credit_risk_analysis_Keras.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 80
    },
    "colab_type": "code",
    "id": "x9MnCTHFMSB9",
    "outputId": "7469f547-3dec-45f6-ba8f-c9eee6c3162d"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import time\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import precision_score, recall_score, accuracy_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import  cross_val_predict\n",
    "from keras import regularizers\n",
    "import pandas as pd \n",
    "from sklearn.feature_selection import RFECV\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import minmax_scale\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import tensorflow as tf\n",
    "import warnings\n",
    "import matplotlib.gridspec as gridspec\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn import preprocessing\n",
    "from sklearn.manifold import TSNE\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from scipy import stats\n",
    "from scipy.stats import shapiro\n",
    "from scipy.stats import anderson\n",
    "from sklearn.model_selection import train_test_split\n",
    "from scipy.stats import mannwhitneyu\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.decomposition import PCA\n",
    "from imblearn.under_sampling import TomekLinks\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.under_sampling import ClusterCentroids\n",
    "from imblearn.pipeline import make_pipeline\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "I2o0FWb_VRUT"
   },
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras.datasets import imdb\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Embedding, SpatialDropout1D, LSTM, BatchNormalization\n",
    "from keras.layers.wrappers import Bidirectional # new! \n",
    "from keras.callbacks import ModelCheckpoint \n",
    "import os\n",
    "from sklearn.metrics import roc_auc_score \n",
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 139
    },
    "colab_type": "code",
    "id": "oph15p7FM042",
    "outputId": "66585eca-5f08-4f33-a909-427d6ff45eb2"
   },
   "outputs": [],
   "source": [
    "## FOR RUNNING IN GOOGLE COLAB\n",
    "#from google.colab import drive\n",
    "#drive.mount('/gdrive')\n",
    "#%cd /gdrive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LOADING THE DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "n5cMl0XgNKqf"
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('/Users/ankitkothari/Documents/loans_2007.csv')\n",
    "data_copy=data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 255
    },
    "colab_type": "code",
    "id": "5kEd7QDEd0zq",
    "outputId": "bb69072c-9f14-4e9b-9764-ff4ad476ada5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'member_id', 'loan_amnt', 'funded_amnt', 'funded_amnt_inv',\n",
       "       'term', 'int_rate', 'installment', 'grade', 'sub_grade', 'emp_title',\n",
       "       'emp_length', 'home_ownership', 'annual_inc', 'verification_status',\n",
       "       'issue_d', 'loan_status', 'pymnt_plan', 'purpose', 'title', 'zip_code',\n",
       "       'addr_state', 'dti', 'delinq_2yrs', 'earliest_cr_line',\n",
       "       'inq_last_6mths', 'open_acc', 'pub_rec', 'revol_bal', 'revol_util',\n",
       "       'total_acc', 'initial_list_status', 'out_prncp', 'out_prncp_inv',\n",
       "       'total_pymnt', 'total_pymnt_inv', 'total_rec_prncp', 'total_rec_int',\n",
       "       'total_rec_late_fee', 'recoveries', 'collection_recovery_fee',\n",
       "       'last_pymnt_d', 'last_pymnt_amnt', 'last_credit_pull_d',\n",
       "       'collections_12_mths_ex_med', 'policy_code', 'application_type',\n",
       "       'acc_now_delinq', 'chargeoff_within_12_mths', 'delinq_amnt',\n",
       "       'pub_rec_bankruptcies', 'tax_liens'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jlVSkEOkdwCc"
   },
   "outputs": [],
   "source": [
    "data = data.drop(columns=['id', 'member_id','funded_amnt', 'funded_amnt_inv','sub_grade','emp_title','issue_d','total_pymnt', 'total_pymnt_inv', 'total_rec_prncp','out_prncp', 'out_prncp_inv','zip_code', 'total_rec_prncp', 'total_rec_int',\n",
    "       'total_rec_late_fee', 'recoveries', 'collection_recovery_fee',\n",
    "       'last_pymnt_d', 'last_pymnt_amnt'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 663
    },
    "colab_type": "code",
    "id": "z9sUmeoyfehn",
    "outputId": "f817dd56-28f1-4a4b-f3b1-58b6fa4f90d4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 42538 entries, 0 to 42537\n",
      "Data columns (total 33 columns):\n",
      "loan_amnt                     42535 non-null float64\n",
      "term                          42535 non-null object\n",
      "int_rate                      42535 non-null object\n",
      "installment                   42535 non-null float64\n",
      "grade                         42535 non-null object\n",
      "emp_length                    41423 non-null object\n",
      "home_ownership                42535 non-null object\n",
      "annual_inc                    42531 non-null float64\n",
      "verification_status           42535 non-null object\n",
      "loan_status                   42535 non-null object\n",
      "pymnt_plan                    42535 non-null object\n",
      "purpose                       42535 non-null object\n",
      "title                         42522 non-null object\n",
      "addr_state                    42535 non-null object\n",
      "dti                           42535 non-null float64\n",
      "delinq_2yrs                   42506 non-null float64\n",
      "earliest_cr_line              42506 non-null object\n",
      "inq_last_6mths                42506 non-null float64\n",
      "open_acc                      42506 non-null float64\n",
      "pub_rec                       42506 non-null float64\n",
      "revol_bal                     42535 non-null float64\n",
      "revol_util                    42445 non-null object\n",
      "total_acc                     42506 non-null float64\n",
      "initial_list_status           42535 non-null object\n",
      "last_credit_pull_d            42531 non-null object\n",
      "collections_12_mths_ex_med    42390 non-null float64\n",
      "policy_code                   42535 non-null float64\n",
      "application_type              42535 non-null object\n",
      "acc_now_delinq                42506 non-null float64\n",
      "chargeoff_within_12_mths      42390 non-null float64\n",
      "delinq_amnt                   42506 non-null float64\n",
      "pub_rec_bankruptcies          41170 non-null float64\n",
      "tax_liens                     42430 non-null float64\n",
      "dtypes: float64(17), object(16)\n",
      "memory usage: 10.7+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 765
    },
    "colab_type": "code",
    "id": "U_MJ2mbkhiGs",
    "outputId": "0be10df2-85c1-4fce-8e04-cddd84101c25"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   loan_amnt        term int_rate  installment grade\n",
      "0     5000.0   36 months   10.65%       162.87     B\n",
      "1     2500.0   60 months   15.27%        59.83     C\n",
      "2     2400.0   36 months   15.96%        84.33     C\n",
      "3    10000.0   36 months   13.49%       339.31     C\n",
      "4     3000.0   60 months   12.69%        67.79     B\n",
      "  emp_length home_ownership  annual_inc verification_status  loan_status\n",
      "0  10+ years           RENT     24000.0            Verified   Fully Paid\n",
      "1   < 1 year           RENT     30000.0     Source Verified  Charged Off\n",
      "2  10+ years           RENT     12252.0        Not Verified   Fully Paid\n",
      "3  10+ years           RENT     49200.0     Source Verified   Fully Paid\n",
      "4     1 year           RENT     80000.0     Source Verified      Current\n",
      "  pymnt_plan         purpose                 title addr_state    dti\n",
      "0          n     credit_card              Computer         AZ  27.65\n",
      "1          n             car                  bike         GA   1.00\n",
      "2          n  small_business  real estate business         IL   8.72\n",
      "3          n           other              personel         CA  20.00\n",
      "4          n           other              Personal         OR  17.94\n",
      "   delinq_2yrs earliest_cr_line  inq_last_6mths  open_acc  pub_rec\n",
      "0          0.0         Jan-1985             1.0       3.0      0.0\n",
      "1          0.0         Apr-1999             5.0       3.0      0.0\n",
      "2          0.0         Nov-2001             2.0       2.0      0.0\n",
      "3          0.0         Feb-1996             1.0      10.0      0.0\n",
      "4          0.0         Jan-1996             0.0      15.0      0.0\n",
      "   revol_bal revol_util  total_acc initial_list_status last_credit_pull_d\n",
      "0    13648.0      83.7%        9.0                   f           Jun-2016\n",
      "1     1687.0       9.4%        4.0                   f           Sep-2013\n",
      "2     2956.0      98.5%       10.0                   f           Jun-2016\n",
      "3     5598.0        21%       37.0                   f           Apr-2016\n",
      "4    27783.0      53.9%       38.0                   f           Jun-2016\n",
      "   collections_12_mths_ex_med  policy_code application_type  acc_now_delinq  \\\n",
      "0                         0.0          1.0       INDIVIDUAL             0.0   \n",
      "1                         0.0          1.0       INDIVIDUAL             0.0   \n",
      "2                         0.0          1.0       INDIVIDUAL             0.0   \n",
      "3                         0.0          1.0       INDIVIDUAL             0.0   \n",
      "4                         0.0          1.0       INDIVIDUAL             0.0   \n",
      "\n",
      "   chargeoff_within_12_mths  \n",
      "0                       0.0  \n",
      "1                       0.0  \n",
      "2                       0.0  \n",
      "3                       0.0  \n",
      "4                       0.0  \n",
      "   delinq_amnt  pub_rec_bankruptcies  tax_liens\n",
      "0          0.0                   0.0        0.0\n",
      "1          0.0                   0.0        0.0\n",
      "2          0.0                   0.0        0.0\n",
      "3          0.0                   0.0        0.0\n",
      "4          0.0                   0.0        0.0\n"
     ]
    }
   ],
   "source": [
    "for i in range(0,33,5):\n",
    "  print(data.iloc[:,i:i+5].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FEATURE ENGINEERING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WEI2QukNptXO"
   },
   "outputs": [],
   "source": [
    "data['earliest_cr_line']= pd.to_datetime(data['earliest_cr_line'], format='%b-%Y').dt.year\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "id": "RpfocLF_ir3V",
    "outputId": "2480e820-6e2d-4348-b651-105f1ed4ef6e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fully Paid                                             33136\n",
       "Charged Off                                             5634\n",
       "Does not meet the credit policy. Status:Fully Paid      1988\n",
       "Current                                                  961\n",
       "Does not meet the credit policy. Status:Charged Off      761\n",
       "Late (31-120 days)                                        24\n",
       "In Grace Period                                           20\n",
       "Late (16-30 days)                                          8\n",
       "Default                                                    3\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['loan_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SrGjxc0Bk_BR"
   },
   "outputs": [],
   "source": [
    "### WE ARE ONLY CONCERNED WITH FULLY PAID OR DEFAULT\n",
    "not_target = ['Current','Late (31-120 days)','In Grace Period','Late (16-30 days)','Default','Does not meet the credit policy. Status:Fully Paid','Does not meet the credit policy. Status:Charged Off']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "SfgYbCpWjjXG",
    "outputId": "9c770f7b-97c8-4ef2-de89-c6cfa88eefec"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(38773, 33)\n",
      "0.0    33136\n",
      "1.0     5634\n",
      "Name: loan_status, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "## NARROWING DOWN OUR TARGET CATEGORIES\n",
    "data = data[~data.loan_status.isin(not_target)]\n",
    "print(data.shape)\n",
    "mapping = {'Fully Paid':0,'Does not meet the credit policy. Status:Fully Paid':0,'Charged Off':1,'Does not meet the credit policy. Status:Charged Off':1}\n",
    "data['loan_status']= data['loan_status'].map(mapping)\n",
    "print(data['loan_status'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 578
    },
    "colab_type": "code",
    "id": "_y1JATtFpSJJ",
    "outputId": "71960a6a-808e-4600-b323-053636b00a6b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "multiple unique values loan_amnt 870\n",
      "multiple unique values term 2\n",
      "multiple unique values int_rate 371\n",
      "multiple unique values installment 15091\n",
      "multiple unique values grade 7\n",
      "multiple unique values emp_length 11\n",
      "multiple unique values home_ownership 5\n",
      "multiple unique values annual_inc 5234\n",
      "multiple unique values verification_status 3\n",
      "multiple unique values loan_status 2\n",
      "only one unique value pymnt_plan ['n']\n",
      "multiple unique values purpose 14\n",
      "multiple unique values title 19365\n",
      "multiple unique values addr_state 50\n",
      "multiple unique values dti 2856\n",
      "multiple unique values delinq_2yrs 11\n",
      "multiple unique values earliest_cr_line 53\n",
      "multiple unique values inq_last_6mths 9\n",
      "multiple unique values open_acc 40\n",
      "multiple unique values pub_rec 5\n",
      "multiple unique values revol_bal 21348\n",
      "multiple unique values revol_util 1088\n",
      "multiple unique values total_acc 82\n",
      "only one unique value initial_list_status ['f']\n",
      "multiple unique values last_credit_pull_d 107\n",
      "only one unique value collections_12_mths_ex_med [0.]\n",
      "only one unique value policy_code [1.]\n",
      "only one unique value application_type ['INDIVIDUAL']\n",
      "only one unique value acc_now_delinq [0.]\n",
      "only one unique value chargeoff_within_12_mths [0.]\n",
      "only one unique value delinq_amnt [0.]\n",
      "multiple unique values pub_rec_bankruptcies 3\n",
      "only one unique value tax_liens [0.]\n"
     ]
    }
   ],
   "source": [
    "###IDENTIFYING WHICH FEATURE COLUMNS HELP IN MAKING BETTER PREDICTIONS\n",
    "\n",
    "for column in data.columns:\n",
    "  uniq= data[column].dropna().unique()\n",
    "  if len(uniq)>=2:\n",
    "    print(f'multiple unique values {column} {len(uniq)}')\n",
    "  else:\n",
    "    data=data.drop(columns=column)\n",
    "    print(f'only one unique value {column} {uniq}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "c52wOGjEtRxr",
    "outputId": "b251b0e5-8dc8-4580-beb2-b29abed5e3e2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "emp_length              1036\n",
      "title                     11\n",
      "revol_util                50\n",
      "last_credit_pull_d         2\n",
      "pub_rec_bankruptcies     697\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "##DROPPING VALUES WITHOUT ANY TARGET VARIABLE INFORMATION\n",
    "data = data.dropna(subset=['loan_status'])\n",
    "print(data.isnull().sum()[data.isnull().sum()>0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "V7toyxvfsE88"
   },
   "outputs": [],
   "source": [
    "data['earliest_cr_line'] = data['earliest_cr_line'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "EH7m6kHd0h9b",
    "outputId": "a4a12525-bc18-439b-e713-0845898a8369"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    36422\n",
       "1.0     1646\n",
       "2.0        5\n",
       "Name: pub_rec_bankruptcies, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['pub_rec_bankruptcies'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 425
    },
    "colab_type": "code",
    "id": "AtP1fmSE07ld",
    "outputId": "264797e7-7105-425b-93e3-be84c0974b12"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loan_amnt              0\n",
      "term                   0\n",
      "int_rate               0\n",
      "installment            0\n",
      "grade                  0\n",
      "emp_length             0\n",
      "home_ownership         0\n",
      "annual_inc             0\n",
      "verification_status    0\n",
      "loan_status            0\n",
      "purpose                0\n",
      "title                  0\n",
      "addr_state             0\n",
      "dti                    0\n",
      "delinq_2yrs            0\n",
      "earliest_cr_line       0\n",
      "inq_last_6mths         0\n",
      "open_acc               0\n",
      "pub_rec                0\n",
      "revol_bal              0\n",
      "revol_util             0\n",
      "total_acc              0\n",
      "last_credit_pull_d     0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "data = data.dropna(subset=['emp_length','title','revol_util','last_credit_pull_d'])\n",
    "data = data.drop(columns=['pub_rec_bankruptcies'], axis=1)\n",
    "print(data.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "uxQXousd1z69",
    "outputId": "2c02a93d-083a-400c-e394-3ed188066873"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float64    12\n",
      "object     11\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data.dtypes.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "colab_type": "code",
    "id": "A8hfDfUz2BSQ",
    "outputId": "47f51a47-56bf-43be-fb50-4141e0b3a74d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>installment</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>earliest_cr_line</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>total_acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>162.87</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.65</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13648.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2500.0</td>\n",
       "      <td>59.83</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1687.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2400.0</td>\n",
       "      <td>84.33</td>\n",
       "      <td>12252.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.72</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2956.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>339.31</td>\n",
       "      <td>49200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5598.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>156.46</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7963.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39781</th>\n",
       "      <td>2500.0</td>\n",
       "      <td>78.42</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7274.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39782</th>\n",
       "      <td>8500.0</td>\n",
       "      <td>275.38</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.40</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8847.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39783</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>156.84</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9698.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39784</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>155.38</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.72</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>85607.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39785</th>\n",
       "      <td>7500.0</td>\n",
       "      <td>255.43</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4175.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>37675 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       loan_amnt  installment  annual_inc  ...  pub_rec  revol_bal  total_acc\n",
       "0         5000.0       162.87     24000.0  ...      0.0    13648.0        9.0\n",
       "1         2500.0        59.83     30000.0  ...      0.0     1687.0        4.0\n",
       "2         2400.0        84.33     12252.0  ...      0.0     2956.0       10.0\n",
       "3        10000.0       339.31     49200.0  ...      0.0     5598.0       37.0\n",
       "5         5000.0       156.46     36000.0  ...      0.0     7963.0       12.0\n",
       "...          ...          ...         ...  ...      ...        ...        ...\n",
       "39781     2500.0        78.42    110000.0  ...      0.0     7274.0       40.0\n",
       "39782     8500.0       275.38     18000.0  ...      0.0     8847.0        9.0\n",
       "39783     5000.0       156.84    100000.0  ...      0.0     9698.0       20.0\n",
       "39784     5000.0       155.38    200000.0  ...      0.0    85607.0       26.0\n",
       "39785     7500.0       255.43     22000.0  ...      0.0     4175.0        8.0\n",
       "\n",
       "[37675 rows x 12 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## CONTINIOUS FEATURES\n",
    "float_df = data.select_dtypes(include=['float'])\n",
    "float_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "colab_type": "code",
    "id": "2Y-SpJpz2eIq",
    "outputId": "f16a4276-b0c6-4f68-cad8-5eac78aee7b1"
   },
   "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>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>grade</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>verification_status</th>\n",
       "      <th>purpose</th>\n",
       "      <th>title</th>\n",
       "      <th>addr_state</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>last_credit_pull_d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36 months</td>\n",
       "      <td>10.65%</td>\n",
       "      <td>B</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>Verified</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>Computer</td>\n",
       "      <td>AZ</td>\n",
       "      <td>83.7%</td>\n",
       "      <td>Jun-2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60 months</td>\n",
       "      <td>15.27%</td>\n",
       "      <td>C</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>RENT</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>car</td>\n",
       "      <td>bike</td>\n",
       "      <td>GA</td>\n",
       "      <td>9.4%</td>\n",
       "      <td>Sep-2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36 months</td>\n",
       "      <td>15.96%</td>\n",
       "      <td>C</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>small_business</td>\n",
       "      <td>real estate business</td>\n",
       "      <td>IL</td>\n",
