lending-club-case-study.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7062158f-e91d-4018-90f4-f191fc89876a",
   "metadata": {},
   "source": [
    "# Lending Club Case Study"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de1ada33-139b-4518-b06d-889d2ae08bee",
   "metadata": {},
   "source": [
    "## Problem Statement"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd7f0463-10bf-4b22-b975-4bec9151aab5",
   "metadata": {},
   "source": [
    "You work for a consumer finance company which specialises in lending various types of loans to urban customers. When the company receives a loan application, the company has to make a decision for loan approval based on the applicant’s profile. Two types of risks are associated with the bank’s decision:\n",
    "\n",
    "- If the applicant is likely to repay the loan, then not approving the loan results in a loss of business to the company\n",
    "\n",
    "- If the applicant is not likely to repay the loan, i.e. he/she is likely to default, then approving the loan may lead to a financial loss for the company"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43d1775c-a2f3-467f-9a1c-67b40f2ede37",
   "metadata": {},
   "source": [
    "## Business Objectives"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ceb2312-d80e-4f45-b4e1-407802ed1ca0",
   "metadata": {},
   "source": [
    "Use EDA to understand how consumer attributes and loan attributes influence the tendency of default."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa90c881-a571-4a2b-ab8e-08e5fd989d4f",
   "metadata": {},
   "source": [
    "## Constraints"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b3ac669-5dfa-4b8e-bddf-77474176ff82",
   "metadata": {},
   "source": [
    "When a person applies for a loan, there are two types of decisions that could be taken by the company:\n",
    "\n",
    "1. <b>Loan accepted</b>: If the company approves the loan, there are 3 possible scenarios described below:\n",
    "\n",
    "    - <b>Fully paid</b>: Applicant has fully paid the loan (the principal and the interest rate)\n",
    "\n",
    "    - <b>Current</b>: Applicant is in the process of paying the instalments, i.e. the tenure of the loan is not yet completed. These candidates are not labelled as 'defaulted'.\n",
    "\n",
    "    - <b>Charged-off</b>: Applicant has not paid the instalments in due time for a long period of time, i.e. he/she has defaulted on the loan \n",
    "\n",
    "2. <b>Loan rejected</b>: The company had rejected the loan (because the candidate does not meet their requirements etc.). Since the loan was rejected, there is no transactional history of those applicants with the company and so this data is not available with the company (and thus in this dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64b3af6a-31f5-448f-a02b-5695907f33b9",
   "metadata": {},
   "source": [
    "### Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d1167df5-b865-43a2-8e3b-bfcc7ceb9095",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "44d85de9-eb8e-478f-94fb-89c04fa215b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Supress warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92d7216d-683c-432d-a08c-ae781339a821",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "7a54bcee-1dc6-47dc-96d4-77a63ca84254",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the Load dataset\n",
    "loan_df = pd.read_csv('loan.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ec087692-959e-48a8-a247-bf66b1a70254",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39717, 111)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the rows and columns in the loan file\n",
    "loan_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "baba648e-4c5e-42d6-9de4-6c130cffc83f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 39717 entries, 0 to 39716\n",
      "Columns: 111 entries, id to total_il_high_credit_limit\n",
      "dtypes: float64(74), int64(13), object(24)\n",
      "memory usage: 33.6+ MB\n"
     ]
    }
   ],
   "source": [
    "# General Information about the dataframe\n",
    "loan_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49a19439-fb61-4257-b70c-692403ff503a",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "bd09c4e2-843b-460e-8462-30d867e85021",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
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       "      <th>...</th>\n",
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       "      <td>4975.0</td>\n",
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       "      <td>B</td>\n",
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       "      <td>2500</td>\n",
       "      <td>2500</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>15.27%</td>\n",
       "      <td>59.83</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
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       "      <td>2400</td>\n",
       "      <td>2400</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>15.96%</td>\n",
       "      <td>84.33</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1076863</td>\n",
       "      <td>1277178</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>13.49%</td>\n",
       "      <td>339.31</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>...</td>\n",
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       "      <th>4</th>\n",
       "      <td>1075358</td>\n",
       "      <td>1311748</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>12.69%</td>\n",
       "      <td>67.79</td>\n",
       "      <td>B</td>\n",
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       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
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       "<p>5 rows × 111 columns</p>\n",
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       "        id  member_id  loan_amnt  funded_amnt  funded_amnt_inv        term  \\\n",
       "0  1077501    1296599       5000         5000           4975.0   36 months   \n",
       "1  1077430    1314167       2500         2500           2500.0   60 months   \n",
       "2  1077175    1313524       2400         2400           2400.0   36 months   \n",
       "3  1076863    1277178      10000        10000          10000.0   36 months   \n",
       "4  1075358    1311748       3000         3000           3000.0   60 months   \n",
       "\n",
       "  int_rate  installment grade sub_grade  ... num_tl_90g_dpd_24m  \\\n",
       "0   10.65%       162.87     B        B2  ...                NaN   \n",
       "1   15.27%        59.83     C        C4  ...                NaN   \n",
       "2   15.96%        84.33     C        C5  ...                NaN   \n",
       "3   13.49%       339.31     C        C1  ...                NaN   \n",
       "4   12.69%        67.79     B        B5  ...                NaN   \n",
       "\n",
       "  num_tl_op_past_12m pct_tl_nvr_dlq  percent_bc_gt_75 pub_rec_bankruptcies  \\\n",
       "0                NaN            NaN               NaN                  0.0   \n",
       "1                NaN            NaN               NaN                  0.0   \n",
       "2                NaN            NaN               NaN                  0.0   \n",
       "3                NaN            NaN               NaN                  0.0   \n",
       "4                NaN            NaN               NaN                  0.0   \n",
       "\n",
       "  tax_liens tot_hi_cred_lim total_bal_ex_mort total_bc_limit  \\\n",
       "0       0.0             NaN               NaN            NaN   \n",
       "1       0.0             NaN               NaN            NaN   \n",
       "2       0.0             NaN               NaN            NaN   \n",
       "3       0.0             NaN               NaN            NaN   \n",
       "4       0.0             NaN               NaN            NaN   \n",
       "\n",
       "  total_il_high_credit_limit  \n",
       "0                        NaN  \n",
       "1                        NaN  \n",
       "2                        NaN  \n",
       "3                        NaN  \n",
       "4                        NaN  \n",
       "\n",
       "[5 rows x 111 columns]"
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     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "loan_df.head()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 53,
   "id": "ee16be9b-b76d-438b-8c95-6e46daebc9cb",
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       "      <td>A</td>\n",
       "      <td>A4</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>39715</th>\n",
       "      <td>90376</td>\n",
       "      <td>89243</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>650.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>7.43%</td>\n",
       "      <td>155.38</td>\n",
       "      <td>A</td>\n",
       "      <td>A2</td>\n",
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       "      <td>86999</td>\n",
       "      <td>7500</td>\n",
       "      <td>7500</td>\n",
       "      <td>800.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>13.75%</td>\n",
       "      <td>255.43</td>\n",
       "      <td>E</td>\n",
       "      <td>E2</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "          id  member_id  loan_amnt  funded_amnt  funded_amnt_inv        term  \\\n",
       "39712  92187      92174       2500         2500           1075.0   36 months   \n",
       "39713  90665      90607       8500         8500            875.0   36 months   \n",
       "39714  90395      90390       5000         5000           1325.0   36 months   \n",
       "39715  90376      89243       5000         5000            650.0   36 months   \n",
       "39716  87023      86999       7500         7500            800.0   36 months   \n",
       "\n",
       "      int_rate  installment grade sub_grade  ... num_tl_90g_dpd_24m  \\\n",
       "39712    8.07%        78.42     A        A4  ...                NaN   \n",
       "39713   10.28%       275.38     C        C1  ...                NaN   \n",
       "39714    8.07%       156.84     A        A4  ...                NaN   \n",
       "39715    7.43%       155.38     A        A2  ...                NaN   \n",
       "39716   13.75%       255.43     E        E2  ...                NaN   \n",
       "\n",
       "      num_tl_op_past_12m pct_tl_nvr_dlq  percent_bc_gt_75  \\\n",
       "39712                NaN            NaN               NaN   \n",
       "39713                NaN            NaN               NaN   \n",
       "39714                NaN            NaN               NaN   \n",
       "39715                NaN            NaN               NaN   \n",
       "39716                NaN            NaN               NaN   \n",
       "\n",
       "      pub_rec_bankruptcies tax_liens tot_hi_cred_lim total_bal_ex_mort  \\\n",
       "39712                  NaN       NaN             NaN               NaN   \n",
       "39713                  NaN       NaN             NaN               NaN   \n",
       "39714                  NaN       NaN             NaN               NaN   \n",
       "39715                  NaN       NaN             NaN               NaN   \n",
       "39716                  NaN       NaN             NaN               NaN   \n",
       "\n",
       "      total_bc_limit total_il_high_credit_limit  \n",
       "39712            NaN                        NaN  \n",
       "39713            NaN                        NaN  \n",
       "39714            NaN                        NaN  \n",
       "39715            NaN                        NaN  \n",
       "39716            NaN                        NaN  \n",
       "\n",
       "[5 rows x 111 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b0f7fc0-c226-4daf-a9ad-93cea590e6c3",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abd5f70e-edeb-4780-b0b0-38053d70fd11",
   "metadata": {},
   "source": [
    "**The data has been read into the dataframe with column names. Now, we can start the data clean up.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "976a1ff1-1913-421f-82c0-482a25e9bc4d",
   "metadata": {},
   "source": [
    "### Data Cleanup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e6cc6a7-2c0d-4f58-822a-b658b319c514",
   "metadata": {},
   "source": [
    "During Data clean up we need to identify the columns that will not add any value to the analysis and remove them from our dataset, like single value across dataset, null values across dataset, high percentage of null values, text or description columns etc.\n",
    "\n",
    "Also, we need to get rid of the data (row) that is not relevant for the analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8300f4e3-5a0a-4888-a394-998567276927",
   "metadata": {},
   "source": [
    "##### Before starting with analysis on the columns, it would be better to filter the data of interest. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "54b8a6e9-bfca-4cf7-9220-d89e2dd92a82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_status\n",
       "Fully Paid     32950\n",
       "Charged Off     5627\n",
       "Current         1140\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_df.loan_status.value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "819c2935-52b9-4f7c-9a3a-c6ef39ccbb27",
   "metadata": {},
   "source": [
    "###"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08d5e3d4-8119-4caa-8eb1-14a48e848adc",
   "metadata": {},
   "source": [
    "As we need to analyse the loan applications that are likely to default or fully paid, the loans that are currently ongoing may not help. So, we need to remove them from dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "id": "b7438c87-deda-408f-8ed5-859aedd88d11",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new dataframe from the loan_df by filtering the Fully Paid and Charged Off loan status.\n",
    "\n",
    "closed_loan_df = loan_df[loan_df.loan_status.isin(['Fully Paid','Charged Off'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "1a73b733-376d-45e0-97c3-a0beece0ff5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38577, 111)"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count of Closed Loan Dataframe\n",
    "\n",
    "closed_loan_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "id": "3b5a1936-e7ad-4840-a25f-a70800b1a9d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Data dropped from initial file: 2.87\n"
     ]
    }
   ],
   "source": [
    "# Percent of data/rows dropped from initial file\n",
    "total_rows_loan_df = loan_df.shape[0]\n",
    "total_rows_closed_loan_df = closed_loan_df.shape[0]\n",
    "print(f'Total Data dropped from initial file: {round((total_rows_loan_df - total_rows_closed_loan_df)*100 /total_rows_loan_df, 2)}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "bdb184cd-0eb4-4960-a92a-a0b9245d66b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                              0.000000\n",
       "member_id                       0.000000\n",
       "loan_amnt                       0.000000\n",
       "funded_amnt                     0.000000\n",
       "funded_amnt_inv                 0.000000\n",
       "                                 ...    \n",
       "tax_liens                       0.101097\n",
       "tot_hi_cred_lim               100.000000\n",
       "total_bal_ex_mort             100.000000\n",
       "total_bc_limit                100.000000\n",
       "total_il_high_credit_limit    100.000000\n",
       "Length: 111, dtype: float64"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the columns with null value count\n",
    "closed_loan_df.isnull().sum() *100/total_rows_closed_loan_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c646e8e-7450-4a92-99c1-5ad9403aeb2f",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91670f1f-4e2f-40a7-9683-44edc2ac73b4",
   "metadata": {},
   "source": [
    " \n",
    "###### It appears that few columns have 100% data as null. We can drop the columns.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "cc204e2c-60a9-4e86-8148-3899e5dd2abc",
   "metadata": {},
   "outputs": [],
   "source": [
    "closed_loan_df.dropna(axis = 1, how='all', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "e6983e28-fb56-4d37-9da2-0bc8d202f155",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38577, 56)"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "closed_loan_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "9b731548-4611-488a-bb60-88c8491cf794",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                             0.000000\n",
       "earliest_cr_line               0.000000\n",
       "open_acc                       0.000000\n",
       "pub_rec                        0.000000\n",
       "revol_bal                      0.000000\n",
       "total_acc                      0.000000\n",
       "initial_list_status            0.000000\n",
       "out_prncp                      0.000000\n",
       "out_prncp_inv                  0.000000\n",
       "total_pymnt                    0.000000\n",
       "delinq_2yrs                    0.000000\n",
       "total_pymnt_inv                0.000000\n",
       "total_rec_int                  0.000000\n",
       "total_rec_late_fee             0.000000\n",
       "recoveries                     0.000000\n",
       "collection_recovery_fee        0.000000\n",
       "last_pymnt_amnt                0.000000\n",
       "policy_code                    0.000000\n",
       "application_type               0.000000\n",
       "acc_now_delinq                 0.000000\n",
       "delinq_amnt                    0.000000\n",
       "total_rec_prncp                0.000000\n",
       "dti                            0.000000\n",
       "inq_last_6mths                 0.000000\n",
       "zip_code                       0.000000\n",
       "grade                          0.000000\n",
       "sub_grade                      0.000000\n",
       "term                           0.000000\n",
       "addr_state                     0.000000\n",
       "home_ownership                 0.000000\n",
       "annual_inc                     0.000000\n",
       "verification_status            0.000000\n",
       "issue_d                        0.000000\n",
       "installment                    0.000000\n",
       "loan_status                    0.000000\n",
       "pymnt_plan                     0.000000\n",
       "url                            0.000000\n",
       "funded_amnt_inv                0.000000\n",
       "funded_amnt                    0.000000\n",
       "loan_amnt                      0.000000\n",
       "member_id                      0.000000\n",
       "purpose                        0.000000\n",
       "int_rate                       0.000000\n",
       "last_credit_pull_d             0.005184\n",
       "title                          0.028514\n",
       "tax_liens                      0.101097\n",
       "revol_util                     0.129611\n",
       "collections_12_mths_ex_med     0.145164\n",
       "chargeoff_within_12_mths       0.145164\n",
       "last_pymnt_d                   0.184047\n",
       "pub_rec_bankruptcies           1.806776\n",
       "emp_length                     2.677761\n",
       "emp_title                      6.185033\n",
       "desc                          32.477901\n",
       "mths_since_last_delinq        64.559193\n",
       "mths_since_last_record        92.897322\n",
       "dtype: float64"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(closed_loan_df.isnull().sum() *100/total_rows_closed_loan_df).sort_values()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b2724131-de23-42c1-8ba3-c325f7ea79a5",
   "metadata": {},
   "source": [
    "The dataset contains two columns with high null percentage:\n",
    "\n",
    "    - mths_since_last_record\n",
    "    - mths_since_last_delinq\n",
    "\n",
    "Also, the 'desc' column has 32% Null values and as per column definition, it is the \"Loan description provided by the borrower\". So, it will not add value to analysis.\n",
    "\n",
    "These columns can be dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "id": "326cd6ce-9045-443e-945b-54fefed58268",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop the three columns with high null value percentage from the datafame.\n",
    "closed_loan_df.drop(columns = ['mths_since_last_record', 'mths_since_last_delinq', 'desc'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "id": "c30e25dc-e62f-4729-9628-68800804f164",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38577, 53)"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "closed_loan_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44b3f250-83e4-4438-9256-4cd624eb4a39",
   "metadata": {},
   "source": [
    "The columns contain a unique value will not add value to the analysis. The columns can be identified and removed from the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "ca05b436-5800-4ebc-a6be-f0ebf26d7a86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tax_liens                         1\n",
       "initial_list_status               1\n",
       "out_prncp                         1\n",
       "out_prncp_inv                     1\n",
       "collections_12_mths_ex_med        1\n",
       "policy_code                       1\n",
       "pymnt_plan                        1\n",
       "application_type                  1\n",
       "acc_now_delinq                    1\n",
       "chargeoff_within_12_mths          1\n",
       "delinq_amnt                       1\n",
       "loan_status                       2\n",
       "term                              2\n",
       "verification_status               3\n",
       "pub_rec_bankruptcies              3\n",
       "home_ownership                    5\n",
       "pub_rec                           5\n",
       "grade                             7\n",
       "inq_last_6mths                    9\n",
       "delinq_2yrs                      11\n",
       "emp_length                       11\n",
       "purpose                          14\n",
       "sub_grade                        35\n",
       "open_acc                         40\n",
       "addr_state                       50\n",
       "issue_d                          55\n",
       "total_acc                        82\n",
       "last_pymnt_d                    101\n",
       "last_credit_pull_d              106\n",
       "int_rate                        370\n",
       "earliest_cr_line                524\n",
       "zip_code                        822\n",
       "loan_amnt                       870\n",
       "funded_amnt                    1019\n",
       "revol_util                     1088\n",
       "total_rec_late_fee             1320\n",
       "collection_recovery_fee        2616\n",
       "dti                            2853\n",
       "recoveries                     4040\n",
       "annual_inc                     5215\n",
       "total_rec_prncp                6841\n",
       "funded_amnt_inv                8050\n",
