BD-CP-Lending-demo_bank_loans_solucion.ipynb

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   "cell_type": "markdown",
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   "source": [
    "## Lending "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YR3-_EEAwrmR"
   },
   "source": [
    "## How Lending Club Works?\n",
    "<img src=\"http://echeck.org/wp-content/uploads/2016/12/Showing-how-the-lending-club-works-and-makes-money-1.png\"><br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 616,
     "status": "ok",
     "timestamp": 1544133064527,
     "user": {
      "displayName": "Carlos Munoz",
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      "userId": "06138933875008493053"
     },
     "user_tz": -60
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    "outputId": "4fc41d2d-01ca-47d2-f84d-0ac55ae81e4b",
    "scrolled": true
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   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# IMPORT LIBRARIES\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import math\n",
    "import scipy.stats as stats \n",
    "import sklearn\n",
    "import random\n",
    "import re\n",
    "import datetime\n",
    "import sys,os\n",
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier      \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_selection import SelectKBest, chi2\n",
    "\n",
    "import chart_studio.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "from plotly.offline import init_notebook_mode,iplot\n",
    "init_notebook_mode(connected=True)\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "pd.set_option('display.max_columns', 100) \n",
    "np.random.seed(1)\n",
    "np.set_printoptions(suppress=True,formatter={'float_kind':'{:f}'.format})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "I2uM8z7IncEx"
   },
   "source": [
    "# 1.- LOAD DATA FROM .CSV FILE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 359
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 53189,
     "status": "ok",
     "timestamp": 1544132939643,
     "user": {
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      "photoUrl": "",
      "userId": "06138933875008493053"
     },
     "user_tz": -60
    },
    "id": "GLg-R5mTncE0",
    "outputId": "c2045f6f-23cc-43a0-fbbc-45ba0cc2357a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This dataset has 887379 rows and 74 columns\n"
     ]
    },
    {
     "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>id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>emp_title</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>verification_status</th>\n",
       "      <th>issue_d</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>pymnt_plan</th>\n",
       "      <th>url</th>\n",
       "      <th>desc</th>\n",
       "      <th>purpose</th>\n",
       "      <th>title</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>addr_state</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>earliest_cr_line</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>mths_since_last_delinq</th>\n",
       "      <th>mths_since_last_record</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>initial_list_status</th>\n",
       "      <th>out_prncp</th>\n",
       "      <th>out_prncp_inv</th>\n",
       "      <th>total_pymnt</th>\n",
       "      <th>total_pymnt_inv</th>\n",
       "      <th>total_rec_prncp</th>\n",
       "      <th>total_rec_int</th>\n",
       "      <th>total_rec_late_fee</th>\n",
       "      <th>recoveries</th>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <th>last_pymnt_d</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>next_pymnt_d</th>\n",
       "      <th>last_credit_pull_d</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <th>mths_since_last_major_derog</th>\n",
       "      <th>policy_code</th>\n",
       "      <th>application_type</th>\n",
       "      <th>annual_inc_joint</th>\n",
       "      <th>dti_joint</th>\n",
       "      <th>verification_status_joint</th>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <th>tot_coll_amt</th>\n",
       "      <th>tot_cur_bal</th>\n",
       "      <th>open_acc_6m</th>\n",
       "      <th>open_il_6m</th>\n",
       "      <th>open_il_12m</th>\n",
       "      <th>open_il_24m</th>\n",
       "      <th>mths_since_rcnt_il</th>\n",
       "      <th>total_bal_il</th>\n",
       "      <th>il_util</th>\n",
       "      <th>open_rv_12m</th>\n",
       "      <th>open_rv_24m</th>\n",
       "      <th>max_bal_bc</th>\n",
       "      <th>all_util</th>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <th>inq_fi</th>\n",
       "      <th>total_cu_tl</th>\n",
       "      <th>inq_last_12m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1077501</td>\n",
       "      <td>1296599</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>4975.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>10.65</td>\n",
       "      <td>162.87</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>Verified</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>n</td>\n",
       "      <td>https://www.lendingclub.com/browse/loanDetail....</td>\n",
       "      <td>Borrower added on 12/22/11 &gt; I need to upgra...</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>Computer</td>\n",
       "      <td>860xx</td>\n",
       "      <td>AZ</td>\n",
       "      <td>27.65</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Jan-1985</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13648.0</td>\n",
       "      <td>83.7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5861.071414</td>\n",
       "      <td>5831.78</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>861.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Jan-2015</td>\n",
       "      <td>171.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1077430</td>\n",
       "      <td>1314167</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>15.27</td>\n",
       "      <td>59.83</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>Ryder</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>RENT</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>https://www.lendingclub.com/browse/loanDetail....</td>\n",
       "      <td>Borrower added on 12/22/11 &gt; I plan to use t...</td>\n",
       "      <td>car</td>\n",
       "      <td>bike</td>\n",
       "      <td>309xx</td>\n",
       "      <td>GA</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Apr-1999</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1687.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1008.710000</td>\n",
       "      <td>1008.71</td>\n",
       "      <td>456.46</td>\n",
       "      <td>435.17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>117.08</td>\n",
       "      <td>1.11</td>\n",
       "      <td>Apr-2013</td>\n",
       "      <td>119.66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sep-2013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  member_id  loan_amnt  funded_amnt  funded_amnt_inv        term  \\\n",
       "0  1077501    1296599     5000.0       5000.0           4975.0   36 months   \n",
       "1  1077430    1314167     2500.0       2500.0           2500.0   60 months   \n",
       "\n",
       "   int_rate  installment grade sub_grade emp_title emp_length home_ownership  \\\n",
       "0     10.65       162.87     B        B2       NaN  10+ years           RENT   \n",
       "1     15.27        59.83     C        C4     Ryder   < 1 year           RENT   \n",
       "\n",
       "   annual_inc verification_status    issue_d  loan_status pymnt_plan  \\\n",
       "0     24000.0            Verified 2011-12-01   Fully Paid          n   \n",
       "1     30000.0     Source Verified 2011-12-01  Charged Off          n   \n",
       "\n",
       "                                                 url  \\\n",
       "0  https://www.lendingclub.com/browse/loanDetail....   \n",
       "1  https://www.lendingclub.com/browse/loanDetail....   \n",
       "\n",
       "                                                desc      purpose     title  \\\n",
       "0    Borrower added on 12/22/11 > I need to upgra...  credit_card  Computer   \n",
       "1    Borrower added on 12/22/11 > I plan to use t...          car      bike   \n",
       "\n",
       "  zip_code addr_state    dti  delinq_2yrs earliest_cr_line  inq_last_6mths  \\\n",
       "0    860xx         AZ  27.65          0.0         Jan-1985             1.0   \n",
       "1    309xx         GA   1.00          0.0         Apr-1999             5.0   \n",
       "\n",
       "   mths_since_last_delinq  mths_since_last_record  open_acc  pub_rec  \\\n",
       "0                     NaN                     NaN       3.0      0.0   \n",
       "1                     NaN                     NaN       3.0      0.0   \n",
       "\n",
       "   revol_bal  revol_util  total_acc initial_list_status  out_prncp  \\\n",
       "0    13648.0        83.7        9.0                   f        0.0   \n",
       "1     1687.0         9.4        4.0                   f        0.0   \n",
       "\n",
       "   out_prncp_inv  total_pymnt  total_pymnt_inv  total_rec_prncp  \\\n",
       "0            0.0  5861.071414          5831.78          5000.00   \n",
       "1            0.0  1008.710000          1008.71           456.46   \n",
       "\n",
       "   total_rec_int  total_rec_late_fee  recoveries  collection_recovery_fee  \\\n",
       "0         861.07                 0.0        0.00                     0.00   \n",
       "1         435.17                 0.0      117.08                     1.11   \n",
       "\n",
       "  last_pymnt_d  last_pymnt_amnt next_pymnt_d last_credit_pull_d  \\\n",
       "0     Jan-2015           171.62          NaN           Jan-2016   \n",
       "1     Apr-2013           119.66          NaN           Sep-2013   \n",
       "\n",
       "   collections_12_mths_ex_med  mths_since_last_major_derog  policy_code  \\\n",
       "0                         0.0                          NaN          1.0   \n",
       "1                         0.0                          NaN          1.0   \n",
       "\n",
       "  application_type  annual_inc_joint  dti_joint verification_status_joint  \\\n",
       "0       INDIVIDUAL               NaN        NaN                       NaN   \n",
       "1       INDIVIDUAL               NaN        NaN                       NaN   \n",
       "\n",
       "   acc_now_delinq  tot_coll_amt  tot_cur_bal  open_acc_6m  open_il_6m  \\\n",
       "0             0.0           NaN          NaN          NaN         NaN   \n",
       "1             0.0           NaN          NaN          NaN         NaN   \n",
       "\n",
       "   open_il_12m  open_il_24m  mths_since_rcnt_il  total_bal_il  il_util  \\\n",
       "0          NaN          NaN                 NaN           NaN      NaN   \n",
       "1          NaN          NaN                 NaN           NaN      NaN   \n",
       "\n",
       "   open_rv_12m  open_rv_24m  max_bal_bc  all_util  total_rev_hi_lim  inq_fi  \\\n",
       "0          NaN          NaN         NaN       NaN               NaN     NaN   \n",
       "1          NaN          NaN         NaN       NaN               NaN     NaN   \n",
       "\n",
       "   total_cu_tl  inq_last_12m  \n",
       "0          NaN           NaN  \n",
       "1          NaN           NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(data_path, parse_dates=['issue_d'], low_memory=False)\n",
    "print ('This dataset has {0} rows and {1} columns'.format(data.shape[0], data.shape[1]))\n",
    "data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XBlHaiOMncE_"
   },
   "source": [
    "## 1.1.- Data preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WN29aqNdncFB"
   },
   "source": [
    "#####  Let´s see if there are variables with more than 80% not informed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 394
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2874,
     "status": "ok",
     "timestamp": 1544132963001,
     "user": {
      "displayName": "Carlos Munoz",
      "photoUrl": "",
      "userId": "06138933875008493053"
     },
     "user_tz": -60
    },
    "id": "Pn_L83KvncFD",
    "outputId": "4473dadc-f1da-4a3c-e067-5c8fffafa277"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 19 variables with more than 80% of empty values\n"
     ]
    }
   ],
   "source": [
    "missing = data.isnull().sum()\n",
    "missing = missing[missing.values >(0.8*len(data))]\n",
    "plt.figure(figsize=(20,4))\n",
    "missing.plot(kind='bar')\n",
    "plt.title('List of Columns & NA counts where NA values are more than 80%')\n",
    "plt.show();\n",
    "print('There are {} variables with more than 80% of empty values'.format(missing.shape[0])) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "86E8shzQncFM"
   },
   "source": [
    "##### As we can see these variables are very poorly informed, I will remove those columns  as it is not feasable to impute missing values for those columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 8330,
     "status": "ok",
     "timestamp": 1544132972505,
     "user": {
      "displayName": "Carlos Munoz",
      "photoUrl": "",
      "userId": "06138933875008493053"
     },
     "user_tz": -60
    },
    "id": "fIdYkzgoncFO",
    "outputId": "5142f020-52b1-47b1-a038-d73d239b8ef0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This dataset has now 887379 rows and 55 columns\n"
     ]
    }
   ],
   "source": [
    "for i in list(missing.index):\n",
    "    data.drop(i,1,inplace=True)\n",
    "print ('This dataset has now {0} rows and {1} columns'.format(data.shape[0], data.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YmgysZoHncFX"
   },
   "source": [
    "##### Now let´s check if there are any variable with unique values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4046,
     "status": "ok",
     "timestamp": 1544132979116,
     "user": {
      "displayName": "Carlos Munoz",
      "photoUrl": "",
      "userId": "06138933875008493053"