       "      <td>98.5%</td>\n",
       "      <td>Jun-2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36 months</td>\n",
       "      <td>13.49%</td>\n",
       "      <td>C</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>other</td>\n",
       "      <td>personel</td>\n",
       "      <td>CA</td>\n",
       "      <td>21%</td>\n",
       "      <td>Apr-2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>36 months</td>\n",
       "      <td>7.90%</td>\n",
       "      <td>A</td>\n",
       "      <td>3 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>wedding</td>\n",
       "      <td>My wedding loan I promise to pay back</td>\n",
       "      <td>AZ</td>\n",
       "      <td>28.3%</td>\n",
       "      <td>Jan-2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39781</th>\n",
       "      <td>36 months</td>\n",
       "      <td>8.07%</td>\n",
       "      <td>A</td>\n",
       "      <td>4 years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>home_improvement</td>\n",
       "      <td>Home Improvement</td>\n",
       "      <td>CO</td>\n",
       "      <td>13.1%</td>\n",
       "      <td>Jun-2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39782</th>\n",
       "      <td>36 months</td>\n",
       "      <td>10.28%</td>\n",
       "      <td>C</td>\n",
       "      <td>3 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>Retiring credit card debt</td>\n",
       "      <td>NC</td>\n",
       "      <td>26.9%</td>\n",
       "      <td>Jul-2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39783</th>\n",
       "      <td>36 months</td>\n",
       "      <td>8.07%</td>\n",
       "      <td>A</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>MBA Loan Consolidation</td>\n",
       "      <td>MA</td>\n",
       "      <td>19.4%</td>\n",
       "      <td>Jun-2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39784</th>\n",
       "      <td>36 months</td>\n",
       "      <td>7.43%</td>\n",
       "      <td>A</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>other</td>\n",
       "      <td>JAL Loan</td>\n",
       "      <td>MD</td>\n",
       "      <td>0.7%</td>\n",
       "      <td>Jun-2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39785</th>\n",
       "      <td>36 months</td>\n",
       "      <td>13.75%</td>\n",
       "      <td>E</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>OWN</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>Consolidation Loan</td>\n",
       "      <td>MA</td>\n",
       "      <td>51.5%</td>\n",
       "      <td>Jun-2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>37675 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             term int_rate grade  ... addr_state revol_util last_credit_pull_d\n",
       "0       36 months   10.65%     B  ...         AZ      83.7%           Jun-2016\n",
       "1       60 months   15.27%     C  ...         GA       9.4%           Sep-2013\n",
       "2       36 months   15.96%     C  ...         IL      98.5%           Jun-2016\n",
       "3       36 months   13.49%     C  ...         CA        21%           Apr-2016\n",
       "5       36 months    7.90%     A  ...         AZ      28.3%           Jan-2016\n",
       "...           ...      ...   ...  ...        ...        ...                ...\n",
       "39781   36 months    8.07%     A  ...         CO      13.1%           Jun-2010\n",
       "39782   36 months   10.28%     C  ...         NC      26.9%           Jul-2010\n",
       "39783   36 months    8.07%     A  ...         MA      19.4%           Jun-2007\n",
       "39784   36 months    7.43%     A  ...         MD       0.7%           Jun-2007\n",
       "39785   36 months   13.75%     E  ...         MA      51.5%           Jun-2010\n",
       "\n",
       "[37675 rows x 11 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##CATEGORICAL FEATURES\n",
    "\n",
    "object_df = data.select_dtypes(include=['object'])\n",
    "object_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "tCkvQAlr4rBa",
    "outputId": "015a584f-1b58-4b72-84b5-cf1780da4872"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RENT        18112\n",
      "MORTGAGE    16686\n",
      "OWN          2778\n",
      "OTHER          96\n",
      "NONE            3\n",
      "Name: home_ownership, dtype: int64\n",
      "Not Verified       16281\n",
      "Verified           11856\n",
      "Source Verified     9538\n",
      "Name: verification_status, dtype: int64\n",
      "10+ years    8545\n",
      "< 1 year     4513\n",
      "2 years      4303\n",
      "3 years      4022\n",
      "4 years      3353\n",
      "5 years      3202\n",
      "1 year       3176\n",
      "6 years      2177\n",
      "7 years      1714\n",
      "8 years      1442\n",
      "9 years      1228\n",
      "Name: emp_length, dtype: int64\n",
      " 36 months    28234\n",
      " 60 months     9441\n",
      "Name: term, dtype: int64\n",
      "CA    6776\n",
      "NY    3614\n",
      "FL    2704\n",
      "TX    2613\n",
      "NJ    1776\n",
      "IL    1447\n",
      "PA    1442\n",
      "VA    1347\n",
      "GA    1323\n",
      "MA    1272\n",
      "OH    1149\n",
      "MD    1008\n",
      "AZ     807\n",
      "WA     788\n",
      "CO     748\n",
      "NC     729\n",
      "CT     711\n",
      "MI     678\n",
      "MO     648\n",
      "MN     581\n",
      "NV     466\n",
      "SC     454\n",
      "WI     427\n",
      "OR     422\n",
      "AL     420\n",
      "LA     420\n",
      "KY     311\n",
      "OK     285\n",
      "KS     249\n",
      "UT     249\n",
      "AR     229\n",
      "DC     209\n",
      "RI     194\n",
      "NM     180\n",
      "WV     164\n",
      "HI     162\n",
      "NH     157\n",
      "DE     110\n",
      "MT      77\n",
      "AK      76\n",
      "WY      76\n",
      "SD      60\n",
      "VT      53\n",
      "MS      19\n",
      "TN      17\n",
      "IN       9\n",
      "ID       6\n",
      "IA       5\n",
      "NE       5\n",
      "ME       3\n",
      "Name: addr_state, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "cols = ['home_ownership', 'verification_status', 'emp_length', 'term', 'addr_state']\n",
    "for col in cols:\n",
    "  print(data[col].value_counts())\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TrXF-FaV9f9Q"
   },
   "outputs": [],
   "source": [
    "data = data.drop(columns=[\"last_credit_pull_d\", \"title\",'grade'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wmlDnEB_-djw"
   },
   "outputs": [],
   "source": [
    "mapping_dict = {\n",
    "        \"10+ years\": \"experienced\",\n",
    "        \"9 years\": \"experienced\",\n",
    "        \"8 years\": \"experienced\",\n",
    "        \"7 years\": \"experienced\",\n",
    "        \"6 years\": \"intermediate\",\n",
    "        \"5 years\": \"intermediate\",\n",
    "        \"4 years\": \"intermediate\",\n",
    "        \"3 years\": \"beginner\",\n",
    "        \"2 years\": \"beginner\",\n",
    "        \"1 year\": \"beginner\",\n",
    "        \"< 1 year\": \"beginner\",\n",
    "        \"n/a\": \"beginner\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 309
    },
    "colab_type": "code",
    "id": "Rtb1ceRD_Dw-",
    "outputId": "8e9f4923-71d6-4050-bdd7-4946f0cb9dcd"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>verification_status</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>purpose</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>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>10.65</td>\n",
       "      <td>162.87</td>\n",
       "      <td>experienced</td>\n",
       "      <td>RENT</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>Verified</td>\n",
       "      <td>0.0</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>AZ</td>\n",
       "      <td>27.65</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>1.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2500.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>15.27</td>\n",
       "      <td>59.83</td>\n",
       "      <td>beginner</td>\n",
       "      <td>RENT</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>1.0</td>\n",
       "      <td>car</td>\n",
       "      <td>GA</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>5.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2400.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>15.96</td>\n",
       "      <td>84.33</td>\n",
       "      <td>experienced</td>\n",
       "      <td>RENT</td>\n",
       "      <td>12252.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>0.0</td>\n",
       "      <td>small_business</td>\n",
       "      <td>IL</td>\n",
       "      <td>8.72</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2956.0</td>\n",
       "      <td>98.5</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>13.49</td>\n",
       "      <td>339.31</td>\n",
       "      <td>experienced</td>\n",
       "      <td>RENT</td>\n",
       "      <td>49200.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>0.0</td>\n",
       "      <td>other</td>\n",
       "      <td>CA</td>\n",
       "      <td>20.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5598.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>7.90</td>\n",
       "      <td>156.46</td>\n",
       "      <td>beginner</td>\n",
       "      <td>RENT</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>0.0</td>\n",
       "      <td>wedding</td>\n",
       "      <td>AZ</td>\n",
       "      <td>11.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7963.0</td>\n",
       "      <td>28.3</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt        term  int_rate  ...  revol_bal revol_util total_acc\n",
       "0     5000.0   36 months     10.65  ...    13648.0       83.7       9.0\n",
       "1     2500.0   60 months     15.27  ...     1687.0        9.4       4.0\n",
       "2     2400.0   36 months     15.96  ...     2956.0       98.5      10.0\n",
       "3    10000.0   36 months     13.49  ...     5598.0       21.0      37.0\n",
       "5     5000.0   36 months      7.90  ...     7963.0       28.3      12.0\n",
       "\n",
       "[5 rows x 20 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['emp_length']= data['emp_length'].map(mapping_dict)\n",
    "data['int_rate']= data['int_rate'].str.rstrip('%').astype(\"float\")\n",
    "data['revol_util']= data['revol_util'].str.rstrip('%').astype(\"float\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 272
    },
    "colab_type": "code",
    "id": "KZ_OgtVoHAPx",
    "outputId": "45836622-eb0a-4245-e2fe-4095998e9202"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "debt_consolidation    17751\n",
       "credit_card            4911\n",
       "other                  3711\n",
       "home_improvement       2808\n",
       "major_purchase         2083\n",
       "small_business         1719\n",
       "car                    1459\n",
       "wedding                 916\n",
       "medical                 655\n",
       "moving                  552\n",
       "house                   356\n",
       "vacation                348\n",
       "educational             312\n",
       "renewable_energy         94\n",
       "Name: purpose, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['purpose'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 473
    },
    "colab_type": "code",
    "id": "74maBfmAH2ZW",
    "outputId": "cee66678-3a27-4cee-9363-b76c160632ee"
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>home_ownership_NONE</th>\n",
       "      <th>home_ownership_OTHER</th>\n",
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       "      <th>home_ownership_RENT</th>\n",
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       "      <th>verification_status_Source Verified</th>\n",
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       "      <th>purpose_renewable_energy</th>\n",
       "      <th>purpose_small_business</th>\n",
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       "      <th>addr_state_OH</th>\n",
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       "      <th>addr_state_SC</th>\n",
       "      <th>addr_state_SD</th>\n",
       "      <th>addr_state_TN</th>\n",
       "      <th>addr_state_TX</th>\n",
       "      <th>addr_state_UT</th>\n",
       "      <th>addr_state_VA</th>\n",
       "      <th>addr_state_VT</th>\n",
       "      <th>addr_state_WA</th>\n",
       "      <th>addr_state_WI</th>\n",
       "      <th>addr_state_WV</th>\n",
       "      <th>addr_state_WY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2400.0</td>\n",
       "      <td>15.96</td>\n",
       "      <td>84.33</td>\n",
       "      <td>12252.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.72</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2956.0</td>\n",
       "      <td>98.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>13.49</td>\n",
       "      <td>339.31</td>\n",
       "      <td>49200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5598.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>7.90</td>\n",
       "      <td>156.46</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7963.0</td>\n",
       "      <td>28.3</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39781</th>\n",
       "      <td>2500.0</td>\n",
       "      <td>8.07</td>\n",
       "      <td>78.42</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7274.0</td>\n",
       "      <td>13.1</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39782</th>\n",
       "      <td>8500.0</td>\n",
       "      <td>10.28</td>\n",
       "      <td>275.38</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.40</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8847.0</td>\n",
       "      <td>26.9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39783</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>8.07</td>\n",
       "      <td>156.84</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9698.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39784</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>7.43</td>\n",
       "      <td>155.38</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.72</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>85607.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39785</th>\n",
       "      <td>7500.0</td>\n",
       "      <td>13.75</td>\n",
       "      <td>255.43</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4175.0</td>\n",
       "      <td>51.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>37675 rows × 91 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       loan_amnt  int_rate  ...  addr_state_WV  addr_state_WY\n",
       "0         5000.0     10.65  ...              0              0\n",
       "1         2500.0     15.27  ...              0              0\n",
       "2         2400.0     15.96  ...              0              0\n",
       "3        10000.0     13.49  ...              0              0\n",
       "5         5000.0      7.90  ...              0              0\n",
       "...          ...       ...  ...            ...            ...\n",
       "39781     2500.0      8.07  ...              0              0\n",
       "39782     8500.0     10.28  ...              0              0\n",
       "39783     5000.0      8.07  ...              0              0\n",
       "39784     5000.0      7.43  ...              0              0\n",
       "39785     7500.0     13.75  ...              0              0\n",
       "\n",
       "[37675 rows x 91 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##ONE_HOT ENCODING FOR DUMMY VARIABLE\n",
    "\n",
    "cat_cols= [\"home_ownership\", \"verification_status\", \"purpose\", \"term\", 'emp_length', \"addr_state\"]\n",
    "dummy_df = pd.get_dummies(data[cat_cols])\n",
    "data = pd.concat([data, dummy_df], axis=1)\n",
    "data = data.drop(cat_cols, axis=1)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "uPM4fXwGMbqu",
    "outputId": "af163b88-97d2-4c0b-c055-106391476542"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 37675 entries, 0 to 39785\n",
      "Data columns (total 91 columns):\n",
      "loan_amnt                              37675 non-null float64\n",
      "int_rate                               37675 non-null float64\n",
      "installment                            37675 non-null float64\n",
      "annual_inc                             37675 non-null float64\n",
      "loan_status                            37675 non-null float64\n",
      "dti                                    37675 non-null float64\n",
      "delinq_2yrs                            37675 non-null float64\n",
      "earliest_cr_line                       37675 non-null float64\n",
      "inq_last_6mths                         37675 non-null float64\n",
      "open_acc                               37675 non-null float64\n",
      "pub_rec                                37675 non-null float64\n",
      "revol_bal                              37675 non-null float64\n",
      "revol_util                             37675 non-null float64\n",
      "total_acc                              37675 non-null float64\n",
      "home_ownership_MORTGAGE                37675 non-null uint8\n",
      "home_ownership_NONE                    37675 non-null uint8\n",
      "home_ownership_OTHER                   37675 non-null uint8\n",
      "home_ownership_OWN                     37675 non-null uint8\n",
      "home_ownership_RENT                    37675 non-null uint8\n",
      "verification_status_Not Verified       37675 non-null uint8\n",
      "verification_status_Source Verified    37675 non-null uint8\n",
      "verification_status_Verified           37675 non-null uint8\n",
      "purpose_car                            37675 non-null uint8\n",
      "purpose_credit_card                    37675 non-null uint8\n",
      "purpose_debt_consolidation             37675 non-null uint8\n",
      "purpose_educational                    37675 non-null uint8\n",
      "purpose_home_improvement               37675 non-null uint8\n",
      "purpose_house                          37675 non-null uint8\n",
      "purpose_major_purchase                 37675 non-null uint8\n",
      "purpose_medical                        37675 non-null uint8\n",
      "purpose_moving                         37675 non-null uint8\n",
      "purpose_other                          37675 non-null uint8\n",
      "purpose_renewable_energy               37675 non-null uint8\n",
      "purpose_small_business                 37675 non-null uint8\n",
      "purpose_vacation                       37675 non-null uint8\n",
      "purpose_wedding                        37675 non-null uint8\n",
      "term_ 36 months                        37675 non-null uint8\n",
      "term_ 60 months                        37675 non-null uint8\n",
      "emp_length_beginner                    37675 non-null uint8\n",
      "emp_length_experienced                 37675 non-null uint8\n",
      "emp_length_intermediate                37675 non-null uint8\n",
      "addr_state_AK                          37675 non-null uint8\n",
      "addr_state_AL                          37675 non-null uint8\n",
      "addr_state_AR                          37675 non-null uint8\n",
      "addr_state_AZ                          37675 non-null uint8\n",
      "addr_state_CA                          37675 non-null uint8\n",
      "addr_state_CO                          37675 non-null uint8\n",
      "addr_state_CT                          37675 non-null uint8\n",
      "addr_state_DC                          37675 non-null uint8\n",
      "addr_state_DE                          37675 non-null uint8\n",
      "addr_state_FL                          37675 non-null uint8\n",
      "addr_state_GA                          37675 non-null uint8\n",
      "addr_state_HI                          37675 non-null uint8\n",
      "addr_state_IA                          37675 non-null uint8\n",
      "addr_state_ID                          37675 non-null uint8\n",
      "addr_state_IL                          37675 non-null uint8\n",
      "addr_state_IN                          37675 non-null uint8\n",
      "addr_state_KS                          37675 non-null uint8\n",
      "addr_state_KY                          37675 non-null uint8\n",
      "addr_state_LA                          37675 non-null uint8\n",
      "addr_state_MA                          37675 non-null uint8\n",
      "addr_state_MD                          37675 non-null uint8\n",
      "addr_state_ME                          37675 non-null uint8\n",
      "addr_state_MI                          37675 non-null uint8\n",
      "addr_state_MN                          37675 non-null uint8\n",
      "addr_state_MO                          37675 non-null uint8\n",
      "addr_state_MS                          37675 non-null uint8\n",
      "addr_state_MT                          37675 non-null uint8\n",
      "addr_state_NC                          37675 non-null uint8\n",
      "addr_state_NE                          37675 non-null uint8\n",
      "addr_state_NH                          37675 non-null uint8\n",
      "addr_state_NJ                          37675 non-null uint8\n",
      "addr_state_NM                          37675 non-null uint8\n",
      "addr_state_NV                          37675 non-null uint8\n",
      "addr_state_NY                          37675 non-null uint8\n",
      "addr_state_OH                          37675 non-null uint8\n",
      "addr_state_OK                          37675 non-null uint8\n",
      "addr_state_OR                          37675 non-null uint8\n",
      "addr_state_PA                          37675 non-null uint8\n",
      "addr_state_RI                          37675 non-null uint8\n",
      "addr_state_SC                          37675 non-null uint8\n",
      "addr_state_SD                          37675 non-null uint8\n",
      "addr_state_TN                          37675 non-null uint8\n",
      "addr_state_TX                          37675 non-null uint8\n",
      "addr_state_UT                          37675 non-null uint8\n",
      "addr_state_VA                          37675 non-null uint8\n",
      "addr_state_VT                          37675 non-null uint8\n",
      "addr_state_WA                          37675 non-null uint8\n",
      "addr_state_WI                          37675 non-null uint8\n",
      "addr_state_WV                          37675 non-null uint8\n",
      "addr_state_WY                          37675 non-null uint8\n",
      "dtypes: float64(14), uint8(77)\n",
      "memory usage: 7.1 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "USqhmeaNPuhW"