       "installment                   15022\n",
       "title                         19297\n",
       "revol_bal                     21275\n",
       "emp_title                     28027\n",
       "total_rec_int                 34025\n",
       "last_pymnt_amnt               34418\n",
       "total_pymnt_inv               36387\n",
       "total_pymnt                   36714\n",
       "member_id                     38577\n",
       "url                           38577\n",
       "id                            38577\n",
       "dtype: int64"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identify the columns and the number of unique values they hold.\n",
    "\n",
    "closed_loan_df.nunique().sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60d19808-9d31-4726-b40d-e632b6eeb981",
   "metadata": {},
   "source": [
    "Drop the columns where the value is unique across columns i.e. Unique values is 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "e9e85f07-9259-436a-81a8-b768a6118aa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['pymnt_plan', 'initial_list_status', 'out_prncp', 'out_prncp_inv',\n",
       "       'collections_12_mths_ex_med', 'policy_code', 'application_type',\n",
       "       'acc_now_delinq', 'chargeoff_within_12_mths', 'delinq_amnt',\n",
       "       'tax_liens'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the list of columns where the unique values exactly 1.\n",
    "columns_with_single_unique_values = closed_loan_df.columns[closed_loan_df.apply(lambda x: x.nunique() == 1)]\n",
    "columns_with_single_unique_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "id": "71b2c411-20cb-401d-b223-59b862ce5303",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the columns from the dataframe\n",
    "closed_loan_df.drop(columns = columns_with_single_unique_values, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "id": "0b077e2b-3b9c-42a1-a6c5-c8e61162b91b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38577, 42)"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "closed_loan_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "id": "47d7bbec-6ade-453d-bf32-be81c3fe1f63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_status                    2\n",
       "term                           2\n",
       "pub_rec_bankruptcies           3\n",
       "verification_status            3\n",
       "pub_rec                        5\n",
       "home_ownership                 5\n",
       "grade                          7\n",
       "inq_last_6mths                 9\n",
       "delinq_2yrs                   11\n",
       "emp_length                    11\n",
       "purpose                       14\n",
       "sub_grade                     35\n",
       "open_acc                      40\n",
       "addr_state                    50\n",
       "issue_d                       55\n",
       "total_acc                     82\n",
       "last_pymnt_d                 101\n",
       "last_credit_pull_d           106\n",
       "int_rate                     370\n",
       "earliest_cr_line             524\n",
       "zip_code                     822\n",
       "loan_amnt                    870\n",
       "funded_amnt                 1019\n",
       "revol_util                  1088\n",
       "total_rec_late_fee          1320\n",
       "collection_recovery_fee     2616\n",
       "dti                         2853\n",
       "recoveries                  4040\n",
       "annual_inc                  5215\n",
       "total_rec_prncp             6841\n",
       "funded_amnt_inv             8050\n",
       "installment                15022\n",
       "title                      19297\n",
       "revol_bal                  21275\n",
       "emp_title                  28027\n",
       "total_rec_int              34025\n",
       "last_pymnt_amnt            34418\n",
       "total_pymnt_inv            36387\n",
       "total_pymnt                36714\n",
       "url                        38577\n",
       "member_id                  38577\n",
       "id                         38577\n",
       "dtype: int64"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the unique values again to verify the columns\n",
    "closed_loan_df.nunique().sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28c3403e-ec54-492c-b6ca-b0b7fbe2d5b2",
   "metadata": {},
   "source": [
    "\n",
    "There are three columns which have unique values for each row of data. Also, these columns have below definition suggesting these values are assinged at the time of application:\n",
    "    <ol> id: A unique LC assigned ID for the loan listing.</ol>\n",
    "    <ol> member_id: A unique LC assigned Id for the borrower member.</ol>\n",
    "    <ol> url: URL for the LC page with listing data.</ol>\n",
    "\n",
    "These do not appear to provide us any insight for the loan application. So, these columns can also be dropped. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "088830b7-54f7-46c6-8e35-7a22440f838e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "id": "9e941337-15b5-460b-8a6c-638988e330bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the three columns from the dataframe\n",
    "closed_loan_df.drop(columns = ['id', 'member_id', 'url'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "id": "6b9921c6-d60c-4d36-a345-eebc1296dfa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38577, 39)"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "closed_loan_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "id": "bfcf1034-8dba-4ea7-8c3c-daedb9855976",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_status                    2\n",
       "term                           2\n",
       "pub_rec_bankruptcies           3\n",
       "verification_status            3\n",
       "pub_rec                        5\n",
       "home_ownership                 5\n",
       "grade                          7\n",
       "inq_last_6mths                 9\n",
       "emp_length                    11\n",
       "delinq_2yrs                   11\n",
       "purpose                       14\n",
       "sub_grade                     35\n",
       "open_acc                      40\n",
       "addr_state                    50\n",
       "issue_d                       55\n",
       "total_acc                     82\n",
       "last_pymnt_d                 101\n",
       "last_credit_pull_d           106\n",
       "int_rate                     370\n",
       "earliest_cr_line             524\n",
       "zip_code                     822\n",
       "loan_amnt                    870\n",
       "funded_amnt                 1019\n",
       "revol_util                  1088\n",
       "total_rec_late_fee          1320\n",
       "collection_recovery_fee     2616\n",
       "dti                         2853\n",
       "recoveries                  4040\n",
       "annual_inc                  5215\n",
       "total_rec_prncp             6841\n",
       "funded_amnt_inv             8050\n",
       "installment                15022\n",
       "title                      19297\n",
       "revol_bal                  21275\n",
       "emp_title                  28027\n",
       "total_rec_int              34025\n",
       "last_pymnt_amnt            34418\n",
       "total_pymnt_inv            36387\n",
       "total_pymnt                36714\n",
       "dtype: int64"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "closed_loan_df.nunique().sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5fa2089-117c-4fe1-b47d-d769e764b3a9",
   "metadata": {},
   "source": [
    "Looking at the above data, it appears that there are text columns like emp_title and title that may not have any significance on our analysis. The column needs to be evaluated separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "id": "5d7c8702-dcf3-496a-aae0-6450f725e1f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Debt Consolidation',\n",
       " 'Debt Consolidation Loan',\n",
       " 'Personal Loan',\n",
       " 'Consolidation',\n",
       " 'debt consolidation',\n",
       " 'Credit Card Consolidation',\n",
       " 'Home Improvement',\n",
       " 'Debt consolidation',\n",
       " 'Small Business Loan',\n",
       " 'Credit Card Loan',\n",
       " 'Personal',\n",
       " 'Consolidation Loan',\n",
       " 'Home Improvement Loan',\n",
       " 'personal loan',\n",
       " 'personal',\n",
       " 'Loan',\n",
       " 'Wedding Loan',\n",
       " 'Car Loan',\n",
       " 'consolidation',\n",
       " 'Other Loan',\n",
       " 'Credit Card Payoff',\n",
       " 'Wedding',\n",
       " 'Credit Card Refinance',\n",
       " 'Major Purchase Loan',\n",
       " 'Consolidate',\n",
       " 'Medical',\n",
       " 'Credit Card',\n",
       " 'home improvement',\n",
       " 'Credit Cards',\n",
       " 'My Loan',\n",
       " 'Debt',\n",
       " 'loan',\n",
       " 'Business Loan',\n",
       " 'Freedom',\n",
       " 'Moving Loan',\n",
       " 'Personal loan',\n",
       " 'consolidate',\n",
       " 'debt_consolidation',\n",
       " 'Motorcycle',\n",
       " 'Debt Consolidation ',\n",
       " 'Debt Loan',\n",
       " 'Credit Card Debt Consolidation',\n",
       " 'Engagement Ring',\n",
       " 'Home improvement',\n",
       " 'Medical Loan',\n",
       " 'Major Purchase',\n",
       " 'Consolidate Debt',\n",
       " 'credit card consolidation',\n",
       " 'debt',\n",
       " 'DEBT CONSOLIDATION',\n",
       " 'Moving',\n",
       " 'Vacation',\n",
       " 'debt consolidation loan',\n",
       " 'Other',\n",
       " 'CC Consolidation',\n",
       " 'Credit Card Debt',\n",
       " 'Wedding Expenses',\n",
       " 'Credit Consolidation',\n",
       " 'my loan',\n",
       " 'freedom',\n",
       " 'Credit Card Consolidation Loan',\n",
       " 'Loan 1',\n",
       " 'payoff',\n",
       " 'Debt Free',\n",
       " 'Debt consolidation loan',\n",
       " 'Refinance',\n",
       " 'consolidation loan',\n",
       " 'Bill Consolidation',\n",
       " 'Payoff',\n",
       " 'Home Improvements',\n",
       " 'Small Business',\n",
       " 'Credit card consolidation',\n",
       " 'Motorcycle Loan',\n",
       " 'Pool Loan',\n",
       " 'Auto Loan',\n",
       " 'Vacation Loan',\n",
       " 'Pool',\n",
       " 'House Loan',\n",
       " 'Consolidation loan',\n",
       " 'credit cards',\n",
       " 'Debt Payoff',\n",
       " 'Home',\n",
       " 'Loan Consolidation',\n",
       " 'motorcycle',\n",
       " 'wedding',\n",
       " 'Credit Card Refi',\n",
       " 'vacation',\n",
       " 'CC Payoff',\n",
       " 'Car',\n",
       " 'Consolidate debt',\n",
       " 'credit card',\n",
       " 'Credit Card Refinancing',\n",
       " 'Lending Club Loan',\n",
       " 'Business',\n",
       " 'My loan',\n",
       " 'debt loan',\n",
       " 'Pay off credit cards',\n",
       " 'car loan',\n",
       " 'Car loan',\n",
       " 'Bills',\n",
       " 'CC Refinance',\n",
       " 'Lending Club',\n",
       " 'car',\n",
       " 'credit card refinance',\n",
       " 'medical',\n",
       " 'Consolidate Credit Card Debt',\n",
       " 'major purchase',\n",
       " 'pay off credit cards',\n",
       " 'Debt Consolodation',\n",
       " 'Boat Loan',\n",
       " 'credit card payoff',\n",
       " 'debt free',\n",
       " 'wedding loan',\n",
       " 'Home Loan',\n",
       " 'Credit card refinance',\n",
       " 'Debt Consolidation loan',\n",
       " 'CC Loan',\n",
       " 'Pay Off',\n",
       " 'Medical Expenses',\n",
       " 'Student Loan',\n",
       " 'bills',\n",
       " 'Moving Expenses',\n",
       " 'Loan1',\n",
       " 'Get out of debt',\n",
       " 'get out of debt',\n",
       " 'consolidate debt',\n",
       " 'Swimming Pool',\n",
       " 'Auto',\n",
       " 'Credit Card Pay Off',\n",
       " 'Fresh Start',\n",
       " 'pay off',\n",
       " 'Freedom Loan',\n",
       " 'Pay Off Credit Cards',\n",
       " 'home',\n",
       " 'Pay off Credit Cards',\n",
       " 'Debt Consolidation loan ',\n",
       " 'Credit Card Refinance Loan',\n",
       " 'Small Business loan',\n",
       " 'PERSONAL LOAN',\n",
       " 'Credit Card consolidation',\n",
       " 'Consolidating Debt',\n",
       " 'credit card loan',\n",
       " 'Credit cards',\n",
       " 'Pay off debt',\n",
       " 'business loan',\n",
       " 'Debit Consolidation',\n",
       " 'pool',\n",
       " 'CC Refi',\n",
       " 'loan 1',\n",
       " 'Credit Card Payoff Loan',\n",
       " 'debt consolidation ',\n",
       " 'Consolidation ',\n",
       " 'Credit card payoff',\n",
       " 'Kitchen Remodel',\n",
       " 'Roof',\n",
       " 'boat',\n",
       " 'Pay off',\n",
       " 'My Personal Loan',\n",
       " 'home improvements',\n",
       " 'Debt Reduction',\n",
       " 'House',\n",
       " 'Consolidate Credit Cards',\n",
       " 'bill consolidation',\n",
       " 'Debt Consolidation 2011',\n",
       " 'Taxes',\n",
       " 'Debt Consolidate',\n",
       " 'Debt consolidation loan ',\n",
       " 'Debt Relief',\n",
       " 'HOME IMPROVEMENT',\n",
       " 'moving loan',\n",
       " 'credit consolidation',\n",
       " 'Paying off credit cards',\n",
       " 'Motorcycle loan',\n",
       " 'Car Repair',\n",
       " 'LOAN',\n",
       " 'Personal Loan ',\n",
       " 'refinance',\n",
       " 'New Start',\n",
       " 'Debt consolidation ',\n",
       " 'CONSOLIDATION',\n",
       " 'vacation loan',\n",
       " 'Debt payoff',\n",
       " 'Financial Freedom',\n",
       " 'CC',\n",
       " 'Loan 2',\n",
       " 'Card Consolidation',\n",
       " 'Boat',\n",
       " 'Debt ',\n",
       " 'Relocation Loan',\n",
       " 'Pay off Debt',\n",
       " 'debt relief',\n",
       " 'PERSONAL',\n",
       " 'Personal ',\n",
       " 'Ring',\n",
       " 'pool loan',\n",
       " 'home improvement loan',\n",
       " 'Truck',\n",
       " 'Credit Card loan',\n",
       " 'moving',\n",
       " 'motorcycle loan',\n",
       " 'CC Debt',\n",
       " 'Refinancing',\n",
       " 'Consolidating Credit Card Debt',\n",
       " 'credit_card',\n",
       " 'auto loan',\n",
       " 'Business Expansion',\n",
       " 'auto',\n",
       " 'Pool loan',\n",
       " 'Dept Consolidation',\n",
       " 'Debt loan',\n",
       " 'Payoff Credit Cards',\n",
       " 'Home improvement loan',\n",
       " 'Debt Consol',\n",
       " 'credit card refinance loan',\n",
       " 'Credit Card payoff',\n",
       " 'Business loan',\n",
       " 'New Roof',\n",
       " 'Investment',\n",
       " 'Credit Refinance',\n",
       " 'Major purchase',\n",
       " 'Help',\n",
       " 'Wedding Fund',\n",
       " 'Purchase',\n",
       " 'Loan consolidation',\n",
       " 'Bike',\n",
       " 'Consolidate Bills',\n",
       " 'lending club loan',\n",
       " 'Credit Card Elimination',\n",
       " 'Truck Loan',\n",
       " 'credit',\n",
       " '2010',\n",
       " 'truck',\n",
       " 'swimming pool',\n",
       " 'Pay off credit card',\n",
       " 'help',\n",
       " 'Home Repairs',\n",
       " 'Wedding ',\n",
       " 'LendingClub Loan',\n",
       " 'Working Capital',\n",
       " 'Small_Business Loan',\n",
       " 'Medical Bills',\n",
       " 'Wedding loan',\n",
       " 'credit card debt',\n",
       " 'Consolidate credit card debt',\n",
       " 'credit card pay off',\n",
       " 'medical expenses',\n",
       " 'business',\n",
       " 'Home loan',\n",
       " 'Relocation',\n",
       " 'PAYOFF',\n",
       " 'Credit card loan',\n",
       " 'DEBT FREE',\n",
       " 'No more credit cards',\n",
       " 'Credit Debt Consolidation',\n",
       " 'loan1',\n",
       " 'DebtConsolidation',\n",
       " 'Remodel',\n",
       " 'Tax Loan',\n",
       " 'Bill consolidation',\n",
       " 'personal ',\n",
       " 'pay bills',\n",
       " 'Refinance Loan',\n",
       " 'Credit Card Repayment',\n",
       " 'Discover',\n",
       " 'Consolidated',\n",
       " 'Medical loan',\n",
       " 'Home_Improvement Loan',\n",
       " 'New Loan',\n",
       " 'debt payoff',\n",
       " 'Education',\n",
       " 'ATV',\n",
       " 'Surgery',\n",
       " 'house',\n",
       " 'other',\n",
       " 'My Debt Consolidation Loan',\n",
       " 'Debt Freedom',\n",
       " 'out of debt',\n",
       " 'Pay Off Debt',\n",
       " 'money',\n",
       " 'CONSOLIDATE',\n",
       " 'CREDIT CARD CONSOLIDATION',\n",
       " 'Debt Consolidation Loan ',\n",
       " 'Bill Pay',\n",
       " 'Starting Over',\n",
       " 'discover',\n",
       " 'bike',\n",
       " 'Credit card',\n",
       " 'Consolodation',\n",
       " 'Debt consol',\n",
       " 'Debt Con',\n",
       " 'Cards',\n",
       " 'bill pay',\n",
       " 'medical loan',\n",
       " 'Get out of Debt',\n",
       " 'Pay off credit card debt',\n",
       " 'credit_card Loan',\n",
       " 'debt_consolidation loan',\n",
       " 'Home Down Payment',\n",
       " 'Credit card debt consolidation',\n",
       " 'car repair',\n",
       " 'Home Improvement ',\n",
       " 'Card Refinance',\n",
       " 'Family',\n",
       " 'LC Loan',\n",
       " 'MY LOAN',\n",
       " 'Credit Card Consolidation ',\n",
       " 'Payoff Loan',\n",
       " 'Home Improvement loan',\n",
       " 'CREDIT CARD PAYOFF',\n",
       " 'Debts',\n",
       " '2011 Loan',\n",
       " 'Emergency',\n",
       " 'Credit Card Payoffs',\n",
       " 'Landscaping',\n",
       " 'Debt Cons',\n",
       " 'pay off debt',\n",
       " 'credit loan',\n",
       " 'ATV Loan',\n",
       " 'CC payoff',\n",
       " 'BILLS',\n",
       " 'Home Improvment',\n",
       " 'Consolidating credit card debt',\n",
       " 'Consolidation Loan ',\n",
       " 'Pay Bills',\n",
       " 'CC loan',\n",
       " 'Engagement',\n",
       " 'Pay It Off',\n",
       " 'Debt Consolidation 1',\n",
       " 'Credit',\n",
       " 'Credit card pay off',\n",
       " 'Debt Cosolidation',\n",
       " 'School',\n",
       " 'home loan',\n",
       " 'Debt Consilidation',\n",
       " 'Pay Off Credit Card',\n",
       " 'Paying off my credit cards',\n",
       " 'FREEDOM',\n",
       " 'Home Improvement Project',\n",
       " 'Truck loan',\n",
       " 'Back on Track',\n",
       " 'Jeep',\n",
       " 'Paying off Credit Cards',\n",
       " 'CREDIT CARD DEBT',\n",
       " 'Credit Card Debt Consolidation Loan',\n",
       " 'wedding expenses',\n",
       " 'Credit Debt',\n",
       " 'pay off credit card',\n",
       " 'pay off bills',\n",
       " 'Refi',\n",
       " 'Wedding Expense',\n",
       " 'Engagement Ring Loan',\n",
       " 'RV',\n",
       " 'Debt Payment',\n",
       " 'Debt relief',\n",
       " 'cards',\n",
       " 'Paying off debt',\n",
       " 'Debt Repayment',\n",
       " 'Debt free',\n",
       " 'Credit Card Payment',\n",
       " 'Debt Help',\n",
       " 'Pay off bills',\n",
       " 'Furniture',\n",
       " 'consolidate bills',\n",
       " 'Credit Loan',\n",
       " 'purchase',\n",
       " 'consolidation ',\n",
       " 'My Debt Consolidation',\n",
       " 'DC',\n",
       " 'Legal Fees',\n",
       " 'Kitchen remodel',\n",
       " 'Debt reduction',\n",
       " 'Auto Repair',\n",
       " 'relief',\n",
       " 'freedom loan',\n",
       " 'Payoff Debt',\n",
       " 'Car payoff',\n",
       " 'new roof',\n",
       " 'Motorcycle Purchase',\n",
       " 'American Express',\n",
       " 'Wedding Ring',\n",
       " 'life saver',\n",
       " 'Garage',\n",
       " 'No more debt',\n",
       " 'Consolidate Debts',\n",
       " 'Down Payment',\n",
       " 'Pay Down Debt',\n",
       " 'New Beginning',\n",
       " 'Home improvements',\n",
       " 'camper',\n",
       " 'personal loan ',\n",
       " 'credit refinance',\n",
       " 'debt consol',\n",
       " 'Credit card consolidation loan',\n",
       " 'New Life',\n",
       " 'Credit Payoff',\n",
       " 'Credit Consolidation Loan',\n",
       " 'Credit Card Payoff loan',\n",
       " 'Equipment Loan',\n",
       " 'CONSOLIDATION LOAN',\n",
       " 'Debt Refinance',\n",
       " 'No More Credit Cards',\n",
       " 'Payback',\n",
       " 'dept consolidation',\n",
       " 'Pay off Credit Card',\n",
       " 'Used Car Loan',\n",
       " 'Refinancing Credit Card',\n",
       " 'Debt consolidation Loan',\n",
       " 'Car Financing',\n",
       " 'Home improvement project',\n",
       " 'Medical Expense',\n",
       " ' Debt Consolidation',\n",
       " 'Other loan',\n",
       " 'Credit Card refinance',\n",
       " 'Roof Repair',\n",
       " 'Expenses',\n",
       " 'HELP',\n",
       " 'Get Out of Debt',\n",
       " 'First Loan',\n",
       " 'Auto Repair Loan',\n",
       " 'Engagement ring',\n",
       " 'Bathroom Remodel',\n",
       " 'Our Wedding',\n",
       " 'lending club',\n",
       " 'Relief',\n",
       " 'Auto loan',\n",
       " 'debt ',\n",
       " 'Refinance Credit Card',\n",
       " 'Improvement',\n",
       " 'Consolidate credit cards',\n",
       " 'CC consolidation',\n",
       " 'Money',\n",
       " 'Moving expenses',\n",
       " 'Amex',\n",
       " 'CC Debt Consolidation',\n",
       " 'Personal Debt Consolidation',\n",
       " 'Wedding expenses',\n",
       " 'refi',\n",
       " 'Renewable Energy',\n",
       " 'MyLoan',\n",
       " 'improvement',\n",
       " 'RV Loan',\n",
       " 'Swimming Pool Loan',\n",
       " 'mine',\n",
       " 'School Loan',\n",
       " 'Consolidation 2011',\n",
       " 'Lending Loan',\n",
       " 'Car Repairs',\n",
       " 'DC Loan',\n",
       " 'Credit Card debt',\n",
       " 'Adoption Loan',\n",
       " 'myloan',\n",
       " 'Consol',\n",
       " 'LendingClubLoan',\n",
       " 'Debt consolidate',\n",
       " 'My personal loan',\n",
       " 'pay it off',\n",
       " 'Credit Cards Consolidation',\n",
       " 'cc consolidation',\n",
       " 'Debt Pay Off',\n",
       " 'Dental',\n",
       " 'Bill payoff',\n",
       " 'Credit Card Payments',\n",
       " 'Smart Loan',\n",
       " 'Excellent Credit',\n",
       " 'major_purchase',\n",
       " 'New Kitchen',\n",
       " 'fresh start',\n",
       " 'Debt pay off',\n",
       " 'debt consilidation',\n",
       " 'Business Growth',\n",
       " 'Basement',\n",
       " '2011 Debt Consolidation',\n",
       " 'DEBT CONSOLIDATION LOAN',\n",
       " 'taxes',\n",
       " 'First',\n",
       " 'home improvment',\n",
       " 'Basement Remodel',\n",
       " 'Debt consolodation',\n",
       " 'dental',\n",
       " 'Consolidate Loan',\n",
       " 'Windows',\n",
       " '2011 Consolidation',\n",
       " 'Capital One Payoff',\n",
       " 'Credit Card ',\n",
       " 'Bye Bye Credit Cards',\n",
       " 'Credit Card Relief',\n",
       " 'Consolidating Credit Cards',\n",
       " 'roof',\n",
       " 'Debt Elimination',\n",
       " 'new start',\n",
       " 'LendingClub',\n",
       " 'Cash',\n",
       " 'debt consolodation',\n",
       " 'truck loan',\n",
       " 'Consolidating credit cards',\n",
       " 'Business Purchase',\n",
       " 'small_business',\n",
       " 'Debt Removal',\n",
       " 'Eliminate Debt',\n",
       " 'Unexpected Expenses',\n",
       " 'Consolidating debt',\n",
       " 'Eliminate Credit Card Debt',\n",
       " 'Family Loan',\n",
