Project 20 Customer_Churn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data processing\n",
    "import pandas as pd\n",
    "\n",
    "#Linear Algebra\n",
    "import numpy as np\n",
    "\n",
    "#Data Visualization\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "\n",
    "#Algorithms\n",
    "from sklearn import model_selection\n",
    "from sklearn import linear_model \n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.preprocessing import StandardScaler,LabelEncoder\n",
    "from sklearn.metrics import confusion_matrix,classification_report,accuracy_score,roc_curve,roc_auc_score\n",
    "from sklearn.model_selection import train_test_split,cross_val_score,GridSearchCV\n",
    "\n",
    "\n",
    "from sklearn import metrics  \n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "cust=pd.read_csv('customer churn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>account length</th>\n",
       "      <th>area code</th>\n",
       "      <th>phone number</th>\n",
       "      <th>international plan</th>\n",
       "      <th>voice mail plan</th>\n",
       "      <th>number vmail messages</th>\n",
       "      <th>total day minutes</th>\n",
       "      <th>total day calls</th>\n",
       "      <th>total day charge</th>\n",
       "      <th>...</th>\n",
       "      <th>total eve calls</th>\n",
       "      <th>total eve charge</th>\n",
       "      <th>total night minutes</th>\n",
       "      <th>total night calls</th>\n",
       "      <th>total night charge</th>\n",
       "      <th>total intl minutes</th>\n",
       "      <th>total intl calls</th>\n",
       "      <th>total intl charge</th>\n",
       "      <th>customer service calls</th>\n",
       "      <th>churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>KS</td>\n",
       "      <td>128</td>\n",
       "      <td>415</td>\n",
       "      <td>382-4657</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>25</td>\n",
       "      <td>265.1</td>\n",
       "      <td>110</td>\n",
       "      <td>45.07</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>16.78</td>\n",
       "      <td>244.7</td>\n",
       "      <td>91</td>\n",
       "      <td>11.01</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>OH</td>\n",
       "      <td>107</td>\n",
       "      <td>415</td>\n",
       "      <td>371-7191</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>26</td>\n",
       "      <td>161.6</td>\n",
       "      <td>123</td>\n",
       "      <td>27.47</td>\n",
       "      <td>...</td>\n",
       "      <td>103</td>\n",
       "      <td>16.62</td>\n",
       "      <td>254.4</td>\n",
       "      <td>103</td>\n",
       "      <td>11.45</td>\n",
       "      <td>13.7</td>\n",
       "      <td>3</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>NJ</td>\n",
       "      <td>137</td>\n",
       "      <td>415</td>\n",
       "      <td>358-1921</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>243.4</td>\n",
       "      <td>114</td>\n",
       "      <td>41.38</td>\n",
       "      <td>...</td>\n",
       "      <td>110</td>\n",
       "      <td>10.30</td>\n",
       "      <td>162.6</td>\n",
       "      <td>104</td>\n",
       "      <td>7.32</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>OH</td>\n",
       "      <td>84</td>\n",
       "      <td>408</td>\n",
       "      <td>375-9999</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>299.4</td>\n",
       "      <td>71</td>\n",
       "      <td>50.90</td>\n",
       "      <td>...</td>\n",
       "      <td>88</td>\n",
       "      <td>5.26</td>\n",
       "      <td>196.9</td>\n",
       "      <td>89</td>\n",
       "      <td>8.86</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>OK</td>\n",
       "      <td>75</td>\n",
       "      <td>415</td>\n",
       "      <td>330-6626</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>166.7</td>\n",
       "      <td>113</td>\n",
       "      <td>28.34</td>\n",
       "      <td>...</td>\n",
       "      <td>122</td>\n",
       "      <td>12.61</td>\n",
       "      <td>186.9</td>\n",
       "      <td>121</td>\n",
       "      <td>8.41</td>\n",
       "      <td>10.1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.73</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  state  account length  area code phone number international plan  \\\n",
       "0    KS             128        415     382-4657                 no   \n",
       "1    OH             107        415     371-7191                 no   \n",
       "2    NJ             137        415     358-1921                 no   \n",
       "3    OH              84        408     375-9999                yes   \n",
       "4    OK              75        415     330-6626                yes   \n",
       "\n",
       "  voice mail plan  number vmail messages  total day minutes  total day calls  \\\n",
       "0             yes                     25              265.1              110   \n",
       "1             yes                     26              161.6              123   \n",
       "2              no                      0              243.4              114   \n",
       "3              no                      0              299.4               71   \n",
       "4              no                      0              166.7              113   \n",
       "\n",
       "   total day charge  ...  total eve calls  total eve charge  \\\n",
       "0             45.07  ...               99             16.78   \n",
       "1             27.47  ...              103             16.62   \n",
       "2             41.38  ...              110             10.30   \n",
       "3             50.90  ...               88              5.26   \n",
       "4             28.34  ...              122             12.61   \n",
       "\n",
       "   total night minutes  total night calls  total night charge  \\\n",
       "0                244.7                 91               11.01   \n",
       "1                254.4                103               11.45   \n",
       "2                162.6                104                7.32   \n",
       "3                196.9                 89                8.86   \n",
       "4                186.9                121                8.41   \n",
       "\n",
       "   total intl minutes  total intl calls  total intl charge  \\\n",
       "0                10.0                 3               2.70   \n",
       "1                13.7                 3               3.70   \n",
       "2                12.2                 5               3.29   \n",
       "3                 6.6                 7               1.78   \n",
       "4                10.1                 3               2.73   \n",
       "\n",
       "   customer service calls  churn  \n",
       "0                       1  False  \n",
       "1                       1  False  \n",
       "2                       0  False  \n",
       "3                       2  False  \n",
       "4                       3  False  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Descriptive Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account length</th>\n",
       "      <th>area code</th>\n",
       "      <th>number vmail messages</th>\n",
       "      <th>total day minutes</th>\n",
       "      <th>total day calls</th>\n",
       "      <th>total day charge</th>\n",
       "      <th>total eve minutes</th>\n",
       "      <th>total eve calls</th>\n",
       "      <th>total eve charge</th>\n",
       "      <th>total night minutes</th>\n",
       "      <th>total night calls</th>\n",
       "      <th>total night charge</th>\n",
       "      <th>total intl minutes</th>\n",
       "      <th>total intl calls</th>\n",
       "      <th>total intl charge</th>\n",
       "      <th>customer service calls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "      <td>3333.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>101.064806</td>\n",
       "      <td>437.182418</td>\n",
       "      <td>8.099010</td>\n",
       "      <td>179.775098</td>\n",
       "      <td>100.435644</td>\n",
       "      <td>30.562307</td>\n",
       "      <td>200.980348</td>\n",
       "      <td>100.114311</td>\n",
       "      <td>17.083540</td>\n",
       "      <td>200.872037</td>\n",
       "      <td>100.107711</td>\n",
       "      <td>9.039325</td>\n",
       "      <td>10.237294</td>\n",
       "      <td>4.479448</td>\n",
       "      <td>2.764581</td>\n",
       "      <td>1.562856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>39.822106</td>\n",
       "      <td>42.371290</td>\n",
       "      <td>13.688365</td>\n",
       "      <td>54.467389</td>\n",
       "      <td>20.069084</td>\n",
       "      <td>9.259435</td>\n",
       "      <td>50.713844</td>\n",
       "      <td>19.922625</td>\n",
       "      <td>4.310668</td>\n",
       "      <td>50.573847</td>\n",
       "      <td>19.568609</td>\n",
       "      <td>2.275873</td>\n",
       "      <td>2.791840</td>\n",
       "      <td>2.461214</td>\n",
       "      <td>0.753773</td>\n",
       "      <td>1.315491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>23.200000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>1.040000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>143.700000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>24.430000</td>\n",
       "      <td>166.600000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>14.160000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>7.520000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>415.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>179.400000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>201.400000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>17.120000</td>\n",
       "      <td>201.200000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>9.050000</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.780000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>510.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>216.400000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>36.790000</td>\n",
       "      <td>235.300000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>235.300000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>10.590000</td>\n",
       "      <td>12.100000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.270000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>243.000000</td>\n",
       "      <td>510.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>350.800000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>59.640000</td>\n",
       "      <td>363.700000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>30.910000</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>17.770000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       account length    area code  number vmail messages  total day minutes  \\\n",
       "count     3333.000000  3333.000000            3333.000000        3333.000000   \n",
       "mean       101.064806   437.182418               8.099010         179.775098   \n",
       "std         39.822106    42.371290              13.688365          54.467389   \n",
       "min          1.000000   408.000000               0.000000           0.000000   \n",
       "25%         74.000000   408.000000               0.000000         143.700000   \n",
       "50%        101.000000   415.000000               0.000000         179.400000   \n",
       "75%        127.000000   510.000000              20.000000         216.400000   \n",
       "max        243.000000   510.000000              51.000000         350.800000   \n",
       "\n",
       "       total day calls  total day charge  total eve minutes  total eve calls  \\\n",
       "count      3333.000000       3333.000000        3333.000000      3333.000000   \n",
       "mean        100.435644         30.562307         200.980348       100.114311   \n",
       "std          20.069084          9.259435          50.713844        19.922625   \n",
       "min           0.000000          0.000000           0.000000         0.000000   \n",
       "25%          87.000000         24.430000         166.600000        87.000000   \n",
       "50%         101.000000         30.500000         201.400000       100.000000   \n",
       "75%         114.000000         36.790000         235.300000       114.000000   \n",
       "max         165.000000         59.640000         363.700000       170.000000   \n",
       "\n",
       "       total eve charge  total night minutes  total night calls  \\\n",
       "count       3333.000000          3333.000000        3333.000000   \n",
       "mean          17.083540           200.872037         100.107711   \n",
       "std            4.310668            50.573847          19.568609   \n",
       "min            0.000000            23.200000          33.000000   \n",
       "25%           14.160000           167.000000          87.000000   \n",
       "50%           17.120000           201.200000         100.000000   \n",
       "75%           20.000000           235.300000         113.000000   \n",
       "max           30.910000           395.000000         175.000000   \n",
       "\n",
       "       total night charge  total intl minutes  total intl calls  \\\n",
       "count         3333.000000         3333.000000       3333.000000   \n",
       "mean             9.039325           10.237294          4.479448   \n",
       "std              2.275873            2.791840          2.461214   \n",
       "min              1.040000            0.000000          0.000000   \n",
       "25%              7.520000            8.500000          3.000000   \n",
       "50%              9.050000           10.300000          4.000000   \n",
       "75%             10.590000           12.100000          6.000000   \n",
       "max             17.770000           20.000000         20.000000   \n",
       "\n",
       "       total intl charge  customer service calls  \n",
       "count        3333.000000             3333.000000  \n",
       "mean            2.764581                1.562856  \n",
       "std             0.753773                1.315491  \n",
       "min             0.000000                0.000000  \n",