     },
     "user_tz": -60
    },
    "id": "PjFTGfzancFa",
    "outputId": "0431b8b5-a829-43a3-a607-70f4b92cafce"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The following variables have been dropped because they have a single value:  ['policy_code', 'pymnt_plan', 'application_type']\n",
      "This dataset has now 887379 rows and 52 columns\n"
     ]
    }
   ],
   "source": [
    "unique_list= []\n",
    "for i in data:\n",
    "    if len(list(data[i].unique()))==1:\n",
    "        data.drop(i,1,inplace=True)\n",
    "        unique_list.append(i)\n",
    "\n",
    "# There are also two other variables with more than 99.999% filled with the same value, we will delete those too.\n",
    "almost_unique = ['pymnt_plan', 'application_type']\n",
    "data.drop(almost_unique, axis=1, inplace=True)\n",
    "\n",
    "print ('The following variables have been dropped because they have a single value: ',unique_list + almost_unique)\n",
    "print ('This dataset has now {0} rows and {1} columns'.format(data.shape[0], data.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fXLhYiJyncFw"
   },
   "source": [
    "#### Previously I have removed columns based on missing and unique values. Now it´s time for a business perspective analysis and for that purpose identifier variables such as 'id´s' are irrelevant. Let´s delete them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 729,
     "status": "ok",
     "timestamp": 1544132981305,
     "user": {
      "displayName": "Carlos Munoz",
      "photoUrl": "",
      "userId": "06138933875008493053"
     },
     "user_tz": -60
    },
    "id": "lvCqzdaYncF0",
    "outputId": "21e7b8bc-b6bc-4838-fc8b-6c3ad9372df2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This dataset has now 887379 rows and 47 columns\n"
     ]
    }
   ],
   "source": [
    "irrelevant_columns = ['id', 'member_id', 'url', 'zip_code', 'title']\n",
    "data.drop(labels = irrelevant_columns, axis =1, inplace=True)\n",
    "print ('This dataset has now {0} rows and {1} columns'.format(data.shape[0], data.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iMWoI9UYncGB"
   },
   "source": [
    "# 2.- EXPLORATORY ANALYSIS\n",
    "##### In this stage I will explore in greater depth the data to have a better understanding of the business. We will use pandas library to find the business insights."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mSW_4bPyncGD"
   },
   "source": [
    "### Analyze Loan amount and Interest rates\n",
    "   - Frequency distribution of loan amount.\n",
    "   - Boxplot and violin plot of loan amount.\n",
    "   - Frequency distribution of interest rates. \n",
    "   - Boxplot and violin plot of interest rates.\n",
    "   \n",
    "More information about normal distribution --> https://en.wikipedia.org/wiki/Normal_distribution\n",
    "   \n",
    "More information about boxplot -->    https://en.wikipedia.org/wiki/Box_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 548
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 11359,
     "status": "ok",
     "timestamp": 1544132995125,
     "user": {
      "displayName": "Carlos Munoz",
      "photoUrl": "",
      "userId": "06138933875008493053"
     },
     "user_tz": -60
    },
    "id": "hQK7Rw7nncGF",
    "outputId": "e43f947f-3424-4000-fda6-160bcaa064e4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmp = data[[\"loan_amnt\", \"int_rate\"]].fillna(0)\n",
    "\n",
    "fig, ax =plt.subplots(2,3, figsize=(14,10))\n",
    "plt.subplots_adjust(hspace = 0.3, top = 0.8)\n",
    "\n",
    "# Loan amount distribution plots\n",
    "sns.distplot(tmp.loan_amnt, fit=stats.norm, axlabel=\"Loan Amount\",\n",
    "             label=\"Loan Amount Frequency distribution\", ax=ax[0][0])\n",
    "sns.boxplot(x=tmp.loan_amnt, ax=ax[0][1])\n",
    "sns.violinplot(x=\"loan_amnt\", data=data, inner=\"quartile\", ax=ax[0][2])\n",
    "\n",
    "# Interest rates distribution plots\n",
    "sns.distplot(tmp.int_rate, fit=stats.norm, axlabel=\"Interest Rate\",\n",
    "             label=\"Interest Frequency distribution\", ax=ax[1][0])\n",
    "sns.boxplot(x=tmp.int_rate, ax=ax[1][1])\n",
    "sns.violinplot(x=\"int_rate\", data=data, inner=\"quartile\", ax=ax[1][2])\n",
    "fig.show()\n",
    "plt.tight_layout;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Oe36551N7f0J"
   },
   "source": [
    "#### As a result  of this analysis, it can be inferred that most of the loans were in the range of 10000 and 15000 usd with a 13% interest rate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UmRN1WYJncHT"
   },
   "source": [
    "### Analyze loans by its purpose\n",
    "- Number of loans by the most common purposes\n",
    "- Loan amount with its distribution pattern by purpose; hues by its term\n",
    "- Interest rate with its distribution pattern by purpose; hues by its term"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1093
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 12602,
     "status": "ok",
     "timestamp": 1544133010417,
     "user": {
      "displayName": "Carlos Munoz",
      "photoUrl": "",
      "userId": "06138933875008493053"
     },
     "user_tz": -60
    },
    "id": "AajmPABancHW",
    "outputId": "cd121123-0ad6-4cf0-e13c-c57cdeb178bc"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1440 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmpdata = data[[\"purpose\", \"int_rate\", \"loan_amnt\", \"term\"]]\n",
    "tmpdata = tmpdata[(tmpdata.purpose).isin(list(tmpdata['purpose'].value_counts().iloc[:10].index))]\n",
    "\n",
    "fig, ax=plt.subplots(3,1,figsize=(18,20))\n",
    "plt.subplots_adjust(hspace = 0.4, top = 0.8)\n",
    "\n",
    "s0 = sns.countplot(x=\"purpose\",data=tmpdata,ax=ax[0])\n",
    "s0.set(xlabel='Purpose',ylabel='Number of Loans')\n",
    "s0.set_xticklabels(s0.get_xticklabels(),rotation=45)\n",
    "\n",
    "s1 = sns.violinplot(x=\"purpose\",y=\"loan_amnt\",data=tmpdata, ax=ax[1], hue=\"term\", split=True)\n",
    "s1.set(xlabel='Purpose',ylabel='Loan Amount')\n",
    "s1.set_xticklabels(s1.get_xticklabels(),rotation=45)\n",
    "\n",
    "s2 = sns.violinplot(x=\"purpose\",y=\"int_rate\",data=tmpdata, ax=ax[2], hue=\"term\", split=True)\n",
    "s2.set(xlabel='Purpose',ylabel='Interest Rate')\n",
    "s2.set_xticklabels(s2.get_xticklabels(),rotation=45)\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jNTqK9vkSDrm"
   },
   "source": [
    "#### As it can be seen, the three main reasons to ask for a loan are debt consolidation, home improvements and credit cards. Also that  small businesses have higher interest rates and loan amounts. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oenSkCj_ncGT"
   },
   "source": [
    "### Analyze  Loans issued and Interest rates over time\n",
    "- Number of loan's growth over time\n",
    "- Total loan book value growth over time\n",
    "- Customers loan requirements over time (Median loan amount)\n",
    "- Median interest rates over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 669
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 5034,
     "status": "ok",
     "timestamp": 1544133018544,
     "user": {
      "displayName": "Carlos Munoz",
      "photoUrl": "",
      "userId": "06138933875008493053"
     },
     "user_tz": -60
    },
    "id": "U_RePr9FncGV",
    "outputId": "921caf5c-f57f-4f46-ed50-fdf77212fa64"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets' transform the issue dates by year.\n",
    "data['issued_year'] = pd.to_datetime(data['issue_d']).dt.year\n",
    "data.drop('issue_d', axis=1, inplace = True)\n",
    "\n",
    "# Number of loan over year\n",
    "loansOverTime = data.groupby(\"issued_year\", sort= True).count()[['loan_amnt']]\n",
    "loansOverTime.columns = [\"Number of Loans\"]\n",
    "\n",
    "fig, ax =plt.subplots(2,2, figsize=(18,12))\n",
    "plt.subplots_adjust(hspace = 0.4, top = 0.8)\n",
    "\n",
    "s0=sns.pointplot(x=loansOverTime.index, y=loansOverTime[\"Number of Loans\"], ax=ax[0][0])\n",
    "s0.set_xticklabels(s0.get_xticklabels(),rotation=90)\n",
    "\n",
    "# loan Amount over year\n",
    "totalloansOverTime = data.groupby(\"issued_year\", sort= True).sum()[['loan_amnt']]\n",
    "totalloansOverTime.columns = [\"Total Loan Amount\"]\n",
    "\n",
    "s1 = sns.pointplot(x=totalloansOverTime.index, y=totalloansOverTime[\"Total Loan Amount\"], capsize=.2, ax=ax[0][1])\n",
    "s1.set_xticklabels(s1.get_xticklabels(),rotation=90)\n",
    "\n",
    "# Median Loan Amount, grouped by issue year\n",
    "meanloanAmtOverTime = data.groupby(\"issued_year\", sort= True).mean()[['loan_amnt']]\n",
    "meanloanAmtOverTime.columns = [\"Mean Loan Amount\"]\n",
    "\n",
    "s2 = sns.pointplot(x=meanloanAmtOverTime.index, y=meanloanAmtOverTime[\"Mean Loan Amount\"], capsize=.2, ax=ax[1][0])\n",
    "s2.set_xticklabels(s2.get_xticklabels(),rotation=90)\n",
    "\n",
    "# Mean Interest rate, grouped by issue year \n",
    "meanIntrateOverTime = data.groupby(\"issued_year\", sort= True).mean()[['int_rate']]\n",
    "meanIntrateOverTime.columns = [\"Mean Interest rate\"]\n",
    "\n",
    "s3 = sns.pointplot(x=meanIntrateOverTime.index, y=meanIntrateOverTime[\"Mean Interest rate\"], capsize=.2, ax=ax[1][1])\n",
    "s3.set_xticklabels(s3.get_xticklabels(),rotation=90)\n",
    "\n",
    "fig.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bCE8jRkTSDr_"
   },
   "source": [
    "#### The number of issued loan was steady during the 2007-2011 period but has rocked from 2012. This trend is reflected also in the total loan amount asked. It is also worth noting that the average interest rates have declined since 2013, what might explain the increase in the volume of loans."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XGHEakZ5ncG3"
   },
   "source": [
    "### Analyze Loans over loan status.\n",
    "- Number of loans over the most common loan status\n",
    "- Distribution of total accounts for each status\n",
    "- Distribution of loan amount with the probability density for each loan value over loan status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1145
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2084,
     "status": "ok",
     "timestamp": 1544133022536,
     "user": {
      "displayName": "Carlos Munoz",
      "photoUrl": "",
      "userId": "06138933875008493053"
     },
     "user_tz": -60
    },
    "id": "-3RqKHa7ncG6",
    "outputId": "9cefd884-6754-441a-8d28-2a3800b8cbd5",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x2160 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmp = data[[\"loan_status\", \"total_acc\", \"loan_amnt\"]]\n",
    "tmp = tmp[tmp.total_acc <= 200]\n",
    "\n",
    "fig, ax =plt.subplots(2,1,figsize=(18,30))\n",
    "plt.subplots_adjust(hspace = 0.4, top = 0.6)\n",
    "\n",
    "s0 = sns.countplot(x=\"loan_status\", data=tmp, ax=ax[0], order=tmp.loan_status.value_counts().iloc[:7].index)\n",
    "s0.set_xticklabels(s0.get_xticklabels(), rotation=45)\n",
    "\n",
    "s1 = sns.boxplot(x=\"loan_status\", y=\"total_acc\", data=tmp, ax=ax[1],  order=tmp.loan_status.value_counts().iloc[:7].index)\n",
    "s1.set_xticklabels(s1.get_xticklabels(), rotation=45)\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "teCg5jRrSDsN"
   },
   "source": [
    "#### In this chart, we can see the volume of loans that remain in force in relation to the rest of status"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yCfI_sv0ncHJ"
   },
   "source": [
    "### Analyze the total loan amount by income range, interest rate and loan status\n",
    "\n",
    "##### Let´s see if there a relation between the annual income and the loan status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 464
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1590,
     "status": "ok",
     "timestamp": 1544133033316,
     "user": {
      "displayName": "Carlos Munoz",
      "photoUrl": "",
      "userId": "06138933875008493053"
     },
     "user_tz": -60
    },
    "id": "anraIx9lSDsS",
    "outputId": "4498e7dd-0b38-49fb-cfeb-d82b33e1b727"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmpdata = data[[\"annual_inc\", \"loan_amnt\", \"loan_status\"]]\n",
    "\n",
    "# Quantile-based discretization function.\n",
    "tmpdata['annual_inc_range'] = pd.qcut(tmpdata[\"annual_inc\"], 5)\n",
    "\n",
    "data_plot = tmpdata[[\"annual_inc_range\", \"loan_amnt\", \"loan_status\"]]\n",
    "data_plot = data_plot[(data_plot.loan_status).isin(list(data_plot['loan_status'].value_counts().iloc[:5].index))]\n",
    "data_plot = data_plot.groupby([\"annual_inc_range\", \"loan_status\"], sort= True).sum().reset_index()\n",
    "\n",
    "fig, ax =plt.subplots(1,1,figsize=(10,6))\n",
    "s0 = sns.barplot(x=\"annual_inc_range\", y=\"loan_amnt\", hue=\"loan_status\", data=data_plot)\n",
    "s0.set(xlabel='Annual Income Range',ylabel='Loan Amount')\n",
    "s0.set_xticklabels(s0.get_xticklabels(),rotation=45)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JTyi47GYSDsZ"
   },
   "source": [
    "#### There is a clear relation between anual income and loan status, the higher the income the greater in loans volume."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "N8lnnzbTSDsc"
   },
   "source": [
    "##### Let´s see if there a relation between interest rates and loan amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 331
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1076,
     "status": "ok",
     "timestamp": 1544133041985,
     "user": {
      "displayName": "Carlos Munoz",
      "photoUrl": "",
      "userId": "06138933875008493053"
     },
     "user_tz": -60
    },
    "id": "lGWQzFL6SDsc",
    "outputId": "41e92c11-e2e2-4d7e-b9db-18ba32d47edf"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmpdata = data[[\"int_rate\", \"loan_amnt\"]]\n",
    "\n",
    "# Quantile-based discretization function.\n",
    "tmpdata['int_rate_range'] = pd.qcut(tmpdata[\"int_rate\"], 5)\n",
    "\n",
    "data_plot = tmpdata.groupby(\"int_rate_range\", sort= True).sum()[['loan_amnt']].reset_index()\n",
