Lending_Club_Loan.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lending Club Project\n",
    "\n",
    "(Using Keras API)\n",
    "\n",
    "LendingClub is a US peer-to-peer lending company, headquartered in San Francisco, California. It was the first peer-to-peer lender to register its offerings as securities with the Securities and Exchange Commission (SEC), and to offer loan trading on a secondary market. LendingClub is the world's largest peer-to-peer lending platform.\n",
    "\n",
    "Given historical data on loans given out with information on whether or not the borrower defaulted (charge-off), can we build a model thatcan predict wether or nor a borrower will pay back their loan? This way in the future when we get a new potential customer we can assess whether or not they are likely to pay back the loan.\n",
    "\n",
    "We will be using a subset of the LendingClub DataSet obtained from Kaggle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_info = pd.read_csv('DATA/lending_club_info.csv', index_col='LoanStatNew')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Revolving line utilization rate, or the amount of credit the borrower is using relative to all available revolving credit.\n"
     ]
    }
   ],
   "source": [
    "print(data_info.loc['revol_util']['Description'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def feat_info(col_name):\n",
    "    print(data_info.loc[col_name]['Description'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of mortgage accounts.\n"
     ]
    }
   ],
   "source": [
    "feat_info('mort_acc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('DATA/lending_club_loan_two.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 396030 entries, 0 to 396029\n",
      "Data columns (total 27 columns):\n",
      " #   Column                Non-Null Count   Dtype  \n",
      "---  ------                --------------   -----  \n",
      " 0   loan_amnt             396030 non-null  float64\n",
      " 1   term                  396030 non-null  object \n",
      " 2   int_rate              396030 non-null  float64\n",
      " 3   installment           396030 non-null  float64\n",
      " 4   grade                 396030 non-null  object \n",
      " 5   sub_grade             396030 non-null  object \n",
      " 6   emp_title             373103 non-null  object \n",
      " 7   emp_length            377729 non-null  object \n",
      " 8   home_ownership        396030 non-null  object \n",
      " 9   annual_inc            396030 non-null  float64\n",
      " 10  verification_status   396030 non-null  object \n",
      " 11  issue_d               396030 non-null  object \n",
      " 12  loan_status           396030 non-null  object \n",
      " 13  purpose               396030 non-null  object \n",
      " 14  title                 394275 non-null  object \n",
      " 15  dti                   396030 non-null  float64\n",
      " 16  earliest_cr_line      396030 non-null  object \n",
      " 17  open_acc              396030 non-null  float64\n",
      " 18  pub_rec               396030 non-null  float64\n",
      " 19  revol_bal             396030 non-null  float64\n",
      " 20  revol_util            395754 non-null  float64\n",
      " 21  total_acc             396030 non-null  float64\n",
      " 22  initial_list_status   396030 non-null  object \n",
      " 23  application_type      396030 non-null  object \n",
      " 24  mort_acc              358235 non-null  float64\n",
      " 25  pub_rec_bankruptcies  395495 non-null  float64\n",
      " 26  address               396030 non-null  object \n",
      "dtypes: float64(12), object(15)\n",
      "memory usage: 81.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b329b02648>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TASK: Since we will be attempting to predict loan_status, create a countplot as shown below\n",
    "\n",
    "sns.countplot(data=df, x='loan_status')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 45000)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TASK: Create a histogram of the loan_amnt column\n",
    "\n",
    "plt.figure(figsize=(12,4))\n",
    "sns.distplot(df['loan_amnt'], kde=False,bins=40)\n",
    "plt.xlim(0,45000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>mort_acc</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>loan_amnt</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.168921</td>\n",
       "      <td>0.953929</td>\n",
       "      <td>0.336887</td>\n",
       "      <td>0.016636</td>\n",
       "      <td>0.198556</td>\n",
       "      <td>-0.077779</td>\n",
       "      <td>0.328320</td>\n",
       "      <td>0.099911</td>\n",
       "      <td>0.223886</td>\n",
       "      <td>0.222315</td>\n",
       "      <td>-0.106539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>int_rate</th>\n",
       "      <td>0.168921</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.162758</td>\n",
       "      <td>-0.056771</td>\n",
       "      <td>0.079038</td>\n",
       "      <td>0.011649</td>\n",
       "      <td>0.060986</td>\n",
       "      <td>-0.011280</td>\n",
       "      <td>0.293659</td>\n",
       "      <td>-0.036404</td>\n",
       "      <td>-0.082583</td>\n",
       "      <td>0.057450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>installment</th>\n",
       "      <td>0.953929</td>\n",
       "      <td>0.162758</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.330381</td>\n",
       "      <td>0.015786</td>\n",
       "      <td>0.188973</td>\n",
       "      <td>-0.067892</td>\n",
       "      <td>0.316455</td>\n",
       "      <td>0.123915</td>\n",
       "      <td>0.202430</td>\n",
       "      <td>0.193694</td>\n",
       "      <td>-0.098628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>annual_inc</th>\n",
       "      <td>0.336887</td>\n",
       "      <td>-0.056771</td>\n",
       "      <td>0.330381</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.081685</td>\n",
       "      <td>0.136150</td>\n",
       "      <td>-0.013720</td>\n",
       "      <td>0.299773</td>\n",
       "      <td>0.027871</td>\n",
       "      <td>0.193023</td>\n",
       "      <td>0.236320</td>\n",
       "      <td>-0.050162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dti</th>\n",
       "      <td>0.016636</td>\n",
       "      <td>0.079038</td>\n",
       "      <td>0.015786</td>\n",
       "      <td>-0.081685</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.136181</td>\n",
       "      <td>-0.017639</td>\n",
       "      <td>0.063571</td>\n",
       "      <td>0.088375</td>\n",
       "      <td>0.102128</td>\n",
       "      <td>-0.025439</td>\n",
       "      <td>-0.014558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open_acc</th>\n",
       "      <td>0.198556</td>\n",
       "      <td>0.011649</td>\n",
       "      <td>0.188973</td>\n",
       "      <td>0.136150</td>\n",
       "      <td>0.136181</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.018392</td>\n",
       "      <td>0.221192</td>\n",
       "      <td>-0.131420</td>\n",
       "      <td>0.680728</td>\n",
       "      <td>0.109205</td>\n",
       "      <td>-0.027732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pub_rec</th>\n",
       "      <td>-0.077779</td>\n",
       "      <td>0.060986</td>\n",
       "      <td>-0.067892</td>\n",
       "      <td>-0.013720</td>\n",
       "      <td>-0.017639</td>\n",
       "      <td>-0.018392</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.101664</td>\n",
       "      <td>-0.075910</td>\n",
       "      <td>0.019723</td>\n",
       "      <td>0.011552</td>\n",
       "      <td>0.699408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revol_bal</th>\n",
       "      <td>0.328320</td>\n",
       "      <td>-0.011280</td>\n",
       "      <td>0.316455</td>\n",
       "      <td>0.299773</td>\n",
       "      <td>0.063571</td>\n",
       "      <td>0.221192</td>\n",
       "      <td>-0.101664</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.226346</td>\n",
       "      <td>0.191616</td>\n",
       "      <td>0.194925</td>\n",
       "      <td>-0.124532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revol_util</th>\n",
       "      <td>0.099911</td>\n",
       "      <td>0.293659</td>\n",
       "      <td>0.123915</td>\n",
       "      <td>0.027871</td>\n",
       "      <td>0.088375</td>\n",
       "      <td>-0.131420</td>\n",
       "      <td>-0.075910</td>\n",
       "      <td>0.226346</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.104273</td>\n",
       "      <td>0.007514</td>\n",
       "      <td>-0.086751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_acc</th>\n",
       "      <td>0.223886</td>\n",
       "      <td>-0.036404</td>\n",
       "      <td>0.202430</td>\n",
       "      <td>0.193023</td>\n",
       "      <td>0.102128</td>\n",
       "      <td>0.680728</td>\n",
       "      <td>0.019723</td>\n",
       "      <td>0.191616</td>\n",
       "      <td>-0.104273</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.381072</td>\n",
       "      <td>0.042035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mort_acc</th>\n",
       "      <td>0.222315</td>\n",
       "      <td>-0.082583</td>\n",
       "      <td>0.193694</td>\n",
       "      <td>0.236320</td>\n",
       "      <td>-0.025439</td>\n",
       "      <td>0.109205</td>\n",
       "      <td>0.011552</td>\n",
       "      <td>0.194925</td>\n",
       "      <td>0.007514</td>\n",
       "      <td>0.381072</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.027239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <td>-0.106539</td>\n",
       "      <td>0.057450</td>\n",
       "      <td>-0.098628</td>\n",
       "      <td>-0.050162</td>\n",
       "      <td>-0.014558</td>\n",
       "      <td>-0.027732</td>\n",
       "      <td>0.699408</td>\n",
       "      <td>-0.124532</td>\n",
       "      <td>-0.086751</td>\n",
       "      <td>0.042035</td>\n",
       "      <td>0.027239</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      loan_amnt  int_rate  installment  annual_inc       dti  \\\n",
       "loan_amnt              1.000000  0.168921     0.953929    0.336887  0.016636   \n",
       "int_rate               0.168921  1.000000     0.162758   -0.056771  0.079038   \n",
       "installment            0.953929  0.162758     1.000000    0.330381  0.015786   \n",