   },
   "source": [
    "False positive rate is the number of false positives divided by the number of false positives plus the number of true negatives. This divides all the cases where we thought a loan would be paid off but it wasn't by all the loans that weren't paid off:\n",
    "\n",
    "fpr = fp / (fp + tn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8hpjdna7Pwld"
   },
   "source": [
    "True positive rate is the number of true positives divided by the number of true positives plus the number of false negatives. This divides all the cases where we thought a loan would be paid off and it was by all the loans that were paid off:\n",
    "\n",
    "tpr = tp / (tp + fn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UMcQmS_ghA36"
   },
   "source": [
    "## Precision = ((True Positive)/(True Positive + False Positive))\n",
    "## Recall = ((True Positive)/(True Positive + False Negative))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QYcP4AN9e-BJ"
   },
   "source": [
    "# SETTING UP MODEL EVALUATION CRITERIA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "V6QIV9AlYjUk"
   },
   "outputs": [],
   "source": [
    "def model_performance(target, predictions):\n",
    "  fig, ((ax1, ax2, ax3)) = plt.subplots(1, 3, sharex=False, sharey=False,figsize=[16,4])\n",
    "  ax1.bar(x=target.value_counts().index.astype(str), height=target.value_counts().to_list(), data=target, color=['b','r'])\n",
    "\n",
    "  metric = metrics.confusion_matrix(target,predictions)\n",
    "  confusion_dataframe = pd.DataFrame(data=metric, index=['Actual_Positive','Actual_Negative'], columns=['Guessed_Positive', 'Guessed_Negative'])\n",
    "  sns.heatmap(confusion_dataframe, annot=True, fmt=\"d\", cmap=\"YlGnBu\", ax = ax2)\n",
    "\n",
    "  fpr, tpr, _ = (metrics.roc_curve(target,predictions))\n",
    "  plt.plot(fpr, tpr)\n",
    "  \n",
    "\n",
    "  fpr, tpr, _ = (metrics.roc_curve(target,predictions))\n",
    "  ax3.plot(fpr, tpr)\n",
    "  roc_auc = auc(fpr, tpr)\n",
    "  ax3.plot([0, 1], [0, 1], 'k--', label='AUC = %0.3f'% roc_auc)\n",
    "  ax3.legend(loc='lower right')\n",
    "  ax3.plot([0,1],[0,1],'r--')\n",
    "  ax3.set_xlim([-0.1,1.0])\n",
    "  ax3.set_ylim([-0.1,1.01])\n",
    "  ax3.set_ylabel('True Positive Rate')\n",
    "  ax3.set_xlabel('False Positive Rate')\n",
    "  plt.tight_layout()\n",
    "  print(f'Area Under the Curve: {round(roc_auc,2)}')\n",
    "\n",
    "\n",
    "    \n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6rrAZyDcP-lB"
   },
   "outputs": [],
   "source": [
    "features = data.drop(columns=['loan_status'],axis=1)\n",
    "target = data['loan_status']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Odtocy2zxyKR"
   },
   "source": [
    "# SCALING AND NORMALIZING THE DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rCDEEf_B12FN"
   },
   "outputs": [],
   "source": [
    "def normalize(subset):\n",
    "   continious_columns = subset.select_dtypes(include=['float']).columns\n",
    "   mm_scaler = preprocessing.MinMaxScaler()\n",
    "   for col in continious_columns:\n",
    "     subset[col]= mm_scaler.fit_transform(subset[[col]])\n",
    "   return subset\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Hkzbl5yQ2MZt"
   },
   "outputs": [],
   "source": [
    "scaled_data = normalize(data)\n",
    "features_scaled = scaled_data.drop(columns=['loan_status'],axis=1)\n",
    "target_scaled = scaled_data['loan_status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qCThv2sg3pkn"
   },
   "outputs": [],
   "source": [
    "rus = RandomUnderSampler(random_state=0)\n",
    "rus.fit(features_scaled, target_scaled)\n",
    "features_sc, target_sc = rus.fit_resample(features_scaled, target_scaled)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "id": "pJ0CFID8L2hJ",
    "outputId": "d2c92e83-c58f-40f9-8e3d-55e3dc732364"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "term_ 36 months                    -0.171194\n",
       "verification_status_Not Verified   -0.044048\n",
       "purpose_credit_card                -0.043337\n",
       "annual_inc                         -0.038175\n",
       "purpose_major_purchase             -0.029175\n",
       "                                      ...   \n",
       "purpose_small_business              0.078515\n",
       "revol_util                          0.099547\n",
       "term_ 60 months                     0.171194\n",
       "int_rate                            0.210814\n",
       "loan_status                         1.000000\n",
       "Name: loan_status, Length: 91, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_data.corr()['loan_status'].sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aggRlbBURAXP"
   },
   "source": [
    "# XGBOOST CLASSIFIER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 703
    },
    "colab_type": "code",
    "id": "3yYHhsvJsGZG",
    "outputId": "243011d2-a5d4-4991-e897-73f1da1cd320"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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eeOGFOzAiEREREakoBfxyX/o+zXaPAb/6tSn46RebsmoNSpZLTb5w0aa8Ue1a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\no+n1+DfeeKPPOcP/3Uzp9umnn/LSSy/h6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VehrsW9PNH31KlTVSrqnDp1ivjeQEUogJgS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\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_correlation_heatmap(df):\n",
    "    corr = df.corr()\n",
    "    sns.set(style=\"white\")\n",
    "    mask = np.zeros_like(corr, dtype=np.bool)\n",
    "    mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "    f, ax = plt.subplots(figsize=(11, 9))\n",
    "    cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "\n",
    "\n",
    "    sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})\n",
    "    plt.show()\n",
    "plot_correlation_heatmap(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 476
    },
    "colab_type": "code",
    "id": "4bipwfl62Fze",
    "outputId": "044974a5-6b78-4d00-b00b-5b9a4d942e2a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['loan_amnt', 'int_rate', 'installment', 'annual_inc', 'dti',\n",
       "       'delinq_2yrs', 'earliest_cr_line', 'inq_last_6mths', 'open_acc',\n",
       "       'pub_rec', 'revol_bal', 'revol_util', 'total_acc',\n",
       "       'home_ownership_MORTGAGE', 'home_ownership_NONE',\n",
       "       'home_ownership_OTHER', 'home_ownership_OWN', 'home_ownership_RENT',\n",
       "       'verification_status_Not Verified',\n",
       "       'verification_status_Source Verified', 'verification_status_Verified',\n",
       "       'purpose_car', 'purpose_credit_card', 'purpose_debt_consolidation',\n",
       "       'purpose_educational', 'purpose_home_improvement', 'purpose_house',\n",
       "       'purpose_major_purchase', 'purpose_medical', 'purpose_moving',\n",
       "       'purpose_other', 'purpose_renewable_energy', 'purpose_small_business',\n",
       "       'purpose_vacation', 'purpose_wedding', 'term_ 36 months',\n",
       "       'term_ 60 months', 'emp_length_beginner', 'emp_length_experienced',\n",
       "       'emp_length_intermediate', 'addr_state_AK', 'addr_state_AL',\n",
       "       'addr_state_AR', 'addr_state_AZ', 'addr_state_CA', 'addr_state_CO',\n",
       "       'addr_state_CT', 'addr_state_DC', 'addr_state_DE', 'addr_state_FL',\n",
       "       'addr_state_GA', 'addr_state_HI', 'addr_state_IA', 'addr_state_ID',\n",
       "       'addr_state_IL', 'addr_state_IN', 'addr_state_KS', 'addr_state_KY',\n",
       "       'addr_state_LA', 'addr_state_MA', 'addr_state_MD', 'addr_state_ME',\n",
       "       'addr_state_MI', 'addr_state_MN', 'addr_state_MO', 'addr_state_MS',\n",
       "       'addr_state_MT', 'addr_state_NC', 'addr_state_NE', 'addr_state_NH',\n",
       "       'addr_state_NJ', 'addr_state_NM', 'addr_state_NV', 'addr_state_NY',\n",
       "       'addr_state_OH', 'addr_state_OK', 'addr_state_OR', 'addr_state_PA',\n",
       "       'addr_state_RI', 'addr_state_SC', 'addr_state_SD', 'addr_state_TN',\n",
       "       'addr_state_TX', 'addr_state_UT', 'addr_state_VA', 'addr_state_VT',\n",
       "       'addr_state_WA', 'addr_state_WI', 'addr_state_WV', 'addr_state_WY'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PF2QsfQP1QZA"
   },
   "outputs": [],
   "source": [
    "features_corr = features.drop(columns=['term_ 60 months','home_ownership_NONE', 'purpose_car', 'purpose_credit_card', 'purpose_debt_consolidation',\n",
    "       'purpose_educational', 'purpose_home_improvement', 'purpose_house',\n",
    "       'purpose_major_purchase', 'purpose_medical', 'purpose_moving',\n",
    "       'purpose_other', 'purpose_renewable_energy', 'purpose_small_business','home_ownership_OTHER',\n",
    "       'purpose_vacation', 'purpose_wedding'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "CFDI7DTF3jKx",
    "outputId": "4c437fe4-2075-4c4a-d90f-7592b30166ad"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(37675, 73)\n",
      "(37675,)\n"
     ]
    }
   ],
   "source": [
    "print(features_corr.shape)\n",
    "print(target.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "8rQ0_vMO2zIS",
    "outputId": "cfb39f12-7362-47b8-bc04-b30abdb0b91c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Duration: 0 seconds\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "smote = SMOTE(ratio='minority')\n",
    "features_c, target_c = smote.fit_sample(features_corr, target)\n",
    "print(f'\\nDuration: {time.time() - start_time:.0f} seconds') # print the time elapsed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 272
    },
    "colab_type": "code",
    "id": "0VeW2EGULuQ5",
    "outputId": "02a4f648-4df5-45c7-8dba-800ed9df5b48"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.0000e+03, 1.1000e+01, 1.6300e+02, 2.4000e+04, 2.8000e+01,\n",
       "       0.0000e+00, 1.9850e+03, 1.0000e+00, 3.0000e+00, 0.0000e+00,\n",
       "       1.3648e+04, 8.4000e+01, 9.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "       1.0000e+00, 0.0000e+00, 0.0000e+00, 1.0000e+00, 1.0000e+00,\n",
       "       0.0000e+00, 1.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "       0.0000e+00, 1.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "       0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "       0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "       0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "       0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "       0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "       0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "       0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "       0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "       0.0000e+00, 0.0000e+00, 0.0000e+00])"
      ]
     },
     "execution_count": 39,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_c[0].round()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7ID5IDVF09zK"
   },
   "source": [
    "# **XGBOOST HYPERPARAMETER**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oQsh_P9CfEqy"
   },
   "source": [
    "*   1)objective='binary:logistic' for binary classification and 'objective': 'multi:softmax' softmax for multiclass classification you also need to set num_class(number of classes)\n",
    "*   2)subsample=0.8 = subsample, which is for each tree the % of rows taken to build the tree.\n",
    "*   3)colsample_bytree: number of columns used by each tree.\n",
    "*   4)max_depth = It represents the depth of each tree, which is the maximum number of different features used in each tree.\n",
    "*   5)n_estimator = maximun number of decision tress.\n",
    "*   6) booster parameter allows you to set the type of model you will use when building the ensemble. The default is gbtree which builds an ensemble of decision trees. If your data isn’t too complicated, you can go with the faster and simpler gblinear option which builds an ensemble of linear models.\n",
    "*   7) The gamma parameter can also help with controlling overfitting. It specifies the minimum reduction in the loss required to make a further partition on a leaf node of the tree.\n",
    "*   8)scoring: \"f1\" pr \"accuracy\"\n",
    "*   9)scale_pos_weight parameter impose greater penalties for errors on the minor class\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "m2C4d-jQG5_O",
    "outputId": "43fd6007-b420-46cb-c7cf-fff3b92be803"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Duration: 0 seconds\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import xgboost as xgb\n",
    "start_time = time.time()\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(features_c, target_c, test_size=0.33)\n",
    "model = xgb.XGBClassifier(learning_rate=0.01, max_depth=7, n_estimators=100,  gamma=5,colsample_bytree=.6, subsample=0.8, scoring = 'f1',scale_pos_weight=1)\n",
    "print(f'\\nDuration: {time.time() - start_time:.0f} seconds') # print the time elapsed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 136
    },
    "colab_type": "code",
    "id": "bL6HAXjKHAGR",
    "outputId": "1f163afb-16b9-4bc7-ac3c-574dc911f48b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=0.6, gamma=5,\n",
       "              learning_rate=0.01, max_delta_step=0, max_depth=7,\n",
       "              min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,\n",
       "              nthread=None, objective='binary:logistic', random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, scoring='f1',\n",
       "              seed=None, silent=None, subsample=0.8, verbosity=1)"
      ]
     },
     "execution_count": 41,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_set = [(X_train, Y_train), (X_test, Y_test)]\n",
    "eval_metric = [\"auc\",\"error\"]\n",
    "model.fit(X_train,Y_train, eval_metric=eval_metric, verbose=False, eval_set=eval_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 285
    },
    "colab_type": "code",
    "id": "lc79U4_v5E7D",
    "outputId": "bc9e81de-e008-46c1-ed68-625cf63e8de9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f06c4658550>]"
      ]
     },
     "execution_count": 42,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lFeIonYWWV4yjuMr+V1aDnlf0gb/r3AU3fGhbC5Y22BuhruMMHXmTwSNvcH7X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\n/x/Mb+7C/cq0awAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "error_train = model.evals_result()['validation_0']['error']\n",
    "error_test =  model.evals_result()['validation_1']['error']\n",
    "plt.plot(range(len(error_train)), error_train)\n",
    "plt.plot(range(len(error_test)), error_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 806
    },
    "colab_type": "code",
    "id": "_jbo3pleHjGR",
    "outputId": "f3881edc-5b47-445e-cd52-cd9bf38268fb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Area Under the Curve: 0.9\n"
     ]
    },
    {
     "data": {
      "image/png": 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E7vsPHk6Iu/gioyN1KpqhIyIiLufl5UVFRUXjF9SffvoJb29vg1O5hsVixtvb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\nESlmVNARERERERERESlm/h/2+xNz5/M7AwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.84      1.00      0.91     10696\n",
      "         1.0       1.00      0.81      0.89     10613\n",
      "\n",
      "    accuracy                           0.90     21309\n",
      "   macro avg       0.92      0.90      0.90     21309\n",
      "weighted avg       0.92      0.90      0.90     21309\n",
      "\n",
      "{'base_score': 0.5, 'booster': 'gbtree', 'colsample_bylevel': 1, 'colsample_bynode': 1, 'colsample_bytree': 0.6, 'gamma': 5, 'learning_rate': 0.01, 'max_delta_step': 0, 'max_depth': 7, 'min_child_weight': 1, 'missing': None, 'n_estimators': 100, 'nthread': 1, 'objective': 'binary:logistic', 'reg_alpha': 0, 'reg_lambda': 1, 'scale_pos_weight': 1, 'seed': 0, 'subsample': 0.8, 'verbosity': 1, 'scoring': 'f1'}\n",
      "[0.00357732 0.02393022 0.00330963 0.00492637 0.00176198 0.00450659\n",
      " 0.00200526 0.13017781 0.00169807 0.00661582 0.00214192 0.00411488\n",
      " 0.00157176 0.03295233 0.00460111 0.04784004 0.07646079 0.02332407\n",
      " 0.03162681 0.3881257  0.04753219 0.04031256 0.02795806 0.00124742\n",
      " 0.         0.0010747  0.00173469 0.01547272 0.00195675 0.00178372\n",
      " 0.         0.         0.00680297 0.00481613 0.00216696 0.\n",
      " 0.         0.00196681 0.         0.00161378 0.00154159 0.00197048\n",
      " 0.00287675 0.00179819 0.         0.00141996 0.00180243 0.00235242\n",
      " 0.00192754 0.         0.00208482 0.         0.00114991 0.00205035\n",
      " 0.00088026 0.00221995 0.00503811 0.00136162 0.00156943 0.00186562\n",
      " 0.00139464 0.00129245 0.00145586 0.00176976 0.         0.00235169\n",
      " 0.         0.00162682 0.         0.00124397 0.00176159 0.00148601\n",
      " 0.        ]\n",
      "Precision = 0.9189639630501845\n",
      "Recall = 0.9028100706426779\n",
      "Accuracy = 0.9031864470411564\n",
      "[[10690     6]\n",
      " [ 2057  8556]]\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(X_test)\n",
    "model_performance(pd.Series(Y_test), pd.Series(predictions))\n",
    "print(metrics.classification_report(Y_test,predictions))\n",
    "print(model.get_xgb_params())\n",
    "print(model.feature_importances_)\n",
    "print(\"Precision = {}\".format(precision_score(Y_test, predictions, average='macro')))\n",
    "print(\"Recall = {}\".format(recall_score(Y_test, predictions, average='macro')))\n",
    "print(\"Accuracy = {}\".format(accuracy_score(Y_test, predictions)))\n",
    "print(metrics.confusion_matrix(Y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bTI0d1jNtA8A"
   },
   "outputs": [],
   "source": [
    "y_hat = model.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PKTruexAu1wy"
   },
   "outputs": [],
   "source": [
    "y_hat = y_hat.reshape(-1,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 268
    },
    "colab_type": "code",
    "id": "ihyj7CgLsnR_",
    "outputId": "976d2c00-f5c3-498d-a196-ea03b72af6bc"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAD7CAYAAABnoJM0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/wLjQRovpp4zgQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(y_hat)\n",
    "_ = plt.axvline(x=0.5, color='orange')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LGBSSyFDYdrT"
   },
   "source": [
    "# MODELING IN KERAS FOR BINARY CLASSIFICATION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VAD6cP_BYj90"
   },
   "source": [
    "## CONVERTING TO NUMPY ARRAYS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KG876MfFI1qt"
   },
   "outputs": [],
   "source": [
    "def standardize(subset):\n",
    "   continious_columns = subset.select_dtypes(include=['float']).columns\n",
    "   mm_scaler = preprocessing.StandardScaler()\n",
    "   for col in continious_columns:\n",
    "     subset[col]= mm_scaler.fit_transform(subset[[col]])\n",
    "   return subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "kdri3ylXYpQt",
    "outputId": "fe39ecd0-8ecf-42dc-f7cb-3f2684ee5aef"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "features shape: (37675, 90)\n",
      "target shape: (37675,)\n"
     ]
    }
   ],
   "source": [
    "features_np = standardize(features).values\n",
    "target_np = target.values\n",
    "print(f'features shape: {features_np.shape}')\n",
    "print(f'target shape: {target_np.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "colab_type": "code",
    "id": "2wJVpEUuJU-k",
    "outputId": "184f7998-7eef-4d71-f8b8-7ad3ba3e3a12"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.83476302, -0.35903013, -0.77647201, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-1.17407407,  0.89345228, -1.26979759, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-1.18764651,  1.08051134, -1.15249871, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       ...,\n",
       "       [-0.83476302, -1.05846836, -0.8053419 , ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-0.83476302, -1.23197242, -0.81233195, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-0.49545196,  0.48138014, -0.33332162, ...,  0.        ,\n",
       "         0.        ,  0.        ]])"
      ]
     },
     "execution_count": 52,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 289
    },
    "colab_type": "code",
    "id": "V_xTRxRKjFGc",
    "outputId": "3873fee6-c6c2-4757-b9f3-66cf42d30f1b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "features shape: (64572, 73)\n",
      "features type: <class 'numpy.ndarray'>\n",
      "target shape: (64572,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[-0.8665028 , -0.5960398 , -0.80631846, ..., -0.11511506,\n",
       "        -0.06655005, -0.03817717],\n",
       "       [-1.1943888 ,  0.69630217, -1.29988344, ..., -0.11511506,\n",
       "        -0.06655005, -0.03817717],\n",
       "       [-1.20750424,  0.88931428, -1.18252764, ..., -0.11511506,\n",
       "        -0.06655005, -0.03817717],\n",
       "       ...,\n",
       "       [-0.23757378,  0.05043853, -0.00598261, ..., -0.11511506,\n",
       "        -0.06655005, -0.03817717],\n",
       "       [-1.08222715,  0.07636797, -1.04372552, ..., -0.11511506,\n",
       "        -0.06655005, -0.03817717],\n",
       "       [-1.1288116 , -0.21141638, -1.10891544, ..., -0.11511506,\n",
       "        -0.06655005, -0.03817717]])"
      ]
     },
     "execution_count": 53,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_np_scaled = standardize(pd.DataFrame(features_c)).values\n",
    "target_np_scaled = target_c\n",
    "print(f'features shape: {features_np_scaled.shape}')\n",
    "print(f'features type: {type(features_np_scaled)}')\n",
    "print(f'target shape: {target_np_scaled.shape}')\n",
    "features_np_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "GvDDQUybexBf",
    "outputId": "2218a447-720c-4099-fc51-2f26163daabb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(51657, 73)\n",
      "(51657,)\n"
     ]
    }
   ],
   "source": [
    "xk_train, xk_test, yk_train, yk_test  = train_test_split(features_np_scaled,target_np_scaled,test_size=0.2,random_state=33)\n",
    "print(xk_train.shape)\n",
    "print(yk_train.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 867
    },
    "colab_type": "code",
    "id": "EoJg1OlPE60f",
    "outputId": "338f0fed-8876-4e3d-a2ae-71dbc8d4d853"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-4.73039605e-01, -3.47082147e-01, -3.22046143e-01,\n",
       "        -2.89953384e-01, -8.88932057e-01,  1.79839527e+00,\n",
       "         3.42491148e-01,  5.06449082e-02, -7.01992861e-02,\n",
       "        -2.80579805e-01, -8.04805889e-01, -1.76352061e+00,\n",
       "        -8.17141217e-01, -9.25677049e-01, -3.06431065e-01,\n",
       "         1.08589636e+00,  1.27479744e+00, -6.31221088e-01,\n",