    "\n",
    "fig, ax =plt.subplots(1,1,figsize=(8,4))\n",
    "\n",
    "s1 = sns.barplot(x=\"int_rate_range\", y=\"loan_amnt\", data=data_plot)\n",
    "s1.set(xlabel='Interest Rate Range',ylabel='Loan Amount')\n",
    "s1.set_xticklabels(s1.get_xticklabels(),rotation=45)\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vOiMB2_fSDsg"
   },
   "source": [
    "#### Acoording to the provided data, greater loan amounts does not strictly correlate with higher interest rates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "E7mPmwAnncHB"
   },
   "source": [
    "### Analyze total loan amount distribution in the US states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2585,
     "status": "ok",
     "timestamp": 1544133085043,
     "user": {
      "displayName": "Carlos Munoz",
      "photoUrl": "",
      "userId": "06138933875008493053"
     },
     "user_tz": -60
    },
    "id": "hhY1WboRncHC",
    "outputId": "86de5c16-4377-4ca8-b895-879b1424f695"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
       "data": [
        {
         "colorbar": {
          "title": {
           "text": "Billions USD"
          }
         },
         "colorscale": [
          [
           0,
           "rgb(242,240,247)"
          ],
          [
           0.2,
           "rgb(218,218,235)"
          ],
          [
           0.4,
           "rgb(188,189,220)"
          ],
          [
           0.6,
           "rgb(158,154,200)"
          ],
          [
           0.8,
           "rgb(117,107,177)"
          ],
          [
           1,
           "rgb(84,39,143)"
          ]
         ],
         "locationmode": "USA-states",
         "locations": [
          "AK",
          "AL",
          "AR",
          "AZ",
          "CA",
          "CO",
          "CT",
          "DC",
          "DE",
          "FL",
          "GA",
          "HI",
          "IA",
          "ID",
          "IL",
          "IN",
          "KS",
          "KY",
          "LA",
          "MA",
          "MD",
          "ME",
          "MI",
          "MN",
          "MO",
          "MS",
          "MT",
          "NC",
          "ND",
          "NE",
          "NH",
          "NJ",
          "NM",
          "NV",
          "NY",
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\"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Total Loan Amount by US States\"}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('aeac0d68-0d94-499b-98b7-5f6063ba6f2c');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
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       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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       "\n",
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       "                        })\n",
       "                };\n",
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       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "totalloansByState = data.groupby(\"addr_state\").sum()[['loan_amnt']].reset_index()\n",
    "totalloansByState.columns = [\"US-State\", \"Total Loan Amount\"]\n",
    "\n",
    "scl = [[0.0, 'rgb(242,240,247)'],[0.2, 'rgb(218,218,235)'],[0.4, 'rgb(188,189,220)'],\n",
    "       [0.6, 'rgb(158,154,200)'],[0.8, 'rgb(117,107,177)'],[1.0, 'rgb(84,39,143)']]\n",
    "\n",
    "data_ = dict(\n",
    "        type='choropleth',\n",
    "        colorscale = scl,\n",
    "        #autocolorscale = False,\n",
    "        locations = totalloansByState['US-State'],\n",
    "        z = totalloansByState['Total Loan Amount'].astype(float),\n",
    "        locationmode = 'USA-states',\n",
    "        text = totalloansByState['US-State'],\n",
    "        marker = dict(\n",
    "            line = dict (\n",
    "                color = 'rgb(255,255,255)',\n",
    "                width = 2\n",
    "            )\n",
    "        ),\n",
    "        colorbar = dict(\n",
    "            title = \"Billions USD\"\n",
    "        ),\n",
    "        #colorscale = 'Viridis', \n",
    "        reversescale = True\n",
    "    )\n",
    "\n",
    "layout = dict(\n",
    "        title = 'Total Loan Amount by US States',\n",
    "        geo = dict(\n",
    "            projection=dict( type='albers usa' ),\n",
    "            showframe=False\n",
    "        ),\n",
    "    )\n",
    "\n",
    "choromap = go.Figure(data = [data_], layout = layout)\n",
    "iplot(choromap, validate=False, image = 'png', filename='StateMapLoanAmount')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "C2YHbRurSDsp"
   },
   "source": [
    "#### As it can be seen, California and Texas together with Florida and NewYork are s states with higher loan amounts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mlhuxSKzSDsr"
   },
   "source": [
    "### Analyze good and bad loans\n",
    "- Amount of bad loans Lending Club has declared so far\n",
    "- Amount of bad loans over the years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 308
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1957,
     "status": "ok",
     "timestamp": 1544133097707,
     "user": {
      "displayName": "Carlos Munoz",
      "photoUrl": "",
      "userId": "06138933875008493053"
     },
     "user_tz": -60
    },
    "id": "faCv6Wc0SDss",
    "outputId": "187c9afb-3f1d-4bc7-c056-75d21535ac52"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bad_status = [\"Charged Off\", \"Default\", \"Does not meet the credit policy. Status:Charged Off\",\n",
    "             \"In Grace Period\", \"Late (16-30 days)\", \"Late (31-120 days)\"]\n",
    "data['loan_condition'] = data['loan_status'].map(lambda x: 'Bad_loan' if x in bad_status else 'Good_loan')\n",
    "\n",
    "f, ax = plt.subplots(1,2, figsize=(12,4))\n",
    "labels =\"Good Loans\", \"Bad Loans\"\n",
    "plt.suptitle('Information on Loan Conditions', fontsize=12)\n",
    "data[\"loan_condition\"].value_counts().plot.pie(explode=[0,0.25], autopct='%1.2f%%', ax=ax[0], shadow=True, colors=['c','r'], \n",
    "                                             labels=labels, fontsize=10, startangle=70)\n",
    "ax[0].set_ylabel('% of Condition of Loans', fontsize=10)\n",
    "\n",
    "data_loan_conditions = data.groupby([\"issued_year\",\"loan_condition\"],\n",
    "                                    sort= True).agg({'loan_amnt': lambda x: len(x) / float(len(data)) * 100})[['loan_amnt']].reset_index()\n",
    "sns.barplot(x=\"issued_year\", y=\"loan_amnt\", hue=\"loan_condition\", data=data_loan_conditions, palette=['r','c'])\n",
    "ax[1].set(ylabel=\"(%)\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_ESwYc9xSDsx"
   },
   "source": [
    "#### Here it can be seen that bad loans accounts for 7.6% of the total but be aware that there is a huge volume of current loans that still have the risk of becoming bad loans. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UGvcFInmSDsy"
   },
   "source": [
    "# Now it´s time for  discover new insights whitin the data, some possibilities are: \n",
    "\n",
    "- Analyze borrowers by employment rate and employment lenght. What profession asks for more loans?\n",
    "- Analyze loan´s grade by loan amount and interest rate. Is there any clear trend?\n",
    "- Analyze the relation between interest rates and good or bad loans. \n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Analyze borrowers by employment rate and employment lenght. What profession asks for more loans?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12d061257b8>"
      ]
     },
     "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": [
    "data['emp_title'].value_counts()[:5].plot.barh()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The profession that asks for more loans is the teachers, followed by the managers.\n",
    "\n",
    "This graph alone is not relevant to the lender, unless it is compared by filtering or eliminating those credits that are good pay.\n",
    "\n",
    "As for the loans that are bad pay, there if we are interested in knowing who is the one who most asks for loans to eliminate that profession and not give loans.\n",
    "\n",
    "Since there is possibly a tendency of that profession not to pay.\n",
    "\n",
    "Or to be placed in circumstances of greater economic vulnerability and that is why they do not pay, that would be the interesting fact"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.Analyze loan´s grade by loan amount and interest rate. Is there any clear trend?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmpdata = data[[\"int_rate\", \"loan_amnt\",'grade']]\n",
    "\n",
    "# Quantile-based discretization function.\n",
    "tmpdata['int_rate_range'] = pd.qcut(tmpdata[\"int_rate\"], 5)\n",
    "\n",
    "data_plot = tmpdata.groupby(\"int_rate_range\", sort= True).sum()[['loan_amnt']].reset_index()\n",
    "\n",
    "fig, ax =plt.subplots(1,1,figsize=(8,4))\n",
    "\n",
    "s1 = sns.barplot(x=\"int_rate_range\", y=\"loan_amnt\", data=tmpdata)\n",
    "s1.set(xlabel='Interest Rate Range',ylabel='Loan Amount')\n",
    "s1.set_xticklabels(s1.get_xticklabels(),rotation=45)\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Filtering by bad_loan**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmpdata = data[[\"int_rate\", \"loan_amnt\",'grade','loan_condition']]\n",
    "\n",
    "# Quantile-based discretization function.\n",
    "tmpdata = tmpdata[tmpdata['loan_condition']=='Bad_loan']\n",
    "tmpdata['int_rate_range'] = pd.qcut(tmpdata[\"int_rate\"], 5)\n",
    "\n",
    "data_plot = data_plot.groupby(\"int_rate_range\", sort= True).sum()[['loan_amnt']].reset_index()\n",
    "\n",
    "fig, ax =plt.subplots(1,1,figsize=(8,4))\n",
    "\n",
    "s1 = sns.barplot(x=\"int_rate_range\", y=\"loan_amnt\", data=tmpdata)\n",
    "s1.set(xlabel='Interest Rate Range',ylabel='Loan Amount')\n",
    "s1.set_xticklabels(s1.get_xticklabels(),rotation=45)\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chart 2.1 and 2.2 indicate a clear trend that says the greater the amount, the higher the rate.\n",
    "\n",
    "2.1 includes all borrowers.\n",
    "\n",
    "And 2.2 includes only those who were poorly paid.\n",
    "\n",
    "But as you can see good or bad pay the interest rate goes up when the amount is higher.\n",
    "\n",
    "This can also be explained because the number of credit providers is lower, and at a lower offer the price rises, according to the principles of the economy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.Analyze the relation between interest rates and good or bad loans."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmpdata = data[[\"int_rate\", \"loan_amnt\",'grade','loan_condition']]\n",
    "\n",
    "# Quantile-based discretization function.\n",
    "data['int_rate_range'] = pd.qcut(data[\"int_rate\"], 5)\n",
    "\n",
    "data_plot = data[data['loan_condition']=='Bad_loan']\n",
    "data_plot = data_plot[[\"int_rate_range\", \"loan_amnt\", \"grade\"]]\n",
    "data_plot = data_plot[(data_plot.grade).isin(list(data_plot['grade'].value_counts().iloc[:5].index))]\n",
    "data_plot = data_plot.groupby([\"int_rate_range\", \"grade\"], sort= True).int_rate_range.agg('count').to_frame('count_users').reset_index()\n",
    "\n",
    "fig, ax =plt.subplots(1,1,figsize=(10,6))\n",
    "s0 = sns.barplot(x=\"grade\", y=\"count_users\", hue=\"int_rate_range\", data=data_plot)\n",
    "s0.set(xlabel='grade',ylabel='count_users')\n",
    "s0.set_xticklabels(s0.get_xticklabels(),rotation=45)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Chart number three indicates that the categories D E F, which were already rated according to the estimated risk of them, were indeed bad pay.\n",
    "\n",
    "Since this graph was made taking into account only the people who were poorly paid, and we observe that not only those who were rated as effective, that is to say the highest risk, were the only ones who stopped paying, we even observed that there were a high number of people who had a B rating, and who also did not pay is the yellow line.\n",
    "\n",
    "We observe that the two high risk factors when combined end up in default, that is to say we see three large columns to the right of purple color that is to say they had high interest rate, and it was combined with that they were also classified as high risk DEF, Y They finally ended up not paying.\n",
    "\n",
    "Calls to attention that people with a C rating, which is not considered as high risk, will stop paying, despite the average interest rate (from 11.99 to 13.99)\n",
    "\n",
    "So we see utility in having an algorithm that predicts if the person we are analyzing is going to be good pay or bad.\n",
    "\n",
    "But there is also benefit in knowing the business rules, which arise from analyzing these latest graphs and variables and which we have just commented."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GyATRBkIncHd"
   },
   "source": [
    "# 3.- Featuring Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tjGZ-OMgncHf"
   },
   "source": [
    "### Data Cleaning & Missing Data Imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MAsrNjKCSDs2"
   },
   "outputs": [],
   "source": [
    "# First we make a copy of the dataset\n",
    "data_model = data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          Fully Paid\n",
       "1         Charged Off\n",
       "2          Fully Paid\n",
       "3          Fully Paid\n",
       "4             Current\n",
       "             ...     \n",
       "887374        Current\n",
       "887375        Current\n",
       "887376        Current\n",
       "887377        Current\n",
       "887378        Current\n",
       "Name: loan_status, Length: 887379, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['loan_status']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2fTjLBqSSDs3"
   },
   "source": [
    "#### In order to fit a model to our data it is necessary to convert categorical variables into numerical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 570