       " 'Fresh Start Loan',\n",
       " 'card consolidation',\n",
       " 'Debt Free!',\n",
       " 'A New Beginning',\n",
       " 'Home Remodel',\n",
       " 'New Beginnings',\n",
       " 'Home Project',\n",
       " 'MOTORCYCLE',\n",
       " 'moving expenses',\n",
       " 'Debt Consolidator',\n",
       " \"Ryan's Loan\",\n",
       " 'Education Loan',\n",
       " 'Credit consolidation',\n",
       " 'Car repairs',\n",
       " 'home repairs',\n",
       " 'Harley',\n",
       " 'Camper',\n",
       " 'Jeep Loan',\n",
       " 'Emergency Loan',\n",
       " 'Chase Payoff',\n",
       " 'back on track',\n",
       " 'loan consolidation',\n",
       " 'pay my bills',\n",
       " 'Finishing Basement',\n",
       " 'Good Investment',\n",
       " 'The Loan',\n",
       " 'Conso Loan',\n",
       " 'small_business Loan',\n",
       " 'card payoff',\n",
       " 'Patio',\n",
       " 'CAR LOAN',\n",
       " 'Getting Married',\n",
       " 'New Car',\n",
       " 'Credit Pay Off',\n",
       " 'School loan',\n",
       " 'catch up',\n",
       " 'amex',\n",
       " 'pay off cards',\n",
       " 'credit card consolidation loan',\n",
       " 'New Home',\n",
       " 'home repair',\n",
       " 'expenses',\n",
       " 'rv loan',\n",
       " 'medical bills',\n",
       " 'Consolidation of Credit Cards',\n",
       " 'AUTO',\n",
       " 'hot tub',\n",
       " 'Lower Interest Rate',\n",
       " 'bmw',\n",
       " 'College Tuition',\n",
       " 'Pay off Loan',\n",
       " 'Line of Credit',\n",
       " 'Debt Consolidation.',\n",
       " 'Small Business loan ',\n",
       " 'Card Payoff',\n",
       " 'Home Improvement loan ',\n",
       " 'Credit Card Killer',\n",
       " 'High Interest Credit Card Consolidation',\n",
       " 'Legal',\n",
       " \"Michael's Loan\",\n",
       " 'Car Refinance',\n",
       " 'Car Financing Loan',\n",
       " 'Boat Purchase',\n",
       " 'Attorney Fees',\n",
       " 'Timeshare',\n",
       " 'investment',\n",
       " 'debt pay off',\n",
       " 'debit',\n",
       " 'Home repairs',\n",
       " 'Plastic Surgery',\n",
       " 'Credit Card Consol',\n",
       " 'debt consolidate',\n",
       " 'Car Loan ',\n",
       " 'BofA',\n",
       " 'loan 2',\n",
       " 'debtconsolidation',\n",
       " 'Dental Loan',\n",
       " 'credit card consolodation',\n",
       " 'Engagment Ring',\n",
       " 'Consolidation!',\n",
       " 'credit card debt consolidation',\n",
       " 'Vehicle',\n",
       " 'my personal loan',\n",
       " 'Helping Hand',\n",
       " 'Debt Consolidaton',\n",
       " 'consolidate credit cards',\n",
       " 'Personal Consolidation Loan',\n",
       " 'Growing Business',\n",
       " 'Personal Expenses',\n",
       " 'Visa Payoff',\n",
       " 'Debt Buster',\n",
       " 'mikes loan',\n",
       " 'windows',\n",
       " 'DEBT',\n",
       " 'credit card refi',\n",
       " 'Ring Loan',\n",
       " 'Final Debt Consolidation',\n",
       " 'bill',\n",
       " 'Bike Loan',\n",
       " 'Unsecured Personal Loan',\n",
       " 'Home Repair',\n",
       " 'stress relief',\n",
       " 'Lending Club Personal Loan',\n",
       " 'Down payment',\n",
       " 'Consolidation 2010',\n",
       " 'Personal debt consolidation',\n",
       " 'Credit Card Freedom',\n",
       " 'Debt Consolidation 2',\n",
       " 'Kitchen Loan',\n",
       " 'LOAN1',\n",
       " 'home_improvement',\n",
       " 'education loan',\n",
       " 'bathroom',\n",
       " 'PAY OFF',\n",
       " 'Mine',\n",
       " 'Divorce',\n",
       " 'One Payment',\n",
       " '2011',\n",
       " 'Credit payoff',\n",
       " 'Home Stuff',\n",
       " 'December Loan',\n",
       " 'debt be gone',\n",
       " 'AMEX',\n",
       " 'Debt Cons.',\n",
       " 'furnace',\n",
       " 'Repairs',\n",
       " 'credit card refinancing',\n",
       " 'Getting out of debt',\n",
       " 'Credit Card Consolodation',\n",
       " 'the loan',\n",
       " 'Backyard',\n",
       " 'debt cons.',\n",
       " 'Jet Ski',\n",
       " 'travel trailer',\n",
       " 'Peace of Mind',\n",
       " 'one payment',\n",
       " 'Green Loan',\n",
       " 'CC Consolidation Loan',\n",
       " 'cc loan',\n",
       " 'Consolidate My Debt',\n",
       " 'Credit card debt',\n",
       " 'Debt Management',\n",
       " 'debtfree',\n",
       " 'Clean Slate',\n",
       " 'Back Yard',\n",
       " 'solar',\n",
       " 'school',\n",
       " 'Chase',\n",
       " 'DEBT PAYOFF',\n",
       " 'Debt refinance',\n",
       " 'Credit Refi',\n",
       " 'Security Deposit',\n",
       " 'Consolidation of Debt',\n",
       " 'credit payoff',\n",
       " 'Wedding Funds',\n",
       " 'IVF',\n",
       " 'credit debt',\n",
       " 'Credit card payment',\n",
       " 'Personal Loan 2010',\n",
       " 'Florida',\n",
       " 'remodel',\n",
       " 'Auto Repairs',\n",
       " 'home improve',\n",
       " 'Debt Free Loan',\n",
       " 'Pay off High Interest Credit Card',\n",
       " 'Golf Cart',\n",
       " 'POOL',\n",
       " 'Loan2',\n",
       " 'Time Share',\n",
       " 'Pay it off',\n",
       " 'Used car loan',\n",
       " 'Credit Card Paydown',\n",
       " 'engagement ring',\n",
       " 'Mustang',\n",
       " 'Home Improvement/Debt Consolidation',\n",
       " 'Home Purchase',\n",
       " 'home improvement ',\n",
       " 'Investment Opportunity',\n",
       " 'Credit card refinancing',\n",
       " 'CREDIT CARD PAY OFF',\n",
       " 'Moving Loan ',\n",
       " 'Lower Interest',\n",
       " 'payoff loan',\n",
       " 'PersonalLoan',\n",
       " 'cash',\n",
       " 'Loan 2011',\n",
       " 'Debit consolidation',\n",
       " 'Startup Loan',\n",
       " 'Hope',\n",
       " 'Paying off Credit Card Debt',\n",
       " 'Reduce Credit Card Debt',\n",
       " 'home addition',\n",
       " 'Used Car Purchase',\n",
       " 'Pay off debts',\n",
       " 'Consolidated Loan',\n",
       " 'help loan',\n",
       " 'LC2',\n",
       " 'surgery loan',\n",
       " 'Travel',\n",
       " 'Credit card refi',\n",
       " 'Credit Debt Payoff',\n",
       " 'House Projects',\n",
       " 'Loan 2010',\n",
       " 'Debt Elimination Loan',\n",
       " 'Faith',\n",
       " 'Consolidating Loan',\n",
       " 'debt consolidation loan ',\n",
       " 'Relocation Expenses',\n",
       " '3 year plan',\n",
       " 'Basement Finishing',\n",
       " 'Main',\n",
       " 'rv',\n",
       " 'House Down Payment',\n",
       " 'consol',\n",
       " 'Pay Off Loan',\n",
       " 'Apartment',\n",
       " 'Pay off Credit Card Debt',\n",
       " 'Credit refinance',\n",
       " 'Pay off my Credit Cards',\n",
       " 'Refinance my credit cards',\n",
       " 'debt loan ',\n",
       " 'Credit Refinance Loan',\n",
       " 'Citibank',\n",
       " 'New Home Improvements',\n",
       " 'Credit Card Pay-Off',\n",
       " 'Debt Consolidation Help',\n",
       " 'Consolidate bills',\n",
       " 'Credit Card Repayment Loan',\n",
       " 'PAY OFF DEBT',\n",
       " 'Housing',\n",
       " 'Debt Consol Loan',\n",
       " 'Life Saver',\n",
       " 'harley',\n",
       " 'Jan-11',\n",
       " 'Scooter',\n",
       " 'debt free soon',\n",
       " 'CC REFI',\n",
       " 'Clean up',\n",
       " 'Debt Consildation',\n",
       " 'New roof',\n",
       " 'Credit Card Refinancing Loan',\n",
       " 'Cargo Van',\n",
       " 'pay credit card',\n",
       " 'Relocating',\n",
       " 'Property Tax',\n",
       " 'education',\n",
       " 'Out of Debt',\n",
       " 'LC',\n",
       " 'home ',\n",
       " 'Citi Card',\n",
       " 'cc payoff',\n",
       " 'operation',\n",
       " 'used car loan',\n",
       " 'personel',\n",
       " 'Nov-11',\n",
       " 'Refinance loan',\n",
       " 'Medical Consolidation',\n",
       " 'Goodbye Debt',\n",
       " 'christmas',\n",
       " '15000',\n",
       " 'Pay Credit Cards',\n",
       " 'Pay off high interest credit card',\n",
       " 'debt consolidaton',\n",
       " 'Moving Expense',\n",
       " 'Home purchase',\n",
       " 'payoffs',\n",
       " 'Clean Up',\n",
       " 'Get out of debt!',\n",
       " 'Get out of Debt!',\n",
       " 'Becoming Debt Free',\n",
       " 'Moving Forward',\n",
       " 'New Garage',\n",
       " 'debt consoladation',\n",
       " 'Kitchen Renovation',\n",
       " 'CC refinance',\n",
       " 'New Business',\n",
       " 'Paying off credit card',\n",
       " 'Nov-10',\n",
       " 'Freedom From Debt',\n",
       " 'savings',\n",
       " 'Move',\n",
       " 'credit card payment',\n",
       " 'Medical Expense Loan',\n",
       " '2010 Debt Consolidation',\n",
       " 'investment loan',\n",
       " 'Credit Card pay off',\n",
       " 'Window Loan',\n",
       " 'car repairs',\n",
       " 'travel',\n",
       " 'Debt Conso',\n",
       " 'Debt consolidator',\n",
       " 'Business ',\n",
       " 'debt free loan',\n",
       " 'Credit Card payoff ',\n",
       " 'Business Investment',\n",
       " 'my debt',\n",
       " \"Mel's Loan\",\n",
       " 'Snowmobile',\n",
       " 'Green',\n",
       " 'debt gone',\n",
       " 'Card Consolidation Loan',\n",
       " 'DEBT RELIEF',\n",
       " 'home purchase',\n",
       " 'spa',\n",
       " 'Total Debt Consolidation',\n",
       " 'Medical ',\n",
       " 'cc',\n",
       " 'Room Addition',\n",
       " 'Consol Loan',\n",
       " 'Travel Trailer',\n",
       " 'Engagement Loan',\n",
       " 'Dental work',\n",
       " 'auto repair',\n",
       " 'Eric',\n",
       " 'Fence',\n",
       " 'Travel Resorts of America',\n",
       " \"Matt's Loan\",\n",
       " 'My Bailout',\n",
       " 'homeloan',\n",
       " 'debt  consolidation',\n",
       " 'Car payment',\n",
       " 'free at last',\n",
       " 'Vacation loan',\n",
       " 'Land Purchase',\n",
       " 'Summer 2011',\n",
       " 'Home improvement ',\n",
       " 'NO MORE DEBT',\n",
       " 'Oct-11',\n",
       " 'Credit Cards Payoff',\n",
       " \"Allen's Debt Consolidation Loan\",\n",
       " 'Lawyer Fees',\n",
       " 'Horse',\n",
       " 'Medical Bill Loan',\n",
       " 'pay credit cards',\n",
       " 'Business Expansion Loan',\n",
       " 'Refinancing Credit Cards',\n",
       " 'surgery',\n",
       " 'Debt Restructure',\n",
       " 'CC refi',\n",
       " 'Paying off bills',\n",
       " 'test',\n",
       " 'lower debt',\n",
       " 'bill payoff',\n",
       " 'save me',\n",
       " 'new loan',\n",
       " 'Credit Card ReFi',\n",
       " 'nice',\n",
       " 'credit card ',\n",
       " 'Tax payment',\n",
       " 'Credit card pay-off',\n",
       " 'Credit Card Debt Elimination',\n",
       " 'Life',\n",
       " 'wedding expense',\n",
       " 'Motorcycle Refinance',\n",
       " 'honda',\n",
       " 'CreditCards',\n",
       " 'Pay Off Bills',\n",
       " 'Lawyer',\n",
       " 'Debit',\n",
       " 'Ryan',\n",
       " 'One payment',\n",
       " 'Refinance high interest credit cards',\n",
       " 'Lending Club loan',\n",
       " 'Debit Consolidation Loan',\n",
       " 'Mazda 3',\n",
       " 'tax payment',\n",
       " 'Car Payoff',\n",
       " 'Responsible Debt Consolidation',\n",
       " 'Loan Refinance',\n",
       " 'repairs',\n",
       " 'Credit Card Reduction',\n",
       " 'Investment property',\n",
       " 'Vehicle Loan',\n",
       " 'Debt Saver',\n",
       " 'Dream Wedding',\n",
       " 'computer',\n",
       " 'all in one',\n",
       " 'Paying off Debt',\n",
       " 'My Debt',\n",
       " 'HVAC',\n",
       " 'Matt',\n",
       " 'debt freedom',\n",
       " 'Pay off High Interest Credit Cards',\n",
       " ' Debt Consolidation Loan',\n",
       " 'bike loan',\n",
       " 'better rate',\n",
       " 'Home repair',\n",
       " 'Mercedes',\n",
       " 'Merry Christmas',\n",
       " 'SCHOOL',\n",
       " 'BOAT',\n",
       " 'Investment Property',\n",
       " 'Get out of debt loan',\n",
       " 'Triumph',\n",
       " 'DebtConsol',\n",
       " 'Credit Card Elimination Loan',\n",
       " 'BMW',\n",
       " 'fence',\n",
       " 'Miscellaneous',\n",
       " 'Ducati',\n",
       " 'get back on track',\n",
       " 'Credit Card Debt Loan',\n",
       " 'Debt Payment Loan',\n",
       " 'Small business loan',\n",
       " 'Roof Replacement',\n",
       " 'Taking Control',\n",
       " 'Credit loan',\n",
       " 'Credit Card refinancing',\n",
       " 'Solid Investment',\n",
       " 'Pay off CC',\n",
       " \"Justin's Loan\",\n",
       " '66 Mustang',\n",
       " 'Credit card refinance loan',\n",
       " 'Condo',\n",
       " 'Freedom loan',\n",
       " 'Refinance credit card debt',\n",
       " 'Out of debt',\n",
       " 'Debt be gone',\n",
       " 'tax loan',\n",
       " 'New Furniture',\n",
       " 'House Repairs',\n",
       " 'Business Start Up',\n",
       " 'Business Equipment',\n",
       " 'Property Investment',\n",
       " 'Last Loan',\n",
       " 'new home',\n",
       " 'Repayment',\n",
       " 'College Debt',\n",
       " 'lower rate',\n",
       " \"Mike's Loan\",\n",
       " 'Empathy Loan',\n",
       " 'helping hand',\n",
       " 'Credit Card Refi Loan',\n",
       " 'bathroom remodel',\n",
       " 'Surgery Loan',\n",
       " 'Improvements',\n",
       " 'Debt Consolidation 101',\n",
       " '2nd loan',\n",
       " 'CCdebt',\n",
       " 'Credit Card Payoff ',\n",
       " 'Payoff loan',\n",
       " 'Salvation',\n",
       " 'consilidation',\n",
       " 'Moving Out Loan',\n",
       " 'Lower Interest Rate Loan',\n",
       " 'General Purpose',\n",
       " 'Loan Request',\n",
       " 'Scott',\n",
       " 'Expansion',\n",
       " 'Auto Refinance',\n",
       " 'Signature Loan',\n",
       " 'Consolidate credit card debts',\n",
       " 'Dedt Consolidation',\n",
       " 'HOME IMPROVEMENTS',\n",
       " '2011 loan',\n",
       " 'Pay off high interest credit cards',\n",
       " 'Remodeling',\n",
       " 'pay off ',\n",
       " 'HOME REMODEL',\n",
       " 'Needed',\n",
       " 'CC Payoff Loan',\n",
       " 'Medical Procedure',\n",
       " 'Lending Tree Loan',\n",
       " 'boat loan',\n",
       " 'Consolidating High Interest Credit Cards',\n",
       " 'Responsible Borrower',\n",
       " 'Medical expenses',\n",
       " 'Jul-11',\n",
       " 'Card payoff',\n",
       " 'Refinancing Loan',\n",
       " 'Kitchen',\n",
       " 'My money',\n",
       " 'Engagement Ring ',\n",
       " 'Dept consolidation',\n",
       " 'Trailer',\n",
       " 'LOAN CONSOLIDATION',\n",
       " 'Credit Card Cleanup',\n",
       " 'Debt Payoff Loan',\n",
       " 'CC Debt Consolidation Loan',\n",
       " 'Getting Out of Debt',\n",
       " 'Lending Club Refinance',\n",
       " 'Moving loan',\n",
       " 'Pay off cards',\n",
       " 'Foundation Repair',\n",
       " 'Rent',\n",
       " 'Bill Consolidation Loan',\n",
       " 'Credit Card PayOff',\n",
       " 'Investment Loan',\n",
       " 'Debt Refinancing',\n",
       " '12',\n",
       " 'Debit ',\n",
       " 'Thank You',\n",
       " 'PayOff',\n",
       " 'Bike loan',\n",
       " 'Roof Loan',\n",
       " 'Credit card Consolidation',\n",
       " 'Debt con',\n",
       " ...]"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "closed_loan_df.title.value_counts().index.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0a1e971-d224-4878-bd63-d3d25deb5923",
   "metadata": {},
   "source": [
    "The values appears to be random. It can be dropped from the dataframe.\n",
    "\n",
    "Similar analysis can be performed on the emp_title column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "id": "666756f6-1340-43f5-ad62-e0c72b05cd57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['US Army',\n",
       " 'Bank of America',\n",
       " 'IBM',\n",
       " 'AT&T',\n",
       " 'Kaiser Permanente',\n",
       " 'USAF',\n",
       " 'Wells Fargo',\n",
       " 'UPS',\n",
       " 'US Air Force',\n",
       " 'Walmart',\n",
       " 'United States Air Force',\n",
       " 'Lockheed Martin',\n",
       " 'Self Employed',\n",
       " 'U.S. Army',\n",
       " 'Verizon Wireless',\n",
       " 'USPS',\n",
       " 'US ARMY',\n",
       " 'Walgreens',\n",
       " 'State of California',\n",
       " 'Target',\n",
       " 'Self',\n",
       " 'Department of Defense',\n",
       " 'JP Morgan Chase',\n",
       " 'JPMorgan Chase',\n",
       " 'Booz Allen Hamilton',\n",
       " 'Northrop Grumman',\n",
       " 'US Navy',\n",
       " 'United States Army',\n",
       " 'Verizon',\n",
       " 'us army',\n",
       " 'Accenture',\n",
       " 'Best Buy',\n",
       " 'Fidelity Investments',\n",
       " 'Comcast',\n",
       " 'Home Depot',\n",
       " 'Social Security Administration',\n",
       " 'US Postal Service',\n",
       " 'Wells Fargo Bank',\n",
       " 'United States Postal Service',\n",
       " 'United Parcel Service',\n",
       " 'self',\n",
       " 'General Electric',\n",
       " 'walmart',\n",
       " 'Morgan Stanley',\n",
       " 'Sprint',\n",
       " 'Citigroup',\n",
       " 'Retired',\n",
       " 'Merrill Lynch',\n",
       " 'American Express',\n",
       " 'Columbia University',\n",
       " 'Raytheon',\n",
       " 'Department of Homeland Security',\n",
       " 'PricewaterhouseCoopers',\n",
       " 'Department of Veterans Affairs',\n",
       " 'General Dynamics',\n",
       " 'US Government',\n",
       " 'usps',\n",
       " 'Internal Revenue Service',\n",
       " 'SAIC',\n",
       " 'United States Marine Corps',\n",
       " 'BAE Systems',\n",
       " 'The Home Depot',\n",
       " 'ADP',\n",
       " 'United States Navy',\n",
       " 'Thomson Reuters',\n",
       " 'US Bank',\n",
       " 'The Boeing Company',\n",
       " 'American Airlines',\n",
       " 'The Hartford',\n",
       " 'Microsoft',\n",
       " 'Nordstrom',\n",
       " 'CSC',\n",
       " 'verizon wireless',\n",
       " 'Union Bank',\n",
       " 'Wal-Mart',\n",
       " 'Citibank',\n",
       " 'Self-employed',\n",
       " 'Morgan Stanley Smith Barney',\n",
       " 'Hewlett Packard',\n",
       " 'T-Mobile',\n",
       " 'Time Warner Cable',\n",
       " 'Department of Justice',\n",
       " 'us postal service',\n",
       " 'USMC',\n",
       " 'walgreens',\n",
       " 'Military',\n",
       " 'U.S. Air Force',\n",
       " \"Macy's\",\n",
       " 'at&t',\n",
       " 'Boeing',\n",
       " 'Fiserv',\n",
       " 'Whole Foods Market',\n",
       " 'Deutsche Bank',\n",
       " 'Self-Employed',\n",
       " 'Federal Aviation Administration',\n",
       " 'United Airlines',\n",
       " 'Costco Wholesale',\n",
       " 'Army',\n",
       " 'Southwest Airlines',\n",
       " 'Bank of America Corp.',\n",
       " 'General Motors',\n",
       " 'bank of america',\n",
       " 'Fifth Third Bank',\n",
       " 'Pfizer',\n",
       " 'Oracle',\n",
       " 'NYC Department of Education',\n",
       " 'Cisco Systems',\n",
       " 'Allstate Insurance',\n",
       " 'home depot',\n",
       " 'Charter Communications',\n",
       " 'Chase',\n",
       " 'University of California',\n",
       " 'FAA',\n",
       " 'US Coast Guard',\n",
       " 'Progressive Insurance',\n",
       " 'Southern California Edison',\n",
       " 'Dell',\n",
       " 'Federal Bureau of Prisons',\n",
       " 'Target Corporation',\n",
       " 'self employed',\n",
       " 'Frito Lay',\n",
       " 'Ernst & Young',\n",
       " 'Apple',\n",
       " 'Siemens',\n",
       " 'Department of Defense (DOD)',\n",
       " 'Ernst & Young LLP',\n",
       " 'State of Connecticut',\n",
       " 'UPMC',\n",
       " 'Chase Bank',\n",
       " 'County of San Diego',\n",
       " 'Harvard University',\n",
       " 'retired',\n",
       " 'TD Bank',\n",
       " 'Continental Airlines',\n",
       " 'Los Angeles Unified School District',\n",
       " 'Marriott International',\n",
       " 'Costco',\n",
       " 'Clark County School District',\n",
       " 'Los Angeles County',\n",
       " 'U.S. Navy',\n",
       " 'wells fargo',\n",
       " 'Edward Jones',\n",
       " 'NYPD',\n",
       " 'Rite Aid',\n",
       " 'Bank of New York Mellon',\n",
       " 'Charles Schwab',\n",
       " 'Credit Suisse',\n",
       " 'BNY Mellon',\n",
       " 'Bank Of America',\n",
       " 'University of Georgia',\n",
       " 'State of Michigan',\n",
       " 'Citi',\n",
       " 'Travelers Insurance',\n",
       " 'Verizon Business',\n",
       " 'City of Chicago',\n",
       " 'KPMG',\n",
       " 'SunTrust Bank',\n",
       " 'TIAA-CREF',\n",
       " 'Honeywell',\n",
       " 'Yale University',\n",
       " 'Amazon.com',\n",
       " 'Texas A&M University',\n",
       " 'URS Corporation',\n",
       " 'Lowes',\n",
       " 'GEICO',\n",
       " 'HSBC',\n",
       " 'Cardinal Health',\n",
       " 'AIG',\n",
       " 'T-Mobile USA',\n",
       " 'Federal Government',\n",
       " 'L-3 Communications',\n",
       " 'verizon',\n",
       " 'Barclays Capital',\n",
       " 'CVS Pharmacy',\n",
       " 'State Farm Insurance',\n",
       " 'Johnson & Johnson',\n",
       " 'Macys',\n",
       " 'MetLife',\n",
       " 'Capital One',\n",
       " 'Starbucks',\n",
       " 'Lockheed Martin Corp.',\n",
       " 'State of Illinois',\n",
       " 'Waste Management',\n",
       " 'Microsoft Corporation',\n",
       " 'School District of Philadelphia',\n",
       " 'ups',\n",
       " 'U.S. Postal Service',\n",
       " 'Union Pacific Railroad',\n",
       " 'FBI',\n",
       " 'Sodexo',\n",
       " 'Dept of Defense',\n",
       " 'GE Healthcare',\n",
       " 'Verizon Communications',\n",
       " 'University of Florida',\n",
       " 'Goldman Sachs',\n",
       " 'Trader Joes',\n",
       " 'Cargill',\n",
       " 'Kroger',\n",
       " 'Bechtel',\n",
       " 'united parcel service',\n",
       " 'ING',\n",
       " 'Sears Holdings Corporation',\n",
       " 'BB&T',\n",
       " 'UBS',\n",
       " 'LPL Financial',\n",
       " 'Farmers Insurance',\n",
       " 'Gap Inc',\n",
       " 'Navy',\n",
       " 'Northwestern Mutual',\n",
       " 'Stanford University',\n",
       " 'Sears',\n",
       " 'Cox Communications',\n",
       " 'State of Texas',\n",
       " 'Cornell University',\n",
       " 'Boeing Company',\n",
       " 'PNC Bank',\n",
       " 'wells fargo bank',\n",
       " 'Air Force',\n",
       " 'Compass Group',\n",
       " 'target',\n",
       " 'Memorial Sloan-Kettering Cancer Center',\n",
       " 'Deloitte',\n",
       " 'HP',\n",
       " 'University of Pennsylvania',\n",
       " 'Chicago Public Schools',\n",
       " 'JPMorgan Chase & Co.',\n",
       " 'Aetna',\n",
       " 'nypd',\n",
       " 'Aerotek',\n",
       " 'Intel',\n",
       " 'Capital One Bank',\n",
       " 'CenturyLink',\n",
       " 'Cognizant Technology Solutions',\n",
       " 'Cablevision',\n",
       " 'Rochester Institute of Technology',\n",
       " 'cvs pharmacy',\n",
       " 'LAUSD',\n",
       " 'Oracle Corporation',\n",
       " 'American Medical Response',\n",
       " 'Boston University',\n",
       " 'KPMG LLP',\n",
       " 'Northern Trust',\n",
       " 'WalMart',\n",
       " 'US Army Corps of Engineers',\n",
       " 'Computer Sciences Corporation',\n",
       " 'San Diego Unified School District',\n",