       "25%             2.300000                1.000000  \n",
       "50%             2.780000                1.000000  \n",
       "75%             3.270000                2.000000  \n",
       "max             5.400000                9.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Minimum total day charge is 0 and maximum total day charge is 59.64  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3333 entries, 0 to 3332\n",
      "Data columns (total 21 columns):\n",
      "state                     3333 non-null object\n",
      "account length            3333 non-null int64\n",
      "area code                 3333 non-null int64\n",
      "phone number              3333 non-null object\n",
      "international plan        3333 non-null object\n",
      "voice mail plan           3333 non-null object\n",
      "number vmail messages     3333 non-null int64\n",
      "total day minutes         3333 non-null float64\n",
      "total day calls           3333 non-null int64\n",
      "total day charge          3333 non-null float64\n",
      "total eve minutes         3333 non-null float64\n",
      "total eve calls           3333 non-null int64\n",
      "total eve charge          3333 non-null float64\n",
      "total night minutes       3333 non-null float64\n",
      "total night calls         3333 non-null int64\n",
      "total night charge        3333 non-null float64\n",
      "total intl minutes        3333 non-null float64\n",
      "total intl calls          3333 non-null int64\n",
      "total intl charge         3333 non-null float64\n",
      "customer service calls    3333 non-null int64\n",
      "churn                     3333 non-null bool\n",
      "dtypes: bool(1), float64(8), int64(8), object(4)\n",
      "memory usage: 524.2+ KB\n"
     ]
    }
   ],
   "source": [
    "cust.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* State ,phone number,international plan,voice mail plan are objects.\n",
    "* Account length,area code,number vmail messages,total day calls,total eve calls,total night calls,total int1 calls,customer service calls are in integer.\n",
    "* Other than these features are floating ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21cc176af48>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking null data\n",
    "sns.heatmap(cust.isnull())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From here we can observe that there is no null data present in this dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21cc85ea388>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cust['churn'].value_counts().plot.pie(autopct=\"%2.2f%%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From here we can observe that the 85 % of churn's are Female and 14 % of the churns are male."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['state', 'account length', 'area code', 'phone number',\n",
       "       'international plan', 'voice mail plan', 'number vmail messages',\n",
       "       'total day minutes', 'total day calls', 'total day charge',\n",
       "       'total eve minutes', 'total eve calls', 'total eve charge',\n",
       "       'total night minutes', 'total night calls', 'total night charge',\n",
       "       'total intl minutes', 'total intl calls', 'total intl charge',\n",
       "       'customer service calls', 'churn'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cleaning columns\n",
    "cust.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cust.columns=['State', 'Account_length', 'Area_code', 'Phone_number',\n",
    "       'International plan', 'Voice_mail_plan', 'Number_vmail_messages',\n",
    "       'Total day minutes', 'Total day calls', 'Total_day_charge',\n",
    "       'Total_eve_minutes', 'Total_eve_calls', 'Total_eve_charge',\n",
    "       'Total_night_minutes', 'Total_night_calls', 'Total_night_charge',\n",
    "       'Total_int_minutes', 'Total_intl_calls', 'Total_intl_charge',\n",
    "       'Customer_service_calls', 'Churn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['State', 'Account_length', 'Area_code', 'Phone_number',\n",
       "       'International plan', 'Voice_mail_plan', 'Number_vmail_messages',\n",
       "       'Total day minutes', 'Total day calls', 'Total_day_charge',\n",
       "       'Total_eve_minutes', 'Total_eve_calls', 'Total_eve_charge',\n",
       "       'Total_night_minutes', 'Total_night_calls', 'Total_night_charge',\n",
       "       'Total_int_minutes', 'Total_intl_calls', 'Total_intl_charge',\n",
       "       'Customer_service_calls', 'Churn'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's check once\n",
    "cust.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21cc864d588>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cust.groupby([\"Area_code\", \"Churn\"]).size().unstack().plot(kind='bar', stacked=True, figsize=(5,5)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21cc86861c8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GVQeAdiALmAOoiGwRkd0i8rejfQMReUBEykWkvKnJppiZS7O1qhmAK8s+3LONNiLCFTOzOHOul+P2ga4hsKI/2tKEI7sMY7XxAFcDn/D/e4eI3PChhqqPqepKVV2Zk5MTQCRjxra1qpnMpFjmR+B+uBdjcVEaibFu3jvW4nQUEwICKfo1QPGw60VA3Vht/OP4aUCr//hbqtqsqt3AZmDFpYY2ZiyqytZjzVwxKwuXK7KXUg5UjNvFqtJMDtV30Nbd53Qc47BAiv5OYLaIzBCRWGA9sGlEm03Aff7LdwFvqG8AcQuwREQS/W8GfwAcxJhJcqypk8aOXhvPH2HNjExEYPtx6+1Hu3GLvn+M/kF8BfwQ8KyqVorIIyJyu7/ZD4EsEakCvgA85L9vG/BtfG8ce4HdqvpK8J+GMT5bq3xF7apZVvSHS0+MZX5+KjtPttE/6HU6jnHQuFM2AVR1M76hmeHHHh52uQe4e4z7Polv2qYxk+6dqmaKMxMoyUp0OkrIuWJmFpV1Hew7fZaVpZlOxzEOiZ5JzCbiDQx62Xa8xXr5Y5iRnURuahzv2fTNqGZF30SMitp2zvUMcJWN54/KN30zm/r2Hqpbu52OYxxiRd9EjHf9UxKvnGXz88eyrDid+BjXB6+ViT5W9E3EeOdoM/PzU8lKjnM6SsiK9bhYOT2Tyrp2Omw7xahkRd9EhPN9g+w61cZV1ssf1+Uzs1CFHSdbnY5iHGBF30SE8lOt9A16uWq2jeePJzMplrl5Kew40WrTN6OQFX0TEbZWteBxCattKmJAVs/IpLN3gDfeP+N0FDPFApqnb0yoe2lfHUUZCby4d+QKIWY0s6elkBLv4Rflp7l5YZ7TccwUsp6+CXtnu/uoO3s+qnfJmii3S1hRksGbh5s409HjdBwzhazom7C3taoFhajdD/diXVaSwaBX2bin1ukoZgpZ0Tdh7+0jTcTHuCjKsKUXJiI7JY5VpRk8W37aztCNIlb0TVhTVd460sSsnGTctpTyhN19WTHHm7rYXd3mdBQzRazom7B29EwnDR09zJmW4nSUsHTrknwSYtxs3G1DPNHCZu+YsPb2Ed/2mrNzbTz/YmzaW8ec3GQ27q5lbl7KBxvJ24bpkct6+iasvXWkibJpyaQnxjodJWwtK07nfP8gRxs7nY5ipoAVfRO2evoH2XGilWtn277Kl6JsWgpJsW72nD7rdBQzBazom7C1/UQrvQNerp1jSy9cCrdLWFKUzvv1HfT0Dzodx0wyK/ombL11uIlYj4s1M2yRtUu1rDidAa9SWdfudBQzyazom7D15uEzXDkri4RYt9NRwl5RRgJZSbE2xBMFrOibsHS8qZMTzV1cP2+a01EigoiwpCiNE01ddPYOOB3HTCIr+iYsDa0Oed1cK/rBsqgwDQUb4olwVvRNWHrj/TPMzU2hONOWXgiWvNR4spNjqai1oh/JrOibsNPR08+OE61cP996+cEkIiwq9A3xNHf2Oh3HTBIr+ibs/PZIMwNe5QYbzw+6xf4hni2VDU5HMZPElmEwYeOp7dUA/KL8NAkxbt5vOMcRO4s0qPJS48lKimVzRT2fWDPd6ThmElhP34QVryqHG88xNy8Fl9iqmsEmIiwuTOO9Yy202BBPRLKib8LKqZZuuvsGmZdnq2pOlkWFaXgVtlQ2Oh3FTAIr+iasHKrvwO0S5uRa0Z8s+WnxlGYlsrmi3ukoZhJY0TdhQ9W3TEBZTjLxMXYW7mQREW5dnM97x1to7epzOo4JMiv6Jmw0dPTQ1t3PgvxUp6NEvFsX5zPoVZvFE4Gs6JuwcbCuAwHm5dvQzmRbWJBqQzwRyoq+CRsH6zsoyUwkJT7G6SgRb2iI591jNsQTaazom7BwurWb+vYeFhTY0M5UGRri+bUN8USUgIq+iKwVkcMiUiUiD41ye5yIPOO/fbuIlI64vUREOkXkb4IT20SbobFlG8+fOgsLUpmelcjmA1b0I8m4RV9E3MCjwC3AAuBeEVkwotn9QJuqlgHfAb4x4vbvAK9eelwTrV6pqCc/LZ6s5Dino0QNEWHtojzerWqmvbvf6TgmSALp6a8GqlT1uKr2AU8D60a0WQf82H/5OeAGEd/pkiLyMeA4UBmcyCba1J09z57qsywuTHM6StS5ZVE+A17ltUN2olakCKToFwKnh12v8R8btY2qDgDtQJaIJAF/B3z10qOaaDU0g2SRFf0pt7QojYK0eH51wGbxRIpAiv5oC5xogG2+CnxHVS+4KpaIPCAi5SJS3tTUFEAkE002V9QzPz+VbBvamXK+IZ583j7SzLkeG+KJBIEU/RqgeNj1IqBurDYi4gHSgFZgDfDPInIS+CvgSyLy4MhvoKqPqepKVV2Zk5Mz4SdhIlfd2fPsrj7LHy7OczpK1LplcR59g94Pdisz4S2QpZV3ArNFZAZQC6wHNoxoswm4D3gPuAt4Q1UVuGaogYh8BehU1e8FIbeJYENLKANsrWoGwOt1Kk10Gv5/4FUlJc7DY28fZ92ykSO7JtyM29P3j9E/CGwBDgHPqmqliDwiIrf7m/0Q3xh+FfAF4EPTOo25GBW17b5t/FJsaMcpLhEWFKRypPEc3X22aXq4C2gTFVXdDGwecezhYZd7gLvHeYyvXEQ+E8Vau/qobu3m5gW5TkeJeosK09h+opW3Djdxy+J8p+OYS2Bn5JqQtff0WQCWFKc7nMSUZiWRGOvmVTtRK+xZ0TchSVXZd/ospVlJZCTGOh0n6rldwoL8VN54/wy9A4NOxzGXwIq+CUl17T00dfayzHr5IWNRYRqdvQO8c7TZ6SjmEljRNyFpb3UbbhEWFdpaO6FiZk4SKfEeNlfYEE84s6JvQo5Xlf217czJSyExNqC5BmYKeFwubpyfy+uHGukftDm04cqKvgk5x5u6ONczYEM7IeiWxfm0n+/nvWMtTkcxF8mKvgk5e0+3EedxMS/PdsgKNdfMzibJZvGENSv6JqT09A9SWdfBooI0Ytz24xlq4mPcXDdvGr+ubGDQO3IJLhMO7LfKhJTXDzXSO+BlWYkN7YSqWxbl09LVx44TrU5HMRfBir4JKS/sqSU13sOM7CSno5gxfGRuDvExLltuOUzZ1AgTMtq6+vjN4SYun5mFS0Zbrds4bWghtpnZyWzcU8vs3JQP/q82rClxMpoJkPX0Tch4paKeAa/arJ0wsKgwlXM9A5xu7XY6ipkgK/omZDy3q4Y5ucnkp8U7HcWMY15eKm6XUFnX4XQUM0FW9E1IONJ4jr2nz/JfVhYjNrQT8uJj3MyelkxFbTtetVk84cSKvgkJvyg/jcclfGy5bdIRLpYUpdF+vp/qFhviCSdW9I3j+ge9bNxdyw3zp9k+uGFkfn4qMW5hX81Zp6OYCbCibxz3xvtnaOnq455VxeM3NiEjzuNmXl4qB2rb7UStMGJF3zju2Z2nmZYSx7Wzc5yOYiZoaVEaXX2DHGvqdDqKCZAVfeOoxo4e3jx8hjsvK8Jjyy6EnTm5KcTHuNhvQzxhw37LjKM27q7Fq3D3ZUVORzEXweN2sTA/jcq6Dnr6bUetcGBF3zhGVflF+WlWlWYwMyfZ6TjmIi0tTqd3wMt/HjrjdBQTAFuGwTjiqe3VnGrp4nhzF8tL0j84vd+En5k5SaTGe3h+dw1/uCTf6ThmHNbTN44pP9VGrMfFosI0p6OYS+ASYVlxBm8daaLpXK/Tccw4rOgbR/QODFJR086SwjTiPG6n45hLtKIknUGv8uLeWqejmHFY0TeOqKhpp2/Qy2XTM5yOYoJgWmo8S4vS2Ljbin6os6JvHLH9RCvTUuIoyUx0OooJko+vKOJgfQeH6m0RtlBmRd9Muf01Z6k9e541MzJtcbUIcvvSAmLcwnO7apyOYi7Air6Zck9trybGLSwvsaGdSJKRFMtNC/LYuLvG5uyHMCv6Zkp19PTz4t46lhalEx9jH+BGmvWri2nr7mdLZYPTUcwYrOibKfXCnlrO9w+yZkaW01HMJLhqVjbFmQk8veO001HMGKzomymjqjy57RRLitIozEhwOo6ZBC6XsH5VCe8db+G4LcIWkqzomylTfqqNI42dfMI20I5od19WhNslPLPTevuhyJZhMFPmZ9tOkRLv4balBbywp87pOCbIhi+lMTc3hZ9uO0VBegL3XVnqXCjzIQH19EVkrYgcFpEqEXlolNvjROQZ/+3bRaTUf/xGEdklIhX+f68PbnwTLlq7+thc0cCdK4pIjLW+RqS7fGYW3X2+s65NaBm36IuIG3gUuAVYANwrIgtGNLsfaFPVMuA7wDf8x5uB21R1MXAf8NNgBTfh5Rflp+kb9LLBhnaiwqycJKalxPHusWbUNk4PKYH09FcDVap6XFX7gKeBdSParAN+7L/8HHCDiIiq7lHVob/jK4F4EbFNUKOM16s8taOa1aWZzMlNcTqOmQIiwhWzsqhr72HnyTan45hhAin6hcDwT2Rq/MdGbaOqA0A7MHJO3p3AHlW1ZfiizFtHmzjV0s0nLrdefjRZXpxBQoybJ7aecDqKGSaQoj/aefIj/167YBsRWYhvyOfTo34DkQdEpFxEypuamgKIZMLJ4++cIDc1jlsW2Vrr0STW42JVaQZbKhuoaet2Oo7xC6To1wDFw64XASOnXnzQRkQ8QBrQ6r9eBPwS+BNVPTbaN1DVx1R1paquzMmxzbEjyXdeO8JvjzaztCid53bV8NT2atswJYpcPjMLEeGH71hvP1QEUvR3ArNFZIaIxALrgU0j2mzC90EtwF3AG6qqIpIOvAJ8UVW3Biu0CR9bq5qJcQurSzOdjmIckJ4Yy7plBfx8RzUtnTayGwrGLfr+MfoHgS3AIeBZVa0UkUdE5HZ/sx8CWSJSBXwBGJrW+SBQBvyDiOz1f00L+rMwIamls5e9p8+yvDiDxDibphmt/vwjs+gd8PK4je2HhIB+E1V1M7B5xLGHh13uAe4e5X5fA752iRlNmPrZ9moGvMqVs2ydnWhWNi2FtQvz+Mm7p3jg2lmkJcQ4HSmq2TIMZlJ09Q7wxNYTzMtLYVpqvNNxjMM+d10Z53oH+Ol7J52OEvWs6JtJ8dT2atq6+/nIXBvNM7CoMI3r5ubw7789QXt3v9NxopoVfRN0Pf2DPPbb41xdlm3bIZoP/I+b59HR08+/vVXldJSoZkXfBN2z5adpOtfLg9eXOR3FhJAFBancsayQJ7aepO7seafjRC0r+iaoevoH+cFvjrFyegZrZtg0TfP7/vrGOaC+8zeMM6zom6D60bsnqWvv4Qs3zrFNz82HFGcm8idXTOe53TUcqLUVOJ1gRd8ETWtXH4++UcUN86ZxZVm203FMiPqL62eTlRTHl35ZwaDXVuCcalb0TdD8n/88Snf/IF+8dZ7TUUwIS0uM4eHbFrC/pp2fvHfS6ThRx06TNEFxvKmTJ7ed4p5VxZRNs+WTze+MttaSqjInN5lvbTnM2kV55KfZnslTxXr65pJ5vcpDGytIiHXzVx+d7XQcEwZEhNuXFjKoyt8+tx+vDfNMGevpm0v2+af3sONEK3euKOT1g2ecjmPCRGZSLF++bSFf3FjB9986xueusym+U8F6+uaSVLd0s6WykdnTkllRkuF0HBNm1q8q5o+W5PPt145QfrLV6ThRwYq+uWh9A16+8OxeROCO5YU2RdNMmIjwvz++mML0BB58ag+NHT1OR4p4VvTNRVFVvrypkvJTbXxseSHpibFORzJh6Knt1by0r57blxbQ2t3Hxx7dyhO24cqksqJvLsqT207x8x3VfPYjs1halO50HBPmCtITuHdVCY0dPTy1o5r+Qa/TkSKWFX0zYa9W1PPVlw5y/bxp/M1Nc52OYyLE3LwU1i0r5OiZTj7/9B4r/JPEZu+YCXl5fx2ff3ovS4vS+Nf1y3C7bBzfBM+q0kx6B7xsrqhnYHA339uwgliP9U2DyV5NE7Bndlbzlz/fw2UlGfzk/jWkxNsOSCb4ri7L5iu3LeDXBxv5s5+U09k74HSkiGJF34yrb8DLP7xwgL97voKryrL50X9dRbLteWsm0aeumsE37lzMO1XN3P2D96hvt6WYg8WKvrmg063dbPj3bfx02ykeuHYmT3xqFYmxVuGyBN8AAA2+SURBVPDN5LtnVQmPf2oVp1u7+dijW9l1qs3pSBHBfnvN7xlaJ0VVKT/VxisV9Qhwz6pivnTrfGfDmagxfL2eP72qlCe3neLuH7zLrYvz+b/3LrdzQi6BFX3zIY0dPWzaV8eJ5i5m5iRx14oim4dvHJOflsCD183muV2neXl/Parw9TsX22dKF8mKvvlAV+8Arx6oZ2tVM3EeN+uWFbCqNBOX9aqMwxJi3fzx5dP57dFmflXZwKH6Dv7tj1cwLy/V6WhhR1RDa3W7lStXanl5udMxooqq8kpFPV97+RANHT2snJ7BTQvz7MNaE5Jm5STx4M/30HG+n79bO49PXVmKy6YOIyK7VHXleO3stzpKDY2Znmrp4tUDDVS3dlOQFs9nrp1JSVaSw+mMGduamVls/streOj5/Tzy8kFeP9TIN+9eSmG6rckfCCv6UarpXC9bKhs4WN9BSryHO5YXctn0DBvKMSFvqMNy/bxppCXE8HJFPdd/6zf8rzsW8/EVtvDfeKzoR5nTrd18/61jPL2jGo/bxUfn53J1Wbad9WjCjoiwsjSTmTnJ/GLXaf77L/bxq8oGvnr7Qgqs1z8mG9OPEseaOvm3N4/x4t5aROCy6RlcPy/Xxu1NRPCqcr5vkH957TBuEf76xjncd2UpMe7o6czYmL4BoKKmnR+8fYzNFfXEeVx88orpPHDtTN58v8npaMYEjUuEP7t2JmsX5fHwiwf42iuHeGpHNV+6ZT43zJ9mQz7DWNGPQN19A7y0r46ntlezr6ad5DgPn/2DWfzXq2eQnRzndDxjJsXQWP9H5+dSnJHI5gMN/LeflFOSmcgj6xbyB3NyrPhjwzsRQ1XZV9POL3fXsHF3Led6B5iTm8zc3BSWFWeQEOt2OqIxU2rQq+w82cpbR5poP9/PwoJUPnn5dG5fVhCRS4kEOrxjRT+MDXqVXafaePVAPVsONFDX3kOs28UfLslnw5oSVk7P4Oc7Tjsd0xhHDXi9xLpdPLH1JIcbz5ES5+HGhbncuiifq2dnEx8TGR0iK/oRSFWpbu3mvWMtbDvewjtVLTR39uJxCWXTkllUkMb8/FTr1RszwoY1Jaj6OklP7zzNrysb6OgZwOMSijMTmZmdxIzsJIozEz/48HfDmhKHU0+MfZAbAbr7BjhY18H+mnYqatvZfryFunbfxtHZyXFcMSuLGxfk0nKul7gI6a0YM1mGpniuLM2k747FvHusmf/47QmON3fyxvtnUMDjEvLS4ilIS0BRFhakMS8vJWL+GoAAe/oishb4V8AN/Ieqfn3E7XHAT4DLgBbgHlU96b/ti8D9wCDwl6q65ULfKxp7+r0Dg5xq6ebYmU6e311L07ke6tt7aDrXy9D/Tkq8h9IsX29kZk4SOclx9qGUMUFyvm+QUy1dnGjuoubseerbz9PT79uu0e0SZuUkffAGMCsnmVnTkinOSMATQlNCg9bTFxE38ChwI1AD7BSRTap6cFiz+4E2VS0TkfXAN4B7RGQBsB5YCBQAr4vIHFUdnPhTmlxerzLgVbzq+3dwUBnwehlUZdCrDAz6bpuIQa/S3TdIR08/nT0DdPYO0NbdT0P7eerbe2ho9xX3ho4eBr2/e+z0hBhyU+NZVJhGUXoCBRkJpNqKgsZMmoRYN/PyU5mX71vATVVp6+6nzv8GUHe2h/881Mgv99R+cJ8Yt1CalURRRgJ5aQnkpcaTnxZPblo8qfEeUuJjSIn3kBLvISHGHTKdtECGd1YDVap6HEBEngbWAcOL/jrgK/7LzwHfE98zXAc8raq9wAkRqfI/3nvBif87B+s6+MyTu/Cqouo7WWPQq3jV9x/oVd/lD9/uK/JT+dFGnMdFcpyH1IQYpqXEMSc3mZyUeHJS4shJjrOzY41xmIiQmRRLZlIsiwrTPjh+vm+Q5dPTOXamk2NNXRxr6qTu7Hkqattp7uy7wOP5ho48Lhcel+B2D7vsEkR8bW6Yl8tXbl84qc8tkKJfCAyfAlIDrBmrjaoOiEg7kOU/vm3EfQtHfgMReQB4wH+1U0QOB5R+amUDzU6HCIDlDC7LGVyW8wLeAb46sbsMzzk9kDsEUvRH+5tkZL94rDaB3BdVfQx4LIAsjhGR8kDGy5xmOYPLcgaX5Qyui8kZyDhCDVA87HoRUDdWGxHxAGlAa4D3NcYYM0UCKfo7gdkiMkNEYvF9MLtpRJtNwH3+y3cBb6hvWtAmYL2IxInIDGA2sCM40Y0xxkzUuMM7/jH6B4Et+KZsPq6qlSLyCFCuqpuAHwI/9X9Q24rvjQF/u2fxfeg7AHwuFGfuBCikh5+GsZzBZTmDy3IG14RzhtwZucYYYyaPzQ00xpgoYkXfGGOiiBX9AIjIWhE5LCJVIvKQ03nGIiInRaRCRPaKSMisZSEij4vIGRE5MOxYpoi8JiJH/f9mOJnRn2m0nF8RkVr/a7pXRG51OGOxiLwpIodEpFJEPu8/HlKv5wVyhtrrGS8iO0Rknz/nV/3HZ4jIdv/r+Yx/Ekso5vyRiJwY9nouG/fBVNW+LvCF78PrY8BMIBbYByxwOtcYWU8C2U7nGCXXtcAK4MCwY/8MPOS//BDwjRDN+RXgb5zONixPPrDCfzkFOAIsCLXX8wI5Q+31FCDZfzkG2A5cDjwLrPcf/wHw2RDN+SPgrok8lvX0x/fBMhSq2gcMLUNhAqSqb+Ob1TXcOuDH/ss/Bj42paFGMUbOkKKq9aq623/5HHAI31nuIfV6XiBnSFGfTv/VGP+XAtfjW1IGQuP1HCvnhFnRH99oy1CE3A+vnwK/FpFd/qUtQlmuqtaDr0AA0xzOcyEPish+//CP48NQQ0SkFFiOr9cXsq/niJwQYq+niLhFZC9wBngN31/2Z1V1wN8kJH7nR+ZU1aHX85/8r+d3/CseX5AV/fEFtJREiLhKVVcAtwCfE5FrnQ4UAb4PzAKWAfXAvzgbx0dEkoHngb9S1Q6n84xllJwh93qq6qCqLsO3YsBqYP5ozaY21SgBRuQUkUXAF4F5wCogE/i78R7Hiv74wmYpCVWt8/97Bvglvh/gUNUoIvkA/n/POJxnVKra6P9l8wL/Tgi8piISg6+Q/kxVN/oPh9zrOVrOUHw9h6jqWeA3+MbK0/1LykCI/c4Py7nWP4ym6lvJ+AkCeD2t6I8vkGUoHCciSSKSMnQZuAk4cOF7OWr40h33AS86mGVMQ4XU7w4cfk39S5b/EDikqt8edlNIvZ5j5QzB1zNHRNL9lxOAj+L7/OFNfEvKQGi8nqPlfH/YG73g+9xh3NfTzsgNgH9a2Xf53TIU/+RwpA8RkZn4evfgW17jqVDJKSI/Bz6CbxnYRuDLwAv4ZkiUANXA3arq6IeoY+T8CL6hCMU3O+rTQ2PnThCRq4HfAhWA13/4S/jGy0Pm9bxAznsJrddzCb4Pat34OsHPquoj/t+np/ENmewB/tjfmw61nG8AOfiGofcCnxn2ge/oj2VF3xhjoocN7xhjTBSxom+MMVHEir4xxkQRK/rGGBNFrOgbY0wUsaJvjDFRxIq+cZSIZA1bFrZhxLK7H1rO1r+E8GcCeFyPiJydnNQXR0SeFBHHF5Yz0W3cPXKNmUyq2oLvZB1E5CtAp6p+6wJ3yQQ+g2+526jhP+NS/MsXGHPRrKdvQpaI/K2IHPB//YX/8NeBuf6/BL4uIqki8oaI7PavNPhHE3j8h/wbU+wXkYf9x/5l+AqlIvK1YRuAfKj9BR77T/3t9onIE8Nuuk5E3hWR4yJyh7/tqM9BRMr8z/0HwG4gX0Q+LSJHROQ3IvIfIvJdf9tcEdkoIuX+jJcH+jqYKOPkxgD2ZV/Dvxi2wQa+haP2AYn4NuE4BCwByoC9w+4TA6T4L08Djvove/AtjzvW97oV+Dd8p6+7gF8BV+JbrfA/h7U7DBSM1X6Mx14KvA9k+q8P/fsk8HP/YywB3h/nOZThW8Jglf96MXACyMC3oc+7wHf9tz0DXO6/XMqwjWDsy76Gf9nwjglV1wDPq2o3gIi8AFwN/HpEOwG+4V/rxQsUi0g2MN54/k34lqDe47+eDMxR1R+Jb6u/XHyrKzaoap2I/O1o7fEV3pGuB55R/9o3+vtr4LygqgrsF5GhNdrHeg4Ax1R1p//yGuANVW3zvybP4VtrB3wLcM31jQIBkCEiCap6fpzXwUQZK/omVI22j8Fo/gRIw7c134CI1ADxAT7+11T1h6Pc9jxwJ74e89MBtB/tscda1Kp3RDu48HPoGqX9WN9ztfp2dzNmTDamb0LV28AdIpLg34hjHb5VG8/hG+4Zkgac8RfLGwl8h6MtwP3+ZagRkaJhveun8S2h/XF8bwDjtR/pdWC9iGT622aOkyXQ57Ad32cC6f616j8+4nt+buiKBLJBtolK1tM3IUlVd/iXOh4a2vi+qlYA+D+srABeAb4NvCQi5fg+7Dwa4ONvFpF5wDb/kMg5YAPQrKr7RCQHOKG+DWku2H6Ux94vIv8MvC0iA8Au4P4LxPlpIM9BVatF5JvADqAWqATa/Td/Dvi+iPwpvt/rNxn2JmDMEFta2ZgwIiLJqtrp7+m/iO/N8CWnc5nwYcM7xoSXfxSRPcB+fDOLXnY4jwkz1tM3Ec0/tv2jEYe7VfXKIDz2ND48mwjgI+rbx9SYkGNF3xhjoogN7xhjTBSxom+MMVHEir4xxkQRK/rGGBNF/j+00SB3Vo5aiAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(cust['Total_eve_charge'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From here we can observe that Total_eve_charge is a normal distributed .In the sense if Total_eve\n",