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4652,
     "status": "ok",
     "timestamp": 1544133108834,
     "user": {
      "displayName": "Carlos Munoz",
      "photoUrl": "",
      "userId": "06138933875008493053"
     },
     "user_tz": -60
    },
    "id": "qZ9t_y1USDs4",
    "outputId": "9034877a-2f8a-4f80-fe95-9ef58ff18f20"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical variables:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Variable</th>\n",
       "      <th>Example</th>\n",
       "      <th>Number of levels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>term</td>\n",
       "      <td>[' 36 months', ' 60 months']</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>grade</td>\n",
       "      <td>['B', 'C']</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>sub_grade</td>\n",
       "      <td>['B3', 'B4']</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>emp_title</td>\n",
       "      <td>['Teacher', 'Manager']</td>\n",
       "      <td>299271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>emp_length</td>\n",
       "      <td>['10+ years', '2 years']</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>home_ownership</td>\n",
       "      <td>['MORTGAGE', 'RENT']</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>verification_status</td>\n",
       "      <td>['Source Verified', 'Verified']</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>loan_status</td>\n",
       "      <td>['Current', 'Fully Paid']</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>purpose</td>\n",
       "      <td>['debt_consolidation', 'credit_card']</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>addr_state</td>\n",
       "      <td>['CA', 'NY']</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>earliest_cr_line</td>\n",
       "      <td>['Aug-2001', 'Aug-2000']</td>\n",
       "      <td>697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>initial_list_status</td>\n",
       "      <td>['f', 'w']</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>last_pymnt_d</td>\n",
       "      <td>['Jan-2016', 'Dec-2015']</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>next_pymnt_d</td>\n",
       "      <td>['Feb-2016', 'Jan-2016']</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>last_credit_pull_d</td>\n",
       "      <td>['Jan-2016', 'Dec-2015']</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>loan_condition</td>\n",
       "      <td>['Good_loan', 'Bad_loan']</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Variable                                Example  \\\n",
       "0                  term           [' 36 months', ' 60 months']   \n",
       "1                 grade                             ['B', 'C']   \n",
       "2             sub_grade                           ['B3', 'B4']   \n",
       "3             emp_title                 ['Teacher', 'Manager']   \n",
       "4            emp_length               ['10+ years', '2 years']   \n",
       "5        home_ownership                   ['MORTGAGE', 'RENT']   \n",
       "6   verification_status        ['Source Verified', 'Verified']   \n",
       "7           loan_status              ['Current', 'Fully Paid']   \n",
       "8               purpose  ['debt_consolidation', 'credit_card']   \n",
       "9            addr_state                           ['CA', 'NY']   \n",
       "10     earliest_cr_line               ['Aug-2001', 'Aug-2000']   \n",
       "11  initial_list_status                             ['f', 'w']   \n",
       "12         last_pymnt_d               ['Jan-2016', 'Dec-2015']   \n",
       "13         next_pymnt_d               ['Feb-2016', 'Jan-2016']   \n",
       "14   last_credit_pull_d               ['Jan-2016', 'Dec-2015']   \n",
       "15       loan_condition              ['Good_loan', 'Bad_loan']   \n",
       "\n",
       "    Number of levels  \n",
       "0                  2  \n",
       "1                  7  \n",
       "2                 35  \n",
       "3             299271  \n",
       "4                 11  \n",
       "5                  6  \n",
       "6                  3  \n",
       "7                 10  \n",
       "8                 14  \n",
       "9                 51  \n",
       "10               697  \n",
       "11                 2  \n",
       "12                98  \n",
       "13               100  \n",
       "14               103  \n",
       "15                 2  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_variables = []\n",
    "for i in data_model.columns:\n",
    "    if data_model[i].dtype == 'object':\n",
    "        categorical_variables.append(i)\n",
    "        \n",
    "print('Categorical variables:')\n",
    "pd.DataFrame(zip(categorical_variables, [str(data_model[x].value_counts().index[0:2].values.tolist()) for x in categorical_variables], [len(data_model[x].value_counts()) for x in categorical_variables]),\n",
    "             columns= ['Variable', 'Example', 'Number of levels'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 331
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1779,
     "status": "ok",
     "timestamp": 1544133111322,
     "user": {
      "displayName": "Carlos Munoz",
      "photoUrl": "",
      "userId": "06138933875008493053"
     },
     "user_tz": -60
    },
    "id": "bEObhZleSDs8",
    "outputId": "1a054c9e-06c7-4970-8c2c-c7925f821a4b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "term                        0\n",
       "grade                       0\n",
       "sub_grade                   0\n",
       "emp_title               51462\n",
       "emp_length              44825\n",
       "home_ownership              0\n",
       "verification_status         0\n",
       "loan_status                 0\n",
       "purpose                     0\n",
       "addr_state                  0\n",
       "earliest_cr_line           29\n",
       "initial_list_status         0\n",
       "last_pymnt_d            17659\n",
       "next_pymnt_d           252971\n",
       "last_credit_pull_d         53\n",
       "loan_condition              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Distribution of missing values in categorical variables\n",
    "data_model[categorical_variables].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          36 months\n",
       "1          60 months\n",
       "2          36 months\n",
       "3          36 months\n",
       "4          60 months\n",
       "             ...    \n",
       "887374     36 months\n",
       "887375     36 months\n",
       "887376     60 months\n",
       "887377     60 months\n",
       "887378     36 months\n",
       "Name: term, Length: 887379, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_model['term']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4127,
     "status": "ok",
     "timestamp": 1544133116122,
     "user": {
      "displayName": "Carlos Munoz",
      "photoUrl": "",
      "userId": "06138933875008493053"
     },
     "user_tz": -60
    },
    "id": "cTXniqihSDs-",
    "outputId": "d1ae85d1-3409-4355-ad56-74b8e140fd2f",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After processing categorical variables this dataset has 887379 rows and 73 columns\n"
     ]
    }
   ],
   "source": [
    "# term Variable --> we extract the number of months\n",
    "data_model['term'] = data_model.term.str.extract('(\\d+)').astype(int)\n",
    "\n",
    "# emp_title, sub_grade, addr_state have to many levels we´ll remove it\n",
    "data_model.drop(['emp_title','addr_state', 'sub_grade'], axis=1, inplace= True)\n",
    "\n",
    "# ['grade', 'sub_grade', 'emp_length', 'home_ownership', 'verification_status', 'purpose', 'initial_list_status'] variables -->dummies\n",
    "dummies_variables = ['grade', 'emp_length', 'home_ownership', 'purpose', 'initial_list_status', 'verification_status']\n",
    "dummy_data_model = pd.get_dummies(data_model[dummies_variables], prefix_sep='_', drop_first=True)\n",
    "data_model = pd.concat([data_model, dummy_data_model], axis=1)\n",
    "data_model.drop(dummies_variables, axis=1, inplace= True)\n",
    "del dummy_data_model\n",
    "\n",
    "# We won´t work with dates so we remove 'earliest_cr_line', 'next_pymnt_d', 'last_pymnt_d', 'last_credit_pull_d'\n",
    "data_model.drop(['earliest_cr_line', 'next_pymnt_d', 'last_pymnt_d', 'last_credit_pull_d'], axis=1, inplace= True)\n",
    "\n",
    "# loan_condition is our target to predict, we convert it to binnary in a new variable called target\n",
    "data_model['target'] = data_model['loan_condition'].map(lambda x: 1 if x=='Bad_loan' else 0)\n",
    "\n",
    "\n",
    "print ('After processing categorical variables this dataset has {0} rows and {1} columns'.format(data_model.shape[0], data_model.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NFeYPH2hSDtD"
   },
   "source": [
    "#### Now we need to transform our loan_condition feature to a binary target in order to be able to predict with a scikit learn built-in algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 72
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1011,
     "status": "ok",
     "timestamp": 1544133117851,
     "user": {
      "displayName": "Carlos Munoz",
      "photoUrl": "",
      "userId": "06138933875008493053"
     },
     "user_tz": -60
    },
    "id": "_ziXRDsbSDtE",
    "outputId": "a6c795bd-f7b9-4996-8c57-cb838de416b7",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    819950\n",
       "1     67429\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loan_condition is our target to predict, we convert it to binnary in a new variable called target\n",
    "data_model['target'] = data_model['loan_condition'].map(lambda x: 1 if x=='Bad_loan' else 0)\n",
    "\n",
    "data_model.target.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7wYzdbmlSDtJ"
   },
   "source": [
    "#### We have 67429 loans classified as bad loans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "diBOJpD_SDtK"
   },
   "source": [
    "#### Once all variables are converted, we need to remove all fields which are related to the current loan to avoid leaking information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "24eR5MnpSDtK"
   },
   "outputs": [],
   "source": [
    "# loan_status and loan_condition need to be removed since are part of the target\n",
    "data_model.drop(['loan_status', 'loan_condition'], axis=1, inplace= True)\n",
    "\n",
    "# Directly related fiels to drop\n",
    "cols = [\"out_prncp\", \"out_prncp_inv\", \"total_pymnt\", \"total_pymnt_inv\", \"total_rec_prncp\", \"total_rec_int\", \"total_rec_late_fee\",\n",
    " \"recoveries\", \"collection_recovery_fee\", \"last_pymnt_amnt\", \"collections_12_mths_ex_med\",\n",
    " \"acc_now_delinq\", \"tot_coll_amt\", \"tot_cur_bal\", \"total_rev_hi_lim\"]\n",
    "\n",
    "data_model.drop(cols, axis=1, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_gsGzJc7SDtR"
   },
   "outputs": [],
   "source": [
    "# we impute 0 to all missing values \n",
    "data_model.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ia4f7I1cSDtc"
   },
   "source": [
    "# MODELLING\n",
    "\n",
    "#### Now it´s time to build a statistical model using machine learning algorithms. The model will use the historic loan data to learn and identify loans/borrowers which are likely to default.\n",
    "\n",
    "#### In order to avoid overfitting or at least minimize it we usually fit the model on a training set and predict on a data that wasn’t trained before. Overfitting occur when we’ve fit the model too much to the training subset so it is not capable to generalize the same patterns in the test set. This usually happens when the model is too complex (i.e. too many features/variables compared to the number of observations). This model will be accurate on the training set but it won´t be able to infer on the test set.\n",
    "\n",
    "#### The opposite to overfitting is underfitting and it means that the model does not fit the training set and therefore misses the patterns in the data. This is the result of fitting a simple model into complex data.\n",
    "\n",
    "\n",
    "#### The goal is to find the middle ground between under and overfitting our model. To do so, the first step is to split the data into two subsets: training data and testing_data, so we can evaluate the statistical model over a indepentent dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BJc9htTmSDtd"
   },
   "outputs": [],
   "source": [
    "data_model_variables = list(data_model.columns)\n",
    "data_model_variables.remove('target')\n",
    "X = data_model[data_model_variables]\n",
    "y = data_model['target']\n",
    "\n",
    "# We will leave a 30% of the dataset to test our model\n",
    "X_train,X_test,y_train,y_test=train_test_split(X, y, stratify=y, test_size=0.3, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 384,
     "status": "ok",
     "timestamp": 1544133179335,
     "user": {
      "displayName": "Carlos Munoz",
      "photoUrl": "",
      "userId": "06138933875008493053"
     },
     "user_tz": -60
    },
    "id": "6ejoiIJ8SDtf",
    "outputId": "e3bbb679-8f22-4297-90e7-f11ca51821ff"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((621165, 55), (266214, 55))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "V_b9pGnOSDtj"
   },
   "source": [
    "#### At this point, our preprocessed dataset has 54 features, let´s feature selection techniques to find the 20 most important variables to predict bad loans. To do so, we will use scikit learn libraries. https://scikit-learn.org/stable/modules/feature_selection.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 677
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 926,
     "status": "ok",
     "timestamp": 1544133183684,
     "user": {
      "displayName": "Carlos Munoz",
      "photoUrl": "",
      "userId": "06138933875008493053"
     },
     "user_tz": -60
    },
    "id": "LypisoFzSDtj",
    "outputId": "bc09e7e9-2293-4301-e990-ba67cc52ab93"
   },