       " 'Schneider Electric',\n",
       " 'City of Los Angeles',\n",
       " 'United States Coast Guard',\n",
       " 'State of Arizona',\n",
       " 'Adecco',\n",
       " 'URS',\n",
       " 'united states postal service',\n",
       " 'Sears Holdings',\n",
       " 'Parsons Brinckerhoff',\n",
       " 'Harris Corporation',\n",
       " 'The Walt Disney Company',\n",
       " 'IRS',\n",
       " 'Office Depot',\n",
       " 'ConocoPhillips',\n",
       " 'Apple Inc.',\n",
       " '3M',\n",
       " \"Trader Joe's\",\n",
       " 'kaiser permanente',\n",
       " 'Schlumberger',\n",
       " 'Sovereign Bank',\n",
       " 'State of Alabama',\n",
       " 'Allstate',\n",
       " 'University of Miami',\n",
       " 'ATK',\n",
       " 'Massachusetts General Hospital',\n",
       " \"Lowe's Home Improvement\",\n",
       " 'EMC Corporation',\n",
       " 'NASA',\n",
       " 'Penn State University',\n",
       " 'University of Chicago',\n",
       " 'International Paper',\n",
       " 'State of Florida',\n",
       " 'self-employed',\n",
       " 'wal mart',\n",
       " 'Army National Guard',\n",
       " 'First Data',\n",
       " 'Fedex Express',\n",
       " 'Turner Broadcasting',\n",
       " 'USDA',\n",
       " 'UCSF',\n",
       " 'Medco Health Solutions',\n",
       " 'Government',\n",
       " 'Publix Supermarkets',\n",
       " 'Ameriprise Financial',\n",
       " 'Autozone',\n",
       " 'PricewaterhouseCoopers LLP',\n",
       " 'CA Technologies',\n",
       " 'Safeway',\n",
       " 'Prudential',\n",
       " 'Sherwin Williams',\n",
       " 'Quest Diagnostics',\n",
       " 'Fannie Mae',\n",
       " 'RR Donnelley',\n",
       " 'Halliburton',\n",
       " 'Mayo Clinic',\n",
       " 'CVS',\n",
       " 'AT and T',\n",
       " 'Outback Steakhouse',\n",
       " 'Walmart Distribution Center',\n",
       " 'AT&T Inc.',\n",
       " 'US NAVY',\n",
       " 'PwC',\n",
       " 'U S Postal Service',\n",
       " 'macys',\n",
       " 'State of Missouri',\n",
       " 'Boston College',\n",
       " 'University of Houston',\n",
       " 'Network Interpreting Service',\n",
       " 'Food Lion',\n",
       " 'FedEx Office',\n",
       " 'Travelers',\n",
       " 'State of Delaware',\n",
       " 'ExxonMobil',\n",
       " 'UCLA',\n",
       " 'MTV Networks',\n",
       " 'Commonwealth of Pennsylvania',\n",
       " 'UnitedHealth Group',\n",
       " 'military',\n",
       " 'Citigroup Inc.',\n",
       " 'Liberty Mutual',\n",
       " 'Chevron',\n",
       " 'lowes',\n",
       " 'GE',\n",
       " 'Electronic Arts',\n",
       " 'Jacobs Engineering',\n",
       " 'Dell Inc',\n",
       " 'Dept. of Homeland Security',\n",
       " 'ACS',\n",
       " 'City of New York',\n",
       " 'comcast',\n",
       " 'First American Title',\n",
       " 'PepsiCo',\n",
       " 'Nationwide Insurance',\n",
       " 'costco wholesale',\n",
       " 'Madison Square Garden',\n",
       " 'Silicon Valley Bank',\n",
       " 'Procter & Gamble',\n",
       " 'Citizens Bank',\n",
       " 'KAISER PERMANENTE',\n",
       " 'Franchise Tax Board',\n",
       " 'State Street Bank',\n",
       " 'Progress Energy',\n",
       " 'Department of Veteran Affairs',\n",
       " 'Department of Education',\n",
       " 'Massage Envy',\n",
       " 'Bloomingdales',\n",
       " 'Emory University',\n",
       " 'Staples',\n",
       " 'VERIZON WIRELESS',\n",
       " 'Carnival Cruise Lines',\n",
       " 'Swedish Medical Center',\n",
       " 'Paychex Inc',\n",
       " 'GE Capital',\n",
       " 'SRA International',\n",
       " 'Covidien',\n",
       " 'Con Edison',\n",
       " 'USAA',\n",
       " 'Department of the Army',\n",
       " 'Starbucks Coffee Company',\n",
       " 'Delta Air Lines',\n",
       " 'Dept of Veterans Affairs',\n",
       " 'Dow Chemical',\n",
       " 'Wells Fargo Advisors',\n",
       " 'United Healthcare',\n",
       " 'State of Georgia',\n",
       " 'University of Virginia',\n",
       " 'University of Maryland',\n",
       " 'Xerox',\n",
       " 'Iron Mountain',\n",
       " 'US Postal Service (USPS)',\n",
       " 'Paychex',\n",
       " 'University of Washington',\n",
       " 'American Electric Power',\n",
       " 'Halliburton Energy Services',\n",
       " 'Transportation Security Administration',\n",
       " 'Baltimore County Public Schools',\n",
       " 'Alcatel-Lucent',\n",
       " 'Cleveland Clinic',\n",
       " 'Dept of Homeland Security',\n",
       " 'State Street Corporation',\n",
       " \"Lowe's\",\n",
       " 'New York Life',\n",
       " 'ICF International',\n",
       " 'Time Inc',\n",
       " 'Chaffey Joint Union High School District',\n",
       " 'New York City Department of Education',\n",
       " 'McKesson Corporation',\n",
       " 'Invesco',\n",
       " 'DOD',\n",
       " 'The Vanguard Group',\n",
       " 'United Health Group',\n",
       " 'McKesson',\n",
       " 'Amtrak',\n",
       " 'Tufts University',\n",
       " 'Wells Fargo Home Mortgage',\n",
       " 'Raytheon Company',\n",
       " 'Self employed',\n",
       " 'County of San Mateo',\n",
       " 'Bloomberg LP',\n",
       " 'Cigna',\n",
       " 'UCSF Medical Center',\n",
       " 'Lending Club',\n",
       " 'University of Minnesota',\n",
       " 'Dish Network',\n",
       " 'Convergys',\n",
       " 'Wegmans Food Markets',\n",
       " 'Caterpillar Inc.',\n",
       " 'Colorado State University',\n",
       " 'Infor Global Solutions',\n",
       " 'HBO',\n",
       " 'EMC',\n",
       " 'Ford Motor Company',\n",
       " 'BBVA Compass',\n",
       " 'Bank of the West',\n",
       " 'National Park Service',\n",
       " 'Commonwealth of Massachusetts',\n",
       " 'Applebees',\n",
       " 'Contra Costa County',\n",
       " 'Wachovia Bank',\n",
       " 'John Hancock',\n",
       " 'TSA',\n",
       " 'Chadbourne & Parke LLP',\n",
       " 'New York Presbyterian Hospital',\n",
       " 'General Dynamics Information Technology',\n",
       " 'Capgemini',\n",
       " 'Weill Cornell Medical College',\n",
       " 'Red Lobster',\n",
       " 'Comerica Bank',\n",
       " 'UPS Freight',\n",
       " 'Genentech',\n",
       " 'Publix',\n",
       " 'General Services Administration',\n",
       " 'Insight Global',\n",
       " 'Webtrends',\n",
       " 'Sycuan Casino',\n",
       " 'Capital One Financial',\n",
       " 'Mercer',\n",
       " 'State of Kansas',\n",
       " 'The Ohio State University Medical Center',\n",
       " 'University of Southern California',\n",
       " 'New York City Police Department',\n",
       " 'Intel Corporation',\n",
       " 'Burger King',\n",
       " 'Department of Motor Vehicles',\n",
       " 'Best Buy Co. Inc.',\n",
       " 'Coldwell Banker',\n",
       " 'Enterprise Rent-A-Car',\n",
       " 'Cerner Corporation',\n",
       " 'CIGNA',\n",
       " 'Cintas Corp',\n",
       " 'OfficeMax',\n",
       " 'General Atomics',\n",
       " 'Crossmark',\n",
       " 'City of Houston',\n",
       " 'EmblemHealth',\n",
       " 'San Bernardino County',\n",
       " 'Social Security Administration (SSA)',\n",
       " 'Denver Health',\n",
       " 'The Shaw Group',\n",
       " 'US Department of Homeland Security',\n",
       " 'County of San Bernardino',\n",
       " 'Ingram Micro',\n",
       " 'Manpower',\n",
       " 'CACI',\n",
       " 'John Deere',\n",
       " 'Saks Fifth Avenue',\n",
       " 'Harris Teeter',\n",
       " 'Ohio State University',\n",
       " 'New York State Unified Court System',\n",
       " 'Hitachi Data Systems',\n",
       " 'Centurylink',\n",
       " 'Pactiv',\n",
       " 'Seagate Technology',\n",
       " 'SSA',\n",
       " 'Clearwire',\n",
       " 'Unilever',\n",
       " 'Department of Army',\n",
       " 'Loyola University New Orleans',\n",
       " 'Jones Walker',\n",
       " 'Bed Bath and Beyond',\n",
       " 'Colorado Mountain College',\n",
       " 'Bed Bath & Beyond',\n",
       " 'Klein ISD',\n",
       " 'Allstate Insurance Company',\n",
       " 'Merrill Corporation',\n",
       " 'SNL Financial',\n",
       " 'department of homeland security',\n",
       " 'University of Colorado',\n",
       " 'First Data Corporation',\n",
       " 'Tampa General Hospital',\n",
       " 'Baptist Health South Florida',\n",
       " 'Williams Lea',\n",
       " 'enterprise rent a car',\n",
       " 'Community Bank',\n",
       " 'ATT',\n",
       " 'Scottrade',\n",
       " 'Geico',\n",
       " 'AT&T Mobility',\n",
       " 'At&t',\n",
       " 'State of Utah',\n",
       " 'Allen & Overy LLP',\n",
       " 'RBC Wealth Management',\n",
       " 'MD Anderson Cancer Center',\n",
       " 'Wellpoint',\n",
       " 'JPMorgan',\n",
       " 'Baylor University Medical Center',\n",
       " 'Heritage Bank',\n",
       " 'Jacobs Technology',\n",
       " 'Procter and Gamble',\n",
       " 'Bank of Oklahoma',\n",
       " \"Brigham and Women's Hospital\",\n",
       " 'Georgia Pacific',\n",
       " 'Walt Disney World',\n",
       " 'University of Pittsburgh',\n",
       " 'Deloitte Consulting',\n",
       " 'State Street',\n",
       " 'PetSmart',\n",
       " 'Apria Healthcare',\n",
       " 'Walgreen',\n",
       " 'Allergan',\n",
       " 'Pizza Hut',\n",
       " 'UNITED STATES POSTAL SERVICE',\n",
       " 'Hewlett-Packard',\n",
       " 'Riverside County',\n",
       " 'tire kingdom',\n",
       " 'Spectrum Health',\n",
       " 'Law Firm',\n",
       " 'Newark Public Schools',\n",
       " 'Prospect Mortgage',\n",
       " 'Capital Group Companies',\n",
       " 'State of New Jersey',\n",
       " 'Apache Corporation',\n",
       " 'U.S. Coast Guard',\n",
       " 'AECOM',\n",
       " 'New York Life Insurance',\n",
       " 'KLA-Tencor',\n",
       " 'Department of Homeland Security (DHS)',\n",
       " 'VA Medical Center',\n",
       " 'Fresenius Medical Care',\n",
       " 'Securitas Security Services',\n",
       " 'Dell Services',\n",
       " 'Northwestern University',\n",
       " 'Department of State',\n",
       " 'Fedex',\n",
       " 'CompuCom',\n",
       " 'Fidelity National Financial',\n",
       " 'Kraft Foods',\n",
       " 'Pentegra Retirement Services',\n",
       " 'Apple, Inc.',\n",
       " 'estes express lines',\n",
       " 'Markit',\n",
       " 'AARP',\n",
       " 'Lennar Corp.',\n",
       " 'Experian',\n",
       " 'Grant Thornton LLP',\n",
       " 'American Red Cross',\n",
       " 'Level 3 Communications',\n",
       " 'Hewitt Associates',\n",
       " 'Florida International University',\n",
       " 'Coca-Cola Refreshments',\n",
       " 'AT&T ',\n",
       " 'New York Life Insurance Company',\n",
       " 'Jones Lang LaSalle',\n",
       " 'BP',\n",
       " 'Pepsico',\n",
       " 'Fed Ex',\n",
       " 'University of California San Francisco',\n",
       " 'TEK Systems',\n",
       " 'wal-mart',\n",
       " 'Fidelity Information Services',\n",
       " 'Carnegie Mellon University',\n",
       " 'Barnes and Noble',\n",
       " 'Western Digital',\n",
       " 'M&T Bank',\n",
       " 'Baxter Healthcare',\n",
       " 'California Pizza Kitchen',\n",
       " 'Global Crossing',\n",
       " 'Pitney Bowes',\n",
       " 'ARAMARK',\n",
       " 'HEB',\n",
       " 'University of Rochester',\n",
       " 'US Military',\n",
       " 'UC Davis',\n",
       " 'usmc',\n",
       " 'Trinity Health',\n",
       " 'Ferguson Enterprises',\n",
       " 'Kohls',\n",
       " 'cablevision',\n",
       " 'Walmart Stores, Inc.',\n",
       " 'Philips Healthcare',\n",
       " 'Aramark',\n",
       " 'Amerigas',\n",
       " 'Intel Corp',\n",
       " 'Baker Hughes',\n",
       " 'Barnes & Noble',\n",
       " 'Constellation Energy',\n",
       " 'JCPenney',\n",
       " 'Spherion',\n",
       " 'Bio-Rad Laboratories',\n",
       " 'Intuit',\n",
       " 'Apple, Inc',\n",
       " 'Humana',\n",
       " 'Verizon wireless',\n",
       " 'DeVry University',\n",
       " 'Johnson Controls',\n",
       " 'Modis',\n",
       " 'U.S Army',\n",
       " 'us navy',\n",
       " 'JPMorgan Chase Bank',\n",
       " 'New York City Transit',\n",
       " 'NBC Universal',\n",
       " 'City and County of San Francisco',\n",
       " 'Kohls Department Stores',\n",
       " 'Deloitte Tax LLP',\n",
       " 'Department of the Navy',\n",
       " 'Duke Energy',\n",
       " 'Bureau of Land Management',\n",
       " 'Franklin Templeton',\n",
       " 'Abbott Laboratories',\n",
       " 'Veterans Administration',\n",
       " 'Kelly Services',\n",
       " 'General Mills',\n",
       " 'CarMax',\n",
       " 'Columbia University Medical Center',\n",
       " 'Gwinnett County Public Schools',\n",
       " \"Applebee's\",\n",
       " 'publix',\n",
       " 'Customs and Border Protection',\n",
       " 'Hot Topic',\n",
       " 'Johns Hopkins University',\n",
       " 'Nielsen',\n",
       " 'Abercrombie & Fitch',\n",
       " 'National Grid',\n",
       " 'Lowes Home Improvement',\n",
       " 'Aspen Dental',\n",
       " 'The Blackstone Group',\n",
       " 'Miami-Dade County Public Schools',\n",
       " 'The Coca-Cola Company',\n",
       " 'Sprint Nextel',\n",
       " 'wachovia',\n",
       " 'FedEx',\n",
       " 'NetSpend',\n",
       " 'Montefiore Medical Center',\n",
       " 'Citibank, N.A.',\n",
       " 'Stifel Nicolaus',\n",
       " 'State of Ohio',\n",
       " 'City of San Francisco',\n",
       " 'state of california',\n",
       " 'Rush University Medical Center',\n",
       " 'Fort Bend ISD',\n",
       " 'GameStop',\n",
       " 'Panera Bread',\n",
       " 'costco',\n",
       " 'The Nielsen Company',\n",
       " 'AREVA',\n",
       " 'US Department of Veterans Affairs',\n",
       " 'Geosyntec Consultants',\n",
       " 'state of ohio',\n",
       " 'WestEd',\n",
       " 'Heald College',\n",
       " 'City of Broken Arrow',\n",
       " 'national grid',\n",
       " 'Bristol-Myers Squibb',\n",
       " 'Battelle Memorial Institute',\n",
       " 'US Department of Education',\n",
       " 'Michigan State University',\n",
       " 'NISCAYAH',\n",
       " 'Bridgepoint Education',\n",
       " 'Darden Restaurants',\n",
       " 'Commonwealth of PA',\n",
       " 'Ulta',\n",
       " 'Lockheed Martin Corporation',\n",
       " 'Eastman Kodak',\n",
       " 'Teledyne Brown Engineering',\n",
       " 'Brown & Brown Insurance',\n",
       " 'Freddie Mac',\n",
       " \"Dick's Sporting Goods\",\n",
       " 'Canadian National Railroad',\n",
       " 'Aflac',\n",
       " 'T. Rowe Price',\n",
       " 'American Savings Bank',\n",
       " 'The UPS Store',\n",
       " 'JPMorganChase',\n",
       " 'Avanade',\n",
       " 'att',\n",
       " 'Publix Super Markets',\n",
       " 'Toyota',\n",
       " 'Central Intelligence Agency',\n",
       " 'chase bank',\n",
       " 'U.S. Department of Education',\n",
       " 'Kmart',\n",
       " 'lockheed martin',\n",
       " 'Nike',\n",
       " 'US Treasury',\n",
       " 'Sage',\n",
       " 'Los Angeles Police Department',\n",
       " 'Brown Brothers Harriman',\n",
       " 'Federal Aviation Administration (FAA)',\n",
       " 'Otis Elevator',\n",
       " 'The Corcoran Group',\n",
       " 'Merced City School District',\n",
       " 'Myriad Genetics',\n",
       " 'Cartus',\n",
       " 'AMC Theatres',\n",
       " 'nj transit',\n",
       " 'Shaw Industries',\n",
       " 'Aurora Diagnostics',\n",
       " 'TriNet',\n",
       " 'Ernst & Young, LLP',\n",
       " 'Maximus',\n",
       " 'Hawaii Pacific Health',\n",
       " 'sears',\n",
       " 'Nestle Purina',\n",
       " 'ecolab',\n",
       " 'Blackbaud',\n",
       " 'Norfolk Southern Railroad',\n",
       " 'Banfield',\n",
       " 'State of Minnesota',\n",
       " 'Sony Computer Entertainment',\n",
       " 'Huron Consulting Group',\n",
       " 'St Lukes Hospital',\n",
       " 'Logan County Schools',\n",
       " 'NYSDOT',\n",
       " 'allied barton',\n",
       " 'DHS/TSA',\n",
       " 'National Institutes of Health',\n",
       " 'Syracuse University',\n",
       " 'Health Net',\n",
       " 'Qualcomm, Inc.',\n",
       " 'Dept of Army',\n",
       " 'BNSF Railway',\n",
       " 'Automatic Data Processing',\n",
       " 'MillerCoors',\n",
       " 'Buffalo Wild Wings',\n",
       " 'southwest airlines',\n",
       " 'Public Health Institute',\n",
       " 'Estes Express Lines',\n",
       " 'Exterran',\n",
       " 'Virginia Commonwealth University',\n",
       " 'Pepsi Beverage Company',\n",
       " 'Black Box Network Services',\n",
       " 'Google',\n",
       " 'army',\n",
       " 'Metro PCS',\n",
       " 'Robert Half International',\n",
       " 'american airlines',\n",
       " 'Express Scripts',\n",
       " 'dept of veterans affairs',\n",
       " 'MGH',\n",
       " 'Guardian Life Insurance Company',\n",
       " 'shaw group',\n",
       " 'Baylor Health Care System',\n",
       " 'Edelman',\n",
       " 'Jefferson Regional Medical Center',\n",
       " 'FEDEX',\n",
       " 'The Methodist Hospital',\n",
       " 'Penske Truck Leasing',\n",
       " 'NYU Medical Center',\n",
       " 'US EPA',\n",
       " 'PNC',\n",
       " 'Wachovia Corp.',\n",
       " 'Apollo Group',\n",
       " 'Vanguard',\n",
       " 'Dillards',\n",
       " 'University of Chicago Medical Center',\n",
       " 'united states navy',\n",
       " 'Inova Health System',\n",
       " 'Sentry Insurance',\n",
       " 'Time Warner',\n",
       " 'Merck',\n",
       " 'Post Office',\n",
       " 'Cricket Communications',\n",
       " 'Acxiom Corporation',\n",
       " 'WELLS FARGO',\n",
       " 'aramark',\n",
       " 'Ingersoll Rand',\n",
       " 'Pearson Education',\n",
       " 'Roswell Park Cancer Institute',\n",
       " 'Pitt County Memorial Hospital',\n",
       " 'FCI Constructors, Inc.',\n",
       " 'ARCADIS',\n",
       " 'CSX TRANSPORTATION',\n",
       " 'Quest Software',\n",
       " 'ymca',\n",
       " 'Wal Mart',\n",
       " 'Temple University',\n",
       " 'Gilead Sciences',\n",
       " 'U.S. Border Patrol',\n",
       " 'Oregon State University',\n",
       " 'Medtronic',\n",
       " 'the home depot',\n",
       " 'Rutgers University',\n",
       " 'G4S Secure Solutions',\n",
       " 'UCSD',\n",
       " 'delta airlines',\n",
       " 'Siemens Industry Inc',\n",
       " 'Att',\n",
       " 'Macys Inc',\n",
       " 'Polo Ralph Lauren',\n",
       " 'Towers Watson',\n",
       " 'University of California, Davis',\n",
       " 'Northern Trust Company',\n",
       " 'Tower Automotive',\n",
       " 'Sun Life Financial',\n",
       " 'Hospital For Special Surgery',\n",
       " 'Mcdonalds',\n",
       " 'OMD',\n",
       " 'Compucom',\n",
       " 'Principal Financial Group',\n",
       " 'Fairchild Semiconductor',\n",
       " 'USN',\n",
       " 'WALMART',\n",
       " 'Hyatt',\n",
       " 'Ace Hardware Corporation',\n",
       " 'Florida Highway Patrol',\n",
       " 'Winn-Dixie',\n",
       " 'Wipro',\n",
       " 'BNSF',\n",
       " 'Cal Fire',\n",
       " 'Village Transportation',\n",
       " 'ABM Industries',\n",
       " 'Applied Materials',\n",
       " 'AXA Equitable',\n",
       " 'The University of Chicago',\n",
       " 'Navy Federal Credit Union',\n",
       " 'Manhattan Beach Unified School District',\n",
       " 'Blockbuster',\n",
       " 'Treasure Island',\n",
       " 'Symantec',\n",
       " 'SourceMedia',\n",
       " 'Beckman Coulter',\n",
       " 'Siemens Healthcare',\n",
       " 'Express Employment Professionals',\n",
       " 'Rackspace',\n",
       " 'Oracle Corp',\n",
       " 'City of Richmond',\n",
       " 'Norfolk Southern',\n",
       " 'Sara Lee',\n",
       " 'McDonalds',\n",
       " 'McGladrey',\n",
       " 'Michelin North America',\n",
       " 'Hormel Foods Corporation',\n",
       " 'Law Office',\n",
       " 'Sharyland I.S.D.',\n",
       " 'Huntington National Bank',\n",
       " 'Calpine',\n",
       " 'Pensacola Christian College',\n",
       " 'Yale New Haven Hospital',\n",
       " 'Wal-mart',\n",
       " 'eBay',\n",
       " 'J.P. Morgan Chase',\n",
       " 'Serco Inc',\n",
       " 'Catholic Healthcare West',\n",
       " 'Hospital for Special Surgery',\n",
       " 'Sharp Healthcare',\n",
       " 'ITT Corporation',\n",
       " 'Mount Sinai Hospital',\n",
       " 'Washington University School of Medicine',\n",
       " 'Epsilon',\n",
       " 'Apex Systems Inc',\n",
       " 'Country Financial',\n",
       " 'Tulalip Casino',\n",
       " 'GM Financial',\n",
       " 'Sams Club',\n",
       " 'UT Southwestern',\n",
       " 'KPMG, LLP',\n",
       " 'Yeshiva University',\n",
       " 'Keiser University',\n",
       " 'Royal Bank of Scotland',\n",
       " 'Travelers Insurance Co.',\n",
       " 'Ericsson',\n",
       " 'Turner Construction',\n",
       " 'Hartford Hospital',\n",
       " 'U.S.P.S.',\n",
       " 'Cambridge Associates',\n",
       " 'Millenium Partners',\n",
       " 'Advanced Technology Services',\n",
       " 'Pricewaterhouse Coopers',\n",
       " 'Qwest',\n",
       " 'jp morgan chase',\n",
       " 'Baltimore County Public Library',\n",
       " 'JP Morgan',\n",
       " 'Multiband',\n",
       " 'UC Berkeley',\n",
       " 'Passaic Board of Education',\n",
       " 'Sikorsky Aircraft Corporation',\n",
       " 'Teksystems',\n",
       " 'Cerner',\n",
       " 'Teleperformance USA',\n",
       " 'Georgetown University',\n",
       " 'U.S. Fish & Wildlife Service',\n",
       " 'King County',\n",
       " 'Flagstar Bank',\n",
       " 'Alameda County',\n",
       " 'The Boston Consulting Group',\n",
       " 'Chartis',\n",
       " 'Consol Energy',\n",
       " 'TIEN TIEN FOOD CO',\n",
       " 'Cengage Learning',\n",
       " 'Henry Ford Hospital',\n",
       " 'Grand Casino',\n",
       " 'Marriott',\n",
       " 'BNP Paribas',\n",
       " 'Janney Montgomery Scott',\n",
       " 'DEPARTMENT OF DEFENSE',\n",
       " 'Blue Shield of California',\n",
       " 'AGIA',\n",
       " 'Colliers International',\n",
       " 'ADT Security Services',\n",
       " 'Banfield Pet Hospital',\n",
       " 'Loyola University Chicago',\n",
       " 'HMS Host',\n",
       " 'U.S. Department of Agriculture',\n",
       " 'Brown University',\n",
       " 'Crowe Horwath, LLP',\n",
       " 'McKesson Corp',\n",
       " 'State of Oregon',\n",
       " 'Gap Inc.',\n",
       " '24 Hour Fitness',\n",
       " 'Avis Budget Group',\n",
       " 'U.S.Postal Service',\n",
       " 'PPG Industries',\n",
       " 'bed bath and beyond',\n",
       " 'Portland Public Schools',\n",
       " 'Orange County Public Schools',\n",
       " 'Goodwin Procter LLP',\n",
       " 'Walden University',\n",
       " 'bloomingdales',\n",
       " 'The Rockefeller University',\n",
       " 'University Hospital',\n",
       " 'Miami University',\n",
       " 'Pacific Gas and Electric Company',\n",
       " 'American Public Media',\n",
       " 'goodyear',\n",
       " 'Waterbury Hospital',\n",
       " 'Juniper Networks',\n",
       " 'Walgreens Pharmacy',\n",
       " 'Universal Health Services',\n",
       " 'Georgia Institute of Technology',\n",
       " 'Dept of Justice',\n",
       " 'Us Army',\n",
       " 'Honeywell International Inc.',\n",