       "annual_inc             0.336887 -0.056771     0.330381    1.000000 -0.081685   \n",
       "dti                    0.016636  0.079038     0.015786   -0.081685  1.000000   \n",
       "open_acc               0.198556  0.011649     0.188973    0.136150  0.136181   \n",
       "pub_rec               -0.077779  0.060986    -0.067892   -0.013720 -0.017639   \n",
       "revol_bal              0.328320 -0.011280     0.316455    0.299773  0.063571   \n",
       "revol_util             0.099911  0.293659     0.123915    0.027871  0.088375   \n",
       "total_acc              0.223886 -0.036404     0.202430    0.193023  0.102128   \n",
       "mort_acc               0.222315 -0.082583     0.193694    0.236320 -0.025439   \n",
       "pub_rec_bankruptcies  -0.106539  0.057450    -0.098628   -0.050162 -0.014558   \n",
       "\n",
       "                      open_acc   pub_rec  revol_bal  revol_util  total_acc  \\\n",
       "loan_amnt             0.198556 -0.077779   0.328320    0.099911   0.223886   \n",
       "int_rate              0.011649  0.060986  -0.011280    0.293659  -0.036404   \n",
       "installment           0.188973 -0.067892   0.316455    0.123915   0.202430   \n",
       "annual_inc            0.136150 -0.013720   0.299773    0.027871   0.193023   \n",
       "dti                   0.136181 -0.017639   0.063571    0.088375   0.102128   \n",
       "open_acc              1.000000 -0.018392   0.221192   -0.131420   0.680728   \n",
       "pub_rec              -0.018392  1.000000  -0.101664   -0.075910   0.019723   \n",
       "revol_bal             0.221192 -0.101664   1.000000    0.226346   0.191616   \n",
       "revol_util           -0.131420 -0.075910   0.226346    1.000000  -0.104273   \n",
       "total_acc             0.680728  0.019723   0.191616   -0.104273   1.000000   \n",
       "mort_acc              0.109205  0.011552   0.194925    0.007514   0.381072   \n",
       "pub_rec_bankruptcies -0.027732  0.699408  -0.124532   -0.086751   0.042035   \n",
       "\n",
       "                      mort_acc  pub_rec_bankruptcies  \n",
       "loan_amnt             0.222315             -0.106539  \n",
       "int_rate             -0.082583              0.057450  \n",
       "installment           0.193694             -0.098628  \n",
       "annual_inc            0.236320             -0.050162  \n",
       "dti                  -0.025439             -0.014558  \n",
       "open_acc              0.109205             -0.027732  \n",
       "pub_rec               0.011552              0.699408  \n",
       "revol_bal             0.194925             -0.124532  \n",
       "revol_util            0.007514             -0.086751  \n",
       "total_acc             0.381072              0.042035  \n",
       "mort_acc              1.000000              0.027239  \n",
       "pub_rec_bankruptcies  0.027239              1.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TASK: Let's explore correlation between the continuous feature variables.\n",
    "# Calculate the correlation between all continuous numeric variables using .corr() method\n",
    "\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 0)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TASK: Visualize this using a heatmap.\n",
    "# Depending on your version of matplotlib, you may need to manually adjust the heatmap\n",
    "\n",
    "plt.figure(figsize=(12,7))\n",
    "sns.heatmap(df.corr(), annot=True, cmap='viridis')\n",
    "plt.ylim(10, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The monthly payment owed by the borrower if the loan originates.\n"
     ]
    }
   ],
   "source": [
    "# TASK: You should have noticed almost perfect correlation with the \"installment\" feature.\n",
    "# Explore this feature further. Print out their descriptions and perform a scatterplot between them.\n",
    "# Does this relationship make sense to you? Do you think there is duplicate information here?\n",
    "\n",
    "feat_info('installment')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The listed amount of the loan applied for by the borrower. If at some point in time, the credit department reduces the loan amount, then it will be reflected in this value.\n"
     ]
    }
   ],
   "source": [
    "feat_info('loan_amnt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b32e1e94c8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=df, x='installment', y='loan_amnt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b32e3d4ec8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TASK: Create a boxplot showing the relationship between the loan_status and the Loan Amount\n",
    "\n",
    "sns.boxplot(data=df, x='loan_status', y='loan_amnt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Charged Off</th>\n",
       "      <td>77673.0</td>\n",
       "      <td>15126.300967</td>\n",
       "      <td>8505.090557</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>8525.0</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>40000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fully Paid</th>\n",
       "      <td>318357.0</td>\n",
       "      <td>13866.878771</td>\n",
       "      <td>8302.319699</td>\n",
       "      <td>500.0</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>19225.0</td>\n",
       "      <td>40000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count          mean          std     min     25%      50%  \\\n",
       "loan_status                                                                 \n",
       "Charged Off   77673.0  15126.300967  8505.090557  1000.0  8525.0  14000.0   \n",
       "Fully Paid   318357.0  13866.878771  8302.319699   500.0  7500.0  12000.0   \n",
       "\n",
       "                 75%      max  \n",
       "loan_status                    \n",
       "Charged Off  20000.0  40000.0  \n",
       "Fully Paid   19225.0  40000.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TASK: Calculate the summary statistics for the loan amount, grouped by the loan_status\n",
    "\n",
    "df.groupby('loan_status')['loan_amnt'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A', 'B', 'C', 'D', 'E', 'F', 'G']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TASK: Let's explore the Grade and SubGrade columns that LendingClub attributes to the loans.\n",
    "# What are the unique possible grades and subgrades?\n",
    "\n",
    "sorted(df['grade'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A1',\n",
       " 'A2',\n",
       " 'A3',\n",
       " 'A4',\n",
       " 'A5',\n",
       " 'B1',\n",
       " 'B2',\n",
       " 'B3',\n",
       " 'B4',\n",
       " 'B5',\n",
       " 'C1',\n",
       " 'C2',\n",
       " 'C3',\n",
       " 'C4',\n",
       " 'C5',\n",
       " 'D1',\n",
       " 'D2',\n",
       " 'D3',\n",
       " 'D4',\n",
       " 'D5',\n",
       " 'E1',\n",
       " 'E2',\n",
       " 'E3',\n",
       " 'E4',\n",
       " 'E5',\n",
       " 'F1',\n",
       " 'F2',\n",
       " 'F3',\n",
       " 'F4',\n",
       " 'F5',\n",
       " 'G1',\n",
       " 'G2',\n",
       " 'G3',\n",
       " 'G4',\n",
       " 'G5']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(df['sub_grade'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b32ea7eb88>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TASK: Create a countplot per grade. Set the hue to the loan_status label\n",
    "\n",
    "sns.countplot(data=df, x='grade', hue='loan_status')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b32eb08d48>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TASK: Display a count plot per subgrade. You may need to resize for this plot and reorder the x axis.\n",
    "# Explore both all loans made per subgrade as well being separated based on the loan_status\n",
    "\n",
    "plt.figure(figsize=(12,4))\n",
    "subgrade_order = sorted(df['sub_grade'].unique())\n",
    "sns.countplot(data=df, x='sub_grade', order = subgrade_order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b32ea81f48>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "subgrade_order = sorted(df['sub_grade'].unique())\n",
    "sns.countplot(data=df, x='sub_grade', order = subgrade_order, hue='loan_status')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b32b796b48>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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55BKuvvpqunXrVn97v5UrVza4Tc+ePZk8eTKVlZVUVFQwbNiwRvd/3XXXcfbZZ3PttdfyhS98gY9//OMAjB49mhdffJHPfvazQN0a6l/84hcMGTKEM888k6qqKj71qU/VX5T5YWPGjGHlypUcccQRRAQ9evTgF7/4Bfvuu+9HnJGmxbarPtuT6urqtHDhwgY/G3rZbSWro2bGOSUbS5IktX8vvvgiAwYMKHcZudm4cSN77LEHEcHs2bO5/fbbmTt3brnLqtfQ/EdETUqpuqH+ntGWJElSq1BTU8PUqVNJKdGzZ09mzpxZ7pI+EoO2JEmSWoWjjz6a559/vtxltBgvhpQkSWpD2uOy37ZgV+bdoC1JktRGdOvWjXXr1hm2SyylxLp16+jWrdtObefSEUmSpDaiX79+rFixgrVr15a7lA6nW7du9OvXb6e2MWhLkiS1EV27dqV///7lLkNFcumIJEmSlAODtiRJkpQDg7YkSZKUA4O2JEmSlAODtiRJkpQDg7YkSZKUA4O2JEmSlAODtiRJkpQDg7YkSZKUA4O2JEmSlAMfwd6BDb3stpKNVTPjnJKNJUmS1Bp4RluSJEnKgUFbkiRJyoFBW5IkScqBQVuSJEnKQW5BOyJmRsSaiHihoO3KiFgZEbXZ10kFn30rIpZFxEsRcUJB++eztmUR8c286pUkSZJaUp5ntG8BPt9A+/dTSlXZ14MAETEQOAsYlG3z44joHBGdgeuBE4GBwPisryRJktSq5XZ7v5TS/IioKLL7qcDslNK7wJ8iYhkwPPtsWUrpjwARMTvr+/sWLleSJElqUeVYoz01In6XLS35RNa2P/BaQZ8VWVtj7TuIiAsiYmFELFy7dm0edUuSJElFK3XQ/gnwaaAKWAVcm7VHA31TE+07NqZ0U0qpOqVU3bt375aoVZIkSdplJX0yZEpp9bbXEXEz8ED2dgXwyYKu/YDXs9eNtUuSJEmtVknPaEfEvgVvxwHb7khyH3BWROweEf2BA4FngQXAgRHRPyJ2o+6CyftKWbMkSZK0K3I7ox0RtwPHAr0iYgVwBXBsRFRRt/xjOfC3ACmlJRFxJ3UXOW4GpqSUtmT7mQr8GugMzEwpLcmrZkmSJKml5HnXkfENNP+sif7fBr7dQPuDwIMtWJokSZKUO58MKUmSJOXAoC1JkiTlwKAtSZIk5cCgLUmSJOXAoC1JkiTlwKAtSZIk5cCgLUmSJOWgpI9g72henV5ZsrEOmLa4ZGNJkiSpeZ7RliRJknJg0JYkSZJyYNCWJEmScmDQliRJknJg0JYkSZJyYNCWJEmScmDQliRJknLgfbQlYOhlt5VsrJoZ55RsLEmSVD6e0ZYkSZJyYNCWJEmSclBU0I6IR4ppkyRJklSnyTXaEdEN+BjQKyI+AUT20V7AfjnXJkmSJLVZzV0M+bfAJdSF6hq2B+3/Aa7PsS5JkiSpTWsyaKeUfgD8ICIuSin9W4lqkiRJktq8om7vl1L6t4g4Aqgo3CalVLp7okmSJEltSFFBOyL+Hfg0UAtsyZoTYNCWJEmSGlDsA2uqgYEppZRnMZIkSVJ7Uex9tF8A9smzEEmSJKk9KfaMdi/g9xHxLPDutsaU0phcqlK78+r0ypKNdcC0xSUbS5IkqTHFBu0r8yxCkiRJam+KvevI43kXIkmSJLUnxd515C3q7jICsBvQFXg7pbRXXoVJkiRJbVmxZ7R7FL6PiLHA8FwqkiRJktqBYu868gEppXuBUS1ciyRJktRuFLt05IsFbztRd19t76ktSZIkNaLYu46cUvB6M7AcOLXFq5EkSZLaiWLXaJ+XdyGSJElSe1LUGu2I6BcRcyJiTUSsjoi7I6Jf3sVJkiRJbVWxF0P+HLgP2A/YH7g/a5MkSZLUgGKDdu+U0s9TSpuzr1uA3jnWJUmSJLVpxQbtNyLi7IjonH2dDazLszBJkiSpLSs2aH8F+BLwZ2AVcDrQ5AWSETEzW9P9QkHb30TEwxGxNPv+iaw9IuKHEbEsIn4XEUMKtpmU9V8aEZN29geUJEmSyqHYoH0VMCml1Dul1Ie64H1lM9vcAnz+Q23fBB5JKR0IPJK9BzgRODD7ugD4CdQFc+AKYAR1T6K8Yls4lyRJklqzYoP2ISmlv257k1L6C3BYUxuklOYDf/lQ86nArdnrW