       "        -7.51501058e-01,  7.46597683e-01,  1.26070132e+00,\n",
       "        -7.89487839e-01, -5.85726793e-01, -5.20421407e-02,\n",
       "        -1.07963175e-01, -7.97127361e-02, -1.58304176e-01,\n",
       "        -5.18425451e-01, -1.47853879e-01, -1.44492974e-01,\n",
       "        -6.89301874e-02, -5.53832983e-02, -3.19309626e-01,\n",
       "        -2.12642837e-01, -7.30295605e-02, -8.79993974e-03,\n",
       "        -1.45782324e-02, -2.14186017e-01, -1.18067238e-02,\n",
       "        -8.15665545e-02, -9.67352720e-02, -1.07917377e-01,\n",
       "        -1.93932238e-01, -1.80911188e-01, -6.81629844e-03,\n",
       "        -1.42834035e-01, -1.33047289e-01, -1.46113148e-01,\n",
       "        -2.20608804e-02, -4.80938147e-02, -1.53813730e-01,\n",
       "        -1.98370400e-02,  1.65193889e+01, -2.46982858e-01,\n",
       "        -7.76042057e-02, -1.31795984e-01, -3.43778097e-01,\n",
       "        -1.87065965e-01, -9.47617346e-02, -1.17815949e-01,\n",
       "        -2.06582914e-01, -7.67136873e-02, -1.18871883e-01,\n",
       "        -4.75199443e-02, -2.19293192e-02, -2.78502264e-01,\n",
       "        -9.11658523e-02, -1.99543988e-01, -3.68160329e-02,\n",
       "        -1.59607010e-01, -1.15115057e-01, -6.65500486e-02,\n",
       "        -3.81771727e-02],\n",
       "       [-1.11748303e-01,  4.57422155e-01,  1.85803964e-01,\n",
       "         1.20528148e+00, -2.94996207e-01, -3.27679865e-01,\n",
       "        -1.25038239e+00,  6.22891579e-01,  3.03482966e+00,\n",
       "         1.74064877e+00,  2.38689959e+00,  5.72123391e-01,\n",
       "         1.24528413e+00,  8.89219420e-02, -3.06431065e-01,\n",
       "         7.77632821e-02,  2.54078950e-01, -6.31221088e-01,\n",
       "         3.12764251e-01,  7.46597683e-01, -9.13123980e-01,\n",
       "         1.45795967e+00, -5.85726793e-01, -5.20421407e-02,\n",
       "        -1.07963175e-01, -7.97127361e-02, -1.58304176e-01,\n",
       "         7.85855636e-01, -1.47853879e-01, -1.44492974e-01,\n",
       "        -6.89301874e-02, -5.53832983e-02, -3.19309626e-01,\n",
       "        -2.12642837e-01, -7.30295605e-02, -8.79993974e-03,\n",
       "        -1.45782324e-02,  2.74897324e+00, -1.18067238e-02,\n",
       "        -8.15665545e-02, -9.67352720e-02, -1.07917377e-01,\n",
       "        -1.93932238e-01, -1.80911188e-01, -6.81629844e-03,\n",
       "        -1.42834035e-01, -1.33047289e-01, -1.46113148e-01,\n",
       "        -2.20608804e-02, -4.80938147e-02, -1.53813730e-01,\n",
       "        -1.98370400e-02, -6.97529100e-02, -2.46982858e-01,\n",
       "        -7.76042057e-02, -1.31795984e-01, -3.43778097e-01,\n",
       "        -1.87065965e-01, -9.47617346e-02, -1.17815949e-01,\n",
       "        -2.06582914e-01, -7.67136873e-02, -1.18871883e-01,\n",
       "        -4.75199443e-02, -2.19293192e-02, -2.78502264e-01,\n",
       "        -9.11658523e-02, -1.99543988e-01, -3.68160329e-02,\n",
       "        -1.59607010e-01, -1.15115057e-01, -6.65500486e-02,\n",
       "        -3.81771727e-02]])"
      ]
     },
     "execution_count": 55,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xk_train[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 513
    },
    "colab_type": "code",
    "id": "6Yzx1nKueSgb",
    "outputId": "21d35c70-3b3f-4d04-e019-73bcc535cc5c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4409: The name tf.random_normal is deprecated. Please use tf.random.normal instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:148: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 78)                5772      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 39)                3081      \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 19)                760       \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 19)                76        \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 1)                 20        \n",
      "=================================================================\n",
      "Total params: 9,709\n",
      "Trainable params: 9,671\n",
      "Non-trainable params: 38\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(78, activation='relu', kernel_initializer='normal', input_shape=(73,)))\n",
    "#model.add(Dropout(0.5))\n",
    "model.add(Dense(39,kernel_initializer='normal', activation='relu'))\n",
    "model.add(Dense(19,kernel_initializer='normal', activation='relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8eBIkffwpOYF"
   },
   "source": [
    "Choosing an optimizer and loss\n",
    "Keep in mind what kind of problem you are trying to solve:\n",
    "\n",
    "# For a multi-class classification problem\n",
    "model.compile(optimizer='rmsprop',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# For a binary classification problem\n",
    "model.compile(optimizer='rmsprop',\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# For a mean squared error regression problem\n",
    "model.compile(optimizer='rmsprop',\n",
    "              loss='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 156
    },
    "colab_type": "code",
    "id": "Y8k_ir-Kenw2",
    "outputId": "346c1803-6ccb-46e5-ff63-9b7a69e5a200"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3657: The name tf.log is deprecated. Please use tf.math.log instead.\n",
      "\n",
      "WARNING:tensorflow:From /tensorflow-1.15.0/python3.6/tensorflow_core/python/ops/nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    }
   ],
   "source": [
    "from keras import optimizers\n",
    "rms = keras.optimizers.RMSprop(lr=0.01, rho=0.9)\n",
    "adam = keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False)\n",
    "sgd = optimizers.SGD(lr=0.01, clipnorm=1.)\n",
    "model.compile(loss='binary_crossentropy', optimizer=rms, metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "V5RsC62wpd1P"
   },
   "source": [
    "Sample: one element of a dataset.\n",
    "Example: one image is a sample in a convolutional network\n",
    "Example: one audio file is a sample for a speech recognition model\n",
    "Batch: a set of N samples. The samples in a batch are processed independently, in parallel. If training, a batch results in only one update to the model.A batch generally approximates the distribution of the input data better than a single input. The larger the batch, the better the approximation; however, it is also true that the batch will take longer to process and will still result in only one update. For inference (evaluate/predict), it is recommended to pick a batch size that is as large as you can afford without going out of memory (since larger batches will usually result in faster evaluation/prediction).\n",
    "Epoch: an arbitrary cutoff, generally defined as \"one pass over the entire dataset\", used to separate training into distinct phases, which is useful for logging and periodic evaluation.\n",
    "When using validation_data or validation_split with the fit method of Keras models, evaluation will be run at the end of every epoch.\n",
    "Within Keras, there is the ability to add callbacks specifically designed to be run at the end of an epoch. Examples of these are learning rate changes and model checkpointing (saving)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "onx0wEnOJDjS"
   },
   "source": [
    "# EARLY STOPPING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lpCAGWrjJHTc"
   },
   "source": [
    "Stop training when a monitored quantity has stopped improving.\n",
    "\n",
    "Arguments:\n",
    "    monitor: Quantity to be monitored.\n",
    "    min_delta: Minimum change in the monitored quantity\n",
    "        to qualify as an improvement, i.e. an absolute\n",
    "        change of less than min_delta, will count as no\n",
    "        improvement.\n",
    "    patience: Number of epochs with no improvement\n",
    "        after which training will be stopped.\n",
    "    verbose: verbosity mode.\n",
    "    mode: One of `{\"auto\", \"min\", \"max\"}`. In `min` mode,\n",
    "        training will stop when the quantity\n",
    "        monitored has stopped decreasing; in `max`\n",
    "        mode it will stop when the quantity\n",
    "        monitored has stopped increasing; in `auto`\n",
    "        mode, the direction is automatically inferred\n",
    "        from the name of the monitored quantity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WmJxR4ZkI9OH"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "early_stop = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "nq-8QJfTff41",
    "outputId": "60011621-7d32-405b-fb3f-e361ce89dbb3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3005: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
      "\n",
      "Train on 51657 samples, validate on 12915 samples\n",
      "Epoch 1/100\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
      "\n",
      "51657/51657 [==============================] - 1s 23us/step - loss: 0.4908 - acc: 0.7587 - val_loss: 1.7788 - val_acc: 0.5636\n",
      "Epoch 2/100\n",
      "51657/51657 [==============================] - 1s 11us/step - loss: 0.3887 - acc: 0.8222 - val_loss: 1.6784 - val_acc: 0.6359\n",
      "Epoch 3/100\n",
      "51657/51657 [==============================] - 1s 11us/step - loss: 0.3625 - acc: 0.8360 - val_loss: 1.1024 - val_acc: 0.7142\n",
      "Epoch 4/100\n",
      "51657/51657 [==============================] - 1s 11us/step - loss: 0.3473 - acc: 0.8460 - val_loss: 1.0996 - val_acc: 0.7237\n",
      "Epoch 5/100\n",
      "51657/51657 [==============================] - 1s 11us/step - loss: 0.3357 - acc: 0.8519 - val_loss: 1.0769 - val_acc: 0.7375\n",
      "Epoch 6/100\n",
      "51657/51657 [==============================] - 1s 11us/step - loss: 0.3299 - acc: 0.8544 - val_loss: 0.7304 - val_acc: 0.7792\n",
      "Epoch 7/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.3197 - acc: 0.8604 - val_loss: 0.6476 - val_acc: 0.7926\n",
      "Epoch 8/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.3158 - acc: 0.8614 - val_loss: 0.4826 - val_acc: 0.8223\n",
      "Epoch 9/100\n",
      "51657/51657 [==============================] - 1s 11us/step - loss: 0.3092 - acc: 0.8652 - val_loss: 0.6070 - val_acc: 0.8067\n",
      "Epoch 10/100\n",
      "51657/51657 [==============================] - 1s 11us/step - loss: 0.3058 - acc: 0.8662 - val_loss: 0.6366 - val_acc: 0.8033\n",
      "Epoch 11/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.3027 - acc: 0.8676 - val_loss: 0.4120 - val_acc: 0.8452\n",
      "Epoch 12/100\n",
      "51657/51657 [==============================] - 1s 11us/step - loss: 0.2999 - acc: 0.8699 - val_loss: 0.3901 - val_acc: 0.8498\n",
      "Epoch 13/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2970 - acc: 0.8702 - val_loss: 0.4102 - val_acc: 0.8442\n",
      "Epoch 14/100\n",
      "51657/51657 [==============================] - 1s 11us/step - loss: 0.2947 - acc: 0.8715 - val_loss: 0.3630 - val_acc: 0.8527\n",
      "Epoch 15/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2934 - acc: 0.8739 - val_loss: 0.3847 - val_acc: 0.8568\n",
      "Epoch 16/100\n",
      "51657/51657 [==============================] - 1s 11us/step - loss: 0.2887 - acc: 0.8748 - val_loss: 0.4543 - val_acc: 0.8418\n",
      "Epoch 17/100\n",
      "51657/51657 [==============================] - 1s 11us/step - loss: 0.2879 - acc: 0.8749 - val_loss: 0.3934 - val_acc: 0.8555\n",
      "Epoch 18/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2872 - acc: 0.8770 - val_loss: 0.4917 - val_acc: 0.8373\n",
      "Epoch 19/100\n",
      "51657/51657 [==============================] - 1s 11us/step - loss: 0.2867 - acc: 0.8767 - val_loss: 0.3825 - val_acc: 0.8532\n",
      "Epoch 20/100\n",
      "51657/51657 [==============================] - 1s 11us/step - loss: 0.2814 - acc: 0.8791 - val_loss: 0.4069 - val_acc: 0.8530\n",
      "Epoch 21/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2801 - acc: 0.8808 - val_loss: 0.4436 - val_acc: 0.8461\n",
      "Epoch 22/100\n",
      "51657/51657 [==============================] - 1s 11us/step - loss: 0.2829 - acc: 0.8775 - val_loss: 0.5302 - val_acc: 0.8345\n",
      "Epoch 23/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2802 - acc: 0.8792 - val_loss: 0.3342 - val_acc: 0.8695\n",
      "Epoch 24/100\n",
      "51657/51657 [==============================] - 1s 11us/step - loss: 0.2778 - acc: 0.8800 - val_loss: 0.3153 - val_acc: 0.8728\n",
      "Epoch 25/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2758 - acc: 0.8813 - val_loss: 0.3258 - val_acc: 0.8722\n",
      "Epoch 26/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2761 - acc: 0.8819 - val_loss: 0.4014 - val_acc: 0.8592\n",
      "Epoch 27/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2767 - acc: 0.8814 - val_loss: 0.3971 - val_acc: 0.8590\n",
      "Epoch 28/100\n",
      "51657/51657 [==============================] - 1s 11us/step - loss: 0.2759 - acc: 0.8810 - val_loss: 0.4129 - val_acc: 0.8564\n",
      "Epoch 29/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2768 - acc: 0.8798 - val_loss: 0.3355 - val_acc: 0.8671\n",
      "Epoch 30/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2753 - acc: 0.8822 - val_loss: 0.3108 - val_acc: 0.8630\n",
      "Epoch 31/100\n",
      "51657/51657 [==============================] - 1s 11us/step - loss: 0.2738 - acc: 0.8836 - val_loss: 0.3310 - val_acc: 0.8705\n",
      "Epoch 32/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2737 - acc: 0.8831 - val_loss: 0.4096 - val_acc: 0.8561\n",
      "Epoch 33/100\n",
      "51657/51657 [==============================] - 1s 11us/step - loss: 0.2753 - acc: 0.8834 - val_loss: 0.3273 - val_acc: 0.8725\n",
      "Epoch 34/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2709 - acc: 0.8842 - val_loss: 0.3176 - val_acc: 0.8533\n",
      "Epoch 35/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2732 - acc: 0.8818 - val_loss: 0.3205 - val_acc: 0.8739\n",
      "Epoch 36/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2736 - acc: 0.8816 - val_loss: 0.3192 - val_acc: 0.8728\n",
      "Epoch 37/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2736 - acc: 0.8826 - val_loss: 0.3843 - val_acc: 0.8646\n",
      "Epoch 38/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2699 - acc: 0.8849 - val_loss: 0.2945 - val_acc: 0.8733\n",
      "Epoch 39/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2723 - acc: 0.8835 - val_loss: 0.5181 - val_acc: 0.8448\n",
      "Epoch 40/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2697 - acc: 0.8847 - val_loss: 0.3230 - val_acc: 0.8755\n",
      "Epoch 41/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2715 - acc: 0.8852 - val_loss: 0.3073 - val_acc: 0.8705\n",
      "Epoch 42/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2705 - acc: 0.8829 - val_loss: 0.3058 - val_acc: 0.8681\n",
      "Epoch 43/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2717 - acc: 0.8844 - val_loss: 0.3498 - val_acc: 0.8317\n",
      "Epoch 44/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2733 - acc: 0.8828 - val_loss: 0.4234 - val_acc: 0.8591\n",
      "Epoch 45/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2711 - acc: 0.8841 - val_loss: 0.3034 - val_acc: 0.8734\n",
      "Epoch 46/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2722 - acc: 0.8839 - val_loss: 0.5080 - val_acc: 0.8513\n",
      "Epoch 47/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2730 - acc: 0.8839 - val_loss: 0.3310 - val_acc: 0.8753\n",
      "Epoch 48/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2700 - acc: 0.8862 - val_loss: 0.3145 - val_acc: 0.8763\n",
      "Epoch 49/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2805 - acc: 0.8820 - val_loss: 0.3032 - val_acc: 0.8786\n",
      "Epoch 50/100\n",
      "51657/51657 [==============================] - 1s 11us/step - loss: 0.2728 - acc: 0.8837 - val_loss: 0.3440 - val_acc: 0.8764\n",
      "Epoch 51/100\n",
      "51657/51657 [==============================] - 0s 9us/step - loss: 0.2801 - acc: 0.8803 - val_loss: 0.3140 - val_acc: 0.8772\n",
      "Epoch 52/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2784 - acc: 0.8834 - val_loss: 0.3109 - val_acc: 0.8774\n",
      "Epoch 53/100\n",
      "51657/51657 [==============================] - 1s 11us/step - loss: 0.2732 - acc: 0.8846 - val_loss: 0.4092 - val_acc: 0.8679\n",
      "Epoch 54/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2722 - acc: 0.8838 - val_loss: 0.3023 - val_acc: 0.8712\n",
      "Epoch 55/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2758 - acc: 0.8838 - val_loss: 0.3022 - val_acc: 0.8766\n",
      "Epoch 56/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2745 - acc: 0.8840 - val_loss: 0.4624 - val_acc: 0.8609\n",
      "Epoch 57/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2783 - acc: 0.8831 - val_loss: 0.5045 - val_acc: 0.8554\n",
      "Epoch 58/100\n",
      "51657/51657 [==============================] - 1s 11us/step - loss: 0.2794 - acc: 0.8817 - val_loss: 0.3232 - val_acc: 0.8778\n",
      "Epoch 59/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2755 - acc: 0.8838 - val_loss: 0.3544 - val_acc: 0.8315\n",
      "Epoch 60/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2765 - acc: 0.8833 - val_loss: 0.5343 - val_acc: 0.8537\n",
      "Epoch 61/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2769 - acc: 0.8836 - val_loss: 0.5508 - val_acc: 0.8519\n",
      "Epoch 62/100\n",
      "51657/51657 [==============================] - 1s 11us/step - loss: 0.2824 - acc: 0.8823 - val_loss: 0.3169 - val_acc: 0.8645\n",
      "Epoch 63/100\n",
      "51657/51657 [==============================] - 1s 10us/step - loss: 0.2815 - acc: 0.8830 - val_loss: 0.3137 - val_acc: 0.8654\n",
      "Epoch 00063: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f06b61dba20>"
      ]
     },
     "execution_count": 59,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(xk_train, yk_train, \n",
    "          batch_size=256, epochs=100, verbose=1, \n",
    "          validation_data=(xk_test, yk_test),\n",
    "          callbacks=[early_stop])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ogt-nCmHBXuV"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "model.save('/gdrive/My Drive/files/full_data_project_model.h5')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tSMwxcM7CAWO"
   },
   "outputs": [],
   "source": [
    "losses = pd.DataFrame(model.history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 285
    },
    "colab_type": "code",
    "id": "11Gei5kaCDSu",
    "outputId": "638f76a7-7539-4706-8e55-dd36e8b6867d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f06af96d400>"
      ]
     },
     "execution_count": 64,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CumTfhAkT+K//+i9mzZrV5jIjRozgtttuw2q1smfPHm688UY2btyIxWIJSaAyo6QQQnRM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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "losses[['loss','val_loss']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TgeKdy4fCODk"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "predictions = model.predict_classes(xk_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "_NNr85yNCybs",
    "outputId": "b77663b5-9846-45a5-b76a-4dbc74433fa0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, ..., 1, 1, 0], dtype=int32)"
      ]
     },
     "execution_count": 66,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions= predictions.reshape(-1,)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 463
    },
    "colab_type": "code",
    "id": "iK7DIxdaCZut",
    "outputId": "f474f4fb-baa9-47d4-f4ab-ddfff6d5d5dd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Area Under the Curve: 0.87\n"
     ]
    },
    {
     "data": {
