insurance-claim-fraud-detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>months_as_customer</th>\n",
       "      <th>age</th>\n",
       "      <th>policy_number</th>\n",
       "      <th>policy_bind_date</th>\n",
       "      <th>policy_state</th>\n",
       "      <th>policy_csl</th>\n",
       "      <th>policy_deductable</th>\n",
       "      <th>policy_annual_premium</th>\n",
       "      <th>umbrella_limit</th>\n",
       "      <th>insured_zip</th>\n",
       "      <th>...</th>\n",
       "      <th>police_report_available</th>\n",
       "      <th>total_claim_amount</th>\n",
       "      <th>injury_claim</th>\n",
       "      <th>property_claim</th>\n",
       "      <th>vehicle_claim</th>\n",
       "      <th>auto_make</th>\n",
       "      <th>auto_model</th>\n",
       "      <th>auto_year</th>\n",
       "      <th>fraud_reported</th>\n",
       "      <th>_c39</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>328</td>\n",
       "      <td>48</td>\n",
       "      <td>521585</td>\n",
       "      <td>2014-10-17</td>\n",
       "      <td>OH</td>\n",
       "      <td>250/500</td>\n",
       "      <td>1000</td>\n",
       "      <td>1406.91</td>\n",
       "      <td>0</td>\n",
       "      <td>466132</td>\n",
       "      <td>...</td>\n",
       "      <td>YES</td>\n",
       "      <td>71610</td>\n",
       "      <td>6510</td>\n",
       "      <td>13020</td>\n",
       "      <td>52080</td>\n",
       "      <td>Saab</td>\n",
       "      <td>92x</td>\n",
       "      <td>2004</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>228</td>\n",
       "      <td>42</td>\n",
       "      <td>342868</td>\n",
       "      <td>2006-06-27</td>\n",
       "      <td>IN</td>\n",
       "      <td>250/500</td>\n",
       "      <td>2000</td>\n",
       "      <td>1197.22</td>\n",
       "      <td>5000000</td>\n",
       "      <td>468176</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>5070</td>\n",
       "      <td>780</td>\n",
       "      <td>780</td>\n",
       "      <td>3510</td>\n",
       "      <td>Mercedes</td>\n",
       "      <td>E400</td>\n",
       "      <td>2007</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>134</td>\n",
       "      <td>29</td>\n",
       "      <td>687698</td>\n",
       "      <td>2000-09-06</td>\n",
       "      <td>OH</td>\n",
       "      <td>100/300</td>\n",
       "      <td>2000</td>\n",
       "      <td>1413.14</td>\n",
       "      <td>5000000</td>\n",
       "      <td>430632</td>\n",
       "      <td>...</td>\n",
       "      <td>NO</td>\n",
       "      <td>34650</td>\n",
       "      <td>7700</td>\n",
       "      <td>3850</td>\n",
       "      <td>23100</td>\n",
       "      <td>Dodge</td>\n",
       "      <td>RAM</td>\n",
       "      <td>2007</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>256</td>\n",
       "      <td>41</td>\n",
       "      <td>227811</td>\n",
       "      <td>1990-05-25</td>\n",
       "      <td>IL</td>\n",
       "      <td>250/500</td>\n",
       "      <td>2000</td>\n",
       "      <td>1415.74</td>\n",
       "      <td>6000000</td>\n",
       "      <td>608117</td>\n",
       "      <td>...</td>\n",
       "      <td>NO</td>\n",
       "      <td>63400</td>\n",
       "      <td>6340</td>\n",
       "      <td>6340</td>\n",
       "      <td>50720</td>\n",
       "      <td>Chevrolet</td>\n",
       "      <td>Tahoe</td>\n",
       "      <td>2014</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>228</td>\n",
       "      <td>44</td>\n",
       "      <td>367455</td>\n",
       "      <td>2014-06-06</td>\n",
       "      <td>IL</td>\n",
       "      <td>500/1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1583.91</td>\n",
       "      <td>6000000</td>\n",
       "      <td>610706</td>\n",
       "      <td>...</td>\n",
       "      <td>NO</td>\n",
       "      <td>6500</td>\n",
       "      <td>1300</td>\n",
       "      <td>650</td>\n",
       "      <td>4550</td>\n",
       "      <td>Accura</td>\n",
       "      <td>RSX</td>\n",
       "      <td>2009</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   months_as_customer  age  policy_number policy_bind_date policy_state  \\\n",
       "0                 328   48         521585       2014-10-17           OH   \n",
       "1                 228   42         342868       2006-06-27           IN   \n",
       "2                 134   29         687698       2000-09-06           OH   \n",
       "3                 256   41         227811       1990-05-25           IL   \n",
       "4                 228   44         367455       2014-06-06           IL   \n",
       "\n",
       "  policy_csl  policy_deductable  policy_annual_premium  umbrella_limit  \\\n",
       "0    250/500               1000                1406.91               0   \n",
       "1    250/500               2000                1197.22         5000000   \n",
       "2    100/300               2000                1413.14         5000000   \n",
       "3    250/500               2000                1415.74         6000000   \n",
       "4   500/1000               1000                1583.91         6000000   \n",
       "\n",
       "   insured_zip  ... police_report_available total_claim_amount injury_claim  \\\n",
       "0       466132  ...                     YES              71610         6510   \n",
       "1       468176  ...                       ?               5070          780   \n",
       "2       430632  ...                      NO              34650         7700   \n",
       "3       608117  ...                      NO              63400         6340   \n",
       "4       610706  ...                      NO               6500         1300   \n",
       "\n",
       "  property_claim vehicle_claim  auto_make  auto_model auto_year  \\\n",
       "0          13020         52080       Saab         92x      2004   \n",
       "1            780          3510   Mercedes        E400      2007   \n",
       "2           3850         23100      Dodge         RAM      2007   \n",
       "3           6340         50720  Chevrolet       Tahoe      2014   \n",
       "4            650          4550     Accura         RSX      2009   \n",
       "\n",
       "  fraud_reported _c39  \n",
       "0              Y  NaN  \n",
       "1              Y  NaN  \n",
       "2              N  NaN  \n",
       "3              Y  NaN  \n",
       "4              N  NaN  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "#Data Loading\n",
    "\n",
    "datapath = \"insurance_claims.csv\"\n",
    "\n",
    "data = pd.read_csv(datapath)\n",
    "\n",
    "original_data = data.copy()\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['months_as_customer', 'age', 'policy_number', 'policy_bind_date',\n",
       "       'policy_state', 'policy_csl', 'policy_deductable',\n",
       "       'policy_annual_premium', 'umbrella_limit', 'insured_zip', 'insured_sex',\n",
       "       'insured_education_level', 'insured_occupation', 'insured_hobbies',\n",
       "       'insured_relationship', 'capital-gains', 'capital-loss',\n",
       "       'incident_date', 'incident_type', 'collision_type', 'incident_severity',\n",
       "       'authorities_contacted', 'incident_state', 'incident_city',\n",
       "       'incident_location', 'incident_hour_of_the_day',\n",
       "       'number_of_vehicles_involved', 'property_damage', 'bodily_injuries',\n",
       "       'witnesses', 'police_report_available', 'total_claim_amount',\n",
       "       'injury_claim', 'property_claim', 'vehicle_claim', 'auto_make',\n",
       "       'auto_model', 'auto_year', 'fraud_reported', '_c39'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data Exploration\n",
    "\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 40)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Null Values: 1\n"
     ]
    }
   ],
   "source": [
    "#check missing or null or any values\n",
    "print(\"Null Values: \" + str(data.isnull().any().sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20c39b2cbe0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Fraud Reported Stats\n",
    "df_count_fraud = data.groupby(['fraud_reported']).count()\n",
    "df_fraud = df_count_fraud['policy_number']\n",
    "df_fraud.plot.bar(x='Fraud Reported', y='Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20c39c31a90>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xJ40xWy859poxZrMxZqMxZpExpmLOcWOMGWWM2Zvz+fBLXnO/MWZPzq/78/7LuU4RAyHluGNun4iIiIiIiBO61pW+yUCnvxwbYVlWmGVZjYEfgJdyjncGauX8GgJ8AmCMCQJeBpoBUcDLxpjAXKXPrVodwK+iGrqIiIiIiIjTuqaiz7KsX4GEvxw7e8mHPoCV87gbMNVyWAOUNMZUADoCP1uWlWBZViLwM/9bSBYsVzfHvX37FkNCrK1RRERERERE8kOu7ukzxrxhjDkM9OX/V/oqAYcveVpczrErHb/ceYcYY6KNMdHx8fG5iXh1TfqDcYH1U/L3OiIiIiIiIjbIVdFnWdYLlmVVAaYBw3MOm8s99W+OX+684y3LirQsK7JMmTK5iXh1AZWgdifY8CVkXsjfa4mIiIiIiBSwvOre+RVwT87jOKDKJZ+rDBz9m+P2ixgI5+Jh1492JxEREREREclTN1z0GWNqXfJhV2BnzuN5wICcLp7NgSTLso4BC4EOxpjAnAYuHXKO2a/mLRBQFaIn2p1EREREREQkT7ldy5OMMdOBdkBpY0wcji6ctxtj6gDZwEHgkZynzwduB/YCqcBAAMuyEowxrwHrcp73qmVZf2oOYxsXV4gYAEteh9P7oFQNuxOJiIiIiIjkCWNZl72trtCIjIy0oqOj8/9CycfhgwbQ/FHo8Hr+X09EbLFiTzxf/HaQQa1DaF69lN1xRERERK7KGBNjWVbkjb4+r+7pK/r8ykOdzrBhGmSm251GRPLYnhPJPDBpLf0/X8vinSfp99nvfLnmoN2xRERERPKdir5LRQ6C8wmwfZ7dSUQkj5xKSeeFOVvo9NEKYg4m8sLt9fj937fQplZpXvxuKy9+t4WMrGy7Y4qIiIjkm2u6p6/YCGkHgcEQMwnC7rM7jYjkQlpGFpNWHWDM0r2cz8iiX7OqPH5rbYJ8PAD47P6mjFi4i0+X72P3iRQ+6RtOKV9Pm1OLiIiI5D2t9F3KxQUiHoCDqyB+l91pROQGWJbFvE1HueW95byzYCfNqwex8Imb+G+30IsFH4Cri+H5znX5sGdjNh0+Q9fRq9h+9KyNyUVERETyh4q+v2rcD1zcIWay3UlE5DrFHEzk7k9W84/pG/D3dmfaQ8347P6m1Czre8XXdG9SiZkPtyAzO5t7PlnNT1uOFWBiERERkfynou+vfMtAvS6wcRpknLc7jYhcg8MJqQz7aj33fLKaI4nneffeMH54rDWtapa+ptc3qlKS74e3pm4FPx6dtp4Pft5Ndnbh7mwsIiIicq10T9/lRA6CbXNg23fQuLfdaUTkCs6mZTBmyV4mrTqAiws8fksthtxUHR/P6//WVtbfi+mDm/Pid1v5aPEedh4/y/s9Gt/QuUREREQKE72buZzgNlCqpqOhi4o+kUInMyub6WsP8cEve0hMvcDdTSrzTMc6lA/wytV5vdxdGXFvGHXL+/Hm/B3c88lqJgyIpEpQiTxKLiIiIlLwtL3zcoxxNHQ5/Duc2G53GhHJYVkWS3aeoNNHK/jP3G3ULufL98Nb816PRrku+P5gjOGhNtWZPDCKo2fO03X0Sn7bdzpPzi0iIiJiBxV9V9KoD7h6OFb7RMR2O46dpf/naxk0OZqsbIvx/SOYPrg5oZUC8uV6N9Uuw9zhrSnl60n/z3/nCw1yFxERkSJKRd+V+JSC+t1h09dw4ZzdaUSKrZNn03hu1mZuH7WCrUeTePnO+ix84iY6NCiPMSZfrx1S2oc5Q1tyU+0y/Oe7rfx7zhYuZGqQu4iIiBQtuqfv70QOhC0zYetsCO9vdxqRYuX8hSwmrNjPp8v3kZGVzYOtQnjs5loElHAv0Bx+Xu5MGBDJyEW7+GTZPvaeSGFsv3BKa5C7iIiIFBHGsgp3W/LIyEgrOjranotbFoxpBp6+MHiJPRlEipnsbIvvNh7h3QW7OH42jU4NyvN857oEl/axOxpzNx7h2VmbKe3ryfgBETSomD9bS0VEREQuZYyJsSwr8kZfr+2df8cYx2rfkRg4tsnuNCJOb83+03Qbs4onZ26irL8nMx9uwaf9IwpFwQfQrXElvnmkBVnZFvd+8hs/btYgdxERESn8VPRdTaNe4OYF0WroIpJfYk+d4+Evouk1fg2nUtL5oGcjvhvaiqiQILuj/Y+wyiWZ91gr6lXwY9hX63l/0S4NchcREZFCTff0XY13IDS4G7Z8Ax1eA08/uxOJOI0zqRcYtXgvX6w5gIerC093qM2Dravj7eFqd7S/VdbPi+lDmvOf77Yyasledh5P5v2ejfHVIHcREREphLTSdy0iB8KFFNgyy+4kIk7hQmY2n6+Mpe2IZUxeHcu9EZVZ+kw7ht9cq9AXfH/wdHPlnXvCePnO+izeeZJ7xq7m0OlUu2OJiIiIM8nOht/H5fo0KvquReWmULYBRE90NHcRkRtiWRYLtx2nwwfLee2H7YRVDmD+42146+4wyvrlzXD1gmSMYWCrEKYMjOL42TS6jlnJ6r2n7I4lIiIizmL52/DTs7k+jYq+a/FHQ5fjm+HoervTiBRJW+KS6Dl+DQ9/EYObqwuTBjZl6qAo6pb3tztarrWuVZq5w1pRxteT/hPXMmX1AQp7Z2QREREp5LZ+C8vfgcZ9c30qFX3XKqwHuJdQQxeR63Qs6TxPztzInaNXsu9kCq91D2XB421oX6dsvg9XL0jBpX2YPbQl7euU4eV52/jXbA1yFxERkRt0JAa+GwpVW0CXD3J9OnUduFZeARB6j6Pi7viG42MRuaJz6ZmMW76P8Sv2k23BI21rMLR9Dfy9Cna4ekHy83JnfP9I3vt5F2OW7mNffAqf9IvQIHcRERG5dmePwvQ+4FMWenwBbrl/H6GVvusRORAyUmHzTLuTiBRaWdkWM9Ydot3IZYxaspfb6pdn8ZNteb5zXacu+P7g4mJ4pmNdRvVuwpYjSXT9eCVbjyTZHUtERESKggup8HUfRxPJPl+Db5k8Oa2KvutRMRzKhzm2eOp+HZH/sXLPKe4YtYLnvt1ClUBvZg9tyce9m1AlqITd0Qpc10YVmfVISyzg3k9X88Pmo3ZHEhERkcLMsmDuMDi6Ee6eAOUa5NmpVfRdD2MgchCc3AZx6+xOI1Jo7D2ZzKDJ6+j3+e+kpGcyuk8Tvn20JeFVA+2OZqvQSgHMG96a0IoBDP9qAyMXapC7iIiIXMHyd2HbbLj1Zah7e56eWkXf9Wp4L3j4qqGLCHA6JZ2X5m6l44crWBebwL861+WXJ9vSJayiUzVpyY0yfp5MG9yMnpFVGL10Lw9/GUNKeqbdsURERKQw2TYHlr0JjXpDqyfy/PRq5HK9PP2g4X2waTp0ehO8i/dKhhRPaRlZTFl9gNFL9pKakUXfZlV5/JZalFLDksvydHPl7XsaUq+CH6/9uIO7x65iwoBIqpXysTuaiIiI2O3oRpjzKFSOgi4fOnYX5jGt9N2IyIGQmQabZtidRKRAWZbFD5uPcuv7y3nrp500DQli4RNteLVbqAq+qzDG8ECrEKYOiuJkcjpdR69ilQa5i4iIFG/Jx2F6byhRCnpNA3evfLmMir4bUaERVIqA6Ilq6CLFxvpDidzzyWqGf7UBX083vnywGRMfaErNsn52RytSWtV0DHIv5+/JgIlrmbQqVoPcRUREiqOM845OnWlnoPd08C2bb5dS0XejIgbCqV1w6De7k4jkq8MJqTw2fQN3j13N4cTzvHNPQ378Rxta1yptd7Qiq1opH2YPbUX7OmX57/fbef7bLaRnZtkdS0RERAqKZcHc4Y4h7HePhwph+Xo53dN3o0LvhoX/djR0qdbS7jQieS45LYOxy/bx+cpYXAz84+aaPNy2Bj6e+raRF3w93RjfP4IPftnNx0v2sjc+hU/7RVDGT9tkRUREnN6KkbB1Ftz8H6h3Z75fTit9N8rDB8J6wva5cO603WlE8kxmVjZfrjlIuxHL+GTZPrqEVWDp0+14skMdFXx5zMXF8FSHOozpE862o0l0Ha1B7iIiIk5vx/ew5HVo2APaPFUgl1TRlxuRAyErHTZ9ZXcSkVyzLIulu07S+aMVvPjdVmqU9WXe8Fa836MxFQK87Y7n1O4Iq8CsR1picAxy/36TBrmLiIg4pWObYfYQR3+Qrh/nS6fOy1HRlxvlGkCVZhAzWQ1dpEjbefwsAyauZeCkdWRkZTOufwQzhjQnrHJJu6MVG6GVApj3WGsaVgrgsekbGLFwpwa5i4iIOJPkE45Ond6B0OurfOvUeTkq+nIrYiCc3gsHVtidROS6nUxO41+zN3P7RyvYHJfEf7rUZ9E/29KxQXkNV7dBaV9Ppj3UnN5RVRizdB9DvogmOS3D7lgiIiKSWxlpMKMvpJ52FHx+5Qv08ir6cqtBd/Aq6WjoIlJEpGVkMWbpXtqPWMY30XE80DKE5c+048HWIXi46duCnTzcXHjzroa82q0BS3fFc9fY1Rw4dc7uWCIiInKjLAu+/wfErYO7x0HFxgUeQV0ZcsvdGxr3gbUTICUefMvYnUjkirKzLeZuOsKIBbs4mpRGxwbleL5zPUJK+9gdTS5hjGFAi2BqlvFl6Ffr6TZmFWP6hGtMhoiISFG06kPYPAPavwD1u9kSQT/SzwsRD0B2Bmz80u4kIle0NjaB7mNX8c8Zmwjy9eDrIc0Z1z9SBV8h1rJmaeYNa015fy8GTPydiSs1yF1ERKRI2TkffvkvhN4DNz1jWwwVfXmhTB2o1srR0CU72+40In9y8PQ5Hv0yhh7jfuPk2XTe79GIecNa07x6KbujyTWoWqoE3w5tya31yvHqD9t5dtZmDXIXEREpCo5vhW8fcmzn7DamwDp1Xo62d+aViIEw+yGIXQY1brY7jQhJqRl8vGQPU347gLurC0/eVpvBbarj7eFqdzS5Tr6ebnzaL4IPF+9h1OI97ItP4dP+EZT1K7iuXyIiInIdUuJhei/w8ode0x23hNlIK315pX5X8A6C6Il2J5FiLiMrm0mrYmk7cimfr4rl7iaVWfZ0O/5xSy0VfEWYi4vhydtqM7ZvODuOJdNt9Cq2xGmQu4iISKGTmQ4z+sG5U45Onf4V7E6koi/PuHlCk76OfbvJx+1OI8WQZVks2nacjh/8yn+/306Div78+Fgb3rk3jLL+WhFyFrc3rMCsR1vgYgz3frqauRuP2B1JRERE/mBZ8MM/4fAa6D4WKoXbnQhQ0Ze3IgaClQUbvrA7iRQzW48k0XvCGoZ8EYMxMPGBSL58sBn1K/rbHU3yQYOKAcwd3opGlUvy+NcbeWfBTrI0yF1ERMR+qz+GjdOg7fMQerfdaS7SPX15qVQNCLkJYqZC6yfBRVvpJH8dT0pjxMJdzN4QR2AJD17r1oBeUVVxd9XPc5xdaV9PvnyoGa98v41Plu1j1/FkPuzVGH8vd7ujiYiIFE+7FsDPLznGMrR9zu40f6J3hnktYiAkHYK9i+1OIk4s9UImH/y8m/Yjl/H9pqMMaVOdpU+3o3+LYBV8xcgfg9xf6x7Kr7vjuWvMKmI1yF1ERKTgndgO3z4IFcKg+6fgUrjej2mlL6/V7QI+ZSBmEtTuYHcacTJZ2Rbfro9j5MJdnExO546wCjzfqS5VgkrYHU1s1L95NWqV9eXRL2PoNnolo/uEc1PtMnbHEhERKR7OnYLpPcHD19Gp06PwvS8rXCWoM3DzgCb9YPcCSFKDBck7q/ee4s6PV/LsrM1ULOnNt4+2YEyfcBV8AkDz6qWYN7w1FUt688CktXy2Yr8GuYuIiOS3zAswcwAkn3B06gyoZHeiy1LRlx/C7wcrWw1dJE/si0/hoSnr6PPZ7ySdz2BU7ybMGdqSiGpBdkeTQqZKUAm+fbQlHeqX5/Ufd/CMBrmLiIjkH8uCH5+Eg6scw9crR9id6Iq0vTM/BIU4BrSvnwptngZX/TbL9Us4d4GPftnNtN8P4eXuynOd6jKwVTBe7moQJFfm4+nG2L7hjFqyhw9/cQxyH9cvQmM7RERE8tqasY5FnjZPQ9h9dqf5W1rpyy+Rg+DsEdizyO4kUsSkZ2Yx4df9tB2xlC/WHKRXVBWWPdOOR9vVUMEn18TFxfDErbX5pG84O48l03X0KjYdPmN3LM31f8wAACAASURBVBEREeex52dY9KKjn0f7F+xOc1VXLfqMMRONMSeNMVsvOTbCGLPTGLPZGDPHGFMy57i7MWaKMWaLMWaHMeZfl7ymkzFmlzFmrzHm+fz5cgqR2p3At7yjoYvINbAsi/lbjnHb+7/yxvwdRFQLZMETN/F694aU9vW0O54UQZ0bVuDbR1vi6mK4b9xvfLdB9xmLiIjk2smdMGsQlGsAd48vdJ06L+daEk4GOv3l2M9AqGVZYcBu4I/i7j7A07KshkAE8LAxJtgY4wqMAToD9YHexpj6eZC/8HJ1h/D+jp8CnDlkdxop5DYePsN9n/7G0Gnr8XZ3ZeqgKCYPjKJ2OT+7o0kRV7+iP/OGt6JJlZI8MWMjb/20Q4PcRUREblRqAkzvBW5eOZ06fexOdE2uWvRZlvUrkPCXY4ssy8rM+XANUPmPTwE+xhg3wBu4AJwFooC9lmXttyzrAvA10C1vvoRCLHyA47/rp9qbQwqtI2fO8/jXG+g+ZhUHTqfy1t0Nmf94G7XblzxVKmeQe7/mVRm3fD8PTVnH2bQMu2OJiIgULX906jx7BHpNg5JV7E50zfJiLXIQ8FPO41nAOeAYcAgYaVlWAlAJOHzJa+Jyjl2WMWaIMSbaGBMdHx+fBxFtUrIq1OrgKPqy9AZL/l9yWgbvLtjJzSOXsWDrcYa3r8myZ9rRO6oqri7G7njihNxdXXi9e0PeuCuUFXtO0X3MKvbHp9gdS0REpGiwLPjpGTiwArqOhipRdie6Lrkq+owxLwCZwLScQ1FAFlARCAGeMsZUBy73LvaK+4ssyxpvWVakZVmRZcoU8RWPyIGQcgJ2/XT154rTy8zK5qvfD9F+5DLGLtvH7Q0rsOTpdjzdsQ6+nuryKvmvb7NqTHuoGWdSM+g2ZhXLdxfhH6yJiIgUlN/HQcxkaP1PaNTT7jTX7YaLPmPM/UAXoK/1/xOA+wALLMvKsCzrJLAKiMSxsnfp+mdl4OiNXrtIqXkb+FdSQxdh+e54bh+1gn/P2UL10r7MHdaKD3o2plJJb7ujSTHTrHop5g5rRaWS3gzUIHcREZG/t3cxLPwX1LkDbn7J7jQ35IaKPmNMJ+A5oKtlWamXfOoQcLNx8AGaAzuBdUAtY0yIMcYD6AXMy130IsLVzXFv374lkBBrdxqxwa7jyQyYuJb7J64lPTObT/uFM+Ph5jSqUtLuaFKMVQkqweyhLenYwDHI/alvNpGWoUHuIiIifxK/G74ZCGXrF5lOnZdzLSMbpgO/AXWMMXHGmAeB0YAf8LMxZqMx5tOcp48BfIGtOAq9SZZlbc5p+jIcWAjsAGZalrUt77+cQqpJfzAujiVhKTbik9P595wtdP7oVzYeSuTFO+qx6J830Sm0Asbovj2xXwkPN8b0Ceeft9Zm9voj9Bq/hhNn0+yOJSIiUjikJsD0no6u/L2ng6ev3YlumCnsW3oiIyOt6Ohou2Pk3vQ+ELcW/rkd3DzsTiP5KC0ji89XxvLJsn2kZWTRr3k1Hr+lFoE++v8uhdeCrcd5cuZGfD3dGD8gksZaiRYRkeIsKwO+vBsOrYH7v4eqzW2NY4yJsSwr8kZfXzTXJ4uiyIFwLh52/mB3EsknlmUxd+MRbnlvOSMW7qJFjVIs+udNvNK1gQo+KfQ6hZZn9tCWeLq70GPcb8xeH2d3JBEREfsseB5if4U7P7K94MsLKvoKSo2bIaCqGro4qegDCXQfu5rHv95IyRLuTB/cnAkDIqlepuhuA5Dip255f+YOa01E1UCenLmJN+drkLuIiBRDayfAus+g5T+gcR+70+QJ9YgvKC6uEDEAlrwOp/ZC6Zp2J5I8cOh0Km8v2MH8Lccp5+/JyPsacXeTSrho1p4UUUE+Hkx9MIrXftjO+F/3s+t4MqN6NyHA293uaCIiIvlv31L46Tmo3QlufcXuNHlGK30FqUl/cHHTap8TSDqfwZvzd3Dr+8tZujOeJ26txdKn23FvRGUVfFLkubu68Gq3UN68qyGr9p7irjGr2KdB7iIi4uxO7YVv7ofSteHuCY5FGyehoq8g+ZWHOrfDxq8gQx3yiqKMrGymrD5AuxFLmbBiP90aV2TZM+144tbalPDQwrk4lz7NqvLV4OYknc+g+5hVLN110u5IIiIi+eN8oqNTp4sb9PkavPztTpSnVPQVtMiBcD4BdnxvdxK5DpZlsXjHCTp++Csvz9tGvQr+/PBYa0bc14hy/l52xxPJN1EhQcwd3ooqgSV4cPI6xv+6T4PcRUTEuWRlOmbxJR6Enl9CYLDdifKcir6CFtLO8QdJWzyLjG1Hk+j72e88OMUxOuSzAZFMe6gZDSoG2JxMpGBUDizBrEdb0Dm0Am/O38mTMzXIXUREnMjCf8P+pdDlfajW0u40+UL70QqaiwtEDIRfXoaTO6FsXbsTyRWcOJvGyIW7mLU+jpLe7vy3awP6NKuKu6t+ViLFTwkPN0b3aULdJX689/Nu9senMK5/JOUDtNItIiJFWPREWDsOmg+D8AF2p8k3evdqh8Z9wcUdYibbnUQuI/VCJh/9sod2I5bx3cYjDG5TnWXPtOf+lsEq+KRYM8bw2C21GN8/gr0nU+g6eiUbDiXaHUtEROTGxP4K85+BmrdBh9fsTpOv9A7WDr5loN6dsOkryDhvdxrJkZ1tMSsmjvYjl/HBL7tpX7cMvzzZln/fXk/t6kUu0aFBeWYPbYWXuys9x6/h2xgNchcRkSLm9D6Y0R+CasC9nztVp87LUdFnl8iBkJYE276zO4kAv+07TdcxK3n6m02UD/Bm1iMtGNs3gmqlfOyOJlIo1Snvx9xhrYisFshT32zi9R+2k5mVbXcsERGRq0tLgum9wJicTp3O36dB9/TZJbgNlKrp2EfcuLfdaYqt/fEpvPXTTn7efoJKJb35qFdj7gyrqFl7Itcg0MeDKYOieOPHHXy2MpbdJ1P4uFcTAkpoZVxERAqprEyYNQgS9kP/7yCout2JCoSKPrsY42josugFOLENyjWwO1GxknjuAh8t3sOXaw7i6ebCMx3r8GDrELzcnXtpXySvubu68ErXBtQt78d/5m6l+9hVTBgQSc2yvnZHExER+V8//wf2/gJdPoSQNnanKTDa3mmnxn3A1ROiNb6hoFzIzOazFftpO2IpU387QI+mVVj2THuGta+pgk8kF3pFOQa5J6dlcNeYVSzdqUHuIiJSyMRMgTVjodkjjlutihEVfXYqEQT1u8HmGXDhnN1pnJplWSzYeozbPljO6z/uoHHVQH56/CbevKshZfw87Y4n4hSaBgcxd3hrqpYqwaAp6/h0uQa5i4hIIXFgJfz4JNS4BTq8YXeaAqeiz26RAyH9LGz91u4kTmtz3Bl6jlvDI1+ux9PNhckDmzJ1UBR1yvvZHU3E6VQq6c2sR1pyR8MKvP3TTp6YsVGD3EVExF4JsY5OnYEhcO9EcC1+d7gVv6+4sKnaAsrUdWzxdOKBkHY4euY8IxbuYs6GI5T29eDNuxrSI7Iybpq1J5KvvD1c+bh3E+pV8Gfkol3EnjrHuP4RVAjwtjuaiIgUN2lnHZ06rWzoMwO8S9qdyBZ692u3Pxq6HF0PxzbZncYppKRnMnLhLtqPXMaPW44xtF0Nlj7djj7NqqrgEykgxhiGta/JhP6R7DuZQtfRq1ivQe4iIlKQsrPg2wfh1B7oMRVK1bA7kW30DrgwaNQT3LzU0CWXsrItpq89RLsRyxi9dC+dQsuz5Km2PNupLn5eaiEvYodb65djzrBWlPBwpde4NXwTfdjuSCIiUlz8/BLsWQS3vwvV29qdxlYq+goD70BocDds+QbSk+1OUyStjU3gjlEr+NfsLQSXKsGcoS35qFcTKgeWsDuaSLFXu5xjkHvTkECembWZ1zTIXURE8tuGL+G30dB0MDR9yO40tlPRV1hEDoILKY7CT65ZdrbFmKV76TX+N85dyGRs33C+eaQFTaoG2h1NRC5RsoQHUwZG8UDLYD5fGcvAyetISs2wO5aIiDijg7/B909A9XbQ6W270xQKKvoKi8qRUC7UscVTLc6vSeK5Cwyaso4RC3fRJawiCx6/idsbVsAYY3c0EbkMt5xB7u/eE8aa/afpNmYle09qd4OIiOShxIMwoy+UrAr3TS6WnTovR0VfYWEMRDwAxzc7mrrI31p/KJE7Rq1g9d7TvNY9lI96NcbHU3+pRYqCHk2r8PWQ5qSkZ9F9zGoW7zhhdySRG3b0zHnmbjzCi99toe9na1ixJ97uSCLFV3qyo1NndmZOp07t/PqDKeyDcyMjI63o6Gi7YxSMtCR4ry6E3gPdRtudplCyLItJqw7w1k87KB/gxdg+ETSsHGB3LBG5AUfPnOfhL2LYejSJZzrW4dG2NbRSL4WaZVnsPZnCugOJrDuQwNrYBI6cOQ+Aj4crvl5uJJy7wMj7GtGtcSWb04oUM9lZ8HVfR+OWfrOgxs12J8pTxpgYy7Iib/T1WhopTLwCoOG9sGUWdHzD8bFcdDYtg+dmbeanrce5rX45Rt7biIAS6sopUlRVLOnNzIdb8Oy3m3l3wS52HkvmnXvC8PZwtTuaCAAZWdlsPZJE9IFE1h5IIPpAAok596KW9vUkKiSQB1uHEBUSRN3yfpy7kMXgqdE8/vVG4pPTeahNdZu/ApFiZPGrsPsn6DzC6Qq+vKCir7CJGAjrp8LmmRA12O40hca2o0kMm7aew4nneeH2ejzUJkQrAiJOwNvDlVG9GlOvgh8jFjoGuY8foEHuYo9z6ZlsOHSGdQcSWHcggQ2HznA+IwuA4FIluLVeOZoGB9E0JIjgUiX+59+hAG8Xpg6K4omvN/L6jzs4mZzO853q4uKif69E8tXG6bDqQ0djRL1/vixt7yyMxt0EWZnw6CrHvX7FmGVZzFh3mJfmbSOohAej+zQhMjjI7lgikg8W7zjB419vxMvdlXH9w4mopr/rkr9Op6Sz7kAi0TlF3tajZ8nKtnAxUK+Cv6PACw6iaXAgZf29rvm8WdkWL8/bypdrDnFXk0q8e28Y7q5qoyCSLw79DlO6QJVm0H8OuDrnLrDcbu9U0VcYRU+CH56AB3+GKlF2p7FN6oVMXpyzldkbjtCmVmk+7NmYUr6edscSkXy050Qyg6dGc/RMGq93D6VH0yp2RxInYVkWcYnnWRubQPRBx/14++LPAeDh5kLjKiWJCg4iMjiQ8GqB+Hvl7o2jZVmMXrKX937ezU21y/BJ33A1HBPJa2cOwYSbwcMXBi+BEs77w0IVfc4oPRneqwf1usBdn9qdxhZ7TyYzdNp69pxM4YlbajP85pq4anuMSLFwJvUCw7/awMq9pxjYKpgXbq+Hm1ZJ5DplZ1vsOpGcs1UzkXWxCRw/mwaAv5cbkZes4jWsHICnW/7cS/r12kP8e84WQisFMPGBppTWDy9F8kZ6Ckzs6Cj8HvoFytSxO1G+UiMXZ+TpB2H3wcavoNNbxa7d7NyNR/jX7C14u7vyxaBmtK5V2u5IIlKASpbwYPLAprw5fycTV8Wy50QKo/s0oWQJD7ujSSGWnpnFlriknIYrji2bZ9MyASjv70XTkCCiggOJDA6iTjm/ArvPrldUVUr5ejL8q/Xc+8lqpg5qRtVSJQrk2iJOKzsb5jwMJ7dDn2+cvuDLC1rpK6yObYZxbaDT29D8UbvTFIi0jCxe+2E7034/RNPgQD7uHU75gGu/h0JEnM830Yd5Yc5WKpT0YsKASGqX87M7khQSyWkZxBxMvLiSt+nwGdIzswGoUcaHqJCgi/fkVQ70tr35V/SBBB6cEo27qwuTBzYltJI6dIvcsMWvwor3itX7ZG3vdGYTbnYsXQ/73ekbuhw6ncrQr2LYeuQsD7etzjMd6mg7l4gAEHMwkUe+jCE1PZOPejXh1vrl7I4kNjiZnMa62MSLnTV3HDtLtgWuLobQiv4Xu2pGVgsstPd/7zmRzICJa0lOy2R8/wha1tROFpHrtnkmzB4M4QPgzlFO/x75Dyr6nNn6L2DecHhgPgS3sjtNvlm47ThPf7MJA7zXozG36Q2diPzFsaTzDJnqGOT+dIc6DG2nQe7OzLIsDpxOZV1swsX5eAdOpwLg7e5Kk6olaRocRFRIEI2rlCxSDVKOJZ3n/olriT11jvd7NObORhXtjiRSdMRFw6TboXJTR6dOt+Kz7V9FnzO7cM7R0KV2B7jnM7vT5LmMrGzeXbCTCStiCascwJg+4VQJ0n0OInJ5aRlZPPftZuZuPEqXsAqMuLeRBrk7iaxsix3HzrI2NuHids1TKekABJZwJzI4iKiclbwGFf2L/PiDpNQMHpq6juiDibzcpT4PtAqxO5JI4ZcUB+Pbg0cJeGgJ+JSyO1GBUiMXZ+bhA416Qsxk6PSOU/3hPpZ0nuFfbSDmYCIDWlTjhTvq5VvnNBFxDl7urnzYszH1KvjzzoKdHDh9jvH9I6lYUoPci5q0jCw2Hj5zcSVvw6EzpKQ7mq5UKulNm1qlc1byAqle2tfphpsHlHDniweb8dj0Dbzy/XZOJqfzTMc6Wr0WuZIL52B6b8g4D/fPc6r3xAVFK32F3Ynt8EkL6PA6tHzM7jR54tfd8TwxYyPpGVm8fU+YtraIyHVbsvMEj0/fiKe7C5/2iyAy2HlnMzmDpNQMx2y8Awmsi01gy5EkMrIc7z/qlPOjaUjgxaYrxamIz8zK5j9ztzF97SHujajMW3c3LPKrmCJ5LjsbvrkfdnwPfWY6dsAVQ9reWRx83gHOnYLHYor0zapZ2RYfLd7Dx0v2ULusH2P7hVOjjK/dsUSkiNp7MpnBU2OIS0zl9e6h9Gxa1e5IkuNY0vn/36oZm8iuE8kAuLsaGlYKyBmfEERktSACSuRuCHpRZ1kWH/6yh48W76F9nTKM6RtOCQ9txBK5aMkb8Ou7TrUAciNU9BUHm752zCIZMA+qt7U7zQ2JT07niRkbWLX3NPdGVOa1bqG6F0dEci0pNYPHvt7Ar7vjeaBlMC/cUU8rJQXMsiz2xaewNqez5trYBI6cOQ+Aj4cr4dUCL96P17hKSbzc9b3/cr5cc5CX5m4lrHJJJj7QlCCf4tOgQuSKtsyCbx+Exv2g2+givfiRWyr6ioOM8/BeXajRHu6bbHea67Y2NoHhX60n6XwGr3UPpUdkFbsjiYgTyczK5p2cplAta5RiTJ9wAvWGOd9kZGWz7ejZP3XWTEzNAKC0r8fFbZpRIUHULe+n8TvXYcHW4/zj6w1UDvRm6qAoKgequZkUY0diHJ06KzaBAXPBrXCOYikoKvqKiwX/grUT4Mnt4FvW7jTXJDvbYvyK/YxYuIuqQSUY2zecehX87Y4lIk7q25g4/jV7C+UDHIPc65TXIPe8kHohkw2Hzlzcrrnh0BnOZ2QBEFyqxJ86awaXKqFmJLm0NjaBh6asw8vdlSmDovTvphRPZ486OnW6ecDgpeCjmZYq+oqL+F0wJgpueRnaPGl3mqs6k3qBp2ZuYvHOk9zRsAJv39MQP6/ifd+GiOS/9YcSefgLxyD3D3o2pkOD8nZHKnJOp6QTfTCRdTlF3tajZ8nKtnAxUK+C/8WVvKbBgZT197I7rlPadTyZ+yeu5dyFTCYMiKR5dXUqlGLkQipM6gyn98KDi6BcA7sTFQoq+oqTSXfA2Th4bAO4FN7tMpsOn2HotPWcTE7jxTvqM6BFNf3kV0QKzPGkNB7+IppNcUk83aE2w9rX1PegK7Asi7jE8zmz8Rz34+2LPweAh5sLjauUpGmwo7NmeLVA/PXDuwJz5Mx5Bnz+O4cTz/NRz8Z0bljB7kgi+c+yYNZA2PYd9J4OdTrbnajQUNFXnPxxM2u/2VDzFrvT/A/Lspj620Fe/3E7Zf28GNM3nMZVStodS0SKobSMLP41ewtzNhzhjrAKjLg3TB0RcWy7330yOed+PMdq3vGzaQD4e7kRGRxEZLCj8UrDygGan2qzxHMXeHDKOjYcPsOr3ULp37ya3ZFE8teyt2HZW3Drf6H1E3anKVQ0nL04qXcnlCgFMZMKXdGXkp7Jc99u5sfNx7ilblne69GIkiXUSEFE7OHl7sr7PRpRr4Ifb/+0k9j4c0y4P5JKxWgGHEB6ZhZb4pJYd8DRWTP6QAJn0xxD0Mv7e9E0JOjiSl6dcn5ONwS9qAv08WDaQ80Z/tV6/vPdVk6eTePJ22pr5Vqc07Y5joKvUW9o9bjdaZyOVvqKmkUvwm9j4Z/bwL9wbPXYcewsw6at52BCKk93qMPDN1XXGwcRKTSW7jrJP6ZvwMPVhU/7R9DUiQe5J6dlEHMwkegDiaw9kMCmw2dIz8wGoEYZH6JCHLPxokKCqBzoreKhiMjMyubfc7YwMzqOXk2r8Hr3UHVFFedydANM7AzlG8IDPxT7Tp2Xo+2dxc3pffBxOLR/Edo+Y3caZkYf5j/fbSXA252PezehmW42F5FCaF98CoOnRHM4MZVXu4XSO8o5BrmfTE5jXc58vHUHEthx7CzZFri6GEIrOpquROY0XSnlqzdRRZllWbz/824+XrKXW+uV4+PeTTTvVpxD8nFHp04XVxi8pMh0qS9oKvqKoyldIWE/PL7J8RfEBucvZPHS3K18ExNHyxql+KhXE8r46Q2FiBReSecz+Mf0DSzfHc+AFtX4T5f6RWqQu2VZHDiderGr5roDCRw4nQqAt7srTaqWvNhZs0nVkvh46g4OZzT1twO8PG8b4VUD+fz+SN1KIUVbxnnHLL74XfDgQsdKn1yWir7iaNsc+OYB6PMN1O5Q4JffF5/CsGnr2XUimcfa1+TxW2vjqu2cIlIEZGVbvLNgJ+N/3U+L6qUY0zecoEI6yD0r22LHsbMXC7x1BxKJT04HILCE+8X5eJHBgYRWCihSBazkzvwtx3ji641ULVWCqYOiqFjM7lUVJ2FZjgaFW7+FntOgXhe7ExVqKvqKo8wL8EF9qNzU0c62AP2w+SjPzdqMp7srH/RsTNvaZQr0+iIieWH2+jien72Fcv6eTBgQSd3y9g/ATsvIYuPhM46VvIOJrD+YSEq6o+lKpZLeRIX8/3y8GmV8de90MffbvtMMmRqNj6cbUx+MonY5P7sjiVyfX0fAktfhlpegzVN2pyn08r3oM8ZMBLoAJy3LCs05NgK4E7gA7AMGWpZ1JudzYcA4wB/IBppalpVmjIkAJgPewHzgcesaKk4VfVfwyyuw6iN4YgsEVM73y6VnZvHmjzuY8ttBIqoFMrpPEyoE6CeLIlJ0bTx8hiFTo0nJGeTesYAHuSelZhB9MIG1BxKIPpDI5rgzZGQ5/lmsU86PpiGBF7draiVHLmf70bM8MGktaRlZfP5AU6duUiROZvs8mNkfGvaAu8eDmkpdVUEUfTcBKcDUS4q+DsASy7IyjTHvAFiW9Zwxxg1YD/S3LGuTMaYUcMayrCxjzFrgcWANjqJvlGVZP10toIq+K0iIhVFNoO1z0P5f+XqpwwmpDP9qPZvikhjcJoRnO9XVNiIRcQonzqYx5IsYNh0+w5O31eaxm/NvkPuxpPOszbkfL/pAIrtOJGNZ4O5qaFgpgKYhju2aEdUCdZ+WXLPDCancP3EtR86cZ1TvJgX+wwuR63ZsE0zsBGXrwwM/gruX3YmKhALZ3mmMCQZ++KPo+8vn7gLutSyrrzHmdqCPZVn9/vKcCsBSy7Lq5nzcG2hnWdbDV7u2ir6/8cXdcHKHY7XPNX9u2F+84wRPztxEtmUx8r5G+sdERJxOWkYW/56zhdnrj3B7w/KMvK9Rrge5W5bFvvgU1sYmEn3AsZoXl3geAB8PV8KrOQagNw0JonGVkni5qwuj3LiEcxcYOHkdW+LO8Hr3hvRp5hzdacUJJZ+ACe0djwcvBb9y9uYpQgrDcPZBwIycx7UByxizECgDfG1Z1rtAJSDuktfE5Ry7LGPMEGAIQNWq+sZ1RZEDYUY/2LMI6t6ep6fOzMpmxKJdjFu+nwYV/RnbN5xqpXzy9BoiIoWBl7sr793XiPoV/Hlz/g5iT6UyYUAElQNLXPM5MrKy2Xb07MXOmtEHE0k4dwGA0r4eNA0OYlCrEKJCgqhb3k8z1iRPBfl4MH1wM4ZOW8+/52zhZHIaj99SS3MYpXDJSIOv+8D5RBi0QAVfActV0WeMeQHIBKZdcr7WQFMg9f/Yu+/4qOrs/+Ovm0pJ6KEmofeeRlVELChNBCGI0lQURde6q+66bv19d9e1Y6GIIgKhCCLN7ipISSF0QocklNBJI3Xu748bNaCYnjszeT8fDx5rLlMOy5DMmXM+5wBfG4YRB6T+yt2vWWI0TXMWMAusSl9ZYnRr7QaDX2OInVuuSV9KahaPLown+uh5xvcK5oWhnfQptIi4NcMwuP+6VrRt5M/0hVsZPuMH3hkfcs3do5k5ecQnXvypXTM+8SKXc/MBaF6/Bjd2aPhTJa9F/Rp68y0VroaPF7MnhPHsxzt57asDnE7L5u8jumi6tjgH04RVj8HxWBgzH5p0tzuiKqfUSZ9hGBOxBrwMKjSQJRn4zjTNswW3WQuEAB8BhaeNBAInSvvcUsDTG0ImWNOPLhyDus3L/JAbDpzld1HxXM7N5/XIHozocc2CrIiI2xnQLoCVj/Tj/g9jGT9nC38d0ZnxvZpzPiPHWptQkOTtOpFKvsPEMKBj41qMDQ/6abJmw1o6nyL28Pb04L93dSPA35d3vzvE2bRs3hjXUx/civ02vAo7FsPAP0Gn4XZHUyWV6kyfYRiDgVeAAaZpnil0u7rA11jVvhzgM+BV0zTXGIYRAzwKbMEa5PKmaZpri3punekrwsUkeL0b9H8SBr1Q6odxOEze/OYgr329nzYBfrxzTwhtGmr8s4hUTalZ1iL3/+07Q2Dd6j+dx/Px8qBHUB3CW1iTNUOa16VWNW+boxX5pbkbjvC31XuIaFGP2RPDqF1dr1OxScIaiBoPXe6EUe9pyuW4EwAAIABJREFUUmcpVcb0zkXADUADIAV4EXgO8AXOFdxss2maDxXc/p6C3zeBtaZp/r7gehg/r2xYBzyqlQ3lZMEYOLkNnthtVf9K6Fx6No8v3sb6A2e5s2cz/jGyS5mHGIiIuLp8h8lb3x5kR/IlQprXIaJFPboG1sbXS1UTcQ2fbj/BU0u20aqBH/OmRNC4tqrQUslO7YT3boWA9jB5LXhr/UxpaTm7wL51sCgSxnwInUaU6K6xR88zfWE85zNz+OvwzkSGB+nsiYiIiJv44eBZHpwfR61q1hJ3dfFIpUk/Y03qdOTDA99ArSZ2R+TSypr0aXyYO2h7C9QKhNj3i30X0zSZ/f1hxs7ajK+3Byse7su4iGAlfCIiIm6kX5sGRE3tTU6+yeh3NxF37ILdIUlVkJdtTZjPOAvjFirhcwJK+tyBh6c10OXwt3D+cJE3v3Q5l6nz4/jn2r3c3LERqx7tT+emtSshUBEREalsXZrVZvm0vtSp7s34OZv5em+K3SGJOzNNWPU4JG2Gke9A0552RyQo6XMfIfeC4Qlx837zZjuTLzH0zfV8m3CaPw/txDv3hGgIgYiIiJsLrl+DZdP60rahP1Pnx7EkJsnukMRdbXwDti+EAc9C55F2RyMFlPS5i1pNrb198R9BXs4vfts0TT7afIxR72wkP99kyUN9mNK/pdo5RUREqogGfr5ETe1N39b1+f3HO5jxzQGcfbaDuJh96+DLF6HTHTDgD3ZHI4Uo6XMnYVMg8ywkrLrickZ2Ho8v3safPtlF3zb1Wf3YdYQE17UpSBEREbFLTV8v3psYzh09mvLfL/bz4qe7yXco8ZNykLIHPr7fWrx+xzvgoTTDmWguvztpfSPUCbYGunQZBcD+lDSmfRTHkbMZPHNre6YNaI2Hh6p7IiIiVZWPlwevjOlBgL8vs9cf4Wx6Nq+M6aEl7lJ6GWdh0Vjw8YNxi8Cnht0RyVWU9LkTDw8ImQjf/B3OHmR5YjX+uGIXNX29+Oj+XvRt3cDuCEVERMQJeHgY/HFIJxr6V+Ofa/dyPiOaWRPCdM5fSi4vBxbfC+mnYdJa68iROB3VXd1Nz3sxPbz4PuolnlyynW6BtVn7WH8lfCIiIvILD1zfitfG9iD26AXGvLuJlNQsu0MSV2KasOYJSNwII96CwFC7I5JrUNLnZo5k+/GDZwRdz6zmsQGBLLi/Fw1rVbM7LBEREXFSd/RsxtxJ4SSez+TOtzdy6Ey63SGJq9j0ljVE8PpnoOtou6OR36Ckz42s23mSYW9uYH7eIOoa6TzZbB9envorFhERkd92fbsAoqb2Jis3n9HvbCQ+UUvcpQj7v4AvX4COw+GG5+2ORoqgjMAN5OQ5+Ouq3UxbsJU2Df3482PToG5La6CLiIiISDF0C6zDx9P64l/Nm7tnb+HbfaftDkmc1ekE+Pg+aNQZRr6rSZ0uQH9DLu74xcuMmbmJ9384yuR+LVjyYB+a1a0JoZOs/urTCXaHKCIiIi6iRYOaLJvWh1YBNbl/XizL4pLtDkmcTcY5a1KnVzUYFwU+Ne2OSIpBSZ8L+zbhNEPeWM/B0+m8PT6EF4d1xser4K+0x3jw8IY4VftERESk+Br6VyNqam96t6rH00u3887/DmmJu1jycmDJBEg9CZELoXag3RFJMSnpc0F5+Q7+81kCkz+IoUnt6qx+tD+3d21y5Y38AqDTcNi+CHIv2xOoiIiIuCT/at7MnRTOsO5N+fdnCfxt9R4cWuJetZkmrH0ajm2AETMgKNzuiKQEtKfPxZxOzeKxqHg2Hz5PZHgQfxne+drLVEMnw66PYfcK6HF35QYqIiIiLs3Xy5PXx/YgwM+XuT8c4UxaNi+P6Y6vl5a4V0lbZsLWedD/Seg2xu5opISU9LmQTYfO8eiieDKy83j5ru6MCi2ipN6iP9Rvaw10UdInIiIiJeThYfDC0I40rOXLv9YlcCEzh3fvCcVfS9yrloNfwefPQfshcOMLdkcjpaD2ThfgcJi89e1Bxs/ZTO3qXqyc3q/ohA/AMKyBLsnRcGpXhccpIiIi7scwDB4a0JqX7+pudRrN2szpNC1xrzLO7IelU6BhJ7hzliZ1uij9rTm5Cxk5TJkXw0uf72Not6Z8Or0/7Rr5F/8BetwNnr4a6CIiIiJlMio0kDkTwzh8JoNR72zkyNkMu0OSipZ5vmBSpw+MWwS+fnZHJKWkpM+JbU28wJA31rPx4Dn+cUcXXo/sQU3fEnbk1qgHne+AHUsgR9+cRUREpPQGtm/Ioqm9yci2lrjvSL5od0hSUfJzYelEuJQMYxdAnWC7I5IyUNLnhEzT5L0NRxjz7iY8PQ0+ntaXe3o3xzCM0j1g6GTITrWGuoiIiIiUQY+gOix7qA/VfTyJnLWZ7/efsTskqQjr/gBHvodhb0BwL7ujkTJS0udkUrNyeXjBVv6+eg8DOzRk9fTr6BpYu2wPGtwbAjpA7NzyCVJERESqtFYBfiyf1pfm9Wsy5YMYPok/bndIUp6iZ0Pse9Dvd9BjnN3RSDlQ0udEdp+4xPA3N/DFnhT+eHtHZt0bSu0a5TAdyzAgbAqciIcT28r+eCIiIlLlNaxVjcUP9iasRV0eX7yNOesP2x2SlIdD31hVvnaDYdCLdkcj5URJnxMwTZNF0YmMfHsjWbkOFk/tzQPXtyp9O+ev6TYWvKproIuIiIiUm1rVvJk3JYIhXZvwjzV7+ecaLXF3aWcPwtJJENAeRs0BD+1kdBfa02ezzJw8/rRiF8vjj3Nd2wa8NrYH9f18y/+JqteBLnfCzmVwyz/AtwQTQEVERESuwdfLkzfG9aSBnw+z11tL3P8zujs+XqotuJTLF6xJnR5eMC5K7xXdjP412ujg6TRGzPiBFduO88RN7fhgckTFJHw/Cp0MOemwc2nFPYeIiEhVdOR7mD8SPnsOjv4Ajny7I6pUnh4GfxnemWdubc8n205w37wYMrLz7A5Liis/z6rwXTgGYz+Cus3tjkjKmWGazl2CDwsLM2NjY+0Oo9yt3Hac55bvpIaPJ69H9qRfmwYV/6SmCe/2t874Pbje+l8REREpvex0+OpFiJkDfo2sakl+DtRoAB1uhw7DoNUA8KrAD3WdzJKYJJ5bsZPOTWsxd1I4DSryA20pH2ufgehZMHwGhNxrdzTyKwzDiDNNM6y091d7ZyXLys3n76v3sGBLIhEt6vHm3T1pVKta5Ty5YUDYZFjzFBzfCoGhlfO8IiIi7ujI97DyEbiYBL0fgRv/BGY+HPgSElbDrhWw9UPw8Yd2t0CHodD2ZrdvmxsTHkR9Px8eWbiV0e9s5MMpvQiuX8PusORaYt6zEr4+05XwuTFV+ipR4rlMHl4Yx67jqTw0oDVP39IOL89K7rDNSoWXO0CXkTDircp9bhEREXdQuLpXr7X187R5n1/eLi8bDn8HCasgYS1kngVPX2h1A3QcCu1vh5qV0Oljk7hjF7hvXgxeHh58MDmcLs3KuIJKyt/h76y25DaDrHN8GtzitMpa6VPSV0k+332Kp5dux8MwePmu7tzUqZF9wXz6qDXQ5cm91oAXERERKZ4rqnsPW9U9n2JUsRz5kLQF9q6CvavhUiIYHhDc10oAOwyFOkEVH38lO3g6jQnvRZOalcfMe0Mr5ziLFM+5QzD7RvBvDPd9CdVq2R2R/AYlfU4uN9/Bv9clMGfDEboF1uatu0MIqmdzi8PxrTB7INz2EvSaam8sIiIirqC41b3iME04tcNK/hJWw+k91vUmPQoSwGHWyHw3OXt/6lIWE+dGc/hsOi+P6cHw7k3tDkkuX4T3boaMs/DAN1Cvpd0RSRGU9Dmxk5cuM31hPHHHLjCxT3OeH9IRXy8nKZvPHGAdNJ+20W1+qIiIiFSI0lb3iuvcIasCmLAakmOsa/XbQMdhVgLYtCd4uPbA9UuZuTzwYSzRR8/z56GdmNJfSYZt8vNg4Rg48h3c+wm0vM7uiKQYlPQ5qe/3n+HxxdvIzs3nX6O6MczZPtWK+wBW/Q6mfAHBveyORkRExPlcUd1rBSPeLn11r7hST0DCGisBPLoBHHng3xQ6DLGSwOb9wNM15/Bl5ebzu6h4Pt+dwkMDWvOHwe0x9MFz5Vv3LGx5B4a9DqGT7I5GiklJn5PJd5i8/tV+3vz2IO0b+fPW+BBaB/jZHdYvZadbA106DoWR79odjYiIiHO5oro3DW58oXyre8Vx+QLs/9yqAh78GvIuQ/W60O426+d36xvBu3rlxlRG+Q6TF1buYuGWRO4Maca/R3XDu7KH2lVlP37o32sa3PYvu6OREtDKBidyJi2bxxfH88PBc9wVGsjfRnShuo+TtHNezdcPut0F8Qvg1v8HNerZHZGIiIj9rq7uTV5X8dW9a6leF7pHWr9yMuHQ19Y5wH1rYPtC8K4BbW6yKoBtb3GJ4WyeHgb/vKMLjfyr8epX+zmfkcPb40Oo4aO3pBXu6AZrbVfrQXDLP+yORiqZKn3lZMvhczy6KJ5Ll3P5+x1dGBPmAhO4Tu20lrXf+n/Q52G7oxEREbHXke9h5XS4mGhfda848nPh6PqCQTBrIP0UeHhbZ7M6DoP2Q8DfxinhxbRwSyJ/+mQnXQPr8P6kcOrV9LE7JPd1/og1qbNmA7j/K6im9RmuwuEw2Xz4HP3aBqi9004Oh8nM7w/z3y/20bxeDd4aH0LHJi408nb2IMhOhUeiNdBFRESqJjvO7pUXhwOOx/48COb8YcCAoAhrDUTHodafyUl9vvsUjy2Kp1md6sybEmH/hHN3lJVqTepMO2VN6qzf2u6IpBjOpGWzLC6ZqJhEjp3L5Ni/hyrps8vFzByeWrKdrxNOM6RbE/51Z1f8q3nbHVbJxH9knVmYtBZa9LM7GhERkcrlKtW94jBNOL23IAFcZXX0ADTq8nMC2KiL033IG3P0PPd9EIOvtyfzJkfQqakLfXju7Bz5sCgSDn0D9yyHVgPsjkh+Q77DZP2BM0RFJ/HV3hTyHCYRLeoRGRHEqNAgJX122JZ0kUcWbOV0WhZ/GtKJCX2au+YEqpxMa6BL25th9Ht2RyMiIlI5XLm6V1wXjlrtn3tXQeJmwIS6LQoSwGEQGOE0qyD2p6QxcW406Vl5zJoQRp/W9e0OyT18/kfYNAOGvALh99kdjVzDqUtZLIlNYnFMEscvXqZeTR9GhTRjbHgwbRpaAyE1vbOSmabJh5uO8Y81e2joX423x4fQPcj5D07/prW/h7j34ckEqKlvsiIi4ubcqbpXXOmnYd9a6xzg4f+BIxdqNixYBTEUWlwPXvaeqTtx8TIT5kaTeC6T1yJ7cHvXJrbG4/J+7OaKmAq3v2R3NHKVvHwH/9t3hkXRiXy77zQOE/q1qU9keDC3dG70i93eSvoqUVpWLs8u38maHScZ1KEhL4/pTp0abnDo+PReeLs33Px36PeY3dGIiIhUjKpQ3SuOrEtw4EurAnjgS8jNAN/a0O4WqwLY5ibwqWlLaBczc7hvXixbEy/w1+GdmdCnhS1xuLxjG2HecOvozviPXXa3oztKOp/JktgklsQmkZKaTQM/X8aEBTI2PIjm9a/9705JXyXZezKVhxdsJfF8Js/c2p6p17XCw8MF2zmv5b1bIeMMPBrndL3+IiIiZVYVq3vFkZtlVf72rrIqgZfPg1c1awdgh6HQ/rZKX+uUlZvP9IXxfLU3hekD2/DULe1c8wiNXS4ctSZ1Vq9rTeqsXtfuiKq83HwHX+1JYVFMEusPnAFgQLsAIsODGdSxYbF2VWpPXyVYEpPECyt3Ubu6Nwvv70WvVm7YAhk2GVY8aP1Q1CFfERFxF7/Yu7cWmve1Oyrn4V0N2g+2fuXnQeKmgkEwa6wk0PC0qkUdhlmtoLWbVXhI1bw9efeeEP70yS5mfHuQ02lZ/L+RXfHSEveiZafBonHgyINxi5Xw2ezI2QyiYhL5OC6Zs+k5NKldjcdubMuY8CCa1aleqbGo0vcbLufk88LKXSyLS6Zfm/q8NrYnAf6+tsRS4XIvWwNdWt0AY+bZHY2IiEjZHVlvnWlSda/kTBNOxFtrIPauhrP7rOvNQn8eBNOgbQWHYPLql/t545uD3NSxIW+OC6G6j2fRd6yqHPkQdbfVsnvPx9B6oN0RVUlZufl8vvsUUdFJbDp8Dk8Pgxs7NGRcRBAD2jXEs5SdgmrvrCCHzqTzyIKt7EtJ49Eb2/K7QW1L/ZfkMj57HqJnwpN7wa+h3dGIiIiUTnY6fPUXiJldcHbvLVX3yurMfmsNxN7VcGKrda1Be2sITMdh0KRHhR0Pmb/5GH9euYueQXV4b2I4dbXE/dd9+Wf44XW4/b8Q8YDd0VQ5B1LSWBSdxPL4ZC5m5hJYtzqR4UHcFRZEo1rVyvz4SvoqwKrtJ3j24x34envy2tgeXN8uoFKf3zZn9sNb4TDoRbjuSbujERERKTlV9yrepeSfV0Ec2whmPtQOKpgEOgyC+4BH+VbkPtt1kseithFcrwbzpkRUemuc09u2ED6ZBmFTrPUMOgNZKS7n5LNm50miohOJPXYBb0+DWzo1JjIiiH6tG5Tr/A8lfeUoOy+ff67Zy4ebjhHavC4z7u5Jk9pV7JvK+0PgUhI8ts1pdveIiIgUSdU9e2Seh33rrATw0DeQnw016lsDYDoMs46NeJe9ygGw+fA5Hvgwlpo+XsybEkH7xv7l8rguL3ELzBsKwb2tBeye3nZH5Pb2nEglKiaRFfHHScvKo2WDmkSGBzEqNJAGfhVzFExJXzlJOp/JIwu3siP5Eg9c15LfD+5QrEk6bmfnMvj4PqsXvM1NdkcjIiJSNFX3nEN2Ohz8yjoHuP9zyE4FHz9oe7N1DrDtLVCtVpmeIuFUKhPnRnM5J585E8OJaFm5k0WdzsVEmDXQ+v/1/q8rfdJqVZKRnceq7SdYFJ3I9uRL+Hh5cFuXxkSGB9O7Vb0KnzCrpK8cfLUnhSeXbMME/ntXd27t3LhCn8+p5WXDKx2t1ozIBXZHIyIicm2q7jmvvBxrInjCKkhYCxmnwdMHWg6wzgG2HwJ+pTs+k3whkwlzo0m+cJk3x/Wsuu/bstNh7q1wMclazRDQzu6I3I5pmuxIvkRUTCKfbjtBRk4+7Rr5ERkezMiezSr1fKmSvjLIy3fw0hf7mPndYbo0q8Xbd4cSXF+fDPLFC7DpLXhiN9RqYnc0IiIiv6Tqnutw5ENSdMEk0FVw8RgYHhDU20oAOwyFus1L9JAXMnKYMi+G7UkX+fsdXRjfq2T3d3kOByy511qrMX6purPKWWpWLivjj7MoOok9J1Op5u3B0G5NGRcRREhwXVv2RlZ40mcYxlxgKHDaNM0uBddeAoYBOcAhYLJpmhcL3ScY2AP8xTTN/xZcGwy8DngCc0zT/FdxAqyopO/UpSweWxRP9NHzjO8VzAtDO1HNW2OAATh3CN4MgYF/ggHP2B2NiIjIz1Tdc22mCSm7rCmge1fB6d3W9cbdrCEwHYZCw47FGkSSmZPH9IXxfJNwmt8NasvjN7WtOkvcv/orbHgFBv/L+tBDysw0TbYmXmDhliTW7DxBVq6DTk1qMa5XMCN6NKVWNXvPSlZG0nc9kA58WCjpuwX4xjTNPMMw/g1gmuYfCt3nY8ABbDFN87+GYXgC+4GbgWQgBhhnmuaeogKsiKRvw4Gz/C4qnsu5+fzfnV0Z0aPiF426nHnDreTv8R3lPoFLRESkVApX93o9BIP+rOqeqzt/2EoAE1ZD0hbrWr1WBQngMGsv4G8MlsvNd/D88p0sjUtmXEQw/7iji/uv2Nq+GFZMhZCJMOx1TeosowsZOSyPP05UdCIHTqdT08eT4T2aMS4iiK7NajvNBwllTfq8irqBaZrfG4bR4qprXxT6cjMwulBAdwCHgYxCt4kADpqmebjgNlHACKxqYKXJd5i8+c0BXv/6AG0C/HjnnhDaNNTkp18VNgWWTrQOZLe71e5oRESkKitc3avbEiavVXXPXdRrBf0es36lnbJWQSSsto6Z/