4GxBe23pTq/AXpGxL7ACcDDKaW/ZOM/zI7hXZIkSWp1ig3anQrPJGdnmot92E2hvimlVQDZ9z5Z+/7AawX9VmRtjbXvICIuiIiFEbFw7dq1u1CaJEmS1HKKDcvXAk9HxF3UPXr9S8C3W7COaKAtNdG+Y2NKNwE3AVRXV/t4eLVar06vLNlYB0xbXLKxJEnSBxV1RjuldBtwGrAaWAt8MaX077sw3upsSQjZ9zVZ+wrgkwX9+gGvN9EuSZIktWpFL/9IKf0e+P1HHO8+YBJwdfZ9bkH71IiYTd2Fj+tTSqsi4tfAvxQsWxkNfOsj1iCpCUMvu61kY9XMOKdkY0mSVGq7ss66KBFxO3As0CsiVlB395CrgTsj4nzgVeCMrPuDwEnAMmAj2a0DU0p/iYirgAVZv+nZhZiSJElSq5Zb0E4pjW/ko+Ma6JuAKY3sZyYwswVLkyRJknJX7F1HJEmSJO0Eg7YkSZKUA4O2JEmSlAODtiRJkpQDg7YkSZKUA4O2JEmSlAODtiRJkpQDg7YkSZKUA4O2JEmSlAODtiRJkpQDg7YkSZKUA4O2JEmSlIMu5S5AUsf16vTKko11wLTFJRtLkiTwjLYkSZKUC4O2JEmSlAODtiRJkpQDg7YkSZKUA4O2JEmSlAODtiRJkpQDg7YkSZKUA4O2JEmSlAMfWCNJjRh62W0lG6tmxjklG0uSVBqe0ZYkSZJyYNCWJEmScmDQliRJknLgGm1JagVenV5ZsrEOmLa4ZGNJUkfmGW1JkiQpBwZtSZIkKQcGbUmSJCkHBm1JkiQpBwZtSZIkKQcGbUmSJCkHBm1JkiQpBwZtSZIkKQcGbUmSJCkHPhlSktSsoZfdVrKxamacU7KxJClPntGWJEmScmDQliRJknJg0JYkSZJyUJagHRHLI2JxRNRGxMKs7W8i4uGIWJp9/0TWHhHxw4hYFhG/i4gh5ahZkiRJ2hnlPKP9uZRSVUqpOnv/TeCRlNKBwCPZe4ATgQOzrwuAn5S8UkmSJGkntaalI6cCt2avbwXGFrTflur8BugZEfuWo0BJkiSpWOUK2gl4KCJqIuKCrK1vSmkVQPa9T9a+P/BawbYrsrYPiIgLImJhRCxcu3ZtjqVLkiRJzSvXfbSPTCm9HhF9gIcj4g9N9I0G2tIODSndBNwEUF1dvcPnkqS24dXplSUb64Bpi0s2lqSOpyxntFNKr2ff1wBzgOHA6m1LQrLva7LuK4BPFmzeD3i9dNVKkiRJO6/kQTsi9oyIHtteA6OBF4D7gElZt0nA3Oz1fcA52d1HDgfWb1tiIkmSJLVW5Vg60heYExHbxv9lSuk/I2IBcGdEnA+8CpyR9X8QOAlYBmwEzit9yZIkSdLOKXnQTin9ETi0gfZ1wHENtCdgSglKkyRJklpMa7q9nyRJktRuGLQlSZKkHBi0JUmSpBwYtCVJkqQcGLQlSZKkHJTryZCSJLVJQy+7rWRj1cw4p2RjSWp5ntGWJEmScuAZbUmSWqlXp1eWZJwDpi0uyThSR+MZbUmSJCkHBm1JkiQpBwZtSZIkKQcGbUmSJCkHBm1JkiQpBwZtSZIkKQcGbUmSJCkH3kdbkiS1et5TXG2RQVuSJO2SUj6Ofk6Pkg0ltRiXjkiSJEk5MGhLkiRJOTBoS5IkSTkwaEuSJEk5MGhLkiRJOfCuI5IkSR9RKe/AUjPjnJKNpY/GM9qSJElSDgzakiRJUg4M2pIkSVIOXKMtSZLUhvg4+rbDoC1JkqQWU8oLQ+f0mFGScXb1fzpcOiJJkiTlwKAtSZIk5cCgLUmSJOXAoC1JkiTlwKAtSZIk5cCgLUmSJOXAoC1JkiTlwKAtSZIk5cCgLUmSJOXAoC1JkiTlwKAtSZIk5aDNBO2I+HxEvBQRyyLim+WuR5IkSWpKmwjaEdEZuB44ERgIjI+IgeWtSpIkSWpcmwjawHBgWUrpjyml94DZwKllrkmSJElqVKSUyl1DsyLidODzKaWvZu8nAiNSSlML+lwAXJC9PQh4qeSF7qgX8Ea5i2glnIvtnIvtnIvtnIvtnIvtnIvtnIvtnIvtWsNcfCql1LuhD7qUupJdFA20feD/EFJKNwE3laac4kTEwpRSdbnraA2ci+2ci+2ci+2ci+2ci+2ci+2ci+2ci+1a+1y0laUjK4BPFrzvB7xeplokSZKkZrWVoL0AODAi+kfEbsBZwH1lrkmSJElqVJtYOpJS2hwRU4FfA52BmSmlJWUuqxitailLmTkX2zkX2zkX2zkX2zkX2zkX2zkX2zkX27XquWgTF0NKkiRJbU1bWToiSZIktSkGbUmSJCkHBu0WEhFbIqK24KsiIvaOiHkRsSEiflTuGkulkbn4PxFRExGLs++jyl1nKTQyF8ML3j8fEePKXWcpNDQXBZ8dkP07ubR8FZZOI78XFRHxTkHbDeWuM28R0TcifhkRf8yOC89ExLiOeOxsYi463LGzibnocMfOxuai4PMOc+xs4veiVR8728TFkG3EOymlqsKGiNgT+CdgcPbVUTQ0F58ATkkpvR4Rg6m7sHX/slRXWg3NxRqgOrvId1/g+Yi4P6W0uTwllswOc1Hg+8B/lLKYMmvo96ICeLmJOWpXIiKAe4FbU0pfzto+BYwBNtGBjp3NzMWTdKBjZzNz8Ws60LGzmbnYpkMcO5uZi+doxcdOg3aOUkpvA09GxP8udy3lllJ6ruDtEqBbROyeUnq3XDWVS0ppY8Hbbnzo4UsdTUSMBf4IvF3uWlRSo4D3Ukr1Z59SSq8A/5a97UjHzubmYpuOcOwsdi46wrGzybnoYMfORuei8K+jrZFLR1rOHgV/tphT7mLKrLm5OA14rh3/h6JQg3MRESMiYgmwGLiwvZ6R+ZAd5iL7q883gH8ub2kl19i/kf4R8VxEPB4RR5etutIYBCwqdxGtRLFz0RGOnU3ORQc7djY6Fx3w2Nncv5FWe+z0jHbLaerP4h1No3MREYOA7wCjS1tS2TQ4Fyml3wKDImIAcGtE/EdKaVPpyyuphubin4Hvp5Q21P1lsMNoaC5WAQeklNZFxFDg3ogYlFL6nzLUV3IRcT1wFHVnrYaVu55yamguOuCxE9hxLjrosRP44FwAj9Mxj53ADnNxFK342GnQVslERD9gDnBOSunlctfTGqSUXoyIt6lbh7qw3PWUwQjg9Ii4BugJbI2ITSmlDnEBXKHsLOW72euaiHgZ+Azt9/diCXVnaAFIKU2JiF6035+3KU3ORQc7dhb1e9FBjp1NzUVHO3Y2Ohet/djp0hGVRET0BH4FfCul9FS56ymniOgfEV2y158CDgKWl7WoMkkpHZ1SqkgpVQDXAf/Sjv9D0aSI6B0RnbPX/ws4kLr1l+3Vo9StN/67graPlauYMmt0LjrgsbOpuehox85G56IDHjub+r1o1cdOnwzZQiJiQ0qpewPty4G9gN2AN4HRKaXfl7i8kmpoLiLi/wLfApYWNI9OKa0paXEl1shcTAS+CbwPbAWmp5TuLUd9pdTYv5GCz68ENqSUvlu6qsqjkd+L04DpwGZgC3BFSun+ctRXKtmdI75P3dm5tdRd1HVDSumOjnbsbGwuqAsNHerY2cRc7EYHO3Y29W+koM+VdIBjZxO/F5tpxcdOg7YkSZKUA5eOSJIkSTkwaEuSJEk5MGhLkiRJOTBoS5IkSTkwaEuSJEk5MGhLkiRJOTBoS1I7ExFXRsSlZRx/Q7nGlqTWxKAtSWrWtievSZKKZ9CWpDYgIvaMiF9FxPMR8UJEnBkRyyOiV/Z5dUQ8VrDJoRHxaEQsjYjJTey3U0T8OCKWRMQDEfFgRJyefbY8IqZFxJPAGRExOSIWZDXcHRGFj8Z+Jvvsqg/t/7Ks/XcR8c8tPjGS1IoZtCWpbfg88HpK6dCU0mDgP5vpfwjwBeCzwLSI2K+Rfl8EKoBK4KtZ/0KbUkpHpZRmA/eklF2jcswAAAHjSURBVIallA4FXgTOz/r8APhJSmkY8OdtG0bEaOoeIT4cqAKGRsTIon5aSWoHDNqS1DYsBo6PiO9ExNEppfXN9J+bUnonpfQGMI+6sNuQo4D/l1LamlL6c9a30B0FrwdHxBMRsRiYAAzK2o8Ebs9e/3tB/9HZ13PAIuBg6oK3JHUIXcpdgCSpeSml/46IocBJwL9GxEPAZrafMOn24U2aeb9NNDP02wWvbwHGppSej4hzgWOb2X8A/5pSurGZMSSpXfKMtiS1AdnSj40ppV8A3wWGAMuBoVmX0z60yakR0S0i9qYuEC9oZNdPAqdla7X78sHw/GE9gFUR0ZW6M9rbPAWclb0ubP818JWI6J79DPtHRJ8m9i9J7YpntCWpbagEZkTEVuB94O+APYCfRcTlwG8/1P9Z4FfAAcBVKaXXG9nv3cBxwAvAf2f7aWxZyj9ln79C3VKWHln7xcAvI+LibH8ApJQeiogBwDMRAbABOBtYU+TPLEltWqTU2F8TJUkdQUR0TyltyM5+Pwscma3XliR9BJ7RliQ9EBE9gd2oO/ttyJakFuAZbUnqACKikg/eEQTg3ZTSiHLUI0kdgUFbkiRJyoF3HZEkSZJyYNCWJEmScmDQliRJknJg0JYkSZJy8P8BGp9pUsBxCC4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TASK: It looks like F and G subgrades don't get paid back that often.\n",
    "# Isloate those and recreate the countplot just for those subgrades.\n",
    "\n",
    "f_and_g = df[(df['grade']=='G') | (df['grade']=='F')]\n",
    "\n",
    "plt.figure(figsize=(12,4))\n",
    "subgrade_order = sorted(f_and_g['sub_grade'].unique())\n",
    "sns.countplot(data=f_and_g, x='sub_grade', order = subgrade_order, hue='loan_status')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Fully Paid', 'Charged Off'], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TASK: Create a new column called 'load_repaid'.\n",
    "# It will contain a 1 if the loan status was \"Fully Paid\" and a 0 if it was \"Charged Off\".\n",
    "\n",
    "df['loan_status'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['loan_repaid'] = df['loan_status'].map({'Fully Paid':1,'Charged Off':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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_repaid</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Charged Off</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396025</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396026</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396027</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396028</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396029</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>396030 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        loan_repaid  loan_status\n",
       "0                 1   Fully Paid\n",
       "1                 1   Fully Paid\n",
       "2                 1   Fully Paid\n",
       "3                 1   Fully Paid\n",
       "4                 0  Charged Off\n",
       "...             ...          ...\n",
       "396025            1   Fully Paid\n",
       "396026            1   Fully Paid\n",
       "396027            1   Fully Paid\n",
       "396028            1   Fully Paid\n",
       "396029            1   Fully Paid\n",
       "\n",
       "[396030 rows x 2 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['loan_repaid','loan_status']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b32e1b6e08>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CHALLENGE TASK: Create a bar plot showing the correlation of the numeric features to the new loan_repaid column.\n",
    "\n",
    "df.corr()['loan_repaid'].sort_values().drop('loan_repaid').plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data PreProcessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>emp_title</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>...</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>initial_list_status</th>\n",
       "      <th>application_type</th>\n",
       "      <th>mort_acc</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>address</th>\n",
       "      <th>loan_repaid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>11.44</td>\n",
       "      <td>329.48</td>\n",
       "      <td>B</td>\n",
       "      <td>B4</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>117000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36369.0</td>\n",
       "      <td>41.8</td>\n",
       "      <td>25.0</td>\n",
       "      <td>w</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0174 Michelle Gateway\\nMendozaberg, OK 22690</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>11.99</td>\n",
       "      <td>265.68</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>Credit analyst</td>\n",
       "      <td>4 years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>65000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20131.0</td>\n",
       "      <td>53.3</td>\n",
       "      <td>27.0</td>\n",
       "      <td>f</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1076 Carney Fort Apt. 347\\nLoganmouth, SD 05113</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15600.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>10.49</td>\n",
       "      <td>506.97</td>\n",
       "      <td>B</td>\n",
       "      <td>B3</td>\n",
       "      <td>Statistician</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>RENT</td>\n",
       "      <td>43057.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11987.0</td>\n",
       "      <td>92.2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>f</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87025 Mark Dale Apt. 269\\nNew Sabrina, WV 05113</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7200.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>6.49</td>\n",
       "      <td>220.65</td>\n",
       "      <td>A</td>\n",
       "      <td>A2</td>\n",
       "      <td>Client Advocate</td>\n",
       "      <td>6 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>54000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5472.0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>13.0</td>\n",
       "      <td>f</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>823 Reid Ford\\nDelacruzside, MA 00813</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24375.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>17.27</td>\n",
       "      <td>609.33</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>Destiny Management Inc.</td>\n",
       "      <td>9 years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24584.0</td>\n",
       "      <td>69.8</td>\n",
       "      <td>43.0</td>\n",
       "      <td>f</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>679 Luna Roads\\nGreggshire, VA 11650</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt        term  int_rate  installment grade sub_grade  \\\n",
       "0    10000.0   36 months     11.44       329.48     B        B4   \n",
       "1     8000.0   36 months     11.99       265.68     B        B5   \n",
