busy": "2023-12-18T18:13:19.374075Z",
     "iopub.status.idle": "2023-12-18T18:13:57.781725Z",
     "shell.execute_reply": "2023-12-18T18:13:57.780614Z"
    },
    "papermill": {
     "duration": 38.456226,
     "end_time": "2023-12-18T18:13:57.802059",
     "exception": false,
     "start_time": "2023-12-18T18:13:19.345833",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Restoring model weights from the end of the best epoch: 66.\n",
      "Epoch 76: early stopping\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KerasClassifier(\n",
       "\tmodel=&lt;keras.src.engine.sequential.Sequential object at 0x7ad3b3bfa9b0&gt;\n",
       "\tbuild_fn=None\n",
       "\twarm_start=False\n",
       "\trandom_state=None\n",
       "\toptimizer=rmsprop\n",
       "\tloss=None\n",
       "\tmetrics=None\n",
       "\tbatch_size=2048\n",
       "\tvalidation_batch_size=None\n",
       "\tverbose=0\n",
       "\tcallbacks=[&lt;keras.src.callbacks.EarlyStopping object at 0x7ad3b4213ca0&gt;]\n",
       "\tvalidation_split=0.1\n",
       "\tshuffle=True\n",
       "\trun_eagerly=False\n",
       "\tepochs=100\n",
       "\tclass_weight=None\n",
       ")</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KerasClassifier</label><div class=\"sk-toggleable__content\"><pre>KerasClassifier(\n",
       "\tmodel=&lt;keras.src.engine.sequential.Sequential object at 0x7ad3b3bfa9b0&gt;\n",
       "\tbuild_fn=None\n",
       "\twarm_start=False\n",
       "\trandom_state=None\n",
       "\toptimizer=rmsprop\n",
       "\tloss=None\n",
       "\tmetrics=None\n",
       "\tbatch_size=2048\n",
       "\tvalidation_batch_size=None\n",
       "\tverbose=0\n",
       "\tcallbacks=[&lt;keras.src.callbacks.EarlyStopping object at 0x7ad3b4213ca0&gt;]\n",
       "\tvalidation_split=0.1\n",
       "\tshuffle=True\n",
       "\trun_eagerly=False\n",
       "\tepochs=100\n",
       "\tclass_weight=None\n",
       ")</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KerasClassifier(\n",
       "\tmodel=<keras.src.engine.sequential.Sequential object at 0x7ad3b3bfa9b0>\n",
       "\tbuild_fn=None\n",
       "\twarm_start=False\n",
       "\trandom_state=None\n",
       "\toptimizer=rmsprop\n",
       "\tloss=None\n",
       "\tmetrics=None\n",
       "\tbatch_size=2048\n",
       "\tvalidation_batch_size=None\n",
       "\tverbose=0\n",
       "\tcallbacks=[<keras.src.callbacks.EarlyStopping object at 0x7ad3b4213ca0>]\n",
       "\tvalidation_split=0.1\n",
       "\tshuffle=True\n",
       "\trun_eagerly=False\n",
       "\tepochs=100\n",
       "\tclass_weight=None\n",
       ")"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_base = make_model(metrics=METRICS, size=X_train_scaled.shape[-1])\n",
    "\n",
    "classifier_base = KerasClassifier(model=model_base,\n",
    "                                  epochs=EPOCHS,\n",
    "                                  batch_size=BATCH_SIZE,\n",
    "                                  callbacks=[early_stopping],\n",
    "                                  validation_split=0.1,\n",
    "                                  verbose=0)\n",
    "\n",
    "classifier_base.fit(X_train_scaled, y_train, class_weight=class_weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "fc5d1f99",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T18:13:57.858454Z",
     "iopub.status.busy": "2023-12-18T18:13:57.858038Z",
     "iopub.status.idle": "2023-12-18T18:13:59.047765Z",
     "shell.execute_reply": "2023-12-18T18:13:59.046329Z"
    },
    "papermill": {
     "duration": 1.221047,
     "end_time": "2023-12-18T18:13:59.050371",
     "exception": false,
     "start_time": "2023-12-18T18:13:57.829324",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29/29 [==============================] - 0s 2ms/step\n",
      "loss :  0.33089134097099304\n",
      "accuracy :  0.8814947009086609\n",
      "precision :  0.20743902027606964\n",
      "recall :  0.807692289352417\n",
      "auc :  0.9224873185157776\n",
      "prc :  0.6526882648468018\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# results output\n",
    "def plot_cm(labels, predictions, p=0.5):\n",
    "    plt.figure(figsize=(8,6))\n",
    "    \n",
    "    cm = confusion_matrix(labels, predictions > p)\n",