   "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>Variables</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>annual_inc</td>\n",
       "      <td>4.143897e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>revol_bal</td>\n",
       "      <td>7.177252e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>loan_amnt</td>\n",
       "      <td>4.755455e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>funded_amnt</td>\n",
       "      <td>3.251804e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>int_rate</td>\n",
       "      <td>2.776753e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>installment</td>\n",
       "      <td>1.789284e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>revol_util</td>\n",
       "      <td>1.681788e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>mths_since_last_major_derog</td>\n",
       "      <td>9.022993e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>inq_last_6mths</td>\n",
       "      <td>5.687963e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>grade_F</td>\n",
       "      <td>2.845534e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>term</td>\n",
       "      <td>2.809483e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>grade_E</td>\n",
       "      <td>2.756164e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>initial_list_status_w</td>\n",
       "      <td>2.443377e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>mths_since_last_delinq</td>\n",
       "      <td>1.987832e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>grade_D</td>\n",
       "      <td>1.976521e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>grade_G</td>\n",
       "      <td>1.395079e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>grade_B</td>\n",
       "      <td>1.330665e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>total_acc</td>\n",
       "      <td>1.128482e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>dti</td>\n",
       "      <td>1.096671e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>verification_status_Verified</td>\n",
       "      <td>9.759773e+02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Variables         score\n",
       "6                     annual_inc  4.143897e+07\n",
       "13                     revol_bal  7.177252e+06\n",
       "0                      loan_amnt  4.755455e+04\n",
       "1                    funded_amnt  3.251804e+04\n",
       "4                       int_rate  2.776753e+04\n",
       "5                    installment  1.789284e+04\n",
       "14                    revol_util  1.681788e+04\n",
       "16   mths_since_last_major_derog  9.022993e+03\n",
       "9                 inq_last_6mths  5.687963e+03\n",
       "22                       grade_F  2.845534e+03\n",
       "3                           term  2.809483e+03\n",
       "21                       grade_E  2.756164e+03\n",
       "52         initial_list_status_w  2.443377e+03\n",
       "10        mths_since_last_delinq  1.987832e+03\n",
       "20                       grade_D  1.976521e+03\n",
       "23                       grade_G  1.395079e+03\n",
       "18                       grade_B  1.330665e+03\n",
       "15                     total_acc  1.128482e+03\n",
       "7                            dti  1.096671e+03\n",
       "54  verification_status_Verified  9.759773e+02"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_relevant = SelectKBest(chi2, k=10).fit(X_train, y_train)\n",
    "most_relevant_df = pd.DataFrame(zip(X_train.columns, most_relevant.scores_), columns= ['Variables', 'score']).sort_values( 'score', ascending=False).head(20)\n",
    "most_relevant_variables = most_relevant_df.Variables.tolist()\n",
    "\n",
    "most_relevant_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bf7UfwCWSDtn"
   },
   "source": [
    "#### Now that I have found the most relevant variables let´s use scikit´s learn classification models to fit it. Here is a tip of which algorithm can be used depending on your date and prediction problem\n",
    "\n",
    "\n",
    "<img src=\"https://i.stack.imgur.com/IMVOl.png\"><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "I9cFQRmsncI5"
   },
   "source": [
    "## Logistic Regression Classifier \n",
    "\n",
    "#### First I will fit a logistic regression model, you can read more information here:\n",
    "https://scikit-learn.org/stable/modules/linear_model.html#logistic-regressio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4331,
     "status": "ok",
     "timestamp": 1544133190811,
     "user": {
      "displayName": "Carlos Munoz",
      "photoUrl": "",
      "userId": "06138933875008493053"
     },
     "user_tz": -60
    },
    "id": "8ZTXV9DoncI6",
    "outputId": "73df9e36-933b-4ad7-d3dc-05577238f685",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logit roc_auc_score metric: 0.651224020064\n"
     ]
    },
    {
     "data": {
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MehX/bVsBsPfqjaNj56oO/byoBKAoik8qsDk4mJDF/mNZHEnOoVEDCx1bRnJ5z+aE1zuP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\nzl3I7bePZfHiBQC88cZrvPjiK7z11mI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EifW5zgR6Aa96y0ep6vepCjSOYr3HRd6/hl5D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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe44b6e2650>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train model with Training Data\n",
    "lr_classifier = LogisticRegression(penalty='l1', random_state=42, max_iter=10)\n",
    "lr_classifier.fit(X_train[most_relevant_variables], y_train)\n",
    "\n",
    "# Test model with Test Data\n",
    "y_predicted = lr_classifier.predict_proba(X_test[most_relevant_variables])[:, 1]\n",
    "\n",
    "# Evaluate model\n",
    "roc_auc = roc_auc_score(y_test, y_predicted.round(4))\n",
    "print('Logit roc_auc_score metric: {}'.format(roc_auc))\n",
    "                        \n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_predicted, pos_label=1)\n",
    "\n",
    "plt.plot(fpr, tpr, lw=1, alpha=0.8, label='ROC {}'.format(roc_auc))\n",
    "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r', label='Random', alpha=.8)\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic example')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show();             "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 677
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 708,
     "status": "ok",
     "timestamp": 1544133193680,
     "user": {
      "displayName": "Carlos Munoz",
      "photoUrl": "",
      "userId": "06138933875008493053"
     },
     "user_tz": -60
    },
    "id": "ZgFtva5SSDtr",
    "outputId": "0b65a980-89ce-4b67-a79c-e5b54f2028f7"
   },
   "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>Variables</th>\n",
       "      <th>Coefficients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>total_acc</td>\n",
       "      <td>2.206182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>inq_last_6mths</td>\n",
       "      <td>1.811310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>open_acc</td>\n",
       "      <td>1.320375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>revol_util</td>\n",
       "      <td>0.885597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>grade_C</td>\n",
       "      <td>0.210400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>delinq_2yrs</td>\n",
       "      <td>0.182836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>annual_inc</td>\n",
       "      <td>0.005021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>installment</td>\n",
       "      <td>0.000316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>funded_amnt_inv</td>\n",
       "      <td>0.000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>term</td>\n",
       "      <td>-0.000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>funded_amnt</td>\n",
       "      <td>-0.000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>loan_amnt</td>\n",
       "      <td>-0.000007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>revol_bal</td>\n",
       "      <td>-0.001054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>dti</td>\n",
       "      <td>-0.003168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>mths_since_last_delinq</td>\n",
       "      <td>-0.007221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>issued_year</td>\n",
       "      <td>-0.007258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>grade_B</td>\n",
       "      <td>-0.010422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>int_rate</td>\n",
       "      <td>-0.062431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>mths_since_last_major_derog</td>\n",
       "      <td>-0.241525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>pub_rec</td>\n",
       "      <td>-0.619574</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Variables  Coefficients\n",
       "15                    total_acc      2.206182\n",
       "9                inq_last_6mths      1.811310\n",
       "11                     open_acc      1.320375\n",
       "14                   revol_util      0.885597\n",
       "19                      grade_C      0.210400\n",
       "8                   delinq_2yrs      0.182836\n",
       "6                    annual_inc      0.005021\n",
       "5                   installment      0.000316\n",
       "2               funded_amnt_inv      0.000002\n",
       "3                          term     -0.000002\n",
       "1                   funded_amnt     -0.000002\n",
       "0                     loan_amnt     -0.000007\n",
       "13                    revol_bal     -0.001054\n",
       "7                           dti     -0.003168\n",
       "10       mths_since_last_delinq     -0.007221\n",
       "17                  issued_year     -0.007258\n",
       "18                      grade_B     -0.010422\n",
       "4                      int_rate     -0.062431\n",
       "16  mths_since_last_major_derog     -0.241525\n",
       "12                      pub_rec     -0.619574"
      ]
     },
     "execution_count": 35,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Let´s see the coeficients\n",
    "logit_coef = pd.DataFrame(zip(X_train.columns, lr_classifier.coef_[0]), columns= ['Variables', 'Coefficients']).sort_values( 'Coefficients', ascending=False)\n",
    "logit_coef"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8MRvUuhvncJJ"
   },
   "source": [
    "## Random Forest Classifier\n",
    "#### Now I will use a random forest to analyze if a tree based algorithm can improve the logit.\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 8556,
     "status": "ok",
     "timestamp": 1544133204242,
     "user": {
      "displayName": "Carlos Munoz",
      "photoUrl": "",
      "userId": "06138933875008493053"
     },
     "user_tz": -60
    },
    "id": "_he4XlHNncJL",
    "outputId": "2d4762eb-97f8-4a6d-cb2f-ac983b197b26",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest roc_auc_score metric: 0.70157346431\n"
     ]
    },
    {
     "data": {
      "image/png": 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GbgltWEKY1T0ffLeNjxZv59S+LRk7sgetmiWEO6zGw+UifvZTJE2bSvSaVcS+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\n7uewdAsAQSxEX2efvcm4uwnmp6JxCRLrfX5KVXu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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe44d286f10>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train model with training set\n",
    "rf_classifier = RandomForestClassifier(max_depth = 3, random_state = 42)\n",
    "rf_classifier.fit(X_train[most_relevant_variables], y_train)\n",
    "\n",
    "# Test model with test Data\n",
    "y_predicted = rf_classifier.predict_proba(X_test[most_relevant_variables])[:, 1]\n",
    "\n",
    "# Evaluate model\n",
    "roc_auc = roc_auc_score(y_test, y_predicted.round(4))\n",
    "print('Random Forest roc_auc_score metric: {}'.format(roc_auc))\n",
    "                        \n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_predicted, pos_label=1)\n",
    "\n",
    "plt.plot(fpr, tpr, lw=1, alpha=0.8, label='ROC {}'.format(roc_auc))\n",
    "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r', label='Random', alpha=.8)\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic example')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show();                 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 620
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 927,
     "status": "ok",
     "timestamp": 1544133209173,
     "user": {
      "displayName": "Carlos Munoz",
      "photoUrl": "",
      "userId": "06138933875008493053"
     },
     "user_tz": -60
    },
    "id": "YvTZWGshSDt7",