    "-charge if increases then churn also going to increase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "le=LabelEncoder()\n",
    "list1=['State','International plan','Voice_mail_plan','Churn']\n",
    "for val in list1:\n",
    "    cust[val]=le.fit_transform(cust[val].astype(str))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3333 entries, 0 to 3332\n",
      "Data columns (total 21 columns):\n",
      "State                     3333 non-null int32\n",
      "Account_length            3333 non-null int64\n",
      "Area_code                 3333 non-null int64\n",
      "Phone_number              3333 non-null object\n",
      "International plan        3333 non-null int32\n",
      "Voice_mail_plan           3333 non-null int32\n",
      "Number_vmail_messages     3333 non-null int64\n",
      "Total day minutes         3333 non-null float64\n",
      "Total day calls           3333 non-null int64\n",
      "Total_day_charge          3333 non-null float64\n",
      "Total_eve_minutes         3333 non-null float64\n",
      "Total_eve_calls           3333 non-null int64\n",
      "Total_eve_charge          3333 non-null float64\n",
      "Total_night_minutes       3333 non-null float64\n",
      "Total_night_calls         3333 non-null int64\n",
      "Total_night_charge        3333 non-null float64\n",
      "Total_int_minutes         3333 non-null float64\n",
      "Total_intl_calls          3333 non-null int64\n",
      "Total_intl_charge         3333 non-null float64\n",
      "Customer_service_calls    3333 non-null int64\n",
      "Churn                     3333 non-null int32\n",
      "dtypes: float64(8), int32(4), int64(8), object(1)\n",
      "memory usage: 494.9+ KB\n"
     ]
    }
   ],
   "source": [
    "cust.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here you can observe that the data had been changed to integer format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "cust.drop('Phone_number',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21cc87c0908>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cust.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check Distribution of Skewness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "collist=cust.columns\n",
    "ncol=20\n",
    "nrows=18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x2160 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,30))\n",
    "for i in range(0,len(collist)):\n",
    "    plt.subplot(nrows,ncol,i+1)\n",
    "    sns.distplot(cust[collist[i]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State                    -0.064618\n",
       "Account_length            0.096606\n",
       "Area_code                 1.126823\n",
       "International plan        2.726332\n",
       "Voice_mail_plan           0.999140\n",
       "Number_vmail_messages     1.264824\n",
       "Total day minutes        -0.029077\n",
       "Total day calls          -0.111787\n",
       "Total_day_charge         -0.029083\n",
       "Total_eve_minutes        -0.023877\n",
       "Total_eve_calls          -0.055563\n",
       "Total_eve_charge         -0.023858\n",
       "Total_night_minutes       0.008921\n",
       "Total_night_calls         0.032500\n",
       "Total_night_charge        0.008886\n",
       "Total_int_minutes        -0.245136\n",
       "Total_intl_calls          1.321478\n",
       "Total_intl_charge        -0.245287\n",
       "Customer_service_calls    1.091359\n",
       "Churn                     2.018356\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3333, 19)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=cust.iloc[:,:-1]\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2850\n",
       "1     483\n",
       "Name: Churn, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=cust['Churn']\n",
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in x.columns:\n",
    "    if x.skew().loc[col]>0.55:\n",
    "        x[col]=np.log1p(x[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State                    -0.064618\n",
       "Account_length            0.096606\n",
       "Area_code                 1.122881\n",
       "International plan        2.726332\n",
       "Voice_mail_plan           0.999140\n",
       "Number_vmail_messages     1.028178\n",
       "Total day minutes        -0.029077\n",
       "Total day calls          -0.111787\n",
       "Total_day_charge         -0.029083\n",
       "Total_eve_minutes        -0.023877\n",
       "Total_eve_calls          -0.055563\n",
       "Total_eve_charge         -0.023858\n",
       "Total_night_minutes       0.008921\n",
       "Total_night_calls         0.032500\n",
       "Total_night_charge        0.008886\n",
       "Total_int_minutes        -0.245136\n",
       "Total_intl_calls         -0.199368\n",
       "Total_intl_charge        -0.245287\n",
       "Customer_service_calls   -0.138568\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting the DataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.1,random_state=43)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2999, 19)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2999,)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(334, 19)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(334,)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Applying Standard Scaling to get optimized result\n",
    "sc=StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=sc.fit_transform(x_train)\n",
    "x_test=sc.fit_transform(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying MachineLearning Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtc=DecisionTreeClassifier()\n",
    "rfc=RandomForestClassifier()\n",
    "lg=LogisticRegression()\n",
    "knc=KNeighborsClassifier()\n",
    "sgc=SGDClassifier()\n",
    "gbc=GradientBoostingClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "************** DecisionTreeClassifier ***********\n",
      "\n",
      "\n",
      "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
      "                       max_features=None, max_leaf_nodes=None,\n",
      "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                       min_samples_leaf=1, min_samples_split=2,\n",
      "                       min_weight_fraction_leaf=0.0, presort=False,\n",
      "                       random_state=None, splitter='best')\n",
      "\n",
      "\n",
      "accuracy score 0.9041916167664671\n",
      "Cross-val-score= 0.9147922957870834\n",
      "\n",
      "\n",
      "roc_auc_score 0.8581939799331104\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.92      0.94       299\n",
      "           1       0.53      0.80      0.64        35\n",
      "\n",
      "    accuracy                           0.90       334\n",
      "   macro avg       0.75      0.86      0.79       334\n",
      "weighted avg       0.93      0.90      0.91       334\n",
      "\n",
      "\n",
      "\n",
      "[[274  25]\n",
      " [  7  28]]\n",
      "\n",
      "\n",
      "AxesSubplot(0.125,0.808774;0.62x0.0712264)\n"
     ]
    },
    {
     "data": {
      "image/png": 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U95S+wOXAbyLyi7vvLuBh4B0RGQmsBC5wj80ABgGZwF5gxOHWubqyQMwYY4wxHppbWL7yql8TPO8L4LQg5RUYU/6a1TwWiBljjDHGo7yBmDl0FogZY4wxxqNwvwVi4WKBmDHGGGM8Cste1chUEAvEjDHGGONhgVj4WCBmjDHGGI+CHJvdKlzskzbGGGOMR0GeeLayiMgrIrJRRBYG7LtPRNaIyC/uNijg2J0ikikiGSJyZiU1o1qwHjFjjDHGeBTklbufZgrwD+C1YvufUtXHA3eISBfgIqAr0Br4TEQ6qmq5Jy+rCaxHzBhjjDEe+bnRnq0sqjoX2Bri5YcA01Q1R1WX4Uzq2vvQa1u9WSBmjDHGGI/8/CjPJiKjRGRewDYqxEtdLyIL3KHLxu6+JGBVQJnV7r5ayQIxY4wxxngU5EV5NlWdpKq9ArZJIVzmBcAHdAfWAU+4+4MlnWkFVb3asRwxY4wxxnjk5pc9HFkWVd1Q9FpEXgI+ct+uBtoGFG0DrD3sG1ZT1TIQq9v6pEhXwZhaKSmxaaSrYIwJg/yCwx8wE5FWqrrOfXseUPRE5XRgqog8iZOs3wH44bBvWE1Vy0DMGGOMMZUnv7B8gZiIvAWcAjQTkdXAvcApItIdZ9hxOTAaQFUXicg7wO9APjCmtj4xCRaIGWOMMaaY3MLyDU2q6sVBdk8upfyDwIPlrFaNZIGYMcYYYzzy1J7lCxcLxIwxxhjjkRf0wUZTGSwQM8YYY4xHjliPWLhYIGaMMcYYjzyxHrFwsUDMGGOMMR4hrPNtKoj1PRpjjDHGI0fEs5XFXcJoo4gsDNjXRERmichS92djd7+IyDMikukuf9SzEptS5VkgZowxxhiPPPFuIZgCDCy276/AbFXtAMx23wOchTOJawdgFM5SSLWWBWLGGGOM8ShvIKaqc4GtxXYPAV51X78KnBuw/zV1fAc0EpFWFVPz6scCMWOMMcZ45Ih3O0QtipY4cn82d/cnAasCyq1299VKFogZY4wxxqN4j5iIjBKReQHbqMO4fLDQTg/jetWaPTVpjDHGGI+8YnGRqk4CJpXzMhuKFv52hx43uvtXA20DyrUB1h5qXas76xEzxhhjjEcFDU1OB4a7r4cD/wnYf4X79GQfYEfREGZtZD1ixhhjjPEo3iNWFhF5CzgFaCYiq4F7gYeBd0RkJLASuMAtPgMYBGQCe4ERFVPr6skCMWOMMcZ4lDcQU9WLSzh0WpCyCow5hGrVSBaIGWOMMcYjl8JIV6HWsEDMGGOMMR7l7REzh84CMWOMMcZ45FmPWNhYIGaMMcYYjxy1QCxcLBAzxhhjjEe+9YiFjQVixhhjjPGwocnwsUDMGGOMMR6HMjQpIsuBXUABkK+qvUSkCfA2kAwsBy5U1W0VVtEawGbWN8YYY4xHvhZ4tnLor6rdVbWX+/6vwGxV7QDMdt+bABaIGWOMMcYjj0LPdhiGAK+6r18Fzj3sytUwFogZY4wxxiO3MN+zicgoEZkXsI0KcpoCM0VkfsDxFkXrSLo/m4erDdWF5YgZY4wxxqN4L5iqTgImlXFaX1VdKyLNgVki8kdl1a8msR4xY4wxxngcSo6Yqq51f24E/g30BjaISCsA9+fGSqpytWWBmDHGGGM8ig9NlkVE6otIYtFr4AxgITAdGO4WGw78p5KqXG3Z0KQxxhhjPMr5pCRAC+DfIgJObDFVVT8RkR+Bd0RkJLASuKBCK1oDWCBmjDHGGI+8wvIFYqqaDRwdZP8W4LQKqlaNZIGYMcYYYzzyCvMiXYVawwIxY4wxxnjklX9o0hwiS9Y3YdGxo495P870b1s3/8GNN1wd6WoZUyO1at2CaR+8zOxvP2DW/95nxKhLAejSLZ1/f/oGM758hw9nv8XRPbtFuKamqsovLPBspvKIqka6DuUWE5dU/Spt/KKioli5fD4nnHg2K1euiXR1TDkkJTaNdBVMCJq3aEbzFkewcMFi6ifU46PZ0xh1xc3c8+DtTH7hDb6c/TX9Tz+R0TeM4KIhIyNdXROCFVsWSDjv1ySxg+ff2a27lob1/rWJDU2asDvt1BPJzl5hQZgxlWTjhs1s3LAZgD2795K5dBktWjVHVUlIrA9AYoNENq7fFMlqmirMesHCp0oFYiIyQlX/Gel6mMp14YVDmPb2B5GuhjG1Qpu2rel6ZCd+mf8bD4x7lNfenci4B24lKkr488ArIl09U0WV96lJc+iqWo7Y/ZGugKlcsbGxDD77DP713keRrooxNV69+nWZOOVJHhj3KLt37eGyERcy/u7HOP6oM3hg3GM8+oz9lWuCyy/I92ym8oQ9R0xEFpR0COioqvElnDcKKFpEdJK77pWpfoZkZ2c/lJqa2jXSFTGVT0Q+Bqap6qtllNsNHOXORVQjiMjpwMuqmlxJ138ZyFbVh9z31wP3APWAJGDN4sWL53fq1Ok/wJMAOTk5e+Pj4+vjLM4swA6gQWXUzxgTmkgEYhuAM4FtxQ8B36hq67BWyITbtJtuuum4CRMmpES6IuYAEVmOMzN2PlAA/A68hvOfnsJSTq0yRGQR0N59WxfIw2kPwENFAUsF37MPcB9wPM7nthR4TlVfq+xArFg96gDbgWNUdRHO36evAluBm4vKZWdn70tNTT0L+BJnks1HgWMqu37GmJJFYmjyIyBBVVcU25bj/OVgaq56wIA33nhje6QrYoIarKqJOMHMw8AdwOTIVil0qtpVVRNUNQH4Cri+6H2wIExEDitHVkROBD4DZgOpQFPgemDQ4Vz3ELUE4t0gDKAvcDlwKvCLuw269tprVwBPAL8CD3FglMHvcD8XY0z5hD0QU9WRqvp1CccuCXd9TFjtBZpu3brVskCrMFXdoarTgWHAcBHpJiLxIvK4iKwUkQ0iMlFE6hadIyJDROQXEdkpIlkiMtDd/6WIXO2+ThOROSKyQ0Q2i8jbAeeriKS5rxuKyGsisklEVojI3SIS5R67UkS+duuyTUSWichZobRLRK4Wkbki8oyIbAXuDtj/h3u9j0WkbcA5XUTkMxHZ6pYZGnDJx4HJqvqYqm5Rx4+qelEJ979bRLJFZJeILBKRcwKOdXTrVvTZTHX3R7n13egeWyAiXdxjb4jIfSLSGVjk7tstIjNF5DsRwb1HdxHpIyKnz5w5M1VE2ojI/0TkZGC+iJwuIstF5C4RWQ+8FMrnaYypGFUtWd/UDpbfVw2o6g/AauAk4BGgI9AdSMPJQboHQER64wxj3gY0AvoBy4NccjwwE2gMtAGeLeHWzwINcXqZTgauAEYEHD8OyACa4QytTRZ3peEQnAAsBo4AHhGR8916D3H3fQ8UBUGJwCy3bc2BS4FJIpLuHusN/CvE+wIswempagg8CEwVkRbusQeB/3Lgs3nO3X8W0Afo4B67CGe40U9VF+Ou8ef2/p0R5N6PAynA7e61koFxAcfbAAlAO+C6crTJGHOYLBAzYWcPWlQra4EmwDXALaq6VVV34QxrFfX8jAReUdVZqlqoqmtU9Y8g18rDGfZsrar7g/WMi0g0Tk/cnaq6y01ZeAJnmK3IClV9SVULcPKgWuHkt4Vipaq+oKoFqroPGI2TP5ahqvnA34DeIpIEnAMsUdXXVDVfVecDHwDnu5+JAOtCvC+q+o6qrnM/o6k4wWqvgM8mGWjlfjb/C9jfAOjkXuN3VV0f6j3B6VUDrgZuVtWnVXUn8HcOfH/g5NLdp6q57udijAkTC8SMMaVJwplvsB7OMNZ2EdkOfILTgwTQFsgK4Vq34wQvP7hDc1cFKdMMiANWBOxb4dajiD8QUdW97suEEO4PsKrY+/bAcwHt2gwU4vQQtQf6Fh1zjw/DCfy24jx52CrE+xYNq/4acK1OOO0FuBWIBeaJyG8iMtxt30xgIvACUDQknBjqPV0tgXgg8N4f4fTyFdmgqrnlvK4xpgJYIGbCRkQGikiGiGSKyF8jXR9TOhE5FicA+gDYB3RV1Ubu1tBNigcnuPGVdT1VXa+q17hPRo8Gni/KCwuwmQM9Z0XaARW1DEPxx8RXASMD2tVIVeuq6vfusdnFjiWo6vVur+APwNDiNwhGRFJxgqlrgaaq2gj4Aycwxe0pu1pVWwFjcIZAU9xjT6tqT6Ab0AUYW842b8AJLvOA1QHfX8NSPhdjTJhYIGbCwh1yeg4n56ULcHFR0rGpWkSkgYicDUwD3lDVX3ESuJ8SkeZumSQROdM9ZTIwQkROc5PLk0SkU5DrXiAibdy323D+8fc8uOEON74DPCgiiSLSHifweKMSmgpOb9M4N+EdEWnk5o0BTAe6isglIhLrbr1FJN09fhtwtYiMFZEm7vk9ihLti0nAae8mp5hcjTvc6J53oTscCs40FAoUuPfrLc6TjHuAXIp9ZmVxP9P/APOBaHG0EZFguWTGmDCzQMyES28gU1Wz3SGQaTgJ0qbq+FBEduH0BI3DmQS0KEn+DiAT+E5EduJM25AO/qT+EcBTOBOEzsHbo1XkWOB7cSZvnQ7cpKrLgpS7ASfoyAa+xkmef6UiGlicqr6L08533XYtwJnnEFXd4b6+DCcXbD1OblW8e/wr4HS3zHL3ScwXgBlB7rMAeAanF20dThD2fUCR44AfRWQP8D4wRlVX4jz8MBknOFvunvvUITT1EmAZTs/lDpyHJjocwnWMMRUs7BO6mtrJ7WUYqKpFUxlcDhynqtdHtmbG1A4ikgx8pKrdIlwVY0yAkHvERKS9ODNFIyJ1DyFh1NRuwaYXsP8FGGOMqdVCCsRE5Bqc+XJedHe1wUngNSZUq3GerivSBmdqBGOMMabWCrVHbAzORIQ7AVR1Kd5Hn40py49ABxFJEZE4nDmMpke4TsYYY0xEhRqI5QTOMeM+wWPDSiZk7mSZ1wOf4sxs/k7AunjGmEokIm8B3wLpIrJaREZGuk7GGEdIyfoi8ijOUztX4DzRdB3wu6qOK+O8V4CzgY3BEkTdZUkm4CySuxe4UlV/Kqs+zZo10+Tk5DLrbYwxxhgTafPnz9+sqkcEOxZqIBaFs4zJGThJ15+qapkLw4pIP2A38FoJgdggnMBuEM7j2xNU9biyrturVy+dN29emfU2xhhjjIk0EZmvqr2CHYsJ8Ro3qOoEnEkdiy56k7uvRKo6131kuiRDcII0xZmfqJGItFLVkNdvM8YYY4wprqBQyckvIDe/kJz8QnLyCsktKGB/XiF79uWwcsUK8lQ59dijaNmwTsTqGWogNhxnCDHQlUH2lVcS3rXfVrv7DgrERGQUMAqgXbt2h3lbY4wxxlQGVSW/UA8EQMWCIc/7/AL3Z9HxAnILisoVuuUKDnrtBFXu+6LXxa6du38f+TvWk79tLQW7t5LY82wAtnz6D3b/OhO0kPrdTufVKf9k6DFtymhV5Sk1EBORi3FmZE4RkcAn3BKBLRVw/5DnllLVScAkcIYmK+DexhhjTI2iquQWFAYEOgEBTLGAJXig4wRDOQHBkDdwKiS3hGAo8HqFFfCvdFxMFPH+LZr4mKiAfdHUjY2iTsEe9u9ey97Nazn2+DOoVy+e+f99g6/+NZld2zZ7rvfsvWNpkFifr5sMZk2fziSnpNL1qKPp3ymyk0CU1SP2DU7vVDPgiYD9u3CWAjlcNreUMcaYGqGwUN3ApKxeoOK9O8V6gQrcYCgwOCqz5+jA8cMlghP0REcRHxvtD4biYg68ToiPoWn9aOJjo4iPjnJ+BgmW/K9jo4iLjva/9hzzn+Nez723iFBYWMjatWvJysqie/fuNGzYkA8//JD777+fzMxMduzY4a/3hOuH0rFjR97LP5YW+Rvx+Xz4fD7S0tLw+Xw0btwYgLOPuu6wP6OKVGogpqorgBXA8ZV0/+nA9SIyDSdZf4flhxljjCmvAv9QmLenZ3+JvUAHB0u5BQWl9AKV0HMU8D634PCDoCjBE5AEC1ga1Y0lPjGe+NhoN1gqFswU70mKjQooFx08UIqJ8l8vNlpwJjUIj7y8PJYvX079Zs1okNiYn3/+mXvvvZesrCyys7PZv38/AJ999hmnnXYaderUoWnTphx33HGeYCslJQWAoUOHMnTo0LDV/3CFlCMmIn2AZ4HOQBwQDexR1QZlnPcWcArQTERWA/cCsQCqOsHtoIcAACAASURBVBFncdxBOIsJ7+XAAsPGGGOqifyCknp6igc3wYOhknJ8igdDBwdOB4Kh/AoYC4uNFk8vUGDAUvQ+IT4meE9PKYGTP7AK2nPkvVdMdMgrD1Yre/bsIT8/n4YNG7J27VrGjx9PZmYmWVlZrFy5koKCAqZMmcLw4cMBWLZsGR06dGDgwIH+Hq1jjjkGgAEDBjBgwIBINqdChTp9xTycmdDfBXrhzCeWVtY8YpXFpq8wxhgnHyivQMvs6Sk9uAme41NiL1CQwKlC8oGiAwOb4kNiwYe9ytvTcyBYOjgYiouJIjoqfL1ANY2qkpeXR1xcHPv37+exxx4jKyvLH2ytX7+e8ePHc/fdd7N+/Xq6du3q780q6tE6+eST/b1aNU1FTF+BqmaKSLSqFgD/FJFvKqyGxhhTzahqKb1AwXN8ggYzQfJ9iucEHZxsXeAfCgvh/9JlCh6wHAhmEuJjiK8fMOxVLFiKC6Gn5+Bg6MD146KjiLIgqFp59dVXycjI8ARbw4YN48UXXyQ2NpaHHnqIZs2a4fP5GDRoED6fj9NOOw2Ali1bsmVLRTzvVzOEGojtddcH/MWdZX8dUL/yqmWMMSULzAcKmsxcLGApsaenxF6gID1HQRKqD1dgPpAnAAoIWBrVjS0x+bl4PtDBAVB00PyhwOuFOx/IVA9z5szht99+Iysry7+lp6fz/vvvA3D//fezatUqkpOTSUtL4/jjj6d///4AREdHs337duLj4yPZhGoj1EDscpx1Ka8HbsF50rH6ZMIZYypMUT5Q8QDIE9wEzfcpPTk6t1gwVVJydG5BIXkFh98NFBMlZQYs9eNjggx1lTYkViw4KpY/VDyZuqbmA5mqb+nSpSxcuNDfm5WVlUVsbCwzZswA4J577mHu3LnUq1cPn89Hx44dOf74A8/tffvttzRt2pSYmOBhhAVhoSszEBORaOBBVb0M2A/cX+m1MsYcJDAfqPSnuUoObsqfP3RwcnRBBSQEFeUDlTRsVSc2ioZ1Y8vs6Qk+JOYNjuqUkD9k+UCmJtu+fbt/6LBo+HD9+vV88skniAj33XcfU6dOBaBp06b4fD6OPPJI//mTJ08mISGBFi1aBO0xbdGiRdjaUtOVGYipaoGIHCEicaqaG45KGVPVFOUDBX+yK9iwVQiPwRfrOSo52brAn0tUUflAxXtm4mIO5PvUj4+hSf1Snv4KliMUYjK15QMZUzEKCwtZs2aNZ+gwMzOTyZMnk5iYyMMPP8wjjzziL9+mTRt8Ph/79++nbt263HXXXYwdOxafz0ejRo0Oun5aWlo4m1OrhTo0uRz4nzu7/p6inar6ZGVUyphAhYVaQu9O6XP6lNjTUxRIlbqMhjdYys2vuEkSDwpmAgKWBm4vUPHH5kt6+ssbUBV/pP7gx/CLJkk0xlR9ubm5rFixwjN8ePPNN9O+fXsmTpzImDFj/GVjYmJISUlh06ZNJCYmctlll9G3b198Ph8pKSnUrVvXc+2uXbuGuzmmBKEGYmvdLQpneSNTS+QXFJYYsJSWHF1a/lBIkysGnFMR+UDRbj5QiQFLTDSN68dRfPbokp7+Kj5hYmnJ1P75gaIsKdoY47V7926ys7P9wdagQYPo2rUrM2fO5KyzzqKw8MB/AuvVq8c555xD+/btOfXUU3nhhRf8c2y1bdvWk6/VrVs3unXrFokmmXIKKRBT1VLzwkTkWVW9oWKqZMpjyYZdZG3cfVBwE/pj9aUFThWTDxQbLaXm8MTHOj1Bh9PT4+058g6JxUVbUrQxJjJUlS1btvh7tDp37kyPHj1YsmQJ/fr1Y8OGDZ7yjRo1omvXrnTu3Jlx48Z5lugJzNfq1KkTnTp1ikSTTAULeR6xMvStoOuYcvg+ewuXvPx9qcFSWYum1o+PoXG9shOePT1Fpa0TFiQYsnwgY0xNVpSvlZmZScOGDenZsyd79uyhX79+ZGZmsnPnTn/ZO++8kx49etCqVSvOPvvsg9ZDbNiwIQBt27blgQceiFSTTBhVVCBmwmzz7hxueOtn2jauyz8u6Um9uOiD84csH8gYYypEbm4uy5YtIzc31/904bBhw/jtt9/Izs4mJycHgMsuu4zXX3+devXq0b59e0444QTPDPJFM8cnJiby8ssvR6w9puqwQKwaKihUbp72Czv25TFlRG+6tC51yU9jjDEh2LVrF5s2bSI1NRWA++67j6+++oqsrCxWrVpFYWEhJ598Ml9++SUA+fn5dOrUiT/96U/+Hq3OnTsDICL+yU+NKU1FBWLW7RJGz36+lK8zN/PI0CMtCDPGmBCpKtu3b6dx48YAvP7668yaNcufKL9x40ZSU1PJysoCnElP9+zZ43/6MC0tjS5duviv995770WkHaZmKVcgJiL1VXVPkEMTKqg+pgxfL93MhNlL+XPPJC7s1TbS1THGmCqlsLAQEecJ5Tlz5vDxxx971kPMzc1l7969REVF8fXXX/PFF1+QlpbG4MGD8fl8pKen+6/15ptvRrAlprYIKRATkROAl4EEoJ2IHA2MVtXrAFR1SqXV0Pht2Lmfm6b9TNoRCfzt3G6W/2WMqdUyMjL49NNPPZOaZmdns3LlSlq0aMGcOXN48skn/eshnnjiifh8PvLy8oiPj2fixIn296iJuFB7xJ4CzgSmA6jqryLSr9JqZQ6SX1DIDVN/Zm9uAW+P7km9OEvvM8bUbJs2bWLu3LmeHq2srCzee+89jjnmGL755htuuukm6tevj8/no0uXLgwePNgfXN12222MGzeO6OjooNe3IMxUBSH/a66qq4r9oS2o+OqYkjwxawk/LN/KU8OOJq25zalrjKn+9u/fz88//+xZoicrK4t77rmHgQMH8uuvv3L++ecD0KxZM3w+H3379vUvKD106FAGDRpE8+bNgwZVxWeTN6YqCjUQW+UOT6qIxAE3Aosrr1om0Od/bOCFL7O4uHc7zuvRJtLVMcaYkBQWFrJ69WpPb1ZmZiYXXHABw4YNY8WKFZxwwgmA0ztVtB5iUVB13HHHMX/+fM/8WoEaNGhAgwb2wJKp3kINxP6Ck5CfBKwGZgJjSj3DVIjV2/Zyy9u/0qVVA+4d3KXsE4wxJoxycnJYvny5J9jq3r07I0aMYN++fbRv395fNjY2ltTUVAYMGABASkoKH374oX9+rTp16niunZiYSM+ePcPaHmPCLdRATFT10kqtiTlIbn4h10/9mYJC5flLe1InNniegzHGVKZdu3Z5hg6bNGnCNddcAzgzwG/atMlfNiEhwX+sfv36TJkyhbZt2+Lz+WjTpo0nXysuLo6zzz47vI0xpooJNRD7RkSWAW8D76nq9kqsk3E9/PEf/LJqO89f2pPkZvUjXR1jTA2lqmzatMnfo5WTk8PIkSMB6N+/v38C0yIDBgzwB1t/+9vfqFu3rn9C0yOOOMKTrzV8+PCwtcOY6khUQ1vUWUR6AxcB5wK/A9NU9Y1KrFuJevXqpfPmzYvErcPm49/Wce2bP3HlCcncd07XSFfHGFPNFRQUsHr1arKysli3bh2XXuoMctx4443885//ZPfu3f6yrVu3Zs2aNQC88MIL7Ny50z+haWpqquVlGVNOIjJfVXsFPRZqIBZwsWbAk8ClqhqRsbKaHogt37yHwc9+TWrzBN4dfTxxMVGRrpIxphrIyclh2bJlZGVlceaZZxITE8Nzzz3Hs88+618nEZzE+H379hEfH89LL73EwoULPYtPJycn+59MNMYcvtICsVAndG0AnIfTI+YD/g30rrAaGr/9eQVc9+ZPREUJz13Sw4IwY4zHzp07ycrKomPHjtSvX58ZM2bw+OOP+9dDLPrPdXZ2NikpKTRs2JBu3boxZMgQ//Chz+cjLi4OwD/EaIyJjFBzxH4FPgAeUNVvK7E+td4DH/3O7+t2Mnl4L9o0rhfp6hhjwqwoX6tevXokJCSwYMECHnvsMX+ifFFi/Ny5cznppJMoKChg//799OvXz9+j5fP5aNWqFQCXXXYZl112WSSbZIwpRaiBWKqWdwzTlNsHP69h6vcr+cvJPk7r3CLS1THGVJKCggLy8vKoU6cO69at4+mnn/Y8lbh7927efPNNLrnkEvbu3cvcuXNJS0vj3HPP9QdbnTt3BmDw4MEMHjw4wi0yxhyqUgMxEXlaVW8GpovIQYGYqp5TaTWrZTI37uKuf/9G7+Qm/N8ZHSNdHWPMYVJVfy7WSy+95Jlna9myZTzwwAPccccd5Ofn8/TTT5OSkkJaWhonn3wyPp+PXr2cdJI+ffqwYsWKCLfGGFNZyuoRe939+fih3kBEBuJMBhsNvKyqDxc7fiXwGLDG3fUPVX35UO9XHe3Nzee6N3+ibmw0z1zcg5hoywszpjp5//33Wbp0qSfYOvfcc5kwYQIxMTGMHTuWevXq4fP56NatG+eeey7HH388AG3atGHv3r0lrodojKnZSg3EVHW++7K7qk4IPCYiNwFzSjtfRKKB54ABODPy/ygi01X192JF31bV68tV8xpCVbn7g4Us3bib167qTcuGdco+yRgTVvPmzeP333/3DB926tSJKVOmAHDTTTexevVqmjdvjs/no1+/fvTu7TzPFBsby4YNG2jSpEnQ9RBFxIIwY2qxUHPEhuP0agW6Msi+4noDmaqaDSAi04AhOPOQGeDdeat5/6c13HRaB07qcESkq2NMrbRmzRoWL17s783KysoiLi6Ot956C4AbbriB7777DhGhXbt2+Hw+UlNT/ed/8cUXtGjRgsTExKDXb9q0aVjaYYypfsrKEbsYuARIEZHpAYcSgS0hXD8JWBXwfjVwXJByQ0WkH7AEuEVVVxUvICKjgFEA7dq1C+HWVd/idTv5f/9ZSN+0ptx4WodIV8eYGmv//v0sW7bMM3S4ceNG3n77bQBuueUW3n33XcBZdic1NZXu3bv7z584cSJ16tQpcX6ttLS08DTEGFPjlNUj9g2wDmgGPBGwfxewIITrH9wPD8WT/j8E3lLVHBH5C/AqcOpBJ6lOAiaBM6FrCPeu0nbtz+O6N3+iYd1Ynh7Wg+ioYB+VMSZUO3bs8AdZRQHXM888Q7169Rg3bhxPPvmkv2xiYiI+n4+cnBzi4+O57bbbuPbaa/H5fCQlJR00VHj00UeHuznGmFqirByxFcAK4PhDvP5qoG3A+zbA2mL3COxZewl45BDvVW2oKne+/xsrtuzhrWv6cESizWBtTFlUlQ0bNniCrb/85S+0bt2a559/njFjxnjKN2/enHHjxpGSksJFF11Ejx49/HNsNWvWzJOvdeyxx4a7OcYYA5Q9NPm1qp4oIrvw9mQJoKpa1oJjPwIdRCQF56nIi3CGOgPv0UpV17lvzwEWl6cB1dHr363gowXruH1gOselWu6IMUXy8/NZtWqVP9g69dRT6dChA7NmzeK8885jz549/rJRUVH079+f1q1b07dvXx555BHPeoiB+VrHHnusBVvGmCqprB6xE92fwTNQy6Cq+SJyPfApzvQVr6jqIhF5AJinqtOBG0XkHCAf2IrzEECN9euq7Yz/6Hf6px/BX/r5Il0dY8Ju//79ZGdnk5WVhc/no0uXLixZsoSzzz6b5cuXk5eX5y87adIkOnToQGpqKldffbV/eR6fz+fJ1zr66KNt+NAYUy2FtOi3iPiA1W4e1ynAUcBrqrq9kusXVHVd9HvH3jz+9OxXqMJHN5xI4/pxka6SMZWiKF+rbt26dO7cmV27djF48GCysrJYs2aNfz3Ee+65h/vvv5+tW7cyevRozxI9JeVrGWNMdXPYi34D7wG9RCQNmAxMB6YCgyqmijWfqnLru7+yYed+3hl9vAVhplorytfau3evfxqHq666ikWLFpGVlcWWLU7q54gRI3jllVdISEjwDyUWBVppaWmkp6cD0KRJE/9Ti8YYU5uEGogVusOM5wFPq+qzIvJzZVaspnn5q2V8tngD95zdhR7tGke6OsaUKT8/n61bt9K8eXMAHnnkEb7//nsyMzPJzs5mz549nH766cyaNQuAlStX0qBBA84//3x/oHXkkUcCzqSln3/+ecTaYowxVVWogVieO6fYcKBoddnYyqlSzTNv+VYe/uQPBnZtyYi+yZGujjF+ubm5xMU5vbPvvvsuc+bM8ayH6PP5+OOPPwD46quvyM7OJi0tjdNOOw2fz8dRRx3lv9Znn30WkTYYY0x1FmogNgL4C/Cgqi5zn4J8o/KqVXNs2Z3D9VN/JqlRXR694KigS5wYEw7fffcdX3zxhWdS0+3bt7Nz505EhBkzZvDvf/8bn89H9+7dOf/88+nUqZP//I8++iiCtTfGmJoppEBMVX8Xkf8DOopINyCj+OLd5mCFhcot7/zK1r25vH/tCTSoY52IpvIsW7aMuXPneiY0zcrKYsmSJTRp0oQZM2Ywfvx4WrZsic/n49RTTyUtLY28vDzi4uKYOHEir7zyiv1nwRhjwiikQMx9UvJVYDnOHGJtRWS4qs6tvKpVf89/mcncJZt48LxudEtqGOnqmGpu+/bt/Pjjj571EDMzM5k6dSrdunVj1qxZjB49mqioKNq3b4/P5+P888/3TwcxduxYbr/9dhISEoJeP9jSPcYYYypXqEOTTwBnqGoGgIh0BN4CjqmsilV332Rt5slZSxjSvTWX9K4Za2OaypWXl8fSpUsP6tG64447OOWUU/j2228ZNMh5UDk+Pt4/xUNhYSEAQ4cOpX///rRv396f9xWoUaNGYW2PMcaYsoUaiMUWBWEAqrpERGycrQQbd+3nxrd+IaVZfR4670gb6jF+27dvP2g9xCFDhnDOOeeQkZHhf8oQoGHDhvh8Pvbu3QtAnz59+PLLL/H5fLRu3ZqoqCjPtZs2bUrTprZSgzHGVCehBmLzRGQy8Lr7/lJgfuVUqXorKFRufOtndufk8ebVx1E/PtSP2NQEqsr69es9wdaRRx7JhRdeyI4dO2jc2Dt1ScuWLenRowcAaWlpvPnmm/55tpo0aeIJ4hs3bszJJ58c1vYYY4ypXKFGCdcCY4AbcXLE5gLPV1alqrOnZi3hu+ytPH7B0aS3PKSVoUwVl5+fz8qVK/2BVqNGjbj44osBaN26NevXr/eXjYqK4tprr+XCCy+kYcOGTJgwgbZt2/rXQ6xfv76/bJ06dbjkkksOup8xxpiaK9SnJnNE5B/AbKAQ56nJ3EqtWTX0RcZG/vFFJsN6teX8Y9pEujrmMOzdu9e/HmJOTg4XXnghAGeddRafffYZ+fn5/rInn3yyPxAbO3Ys9erV8+dvFc/XuvHGG8PbEGOMMVVaqGtN/gmYCGTh9IilAKNV9ePKrV5wVXGtybXb9/GnZ76iRYM6fDCmL3VibX28qm7btm1kZWWxbt06Bg925in+v//7P9566y3Wrl3rL9e2bVtWrlwJwKOPPsr27ds96yEGy9cyxhhjilTEWpNPAP1VNdO9oA/4LxCRQKyqySso5PqpP5GbX8jzl/a0IKyKUFXWrVtHVlYWffv2JSoqikmTJvHSSy+RlZXFtm3bAIiJiWHfvn3ExMSQlJTEgAEDDlp8usjtt98eqeYYY4ypgUINxDYWBWGubGBjJdSnWnr0kz/4aeV2nr24B6lHBJ+jyVSO/Px8VqxYQVJSEnXq1GHmzJk8//zz/mT5ffv2AbB69WqSkpJQVRo3bsywYcP8QVZaWpq/R+uWW26JZHOMMcbUMqEGYotEZAbwDqDABcCPIvJnAFV9v5LqV+XNXLSel75axhXHt2fw0a0jXZ0aae/evURFRVGnTh0WLlzoCbSWL19OQUEB3377LX369GH79u0sXbqUtLQ0f8+Wz+fzP604evRoRo8eHeEWGWOMMY5Qc8T+WcphVdWrKq5KZasqOWIrt+zlT89+RXLT+vzr2uOJj7EhyUNVUFBAdHQ0GzZs4OWXX/ZMaLp27VreeecdLrjgAubMmcO5557r6c3y+XwMGjSIli1bRroZxhhjzEEOO0dMVUeUcYM7VfXvh1K56ionv4AxU38C4PlLe1oQFqJ9+/Yxbdq0g2aPv/vuuxk7dix79uzh7rvvplWrVvh8PgYMGEBaWhpdunQBoF+/fv7cLmOMMaa6q6jZRi8AalUg9uB/F/Pbmh1MuvwY2japF+nqVBmqyqxZszxBVmZmJueccw4PPfQQUVFRjBw50rMe4rBhwzjqqKMASE5OZvfu3Z75tQLZKgXGGGNqkooKxGrVv44f/rqW175dwTUnpXBG19o3HLZ48WIyMjI8i0+np6fzzDPPICJcfvnlbNy4kbp16/qHD9PS0gBnjcSsrCzatGlDbOzBq2RFRUWVGIQZY4wxNU1FBWJlJ5rVENmbdvPX9xbQs10jbh/YKdLVqRTbtm3z92gV/YyPj2fixIkAXH755cyf76xw1bhxY3w+HwkJB54W/fTTT2nevDmtWrUK2oOVkpISnoYYY4wxVZz1iJXD/rwCrnvzJ+JiovjHJT2Jja6ek3gWFhb659cqCra2bNniD7SuuuoqPvjgA3/51q1bc+yxx/rfP/PMM8TFxXmeRgzUvXv3ym+EMcYYUwNUVCD2bgVdp0q75z8Lydiwi39eeSytG9WNdHVKlZeXx4oVKzzDh3//+9+Ji4vj1ltv5emnn/aXjY6OJiUlhdzcXOLi4rjlllu48sor8fl8pKamUq+eNwfuhBNOCHdzjDHGmBoppEBMRDoCLwAtVLWbiBwFnKOqfwNQ1YcqsY5VwrvzVvHOvNXccGoap6Q3j3R1AGd+rcBA64orruCII47gxRdfZMyYMRQUFPjL1q1blxtuuIHk5GT+/Oc/07FjR/8UEO3atfPka/Xr1y8SzTHGGGNqnVDnEZsD3Aa8qKo93H0LVbVbJdcvqHDPI5axfhdDnvuaHm0b88bVxxEdFb6R2K1bt/qHD/v27Uu7du347LPPuOKKK1i3bp2n7Oeff07//v354YcfmD59umeJnpLytYwxxhhTuSpircl6qvpDsX/I8w+7ZtXAnpx8rn1zPgnxsUy4uHuFB2GB+Vpt2rQhNTWVJUuWcOmll5KZmcn27dv9ZadMmcLw4cNJSkpi4MCBB62HWJSv1bt3b3r37l2h9TTGGGNMxQs1ENvsLvStACJyPrCu9FOqP1Xlrn//xvLNe3jj6uNonljnkK5TlK8VExNDcnIyO3bs4IorrvAPKe7fvx+A8ePHc/fdd9OoUSOaNm1K7969D5pBHqBz58688sorFdZOY4wxxkRGqIHYGGAS0ElE1gDLgMtCOVFEBgITgGjgZVV9uNjxeOA14BhgCzBMVZeHWK9KNfWHlfznl7X83xkdOcHXrNSye/bsYdeuXbRs2RJV5cYbb/TPtbVixQoKCgoYNWoUL774IgkJCaxatYq0tDR/z5bP5/NPatq8eXM++eSTcDTRGGOMMREU6hJH2cDpIlIfiFLVXaGcJyLRwHPAAGA1zkLh01X194BiI4FtqpomIhcBjwDDytOIyrBwzQ7un/47/ToewXWnpKGq7N271z/Z6LPPPsv8+fP9vVrr1q3jrLPOYsaMGYgI3377LVFRUfTu3ZtLLrkEn89Hz549AecpxZ9++imSzTPGGGNMFRDqU5P3FHsPgKo+UMapvYFMN5BDRKYBQ4DAQGwIcJ/7+l/AP0RENJSnCCrJzv15DLv7OfYsW8jOxbkc+9wysrKyaNu2Lb/99hsA7733HpmZmfh8PgYOHEhaWho9evTwX6MqLEpujDHGmKot1KHJPQGv6wBnA4tDOC8JWBXwfjVwXEllVDVfRHYATYHNgYVEZBQwCqBdu3YhVvvQTP1+JSt/mMW+JV+zKDkZn89Hnz596Nq1q7/M7NmziY62hb6NMcYYc+hCHZp8IvC9iDwOTA/h1GCPGBbv6QqlDKo6CSdPjV69elVqb9mok1Lp9s4U+qQnERMT/COyIMwYY4wxh+tQ1+ipB6SGUG410DbgfRtgbUllRCQGaAhsPcR6VYioKOHEru1LDMKMMcYYYypCqDliv3GglyoaOAIoKz8M4Eegg4ikAGuAi4BLipWZDgwHvgXOBz6PZH6YMcYYY0y4hDqzfvuAt/nABlUNaUJXERkEPI0TwL2iqg+KyAPAPFWdLiJ1gNeBHjg9YRcVJfeXcs1NwIpQ7n8YmlEsT81UCfa9VD32nVRN9r1UPfadVE3h+F7aq+oRwQ6UGYiJSBSwIFLLGUWKiMwraTkCEzn2vVQ99p1UTfa9VD32nVRNkf5eyswRU9VC4FcRqdxHFY0xxhhjaplQs9FbAYtE5AcCprJQ1XMqpVbGGGOMMbVAqIFYAs7cYUUEZwb8mmxSpCtggrLvpeqx76Rqsu+l6rHvpGqK6PcSarL+T6ras9i+Bap6VKXVzBhjjDGmhiu1R0xErgWuA1JFZEHAoUTgf5VZMWOMMcaYmq6sZP2pwGCcub4GB2zHqOpllVy3sBCRgSKSISKZIvLXIMfjReRt9/j3IpIc/lrWLiF8J2NF5HcRWSAis4tNr2IqSVnfS0C580VERcSeDqtkoXwnInKh+/uySESmhruOtVEIf4e1E5EvRORn9++xQZGoZ20iIq+IyEYRWVjCcRGRZ9zvbIGI9AxWrlKoaq3dcOY2y8JZJSAO+BXoUqzMdcBE9/VFwNuRrndN3kL8TvoD9dzX19p3UjW+F7dcIjAX+A7oFel61+QtxN+VDsDPQGP3ffNI17umbyF+L5OAa93XXYDlka53Td+AfkBPYGEJxwcBH+PkwPcBvg9X3Q51iaOaojeQqarZqpoLTAOGFCszBHjVff0v4DQRCbY+pqkYZX4nqvqFqu51336Hs3SWqVyh/K4AjAceBfaHs3K1VCjfyTXAc6q6DUBVN4a5jrVRKN+LAg3c1w05eOk/U8FUdS6lL584BHhNHd8BjUSkVTjqVtsDsSRgVcD71e6+oGXUWU1gB9A0LLWrnUL5TgKNxPlfjKlcZX4vSAffsAAABoFJREFUItIDaKuqH4WzYrVYKL8rHYGOIvI/kf/f3r3HyFnVYRz/PlKkqE1JrSYqakVNEaqpaUUatVBLGjWGCNlYCAUW9Q9uElCIMRKseEO8YMRIC9i0QWKq9UKjmGrAEqlcWi2xF0AJIGBMRCIosSW0Pv5xzprtMjt9GzvzbjvPJ9nseznznt/Myc789pzzztHdkt7Xt+gGV5N2WQoskfQEcCvw8f6EFl3s62fPfjPoq1p36tkaextpkzKx/zR+vSUtAeYCJ/Q0ooC9tEtdgeMaYLhfAUWjv5VJlOHJEyk9x7+RNMv20z2ObZA1aZfTgZW2vy5pHnBTbZf/9D68GEdrn/WD3iP2BPDaUftH8sIu4v+VkTSJ0o3crXsz/j9N2gRJJwGfAU62/VyfYhtke2uXKcAsYL2kRylzLNZmwn5PNX3/usX287YfAR6kJGbRO03a5aPADwBs3wVMpqx3GO1p9NnTC4OeiG0E3izpDZJeTJmMv3ZMmbXA2XV7CLjddWZf9MRe26QOgS2nJGGZ89IfXdvF9jO2p9ueYXsGZe7eybY3tRPuQGjy/vVTys0tSJpOGap8uK9RDp4m7fIYsBBA0lsoidiTfY0yxloLnFXvnjweeMb2X/tR8UAPTdreJelCYB3lTpcVtrdJuhLYZHst8F1Kt/FDlJ6w09qL+ODXsE2+Slnt4Yf1vonHnOW2eqphu0QfNWyTdcAiSduB3cBltp9qL+qDX8N2+SRwg6RLKMNfw/kHv7ckfZ8yRD+9zs37LHAogO1llLl6HwAeAv4NnNO32NL2EREREe0Y9KHJiIiIiNYkEYuIiIhoSRKxiIiIiJYkEYuIiIhoSRKxiIiIiJYkEYuIiIhoSRKxiOgJSRdJul/SzeOcP1HShFqXUtK5ks6q28OSXj3q3I2Sjmkhpj3iiIiDy0B/oWtE9NT5wPvr0joHhPrFjiOGga3UZU5sf6xX9Uo6xPbucU7vEUdEHFzSIxYR+52kZcBRlPUmPyXpt5I2198zO5Q/QdJ99WezpCn1+GWSNkr6g6TPdalvhqQHJK2qZddIekk9t7Bec4ukFZIOq8evkrS9lv9aPbZU0qWShigLyt9cYzpc0npJcyWdJ+nqUXUPS7q2bi+RdG99zHJJh3SJ+VlJV0q6B5gn6Yr6XLdKur4utdIpjjmS7pD0O0nrJL1qnxsoIiaMJGIRsd/ZPpfSg7MAuA6Yb/vtwBXAlzo85FLgAtuzgfcAOyQtoixQfRwwG5gjaX6XamcC19t+G/BP4HxJk4GVwGLbb6WMApwnaRpwCnBsLf+FMfGvATYBZ9iebXvHqNNrgFNH7S8GVtc1AxcD76rPYzdwRpd4Xwpstf1O23cC37b9DtuzgMOBD46NA9gFXAsM2Z4DrAC+2KWOiJjgkohFRK9NpawLuhW4Bji2Q5kNwDckXQQcYXsXsKj+bAZ+DxxNSczG87jtDXX7e8C7KcnZI7b/WI+vAuZTErWdwI2STqWsLdeI7SeBhyUdL+nltY4NlEWc5wAbJd1X94/qcqndwI9G7S+QdI+kLcB76fw6zQRmAb+qdVwOHNk09oiYeDJHLCJ67fPAr22fImkGsH5sAdtXSfo5ZdHduyWdBAj4su3lDesZu3Cu6zVeWLAszHwcJVk6DbiQkvw0tRr4MPAA8BPbVlmBfpXtTze8xs6ReWG15+47wFzbj0taCkzu8BgB22zP24dYI2ICS49YRPTaVOAvdXu4UwFJb7S9xfZXKENxRwPrgI9Ielkt8xpJr+xSz+skjSQopwN3UhKlGZLeVI+fCdxRrznV9q3AxZShz7H+BUwZp64fAx+q9ayux24DhkZilDRN0uu7xDvaSNL19xrb0DhxPAi8YuR5SjpUUqees4g4QKRHLCJ67WpglaRPALePU+ZiSQsow3XbgV/Yfq7Ou7qrdDbxLLAE+Ns417gfOFvScuBPwHW2d0o6hzI0OgnYCCwDpgG31J4oAZd0uN5KYJmkHcAePVC2/yFpO3CM7Xvrse2SLgd+KelFwPPABcCf9/L6YPtpSTcAW4BHa5zjxTEEfEvSVMp7+DeBbXurIyImJtlje/MjIg4sdcjzZ3Wie0TEASNDkxEREREtSY9YRBww6l2Kt3U4tdD2U/2Op4n6PWGHjTl8pu0tbcQTERNLErGIiIiIlmRoMiIiIqIlScQiIiIiWpJELCIiIqIlScQiIiIiWpJELCIiIqIl/wUVOA2RJtsxwQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "************** RandomForestClassifier ***********\n",
      "\n",
      "\n",
      "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
      "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                       min_samples_leaf=1, min_samples_split=2,\n",
      "                       min_weight_fraction_leaf=0.0, n_estimators=10,\n",
      "                       n_jobs=None, oob_score=False, random_state=None,\n",