PA6+DexVkF0GAot+v9iMqW3pwf/Gd2NhrV8eevbQ5xLz+aNcT3dt3srKQY+fRSa97f28TlJQuJqTNNk8+HzRMUksm7XKXLyHHQPqsO/7uzKsO5NqelbZIrkcsrjTzQFWAxgGEZN4A9YFb2nC92mGZBU6OtkoNe1HtAwjKnAVIDg4OByCBHOpWfz+OJtrD9wljt7NuMfI7tQw8f9/kLLTYchULMhxL6vpE9EROxzRXVvmqp77sy/MYTfZ/26fAH2f2ENgtm2EGLmQLU60G6wdQ6w9aCfXgeGYfDMrR0I8PPlr6v3cO97W5gzIZzaNdxsdcGlZIi625q3MHY+eGlJfUmdTc9mWVwyi2OSOHI2A/9qXowLDyIyIpiOTco2WdbZlSnrMQzjj0Ae8OOYx78Cr5qmmX5VKfTXPoa4Zl+paZqzgFlgtXeWJUaA2KPnmb4wnvOZOfzrzq6MDQ9ymlKt0/L0hp73wA+vWd9kagfaHZGIiFQl2enw9V8hepaqe1VR9brQfaz1KycTDn9rnQHctw52RIFXdWgzyGoDbXcrVK/LpH4taeDvy5OLt3PXzI3MmxLhPvuWczJgUSTkXoaJq7SaoQQcDpMNB88SFZPIl3tSyM03CW9Rl+kD23B71yZU93HTqvBVSp30GYYxEWvAyyDz54OBvYDRhmH8B6gDOAzDyALigKBCdw8ETpT2uYvLNE3mrD/Cvz5LILBudVY83JfOTWtX9NO6j9CJsOFV2PohDHze7mhEysfpvfDDGxAUAd0jwdtN3hCIuBNV96QwnxoFLZ5DID8Xjv3w8znAhNXg4QUtroOOQxnaYSj1poQz9cM4Rr1tJX5tG7n4UR6HA1Y8CCm7YdxiaNjB7ohcQkpqFktiklgcm0TyhcvUreHNhD4tiAwPcv3XRCkUa2VDwZm+1YUGuQwGXgEGmKZ55hr3+QuQXjDIxQtrkMsg4DjWIJe7TdPcXdRzl3aQy6XMXJ5etp0v96RwW5fG/Ht0N9un7rikj0ZZ32Qe3wWeaocVFxe/ANY8BWY+5OdAjQYQ8QCE3w81G9gdnYhcXd27421V9+TaHA44sdWqACashnMHreuB4ZxqdjPT4ppyOL8RcyeFEdrchStj3/wDvn8Jbvkn9J1udzROLd9h8r99p1kUncS3+06T7zDp27o+kRHB3Nq5Eb5erlvVq4zpnYuAG4AGQArwIvAc4AucK7jZZtM0H7rqfn+hIOkr+Pp24DWslQ1zTdP8Z3ECLE3StzP5Eg8vjOPkxSyev70jk/u1UDtnae1dDYvHQ+RC6xM2EVeUkwFrnobtC61Pg0fNgbMHYNMM2P8ZeFWD7uOgzyPQoK3d0YpUTZrMKWVhmnBmX0ECuApObgfgsEdz1uWFEnLrBPr0vcH1Bp/sXAYf32cduRk+w/XiryTHL15mcUwSS2OTOHkpiwZ+PowODSIyPIgWDWraHV650HL2QkzT5KMtifx91R4a+PkwY3wIIcF1KzhCN5efB691gUZd4J5ldkcjUnKn98KSiXB2Pwz4Awz4/ZVrSM7ss6bEbY+yqn/tb4M+063qgn64ilQ8VfekIlw4BglryN39KR7JW/DEQXr1Zvh1v8MaBBPUy/lXUiXHwQe3Q9MQmLBSg1uukpvv4Ou9KSyKTuL7A1bj4XVtAxgXHsSgjo3w8br2qg9XpKSvQEZ2Hs8t38mn209wQ/sAXh3Tg7o19Y+jXHzzT6ut4HfboW5zu6MRKb4f2zl9/WHUbGh1w7Vvm37amg4XPRsun7d+yPZ9FDoOV2uzSEVRdU8qQcaFUyyaP5OWZ77lBq9deJq5UDMA2t9uDYJpeT14+dod5pVST8CsgVai98C3OoJQyLFzGUTFJLE0Npmz6dk0rlWNMWGB3BUWRFA99/3+oaQP2HcqjYcXxHHkbAZP3dKeaQNa4+Huizkr08UkeL0b9H/C+oEs4ux+0c75Hvg3KuZ9M2H7Iqv6d/4Q1A6GPg9brTW+Ve/gt0iFUHVPKlluvoM/LNvBF/EHeKH9ce7y247HgS8gJx18a0HbW6wKYJubwdfP3mBzMuH926wzivd9CY062RuPE8jOy+eL3SlExSTyw8FzeBhwY4eGRIYHc0P7ALw83auq92uqfNL3cVwyf/xkJ36+3rwxrgd9W+uTkAqxcCyciIcndv9iMaqIUymqnbO4HA7Yvw42vgmJm8C3NoRNhl4PQq2m5R+3SFXxU3XvWKHqnnucuRHn5nCY/PuzBGZ+f5jbujTm1VEdqZa0AfZ+CvvWQuY58PSF1gOtZfDtb4ea9Ss7SFg2GfashHFR0H5w5T6/kzl4Op2o6ESWxx/nfEYOzepUJzI8iLvCgmhcu5rd4VWqsiZ9LtuzlJWbz4srd7M4NonererxxrieNPSvWn/5lSp0sjXwYt9a6DTC7mhEfl38R1aFz9cfJnzy2+2cRfHw+HlEeHKslfxtfMOqAHYdbZ37a9ylvCIXcX85GfDVX36u7k1aCy362R2VVCEeHgbP3d6RAH9f/rFmL+czcpg1YSC1290CjnxI3PzzJND9n4HhAc37WQlgx6GVs7P4+//Ank/g5r9V2YQvKzeftTtPEhWdRPTR83h5GNzcqRGREcH0b9MAT3XzlYpLVvqOnM3g4QVb2XsylUcGtuaJm9pVibKurRz58Fo3a7LhhE/sjkbkSjkZ1tm97YtK3s5ZEheOwuZ3YOt8yM2AVgOt8dmtB2noi8hvObrBqu5dOKrqnjiFlduO8/TS7bQO8GPelAga1SpUODBNa/pnwmprivmZvdb1pj0LEsBhENC+/IPavQKWToLud1stz1Xs50rCqVSiopNYvjWZ1Kw8WtSvwdjwYEaHBhLg72RnLm1Q5do71+08yTPLduDlafDq2B4MbN/QxuiqmP/9G/73/+DRrVC/td3RiFhS9lg/JMvazlkSly9A7PsxkkNaAAAgAElEQVSwZSakn4KGna11D11HO98wABE7XV3dG/GWqnviNNYfOMND8+OoU8OHD++LoHXANc7ynT1orYHYuxqOF7wnrd/WSv46DrUGf5U1QTsRD3NvgybdYOKqKvOzJCM7j9U7TrAoOoltSRfx8fRgcJfGREYE0btlfc3oKKTKJH05eQ7+b91e3v/hKD2C6vDW+BCa1alud3hVS+oJeLWLVdm4+W92RyNVnWnCtgU/t3MWNZ2zIuTlwK5lVuvn6T3g19g68xc2GaprXYxUcaruiQvYmXyJyR9Ek+8wmTspnJ5FrfpKPQEJa6w20KMbwMyHWs2sowAdh0Fw35JPfE49CbMHgoeXNanTL6D0fyAXsTP5EotiEvl02wnSs/No09CPyPAg7gwJpJ6m7/+qKpH0rfxqPY8s2Mq2pItM6deSZ2/r4Ha7N1xG1Hir5/3JvdoXI/Yp3M7Z8nq4c07FtHMWl2nCoW+s5O/wt+BdE0Luhd7ToG4L++ISsYOqe+Jijp3LYMLcaE6nZvP2+BAGdihmF1nmedj/uZUAHvoa8rKgej1r32uHodZAGO8iChS5l+H9262dsfd94dZnxVOzclm57QRR0YnsPpGKr5cHQ7s1ZVxEEKHN62JUsXbWknL7pK99lx5m9TH/IT/f5D+ju3Fb1yZ2h1S1HfgKFoyC0XOhyyi7o5GqqHA75w3PwvXPONeC3VM7rWEvO5dZnwB3HG7t+wss9fdpEdeh6p64qDNp2Uz+IJq9J9P4151duSssqGQPkJMBB7+2zgHu+wyyL1kfALa9CToMg3a3QLXaV97HNOHj+2DXcohcYFUL3YxpmmxNvEhUdCKrd5zkcm4+HRr7c3evYEb0aEbt6poIX1xun/T5Nmlr3vjsXN4ZH0KLBvrBYTuHA97oDnWaw6TVdkcjVYkztHOWROoJ68xf7PvWD//gPtbEz/a3OVeSKlIeVN0TN5CencdD8+PYcPAsvx9s7X0uVfUpLweOrrcSwIQ1kJ4CHt7QaoBVAewwBPwawncvwbf/gEEvwnVPlv8fyEYXM3NYEX+cqOgk9qWkUcPHk+HdmxIZEUz3wNqq6pWC2yd9TVp3No8k7KCat94kOY31L8PXf4PpsdY0T5GK5mztnCWRnWatktj8NlxMhHqtrWXv3e8Gnxp2RydSdqruiRvJyXPw9NLtfLr9BJP6tuDPQzuVbZiIwwHJMT8PgrlwBDCsSaAntkK3sTBypltM6jRNky1HzhMVncjaXafIyXPQPbA2kRHBDOveFD9fl90U5xTcPukrajm72CAtBV7tZP1wv/Wfdkcj7s7Z2zmLKz/PWgC88U3rB331ehB+P0Q8YH3iK+JqrqjutYARb6u6J27B4TD559q9vLfhCEO6NeGVMd3x9SqHnzumaQ392rvaSgJrNLAWsHu79p7pc+nZfLw1maiYJA6fycDf14s7ejYjMiKIzk1rF/0AUixK+sQeSybAke/hyQSX/2YlTuoX7ZxzrNYYV2eakLgJNs6AfWvB0we6j7VaPyti75NIRVB1T6qAWd8f4v+tTaBv6/rMvDcU/2o6f/Yjh8Nk46FzLIpJ5Ivdp8jNNwltXpfI8CCGdGtCDR9V9cqbkj6xx6FvYf4dMHKW9YZVpDxlp1vtnDuiXK+dsyTOHoTNb8G2hdbUt7a3WkNfWvR3i1YfcUM5GfDVXyF6pqp7UiUs35rM75ftoF0jfz6YEk5D/6r9Qffp1CyWxiWzOCaJxPOZ1KnhzZ09A4mMCKJdI3+7w3NrSvrEHg4HzAgFv0Yw5TO7oxF3krIHlk6Eswdcu52zJDLOQswciJ4NmWehSQ8r+es0Ajz1ybI4CVX3pIr6377TPLxgK/X9fPhwSi9aVrHBgvkOk+/3n2FRdCJfJ5wm32HSu1U9xkUEc2vnxpq7UUmU9Il9fngdvvwzPLwZGna0OxpxdaZpDTxZ+4x7tXOWRO5l2B5lrXw4dwBqB1lvrkMmQLVadkcnVZWqeyJsT7rI5A9iAHh/Ujjdg+rYHFHFO3HxMktik1gSk8SJS1nUr+nD6NBAxoYH0SrAz+7wqhwlfWKfjLPwSkcImwK3/dvuaMSVVZV2zuJyOODA59a5v2MbwLcWhE60EsDagXZHJ1WJqnsiPzlyNoMJc7dwLj2Hd+4JZUC7ALtDKne5+Q6+SThNVHQi3+0/gwn0b9OAcRHB3NSxET5eHnaHWGUp6RN7LZtiLWx/KkHj56V0rmjnfA6uf9r92zlL4vhW2DQDdn9infPrfCf0nQ5NutsdmbgzVfdEftXptCwmzY1hf0oaL93VjZE93eODuMRzmSyOTWRJbDJn0rJp6O/LmLAgxoYHEVRP7++cgZI+sdeR9TBvqPWGoOd4u6MRV6J2zpK5mAib34Wt8yAn3aqI9n0M2tykoS9SvlTdE/lNaVm5PDg/jo2HzvH87R2Yen1ru0MqlZw8B1/sOUVUdBIbDp7Fw4CB7RsSGRHMwPYBeHmqqudMlPSJvUwTZoRD9Tpw/1d2RyOu4op2zgFw5+yq3c5ZEpcvWonf5nch7QQEdLDWPXQbA16+dkcnrkzVPZFiy87L58kl21mz4yT392/J87d3LNsS90p0+Ew6UTFJfByXzLmMHJrVqc6YsCDGhAfSpHZ1u8OTa1DSJ/bb9BZ8/jw89AM07mJ3NOLs1M5ZPvJyYPcKa9l7yk6o2RB6TYWw+6BGPbujE1ej6p5IiTkcJn9bvYcPNh5lRI+mvDS6u9OeecvKzeezXadYGJ1I9JHzeHkYDOpoVfWubxuAp4skrFWZkj6xX+Z5eLkDhNwLQ162OxpxVmrnrBimCUe+s5K/g1+Bdw3oMR76PAz1WtkdnTg7VfdEysQ0Td757hD/+Wwf17VtwDv3hOLn6zyLyfedSmNRdCIr4o9z6XIuzevXYGx4EKNDA6v8zkFXo6RPnMPyqZCw1hro4qsxvnIVtXNWjpQ9VuV9x2Jw5EHHoda5v6AIuyMTZ3T0h4Lq3hGIeBBuelHVPZFSWhqbxLPLd9KpSS3mTgonwN++dvvMnDxW7zhJVHQiWxMv4u1pcGvnxoyLCKZPq/ou04YqV1LSJ84hcTPMvRWGvWGNlhf5kdo5K1/aKYieBTHvQdZFCIywlr13GKL/78Wq7n39N9jybkF17y1o0d/uqERc3rcJ1hL3hrV8+XBKBM3rV+6HKLuOXyIqJpGV8SdIy86jVUBNxoUHc2dIM+r76cy3q1PSJ87BNOHtPuBdDab+z+5oxBmYJsTPh7W/VzunXXIyIH6BtfLh4jGo2xL6PAI97lZFp6pSdU+kQsUnXmDKBzF4ehh8MDmCLs1qV+jzpWXl8un2E0RFJ7Hz+CV8vTwY0rUJkRHBhLeoi6Hpzm5DSZ84jy0zYd3vraSvaU+7oxE7ZafDmietNkO1c9rPkQ8Jq61zf8kxUL2uNfAlYqr+XqoKVfdEKs2hM+lMeC+ai5k5zLw3jP5tG5Tr45umybaki0RFJ7Fqxwkyc/Lp0NifyPAgRvYMpHYN73J9PnEOSvrEeVy+aA106TYGhr9hdzRil5TdsHSS2jmdVeIW2PgGJKwBT2/r32uf6dCwo92RSUVRdU+k0qWkZjFxbjSHzqTz37u6M6JHszI/5qXMXD7ZdpxF0YkknEqjurcnw7o3YVxEMD2C6qiq5+aU9Ilz+eQRa4z8UwlQrZbd0Uhl+qmd8xmoVttq52x5vd1RybWcOwSb37baP/MuQ5uboe90qzKrNw7uQdU9EVtdupzL1A9j2XLkPC8M7cR9/VuW+DFM0yTm6AWiohNZs/Mk2XkOujarTWREEMO7N8W/mqp6VYWSPnEuybEwZxAMeQXC77M7GqksV7dzjpoDfg3tjkqKI/O8NfAlehZknIbGXa2Jn51HWpVAcU2q7ok4hazcfJ5YvI11u07x4IBWPDu4Q7Eqcuczcli+NZlF0YkcOpOBn68XI3o0ZVxEcIWfExTnpKRPnItpwrvXWf/90HpVDKqClN2wZCKcP2S1c173lNo5XVFuFuxcAhtnwNl9UKuZtaQ7dKJVuRXXoOqeiNPJd5j85dPdzN98jDtDmvHvUd3w9vzlEneHw2TT4XMsik7ki90p5OQ7CAmuQ2REMEO7NaGGj/Ps/5PKp6RPnE/MHGsn2/1fQ2CpX5vi7NTO6Z4cDmvJ+8Y34Oh68PG3Er9eD0GdILujk9+i6p6I0zJNkxnfHOTlL/czoF0Ab48PoWbBEvfTaVksi0tmcUwSx85lUru6NyN7NmNcRDDtG/vbHLk4CyV94nyyUq2BLp1Hwh1v2R2NVITC7ZytbrCmc6qd0/2c2Gate9i13Pq680jr3J+m8zoXVfdEXEZUdCLPr9hJ12a1mXZDGz6JP85Xe1PIc5hEtKzH3RHBDO7SmGre6piRKynpE+f06WOwY4k10KV6HbujkfKkds6q52KSlVDEzYOcNGhxnTXxs+0t4PHLFiWpRMc2wicPF1T3psJNf1F1T8TJfbknhekLt5Kd56BeTR9GhwYyNjyI1gF+docmTkxJnzinE/Ew6wa47SXoNdXuaKQ8mCZs/dDaxah2zqop65L1Gtj8LqQmQ4N2VvLXbSx4V7M7uqrlp+reTKjbXNU9ERez6/glki9kMrBDQ3y99MGpFE1JnzivmQMgLxse3qSBLq5O7ZxSWH4u7P4ENr0JJ7dDzQCryhR2H9Ssb3d07k/VPRGRKqesSZ/6cqTihE2GM3shaYvdkUhZpOy2qrY7l8LAP8I9y5XwVXWe3tDtLpj6HUxcBU1D4Nt/wqudYfWT1g5AKX85GbDuD/D+7YAJk9bA7S8p4RMRkSIp6ZOK02W0Nfkv9n27I5HSME3rDNfsGyE7FSashAG/1/k9+ZlhWC2+45fAw1usRDB+PrwZClHj4dgm63UkZXdsI7zTzzpbGfEATNuodk4RESk2JX1ScXz9oNsY2L3CWgAtriM7HZZPhVWPQXBveGiDzu/Jb2vYAYa/CY/vguufhmM/wPuDYc5N1veA/Dy7I3RNqu6JiEg5UNInFStsMuRnw/YouyOR4jq1y2rn3LVM7ZxScv6N4MY/wRN7YMjLcPk8LJ0Eb4ZYA2Cy0+2O0HWouiciIuVEg1yk4s0eZE39mx6jgS7O7BfTOd+DltfZHZW4Okc+7FsLG2dA0mbrtRU2xVoeXquJ3dE5J03mFBGRq2iQizi/sClw7oDV7iXO6VfbOZXwSTnw8ISOw+C+z+G+r6zprz+8Dq91hRXTrEFB8jNV90REpAIo6ZOK13kk+NbWQBdndUU755/UzikVJygcxnwIj261Pgza8wm80xfm3wmHvqnaQ19yMmHds9bZPdMBE1fr7J6IiJQbL7sDkCrApwZ0j4S49yHjLNRsYHdEAr9s55zwqap7UjnqtYTb/wM3PGt9X9gyE+aPhEZdrGXvXUaBl4/dUVaeq/fuDXrRGoQlIiJSTlTpk8oRNhnyc2DbArsjEYDsNFj+gNo5xV416sF1T8HjO2HE29b5v08egte7wYZX4fJFuyOsWNeq7inhExGRcqZBLlJ55g6G9BSYHgce+rzBNqd2wdKJcP4w3PA8XPekdu+JczBNOPQ1bHwTDv8PfPyg573Qe5o10MSdqLonIiIloEEu4jpCJ1uJxtHv7Y6kajJNiPsA5gyyKn0TPoUBzyjhE+dhGNDmJpiw0qo+dxgKMbPhjR7W2ofjcXZHWHaq7omIiA1U6ZPKk5sFr3SAlgNgzDy7o6lastNg9ROwc6k1PfHO2RrWIq7h0nGIngmxH0D2JWjezzr3126w63UMqLonIiKlpEqfuA7vatD9bkhYDWkpdkdTdfw0nfNjTecU11O7Gdz8N3hyN9z6f3AxEaLGwVvhEDsXci/bHWHRVN0TERGbKemTyhU2GRx5sO0juyNxf2rnFHfi6w99HobHtsHoudbXq5+AVzvDt/8H6WfsjvDXHdtoraXY8s7Pe/c0NElERCpZkUmfYRhzDcM4bRjGrkLXXjIMI8EwjB2GYawwDKNOwfWbDcOIMwxjZ8H/3ljoPqEF1w8ahvGGYRhGxfyRxKk1aAstroO4eeBw2B2N+/ppOufvILiPpnOK+/D0slY6PPAtTFoLgRHw3b/gtS7W6/3sAbsjtKi6JyIiTqQ4lb4PgMFXXfsS6GKaZjdgP/BcwfWzwDDTNLsCE4H5he7zDjAVaFvw6+rHlKoidBJcPAaHv7E7EvdUuJ3zRrVzipsyDGjRD+6OgkdirF2g2xbBjDBYGAlHf7Bv2fuxTfBuP6u6F36/qnsiImK7IpM+0zS/B85fde0L0zTzCr7cDAQWXI83TfNEwfXdQDXDMHwNw2gC1DJNc5NpTY75ELijvP4Q4mI6DoMa9SH2fbsjcS9XtHOmw8RVcP0zrjfsQqSkAtrBsNfhid0w4FlIjoYPbofZA60PP/Lzin6M8pCTCZ89B+/fZu0cnLgKhvxX1T0REbFdebwbnAKs+5Xro4B40zSzgWZAcqHfSy64JlWRly/0GA/71kHqSbujcQ+/1s7Zor/dUYlULr8AGPiclfwNfdX6d7FsCrzREza9bX1dUX6s7m1+u1B17/qKez4REZESKFPSZxjGH4E8YMFV1zsD/wYe/PHSr9z9mn03hmFMNQwj1jCM2DNnnPRwvpRN6CQw8yF+fpE3lSL8ajtngN1RidjHuzqETbHaPiMXQZ0g+Pw5eKUzfPlnSD1R9GMUl6p7IiLiAkqd9BmGMREYCow3Cy37MwwjEFgBTDBN81DB5WQKWkALBALX/KlrmuYs0zTDTNMMCwjQm1e3VL+1tS8ubp71RklKzjStFlm1c4r8Og8P6HA7TF4L938DbW6EjW/Ca11h+YNwamfZHl/VPRERcRGlendoGMZg4A/AcNM0MwtdrwOsAZ4zTfOHH6+bpnkSSDMMo3fB1M4JwMoyRS6uL3QypCbDgS/tjsT1ZKfBx/fD6sfVzilSHIGhcNcH8Fg8hD8Ae1fBu/3hwxFw4KuSDX1RdU9ERFyMYRbxg84wjEXADUADIAV4EWtapy9wruBmm03TfMgwjD8V/F7hmdm3mKZ52jCMMKxJoNWxzgA+ahb15EBYWJgZGxtbkj+TuIr8XHilEzQLgbsX2x2N6zi1E5ZOgvOHYeDz0P8pVfdESuryRWvw0ZZ3Ie0kNOwEfaZD19HWueNrObYJVj5s/fsLfwBu+ouSPRERqXCGYcSZphlW6vsXI++ylZI+N/f132DDq/D4TqgdWPTtq7Ifp3Ou+wNUrwuj31N1T6Ss8nKs87CbZkDKLvBrBL0etDoRatT7+XY5mfDN32HzO1AnGEbMUCuniIhUGiV94touHIPXu8OA31tVK/l12Wmw6nHYtQxaDYQ7Z2tYi0h5Mk04/C1snAGHvgbvGtDzXug9DdJOqbonIiK2UtInru+jUZCyGx7fBZ5edkfjfE7thCUT4cIRtXOKVIaU3bDpLdixxJoybJqq7omIiK3KmvTpHbbYL3QyLB4P+z+DjkPtjsZ5XN3OOXGV2jlFKkOjznDH23DjCxD7nnWt3+Oq7omIiMtS0if2azcY/JtA3PtK+n5UuJ2z9Y0wcpbaOUUqW60m1u5LERERF6ceMbGfpxeETICDX1tn/Kq6Uzth5gDYvdyqNIz/WAmfiIiIiJSakj5xDiETwDBg6zy7I7HPj8vWZw+CnAyYuBquf1rn90RERESkTPRuUpxD7UBoewtsnW/t76tqCi9bb9GvYNl6P7ujEhERERE3oKRPnEfYFMg4DQlr7I6kcqmdU0REREQqkJI+cR5tboLaQdZAl6rANCF2rtXOmZupdk4RERERqRB6dynOw8PTOtt3+H9w7pDd0VSsrFT4+D5Y/YTVxvngerVzioiIiEiFUNInzqXnvWB4Wvvp3NXJHTDrBti9Qu2cIiIiIlLhlPSJc6nVBNrfBtsWQF623dGUrx/bOefcpHZOEREREak0ercpzidsMmSeg72r7I6k/KidU0RERERsoqRPnE+rG6FOc/dp8VQ7p4iIiIjYSEmfOB8PDwidCEfXw5n9dkdTemrnFBEREREnoHef4px63AMeXq5b7buinbO/lq2LiIiIiG2U9Ilz8m8EHYbC9oWQm2V3NCVTuJ1z0J9h/DKo2cDuqERERESkilLSJ84rbDJcvgB7VtodSfH8WjvndU+pnVNEREREbKV3o+K8WlwP9VpB3Pt2R1K0rFRYNkXtnCIiIiLidJT0ifPy8IDQSZC4CU7vtTuaazu5A2YNgD2fqJ1TRERERJyOkj5xbj3Gg6cPxDphtc80Iea9gnbOyzBpjdo5RURERMTp6N2pOLeaDaDjcNgeBTmZdkfzsx/bOdc8+XM7Z/O+dkclIiIiIvILSvrE+YVNhuxL1jRMZ3Byu9o5RURERMRlKOkT59e8HzRoZ03GtNNP7Zw3q51TRERERFyG3q2K8zMMCJ0Mx2Ph1E57YlA7p4iIiIi4KCV94hq6R4Knrz0DXX5q51wJg15UO6eIiIiIuBQlfeIaatSDziNhxxLITq+c5zRNiJlT0M6ZBZNWw3VPqp1TRERERFyK3r2K6wibDDlpsGtZxT9XViosmwxrnoKW18FD69XOKSIiIiIuSUmfuI6gXhDQseJbPH9q5/zUaue8e6naOUVERETEZSnpE9dhGBA2BU5ugxPx5f/4aucUERERETekd7PiWrqNAa/q5V/tUzuniIiIiLgpJX3iWqrXgS6jYOcyK1ErD2rnFBERERE3pqRPXE/YZMjNgJ1LyvY4P7Vz3qR2ThERERFxW3p3K66nWSg07gqxH1iJW2lc0c55vdo5RURERMRtKekT12MYEDoZUnbC8biS3//kdph5vdo5RURERKRKUNInrqnrXeBdE2LnFv8+hds587Jh0hq1c4qIiIiI29O7XXFN1WpB19Gwazlcvlj07X/RzrkBmvep+DhFRERERGympE9cV9hkyLsMOxb/9u0Kt3Pe9JeCds76lRGhiIiIiIjtlPSJ62ra0/oV+/6vD3QxTYiefWU7Z/8n1M4pIiIiIlWK3v2KawudDGf2QtKWK69nXYKlk2Dt09BygNo5RURERKTKUtInrq3LKPDxv3Kgy4ltMHMA7F1V0M65RO2cIiIiIlJlKekT1+brB93Hwu5PIPO81c753s1q5xQRERERKeBldwAiZRY6uWAVwyA4fxja3AwjZ6q6JyIiIiKCkj5xB427QFAvSI612jn7/k7VPRERERGRAkr6xD2M+dAa3hLQ3u5IREREREScipI+cQ/+ja1fIiIiIiJyBfXAiYiIiIiIuDElfSIiIiIiIm5MSZ+IiIiIiIgbKzLpMwxjrmEYpw3D2FXo2kuGYSQYhrHDMIwVhmHUKfR7zxmGcdAwjH2GYdxa6PrggmsHDcN4tvz/KCIiIiIiInK14lT6PgAGX3XtS6CLaZrdgP3AcwCGYXQCIoHOBfd52zAMT8MwPIG3gNuATsC4gtuKiIiIiIhIBSoy6TNN83vg/FXXvjBNM6/gy81AYMF/jwCiTNPMNk3zCHAQiCj4ddA0zcOmaeYAUQW3FRERERERkQpUHmf6pgDrCv67GZBU6PeSC65d67qIiIiIiIhUoDIlfYZh/BHIAxb8eOlXbmb+xvVrPe5UwzBiDcOIPXPmTFlCFBERERERqdJKnfQZhjERGAqMN03zxwQuGQgqdLNA4MRvXP9VpmnOMk0zzDTNsICAgNKGKCIiIiIiUuWVKukzDGMw8AdguGmamYV+61Mg0jAMX8MwWgJtgWggBmhrGEZLwzB8sIa9fFq20EVERERERKQoXkXdwDCMRcANQAPDMJKBF7GmdfoCXxqGAbDZNM2HTNPcbRjGEmAPVtvnI6Zp5hc8znTgc8ATmGua5u4K+POIiIiIiIhIIcbPnZnOKSwszIyNjbU7DBEREREREVsYhhFnmmZYae9fHtM7RURERERExEkp6RMREREREXFjSvpERERERETcmNOf6TMMIw3YZ3cc4hIaAGftDkJchl4vUlx6rUhJ6PUixaXXipREe9M0/Ut75yKndzqBfWU5tChVh2EYsXqtSHHp9SLFpdeKlIReL1Jceq1ISRiGUabJlmrvFBERERERcWNK+kRERERERNyYKyR9s+wOQFyGXitSEnq9SHHptSIlodeLFJdeK1ISZXq9OP0gFxERERERESk9V6j0iYiIiIiISCkp6RMREREREXFj5Zb0GYYx0jAM0zCMDuX1mNd4nqaGYSwr4jYtDMO4uyLjkPJlGEZjwzCiDMM4ZBjGHsMw1hqG0e4at21hGMaugv++wTCM1ZUbrZQ3wzDSr/p6kmEYMwr++yHDMCYUcf+fbl/E7YYahhFvGMb2gtfZg79x259eZ2VlGMYHhmGMLo/HkopnGEZ6wd//5YLXy17DMKINw5hod2xiv4L3OvMLfe1lGMaZH38WFXw/OlPw2jlgGMbnhmH0tS9iKU9F/f3///buPdjK6rzj+PcnIkogkBi1SA1UjI7I5YCXGlEiqdLJJK0aNchEWxKTDiaNiRlNtZrGS5s6YscoiSMl1WhHDfXWsZqokQDxgmAKCEiNjoAJjbeJ1CgjBvHpH+vZunPYe59zwoZ9zvb3mWHcZ+31Pu96L757Pe9a7949jDVU0pea20LriyRtlbRC0mpJt0ka2Kgfkv2KddmfeVrSTZKGN1pHM0f6pgMPA6c1MeY2IuLXEdFV52kk4KSvj5Ak4C5gYUSMiojRwN8D+7S2ZdYbRMR1EXHT9saR1J/yEPRfRMR4YAKwcHvjWlt7NiImRMTBlM+2cyR9rtWNspbbBIyRtEf+fTzwv53qzMtz5yPA5cCdkg7emY20HaY7x7+7hgI9SvpUeKZe+3kjIjoiYgzwO2BmN5Y5L/szBwHLgQWSdqtXuSknjaRBwCTgTDLpkzRM0s+qstZjJPXLzHS1pFWSzsm6HZIek7RS0l2SPpDlB0h6MLPYZZJGdRrlGSnpoU8LaYgAAAuLSURBVHxvWdWdtMuBY3Ld5+R6Z0l6PNdR9+6+tcQUYEtEXFcpiIgVwMN53Crny7RGQSS9T9L1eZyXSzohywdK+o889vMkLZF0WL43VdLiPH9uy3PZehFJF0s6N18fnsdxceXcqKq6r6T78s76FTVCDQZ2BX4DEBFvRsQvMu4+ee15Iv9VriX9JM2V9KSkByof8g2uWTXLre+LiLXA14GzW90W6xV+DHwyX08Hbq1XMSIWUG44/c1OaJftHHWPv6QjJD2a/ZBHJR2U5YeozBhYkZ8RlRsCo7JsVtY7r6q/ekmWjVSZcXAtsAzYr1Z/2trGQ8AB+bpmP6RaFFcBLwCfqBe0WXcKTgTui4ingVckTaSMtN0fER3AeGAF0AEMj4gxETEWuCGXvwn4u4gYB6wCvpXlNwPfyyz2KOD5Tut9CTg+IiYC04Brsvx84KHMmK+iJKOvRsThwOHAFyX9SZO23bbfGOC/a5R/mnLOjAeOA2ZJGtYgzoXAT/M4T8n676PcRduY59dlwKEAkj4EXAQcl+fQzymdOtv59sgPvRWSVgCX1ql3AzAzIj4KbO30XgflOjAWmCZpv+o3I+IV4G7gOUm3Svps1d3Sa4BFea2ZCDyZ5R+hXIMOAf4PODnL612z6pVbe1gG7NBHGKzP+CFwmqTdgXHAki7q+9xpL42O/1PA5IiYAPwD8O0snwlcnf3iw4ANlP7qs9lfPU/SVMrnzhGUz7RDJU3O5Q8Cbsq4H6J2f9r6OEm7UhK3VVlUrx9SS8PrzK5NauN04Dv5+of5938B16tMqfrPiFghaS2wv6TZwL3AA5KGAEMjYlEufyNwm6TBlBP6LoCI2AwgqXq9/YHvSuqgdABrPgMGTAXG6d1naoZQduK67dxu27GOBm6NiK3Ai5IWUZL2lXXqTwX+sjIqBOwOfDjjXA0QEaslVZY/EhgNPJLn1W7A4h2xIdalN/KDECjPxFA+FKkqGwoMjohHs+gW4FNVVeZHxKtZdw0wAvhVdYyI+IKksZSbCOdSpuXMAD4O/FXW2Qq8mqN063LUGcqNiZENrlk1y/+AfWG9l7quYu8FEbFS0khKf+dH3VjE504b6eL4DwFuzJG8oPRVofQvLpT0x8CdEfFMpz4tlH7MVMpUPYBBlP7qL4HnIuKxLN+mP92kTbPW2SNvekMZ6fs3YF9q9EMaxGh4ndnupE/SnpQO0xhJAfSjnOTfACZThr//XdKsiLhJ0njgz4EvA58B6g1Jd+cCeQ7wImUkaBdgc4NYX4mI+7u3VbaTPQnUek6zpx+SAk6uTNl7p7DGVbWq/k8iYnoP12Ot0dX58GbV663Uub5FxCpglcqD+OsoSV93Y24zrcLeUyYA/9PqRlivcTdwJXAssGcXdX3utJ96x/8yYEFEnJSJ4UKAiLhF0hJKv/h+SV+gJG/VBPxzRMz5vcISZ1Pl74jYWKM//fnmbJa1yO/d/IZ3Brp60g+ZAMyv92YzpneeQhluHhERIyNiP0pHajLwUkTMpWSrE3M63S4RcQfwTWBi3pnfKOmYjHcGZZrVb4ENkk4EkDRA0sBO6x4CPB8Rb+dy/bL8NcrzOxX3A2flqCOSDsxpf9Y7/BQYIOmLlQJJhwMbKdP0+knai3JOLW0Q537gK5UkT9KELH+YckFE0mjK9D+Ax4BJkg7I9waqzjeGWutFxEbgNUlHZlGPvjRK0iBJx1YVdQDP5ev5wFlZr5+k9zdoR71rVs3ynrTReq/sdF0JzG5tS6wXuR64NG8k1SXpY5Tn+ebulFbZzlLv+A/h3S92mVEplLQ/sDYirqEkjOOo3V/9vPL7BSQNl7R35xXX6k83ZYusT1JxNjAMuK9evWZM75xOeRC12h3AD4BNkrYAr1OmTg0Hbqh6juaC/O9fA9dlUrcWqHw72hnAHEmXAluAU4G3q9ZzLXCHpFOBBbx7F2Ql8JakJ7IdV1OGQ5dlQvAy5TlE6wUiIiSdBHxH0vmUEdv1wNcoUxueIEePI+KF7HzVchllmvHKPM7rKdP/rqVMtVhJmTKxkvKM58s5jfBWSQMyxkXA083eRmuaM4G5kjZR7p6+2oNlBXxD0hzgDcr1Yka+91XgXyWdSbmTdhbbPkNcrd41q1659RH5PEXlzuooScspU8VfA2ZHhJ+dMQAiYgP56EAN0yQdDQyk3Ag/OSI80tdGGhz/Kyh9jq9TbmpXTANOz37xC5SE8RVJj6h8KdmP87m+g4HFef/6deB0tn2GvV5/2t5bZkn6JuU68xgwJSJ+V6+yImKntcysFST1A/pHxGZJoyijOgc2+h/DeidJgyLi9Xx9PjAsIr7a4mZZG8kpU3Mj4ohWt8XMzKxZmvVFLma92UDKb5f0p4z2nOWEr8/6pKQLKNeu52j8PJ5Zj0iaSflJhq+1ui1mZmbN5JE+MzMzMzOzNtas3+kzMzMzMzOzXshJn5mZmZmZWRtz0mdmZmZmZtbGnPSZmZmZmZm1MSd9ZmbWY5IebXUbACTNkPTdJsRZnz943BSShkr6UtXf+0q6vVnxM2ZT27yjYpqZWes56TMzsx6LiKN2VOz8bc2+bijwTtIXEb+OiFNa2B4zM3sPc9JnZmY9Jun1/O+xkhZKul3SU5JulqR873JJayStlHRllv1A0il14iyQdAuwKstOl7RU0gpJcyrJoKTPSXpa0iJgUhft3EvSHZIez3+TsnxPSQ9IWi5pDuU3PJE0UtLqquXPlXRxvj5A0oOSnpC0TNIoSYMkzc+/V0k6IRe9HBiVbZ9VHVfS7pJuyPrLJU3J8hmS7pR0n6RnJF3Rg+Oxzb6SdFZ1jIw/u9G+NTOz9uSkz8zMttcEyg+ajwb2ByZJ+iBwEnBIRIwD/rEbcY4ALoyI0ZIOBqYBkyKiA9gKfFbSMOASSrJ3fK6zkauBqyLicOBk4PtZ/i3g4YiYANwNfLgb7bsZ+F5EjAeOAp4HNgMnRcREYArwL5n0ng88GxEdEXFepzhfBoiIscB04EZJu+d7HbndY4FpkvbrqlH19hVwO/DpqqrTgHkN6puZWZvatdUNMDOzPm9pRGwAkLQCGAk8RkmIvi/pXuCebsZZl6//DDgUeDwHDvcAXgL+FFgYES/n+uYBBzaIeRwwOmMAvF/SYGAymRBFxL2SNjZqWC4zPCLuymU2Z3l/4NuSJgNvA8OBfbrYzqOB2RnnKUnPVW3D/Ih4NWOvAUYAv+oiXs19FREvS1or6UjgGeAg4BFK0llr35qZWZty0mdmZtvrzarXW4FdI+ItSUdQEpLTgL8FPg68Rc4yyRGx3aqW3VT1WsCNEXFB9YoknQhED9q2C/DRiHijUxzqxHmnfakyAqcadaGMkO0FHBoRWyStr1qmnnqxoMa+7CJWJd42+yrNAz4DPAXcFRGR+71efTMza0Oe3mlmZk0naRAwJCJ+RJn62ZFvraeMMgGcAPSvE2I+cIqkvTPeByWNAJYAx+Yzef2BU7toygOUhLPSrko7fkZOaZT0CeADWf4isHfGHwB8CiAifgtsyKQTSQMkDQSGUEbVtuSzeSMyzmvA4Dptql73gZSppb/oYjsaqbevAO4ETqRMI53XjfpmZtaGnPSZmdmOMBi4R9JKYBFwTpbPBT4maSllquamWgtHxBrgIuCBjPETYFhEPA9cDCwGHgSWddGOs4HD8stk1gAzs/wSYLKkZcBU4Je53i3ApZTk8h7KCFnFGcDZ2Z5HgT+iPOd3mKSfUxK5pzLOb4BHJK2WNKtTm64F+klaRUnEZkTEm/yB6u2rfG8jsAYYERFLu6pvZmbtSRE9mSVjZmZmZmZmfYlH+szMzMzMzNqYv8jFzMz6PEkXsu3zfbdFxD+1oj3NJGkJMKBT8RkRsaoV7TEzs77H0zvNzMzMzMzamKd3mpmZmZmZtTEnfWZmZmZmZm3MSZ+ZmZmZmVkbc9JnZmZmZmbWxv4fN+qqmleZWPoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Annual premium of education level policy_annual_premium\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# plot data\n",
    "fig, ax = plt.subplots(figsize=(15,7))\n",
    "df_avg_prem = data.groupby(['insured_education_level', 'fraud_reported']).mean()['policy_annual_premium']\n",
    "df_avg_prem.unstack().plot(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20c39cad278>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,7))\n",
    "data.groupby(['insured_occupation', 'fraud_reported']).mean()['total_claim_amount'].unstack().plot(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np \n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(20, 9))\n",
    "\n",
    "corr = data.corr()\n",
    "ax = sns.heatmap(\n",
    "    corr, \n",
    "    vmin=-1, vmax=1, center=0,\n",
    "    cmap=sns.diverging_palette(220, 10, as_cmap=True),\n",
    "    square=True\n",
    ")\n",
    "ax.set_xticklabels(\n",
    "    ax.get_xticklabels(),\n",
    "    rotation=45,\n",
    "    horizontalalignment='right'\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "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>months_as_customer</th>\n",
       "      <th>age</th>\n",
       "      <th>policy_state</th>\n",
       "      <th>policy_csl</th>\n",
       "      <th>policy_deductable</th>\n",
       "      <th>policy_annual_premium</th>\n",
       "      <th>umbrella_limit</th>\n",
       "      <th>insured_sex</th>\n",
       "      <th>insured_education_level</th>\n",
       "      <th>insured_occupation</th>\n",
       "      <th>...</th>\n",
       "      <th>witnesses</th>\n",
       "      <th>police_report_available</th>\n",
       "      <th>total_claim_amount</th>\n",
       "      <th>injury_claim</th>\n",
       "      <th>property_claim</th>\n",
       "      <th>vehicle_claim</th>\n",
       "      <th>auto_make</th>\n",
       "      <th>auto_model</th>\n",
       "      <th>auto_year</th>\n",
       "      <th>fraud_reported</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>328</td>\n",
       "      <td>48</td>\n",
       "      <td>OH</td>\n",
       "      <td>250/500</td>\n",
       "      <td>1000</td>\n",
       "      <td>1406.91</td>\n",
       "      <td>0</td>\n",
       "      <td>MALE</td>\n",
       "      <td>MD</td>\n",
       "      <td>craft-repair</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>YES</td>\n",
       "      <td>71610</td>\n",
       "      <td>6510</td>\n",
       "      <td>13020</td>\n",
       "      <td>52080</td>\n",
       "      <td>Saab</td>\n",
       "      <td>92x</td>\n",
       "      <td>2004</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>228</td>\n",
       "      <td>42</td>\n",
       "      <td>IN</td>\n",
       "      <td>250/500</td>\n",
       "      <td>2000</td>\n",
       "      <td>1197.22</td>\n",
       "      <td>5000000</td>\n",
       "      <td>MALE</td>\n",
       "      <td>MD</td>\n",
       "      <td>machine-op-inspct</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>5070</td>\n",
       "      <td>780</td>\n",
       "      <td>780</td>\n",
       "      <td>3510</td>\n",
       "      <td>Mercedes</td>\n",
       "      <td>E400</td>\n",
       "      <td>2007</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>134</td>\n",
       "      <td>29</td>\n",
       "      <td>OH</td>\n",
       "      <td>100/300</td>\n",
       "      <td>2000</td>\n",
       "      <td>1413.14</td>\n",
       "      <td>5000000</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>PhD</td>\n",
       "      <td>sales</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>NO</td>\n",
       "      <td>34650</td>\n",
       "      <td>7700</td>\n",
       "      <td>3850</td>\n",
       "      <td>23100</td>\n",
       "      <td>Dodge</td>\n",
       "      <td>RAM</td>\n",
       "      <td>2007</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>256</td>\n",
       "      <td>41</td>\n",
       "      <td>IL</td>\n",
       "      <td>250/500</td>\n",
       "      <td>2000</td>\n",
       "      <td>1415.74</td>\n",
       "      <td>6000000</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>PhD</td>\n",
       "      <td>armed-forces</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>NO</td>\n",
       "      <td>63400</td>\n",
       "      <td>6340</td>\n",
       "      <td>6340</td>\n",
       "      <td>50720</td>\n",
       "      <td>Chevrolet</td>\n",
       "      <td>Tahoe</td>\n",