    "outputId": "d3bf3bef-f070-43fd-b1d3-869af4c78c39"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fysrH+M3KzLc0u5YuEbEhsFJm3hMR+wMTM/Pwvq455crtF+sb8ciJVy/O5cNS\ne/soOjpmN7uMYcdxbw7HvTkc9+Zw3BuvvX1U92XNQAMft1e1iNiVnp8DfVZmXtcqfUbEWvT8Acvp\nmXnyIjR5Kz2vK++p72/T85KaHTPzjUXouzezge9ERCfFEphDK2xbkiRpUHLGWoOCM9YDzxmN5nDc\nm8Nxbw7HvTkc98brbca6mWusJUmSpCHDYC1JkiRVwGAtSZIkVaBlPryooe2UfW91PZgkSWppzlhL\nkiRJFTBYS5IkSRUwWEuSJEkVMFhLkiRJFfDDixoUdrx+r7rPvXTsxY0rRJIkaRE5Yy1JkiRVwGAt\nSZIkVcBgLUmSJFXAYC1JkiRVwGAtSZIkVcBgLUmSJFXAYC1JkiRVYFgF64jYu3wdExEPNaD966tu\ns1v7Ly/CNXsvxLlHRcQpfRy/OCJ2jogdIuKIha1FkiRpKBtufyDm/wFXN6rxzNytUW0viogYCRxL\nxfecmVOrbE+SJGkoGFLBOiImA+OB0cCGwBeA/YENgAuBjSPiWoqwuUREnAdsATycmYdHxIeALwNv\nAC8CB2bm3F76OhvYDBgBnJeZF0fEy5k5OiKmAT8Hti1r2SUzZ0XEWcCWwDzgk5n5eEScBowr2zk3\nM39Ux31uB3wJmAO8CuwLvAm4Eli6/DoS+BiwUUR8OzM/1Utbk4BvAS8AzwO/L/f3Wlc5zu8GzgUu\nAWYCGwOPZubHI+I9wKXAn4AngeUyc3J/9yVJktTKhuJSkHWBXYGvAMcDe5TbqwOvZeae5XnrAV8E\nNgd2ioiVgKOAz2bmeOAKYNWeOoiIVYAPZ+ZYYBtgqR5O+0tmTgJuAfYsw/CambkV8Hlgv4gYB6yd\nmR+gCOEnRMSb6rjHlYEDyjr/AmwPTAKezcwJwIHAasCZQPYWqktfAQ7KzA9S/BDAQta1aXk/teN4\nInBCef8j6rgfSZKkljcUg/VDmdlJMfv6q8ycTzH7vGK3857KzBcycwHFbO2KwFXA+RHxeYrZ1xd6\n6iAzXwF+V66p3o9idra7u8rXZ8u23wfcU15/Z2aeCIwFtipnuG+l+PdYvY577AC+GxHTgYkUPwDM\nALaOiPOBdy7Eco0xmfnLcnt6+bowddWO43Plva4P3Fcen1ZnHZIkSS1tKAbreb1st/VxHkBbZl5G\nEVRfBm6MiHf11klm7kgx4/1e4MZ+6mgD5vPv4z0H+F5mTii/1s/M3/fWZ43vA0eVM9bXl/U8T7Ec\n41rgiIg4qY52ABbUbHfVtzB1/ds4ll+dvRyXJEkakoZisO5Ln/cbEScCczPzAoqlIBv0ct6YiDgm\nMx/JzOPoZclINw9ShHYiYpPNtJ6nAAAgAElEQVSImALcD+wSEUtExDIRcU6d97EiMKtcdjERGFku\nNdkuM38GHE2x/nsB/a+j/2MU2oAJ5b5FravLkxRryQG2W8hrJUmSWtJwC9aPRsQDfRyfBdwWEbdR\nzP72tpziOWBsRNwbEXdQzCD3KTPvBJ6IiLuAs4HzM/Ne4A6KZRx3Ag/XeR9TKJaVXAB8jWIt+V+B\nL5TLNy6lWF/9PEXovqqPtr5A8dSQG4FnyloXta4upwFfKcdRkiRpWGjr7Ozs/yxpEUXEzsDe/T0V\nZMfr96r7G/HSsRcvZlUCaG8fRUfH7GaXMew47s3huDeH494cjnvjtbeP6r7EGBhij9urWkTsSvFo\nvu7OyszrWqXPiFiLnj9gOT0zT16UNiVJkvSvDNZ9yMwbgBtavc/MnMU/108PqMy8CbipGX1LkiQN\npOG2xlqSJElqCIO1JEmSVAGDtSRJklQB11hrULhlt2v8BLMkSWppzlhLkiRJFTBYS5IkSRUwWEuS\nJEkVMFhLkiRJFfDDixoUdrruy3Wfe8k2n25gJZIkSYvGGWtJkiSpAgZrSZIkqQIGa0mSJKkCBmtJ\nkiSpAgZrSZIkqQIGa0mSJKkCBmtJkiSpAgZrSZIkqQIG6yEmIvbq49iuETGyj+MXR8TOjalMkiRp\naDNYDyERMQbYv49TjgV6DdaSJEladP5J86FlCrBFRJwMvBdYCVgKOAbYENgKuCUiJgFfBbYAlgHO\nz8zv9td4RGwHfAmYA7wK7JuZcyLiLGBLYB7wycx8vKd91d6qJEnS4OKM9dByJjAd6ATuy8yJwGeA\nb2bmZcALwI4U/+5PZ+Y2wDjg1DrbXxk4IDPHA38Bti/D9pqZuRXweWC/nvZVdoeSJEmDlDPWQ9Nm\nwGkAmflQRLyz9mBm/j0iVomIeylmn9vrbLcD+G5ELAmsA9wOrAbcU7Z7J3BnRHyu+77FvyVJkqTB\nzRnroakTaKt5P6L2YESMB7YFxmfmBOAfdbb7feCocsb6+nLffP79+6infZIkSUOa4WdoWUDxW4gH\ngYkAEbEV8Hi346OBZzJzbkTsCozo62khNVYEZkXESmX7I7v1tUlETOllnyRJ0pDmUpCh5QngfcAf\ngDUj4naKH56OLI9PA+4GPgz8V0RMB34C3AScV0f7UyiWePwO+BpwCjAWeCIi7irP+VRm/joidqvd\nt5j3JUmSNOi1dXZ2NrsGiZ2u+3Ld34iXbPPpRpYybLS3j6KjY3azyxh2HPfmcNybw3FvDse98drb\nR7X1tN8Za/2LiFgLuLSHQ9Mz8+SBrkeSJKlVGKz1LzJzFjCh2XVIkiS1Gj+8KEmSJFXAYC1JkiRV\nwKUgGhRu3uMEP2ghSZJamjPWkiRJUgUM1pIkSVIFDNaSJElSBQzWkiRJUgUM1pIkSVIFfCqIBoUP\nX3vev+27eNxBTahEkiRp0ThjLUmSJFXAYC1JkiRVwGAtSZIkVaDfNdYRMQZ4a2beExGHAVsBX8/M\nJxpdnCRJktQq6pmxvgiYExGbAB8HrgHObmhVkiRJUoupJ1h3ZuaDwB7AuZl5M9DW2LIkSZKk1lLP\n4/aWj4jNgb2B8RGxNLByY8tSs0XEQ8Demfn0QlwzBvg18DDQCSwD/Gdm3t2IGiVJkgaTemasvwFc\nCHwnMzuAU4AfNrIotbTMzAmZORH4L+DEZhckSZI0EPqdsc7MH0fE1UB7uesLmbmgsWVpcUTEisDV\nwJuAm4HDgHnl9kvATcAUYC6wANgnM1+JiLOBrYEERpZtrQF8r3w/H/h4Zs6qs5Q3A3+s6LYkSZIG\ntX5nrCNiW+ApYFq56xsRsXMji9JiOxj4bWZuA/yZYk38UsAtmXkasBpwdDmrfA9wYERsAIwFtgSO\nB6Js60vANzJzEvAt+p+BjoiYFhH3Af8NfL3aW5MkSRqc6lkKcjrFI/aeL9+fBpzQsIpUhfUpAjPA\nDTX7HyhfXwROj4jpwP7AqsAGwP2ZuSAznwF+X547FjglIqZRBO5V++m7aynIVsAHgR9HRD1r+SVJ\nklpaPcH69cx8setNZr4MzGlcSapAG8USDyg+RNil69/tLOCszBwPfKeHa+Cf3xtzKJaKTMjMcZm5\nZ71FZOaTwBvAmgtZvyRJUsupJ1i/ERHjgbaIWDkijgD+3uC6tHhmApuV2zv2cHw0MLN8wstOFOun\nE9g0ItoiYm3g7eW59wO7Q7EsKCIOqLeIiFgFWB3XWUuSpGGgnl/Rfwo4D9icIrDdBRzeyKK02C4G\nri+Xb/yc4kOHI2qOnwP8hOLf8xzgXODHFI/KmwH8DnisPPcU4KKI2J9i9ntyP31H2S8Uj9s7KjP9\nDYckSRry6nkqyDOAH1ZsLcsBp2bmrRGxNTA+Mz/UdTAzLwAuqDn/uvL1E720t309nZbPvB618OVK\nkiS1vl6DdUTcxb+uz/0XmfmBhlSkKrwGHBsRJ1GsnT6mysYj4tsUH3bsbsfMfKPKviRJklpFXzPW\nPvmjRWXmn6lzlnkR2/9Uo9qWJElqVb0G68ycDhARbRQfgNuQYgb715l568CUJ0mSJLWGep4KchnF\nn6ZeleKvL34hIi5qaFWSJElSi6nnqSDrZeYWXW/KGez7GleSJEmS1HrqCdazImLZzPxb+X4Zise0\nSZX56Z5H0NExu9llSJIkLbK+ngpyGcWa6mWBpyLiPoq/zLcl8NDAlCdJkiS1hr5mrG+r2f5xzfaN\n9PEYPkmSJGk46uupIJf0tD8ilgJ+CFzaqKIkSZKkVtPvGuuI+Ajw38Aq5a4FwC8aWZQkSZLUaur5\n8OIxwEbAFcCHgQMp/rKfJEmSpFI9z7F+LTNfAEZk5l8z8wLgow2uS8PMzlf/oNklSJIkLZZ6Zqzn\nR8TOwDMRcQrwG2DthlYlSZIktZh6Zqw/AjwLfAZYAzgIOLqRRUmSJEmtpq/nWLdlZifwcvkF8MkB\nqUqSJElqMX3NWHc9+WMeMLfmq+u9JEmSpFJfz7HetuuczFwwQPVIkiRJLameNdY+s1qSJEnqRz1P\nBXksIk4F7gXmdO3MzNsbVpUkSZLUYuoJ1u8tX8fV7OsEDNb6FxGxPPA4cDDwZGa+FBHXZ+ZuTS5N\nkiSp4foN1pk5sfu+iNirMeVoiPgo8HXgJUO1JEkaLvoN1hGxFnAUMLrctTSwLXBNA+tSi4iIFSi+\nF5YB7gaWAnYHNix/AHskM0f30YQkSdKQUM+HFy8DXgG2Bh4G2in+aIwExR8MejwzxwGPUTyK8THg\n0Myc1dTKJEmSBlA9wXpeZn4VeDEzpwC7Akc2tiy1kA0oPtgKMK2JdUiSJDVVPcH6TRHxNmBBRKxD\nMSM5pqFVqZW0AV3POa/n+0mSJGlI6jUIRcRby82vAdsBZ1L8iv9l/jlDKSWwWbnd9UHXBdT3xBlJ\nkqQho68Zxl9HxE/Lcy7PzFuAVYB1MtOlIOpyKbBVRPwCCIpHMU4Hro6IDZtamSRJ0gDqa1ZxDWAP\n4DDg3Ij4IfDdzHxyQCpTS8jMP/PPmWqAk8vXL5avPhFEkiQNC70G68z8O/Aj4EcRsTpwIPDjiPgr\nRcD+/gDVKEmSJA16dX3YLDOfz8yvA/sBfwCmNLQqSZIkqcXU8wdiVgb2ByZT/HGY7wHHNLYsSZIk\nqbX0GqwjYheKML0NcC1wZGY+OEB1SZIkSS2lrxnr4yhmpw/KzDcGqB4NUzftfSAdHbObXYYkSdIi\n6+vDi+MHshBJkiSplfmX8iRJkqQKGKwlSZKkChisJUmSpAoYrCVJkqQKGKwlSZKkChisJUmSpAoY\nrCVJkqQKGKwlSZKkChisJUmSpAoYrCVJkqQKGKwlSZKkChisJUmSpAoYrNWniNir2TVIkiS1AoO1\nehURY4D9m12HJElSK1iy2QVoUJsCbBERJwMbAStTfM8cnZm/ioj/AW4GXgLWLV83BdqBM4BDgdHA\n+Mx8rQn1S5IkDRhnrNWXM4HpwAJgamZOAo4AvlEeXwq4JTNPK9/PK8/5NTA2M7crtycObNmSJEkD\nzxlr1WMs0B4RB5Xvl6059kAP288DT5bbLwIrNrY8SZKk5jNYqx5zKJZ/zOjlWJd5vWy3NaQqSZKk\nQcSlIOrLAoofvu4HdgeIiA0i4timViVJkjQIOWOtvjwBvA/4A7BWRNwFjACOaWpVkiRJg1BbZ2dn\ns2uQADo7OmY3u4Zhpb19FI75wHPcm8Nxbw7HvTkc98Zrbx/V4zJXl4JIkiRJFTBYS5IkSRUwWEuS\nJEkVMFhLkiRJFTBYS5IkSRUwWEuSJEkVMFhLkiRJFTBYS5IkSRUwWEuSJEkVMFhLkiRJFTBYS5Ik\nSRUwWEuSJEkVMFhLkiRJFTBYS5IkSRUwWEuSJEkVMFhLkiRJFTBYS5IkSRUwWEuSJEkVWLLZBWhw\nioiHgL0z8+mFvG5d4FtAOzACuBc4LjP/UXmRkiRJg4gz1qpMRIwArgG+lplbAJuVh05qXlWSJEkD\nwxnrISgiVgSuBt4E3AwcBswrt18CbgKmAHOBBcA+mflKRJwNbA0kMLJsaw3ge+X7+cDHM3NWL11/\nEHgyM6cDZGZnRHyu7EOSJGlIc8Z6aDoY+G1mbgP8GWgDlgJuyczTgNWAozNzInAPcGBEbACMBbYE\njgeibOtLwDcycxLFEo8T++j3XcBjtTsy8w2XgUiSpOHAGeuhaX1gWrl9A/C5cvuB8vVF4IyIWBZY\nA/gBsAFwf2YuAJ6JiN+X544FIiJOoFgz3dFHv53lOZIkScOOwXpoauOfyy86a/bPKV/PAs7IzKkR\ncRywfLdr4J+/zZhDsVTk+Tr6fRI4qnZHRCwNrJuZjy/cLUiSJLUWl4IMTTP55wcHd+zh+GhgZhl6\nd6JYP53AphHRFhFrA28vz70f2B0gIraNiAP66PfnwNoRsUt5/hLAGcB+i3k/kiRJg57Bemi6GBgX\nEdOAN1N86LDWOcBPgKvK7UMoZqx/DcygWFfdtVb6FGD3iLgTOLk83qNyGcn2wOHl4/ruBl4rr5Mk\nSRrS2jo7O/s/Sy2lnHF+V2beGhFbA1/MzA81u65+dHZ0zG52DcNKe/soHPOB57g3h+PeHI57czju\njdfePqqtp/2usR6aXgOOjYiTKGaij6my8Yj4NsWHHbvbMTPfqLIvSZKkVmGwHoIy888USzIa1f6n\nGtW2JElSq3KNtSRJklQBg7UkSZJUAYO1JEmSVAGDtSRJklQBg7UkSZJUAYO1JEmSVAGDtSRJklQB\ng7UkSZJUAYO1JEmSVAGDtSRJklQBg7UkSZJUAYO1JEmSVAGDtSRJklQBg7UkSZJUAYO1JEmSVAGD\ntSRJklQBg7UkSZJUAYO1ehQRL/dxbExEPFRnO3WfK0mS1MoM1pIkSVIFlmx2AapOREwGdgTWAKYC\nOwELgJ8A3wJ+D0Rm/j0ixgOfBg4FLgZWApYCjsnMR+robqmIuBxYD3g0Mz8RERsDU4C5Zb/7VHd3\nkiRJg5sz1kPPWsDBwHbANsAHgL2AtwK3AZPK83YDrqYI1/dl5kTgM8A36+xnA+B4YEvgfRGxEbAa\ncHTZ1j3AgVXckCRJUiswWA89DwJbAOsCd5Rfo4AxwLXALuV52wM3ApsB0wAy8yHgnXX281RmPpOZ\nnWWfAbwInB4R04H9gVUX/3YkSZJag0tBhp455ddPM/MTtQciYmngzHJ2eWZmzo6ITqCt5rQRdfbT\n2cP7s4AzMnNqRBwHLL9IdyBJktSCnLEemh4GJkbEshHRFhFnRcSbMvMfwC+B/6RYBgLFbPNEgIjY\nCni8zj7eERGrR8QSwObAE8BoYGYZ4HcCRlZ3S5IkSYObwXoIysxZFB9WvBO4D3ghM98oD18L7A3c\nUL4/C9g0Im4Hvkqx5roevwROA2YAMzLzt8A5FB+UvKrcPgRYcbFvSJIkqQW0dXZ2/42+1BSdHR2z\nm13DsNLePgrHfOA57s3huDeH494cjnvjtbePautpv2us1auIOBw4oIdDx2fmjIGuR5IkaTAzWKtX\nmXkBcEGz65AkSWoFrrGWJEmSKmCwliRJkipgsJYkSZIqYLCWJEmSKmCwliRJkipgsJYkSZIqYLCW\nJEmSKmCwliRJkipgsJYkSZIqYLCWJEmSKmCwliRJkipgsJYkSZIqYLCWJEmSKmCwliRJkipgsJYk\nSZIqYLAehCJih4g4YgD6uToiJjS6H0mSpOFgyWYXoH+XmVObXYMkSZIWjsF6EIqIycBGwNuA1YGl\ngZOBXwCXd9v3JHB1Zm5WXvsQsDcwB/geMBKYD3w8M2dFxOeA/YH/BVboo4YdgQMy8yPl+wuBG4FX\ngdOBucAzwGHAAuCSst7lgFMy86aImAY8DpCZRy3+yEiSJA1eLgUZvDYBRmfmB4DtgVUownb3fb35\nEvCNzJwEfAs4MSJWAj4FbA18BHh3H9f/DNgyIpaJiCWA9wNTgbOB3TJzW+BFYJ+yjp9l5nhgX+CL\nNe08bqiWJEnDgTPWg9djwDYRcRlwHXAFsAwwqtu+tXq5fiwQEXHC/9/evUfZWdf3Hn+PSYCFTMIl\nw1WQgvZ7DOA5ImAoAXJBuYlItHqKtAQvKEiEQo+tVi7ipVZKEZpWZalArGgFogWLBFDC/Q7iwkW/\n54CJGAk4VDAogZDMPn88T8pmnNmTmXlm79l73q+1Zs0zz/N7fr/v/rkNn/nNb+8NTAJ6gdcBP8vM\nF4AXIuL+wQbPzPUR8QPgCGAVcCuwFfB6YElEQLE6/TTFKva+EXEixer1NnVd3TOCxy5JktR2DNbj\nVx8wkyIgLwDenpnvj4hXnOOVq8MAU8rva4E/zcxVGy5ExL5lvxsM9ReLxcBfAyuAy8s+f5WZs+sb\nRcTxFKvWB5bf76u7vHaIMSRJkjqCW0HGr70p9jjfBpwEzIiIPzgHrAa2i4iuiNge2L28/27gnQAR\nMTcijgUeA94QEZtExFTgzY0KyMyfADsB+wG3ZOYzZX8zyu8LI+KNwHRgeWb2AfMp9nVLkiRNKK5Y\nj1/LgeMi4sMULz48rzz3+fpzmflMRNwI3As8BDxY3n8OcElE/BlQAxZk5m8i4jLgTuDn5T1DuR7o\nzsxa+fMHyn7XAk8AF1OE+6vL1fRvACsj4qzRPXxJkqT20lWr1YZupQkpIrqAG4CPZOajYzxcrbf3\nuTEeQvV6erpxzpvPeW8N5701nPfWcN7HXk9Pd9dA512xnuAiYj/giwNc+jfgg8B3mxCqJUmS2p7B\neoLLzHuA2YNc/nITS5EkSWprvnhRkiRJqoDBWpIkSaqAwVqSJEmqgMFakiRJqoDBWpIkSaqAwVqS\nJEmqgMFakiRJqoDBWpIkSaqAwVqSJEmqgMFakiRJqoDBWpIkSaqAwVqSJEmqgMFakiRJqoDBWpIk\nSaqAwVqSJEmqgMFakiRJqoDBWpIkSaqAwbrFImJKRNwdEZeN8P49I2LZRrbdIiJWjGSckYiId0TE\nJs0aT5IkqZUM1q23A7BpZh7f6kLGwOmAwVqSJE0Ik1tdgLgA2D0iLgHuz8xFEbEnsCgzZ0fEo8D3\ngQOAZ4EjgR2BK4AXgYc2dBQR84EzgHXAfZl5RkRMBa4CNgNua1RIREwGLgNeA7waOCczf1CuiN8E\nvBXoK9ssANYD84AzgWlAALsDpwHTgZnADyNiXmauHc0kSZIkjXeuWLfeGUACvxjk+m7A4szcH9gK\neCPwMeA7mTkbeAKKbR7Ap4C5mXkwsHNEHAAcBzycmQcCPxmilq2B68v73wN8uu7aqsycBUwCti77\nmwTsVV7fOTOPAE4FPpyZ3wSeBA43VEuSpInAFevxb3Vm/rQ8XkmxMjyDYsUaYBlwOLAHsAuwNCIo\n2722bHtzXdtGngH2jYgTKVamt6m7dk/5fRXwYHn8VDkOvLwavrLunCRJ0oRhsB4/anXHU+qO1/Vr\n11V+9ZU/b/irw1qKrSSH1jcuV637tx3MsRSr1geW3+8bpI76464G5yRJkiYMt4KMH6spXsgIMGuI\ntgnsUx7PqTv3hojYFiAiPh0ROw3SdjDTgeWZ2QfMZ/QvPOzDX94kSdIEYbAeP5YAR0fEDcCWQ7S9\nEHh/RCyl2HdNZj5P8aLBayPidoptHE8Ai4GZEfEjihcX1gbuEihe5HhU2fb3wMqIOGsUj2kZcFtE\nTB9FH5IkSW2hq1ZrlLOkpqn19j7X6homlJ6ebpzz5nPeW8N5bw3nvTWc97HX09M94LZX/0w/AZWr\n0HMHuHRCZi5vdj2SJEmdwGA9AWXmucC5ra5DkiSpk7jHWpIkSaqAwVqSJEmqgMFakiRJqoDBWpIk\nSaqAwVqSJEmqgMFakiRJqoDBWpIkSaqAwVqSJEmqgMFakiRJqoDBWpIkSaqAwVqSJEmqgMFakiRJ\nqoDBWpIkSaqAwVqSJEmqgMFakiRJqoDBWpIkSaqAwVqSJEmqgMFaA4qIFRGxRYPr/97MeiRJksY7\ng7VGJDOPbnUNkiRJ48nkVhegjRcRU4HLgVcDmwMLy5+/ChwFbAocArwLmAX0AAGcl5lfj4gVwJ6Z\n+buI+AfgYWBJ/z4z856NqOXpzJweEcuAG4C5wHTgqMx8PCIuBN4CrAM+kpkPVzIJkiRJ45Qr1u1l\ne+BrmTkH+ATw1xS/HP1nZh4ELAfmlW33AuYD76QI4MPpc7hWZ+Y84IfA/Ig4BNg5M2cCnwTeO4I+\nJUmS2oor1u3lKeDMiPgritXp35fnby2/rwSmlcd3Zub6iKg/N5w+h6N+/G2AvYHbATLzFuCWEfQp\nSZLUVlyxbi+nAb/KzFnASXXn19UddzU4V6s7N2WIPoej/1jr8bklSZImGMNPe5kOPFYeHwNsMsz7\nVwM7RMQkYGZFfQ7kXmAOQES8KSL+uYI+JUmSxjWDdXtZDJweEdcDd1Psj+5qfMsrLAKuoXjB4s8G\n6zMiThhNkeX2j0ci4lbgIuAro+lPkiSpHXTVarWhW0ljr9bb+1yra5hQenq6cc6bz3lvDee9NZz3\n1nDex15PT/eAC5u+eFGDioh3AKcPcOnCzPxes+uRJEkazwzWGlRmXg1c3eo6JEmS2oF7rCVJkqQK\nGKwlSZKkChisJUmSpAoYrCVJkqQKGKwlSZKkChisJUmSpAoYrCVJkqQKGKwlSZKkChisJUmSpAoY\nrCVJkqQKGKwlSZKkChisJUmSpAoYrCVJkqQKGKwlSZKkChisJUmSpAoYrNtIRBwWEScNo/0uEbFf\ng+uzI+LK8vjpKmocYIx3jUW/kiRJ483kVhegjZeZ1w3zlrnAFsA9Y1DOkCJiV+DPgKtaMb4kSVIz\nGazbSEQsAN4O9ACPAf8TeDAzPxgRbwM+C6wBngI+CpwDvBQRjwPPA58B1gLPAO8ZZIxlwE3AW4E+\n4DJgAbAemAdsDlwCbEXx/FmYmT+NiEeBrwJHAZsChwD/DOwXEWdl5rnVzYQkSdL441aQ9vRm4JPA\nvsAREbElcApwRmYeDHwHmARcClyYmVdTBOFjy+urgUMb9L8qM2eVfWydmQeWx3sBpwHXZeY84CTg\n/PKeycB/ZuZBwHKKEH4ecLOhWpIkTQSuWLenRzPzSYCIeAKYBlwBfCUivgV8OzOfjIj6e3qBr0XE\nZGA34MfAc4P0v2HryCrgwfL4qXKcPwF6IuK48vzmdffdWn5fWbZ9dmQPT5Ikqf0YrNvTun4/d2Xm\nNyNiKfBO4JqIeHe/Nt8AjszMRyJi0TD6rz/uothKsjAz7xzivq4hxpAkSeoobgXpEBFxJvBSZl5M\nsRVkBsUe6Q2/PE0DHi+3jcwBNhnhUHdThHciYkZEnN6gbf34kiRJHc1g3TkeB26MiBspXtR4HXAn\n8PGIeB/FCwlvBy4Gvgh8AthhBOP8E/C6iLgV+BpwS4O2jwB7R8QFIxhHkiSprXTVarVW1yAB1Hp7\nB9vyrbHQ09ONc958zntrOO+t4by3hvM+9np6ugfc8uqKtSRJklQBg7UkSZJUAYO1JEmSVAGDtSRJ\nklQBg7UkSZJUAYO1JEmSVAGDtSRJklQBg7UkSZJUAYO1JEmSVAGDtSRJklQBg7UkSZJUAYO1JEmS\nVAGDtSRJklQBg7UkSZJUAYO1JEmSVAGDtSRJklQBg7UkSZJUAYO1JEmSVAGDtSRJklSBya0uQONT\nRDydmdOHec8uwPaZeU9EfAm4EDgeeDozF41FnZIkSeOFwVpVmgtsAdyTmacBRERrK5IkSWoSg3UH\niYgFwOHAjsB1wBFAH/B94EvAz4HIzBci4mDgVOAE4FJgS2AK8LHMfGCIcXYFrszMfcqf7wNOAs4B\nXoqIx4HTgVMqfYCSJEnjmHusO88uwF8AhwCzgIOAdwE7ATcC88p2RwNXUoTruzJzDnAacMEIx+2l\nCOgXZubVIy1ekiSpXRmsO8+9wH7A64Gbyq9uYFdgCXBU2e5Q4BpgH2AZQGbeB7yuqdVKkiR1CLeC\ndJ615dd/ZOaH6y9ExKbAeRGxF/BYZj4XETWgq67ZpI0Yo9bv5ymjKViSJKkTGKw70/3A30fE5sAa\niv3Vf5OZayLiIeD/UGwDgWKFew5wV0TMBB7eiP5XA9tFRBewHbB7eb4Pn1OSJGmCcitIB8rMxynC\n9C3AXcCTmbmmvLwEeDewYR/0hcCbI+LHwBco9lwP1f8zFPu17wU+BzxYXroT+HhEvK+ihyJJktQ2\numq1/n/Vl1qi1tv7XKtrmFB6erpxzpvPeW8N5701nPfWcN7HXk9Pd9dA5/2zvQYVEScCxw5w6ROZ\neWez65EkSRrPDNYaVGZeDFzc6jokSZLagXusJUmSpAoYrCVJkqQKGKwlSZKkChisJUmSpAoYrCVJ\nkqQKGKwlSZKkChisJUmSpAoYrCVJkqQKGKwlSZKkChisJUmSpAoYrCVJkqQKGKwlSZKkChisJUmS\npAoYrCVJkqQKGKwlSZKkChisJUmSpAoYrEcoIqZExN0RcdkI798zIpZtZNstImLFSMYZiYh4R0Rs\n0uD6gog4pln1SJIktVQ/rmAAAAqkSURBVIPJrS6gje0AbJqZx7e6kDFwOvBjYO1AFzPz0qZWI0mS\n1AYM1iN3AbB7RFwC3J+ZiyJiT2BRZs6OiEeB7wMHAM8CRwI7AlcALwIPbegoIuYDZwDrgPsy84yI\nmApcBWwG3NaokIiYDFwGvAZ4NXBOZv6gXBG/CXgr0Fe2WQCsB+YBZwLTgAB2B04DpgMzgR9GxLzM\n/INwHRHnAE8DDwOnlH2/AbiyfMwXZObcsu3ZwDOZedHQUypJktS+3AoycmcACfxikOu7AYszc39g\nK+CNwMeA72TmbOAJKLZ5AJ8C5mbmwcDOEXEAcBzwcGYeCPxkiFq2Bq4v738P8Om6a6sycxYwCdi6\n7G8SsFd5fefMPAI4FfhwZn4TeBI4fKBQPYD9KML6/sDCzHwI2DEitiyvv4PiFwRJkqSO5or12Fmd\nmT8tj1dSrAzPoFixBlgGHA7sAewCLI0IynavLdveXNe2kWeAfSPiRIrV423qrt1Tfl8FPFgeP1WO\nAy+vhq+sOzccD2Tm8wBl/QDXAIdFxB3AC5n5qxH0K0mS1FYM1qNXqzueUne8rl+7rvKrr/x5w18L\n1lJsJTm0vnG5at2/7WCOpVi1PrD8ft8gddQfdzU4Nxz9HyfAEootItNxtVqSJE0QbgUZvdUUL2QE\nmDVE2wT2KY/n1J17Q0RsCxARn46InQZpO5jpwPLM7APmA4O+o8dG6mN0v3TdRbHifiTFvmtJkqSO\nZ7AevSXA0RFxA7DlEG0vBN4fEUsp9l1TbqM4Dbg2Im6n2MbxBLAYmBkRP6J4cWFt4C6BYlX4qLLt\n74GVEXHWKB7TMuC2iJg+kpszswbcAUzLzMdHUYckSVLb6KrVGuU1qWlqvb3PtbqGCaWnpxvnvPmc\n99Zw3lvDeW8N533s9fR0D7h91j3WbaRchZ47wKUTMnP5GIy3hGLPdr3fZubRVY8lSZLU7gzWbSQz\nzwXObeJ485s1liRJUrtzj7UkSZJUAYO1JEmSVAGDtSRJklQBg7UkSZJUAYO1JEmSVAHfx1qSJEmq\ngCvWkiRJUgUM1pIkSVIFDNaSJElSBQzWkiRJUgUM1pIkSVIFDNaSJElSBQzWkiRJUgUmt7oATSwR\ncQEwE6gBp2bmvXXXDgE+D6wHrs3Mz7Smys4zxLxvBnwV2CMz92lRiR1piHmfA/wdxfM9gQ9mZl9L\nCu0wQ8z7h4APUMz7Q8BHM9MPdBilRnNe1+bvgP0zc3aTy+tYQzzXVwC/pHiuA7wvM3/V7BonGles\n1TQRcTDw+szcn+I/bBf1a3IR8C7gAOBtETGjySV2pI2Y9/OAnzS9sA63EfN+MfDuzDwA6AYOa3KJ\nHanRvEfE5sD/Bg4s5/1/APu3pNAOshHPdcp/zw9qdm2dbGPmHTg8M2eXX4bqJjBYq5nmAd8HyMxH\ngK0iYipAROwG/CYzf1mu2l1bttfoDTrvpU8C32tFYR1uqHl/c2auLI97gW2aXF+nGnTeM/P5zJyX\nmS+VIXsa8GTrSu0YQz3XAc4H/rbZhXW4jZl3NZnBWs20PUWA2KC3PDfQtV8DOzSprk7XaN7JzOea\nXtHEMNS8rwaIiB2At1H8MqnRazjvABHxN8BjwHcz8+dNrK1TNZzziFgA3AysaGpVnW/I5zrwlYi4\nLSK+EBFdzStt4jJYq5Ua/Z/cfwDGjnPbGn8w7xGxLXANcHJm/lfzS5oQ/mDeM/MLwG7AYRFxQPNL\n6nj/PecRsTVwAsWKtcZW/+f6WcDpwGxgT4qtlhpjBms10xO88rfpHYFVg1zbqTyn0Ws07xo7Dee9\n/JPtD4FPZeb1Ta6tkw067xGxdUQcBJCZayjm32A9eo2e63OBHuBWii1ne5cvuNPoNfw3JjMXZ+av\nM3MdxV/E9mpyfROSwVrNdD3wboCI2Bt4YsM2hMxcAUyNiF0jYjLw9rK9Rm/QedeYGmrezwcuyMzr\nWlFcB2s071OASyNii/Ln/SjekUWj0+jf9iszc0ZmzgSOAR7IzL9sXakdZdB5j4hpEbE0IjYp2x4M\nPNyaMieWrlrNdxlS80TEFyheGd4HfBR4E/DbzPxeuZL092XTqzLzH1pUZscZYt6vAHYG9gDuBy7O\nzMtbVmwHGWzegaXAM8Cddc0vz8yLm15kBxri+b6gPLeO4u32TvLt9kav0ZzXtdkVuNS326vOEM/1\nU4HjgTXAg8BCn+tjz2AtSZIkVcCtIJIkSVIFDNaSJElSBQzWkiRJUgUM1pIkSVIFDNaSJElSBSa3\nugBJkgYSEXsAi4DDKN6i8LOZeWOTxp4BbJaZD4z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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe44b41a210>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Plot the feature importances of the forest\n",
    "rf_importances = pd.DataFrame(zip(X_train.columns, rf_classifier.feature_importances_), columns= ['Variables', 'Importance']).sort_values( 'Importance', ascending=False)\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.title(\"Feature importances\")\n",