      "image/png": 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zul07tWbH0XFpmtas2cSIEf2tjuGdbEbdN5FGVpz2E24TEjt1sjqKyG/mqqkB\noNOVl5Pd7XeMeuM1Es7qaHGqlqXea+hs374df39/evXqBcDo0aNZv379bxoTERHv4efnR2lpae36\nIHv37sXf39/iVNYJDwtiwB+74e/vi91uY/SI87mgdyfe/eA7tnz/E4P7nUPb1rEA9LuwK2e2c/LD\n7iOlzPK3PuKWGy4lPi4CZ1wEt078M8ve2AzA5s934HK5+fv1g/Hz8+HGay8B4INPt1tzonJK33yz\nk6ys3GPubvWzqqpqKiurgCOlaGVlFaZpAkd+KVZZWUV1dQ2maVJZWUVVVXWjZm8WVOhIE2bmHqLA\nCCckLMzqKCL1VpiTw/Krr2XFDX8DoOefL2XknNknXNBfPKvOa+hMnToV0zTp2bMnU6ZMISMjg/j4\n/y7mFRkZidvtpqCgoN5jDoejgU5LRESsduONNzJ+/Hiys7OZPn06H330EQsXLrQ6lmV8fezMmnYV\nHdvH43K52bMvnasmPMze/Zns3Z9Ju9axvPvafTjCg0nLyOPmu55nz750AJ5/5T3OaB3LV/9ZAMBL\nK97n+VfeA6C62sVVEx7mqQUTmDN9DLt+TOOqCQ9TXe2y7Fzl5Fav3sjAgX0ICQk6bmzw4BtJS8sG\nYPz4mQBs3Pg8iYlxfPXVD1xzzd21z+3W7XJ+//suLFs2r3GCNxOmehppwkIqMikJdFodQ6Tevly9\nmsznl9DaZic1MR63260ix0J1KnSWL1+O0+mkqqqKuXPnMnv2bAYOHOjpbCIi0oz17duXdu3a8dFH\nH2GaJpMmTaJNmzanPtBLHc4r5oKh9/7q+JyH32DOw2/86vg9//gX9/zjXycc++6HA5z/53t+c0Zp\nHLNn3/yrY5s2vfCrY717d2X37rWeiORdNPPmlD755BPefvtt8vLyeOaZZ9i2bRslJSX06dPH6mhe\nraSwkAiKKI38vdVRRE5bZXk5q6bdScKhVAzA9+qxjLnyCqtjtXh1qtKcziMtsp+fH2PHjuWbb77B\n6XSSnp5e+5y8vDxsNhsOh6PeYyIi4j1Wr15NTEwMY8eOZdy4cS26zBGRRmS31X1rgZYtW8asWbNo\n27YtX331FQABAQE8+uijFifzfim7dwEQmtThFM8UaXrSdu4i5uAhDoUGc+ELizlPZU6TcMqfZGVl\nZRQXFwNHFm985513SE5OpkuXLlRUVLBlyxYAVqxYweDBgwHqPSYiIt5j06ZNXHzxxdxzzz18/fXX\nVscRkZbCdhpbC/Tyyy+zZMkSJk6cWHuZRLt27di/f7/Fybxf/v49AMSfmWxxEpG6cbvdvP/Ci7jd\nbtr1OIf2D8xm3PJlOOJirXiy8rYAACAASURBVI4mR53ykqvc3FwmT56My+XC7XbTvn17Zs6cic1m\nY8GCBcycOZPKykoSEhJq10ao75iIiHiPxx57jIKCAtauXcvcuXMpLS3lsssu429/+5vV0UTEmxm6\n5OpkSktLa2ff/7xofU1Nje5C2Aiqcg5SZvrTNjH+1E8WsVjG3n28f+ddtKlx8YHbpN+E8bTt3s3q\nWPI/TlnoJCUlsXr16hOO9ejRg7VrT3wtd33HRETEezgcDq6++mqGDh3Kww8/zD//+U8VOiLiWVpD\n56TOPfdcnnvuOSZNmlS7b+nSpfTu3dvCVC2Df3E6Bb6xWkBWmryNzz5H9dq3cRo2srp1Yfhfr7U6\nkvyKOt/lSkRE5HS4XC42b97MW2+9xddff03//v155ZVXrI4lIl7O1Aydk7r33nu58cYbWblyJaWl\npQwaNIjg4GCeffZZq6N5tZrqaiJdh8mI0ILI0rS9OunvtE7PJN8wSJp6Gxf3/aPVkeQkVOiIiIhH\n9O3bl44dOzJixAgWLlxIQECA1ZFEpCXQ5IeTio2N5c0332Tbtm2kpaXhdDrp1q2bZo14WPq+n/A1\nXAQ6z7A6ishJxZ7dnUOmyciHFhEQHGR1HDkFFToiIuIRK1eurF2nQUSk0bTQu1fV1aRJk3j66afp\n1q0b3br9dz2Mm2++mSeeeMLCZN4ta99uooCY9h2tjiJyjMryclbfMR1bQABXLpxP/79NtDqSnAYV\nOiIi0qC+/vprevbsyZ49e9izZ89x43379rUglYi0GFpD56S++OKLE+7/8ssvGzlJy1Kato9w00bb\nM1XoSNOx54sv+G7ugyQaNg6EheB2uzVbr5lRoSMiIg1q1apV9OzZk+eff/64McMwVOiIiGepzzmh\nRx99FIDq6urar3+WkpJCfLzuvORJtsJU8m2RdPT3tzqKCG63m7UP/IPQr7YQbkLxgL6M+3+3WB1L\n6kGFjoiINKgHHngAgGXLllmcRERaIlMzdE4oMzMTANM0a7/+mdPpZPLkyVbEajHCK7PIDz3T6hgi\nAOz+9DMitnxDmo8PF8ydQ1LnZKsjST2p0BEREY8YM2YMr7766in3iYg0KBU6JzRv3jwAzjnnHK66\n6iqL07QsuVlZhBrlFMe0tjqKtHA/fLiZ3/X9I8kXnE9Jbi5X/flS7D6qBJoz/e2JiIhHVFRUHPPY\n5XJRWFhoURoRaTF02/KT+rnMKSkpIT8//5ixpKQkKyJ5vbRdOwkBItpoho5Yozgvj3/fdjttC4r4\nJCOD80eP4tzhw6yOJQ1AhY6IiDSo559/nueff56SkhL69OlTu7+iooKhQ4damExEWgSt53lS+/bt\n4/bbb2fXrl0YhoFpmhhHS7CdO3danM47FabsJQRITO5sdRRpgbasXUfqs4tpbbNzyBnHiCF/tjqS\nNCAVOiIi0qBGjRrF4MGDmTNnDjNmzKjdHxISQnh4uIXJRKRF0G3LT2rWrFn07t2bpUuX0r9/fzZt\n2sRDDz3EOeecY3U0r+XKOUShGUK7yEiro0gL88YddxK760d8TLCNHcWY0aOsjiQNTD/xRESkQYWG\nhpKYmMizzz5LQkJC7aYyR0Qahc2o+1YPTzzxBGeddRZ79uwBYOvWrQwbNoxBgwZx/fXXk5ubW/vc\n+o550q5du5g6dSphYWGYpkloaCh33HHHcXe+koYTVJ5BcUCc1TGkBfIJCiIlOIg/LH6G81XmeCUV\nOiIi0qCmTZsGwOWXX84VV1xx3CYi4kmmYdR5O10//PADW7duJSEhAThy699p06YxY8YMNmzYQK9e\nvVi0aNFvGvM0f39/ampqAIiIiCA9PR23201BQUGjvH9LU1leToQ7HzNC6xOJ57ndbv49Zy7vPPQw\nAMNm3Me4V18hMt5pcTLxFF1yJSIiDeraa68F4M4777Q4iYi0SB76dWVVVRWzZ8/moYce4pprrgFg\n+/bt+Pv706tXLwBGjx5N//79mTdvXr3HPK1nz5783//9H5dddhmDBg1iwoQJ+Pn5cd5553n8vVui\nlN27sRsmIfHtrI4iXi5r/wE2TruTNtU1HPTzBcBm0/wNb6dCR0REGlSXLl0A+P3vf1+7r6qqisLC\nQmJiYqyKJSItxWnMvCkqKqKoqOi4/WFhYYSFhR2z79FHH2XYsGEkJibW7svIyCA+Pr72cWRkZO1s\nl/qOORyOOuevj19eWjVlyhQ6dOhAWVkZI0eO9Oj7tlQ5P+0mDojr2MnqKOLF3n/hRSpWrSHesJHR\nuROj5s6xOpI0ElV2IiLiEbfddhvFxcW1d7f685//zAsvvGB1LBHxdqexhs7LL79M//79j9tefvnl\nY17y22+/Zfv27YwdO9aik/IMm83GiBEjuOKKK3jrrbfqdMz+/fsZNWoUgwYNYtSoURw4cOCEz3vn\nnXcYOnQoQ4YMYejQoRw+fLgBkzcflVkHqDR9cLZta3UU8VLfb9yE37/fpswwiJpyC1fMn4fdR/M2\nWgr9TYuIiEfs37+f0NBQ1q9fT+/evbnrrru46qqrGD9+vNXRRMSbncZix9dee+0JZ6b87+ycr776\nin379tG/f38AMjMzGT9+PFdffTXp6em1z8vLy8Nms+FwOHA6nfUa86TPPvuMnTt30rp1awYMGEBN\nTQ3/+te/WLx4MQ6Hg3Hjxp3yNWbOnMnYsWMZPnw4a9asYcaMGSxduvSY52zbto0nnniCl19+mZiY\nGIqLi/Hz8/PUaTVpvoVp5PvEYLfbrY4iXib74EFi27ShW/9+rN/+A3/667UE/c/3LvF+KnRERMQj\nfl5086uvvqJv374EBgbqWm4R8TjTXvdC50SXVp3IxIkTmThxYu3jfv368cwzz9ChQwdef/11tmzZ\nQq9evVixYgWDBw8Gjlx+WlFRcdpjnvLcc8/x9NNP06FDB/bu3cuYMWP48ssv8fPzY86cOVx00UWn\nfI3c3Fx27NjBkiVLABgyZAhz5swhLy+PyF/ckvull17i+uuvr73MNjQ01CPn1NS53W4iarLJjuhu\ndRTxItWVlbx1513E7PuJqMl/p/slAxl862SrY4lFVOiIiIhHtG/fnhtuuIGffvqJ22+/nYqKCqsj\niUhLUI+7V9WXzWZjwYIFzJw5k8rKShISEli4cOFvGvOU1157jWXLltGlSxe2bt3KmDFjuPPOO7nu\nuuvq/BoZGRnExcXVzjax2+3ExsaSkZFxTKGzb98+EhMTGTduHGVlZQwcOJBJkyZhNOLfTVOQefAQ\nAUY1/nFtrY4iXmLvli18M3suiYaNA8HBdP9dZ6sjicVU6IiIiEfMnz+fjz/+mLPOOougoCCysrK4\n/fbbrY4lIt7uNC65qq9NmzbVft2jRw/Wrl17wufVd8wT8vPzaxetP/vss/Hz86u9K2FDc7lc7N69\nmyVLllBVVcUNN9xAfHw8I0aM8Mj7NVWZP+7CAUSdcabVUcQLrHtwPgEff0YEUHjx+Yy7fYrVkaQJ\n0Nx3ERHxiICAAHr27Mm+ffv44IMP8PPz449//KPVsUTE2xmnsbUwpmnidrtxuVz4+/sDRy4L+nk7\nFafTSVZWFi6XCzhS3GRnZ+N0Oo95Xnx8PIMHD8bPz4+QkBD69+/P999/3/An1MSVpO7DbRokdkq2\nOop4gZKUVA772Ok4bw6XqsyRozRDR0REPOKjjz5i2rRpdO7cGdM02b17NwsXLuT888+3OpqIeDEt\n1XViZWVldO7838szTNOsfWyaJoZhsHPnzpO+RlRUFMnJyaxbt47hw4ezbt06kpOTj7ncCo6srfPh\nhx8yfPhwampq+Pzzzxk0aFDDn1QTZ+ankG+E0yE42Ooo0kx98OISfIOCOH/0KC5/eBE2u113sJJj\n6F+DiIh4xCOPPMLy5ctp3749cGRNhWnTpqnQERGPamHLtNTZxo0bG+R1Zs2axfTp03nqqacICwtj\n/vz5AEyYMIFbbrmFrl278uc//5nt27dz6aWXYrPZuOCCC7jiiisa5P2bk9CKTIqDEq2OIc1QaUEB\nq2+7nbZ5BRyy2zh/9Ch8j86qE/klFToiIuIRNTU1tWUOHFkk+ec7X4mIeIoKnRNLSEhokNdp3749\nK1euPG7/4sWLa7+22Wzcdddd3HXXXQ3yns1RUX4BDoopjWptdRRpZrZu2MD+x5+ird2HgzFRjHh4\nkdWRpAnTpFQREfGIyMhI3nrrrdrHq1atOm5avohIQ7PZjDpvIp6SumsHAGFJHSxOIs3J1g0bKHzy\nGfxMcF0xkrHPP0dQWJjVsaQJO61C54knnuCss85iz549AGzdupVhw4YxaNAgrr/+enJzc2ufW98x\nERHxDrNnz2bFihV07dqVbt26sWLFCubMmWN1LBHxcoZR903EU/IO/ghAfMdOFieR5qCitAyAbgMH\nkn1WR3o/8wR/vPovFqeS5qDOhc4PP/zA1q1ba6drut1upk2bxowZM9iwYQO9evVi0aJFv2lMRES8\nQ0FBAUVFRbz44ot8/vnnfPbZZ7z22mskJSVZHU1EvJxhq/sm4ik12QcpMQOIcrayOoo0YW63m3UP\nzue9q8ZwcNs2bDYbVy6cT7Q+L0kd1elHWVVVFbNnz2bWrFm1+7Zv346/vz+9evUCYPTo0axfv/43\njYmISPP3zjvv0LdvXyZOnMhFF13E999/T7Du8CEijUQzdOomIyODrVu3Wh3DawWUpFPoF4tNt12T\nX5F96BCvjh5LxGdfUurjA7Twb0pSL3VaFPnRRx9l2LBhJCb+d5X2jIwM4uPjax9HRkbidrspKCio\n95jD4WiIcxIREQs9/fTTrFixguTkZD7//HOefPJJ+vTpY3UsEWkhtDTOyaWnpzNlyhR27dqFYRh8\n++23rF+/no8++oi5c+daHc8rVFdVEeHOJSNcP/vkxD586WVK33iLRMNGeqeOXD73AXz8fK2OJc3Q\nKSvjb7/9lu3btzN27NjGyCMiIs2czWYjOTkZgPPOO4/i4mKLE4lIS6IZOic3Y8YMLrroIr755ht8\nfI78bvf888/n008/tTiZ90jbuxdfw01Q/BlWR5EmKvU/G6kAHJNv4sqF81XmSL2dcobOV199xb59\n++jfvz8AmZmZjB8/nquvvpr09PTa5+Xl5WGz2XA4HDidznqNiYhI81ddXc2+ffswTRM4ctnuLx93\n6KA7foiI57TUoqautm3bxnPPPYfNZsM4+ocVGhqq8r0BZe/bTTQQ2/4sq6NIE7J1wwZ8/APoclFf\nhj72CHYfH4LDw62OJc3cKQudiRMnMnHixNrH/fr145lnnqFDhw68/vrrbNmyhV69erFixQoGDx4M\nQJcuXaioqDjtMRERaf4qKiqYMGHCMft+fmwYBhs3brQiloi0EDa7Gp2TiYqK4uDBg5xxxn9nj+zd\nuxen02lhKu9Slr6fGtPGGfoFhgA1VdWsuutuYvfsJd3XTpeL+hIWFWV1LPESdVpD50RsNhsLFixg\n5syZVFZWkpCQwMKFC3/TmIiINH+bNm2q0/Py8vKIjIz0cBoRaWk0Q+fkrr/+em688UYmTpxITU0N\n69at49lnnz2uiJf6sxemkmeLoqOfn9VRxGL7t27ly5mzScTgQGAAAxfNtzqSeJnTLnR++UG9R48e\nrF279oTPq++YiIi0DOPHj2fVqlVWxxARL6NC5+SuuOIKHA4Hr732Gk6nk9WrV3PrrbcyYMAAq6N5\nBbfbTXhVFnlhutyqpdu6YQOHH3+aKCD/wj8wZtpU3fVMGly9Z+iIiIj8Fj+vqSMi0pBU6Jycy+Vi\nwIABKnA8JC8rixCjgqLYNlZHEYu43W5sNhudLriQN//1Gn+47RbOOPtsq2OJl1JFKCIiljD0vy4R\n8QCbUfetJTr//POZNWsWX3/9tdVRvFLarp0ARLQ50+IkYoXNy17hjRGXk5uWRkBwEONeflFljniU\nCh0RERER8Rq6bfnJvfjiiwQFBXH77bfTr18/HnroIXbv3m11LK9RmLIXgKROnS1OIo2prKiIf90w\nEfsbq/B1uzl8KMXqSNJC6JIrERGxhC65EhFPaKlFTV117tyZzp07c8cdd/Dll1+ybt06rr32WmJi\nYrTGZQMwcw9RQCjtIhxWR5FG8v17G9n7z8dpY7dzMCqS4Y8s0u3IpdGo0BEREUsMHDjQ6ggi4oV0\n2/K6a9euHe3btyc+Pp4DBw5YHccrBJVlUOwfZ3UMaUTbn34WByY1I4cx9rprrY4jLYwKHRERaVDL\nly8/6fi4ceMA+Pvf/94YcUSkhdEMnZMrKipiw4YNrFu3ju+++47zzz+fG264gf79+1sdrdkrKy0l\nwiykPLKH1VHEww5u24bN7kNS52QuenAuPv7+xLZubXUsaYFU6IiISIPavn271RFEpAVToXNyF154\nIeeccw5Dhgzh8ccfJywszOpIXiN19y58DJOQxHZWRxEPcbvdrH/4EXw/+IicAH/GvvEa8WdqAWyx\njgodERFpUPPmzbM6goi0YC317lV19Z///IfY2FirY3il3J/2EAe0OjPZ6ijiAblpaayfMo22FZWk\n2m30vutOqyOJqNARERHP+emnn9i1axdVVVW1+0aMGGFhIhHxdpqhc7yvvvqKc889F4B9+/axb9++\nEz6vT58+jRnL61RmHaDC9KVtG116422+f28jhx55jES7jbQO7bj8wQfx8fO1OpaICh0REfGMpUuX\n8tprr5GTk0PXrl3ZsmUL5557rgodEfEow2Z1gqbn/vvvZ926dQDcc889J3yOYRhs3LixMWN5Hb/i\nNPJ9YrHZ9I/Q2yR1+R0/+PuRMGE8ff802Oo4IrVU6IiIiEe8/vrrrFy5kjFjxvDCCy+wZ88ennzy\nSatjiYiX0wyd4/1c5gBs2rTJwiTey+VyEVGTQ1bkOVZHkQby/cZNbFv8Apc9/ywRrVox5q2VVkcS\nOY7qYxER8Qg/Pz+CgoJwu92YpknHjh11W1wR8Tibzajz1hJNmjTphPtvvvnmRk7iXTIOHMDfqMG/\n1RlWR5HfyFVTwxt3Tifvn48TWVLCT19/Y3UkkV+lGToiIuIRgYGBVFdX06lTJxYuXIjT6cTtdlsd\nS0S8nGbonNwXX3xxwv1ffvllIyfxLlk/7iYCiD6jo9VR5Dc4uG07n903kyQTDgb403/Bg8Sd0dbq\nWCK/SoWOiIh4xMyZM6murmb69Ok8/PDDpKamsmDBgnq9Vv7+Wxo4nTQHiV1ftTqCWCB128zfdLwK\nnRN79NFHAaiurq79+mcpKSnEx8dbEctrlKTtI8w0SDqrk9VR5Df47L6ZxLhc5Pb+PaPvnq71kKTJ\nU6EjIiIe0bHjkd9SBgUFMXfuXIvTiEhL0UKvpDqlzMxMAEzTrP36Z06nk8mTJ1sRy2sY+SnkGw7O\nDAq0Ooqcprz0DGx2O464WM6edjs+/n506NXL6lgidaJCR0REPOKWW27BOMGvyv/3N8MiIg1Jhc6J\nzZs3D4BzzjmHq666yuI03ie0IovC4DZWx5DT9Mm/XiV/+QoKHWGMW76MTuf/wepIIqdFhY6IiHjE\nxRdfXPt1ZWUlGzZsoH379hYmEpGWwGaYVkdoclJTU0lMTASgT58+pKSknPB5SUlJjRnLaxTm5RFu\nlFAS3drqKFJH5SUlrJoylaTMbGoM6DR2jNWRROpFhY6IiHjEyJEjj3l82WWXMX78eIvSiEhLoRk6\nxxs6dCjffvstAAMHDsQwDEzz2OLLMAx27txpRbxmL3XnDgKB8NYdrI4idbDr08/44R/zaWO3cyDC\nwbBHFhEaGWl1LJF6UaEjIiKNwjAMsrKyrI4hIl7ORzN0jvNzmQOwa9cuC5N4p/xDewkEEjolWx1F\n6iAwNBQbJpVDLmXcBP2iSZo3FToiIuIRv1xDxzRNdu/eTZ8+fSxOJSLeTjN0Tk9KSgqGYdRekiWn\nryb7IMVmIO3i4qyOIr/i0A8/8MlTz3DVo4/QpmsXEt56Ax8/X6tjifxmKnRERMQjfrmGjt1uZ/z4\n8XTv3t3CRCLSEugmwyc3ZcoU/vKXv9CjRw/efPNN7r//fmw2G/fccw9XXnml1fGapcDSdIr8VOY0\nVf/3yD+xb/yAWAN2ffIpv+v7R5U54jX0M09ERDzCZrMxcuRIRo4cybBhw+jevTtr1qyxOpaIeDmb\nUfftdN10000MGzaMESNGMHbs2No1Z/bv38+oUaMYNGgQo0aN4sCBA7XH1HfMUz777DO6dOkCwEsv\nvcSSJUtYuXIlixcvrvNrnE7un376ie7duzN//vzfGr1JqqqsJMKdh8uhGU5NTV56BsvH/IWwDz6i\nwGbQ+r57+F3fP1odS6RBqdARERGPeOmll+q0T0SkIRmGWeftdM2fP59///vfrF69muuvv567774b\ngJkzZzJ27Fg2bNjA2LFjmTFjRu0x9R3zlOrqavz8/MjKyqKgoICePXty5plncvjw4Tq/Rl1zu1wu\nZs6cyYABAxoqfpOT+uMefAw3wfHtrI4iv+B2u/nPpL+TVFpGapskRq5cwZm/P9fqWCINTpdciYhI\ng9q2bRvff/89+fn5LF++vHZ/SUkJ1dXVFiYTkZbAk2vohIaG1n5dUlKCYRjk5uayY8cOlixZAsCQ\nIUOYM2cOeXl5mKZZr7FID95xJzk5mWeffZa0tDQuuugiALKysggJCanT8Sc73//N/dxzz3HRRRdR\nVlZGWVlZg55HU5Gzbw8xQFyHTlZHEY7cjtwwbAQEB9Hmmr/g4+vLqGFDrY4l4jF1KnRuuukmUlNT\nsdlsBAUFcd9995GcnMz+/fuZPn06BQUFOBwO5s+fT9u2bQHqPSYiIs1bVlYW27dvp7y8nO3bt9fu\nDw4OZt68eRYmE5GW4HSmnxcVFVFUVHTc/rCwMMLCwk54zD333MMnn3yCaZo8//zzZGRkEBcXh91u\nB46sGRYbG0tGRgamadZrzJOFzty5c3n00Ufx8fFh2rRpwJG7YA0dWrf/9J7sfH+Ze9euXXz88ccs\nXbqUp556quFPpIkoz9hPtWmndXvN0LHaDx9uZteih6lKTGDM009y3uWXWR1JxOPqVOjMnz+/9jcS\n7733HnfffTerVq2qnW45fPhw1qxZw4wZM1i6dClAvcdERKR5GzBgAAMGDODjjz/mggsusDqOiLQw\nPra6X0r18ssv88QTTxy3/+abb2by5MknPGbu3LkArF69mgULFnDrrbfWL6hFWrduzUMPPXTMvsGD\nBzN48OAGe4/q6mruu+8+5s2bV1v8eCufwlTy7NGc5atFdq3iqqlh9YxZRG77gWDTxHG2bsAgLUed\nCh1vmF4qIiKNa8eOHXTp0gWHwwFAfn4+b775JjfccIPFyUTEm53ODJ1rr72WkSNHHrf/12bn/NKI\nESOYMWMGrVq1IisrC5fLhd1ux+VykZ2djdPpxDTNeo152ptvvsmaNWvIysoiLi6O4cOHc/nll9fp\nWKfTecrcOTk5HDp0iIkTJwJHZkKZpklJSQlz5szxyDlZwe1246jO5nB4Z6ujtFgpO3by8T330dpt\nctDPl4vnz8PZob3VsUQaTZ3X0Gnu00tFRKRxvf3227Uf5gEiIiJYt26dCh0R8ajTWUPnZJdW/a/S\n0lKKiopqi4tNmzYRHh5OVFQUycnJrFu3juHDh7Nu3TqSk5NrP9fWd8xTnn766dpFnePj40lPT+f5\n558nOzubSZMmnfL4U50vQHx8PF988UXt48cff5yysjLuvPNOj5yTVQ6npxNkVOIX28bqKC1WfkYG\nUdU15Jzbg9H33YvNpnv+SMtS50KnuU8vFRGRxmWax1/24HK5LEgiIi2JrR53r6qL8vJybr31VsrL\ny7HZbISHh/PMM89gGAazZs1i+vTpPPXUU4SFhR1zi+76jnnKypUrWbZsGQkJCbX7LrjgAv7yl7/U\nqdCBX889YcIEbrnlFrp27eqR7E1N2u6dhAMRbc+0OkqLkp+ZyUfPv8iwe++mW/9+tOnWlfCYGKtj\niVjitO9y1Zynl4qISONp27YtS5Ys4brrrsM0TZYsWULr1q2tjiUiXs5Td7mKjo7m9ddfP+FY+/bt\nWblyZYOOeUp5eflxs4AcDgcVFRV1fo1fy7148eITPv/X1iNq7opT9xEOJHVKtjpKi/H5yjfIeXkZ\nDpudXZ98Sqfz/6AyR1q0U85JKy0tJSMjo/bxiaaXAsdMt6zvmIiIeI977rmH999/n27dunH22Wez\nefNm/vrXv1odS0S8nO00tpbowgsvZOrUqfz0009UVFSwb98+pk+frkXs68HMPUQ+YYSEh1sdxetV\nlJbx6qS/U73sX7iBgOuvodP5f7A6lojlTjlDx1uml4qISOOKi4tj6dKlpKen89Zbb7F27Vruvvtu\n3n33XaujiYgXO527XLVEM2bMYPbs2QwbNgyXy4WPjw9/+tOfuPfee62O1uwEl2dSEtDK6hhez+12\ns3rc1bQ24UB4GEP/+RBhUVFWxxJpEk5Z6HjL9FIREWk8NTU1bNy4kTfffJPvvvuOmpoaXnzxRbp3\n161ERcSzPHXJlTcoLi7m0KFDzJgxgwcffJD8/HwiIiK0kGw9lBaX4DALKYvsZXUUr+V2uwGw2WxE\n9LuYcl8fxk260eJUIk2LvnuLiEiD+sc//kHfvn157bXXGDp0KB9++CHh4eEqc0SkUeiSqxP74IMP\nuPDCC7n88svp27cvX375JVFRUSpz6il1105sBoQm6hbZnpC6axf/n707D4/x3v8//pzJIoKIIBGN\n2kWUqoqtpdQWSyKhlBJtddGjLT1d7BpbVKluimr10IWiamtCcdAqbSmqVVVL7CQSREQikWRmfn/4\ndU7zjSX7neX1cM11Ze71dWfGZOY9n2V53/6snTQZgIARL9JZxRyRLPQKLiIi+Wr58uXUq1ePoUOH\nEhQUhIuLCyaTvjIXkcJhNtmyfStN3n//fV577TX27dvHiBEjeO+994yOVKxdOnkEAO8GGhA5v236\nYC5/vjYGr/QMHJycqE6+wQAAIABJREFUjY4jUqTleJYrERGR29m+fTsRERHMnDmTK1euEBISounK\nRaTQqMvVzZ05c4bQ0FAABg0axPz58w1OVLylxZ4ixeZMrRo+RkcpMRJi41j3ymvUSkom2mSiydhR\nNHygjdGxRIo0tdAREZF85ebmxqBBg1i1ahVz584lMTGR69evM2jQIJYtW2Z0PBEp4dTl6ub+Ho8E\nwNHRUYX2PHK+eo7LTp7qspaP/tyyBZ+rVznjU51eX32pYo5INqiFjoiIFJiGDRsyfvx4Ro0axebN\nm1m1ahUDBgwwOpaIlGBqoXNzqampDBo0yH4/OTk5032AJUuWFHasYikjIwMPy0XOuzc3Okqxl5p8\njV9WreKhwaE8OPAxTt5zD+2b3mt0LJFiQwUdEREpcE5OTnTv3p3u3bsbHUVESjgHTVt+U9OmTct0\nv2/fvgYlKf5ijh/H2ZSBS7XaRkcp1g79+BMH3pyJl8nMUV9f6rdsQS0Vc0RyRAUdERERESkx1AHm\n5nr37m10hBIjNuowHkDVOg2MjlIsWa1W1k6aTMV9+6lgs5HavQv1W7YwOpZIsaSCjoiIiIiUGKVt\n9iopfMnRx6loM1HT19foKMWO1Wpl2aOPUTM9g9NOjjw0fRp3+aowJpJbKuiIiIiISImhMXSkoJku\nnyXe7EF9FxejoxQ7ZrOZMvXrEefkSP9JEzWotEgeqaAjIiIiIiWGCjpS0NyunyehQh2jYxQbVy5c\nIPLl1/B6sA2dh/2LR6ZPu/NOIpItKuiIiIiISInhYHQAKdEux13AzXSNpCo1jY5SLOxatZrYhZ9y\nt9mB88dPGB1HpMRRQUdERERESgyNoXN7aWlpzJ07l8jISBISEti7dy87duzg5MmThIaGGh2vyDt7\n+CDlAPe76xsdpUi7npLC6tdGcdeZcwA4PRFKv0f6GJxKpORRp0URERERKTEczdm/lUZvvPEGR44c\nYdasWZhMN/qn1a9fn6VLlxqcrHi4cvoYAHc1bGhwkqJt+6efUeNsNKcrlOOh/yygtYo5IgVCLXRE\nREREpMRw0Bg6t7V582Y2bdqEq6urfUBaLy8vYmNjDU5WPGRcOEWirRx1qlY1OkqRY7VaOfzTz/i1\nfZCOzw1lt5cXg/r0NjqWSImmgo6IiIiIlBgaFPn2nJycsFgsmZbFx8fj7u5uUKLipey1GBLLeBkd\no8iJPnqU78eMp1paGmUrVKBW03tppWKOSIErpY1NRURERKQkMpts2b6VRt26dWP06NGcOXMGgLi4\nOKZMmULPnj0NTlb0paWm4mGNx1bJx+goRcrm+R+x/+XX8E5LJ6FpE2rc08joSCKlhgo6IiIiIlJi\nmE3Zv5VGL7/8Mj4+PvTq1YvExEQCAgLw9PTkhRdeMDpakXfm8GEcTDbKVdeU5QCWjAyWPP4kZb/d\nRJLJhNeoV+gTPhUHR3UCESks+t8mIiIiIiWGpi2/PWdnZ8aNG8e4ceOIj4+nUqVK9sGR5fYuHD+C\nJ+BV38/oKEWCg6MjJicnTlevRu9Zb+FSztXoSCKljgo6IiIiIlJilNaWN9n1d1ervyUnJ9t/rlGj\nRmHHKVZSz58gzebI3bVrGR3FMNdTUlg9agz1unTGv1cQAxZ8ZB9cW0QKnwo6IiIiIlJiOJlL59g4\n2dWlSxdMJhM22/9+T3+30Pnrr7+MilUsOCaeI96hCo6ltEvR4Z93sn/6DGqYzBzdsBH/XkEq5ogY\nrHS+GomIiIhIiaQWOrd36NChTPcvXLjAnDlz8Pf3NyhR8WC1WqmUHscF9yZGRyl0VquViPBpVNi9\nl4o2uNqlA4+9NNzoWCKCCjoiIiIiUoKooJMzVatWZfz48QQEBBAUFGR0nCIr7sxZyprScPaqaXSU\nQrfx/dlU2fsbpx0daTttKjUaaQwhkaJCBR0RERERKTFU0Mm548ePk5KSYnSMIi36yF+4A5VrNTA6\nSqGJO3UKz5o16fLCC2yyWnn0pRGawUqkiLnj/8jLly8zatQoTp8+jbOzMzVr1mTKlCl4eHjw22+/\nERYWxvXr17nrrrt46623qFy5MkCu14mIiIiI5JaDSWPo3M7AgQMzzWqVkpJCVFSUpi2/g6Szx3Cz\ngU/Dkt865Wp8PN/8+1U84uNpNvtdqtWpQ49XXzE6lojcxB1HsTKZTDzzzDNs3LiRiIgIatSowaxZ\ns7BarYwcOZKwsDA2btyIv78/s2bNAsj1OhERERGRvDDn4FYa9evXj759+9pvQ4cO5ZtvvuG5554z\nOlqRZos/Q4KpIuUqlDc6SoHaExHJlseHcHfCFRLuqk5FT0+jI4nIbdzxb5m7uzutWrWy37/vvvuI\njo7mwIEDlClTxj6A2oABA9iwYQNArteJiIiIiOSF2ZT9W2ljsVjYuXMnPXv2pHfv3vTu3Ztu3bpR\nq1Yto6MVeeVTz5Nc1tvoGAXGkpHB8pde5tqChTjYwGHQAB776EPKli/ZBSyR4i5HnSCtVitLly6l\nY8eOxMTEUL16dfs6Dw8PrFYrCQkJuV7n7u6eD5ckIiIiIqWVpi2/NQcHB3788cdMXa7kzpKuXKES\niSRXbnXnjYspk9mMJTqGM+Vc6f72W3hUL7nFK5GSJEetTadOnYqrqyuhoaEFlUdEREREJNfUQuf2\nnnjiCT744APS09ONjlJsnDn0FwBuNeoZnCR/Wa1Wvgl/g6O/7MZsNtN70ScMWrpYxRyRYiTbLXRm\nzJjBqVOnmD9/PmazGW9vb6Kjo+3r4+PjMZvNuLu753qdiIiIiEhelNZCzZ1ERkYSGBjI4sWLuXjx\nIosWLcLDwyNTa53vv/8+W8c6ceIEY8aMsbewnzFjRpZuW3PnzmX9+vWYzWacnJx4+eWXadeuXT5e\nUeGJP3kUb6B6g4ZGR8k3sSdOsmXkaGqmZ7D75Enqt2yh7lUixVC2CjrvvPMOBw4c4OOPP8bZ2RmA\nxo0bk5qayp49e/D392fZsmV069YtT+tERERERPJCBZ2bCwsLIzAwkLfeeivPx5o4cSIDBw4kODiY\ntWvXEhYWxueff55pm3vvvZennnqKsmXLcujQIUJDQ9mxYwcuLi55Pn9hS487RbKtDLX+MWxEcfbd\nfxaSunot1U1mYho1pP+0qUZHEpFcumNB5+jRo3z00UfUqlWLAQMGAODj48PcuXOZOXMmEydOzDT9\nOIDZbM7VOhERERGRvHBQQeembLYbYwu1bNkyT8e5dOkSBw8eZNGiRQAEBgYydepU4uPj8fDwsG/3\nz9Y4vr6+2Gw2EhISqFatWp7Ob4QySee44uSJ2Vz850b79t33cPt+O/EmEz6vjKDDww8bHUlE8uCO\nBZ369etz+PDhm667//77iYiIyNd1IiIiJdHSJZv5Zs0Ojh45S/eerZj6xrP2dbt+Psgb4Z9zPiae\nxvfWYeq0Z6h+VxUA3p65jO+37uPixSt4ernzzNAggoIf/N++Ow/yzlvLOXM6FvdKFXjqmZ70fbRD\nYV+e3MaKhU/Q7F4fLBYrAOdjE2nfay4PtKjFlLHd8PaqiNVqZdfeU0x441vOx10FYMvqYfhU/1+X\n9DLOjny34yhDhi8DwGw28eoLHegf0ozy5Zw5eTqeR5/+jMSr1wv/IosQs0mDIt+M1Wpl586d9sLO\nzbRp0+aOx4mJicHLywsHBwfgxkDLnp6exMTEZCro/NOaNWu4++67i2UxJyM9HQ/LJWI88lYIM1pq\n8jVcyrny0JAnWXfmLEFTJqmLlUgJkKNZrkRERCR3qnq68+xzQfz04wGuX0+zL798+SqvvPQBE6cM\nof3D9zF39ipGvTqPxcvCAChbtgyz571EzVrVOPDHCZ5/7m1q3O3Jfc3qk56ewSsjPuDfrz5K30c7\n8OeBEzzz5Aya3FsH34Z3G3WpchOvv7Gepav2ZVp25PgFBj23mNgLSTg7OTDyxYd5Y0JPnhpxo2DT\nqfeHmbb/6dsRRG46aL//6gsd8G9ag+DQ/3Au5gq+9apy/XpGwV9MEVdQbSguX77MqFGjOH36NM7O\nztSsWZMpU6bg4eHBb7/9RlhYWKbW55UrVwbI9br8lpaWxvjx429Z0DGZTGzZsiXfz/vLL7/w/vvv\ns3Dhwnw/dmE4FxWFk8lC2Wq1jY6SK+nXr7Nq9Ficoo7R5fNFVPDw4NF3ZhkdS0TySfFvNygiIlIM\ndO7iT8fOzXF3z/yN6Jb/7qVuvbvo2q0lZco4868XenPk8BlOHL8xgcDzw3tTu051zGYz9zaty/3N\nG/D7b1EAJF5JJikphcBeD2AymWjcpA516lbn+LHoLOeXoufipWRiLyTZ71usNmrdffMWDq39a+JR\nyZX1m2/MtlPRzYVnQlszalIE52KuAHA46gLX0ywFH7yIczJn/5YTJpOJZ555ho0bNxIREUGNGjWY\nNWsWVquVkSNHEhYWxsaNG/H392fWrBsfmHO7riCULVuWLVu2sHXr1pveslvM8fb2JjY2FovlxnPN\nYrEQFxeHt3fWmZH27dvHyJEjmTt3LnXq1MnX6ykscceOAFC1rq/BSXIuas8eVvcbgM+JU6SUL09G\nWtqddxKRYkUFHREREQMdizpHA98a9vuurmXwqeHJsahzWbZNTU3jzz9OUK/eXQBUrlKR7j1bs3b1\ndiwWK7//FkV09EWa3V+/0PJL9ox5qRP7fxjJ6s+H0Ma/pn159Wpu/PnjaKL2jOe5J9rw4aIfb7p/\n315NWb/5L1JSbkw13bC+JxkZVnp2bcSv373KDxEv8sSAFoVyLUWd2WTL9i0n3N3dadWqlf3+fffd\nR3R0NAcOHKBMmTL4+/sDMGDAADZs2ACQ63VFWeXKlfHz8yMyMhK4MXuWn59flu5W+/fv5+WXX2b2\n7Nncc889RkTNF9eij5NhM+NTv/i8rlqtViKmTefkpHAqWW1ceehBBi1bQqVi2OVNRG5PXa5ERKRA\n/d+BMiWza9dSqeRRIdOyChXKkpycmmXb8Mmf0aBhDR5o28S+rFuPVkwOW8TM6V8CMD7scap5F0yX\nDcmdN97dzJFjF0hPt9Cre2MWzXmMgL4fcersZaLPJ3LPgzNwd3NhYN/mHDtxMcv+Li6O9OzSiKeG\nL7Uv8/Zyo6KbC3VqVuaBbu9Tu6YHyxY8zvFTl9j+8/HCvLwiJyezXCUmJpKYmJhluZubG25ubrfc\nz2q1snTpUjp27EhMTAzV/zH7kYeHB1arlYSEhFyvc3d3J7/dbuycnJo0aRJjxoxh3rx5uLm5MWPG\nDACeffZZRowYQZMmTZg8eTKpqamEhYXZ95s5cya+vsWrpYs54SzxZg8alCljdJRss2ZYSNn1C8mO\nDrSZOomaTZrceScRKZZU0BERkQLx+++/8+9//xur1cq2bdv4448/+Oqrr5g6VdOj/pOrqwvJSZmL\nN0lJqZQrl3lq33feWkbU0bN88ukYTKYbn1hPHI9m9Gsf8s77w2nzwD2cPhXL8GHvUdXTnYfa31do\n1yC3t++P/7W2+vqb3wnp3piOD9Vn0Ze/2JcnJKayYu1vbFr5L/w7vYPF8r8P3z06+5FwJYWf95yy\nL0tNvTFWzrvzt5F6PYO/jsTxzYY/6diuvgo6OSjofPbZZ8yZMyfL8hdffJHhw4ffcr+pU6fi6upK\naGgo//3vf3MTs9Dt27fvzhtlU926dVmxYkWW5QsWLLD/vHLlynw7n5Hc0mK5XKF4tM7Z9ulnNGzf\nHq/ategw530qV6+Og6M+7omUZPofLiIiBWL69OksWLCA1157DYAmTZowZswYg1MVPXXr3UXE2h32\n+9euXefsmTjq/v9uVQDzPljNju1/sPDzsZQvX9a+POroOWrWrMaD/7/FTq3a3rRrfy87tv+hgk4R\nZsPGzWoOjo5mqlYuT4VyZUhI/F+Rr2+vpqyM+D3Ttn8dib1xrH+0usjPFhjFWU7GE3jiiSfo3bt3\nluW3a50zY8YMTp06xfz58zGbzXh7exMd/b9xq+Lj4zGbzbi7u+d6nRQNl2LOU8GUwlXPmnfe2EDJ\nCQms+fer1LqcwJYdPzLwk4/xvFsD44uUBhpDR0RECkR6ejr16tXLtMzJycmgNMbLyLBw/XoaFosV\ni8XK9etpZGRY6Nj5fqKOnmPzpt1cv57GRx+upX4DH2rXudEV4z8fR/Ltup18/J+RWQZUbuhXk9On\nYtm18yA2m40zp+P44fvfadCgxs0iiAHcKpSh/QN1KePsgIODid49m9Dq/pp892MU3Ts1pE6typhM\n4FHJlbCRAfxxMCZTMcfbqwIPtKjNirWZCzqnzl5m595TjBj6EM5ODtSrXYVe3RqzeduRwr7EIsdk\nyv7Nzc0NHx+fLLdbFXTeeecdDhw4wNy5c3F2dgagcePGpKamsmfPHgCWLVtGt27d8rROioZzhw8B\nUOnuenfY0jj7vt3AptAnqHU5gVOeVQjRDFYipYpa6IiISIFwdnYmOTnZ3j0oKiqKMsVoDIL8tmD+\nN8yft9Z+f13Ez/zr+WCGvdibt997kenTvmDc6I9pcm8dZrw9zL7d7Pe+xsnJkcBuo+3LnhkayDPP\nBVHjbk8mhz/NjDeWEBN9ifIVytIjsA19+j5UqNcmt+bo6MDI4Q9Tr3YVLBYbx05c5Ol/L+fEqXg6\nPFiP11/rShWPciRdS+Pn3Sd55t/LM+3fJ7Ape38/w6mzl7Mc+8VRK5k1uRd/7BjFxfhk3pr7HT/u\nOlFYl1Zk5aDHVY4cPXqUjz76iFq1ajFgwAAAfHx8mDt3LjNnzmTixImZph8HMJvNuVonRcOVM1GU\nB3z8Ghkd5aY2fTCXMv/djLMNrP36MDB0kNGRRKSQmWzFrH3u2bNn6dSpE1u2bMHHxyfH+we9uvbO\nG0mJFPF2sNER+DH4EaMjiAEeXJu7cQTy+npntG3btvHhhx9y5swZ2rVrx/bt23nrrbd44IEHcnys\nVMvPBZBQirp6920yOoIY4OwfE/O0/68X12V72/ur9MzTuaRgFJW/f9+/+zoVk0/TbMJnhmW4GavV\nitls5vzx42yZPJWA8ClUqaGWmSLFUV5f79RCR0RECkT79u2pU6cO27dvx2azMWzYMGrWLNrjEIhI\n8WfK4XTkIrfiei2Gqy5eRsews1qtrJsxk6Tde+m7dDHV6tRh0GeLjI4lIgZSQUdERArEmjVr6Nat\nGwMHDjQ6ioiUIgXV5UpKl9RrKXjYEjhXqanRUQCIO32a/742ilrX07kGXI6N1cDHIqJBkUVEpGBs\n3bqVhx9+mPHjx7N3716j44hIKZGTQZFFbuXM4UOYTTbK31XX6Chs+/Qz9jw/HJ+U60Q3qMcjK5ap\nmCMigFroiIhIAZk9ezYJCQlEREQwbdo0kpOT6dOnD88995zR0USkBFOdRvLDxeNH8AKq1W9oaI7r\nKSlc/noVVkzcNfx52nftYmgeESla1EJHREQKjLu7O4MHD2bhwoW0atWK9957z+hIIlLCmU3Zv4nc\nyvXYE1y3OVKtljFjv+3fvIXkhATKlC3LPWETCFjyGU1VzBGR/0MtdEREpEBYLBZ++OEHVq1axd69\ne+nUqROLFy82OpaIlHCq00h+cEo8R7yjJw4ODoV63oy0dFaPHYfnkSi+8anOYx/OpX7LFoWaQUSK\nDxV0RESkQLRv354GDRoQEhLCW2+9hYuLi9GRRKQUUMsbySuLxUKljAvEFfKAyCd++41fJk7BBxMn\nXV0IGDemUM8vIsWPCjoiIlIgVqxYgbe3t9ExRKSUUT1H8ir29GlcTOmUqVa70M753X8WYln9DZWB\nhIce5LHXXsVs1ugYInJ7KuiIiEi+2rt3L82bN+fIkSMcOXIky/r27dsbkEpESgu10JG8ijnyF5WA\nyrXrF9o5qzdqxK/rvqV12Hhq33dfoZ1XRIo3FXRERCRfrV69mubNm/PJJ59kWWcymVTQEZECpXqO\n5FXSueNUtJnw8S3YGa5++PwLznz3PY/9ZwG+bVrju2pFgZ5PREoeFXRERCRfhYeHA/DFF18YnERE\nSiOTyWZ0BCnmTPFnuGyqSL1y5Qrk+MlXrrD2ldeoeTEeV0sG8efOUaVGjQI5l4iUbOqYKSIiBeKx\nxx7L1jIRkfxkysFN5GbKp57nmmvBjAH3+6b/snHQ49S8GM+pyh50XfK5ijkikmtqoSMiIgUiNTU1\n032LxcKVK1cMSiMipYVJlRrJg8T4eNxNSSRVvjvfj52SlMTZ9+fgYoKM3r0Y+OQT+X4OESldVNAR\nEZF89cknn/DJJ5+QlJREmzZt7MtTU1MJCgoyMJmIlAYOKuhIHpw5fIiyQMW76+XfMQ/+hXe9epQt\nXx7PoU9Ts9l9eN6d/wUjESl9VNAREZF81b9/f7p168bUqVMJCwuzLy9fvjwVK1Y0MJmIlAaq50he\nJJw8SlngLl+/PB/LarXy7dvv4LxtB7sa+dJ35gxaBPfKe0gRkf/vjmPozJgxg44dO+Lr65tp+tkT\nJ07Qv39/AgIC6N+/PydPnszzOhERKf4qVKiAj48PH330EXfddZf9pmKOiBQGkyn7N5H/K/3CKZJs\nZfHw8srTcS6dO8fSx0Jx3/Ez8Q5mmvXvn08JRUT+544FnU6dOrFkyRLuuuuuTMsnTpzIwIED2bhx\nIwMHDsz0LWxu14mISMkRExPDK6+8Qo8ePejUqZP9JiJSkDQosuSFS1I0V5w9MZtzP3fMzhVfs3Po\n8/ikpHCubi0eWbGcus3vz8eUIiI33PGVyt/fH2/vzKO8X7p0iYMHDxIYGAhAYGAgBw8eJD4+Ptfr\nRESkZBk3bhxt2rTBZrMxa9YsmjdvTu/evY2OJSIlnNmU/ZvIP6WnpeFhvYTV3SdPxzE7OXLdZMJt\n2HM8+s7bODo75VNCEZHMclV6jomJwcvLCwcHBwAcHBzw9PQkJiYm1+tERKRkuXz5Mv369cPR0ZFm\nzZrx5ptvsm3bNqNjiUgJpxY6kltnjx7F0WSlrHftHO+7f8tWVo4ZB0DLkBB6rVpBs+7d8juiiEgm\nGhRZREQKhJPTjW8kXV1diY6OpkqVKmqRKSIFzmyyGR1BiqkLx49QBfCs1zDb+1gyMlg9bgJV/jpM\nWZuV+OgYPKp7q1WOiBSKXBV0vL29iY2NxWKx4ODggMViIS4uDm9vb2w2W67WiYhIyeLv709CQgKP\nPfYYffr0wdnZmW7d9G2liBQsDXYsuXUt+jjpNjN162VvyvJTfxzg59cnUsMGp1zK0HnWDDyq63ON\niBSeXBV0KleujJ+fH5GRkQQHBxMZGYmfnx8eHh4AuV4nIiIlx+jRowEICQmhZcuWJCUl0aBBA4NT\niUhJp3qO5JbDlbPEO1TB1+nOrWuuJSZyYOx4qmAivk0rBowdnaeBlEVEcuOOBZ3w8HA2bdrExYsX\nGTJkCO7u7qxbt45JkyYxZswY5s2bh5ubGzNmzLDvk9t1IiJSckRFRWW6bzabiY2NxSuPU8GKiNyO\nPlJLblitVtzT4rjk5nvb7a7Gx1PO3R1XNzece3SjVssW1PP3L6SUIiKZ3bGgM2HCBCZMmJBled26\ndVmxYsVN98ntOhERKTmGDh1KTEwMFSpUAODq1atUrlwZZ2dn3nnnHe677z6DE4pISaQuV5Ibl2LO\nU86USqJnrVtu8+OXS7m8ZBnmhx+ix2uv0uX5YYUXUETkJjQosoiIFIhOnTrRqlUrOnfuDMDmzZvZ\nuXMnXbp0Ydq0aSrui0gBUUVHci76yF9UACrVzDp+TkpSEqtfeY0a5+PIMEENP7/CDygichNqlSoi\nIgXil19+sRdzADp37szu3btp1aoVqampBiYTkZLMlIN/In9LPHMMgBp+92RafuD7bawfEErN2Auc\n9nCn0+eLaN6zhxERRUSyUEFHREQKhNVq5ddff7Xf37dvH1arFUADR4pIgTGZHLJ9E/mb9eIpEqhA\nBfeKmZZHH/gTV2xcD+zBoE8XUkGTuYhIEaIuVyIiUiAmTpzIyy+/jIuLCwCpqam8/fbbJCcn8+ST\nTxobTkRKLLW8kdxwTTnPVZdqAJz+80/+2vIdASNepOuLz3Olfz8qVq1qcEKR/0lPT+fs2bNq8VyM\nuLi44OPjg1M2ZtHLCRV0RESkQPj7+/Pf//6XEydOAFC7dm2cnZ0B6N27t5HRRKREU0GnIJ04cYIx\nY8aQkJCAu7s7M2bMoFatWpm2sVgshIeHs337dkwmE0OHDqVfv37GBM6Ga1eTqGRLIKXS/Xz77ns4\nbPkegIT+j+Lu5alijhQ5Z8+epUKFCtSqVQuTRoIv8mw2G5cuXeLs2bPUrl07X4+tNu8iIlJg9u7d\ny+7du/H19eXq1av24o6ISEExmczZvknOTZw4kYEDB7Jx40YGDhxIWFhYlm0iIiI4ffo0mzZtYvny\n5XzwwQecPXvWgLTZc/bIIZJSMjj2zVbcvt9OgtlEnbDxuHt5Gh1N5KZSU1OpXLmyijnFhMlkonLl\nygXSokp/yUREpEB8/PHHzJkzh88//xy40Tx43LhxBqcSkZLPlIOb5MSlS5c4ePAggYGBAAQGBnLw\n4EHi4+Mzbbd+/Xr69euH2WzGw8ODzp07s2HDBiMiZ8u5P3/n1K8W6lptnK1Zg94rllG/ZQujY4nc\nloo5xUtBPV4q6IiISIGIjIzk008/xdXVFYBq1aqRlJRkcCoRKek0y1XBiYmJwcvLCweHGwNKOzg4\n4OnpSUxMTJbtqlevbr/v7e3N+fPnCzVrTnjcXZvY8o6UffpJ+s9+D6cyZYyOJCKSLRpDR0RECoSL\ni0uWgd/0bZKIFDQVaiSnmnboSNMOHY2OIVKsXblyhXbt2vHoo48yYcIE+/IPPviAa9euMXr0aPuy\nxYsXc+DAAd58803gxthcs2bN4tChQ1SsWBFnZ2eeeeYZOnfunC/Z5s6dy+rVq4Eb4zi+8MILN93u\n66+/5rPPPsNsNuPg4MC4cePw9/cHICEhgSlTpvDnn3/i6OhI9+7defHFFwHw9fWlQYMG9llcZ86c\nia+vb75kvxNW6SJ6AAAgAElEQVQVdEREpEBUq1aNPXv2YDKZsFqtzJ8/n/r16xsdS0RKOE1HXnC8\nvb2JjY3FYrHg4OCAxWIhLi4Ob2/vLNtFR0dz7733Allb7IhIyRMZGUnTpk1Zt24do0aNsk+EcSdx\ncXGEhoYycuRI5s6dC8CFCxf48ccf8yXX7t272bBhA5GRkQD069ePli1b0qJF5m6Vly9f5o033mDT\npk1UqVKFLVu2EBYWxvr16wEYM2YMrVu35p133rFn/Kdly5ZRrly5fMmcE+pyJSIiBeL1119n3rx5\nHD16lKZNm7J7926NoSMihUBj6BSUypUr4+fnZ/9gFBkZiZ+fHx4eHpm269atGytWrMBqtRIfH8/m\nzZsJCAgwIrKIFJKVK1fy/PPP4+vry5YtW7K935IlS2jVqhUhISH2ZVWrVs10Py/Wr19PSEgILi4u\nuLi4EBISYi/S/JPNZsNms5GcnAzA1atXqVatGgAnT57kyJEjPPHEE5kyFgVqoSMiIgWiatWqLFy4\nkJSUFKxWqyHfWohI6VNQXa5mzJjBxo0bOXfuHBERETRo0AC4/TTeuV1XlE2aNIkxY8Ywb9483Nzc\nmDFjBgDPPvssI0aMoEmTJgQHB/P777/TtWtXAF544QVq1KhhZGyREq1v375ZlgUGBvLkk0+SkpLC\n4MGDs6zv168f/fv3Jz4+nqFDh2ZZP3jwYIKDg7N1/kOHDpGQkEDr1q25cOECK1eupHv37tna9+DB\ngzz44IPZ2jYxMfGm1wJQr1493n777SzLY2JiaNmypf2+t7c3u3fvzrKdh4cHU6ZMoXfv3ri5uWG1\nWvniiy8AiIqKwsvLi/Hjx/PXX39RpUoVRo0alanl+eDBg7FYLDz00EMMHz482y2U8koFHRERyVdR\nUVG3XV+vXr1CSiIipVPBNEDv1KkTjz/+OIMGDcq0/O9pvIODg1m7di1hYWH22f1yu64oq1u3LitW\nrMiyfMGCBfafHRwcmDx5cmHGEhEDff311wQHB2MymejatSvh4eHExsbi5eV1y/ETczOuopubG2vX\nrs1r3JtKSkpiyZIlfP3119SpU4f169fz4osv8s0332C1Wvn999959dVX8ff3Z9OmTQwbNozNmzcD\n8P333+Pt7U1SUpK969jLL79cIDn/LxV0REQkX93sWx6TyURycjJXrlzhr7/+MiCViJQWBdVC5++B\nMf/p72m8Fy1aBNz4Rnzq1KnEx8djs9lyte7/dl8SEbmTr7/++pbrypYte9v1Hh4et11/J2lpaURG\nRuLs7GwvtqSnp7Nq1SqGDRtGpUqVOHfuXKZ9Ll++bH+ta9SoEX/88Ue2zpWbFjp/j+n1t5iYmCzj\nfgHs2LGDChUqUKdOHQB69OjB2LFjuXz5Mt7e3nh7e9v/DnTt2pWRI0faX7P/Pl758uXp16+f/bW9\nMKigIyIi+Wrr1q2Z7l+7do1Fixbx5Zdf8uSTTxoTSkRKjZx865uYmEhiYmKW5W5ubri5ud1x/9tN\n422z2XK1TgUdESlOtmzZQu3atVm6dKl92b59+xg9ejTDhg2jdevWfPjhh5w/f55q1aqRkJDA+vXr\nCQsLA2DgwIH07t2biIgIgoKCgBvF8u3bt2cZRyc3LXS6detGeHi4vXXlmjVreP3117Ns5+Pjw8GD\nB7l06RKVK1dm586dlC9fnkqVKlGpUiVcXV05evQo9evXZ/fu3VSsWJFKlSpx5coVypQpg4uLCxkZ\nGWzcuBE/P78cZcwLFXRERKRAZGRksHTpUhYsWED79u1ZtWoVXl5eRscSkRIv+wWdzz77jDlz5mRZ\n/uKLLzJ8+PD8DCUiUiKtXLnSXoj5W7NmzbBarfzyyy+0bNmScePG8fzzz2OxWLDZbISGhtKmTRsA\nvLy