    "    sns.heatmap(cm, annot=True, fmt=\"d\")\n",
    "    plt.title('Confusion matrix @{:.2f}'.format(p))\n",
    "    plt.ylabel('Actual label')\n",
    "    plt.xlabel('Predicted label')\n",
    "\n",
    "prediction_base = model_base.predict(X_test_scaled, batch_size=BATCH_SIZE)\n",
    "evaluation_base = model_base.evaluate(X_test_scaled, y_test, batch_size=BATCH_SIZE, verbose=0)\n",
    "\n",
    "for name, value in zip(model_base.metrics_names, evaluation_base):\n",
    "    print(name, ': ', value)\n",
    "print()\n",
    "\n",
    "plot_cm(y_test, prediction_base)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec2a1ff7",
   "metadata": {
    "papermill": {
     "duration": 0.030479,
     "end_time": "2023-12-18T18:13:59.109054",
     "exception": false,
     "start_time": "2023-12-18T18:13:59.078575",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Checking the correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1b7e36ce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T18:13:59.167345Z",
     "iopub.status.busy": "2023-12-18T18:13:59.166855Z",
     "iopub.status.idle": "2023-12-18T18:14:02.722954Z",
     "shell.execute_reply": "2023-12-18T18:14:02.721712Z"
    },
    "papermill": {
     "duration": 3.59065,
     "end_time": "2023-12-18T18:14:02.727860",
     "exception": false,
     "start_time": "2023-12-18T18:13:59.137210",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2500x2000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking correlations\n",
    "corr = df_final.corr().round(1)\n",
    "\n",
    "plt.figure(figsize=(25,20))\n",
    "mask = np.zeros_like(corr)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "sns.heatmap(corr, annot=True, mask=mask, cbar=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1b4c8f0",
   "metadata": {
    "papermill": {
     "duration": 0.030929,
     "end_time": "2023-12-18T18:14:02.790147",
     "exception": false,
     "start_time": "2023-12-18T18:14:02.759218",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Plotting the ROC and the AUC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d33b25de",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-18T18:14:02.857128Z",
     "iopub.status.busy": "2023-12-18T18:14:02.856733Z",
     "iopub.status.idle": "2023-12-18T18:14:03.455452Z",
     "shell.execute_reply": "2023-12-18T18:14:03.454110Z"
    },
    "papermill": {
     "duration": 0.636727,
     "end_time": "2023-12-18T18:14:03.458196",
     "exception": false,
     "start_time": "2023-12-18T18:14:02.821469",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ...\n",
    "\n",
    "# Predictions on the test set\n",
    "prediction_base = classifier_base.predict(X_test_scaled)\n",
    "\n",
    "# Get predicted probabilities for the positive class\n",
    "y_scores_base = classifier_base.predict_proba(X_test_scaled)[:, 1]\n",
    "\n",
    "# Plot ROC curve\n",
    "fpr_base, tpr_base, _ = roc_curve(y_test, y_scores_base)\n",
    "roc_auc_base = auc(fpr_base, tpr_base)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr_base, tpr_base, color='navy', lw=2, label=f'AUC = {roc_auc_base:.2f}')\n",
    "plt.plot([0, 1], [0, 1], color='orange', lw=2, linestyle='--')\n",
    "plt.xlabel('False Positive Rate (FPR)')\n",
    "plt.ylabel('True Positive Rate (TPR)')\n",
    "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a509427",
   "metadata": {
    "papermill": {
     "duration": 0.032088,
     "end_time": "2023-12-18T18:14:03.523205",
     "exception": false,
     "start_time": "2023-12-18T18:14:03.491117",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Thank you!"
   ]
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "none",
   "dataSources": [
    {
     "datasetId": 1404394,
     "sourceId": 2327131,
     "sourceType": "datasetVersion"
    },
    {
     "datasetId": 4182762,
     "sourceId": 7225487,
     "sourceType": "datasetVersion"
    }
   ],
   "dockerImageVersionId": 30626,
   "isGpuEnabled": false,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 121.709637,
   "end_time": "2023-12-18T18:14:06.894139",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2023-12-18T18:12:05.184502",
   "version": "2.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}