    "ax = sns.barplot(x=\"Importance\", y=\"Variables\", data=rf_importances)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lLOhAxfpncJB"
   },
   "source": [
    "## Decision Tree Classifier\n",
    "#### Now I will use a Decision Tree Classifier to analyze if a tree based algorithm can improve the logit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aUgRlcN3ncJD"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest roc_auc_score metric: 0.6956403376714387\n"
     ]
    },
    {
     "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": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "# Train model with training set\n",
    "rf_classifier = DecisionTreeClassifier(max_depth = 3, random_state = 42)\n",
    "rf_classifier.fit(X_train[most_relevant_variables], y_train)\n",
    "\n",
    "# Test model with test Data\n",
    "y_predicted = rf_classifier.predict_proba(X_test[most_relevant_variables])[:, 1]\n",
    "\n",
    "# Evaluate model\n",
    "roc_auc = roc_auc_score(y_test, y_predicted.round(4))\n",
    "print('Random Forest roc_auc_score metric: {}'.format(roc_auc))\n",
    "                        \n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_predicted, pos_label=1)\n",
    "\n",
    "plt.plot(fpr, tpr, lw=1, alpha=0.8, label='ROC {}'.format(roc_auc))\n",
    "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r', label='Random', alpha=.8)\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic example')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show();                 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Plot the feature importances of the forest\n",
    "rf_importances = pd.DataFrame(zip(X_train.columns, rf_classifier.feature_importances_), columns= ['Variables', 'Importance']).sort_values( 'Importance', ascending=False)\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.title(\"Feature importances\")\n",
    "ax = sns.barplot(x=\"Importance\", y=\"Variables\", data=rf_importances)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BaggingClassifier\n",
    "#### Now I will use a ExtraTreeClassifier to analyze if a tree based algorithm can improve the logit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "92ZP6u4MSDuB"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logit roc_auc_score metric: 0.6302505779523884\n"
     ]
    },
    {
     "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": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.tree import ExtraTreeClassifier\n",
    "\n",
    "# Train model with Training Data\n",
    "lr_classifier = BaggingClassifier(base_estimator=ExtraTreeClassifier())\n",
    "lr_classifier.fit(X_train[most_relevant_variables], y_train)\n",
    "\n",
    "# Test model with Test Data\n",
    "y_predicted = lr_classifier.predict_proba(X_test[most_relevant_variables])[:, 1]\n",
    "\n",
    "# Evaluate model\n",
    "roc_auc = roc_auc_score(y_test, y_predicted.round(4))\n",
    "print('Logit roc_auc_score metric: {}'.format(roc_auc))\n",
    "                        \n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_predicted, pos_label=1)\n",
    "\n",
    "plt.plot(fpr, tpr, lw=1, alpha=0.8, label='ROC {}'.format(roc_auc))\n",
    "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r', label='Random', alpha=.8)\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic example')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show();\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## xgboost\n",
    "#### Now I will use a XGB Classifier to analyze if a tree based algorithm can improve the logit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logit roc_auc_score metric: 0.7485447049298822\n"
     ]
    },
    {
     "data": {
      "image/png": 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ll8HEie6MIAAsERhj/CY9O5e/dqSzMy2bLYmZrN2RzvbULKKb1aNTc/fv7D6tiWpal5ohVfgHf789e2DOHJg0CUSgUyc3Xc8/t4WWliUCY0y52LN3H1sSM9i4O4N1O9KJTcwgLSuP9k3q0Lx+Lbq1rM8pXZvTqXm9qn2UXxRV+OILmDHjQCPw8OFuXoCTAFgiMMYcAVUlITWb5dtSWBGfyor4VHLzPXRoWo/oZnU5sXNTLh4cRasGtQmtDkf6JUlIcI3Bf/7ppgcPhl69AhtTIZYIjDGHlZfvYUtSJqsS0li7I40V8WnUEOjVpgF92jZk8qAoWtSvVfnv0KlIHo+7HXTmTMjOhvr1YepUGDnSXRaqRCwRGGMOsXdfHqsT0lgel8JfO9JZtzOdiNqhHN+hCb0jGzLpePvhP6z334fHH3ePhw+Hm2+Gxo0DG1MxLBEYY1BVNuzay++bElmyNYX45Cyim9ejd2QDLhkcRXSzeuVTPqE6Oecc+O47uOgiGDo00NGUyBKBMdWQqhKXnMXircms3ZHOivhU6oSFMDi6KZeeEEW3lvWrX4Pu0VqzBp57Dv7v/6BuXahdG154odJdBiqKJQJjqglVZVlcKl+v2sHyuFRCQ4Rj2zXimHaNmDwoipYNagc6xOC0bx88/zy89ZZrF3j9dbjmGjcvCJIAWCIwpkrLzfewKiGNX9fvZn5sMuE1azC6T2suGRxFswi7xn/UFi+GBx6ArVvdj/7f/gaXBl/ZNEsExlQxe/buY3lcCku2prAgNonWDcIZ2LExD53XizYNwwMdXtWQkQHPPAMffuimO3aEO++sdLeFlpYlAmOCnKoSm5jJgtgkFsYmsS0pi96RDegd2ZBLBkfRpF6tQIdY9Sxb5pJASAj8/e/uLKACi8SVN0sExgSh1MxcFm9NZsnWZJbGpRIWUoNebRpwTt829I9qbA29/pCTc+DHfvBguPpqOPlk6Nw5sHGVA0sExgSJfI/y56ZEftmwhyVbk+kd2ZA+kQ2ZMLAdLevXtuv9/qIK33wDTzwB06dDTIx7/rLLAhtXObJEYEwltys9mz83JfHf5QnUr12TYd2bc83QaCJq1wx0aFXf7t3w0EPw889u+rPPDiSCKsQSgTGV0I7UbH5cu4v5sUnsSM2mf/tG/POUzvRsU9+O/CuCqvvRf/JJ2LsX6tSBG25wncSqIEsExlQSqVm5/LEpkR/X7iJ2TyZDujZj8qAoeraub4XbKtKOHXDvvbBggZs+8US4/XZo3jywcfmRJQJjAigrJ58f1u7il/V72Lh7L73bNGB079bW4BtIoaGul3DDhq4+0OmnB03HsCNlicCYANiamMnny+L5dcMeerRqwLnHtKF7qwi77h8oW7ZAZKS7HbRpU1csLjoaGjUKdGQVwhKBMRUkPTuXb9fsZN6GRHakZTOyZytm/e1Yu88/kHJz3VCRr7wC117rRg4D6N8/sHFVMEsExvjZtqRM5q7awXdrdtGnbUP+NrAdMa0b2KWfQFu92rUFbNzopnfvDmw8AWSJwBg/2Lsvj983JvLTOtfwO6x7c566sC/N61tht4DLznZVQt95xxWJi4yEadOq3VmAL0sExpSjxL37+HhxPN+u2UmvNg04I6Ylx0U1pnZNq+VfKezaBVdeCXFxUKOGuxT0j3+4ktHVmCUCY45SvkdZui2ZL5ZtZ/X2NIZ3b8Gsi46lqV37r3yaNoUmTaBWLbjrrirZOexIWCIw5gglZ+Qwd9UOvl2zk/q1azKiZ0tuHdHNRvKqbH79FTp1gpYt3VnAo4+68YNr2h1a+1kiMKaMsnPz+WxpPJ8uSWBQdBNuGdGNzs3rWY/fyiY52dUH+t//XJG4p55y/QGaNAl0ZJWOJQJjSik1M5cvV2znq5Xb6dGqPo+e35u2jesEOixTmCp8/TU89hikpLjLQMcf7563ZF0kSwTGHEZKZg6fLonnf6t2MDi6KfeeHUPHZvUCHZYpyq5drkjcL7+46eOOc3cEtWkT2LgqOUsExhRj/c50Plwcx5KtKQzp0ozp4/vS2kb4qrwyM2HCBEhNdYPH/+tfMGaMnQWUgiUCY3zk5Hn4c3Mic1ftYEtiJuP6t+X6YZ2pE2ZflUqvTh0YOxY2bIDbbqvSReLKm18/3SIyAngKCAFeUtWHC81vALwFtPPG8riqvurPmIwpSlZOPl8sS+DLFdtp3bA2p/doyQmdmlrv38rM43GdwiIjYehQ99xVV7kzADsLKBO/JQIRCQFmAcOBOGCBiHyuqqt9FrsWWK2qo0WkGbBWRN5W1Rx/xWWMr3yP8s3qHbz951Z6tWnAnaN60Km5Xf+v9DZsgPvuc2UiGjeGgQMhPNzdHmrKzJ9nBAOADaq6CUBE3gPGAL6JQIEIcffd1QOSgDw/xmRMgcVbk3npl01E1KrJXaN60LlFRKBDMoeTk3OgSFx+vrv8c8cdLgmYI+bPRNAG2OYzHQcMLLTMTOBzIAGIAC5QVU/hFYnIlcCVAO3atfNLsKb6iEvO5LXfYolNzODvJ3RgUHQT6wMQDFaudGcBmza56fPPhylTXMOwOSr+TARFfbO00PQZwFLgVCAa+EZEflHVtINepPoC8AJA//79C6/DmFLZmZbNu/O3Mn9zEucc04abR3SlVqj1Ag4K+flw552wbRu0a+duCT322EBHVWX4MxHEAW19piNxR/6+LgUeVlUFNojIZqAbMN+PcZlqJmNfHp8tTeCLZQmcHtOCly8+zspABAuPx133DwmBf/8b/vjDFYmrZXWcypM/E8ECoLOIdADigQuBvxVaZiswDPhFRFoAXYFNfozJVCOqysItycz6YQM9WtXnyQv70sLKQAeH9HRXEqJWLTdcJMCAAe6fKXd+SwSqmici/wTm4m4ffUVVV4nIVd75zwH3A6+JyArcpaRbVXWPv2Iy1Ud8ShZPfL2WzH35TDm1M/3aV48hB6uEn35yvYP37IGwMLjkEmjWLNBRVWl+7UegqnOAOYWee87ncQJwuj9jMNVLdm4+Xy7fzsdL4vjbgPaM7NmSGjWsITgoJCW5sYK//tpN9+7t2gUsCfiddZc0VYKqMn9zEs//vImOTevy0Lm9adfECsIFjTlzXBJIS3ODxPzznzB+vPULqCCWCEzQ252+j5d/3cym3Xu5blhn+rZtGOiQTFn9+qtLAgMGuDuCWrcOdETViiUCE7Ty8j18viyBDxbGcWavltxwWmcbEjJYeDyuRHTjxm765pvdmAFnnWXlIQLAEoEJSr9t2MObv2+hUd2adjdQsNm6FR54wN0Z9OabEBoKjRrBqFGBjqzaskRggsqGXem8N38bW5MyuWpoNMe0bWi9goNFfj68/TY895wrFdG4sUsKHTsGOrJqzxKBCQo5eR7e+D2Wn9btZuyxkUw9o6tdBgom69a58hB//eWmR42CG290YwebgLNEYCq99TvTeeLrdUQ2CmfWRcdSv7YNOh5UXn8dnn3WnRG0bOmKxA0aFOiojA9LBKbS8niU/67Yznvzt3LVkGhO6tzULgMFo/r1XePw+PHuttA6dltvZWOJwFRKS7Ym8/Kvm6kbFsojY22Q+KCSmQlr1kC/fm76nHMgJga6dAlsXKZYpUoEIhIGtFPVDX6Ox1Rze/fl8cJPG1mZkMYVJ3Xk+I6N7SwgmPz5Jzz4ICQmwvvvu0HjRSwJVHKH7bYnImcBK4BvvNN9ReQTfwdmqp+/dqQx5Z3FhIbU4NmLjrVxAoJJWpprDL72WkhIgPbtYd++QEdlSqk0ZwT34QaU+QFAVZeKSCe/RmWqldx8Dx8sjOPLFQlcO7QTgzs1DXRIpiy+/x4eecSdBYSFwRVXwKRJrn+ACQql+UvlqmpKoSMzGxzGlIsNu9KZ8e16GtcJY8YFfWkeYR3DgsoLL7h/AH36uCJxUVEBDcmUXWkSwRoRGQ/U8I4tcD3wh3/DMlVdbr6H1+fF8t2aXVxxcgdO6drcLgMFo9NOg/feg6uuckNHWpG4oFSaRPBP4C7AA3yMG1/g3/4MylRt8SlZTP96HQ3Ca/LcpH40CLd+AUFj+3b48ku47DLXCNyxo5u2weODWmkSwRmqeitw6/4nROQ8XFIwptRUlR/X7ealXzZx3jGRnHtMGxsrIFh4PPDhhzBzprs9tG1bOOMMN8+SQNArTSKYxqE/+ncU8ZwxxdqVls3snzayZ28O087qQfdWVlogaGzZ4u4IWrbMTQ8bBscdF9iYTLkqNhGIyBnACKCNiEz3mVUfd5nImFL5c1MiT3+/ntG9W/PvkZGEhdp15KCQl+eqg774oisS16QJ3HornHpqoCMz5aykM4JdwEogG1jl83w6cJs/gzJVQ06ehxd+3sjSbSncNqI7vSIbBDokUxbvvw+zZrnHZ58NN9xgReKqqGITgaouAZaIyNuqml2BMZkqYP/g8U3qhvHMhGMJD7NKoUFn7Fj4/XeYOBEGDgx0NMaPStNG0EZEHgR6AAU3eauq9Rk3Rfpl/W6e+2kj4/u3ZXTv1tYgHCyWLoXnn4dHH4WICKhVC555JtBRmQpQmou1rwGvAgKMBN4H3vNjTCaIzduwh5d/3cy9Z8cwpq/dFRQUMjPdj//ll8OCBa5dwFQrpUkEdVR1LoCqblTVacAp/g3LBBtV5ZMlcTz740buOLM7nZpHBDokUxrz5rny0O+/DyEhrn/A5ZcHOipTwUpzaWifuC6fG0XkKiAeaO7fsEww8XiUl37dxLK4VJ4Y38fGDw4GqakwfbrrDAbQvbsrD2FVQqul0iSCfwH1gOuAB4EGwN/9GZQJHunZuTzx9Tr25eXz8Hm9iLDRw4LDX3+5JBAW5spDXHSROyMw1dJhE4Gq/ul9mA5MAhCRSH8GZYLDup3pTP96HX3aNuTykzpQM8T6B1RqWVkHegEPHAjXXw9DhkC7doGNywRcid9cETlORM4Rkabe6RgReQMrOletqSrvL9jGPZ+v4sIBbblqSEdLApWZKnz+OZx1FixffuD5SZMsCRig5J7FDwFjgWXANO9gNNcDjwBXVUx4prLJysln9o8b2LQng5l/O5bGdcMCHZIpSUICPPAAzJ/vpufOhd69AxuTqXRKujQ0Buijqlki0hhI8E6vrZjQTGWTmpnL3Z+vpF3jOjx2fh/rJFaZeTzuTqCZMyE7Gxo0gKlTYcSIQEdmKqGSEkG2qmYBqGqSiPxlSaD6yszJ497/rqJP24ZcMjjKxg6ozOLj3R1A+y8DnX66SwKNGwc2LlNplZQIOorI/gqjAkT5TKOq5x1u5SIyAngKCAFeUtWHi1hmKPAkUBPYo6pDSh++qQh79u7jvi9W071VfUsCwSA8HGJjoVkz+Pe/4eSTAx2RqeRKSgRjC03PLMuKRSQEmAUMB+KABSLyuaqu9lmmIfAsMEJVt4qI9U+oZDbu3ssD/13NyF6tGNcv0pJAZbV+PXTo4MYJbtwYnnzSTUdYxz5zeCUVnfvuKNc9ANigqpsAROQ9XLvDap9l/gZ8rKpbvdvcdZTbNOVow6507v58FVeeHM2QLs0CHY4pyr59rj7QW2/BNdfAJZe4561B2JSBP+/5awNs85mO8z7nqwvQSER+FJFFIjK5qBWJyJUislBEFu7evdtP4Rpfa7ance8Xq/mHJYHKa/FimDAB3njDTWdkBDYeE7RK07P4SBV1DUGL2H4/YBgQDvwuIn+o6rqDXqT6AvACQP/+/Quvw5Sz+ZuTePLbddxwWhcGdLAGxkonI8NVBf3wQzfdsSPcdRf07BnYuEzQKnUiEJFaqrqvDOuOA9r6TEfibkEtvMweVc0AMkTkZ6APsA4TEN+s3snr82K5c5QNJ1kpbd/uCsPt2nWgSNwll7hSEcYcocNeGhKRASKyAljvne4jIqUpUr4A6CwiHUQkDLgQ+LzQMp8BJ4lIqIjUAQYCa8q0B6bcfLY0nrf/3MJD5/WyJFBZtWgBkZHQowe8/TZceaUlAXPUSnNG8DQwCvgUQFWXichhy1Crap6I/BOYi7t99BVVXeWtYIqqPqeqa0Tkf8By3DjIL6nqyiPcF3OEVJXX5sXyx6ZEHhnb26qHViaq8O23EBMDrVtDjRoHBo6xInGmnJQmEdRQ1S2FbhvML83KVXUOMOTy3c4AACAASURBVKfQc88Vmn4MeKw06zPlLyfPw5PfriMuOYvHx/Wx6qGVye7d8PDD8NNPMGCAGz9YBBo2DHRkpoopTSLYJiIDAPX2DZiCXcOvEtKzc7n7s1U0i6jF4+P6EBZqheMqhf1F4mbMgL17oW5dOO20QEdlqrDSJIKrcZeH2gE7gW+9z5kglpKZU9Bb+PKTOlhHscoiPt4ViVuwwE2fdJLrHdzc+loa/ylNIshT1Qv9HompMIl793HHJysZ0KGxlYyoTPbuhYkTIT3dXf65+WZXJ8j+PsbPSpMIFojIWuA/uF7A6X6OyfjRrrRs7vh0Jad1b874/m0tCVQm9eq5DmJbt8JNN0GjRoGOyFQTpRmhLFpEBuNu/7xXRJYC76nqe36PzpSr1Mxcpn26kjN7teTcY2yQuYDLzYXXXnMdwoYNc89dcYWdAZgKV6rWQVWdp6rXAccCacDbfo3KlLucPA+Pzv2LQdFNLAlUBqtXu8tAzz8PjzzixgwASwImIA57RiAi9XDF4i4EuuM6gQ32c1ymHKVl5/LQnL9oVKcmkwdFBTqc6i07G557Dt55xw0eExnpxg6obX03TOCUpo1gJfAF8Kiq/uLneEw5S87IYdqnK+kd2YDLT+pISA074gyYRYvg/vshLs51DJs0Cf7xD0sCJuBKkwg6qqrH75GYcpeSmcPNHy5jWPcWTBhgg5QHVH4+PPigSwKdOrkicT16BDoqY4CSB69/QlVvAj4SkUMqfpZmhDITONm5+Tw6dy2Do5taEggkj8cd/YeEwLRp7qzgkkugpvXgNpVHSWcE//H+X6aRyUzgeTzKjG/X0ahOTS4ZHBXocKqn5GR4/HHXK/j2291zxx7r/hlTyRR715Cqzvc+7K6q3/n+wzUam0rI41Fe+nUTSXtzmHJqZ2pYm0DFUoW5c2HcOPf/V19BYmKgozKmRKW5ffTvRTx3WXkHYo6eqvLCL5tYsz2du0b3oHZNq05ZoXbtghtvhDvugJQUVyjuvfegSZNAR2ZMiUpqI7gAd8toBxH52GdWBJDi78BM2f1nwTbWbE/jgXN6WhXRivbxx/DUU270sHr1XEIYPdr6BZigUFIbwXwgETey2Cyf59OBJf4MypTdkq3JfLliO09feIwlgUBYutQlgSFD4LbboJmN82yCR7GJQFU3A5tx1UZNJbYzLZsnvl7HLSO60qiujVZVIfLz3bX//VVBb7rJJYFTT7WzABN0im0jEJGfvP8ni0iSz79kEUmquBBNSXamZXPrR8u5aGA7ekfagCUVYsMGuPRS+Oc/ISfHPdeggasXZEnABKGSLg3tH46yaUUEYsouJTOHuz9bxZi+rRnZq1Wgw6n6cnLg1VfhlVfcGUGLFpCQAFFRgY7MmKNS0u2j+3sTtwVCVDUfGAT8A6hbAbGZEuTle3jwyzUc37GxFZGrCCtXuiJxL77oksC4cfD++5YETJVQmhITnwLHiUg08AbwJfAObkB7EwCqyrM/bqRe7VArIlcRXnjBJQBVaNfO9RC2jmGmCilNIvCoaq6InAc8qapPi4jdNRRAny6NZ/2uvTw6trd1GKsIrVq5a/+TJ8OVV0KtWoGOyJhyVaqhKkVkHDAJOMf7nN2fGCALYpP4aFE8j43rTXiYdRjzi/R0WLECBnurrY8aBb162WUgU2WVtmfxKbgy1JtEpAPwrn/DMkXZlpTJk9+u47aR3WjVIDzQ4VRNP/3krv9PneqGjAR3NmBJwFRhpRmqcqWIXAd0EpFuwAZVfdD/oRlf6dm53PvFai4Z3IGebRoEOpyqJynJFYn7+ms33bt3YOMxpgKVZoSyk4A3gXhAgJYiMklVf/N3cMbxeJSHvvqLgR0aM7xHi0CHU7WousJwjz8OaWkQHu76B4wb58pHG1MNlKaNYAZwpqquBhCR7rjE0N+fgZkD3v5zC/n5ymUndgh0KFXP7NmuXwDAwIGuYFzr1oGNyZgKVppEELY/CQCo6hoRsToGFWTehj38sHY308f3sTuE/OGss+Dzz+Haa12jsPUMNtVQaRLBYhF5HncWAHARVnSuQmzek8HMHzZw9+gYGtax3Fsutm6FL76Aa65xP/rt27tEEGbvr6m+SpMIrgKuA27BtRH8DDzjz6CMG2ry/v+u5qoh0XRtGRHocIJffj68/TY895wrFdGhA5x5pptnScBUcyUmAhHpBUQDn6jqoxUTkgF49seNxLSuz8ldrJzxUVu3Du67D/76y02PGgUnnhjYmIypREoamOZ23Ehki3ElJu5T1VcqLLJq7LcNe1gZn8rsiVbG4Kjk5MBLL8Hrr7szgpYtXWPwoEGBjsyYSqWk++MuAnqr6jjgOODqsq5cREaIyFoR2SAit5Ww3HEiki8i55d1G1XNqoRUZv2wgdvP7EatUOs5fFQ++MDdEeTxwAUXuCJxlgSMOURJl4b2qWoGgKruFpEy3VQtIiG4kc2GA3HAAhH53PcOJJ/lHgHmlinyKmjtjnT+b84abhzehU7NrV3giKgeuPNn3DhYsgQmTYI+fQIblzGVWEmJoKPPWMUCRPuOXayq5x1m3QNwvZA3AYjIe8AYYHWh5aYAH+HOOqqt3HwPj81dy7WndKJ/VONAhxOc/vjDNQY//TTUr+8agR9/PNBRGVPplZQIxhaanlnGdbcBtvlMxwEDfRcQkTbAucCplJAIRORK4EqAdu3alTGM4PDJ4ngiG4UzONrGASqztDSYMcPdFgrw7rvwj38ENiZjgkhJYxZ/d5TrLqpnjhaafhK4VVXzpYSOPKr6AvACQP/+/QuvI+it2Z7GZ8vimT6+b6BDCT7ffw+PPOLGDw4LcwngoosCHZUxQaU0/QiOVBxudLP9IoGEQsv0B97zJoGmwJkikqeqn/oxrkolLjmTB79cw3WndqZF/dqBDid4JCa6BPD99266b1+4807XQcwYUyb+TAQLgM7estXxwIXA33wXUNWC4jki8hrw3+qUBHLyPDz57XrGH9eWgR2bBDqc4LJpk0sCderAlCkwdqwViTPmCJU6EYhILVXdV9rlVTVPRP6JuxsoBHhFVVeJyFXe+c+VOdoq5pXfNtO4bhijbOD50klPhwjv3VTHHQe33AInneRGEDPGHLHDHkKJyAARWQGs9073EZFSlZhQ1Tmq2kVVo/ePYaCqzxWVBFT1ElX9sIzxB60lW5OZvzmJa0/pZMXkDsfjgf/8xxWIW+JT5mr8eEsCxpSD0pxLP40bqD4RQFWX4UYsM0coPTuXp75bz7WndKJBuI36WaLYWLjiCnjsMcjMhF9+CXRExlQ5pbk0VENVtxS6qyffT/FUC7N+2MiJnZrSr32jQIdSeeXlwRtvwIsvQm4uNGkC//43DB0a6MiMqXJKkwi2icgAQL29gKcA6/wbVtX156ZENuxK54bTrI5QsbZuhdtuc8XiAM4+G264wXUSM8aUu9Ikgqtxl4faATuBbzmCukMGkjNymP3TRm4c3oXaNa2OULEiImDXLjdS2LRpMGBAoCMypkorzeD1u3C3fpqjoKo8/f16hnVrTu/IhoEOp/JZuRK6doWaNaFRI1cmIirK3R5qjPGr0gxe/yKH9ghGVa/0S0RV1GdLE0jNzOWC46pmiYwjlpkJM2e6yqD/+IdrGAbo0SOwcRlTjZTm0tC3Po9r42oDbStmWVOE1QlpfLgojkfO701YqHV6KjBvHvzf/8GOHRASYuMFGxMgpbk09B/faRF5E/jGbxFVMalZuTz+9VquGRpNm4bhgQ6nckhNhenT4csv3XT37q48RJcugY3LmGrqSEpMdACsoEspqCrP/7SRAR0aM7iTVRUFICEBLrkEkpJckbirrnJF4kKs8dyYQClNG0EyB9oIagBJQLGjjZkD5q7awZakTJ4YZ4OiFGjVCjp1cv0Epk2DKlpW3JhgcrjB6wXogysaB+BR1SpXBtofUjJzeOuPrTx4bs/qfauoqhsn4JhjoG1b1w7wyCNQt64ViTOmkigxEaiqisgnqtqvogKqKl6bF8sp3ZrTvkndQIcSOAkJ8MADMH8+9OsHs2e7H/