8+OKLL5g1axbvvfcerq6uuLq68uyzz+ZLvlatWtG1a1d69uwJQEhIiH1MnS1btrB161amTZtG\n48aNeeaZZwgNDcXJyQlnZ2fef/99+5cEb7zxBmPHjiUtLY2yZcsyZ84cTCYTx48fJywsDJPJREZG\nBs2aNeOll17Kl+zZoYKOiIjkuzVr1jBnzhwaN27MZ599Ru3atY2OJCKlhInsT1v+xBNP0Lt37yzL\ns9M6B24/jbfNZsvVOhGR4uSTTz656fK/x5eBG92XevTocctj1K1blw8//DDfs/1t+PDhNy3Sd+rU\niU6dOtnvDxkyhCFDhtz0GE2aNLlp17RmzZoRERGRf2FzSAUdERHJV0FBQVy7do3hw4fTuHFjLBZL\npoGSNSiyiBSknHS5ym7Xqlv55zTewcHBWabxzu06ERGR7FBBR0RE8lVycjIAs2fPxmQyYbPZ7OtM\nJhNbtmwxKpqIlAoFMyhyeHg4mzZt4uLFiwwZMgR3d3fWrVt3y2m84dZTfN9pnYiISHaooCMiIvnq\n/w6KfCuazUVECoKpgKYtnzBhAhMmTMiy/FbTeOdlnYiISHYUzF88ERGRO3j66aeNjiAiJZIpBzcR\nkeLpny2gpegrqMdLBR0RETGE3oiISEEw5eCfiEhx5OLiwqVLl/Reqpiw2WxcunQJFxeXfD+2ulyJ\niIghcjJwqYhIdum1RURKOh8fH86ePcuFCxeMjiLZ5OLigo+PT74fVwUdERERESkxcjJtuYhIceTk\n5ETt2rWNjiFFgGFdrk6cOEH//v0JCAigf//+nDx50qgoIiJiADUTFpGCoTF0RESkdDCsoDNx4kQG\nDhzIxo0bGThwIGFhYUZFERERA3Tp0sXoCCJSAplMpmzfREREijNDulxdunSJgwcPsmjRIgACAwOZ\nOnVqtqawtVgsAJw/fz5X506/Fp+r/aT4O3v2rNERuJiWZnQEMUBun3t/v879/bpXXCxZsuS26wcN\nGgTACy+8UBhxRKTU0ZwfxV1e3++LiBQXeX2/b0hBJyYmBi8vLxwcbvRxdnBwwNPTk5iYmDsWdP4e\n+OnvDwQi2dVp65tGR5DSqlOnPO1+4cIFatasmU9hCt6BAwfy/ZguDm3y/ZhS9J39Q4+75JwJX6Mj\nSB7p/b6IlDa5fb9f7AZFbty4MUuWLKFq1ar2gpCISElksVi4cOECjRs3NjpKjkyfPt3oCCIiUozp\n/b6IlBZ5fb9vSEHH29ub2NhYLBYLDg4OWCwW4uLi8Pb2vuO+Li4u+Pv7F0JKERHjFaeWOTdz/Phx\nDh06RNo/uhuGhIQYmEhERIo6vd8XkdIkL+/3DSnoVK5cGT8/PyIjIwkODiYyMhI/P787drcSEZHi\n4/PPP2f58uVcuHCBJk2asGfPHlq0aKGCjoiIiIhIPjBs1LhJkyaxePFiAgICWLx4MZMnTzYqioiI\nFICvvvqKFStW4O3tzX/+8x9WrFhBuXLljI4lIiIiIlIiGDaGTt26dVmxYoVRpxcRkQLm7OyMq6sr\nVqsVm81GgwYNOHnypNGxRERERERKhGI3KLKIiBQPZcuWJT09nYYNG/LWW2/h7e2N1Wo1OpaIiIiI\nSIlgstlsNqNDiIhIyXPkyBF8fHxISUnhnXfe4erVqwwbNgw/Pz+jo4mIiIiIFHsq6IiIiIiIiIiI\nFDPqciUiIgVixIgRmEymLMvff/99A9KIiIiIiJQshs1yJQXjxIkT9O/fn4CAAPr373/TAUgtFguT\nJ0+mc+fOdOnSRYNTS76YMWMGHTt2xNfXlyNHjtx0Gz33SpeHH36YDh060KFDB9q0aUNiYiJVq1Y1\nOpaIiJRSpeV9cnauc+7cufTs2ZOgoCD69OnD9u3bCz9oHmTnGv92/PhxmjZtyowZMwovYD7J7nWu\nX7+eoKAgAgMDCQoK4uLFi4UbNA+yc42XLl1i6NChBAUF0b17dyZNmkRGRkbhh82lAv2cZJMSZfDg\nwbY1a9bYbDabbc2aNbbBgwdn2Wb16tW2p556ymaxWGyXLl2ytWvXznbmzJnCjiolzO7du23R0dG2\nhx9+2Hb48OGbbqPnXul2/fp1W2hoqGHnT0tLs82ePdvWtWtXW48ePWxBQUG24cOH244ePWpYpltZ\nuXKlbfjw4bfdJjQ01NaxY0dbr169bAEBAbZPP/00V+c6f/58psdl9uzZtuvXr9vvv/fee7Z169bl\n6thGKImPc+vWrW1JSUmZlm3durXAchX354DIrZSW98nZuc4ffvjBdu3aNZvNZrP99ddftubNm9tS\nUlIKNWdeZOcabTabLSMjwxYaGmp75ZVXbG+++WZhRswX2bnO/fv327p3726Li4uz2Ww2W2Jioi01\nNbVQc+ZFdq4xPDzc/vilpaXZ+vbtW6z+LhXk5yS10ClBLl26xMGDBwkMDAQgMDCQgwcPEh8fn2m7\n9evX069fP8xmMx4eHnTu3JkNGzYYEVlKEH9/f7y9vW+7jZ57pZvJZCI2Ntaw848dO5bDhw+zYsUK\n1q1bx9q1a+nTpw8nTpwwLFNeTZgwgbVr17Jo0SJmz57NoUOHcnwMLy8vvvjiC/v9OXPmkJ6ebr//\n0ksv0aNHj3zJWxhK4uNctmxZFi1aVGjnK+7PAZGbKS3vk7N7ne3ataNs2bIA+Pr6YrPZSEhIKPS8\nuZHdawT4+OOP6dChA7Vq1SrklHmX3ev89NNPeeqpp+ytoCtUqECZMmUKPW9uZPcaTSYTycnJWK1W\n0tLSSE9Px8vLy4jIuVKQn5M0hk4JEhMTg5eXFw4ODgA4ODjg6elJTEwMHh4embarXr26/b63tzfn\nz58v9LxS+ui5V7r8cwwdm83G4cOHadOmjSFZTp48yebNm9m2bRtubm7AjTcHHTp0AGDMmDE0btyY\n0NDQLPeTkpKYPn06hw8f5vr167Rq1YqxY8fi4ODAnDlziIyMpEyZMphMJj7//HOcnJwYPXo0UVFR\nODo6Urt2bfu4QatXr+bLL7/EYrFQvnx5Jk2aRJ06dUhLSyM8PJydO3dSqVKlHM8E5u3tTe3atTlx\n4gRVqlRh4sSJnD59GoCnn36akJAQrFYrU6ZMYefOnTg7O+Pq6sqyZcs4e/YsjzzyCLt27WLy5MkA\nDBgwALPZzBdffMEbb7xB48aNeeSRR+jQoQPffvut/W/KjBkzKFeuHC+++CK///47s2bNIjk5Gbjx\n+P/9+y0sJfVxHjp0KLNnz2bgwIGZ/p4Dt80dFRXF2LFjSUlJoWHDhpw+fZphw4bx8MMPs3DhQtat\nW4fFYqFMmTJMmjQJPz+/Yv8cELmV0vI+ObvX+U9r1qzh7rvvplq1aoUZNdeye42HDh1ix44dfP75\n58ybN8+ouLmW3es8duwYPj4+DBo0iGvXrtGlSxeGDRt203EMi5rsXuPzzz/P8OHDadu2LSkpKQwa\nNIjmzZsbFbtA5Pa1RwUdEREpEA8//LD9ZwcHB55++mmaNm1qSJaDBw9Ss2ZNKlasmON9p0+fTosW\nLZg2bRpWq5XXXnuNlStX0rVrVz799FN27NiBi4sLSUlJuLi48N1335GcnMz69esBuHLlCgB79uzh\n22+/ZcmSJTg7O7Nt2zbGjRvHsmXLWL58OWfPnmXdunVkZGQwaNAgfHx8sp0xKiqK48eP4+vrS3h4\nOPXr12fu3LnExcXRp08fGjVqREZGBrt27WL9+vWYzWZ7rn+aOHEiX375JcuWLaNcuXKZ1pUtW5bO\nnTsTGRnJ448/TkZGBhERESxbtozExEQmTpzIxx9/jKenJ3FxcfTt25fIyEh7YaUwlNTH2cvLi+Dg\nYObPn8+4ceOylfvRRx9l1KhRPPHEEwQHB/PHH3/w6KOP2vcLCQnhqaeeAuCnn35i4sSJfPXVV8X+\nOSAiOfPLL7/w/vvvs3DhQqOj5Kv09HRef/11pk+fbi8WlFQWi4XDhw+zaNEi0tLSeOaZZ6hevToh\nISFGR8s3GzZswNfXl88++4zk5GSeffZZNmzYQLdu3YyOZjgVdEoQb29vYmNjsVgsODg4YLFYiIuL\ny9K8y9vbm+joaO69914gazVQpKDouVe6mM1mgoODMy1bu3ZtlmVGiIqK4tVXXyU1NZV27drddtut\nW7eyf/9+e5eX1NRUvLy8qFChAnfffTejRo2ibdu2dOjQgfLly9OwYUOOHTvG5MmTadmypb2Fwtat\nWzl06BD9+vUDbrRaSkxMBGDXrl2EhITg5OSEk5MTvXr14tdff73jdYSHh/Pee+9RpkwZpkyZQp06\ndfj5558ZM2YMAJ6enrRv395+/IyMDMaPH0+rVq0yFdyyq3fv3kybNo3HH3+cH374gTp16uDj48O2\nbds4e/Yszz77rH1bk8nEqVOnaNKkSY7Pk19KyuMMN1rp9OzZkyFDhmQrd1JSEkeOHCEoKAiAJk2a\n4Ovra9/vwIEDfPTRR1y5cgWTyXTbAUX/qbg9B0T+VlreJ2f3OgH27dvHyJEjmTdvHnXq1DEgbe5k\n5xovXLjA6dOnGTp0KACJiYnYbDaSkpKYOnWqUdFzJLuPZfXq1enWrRvOzs44OzvTqVMn9u/fXywK\nOtm9xsWLF/PGG29gNpupUKECHTt2ZNeuXSWqoJPb1x4VdEqQypUr4+fnR2RkJMHBwURGRuLn55el\neWW3bt1YsWIFXbt2JSEhgc2bN7NkyRKDUktpoude6fLpp59mKd7cbFlhaNSoEadOnSIxMRE3Nzfq\n1avH2rVrWbx4MQcOHMDBwQGr1Wrf/vr16/afbTYb8+bNo0aNGlmO+9VXX/Hrr7+yc+dO+vTpwyef\nfELDhg2JjIxk586d/PDDD7z77rtERERgs9l45JFHeOmll/LtuiZMmJDtwkyFChVYt24du3bt4qef\nfmLWrFmsXr06R+fz9/cnOTmZw4cPs3r1avr06QPc+B35+voa/v+5pD7OAJUqVSI0NJTZs2dnWn6r\n3ElJSQA3bXKflpbGSy+9xOLFi7nnnnuIjY3loYceylaOov4cELmV0vI+ObvXuX//fl5++WVmz57N\nPffcY1Da3MnONVavXp1du3bZ73/wwQdcu3aN0aNHGxE5V7L7WAYGBrJt2zaCg4PJyMhg586dBAQE\nGJQ6Z7J7jT4+Pvzwww/ce++9pKWl8fPPP9OlSxeDUheM3L72aFDkEmbSpEksXryYgIAAFi9ebO8L\n/+yzz/LHH38AEBwcjI+PD127duXRRx/lhRdeuOkbWJGcCA8P56GHHuL8+fMMGTKEnj17AnrulUZ/\n/PEHS5Ys4fLlyyxZssR+++ijjzINtFqYatWqRadOnZgwYQJXr161L7927RoANWvWtD9P4+LiMr0J\n7NixIx9//DEWiwWA+Ph4zpw5Q1JSEvHx8bRs2ZIRI0bQoEEDjh49yvnz53FwcKBz586MHTuW+Ph4\nEhIS6NixI2vXrrX3h7ZYLBw4cACA1q1bs3btWjIyMkhNTSUyMjLX19qmTRu++uor4MY3lNu2baN1\n69bEx8eTkpJCu3bteO2116hQoQJnzpzJsn+5cuXsxYCbCQkJYdGiRezevdv+hrFZs2acOnWKnTt3\n2rfbv38/Npst19eRGyX9cX7yySfZsWNHpsftVrnLly9P/fr17ef4888/7VOlpqWlkZGRYf8G9Msv\nv8x0nuL8HBC5ndLyPjk71zl58mRSU1MJCwsjODiY4OBgDh8+bGTsHMnONZYE2bnOnj17UrlyZXr0\n6EFISAj16tWjb9++RsbOkexc47hx49i7dy9BQUGEhIRQq1atTN2Ii7qC/JxksukvrYiI5KPNmzez\nZcsWtm7dSseOHe3Ly5UrR3BwsGHdL9LS0pg3bx7ffvstjo6OuLm54enpydChQ6levTojRozg0qVL\n1KpVC6vVStu2be2D5b711lvs3bsXk8mEk5MT48aNw8fHh+HDh5OamorNZqNRo0ZMnTqVnTt38vbb\nbwNgtVrp1auXvcn3N998w6JFi7BYLKSnp9OtWzdeeukl0tLSmDp1Krt27aJSpUo0atSIS5cuZWmN\n8U+DBw/mqaeeytJC5+LFi4SFhdk/9P89KPKff/7J66+/TkZGBhaLhbZt2zJ69Giio6PtgyLDjRmO\nIiIicHFxyTQg7t8DCUdHR9OpUyf69OnDtGnT7Ofdv38/b731FleuXCE9PZ0aNWowf/58zObC/e6o\npD/OixcvZurUqcyfP5+HH374lrn9/f05cuQI48aN4/r16zRo0IBjx44xYcIE/P39WbBgAUuXLsXd\n3Z2AgADeeecd+4e54v4cEBERKS1U0BERkQKxY8cO2rZta3QMkVIrOTkZV1dXTCYTUVFRDB48mA0b\nNuRq0GgREREpejSGjoiIFIiDBw/SuHFj3N3dAbh8+TIrV67kmWeeMTiZSOmwb98+Zs6cae/2NHXq\nVBVzREREShC10BERkQIRHBzM2rVrMy0LCQlhzZo1BiUqflasWMHixYuzLH/zzTfx8/MzIJEUBD3O\nIiIikhsq6IiISIHo1asX33zzTaZlQUFBREREGJRIRERERKTk0Ch1IiJSIGrVqsWiRYuw2WxYrVb+\n85//cPfddxsdS0RERESkRFBBR0RECsT48eP57rvvuPfee7nvvvv44YcfGDJkiNGxREREpJANHjyY\nFStWGB3jtr755hueeuqpW67fs2cPAQEBhZhI5M7U5UpERApUdHQ0q1atIiIiApvNxqZNm4yOJCIi\nIrnUsWNHLl68iIODg33Zhg0b8PLyuuU+gwcPplevXvTr1y/fcgwePJjffvsNR0dHnJ2dadGiBWFh\nYXh6eubL8X19fdm0aRM1a9bMl+PdygcffMD8+fNxdnbGwcGBevXqMXr0aJo1a1akckrRpBY6IiKS\n7zIyMti4cSNDhw6ld+/eLFq0iJkzZ6qYIyIiUgLMnz+fffv22W+3K+YUpLCwMPbt28fGjRtJTExk\n+vTphuTIq+7du7Nv3z527txJq1ateOmll4yOJMWECjoiIpKv3njjDdq3b8/y5csJCgpi27ZtVKxY\nkaZNmxodTURERArAlStXeO6552jdujUtWrTgueee4/z58zfd9tSpU4SGhtK8eXNatWrFv//9b/u6\nY8eOMWTIEFq2bElAQADr16/P1vnd3d0JCAjg6NGjAPz666888sgjNG/enEceeYRff/3Vvu2qVavo\n1KkTzZo1o2PHjvYJHFatWsVjjz0GwKBBg4AbM3Y2a9aM9evXs2vXLh566CEAPv74Y0aMGJEpQ3h4\nOOHh4QBcvXqVcePG0bZtW9q1a8e7776LxWK543U4OjoSFBREbGws8fHxAOzfv5/+/fvj7+9P27Zt\nmTJlCmlpabfMCfDdd98RHByMv78/AwYM4NChQ9n6PUrxo4KOiIjkq+XLl1OvXj2GDh1KUFAQLi4u\nmEwmo2OJiIhIAbFarfTp04fvvvuO7777jjJlyjBlypSbbvv+++/z4IMPsnv3bn744QdCQ0MBuHbt\nGk899RSBgYH89NNPvPvuu0yePJmoqKg7nj8+Pp6NGzfi5+dHQkICzz33HIMHD2bXrl0MGTKE5557\njsuXL3Pt2jXCw8NZsGAB+/btY9myZfj5+WU53pIlSwBYu3Yt+/bto0ePHpnW9+zZk23btpGUlASA\nxWJhw4YNBAYGAjBmzBgcHR3ZtGkTa9as4ccff8zWGEJpaWmsWbMGd3d33NzcADCbzYwdO5adO3ey\nbNkyfv75Z7788stb5jx48CDjxo1jypQp7Nq1i/79+/P888/bi0BSsqigIyIi+Wr79u107dqVmTNn\n0qlTJz744INsfSslIiIixcMLL7yAv78//v7+PP/881SqVImAgADKli1L+fLlGTZsGLt3777pvo6O\njkRHRxMXF0eZMmXw9/cH4Pvvv+euu+7ikUcewdHRkUaNGhEQEMCGDRtumSM8PBx/f3+Cg4OpWrUq\nY8eO5fvvv6dmzZqEhITg6OhIYGAgderU4bvvvgNuFEiOHj1Kamoqnp6e1K9fP8fXf9ddd9GoUSM2\nb94MwM6dO3FxceG+++7j4sWLbNu2jXHjxuHq6krlypV58sknWbdu3S2Pt2HDBvz9/WnatCkrVqxg\n9uzZODo6AtC4cWPuu+8+HB0d8fHxoX///rf83cKNL9b69+9P06ZNcXBwoHfv3jg5OfHbb7/l+Dql\n6HM0OoCIiJQsbm5uDBo0iEGDBnHo0CFWrlzJ9evXGTRoEEFBQQwYMMDoiCIiIpIHc+fO5YEHHrDf\nT0lJYfr06Wzfvp0rV64AkJycjMViyTR4MsDIkSN5//336du3LxUrVmTIkCH07duXc+fOsX//fnuB\nB260fOnVq9ctc0yYMCHLQMtxcXFUr14907Lq1asTGxuLq6sr7777LgsXLmT8+PHcf//9jB49mrp1\n6+b4dxAYGEhkZCQhISFERkbaW+dER0eTkZFB27Zt7dtarVa8vb1veaxu3boxa9Ys4uPjGTFiBH/+\n+SetWrUC4MSJE7z55pscOHCAlJQULBYL99xzzy2PFR0dzZo1a1i8eLF9WXp6OnFxcTm+Rin6VNAR\nEZEC07BhQ8aPH8+oUaPYvHkzq1atUkFHRESkhFm4cCEnTpzgq6++omrVqvz111+EhIRwswmVq1at\nah9rZs+ePQwZMoQWLVrg7e1NixYtWLRoUZ6yeHp6Eh0dnWlZTEwM7dq1A6Bdu3a0a9eO1NRU3nvv\nPV5//XV7F6ac6N69OzNmzOD8+fP897//Zfny5QBUq1YNZ2dndu7caW9lk10eHh5MmTKFRx55hMDA\nQDw9PZk0aRKNGjXi7bffpnz58nz66ads3Ljxlsfw9vbmX//6F8OGDcvxNUnxoy5XIiJS4JycnOje\nvTsLFiwwOoqIiIjks+TkZMqUKYObmxsJCQnMmTPnltt+++239gGTK1asiMlkwmw206FDB06ePMma\nNWtIT08nPT2d/fv3c+zYsRxlad++PSdPniQiIoKMjAzWr19PVFQUHTp04OLFi2zevJlr167h7OyM\nq6srZvPNPxJXqVKFM2fO3PI8Hh4etGzZkrFjx+Lj42Nv5ePp6cmDDz7Im2++SVJSElarldOnT/PL\nL79kK9Te6KsAAAGYSURBVH+dOnVo164dn3zyCXDjd1uuXDnKlSvHsWPHWLp06W1z9uvXj2XLlvH7\n779js9m4du0a33//vX28HylZVNARERERERGR/9fe/aIsFkYBHD7TLdpdgkHxX/BWg12MBpMgVgUR\nRJu4AIOi2WASN2F0GeICRHAmOWHGgW8mzYXnWcB771t/nHvuP+t2u/F4PKJer0en0/k5DfPJ9XqN\ndrsdxWIx+v1+TCaTyOfzkclkYrvdxvl8jiRJotFoxGq1+utlvtlsNtbrdex2u6jVarHZbGK9Xkcu\nl4vX6xX7/T6SJIlqtRqXyyVms9nHcwaDQYzH4yiXy3/829Z7gfP7c6u35XIZz+czWq1WVCqVGA6H\ncbvdvnyHXq8Xh8Mh7vd7jEajOJ1OUSqVYjqd/rag+df3LBQKsVgsYj6fR6VSiWazGcfj8cvPJl2+\nff80BwcAAADAf8uEDgAAAEDKCDoAAAAAKSPoAAAAAKSMoAMAAACQMoIOAAAAQMoIOgAAAAApI+gA\nAAAApIygAwAAAJAygg4AAABAyvwAxZ/FBwutlSwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.85      0.89      0.87      6511\n",
      "         1.0       0.88      0.84      0.86      6404\n",
      "\n",
      "    accuracy                           0.87     12915\n",
      "   macro avg       0.87      0.87      0.87     12915\n",
      "weighted avg       0.87      0.87      0.87     12915\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model_performance(pd.Series(yk_test), pd.Series(predictions))\n",
    "print(metrics.classification_report(yk_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kN98V7yqr9iH"
   },
   "outputs": [],
   "source": [
    "y_hat = model.predict_proba(xk_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 270
    },
    "colab_type": "code",
    "id": "hN49h4eAsGoZ",
    "outputId": "6d6af439-d3c9-4f31-a27b-94e44219049a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZP36ZUWju3Llmw4YNxhhjNmzYYObOnfu3bd555x1TV1dnEomEicfjZurUqWb//v1uTzVr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\n/wKDN+hO4PkH7wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(y_hat)\n",
    "_ = plt.axvline(x=0.5, color='orange')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "swY8iCK_4oDe"
   },
   "source": [
    "## EVALUATING THE PREDICTIONS AND IDENTIFYING WHERE IS THE MODEL DOING A BAD JOB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zy6IlnV4woD2"
   },
   "outputs": [],
   "source": [
    "y_hat = pd.Series(y_hat.reshape(-1,), name='y_hat')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 493
    },
    "colab_type": "code",
    "id": "vVDA-F4zxCLJ",
    "outputId": "9ebb3cfb-1f77-4821-c0aa-a1969c3f6b65"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['loan_amnt', 'int_rate', 'installment', 'annual_inc', 'loan_status',\n",
       "       'dti', 'delinq_2yrs', 'earliest_cr_line', 'inq_last_6mths', 'open_acc',\n",
       "       'pub_rec', 'revol_bal', 'revol_util', 'total_acc',\n",
       "       'home_ownership_MORTGAGE', 'home_ownership_NONE',\n",
       "       'home_ownership_OTHER', 'home_ownership_OWN', 'home_ownership_RENT',\n",
       "       'verification_status_Not Verified',\n",
       "       'verification_status_Source Verified', 'verification_status_Verified',\n",
       "       'purpose_car', 'purpose_credit_card', 'purpose_debt_consolidation',\n",
       "       'purpose_educational', 'purpose_home_improvement', 'purpose_house',\n",
       "       'purpose_major_purchase', 'purpose_medical', 'purpose_moving',\n",
       "       'purpose_other', 'purpose_renewable_energy', 'purpose_small_business',\n",
       "       'purpose_vacation', 'purpose_wedding', 'term_ 36 months',\n",
       "       'term_ 60 months', 'emp_length_beginner', 'emp_length_experienced',\n",
       "       'emp_length_intermediate', 'addr_state_AK', 'addr_state_AL',\n",
       "       'addr_state_AR', 'addr_state_AZ', 'addr_state_CA', 'addr_state_CO',\n",
       "       'addr_state_CT', 'addr_state_DC', 'addr_state_DE', 'addr_state_FL',\n",
       "       'addr_state_GA', 'addr_state_HI', 'addr_state_IA', 'addr_state_ID',\n",
       "       'addr_state_IL', 'addr_state_IN', 'addr_state_KS', 'addr_state_KY',\n",
       "       'addr_state_LA', 'addr_state_MA', 'addr_state_MD', 'addr_state_ME',\n",
       "       'addr_state_MI', 'addr_state_MN', 'addr_state_MO', 'addr_state_MS',\n",
       "       'addr_state_MT', 'addr_state_NC', 'addr_state_NE', 'addr_state_NH',\n",
       "       'addr_state_NJ', 'addr_state_NM', 'addr_state_NV', 'addr_state_NY',\n",
       "       'addr_state_OH', 'addr_state_OK', 'addr_state_OR', 'addr_state_PA',\n",
       "       'addr_state_RI', 'addr_state_SC', 'addr_state_SD', 'addr_state_TN',\n",
       "       'addr_state_TX', 'addr_state_UT', 'addr_state_VA', 'addr_state_VT',\n",
       "       'addr_state_WA', 'addr_state_WI', 'addr_state_WV', 'addr_state_WY',\n",
       "       'y_hat'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 71,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data = pd.concat([data,y_hat], axis=1)\n",
    "new_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "N7ReSTh1xdDc",
    "outputId": "3fc8d7c0-46fb-4783-80bc-5284803269d4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 39067 entries, 0 to 39785\n",
      "Data columns (total 92 columns):\n",
      "loan_amnt                              37675 non-null float64\n",
      "int_rate                               37675 non-null float64\n",
      "installment                            37675 non-null float64\n",
      "annual_inc                             37675 non-null float64\n",
      "loan_status                            37675 non-null float64\n",
      "dti                                    37675 non-null float64\n",
      "delinq_2yrs                            37675 non-null float64\n",
      "earliest_cr_line                       37675 non-null float64\n",
      "inq_last_6mths                         37675 non-null float64\n",
      "open_acc                               37675 non-null float64\n",
      "pub_rec                                37675 non-null float64\n",
      "revol_bal                              37675 non-null float64\n",
      "revol_util                             37675 non-null float64\n",
      "total_acc                              37675 non-null float64\n",
      "home_ownership_MORTGAGE                37675 non-null float64\n",
      "home_ownership_NONE                    37675 non-null float64\n",
      "home_ownership_OTHER                   37675 non-null float64\n",
      "home_ownership_OWN                     37675 non-null float64\n",
      "home_ownership_RENT                    37675 non-null float64\n",
      "verification_status_Not Verified       37675 non-null float64\n",
      "verification_status_Source Verified    37675 non-null float64\n",
      "verification_status_Verified           37675 non-null float64\n",
      "purpose_car                            37675 non-null float64\n",
      "purpose_credit_card                    37675 non-null float64\n",
      "purpose_debt_consolidation             37675 non-null float64\n",
      "purpose_educational                    37675 non-null float64\n",
      "purpose_home_improvement               37675 non-null float64\n",
      "purpose_house                          37675 non-null float64\n",
      "purpose_major_purchase                 37675 non-null float64\n",
      "purpose_medical                        37675 non-null float64\n",
      "purpose_moving                         37675 non-null float64\n",
      "purpose_other                          37675 non-null float64\n",
      "purpose_renewable_energy               37675 non-null float64\n",
      "purpose_small_business                 37675 non-null float64\n",
      "purpose_vacation                       37675 non-null float64\n",
      "purpose_wedding                        37675 non-null float64\n",
      "term_ 36 months                        37675 non-null float64\n",
      "term_ 60 months                        37675 non-null float64\n",
      "emp_length_beginner                    37675 non-null float64\n",
      "emp_length_experienced                 37675 non-null float64\n",
      "emp_length_intermediate                37675 non-null float64\n",
      "addr_state_AK                          37675 non-null float64\n",
      "addr_state_AL                          37675 non-null float64\n",
      "addr_state_AR                          37675 non-null float64\n",
      "addr_state_AZ                          37675 non-null float64\n",
      "addr_state_CA                          37675 non-null float64\n",
      "addr_state_CO                          37675 non-null float64\n",
      "addr_state_CT                          37675 non-null float64\n",
      "addr_state_DC                          37675 non-null float64\n",
      "addr_state_DE                          37675 non-null float64\n",
      "addr_state_FL                          37675 non-null float64\n",
      "addr_state_GA                          37675 non-null float64\n",
      "addr_state_HI                          37675 non-null float64\n",
      "addr_state_IA                          37675 non-null float64\n",
      "addr_state_ID                          37675 non-null float64\n",
      "addr_state_IL                          37675 non-null float64\n",
      "addr_state_IN                          37675 non-null float64\n",
      "addr_state_KS                          37675 non-null float64\n",
      "addr_state_KY                          37675 non-null float64\n",
      "addr_state_LA                          37675 non-null float64\n",
      "addr_state_MA                          37675 non-null float64\n",
      "addr_state_MD                          37675 non-null float64\n",
      "addr_state_ME                          37675 non-null float64\n",
      "addr_state_MI                          37675 non-null float64\n",
      "addr_state_MN                          37675 non-null float64\n",
      "addr_state_MO                          37675 non-null float64\n",
      "addr_state_MS                          37675 non-null float64\n",
      "addr_state_MT                          37675 non-null float64\n",
      "addr_state_NC                          37675 non-null float64\n",
      "addr_state_NE                          37675 non-null float64\n",
      "addr_state_NH                          37675 non-null float64\n",
      "addr_state_NJ                          37675 non-null float64\n",
      "addr_state_NM                          37675 non-null float64\n",
      "addr_state_NV                          37675 non-null float64\n",
      "addr_state_NY                          37675 non-null float64\n",
      "addr_state_OH                          37675 non-null float64\n",
      "addr_state_OK                          37675 non-null float64\n",
      "addr_state_OR                          37675 non-null float64\n",
      "addr_state_PA                          37675 non-null float64\n",
      "addr_state_RI                          37675 non-null float64\n",
      "addr_state_SC                          37675 non-null float64\n",
      "addr_state_SD                          37675 non-null float64\n",
      "addr_state_TN                          37675 non-null float64\n",
      "addr_state_TX                          37675 non-null float64\n",
      "addr_state_UT                          37675 non-null float64\n",
      "addr_state_VA                          37675 non-null float64\n",
      "addr_state_VT                          37675 non-null float64\n",
      "addr_state_WA                          37675 non-null float64\n",
      "addr_state_WI                          37675 non-null float64\n",
      "addr_state_WV                          37675 non-null float64\n",
      "addr_state_WY                          37675 non-null float64\n",
      "y_hat                                  12915 non-null float32\n",
      "dtypes: float32(1), float64(91)\n",
      "memory usage: 27.6 MB\n"
     ]
    }
   ],
   "source": [
    "new_data = new_data.rename(columns={0:'y_hat'})\n",
    "new_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 677
    },
    "colab_type": "code",
    "id": "ZlfJbAmYyYqI",
    "outputId": "666cde86-30ac-4024-b01e-8522d2fd2680"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    .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>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>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>last_credit_pull_d</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <th>policy_code</th>\n",
       "      <th>application_type</th>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <th>chargeoff_within_12_mths</th>\n",
       "      <th>delinq_amnt</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>tax_liens</th>\n",
       "      <th>y_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1077430</td>\n",
       "      <td>1314167.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>2500.000000</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>Dec-2011</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</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>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>1008.71</td>\n",
       "      <td>1008.71</td>\n",
       "      <td>456.46</td>\n",
       "      <td>435.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>117.08</td>\n",
       "      <td>1.1100</td>\n",
       "      <td>Apr-2013</td>\n",
       "      <td>119.66</td>\n",
       "      <td>Sep-2013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.137484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1071570</td>\n",
       "      <td>1306721.0</td>\n",
       "      <td>5375.0</td>\n",
       "      <td>5375.0</td>\n",
       "      <td>5350.000000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>12.69%</td>\n",