       " 'JPMORGAN CHASE',\n",
       " 'RBC Bank',\n",
       " 'Stop and Shop',\n",
       " 'Battelle',\n",
       " 'City of Mesa',\n",
       " 'Arizona State University',\n",
       " 'AllianceBernstein',\n",
       " 'Oregon Health & Science University',\n",
       " 'Tech Data Corporation',\n",
       " 'Us army',\n",
       " 'Travelers Insurance Company',\n",
       " 'Carroll County Public Schools',\n",
       " 'Nestle',\n",
       " 'Burlington Coat Factory',\n",
       " 'Luxottica Retail',\n",
       " 'Fairfax County Public Schools',\n",
       " 'Mass General Hospital',\n",
       " 'Affinity Marketing Group',\n",
       " 'Apple Inc',\n",
       " \"O'Reilly Auto Parts\",\n",
       " 'Torrance Memorial Medical Center',\n",
       " 'PC-Doctor, Inc.',\n",
       " 'Nestle Waters North America',\n",
       " 'Dicks Sporting Goods',\n",
       " 'CH2M Hill',\n",
       " 'Copart',\n",
       " 'NetApp',\n",
       " 'Kohler Company',\n",
       " 'State Farm',\n",
       " 'Doubletree Hotel',\n",
       " 'Education Management Corporation',\n",
       " 'New York University',\n",
       " 'Ohio State University Medical Center',\n",
       " 'nyc transit',\n",
       " 'Dallas ISD',\n",
       " 'United States Department of State',\n",
       " 'Genzyme',\n",
       " 'FDNY',\n",
       " 'Las Vegas Valley Water District',\n",
       " 'Memorial Sloan Kettering Cancer Center',\n",
       " 'Demand Media',\n",
       " 'Nielsen Company',\n",
       " 'PG&E',\n",
       " 'fedex express',\n",
       " 'HP Enterprise Services',\n",
       " 'Hewlett-Packard Company',\n",
       " 'board of ed',\n",
       " 'Assurant',\n",
       " 'SPR Industries',\n",
       " 'University of California, San Diego',\n",
       " 'University of South Carolina',\n",
       " 'Fairview Health Services',\n",
       " ...]"
      ]
     },
     "execution_count": 343,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "closed_loan_df.emp_title.value_counts().index.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b681bac9-f253-474c-af63-ba783ca0ec70",
   "metadata": {},
   "source": [
    "The emp_title also appears to be random values. These two columns can be dropped from dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "id": "98cf4b75-7e1f-4fab-a8a4-71bdfd5a8803",
   "metadata": {},
   "outputs": [],
   "source": [
    "closed_loan_df.drop(columns = ['title', 'emp_title'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "id": "d72f8b97-790a-4fb0-acab-67a09026ac9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38577, 37)"
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "closed_loan_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "id": "7b859394-942b-4f0e-b69e-3ed1dba6bf4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "term                           2\n",
       "loan_status                    2\n",
       "pub_rec_bankruptcies           3\n",
       "verification_status            3\n",
       "pub_rec                        5\n",
       "home_ownership                 5\n",
       "grade                          7\n",
       "inq_last_6mths                 9\n",
       "delinq_2yrs                   11\n",
       "emp_length                    11\n",
       "purpose                       14\n",
       "sub_grade                     35\n",
       "open_acc                      40\n",
       "addr_state                    50\n",
       "issue_d                       55\n",
       "total_acc                     82\n",
       "last_pymnt_d                 101\n",
       "last_credit_pull_d           106\n",
       "int_rate                     370\n",
       "earliest_cr_line             524\n",
       "zip_code                     822\n",
       "loan_amnt                    870\n",
       "funded_amnt                 1019\n",
       "revol_util                  1088\n",
       "total_rec_late_fee          1320\n",
       "collection_recovery_fee     2616\n",
       "dti                         2853\n",
       "recoveries                  4040\n",
       "annual_inc                  5215\n",
       "total_rec_prncp             6841\n",
       "funded_amnt_inv             8050\n",
       "installment                15022\n",
       "revol_bal                  21275\n",
       "total_rec_int              34025\n",
       "last_pymnt_amnt            34418\n",
       "total_pymnt_inv            36387\n",
       "total_pymnt                36714\n",
       "dtype: int64"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "closed_loan_df.nunique().sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a24ca09c-acb5-40a7-959b-faf7439fa918",
   "metadata": {},
   "source": [
    "Now, going through the individual columns and their definition, it can be understood that the below column values are available either after the loan application is approved, or it has no signifiance with respect to analysis.\n",
    "\n",
    "- **delinq_2yrs**: The number of 30+ days past-due incidences of delinquency in the borrower's credit file for the past 2 years - Post Loan application data\n",
    "- **last_pymnt_d**: Last month payment was received - Post Loan application data\n",
    "- **last_credit_pull_d**: The most recent month LC pulled credit for this loan - Post Loan application data\n",
    "- **total_rec_late_fee**: Late fees received to date - Post Loan application data\n",
    "- **collection_recovery_fee**: Post charge off collection fee - Post Loan application data\n",
    "- **recoveries**: Post charge off gross recovery - Post Loan application data\n",
    "- **total_rec_prncp**: Principal received to date - Post Loan application data\n",
    "- **revol_bal**: Total credit revolving balance - Post Loan application data\n",
    "- **total_rec_int**: Interest received to date - Post Loan application data\n",
    "- **last_pymnt_amnt**: Last total payment amount received - Post Loan application data\n",
    "- **total_pymnt_inv**: Payments received to date for portion of total amount funded by investors - Post Loan application data\n",
    "- **total_pymnt**: Payments received to date for total amount funded - Post Loan application data\n",
    "- **inq_last_6mths**: The number of inquiries in past 6 months (excluding auto and mortgage inquiries) - Post Loan application data\n",
    "- **zip_code**: The first 3 numbers of the zip code provided by the borrower in the loan application - has similar significance as the 'addr_state' column\n",
    "- **earliest_cr_line**: The month the borrower's earliest reported credit line was opened - No impact on loan repayment\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "id": "cba27823-c680-485a-a0fc-21588907479f",
   "metadata": {},
   "outputs": [],
   "source": [
    "closed_loan_df.drop(columns = ['delinq_2yrs', 'last_pymnt_d', 'last_credit_pull_d', 'total_rec_late_fee', 'collection_recovery_fee', 'recoveries', 'total_rec_prncp', 'revol_bal', 'total_rec_int', 'last_pymnt_amnt', 'total_pymnt_inv', 'total_pymnt', 'inq_last_6mths', 'zip_code', 'earliest_cr_line'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "id": "388ec8cb-568d-403a-a574-3dcb581c221e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38577, 22)"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "closed_loan_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "id": "768f5a98-96e7-4dc2-94ad-5af18a25d6fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt                  0\n",
       "funded_amnt                0\n",
       "funded_amnt_inv            0\n",
       "term                       0\n",
       "int_rate                   0\n",
       "installment                0\n",
       "grade                      0\n",
       "sub_grade                  0\n",
       "emp_length              1033\n",
       "home_ownership             0\n",
       "annual_inc                 0\n",
       "verification_status        0\n",
       "issue_d                    0\n",
       "loan_status                0\n",
       "purpose                    0\n",
       "addr_state                 0\n",
       "dti                        0\n",
       "open_acc                   0\n",
       "pub_rec                    0\n",
       "revol_util                50\n",
       "total_acc                  0\n",
       "pub_rec_bankruptcies     697\n",
       "dtype: int64"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "closed_loan_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "af619929-3fe9-4bb0-8879-d53249b8b031",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "emp_length\n",
       "10+ years    8488\n",
       "< 1 year     4508\n",
       "2 years      4291\n",
       "3 years      4012\n",
       "4 years      3342\n",
       "5 years      3194\n",
       "1 year       3169\n",
       "6 years      2168\n",
       "7 years      1711\n",
       "8 years      1435\n",
       "9 years      1226\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "closed_loan_df.emp_length.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "id": "05ccf4cc-7178-42d0-93ab-b6be7924a55d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pub_rec_bankruptcies\n",
       "0.0    36238\n",
       "1.0     1637\n",
       "2.0        5\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "closed_loan_df.pub_rec_bankruptcies.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "id": "7cf99838-9386-43fe-80bb-318ffad41502",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "term                        2\n",
       "loan_status                 2\n",
       "pub_rec_bankruptcies        3\n",
       "verification_status         3\n",
       "pub_rec                     5\n",
       "home_ownership              5\n",
       "grade                       7\n",
       "emp_length                 11\n",
       "purpose                    14\n",
       "sub_grade                  35\n",
       "open_acc                   40\n",
       "addr_state                 50\n",
       "issue_d                    55\n",
       "total_acc                  82\n",
       "int_rate                  370\n",
       "loan_amnt                 870\n",
       "funded_amnt              1019\n",
       "revol_util               1088\n",
       "dti                      2853\n",
       "annual_inc               5215\n",
       "funded_amnt_inv          8050\n",
       "installment             15022\n",
       "dtype: int64"
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "closed_loan_df.nunique().sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42182d28-05fd-499c-a4a9-90dc86b3d3a6",
   "metadata": {},
   "source": [
    "Now we have the dataframe with columns that are of our interest. We need to clean up the data for our analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03799c7f-ae93-49ca-9d5b-ae141c8b3154",
   "metadata": {},
   "source": [
    "### Data Cleanup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "id": "77d95b39-4cf7-4c47-af63-4de65c6c4576",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='emp_length'>"
      ]
     },
     "execution_count": 451,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "closed_loan_df.emp_length.value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "id": "9bb7dc32-e7c6-4a02-a234-9c73c726badd",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_df = closed_loan_df.copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "id": "9a66a143-14b6-4a1e-a5c4-54b475a79d22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  1.,  2., nan])"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_df.pub_rec_bankruptcies.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "id": "dc773883-fdc7-4378-bfa8-79d2a56b6a6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['10+ years', '< 1 year', '3 years', '8 years', '9 years',\n",
       "       '4 years', '5 years', '1 year', '6 years', '2 years', '7 years',\n",
       "       nan], dtype=object)"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_df.emp_length.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "id": "d6b397f7-e21b-4c15-9db0-262c7b30324e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt               0.00\n",
       "funded_amnt             0.00\n",
       "funded_amnt_inv         0.00\n",
       "term                    0.00\n",
       "int_rate                0.00\n",
       "installment             0.00\n",
       "grade                   0.00\n",
       "sub_grade               0.00\n",
       "emp_length              2.68\n",
       "home_ownership          0.00\n",
       "annual_inc              0.00\n",
       "verification_status     0.00\n",
       "issue_d                 0.00\n",
       "loan_status             0.00\n",
       "purpose                 0.00\n",
       "addr_state              0.00\n",
       "dti                     0.00\n",
       "open_acc                0.00\n",
       "pub_rec                 0.00\n",
       "revol_util              0.13\n",
       "total_acc               0.00\n",
       "pub_rec_bankruptcies    1.81\n",
       "dtype: float64"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round((features_df.isnull().sum()*100)/features_df.shape[0],2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "id": "abb6f8b2-cf82-480d-a196-f91d906ba018",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38577"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_row_count_before_dropping_na = features_df.shape[0]\n",
    "total_row_count_before_dropping_na"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "id": "f84bbfe3-c89e-413b-862c-48f4f3f7a750",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_df.dropna(axis = 0, how = 'any', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "id": "9447c51a-529d-4bf0-b85e-aa25b415b7c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36800"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_row_count_after_dropping_na = features_df.shape[0]\n",
    "total_row_count_after_dropping_na"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "id": "4eb0f78a-eb7e-47bc-8a34-3b6735e1e56e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95.39"
      ]
     },
     "execution_count": 360,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(total_row_count_after_dropping_na*100/total_row_count_before_dropping_na, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "769785e2-a784-45a6-a172-9df790f70d8b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "484f90fe-1ae0-4f84-bbba-8ef314ee2da5",
   "metadata": {},
   "source": [
    "Clean up Employee length column data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "id": "fc22d63c-baa2-4e12-9b19-e4af07a93ddf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "emp_length\n",
       "10+ years    8368\n",
       "< 1 year     4329\n",
       "2 years      4202\n",
       "3 years      3947\n",
       "4 years      3290\n",
       "5 years      3157\n",
       "1 year       3071\n",
       "6 years      2133\n",
       "7 years      1686\n",
       "8 years      1408\n",
       "9 years      1209\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_df.emp_length.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "id": "4b53259d-b318-46ae-9d39-9cebf54346c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_df.emp_length = features_df.emp_length.apply(lambda x: 0 if x == '< 1 year' else (10 if x == '10+ years' else str(x).split()[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "id": "d9e61d80-dc3b-491f-bc96-d692c84cfdf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "emp_length\n",
       "10    8368\n",
       "0     4329\n",
       "2     4202\n",
       "3     3947\n",
       "4     3290\n",
       "5     3157\n",
       "1     3071\n",
       "6     2133\n",
       "7     1686\n",
       "8     1408\n",
       "9     1209\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_df.emp_length.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "id": "de8fe4ad-744d-4fd6-bb2a-24fb6d31c5af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        83.70%\n",
       "1         9.40%\n",
       "2        98.50%\n",
       "3           21%\n",
       "5        28.30%\n",
       "          ...  \n",
       "39562    68.70%\n",
       "39573       79%\n",
       "39623    62.90%\n",
       "39666    34.30%\n",
       "39680    70.90%\n",
       "Name: revol_util, Length: 36800, dtype: object"
      ]
     },
     "execution_count": 366,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_df.revol_util"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "id": "e4da5f3c-c8ab-4d79-9808-0dfd840ca9c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_df['revol_util'] = features_df.revol_util.apply(lambda x: round(float(x.split('%')[0]), 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "id": "373808b1-dfd4-42d4-8c49-1d144db5f790",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt               0\n",
       "revol_util              0\n",
       "pub_rec                 0\n",
       "open_acc                0\n",
       "dti                     0\n",
       "addr_state              0\n",
       "purpose                 0\n",
       "loan_status             0\n",
       "issue_d                 0\n",
       "verification_status     0\n",
       "annual_inc              0\n",
       "home_ownership          0\n",
       "emp_length              0\n",
       "sub_grade               0\n",
       "grade                   0\n",
       "installment             0\n",
       "int_rate                0\n",
       "term                    0\n",
       "funded_amnt_inv         0\n",
       "funded_amnt             0\n",
       "total_acc               0\n",
       "pub_rec_bankruptcies    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_df.isnull().sum().sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df50f982-731e-4330-8928-3f584810dc9f",
   "metadata": {},
   "source": [
    "Now there are no null values in the dataset. The dataset can now enhanced with more features and corrected datatypes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "id": "3ba6fb75-509a-4f20-8b72-1d434554487e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 36800 entries, 0 to 39680\n",
      "Data columns (total 22 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   loan_amnt             36800 non-null  int64  \n",
      " 1   funded_amnt           36800 non-null  int64  \n",
      " 2   funded_amnt_inv       36800 non-null  float64\n",
      " 3   term                  36800 non-null  object \n",
      " 4   int_rate              36800 non-null  object \n",
      " 5   installment           36800 non-null  float64\n",
      " 6   grade                 36800 non-null  object \n",
      " 7   sub_grade             36800 non-null  object \n",
      " 8   emp_length            36800 non-null  object \n",
      " 9   home_ownership        36800 non-null  object \n",
      " 10  annual_inc            36800 non-null  float64\n",
      " 11  verification_status   36800 non-null  object \n",
      " 12  issue_d               36800 non-null  object \n",
      " 13  loan_status           36800 non-null  object \n",
      " 14  purpose               36800 non-null  object \n",
      " 15  addr_state            36800 non-null  object \n",
      " 16  dti                   36800 non-null  float64\n",
      " 17  open_acc              36800 non-null  int64  \n",
      " 18  pub_rec               36800 non-null  int64  \n",
      " 19  revol_util            36800 non-null  float64\n",
      " 20  total_acc             36800 non-null  int64  \n",
      " 21  pub_rec_bankruptcies  36800 non-null  float64\n",
      "dtypes: float64(6), int64(5), object(11)\n",
      "memory usage: 6.5+ MB\n"
     ]
    }
   ],
   "source": [
    "features_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "id": "6497d667-eeec-47b3-ac69-6ffe0f18e226",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_df.funded_amnt_inv = features_df.funded_amnt_inv.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "id": "bb3e62fb-649c-4dd1-8054-d2a9d1ee9e6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_df.term = features_df.term.apply(lambda x: x.split()[0]).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "id": "e053388b-7653-4eef-bf30-5bfa66ccc62c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([36, 60])"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_df.term.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "id": "1be0380a-95b1-47c2-af68-120ad24b62d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>...</th>\n",
       "      <th>issue_d</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>purpose</th>\n",
       "      <th>addr_state</th>\n",
       "      <th>dti</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>4975</td>\n",
       "      <td>36</td>\n",
       "      <td>10.65%</td>\n",
       "      <td>162.87</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>10</td>\n",
       "      <td>RENT</td>\n",
       "      <td>...</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>AZ</td>\n",
       "      <td>27.65</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>83.7</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2500</td>\n",
       "      <td>2500</td>\n",
       "      <td>2500</td>\n",
       "      <td>60</td>\n",
       "      <td>15.27%</td>\n",
       "      <td>59.83</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>0</td>\n",
       "      <td>RENT</td>\n",
       "      <td>...</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>car</td>\n",
       "      <td>GA</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2400</td>\n",
       "      <td>2400</td>\n",
       "      <td>2400</td>\n",
       "      <td>36</td>\n",
       "      <td>15.96%</td>\n",
       "      <td>84.33</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>10</td>\n",
       "      <td>RENT</td>\n",
       "      <td>...</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>small_business</td>\n",
       "      <td>IL</td>\n",
       "      <td>8.72</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>98.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>36</td>\n",
       "      <td>13.49%</td>\n",
       "      <td>339.31</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>10</td>\n",
       "      <td>RENT</td>\n",
       "      <td>...</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>other</td>\n",
       "      <td>CA</td>\n",
       "      <td>20.00</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>37</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>36</td>\n",
       "      <td>7.90%</td>\n",
       "      <td>156.46</td>\n",
       "      <td>A</td>\n",
       "      <td>A4</td>\n",
       "      <td>3</td>\n",
       "      <td>RENT</td>\n",
       "      <td>...</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>wedding</td>\n",
       "      <td>AZ</td>\n",
       "      <td>11.20</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>28.3</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39562</th>\n",
       "      <td>4800</td>\n",
       "      <td>4800</td>\n",
       "      <td>1100</td>\n",
       "      <td>36</td>\n",