       "      <td>2014</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>228</td>\n",
       "      <td>44</td>\n",
       "      <td>IL</td>\n",
       "      <td>500/1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1583.91</td>\n",
       "      <td>6000000</td>\n",
       "      <td>MALE</td>\n",
       "      <td>Associate</td>\n",
       "      <td>sales</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>NO</td>\n",
       "      <td>6500</td>\n",
       "      <td>1300</td>\n",
       "      <td>650</td>\n",
       "      <td>4550</td>\n",
       "      <td>Accura</td>\n",
       "      <td>RSX</td>\n",
       "      <td>2009</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>995</td>\n",
       "      <td>3</td>\n",
       "      <td>38</td>\n",
       "      <td>OH</td>\n",
       "      <td>500/1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1310.80</td>\n",
       "      <td>0</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>Masters</td>\n",
       "      <td>craft-repair</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>87200</td>\n",
       "      <td>17440</td>\n",
       "      <td>8720</td>\n",
       "      <td>61040</td>\n",
       "      <td>Honda</td>\n",
       "      <td>Accord</td>\n",
       "      <td>2006</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>996</td>\n",
       "      <td>285</td>\n",
       "      <td>41</td>\n",
       "      <td>IL</td>\n",
       "      <td>100/300</td>\n",
       "      <td>1000</td>\n",
       "      <td>1436.79</td>\n",
       "      <td>0</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>PhD</td>\n",
       "      <td>prof-specialty</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>108480</td>\n",
       "      <td>18080</td>\n",
       "      <td>18080</td>\n",
       "      <td>72320</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>Passat</td>\n",
       "      <td>2015</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>997</td>\n",
       "      <td>130</td>\n",
       "      <td>34</td>\n",
       "      <td>OH</td>\n",
       "      <td>250/500</td>\n",
       "      <td>500</td>\n",
       "      <td>1383.49</td>\n",
       "      <td>3000000</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>Masters</td>\n",
       "      <td>armed-forces</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>YES</td>\n",
       "      <td>67500</td>\n",
       "      <td>7500</td>\n",
       "      <td>7500</td>\n",
       "      <td>52500</td>\n",
       "      <td>Suburu</td>\n",
       "      <td>Impreza</td>\n",
       "      <td>1996</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>998</td>\n",
       "      <td>458</td>\n",
       "      <td>62</td>\n",
       "      <td>IL</td>\n",
       "      <td>500/1000</td>\n",
       "      <td>2000</td>\n",
       "      <td>1356.92</td>\n",
       "      <td>5000000</td>\n",
       "      <td>MALE</td>\n",
       "      <td>Associate</td>\n",
       "      <td>handlers-cleaners</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>YES</td>\n",
       "      <td>46980</td>\n",
       "      <td>5220</td>\n",
       "      <td>5220</td>\n",
       "      <td>36540</td>\n",
       "      <td>Audi</td>\n",
       "      <td>A5</td>\n",
       "      <td>1998</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>999</td>\n",
       "      <td>456</td>\n",
       "      <td>60</td>\n",
       "      <td>OH</td>\n",
       "      <td>250/500</td>\n",
       "      <td>1000</td>\n",
       "      <td>766.19</td>\n",
       "      <td>0</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>Associate</td>\n",
       "      <td>sales</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>5060</td>\n",
       "      <td>460</td>\n",
       "      <td>920</td>\n",
       "      <td>3680</td>\n",
       "      <td>Mercedes</td>\n",
       "      <td>E400</td>\n",
       "      <td>2007</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     months_as_customer  age policy_state policy_csl  policy_deductable  \\\n",
       "0                   328   48           OH    250/500               1000   \n",
       "1                   228   42           IN    250/500               2000   \n",
       "2                   134   29           OH    100/300               2000   \n",
       "3                   256   41           IL    250/500               2000   \n",
       "4                   228   44           IL   500/1000               1000   \n",
       "..                  ...  ...          ...        ...                ...   \n",
       "995                   3   38           OH   500/1000               1000   \n",
       "996                 285   41           IL    100/300               1000   \n",
       "997                 130   34           OH    250/500                500   \n",
       "998                 458   62           IL   500/1000               2000   \n",
       "999                 456   60           OH    250/500               1000   \n",
       "\n",
       "     policy_annual_premium  umbrella_limit insured_sex  \\\n",
       "0                  1406.91               0        MALE   \n",
       "1                  1197.22         5000000        MALE   \n",
       "2                  1413.14         5000000      FEMALE   \n",
       "3                  1415.74         6000000      FEMALE   \n",
       "4                  1583.91         6000000        MALE   \n",
       "..                     ...             ...         ...   \n",
       "995                1310.80               0      FEMALE   \n",
       "996                1436.79               0      FEMALE   \n",
       "997                1383.49         3000000      FEMALE   \n",
       "998                1356.92         5000000        MALE   \n",
       "999                 766.19               0      FEMALE   \n",
       "\n",
       "    insured_education_level insured_occupation  ... witnesses  \\\n",
       "0                        MD       craft-repair  ...         2   \n",
       "1                        MD  machine-op-inspct  ...         0   \n",
       "2                       PhD              sales  ...         3   \n",
       "3                       PhD       armed-forces  ...         2   \n",
       "4                 Associate              sales  ...         1   \n",
       "..                      ...                ...  ...       ...   \n",
       "995                 Masters       craft-repair  ...         1   \n",
       "996                     PhD     prof-specialty  ...         3   \n",
       "997                 Masters       armed-forces  ...         3   \n",
       "998               Associate  handlers-cleaners  ...         1   \n",
       "999               Associate              sales  ...         3   \n",
       "\n",
       "    police_report_available  total_claim_amount  injury_claim property_claim  \\\n",
       "0                       YES               71610          6510          13020   \n",
       "1                         ?                5070           780            780   \n",
       "2                        NO               34650          7700           3850   \n",
       "3                        NO               63400          6340           6340   \n",
       "4                        NO                6500          1300            650   \n",
       "..                      ...                 ...           ...            ...   \n",
       "995                       ?               87200         17440           8720   \n",
       "996                       ?              108480         18080          18080   \n",
       "997                     YES               67500          7500           7500   \n",
       "998                     YES               46980          5220           5220   \n",
       "999                       ?                5060           460            920   \n",
       "\n",
       "    vehicle_claim   auto_make auto_model auto_year fraud_reported  \n",
       "0           52080        Saab        92x      2004              Y  \n",
       "1            3510    Mercedes       E400      2007              Y  \n",
       "2           23100       Dodge        RAM      2007              N  \n",
       "3           50720   Chevrolet      Tahoe      2014              Y  \n",
       "4            4550      Accura        RSX      2009              N  \n",
       "..            ...         ...        ...       ...            ...  \n",
       "995         61040       Honda     Accord      2006              N  \n",
       "996         72320  Volkswagen     Passat      2015              N  \n",
       "997         52500      Suburu    Impreza      1996              N  \n",
       "998         36540        Audi         A5      1998              N  \n",
       "999          3680    Mercedes       E400      2007              N  \n",
       "\n",
       "[1000 rows x 34 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Remove Less Correlated Columns\n",
    "deleteCols = [\"policy_number\", \"policy_bind_date\", \"insured_zip\", \"incident_location\", \"incident_date\", \"_c39\"]\n",
    "data = data.drop(deleteCols, axis=1)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "months_as_customer               int64\n",
       "age                              int64\n",
       "policy_state                    object\n",
       "policy_csl                      object\n",
       "policy_deductable                int64\n",
       "policy_annual_premium          float64\n",
       "umbrella_limit                   int64\n",
       "insured_sex                     object\n",
       "insured_education_level         object\n",
       "insured_occupation              object\n",
       "insured_hobbies                 object\n",
       "insured_relationship            object\n",
       "capital-gains                    int64\n",
       "capital-loss                     int64\n",
       "incident_type                   object\n",
       "collision_type                  object\n",
       "incident_severity               object\n",
       "authorities_contacted           object\n",
       "incident_state                  object\n",
       "incident_city                   object\n",
       "incident_hour_of_the_day         int64\n",
       "number_of_vehicles_involved      int64\n",
       "property_damage                 object\n",
       "bodily_injuries                  int64\n",
       "witnesses                        int64\n",
       "police_report_available         object\n",
       "total_claim_amount               int64\n",
       "injury_claim                     int64\n",
       "property_claim                   int64\n",
       "vehicle_claim                    int64\n",
       "auto_make                       object\n",
       "auto_model                      object\n",
       "auto_year                        int64\n",
       "fraud_reported                  object\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Handle Categorical Data\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>months_as_customer</th>\n",
       "      <th>age</th>\n",