       "      <td>121.45</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>Starbucks</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>RENT</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Dec-2011</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>other</td>\n",
       "      <td>Building my credit history.</td>\n",
       "      <td>774xx</td>\n",
       "      <td>TX</td>\n",
       "      <td>18.08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sep-2004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9279.0</td>\n",
       "      <td>36.5%</td>\n",
       "      <td>3.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1476.19</td>\n",
       "      <td>1469.34</td>\n",
       "      <td>673.48</td>\n",
       "      <td>533.42</td>\n",
       "      <td>0.00</td>\n",
       "      <td>269.29</td>\n",
       "      <td>2.5200</td>\n",
       "      <td>Nov-2012</td>\n",
       "      <td>121.45</td>\n",
       "      <td>Mar-2013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.080214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>1069126</td>\n",
       "      <td>1303778.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9975.000000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>10.65%</td>\n",
       "      <td>215.69</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>CEC ENTERTAINMENT INC</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>45996.0</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Dec-2011</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>Consolidation</td>\n",
       "      <td>440xx</td>\n",
       "      <td>OH</td>\n",
       "      <td>21.73</td>\n",
       "      <td>0.0</td>\n",
       "      <td>May-1997</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11170.0</td>\n",
       "      <td>30.1%</td>\n",
       "      <td>15.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8772.91</td>\n",
       "      <td>8751.08</td>\n",
       "      <td>5495.38</td>\n",
       "      <td>2429.23</td>\n",
       "      <td>1.00</td>\n",
       "      <td>847.30</td>\n",
       "      <td>152.5140</td>\n",
       "      <td>Apr-2015</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Jun-2015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.199193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1068542</td>\n",
       "      <td>1303143.0</td>\n",
       "      <td>17500.0</td>\n",
       "      <td>17500.0</td>\n",
       "      <td>17500.000000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>17.27%</td>\n",
       "      <td>437.47</td>\n",
       "      <td>D</td>\n",
       "      <td>D3</td>\n",
       "      <td>Travelers Insurance</td>\n",
       "      <td>7 years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Dec-2011</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>Debit Free</td>\n",
       "      <td>992xx</td>\n",
       "      <td>WA</td>\n",
       "      <td>21.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sep-2002</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20129.0</td>\n",
       "      <td>86.1%</td>\n",
       "      <td>26.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10037.10</td>\n",
       "      <td>10037.10</td>\n",
       "      <td>3077.85</td>\n",
       "      <td>3473.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3486.00</td>\n",
       "      <td>625.6170</td>\n",
       "      <td>Apr-2013</td>\n",
       "      <td>437.47</td>\n",
       "      <td>Dec-2015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.104872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>1068202</td>\n",
       "      <td>1302588.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>23088.161058</td>\n",
       "      <td>60 months</td>\n",
       "      <td>13.49%</td>\n",
       "      <td>575.12</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>Midnight</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>67000.0</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Dec-2011</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>Pay off credit card, refinance house</td>\n",
       "      <td>996xx</td>\n",
       "      <td>AK</td>\n",
       "      <td>14.15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Aug-1999</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15319.0</td>\n",
       "      <td>91.2%</td>\n",
       "      <td>21.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23880.65</td>\n",
       "      <td>20309.60</td>\n",
       "      <td>13833.20</td>\n",
       "      <td>8008.11</td>\n",
       "      <td>28.76</td>\n",
       "      <td>2010.58</td>\n",
       "      <td>361.9044</td>\n",
       "      <td>Mar-2015</td>\n",
       "      <td>1179.00</td>\n",
       "      <td>Jul-2015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.018823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12841</th>\n",
       "      <td>791842</td>\n",
       "      <td>996205.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>7000.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>9.99%</td>\n",
       "      <td>225.84</td>\n",
       "      <td>B</td>\n",
       "      <td>B1</td>\n",
       "      <td>SHALIMAR JEWELERS</td>\n",
       "      <td>5 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>42614.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Jun-2011</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>337xx</td>\n",
       "      <td>FL</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb-2007</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1924.0</td>\n",
       "      <td>10%</td>\n",
       "      <td>15.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5404.04</td>\n",
       "      <td>5404.04</td>\n",
       "      <td>4234.50</td>\n",
       "      <td>1011.39</td>\n",
       "      <td>0.00</td>\n",
       "      <td>158.15</td>\n",
       "      <td>1.5815</td>\n",
       "      <td>Aug-2013</td>\n",
       "      <td>126.13</td>\n",
       "      <td>Nov-2013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.174470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12847</th>\n",
       "      <td>791741</td>\n",
       "      <td>996093.0</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>22.11%</td>\n",
       "      <td>498.27</td>\n",
       "      <td>G</td>\n",
       "      <td>G1</td>\n",
       "      <td>JP Morgan Chase</td>\n",
       "      <td>4 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>81000.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Jun-2011</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>Refi Loan</td>\n",
       "      <td>100xx</td>\n",
       "      <td>NY</td>\n",
       "      <td>18.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Nov-2003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16753.0</td>\n",
       "      <td>90.6%</td>\n",
       "      <td>24.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19367.87</td>\n",
       "      <td>19367.87</td>\n",
       "      <td>8035.00</td>\n",
       "      <td>9902.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1430.15</td>\n",
       "      <td>14.3015</td>\n",
       "      <td>Sep-2014</td>\n",
       "      <td>498.27</td>\n",
       "      <td>Jan-2015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.056063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12861</th>\n",
       "      <td>775996</td>\n",
       "      <td>978302.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>11984.210942</td>\n",
       "      <td>60 months</td>\n",
       "      <td>10.99%</td>\n",
       "      <td>260.85</td>\n",
       "      <td>B</td>\n",
       "      <td>B3</td>\n",
       "      <td>awnings of hollywood</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Jun-2011</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>major_purchase</td>\n",
       "      <td>rv loan</td>\n",
       "      <td>333xx</td>\n",
       "      <td>FL</td>\n",
       "      <td>7.24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Jun-1992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15468.0</td>\n",
       "      <td>77.3%</td>\n",
       "      <td>16.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9006.52</td>\n",
       "      <td>8979.36</td>\n",
       "      <td>5371.48</td>\n",
       "      <td>2697.51</td>\n",
       "      <td>0.00</td>\n",
       "      <td>937.53</td>\n",
       "      <td>9.2017</td>\n",
       "      <td>Feb-2014</td>\n",
       "      <td>260.85</td>\n",
       "      <td>Jul-2014</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.043458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12895</th>\n",
       "      <td>790863</td>\n",
       "      <td>995087.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>14.79%</td>\n",
       "      <td>172.82</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>paccar parts</td>\n",
       "      <td>6 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>41000.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Jun-2011</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>debt loan</td>\n",
       "      <td>980xx</td>\n",
       "      <td>WA</td>\n",
       "      <td>8.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Mar-1993</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6396.0</td>\n",
       "      <td>89.4%</td>\n",
       "      <td>12.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1279.23</td>\n",
       "      <td>1279.23</td>\n",
       "      <td>685.96</td>\n",
       "      <td>347.78</td>\n",
       "      <td>0.00</td>\n",
       "      <td>245.49</td>\n",
       "      <td>44.1882</td>\n",
       "      <td>Jan-2012</td>\n",
       "      <td>172.82</td>\n",
       "      <td>May-2012</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.007242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12914</th>\n",
       "      <td>790556</td>\n",
       "      <td>994712.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>24975.000000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>15.99%</td>\n",
       "      <td>607.82</td>\n",
       "      <td>D</td>\n",
       "      <td>D2</td>\n",
       "      <td>NORTHWEST SURGERY CENTER</td>\n",
       "      <td>3 years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Jun-2011</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>Debt Consolidation</td>\n",
       "      <td>786xx</td>\n",
       "      <td>TX</td>\n",
       "      <td>19.16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb-2001</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24572.0</td>\n",
       "      <td>33.4%</td>\n",
       "      <td>29.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18709.97</td>\n",
       "      <td>18691.44</td>\n",
       "      <td>8837.90</td>\n",
       "      <td>7572.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2300.01</td>\n",
       "      <td>23.0001</td>\n",
       "      <td>Nov-2013</td>\n",
       "      <td>36.42</td>\n",
       "      <td>Mar-2014</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.070435</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>669 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  member_id  loan_amnt  ...  pub_rec_bankruptcies  tax_liens     y_hat\n",
       "1      1077430  1314167.0     2500.0  ...                   0.0        0.0  0.137484\n",
       "9      1071570  1306721.0     5375.0  ...                   0.0        0.0  0.080214\n",
       "64     1069126  1303778.0    10000.0  ...                   0.0        0.0  0.199193\n",
       "97     1068542  1303143.0    17500.0  ...                   0.0        0.0  0.104872\n",
       "120    1068202  1302588.0    25000.0  ...                   0.0        0.0  0.018823\n",
       "...        ...        ...        ...  ...                   ...        ...       ...\n",
       "12841   791842   996205.0     7000.0  ...                   0.0        0.0  0.174470\n",
       "12847   791741   996093.0    18000.0  ...                   0.0        0.0  0.056063\n",
       "12861   775996   978302.0    12000.0  ...                   0.0        0.0  0.043458\n",
       "12895   790863   995087.0     5000.0  ...                   0.0        0.0  0.007242\n",
       "12914   790556   994712.0    25000.0  ...                   0.0        0.0  0.070435\n",
       "\n",
       "[669 rows x 53 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data_fn = new_data[(new_data['loan_status']==1) & (new_data['y_hat']<0.2)].index\n",
    "new_data_fn = data_copy.iloc[new_data_fn]\n",
    "new_data_fn = pd.concat([new_data_fn, y_hat], axis=1)\n",
    "new_data_fn.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 153
    },
    "colab_type": "code",
    "id": "BT9wLGwwIAWE",
    "outputId": "f4ac48c9-49bc-4125-8348-4c92e2df7132"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B    176\n",
       "C    160\n",
       "D    132\n",
       "E     85\n",
       "A     84\n",
       "F     52\n",
       "G     15\n",
       "Name: grade, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data_fn['grade'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 285
    },
    "colab_type": "code",
    "id": "hAUSJQXP5RsX",
    "outputId": "ca7b8ee5-55d5-46af-c900-566bfd4bff0a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f06b6159240>"
      ]
     },
     "execution_count": 75,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SGhsbVVlZmXUwAIA7XLl75fbt2zV79mw3VgUAcBmnbgDAcq4c0Tc2NsoYo/Lycq1Zs0bF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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_data_fn[new_data_fn['addr_state']=='CA']['y_hat'].hist()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 677
    },
    "colab_type": "code",
    "id": "bsHVThXE9H2N",
    "outputId": "ec143f0a-9bf1-4e00-e3f9-de4474700c87"
   },
   "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>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>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>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>last_credit_pull_d</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <th>policy_code</th>\n",
       "      <th>application_type</th>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <th>chargeoff_within_12_mths</th>\n",
       "      <th>delinq_amnt</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>tax_liens</th>\n",
       "      <th>y_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1077430</td>\n",
       "      <td>1314167.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>2500.000000</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>Dec-2011</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</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>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>1008.71</td>\n",
       "      <td>1008.71</td>\n",
       "      <td>456.46</td>\n",
       "      <td>435.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>117.08</td>\n",
       "      <td>1.1100</td>\n",
       "      <td>Apr-2013</td>\n",
       "      <td>119.66</td>\n",
       "      <td>Sep-2013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.137484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1071570</td>\n",
       "      <td>1306721.0</td>\n",
       "      <td>5375.0</td>\n",
       "      <td>5375.0</td>\n",
       "      <td>5350.000000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>12.69%</td>\n",
       "      <td>121.45</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>Starbucks</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>RENT</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Dec-2011</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>other</td>\n",
       "      <td>Building my credit history.</td>\n",
       "      <td>774xx</td>\n",
       "      <td>TX</td>\n",
       "      <td>18.08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sep-2004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9279.0</td>\n",
       "      <td>36.5%</td>\n",
       "      <td>3.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1476.19</td>\n",
       "      <td>1469.34</td>\n",
       "      <td>673.48</td>\n",
       "      <td>533.42</td>\n",
       "      <td>0.00</td>\n",
       "      <td>269.29</td>\n",
       "      <td>2.5200</td>\n",
       "      <td>Nov-2012</td>\n",
       "      <td>121.45</td>\n",
       "      <td>Mar-2013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.080214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>1069126</td>\n",
       "      <td>1303778.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9975.000000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>10.65%</td>\n",
       "      <td>215.69</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>CEC ENTERTAINMENT INC</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>45996.0</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Dec-2011</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>Consolidation</td>\n",
       "      <td>440xx</td>\n",
       "      <td>OH</td>\n",
       "      <td>21.73</td>\n",
       "      <td>0.0</td>\n",
       "      <td>May-1997</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11170.0</td>\n",
       "      <td>30.1%</td>\n",
       "      <td>15.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8772.91</td>\n",
       "      <td>8751.08</td>\n",
       "      <td>5495.38</td>\n",
       "      <td>2429.23</td>\n",
       "      <td>1.00</td>\n",
       "      <td>847.30</td>\n",
       "      <td>152.5140</td>\n",
       "      <td>Apr-2015</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Jun-2015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.199193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1068542</td>\n",
       "      <td>1303143.0</td>\n",
       "      <td>17500.0</td>\n",
       "      <td>17500.0</td>\n",
       "      <td>17500.000000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>17.27%</td>\n",
       "      <td>437.47</td>\n",
       "      <td>D</td>\n",
       "      <td>D3</td>\n",
       "      <td>Travelers Insurance</td>\n",
       "      <td>7 years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Dec-2011</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>Debit Free</td>\n",
       "      <td>992xx</td>\n",
       "      <td>WA</td>\n",
       "      <td>21.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sep-2002</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20129.0</td>\n",
       "      <td>86.1%</td>\n",
       "      <td>26.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10037.10</td>\n",
       "      <td>10037.10</td>\n",
       "      <td>3077.85</td>\n",
       "      <td>3473.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3486.00</td>\n",
       "      <td>625.6170</td>\n",
       "      <td>Apr-2013</td>\n",
       "      <td>437.47</td>\n",
       "      <td>Dec-2015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.104872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>1068202</td>\n",
       "      <td>1302588.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>23088.161058</td>\n",
       "      <td>60 months</td>\n",
       "      <td>13.49%</td>\n",
       "      <td>575.12</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>Midnight</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>67000.0</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Dec-2011</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>Pay off credit card, refinance house</td>\n",
       "      <td>996xx</td>\n",
       "      <td>AK</td>\n",
       "      <td>14.15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Aug-1999</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15319.0</td>\n",
       "      <td>91.2%</td>\n",
       "      <td>21.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23880.65</td>\n",
       "      <td>20309.60</td>\n",
       "      <td>13833.20</td>\n",
       "      <td>8008.11</td>\n",
       "      <td>28.76</td>\n",
       "      <td>2010.58</td>\n",
       "      <td>361.9044</td>\n",
       "      <td>Mar-2015</td>\n",
       "      <td>1179.00</td>\n",
       "      <td>Jul-2015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.018823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12841</th>\n",
       "      <td>791842</td>\n",
       "      <td>996205.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>7000.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>9.99%</td>\n",
       "      <td>225.84</td>\n",
       "      <td>B</td>\n",
       "      <td>B1</td>\n",
       "      <td>SHALIMAR JEWELERS</td>\n",
       "      <td>5 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>42614.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Jun-2011</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>337xx</td>\n",
       "      <td>FL</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb-2007</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1924.0</td>\n",
       "      <td>10%</td>\n",
       "      <td>15.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5404.04</td>\n",
       "      <td>5404.04</td>\n",
       "      <td>4234.50</td>\n",
       "      <td>1011.39</td>\n",
       "      <td>0.00</td>\n",
       "      <td>158.15</td>\n",
       "      <td>1.5815</td>\n",
       "      <td>Aug-2013</td>\n",
       "      <td>126.13</td>\n",
       "      <td>Nov-2013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.174470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12847</th>\n",
       "      <td>791741</td>\n",
       "      <td>996093.0</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>22.11%</td>\n",
       "      <td>498.27</td>\n",
       "      <td>G</td>\n",
       "      <td>G1</td>\n",
       "      <td>JP Morgan Chase</td>\n",
       "      <td>4 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>81000.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Jun-2011</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>Refi Loan</td>\n",
       "      <td>100xx</td>\n",
       "      <td>NY</td>\n",
       "      <td>18.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Nov-2003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16753.0</td>\n",
       "      <td>90.6%</td>\n",
       "      <td>24.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19367.87</td>\n",
       "      <td>19367.87</td>\n",
       "      <td>8035.00</td>\n",
       "      <td>9902.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1430.15</td>\n",
       "      <td>14.3015</td>\n",
       "      <td>Sep-2014</td>\n",
       "      <td>498.27</td>\n",
       "      <td>Jan-2015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.056063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12861</th>\n",
       "      <td>775996</td>\n",