      "                       verbose=0, warm_start=False)\n",
      "\n",
      "\n",
      "accuracy score 0.9580838323353293\n",
      "Cross-val-score= 0.9420995143963363\n",
      "\n",
      "\n",
      "roc_auc_score 0.8504538939321549\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.99      0.98       299\n",
      "           1       0.86      0.71      0.78        35\n",
      "\n",
      "    accuracy                           0.96       334\n",
      "   macro avg       0.91      0.85      0.88       334\n",
      "weighted avg       0.96      0.96      0.96       334\n",
      "\n",
      "\n",
      "\n",
      "[[295   4]\n",
      " [ 10  25]]\n",
      "\n",
      "\n",
      "AxesSubplot(0.125,0.808774;0.62x0.0712264)\n"
     ]
    },
    {
     "data": {
      "image/png": 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9smnR7tpUIiYiIiJxyirS45aGuPvrwLoELz8DeNzdt7n7YoJBXSc0Pdpd2y5ZItZ18ORUhyDSLvXr3CPVIYhIC6iojC+nCSf5jp3oe044/2RDfmBmZwDzgR+5+3pgIPB2zDHLw23tkkrEREREJE5FVVrc4u5z3H18zJJIEvZHIAKMBVYCvw231zY2hjdT6LucXbJETERERJKnrGrne026+1fVn83sHuC5cHU5MDjm0EHAip2+4S5KJWIiIiISp9zT4pamMLP+MavfAap7VD4LnGxmHcxsODASeHenAt6FqURMRERE4pTXOX937czsMeAwoLeZLQd+DhxmZmMJqh2XAOcCuPsnZvYk8ClQAVzYXntMghIxERERqWGbNa4UzN1PqWXzffUcfyNwYyPDapOUiImIiEicctNcky1FiZiIiIjE0ZzfLUeJmIiIiMTZphKxFqNETEREROKoRKzlaPgKERERiVNu8UtD6pj0u6eZvWhmC8OfPcLtZmZ3hJN+LzCzfZP3JK2fEjERERGJs83ilwTMBabW2PZT4GV3Hwm8HK4DHEUwdthIgmmT/tgcMe+qlIiJiIhInMaWiNUx6fcM4IHw8wPAsTHbH/TA20D3GoO/titKxERERCROOR63mNk5ZjY/Zjmn4avQ191XAoQ/+4TbBwLLYo5r15N+q7G+iIiIxKlZHRlO8p3IRN+J0KTfMVQiJiIiInFqlog10VfVVY7hz9Xhdk36HUOJmIiIiMRppkTsWWBW+HkW8H8x288Ie09OBDZWV2G2R6qaFBERkThlVDXq+Dom/b4JeNLMzgaWAieEh88DpgEFQAlwVvNEvWtSIiYiIiJxGlsKVsek3wBH1HKsAxc2Iaw2SYmYiIiIxClvZImYNJ0SMREREYmzzZWItRQlYiIiIhKnQiViLUaJmIiIiMRR1WTLUSImIiIicZpSNWlmS4DNQCVQ4e7jzawn8AQwDFgCnOju65st0DZA44iJiIhInAqvjFsaYbK7j3X38eF6XRN/S0iJmIiIiMQppypu2Ql1TfwtISViIiIiEqesqiJuSXDSbwdeMLP3Y/bXNfG3hNRGTEREROLULAVLcNLvg919hZn1AV40s8+TFV9bohIxERERidOUNmLuviL8uRp4GphA3RN/S0iJmIiIiMSpWTXZEDPrZGZdqj8D3wI+pu6JvyWkqkkRERGJ08iekgB9gafNDILc4lF3/4eZvUftE39LSImYiIiIxCmvalwi5u6LgH1q2b6WWib+lu2UiImIiEic8qryVIfQbigRExERkTjlja+alCZSY31Jmtmzf0NR0fvMn/9CdFuPHt147rmH+eijV3nuuYfp3r1rCiMUaZv6D+zHk/93P6+8/Swvv/kMZ587E4DLrriA+R+/zPOv/ZnnX/szh3/zkBRHKq1VRVVl3CLJY+6e6hgaLSdn6K4XdDt08METKC4u4d57b2X8+G8BcOONV7J+/QZuueWP/PjH59O9ezeuvvqmFEcqieqV0yXVIUgC+vTtTZ++u/Hxgs/o1Lkjf//nk5x9+sVMP3YqxcUl3P37uakOURpp+bqPrSXv17PLyLjfs+s2L2zR+7cnKhGTpHnjjXdZt25D3Lajj57Cww8/BcDDDz/F9OnfSkVoIm3a6q++5uMFnwFQvKWEhV8sol//vimOSnYlKhFrOa0qETOzs1IdgyRXnz69WbUqGM9v1arV7LZb7xRHJNK2DRo8gDF7j+aD9xcAcOb3T+HFf/2FW+68gW7d1DRAaldeVRm3SPK0qkQMuC7VAYiItBUdO+Uw54Hfce3PbmbL5mIevP8JDt73KL416ThWr1rD//vF5akOUVqpisqKuEWSp8XbiJnZgrp2AaPcvUMd550DVE8iOiec90pav2HAc8CYcD3/gAMOuPedd975DdAfeBXIS01okmxmdhjwsLsPSnUsrYWZOTDS3QuScO3TgFnu/i0g829/+9u/Tz/99BHr16/PAWYCvwB+4+4PsOPfTRFJgVSUiPUFzgCm17Ksreskd5/j7uPDRUnYruvZGTNmXBJ+1nQXKWJmS8xsq5ltMbNVZjbXzDqnOq7GqPEM1cuAFrz/MDNzM8uosb2/md1nZivNbLOZfW5m14XTviSVuz8SJmEG3PeTn/yk//r163/h7p3d/ZkhQ4ZUhkkYwHcIpqARkRRKRSL2HNDZ3YtqLEsISkek7XgMeIugxGs5cDZw0+TJk7sCC4EpgLpMps50d+8MjAXGAVemOJ6mmB4mGdXLisacXDOJ2llm1pPgz3wOcKC7dyH4c94diDTnvRpwMHD6li1b+j7zzDMXAf8Fpt1+++2DgI+ABcBk4NJELtbc35OIxHB3LVpadAHmpzqG9r4AS4Bvxqz/Gvhb+PnbwAfAJmAZcG3MccMAJyjNXAp8DVwVsz8HmAusBz4FLgeWx+wfTfAfrg3AJ8AxMfvmAncBfwe2AG8A/YDbwut9Doyr6xlqPN8x4fU3hPcbXeO8KwiSkW0EA1sPAJ4C1gCLgYtjjp8AzA+/j6+AW8PtS8PvYku4HEhQ9fcRkFbPd+9AbgLfdTbwMEFNwQbgPaBvuO9MYBGwOYz3tJjt/w4/FwJVwNYwvg7h8d+Pucf3gM/C7/d5YGiNOC8k+E/T4lT/mdWipa0ura2xvrQPqlpuRcxsEHAUUN1mqZig+UB3gkThfDM7tsZp3yAo6TwCuMbMRofbf05Q8hMBjiRI2Krvkwn8FXgB6ANcBDxiZrFtBE8ErgZ6EyRJbwH/Cdf/DNyawPOMIiiN/SGwGzAP+KuZZcUcdkr4bN0JkpW/Ah8CA8Nn+qGZHRkeeztwu7t3DZ/ryXD7pPBndw9K494Cvgn8xd2rGoozVN93PQvoBgwGegHnAVvDKs47gKM8KHE7iKDEK467RwiSxepSw21AtMQwvM/PgO+G39O/wu8t1rHAAcAeCT6PiDSSEjFpca42fq3FM2a2maAkZjVBEoW7v+ruH7l7lbsvIPjlfGiNc69z963u/iFBAlM92e+JwI3uvs7dlxEkDNUmAp2Bm9y9zN3/SdBU4ZSYY5529/fdvRR4Gih19wfdvRJ4gqAKteYzbAiXZ8JtJxGU7r3o7uXALQQldQfFnHeHuy9z963A/sBu7n59GNci4B7g5PDYciDXzHq7+xZ3f7ue77QXsLKe/XEa+K7Lw+vluntl+L1sCvdVAWPMLMfdV7r7JwneMja2c4Ffuftn7l4B/BIYa2ZDY475Vfgutyb6TCLSOErERNqvY8MSlcOA3QlKnTCzA8zsFTNbY2YbCUpiag74tirmcwlBggVBFd+ymH1FMZ8HAMtqlBYVEZRCVfsq5vPWWtZrdig41t27h0t1SdKA2PuG91tW4z6xMQ4FBsQkdBsISoqqR0A9GxgFfG5m75nZ0dRtLUFv4IQ08F0/RFBd+LiZrTCzX5tZprsXEySb5wErzexvZrZ7oveMMRS4PeaZ1xE08q/rexKRJFAiJi3GzKaaWb6ZFZjZT1MdjwTc/TWC9lm3hJseBZ4FBrt7N2A2wS/oRKwkqEqrNiTm8wpgsJml1dj/ZRPCrs8KgiQDADOzMKbY+8SO27OMoA1U95ili7tPA3D3he5+CkF16s3An8PqwdrG/nkJ+E6NZ6xPnd+1u5e7+3XuvgdBad7RBNWYuPvz7j6FIOn7nKAEr05mdr+ZrSYo/Yt97nNrPHeOu78Zc4ymkxNJMiVi0iLMLB34A0FbpD2AU8xM7U5aj9uAKWY2FugCrHP3UjObAJzaiOs8CVxpZj3CtmcXxex7h6BN1E/MLDMcY2w68HizPEF8DN82syPCdmk/Imhv9mYdx78LbDKzK8wsx8zSzWyMme0PYGYzzWy3sGStes6uSoKG/VXAiJhr3Qp0BR6oruIzs4FmdquZ7V3Lvev8rs1sspntFf7d2URQVVlpZn3N7JgwGdxG0BC/oaHP5wJTa2ybTfCu9gzv183MTmjgOiLSzJSISUuZABS4+yJ3LyP45TsjxTFJyN3XAA8C/w+4ALg+bD92DdsbpyfiOoJqwcUEjfIfirlHGUFvxqMIelveBZzh7p83xzPE3CefYPDSO8P7TCdosF5Wx/GV4TFjw7i/Bu4laCgPQQLziZltIWi4f7K7l7p7CXAj8EZYvTfR3dcRlF6VA++E3+HLwEa2d4aIVd933Y+gg8Imgp6NrxH0okwjSC5XEFQnHhpep77v5PXw2NhtTxOU8D1uZpsIxhQ7qr7riEjza/GR9aV9MrPjganu/v1w/XTgAHf/QWojE2kfzGwY8Jy7ayR9kVYk4RIxMxtqZt8MP+eYWZfkhSVtUG1tjPS/ABERadcSSsTM7H8IisjvDjcNAp6p+wyRHSwnvhH3IGLGNBIREWmPEi0Ru5BgyoxNEPQiIuhBJJKo94CRZjY8HFjzZILeYiIiIu1WoonYttiGruG8Y6pWkoSFA0b+gGBcpM+AJxsxCKWI7AQzi877ambLzezsVMckIoGEGuub2a8Jum2fQdAd/QLgU3e/qoHz7icY+2Z1bQ1Ew/F9bgemEQwKeaa7/6eheHr37u3Dhg1rMG4RERGRVHv//fe/dvfdatuXaCKWRjC69LcIGl0/7+71DiAYnjeJYIybB+tIxKYRJHbTCOYzu93dD2jouuPHj/f58+c3GLeIiIhIqpnZ++4+vrZ9GQle4yJ3v52Y0ZvN7JJwW53c/fWwy3RdZhAkaQ68bWbdzay/uyc8V5uIiIhIXaqqnNKKSraWVbK1vJLS8ko2FpeyePESyqqcw/ffm37dslMWX6KJ2CyCKsRYZ9ayrbEGEj+X2fJw2w6JmJmdA5wDMGTIkJq7RUREZBdTnSSVlAWJUml5kCxtLaukpLyS0jB5qt62NWa9tHz7edXrW8NtpWWVbCkuZvOa5RSv+ZLKLevosm8wTeza53/Plg9fAK+i05hvMnfu/3L8foNS9h3Um4iZ2SkEU24MN7PYHm5dCCa33VkJjy3l7nOAORBUTTbDvUVERKQOlVUelxhFE5/qRKhs+3pp+faEqKRGQhX3s/q8cNu2iqpGx5WZbmRnppOdkUZWRTGVG7+icuNKRux/BP26ZpP/0hMseO4Bijd8HXfeTVdeTLcunXm3+9GsPGB3hg0fwV77jOXw3VM7CERDJWJvEpRO9QZ+G7N9M7CgGe6vsaVEREQaqTpJqjfpqSUJqplQVSdQcdfZySQpJzOdnKx0cjLTyc5Mp2NWsN69YybZmcH2jlnpZIfHRNdjzqu+RocMY9Pa1axaVsT4/cbRt3dP/jHvb1x33TUsLChg48aN0Xs/8tNTGTVqFE91XsrfMtcRiUSIRCLk5uYSiUTo0aMHACeOv7DZ3kNzqDcRc/cignnjDkzS/Z8FfmBmjxM01t+o9mEiIrIrq6zy2kuRyuKrz6pLh+JKkWpWxdVRGlXWhCQpKz2N7Mw0crLS6ZiVESZFadEkKScrI1jPDJKkjpkZ5GSlRROq4Lz0aDKVEx6THXNMZnrjp7AuLy9nyZIl9O7djR49evDBBx/w8yt+TmFhIYsWLaK0tBSAl156iUFHHEF2dja9evXigAMOiEu2hg8fDsBxxx3Hcccd1+g4UiWhNmJmNpFgAt3RQBaQDhS7e9cGznsMOAzobWbLgZ8DmQDuPhuYR9BjsoBg+IqzmvQUIiIiCaiorKK0ooqSsgpKy6qiiU1JWUWYCFXFJEIV0fVoyVEtCVXNBKqpSVK0NCgrpiQpM50eHbPCfWkxCVR6kCRlZcSUIKWF523fVp0k5WSmk9GEJKm5FBcXU1FRQbdu3VixYgU33HADBQUFFBYWsnTpUiorK5k7dy6zZs0CYPHixYwcOZKpU6dGS7T2228/AKZMmcKUKVNS9izNLdHhK+YTjIT+J2A8wXhiuQ2NI5YsGr5CRKTtqaisimlHVEVJeUV8Q+yyqu0JU/V6eUVMg+6q8Pjq86rCkqSKMHmqoqyyCUlSRlqdVWixCVPNBKq+qrfY9eyMtJQmSc3B3SkvLycrK4vS0lJ+85vfUFhYGE22Vq1axQ033MDVV1/NqlWr2HPPPaOlWdUlWoceemi0VKutaY7hK3D3AjNLd/dK4H/N7M1mi1BERFq1aJJUTy+2hBp019YmKdxfXtn4flhZGa96bAUAACAASURBVGnbE6GYKrROHTLo1Tm+vVFs+6Og6q32tkyx18nJTCc9rbZ+Ze3bAw88QH5+flyyddJJJ3H33XeTmZnJL3/5S3r37k0kEmHatGlEIhGOOOIIAPr168fatc3R369tSDQRKwnnB/xvOMr+SqBT8sISEZFElYdJUmx7o1rXd6JBd1OSpA4ZaTs0vs7JrE6SOmwvOaqtpKiW5Cm26q76PCVJyfHaa6/x0UcfUVhYGF3y8vL4y1/+AsB1113HsmXLGDZsGLm5uRx44IFMnjwZgPT0dDZs2ECHDh1S+Qi7jEQTsdMJ5qX8AXApQU/HXaclnIhIipRXVtXdELuehtm1JVR1JVAVVU1PkjrWqELr3CGD3Tp32OmqNyVJrdvChQv5+OOPo6VZhYWFZGZmMm/ePACuueYaXn/9dTp27EgkEmHUqFEceOD2fntvvfUWvXr1IiOj9jRCSVjiGkzEzCwduNHdZwKlwHVJj0pEJMncnfJKr3tgyLoGlKwnoao5oOTW8qYlSdmZafFVaFk7JklxJUeNrHrLzkgnTUlSm7Zhw4Zo1WF19eGqVav4xz/+gZlx7bXX8uijjwLQq1cvIpEIe+21V/T8++67j86dO9O3b1+CaaHj9e3bt8Wepa1rMBFz90oz283Msty9rCWCEpH2LZokNZAEBe2PKoJG2nWNiVRLQlU9oGTlTiRJQe+17VVvXbIz6Nu1Q+0lR/VUvdVcV5IkiaiqquLLL7+MqzosKCjgvvvuo0uXLtx0003cfPPN0eMHDRpEJBKhtLSUnJwcfvazn3HZZZcRiUTo3r37DtfPzc1tycdp1xKtmlwCvBGOrl9cvdHdb01GUCLSerk7ZZVV0a7/QSJUGdf1f/tQANU92SpiEqqYnmxhT7cgMaqI7tvaxCQpvv1QWrQbf7ecTPpFk6SM7V3/a1mP6/5fYziADhlpSpKkxZSVlVFUVBRXffjDH/6QoUOHMnv2bC68cPvApBkZGQwfPpw1a9bQpUsXZs6cycEHH0wkEmH48OHk5OTEXXvPPfds6ceROiSaiK0IlzSC6Y1EpBWqTpIaapAd1xA7kQbdNRKoJuRItTa2rk6S+nfNjpYi5cRWocWOl5SZsWPD7Zh1JUmyK9qyZQuLFi2KJlvTpk1jzz335IUXXuCoo46iqmr7cBsdO3bkmGOOYejQoRx++OH88Y9/jI6xNXjw4Lj2WmPGjGHMmDGpeCRppIQSMXevt12Ymd3p7hc1T0gibZO7s62iKr4hdgMDQ9Y3yW1d5zU2STJjh67/1YlO945Z9K+j91ptVW91VcV1yEirtZ2JSFvn7qxduzZaojV69GjGjRvHF198waRJk/jqq6/iju/evTt77rkno0eP5qqrroqboie2vdbuu+/O7rvvnopHkmaW8DhiDTi4ma4jkhLVSVJ9bZJi2x81pkF3bOlTAuMnx6mZJMWWFHXvmMWAWnqv7VDqVEuX/9hecEqSRHZOdXutgoICunXrxr777ktxcTGTJk2ioKCATZs2RY+98sorGTduHP379+foo4/eYT7Ebt26ATB48GCuv/76VD2StKDmSsREkqZmkrRDQ+z6Jr2tMaBkfZPcNjVJ2mHco8x0enbKIqf7jr3X6q5627HKTkmSSOtRVlbG4sWLKSsri/YuPOmkk/joo49YtGgR27ZtA2DmzJk89NBDdOzYkaFDh3LQQQfFjSBfPXJ8ly5duPfee1P2PNJ6KBGTneLulJZXxZUG1TbJbW3tj3aoiqtjQMnSiqYlSbHJT2yVWTRJqqe9USJVb0qSRNqWzZs3s2bNGkaMGAHAtddey7/+9S8KCwtZtmwZVVVVHHroobz66qsAVFRUsPvuu/Ptb387WqI1evRoAMwsOvipSH2aKxHTb6NWqKoqLEmK68VWY0Lb2G011rf3bKuqt+qtsdKqq9uyMrb3XAsTnd6ds3YsOWpC1ZuSJBGpyd3ZsGEDPXr0AOChhx7ixRdfjDaUX716NSNGjKCwsBAIBj0tLi6O9j7Mzc1ljz32iF7vqaeeSslzSNvSqETMzDq5e3Etu25vpnjajaoqp7SilnZEtQwMuTWu6q0qZkLbGkMDhJPabk+8Gj+5bZoRjo8U9FTrmJkRJkFp0SQp6L0Wm0BlkBOOp7Q9gcqIdv2Prmemk52VRla6kiQRSY6qqirMDDPjtdde4+9//3vcfIhlZWWUlJSQlpbGv//9b1555RVyc3OZPn06kUiEvLy86LUeeeSRFD6JtBcJJWJmdhBwL9AZGGJm+wDnuvsFAO4+N2kRtjLri8t4Z/HauKq3mr3admjQXWOk7WB705OkmqNn52Sm0adLdkwVWtr2ZCozPZokRcdLqmM4ACVJIrIryc/P5/nnn48b1HTRokUsXbqUvn378tprr3HrrbdG50P8xje+QSQSoby8nA4dOjB79mz9eycpl2iJ2O+AI4FnAdz9QzOblLSoWrFf/f0znpy/fIft6Wm2w5xt1UlOny6ZO1Sh1VX1VlvD7+qfmemmfzREpN1Ys2YNr7/+elyJVmFhIU899RT77bcfb775JpdccgmdOnUiEomwxx57MH369Oi/k5dffjlXXXUV6enptV5f/55Ka5Bw1aS7L6vxh7bxjYPagILVWxg3pDu/O3FsXEKVlZGW6tBERHYppaWlfPDBB3FT9BQWFnLNNdcwdepUPvzwQ44//ngAevfuTSQS4eCDD45OKH3ccccxbdo0+vTpU2tSVXM0eZHWKNFEbFlYPelmlgVcDHyWvLBar6XrSjhi974M690p1aGIiLRqVVVVLF++PK40q6CggBNOOIGTTjqJoqIiDjroICAonaqeD7E6qTrggAN4//3348bXitW1a1e6du3aos8k0twSTcTOI2iQPxBYDrwAXFjvGW3Qlm0VfL2ljCG9OqY6FBGRVmHbtm0sWbIkLtkaO3YsZ511Flu3bmXo0KHRYzMzMxkxYgRTpkwBYPjw4fz1r3+Njq+VnZ0dd+0uXbqw7777tujziLS0RBMxc/fTkhrJLmDZuhIAhioRE5F2ZPPmzXFVhz179uR//ud/gGAE+DVr1kSP7dy5c3Rfp06dmDt3LoMHDyYSiTBo0KC49lpZWVkcffTRLfswIq1MoonYm2a2GHgCeMrdNyQxplaraG2YiPVUtaSItB3uzpo1a6IlWtu2bePss88GYPLkydEBTKtNmTIlmmz94he/ICcnJzqg6W677RbXXmvWrFkt9hwiu6JEJ/0eaWYTgJOBq8zsU+Bxd384qdG1MkvXBUOoDempEjER2bVUVlayfPlyCgsLWblyJaedFlRyXHzxxfzv//4vW7ZsiR47YMCAaCJ24oknMnXq1OiApiNGjIhrl3XOOee07IOItDGN6TX5LvCumf0SuBV4AGhXiVjR2hK65WTSrWNmqkMREdnBtm3bWLx4MYWFhRx55JFkZGTwhz/8gTvvvDM6TyIEDeOPP/54OnTowF577cX3vve9uMmnhw0bFr3m+eefn6KnEWkfEh3QtSvwHYISsQjwNDAhiXG1SkvXlah9mIik1KZNmygsLGTUqFF06tSJefPmccstt0TnQ/RwYtZFixYxfPhwunXrxpgxY5gxY0a0+jASiZCVlQUQrWIUkdRItETsQ+AZ4Hp3fyuJ8bRqS9eVsNfAHbtQi4g0l+r2Wh07dqRz584sWLCA3/zmN9GG8tUN419//XUOOeQQKisrKS0tZdKkSdESrUgkQv/+/QGYOXMmM2fOTOUjiUg9Ek3ERnj1f7PaqYrKKr5cv5Vv79U/1aGIyC6usrKS8vJysrOzWblyJbfddltcr8QtW7bwyCOPcOqpp1JSUsLrr79Obm4uxx57bDTZGj16NADTp09n+vTpKX4iEWmqehMxM7vN3X8IPGtmOyRi7n5M0iJrZVZsKKWiylU1KSIJcXfMjK1bt3LPPffEjbO1ePFirr/+eq644goqKiq47bbbGD58OLm5uRx66KFEIhHGjx8PwMSJEykqKkrx04hIsjRUIvZQ+POWpt7AzKYSDAabDtzr7jfV2H8m8Bvgy3DT79393qbeL1mKoj0mNXSFiMT7y1/+wsKFC+OSrWOPPZbbb7+djIwMLrvsMjp27EgkEmHMmDEce+yxHHjggQAMGjSIkpKSOudDFJG2rd5EzN3fDz+OdffbY/eZ2SXAa/Wdb2bpwB+AKQQj8r9nZs+6+6c1Dn3C3X/QqMhb2FIN5irSbs2fP59PP/00rvpw9913Z+7cuQBccsklLF++nD59+hCJRJg0aRITJgT9mTIzM/nqq6/o2bNnrfMhmpmSMJF2LNE2YrMISrVinVnLtpomAAXuvgjAzB4HZgA1E7FWb+naErLS0+jXNbvhg0Vkl/Lll1/y2WefRUuzCgsLycrK4rHHHgPgoosu4u2338bMGDJkCJFIhBEjRkTPf+WVV+jbty9dunSp9fq9evVqkecQkV1PQ23ETgFOBYab2bMxu7oAaxO4/kBgWcz6cuCAWo47zswmAV8Al7r7spoHmNk5wDkAQ4YMSeDWzatobQmDeuaQlrbj/2hFpHUrLS1l8eLFcVWHq1ev5oknngDg0ksv5U9/+hMQTLszYsQIxo4dGz1/9uzZZGdnM2zYMDp06LDD9XNzc1vmQUSkzWmoROxNYCXQG/htzPbNwIIErl9b1lKz0f9fgcfcfZuZnUcwUOzhO5zkPgeYAzB+/PgW78FZtK6EoRpRX6TV2rhxYzTJqk647rjjDjp27MhVV13FrbfeGj22S5cuRCIRtm3bRocOHbj88ss5//zziUQiDBw4cIeqwn322aelH0dE2omG2ogVAUXAgU28/nJgcMz6IGBFjXvElqzdA9zcxHsljbuzdG0xBwzvmepQRNotd+err76KS7bOO+88BgwYwF133cWFF14Yd3yfPn246qqrGD58OCeffDLjxo2LjrHVu3fvuPZa+++/f0s/jogI0HDV5L/d/Rtmtpn4kiwD3N271nFqtfeAkWY2nKBX5MkEVZ2x9+jv7ivD1WOAzxrzAC1hXXEZxWWVmmNSJMkqKipYtmxZNNk6/PDDGTlyJC+++CLf+c53KC4ujh6blpbG5MmTGTBgAAcffDA333xz3HyIse219t9/fyVbItIqNVQi9o3wZ+0tUBvg7hVm9gPgeYLhK+5390/M7Hpgvrs/C1xsZscAFcA6gk4ArUpR2GNSiZjIzistLWXRokUUFhYSiUTYY489+OKLLzj66KNZsmQJ5eXl0WPnzJnDyJEjGTFiBN///vej0/NEIpG49lr77LOPqg9FZJeU6FyTEWB52I7rMGBv4EF339DQue4+D5hXY9s1MZ+vBK5sTNAtbelaDV0h0hjV7bVycnIYPXo0mzdvZvr06RQWFvLll19G50O85ppruO666+jduzf77LMP3/3ud+PmQxw4cCAAkUiE2267LZWPJCKSFIkOX/EUMN7McoH7gGeBR4FpyQqsNSkKE7HBKhETAba31yopKYkO4/C9732PTz75hMLCQtauDZp+nnXWWdx///107tw5WpVYnWjl5uaSl5cHQM+ePaO9FkVE2pNEE7GqsJrxO8Bt7n6nmX2QzMBak6XrSujXNZvsTA26KO1HRUUF69ato0+fPgDcfPPNvPPOOxQUFLBo0SKKi4v55je/yYsvvgjA0qVL6dq1K8cff3w00dprr72AYNDSf/7znyl7FhGR1irRRKw8HFNsFlA9u2xmckJqfZauK1b7MGmTysrKyMrKAuBPf/oTr732Wtx8iJFIhM8//xyAf/3rXyxatIjc3FyOOOIIIpEIe++9d/RaL730UkqeQURkV5ZoInYWcB5wo7svDntBPpy8sFqXorUlTBq1W6rDENkpb7/9Nq+88krcoKYbNmxg06ZNmBnz5s3j6aefJhKJMHbsWI4//nh233336PnPPfdcCqMXEWmbEkrE3P1TM/sxMMrMxgD5NSfvbqu2llWyevM2DeYqrd7ixYt5/fXX4wY0LSws5IsvvqBnz57MmzePG264gX79+hGJRDj88MPJzc2lvLycrKwsZs+ezf3331/rfIgiIpIcifaaPIxgxPslBGOIDTazWe7+evJCax2qJ/seoh6TkmIbNmzgvffei5sPsaCggEcffZQxY8bw4osvcu6555KWlsbQoUOJRCIcf/zx0eEgLrvsMn7yk5/QuXPnWq9f29Q9IiKSXIlWTf4W+Ja75wOY2SjgMWC/ZAXWWlQnYkN7dUpxJNLWlZeXs3Dhwh1KtK644goOO+ww3nrrLaZNCzoqd+jQITrEQ1VVFQDHHXcckydPZujQodF2X7G6d+/eos8jIiINSzQRy6xOwgDc/QszaxeN9YvWBiN5q7G+NIcNGzbsMB/ijBkzOOaYY8jPz4/2MgTo1q0bkUiEkpLgPwMTJ07k1VdfJRKJMGDAANLS0uKu3atXL3r16tWizyMiIjsn0URsvpndBzwUrp8GvJ+ckFqXpetK6NIhgx4d20XeKTvJ3Vm1alVcsrXXXntx4oknsnHjRnr06BF3fL9+/Rg3bhwAubm5PPLII9Fxtnr27BnXXqtHjx4ceuihLfo8IiKSXIkmYucDFwIXE7QRex24K1lBtSZFa0sY0qujGjBLVEVFBUuXLo0mWt27d+eUU04BYMCAAaxatSp6bFpaGueffz4nnngi3bp14/bbb2fw4MHR+RA7ddpe5Z2dnc2pp566w/1ERKTtSrTX5DYz+z3wMlBF0GuyLKmRtRLL1pWwe/8mTbUpu7CSkpLofIjbtm3jxBNPBOCoo47ipZdeoqKiInrsoYceGk3ELrvsMjp27Bhtv1WzvdbFF1/csg8iIiKtWqK9Jr8NzAYKCUrEhpvZue7+92QGl2qVVc6y9SVM2bNvqkORJFi/fj2FhYWsXLmS6dODcYp//OMf89hjj7FixYrocYMHD44mYpMnT2bcuHFx8yEOGDAgeuzll1/esg8hIiK7tMb0mpzs7gUQnQT8b0CbTsRWbtxKeaUztKd6TO6K3J2VK1dSWFjIwQcfTFpaGnPmzOGee+6hsLCQ9evXA5CRkcHWrVvJyMhg4MCBTJkyJTpFT3WyVe0nP/lJqh5HRETaoEQTsdXVSVhoEbA6CfG0KkvXVg9doR6TrVVFRQVFRUUMHDiQ7OxsXnjhBe66665oY/mtW7cCsHz5cgYOHIi706NHD0466aRokpWbmxvtgXjppZem8nFERKSdSTQR+8TM5gFPAg6cALxnZt8FcPe/JCm+lIoO5qqhK1KqpKSEtLQ0srOz+fjjj+MSrSVLllBZWclbb73FxIkT2bBhAwsXLiQ3NzdashWJRKK9Fc8991zOPffcFD+RiIhIINFELBv4CqjuO78G6EkwAbgDbTIRK1pXQkaaMaB7TqpDafMqKytJT0/nq6++4t57740b0HTFihU8+eSTnHDCCaxdu5bHHnuMSCTCfvvtFy3ZGjZsGAAnnnhitD2XiIhIa5dor8mz6ttvZle6+6+aJ6TWY+naEgb1yCE9TUNXNJetW7fy+OOP7zB6/NVXX81ll11GcXExV199Nf379ycSiTBlyhRyc3PZY489AJg0aVK0bZeIiMiuLtESsYacALS5RKxoXTFDNLVRo7g7L774YlySVVBQwDHHHMMvf/lL0tLSOPvss+PmQzzppJPYe++9ARg2bBhbtmyJG18rlsZzExGRtqS5ErE299vR3SlaW8K4wT0aPrid+eyzz8jPz4+bfDovL4877rgDM+P0009n9erV5OTkRBvD5+bmAsEciYWFhQwaNIjMzB1nK0hLS6szCRMREWlrmisR82a6TquxcWs5m0sr2mWPyfXr10dLtKp/dujQgdmzZwNw+umn8/77wQxXPXr0IBKJ0Llz5+j5zz//PH369KF///61lmANHz68ZR5ERESklVOJWB2KwqErBrfBHpNVVVXR8bWqk621a9dGE63vfe97PPPMM9HjBwwYwP777x9dv+OOO8jKyorrjRhr7NixyX8IERGRNqC5ErE/NdN1Wo2idbv2GGLl5eUUFRXFVR/+6le/Iisrix/96Efcdttt0WPT09MZPnw4ZWVlZGVlcemll3LmmWcSiUQYMWIEHTvGfwcHHXRQSz+OiIhIm5ToFEejgD8Cfd19jJntDRzj7r8AcPdfJjHGlFi6thho3WOIlZSUxCVaZ5xxBrvttht33303F154IZWVldFjc3JyuOiiixg2bBjf/e53GTVqVHSMrSFDhsS115o0aVIqHkdERKTdSbRE7B7gcuBuAHdfYGaPAr9IVmCptnRdCbt16UDHrOYqNGyadevWRasPDz74YIYMGcJLL73EGWecwcqVK+OO3XfffaNzIf70pz+Nm6Intr3WIYccwiGHHJKKxxEREZEYiWYZHd393RoNryuSEE+rUbS2pEVKw2Lbaw0aNIgRI0bwxRdfcNppp1FQUMCGDRuix86dO5dZs2YxcOBApk6dusN8iNXttSZMmMCECROSHruIiIjsnEQTsa/Dib4dwMyOB1bWf8qubem6Eg4c0atZrlXdXisjI4Nhw4axceNGzjjjjGiVYmlpKQA33HADV199Nd27d6dXr15MmDAhbj7E6smnR48ezf33398ssYmIiEjqJJqIXQjMAXY3sy+BxcDMRE40s6nA7UA6cK+731RjfwfgQWA/YC1wkrsvSTCupCgtr2TVplKGNKKhfnFxMZs3b6Zfv364OxdffHF0rK2ioiIqKys555xzuPvuu+ncuTPLli0jNzc3WrIViUSig5r26dOHf/zjH8l6PBEREWklEp3iaBHwTTPrBKS5++ZEzjOzdOAPwBRgOcFE4c+6+6cxh50NrHf3XDM7GbgZOKkxD9Hclq8vwT2+x6S7U1JSEh1s9M477+T999+PlmqtXLmSo446innz5mFmvPXWW6SlpTFhwgROPfVUIpEI++67LxD0UvzPf/6TkmcTERGR1iPRXpPX1FgHwN2vb+DUCUBBmMhhZo8DM4DYRGwGcG34+c/A783M3D1lg8QuXVdCScG7/PXel3lszfJosjV48GA++ugjAJ566ikKCgqIRCJMnTqV3Nxcxo0bF73G/PnzUxW+iIiI7CISrZosjvmcDRwNfJbAeQOBZTHry4ED6jrG3SvMbCPQC/g69iAzOwc4B2DIkCEJht00RWtLKP7sNR76vzcYNmwYkUiEiRMnsueee0aPefnll0lPT09qHCIiItK2JVo1+dvYdTO7BXg2gVNrG3G/ZklXIsfg7nMI2qkxfvz4pJaWnbz/EMY8/RBjh/erdT5EQEmYiIiI7LS0Jp7XERiRwHHLgcEx64OAFXUdY2YZQDdgXRPjahY5WensP2pwnUmYiIiISHNItI3YR2wvpUoHdgMaah8G8B4w0syGA18CJwOn1jjmWWAW8BZwPPDPVLYPExEREWkplkjOY2ZDY1YrgK/cPaEBXc1sGnAbQQJ3v7vfaGbXA/Pd/VkzywYeAsYRlISdXN24v55rrgGKErn/TuhNjXZq0irovbQ+eietk95L66N30jq1xHsZ6u671bajwUTMzNKABe4+JhmRtVZmNt/dx6c6Domn99L66J20TnovrY/eSeuU6vfSYBsxd68CPjSz5HZVFBEREWlnEh2+oj/wiZm9S8xQFu5+TFKiEhEREWkHEk3EOhOMHVbNCEbAb8vmpDoAqZXeS+ujd9I66b20PnonrVNK30uijfX/4+771ti2wN33TlpkIiIiIm1cvSViZnY+cAEwwswWxOzqAryRzMBERERE2rqGGus/CkwnGOtresyyn7vPTHJsLcLMpppZvpkVmNlPa9nfwcyeCPe/Y2bDWj7K9iWBd3KZmX1qZgvM7OUaw6tIkjT0XmKOO97M3MzUOyzJEnknZnZi+PflEzN7tKVjbI8S+DdsiJm9YmYfhP+OTUtFnO2Jmd1vZqvN7OM69puZ3RG+swVmtm9txyWFu7fbhWBss0KCWQKygA+BPWoccwEwO/x8MvBEquNuy0uC72Qy0DH8fL7eSet4L+FxXYDXgbeB8amOuy0vCf5dGQl8APQI1/ukOu62viT4XuYA54ef9wCWpDrutr4Ak4B9gY/r2D8N+DtBG/iJwDstFVtTpzhqKyYABe6+yN3LgMeBGTWOmQE8EH7+M3CEmdU2P6Y0jwbfibu/4u4l4erbBFNnSXIl8ncF4Abg10BpSwbXTiXyTv4H+IO7rwdw99UtHGN7lMh7caBr+LkbO079J83M3V+n/ukTZwAPeuBtoLuZ9W+J2Np7IjYQWBazvjzcVusxHswmsBHo1SLRtU+JvJNYZxP8L0aSq8H3YmbjgMHu/lxLBtaOJfJ3ZRQwyszeMLO3zWxqi0XXfiXyXq4FZprZcmAecFHLhCb1aOzvnmaT6PAVbVVtJVs1u5Emcow0n4S/bzObCYwHDk1qRAINvJdwBo7fAWe2VECS0N+VDILqycMISo7/ZWZj3H1DkmNrzxJ5L6cAc939t2Z2IPBQ+F6qkh+e1CFlv+vbe4nYcmBwzPogdiwijh5jZhkExcj1FW/KzknknWBm3wSuAo5x920tFFt71tB76QKMAV41syUEbSyeVYP9pEr036//c/dyd18M5BMkZpI8ibyXs4EnAdz9LSCbYL5DSZ2EfvckQ3tPxN4DRprZcDPLImiM/2yNY54FZoWfjwf+6WHLPkmKBt9JWAV2N0ESpjYvLaPe9+LuG929t7sPc/dhBG33jnH3+akJt11I5N+vZwg6t2BmvQmqKhe1aJTtTyLvZSlwBICZjSZIxNa0aJRS07PAGWHvyYnARndf2RI3btdVk+5eYWY/AJ4n6Olyv7t/YmbXA/Pd/VngPoJi4wKCkrCTUxdx25fgO/kNwWwPfwr7TSx1TbeVVAm+F2lBCb6T54FvmdmnQCVwubuvTV3UbV+C7+VHwD1mdilB9deZ+g9+cpnZYwRV9L3Dtnk/BzIB3H02MFskegAABKxJREFUQVu9aUABUAKc1WKx6d2LiIiIpEZ7r5oUERERSRklYiIiIiIpokRMREREJEWUiImIiIikiBIxERERkRRRIiYiIiKSIkrERCQpzOxiM/vMzB6pY/9hZtaq5qU0s/PM7Izw85lmNiBm371mtkcKYoqLQ0TalnY9oKuIJNUFwFHh1Dq7hHBgx2pnAh8TTnPi7t9P1n3NLN3dK+vYHReHiLQtKhETkWZnZrOBEQTzTV5hZm+a2Qfhz7xajj/UzP4bLh+YWZdw++Vm9p6ZLTCz6+q53zAz+9zMHgiP/bOZdQz3HRFe8yMzu9/MOoTbbzKzT8Pjbwm3XWtmPzaz4wkmlH8kjCnHzF41s/Fmdr6Z/Trm3mea2Z3h55lm9m54zt1mll5PzFvM7Hozewc40MyuCZ/1YzObE061Ulsc+5nZa2b2vpk9b2b9G/2CRKTVUCImIs3O3c8jKMGZDPwRmOTu4+D/t3d3IVJXYRzHv78oMio2NuqmKKlASYuFLS1KwQqvukhZ2sKk7K6MsOgmEBEqrS4qKkorwoVuFqwIeqHCXsClF6OC1e0NekG6KSOpYDdSfl2cM7BNM+N0sc0s/D4wMP8zZ855Zi6Gh+f8dx+2ANtavOUeYKPtIWAFMC1pNaVB9TJgCBiWtLLDtouAZ2xfDPwG3C5pAbALGLV9EeUU4DZJg8AaYEmdf39T/LuBT4B1todsT896eTewdtb1KDBeewaOAlfUz3EUWNch3pOB/baX294LPGn7UttLgZOAa5vjAI4ATwAjtoeB54EHOuwREX0uiVhEzLUBSl/Q/cCjwJIWcyaARyTdCZxm+wiwuj4+Az4FFlMSs3YO2p6oz18ArqQkZ9/Z/rqOjwErKYnaDPCcpLWU3nJdsf0z8K2kyySdXveYoDRxHgb2Sfq8Xp/XYamjwIuzrldJ+kjSJHAVrb+nRcBS4O26x2bg7G5jj4j+k3vEImKu3Qe8a3uNpIXAe80TbD8o6TVK090PJV0DCNhue2eX+zQ3znVd498TS2PmZZRk6QbgDkry061x4HrgS+Bl21bpQD9m+94u15hp3BdWK3dPAZfYPihpK7CgxXsEHLB9+X+INSL6WCpiETHXBoAf6/NbWk2QdL7tSdsPUY7iFgNvArdKOqXOOUvSmR32OUdSI0G5EdhLSZQWSrqgjq8H3q9rDth+HdhEOfps9jtwapu9XgKuq/uM17E9wEgjRkmDks7tEO9sjaTrUI1tpE0cXwFnND6npBMktaqcRcQ8kYpYRMy1h4ExSXcD77SZs0nSKspx3RTwhu0/631XH5RiE38ANwE/tVnjC+BmSTuBb4Cnbc9I2kA5Gj0e2AfsAAaBV2olSsBdLdbbBeyQNA38owJl+1dJU8CFtj+uY1OSNgNvSToO+AvYCPxwjO8H24clPQtMAt/XONvFMQI8LmmA8hv+GHDgWHtERH+S3VzNj4iYX+qR56v1RveIiHkjR5MRERERPZKKWETMG/WvFPe0eOlq27/83/F0o/6fsBObhtfbnuxFPBHRX5KIRURERPRIjiYjIiIieiSJWERERESPJBGLiIiI6JEkYhERERE9kkQsIiIiokf+Bs8AsGNyiKVCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "************** KNeighborsClassifier ***********\n",
      "\n",
      "\n",
      "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
      "                     weights='uniform')\n",
      "\n",
      "\n",
      "accuracy score 0.9041916167664671\n",
      "Cross-val-score= 0.8661986268427002\n",
      "\n",
      "\n",
      "roc_auc_score 0.6563784042044912\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.97      0.95       299\n",
      "           1       0.57      0.34      0.43        35\n",
      "\n",
      "    accuracy                           0.90       334\n",
      "   macro avg       0.75      0.66      0.69       334\n",
      "weighted avg       0.89      0.90      0.89       334\n",
      "\n",
      "\n",
      "\n",
      "[[290   9]\n",
      " [ 23  12]]\n",
      "\n",
      "\n",
      "AxesSubplot(0.125,0.808774;0.62x0.0712264)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "************** SGDClassifier ***********\n",
      "\n",
      "\n",
      "SGDClassifier(alpha=0.0001, average=False, class_weight=None,\n",
      "              early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n",
      "              l1_ratio=0.15, learning_rate='optimal', loss='hinge',\n",
      "              max_iter=1000, n_iter_no_change=5, n_jobs=None, penalty='l2',\n",
      "              power_t=0.5, random_state=None, shuffle=True, tol=0.001,\n",
      "              validation_fraction=0.1, verbose=0, warm_start=False)\n",
      "\n",
      "\n",
      "accuracy score 0.8652694610778443\n",
      "Cross-val-score= 0.7788852802140985\n",
      "\n",
      "\n",
      "roc_auc_score 0.5211180124223602\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.96      0.93       299\n",
      "           1       0.19      0.09      0.12        35\n",
      "\n",
      "    accuracy                           0.87       334\n",
      "   macro avg       0.54      0.52      0.52       334\n",
      "weighted avg       0.82      0.87      0.84       334\n",
      "\n",
      "\n",
      "\n",
      "[[286  13]\n",
      " [ 32   3]]\n",
      "\n",
      "\n",
      "AxesSubplot(0.125,0.808774;0.62x0.0712264)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "************** LogisticRegression ***********\n",
      "\n",
      "\n",
      "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
      "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
      "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
      "                   warm_start=False)\n",
      "\n",
      "\n",
      "accuracy score 0.8862275449101796\n",
      "Cross-val-score= 0.8643860300053937\n",
      "\n",
      "\n",
      "roc_auc_score 0.5958910654562829\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.96      0.94       299\n",
      "           1       0.42      0.23      0.30        35\n",
      "\n",
      "    accuracy                           0.89       334\n",
      "   macro avg       0.67      0.60      0.62       334\n",
      "weighted avg       0.86      0.89      0.87       334\n",
      "\n",
      "\n",
      "\n",
      "[[288  11]\n",
      " [ 27   8]]\n",
      "\n",
      "\n",
      "AxesSubplot(0.125,0.808774;0.62x0.0712264)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "************** GradientBoostingClassifier ***********\n",
      "\n",
      "\n",
      "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
      "                           learning_rate=0.1, loss='deviance', max_depth=3,\n",
      "                           max_features=None, max_leaf_nodes=None,\n",
      "                           min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                           min_samples_leaf=1, min_samples_split=2,\n",
      "                           min_weight_fraction_leaf=0.0, n_estimators=100,\n",
      "                           n_iter_no_change=None, presort='auto',\n",
      "                           random_state=None, subsample=1.0, tol=0.0001,\n",
      "                           validation_fraction=0.1, verbose=0,\n",
      "                           warm_start=False)\n",
      "\n",
      "\n",
      "accuracy score 0.9491017964071856\n",
      "Cross-val-score= 0.9502122261501093\n",
      "\n",
      "\n",
      "roc_auc_score 0.8832775919732442\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.97      0.97       299\n",
      "           1       0.74      0.80      0.77        35\n",
      "\n",
      "    accuracy                           0.95       334\n",
      "   macro avg       0.86      0.88      0.87       334\n",
      "weighted avg       0.95      0.95      0.95       334\n",
      "\n",
      "\n",
      "\n",
      "[[289  10]\n",
      " [  7  28]]\n",
      "\n",
      "\n",
      "AxesSubplot(0.125,0.808774;0.62x0.0712264)\n"
     ]
    },
    {
     "data": {
      "image/png": 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lYsYYY4zx0Ai6I031sETMGGOMMV6WiEWNJWLGGGOM8Sq0Tb+jxRIxY4wxxnhY12T02PIVxhhjjPEqLvEelahg0+9WIrJARJa7P1u650VEHnQ3/f5BRAbWcI1qLUvEjDHGGONVWOQ9Kuff9Ls3MASY4W7s/Rdgkar2ABa57wFOxlk7rAcwDWcF/gbJEjFjjDHGeGhRieeotLzqelX91n29E/Bv+j0eeNot9jRwqvt6PPCMOr4AWoQs/tpg2BgxY4wxxnhoBN2R5QnZ9Ludqq4HJ1kTkbZusfI2/V6/319cR1mLmDHGGGO8Coo9h4hME5GlQce0cB8Ls+l3eWzTb5e1iBljjDHGQ4u9uw2p6hxgTkWfCbfpN5ArIu3d1rD2QJ573jb9dlmLmDHGGGM8tKjMc1SmvE2/cTb3nuq+ngq8FXT+XHf25BBgu78Ls6GxFjFjjDHGeESSfIUob9PvO4FXRORCYDVwhnttHjAGyATygfMPNOa6yhIxY4wxxniUFVQtEatg02+AkWHKKzCj6pHVP5aIGWOMMcajzHY4ihpLxIwxxhjjYYlY9FgiZowxxhiP0kKbyxctlogZY4wxxqO0uLzhXqa6WSJmjDHGGI/SYmsRixb7TRtjjDHGo6Qo3nNURkSeEpE8Efkp6NwtIrJWRP7nHmOCrl0vIpkikiEiJ9VQNeoEaxEzxhhjjEdJSZXbaeYC/wCeCTn/f6p6T/AJEekDnAn0BToAC0Wkp6qW0gBZi5gxxhhjPEqL4zxHZVR1MbAlwtuPB15S1UJVXYGzqOvg/Y+2brNEzBhjjDEeRSXxnuMAXCoiP7hdly3dcx2BNUFlctxzDVKd7Jps0uHYWIdgTIPUoVmrWIdgjImCklJvO42ITAOmBZ2a424EXpFHgNsAdX/eC1xA+BX4db+DrePqZCJmjDHGmJpTUuZNxNykq7LEK/Qzuf7XIvI48K77NgfoHFS0E7BuvwKtB6xr0hhjjDEeRWXxnmN/iEj7oLenAf4ZlW8DZ4pIkoh0B3oAXx1QwHWYtYgZY4wxxqNYq9ZOIyIvAscDbUQkB7gZOF5E+uN0O64ELgJQ1Z9F5BXgF6AEmNFQZ0yCJWLGGGOMCVEcdhhX+VT1rDCnn6yg/O3A7VUMq16yRMwYY4wxHoViI5eixRIxY4wxxngUi+01GS2WiBljjDHGw/b8jh5LxIwxxhjjUWgtYlFjiZgxxhhjPKxFLHpsNJ4xxhhjPIrFe1TG3cIoT0R+CjrXSkQWiMhy92dL97yIyIMikulufzSw5mpS+1kiZowxxhiPQvEeEZgLjA459xdgkar2ABa57wFOxlnEtQfOtkmPVEfMdZUlYsYYY4zxqGqLmKouBraEnB4PPO2+fho4Nej8M+r4AmgRsgp/g2KJmDHGGGM8ilHPISLTRGRp0DGt8rvQTlXXA7g/27rnOwJrgsrluOcaJBusb4wxxhiP0O7I/dn0uwLh2ti0mu5d51iLmDHGGGM8QlvE9lOuv8vR/Znnns8BOgeV6wSs2+9g6zhLxIwxxhjjUU2J2NvAVPf1VOCtoPPnurMnhwDb/V2YDZF1TRpjjDHGo4iyKpUXkReB44E2IpID3AzcCbwiIhcCq4Ez3OLzgDFAJpAPnF89UddNlogZY4wxxqOqrWCqelY5l0aGKavAjP0Iq16yRMwYY4wxHsVVbBEz+88SMWOMMcZ4FKolYtFiiZgxxhhjPEqsRSxqLBEzxhhjjId1TUaPJWLGGGOM8difrkkRWQnsBEqBElUdJCKtgJeBbsBKYKKqbq22QOsBW0fMGGOMMR4lWuo5qmCEqvZX1UHu+/I2/jYuS8SMMcYY41FMmec4AOVt/G1clogZY4wxxqOorMRzREiB+SLyTdCm4OVt/G1cNkbMGGOMMR6hrWBuYjUt6NQcdyPwYMNUdZ2ItAUWiMivNRxmvWCJmDHGGGM8QseFuUlXaOIVWmad+zNPRN4ABuNu/K2q60M2/jYu65o0xhhjjEdVuyZFpKmIpPhfA78DfqL8jb+Ny1rEjDHGGONRxZmSAO2AN0QEnNziBVX9r4h8TfiNv43LEjFjjDHGeBSXVS0RU9Vs4PAw5zcTZuNvs5clYsYYY4zxKC4rjnUIDYYlYsYYY4zxKK5616TZTzZY30RFz54+ln49P3Bs2fQrl1/2x1iHZUy91L5jO15660kWffEWCz9/gwsuOgeAPv3SeXP+c7z38au8u+glDh/YL8aRmtqqpKzUc5iaI6oa6xiqLCGxY90L2gTExcWxeuU3HH3MKaxevTbW4Zgq6NCsVaxDMBFo264NbdsdzE8/LKNps2T+88HL/GnKFdx8x3U88cizfLTwU0aceCwXX34+k8ZdEOtwTQRWb/lRovl9rVJ6eP6e3bJzeVS/vyGxrkkTdSNPOIbs7FWWhBlTQ/JyN5GXuwmA3bvyyfxtBYe0b4eqkpLSFICUg5qRu2FjLMM0tZi1gkVPrUrEROR8Vf1XrOMwNWvixPG89PKbsQ7DmAahU+cO9D2sF9998wO33nAXz772GLNm/5k4EU4bPSXW4ZlaqqqzJs3+q21jxG6NdQCmZjVq1Iixp/yO115/N9ahGFPvJTdtwmNP/x+33nAXu3buZsr5k5g96+8MOXQUs2+8m7sfnB3rEE0tVVJa4jlMzYn6GDER+aG8S0BPVU0q53PB+1yF2+PK1A3js7Oz70hNTe0b60DM/hORlcAfVXWhiNwApKpqvZ19Ec06isgtQJqqTj7AWzUC3gXeB+4DKCwszG/cuPEKYIaqflxWVrY9Pj7+deBUYDlwNfCEqqYf4HcbYyIUixaxdsC5wNgwx+byPqSqc1R1kHtYElZ3nfXAAw8kxzqI+k5EzhSRL0Vkt4jkua8vEXfZ6+qkqndUR4IiIt1EREUkIejceSJSKiK73CNbRKYf6HdVEsfxIpITfK666hj0HWeLyFK3TutF5D0ROaa67o/zD9sngWW4SRjA2rVrRVVnqOpHwAnz589fD4wCOqnqYFX9xJIwY6IrFonYu0AzVV0VcqwEPopBPCZ6koFRzz333LZYB1KficjVwAPA3cAhOP/4uRgYBiSGKR8f1QCrbomqNlPVZsAE4O8iMiDWQe0vEZkJ3A/cgfNsugAPA+Or8WuGAVOAE4D/uceY6dOnrwLuBb4H7rj77rufBVaq6u4D/cLgBNoYUwWqaocdUT2ApbGOob4eQHNgN3B6BWXmAo8A89yyJwK/B74DdgBrgFtCPjMFWIXTaj0LWAmc6F67BXguqOwQ4HNgG85f+McHXfsIuA34DNgJzAfauNdWAwrsco+hwHnApyGxfAWcHfR+HPCz+30fAb2DrvV2z21zy4wLujYG+MWNYy3wZ6ApsAcoC4qjQ3AdgW5unFPdmDcBs4Lu2wR4GtiK0yJ1LZAT9Hx2AWdU8HxCf5+vAhuA7cBioG9FdXDPt8H5R+82YAvwCc4/vJf6nx1wIVAAlLox3Qoc74/VvU8H4HVgI7ACuDwkzteA53D+u/ljrP/7t8OOunjUtsH6pmGwruWaMxRIAt6qpNzZwO1ACvApTkJ2LtACJymbLiKnAohIH5zEbQrOX8ytgU7hbioiHYH/AH8FWuEkN6+LyMEh330+0Banhe7P7vnh7s8W6rSALQlz/yOBnjgJBSLSE3gRuBI4GCe5fEdEEkWkEfAOTrLXFrgMeF5E/F1vTwIXqWoK0A/4QJ2WoZOBdW4MzVR1XTm/w2OAdJx99G4Skd7u+ZtxkrVUnG6/4LFeQ4HGwBvl3DOc94Aebh2+BZ4PurZPHdzzVwM57u+kHXADTvIY+LOnqk/itJT6WxxvDv5SEYnD+f19D3R063mliJwUVGw8TjLWIiQuY0yELBEzUac2xq8mtQE2qWpgmpOIfC4i20Rkj4j4k523VPUzVS1T1QJV/UhVf3Tf/4CT3Bznlp0AvKuqi1W1EPh/OC1G4UwG5qnqPPdeC3CSpjFBZf6lqr+p6h7gFaB/JXUa4sa/C6c17FmcgeUAk4D/qOoCVS0G7sFpkToap2WuGXCnqhap6gc4rURnuZ8tBvqIyEGqulVVv60kjlC3quoeVf0eJ1nxb3g8EbjDvWcO8GDQZ1oT8nwqo6pPqepO93d/C3C4iDSvpA7FQHugq6oWqzP2S6v4Z+9I4GBVne3+/rKBx4Ezg8osUdU33We9pwr3Nsa4LBEzpn7ZDLQJHq+jqkeragv3