       "      <td>10.28%</td>\n",
       "      <td>155.52</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>1</td>\n",
       "      <td>RENT</td>\n",
       "      <td>...</td>\n",
       "      <td>Nov-07</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>VA</td>\n",
       "      <td>7.51</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>68.7</td>\n",
       "      <td>12</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39573</th>\n",
       "      <td>7000</td>\n",
       "      <td>7000</td>\n",
       "      <td>1000</td>\n",
       "      <td>36</td>\n",
       "      <td>10.59%</td>\n",
       "      <td>227.82</td>\n",
       "      <td>C</td>\n",
       "      <td>C2</td>\n",
       "      <td>3</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>...</td>\n",
       "      <td>Nov-07</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>AZ</td>\n",
       "      <td>8.50</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>79.0</td>\n",
       "      <td>21</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39623</th>\n",
       "      <td>9000</td>\n",
       "      <td>9000</td>\n",
       "      <td>700</td>\n",
       "      <td>36</td>\n",
       "      <td>12.49%</td>\n",
       "      <td>301.04</td>\n",
       "      <td>D</td>\n",
       "      <td>D3</td>\n",
       "      <td>8</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>...</td>\n",
       "      <td>Oct-07</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>MD</td>\n",
       "      <td>5.08</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>62.9</td>\n",
       "      <td>37</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39666</th>\n",
       "      <td>15450</td>\n",
       "      <td>15450</td>\n",
       "      <td>600</td>\n",
       "      <td>36</td>\n",
       "      <td>11.22%</td>\n",
       "      <td>507.46</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>2</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>...</td>\n",
       "      <td>Aug-07</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>VA</td>\n",
       "      <td>13.50</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>34.3</td>\n",
       "      <td>29</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39680</th>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>525</td>\n",
       "      <td>36</td>\n",
       "      <td>11.86%</td>\n",
       "      <td>99.44</td>\n",
       "      <td>D</td>\n",
       "      <td>D1</td>\n",
       "      <td>2</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>...</td>\n",
       "      <td>Aug-07</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>IN</td>\n",
       "      <td>7.47</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>70.9</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36800 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       loan_amnt  funded_amnt  funded_amnt_inv  term int_rate  installment  \\\n",
       "0           5000         5000             4975    36   10.65%       162.87   \n",
       "1           2500         2500             2500    60   15.27%        59.83   \n",
       "2           2400         2400             2400    36   15.96%        84.33   \n",
       "3          10000        10000            10000    36   13.49%       339.31   \n",
       "5           5000         5000             5000    36    7.90%       156.46   \n",
       "...          ...          ...              ...   ...      ...          ...   \n",
       "39562       4800         4800             1100    36   10.28%       155.52   \n",
       "39573       7000         7000             1000    36   10.59%       227.82   \n",
       "39623       9000         9000              700    36   12.49%       301.04   \n",
       "39666      15450        15450              600    36   11.22%       507.46   \n",
       "39680       3000         3000              525    36   11.86%        99.44   \n",
       "\n",
       "      grade sub_grade emp_length home_ownership  ...  issue_d  loan_status  \\\n",
       "0         B        B2         10           RENT  ...   Dec-11   Fully Paid   \n",
       "1         C        C4          0           RENT  ...   Dec-11  Charged Off   \n",
       "2         C        C5         10           RENT  ...   Dec-11   Fully Paid   \n",
       "3         C        C1         10           RENT  ...   Dec-11   Fully Paid   \n",
       "5         A        A4          3           RENT  ...   Dec-11   Fully Paid   \n",
       "...     ...       ...        ...            ...  ...      ...          ...   \n",
       "39562     C        C1          1           RENT  ...   Nov-07   Fully Paid   \n",
       "39573     C        C2          3       MORTGAGE  ...   Nov-07   Fully Paid   \n",
       "39623     D        D3          8       MORTGAGE  ...   Oct-07  Charged Off   \n",
       "39666     C        C4          2       MORTGAGE  ...   Aug-07  Charged Off   \n",
       "39680     D        D1          2       MORTGAGE  ...   Aug-07   Fully Paid   \n",
       "\n",
       "                  purpose addr_state    dti open_acc  pub_rec  revol_util  \\\n",
       "0             credit_card         AZ  27.65        3        0        83.7   \n",
       "1                     car         GA   1.00        3        0         9.4   \n",
       "2          small_business         IL   8.72        2        0        98.5   \n",
       "3                   other         CA  20.00       10        0        21.0   \n",
       "5                 wedding         AZ  11.20        9        0        28.3   \n",
       "...                   ...        ...    ...      ...      ...         ...   \n",
       "39562  debt_consolidation         VA   7.51       11        1        68.7   \n",
       "39573  debt_consolidation         AZ   8.50        9        1        79.0   \n",
       "39623  debt_consolidation         MD   5.08        9        1        62.9   \n",
       "39666  debt_consolidation         VA  13.50       13        1        34.3   \n",
       "39680  debt_consolidation         IN   7.47        3        1        70.9   \n",
       "\n",
       "       total_acc  pub_rec_bankruptcies  \n",
       "0              9                   0.0  \n",
       "1              4                   0.0  \n",
       "2             10                   0.0  \n",
       "3             37                   0.0  \n",
       "5             12                   0.0  \n",
       "...          ...                   ...  \n",
       "39562         12                   1.0  \n",
       "39573         21                   1.0  \n",
       "39623         37                   1.0  \n",
       "39666         29                   1.0  \n",
       "39680          6                   1.0  \n",
       "\n",
       "[36800 rows x 22 columns]"
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "id": "e036c730-1420-47e3-804a-3eec61124f83",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_df.int_rate = features_df.int_rate.apply(lambda x: x.replace('%', '')).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "id": "7e1a4d19-3649-49ca-ae13-653901c028ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int_rate\n",
       "10.99    891\n",
       "11.49    766\n",
       "7.51     756\n",
       "13.49    736\n",
       "7.88     701\n",
       "        ... \n",
       "16.96      1\n",
       "17.46      1\n",
       "16.20      1\n",
       "18.72      1\n",
       "11.22      1\n",
       "Name: count, Length: 336, dtype: int64"
      ]
     },
     "execution_count": 376,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_df.int_rate.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "id": "64ad6c30-7921-47c5-ba0c-2795f613c7c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>...</th>\n",
       "      <th>issue_d</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>purpose</th>\n",
       "      <th>addr_state</th>\n",
       "      <th>dti</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>4975</td>\n",
       "      <td>36</td>\n",
       "      <td>10.65</td>\n",
       "      <td>162.87</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>10</td>\n",
       "      <td>RENT</td>\n",
       "      <td>...</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>AZ</td>\n",
       "      <td>27.65</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>83.7</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2500</td>\n",
       "      <td>2500</td>\n",
       "      <td>2500</td>\n",
       "      <td>60</td>\n",
       "      <td>15.27</td>\n",
       "      <td>59.83</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>0</td>\n",
       "      <td>RENT</td>\n",
       "      <td>...</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>car</td>\n",
       "      <td>GA</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2400</td>\n",
       "      <td>2400</td>\n",
       "      <td>2400</td>\n",
       "      <td>36</td>\n",
       "      <td>15.96</td>\n",
       "      <td>84.33</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>10</td>\n",
       "      <td>RENT</td>\n",
       "      <td>...</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>small_business</td>\n",
       "      <td>IL</td>\n",
       "      <td>8.72</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>98.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>36</td>\n",
       "      <td>13.49</td>\n",
       "      <td>339.31</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>10</td>\n",
       "      <td>RENT</td>\n",
       "      <td>...</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>other</td>\n",
       "      <td>CA</td>\n",
       "      <td>20.00</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>37</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>36</td>\n",
       "      <td>7.90</td>\n",
       "      <td>156.46</td>\n",
       "      <td>A</td>\n",
       "      <td>A4</td>\n",
       "      <td>3</td>\n",
       "      <td>RENT</td>\n",
       "      <td>...</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>wedding</td>\n",
       "      <td>AZ</td>\n",
       "      <td>11.20</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>28.3</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39562</th>\n",
       "      <td>4800</td>\n",
       "      <td>4800</td>\n",
       "      <td>1100</td>\n",
       "      <td>36</td>\n",
       "      <td>10.28</td>\n",
       "      <td>155.52</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>1</td>\n",
       "      <td>RENT</td>\n",
       "      <td>...</td>\n",
       "      <td>Nov-07</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>VA</td>\n",
       "      <td>7.51</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>68.7</td>\n",
       "      <td>12</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39573</th>\n",
       "      <td>7000</td>\n",
       "      <td>7000</td>\n",
       "      <td>1000</td>\n",
       "      <td>36</td>\n",
       "      <td>10.59</td>\n",
       "      <td>227.82</td>\n",
       "      <td>C</td>\n",
       "      <td>C2</td>\n",
       "      <td>3</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>...</td>\n",
       "      <td>Nov-07</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>AZ</td>\n",
       "      <td>8.50</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>79.0</td>\n",
       "      <td>21</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39623</th>\n",
       "      <td>9000</td>\n",
       "      <td>9000</td>\n",
       "      <td>700</td>\n",
       "      <td>36</td>\n",
       "      <td>12.49</td>\n",
       "      <td>301.04</td>\n",
       "      <td>D</td>\n",
       "      <td>D3</td>\n",
       "      <td>8</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>...</td>\n",
       "      <td>Oct-07</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>MD</td>\n",
       "      <td>5.08</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>62.9</td>\n",
       "      <td>37</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39666</th>\n",
       "      <td>15450</td>\n",
       "      <td>15450</td>\n",
       "      <td>600</td>\n",
       "      <td>36</td>\n",
       "      <td>11.22</td>\n",
       "      <td>507.46</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>2</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>...</td>\n",
       "      <td>Aug-07</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>VA</td>\n",
       "      <td>13.50</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>34.3</td>\n",
       "      <td>29</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39680</th>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>525</td>\n",
       "      <td>36</td>\n",
       "      <td>11.86</td>\n",
       "      <td>99.44</td>\n",
       "      <td>D</td>\n",
       "      <td>D1</td>\n",
       "      <td>2</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>...</td>\n",
       "      <td>Aug-07</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>IN</td>\n",
       "      <td>7.47</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>70.9</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36800 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       loan_amnt  funded_amnt  funded_amnt_inv  term  int_rate  installment  \\\n",
       "0           5000         5000             4975    36     10.65       162.87   \n",
       "1           2500         2500             2500    60     15.27        59.83   \n",
       "2           2400         2400             2400    36     15.96        84.33   \n",
       "3          10000        10000            10000    36     13.49       339.31   \n",
       "5           5000         5000             5000    36      7.90       156.46   \n",
       "...          ...          ...              ...   ...       ...          ...   \n",
       "39562       4800         4800             1100    36     10.28       155.52   \n",
       "39573       7000         7000             1000    36     10.59       227.82   \n",
       "39623       9000         9000              700    36     12.49       301.04   \n",
       "39666      15450        15450              600    36     11.22       507.46   \n",
       "39680       3000         3000              525    36     11.86        99.44   \n",
       "\n",
       "      grade sub_grade emp_length home_ownership  ...  issue_d  loan_status  \\\n",
       "0         B        B2         10           RENT  ...   Dec-11   Fully Paid   \n",
       "1         C        C4          0           RENT  ...   Dec-11  Charged Off   \n",
       "2         C        C5         10           RENT  ...   Dec-11   Fully Paid   \n",
       "3         C        C1         10           RENT  ...   Dec-11   Fully Paid   \n",
       "5         A        A4          3           RENT  ...   Dec-11   Fully Paid   \n",
       "...     ...       ...        ...            ...  ...      ...          ...   \n",
       "39562     C        C1          1           RENT  ...   Nov-07   Fully Paid   \n",
       "39573     C        C2          3       MORTGAGE  ...   Nov-07   Fully Paid   \n",
       "39623     D        D3          8       MORTGAGE  ...   Oct-07  Charged Off   \n",
       "39666     C        C4          2       MORTGAGE  ...   Aug-07  Charged Off   \n",
       "39680     D        D1          2       MORTGAGE  ...   Aug-07   Fully Paid   \n",
       "\n",
       "                  purpose addr_state    dti open_acc  pub_rec  revol_util  \\\n",
       "0             credit_card         AZ  27.65        3        0        83.7   \n",
       "1                     car         GA   1.00        3        0         9.4   \n",
       "2          small_business         IL   8.72        2        0        98.5   \n",
       "3                   other         CA  20.00       10        0        21.0   \n",
       "5                 wedding         AZ  11.20        9        0        28.3   \n",
       "...                   ...        ...    ...      ...      ...         ...   \n",
       "39562  debt_consolidation         VA   7.51       11        1        68.7   \n",
       "39573  debt_consolidation         AZ   8.50        9        1        79.0   \n",
       "39623  debt_consolidation         MD   5.08        9        1        62.9   \n",
       "39666  debt_consolidation         VA  13.50       13        1        34.3   \n",
       "39680  debt_consolidation         IN   7.47        3        1        70.9   \n",
       "\n",
       "       total_acc  pub_rec_bankruptcies  \n",
       "0              9                   0.0  \n",
       "1              4                   0.0  \n",
       "2             10                   0.0  \n",
       "3             37                   0.0  \n",
       "5             12                   0.0  \n",
       "...          ...                   ...  \n",
       "39562         12                   1.0  \n",
       "39573         21                   1.0  \n",
       "39623         37                   1.0  \n",
       "39666         29                   1.0  \n",
       "39680          6                   1.0  \n",
       "\n",
       "[36800 rows x 22 columns]"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "id": "cf9ffb97-d343-497d-8c66-19dd7c3860dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pub_rec_bankruptcies\n",
       "0.0    35266\n",
       "1.0     1529\n",
       "2.0        5\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_df.pub_rec_bankruptcies.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "id": "62783915-1a74-498a-a61e-dbfce5f86b43",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_df.pub_rec_bankruptcies = features_df.pub_rec_bankruptcies.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "id": "086b7fce-f6ac-4ba4-8954-f253ad8b6bad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 36800 entries, 0 to 39680\n",
      "Data columns (total 22 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   loan_amnt             36800 non-null  int64  \n",
      " 1   funded_amnt           36800 non-null  int64  \n",
      " 2   funded_amnt_inv       36800 non-null  int32  \n",
      " 3   term                  36800 non-null  int32  \n",
      " 4   int_rate              36800 non-null  float64\n",
      " 5   installment           36800 non-null  float64\n",
      " 6   grade                 36800 non-null  object \n",
      " 7   sub_grade             36800 non-null  object \n",
      " 8   emp_length            36800 non-null  object \n",
      " 9   home_ownership        36800 non-null  object \n",
      " 10  annual_inc            36800 non-null  float64\n",
      " 11  verification_status   36800 non-null  object \n",
      " 12  issue_d               36800 non-null  object \n",
      " 13  loan_status           36800 non-null  object \n",
      " 14  purpose               36800 non-null  object \n",
      " 15  addr_state            36800 non-null  object \n",
      " 16  dti                   36800 non-null  float64\n",
      " 17  open_acc              36800 non-null  int64  \n",
      " 18  pub_rec               36800 non-null  int64  \n",
      " 19  revol_util            36800 non-null  float64\n",
      " 20  total_acc             36800 non-null  int64  \n",
      " 21  pub_rec_bankruptcies  36800 non-null  int32  \n",
      "dtypes: float64(5), int32(3), int64(5), object(9)\n",
      "memory usage: 6.0+ MB\n"
     ]
    }
   ],
   "source": [
    "features_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "id": "d0c3e3fe-561b-41e3-a71d-1ceea467ca5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# features_df[['grade', 'sub_grade']] = features_df[['grade', 'sub_grade']].astype(str)\n",
    "features_df.emp_length = features_df.emp_length.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "id": "055ee46d-5bca-4f8d-9711-7fc1fdf22c02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 36800 entries, 0 to 39680\n",
      "Data columns (total 22 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   loan_amnt             36800 non-null  int64  \n",
      " 1   funded_amnt           36800 non-null  int64  \n",
      " 2   funded_amnt_inv       36800 non-null  int32  \n",
      " 3   term                  36800 non-null  int32  \n",
      " 4   int_rate              36800 non-null  float64\n",
      " 5   installment           36800 non-null  float64\n",
      " 6   grade                 36800 non-null  object \n",
      " 7   sub_grade             36800 non-null  object \n",
      " 8   emp_length            36800 non-null  int32  \n",
      " 9   home_ownership        36800 non-null  object \n",
      " 10  annual_inc            36800 non-null  float64\n",
      " 11  verification_status   36800 non-null  object \n",
      " 12  issue_d               36800 non-null  object \n",
      " 13  loan_status           36800 non-null  object \n",
      " 14  purpose               36800 non-null  object \n",
      " 15  addr_state            36800 non-null  object \n",
      " 16  dti                   36800 non-null  float64\n",
      " 17  open_acc              36800 non-null  int64  \n",
      " 18  pub_rec               36800 non-null  int64  \n",
      " 19  revol_util            36800 non-null  float64\n",
      " 20  total_acc             36800 non-null  int64  \n",
      " 21  pub_rec_bankruptcies  36800 non-null  int32  \n",
      "dtypes: float64(5), int32(4), int64(5), object(8)\n",
      "memory usage: 5.9+ MB\n"
     ]
    }
   ],
   "source": [
    "features_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "id": "aa2b0b07-382b-4f5b-b10e-ad8b67fd5ad9",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_df['issue_date'] = pd.to_datetime(features_df.issue_d, format = '%b-%y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "id": "07cb8a35-1ed6-4b2c-bc1a-3e744244c8ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>issue_date</th>\n",
       "      <th>issue_d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>Dec-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>Dec-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>Dec-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>Dec-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>Dec-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39562</th>\n",
       "      <td>2007-11-01</td>\n",
       "      <td>Nov-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39573</th>\n",
       "      <td>2007-11-01</td>\n",
       "      <td>Nov-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39623</th>\n",
       "      <td>2007-10-01</td>\n",
       "      <td>Oct-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39666</th>\n",
       "      <td>2007-08-01</td>\n",
       "      <td>Aug-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39680</th>\n",
       "      <td>2007-08-01</td>\n",
       "      <td>Aug-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36800 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      issue_date issue_d\n",
       "0     2011-12-01  Dec-11\n",