       "2    15600.0   36 months     10.49       506.97     B        B3   \n",
       "3     7200.0   36 months      6.49       220.65     A        A2   \n",
       "4    24375.0   60 months     17.27       609.33     C        C5   \n",
       "\n",
       "                 emp_title emp_length home_ownership  annual_inc  ... pub_rec  \\\n",
       "0                Marketing  10+ years           RENT    117000.0  ...     0.0   \n",
       "1          Credit analyst     4 years       MORTGAGE     65000.0  ...     0.0   \n",
       "2             Statistician   < 1 year           RENT     43057.0  ...     0.0   \n",
       "3          Client Advocate    6 years           RENT     54000.0  ...     0.0   \n",
       "4  Destiny Management Inc.    9 years       MORTGAGE     55000.0  ...     0.0   \n",
       "\n",
       "  revol_bal revol_util total_acc initial_list_status  application_type  \\\n",
       "0   36369.0       41.8      25.0                   w        INDIVIDUAL   \n",
       "1   20131.0       53.3      27.0                   f        INDIVIDUAL   \n",
       "2   11987.0       92.2      26.0                   f        INDIVIDUAL   \n",
       "3    5472.0       21.5      13.0                   f        INDIVIDUAL   \n",
       "4   24584.0       69.8      43.0                   f        INDIVIDUAL   \n",
       "\n",
       "  mort_acc  pub_rec_bankruptcies  \\\n",
       "0      0.0                   0.0   \n",
       "1      3.0                   0.0   \n",
       "2      0.0                   0.0   \n",
       "3      0.0                   0.0   \n",
       "4      1.0                   0.0   \n",
       "\n",
       "                                           address  loan_repaid  \n",
       "0     0174 Michelle Gateway\\nMendozaberg, OK 22690            1  \n",
       "1  1076 Carney Fort Apt. 347\\nLoganmouth, SD 05113            1  \n",
       "2  87025 Mark Dale Apt. 269\\nNew Sabrina, WV 05113            1  \n",
       "3            823 Reid Ford\\nDelacruzside, MA 00813            1  \n",
       "4             679 Luna Roads\\nGreggshire, VA 11650            0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove or fill any missing data.\n",
    "# Remove unnecessary or repetitive features. Convert categorical string features to dummy variables.\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "396030"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TASK: What is the length of the dataframe?\n",
    "\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt                   0\n",
       "term                        0\n",
       "int_rate                    0\n",
       "installment                 0\n",
       "grade                       0\n",
       "sub_grade                   0\n",
       "emp_title               22927\n",
       "emp_length              18301\n",
       "home_ownership              0\n",
       "annual_inc                  0\n",
       "verification_status         0\n",
       "issue_d                     0\n",
       "loan_status                 0\n",
       "purpose                     0\n",
       "title                    1755\n",
       "dti                         0\n",
       "earliest_cr_line            0\n",
       "open_acc                    0\n",
       "pub_rec                     0\n",
       "revol_bal                   0\n",
       "revol_util                276\n",
       "total_acc                   0\n",
       "initial_list_status         0\n",
       "application_type            0\n",
       "mort_acc                37795\n",
       "pub_rec_bankruptcies      535\n",
       "address                     0\n",
       "loan_repaid                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TASK: Create a Series that displays the total count of missing values per column.\n",
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt               0.000000\n",
       "term                    0.000000\n",
       "int_rate                0.000000\n",
       "installment             0.000000\n",
       "grade                   0.000000\n",
       "sub_grade               0.000000\n",
       "emp_title               5.789208\n",
       "emp_length              4.621115\n",
       "home_ownership          0.000000\n",
       "annual_inc              0.000000\n",
       "verification_status     0.000000\n",
       "issue_d                 0.000000\n",
       "loan_status             0.000000\n",
       "purpose                 0.000000\n",
       "title                   0.443148\n",
       "dti                     0.000000\n",
       "earliest_cr_line        0.000000\n",
       "open_acc                0.000000\n",
       "pub_rec                 0.000000\n",
       "revol_bal               0.000000\n",
       "revol_util              0.069692\n",
       "total_acc               0.000000\n",
       "initial_list_status     0.000000\n",
       "application_type        0.000000\n",
       "mort_acc                9.543469\n",
       "pub_rec_bankruptcies    0.135091\n",
       "address                 0.000000\n",
       "loan_repaid             0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TASK: Convert this Series to be in term of percentage of the total DataFrame\n",
    "\n",
    "100 * df.isnull().sum()/len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job title supplied by the Borrower when applying for the loan.*\n",
      "\n",
      "\n",
      "Employment length in years. Possible values are between 0 and 10 where 0 means less than one year and 10 means ten or more years. \n"
     ]
    }
   ],
   "source": [
    "# TASK: Let's examine emp_title and emp_length to see whether it will be okay to drop them.\n",
    "# Print out their feature information using the feat_info() function from the top of this notebook.\n",
    "\n",
    "feat_info('emp_title')\n",
    "print('\\n')\n",
    "feat_info('emp_length')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "173105"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TASK: How many unique employment job titles are there?\n",
    "\n",
    "df['emp_title'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Teacher                          4389\n",
       "Manager                          4250\n",
       "Registered Nurse                 1856\n",
       "RN                               1846\n",
       "Supervisor                       1830\n",
       "                                 ... \n",
       "Community worker                    1\n",
       "Retail associate                    1\n",
       "ram reality services                1\n",
       "performance foodservice             1\n",
       "SAN DIEGO COUNTY CREDIT UNION       1\n",
       "Name: emp_title, Length: 173105, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['emp_title'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TASK: Realistically there are too many unique job titles to try to convert this to a dummy variable feature.\n",
    "# Let's remove that emp_title column.\n",
    "\n",
    "df = df.drop('emp_title', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1 year',\n",
       " '10+ years',\n",
       " '2 years',\n",
       " '3 years',\n",
       " '4 years',\n",
       " '5 years',\n",
       " '6 years',\n",
       " '7 years',\n",
       " '8 years',\n",
       " '9 years',\n",
       " '< 1 year']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TASK: Create a count plot of the emp_length feature column. Challenge: Sort the order of the values.\n",
    "\n",
    "sorted(df['emp_length'].dropna().unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "emp_length_order = [ '< 1 year','1 year','2 years','3 years','4 years','5 years','6 years','7 years','8 years','9 years',\n",
    "                     '10+ years']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b32e327308>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "sns.countplot(data=df, x='emp_length', order=emp_length_order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b32e311048>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NfvGLXzRZ7te+9jW+/vWv8+qrrwLFBavjx4/ni1/8YgtbLkmS1PE4xnwdtGR6w40pIpg0aRLnnHMOl1xyCV27dl05XeJLL73U5D7du3dn9OjRDBgwgLq6Ovbcc89my7/yyis5+eST+d73vseRRx7J1ltvDcAhhxzC3Llz+fSnPw0UY8B/9rOfMXDgQE444QTq6+v5+Mc/vvIi1FUdc8wxvPTSS+yzzz5EBFtuuSU/+9nP2GmnnTbwjEiSJLVfsWLGjo5u8ODBOX369L9Jmzt3Ln379q1RjVrfm2++yeabb05EMHHiRH7+859z55131rpaK7X38y9Jkpo36NybWrX8GZd9rlXLb05EzMjMwU1ts8e8A5sxYwZjx44lM+nevTvjxo2rdZUkSZI6LAPzDmz//ffnqaeeqnU1JEmShBd/rpVDfWrD8y5JkjoaA/M16Nq1K6+++qpBYhvLTF599VW6du1a66pIkiS1mZoMZYmILwNfABKYDZwG7ARMBLYFZgKnZOa7EbEZcBMwCHgVOCEzF5TlnAecDrwPfCkz7y3TDwOuAjoBN2TmJetTz169erFw4UKWLFmyvk3VeuratSu9evWqdTUkSZLaTJsH5hGxM/AloF9mvhURtwAnAkcAV2TmxIi4liLg/mH598+Z+d8j4kTgUuCEiOhX7rcb8FHgNxHxyfIw1wB/DywEpkXEXZn57LrWtUuXLvTu3XuD2itJkiS1RK2GsnQGNo+IzsBHgEXAMGDF3XBuBEaUy8PLdcrtB0Vx+8nhwMTMfCcz/wjMB4aUj/mZ+YfMfJeiF354G7RJkiRJWm9tHphn5kvAd4EXKALypcAM4PXMXF5mWwjsXC7vDLxY7ru8zL9d4/RV9mkufTURcUZETI+I6Q5XkSRJUi21eWAeEdtQ9GD3phiCsgVweBNZV1xxGc1sW9f01RMzr8/MwZk5uGfPnmuruiRJktRqajGU5WDgj5m5JDPfA24H9gG6l0NbAHoBL5fLC4GPAZTbtwZea5y+yj7NpUuSJEmVVYvA/AVg74j4SDlW/CDgWWAycFyZZxSw4t7wd5XrlNsfzGL+wruAEyNis4joDewCPAlMA3aJiN4RsSnFBaJ3tUG7JEmSpPXW5rOyZOYTEXErxZSIy4FZwPXAL4GJEfGtMu3H5S4/Bn4aEfMpespPLMuZU87o8mxZzpjMfB8gIsYC91JMlzguM+e0VfskSZKk9VGTecwz80LgwlWS/0Axo8qqed8Gjm+mnG8D324i/R7gng2vqSRJktQ2vPOnJEmSVAEG5pIkSVIFGJhLkiRJFWBgLkmSJFWAgbkkSZJUAQbmkiRJUgUYmEuSJEkVYGAuSZIkVYCBuSRJklQBBuaSJElSBRiYS5IkSRVgYC5JkiRVgIG5JEmSVAEG5pIkSVIFGJhLkiRJFWBgLkmSJFWAgbkkSZJUAQbmkiRJUgUYmEuSJEkVYGAuSZIkVYCBuSRJklQBBuaSJElSBRiYS5IkSRVgYC5JkiRVgIG5JEmSVAEG5pIkSVIFGJhLkiRJFWBgLkmSJFWAgbkkSZJUAQbmkiRJUgUYmEuSJEkVUJPAPCK6R8StEfG7iJgbEZ+OiG0j4v6ImFf+3abMGxFxdUTMj4inI2Jgo3JGlfnnRcSoRumDImJ2uc/VERG1aKckSZLUUrXqMb8K+HVm9gE+BcwFvgE8kJm7AA+U6wCHA7uUjzOAHwJExLbAhcBewBDgwhXBfJnnjEb7HdYGbZIkSZLWW5sH5hGxFTAU+DFAZr6bma8Dw4Eby2w3AiPK5eHATVn4LdA9InYCDgXuz8zXMvPPwP3AYeW2rTLz8cxM4KZGZUmSJEmVVIse8/8GLAF+EhGzIuKGiNgC2CEzFwGUf7cv8+8MvNho/4Vl2prSFzaRvpqIOCMipkfE9CVLlmx4yyRJkqT11KLAPCIeaElaC3UGBgI/zMw9gL/w12ErTR6+ibRcj/TVEzOvz8zBmTm4Z8+ea661JEmS1IrWGJhHRNdyLHePiNimvEBz24ioAz66nsdcCCzMzCfK9VspAvVXymEolH8XN8r/sUb79wJeXkt6rybSJUmSpMpaW4/5/wRmAH3KvysedwLXrM8BM/NPwIsRsWuZdBDwLHAXsGJmlVHlMSjTP1fOzrI3sLQc6nIvcEj5hWEb4BDg3nLbGxGxdzkby+calSVJkiRVUuc1bczMq4CrIuKszPz+RjzuWcCEiNgU+ANwGsWXhFsi4nTgBeD4Mu89wBHAfODNMi+Z+VpEfBOYVua7ODNfK5f/CRgPbA78qnxIkiRJlbXGwHyFzPx+ROwD1DXeJzNvWp+DZmYDMLiJTQc1kTeBMc2UMw4Y10T6dKD/+tRNkiRJqoUWBeYR8VPgE0AD8H6ZvGIqQkmSJEkbqEWBOUXvdr+y91qSJEnSRtbSecyfAXZszYpIkiRJHVlLe8x7AM