mv/P/JrgD4nIUSLyoYhsFJHtOC0lbdzLHYLLu61G5U2s6Qqc4SZO20RkG07LUfugMhuCXufjJEsV+UJVW6gzRuwQoC/O+Cp/bKuCYitzY+3oj9s957fKvQZwOk6CuEpEPhaRoZXEEaq8enh+XyGv93k+FRGReBG5U0SyRGQHTrci7H025dXhbiATmO9OcPhLpJUK0hXoEPIsb8BpYQtXN2PMfrBEzESNiIwWkQwRydzPvxhM5ZYAhVQ+8Dt03ZoXgLeBzqraHHgUZ+YdwHqgs7+giCTjtOyEswZ41k2c/EdTVb0zgtgrXUtHVXNxxiyNdU+tw0kY/LGJG+ta91pnt4vNr4t7DVX9WlXH43T5vYnTOhdRHJVYj7frtnPQ6yU447JOjfBeZ+M8yxNxxpd1c88LlF8HtwXtalVNxfld3SwiW0XkpyrUYw2wIuRZpqhqcOumbTdnzAGyRMxEhTsz758442/6AGe5Y49MNVLVbTiDrh8WkQki0kxE4kSkP85A9PKkAFtUtUBEBuMkAH6vAaeIyDEikgjMpvz/dzwHjBWRk9zWnMbuchBhx5SF2IjT5ZlaXgERaQ2chjPwHpzE4/ciMtIdE3Y1TiL6OfAlzti3a0WkkYgcj5OUvOSOITtHRJq7XZo7cAatA+QCrYO6/6rqFeB6EWnpjpm71H9BVbcDNwH/FJFTRSTZje1kEfl7mHuluPXZjDPr2N8SSEV1EJFTRCTNTUx34LTYXVvFenwF7BCR60Skifs8+7nj9Iwx1cQSMRMtg4FMVc1W1SLgJap3ur5xqerfgZk4f/Hm4SQWjwHX4SQo4VwCzBaRnTiJgr91CFX9GZiB02q2Hmc2YE64m6jqGpznegNOYrUGuIYI/l+jqvk4Ewg+c7vChriXhvrXEcOZhbgRZ+A9qpqBMy7tIZzZi2OBse6YpiKcGZUnu9ceBs5V1V/d+04BVrpdfhe798G9/iKQ7cbRobLYQ8zG+f2sABbiJLKFQfW8D+f53Bj0O7oUp0Ur1DM43alrcWZHfhFyPWwdcAb3L8SZDbkEZ7mMBVWphKqW4vw++7t12QQ8gdMyZ4ypJlFfWd80TCIyARit7qKYIjIFOEpVL634k8bUbe4CtGeq6nGVFq7ZOLrhTLroF8s4jDFeEbeIiUhXETnRfd1ERFJqLixTD4Vb0d3+FWDqHRFpLyLD3C7hdJzu0qosV2GMaUAiSsRE5E84zeuPuac6Eb4Z3Zjy5OAdtNwJZzC1MfVNIs7/K3firOv1Fk63qDHG7CPSLSlm4Izx+RJAVZeLSNsai8rUR18DPUSkO854lzPxDgg3pl5Q1VU4i6saY0ylIu2aLHQHvgKBPcWsW8lEzF3A8lLgfZwB16+4g8CNMTVMRF7EGbSfLiI5InJhrGMyxjgiGqzvTqvehrMFymU4M6x+UdVZlXzuKeAUIC/cAFF3avUDOAsS5gPnRbK6dZs2bbRbt26Vxm2MMcYYE2vffPPNJlU9ONy1SBOxOJwNYn+HM+j6fVV9PILPDceZPv1MOYnYGJzEbgxwFPCAqh5V2X0HDRqkS5curTRuY4wxxphYE5FvVHVQuGuRjhG7TFUfwNlnzH/TK9xz5VLVxe6U6fKMx0nSFPhCRFqISHtVXR9hXMYYY4wxHqpKUWkZRSVlFJY4P4tKyigqLaOwuIz8ggJWrVpFscIJRx5K++ZNYhZrpInYVJwuxGDnhTlXVR3x7lWW457bJxETkWnANIAuXboc4NcaY4wxpjr5k5/gxMebBJVSWFxGYZgEqbCk1PuZoDL7XPMnVEHng6/5719WVEDJ9g2UbF1H6a4tpAw8BYDN7/+DXd/PBy2jab8TmTv3X0w4IpLNP2pGhYmYiJyFM7Otu4i8HXQphfI3/a2KiNeWUtU5wBxwuiar4buNMTlH/CsAACAASURBVMaYOk1V90lcwiY3bkvQ3nLlJDfFpZ5kap/7hWtlCkqeqoMIJCXEkRgfR2JCPEkJcc77oJ+NG8VxUOMEdM9O8netY/emtRw67Hc0aZLE1+8+x+LXnmTnlo2e+/7j5pmkpDTls1bjyBnSm67dU+l32OGc0Cu2i0BU1iL2OU7rVBvg3qDzO4EfquH7bW0pY4wxdUpZmdvtFUFy42nRqShZcu/lb80Jd628rrbqECe4iU48iW4SFJr8JCcm0MJ/rZE/UYrzfC44adpbLj6o3N5rjYOuBc4nxJEQJzhz+aCsrIx169aRlZVF//6H07x5c9555x1uvfVWMjMz2b59e6AO/zfjD/Ts2ZPXiwbRtjgXn8+Hz+cjLS0Nn89Hy5YtATjlsOnV8jurLhUmYu56OKuAoTX0/W8Dl4rISziD9bfb+DBjjDGh/MlPuNabcMnNPi06FSVLnkSnNEwrkffzxaXV0ykTJ+xNfEITGDe58Sc/lSU3oUlTYny8J7nxlIuP9yRSSQlxJMTHbuvp4uJiVq5cSZs2bWjasiXfffcdN998M1lZWWRnZ1NQUADAwoULGTlyJI0bN6Z169YcddRRnmSre/fuAJx++umcfvrpMatPVUU0RszdfPchoDfOqtHxwG5VPaiSz70IHA+0EZEc4GagEYCqPgrMw5kxmYmzfMX5+1ULY4wx1S44+SmvKys4uSncJyHaOzZo32Rpb3dWcEJVXldZTSQ/SQlhWnTinZaflslxYRKkfROYfe4RHxf23vsmUrFNfqJt9+7dlJSU0Lx5c9atW8dtt91GZmYmWVlZrF69mtLSUubOncvUqVMBWLFiBT169GD06NGBFq0jjjgCgFGjRjFq1KhYVqdaRbp8xVKcldBfBQbhrCeWVtk6YjXFlq8wxtRXgeSnuIzCchKY0OSm3PE8YbrDQrvKglt7Qj9XXclPfJzs053lJCYh3VnlJDGhCUxS8PkwXWVhu8kSGl7yE02qSnFxMYmJiRQUFHD33XeTlZUVSLY2bNjAbbfdxo033siGDRvo27dvoDXL36J13HHHBVq16pvqWL4CVc0UkXhVLQX+JSKfV1uExhgTQ6VlGjSoubTc8TieBKaigdFhWozCdZWFm/VVUlY9yU9CnJSTqOxtnWmWlEDrpnvLJIYkON5WnHiSwiRK/mvhWon894qPCzcvy9RlTz/9NBkZGZ5ka9KkSTz22GM0atSIO+64gzZt2uDz+RgzZgw+n4+RI0cCcMghh7B5c3XM96sfIk3E8kUkEfifu8r+eqBpzYVljKnv/MlPcLdWRIOfK5r1FWZmVySzvqo7+dknUQlKblIaJ3gTlXDJTUhXWbjWnspaiSz5MQfi448/5scffyQrKytwpKen8+9//xuAW2+9lTVr1tCtWzfS0tIYOnQoI0aMACA+Pp5t27aRlJQUyyrUGZEmYlNw9qW8FLgKZ6Zj3RkJZ4wBnOQnNIGp0uDnCGZ97dtaVBp21ldpNSY/lXVl+ZOfcNf2SW5CusqSypnZ5b+WFG/Jj6l7li9fzk8//RRozcrKyqJRo0bMmzcPgJtuuonFixeTnJyMz+ejZ8+eDB26d97ekiVLaN26NQkJ4dMIS8IiV2kiJiLxwO2qOhkoAG6t8aiMqUdKSisZp1NBS1BoolTx+j/hW4uC711dyU+jeKm0K6t5k0Zhp8BXlNx4p77vOzA6UC7oWpwlP8bsY9u2bYGuQ3/34YYNG/jvf/+LiHDLLbfwwgsvANC6dWt8Ph+HHnpo4PNPPvkkzZo1o127doGlJIK1a9cuanWp7ypNxFS1VEQOFpFEVS2KRlDGHCh/8hOa3IQb1Bw6Vifs4OeQcUP7Tn0vZ6HDak5+glt0wiUxzRMbBbXURDbry/uz4hYjfyuRJT/GxFZZWRlr1671dB1mZmby5JNPkpKSwp133sldd90VKN+pUyd8Ph8FBQU0adKEG264gZkzZ+Lz+WjRosU+909LS4tmdRq0SLsmVwKfuavr7/afVNX7aiIoU/eoKiVBA573JkGlEU9pL3fwczkzuyoaU1RNuU/IgoWhg5qdn80TG5GUkhR2PE9lyc0+rUXlDH625MeYhqeoqIhVq1Z5ug+vvPJKunbtyqOPPsqMGTMCZRMSEujevTsbN24kJSWFyZMnM2zYMHw+H927d6dJE+9ein379o12dUw5Ik3E1rlHHM72RqYWCE5+QvfzKohg1te+rT+V7+flaSkKaQmq7uQntLUnOAlqkZxYznie+HKTJ/+1sGv/hBn8bMmPMaam7dq1i+zs7ECyNWbMGPr27cv8+fM5+eSTKSvbu3J+cnIy48aNo2vXrpxwwgk88sgjgTW2Onfu7Bmv1a9fP/r16xeLKpkqiigRU9UKx4WJyEOqeln1hFQ/FBSX8nnWJvKLyt/PqzBMN1dFM7v2Jk179wKLYBm4iASP5Qm7jk98HMnJCft0Z4XbDqO85CYpft9roS1BlvwYY+oTVWXz5s2BFq3evXszYMAAfvvtN4YPH05ubq6nfIsWLejbty+9e/dm1qxZni16gsdr9erVi169esWiSqaaRbyOWCWGVdN96o0nPsnmnvm/VVjG35VV0QKGTZsmhB+nE9QKtPdcmMHPlQyM9n9PuMGYxhhjKucfr5WZmUnz5s0ZOHAgu3fvZvjw4WRmZrJjx45A2euvv54BAwbQvn17TjnllH32Q2zevDkAnTt3Zvbs2bGqkomi6krETIj5v+RyaMfm3Dvx8PDrBFnyY4wxdUZRURErVqygqKgoMLtw0qRJ/Pjjj2RnZ1NYWAjA5MmTefbZZ0lOTqZr164cffTRnhXk/SvHp6Sk8MQTT8SsPqb2sESsBuTuKOCHnO1cc1I6PdvZkDpjjKkLdu7cycaNG0lNTQXglltu4ZNPPiErK4s1a9ZQVlbGcccdx0cffQRASUkJvXr14ve//32gRat3794AiEhg8VNjKlJdiZg17QRZtCwPgFF9bJ0VY4ypLVSVbdu20bJlSwCeffZZFixYEBgon5eXR2pqKllZWYCz6Onu3bsDsw/T0tLo06dP4H6vv/56TOph6pcqJWIi0lRVd4e59EA1xVMvLFyWS+dWTejRtlmsQzHGmAalrKwMEUFE+Pjjj3nvvfc8+yEWFRWRn59PXFwcn376KR9++CFpaWmMHTsWn89Henp64F7PP/98DGtiGoqIEjERORp4AmgGdBGRw4GLVPUSAFWdW2MR1jH5RSV8lrmJs4/qYmPAjDGmBmVkZPD+++97FjXNzs5m9erVtGvXjo8//pj77rsvsB/iMcccg8/no7i4mKSkJB599FH7/7SJuUhbxP4POAl4G0BVvxeR4TUWVR326fJNFJaUMaq3dUsaY8yB2LhxI4sXL/a0aGVlZfH6669zxBFH8Pnnn3PFFVfQtGlTfD4fffr0YezYsYHk6pprrmHWrFnEx8eHvb8lYaY2iLhrUlXXhPxHW1r94dR9C5flktI4gSO7t4p1KMYYU6sVFBTw3XffebboycrK4qabbmL06NF8//33TJgwAYA2bdrg8/kYNmxYYEPp008/nTFjxtC2bduwSVXoavLG1EaRJmJr3O5JFZFE4HJgWc2FVTeVlSkf/JrH8eltaRQfF+twjDEmpsrKysjJyfG0ZmVmZnLGGWcwadIkVq1axdFHHw04rVP+/RD9SdVRRx3FN99841lfK9hBBx3EQQcdFNU6GVPdIk3ELsYZkN8RyAHmAzMq/EQD9L+cbWzaVcSJvdvGOhRjjImKwsJCVq5c6Um2+vfvz/nnn8+ePXvo2rVroGyjRo1ITU1l1KhRAHTv3p133nknsL5W48aNPfdOSUlh4MCBUa2PMdEWaSImqnpOjUZSDyz8JZf4OOH4npaIGWPqj507d3q6Dlu1asWf/vQnwFkBfuPGjYGyzZo1C1xr2rQpc+fOpXPnzvh8Pjp16uQZr5WYmMgpp5wS3coYU8tEmoh9LiIrgJeB11V1Ww3GVGctWpbH4G6taJ7cKNahGGNMxFSVjRs3Blq0CgsLufDCCwEYMWJEYAFTv1GjRgWSrb/+9a80adIksKDpwQcf7BmvNXXq1KjVw5i6KNJNv3uIyGDgTGCWiPwCvKSqz9VodHXI6s35ZOTu5P+d0qfywsYYE2WlpaXk5OSQlZXF+vXrOeccp5Pj8ssv51//+he7du0KlO3QoUMgEZs4cSKjR48OLGiamprqGZc1bdq06FbEmHqmKrMmvwK+EpE7gPuApwFLxFwLl+UC2PgwY0zMFBYWsmLFCrKysjjppJNISEjgn//8Jw899FBgn0RwBsZPmDCBpKQkDj30UC644ALP5tPdunUL3HP69Okxqo0xDUOkC7oeBJyG0yLmA94ABtdgXHXOol9z6dG2GV1bN411KMaYemzHjh1kZWXRs2dPmjZtyrx587jnnnsC+yGqKgDZ2dl0796d5s2b069fP8aPHx/oPvT5fCQmJgIEuhiNMbERaYvY98CbwGxVXVKD8dRJOwqK+TJ7C38anhrrUIwxdZx/vFZycjLNmjXjhx9+4O677w4MlPcPjF+8eDHHHnsspaWlFBQUMHz48ECLls/no3379gBMnjyZyZMnx7JKxpgKRJqIpar/n1lmHx9nbKSkTK1b0hgTkdLSUoqLi2ncuDHr16/n/vvv98xK3LVrF88//zxnn302+fn5LF68mLS0NE499dRAstW7d28Axo4dy9ixY2NcI2PM/qowEROR+1X1SuBtEdknEVPVcTUWWR2ycFkurZsm0r9zy1iHYoypJVQVEWHPnj08/vjjnnW2VqxYwezZs7nuuusoKSnh/vvvp3v37qSlpXHcccfh8/kYNGgQAEOGDGHVqlUxro0xpqZU1iL2rPvznv39AhEZjbMYbDzwhKreGXL9POBuYK176h+q+sT+fl+0FZeW8eGvefyu7yHEx9m+ZcY0RP/+979Zvny5J9k69dRTeeCBB0hISGDmzJkkJyfj8/no168fp556KkOHDgWgU6dO5Ofnl7sfojGmfqswEVPVb9yX/VX1geBrInIF8HFFnxeReOCfwCicFfm/FpG3VfWXkKIvq+qlVYq8lli6cis7Cko40Tb5NqbeWrp0Kb/88oun+7BXr17MnTsXgCuuuIKcnBzatm2Lz+dj+PDhDB7szGdq1KgRubm5tGrVKux+iCJiSZgxDVikY8Sm4rRqBTsvzLlQg4FMVc0GEJGXgPFAaCJWZy1clktiQhzH9mgT61CMMftp7dq1LFu2LNCalZWVRWJiIi+++CIAl112GV988QUiQpcuXfD5fKSm7p2c8+GHH9KuXTtSUlLC3r9169ZRqYcxpu6pbIzYWcDZQHcReTvoUgqwOYL7dwTWBL3PAY4KU+50ERkO/AZcpaprQguIyDRgGkCXLl0i+Oqap6osXJbL0b7WNE2KeEk2Y0yUFRQUsGLFCk/XYV5eHi+//DIAV111Fa+++irgbLuTmppK//79A59/9NFHady4Md26dSMpKWmf+6elpUWnIsaYeqey7OFzYD3QBrg36PxO4IcI7h9u0FTooP93gBdVtVBELsZZKPaEfT6kOgeYAzBo0KBaMYMza+MuVm3O50/H2rIVxsTa9u3bA0mWP+F68MEHSU5OZtasWdx3332BsikpKfh8PgoLC0lKSuKaa65h+vTp+Hw+OnbsuE9X4eGHHx7t6hhjGojKxoitAlYBQ/fz/jlA56D3nYB1Id8R3LL2OHDXfn5X1C34JQ+AkbZshTE1TlXJzc31JFsXX3wxHTp04OGHH2bGjBme8m3btmXWrFl0796dM888kwEDBgTW2GrTpo1nvNaRRx4Z7eoYYwxQedfkp6p6jIjsxNuSJYCq6kHlfNTva6CHiHTHmRV5Jk5XZ/B3tFfV9e7bccCyqlQglhYty6Vfx4No37xJrEMxpl4oKSlhzZo1gWTrhBNOoEePHixYsIDTTjuN3bt3B8rGxcUxYsQIOnTowLBhw7jrrrs8+yEGj9c68sgjLdkyxtRKlbWIHeP+DD8CtRKqWiIilwLv4yxf8ZSq/iwis4Glqvo2cLmIjANKgC04kwBqvc27Cvlm9VauGNkj1qEYU6cUFBSQnZ1NVlYWPp+PPn368Ntvv3HKKaewcuVKiouLA2XnzJlDjx49SE1N5Y9//GNgex6fz+cZr3X44Ydb96Expk6SSBbMFxEfkOOO4zoeOAx4RlW31XB8YQ0aNEiXLl0ai68OeHXpGq557QfevewY+nVsHtNYjKlt/OO1mjRpQu/evdm5cydjx44lKyuLtWvXBvZDvOmmm7j11lvZsmULF110kWeLnvLGaxljTF0jIt+o6qBw1yKd6vc6MEhE0oAngbeBF4Ax1RNi3bNoWR7tmzemb4fKemeNqX/847Xy8/MDyzhccMEF/Pzzz2RlZbF5szP08/zzz+epp56iWbNmga5Ef6KVlpZGeno6AK1atQrMWjTGmIYk0kSszO1mPA24X1UfEpHvajKw2qyguJTFyzfyh4Edwy7QaEx9UFJSwpYtW2jb1pmMctddd/Hll1+SmZlJdnY2u3fv5sQTT2TBggUArF69moMOOogJEyYEEq1DDz0UcBYt/eCDD2JWF2OMqa0iTcSK3TXFpgL+3WUb1UxItd+S7M3kF5Uy0lbTN3VcUVERiYmJALz66qt8/PHHnv0QfT4fv/76KwCffPIJ2dnZpKWlMXLkSHw+H4cddljgXgsXLoxJHYwxpi6LNBE7H7gYuF1VV7izIJ+rubBqt0XLcklOjGdoqq2WbeqOL774gg8//NCzqOm2bdvYsWMHIsK8efN444038Pl89O/fnwkTJtCrV6/A5999990YRm+MMfVTRImYqv4iIn8GeopIPyAjdPPuhkJVWfhLHsN7HEzjRjaI2NQeK1asYPHixZ4FTbOysvjtt99o1aoV8+bN47bbbuOQQw7B5/NxwgknkJaWRnFxMYmJiTz66KM89dRT1t1ujDFRFFEi5s6UfBpYibOGWGcRmaqqi2sutNrp53U72LCjwBZxNVG3bds2vv76a89+iJmZmbzwwgv069ePBQsWcNFFFxEXF0fXrl3x+XxMmDAhsBzEzJkzufbaa2nWrFnY+4fbuscYY0zNirRr8l7gd6qaASAiPYEXgSNqKrDaauGyXETghF6WiJnqVVxczPLly/dp0bruuus4/vjjWbJkCWPGOBOVk5KSAks8lJWVAXD66aczYsQIunbtGhj3FaxFixZRrY8xxpjKRZqINfInYQCq+puINMjB+guX5XJEl5a0bmatB6bqtm3bts9+iOPHj2fcuHFkZGQEZhkCNG/eHJ/PR35+PgBDhgzho48+wufz0aFDB+Li4jz3bt26Na1b27hFY4ypSyJNxJaKyJPAs+77c4Bvaiak2mv99j38tHYH143uVXlh0yCpKhs2bPAkW4ceeigTJ05k+/bttGzZ0lP+kEMOYcCAAQCkpaXx/PPPB9bZatWqlWe8VsuWLTnuuOOiWh9jjDE1K9JEbDowA7gcZ4zYYuDhmgqqtlq0zNnke1Qf65ZsyEpKSli9enUg0WrRogVnnXUWAB06dGDDhg2BsnFxcUyfPp2JEyfSvHlzHnjgATp37hzYD7Fp06aBso0bN+bss8/e5/uMMcbUX5HOmiwUkX8Ai4AynFmTRTUaWS20cFku3Von4zs4/GBnU3/k5+cH9kMsLCxk4sSJAJx88sksXLiQkpKSQNnjjjsukIjNnDmT5OTkwPit0PFal19+eXQrYowxplaLdNbk74FHgSycFrHuInKRqr5Xk8HVJrsLS/g8azNThnS16f31xNatW8nKymL9+vWMHeusU/znP/+ZF198kXXr1gXKde7cOZCIjRgxggEDBnj2Q+zQoUOg7DXXXBPdShhjjKnTqjJrcoSqZkJgE/D/AA0mEftk+SaKSso40VbTrzNUlfXr15OVlcWwYcOIi4tjzpw5PP7442RlZbF161YAEhIS2LNnDwkJCXTs2JFRo0bts/m037XXXhur6hhjjKmHIk3E8vxJmCsbyKuBeGqthctyOahxAoO6tay8sImakpISVq1aRceOHWncuDHz58/n4YcfDgyW37NnDwA5OTl07NgRVaVly5ZMmjQpkGSlpaUFZiBeddVVsayOMcaYBibSROxnEZkHvAIocAbwtYj8AUBV/11D8dUKpWXKh7/mMaJXWxrFx1X+AVOt8vPziYuLo3Hjxvz000+eRGvlypWUlpayZMkShgwZwrZt21i+fDlpaWmBli2fzxeYrXjRRRdx0UUXxbhGxhhjjCPSRKwxkAv4585vBFrhbACuQL1OxP63ZiubdxdZt2QNKi0tJT4+ntzcXJ544gnPgqbr1q3jlVde4YwzzmDz5s28+OKL+Hw+jjjiiEDLVrdu3QCYOHFiYDyXMcYYU9tFOmvy/Iqui8j1qvq36gmp9lnwSx4JccJx6QfHOpQ6b8+ePbz00kv7rB5/4403MnPmTHbv3s2NN95I+/bt8fl8jBo1irS0NPr06QPA8OHDA2O7jDHGmLou0haxypwB1NtEbNGyXI5KbcVBjRvkZgJVoqosWLDAk2RlZmYybtw47rjjDuLi4rjwwgs9+yFOmjSJww47DIBu3bqxa9cuz/pawWzGqjHGmPqkuhKxevu346rNu1met4uzj+oS61BqjWXLlpGRkeHZfDo9PZ0HH3wQEWHKlCnk5eXRpEmTwGD4tLQ0wNkjMSsri06dOtGo0b6JbVxcXLlJmDHGGFPfVFciptV0n1pnobuafkMaH7Z169ZAi5b/Z1JSEo8++igAU6ZM4ZtvnB2uWrZsic/no1mzvYvcvv/++7Rt25b27duHbcHq3r17dCpijDHG1HLWIlaJhb/kkt4uhc6tkmMdSrUpKysLrK/lT7Y2b94cSLQuuOAC3nzzzUD5Dh06cOSRRwbeP/jggyQmJnpmIwbr379/zVfCGGOMqQeqKxF7tZruU6tszy/mq5VbuPi41FiHUmXFxcWsWrXK0334t7/9jcTERK6++mruv//+QNn4+Hi6d+9OUVERiYmJXHXVVZx33nn4fD5SU1NJTvYmoUcffXS0q2OMMcbUS5FucdQTeARop6r9ROQwYJyq/hVAVe+owRhj5qPf8igtU0bW0m7J/Px8T6J17rnncvDBB/PYY48xY8YMSktLA2WbNGnCZZddRrdu3fjDH/5Az549A2tsdenSxTNea/jw4bGojjHGGNPgRNoi9jhwDfAYgKr+ICIvAH+tqcBqg4XL8mjTLJH+nVrELIYtW7YEug+HDRtGly5dWLhwIeeeey7r16/3lB04cGBgL8S//OUvni16gsdrHXvssRx77LGxqI4xxhhjgkSaiCWr6lchA69LaiCeWqO4tIyPMvIY0689cXE1NwQueLxWp06dSE1N5bfffuOcc84hMzOTbdu2BcrOnTuXqVOn0rFjR0aPHr3Pfoj+8VqDBw9m8ODBNRazMcYYY6pHpInYJnejbwUQkQnA+oo/Urd9vWILOwtKGNm77QHfyz9eKyEhgW7durF9+3bOPffcQJdiQUEBALfddhs33ngjLVq0oHXr1gwePNizH6J/8+nevXvz1FNPHXBcxhhjjImtSBOxGcAcoJeIrAVWAJMj+aCIjAYeAOKBJ1T1zpDrScAzwBHAZmCSqq6MMK4as2BZLkkJcRzTo01E5Xfv3s3OnTs55JBDUFUuv/zywFpbq1atorS0lGnTpvHYY4/RrFkz1qxZQ1paWqBly+fzBRY1bdu2Lf/9739rsnrGGGOMqQUi3eIoGzhRRJoCcaq6M5LPiUg88E9gFJCDs1H426r6S1CxC4GtqpomImcCdwGTqlKJ6qaqLFyWy7C0NiQnJgTO5efnBxYbfeihh/jmm28CrVrr16/n5JNPZt68eYgIS5YsIS4ujsGDB3P22Wfj8/kYOHAg4MxS/Pbbb2NWP2OMMcbUDpHOmrwp5D0Aqjq7ko8OBjLdRA4ReQkYDwQnYuOBW9zXrwH/EBFR1ZgtErs8bxcZX31Mq2abmTjv7kCy1blzZ3788UcAXn/9dTIzM/H5fIwePZq0tDQGDBgQuMfSpUtjFb4xxhhj6ohIuyZ3B71uDJwCLIvgcx2BNUHvc4CjyiujqiUish1oDWwKLiQi04BpAF261Ox2Qx9l5LF72cfM++0zunXrhs/nY8iQIfTt2zdQZtGiRcTHx9doHMYYY4yp3yLtmrw3+L2I3AO8HcFHw003DG3piqQMqjoHZ5wagwYNqtHWsguPSeXQ157hyB4dSEgI/yuyJMwYY4wxBypuPz+XDESy3HwO0DnofSdgXXllRCQBaA5s2c+4qkV8nDC0d5dykzBjjDHGmOoQ6RixH9nbShUPHAxUNj4M4Gugh4h0B9YCZwJnh5R5G5gKLAEmAB/EcnyYMcYYY0y0SCQ5j4h0DXpbAuSqakQLuorIGOB+nATuKVW9XURmA0tV9W0RaQw8CwzAaQk70z+4v4J7bgRWRfL9B6ANIePUTK1gz6X2sWdSO9lzqX3smdRO0XguXVX14HAXKk3ERCQO+EFV+9VEZLWViCxV1UGxjsN42XOpfeyZ1E72XGofeya1U6yfS6VjxFS1DPheRGp2qqIxxhhjTAMT6Wj09sDPIvIVQUtZqOq4GonKGGOMMaYBiDQRa4azdpif4KyAX5/NiXUAJix7LrWPPZPayZ5L7WPPpHaK6XOJdLD+t6o6MOTcD6p6WI1FZowxxhhTz1XYIiYi04FLgFQR+SHoUgrwWU0GZowxxhhT31U2WP8FYCzOWl9jg44jVHVyDccWFSIyWkQyRCRTRP4S5nqSiLzsXv9SRLpFP8qGJYJnMlNEfhGRH0RkUcjyKqaGVPZcgspNEBEVEZsdVsMieSYiMtH98/KziLwQ7Rgbogj+H9ZFRD4Uke/c/4+NiUWcDYmIPCUieSLyUznXRUQedJ/ZDyIyMFy5cMNBBAAAB41JREFUGqGqDfbAWdssC2eXgETge6BPSJlLgEfd12cCL8c67vp8RPhMRgDJ7uvp9kxqx3Nxy6UAi4EvgEGxjrs+HxH+WekBfAe0dN+3jXXc9f2I8LnMAaa7r/sAK2Mdd30/gOHAQOCncq6PAd7DGQM/BPgyWrHt7xZH9cVgIFNVs1W1CHgJGB9SZjzwtPv6NWCkiITbH9NUj0qfiap+qKr57tsvcLbOMjUrkj8rALcBfwcKohlcAxXJM/kT8E9V3QqgqnlRjrEhiuS5KHCQ+7o5+279Z6qZqi6m4u0TxwPPqOMLoIWItI9GbA09EesIrAl6n+OeC1tGnd0EtgOtoxJdwxTJMwl2Ic6/YkzNqvS5iMgAoLOqvhvNwBqwSP6s9AR6ishnIvKFiIyOWnQNVyTP5RZgsojkAPOAy6ITmvn/7d17jJxVHcbx7yNFitrU1Gqioq6oKUI1Na1IoxZqSROJIUI2FkKBRf2DWwgoxBgJIt4QLxgx0gI2bZSYar2w8ZJqwBKpCK2WuO0CSgBBYyISQYktofXxj3PWbIfZ2Wns7LvbeT7JZt/Lmff8Zk525rfnnHdOBwf62XPQ9Puq1u16tlpvI+2mTBw8Xb/eklYDS4ATexpRwCTtUlfguB4YmqqAoqu/lVmU4cmTKD3Hv5K00PZTPY6tn3XTLmcC621/WdJS4Fu1Xf7T+/BiAo191vd7j9ifgdeM2z+K53cR/6+MpFmUbuRO3Zvx/+mmTZB0MvAJ4FTbz05RbP1ssnaZAywEtkh6lDLHYjgT9nuq2/ev22w/Z/sR4EFKYha90027fAj4LoDtu4HZlPUOozldffb0Qr8nYtuAN0l6vaQXUibjD7eUGQbOrduDwB2uM/uiJyZtkzoEtpaShGXOy9To2C62n7Y93/aA7QHK3L1TbW9vJty+0M37148oN7cgaT5lqPLhKY2y/3TTLo8BKwAkvZmSiD0xpVFGq2HgnHr35AnA07b/OhUV9/XQpO29ki4GNlPudFlne5eka4DttoeBb1K6jR+i9ISd0VzEh74u2+SLlNUevlfvm3jMWW6rp7psl5hCXbbJZmClpFFgH3CF7Sebi/rQ12W7fBS4WdJllOGvofyD31uSvkMZop9f5+Z9EjgcwPYayly9U4CHgH8D501ZbGn7iIiIiGb0+9BkRERERGOSiEVEREQ0JIlYREREREOSiEVEREQ0JIlYREREREOSiEVEREQ0JIlYRPSEpEsk3S/p1gnOnyRpWq1LKel8SefU7SFJrxp37hZJxzYQ035xRMShpa+/0DUieupC4L11aZ0ZoX6x45ghYCd1mRPbH+5VvZIOs71vgtP7xRERh5b0iEXEQSdpDXA0Zb3Jj0n6taQd9feCNuVPlHRf/dkhaU49foWkbZJ+L+lTHeobkPSApA217CZJL6rnVtRrjkhaJ+mIevxaSaO1/JfqsaslXS5pkLKg/K01piMlbZG0RNIFkq4bV/eQpBvq9mpJ99bHrJV0WIeYn5F0jaR7gKWSrqrPdaekm+pSK+3iWCzpTkm/lbRZ0isPuIEiYtpIIhYRB53t8yk9OMuBG4Fltt8GXAV8rs1DLgcusr0IeDewW9JKygLVxwOLgMWSlnWodgFwk+23Av8ELpQ0G1gPrLL9FsoowAWS5gGnAcfV8p9piX8TsB04y/Yi27vHnd4EnD5ufxWwsa4ZuAp4Z30e+4CzOsT7YmCn7XfYvgv4uu23214IHAm8rzUOYC9wAzBoezGwDvhshzoiYppLIhYRvTaXsi7oTuB64Lg2ZbYCX5F0CfBS23uBlfVnB/A74BhKYjaRx21vrdvfBt5FSc4esf2HenwDsIySqO0BbpF0OmVtua7YfgJ4WNIJkl5W69hKWcR5MbBN0n11/+gOl9oHfH/c/nJJ90gaAd5D+9dpAbAQ+EWt40rgqG5jj4jpJ3PEIqLXPg380vZpkgaALa0FbF8r6SeURXd/I+lkQMDnba/tsp7WhXNdr/H8gmVh5uMpydIZwMWU5KdbG4EPAA8AP7RtlRXoN9j+eJfX2DM2L6z23H0DWGL7cUlXA7PbPEbALttLDyDWiJjG0iMWEb02F/hL3R5qV0DSG2yP2P4CZSjuGGAz8EFJL6llXi3pFR3qea2ksQTlTOAuSqI0IOmN9fjZwJ31mnNt/xS4lDL02epfwJwJ6voB8P5az8Z67HZgcCxGSfMkva5DvOONJV1/r7ENThDHg8DLx56npMMltes5i4gZIj1iEdFr1wEbJH0EuGOCMpdKWk4ZrhsFfmb72Trv6u7S2cQzwGrgbxNc437gXElrgT8CN9reI+k8ytDoLGAbsAaYB9xWe6IEXNbmeuuBNZJ2A/v1QNn+h6RR4Fjb99Zjo5KuBH4u6QXAc8BFwJ8meX2w/ZSkm4ER4NEa50RxDAJfkzSX8h7+VWDXZHVExPQku7U3PyJiZqlDnj+uE90jImaMDE1GRERENCQ9YhExY9S7FG9vc2qF7SenOp5u1O8JO6Ll8Nm2R5qIJyKmlyRiEREREQ3J0GREREREQ5KIRURERDQkiVhEREREQ5KIRURERDQkiVhEREREQ/4LIu2dEdtTYIAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seed=7\n",