       "1     2011-12-01  Dec-11\n",
       "2     2011-12-01  Dec-11\n",
       "3     2011-12-01  Dec-11\n",
       "5     2011-12-01  Dec-11\n",
       "...          ...     ...\n",
       "39562 2007-11-01  Nov-07\n",
       "39573 2007-11-01  Nov-07\n",
       "39623 2007-10-01  Oct-07\n",
       "39666 2007-08-01  Aug-07\n",
       "39680 2007-08-01  Aug-07\n",
       "\n",
       "[36800 rows x 2 columns]"
      ]
     },
     "execution_count": 384,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_df[['issue_date', 'issue_d']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "id": "348a0e12-5e41-4a85-8aff-b603a3d82170",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 36800 entries, 0 to 39680\n",
      "Data columns (total 23 columns):\n",
      " #   Column                Non-Null Count  Dtype         \n",
      "---  ------                --------------  -----         \n",
      " 0   loan_amnt             36800 non-null  int64         \n",
      " 1   funded_amnt           36800 non-null  int64         \n",
      " 2   funded_amnt_inv       36800 non-null  int32         \n",
      " 3   term                  36800 non-null  int32         \n",
      " 4   int_rate              36800 non-null  float64       \n",
      " 5   installment           36800 non-null  float64       \n",
      " 6   grade                 36800 non-null  object        \n",
      " 7   sub_grade             36800 non-null  object        \n",
      " 8   emp_length            36800 non-null  int32         \n",
      " 9   home_ownership        36800 non-null  object        \n",
      " 10  annual_inc            36800 non-null  float64       \n",
      " 11  verification_status   36800 non-null  object        \n",
      " 12  issue_d               36800 non-null  object        \n",
      " 13  loan_status           36800 non-null  object        \n",
      " 14  purpose               36800 non-null  object        \n",
      " 15  addr_state            36800 non-null  object        \n",
      " 16  dti                   36800 non-null  float64       \n",
      " 17  open_acc              36800 non-null  int64         \n",
      " 18  pub_rec               36800 non-null  int64         \n",
      " 19  revol_util            36800 non-null  float64       \n",
      " 20  total_acc             36800 non-null  int64         \n",
      " 21  pub_rec_bankruptcies  36800 non-null  int32         \n",
      " 22  issue_date            36800 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](1), float64(5), int32(4), int64(5), object(8)\n",
      "memory usage: 6.2+ MB\n"
     ]
    }
   ],
   "source": [
    "features_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "id": "b478d6f6-cc0e-4cb8-843d-a413d2a71b5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pub_rec\n",
       "0    34871\n",
       "1     1875\n",
       "2       45\n",
       "3        7\n",
       "4        2\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 386,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_df.pub_rec.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "706b165f-c272-487a-a675-3ac07b71d1b8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "302301c8-5b51-4aa2-8047-382968b54108",
   "metadata": {},
   "source": [
    "The data has been cleaned up, few derived columns can be created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "id": "e6a17cbf-58a3-4b67-a1a4-d95878735488",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 387,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features_df[['loan_amnt', 'funded_amnt', 'funded_amnt_inv']].plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "id": "eaaec135-8273-483b-9ed3-666cb6dfdd4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_status\n",
       "Fully Paid     0.758333\n",
       "Charged Off    0.241667\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_df[features_df['loan_amnt']>30000]['loan_status'].value_counts()/features_df[features_df['loan_amnt']>30000]['loan_status'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "id": "afe5924e-f97c-414f-85c9-99c372cc716b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>...</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>purpose</th>\n",
       "      <th>addr_state</th>\n",
       "      <th>dti</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>issue_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>17500</td>\n",
       "      <td>8950</td>\n",
       "      <td>8925</td>\n",
       "      <td>60</td>\n",
       "      <td>17.27</td>\n",
       "      <td>223.74</td>\n",
       "      <td>D</td>\n",
       "      <td>D3</td>\n",
       "      <td>6</td>\n",
       "      <td>RENT</td>\n",
       "      <td>...</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>small_business</td>\n",
       "      <td>CA</td>\n",
       "      <td>19.47</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>58.9</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>12500</td>\n",
       "      <td>8925</td>\n",
       "      <td>8925</td>\n",
       "      <td>60</td>\n",
       "      <td>17.27</td>\n",
       "      <td>223.11</td>\n",
       "      <td>D</td>\n",
       "      <td>D3</td>\n",
       "      <td>5</td>\n",
       "      <td>RENT</td>\n",
       "      <td>...</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>MN</td>\n",
       "      <td>13.16</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>70.8</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>20975</td>\n",
       "      <td>13575</td>\n",
       "      <td>13550</td>\n",
       "      <td>60</td>\n",
       "      <td>17.58</td>\n",
       "      <td>341.63</td>\n",
       "      <td>D</td>\n",
       "      <td>D4</td>\n",
       "      <td>5</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>...</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>GA</td>\n",
       "      <td>18.79</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>79.4</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>7250</td>\n",
       "      <td>7200</td>\n",
       "      <td>7175</td>\n",
       "      <td>60</td>\n",
       "      <td>21.67</td>\n",
       "      <td>197.51</td>\n",
       "      <td>F</td>\n",
       "      <td>F3</td>\n",
       "      <td>0</td>\n",
       "      <td>RENT</td>\n",
       "      <td>...</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>CA</td>\n",
       "      <td>24.82</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>91.2</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>35000</td>\n",
       "      <td>22075</td>\n",
       "      <td>22050</td>\n",
       "      <td>60</td>\n",
       "      <td>17.27</td>\n",
       "      <td>551.84</td>\n",
       "      <td>D</td>\n",
       "      <td>D3</td>\n",
       "      <td>3</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>...</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>home_improvement</td>\n",
       "      <td>NY</td>\n",
       "      <td>7.51</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>53.3</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38886</th>\n",
       "      <td>25000</td>\n",
       "      <td>18175</td>\n",
       "      <td>14903</td>\n",
       "      <td>36</td>\n",
       "      <td>10.08</td>\n",
       "      <td>587.14</td>\n",
       "      <td>B</td>\n",
       "      <td>B3</td>\n",
       "      <td>2</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>...</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>major_purchase</td>\n",
       "      <td>NJ</td>\n",
       "      <td>4.51</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>39.5</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>2008-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38889</th>\n",
       "      <td>12000</td>\n",
       "      <td>6500</td>\n",
       "      <td>4250</td>\n",
       "      <td>36</td>\n",
       "      <td>11.34</td>\n",
       "      <td>213.85</td>\n",
       "      <td>C</td>\n",
       "      <td>C2</td>\n",
       "      <td>10</td>\n",
       "      <td>OWN</td>\n",
       "      <td>...</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>FL</td>\n",
       "      <td>20.82</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>50.6</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38890</th>\n",
       "      <td>25000</td>\n",
       "      <td>18825</td>\n",
       "      <td>14575</td>\n",
       "      <td>36</td>\n",
       "      <td>15.76</td>\n",
       "      <td>659.61</td>\n",
       "      <td>F</td>\n",
       "      <td>F1</td>\n",
       "      <td>7</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>...</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>CA</td>\n",
       "      <td>14.23</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>94.6</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>2008-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38893</th>\n",
       "      <td>20000</td>\n",
       "      <td>6875</td>\n",
       "      <td>3775</td>\n",
       "      <td>36</td>\n",
       "      <td>10.71</td>\n",
       "      <td>224.14</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>4</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>...</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>TX</td>\n",
       "      <td>9.66</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>90.2</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>2008-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38897</th>\n",
       "      <td>23000</td>\n",
       "      <td>4325</td>\n",
       "      <td>1684</td>\n",
       "      <td>36</td>\n",
       "      <td>11.03</td>\n",
       "      <td>141.66</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>0</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>...</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>other</td>\n",
       "      <td>CA</td>\n",
       "      <td>15.07</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>88.7</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>2008-04-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1717 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       loan_amnt  funded_amnt  funded_amnt_inv  term  int_rate  installment  \\\n",
       "61         17500         8950             8925    60     17.27       223.74   \n",
       "66         12500         8925             8925    60     17.27       223.11   \n",
       "70         20975        13575            13550    60     17.58       341.63   \n",
       "77          7250         7200             7175    60     21.67       197.51   \n",
       "80         35000        22075            22050    60     17.27       551.84   \n",
       "...          ...          ...              ...   ...       ...          ...   \n",
       "38886      25000        18175            14903    36     10.08       587.14   \n",
       "38889      12000         6500             4250    36     11.34       213.85   \n",
       "38890      25000        18825            14575    36     15.76       659.61   \n",
       "38893      20000         6875             3775    36     10.71       224.14   \n",
       "38897      23000         4325             1684    36     11.03       141.66   \n",
       "\n",
       "      grade sub_grade  emp_length home_ownership  ...  loan_status  \\\n",
       "61        D        D3           6           RENT  ...   Fully Paid   \n",
       "66        D        D3           5           RENT  ...  Charged Off   \n",
       "70        D        D4           5       MORTGAGE  ...   Fully Paid   \n",
       "77        F        F3           0           RENT  ...   Fully Paid   \n",
       "80        D        D3           3       MORTGAGE  ...   Fully Paid   \n",
       "...     ...       ...         ...            ...  ...          ...   \n",
       "38886     B        B3           2       MORTGAGE  ...   Fully Paid   \n",
       "38889     C        C2          10            OWN  ...   Fully Paid   \n",
       "38890     F        F1           7       MORTGAGE  ...   Fully Paid   \n",
       "38893     B        B5           4       MORTGAGE  ...   Fully Paid   \n",
       "38897     C        C1           0       MORTGAGE  ...   Fully Paid   \n",
       "\n",
       "                  purpose addr_state    dti open_acc pub_rec  revol_util  \\\n",
       "61         small_business         CA  19.47        5       0        58.9   \n",
       "66     debt_consolidation         MN  13.16        7       1        70.8   \n",
       "70            credit_card         GA  18.79       13       1        79.4   \n",
       "77     debt_consolidation         CA  24.82        9       1        91.2   \n",
       "80       home_improvement         NY   7.51       11       0        53.3   \n",
       "...                   ...        ...    ...      ...     ...         ...   \n",
       "38886      major_purchase         NJ   4.51        9       0        39.5   \n",
       "38889  debt_consolidation         FL  20.82       14       1        50.6   \n",
       "38890  debt_consolidation         CA  14.23        9       1        94.6   \n",
       "38893         credit_card         TX   9.66        3       0        90.2   \n",
       "38897               other         CA  15.07       11       0        88.7   \n",
       "\n",
       "       total_acc  pub_rec_bankruptcies  issue_date  \n",
       "61             6                     0  2011-12-01  \n",
       "66            28                     1  2011-12-01  \n",
       "70            21                     1  2011-12-01  \n",
       "77            19                     1  2011-12-01  \n",
       "80            31                     0  2011-12-01  \n",
       "...          ...                   ...         ...  \n",
       "38886         39                     0  2008-04-01  \n",
       "38889         22                     1  2008-04-01  \n",
       "38890         30                     1  2008-04-01  \n",
       "38893         25                     0  2008-04-01  \n",
       "38897         30                     0  2008-04-01  \n",
       "\n",
       "[1717 rows x 23 columns]"
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Need to analyze if there is a coorelation between % of amount funded vs loan on the defaults\n",
    "features_df[features_df['loan_amnt'] != features_df['funded_amnt'] ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "id": "2d496bfe-8279-4f90-8d18-4fc00ec4a31d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>...</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>purpose</th>\n",
       "      <th>addr_state</th>\n",
       "      <th>dti</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>issue_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>4975</td>\n",
       "      <td>36</td>\n",
       "      <td>10.65</td>\n",
       "      <td>162.87</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>10</td>\n",
       "      <td>RENT</td>\n",
       "      <td>...</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>AZ</td>\n",
       "      <td>27.65</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>83.7</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5375</td>\n",
       "      <td>5375</td>\n",
       "      <td>5350</td>\n",
       "      <td>60</td>\n",
       "      <td>12.69</td>\n",
       "      <td>121.45</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>0</td>\n",
       "      <td>RENT</td>\n",
       "      <td>...</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>other</td>\n",
       "      <td>TX</td>\n",
       "      <td>18.08</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>36.5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20250</td>\n",
       "      <td>20250</td>\n",
       "      <td>19142</td>\n",
       "      <td>60</td>\n",
       "      <td>15.27</td>\n",
       "      <td>484.63</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>3</td>\n",
       "      <td>RENT</td>\n",
       "      <td>...</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>TX</td>\n",
       "      <td>26.53</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>85.6</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>15000</td>\n",
       "      <td>15000</td>\n",
       "      <td>8725</td>\n",
       "      <td>36</td>\n",
       "      <td>14.27</td>\n",
       "      <td>514.64</td>\n",
       "      <td>C</td>\n",
       "      <td>C2</td>\n",
       "      <td>9</td>\n",
       "      <td>RENT</td>\n",
       "      <td>...</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>NY</td>\n",
       "      <td>15.22</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>57.6</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>25600</td>\n",
       "      <td>25600</td>\n",
       "      <td>25350</td>\n",
       "      <td>36</td>\n",
       "      <td>9.91</td>\n",
       "      <td>824.96</td>\n",
       "      <td>B</td>\n",
       "      <td>B1</td>\n",
       "      <td>9</td>\n",
       "      <td>RENT</td>\n",
       "      <td>...</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>CA</td>\n",
       "      <td>15.71</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39562</th>\n",
       "      <td>4800</td>\n",
       "      <td>4800</td>\n",
       "      <td>1100</td>\n",
       "      <td>36</td>\n",
       "      <td>10.28</td>\n",
       "      <td>155.52</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>1</td>\n",
       "      <td>RENT</td>\n",
       "      <td>...</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>VA</td>\n",
       "      <td>7.51</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>68.7</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>2007-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39573</th>\n",
       "      <td>7000</td>\n",
       "      <td>7000</td>\n",
       "      <td>1000</td>\n",
       "      <td>36</td>\n",
       "      <td>10.59</td>\n",
       "      <td>227.82</td>\n",
       "      <td>C</td>\n",
       "      <td>C2</td>\n",
       "      <td>3</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>...</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>AZ</td>\n",
       "      <td>8.50</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>79.0</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>2007-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39623</th>\n",
       "      <td>9000</td>\n",
       "      <td>9000</td>\n",
       "      <td>700</td>\n",
       "      <td>36</td>\n",
       "      <td>12.49</td>\n",
       "      <td>301.04</td>\n",
       "      <td>D</td>\n",
       "      <td>D3</td>\n",
       "      <td>8</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>...</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>MD</td>\n",
       "      <td>5.08</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>62.9</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2007-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39666</th>\n",
       "      <td>15450</td>\n",
       "      <td>15450</td>\n",
       "      <td>600</td>\n",
       "      <td>36</td>\n",
       "      <td>11.22</td>\n",
       "      <td>507.46</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>2</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>...</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>VA</td>\n",
       "      <td>13.50</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>34.3</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>2007-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39680</th>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>525</td>\n",
       "      <td>36</td>\n",
       "      <td>11.86</td>\n",
       "      <td>99.44</td>\n",
       "      <td>D</td>\n",
       "      <td>D1</td>\n",
       "      <td>2</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>...</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>IN</td>\n",
       "      <td>7.47</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>70.9</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2007-08-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18189 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       loan_amnt  funded_amnt  funded_amnt_inv  term  int_rate  installment  \\\n",
       "0           5000         5000             4975    36     10.65       162.87   \n",
       "9           5375         5375             5350    60     12.69       121.45   \n",
       "20         20250        20250            19142    60     15.27       484.63   \n",
       "26         15000        15000             8725    36     14.27       514.64   \n",
       "51         25600        25600            25350    36      9.91       824.96   \n",
       "...          ...          ...              ...   ...       ...          ...   \n",
       "39562       4800         4800             1100    36     10.28       155.52   \n",
       "39573       7000         7000             1000    36     10.59       227.82   \n",
       "39623       9000         9000              700    36     12.49       301.04   \n",
       "39666      15450        15450              600    36     11.22       507.46   \n",
       "39680       3000         3000              525    36     11.86        99.44   \n",
       "\n",
       "      grade sub_grade  emp_length home_ownership  ...  loan_status  \\\n",
       "0         B        B2          10           RENT  ...   Fully Paid   \n",
       "9         B        B5           0           RENT  ...  Charged Off   \n",
       "20        C        C4           3           RENT  ...   Fully Paid   \n",
       "26        C        C2           9           RENT  ...  Charged Off   \n",
       "51        B        B1           9           RENT  ...   Fully Paid   \n",
       "...     ...       ...         ...            ...  ...          ...   \n",
       "39562     C        C1           1           RENT  ...   Fully Paid   \n",
       "39573     C        C2           3       MORTGAGE  ...   Fully Paid   \n",
       "39623     D        D3           8       MORTGAGE  ...  Charged Off   \n",
       "39666     C        C4           2       MORTGAGE  ...  Charged Off   \n",
       "39680     D        D1           2       MORTGAGE  ...   Fully Paid   \n",
       "\n",
       "                  purpose addr_state    dti open_acc pub_rec  revol_util  \\\n",
       "0             credit_card         AZ  27.65        3       0        83.7   \n",
       "9                   other         TX  18.08        2       0        36.5   \n",
       "20     debt_consolidation         TX  26.53        8       0        85.6   \n",
       "26     debt_consolidation         NY  15.22        7       0        57.6   \n",
       "51            credit_card         CA  15.71       11       0        62.0   \n",
       "...                   ...        ...    ...      ...     ...         ...   \n",
       "39562  debt_consolidation         VA   7.51       11       1        68.7   \n",
       "39573  debt_consolidation         AZ   8.50        9       1        79.0   \n",