9GxJPAOysSM/OYVqmVJEmS1MG0NDC/qDUrIUmSJHV0LZ2V5aHWrogkSZLUkbV0VpY3+Ott7TcFugB/ycytWqtikiRJUkfS0h7zLRuvR8QIYEir1EiSJEnqgFo6K8vfyMw7gGEbuS6SJElSh9XSoSyfbbS6CcW85s5pLkmSJG0kLZ2V5ehGy8uBBcDwjV4bSZIkqYNq6Rjz01q7IpIkSVJH1qIx5hHRKyImRcTiiHglIm6LiF6tXTlJkiSpo2jpxZ8/Ae4CPgrsDPyiTJMkSZK0EbQ0MO+ZmT/JzOXlYzzQsxXrJUmSJHUoLQ3M/zMiTo6ITuXjZODV1qyYJEmS1JG0NDD/PPA/gD8Bi4DjAC8IlSRJkjaSlk6X+E1gVGb+GSAitgW+SxGwS5IkSdpALe0x331FUA6Qma8Be7ROlSRJkqSOp6WB+SYRsc2KlbLHvKW97ZIkSZLWoqXB9feAxyLiViApxpt/u9VqJUmSJHUwLb3z500RMR0YBgTw2cx8tlVrJkmSJHUgLR6OUgbiBuOSJElSK2jpGHNJkiRJrcjAXJIkSaoAA3NJkiSpAgzMJUmSpAowMJckSZIqwMBckiRJqgADc0mSJKkCDMwlSZKkCqhZYB4RnSJiVkTcXa73jognImJeRNwcEZuW6ZuV6/PL7XWNyjivTH8uIg5tlH5YmTY/Ir7R1m2TJEmS1lUte8zPBuY2Wr8UuCIzdwH+DJxepp8O/Dkz/ztwRZmPiOgHnAjsBhwG/KAM9jsB1wCHA/2Ak8q8kiRJUmXVJDCPiF7AkcAN5XoAw4Bbyyw3AiPK5eHlOuX2g8r8w4GJmflOZv4RmA8MKR/zM/MPmfkuMLHMK0mSJFVWrXrMrwS+BnxQrm8HvJ6Zy8v1hcDO5fLOwIsA5falZf6V6avs01z6aiLijIiYHhHTlyxZsqFtkiRJktZbmwfmEXEUsDgzZzRObiJrrmXbuqavnph5fWYOzszBPXv2XEOtJUmSpNbVuQbH3Bc4JiKOALoCW1H0oHePiM5lr3gv4OUy/0LgY8DCiOgMbA281ih9hcb7NJcuSZIkVVKb95hn5nmZ2Ssz6ygu3nwwM0cCk4HjymyjgDvL5bvKdcrtD2ZmluknlrO29AZ2AZ4EpgG7lLO8bFoe4642aJokSZK03mrRY96crwMTI+JbwCzgx2X6j4GfRsR8ip7yEwEyc05E3AI8CywHxmTm+wARMRa4F+gEjMvMOW3aEkmSJGkd1TQwz8wpwJRy+Q8UM6qsmudt4Phm9v828O0m0u8B7tmIVZUkSZJalXf+lCRJkirAwFySJEmqAANzSZIkqQIMzCVJkqQKMDCXJEmSKsDAXJIkSaoAA3NJkiSpAgzMJUmSpAowMJckSZIqwMBckiRJqgADc0mSJKkCDMwlSZKkCjAwlyRJkirAwFySJEmqAANzSZIkqQIMzCVJkqQKMDCXJEmSKqBzrSsgVcGgc29q1fJnXPa5Vi1fkiR9+NljLkmSJFWAgbkkSZJUAQbmkiRJUgUYmEuSJEkVYGAuSZIkVYCBuSRJklQBTpe4Fk6jp/bK17YkSdVij7kkSZJUAQbmkiRJUgUYmEuSJEkV4BhzSZIkdTgvXDygVcv/uwtmr/M+9phLkiRJFWBgLkmSJFVAmwfmEfGxiJgcEXMjYk5EnF2mbxsR90fEvPLvNmV6RMTVETE/Ip6OiIGNyhpV5p8XEaMapQ+KiNnlPldHRLR1OyVJkqR1UYsx5suBr2TmzIjYEpgREfcDpwIPZOYlEfEN4BvA14HDgV3Kx17AD4G9ImJb4EJgMJBlOXdl5p/LPGcAvwXuAQ4DftWGbZSkmmvtuerB+eolaWNq88A8MxcBi8rlNyJiLrAzMBw4sMx2IzCFIjAfDtyUmQn8NiK6R8ROZd77M/M1gDK4PywipgBbZebjZfpNwAgMzFvMG89IkiS1vZqOMY+IOmAP4AlghzJoXxG8b19m2xl4sdFuC8u0NaUvbCJdkiRJqqyaBeYR0Q24DTgnM/9rTVmbSMv1SG+qDmdExPSImL5kyZK1VVmSJElqNTWZxzwiulAE5RMy8/Yy+ZWI2CkzF5VDVRaX6QuBjzXavRfwcpl+4CrpU8r0Xk3kX01mXg9cDzB48OAmg3dJ7YPjrSVJVVeLWVkC+DEwNzMvb7TpLmDFzCqjgDsbpX+unJ1lb2BpOdTlXuCQiNimnMHlEODectsbEbF3eazPNSpLkiRJqqRa9JjvC5wCzI6IhjLtfOAS4JaIOB14ATi+3HYPcAQwH3gTOA0gM1+LiG8C08p8F6+4EBT4J2A8sDnFRZ9e+ClJkqRKq8WsLI/Q9DhwgIOayJ/AmGbKGgeMayJ9OtB/A6opSZIktSnv/ClJkiRVQE0u/pQkqTV0xPswdMQ2S+2VPeaSJElSBRiYS5IkSRXgUBZJkvSh4vAdtVf2mEuSJEkVYGAuSZIkVYCBuSRJklQBBuaSJElSBRiYS5IkSRXgrCySJEkV50w0HYM95pIkSVIFGJhLkiRJFWBgLkmSJFWAgbkkSZJUAQbmkiRJUgUYmEuSJEkVYGAuSZIkVYCBuSRJklQBBuaSJElSBRiYS5IkSRXQudYVkDqCFy4e0Krl/90Fs1u1fEmS1PoMzGustQM2MGhTbfhlRJKkdeNQFkmSJKkCDMwlSZKkCnAoi9qcQxyk9sP3syRtPAbmkrSRGKRKkjaEgbkkSS3UES/Yt82to2ptVjU4xlySJEmqAHvMJUmSOjiH4lWDPeaSJElSBRiYS5IkSRXQbgPziDgsIp6LiPkR8Y1a10eSJElak3YZmEdEJ+Aa4HCgH3BSRPSrba0kSZKk5rXLwBwYAszPzD9k5rvARGB4jeskSZIkNSsys9Z12Ogi4jjgsMz8Qrl+CrBXZo5dJd8ZwBnl6q7Ac21a0UIP4D9rcNxass0dg23uGDpamztae8E2dxS2ue18PDN7NrWhvU6XGE2krfYNJDOvB65v/eo0LyKmZ+bgWtahrdnmjsE2dwwdrc0drb1gmzsK21wN7XUoy0LgY43WewEv16gukiRJ0lq118B8GrBLRPSOiE2BE4G7alwnSZIkqVntcihLZi6PiLHAvUAnYFxmzqlxtZpT06E0NWKbOwbb3DF0tDZ3tPaCbe4obHMFtMuLPyVJkqQPm/Y6lEWSJEn6UDEwlyRJkirAwLwVRcTxETEnIj6IiEpNx7OhImJcRCyOiGdqXZfWFBEfi4jJETG3fC7PrnWdWlNEdI2IJyPiqbK9/7fWdWorEdEpImZFxN21rktbiIgFETE7IhoiYnqt69MWIqJ7RNwaEb8r39OfrnWdWlNE7Fo+vyse/xUR59S6Xq0tIr5cfn49ExE/j4iuta5Ta4uIs8v2zmkvz3FzcUZEbBsR90fEvPLvNrWqY2swMN8IImLTiNiiiU3PAJ8FprZxfSIiWvu5HQ8c1srHWE1EtPUFy8uBr2RmX2BvYExE9GuLA9egrQDvAMMy81NAPXBYROzdFgeuUXsbOxuY25YHrECbP5OZ9W05j2+N23wV8OvM7AN8ijZ6vmvV5sx8rnx+64FBwJvApLY4dq3aHBE7A18CBmdmf4oJIE5so2PXqs39gdEUdz3/FHBUROzSRsduzTaPp+k44xvAA5m5C/BAub5qvcZHxIGtWLcmRUSnDS3DwHwDRKvd7SYAAAuNSURBVETfiPgexR1DP7nq9sycm5lrvJtoRPw0IoY3Wp8QEceUvXeXRcS0iHg6Iv5nub1bRDwQETPL3q7hZXpd2QP0A2AmfzuP+0aXmVOB19bQri0j4o8R0aVc36rsoesSEZ+IiF9HxIyIeDgi+pR5jo6IJ8pey99ExA5l+kURcX1E3Afc1JrtWlVmLsrMmeXyGxT/yHduq7ZGxG5lD3ZD+Tpo1Q/bLCwrV7uUj7+5Qrxs08xG67tExIxyeVBEPFS2996I2KlMH12+lp+KiNsi4iNl+viIuDwiJgOXRsQBjXr3ZkXElq3Z3kZt6AUcCdzQzPZ21+a1aU+v6xX1B4YCPwbIzHcz8/X23OZVHAT8PjOf7wBt7gxsHkXQ+BFWuY9JO3w/9wV+m5lvZuZy4CHg2FXa/KF7ntcQZwwHbiyXbwRGrGvZEXF6RFzRaH10RFxeLp/cqD3XRRlsR8QPI2J6rPJrcnkeL4iIR4DjI+JLEfFseS4mrmvdyEwf6/AAtgBOAx4BHgW+AGy5ln2mUHx7b2rbAcAd5fLWwB8pPlTOAP65TN8MmA70LrdtVab3AOZT3Om0DvgA2LsNz0Ud8Mwatv8EGFEunwF8r1x+ANilXN4LeLBc3oa/zhT0hUb5LwJmAJvX+LmvA15Ycf7boq3A94GR5fKmbXEOKHqYGoBlwKXN5JkM1JfL/wKcRRHEPwb0LNNPoJiqFGC7Rvt+CzirXB4P3A10Ktd/AexbLncDOrfRc3srRY/igcDdHaTNf6T4Ej8DOKOZPO3pdV0PPFme/1kUX8K2aM9tXqVd44Cx7f15Lo91NsXn1xJgQjN52s37mSIw/3/AdhRfRB4Hvt8enmeaiDOA11dZ/3MT+40HDlxDuVsAvwe6lOuPAQPKc/mLRuk/AD5XLm9b/u1EEdftXq4vAL7WqOyXgc3K5e7r2uZa/4z6YbQIeBr4Qmb+bkMLy8yHIuKaiNieYtjLbVnMw34IsHtEHFdm3RrYheKupv8SEUMpAvGdgR3KPM9n5m83tE4b0Q3A14A7KL7MjI6IbsA+wH9ExIp8m5V/ewE3lz0Um1IEDivclZlvtUmtm1DW+zbgnMz8ryaytFZbHwf+dxQ9urdn5ryN2KwmZeb7QH1EdAcmRUT/zFz1WoIbgNMi4n9R/PMaAuwK9AfuL9vbieL9AtA/Ir4FdKf4Z3Vvo7L+ozwmFF92L4+ICRTtXbjxW/i3IuIoYHFmzog1//TZbtpc2jczXy4/e+6PiN9l0UPVWLt5XVN0agykCKieiIirKH4C/z+r5GtPbQaK4ZbAMcB5zWRpN22OYrzxcIqOrNfL+p+cmT9bJWu7eT9n5tyIuBS4n+ILyVMUwzBX1W6e56ZExKHApeXq3wH7RcQy4J3M3Ktx3sz8S0Q8SDHsZy5FID47invgDAKmledjc2Bxudv/iIgzKD5LdgL6UcSDADc3Kv5pYEJE3EFxrtdNa3+Ta28P4JDyCZgLXAB8vAX7TKGZHvNy+9eBLwNPALuVabcBhzaR99Ty+Cu+zS2g+EZZxxp6r1vpXKz1mBQfEAcAT5brWwGL1nCejimXDwSmlMsXAV+t4XPeheJD+H/Voq3AJyjGTP6BYvx3W7b9wqbOPdCVoodmOHBLmTYAeLyZcv4IfKpcPhUYXy6PB45bJe+A8j2xEOjTBm381/JYC4A/UYzD/Vl7bnMTdW32PdZeXtfAjsCCRuv7A79sz21udNzhwH1rydMu2gwcD/y40frngB80ka89v5//Bfhie3ieabrH/Dlgp3J5J+C5JvYbzxp6zMs8ewF3UgTzXyzTzgL+tYm8vSlGKGzTqPxTy+UFQI9GeTsBnwGuKOu6Tr+aOMZ8HWXmfZl5ArAfsBS4sxx7VbcBxY4HzinLX3GH0nuBf2o0HuyTUVxgujVF7957EfEZ4OMbcNy2cBPwc4qf0Miit/mPEXE8rLxQ9VNl3q2Bl8rlUW1d0aZE8ZX5x8DczLx8Ldk3elsj4r8Bf8jMq4G7gN3Xty0tERE9y55yImJz4GBgtV+GMvNtitfoDynbS/EB1DPKmS7KsYu7ldu2BBaVr+eRazj+JzJzdmZeSjF8q8/GaVnzMvO8zOyVmXUUF4k9mJknN5Gv3bQ5IraIcuxr+blyCMXF6k350L+uATLzT8CLEbFrmXQQ8Gwz2dtFmxs5iaI9a9Je2vwCsHdEfKT8/D6IJi7ybU/v5/K425d//47i1/fmnu/28Dzf1ag+oyiC63WWmU9QXI/3j/z1fD0AHNfofG4bER+n+ALzF2BpFGPtD2+qzCgm3vhYZk6m+HVixS8sLWZgvp4y89XMvCqLq93PB95fNU9EHBsRC4FPA7+MiHtXzVOW9QrFB8dPGiXfQPFPY2YUUwVdR/HzyQRgcBTTm42kiaCpLUTEzyl+vto1IhZGxOnNZJ1AMU6t8YfESOD0iHgKmEPRYwHFt/D/iIiHgf9slYqvu32BU4Bh8dcLeo5oJm9rtPUE4JmIaKD4gG/ti193AiZHxNPANOD+zGxu+sAJFBeG3gfFxXTAcRQXQD1FMU59nzLv/6H4Reh+1vyaPSeKKb+eAt4CfrWB7dnY2kubdwAeKY/5JEXP8a+bydseXtcrnEXxE/PTFGPO/6WZfO2mzVFcpPj3wO1rydou2lwGW7dSXD8xmyLOae626+3l/QxwW0Q8SzE+ekxm/rmZfB+a53kNccYlwN9HxDyK1/YlG3CYW4BHV5yvzHwW+GfgvvJz4n6K3vmnKK5NmUNxvcajzZTXCfhZRMwu81+Rq1xkvjYrBvWrhsoPztnAwMxcWuv6bExRjJEfnpmn1Loura0jtRUgIr4KbJ2Zq47Rbbc6aJs71OsabHOt69JWfD8rivtWXJGZD9S6Lit48WeNRcTBFN++Lm+HQfn3KX7uaa6Hud3oSG0FiIhJFGMKh9W6Lm2lg7a5Q72uwTbXui5txfdzx1YO2XwSeKpKQTnYYy5JkiRVgmPMJUmSpAowMJckSZIqwMBckiRJqgADc0mSJKkCDMwlSessiltdb+wy6xvfJyAiLiqntJOkDsHAXJJUFfU4lZukDszAXJLakYg4OSKeLO9Se11EdIqIZRFxaUTMiIjfRMSQiJgSEX+IiGPK/U6NiDsj4tcR8VxEXLgOxzw3IqZFxNMR8X/LtLqImBsRP4qIORFxX0RsXm7bs8z7eERcVt4pcVPgYuCEsu4nlMX3a1TXL23k0yVJlWJgLkntRET0pbg99r6ZWQ+8T3G77S2AKZk5CHgD+BbFrayPpQiGVxhS5q8Hjo+IwS045iHALuW+9cCgiBhabt4FuCYzdwNeB/6hTP8JcGZmfrqs44rboV8A3JyZ9Zl5c5m3D3BoWf6FEdFl3c6KJH14eOdPSWo/DgIGAdMiAmBzYDHwLvDrMs9s4J3MfC8iZgN1jfa/PzNfBYiI24H9gOlrOeYh5WNWud6NIiB/AfhjZjaU6TOAuvKOe1tm5mNl+r8DR62h/F9m5jvAOxGxGNgBWLiWOknSh5KBuSS1HwHcmJnn/U1ixFfzr7d5/gB4ByAzP4iIxv8HVr0VdEtuDR3Av2bmdascs27FcUrvU3xRiBaU2diqZfh/S1K75VAWSWo/HgCOi4jtASJi24j4+Drs//flPpsDI4BHW7DPvcDnI6JbecydVxy/KZn5Z+CNiNi7TDqx0eY3gC3Xob6S1K4YmEtSO5GZzwL/DNwXEU8D9wM7rUMRjwA/BRqA2zJzbcNYyMz7KIajPF4OjbmVtQfXpwPXR8TjFD3oS8v0yRQXeza++FOSOoz466+bkqSOKiJOBQZn5tg2OFa3zFxWLn8D2Ckzz27t40pS1TlWT5LU1o6MiPMo/gc9D5xa2+pIUjXYYy5JalJEbEcxbn1VB62YvUWStPEYmEuSJEkV4MWfkiRJUgUYmEuSJEkVYGAuSZIkVYCBuSRJklQB/x8mYX28nm9rxgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TASK: Plot out the countplot with a hue separating Fully Paid vs Charged Off\n",
    "\n",
    "plt.figure(figsize=(12,4))\n",
    "sns.countplot(data=df, x='emp_length', order=emp_length_order, hue='loan_status')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CHALLENGE TASK: What is the percentage of charge offs per category.\n",
    "# Essentially informing us what percent of people per employment category didn't pay back their loan.\n",
    "\n",
    "emp_co = df[df['loan_status']==\"Charged Off\"].groupby(\"emp_length\").count()['loan_status']\n",
    "emp_fp = df[df['loan_status']==\"Fully Paid\"].groupby(\"emp_length\").count()['loan_status']\n",
    "\n",
    "emp_len = emp_co/emp_fp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "emp_length\n",
       "1 year       0.248649\n",
       "10+ years    0.225770\n",
       "2 years      0.239560\n",
       "3 years      0.242593\n",
       "4 years      0.238213\n",
       "5 years      0.237911\n",
       "6 years      0.233341\n",
       "7 years      0.241887\n",
       "8 years      0.249625\n",
       "9 years      0.250735\n",
       "< 1 year     0.260830\n",
       "Name: loan_status, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b32ec3e1c8>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAExCAYAAAByP2k/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAActElEQVR4nO3de7gcdZ3n8feHBJCbyCWiA4SABjUqxuEYVGaRWQTjLchMHII6gqBZd4fVZ1ydRZ2FMeoMyNznYXaIEsfroKBoxgkCcnNE0BwuBgJGQ0SIUYkEBYQFEj77R9XR9tjh9OF0deX8zuf1PP10d136W13d59N1qn71K9kmIiLKtV3bCxAREc1K0EdEFC5BHxFRuAR9REThEvQREYVL0EdEFG562wsw2t577+1Zs2a1vRgREZPK9ddf/zPbM7qN2+aCftasWQwPD7e9GBERk4qkH25tXHbdREQULkEfEVG4BH1EROES9BERhUvQR0QULkEfEVG4BH1EROES9BERhdvmTpiKiCjVrNP+Y0Lz33Hmq5/QfNmij4goXII+IqJwCfqIiMIl6CMiCtdT0EuaL2mNpLWSTusy/l2SbpW0StLlkg7oGLdF0k31bXk/Fz4iIsY2ZqsbSdOAc4CjgfXASknLbd/aMdmNwJDtByX9d+AjwPH1uIdsz+3zckdERI962aKfB6y1vc72I8D5wLGdE9i+0vaD9dPrgP36u5gREfFE9dKOfl/gro7n64HDHmf6U4CLO54/SdIwsBk40/aXxr2UERF9NJH27E+0LXubegl6dRnmrhNKbwKGgJd1DJ5pe4Okg4ArJN1s+/ZR8y0GFgPMnDmzpwWPiMlvqgVuW3oJ+vXA/h3P9wM2jJ5I0suB9wMvs/3wyHDbG+r7dZKuAl4I/EbQ214KLAUYGhrq+iMyIl+MiIjx6WUf/UpgtqQDJe0ALAJ+o/WMpBcC5wILbN/dMXwPSTvWj/cGDgc6D+JGRETDxtyit71Z0qnAJcA0YJnt1ZKWAMO2lwNnA7sCF0gCuNP2AuA5wLmSHqP6UTlzVGudiIhoWE+dmtleAawYNez0jscv38p83wSeP5EFjIiIicmZsRERhUs3xbFVbXWpGoOVBg7lyxZ9REThskU/CWSLKyImIkEf26S2ftzyoxolyq6biIjCJegjIgqXoI+IKFz20UdsI3J8IJqSLfqIiMIl6CMiCpegj4goXII+IqJwCfqIiMKl1U2P0sFXRExW2aKPiChcgj4ionAJ+oiIwiXoIyIKl6CPiChcgj4ionAJ+oiIwiXoIyIKl6CPiChcgj4ionAJ+oiIwiXoIyIKl6CPiChcgj4ionAJ+oiIwiXoIyIK11PQS5ovaY2ktZJO6zL+XZJulbRK0uWSDugYd6Kk79e3E/u58BERMbYxg17SNOAc4JXAHOAESXNGTXYjMGT7EOBC4CP1vHsCZwCHAfOAMyTt0b/Fj4iIsfSyRT8PWGt7ne1HgPOBYzsnsH2l7Qfrp9cB+9WPXwFcZnuT7XuBy4D5/Vn0iIjoRS9Bvy9wV8fz9fWwrTkFuHg880paLGlY0vDGjRt7WKSIiOhVL0GvLsPcdULpTcAQcPZ45rW91PaQ7aEZM2b0sEgREdGrXoJ+PbB/x/