       "      <td>978302.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>11984.210942</td>\n",
       "      <td>60 months</td>\n",
       "      <td>10.99%</td>\n",
       "      <td>260.85</td>\n",
       "      <td>B</td>\n",
       "      <td>B3</td>\n",
       "      <td>awnings of hollywood</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Jun-2011</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>major_purchase</td>\n",
       "      <td>rv loan</td>\n",
       "      <td>333xx</td>\n",
       "      <td>FL</td>\n",
       "      <td>7.24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Jun-1992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15468.0</td>\n",
       "      <td>77.3%</td>\n",
       "      <td>16.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9006.52</td>\n",
       "      <td>8979.36</td>\n",
       "      <td>5371.48</td>\n",
       "      <td>2697.51</td>\n",
       "      <td>0.00</td>\n",
       "      <td>937.53</td>\n",
       "      <td>9.2017</td>\n",
       "      <td>Feb-2014</td>\n",
       "      <td>260.85</td>\n",
       "      <td>Jul-2014</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.043458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12895</th>\n",
       "      <td>790863</td>\n",
       "      <td>995087.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>14.79%</td>\n",
       "      <td>172.82</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>paccar parts</td>\n",
       "      <td>6 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>41000.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Jun-2011</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>debt loan</td>\n",
       "      <td>980xx</td>\n",
       "      <td>WA</td>\n",
       "      <td>8.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Mar-1993</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6396.0</td>\n",
       "      <td>89.4%</td>\n",
       "      <td>12.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1279.23</td>\n",
       "      <td>1279.23</td>\n",
       "      <td>685.96</td>\n",
       "      <td>347.78</td>\n",
       "      <td>0.00</td>\n",
       "      <td>245.49</td>\n",
       "      <td>44.1882</td>\n",
       "      <td>Jan-2012</td>\n",
       "      <td>172.82</td>\n",
       "      <td>May-2012</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.007242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12914</th>\n",
       "      <td>790556</td>\n",
       "      <td>994712.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>24975.000000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>15.99%</td>\n",
       "      <td>607.82</td>\n",
       "      <td>D</td>\n",
       "      <td>D2</td>\n",
       "      <td>NORTHWEST SURGERY CENTER</td>\n",
       "      <td>3 years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Jun-2011</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>Debt Consolidation</td>\n",
       "      <td>786xx</td>\n",
       "      <td>TX</td>\n",
       "      <td>19.16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb-2001</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24572.0</td>\n",
       "      <td>33.4%</td>\n",
       "      <td>29.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18709.97</td>\n",
       "      <td>18691.44</td>\n",
       "      <td>8837.90</td>\n",
       "      <td>7572.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2300.01</td>\n",
       "      <td>23.0001</td>\n",
       "      <td>Nov-2013</td>\n",
       "      <td>36.42</td>\n",
       "      <td>Mar-2014</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.070435</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>669 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  member_id  loan_amnt  ...  pub_rec_bankruptcies  tax_liens     y_hat\n",
       "1      1077430  1314167.0     2500.0  ...                   0.0        0.0  0.137484\n",
       "9      1071570  1306721.0     5375.0  ...                   0.0        0.0  0.080214\n",
       "64     1069126  1303778.0    10000.0  ...                   0.0        0.0  0.199193\n",
       "97     1068542  1303143.0    17500.0  ...                   0.0        0.0  0.104872\n",
       "120    1068202  1302588.0    25000.0  ...                   0.0        0.0  0.018823\n",
       "...        ...        ...        ...  ...                   ...        ...       ...\n",
       "12841   791842   996205.0     7000.0  ...                   0.0        0.0  0.174470\n",
       "12847   791741   996093.0    18000.0  ...                   0.0        0.0  0.056063\n",
       "12861   775996   978302.0    12000.0  ...                   0.0        0.0  0.043458\n",
       "12895   790863   995087.0     5000.0  ...                   0.0        0.0  0.007242\n",
       "12914   790556   994712.0    25000.0  ...                   0.0        0.0  0.070435\n",
       "\n",
       "[669 rows x 53 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data_fn.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 285
    },
    "colab_type": "code",
    "id": "74Awnt6D5gzK",
    "outputId": "cb0e48b1-9ab1-4ba5-9f4f-a317905c9351"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f06af95ae80>"
      ]
     },
     "execution_count": 77,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NjenMmTOSpAMHDqi6ujrZqQAACUpqZf/uu+9q3759WrhwoRoaGiRJLpdLe/bsUWtrq5qb\nm+946SUAIDuSCvvHH39cFy9enHRs6dKl6urqSubyAIAU4R20AGAAwh4ADEDYA4ABCHsAMABhDwAG\nIOwBwACEPQAYgLAHAAMQ9gBgAMIeAAxA2AOAAQh7ADAAYQ8ABiDsAcAAhD0eWDdvRY2aF0hGyn4H\nLZBpeY/YVfPjQxmft+t/6jI+J5AsVvYAYADCHgAM8FCGfbb2VMfTPO+9fuN8uueeCvvXZsjGfS4v\nL+f/Vwo9lHv22dzLzca82Zyb/Wsz8PORB99DubIH0imTq83//92caStd0/pNp4dyZQ+kU7ZWuZJ5\nK13+rVOHlT0AGCCtYd/f36/6+npVVVWpvr5ely9fTud0wEOPbQ0kKq3bOM3NzVqzZo3q6up06NAh\nNTU1af/+/emcEnio8YNSJCptYR8Oh9Xb26tXX31VkuT1erV9+3ZFIhE5HI57nhuNfrB6uXbt2qTj\nw8PDGhgYuOc1bv1vJIGqkzMwMJCVebM5d//lf+uR3PTvBhYVFU16z7nPzPuwzX2/bLuXqTJTknIs\ny7ISvvI9BAIBvfDCCzp8+HDs2KpVq7Rr1y498cQT9zz3zJkzWrt2bTrKAoCH3vHjx+Vyue44NiNf\njVNWVqaOjg4VFhbKbrdnuxwAeCB8uCsyf/78u8bSFvZOp1OhUEjRaFR2u13RaFRDQ0NyOp33PXfW\nrFnyeDzpKg0AjJO2zda5c+fK7Xaru7tbktTd3S23233f/XoAQOqlbc9eki5duqTGxka99957mjNn\njnw+nz71qU+lazoAwBTSGvYAgJmBd9ACgAEIewAwAGEPAAYg7AHAADM27ON5iNovf/lLPfnkk6qr\nq1NdXZ22bduW+ULTIN4HyB05ckQ1NTXyer2qqanRyMhIZgtNsXj63rJlS+x+19XVafHixTp+/Hjm\ni02hePoOh8N67rnnVFNTo5UrV6qlpUW3b9/OfLEpFk/vw8PD+sEPfhDr/dCh7DzyOJV8Pp8qKipU\nWlqqvr6+ST8nGo1q27ZtWrFihZ566il1dnYmN6k1Q61bt846ePCgZVmWdfDgQWvdunV3fc4vfvEL\n6+c//3mmS0u7eHo/f/68tXLlSmtoaMiyLMt67733rLGxsYzWmWrx9P3fLly4YH3+85+3xsfHM1Fe\n2sTT944dO2L/12/evGmtXr3aOnz4cEbrTId4ev/Rj35k7d6927IsywqHw9aXv/xla3BwMKN1ptrb\nb79tDQ4OWl/5ylesixcvTvo5f/jDH6z169db0WjUCofD1vLly62rV68mPOeMXNl/+BA1r9cr6YOH\nqPX29ioSyc4DkTIp3t5/85vfaP369SosLJQkPfroo8rPz894vamSyD3/3e9+p5qaGuXl5WWqzJSL\nt++cnBzduHFDExMTunnzpm7duqWioqJslJwy8fb+zjvvaPny5ZIkh8OhxYsX609/+lPG600lj8dz\n36cJHDlyRN/+9rdls9nkcDi0YsUKHT16NOE5Z2TYB4NBFRUVxZ6LY7fbNW/ePAWDwbs+9/Dhw6qp\nqdH69et19uzZTJeacvH2funSJV29elVr167VN77xDbW3t8t6gN8yMZ17Lkk3b95UV1eXvvWtb2Wy\nzJSLt++NGzeqv79fX/rSl2J/7vUL6B8E8fb+xBNP6MiRI7IsS1evXtXZs2c1ODiYjZIzKhgMqri4\nOPax0+m851Mt72dGhn28GhoadPz4cXV1denZZ5/Vxo0bNTo6mu2yMiIajerixYt69dVX9dprr+nk\nyZMPxV5mvN544w0VFxfL7XZnu5SMOHr0qEpLS3Xq1CmdPHlSZ86cSWqV9yBpbGzUyMiI6urq9LOf\n/UxPPvkkD0hMwIwM+/9+iJqkKR+iVlhYqEceeUSS9MUvflFOp1PvvvtuxutNpXh7Ly4uVnV1tfLy\n8vSxj31MlZWVOn/+fDZKTol4+/7Q73//+wd+VS/F3/dvf/tb1dbWymaz6dFHH1VFRYXeeuutbJSc\nMvH27nA41NbWpj/+8Y/au3evbty4oU9/+tPZKDmjnE7nHd/BBIPBSZ9mGa8ZGfbxPkQtFArF/n7h\nwgX95z//0Sc/+cmM1ppq8fbu9Xp16tQpWZalW7du6W9/+5sWL16cjZJTYjoPzrt27Zr8fr9qamoy\nXWbKxdu3y+XSyZMnJX2whXX69Gk9/vjjGa83leLtfXR0NPbKo9OnT6uvry+2z/8wq66uVmdnpyYm\nJhSJRPTGG2+oqqoq8Qsm/KPdNPvnP/9prV692vrqV79qrV692rp06ZJlWZa1YcMG6/z585ZlWdaW\nLVusr33ta1ZNTY31zW9+03rzzTezWXLKxNN7NBq1du7caVVXV1urVq2ydu7caUWj0WyWnbR4+rYs\ny2pvb7eef/75bJWZcvH0feXKFeu73/2u5fV6rZUrV1otLS3WrVu3sll2SsTT+5tvvmk99dRTVlVV\nldXQ0GD19vZms+SU2L59u7V8+XLL7XZbX/jCF6xVq1ZZlnVn37dv37aampqsyspKq7Ky0jpw4EBS\nc/IgNAAwwIzcxgEApBZhDwAGIOwBwACEPQAYgLAHAAMQ9gBgAMIeAAxA2AOAAf4PTbYqncvjBMIA\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_data_tp = new_data[(new_data['loan_status']==1) & (new_data['y_hat']>0.5)].index\n",
    "new_data_tp = data_copy.iloc[new_data_tp]\n",
    "new_data_tp = pd.concat([new_data_tp, y_hat], axis=1)\n",
    "new_data_tp['addr_state'].value_counts()\n",
    "new_data_tp[new_data_tp['addr_state']=='CA']['y_hat'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 677
    },
    "colab_type": "code",
    "id": "Z764oRA9_TfG",
    "outputId": "80ca8cf5-5dd5-4b7b-b48a-0b17434db53b"
   },
   "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>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>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>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>last_credit_pull_d</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <th>policy_code</th>\n",
       "      <th>application_type</th>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <th>chargeoff_within_12_mths</th>\n",
       "      <th>delinq_amnt</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>tax_liens</th>\n",
       "      <th>y_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1039153</td>\n",
       "      <td>1269083.0</td>\n",
       "      <td>21000.0</td>\n",
       "      <td>21000.0</td>\n",
       "      <td>21000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>12.42%</td>\n",
       "      <td>701.73</td>\n",
       "      <td>B</td>\n",
       "      <td>B4</td>\n",
       "      <td>Osram Sylvania</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>105000.0</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Dec-2011</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>Debt Cleanup</td>\n",
       "      <td>335xx</td>\n",
       "      <td>FL</td>\n",
       "      <td>13.22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb-1983</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32135.0</td>\n",
       "      <td>90.3%</td>\n",
       "      <td>38.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14025.40</td>\n",
       "      <td>14025.40</td>\n",
       "      <td>10694.96</td>\n",
       "      <td>3330.44</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Sep-2013</td>\n",
       "      <td>701.73</td>\n",
       "      <td>Jun-2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.595776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1069657</td>\n",
       "      <td>1304764.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>16.77%</td>\n",
       "      <td>123.65</td>\n",
       "      <td>D</td>\n",
       "      <td>D2</td>\n",
       "      <td>Frito Lay</td>\n",
       "      <td>2 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>50004.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Dec-2011</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>other</td>\n",
       "      <td>Other</td>\n",
       "      <td>150xx</td>\n",
       "      <td>PA</td>\n",
       "      <td>13.97</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Oct-2003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4345.0</td>\n",
       "      <td>59.5%</td>\n",
       "      <td>22.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1609.12</td>\n",
       "      <td>1609.12</td>\n",
       "      <td>629.05</td>\n",
       "      <td>719.11</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>260.96</td>\n",
       "      <td>2.3000</td>\n",
       "      <td>Dec-2012</td>\n",
       "      <td>123.65</td>\n",
       "      <td>May-2013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.999952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1069465</td>\n",
       "      <td>1304521.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>8.90%</td>\n",
       "      <td>158.77</td>\n",
       "      <td>A</td>\n",
       "      <td>A5</td>\n",
       "      <td>STERIS Corporation</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Dec-2011</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>Debt Consolidation for a better life</td>\n",
       "      <td>441xx</td>\n",
       "      <td>OH</td>\n",
       "      <td>16.33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Jun-1995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74351.0</td>\n",
       "      <td>62.1%</td>\n",
       "      <td>35.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5021.37</td>\n",
       "      <td>5021.37</td>\n",
       "      <td>4217.38</td>\n",
       "      <td>696.99</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>107.00</td>\n",
       "      <td>1.0700</td>\n",
       "      <td>Aug-2014</td>\n",
       "      <td>158.77</td>\n",
       "      <td>Dec-2014</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.665908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>1069243</td>\n",
       "      <td>1304116.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>15.96%</td>\n",
       "      <td>421.65</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>Chemat Technology Inc</td>\n",
       "      <td>4 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Dec-2011</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>car</td>\n",
       "      <td>Car Loan</td>\n",
       "      <td>914xx</td>\n",
       "      <td>CA</td>\n",
       "      <td>20.88</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Oct-2006</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15793.0</td>\n",
       "      <td>90.8%</td>\n",
       "      <td>11.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3521.95</td>\n",
       "      <td>3521.95</td>\n",
       "      <td>1903.66</td>\n",
       "      <td>1039.35</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>578.94</td>\n",
       "      <td>5.4900</td>\n",
       "      <td>Aug-2012</td>\n",
       "      <td>421.65</td>\n",
       "      <td>Dec-2012</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>1069410</td>\n",
       "      <td>1303652.0</td>\n",
       "      <td>21000.0</td>\n",
       "      <td>21000.0</td>\n",
       "      <td>20975.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>19.91%</td>\n",
       "      <td>555.33</td>\n",
       "      <td>E</td>\n",
       "      <td>E4</td>\n",
       "      <td>Costco</td>\n",
       "      <td>7 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Dec-2011</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>Bill pay of</td>\n",
       "      <td>980xx</td>\n",
       "      <td>WA</td>\n",
       "      <td>21.58</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sep-1998</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19448.0</td>\n",
       "      <td>97.6%</td>\n",
       "      <td>14.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18319.14</td>\n",
       "      <td>18297.35</td>\n",
       "      <td>8990.81</td>\n",
       "      <td>9328.33</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Oct-2014</td>\n",
       "      <td>555.33</td>\n",
       "      <td>Jun-2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12824</th>\n",
       "      <td>792146</td>\n",
       "      <td>996544.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>19975.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>14.79%</td>\n",
       "      <td>473.60</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>The Matworks</td>\n",
       "      <td>3 years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>66950.0</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Jun-2011</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>JB</td>\n",
       "      <td>212xx</td>\n",
       "      <td>MD</td>\n",
       "      <td>24.82</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Jul-1995</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27240.0</td>\n",
       "      <td>74.2%</td>\n",
       "      <td>19.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3335.58</td>\n",
       "      <td>3331.42</td>\n",
       "      <td>1645.10</td>\n",
       "      <td>1666.82</td>\n",
       "      <td>23.661707</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Feb-2012</td>\n",
       "      <td>970.88</td>\n",
       "      <td>Jun-2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12828</th>\n",
       "      <td>792036</td>\n",
       "      <td>996420.0</td>\n",
       "      <td>22400.0</td>\n",
       "      <td>22400.0</td>\n",
       "      <td>22375.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>12.99%</td>\n",
       "      <td>509.56</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>Monument Medical Consultants</td>\n",
       "      <td>2 years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>52800.0</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Jun-2011</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>home_improvement</td>\n",
       "      <td>Well Loan</td>\n",
       "      <td>815xx</td>\n",
       "      <td>CO</td>\n",
       "      <td>2.70</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Jan-1999</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1761.0</td>\n",
       "      <td>16.5%</td>\n",
       "      <td>13.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8093.66</td>\n",
       "      <td>8084.84</td>\n",
       "      <td>574.78</td>\n",
       "      <td>5859.45</td>\n",
       "      <td>25.282240</td>\n",
       "      <td>1634.15</td>\n",
       "      <td>145.9582</td>\n",
       "      <td>Oct-2013</td>\n",
       "      <td>35.91</td>\n",
       "      <td>Nov-2013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.999204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12878</th>\n",
       "      <td>791139</td>\n",
       "      <td>995412.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>11.99%</td>\n",
       "      <td>355.84</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>City of Zeigler</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Jun-2011</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>Debt Reconsolidation</td>\n",
       "      <td>628xx</td>\n",
       "      <td>IL</td>\n",
       "      <td>19.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>May-1999</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1904.0</td>\n",
       "      <td>9%</td>\n",
       "      <td>21.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10354.32</td>\n",
       "      <td>10354.32</td>\n",
       "      <td>5523.30</td>\n",
       "      <td>3354.95</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1476.07</td>\n",
       "      <td>20.8525</td>\n",
       "      <td>Aug-2013</td>\n",
       "      <td>355.84</td>\n",
       "      <td>Jun-2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12888</th>\n",
       "      <td>790956</td>\n",
       "      <td>995195.0</td>\n",
       "      <td>6500.0</td>\n",
       "      <td>6500.0</td>\n",
       "      <td>6500.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>16.49%</td>\n",
       "      <td>159.77</td>\n",
       "      <td>D</td>\n",
       "      <td>D3</td>\n",
       "      <td>Raytheon</td>\n",
       "      <td>7 years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>160000.0</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Jun-2011</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>car</td>\n",
       "      <td>motorcycle</td>\n",
       "      <td>017xx</td>\n",
       "      <td>MA</td>\n",
       "      <td>14.97</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Aug-1979</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>147365.0</td>\n",
       "      <td>71.4%</td>\n",
       "      <td>33.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7916.01</td>\n",
       "      <td>7916.01</td>\n",
       "      <td>4598.02</td>\n",
       "      <td>2888.85</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>429.14</td>\n",
       "      <td>77.2452</td>\n",
       "      <td>Jun-2015</td>\n",
       "      <td>159.77</td>\n",
       "      <td>Sep-2015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.999981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12905</th>\n",
       "      <td>790666</td>\n",
       "      <td>994843.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>15975.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>17.49%</td>\n",
       "      <td>401.87</td>\n",
       "      <td>D</td>\n",
       "      <td>D5</td>\n",
       "      <td>k&amp;s tool and manufacturing co.</td>\n",
       "      <td>5 years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>57000.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Jun-2011</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>debt loan</td>\n",
       "      <td>273xx</td>\n",
       "      <td>NC</td>\n",
       "      <td>11.96</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Jul-1997</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6168.0</td>\n",
       "      <td>88.1%</td>\n",
       "      <td>15.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10334.38</td>\n",
       "      <td>10318.37</td>\n",
       "      <td>4788.74</td>\n",
       "      <td>4855.83</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>689.81</td>\n",
       "      <td>6.8981</td>\n",
       "      <td>Aug-2013</td>\n",
       "      <td>32.64</td>\n",
       "      <td>Jun-2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.508096</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>848 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  member_id  loan_amnt  ...  pub_rec_bankruptcies  tax_liens     y_hat\n",
       "21     1039153  1269083.0    21000.0  ...                   0.0        0.0  0.595776\n",
       "27     1069657  1304764.0     5000.0  ...                   0.0        0.0  0.999952\n",
       "46     1069465  1304521.0     5000.0  ...                   0.0        0.0  0.665908\n",
       "53     1069243  1304116.0    12000.0  ...                   0.0        0.0  1.000000\n",
       "55     1069410  1303652.0    21000.0  ...                   0.0        0.0  1.000000\n",
       "...        ...        ...        ...  ...                   ...        ...       ...\n",
       "12824   792146   996544.0    20000.0  ...                   0.0        0.0  1.000000\n",
       "12828   792036   996420.0    22400.0  ...                   0.0        0.0  0.999204\n",
       "12878   791139   995412.0    16000.0  ...                   0.0        0.0  0.999999\n",
       "12888   790956   995195.0     6500.0  ...                   0.0        0.0  0.999981\n",
       "12905   790666   994843.0    16000.0  ...                   0.0        0.0  0.508096\n",
       "\n",
       "[848 rows x 53 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data_tp.dropna()"
   ]
  }
 ],
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