       "      <th>policy_deductable</th>\n",
       "      <th>policy_annual_premium</th>\n",
       "      <th>umbrella_limit</th>\n",
       "      <th>capital-gains</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>incident_hour_of_the_day</th>\n",
       "      <th>number_of_vehicles_involved</th>\n",
       "      <th>bodily_injuries</th>\n",
       "      <th>...</th>\n",
       "      <th>auto_model_Pathfinder</th>\n",
       "      <th>auto_model_RAM</th>\n",
       "      <th>auto_model_RSX</th>\n",
       "      <th>auto_model_Silverado</th>\n",
       "      <th>auto_model_TL</th>\n",
       "      <th>auto_model_Tahoe</th>\n",
       "      <th>auto_model_Ultima</th>\n",
       "      <th>auto_model_Wrangler</th>\n",
       "      <th>auto_model_X5</th>\n",
       "      <th>auto_model_X6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>328</td>\n",
       "      <td>48</td>\n",
       "      <td>1000</td>\n",
       "      <td>1406.91</td>\n",
       "      <td>0</td>\n",
       "      <td>53300</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>228</td>\n",
       "      <td>42</td>\n",
       "      <td>2000</td>\n",
       "      <td>1197.22</td>\n",
       "      <td>5000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>134</td>\n",
       "      <td>29</td>\n",
       "      <td>2000</td>\n",
       "      <td>1413.14</td>\n",
       "      <td>5000000</td>\n",
       "      <td>35100</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>256</td>\n",
       "      <td>41</td>\n",
       "      <td>2000</td>\n",
       "      <td>1415.74</td>\n",
       "      <td>6000000</td>\n",
       "      <td>48900</td>\n",
       "      <td>-62400</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>228</td>\n",
       "      <td>44</td>\n",
       "      <td>1000</td>\n",
       "      <td>1583.91</td>\n",
       "      <td>6000000</td>\n",
       "      <td>66000</td>\n",
       "      <td>-46000</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>995</td>\n",
       "      <td>3</td>\n",
       "      <td>38</td>\n",
       "      <td>1000</td>\n",
       "      <td>1310.80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>996</td>\n",
       "      <td>285</td>\n",
       "      <td>41</td>\n",
       "      <td>1000</td>\n",
       "      <td>1436.79</td>\n",
       "      <td>0</td>\n",
       "      <td>70900</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>997</td>\n",
       "      <td>130</td>\n",
       "      <td>34</td>\n",
       "      <td>500</td>\n",
       "      <td>1383.49</td>\n",
       "      <td>3000000</td>\n",
       "      <td>35100</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>998</td>\n",
       "      <td>458</td>\n",
       "      <td>62</td>\n",
       "      <td>2000</td>\n",
       "      <td>1356.92</td>\n",
       "      <td>5000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>999</td>\n",
       "      <td>456</td>\n",
       "      <td>60</td>\n",
       "      <td>1000</td>\n",
       "      <td>766.19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 162 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     months_as_customer  age  policy_deductable  policy_annual_premium  \\\n",
       "0                   328   48               1000                1406.91   \n",
       "1                   228   42               2000                1197.22   \n",
       "2                   134   29               2000                1413.14   \n",
       "3                   256   41               2000                1415.74   \n",
       "4                   228   44               1000                1583.91   \n",
       "..                  ...  ...                ...                    ...   \n",
       "995                   3   38               1000                1310.80   \n",
       "996                 285   41               1000                1436.79   \n",
       "997                 130   34                500                1383.49   \n",
       "998                 458   62               2000                1356.92   \n",
       "999                 456   60               1000                 766.19   \n",
       "\n",
       "     umbrella_limit  capital-gains  capital-loss  incident_hour_of_the_day  \\\n",
       "0                 0          53300             0                         5   \n",
       "1           5000000              0             0                         8   \n",
       "2           5000000          35100             0                         7   \n",
       "3           6000000          48900        -62400                         5   \n",
       "4           6000000          66000        -46000                        20   \n",
       "..              ...            ...           ...                       ...   \n",
       "995               0              0             0                        20   \n",
       "996               0          70900             0                        23   \n",
       "997         3000000          35100             0                         4   \n",
       "998         5000000              0             0                         2   \n",
       "999               0              0             0                         6   \n",
       "\n",
       "     number_of_vehicles_involved  bodily_injuries  ...  auto_model_Pathfinder  \\\n",
       "0                              1                1  ...                      0   \n",
       "1                              1                0  ...                      0   \n",
       "2                              3                2  ...                      0   \n",
       "3                              1                1  ...                      0   \n",
       "4                              1                0  ...                      0   \n",
       "..                           ...              ...  ...                    ...   \n",
       "995                            1                0  ...                      0   \n",
       "996                            1                2  ...                      0   \n",
       "997                            3                2  ...                      0   \n",
       "998                            1                0  ...                      0   \n",
       "999                            1                0  ...                      0   \n",
       "\n",
       "     auto_model_RAM  auto_model_RSX  auto_model_Silverado  auto_model_TL  \\\n",
       "0                 0               0                     0              0   \n",
       "1                 0               0                     0              0   \n",
       "2                 1               0                     0              0   \n",
       "3                 0               0                     0              0   \n",
       "4                 0               1                     0              0   \n",
       "..              ...             ...                   ...            ...   \n",
       "995               0               0                     0              0   \n",
       "996               0               0                     0              0   \n",
       "997               0               0                     0              0   \n",
       "998               0               0                     0              0   \n",
       "999               0               0                     0              0   \n",
       "\n",
       "     auto_model_Tahoe  auto_model_Ultima  auto_model_Wrangler  auto_model_X5  \\\n",
       "0                   0                  0                    0              0   \n",
       "1                   0                  0                    0              0   \n",
       "2                   0                  0                    0              0   \n",
       "3                   1                  0                    0              0   \n",
       "4                   0                  0                    0              0   \n",
       "..                ...                ...                  ...            ...   \n",
       "995                 0                  0                    0              0   \n",
       "996                 0                  0                    0              0   \n",
       "997                 0                  0                    0              0   \n",
       "998                 0                  0                    0              0   \n",
       "999                 0                  0                    0              0   \n",
       "\n",
       "     auto_model_X6  \n",
       "0                0  \n",
       "1                0  \n",
       "2                0  \n",
       "3                0  \n",
       "4                0  \n",
       "..             ...  \n",
       "995              0  \n",
       "996              0  \n",
       "997              0  \n",
       "998              0  \n",
       "999              0  \n",
       "\n",
       "[1000 rows x 162 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#One-hot encoding all categorical columns except output column(i.e. fraud_reported)\n",
    "list_hot_encoded = []\n",
    "for column in data.columns:\n",
    "    if(data[column].dtypes==object and column != 'fraud_reported'):\n",
    "        data = pd.concat([data, pd.get_dummies(data[column], prefix=column)], axis=1)\n",
    "        list_hot_encoded.append(column)\n",
    "#Drop hot-encoded columns\n",
    "data = data.drop(list_hot_encoded, axis=1)\n",
    "\n",
    "#Binary encoder for output column\n",
    "data['fraud_reported'] = data['fraud_reported'].map( {'Y':1, 'N':0})\n",
    "\n",
    "data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Model Training\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "y = data['fraud_reported']\n",
    "X = data.drop(['fraud_reported'], axis=1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN Score :\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7333333333333333"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import neighbors\n",
    "\n",
    "#K-Nearest Neighbors\n",
    "print(\"KNN Score :\")\n",
    "KNNClassifier = neighbors.KNeighborsClassifier(n_neighbors=12, weights='distance')\n",
    "KNNClassifier.fit(X=X_train,y=y_train)\n",
    "KNNClassifier.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[108,   5],\n",
       "       [ 35,   2]], dtype=int64)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KNN_y_predicted = KNNClassifier.predict(X_test)\n",
    "class_names = np.unique(np.array(y_test))\n",
    "confusion_matrix(y_test, KNN_y_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.96      0.84       113\n",
      "           1       0.29      0.05      0.09        37\n",
      "\n",
      "    accuracy                           0.73       150\n",
      "   macro avg       0.52      0.50      0.47       150\n",
      "weighted avg       0.64      0.73      0.66       150\n",
      "\n",
      "Cross-Validated Accuracy: 0.73\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "print(classification_report(y_test, KNN_y_predicted))\n",
    "\n",