       "39623  debt_consolidation         MD   5.08        9       1        62.9   \n",
       "39666  debt_consolidation         VA  13.50       13       1        34.3   \n",
       "39680  debt_consolidation         IN   7.47        3       1        70.9   \n",
       "\n",
       "       total_acc  pub_rec_bankruptcies  issue_date  \n",
       "0              9                     0  2011-12-01  \n",
       "9              3                     0  2011-12-01  \n",
       "20            22                     0  2011-12-01  \n",
       "26            11                     0  2011-12-01  \n",
       "51            27                     0  2011-12-01  \n",
       "...          ...                   ...         ...  \n",
       "39562         12                     1  2007-11-01  \n",
       "39573         21                     1  2007-11-01  \n",
       "39623         37                     1  2007-10-01  \n",
       "39666         29                     1  2007-08-01  \n",
       "39680          6                     1  2007-08-01  \n",
       "\n",
       "[18189 rows x 23 columns]"
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_df[features_df['funded_amnt_inv'] != features_df['funded_amnt'] ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "id": "f18cdb9b-ec69-418d-bbec-88d71595aa43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int_rate\n",
       "Low          9258\n",
       "High         9198\n",
       "Very High    9185\n",
       "Moderate     9159\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 455,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.qcut(features_df['int_rate'], q=4, precision =0, labels = ['Low', 'Moderate', 'High', 'Very High']).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "id": "dc088770-5b9a-44aa-8437-af1f72546c5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# features_df['int_rate'].plot.box()\n",
    "# features_df['int_rate'].describe()\n",
    "features_df['int_rate_group'] = pd.qcut(features_df['int_rate'], q=4, precision =0, labels = ['Low', 'Moderate', 'High', 'Very High'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "id": "d52c6081-cfc4-49df-81e4-68940d01e6b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_df['issue_date_month'] = features_df['issue_date'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "id": "c06a5f07-da24-4f2a-9e76-92f44232923b",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_df['issue_date_year'] = features_df['issue_date'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "id": "e18f9002-f7d2-4cab-865c-bd3a3153cb6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_df['annual_inc_group'] = pd.qcut(features_df['annual_inc']/1000, q=4, precision =0, labels = ['3-41K', '41-60K', '60-83K', '83-6000K'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "id": "85270b52-3618-4b65-95fa-f35bc0c2ee97",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "features_df['dti_group'] = pd.qcut(features_df['dti'], q=4, precision =0, labels = ['Low', 'Moderate', 'High', 'Very High'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "id": "5e648e7d-ae02-4d63-ab14-d05d8f354e9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_df['open_acc_group'] = pd.qcut(features_df['open_acc'], q=4, precision =0, labels = ['Low', 'Moderate', 'High', 'Very High'])\n",
    "features_df['total_acc_group'] = pd.qcut(features_df['total_acc'], q=4, precision =0, labels = ['Low', 'Moderate', 'High', 'Very High'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "id": "1eed9a94-51c0-4cd6-8bb5-2fd7eb771430",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_df['revol_util_group'] = pd.qcut(features_df['revol_util'], q=4, precision =0, labels = ['Low', 'Moderate', 'High', 'Very High'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "id": "16b43e6c-c8c4-494e-8f04-9b4139b9999a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pub_rec\n",
      "0    34871\n",
      "1     1875\n",
      "2       45\n",
      "3        7\n",
      "4        2\n",
      "Name: count, dtype: int64\n",
      "pub_rec_bankruptcies\n",
      "0    35266\n",
      "1     1529\n",
      "2        5\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# features_df[features_df.pub_rec_bankruptcies < features_df.pub_rec]\n",
    "print(features_df.pub_rec.value_counts())\n",
    "print(features_df.pub_rec_bankruptcies.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "id": "e35d607b-bc63-4956-b88b-f3859163e06d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['loan_amnt', 'funded_amnt', 'funded_amnt_inv', 'term', 'int_rate',\n",
       "       'installment', 'grade', 'sub_grade', 'emp_length', 'home_ownership',\n",
       "       'annual_inc', 'verification_status', 'issue_d', 'loan_status',\n",
       "       'purpose', 'addr_state', 'dti', 'open_acc', 'pub_rec', 'revol_util',\n",
       "       'total_acc', 'pub_rec_bankruptcies', 'issue_date', 'int_rate_group',\n",
       "       'issue_date_month', 'issue_date_year', 'annual_inc_group', 'dti_group',\n",
       "       'open_acc_group', 'total_acc_group', 'revol_util_group'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 402,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "id": "1faa7c99-d3e0-4f0b-b665-dbd0be4afb59",
   "metadata": {},
   "outputs": [],
   "source": [
    "column_names = {\t\n",
    "'loan_amnt'\t:\t'Loan Amount',\n",
    "'funded_amnt':'Funded Loan Amount',\n",
    "'installment':'Installment Amount',\n",
    "'annual_inc':'Borrower Annual Income',\n",
    "'purpose':'Purpose of Loan',\n",
    "'total_acc':'Total Credit Accounts',\n",
    "'issue_date_month':'Loan Funded Month',\n",
    "'open_acc_group':'Open Credit Accounts - Group',\n",
    "'funded_amnt_inv':'Funded Amount by Investor',\n",
    "'grade':'Grade',\n",
    "'verification_status':'Verification Status',\n",
    "'addr_state':'Address State of Borrower',\n",
    "'pub_rec_bankruptcies':'Public Record of Bankrupcies',\n",
    "'issue_date_year':'Loan Funded Year',\n",
    "'total_acc_group':'Total Credit Accounts - Group',\n",
    "'term':'Loan Term - Months',\n",
    "'sub_grade':'Sub-Grade',\n",
    "'issue_d':'Loan Funded Date - Short',\n",
    "'dti':'DTI',\n",
    "'issue_date':'Loan Funded Date',\n",
    "'annual_inc_group':'Borrower Annual Income - Group',\n",
    "'revol_util_group':'Revolving Line Utilization - Group',\n",
    "'int_rate':'Interest Rate',\n",
    "'emp_length':'Employee Tenure',\n",
    "'loan_status':'Loan Status',\n",
    "'open_acc':'Open Line of Credit',\n",
    "'int_rate_group':'Interest Rate - Group',\n",
    "'dti_group':'DTI - Group',\n",
    "'pub_rec':'Public Records',\n",
    "'revol_util':'Revolving Line Utilization',\n",
    "'home_ownership':'Home Ownership'\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "id": "7a905638-51a1-485f-b178-b0880f3c2383",
   "metadata": {},
   "outputs": [],
   "source": [
    "transformed_df = features_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "id": "7a583109-06ad-4fbe-b68f-6a4de0cadd9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "transformed_df.rename(columns = column_names, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "id": "33d97878-ba8a-48d7-937d-628363f3c872",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan Amount</th>\n",
       "      <th>Funded Loan Amount</th>\n",
       "      <th>Funded Amount by Investor</th>\n",
       "      <th>Loan Term - Months</th>\n",
       "      <th>Interest Rate</th>\n",
       "      <th>Installment Amount</th>\n",
       "      <th>Grade</th>\n",
       "      <th>Sub-Grade</th>\n",
       "      <th>Employee Tenure</th>\n",
       "      <th>Home Ownership</th>\n",
       "      <th>...</th>\n",
       "      <th>Public Record of Bankrupcies</th>\n",
       "      <th>Loan Funded Date</th>\n",
       "      <th>Interest Rate - Group</th>\n",
       "      <th>Loan Funded Month</th>\n",
       "      <th>Loan Funded Year</th>\n",
       "      <th>Borrower Annual Income - Group</th>\n",
       "      <th>DTI - Group</th>\n",
       "      <th>Open Credit Accounts - Group</th>\n",
       "      <th>Total Credit Accounts - Group</th>\n",
       "      <th>Revolving Line Utilization - Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>4975</td>\n",
       "      <td>36</td>\n",
       "      <td>10.65</td>\n",
       "      <td>162.87</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>10</td>\n",
       "      <td>RENT</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>12</td>\n",
       "      <td>2011</td>\n",
       "      <td>3-41K</td>\n",
       "      <td>Very High</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>Very High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2500</td>\n",
       "      <td>2500</td>\n",
       "      <td>2500</td>\n",
       "      <td>60</td>\n",
       "      <td>15.27</td>\n",
       "      <td>59.83</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>0</td>\n",
       "      <td>RENT</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>Very High</td>\n",
       "      <td>12</td>\n",
       "      <td>2011</td>\n",
       "      <td>3-41K</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2400</td>\n",
       "      <td>2400</td>\n",
       "      <td>2400</td>\n",
       "      <td>36</td>\n",
       "      <td>15.96</td>\n",
       "      <td>84.33</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>10</td>\n",
       "      <td>RENT</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>Very High</td>\n",
       "      <td>12</td>\n",
       "      <td>2011</td>\n",
       "      <td>3-41K</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>Very High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>36</td>\n",
       "      <td>13.49</td>\n",
       "      <td>339.31</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>10</td>\n",
       "      <td>RENT</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>High</td>\n",
       "      <td>12</td>\n",
       "      <td>2011</td>\n",
       "      <td>41-60K</td>\n",
       "      <td>Very High</td>\n",
       "      <td>High</td>\n",
       "      <td>Very High</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>36</td>\n",
       "      <td>7.90</td>\n",
       "      <td>156.46</td>\n",
       "      <td>A</td>\n",
       "      <td>A4</td>\n",
       "      <td>3</td>\n",
       "      <td>RENT</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>Low</td>\n",
       "      <td>12</td>\n",
       "      <td>2011</td>\n",
       "      <td>3-41K</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Low</td>\n",
       "      <td>Moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39562</th>\n",
       "      <td>4800</td>\n",
       "      <td>4800</td>\n",
       "      <td>1100</td>\n",
       "      <td>36</td>\n",
       "      <td>10.28</td>\n",
       "      <td>155.52</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>1</td>\n",
       "      <td>RENT</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2007-11-01</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>11</td>\n",
       "      <td>2007</td>\n",
       "      <td>3-41K</td>\n",
       "      <td>Low</td>\n",
       "      <td>High</td>\n",
       "      <td>Low</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39573</th>\n",
       "      <td>7000</td>\n",
       "      <td>7000</td>\n",
       "      <td>1000</td>\n",
       "      <td>36</td>\n",
       "      <td>10.59</td>\n",
       "      <td>227.82</td>\n",
       "      <td>C</td>\n",
       "      <td>C2</td>\n",
       "      <td>3</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2007-11-01</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>11</td>\n",
       "      <td>2007</td>\n",
       "      <td>60-83K</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>High</td>\n",
       "      <td>Very High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39623</th>\n",
       "      <td>9000</td>\n",
       "      <td>9000</td>\n",
       "      <td>700</td>\n",
       "      <td>36</td>\n",
       "      <td>12.49</td>\n",
       "      <td>301.04</td>\n",
       "      <td>D</td>\n",
       "      <td>D3</td>\n",
       "      <td>8</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2007-10-01</td>\n",
       "      <td>High</td>\n",
       "      <td>10</td>\n",
       "      <td>2007</td>\n",
       "      <td>3-41K</td>\n",
       "      <td>Low</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Very High</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39666</th>\n",
       "      <td>15450</td>\n",
       "      <td>15450</td>\n",
       "      <td>600</td>\n",
       "      <td>36</td>\n",
       "      <td>11.22</td>\n",
       "      <td>507.46</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>2</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2007-08-01</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>8</td>\n",
       "      <td>2007</td>\n",
       "      <td>3-41K</td>\n",
       "      <td>High</td>\n",
       "      <td>Very High</td>\n",
       "      <td>High</td>\n",
       "      <td>Moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39680</th>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>525</td>\n",
       "      <td>36</td>\n",
       "      <td>11.86</td>\n",
       "      <td>99.44</td>\n",
       "      <td>D</td>\n",
       "      <td>D1</td>\n",
       "      <td>2</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2007-08-01</td>\n",
       "      <td>High</td>\n",
       "      <td>8</td>\n",
       "      <td>2007</td>\n",
       "      <td>3-41K</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36800 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Loan Amount  Funded Loan Amount  Funded Amount by Investor  \\\n",
       "0             5000                5000                       4975   \n",
       "1             2500                2500                       2500   \n",
       "2             2400                2400                       2400   \n",
       "3            10000               10000                      10000   \n",
       "5             5000                5000                       5000   \n",
       "...            ...                 ...                        ...   \n",
       "39562         4800                4800                       1100   \n",
       "39573         7000                7000                       1000   \n",
       "39623         9000                9000                        700   \n",
       "39666        15450               15450                        600   \n",
       "39680         3000                3000                        525   \n",
       "\n",
       "       Loan Term - Months  Interest Rate  Installment Amount Grade Sub-Grade  \\\n",
       "0                      36          10.65              162.87     B        B2   \n",
       "1                      60          15.27               59.83     C        C4   \n",
       "2                      36          15.96               84.33     C        C5   \n",
       "3                      36          13.49              339.31     C        C1   \n",
       "5                      36           7.90              156.46     A        A4   \n",
       "...                   ...            ...                 ...   ...       ...   \n",
       "39562                  36          10.28              155.52     C        C1   \n",
       "39573                  36          10.59              227.82     C        C2   \n",
       "39623                  36          12.49              301.04     D        D3   \n",
       "39666                  36          11.22              507.46     C        C4   \n",
       "39680                  36          11.86               99.44     D        D1   \n",
       "\n",
       "       Employee Tenure Home Ownership  ...  Public Record of Bankrupcies  \\\n",
       "0                   10           RENT  ...                             0   \n",
       "1                    0           RENT  ...                             0   \n",
       "2                   10           RENT  ...                             0   \n",
       "3                   10           RENT  ...                             0   \n",
       "5                    3           RENT  ...                             0   \n",
       "...                ...            ...  ...                           ...   \n",
       "39562                1           RENT  ...                             1   \n",
       "39573                3       MORTGAGE  ...                             1   \n",
       "39623                8       MORTGAGE  ...                             1   \n",
       "39666                2       MORTGAGE  ...                             1   \n",
       "39680                2       MORTGAGE  ...                             1   \n",
       "\n",
       "      Loan Funded Date Interest Rate - Group Loan Funded Month  \\\n",
       "0           2011-12-01              Moderate                12   \n",
       "1           2011-12-01             Very High                12   \n",
       "2           2011-12-01             Very High                12   \n",
       "3           2011-12-01                  High                12   \n",
       "5           2011-12-01                   Low                12   \n",
       "...                ...                   ...               ...   \n",
       "39562       2007-11-01              Moderate                11   \n",
       "39573       2007-11-01              Moderate                11   \n",
       "39623       2007-10-01                  High                10   \n",
       "39666       2007-08-01              Moderate                 8   \n",
       "39680       2007-08-01                  High                 8   \n",
       "\n",
       "      Loan Funded Year Borrower Annual Income - Group  DTI - Group  \\\n",
       "0                 2011                          3-41K    Very High   \n",
       "1                 2011                          3-41K          Low   \n",
       "2                 2011                          3-41K     Moderate   \n",
       "3                 2011                         41-60K    Very High   \n",
       "5                 2011                          3-41K     Moderate   \n",
       "...                ...                            ...          ...   \n",
       "39562             2007                          3-41K          Low   \n",
       "39573             2007                         60-83K     Moderate   \n",
       "39623             2007                          3-41K          Low   \n",
       "39666             2007                          3-41K         High   \n",
       "39680             2007                          3-41K          Low   \n",
       "\n",
       "       Open Credit Accounts - Group  Total Credit Accounts - Group  \\\n",
       "0                               Low                            Low   \n",
       "1                               Low                            Low   \n",
       "2                               Low                            Low   \n",
       "3                              High                      Very High   \n",
       "5                          Moderate                            Low   \n",
       "...                             ...                            ...   \n",
       "39562                          High                            Low   \n",
       "39573                      Moderate                           High   \n",
       "39623                      Moderate                      Very High   \n",
       "39666                     Very High                           High   \n",
       "39680                           Low                            Low   \n",
       "\n",
       "       Revolving Line Utilization - Group  \n",
       "0                               Very High  \n",
       "1                                     Low  \n",
       "2                               Very High  \n",
       "3                                     Low  \n",
       "5                                Moderate  \n",
       "...                                   ...  \n",
       "39562                                High  \n",
       "39573                           Very High  \n",
       "39623                                High  \n",
       "39666                            Moderate  \n",
       "39680                                High  \n",
       "\n",
       "[36800 rows x 31 columns]"
      ]
     },
     "execution_count": 406,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformed_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "id": "64457a38-7475-420e-87d9-e991a079fc6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "transformed_df['Loan Status'].value_counts().plot.pie(autopct='%1.0f%%', \n",
    "                                                      explode = (0, 0.1), \n",
    "                                                      shadow =True\n",
    "                                                      )\n",
    "plt.title('Loan Distribution by Status')\n",
    "plt.legend()\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "id": "06903d6b-cc9a-46cb-865e-0df3a20d373e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "transformed_df['Loan Funded Year'].value_counts().sort_index().plot.bar()\n",
    "plt.title('Loan Distribution by Year')\n",
    "plt.ylabel('Count of Loans')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "id": "52f27360-7bf5-47a0-b041-fd23ec0c40da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# transformed_df['Loan Funded Month'].value_counts().sort_index().plot.bar()\n",
    "# plt.title('Loan Distribution by Year')\n",
    "# plt.ylabel('Count of Loans')\n",
    "# plt.show()\n",
    "\n",
    "# figure, axes = plt.subplots(1,3)\n",
    "\n",
    "# transformed_df['Loan Funded Year'].value_counts().sort_index().plot.bar(ax=axes[0])\n",
    "# transformed_df['Loan Funded Month'].value_counts().sort_index().plot.bar(ax=axes[1])\n",
    "# transformed_df['Loan Funded Month'].value_counts().sort_index().plot.bar(ax=axes[2])\n",
    "\n",
    "# plt.show()\n",
    "\n",
    "fig, axs = plt.subplots(nrows=1, ncols=3, figsize=(18, 6))\n",
    "\n",
    "# Plot each metric on a different subplot\n",
    "transformed_df['Loan Funded Month'].value_counts().sort_index().plot(kind='bar', ax=axs[0], color='#1f77b4', title='Interest Rate 2023')\n",
    "transformed_df['Loan Funded Year'].value_counts().sort_index().plot(kind='bar', ax=axs[1], color='#ff7f0e', title='Interest Rate 2024')\n",
    "transformed_df['Loan Status'].value_counts().sort_index().plot(kind='bar', ax=axs[2], color='#2ca02c', title='Loan Amount')\n",
    "\n",
    "# Set labels\n",
    "# for ax in axs:\n",
    "#     ax.set_xlabel('Loan Type')\n",
    "#     ax.set_ylabel('Value')\n",
    "#     ax.set_xticklabels(transformed_df.index, rotation=0)\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "937caa17-d9b6-4ad8-bfed-ca40835c65bd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}