P9gA2jJ5L0cuD9wALbD49n3oiIaE4vQb8SmC3pQEk7AIuA5Z0TSHohcC5VyN/dMeoS4BhJe9QHYY+ph0VExIBMH2sC25slnUoV0NOAZbZXS1oCDNteTrWrZlfgAkkAd9peYHuTpA9S/VgALLG9qZF3EhERXY0Z9AC2VwArRg07vePxyx9n3mXAsie6gBERMTE5MzYionAJ+oiIwiXoIyIKl6CPiChcgj4ionAJ+oiIwiXoIyIKl6CPiChcgj4ionAJ+oiIwiXoIyIKl6CPiChcgj4ionAJ+oiIwiXoIyIKl6CPiChcgj4ionAJ+oiIwiXoIyIKl6CPiChcgj4ionAJ+oiIwiXoIyIKl6CPiChcgj4ionAJ+oiIwiXoIyIKl6CPiChcgj4ionAJ+oiIwiXoIyIK11PQS5ovaY2ktZJO6zL+CEk3SNosaeGocVsk3VTflvdrwSMiojfTx5pA0jTgHOBoYD2wUtJy27d2THYncBLw7i4v8ZDtuX1Y1oiIeALGDHpgHrDW9joASecDxwK/Cnrbd9TjHmtgGSMiYgJ62XWzL3BXx/P19bBePUnSsKTrJL2u2wSSFtfTDG/cuHEcLx0REWPpJejVZZjHUWOm7SHgDcDfS3rGb72YvdT2kO2hGTNmjOOlIyJiLL0E/Xpg/47n+wEbei1ge0N9vw64CnjhOJYvIiImqJegXwnMlnSgpB2ARUBPrWck7SFpx/rx3sDhdOzbj4iI5o0Z9LY3A6cClwC3AZ+3vVrSEkkLACS9SNJ64PXAuZJW17M/BxiW9B3gSuDMUa11IiKiYb20usH2CmDFqGGndzxeSbVLZ/R83wSeP8FljIiICciZsRERhUvQR0QULkEfEVG4BH1EROES9BERhUvQR0QULkEfEVG4BH1EROES9BERhUvQR0QULkEfEVG4BH1EROES9BERhUvQR0QULkEfEVG4BH1EROES9BERhUvQR0QULkEfEVG4BH1EROES9BERhUvQR0QULkEfEVG4BH1EROES9BERhUvQR0QULkEfEVG4BH1EROES9BERhUvQR0QUrqeglzRf0hpJayWd1mX8EZJukLRZ0sJR406U9P36dmK/FjwiInozZtBLmgacA7wSmAOcIGnOqMnuBE4CPjtq3j2BM4DDgHnAGZL2mPhiR0REr3rZop8HrLW9zvYjwPnAsZ0T2L7D9irgsVHzvgK4zPYm2/cClwHz+7DcERHRo16Cfl/gro7n6+thvZjIvBER0Qe9BL26DHOPr9/TvJIWSxqWNLxx48YeXzoiInrRS9CvB/bveL4fsKHH1+9pXttLbQ/ZHpoxY0aPLx0REb3oJehXArMlHShpB2ARsLzH178EOEbSHvVB2GPqYRERMSBjBr3tzcCpVAF9G/B526slLZG0AEDSiyStB14PnCtpdT3vJuCDVD8WK4El9bCIiBiQ6b1MZHsFsGLUsNM7Hq+k2i3Tbd5lwLIJLGNERExAzoyNiChcgj4ionAJ+oiIwiXoIyIKl6CPiChcgj4ionAJ+oiIwiXoIyIKl6CPiChcgj4ionAJ+oiIwiXoIyIKl6CPiChcgj4ionAJ+oiIwiXoIyIKl6CPiChcgj4ionAJ+oiIwiXoIyIKl6CPiChcgj4ionAJ+oiIwiXoIyIKl6CPiChcgj4ionAJ+oiIwiXoIyIKl6CPiChcgj4ionAJ+oiIwvUU9JLmS1ojaa2k07qM31HS5+rx35I0qx4+S9JDkm6qb//S38WPiIixTB9rAknTgHOAo4H1wEpJy23f2jHZKcC9tp8paRFwFnB8Pe5223P7vNwREdGjXrbo5wFrba+z/QhwPnDsqGmOBT5RP74QOEqS+reYERHxRPUS9PsCd3U8X18P6zqN7c3AL4C96nEHSrpR0tWS/ssElzciIsZpzF03QLctc/c4zY+BmbbvkXQo8CVJz7V932/MLC0GFgPMnDmzh0WKiIhe9bJFvx7Yv+P5fsCGrU0jaTqwO7DJ9sO27wGwfT1wO3Dw6AK2l9oesj00Y8aM8b+LiIjYql6CfiUwW9KBknYAFgHLR02zHDixfrwQuMK2Jc2oD+Yi6SBgNrCuP4seERG9GHPXje3Nkk4FLgGmActsr5a0BBi2vRw4D/iUpLXAJqofA4AjgCWSNgNbgLfb3tTEG4mIiO562UeP7RXAilHDTu94/P+A13eZ7wvAFya4jBERMQE5MzYionAJ+oiIwiXoIyIKl6CPiChcgj4ionAJ+oiIwiXoIyIKl6CPiChcgj4ionAJ+oiIwiXoIyIKl6CPiChcgj4ionAJ+oiIwiXoIyIKl6CPiChcgj4ionAJ+oiIwiXoIyIKl6CPiChcgj4ionAJ+oiIwiXoIyIKl6CPiChcgj4ionAJ+oiIwiXoIyIKl6CPiChcgj4ionAJ+oiIwiXoIyIK11PQS5ovaY2ktZJO6zJ+R0mfq8d/S9KsjnHvrYevkfSK/i16RET0YsyglzQNOAd4JTAHOEHSnFGTnQLca/uZwN8BZ9XzzgEWAc8F5gP/XL9eREQMSC9b9POAtbbX2X4EOB84dtQ0xwKfqB9fCBwlSfXw820/bPsHwNr69SIiYkBk+/EnkBYC822/tX7+x8Bhtk/tmOaWepr19fPbgcOAvwCus/3pevh5wMW2LxxVYzGwuH76LGDNBN7T3sDPJjD/ZKvbZu2pVrfN2nnPU6P2ROoeYHtGtxHTe5hZXYaN/nXY2jS9zIvtpcDSHpZlTJKGbQ/147UmQ902a0+1um3WznueGrWbqtvLrpv1wP4dz/cDNmxtGknTgd2BTT3OGxERDeol6FcCsyUdKGkHqoOry0dNsxw4sX68ELjC1T6h5cCiulXOgcBs4Nv9WfSIiOjFmLtubG+WdCpwCTANWGZ7taQlwLDt5cB5wKckraXakl9Uz7ta0ueBW4HNwJ/Y3tLQexnRl11Ak6hum7WnWt02a+c9T43ajdQd82BsRERMbjkzNiKicAn6iIjCTeqglzRN0qe3geXYTtKT216OiJg8VNl/7CknblIHfX1gd0bdGmigJH1W0pMl7UJ1sHmNpPcMqPYzJO1YPz5S0jskPWUAdXeRtF39+GBJCyRtX2rdul5b67qVum3Wnmqfc90y8UtN1hgxqYO+dgdwjaT/I+ldI7cB1J1j+z7gdcAKYCbwxwOoC/AFYIukZ1K1eDoQ+OwA6n4deJKkfYHLgbcA/1pwXWhvXbdVt83aU/Fzvk7Si5ouUkLQbwC+QvVeduu4NW37emvjdcCXbT9Kl7N+G/KY7c3AccDf2/5T4OkDqCvbDwJ/APyT7eOoOrortS60t67bqttm7an4Of8+cK2k2yWtknSzpFX9LtJLFwjbNNsfaKn0v1D9N/Ed4OuSDgDuG1DtRyWdQHWS2mvrYYP4F1eSXgK8karHUhjMd6itutDeum6rbpu1p+Ln/MoB1Jj8QS9pBvBnVF0hP2lkuO3/2mDN7YCf2t63Y9idVL/Og/AW4O3Ah23/oD7reBAHpd8JvBe4qD4Z7iDgyoLrQnvruq26bdaecp+z7R8CSHoqHfnVRKFJfQMupfr1vw14GbAMOGsAdb/e0vudBny6pbpnT5W628C6HnjdbeA9T6nPua69APg+8EvgB8BjwOp+1ylhH/1ets8DHrV9te2TgRcPoO5lkt4taX9Je47cmi7qlloa1XUPHWTNNut21G5rXbfSmmyqfb86areyvoEPUuXV92wfCBwFXNPvIpN+1w3waH3/Y0mvpjo4u98A6p5c3/9JxzADBw2g9h1ULY2WU20JVMXtv2247o11zQtG1f1ioXWhvXXdVt02a0/Fz/lR2/fU5+JsZ/tKSWf1u0gJQf8hSbsD/wv4J+DJwJ82XbT+9W3Lhvo20tJoUPYE7gE6j38YaPoPsa260N66bqtum7Wn4uf8c0m7Av8JfEbS3VQdQPZVOjWbAEnPo2r+1XkQ+JPtLVFETCb1CZcPUf3AvJHqWh6fsX1PX+tM9qCXdDDwf4F9bD9P0iHAAtsfarjuGcCRVEG/gqqZ1DdsL2yybl174C2N6rpPojrwPbruyVudaRLXrWu3ta5bqdtm7an4Ode1DwBm2/6apJ2Babbv72eNEg7GfpSqSdajALZXUfeH37CFVAdOfmL7LcALgB0HUBfgM8B3qc7e+wDV/sWVA6j7KeBpwCuAq6mOhfT1C7mN1YX21nVbddusPeU+Z0lvAy4Ezq0H7UsT3SK00aSoz82TVtb3N3YMu2kAdb9d319PdVxANNAsaiu1r6/vV3UMu3oAdW/srEt1QskVpdZteV23Urfl9zwVP+ebgB1G5dfN/a5TwsHYn0l6BnX3A5IWAj8eQN3hutOjj1KF/QMM7jKJbbU0Gqn78/r4xE+AWQXX7azd1roedN02a0/Fz/lh249IAn51ze3+708fxK9lw7+IBwFfAx4EfgR8AzhgwMswCzhkgPVeQ3XQ5nlUZw5eT3Vcoum6bwX2oDoxbR1wN/D2Uuu2vK5bqTsVv18tv+ePAO+j2m10NHAR1dm5fa1TwsHYaba31Eevt3OfD2I8Tl1RHSU/yPYSSTOBp9nOxc8joid1dyqnAMdQ7f69BPiY+xzMJRyMXSvpbGDmoEK+9s/AS4AT6uf3A+cMonDdV/flkm6pnx8i6c8HUHcfSedJurh+PkfSKWPNN1nr1rXaWtet1G2z9lT8nIFXAefZfr3thbY/2u+QB4rYdbMb8Dbgm8B1wGLgyQOoe0N933kQ5TsDes9XA/NG1b5lAHUvBv5o5H1SnXDX9wNH20rdltd1K3Vbfs9T8XP+NHA71S6c5zRVZ9Jv0du+39Wv4Eup2sGeQXVA5ROqLiLQlEclTePXB4FnUHVINAg7+7d3EfX9bLou9rb9eer36ar/7i0F14X21nVbddusPeU+Z9tvAl5IFfYfl3StpMWS+np27qQPelXXjV0g6SLgH4C/oTpA++9UJzI15R+pDpw8VdKHqQ4C/2WD9Tq11dLol5L26qj7YuAXBdeF9tZ1W3XbrD0VP2dcXanuC8D5VBc7OQ64QdL/7GeRSX2jOjp/HvDSLuP+seHaz6bq1OxUGvy3q0vdVloaUfUueA3VH981wPcYQGujtuq2vK5ba0021b5fLb/n11JtMK4C3gM8tR6+M/DDftUpodXNrrYfaKHuXwMft726hdqttDSqa08HnkXVQmCNq0solly3rVZdbX7GU/H71dbn/EmqVjZf7zLuKNuX96POpN9100bI174LLJX0LUlvr3vQHJRWWhpJGqY62L3B9i0D/CNspW6trVZdbdVtrfZU/Jxtv7lbyNfj+hLyUEDQt8X2x2wfDryZ6oSpVZI+K2kQlxM8hOrf2vMkXVcfvHnyAOououqLY6Wk8yW9QiOn9JVZF9pb123VbbP2VPycB2MQ+79KvVFdguxYqk6Irgf+N9VB4PMHuAxHUO1T/CXwCeCZA6i5HdUl0H4E3EXVCdSepdZtc123WXeqfb+2hfXd1K3ILXpJbxlAjb+l2n3zKuAvbR9q+yzbr6VqLtVk7bZaGqGqG+i/Ac6maimwELgPuKLQuq2s65Y/46n4/WrtPQ9E2780Df0i3zmAGidTtb3tNm73hmu30tKI6r+Wy4E3ADuOGvfF0uq2vK7bbE02pb5fba/vLvUu7vdrTtpWN5JWbW0UcLDtQfUNP3AttjQ6yPa6qVK3rt3Wum6lbpu1p9LnLOl3tzYK+Irtp/e13iQO+p9SXaDg3tGjgG/a/p3BL1VExNgkbaHqdqHbweYX296pn/Umc3/0XwF2tX3T6BGSrhr84kRE9Ow24L/Z/v7oEZLu6nexSbtFHxExWdVdLNxse02Xca+z3dfLCSboJ0jStbZfMsB6z6Zqa/ytzn2Kkubb/mqDdecBtr1S0hxgPvBd2wNtkSDpk7bfPMiadd3fo+rd8BbblzZY5zDgNtv3SdoJOA34XeBWqtZdjfX9IukdwEW2+75FOUbdHaja0G9wdYHsNwAvpdrqXeqGT5yq+7g5DtifqiOz7wP/1uS6HrQE/QRJutF2o80pO2q9g6pvnduAucA7bX+5HneD7a0d4Jlo3TOAV1Lt6rsMOAy4Cng5cIntDzdUd/noQcDvUze1s72gibp17W/bnlc/fhvVer+I6gIR/277zIbqrgZeYHuzpKVUfa9cSHUh+hfY/oMm6ta1f0HVdvx24N+AC2xvbKpeR93PUH23dgZ+DuwKfJHqPcv2iQ3WfgdVfzNXUzWVvonquN9xwP+wfVVTtQdqkM2GSrlRnVBxBNUlz77X8fyIhuveTHVcAqqzcYepwh46+tFuqO40qj/E+6j7+wd2ouNiyg3UvYGqv+4j63V9JFWPgi8DXtbwuu7sl3wlMKN+vAsN9pFOtTX/q/c/alyjF70HbqQ6WekYqqaGG4GvAicCuzVYd+Ri4NOBnwLT6udq8vtV17i5o97OwFX145lN/k0N+jaZD8a2qfOErL2Ak6i+lAa69lvRJ9Nc766xfYekI4ELJR1A96P3/bLZ9hbgQUm3u+pWFdsPSWqyD/4h4J3A+4H32L5J0kO2r26w5ojtJO1BFXxyvWVr+5eSmuyn/BZJb7H9ceA7koZsD0s6mF9fwLoptv0YcClwqaTtqf6TOwH4a2BGQ3W3q3ff7EIVtrsDm4Adge0bqtlpOlW/9ztSXcgI23fW778ICfonwPavgr7eZXLygEr/RNJc1y2NbD8g6TXAMuD5DdZ9RNLOth+k6koWgLojt8aCvg6dv5N0QX3/Uwb3nd2d6gQeAZb0NNs/kbQrzf6ovhX4B1WXsfsZcG3dCuOuelyTfuN9udo3vhxYXh8vaMp5VGeZT6P6Ub9A0jrgxVR9tDfpY1R961xH9V/5WfCrCwltarKwpJ2B2ba/0zFsJrDF9o/6Wqv+NyWeoAHvo9+Pauv6J13GHW77mobq7mj74S7D9waebvvmJup2qfdq4HDb7xtEva0sw87APrZ/0HCd3ahOwZ8OrLf90ybr1TUPtv29putspfbvANjeIOkpVMd/7vRvX/WpidrPBZ5DdaD9u03X66i7PdUP3CG2f1kPuxR4n+3hvtZK0E+MpGPcYCuMiCiXquta3Gp7Wb01/+UmNhyL7NRskBLyETEBH+PXx/zeDHy8iSIJ+nGStLukMyV9V9I99e22ethT2l6+iJg8RnYV1QfbTwA+1USdBP34fZ6qne2RtveyvRdV2+57gQtaXbKImIzOo9qyX2V7dN9dfZF99OMkaY3tZ413XEREN/UB/h8Df2j7a03USPPK8fuhpD8DPjHSEkLSPlRt6Qd66nhETH51s+VGrzmdXTfjdzzVSVJXS9okaRNVdwB7An/U5oJFRHSTXTcREYXLFn0fDeJatRER45Ut+j6SdKftmW0vR0REpxyMHacxrlW7zyCXJSKiFwn68duHx7lW7eAXJyLi8SXoxy/Xqo2ISSX76CMiCpdWNxERhUvQR0QULkEfEVG4BH1EROES9BE9kPRAA685V9KrOp7/haR397tORII+oj1zgVeNOVXEBCXoY1KT9CZJ35Z0k6RzJU2T9ICksyRdL+lrkuZJukrSOkkL6vlOkvRlSV+VtEbSGeOo+R5JKyWtkvSBetis+kpjH5W0WtKlknaqx72onvZaSWdLukXSDsAS4Ph62Y+vX35Ox7K+o8+rK6aoBH1MWpKeQ9Vt9OG25wJbgDcCuwBX2T4UuB/4EHA0cBxVuI6YV08/F3i9pKEeah4DzK7nnQscKumIevRs4BzbzwV+DvxhPfzjwNttv6ReRmw/ApwOfM72XNufq6d9NtWZ1/OAMyRtP761EvHbcmZsTGZHAYcCKyUB7ATcDTwCfLWe5mbgYduPSroZmNUx/2W27wGQ9EXg94DhMWoeU99urJ/vShXwdwI/6Dhj+npgVn0d4d1sj3SP8VngNY/z+v9h+2HgYUl3U3W5sX6MZYp4XAn6mMxEdaWv9/7GQOnd/vUp348BDwPYfkxS53d+9GnhvZwmLuCvbJ87quaskTq1LVQ/POrhNTuNfo38jcaEZddNTGaXAwslPRVA0p6SDhjH/EfX8+wEvA64pod5LgFOlrRrXXPfkfrd1Bd7vl/Si+tBizpG3w/sNo7ljXhCEvQxadm+Ffhz4NK6++jLgKeP4yW+AXwKuAn4gu2xdttg+1Kq3S/X1ruCLmTssD4FWCrpWqot/F/Uw6+kOvjaeTA2ou/SqVlMSZJOAoZsnzqAWrvafqB+fBrwdNvvbLpuxIjs/4to3qslvZfq7+2HwEntLk5MNdmij6hJ2otqv/9oR420zomYjBL0ERGFy8HYiIjCJegjIgqXoI+IKFyCPiKicAn6iIjC/X/lAl1E7oBFWAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "emp_len.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TASK: Charge off rates are extremely similar across all employment lengths. Go ahead and drop the emp_length column.\n",
    "\n",
    "df = df.drop('emp_length', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt                   0\n",
       "term                        0\n",
       "int_rate                    0\n",
       "installment                 0\n",
       "grade                       0\n",
       "sub_grade                   0\n",
       "home_ownership              0\n",
       "annual_inc                  0\n",
       "verification_status         0\n",
       "issue_d                     0\n",
       "loan_status                 0\n",
       "purpose                     0\n",
       "title                    1755\n",
       "dti                         0\n",
       "earliest_cr_line            0\n",
       "open_acc                    0\n",
       "pub_rec                     0\n",
       "revol_bal                   0\n",
       "revol_util                276\n",
       "total_acc                   0\n",
       "initial_list_status         0\n",
       "application_type            0\n",
       "mort_acc                37795\n",
       "pub_rec_bankruptcies      535\n",
       "address                     0\n",
       "loan_repaid                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TASK: Revisit the DataFrame to see what feature columns still have missing data.\n",
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0              vacation\n",
       "1    debt_consolidation\n",
       "2           credit_card\n",
       "3           credit_card\n",
       "4           credit_card\n",
       "5    debt_consolidation\n",
       "6      home_improvement\n",
       "7           credit_card\n",
       "8    debt_consolidation\n",
       "9    debt_consolidation\n",
       "Name: purpose, dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TASK: Review the title column vs the purpose column. Is this repeated information?\n",
    "\n",
    "df['purpose'].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                   Vacation\n",