    "#prepare models\n",
    "models=[]\n",
    "models.append(('DecisionTreeClassifier',dtc))\n",
    "models.append(('RandomForestClassifier',rfc))\n",
    "models.append(('KNeighborsClassifier',knc))\n",
    "models.append(('SGDClassifier',sgc))\n",
    "models.append(('LogisticRegression',lg))\n",
    "models.append(('GradientBoostingClassifier',gbc))\n",
    "\n",
    "\n",
    "#evaluate each model in turn\n",
    "Model=[]\n",
    "cvs=[]\n",
    "score=[]\n",
    "rocscore=[]\n",
    "for name,model in models:\n",
    "    print('**************',name,'***********')\n",
    "    print('\\n')\n",
    "    Model.append(name)\n",
    "    model.fit(x_train,y_train)\n",
    "    print(model)\n",
    "    pred=model.predict(x_test)\n",
    "    print('\\n')\n",
    "    acc=accuracy_score(y_test,pred)\n",
    "    print('accuracy score',acc)\n",
    "    score.append(acc*100)\n",
    "    kfold=model_selection.KFold(n_splits=10,random_state=seed)\n",
    "    cv=model_selection.cross_val_score(model,x,y,cv=20,scoring='accuracy').mean()\n",
    "    print('Cross-val-score=',cv)\n",
    "    cvs.append(cv*100)\n",
    "    print('\\n')\n",
    "    false_positive_rate,true_positive_rate,thresholds=roc_curve(y_test,pred)\n",
    "    roc_auc=roc_auc_score(y_test,pred)\n",
    "    print('roc_auc_score',roc_auc)\n",
    "    rocscore.append(roc_auc*100)\n",
    "    print('\\n')\n",
    "    print(classification_report(y_test,pred))\n",
    "    print('\\n')\n",
    "    cm=confusion_matrix(y_test,pred)\n",
    "    print(cm)\n",
    "    print('\\n')\n",
    "    plt.figure(figsize=(10,15))\n",
    "    plt.subplot(911)\n",
    "    plt.title(name)\n",
    "    print(sns.heatmap(cm,annot=True))\n",
    "    plt.subplot(912)\n",
    "    plt.title(name)\n",
    "    plt.plot(false_positive_rate,true_positive_rate,label='AUC'%roc_auc)\n",
    "    plt.plot([0,1],[0,1],'k--')\n",
    "    plt.xlabel('false_positive_rate')\n",
    "    plt.ylabel('true_positive_rate')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>cvs</th>\n",
       "      <th>score</th>\n",
       "      <th>rocscore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>91.479230</td>\n",
       "      <td>90.419162</td>\n",
       "      <td>85.819398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>94.209951</td>\n",
       "      <td>95.808383</td>\n",
       "      <td>85.045389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>86.619863</td>\n",
       "      <td>90.419162</td>\n",
       "      <td>65.637840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>77.888528</td>\n",
       "      <td>86.526946</td>\n",
       "      <td>52.111801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>86.438603</td>\n",
       "      <td>88.622754</td>\n",
       "      <td>59.589107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>95.021223</td>\n",
       "      <td>94.910180</td>\n",
       "      <td>88.327759</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Model        cvs      score   rocscore\n",
       "0      DecisionTreeClassifier  91.479230  90.419162  85.819398\n",
       "1      RandomForestClassifier  94.209951  95.808383  85.045389\n",
       "2        KNeighborsClassifier  86.619863  90.419162  65.637840\n",
       "3               SGDClassifier  77.888528  86.526946  52.111801\n",
       "4          LogisticRegression  86.438603  88.622754  59.589107\n",
       "5  GradientBoostingClassifier  95.021223  94.910180  88.327759"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result=pd.DataFrame({'Model':Model,'cvs':cvs,'score':score,'rocscore':rocscore},columns=['Model','cvs','score','rocscore'])\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Selecting Best model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From here we can observe that RandomForestClassifier is best algorithm for this dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0248422  0.02784227 0.00722443 0.0673095  0.02824011 0.02756666\n",
      " 0.17354834 0.02854469 0.09871735 0.06738187 0.02794899 0.07164972\n",
      " 0.03727176 0.02674761 0.03881063 0.04487979 0.04669402 0.03080527\n",
      " 0.12397479]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21cca370b88>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importance = rfc.feature_importances_\n",
    "print (rfc.feature_importances_)\n",
    "feat_importances = pd.Series(rfc.feature_importances_, index=x.columns)\n",
    "feat_importances = feat_importances.nlargest(19)\n",
    "feat_importances.plot(kind='barh' , figsize=(10,10)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From here we can observe that Area_code feature has less contribution so t's better to this feature and run it again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Account_length</th>\n",
       "      <th>International plan</th>\n",
       "      <th>Voice_mail_plan</th>\n",
       "      <th>Number_vmail_messages</th>\n",
       "      <th>Total day minutes</th>\n",
       "      <th>Total day calls</th>\n",
       "      <th>Total_day_charge</th>\n",
       "      <th>Total_eve_minutes</th>\n",
       "      <th>Total_eve_calls</th>\n",
       "      <th>Total_eve_charge</th>\n",
       "      <th>Total_night_minutes</th>\n",
       "      <th>Total_night_calls</th>\n",
       "      <th>Total_night_charge</th>\n",
       "      <th>Total_int_minutes</th>\n",
       "      <th>Total_intl_calls</th>\n",
       "      <th>Total_intl_charge</th>\n",
       "      <th>Customer_service_calls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>128</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>3.258097</td>\n",
       "      <td>265.1</td>\n",
       "      <td>110</td>\n",
       "      <td>45.07</td>\n",
       "      <td>197.4</td>\n",
       "      <td>99</td>\n",
       "      <td>16.78</td>\n",
       "      <td>244.7</td>\n",
       "      <td>91</td>\n",
       "      <td>11.01</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>2.70</td>\n",
       "      <td>0.693147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>107</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>3.295837</td>\n",
       "      <td>161.6</td>\n",
       "      <td>123</td>\n",
       "      <td>27.47</td>\n",
       "      <td>195.5</td>\n",
       "      <td>103</td>\n",
       "      <td>16.62</td>\n",
       "      <td>254.4</td>\n",
       "      <td>103</td>\n",
       "      <td>11.45</td>\n",
       "      <td>13.7</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>3.70</td>\n",
       "      <td>0.693147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>137</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>243.4</td>\n",
       "      <td>114</td>\n",
       "      <td>41.38</td>\n",
       "      <td>121.2</td>\n",
       "      <td>110</td>\n",
       "      <td>10.30</td>\n",
       "      <td>162.6</td>\n",
       "      <td>104</td>\n",
       "      <td>7.32</td>\n",
       "      <td>12.2</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>35</td>\n",
       "      <td>84</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>299.4</td>\n",
       "      <td>71</td>\n",
       "      <td>50.90</td>\n",
       "      <td>61.9</td>\n",
       "      <td>88</td>\n",
       "      <td>5.26</td>\n",
       "      <td>196.9</td>\n",
       "      <td>89</td>\n",
       "      <td>8.86</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.079442</td>\n",
       "      <td>1.78</td>\n",
       "      <td>1.098612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>36</td>\n",
       "      <td>75</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>166.7</td>\n",
       "      <td>113</td>\n",
       "      <td>28.34</td>\n",
       "      <td>148.3</td>\n",
       "      <td>122</td>\n",
       "      <td>12.61</td>\n",
       "      <td>186.9</td>\n",
       "      <td>121</td>\n",
       "      <td>8.41</td>\n",
       "      <td>10.1</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>2.73</td>\n",
       "      <td>1.386294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   State  Account_length  International plan  Voice_mail_plan  \\\n",
       "0     16             128            0.000000         0.693147   \n",
       "1     35             107            0.000000         0.693147   \n",
       "2     31             137            0.000000         0.000000   \n",
       "3     35              84            0.693147         0.000000   \n",
       "4     36              75            0.693147         0.000000   \n",
       "\n",
       "   Number_vmail_messages  Total day minutes  Total day calls  \\\n",
       "0               3.258097              265.1              110   \n",
       "1               3.295837              161.6              123   \n",
       "2               0.000000              243.4              114   \n",
       "3               0.000000              299.4               71   \n",
       "4               0.000000              166.7              113   \n",
       "\n",
       "   Total_day_charge  Total_eve_minutes  Total_eve_calls  Total_eve_charge  \\\n",
       "0             45.07              197.4               99             16.78   \n",
       "1             27.47              195.5              103             16.62   \n",
       "2             41.38              121.2              110             10.30   \n",
       "3             50.90               61.9               88              5.26   \n",
       "4             28.34              148.3              122             12.61   \n",
       "\n",
       "   Total_night_minutes  Total_night_calls  Total_night_charge  \\\n",
       "0                244.7                 91               11.01   \n",
       "1                254.4                103               11.45   \n",
       "2                162.6                104                7.32   \n",
       "3                196.9                 89                8.86   \n",
       "4                186.9                121                8.41   \n",
       "\n",
       "   Total_int_minutes  Total_intl_calls  Total_intl_charge  \\\n",
       "0               10.0          1.386294               2.70   \n",
       "1               13.7          1.386294               3.70   \n",
       "2               12.2          1.791759               3.29   \n",
       "3                6.6          2.079442               1.78   \n",
       "4               10.1          1.386294               2.73   \n",
       "\n",
       "   Customer_service_calls  \n",
       "0                0.693147  \n",
       "1                0.693147  \n",
       "2                0.000000  \n",
       "3                1.098612  \n",
       "4                1.386294  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=x.drop('Area_code',axis=1)\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
      "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                       min_samples_leaf=1, min_samples_split=2,\n",
      "                       min_weight_fraction_leaf=0.0, n_estimators=10,\n",
      "                       n_jobs=None, oob_score=False, random_state=None,\n",
      "                       verbose=0, warm_start=False)\n",
      "\n",
      "\n",
      "accuracy score 0.9491017964071856\n"
     ]
    }
   ],
   "source": [
    "rfc.fit(x_train,y_train)\n",
    "print(rfc)\n",
    "pred=rfc.predict(x_test)\n",
    "print('\\n')\n",
    "acc=accuracy_score(y_test,pred)\n",
    "print('accuracy score',acc)\n",
    "score.append(acc*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "you can see difference before removeing Are_code feature and after ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted insurance claim: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0\n",
      " 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 1\n",
      " 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0\n",
      " 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 1 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0\n",
      " 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0]\n",
      "actual insurance claim 716     0\n",
      "2382    0\n",
      "1479    0\n",
      "2418    0\n",
      "1799    0\n",
      "       ..\n",
      "2454    0\n",
      "962     0\n",
      "1395    0\n",
      "80      0\n",
      "269     0\n",
      "Name: Churn, Length: 334, dtype: int32\n"
     ]
    }
   ],
   "source": [
    "pred=rfc.predict(x_test)\n",
    "print('predicted insurance claim:',pred)\n",
    "print('actual insurance claim',y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saving Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "cust=pd.DataFrame(pred)\n",
    "cust.to_csv('rfc_cust.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.externals import joblib\n",
    "#save the model as a pickle in a file\n",
    "joblib.dump(rfc,'cust_rfc.obj')\n",
    "#load the model from a file\n",
    "rfc_from_joblib=joblib.load('cust_rfc.obj')\n",
    "#use the loaded model to make prediction\n",
    "rfc_from_joblib.predict(x_test)\n"
   ]
  }
 ],
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