8IG4bTmMqkNIdk80XEusGWwfqd6SzakswFx7U9/MJVkcfjRgkbP94lgQYN4Jxz7K4gYyqpYs8IRCRUVfOAE4ErRGQjkIEbeUxV1ZJDEVSVV37bzEUD21Gvlj+He6ikNm1yZwHLl7vp00+HqVOhsY3DbExlVdIv1XzgWOCcCoqlSvhm9U7Ss/MY3qNloEOpeHv3ujuCMjOhWTNXJO7kkwMdlTHmMEpKBAKgqhsrKJagl7Evj7f/3Mq0s7oTUh3HGKhXzyWC7dvh+uvdtDGm0ispETQTkRuLm6mq0/0QT1D7YOE2+kQ2oHOLatIYum8fPP+8qxF0xhnuuUsvtbYAY4JMSYkgBKiH98zAlGxbUiZfr97JMxOOCXQoFWPxYrj/fti2zV3/HzoUatWyJGBMECopEWxX1fsqLJIgpqo8//NGLjiuLU3q1Qp0OP6VkeEqg370kZvu2BHuusslAWNMUDpsG4E5vLmrdpCSmctZVX0Q+t9+gwcfdGMFhIbC3//uLgXVtOE2jQlmJSWCYRUWRRBLyczh9XlbeGRsb0JDqnBP2bw8Vyhu1y6IiXFnAdHRgY7KGFMOik0EqppUkYEEqxd/2cTpMS1o16QKDqCi6hJAzZruDOCuu9wAMhMmWHkIY6oQ+zYfhVUJqaxOSGPCgCpYOG3XLrjpJnj44QPP9enjKoVaEjCmSrFv9BHyeJRXf4tl4vHtq1ZROVX45BMYNw5+/hm++86VjDbGVFnVsAZC+fhp/W48HuWUrs0DHUr5iYtz5SEWLnTTJ58Mt91m5SGMqeIsERyB3HwP7/y5lWtP6USNqtCDWNUViZs1y3USa9gQbrkFhg+3fgHGVAOWCI7AN6t30rReLfpENgh0KOVDBDZscElgxAhXJK5hw0BHZYypIJYIysjjUT5eHMfUM7oiwXy0nJsLu3dD69Zu+oYbYNgwOOGEwMZljKlw1lhcRj+v30392jXpGsz1hFavhokTYcoUyMlxz9Wvb0nAmGrKEkEZ5OR5eH1eLJed1CE4zways+HJJ12F0I0b3QAyO3YEOipjTID5NRGIyAgRWSsiG0TkkAHvReQiEVnu/TdPRCr1KO9zV+2gfZO6xLQOwraBhQvhggvgrbfc9OTJroHYBo83ptrzWxuBiIQAs4DhQBywQEQ+V9XVPottBoaoarKIjAReAAb6K6ajkZ6dy3sLtvLgOb0CHUrZPfMMvP66e9ypk+sh3KNHYGMyxlQa/mwsHgBsUNVNACLyHjAGKEgEqjrPZ/k/gEg/xnNU/rt8O/3aNSKqaRAORh8d7UpEXH45XHyxFYkzxhzEn4mgDbDNZzqOko/2LwO+KmqGiFwJXAnQLgCXMtKzc/l8aQJPjK/UV64OSE52YwYPGeKmR46Evn0P3CFkjDE+/NlGUFRrqha5oMgpuERwa1HzVfUFVe2vqv2bNWtWjiGWztxVOzkuqhGtG4ZX+LbLRBX+9z84/3zXI3jzZve8iCUBY0yx/HlGEAe09ZmOBBIKLyQivYGXgJGqmujHeI6Ix6N8tWI7t4zoFuhQSrZzJzz0EPz6q5seMMAGizHGlIo/E8ECoLOIdADigQuBv/kuICLtgI+BSaq6zo+xHLE/NifSuG4YXVtW0n4DHg98+qm7LTQz0w0Yf+ONMHq0lYcwxpSK3xKBquaJyD+Bubjxj19R1VUicpV3/nPAXUAT4Fnvffl5qtrfXzEdiS+WJXBmZR557Jln4M033eOhQ+HWWyEAl8+MMcHLryUmVHUOMKfQc8/5PL4cuNyfMRyNLYkZJKRkc3KXSvzDet55rlT0dde5EhF2FmCMKSPrWVyCDxbGcUZMS0IqU4XR9evhiSdcwzBA27Zu/IDTTrMkYIw5IpYIipGamcv82CTG9K0kd9vk5MBzz7kaQe++C1/53GkbUoUGxjHGVDirPlqML1ds54ToptStVQneohUr4P77YdMmNz1+vGsPMMaYclAJfuUqn+zcfL5auZ0HzukZ2ECysmD2bHcGoOrqAt15JxxzTGDjMsZUKZYIivDdml10aRFB+yYBLifx8cfwzjtusPiLL4Yrr4SwsMDGZIypciwRFOI78ExAqB5o9B0/Htasce0C3Sp5hzZjTNCyRFDIH5sSiagdSrdAdCD78Ud44QV49lk3VGTNmm4weRNwubm5xMXFkZ2dHehQjClR7dq1iYyMpGYZiktaIvChqnyyJJ7z+7Wt2IFnkpLg0Ufh22/d9AcfwBVXVNz2zWHFxcURERFBVFRUcA5KZKoFVSUxMZG4uDg6dOhQ6tdZIvCxKiGN1KxcBkc3qZgNqrrbQB9/HNLSIDzcDR95/vkVs31TatnZ2ZYETKUnIjRp0oTdu3eX6XWWCHx8vXonZ/VuRY2K6EC2Ywf83//BPO+QDMcfD7ffblVCKzFLAiYYHMnn1BKBV1p2Lgs2J3HJ4KiK2WBCgksCERGuSNyoUdYz2BgTENaz2OvHtbvpH9WIxnX9eHtmcvKBx8ce64aM/PBDqxRqSiUkJIS+ffvSs2dPRo8eTUpKSsG8VatWceqpp9KlSxc6d+7M/fffj+qB4T+++uor+vfvT/fu3enWrRtTp049ZP2qynXXXUenTp3o3bs3ixcvLjKOk046ib59+9K3b19at27NOeecc9D8BQsWEBISwocffljw3IwZM4iJiaFnz55MmDChoNH9nnvuoU2bNgXrmzPnoNJkbN26lXr16vH4448fEsfZZ59Nz54H+vr861//KlhPly5daNiwYcG8119/nc6dO9O5c2de3z9sq48pU6ZQr169gunk5GTOPfdcevfuzYABA1i5ciUA27Zt45RTTqF79+7ExMTw1FNPFbxm2bJlDBo0iF69ejF69GjS0tIAd6PBxRdfTK9evejevTsPPfRQwWveffddevXqRe/evRkxYgR79uwBYPr06fTo0YPevXszbNgwtmzZAsDSpUsZNGgQMTEx9O7dm//85z9F/o3KTFWD6l+/fv3UH6a+v1QXbE70y7o1L0/1tddUBw1SnT/fP9swfrV69epAh6B169YteDx58mR94IEHVFU1MzNTO3bsqHPnzlVV1YyMDB0xYoTOnDlTVVVXrFihHTt21DVr1qiqam5urs6aNeuQ9X/55Zc6YsQI9Xg8+vvvv+uAAQMOG9N5552nr7/+esF0Xl6ennLKKTpy5Ej94IMPVFU1Li5Oo6KiNDMzU1VVx40bp6+++qqqqt5999362GOPlbj+888//5BlPvroI50wYYLGxMQU+bqnn35aL730UlVVTUxM1A4dOmhiYqImJSVphw4dNCkpqWDZBQsW6MSJEw96f6dOnar33HOPqqquWbNGTz31VFVVTUhI0EWLFqmqalpamnbu3FlXrVqlqqr9+/fXH3/8UVVVX375ZZ02bZqqqr799tt6wQUXqKr727Rv3143b96subm52qxZM929e7eqqt5888169913q6rq999/rxkZGaqq+uyzz+r48eNVVXXt2rW6bt06VVWNj4/Xli1banJy8iH7X9TnFVioxfyu2hkBsC0pkx1p2fRp2/DwC5fVunWuM9gzz7h6QQsWlP82TLUzaNAg4uPjAXjnnXc44YQTOP300wGoU6cOM2fO5OGHHwbg0Ucf5Y477qCbty9KaGgo11xzzSHr/Oyzz5g8eTIiwvHHH09KSgrbt28vNob09HS+//77g84InnnmGcaOHUvz5s0PWjYvL4+srCzy8vLIzMykdSnawj799FM6duxITEzMQc/v3buX6dOnM23atGJf++677zJhwgQA5s6dy/Dhw2ncuDGNGjVi+PDh/O9//wMgPz+fm2++mUcfffSg169evZphw4YB0K1bN2JjY9m5cyetWrXi2GOPBSAiIoLu3bsX/B3Wrl3LySefDMDw4cP56KOPAHfNPiMjo+A9CAsLo379+gU/whkZGagqaWlpBe/LKaecQp06dQA4/vjjiYuLAyg44wNo3bo1zZs3L3PDcFGsjQD4eHE8Z/ZqRc2QcsyLOTnw0kvw2mtu8JhWreCOO1yjsAl6o5/5tdzX+cWUE0u1XH5+Pt999x2XXXYZ4C4L9evX76BloqOj2bt3L2lpaaxcuZKbbrrpsOuNj4+nbdsDgwpGRkYSHx9Pq1ZFj8fxySefMGzYMOrXr1/w+k8++YTvv/+eBT4HPG3atGHq1Km0a9eO8PBwTj/99IKkBTBz5kzeeOMN+vfvzxNPPEGjRo3IyMjgkUce4ZtvvjnkstCdd97JTTfdVPBDWdiWLVvYvHkzp556aon7tX/bZ5999iH72KdPHz7++GNOPPFE5s+fz5YtW4iLi6NFixYFy8TGxrJkyRIGDnRDsffs2ZPPP/+cMWPG8MEHH7Btmxuy/fzzz+ezzz6jVatWZGZmMmPGDBo3bgzA7Nmz6dWrF3Xr1qVz587MmjXrkP15+eWXGTly5CHPz58/n5ycHKKjo4t8H8qi2ieCrJx85m3cw/OT+h1+4dLatAluuQViY921/wsugGuvhWI+uCb4lPZHuzxlZWXRt29fYmNj6devH8OHDwfc5d3i7hQpyx0kqocOKV7S6999910uv/zAcCI33HADjzzyCCGFquEmJyfz2WefsXnzZho2bMi4ceN46623mDhxIldffTV33nknIlLwA//KK69w9913869//eug6/bgrpFv2LCBGTNmEBsbW2Rc7733Hueff35BHMXtV0JCAh988AE//vjjIfNvu+02rr/+evr27UuvXr045phjCA098HO5d+9exo4dy5NPPlmQCF955RWuu+467rvvPs4++2zCvOVg5s+fT0hICAkJCSQnJ3PSSSdx2mmn0bZtW2bPns2SJUvo2LEjU6ZM4aGHHjroTOett95i4cKF/PTTTwfFt337diZNmsTrr79OjRpHfwBb7RPBrxv2ENO6AQ3rlGMjcdOmrl9AVJQrEtenT/mt21Rb4eHhLF26lNTUVEaNGsWsWbO47rrriImJ4eeffz5o2U2bNlGvXj0iIiKIiYlh0aJF9DnM5zAyMrLgKBZcJ7riLuEkJiYyf/58Pvnkk4LnFi5cyIUXXgjAnj17mDNnDqGhoeTm5tKhQweaeUfOO++885g3bx4TJ0486Aj7iiuuYNSoUQD8+eeffPjhh9xyyy2kpKRQo0YNateuTUhICIsWLSIqKoq8vDx27drF0KFDD/oxf++99w46so6MjDxoflxcHEOHDmXJkiVs2LCBTp06AZCZmUmnTp3YsGED9evX59VXXwVcIunQoUNBB63c3FzGjh3LRRddxHnnnVew3m7duvH1118DsG7dOr788kvAXbobMWIENWvWpHnz5pxwwgksXLiQxEQ3RPv+I/rx48cXXM4D+Pbbb3nwwQf56aefqOUz/nhaWhpnnXUWDzzwAMeX1xWG4hoPKuu/8m4snvr+Uv19456jX9HChar79h2YXrfu4GkT1CpbY/HixYu1bdu2mpOTo5mZmdqhQwf95ptvVNU1Hp911ln69NNPq6rqsmXLNDo6WteuXauqqvn5+frEE08csv7//ve/BzUWH3fcccXGMnv2bJ08eXKx8y+++OKCxuI//vhDe/TooRkZGerxeHTy5MkFsSUkJBS8Zvr06QWNqr6Ka1DevHnzIY3Ff/31l7Zv3149Hk/Bc4mJiRoVFaVJSUmalJSkUVFRmph46I0hvu9vcnKy7vN+f1944QWdNGmSqqp6PB6dNGmSXn/99Ye8fufOnarq3t9Jkybpyy+/rKqqDz/8sF5yySXq8Xh079692r17d122bFlBY++uXbtUVXXatGl64403qqr7+3bs2LGgYXi/ffv26amnnqozZsw4ZPu+ytpYHPAf9rL+K89EsDMtSye88Lvm5XsOv3BxUlNV77lHtV8/1dmzyy02U7lUtkSgqjpq1Ch94403VFV1+fLlOmTIEO3SpYtGR0frPffcc9CP4RdffKHHHnusduvWTbt3765Tp049ZP0ej0evueYa7dixo/bs2VMXLFhQMG/kyJEaHx9fMD1kyBD96quvio3VNxGoqt51113atWtXjYmJ0YkTJ2p2draqqk6cOFF79uypvXr10tGjRx+UGPYrSyK4++679dZbbz1k2Zdfflmjo6M1OjpaX3nllSJj9n1/582bp506ddKuXbvqueeeW3CX0S+//KKA9urVS/v06aN9+vTRL7/8UlVVn3zySe3cubN27txZb7311oL3Pz09Xc8//3zt0aOHdu/eXR999NGC7cyePVu7deumvXr10lGjRumePe6gdNiwYdq8efOCbYwePVpVVd98800NDQ0teL5Pnz66ZMmSQ/alrIlAtIjrZ5VZ//79deHCheWyrg8WbiM+JYsbTutyZCv4/nt4+GFXKygsDK65xlUKNVXOmjVr6N69e6DDMKZUivq8isgiVe1f1PLVto3A41G+WL6de8+OOfzChSUmwiOPuEQAbqCYadOgffvyDdIYYypAtU0Ea3akEVE7lA5Nyzj4THw8TJrkGoPr1HFF4saOdYPHGGNMEKq2ieD3jYmcEN207C9s3RpiYtxtobffDi1bln9wxhhTgaplIlBVflm/h/vHlGJMYo/HjQ9w/PHu0o+IuywUHm71gYwxVUK1TAQr4lOpVyuUdk0O08Fr82a4/35Yvhz69oUXX3Q//tYxzBhThVTLRPDdml0M79Gi+AXy8uCNN9wPf26u6yA2caKdARhjqqRq18KZk+fhz82JDOnSrOgF/voLJk924wbn5sKYMe7S0NChFRqnMYWVVIb6aMTGxh5UztlUP9UuESyPS6Ftozo0KmrcgfR0uPJKVzG0dWuXDO680w0eY0yA7S8xsXLlSho3blxkgTJjjkS1uzT0x6ZEBncqZkziiAg3aPzu3XD11a5B2Jii9C+yX45z++2w69V8bwAAC+tJREFUvwbNxx+7IUmLc4SdIwcNGsTy5csBVwBtzJgxJCcnk5ubywMPPMCYMWOIjY1l5MiRnHjiicybN482bdrw2WefER4ezqJFi/j73/9OnTp1OPHEAwX0srOzufrqq1m4cCGhoaFMnz6dU045hddee41PP/2U/Pz8gmqmOTk5vPnmm9SqVYs5c+YUVNQ0wafanREs2ZpC//beD2xmprsDyFscCnB9BG680ZKAqbT2l6E+++yzAahduzaffPIJixcv5ocffuCmm24qqLi5fv16rr32WlatWkXDhg0LauRfeumlPP300/z+++8HrXv/WcaKFSt49913ufjiiwtGE1u5ciXvvPMO8+fP54477qBOnTosWbKEQYMG8cYbb1TU7hs/qFZnBAkpWeR6lMhG4W684AcfhJ074bvvYPhwVybCmNIo7ZH8eecdODs4SiWVob799tv5+eefqVGjBvHx8ezcuROADh060LdvXwD69etHbGwsqamppKSkMGTIEAAmTZrEV199BcCvv/7KlClTAFdNs3379qxbtw5wg6VEREQQERFBgwYNGD16NAC9evUqODsxwcmvZwQiMkJE1orIBhG5rYj5IiJPe+cvF5Fj/RnP4q3JDGgcgtx9N1x3nUsCPXrArFmWBEylt7+NYMuWLeTk5BQcvb/99tvs3r2bRYsWsXTpUlq0aFFwFO9bvjgkJIS8vLwSxy8oqfaY77pq1KhRMF2jRg3y8vKOev9M4PgtEYhICDALGAn0ACaISI9Ci40EOnv/XQnM9lc8qJL06RwuevwmmDPH/fBffz28+ip4h34zJhg0aNCAp59+mscff5zc3FxSU1Np3rw5NWvW5IcffigY6Lw4DRs2pEGDBvz6qxtl7e233y6Yd/LJJxdMr1u3jq1bt9K1a1f/7YypFP6/vbsPsqqu4zj+/oRLi4mgkI2KwJpE4vAo8qA9YPQANIU5jottEE6Mg0BljTNUOj1M/UFPM2lGxDCwMEPASJhkPsQYCiErbMmzloSO7cSErgw5iAHLtz9+P+xyu+ueu9x7l3PP9zVzZzjn/O493+/ucn7n/M653185rwjGAPvN7ICZHQdWA1Pz2kwFVsQqqU1Ab0mF58U7S8f/c4JBD6/iwmNvwKhRsGZNuB+QN5uSc2kwcuRIhg8fzurVq2loaKC5uZnRo0ezcuXKt+cmfifLli1j7ty5jB8/nh4598PmzJlDW1sbQ4cOpb6+nsbGxjOuBFx1KlsZakm3AJPMbFZcng6MNbN5OW0eARaY2Z/i8pPAfDNrzvusOwhXDPTv3//ajs54Cjl2vI1dj29m7MnX4aabvEicK4qXoXZpci6VoS40CJnf6yRpg5ktBhZDmI+gM8H06N6NsZ+d0Jm3OudcVSvnaXELcEXOcj/gn51o45xzrozK2RFsBwZJqpPUHZgGrM9rsx6YEZ8eGgccMbODZYzJuU5L22x+Lps683datqEhMzspaR7wBNANWGpmeyXNjtsXAY8CU4D9wJvA7eWKx7mzUVtbS2trK3369Gn30UvnupqZ0draSm1tbVHvy/Scxc4ldeLECVpaWt5+Pt+5c1VtbS39+vWjpqbmjPU+Z7FzZ6mmpoa6urquDsO5svBnKJ1zLuO8I3DOuYzzjsA55zIudTeLJb0KFP/V4qAv8FoJw0kDzzkbPOdsOJucB5hZwakZU9cRnA1Jze3dNa9WnnM2eM7ZUK6cfWjIOecyzjsC55zLuKx1BIu7OoAu4Dlng+ecDWXJOVP3CJxzzv2/rF0ROOecy+MdgXPOZVxVdgSSJkn6q6T9kr5RYLsk3R+375I0qiviLKUEOTfEXHdJekbS8K6Is5Q6yjmn3XWS2uKseamWJGdJEyTtkLRX0tOVjrHUEvxt95L0O0k7Y86prmIsaamkQ5L2tLO99McvM6uqF6Hk9d+BK4HuwE5gSF6bKcBjhBnSxgHPdnXcFcj5euCi+O/JWcg5p90fCSXPb+nquCvwe+4N7AP6x+VLujruCuT8LeCH8d/vBV4Hund17GeR80eAUcCedraX/PhVjVcEY4D9ZnbAzI4Dq4GpeW2mAissaAJ6S7q00oGWUIc5m9kzZnY4LjYRZoNLsyS/Z4AvA78BDlUyuDJJkvPngXVm9gqAmaU97yQ5G9BTYaKICwgdwcnKhlk6ZraJkEN7Sn78qsaO4HLgHznLLXFdsW3SpNh8vkQ4o0izDnOWdDnwOWBRBeMqpyS/5w8AF0l6StKfJc2oWHTlkSTnB4CrCdPc7ga+amanKhNelyj58asa5yMoNH1U/jOySdqkSeJ8JN1I6Ag+VNaIyi9Jzj8D5ptZW5XMKpYk5/OAa4GJQA9gq6QmM/tbuYMrkyQ5fwrYAXwMeD+wQdJmM/t3uYPrIiU/flVjR9ACXJGz3I9wplBsmzRJlI+kYcASYLKZtVYotnJJkvNoYHXsBPoCUySdNLPfVibEkkv6t/2amR0FjkraBAwH0toRJMn5dmCBhQH0/ZJeAj4IbKtMiBVX8uNXNQ4NbQcGSaqT1B2YBqzPa7MemBHvvo8DjpjZwUoHWkId5iypP7AOmJ7is8NcHeZsZnVmNtDMBgJrgTkp7gQg2d/2w8CHJZ0n6XxgLPB8heMspSQ5v0K4AkLS+4DBwIGKRllZJT9+Vd0VgZmdlDQPeILwxMFSM9sraXbcvojwBMkUYD/wJuGMIrUS5vxtoA+wMJ4hn7QUV25MmHNVSZKzmT0v6XFgF3AKWGJmBR9DTIOEv+fvA42SdhOGTeabWWrLU0taBUwA+kpqAb4D1ED5jl9eYsI55zKuGoeGnHPOFcE7AuecyzjvCJxzLuO8I3DOuYzzjsA55zLOOwJ3zomVQnfkvAa+Q9uB7VVpLHKfT8UKlzslbZE0uBOfMft0SQdJMyVdlrNtiaQhJY5zu6QRCd5zV/xOgXMFeUfgzkXHzGxEzuvlCu23wcyGA8uBHxf75vgc/4q4OBO4LGfbLDPbV5Io/xfnQpLFeRfgHYFrl3cELhXimf9mSX+Jr+sLtLlG0rZ4FbFL0qC4/gs5638lqVsHu9sEXBXfO1HSc5J2xzrx747rF0jaF/fzk7juu5LuVpj3YDSwMu6zRzyTHy3pTkk/yol5pqSfdzLOreQUG5P0S0nNCjX5vxfXfYXQIW2UtDGu+6SkrfHn+KCkCzrYj6ty3hG4c1GPnGGhh+K6Q8AnzGwUUA/cX+B9s4H7zGwE4UDcIunq2P6GuL4NaOhg/58BdkuqBRqBejMbSvgm/p2SLiZUNb3GzIYBP8h9s5mtBZoJZ+4jzOxYzua1wM05y/XAmk7GOQnILZlxT/y2+DDgo5KGmdn9hDo0N5rZjZL6AvcCH48/y2bg6x3sx1W5qisx4arCsXgwzFUDPBDHxNsI5ZbzbQXukdSPUJP/RUkTCdU4t8fSGj1of26ClZKOAS8T5jEYDLyUU5tpOTCXUPb4LWCJpN8DjyRNzMxelXQg1oh5Me5jS/zcYuJ8D6HkQu7sVLdKuoPw//pSYAih1ESucXH9lrif7oSfm8sw7whcWnwN+Behkua7CAfiM5jZryU9C3waeELSLELtmeVm9s0E+2gws+bTC5L6FGoU69+MIRQ6mwbMI5RATmoNcCvwAvCQmZnCUTlxnISZuhYAvwBullQH3A1cZ2aHJTUCtQXeK2CDmd1WRLyuyvnQkEuLXsDBOOHIdMLZ8BkkXQkciMMh6wlDJE8Ct0i6JLa5WNKAhPt8ARgo6aq4PB14Oo6p9zKzRwk3Ygs9ufMG0LOdz10H3ATcRugUKDZOMztBGOIZF4eVLgSOAkcUKnBObieWJuCG0zlJOl9SoasrlyHeEbi0WAh8UVITYVjoaIE29cAeSTsI9ehXxCd17gX+IGkXsIEwbNIhM3uLUNnxwVjZ8hRhtrOewCPx854mXK3kawQWnb5ZnPe5hwnzCg8ws21xXdFxxnsPPwXuNrOdwHPAXmApYbjptMXAY5I2mtmrhCeaVsX9NBF+Vi7DvPqoc85lnF8ROOdcxnlH4JxzGecdgXPOZZx3BM45l3HeETjnXMZ5R+CccxnnHYFzzmXcfwH6SjhjIkIBjAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "\n",
    "# Train model with Training Data\n",
    "lr_classifier = XGBClassifier(n_jobs=3, max_depth=10 )\n",
    "lr_classifier.fit(X_train[most_relevant_variables], y_train)\n",
    "\n",
    "# Test model with Test Data\n",
    "y_predicted = lr_classifier.predict_proba(X_test[most_relevant_variables])[:, 1]\n",
    "\n",
    "# Evaluate model\n",
    "roc_auc = roc_auc_score(y_test, y_predicted.round(4))\n",
    "print('Logit roc_auc_score metric: {}'.format(roc_auc))\n",
    "                        \n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_predicted, pos_label=1)\n",
    "\n",
    "plt.plot(fpr, tpr, lw=1, alpha=0.8, label='ROC {}'.format(roc_auc))\n",
    "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r', label='Random', alpha=.8)\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic example')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Plot the feature importances of the forest\n",
    "rf_importances = pd.DataFrame(zip(X_train.columns, lr_classifier.feature_importances_), columns= ['Variables', 'Importance']).sort_values( 'Importance', ascending=False)\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.title(\"Feature importances\")\n",
    "ax = sns.barplot(x=\"Importance\", y=\"Variables\", data=rf_importances)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zHEhqEyMg4xm"
   },
   "source": [
    "**What algorithm gave the best results?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "The contingency table can provide several evaluation measures (see terminology box). To draw an ROC curve, only the Positive True (VPR) and False Positive (FPR) ratios are necessary. The VPR measures the extent to which a classifier or diagnostic test is able to detect or classify positive cases correctly, among all the positive cases available during the test. The RPF defines how many positive results are incorrect among all the negative cases available during the test.\n",
    "\n",
    "The best possible method of prediction would be located at a point in the upper left corner, or coordinate (0.1) of the ROC space, representing 100% sensitivity (no false negatives) and 100% also specificity (no false positives). ). This point (0,1) is also called a perfect classification. On the contrary, a totally random classification (or random divination) would give a point along the diagonal line, which is also called a non-discrimination line, from the lower left end to the upper right corner (regardless of the types of positive and negative basis). A typical example of random divination would be to decide from the results of throwing a coin in the air, as the sample size increases, the point of a random ROC classifier will shift to the position (0.5, 0.5)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As described in the previous paragraph, the algorithm that best classifies in this case is Xgboos classifier with an area under the curve of 0.74"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "demo_bank_loans_Example.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}