       "1         Debt consolidation\n",
       "2    Credit card refinancing\n",
       "3    Credit card refinancing\n",
       "4      Credit Card Refinance\n",
       "5         Debt consolidation\n",
       "6           Home improvement\n",
       "7       No More Credit Cards\n",
       "8         Debt consolidation\n",
       "9         Debt Consolidation\n",
       "Name: title, dtype: object"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['title'].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TASK: The title column is simply a string subcategory/description of the purpose column. Go ahead and drop the title column.\n",
    "\n",
    "df = df.drop('title', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of mortgage accounts.\n"
     ]
    }
   ],
   "source": [
    "# TASK: Find out what the mort_acc feature represents\n",
    "\n",
    "feat_info('mort_acc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     139777\n",
       "1.0      60416\n",
       "2.0      49948\n",
       "3.0      38049\n",
       "4.0      27887\n",
       "5.0      18194\n",
       "6.0      11069\n",
       "7.0       6052\n",
       "8.0       3121\n",
       "9.0       1656\n",
       "10.0       865\n",
       "11.0       479\n",
       "12.0       264\n",
       "13.0       146\n",
       "14.0       107\n",
       "15.0        61\n",
       "16.0        37\n",
       "17.0        22\n",
       "18.0        18\n",
       "19.0        15\n",
       "20.0        13\n",
       "24.0        10\n",
       "22.0         7\n",
       "21.0         4\n",
       "25.0         4\n",
       "27.0         3\n",
       "23.0         2\n",
       "32.0         2\n",
       "26.0         2\n",
       "31.0         2\n",
       "30.0         1\n",
       "28.0         1\n",
       "34.0         1\n",
       "Name: mort_acc, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TASK: Create a value_counts of the mort_acc column.\n",
    "\n",
    "df['mort_acc'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation with the mort_acc column\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "int_rate               -0.082583\n",
       "dti                    -0.025439\n",
       "revol_util              0.007514\n",
       "pub_rec                 0.011552\n",
       "pub_rec_bankruptcies    0.027239\n",
       "loan_repaid             0.073111\n",
       "open_acc                0.109205\n",
       "installment             0.193694\n",
       "revol_bal               0.194925\n",
       "loan_amnt               0.222315\n",
       "annual_inc              0.236320\n",
       "total_acc               0.381072\n",
       "mort_acc                1.000000\n",
       "Name: mort_acc, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TASK: Let's review the other columns to see which most highly correlates to mort_acc\n",
    "\n",
    "print(\"Correlation with the mort_acc column\")\n",
    "df.corr()['mort_acc'].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean of mort_acc column per total_acc\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "total_acc\n",
       "2.0      0.000000\n",
       "3.0      0.052023\n",
       "4.0      0.066743\n",
       "5.0      0.103289\n",
       "6.0      0.151293\n",
       "           ...   \n",
       "124.0    1.000000\n",
       "129.0    1.000000\n",
       "135.0    3.000000\n",
       "150.0    2.000000\n",
       "151.0    0.000000\n",
       "Name: mort_acc, Length: 118, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TASK: Looks like the total_acc feature correlates with the mort_acc. Let's try this fillna() approach.\n",
    "# We will group the dataframe by the total_acc and calculate the mean value for the mort_acc per total_acc entry.\n",
    "\n",
    "print(\"Mean of mort_acc column per total_acc\")\n",
    "df.groupby('total_acc').mean()['mort_acc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CHALLENGE TASK: Let's fill in the missing mort_acc values based on their total_acc value.\n",
    "\n",
    "total_acc_avg = df.groupby('total_acc').mean()['mort_acc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_acc_avg[2.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fill_mort_acc(total_acc,mort_acc):\n",
    "    '''\n",
    "    Accepts the total_acc and mort_acc values for the row.\n",
    "    Checks if the mort_acc is NaN , if so, it returns the avg mort_acc value\n",
    "    for the corresponding total_acc value for that row.\n",
    "    \n",
    "    total_acc_avg here should be a Series or dictionary containing the mapping of the\n",
    "    groupby averages of mort_acc per total_acc values.\n",
    "    '''\n",
    "    if np.isnan(mort_acc):\n",
    "        return total_acc_avg[total_acc]\n",
    "    else:\n",
    "        return mort_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['mort_acc'] = df.apply(lambda x: fill_mort_acc(x['total_acc'], x['mort_acc']), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt                 0\n",
       "term                      0\n",
       "int_rate                  0\n",
       "installment               0\n",
       "grade                     0\n",
       "sub_grade                 0\n",
       "home_ownership            0\n",
       "annual_inc                0\n",
       "verification_status       0\n",
       "issue_d                   0\n",
       "loan_status               0\n",
       "purpose                   0\n",
       "dti                       0\n",
       "earliest_cr_line          0\n",
       "open_acc                  0\n",
       "pub_rec                   0\n",
       "revol_bal                 0\n",
       "revol_util              276\n",
       "total_acc                 0\n",
       "initial_list_status       0\n",
       "application_type          0\n",
       "mort_acc                  0\n",
       "pub_rec_bankruptcies    535\n",
       "address                   0\n",
       "loan_repaid               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TASK: revol_util and the pub_rec_bankruptcies have missing data points, but they account for less than 0.5% of the total data.\n",
    "# Go ahead and remove the rows that are missing those values in those columns with dropna().\n",
    "\n",
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt               0\n",
       "term                    0\n",
       "int_rate                0\n",
       "installment             0\n",
       "grade                   0\n",
       "sub_grade               0\n",
       "home_ownership          0\n",
       "annual_inc              0\n",
       "verification_status     0\n",
       "issue_d                 0\n",
       "loan_status             0\n",
       "purpose                 0\n",
       "dti                     0\n",
       "earliest_cr_line        0\n",
       "open_acc                0\n",
       "pub_rec                 0\n",
       "revol_bal               0\n",
       "revol_util              0\n",
       "total_acc               0\n",
       "initial_list_status     0\n",
       "application_type        0\n",
       "mort_acc                0\n",
       "pub_rec_bankruptcies    0\n",
       "address                 0\n",
       "loan_repaid             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Categorical Variables and Dummy Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['term', 'grade', 'sub_grade', 'home_ownership', 'verification_status',\n",
       "       'issue_d', 'loan_status', 'purpose', 'earliest_cr_line',\n",
       "       'initial_list_status', 'application_type', 'address'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TASK: List all the columns that are currently non-numeric\n",
    "\n",
    "df.select_dtypes(['object']).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 36 months    301247\n",
       " 60 months     93972\n",
       "Name: term, dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TASK: Convert the term feature into either a 36 or 60 integer numeric data type using .apply() or .map().\n",
    "\n",
    "df['term'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['term'] = df['term'].apply(lambda term: int(term[:3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TASK: We already know grade is part of sub_grade, so just drop the grade feature.\n",
    "\n",
    "df = df.drop('grade', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TASK: Convert the subgrade into dummy variables. Then concatenate these new columns to the original dataframe.\n",
    "# Remember to drop the original subgrade column and to add drop_first=True to your get_dummies call.\n",
    "\n",
    "subgrade_dummies = pd.get_dummies(df['sub_grade'], drop_first=True)\n",
    "df = pd.concat([df.drop('sub_grade',axis=1),subgrade_dummies],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['loan_amnt', 'term', 'int_rate', 'installment', 'home_ownership',\n",
       "       'annual_inc', 'verification_status', 'issue_d', 'loan_status',\n",
       "       'purpose', 'dti', 'earliest_cr_line', 'open_acc', 'pub_rec',\n",
       "       'revol_bal', 'revol_util', 'total_acc', 'initial_list_status',\n",
       "       'application_type', 'mort_acc', 'pub_rec_bankruptcies', 'address',\n",
       "       'loan_repaid', 'A2', 'A3', 'A4', 'A5', 'B1', 'B2', 'B3', 'B4', 'B5',\n",
       "       'C1', 'C2', 'C3', 'C4', 'C5', 'D1', 'D2', 'D3', 'D4', 'D5', 'E1', 'E2',\n",
       "       'E3', 'E4', 'E5', 'F1', 'F2', 'F3', 'F4', 'F5', 'G1', 'G2', 'G3', 'G4',\n",
       "       'G5'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['home_ownership', 'verification_status', 'issue_d', 'loan_status',\n",
       "       'purpose', 'earliest_cr_line', 'initial_list_status',\n",
       "       'application_type', 'address'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(['object']).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TASK: Convert these columns: ['verification_status', 'application_type','initial_list_status','purpose']\n",
    "# into dummy variables and concatenate them with the original dataframe.\n",
    "# Remember to set drop_first=True and to drop the original columns.\n",
    "\n",
    "dummies = pd.get_dummies(df[['verification_status', 'application_type','initial_list_status','purpose' ]], drop_first=True)\n",
    "df = df.drop(['verification_status', 'application_type','initial_list_status','purpose'], axis=1)\n",
    "df = pd.concat([df,dummies], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MORTGAGE    198022\n",
       "RENT        159395\n",
       "OWN          37660\n",
       "OTHER          110\n",
       "NONE            29\n",
       "ANY              3\n",
       "Name: home_ownership, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TASK:Review the value_counts for the home_ownership column.\n",
    "\n",
    "df['home_ownership'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TASK: Convert these to dummy variables, but replace NONE and ANY with OTHER.\n",
    "# So that we end up with just 4 categories, MORTGAGE, RENT, OWN, OTHER. Then concatenate them with the original dataframe.\n",
    "# Remember to set drop_first=True and to drop the original columns.\n",
    "\n",
    "df['home_ownership'] = df['home_ownership'].replace(['NONE', 'ANY'], 'OTHER')\n",
    "\n",
    "dummies = pd.get_dummies(df['home_ownership'], drop_first=True)\n",
    "df = df.drop('home_ownership', axis=1)\n",
    "df = pd.concat([df,dummies], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TASK: Let's feature engineer a zip code column from the address in the data set.\n",
    "# Create a column called 'zip_code' that extracts the zip code from the address column.\n",
    "\n",
    "df['zip_code'] = df['address'].apply(lambda address:address[-5:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TASK: Now make this zip_code column into dummy variables using pandas.\n",
    "# Concatenate the result and drop the original zip_code column along with dropping the address column.\n",
    "\n",
    "dummies = pd.get_dummies(df['zip_code'], drop_first=True)\n",
    "df = df.drop(['zip_code','address'], axis=1)\n",
    "df = pd.concat([df,dummies], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TASK: This would be data leakage, we wouldn't know beforehand whether or not a loan would be issued when using our model.\n",
    "# So in theory we wouldn't have an issue_date, drop this feature.\n",
    "\n",
    "df = df.drop('issue_d', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TASK: This appears to be a historical time stamp feature.\n",
    "# Extract the year from this feature using apply function, then convert it to a numeric feature.\n",
    "# Set this new data to a feature column called 'earliest_cr_year'. Then drop the earliest_cr_line feature.\n",
    "\n",
    "df['earliest_cr_year'] = df['earliest_cr_line'].apply(lambda date:int(date[-4:]))\n",
    "df = df.drop('earliest_cr_line', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['loan_status'], dtype='object')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(['object']).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TASK: Import train_test_split from sklearn.\n",
    "\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TASK: drop the load_status column we created earlier, since its a duplicate of the loan_repaid column.\n",
    "# We'll use the loan_repaid column since its already in 0s and 1s.\n",
    "\n",
    "df = df.drop('loan_status', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TASK: Set X and y variables to the .values of the features and label.\n",
    "\n",
    "X = df.drop('loan_repaid',axis=1).values\n",
    "y = df['loan_repaid'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TASK: Perform a train/test split with test_size=0.2 and a random_state of 101.\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalizing the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TASK: Use a MinMaxScaler to normalize the feature data X_train and X_test.\n",
    "# Recall we don't want data leakge from the test set so we only fit on the X_train data.\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Activation,Dropout\n",
    "from tensorflow.keras.constraints import max_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TASK: Build a sequential model to will be trained on the data.\n",
    "# You have unlimited options here, but here is what the solution uses: a model that goes 78 --> 39 --> 19--> 1 output neuron\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "# input layer\n",
    "model.add(Dense(78,  activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "# hidden layer\n",
    "model.add(Dense(39, activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "# hidden layer\n",
    "model.add(Dense(19, activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "# output layer\n",
    "model.add(Dense(units=1, activation='sigmoid'))\n",
    "\n",
    "# Compile model\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "1236/1236 [==============================] - 2s 2ms/step - loss: 0.3045 - val_loss: 0.2653\n",
      "Epoch 2/25\n",
      "1236/1236 [==============================] - 2s 2ms/step - loss: 0.2658 - val_loss: 0.2629\n",
      "Epoch 3/25\n",
      "1236/1236 [==============================] - 2s 2ms/step - loss: 0.2631 - val_loss: 0.2627\n",
      "Epoch 4/25\n",
      "1236/1236 [==============================] - 2s 2ms/step - loss: 0.2618 - val_loss: 0.2620\n",
      "Epoch 5/25\n",
      "1236/1236 [==============================] - 2s 2ms/step - loss: 0.2610 - val_loss: 0.2619\n",
      "Epoch 6/25\n",
      "1236/1236 [==============================] - 2s 2ms/step - loss: 0.2604 - val_loss: 0.2621\n",
      "Epoch 7/25\n",
      "1236/1236 [==============================] - 2s 2ms/step - loss: 0.2600 - val_loss: 0.2620\n",
      "Epoch 8/25\n",
      "1236/1236 [==============================] - 2s 2ms/step - loss: 0.2595 - val_loss: 0.2615\n",
      "Epoch 9/25\n",
      "1236/1236 [==============================] - 2s 2ms/step - loss: 0.2593 - val_loss: 0.2612\n",
      "Epoch 10/25\n",
      "1236/1236 [==============================] - 2s 2ms/step - loss: 0.2591 - val_loss: 0.2611\n",
      "Epoch 11/25\n",
      "1236/1236 [==============================] - 2s 2ms/step - loss: 0.2586 - val_loss: 0.2615\n",
      "Epoch 12/25\n",
      "1236/1236 [==============================] - 2s 2ms/step - loss: 0.2586 - val_loss: 0.2611\n",
      "Epoch 13/25\n",
      "1236/1236 [==============================] - 2s 2ms/step - loss: 0.2581 - val_loss: 0.2611\n",
      "Epoch 14/25\n",
      "1236/1236 [==============================] - 2s 2ms/step - loss: 0.2581 - val_loss: 0.2614\n",
      "Epoch 15/25\n",
      "1236/1236 [==============================] - 2s 2ms/step - loss: 0.2577 - val_loss: 0.2614\n",
      "Epoch 16/25\n",
      "1236/1236 [==============================] - 2s 2ms/step - loss: 0.2575 - val_loss: 0.2611\n",
      "Epoch 17/25\n",
      "1236/1236 [==============================] - 2s 2ms/step - loss: 0.2574 - val_loss: 0.2617\n",
      "Epoch 18/25\n",
      "1236/1236 [==============================] - 2s 2ms/step - loss: 0.2572 - val_loss: 0.2612\n",
      "Epoch 19/25\n",
      "1236/1236 [==============================] - 2s 2ms/step - loss: 0.2568 - val_loss: 0.2617\n",
      "Epoch 20/25\n",
      "1236/1236 [==============================] - 2s 2ms/step - loss: 0.2570 - val_loss: 0.2609\n",
      "Epoch 21/25\n",
      "1236/1236 [==============================] - 2s 2ms/step - loss: 0.2568 - val_loss: 0.2613\n",
      "Epoch 22/25\n",
      "1236/1236 [==============================] - 2s 2ms/step - loss: 0.2563 - val_loss: 0.2610\n",
      "Epoch 23/25\n",
      "1236/1236 [==============================] - 2s 2ms/step - loss: 0.2562 - val_loss: 0.2614\n",
      "Epoch 24/25\n",
      "1236/1236 [==============================] - 2s 2ms/step - loss: 0.2562 - val_loss: 0.2609\n",
      "Epoch 25/25\n",
      "1236/1236 [==============================] - 2s 2ms/step - loss: 0.2561 - val_loss: 0.2612\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1b34570ef48>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TASK: Fit the model to the training data for at least 25 epochs. Also add in the validation data for later plotting.\n",
    "# Optional: add in a batch_size of 256.\n",
    "\n",
    "model.fit(x=X_train, y=y_train, epochs=25, batch_size=256, validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TASK: Save your model.\n",
    "\n",
    "from tensorflow.keras.models import load_model\n",
    "model.save('kr_lendingclub_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluating Model Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b34125dd88>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TASK: Plot out the validation loss versus the training loss.\n",
    "\n",
    "losses = pd.DataFrame(model.history.history)\n",
    "losses[['loss','val_loss']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TASK: Create predictions from the X_test set and display a classification report and confusion matrix for the X_test set.\n",
    "\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "predictions = model.predict_classes(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.46      0.62     15658\n",
      "           1       0.88      0.99      0.93     63386\n",
      "\n",
      "    accuracy                           0.89     79044\n",
      "   macro avg       0.92      0.73      0.78     79044\n",
      "weighted avg       0.90      0.89      0.87     79044\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 7205,  8453],\n",
       "       [  386, 63000]], dtype=int64)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt           25000.00\n",
       "term                   60.00\n",
       "int_rate               18.24\n",
       "installment           638.11\n",
       "annual_inc          61665.00\n",
       "                      ...   \n",
       "48052                   0.00\n",
       "70466                   0.00\n",
       "86630                   0.00\n",
       "93700                   0.00\n",
       "earliest_cr_year     1996.00\n",
       "Name: 305323, Length: 78, dtype: float64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TASK: Given the customer below, would you offer this person a loan?\n",
    "\n",
    "import random\n",
    "random.seed(101)\n",
    "random_ind = random.randint(0,len(df))\n",
    "\n",
    "new_customer = df.drop('loan_repaid',axis=1).iloc[random_ind]\n",
    "new_customer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1]])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_classes(new_customer.values.reshape(1,78))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TASK: Now check, did this person actually end up paying back their loan?\n",
    "\n",
    "df.iloc[random_ind]['loan_repaid']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}