    "scores = cross_val_score(KNNClassifier, X, y, cv=10, scoring='accuracy')\n",
    "knn_accuracy = scores.mean()\n",
    "print('Cross-Validated Accuracy: %0.2f' % knn_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "knn_pred_prob = KNNClassifier.predict_proba(X_test)[:, 1]\n",
    "fpr, tpr, thresholds = roc_curve(y_test, knn_pred_prob)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "lw = 2\n",
    "plt.plot(fpr, tpr,label='ROC curve KNN (AUC = %0.2f)' % roc_auc)\n",
    "plt.xlim([0.0, 1])\n",
    "plt.ylim([0.0, 1])\n",
    "plt.title('ROC curve for KNN CLASS 1')\n",
    "plt.xlabel('(1 - Specificity)')\n",
    "plt.ylabel('(Sensitivity)')\n",
    "plt.grid(True)\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Score :\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8066666666666666"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "\n",
    "#DecisionTree\n",
    "DTClassifier = tree.DecisionTreeClassifier()\n",
    "DTClassifier.fit(X_train, y_train) \n",
    "\n",
    "print(\"Decision Tree Score :\")\n",
    "DTClassifier.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[96, 17],\n",
       "       [12, 25]], dtype=int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DT_y_predicted = DTClassifier.predict(X_test)\n",
    "class_names = np.unique(np.array(y_test))\n",
    "confusion_matrix(y_test, DT_y_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.85      0.87       113\n",
      "           1       0.60      0.68      0.63        37\n",
      "\n",
      "    accuracy                           0.81       150\n",
      "   macro avg       0.74      0.76      0.75       150\n",
      "weighted avg       0.82      0.81      0.81       150\n",
      "\n",
      "Cross-Validated Accuracy: 0.78\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, DT_y_predicted))\n",
    "\n",
    "scores = cross_val_score(DTClassifier, X, y, cv=10, scoring='accuracy')\n",
    "dt_accuracy = scores.mean()\n",
    "print('Cross-Validated Accuracy: %0.2f' % dt_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "dt_pred_prob = DTClassifier.predict_proba(X_test)[:, 1]\n",
    "fpr, tpr, thresholds = roc_curve(y_test, dt_pred_prob)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "lw = 2\n",
    "plt.plot(fpr, tpr,label='ROC curve Decision Tree (AUC = %0.2f)' % roc_auc)\n",
    "plt.xlim([0.0, 1])\n",
    "plt.ylim([0.0, 1])\n",
    "plt.title('ROC curve for Decision Tree CLASS 1')\n",
    "plt.xlabel('(1 - Specificity)')\n",
    "plt.ylabel('(Sensitivity)')\n",
    "plt.grid(True)\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM Score :\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7533333333333333"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "#Support Vector Machine\n",
    "SVMClassifier = SVC(kernel='rbf',probability=True,random_state=42, gamma='auto') \n",
    "SVMClassifier.fit(X_train, y_train) \n",
    "print(\"SVM Score :\")\n",
    "SVMClassifier.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[113,   0],\n",
       "       [ 37,   0]], dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVM_y_predicted = SVMClassifier.predict(X_test)\n",
    "class_names = np.unique(np.array(y_test))\n",
    "confusion_matrix(y_test, SVM_y_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      1.00      0.86       113\n",
      "           1       0.00      0.00      0.00        37\n",
      "\n",
      "    accuracy                           0.75       150\n",
      "   macro avg       0.38      0.50      0.43       150\n",
      "weighted avg       0.57      0.75      0.65       150\n",
      "\n",
      "Cross-Validated Accuracy: 0.75\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "report = classification_report(y_test, SVM_y_predicted)\n",
    "print(report)\n",
    "\n",
    "scores = cross_val_score(SVMClassifier, X, y, cv=10, scoring='accuracy')\n",
    "svm_accuracy = scores.mean()\n",
    "print('Cross-Validated Accuracy: %0.2f' % svm_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JyAhgBEC9evUICwsr7FhLpISEBOsLF+uL08pyX6RlKIu2pbJwcyqVysPQAKVfixNsWvsHm7wdXAnmyUSR0/6dZhu+DZimqm+JSC/gUxEJVtWMM2ZSnQJMAQgMDNTQ0FBPxFvihIWFYX3hsL44raz2RUTcMZ6evY4NexO5pmMjXrq+A5GrV5bJvihsnkwUcUDTLMP+nH1o6V5gAICqrhSRykBdYL8H4zLGlCLJqem8s2QjH/6yjTrVKvJ/d3Xlyg4NvR1WqeLJRLEKaCsiLYFdwBDg9mxtdgCXAdNEpB1QGTjgwZiMMaXIH1sPMXpuBNsOnmBI96Y8e3U7/KpYEb/C5rFEoappIvII8APOpa9TVTVKRF4GVqvqQuCfwP9E5Amcw1J3q2r2w1PGGHOG48mpvPb9Bj77fQdNa1fh8/t60rtNXW+HVWp59D4K1z0Ri7KNeyHL+2igtydjMMaULss27GfMvAj2xCdzb5+W/POKAKpWtHuHPcl61xhTIhw+kcL4b6KZ9/cu2tavzpyHLqJLs1reDqtMsERhjCnWVJVvI/bw4oIojiWl8thlbRnZrzWVylsRv6JiicIYU2zti0/m+fmR/Bi9j47+fnx2X0/aNaqR/4ymUFmiMMYUO6rKl6t28q9F60lJy2DM1e0Y3ruFFfHzEksUxphiZcehREbPDee3LYfo2bI2r93UkRZ1q3k7rDLNEoUxplhIz1A+XrGNNxfHUL5cOSYOCmFI96ZWxK8YsERhjPG6jfucIn5rdx7l0qD6/GtQMI38rIhfcWGJwhjjNSlpGfw3bAvvL9uEb+UKvDukE9df0BgR24soTixRGGO8Yt3Oo4yaHU7MvuMM7NSYF65tT53qlbwdlsmBJQpjTJFKSknn7R9j+OjXbdT3rcyHQ7vRv30Db4dl8mCJwhhTZFZuOcToueFsP5TI7T2bMfqqIGpUtiJ+xZ0lCmOMx8Unp/LKog3M+HMHzetU5Yv7e3JRayviV1JYojDGeNRP6/cxZl4k+48nM+KSVjzRP4AqFa38RkliicIY4xGHEk7y0tfRLFy3m8AGvnxwV1c6Na3p7bBMAViiMMYUKlVl4brdjFsYRcLJNJ7oH8BDoa2pWN7Kb5RUliiMMYVmz7Eknp8XyU8b9tOpaU1ev7kjAQ18vR2WOU+WKIwx5y0jQ5mxagevLNpAWkYGz1/TjuG9W+Jj5TdKBUsUxpjzEnvwBKPnhvP71sNc1LoOr97YkWZ1qno7LFOILFEYYwokLT2DqSu28dbijVT0KcerN4YwuHtTK79RClmiMMacsw1743lmdjjr4o7Rv10DJtwQTEO/yt4Oy3iIJQpjjNtOpqUzadkWJi/bjF+VCrx/e2euCWlkexGlnCUKY4xb/tpxhGdmh7NpfwKDOjfhhWvbU6taRW+HZYqAJQpjTJ4SU9J4a/FGpq7YRsMalfn47u70C6rv7bBMEbJEYYzJ1YrNBxk9N5ydh5O488JmPDMgCF8r4lfmWKIwxpzlWFIqryxaz8xVO2lZtxpfjriQnq3qeDss4yWWKIwxZ1gctZfn50dyMOEkD/R1ivhVrmBF/MoySxTGGAAOHD/JuK+j+DZ8D0ENfflwWDc6+lsRP2OJwpgyT1WZv3YXL30dTeLJdJ66IoAH+ramgo8V8TMOSxTGlGG7jiYxZl4EYTEH6NLMKeLXpr4V8TNnskRhTBmUkaF8/ucOXl20ngyFF69rz9BeLayIn8mRJQpjypitBxIYPSeCP2MP06dNXV65MYSmta2In8mdJQpjyoi09Aw+/HUb7/y4kUrly/H6zR25pau/ld8w+TqnRCEi1YBkVU33UDzGGA+I3h3PqDnriNwVz5UdGjB+YDD1a1gRP+OePBOFiJQDhgB3AN2Bk0AlETkALAKmqOomj0dpjCmQ5NR03l+6mQ9+3kLNqhX57x1duCqkkbfDMiVMfte/LQNaA88CDVW1qarWBy4GfgdeFZE7c5tZRAaISIyIbBaR0bm0uVVEokUkSkS+KODnMMZks2b7Ya557xfeX7aZgZ2asOTJSyxJmALJ79BTf1VNzT5SVQ8Dc4A5IpJj4RcR8QEmAZcDccAqEVmoqtFZ2rTFSUK9VfWIiFilMWPOU3KaMm5hFNNXxtLYrwrT7+lB34B63g7LlGB5JopTSUJE3gQ+VtWo3NrkoAewWVW3upYxExgIRGdpcz8wSVWPuJa1/5w/gTEm0/KNBxjzaxKHkmMZ1qs5Tw8Ionolu2bFnB93v0EbgCkiUh74GJihqsfymacJsDPLcBzQM1ubAAARWQH4AONU9fvsCxKREcAIgHr16hEWFuZm2KVbQkKC9YVLWe+LE6nKjA0p/LorjQZVlOd6ViHA7yCrV/7q7dC8qqx/LwqLW4lCVT8EPhSRQGA4EO7auP9PVZflMltO19xpDutvC4QC/sAvIhKsqkezrX8KMAUgMDBQQ0ND3Qm71AsLC8P6wlGW++L7yD2MWxDF4RPpPBzamk4V9nDFZf28HVaxUJa/F4XJ7WIurnMOQa7XQWAd8KTrkFJO4oCmWYb9gd05tFmgqqmqug2IwUkcxph87D+ezEOfreHBz/6iXvVKLBjZm1EDgqjoY/dFmMLl1h6FiLwNXAcsBSaq6p+uSa+JSEwus60C2opIS2AXzmW2t2drMx+4DZgmInVxDkVtPbePYEzZoqrM+WsX47+JJik1naevDGTEJa2siJ/xGHfPUUQCz6tqYg7TeuQ0g6qmicgjwA845x+mqmqUiLwMrFbVha5pV4hINJAOPK2qh875UxhTRsQdSeS5eZEs33iAbs1r8epNHWlTv7q3wzKlnLuJ4g5VnZp1hIj8pKqX5XVSW1UX4dyYl3XcC1neK/Ck62WMyUVGhvLp79t57fsNALx0fQfuurA55ayInykC+d2ZXRmoCtQVkVqcPkFdA2js4diMMcDm/QmMnhPO6u1HuCSgHhMHBeNfy4r4maKT3x7FA8A/cJLCX1nGx+PcTGeM8ZDU9AymLN/Ku0s2UaWiD2/dcgE3dmliRfxMkcvvhrt3gXdF5FFV/U8RxWRMmRe56xijZocTvSeeq0Ma8tL1wdTzreTtsEwZld+hp0tVdSmwS0RuzD5dVed6LDJjyqDk1HTe/WkTU5ZvpXa1inxwZxcGBFt9JuNd+R166otzSex1OUxTwBKFMYVkVexhnpkdztaDJ7ilqz/PX9Mev6o5llIzpkjld+jpRdfb++wZFMZ4RsLJNF7/fgOfrNyOf60qfHpvDy5ua0X8TPHh7uWx20Tke+BLYKnrslZjzHkKi9nPmHmR7D6WxPDeLXjqikCqWRE/U8y4+40MxDn8NBL4SES+AWaqatmuOGZMAR05kcL4b6OZ+9cu2tSvzuwHL6Jr81reDsuYHLlbFDAJ+Ar4ynU/xbvAzzh3XBtj3KSqfBe5lxcWRHI0MZVHL23DI5e2oVJ5+69kii+393FFpC8wGLgKp47TrZ4KypjSaH98MmMXRPJD1D5CmvjxyT09ad+4hrfDMiZf7hYF3AasxdmreFpVT3g0KmNKEVVl1po4JnwTzcm0DEZfFcR9fVpS3or4mRLC3T2KC1Q13qORGFMK7TycyLNzI/h180F6tKjNqzeF0KqeFfEzJUt+N9yNUtXXgX+JyFlXOqnqYx6LzJgSLD1Dmf5bLG/8EINPOWH8DcHc0aOZFfEzJVJ+exTrXf+u9nQgxpQWm/Yd55k54fy14yihgfWYOCiExjWreDssYwosvxvuvna9TVTVWVmnicgtHovKmBIoNT2DD8K28J+lm6lWyYd/D+7EwE6NrYifKfHcPUfxLDDLjXHGlEkRccd4evY6Nuw9zrUdGzHu+g7UrW5F/EzpkN85iquAq4EmIvJelkk1gDRPBmZMSZCcms47Szbyv+VbqVu9ElPu6soVHRp6OyxjClV+exS7cc5PXA+syTL+OPCEp4IypiT4feshnp0bwbaDJxjSvSnPXt0OvypWxM+UPvmdo1gHrBORz1XV9iCMAY4np/Lqdxv4/I8dNKtdlc/v60nvNnW9HZYxHpPfoaevVPVW4O9sl8cKziOvO3o0OmOKmWUb9vPcvAj2xSdzX5+WPHlFAFUrWhE/U7rl9w1/3PXvtZ4OxJji7PCJFF7+Oor5a3fTtn51Jj90EZ2bWRE/Uzbkd+hpj+vtQSBJVTNEJAAIAr7zdHDGeJuq8k34HsYtjOJYUiqPX9aWh/u1tiJ+pkxxd595OXCxq3LsTzgnuAcDd3gqMGO8be+xZJ6fH8mS9fvo6O/H5/f3JKihFfEzZY+7iUJUNVFE7gX+o6qvi8jfngzMGG9RVWau2snEb9eTkp7BmKvbMbx3CyviZ8ostxOFiPTC2YO49xznNabE2H7oBKPnRLBy6yEubFWbV2/sSIu61bwdljFe5e7G/nGcO7HnqWqUiLQClnkuLGOKVnqG8vGKbby5OIYK5coxcVAIQ7o3tSJ+xuD+E+6W45ynODW8FbDKsaZUiNl7nFFzwlm38yiXBdVnwqBgGvlZET9jTnH3wUUBwFNAi6zzqOqlngnLGM9LSctgcthmJi3bjG/lCrw7pBPXX2BF/IzJzt1DT7OAD4APgXTPhWNM0Vi78yjPzA4nZt9xBnZqzAvXtqeOFfEzJkfuJoo0Vf2vRyMxpggkpaTz9o8xfPTrNur7VubDod3o376Bt8MyplhzN1F8LSIPA/OAk6dGquphj0RljAf8tuUgo+dEsONwIrf3bMboq4KoUdmK+BmTH3cTxTDXv09nGadAq8INx5jCF5+cyiuLNjDjzx00r1OVGfdfSK/WdbwdljElhrtXPbX0dCDGeMKS6H2MmR/BgeMnGXFJK57oH0CVilZ+w5hz4e5VT1WBJ4FmqjpCRNoCgar6jUejM6aADiWc5KWvo1m4bjdBDX2Zclc3Lmha09thGVMiuXvo6WOcBxdd5BqOw7kSyhKFKVZUlYXrdjNuYRQJJ9N4on8AD4W2pmJ5K79hTEG5+7+ntaq+DqQCqGoSzjMp8iQiA0QkRkQ2i8joPNrdLCIqIt3cjMeYs+w+msS901fz+My1NK9TjW8fu5jH+7e1JGHMeXJ3jyJFRKrgnMBGRFqT5eqnnIiIDzAJuBxnD2SViCxU1ehs7Xxx7vL+4xxjNwaAjAxl2Y5UHlm2nPQMZey17bn7ohb4WPkNYwqFu4niReB7oKmIfA70Bu7OZ54ewGZXuQ9EZCYwEIjO1m488DrOnd/GnJNtB08wek44f2xLoXebOrwyqCPN6lT1dljGlCruXvX0o4j8BVyIc8jpcVU9mM9sTYCdWYbjgJ5ZG4hIZ6Cpqn4jIrkmChEZAYwAqFevHmFhYe6EXeolJCSU2b5Iz1AWb09j7qYUypeD29sol7dOYmvEn2z1dnBeVpa/F9lZXxSO/J6Z3Rw4qqrHVPWQiCQCNwABIvK+qqbkNXsO4zKfuy0i5YB3yH/PBFWdAkwBCAwM1NDQ0PxmKRPCwsIoi32xfk88z8wJJzwukcvbN2DCDcGs/+v3MtkXOSmr34ucWF8UjvzO8n0FVAMQkU44VzrtAC4AJuczbxzQNMuwP7A7y7AvEAyEiUgszt7KQjuhbXJzMi2dtxfHcN1/fmXXkSTev70zU+7qSoMalb0dmjGlWn6Hnqqo6qmN+53AVFV9y7U3sDafeVcBbUWkJbALGALcfmqiqh4D6p4aFpEw4ClVXX1uH8GUBX/tOMIzs8PZtD+BQZ2b8MK17alVraK3wzKmTMgvUWQ9fHQpzsOLUNWM/Eoxq2qaiDwC/AD44CSZKBF5GVitqgsLHrYpKxJT0njzh418/Ns2GtWozMd3d6dfUH1vh2VMmZJfolgmIl8Be4BawFIAEWkE5HV+AgBVXQQsyjbuhVzahroRrylDVmw+yOi54ew8nMRdFzZn1IBAfK2InzFFLr9E8TgwGGgE9FHVVNf4hsAYcB6mraqay/zGnLNjSalM/HY9X67eScu61fhyxIX0bGVF/Izxlnz3KIA5wBxV3ZVlfBRQW0Smu9pM80x4pqz5IWovY+dHcuhECg/2bc0/+relcgUr4meMN+WXKAYA9wAzXCeljwKVcc45LAbeUdX8Tmobk68Dx08ybmEU30bsoV2jGnw0rDsh/o4SMhYAABzVSURBVH7eDssYQz6JQlWTcS6DnSwiFXCuUkpS1aNFEZwp/VSVeX/v4uVvokk8mc5TVwTwQN/WVPCx+kzGFBfulvDAdX5ijwdjMWXMrqNJjJkXQVjMAbo0q8nrN3ekTX1fb4dljMnG7URhTGHJyFA+/2M7r363AQXGXdeeu3pZET9jiitLFKZIbT2QwOg5EfwZe5iL29Zl4qAQmta2In7GFGeWKEyRSEvP4H+/bOOdJRupXL4cb9zckZu7+pPfjZvGGO+zRGE8Lmr3MZ6ZE07krniu7NCA8QODqW/1mYwpMSxRGI9JTk3nP0s38cHPW6lVtSL/vaMLV4U08nZYxphzZInCeMSa7YcZNTucLQdOcFMXf8Ze246aVa2InzElkSUKU6hOnEzjjR9imL4ylsZ+VZh+Tw/6BtTzdljGmPNgicIUmuUbD/Ds3Ah2H0ti6IXNeXpAENUr2VfMmJLO/heb83Y0MYUJ365n9po4WtWrxlcP9KJ7i9reDssYU0gsUZjz8l3EHsYuiOJIYgoPh7bmscusiJ8xpY0lClMg+48n8+KCKL6L3Ev7RjWYNrw7wU2siJ8xpZElCnNOVJXZa+KY8O16klLTGTUgkPsvbmVF/IwpxSxRGLftPJzIc/Mi+GXTQbq3qMWrN3Wkdb3q3g7LGONhlihMvjIylE9WxvL6DzEI8PLADtzZsznlrIifMWWCJQqTp837Exg9J5zV249wSUA9Jg4Kxr+WFfEzpiyxRGFylJqewZTlW3l3ySaqVPThrVsu4MYuTayInzFlkCUKc5bIXccYNTuc6D3xXBPSiHHXd6CebyVvh2WM8RJLFCZTcmo67/60iSnLt1K7WkU+uLMrA4IbejssY4yXWaIwAKyKPcwzs8PZevAEt3bzZ8zV7fGrWsHbYRljigFLFGVcwsk0Xv9+A5+s3I5/rSp8dm9P+rSt6+2wjDHFiCWKMmxZzH7GzI1gT3wyw3u34KkrAqlmRfyMMdnYVqEMOnIihfHfRDP37120qV+d2Q9eRNfmtbwdljGmmLJEUYaoKosi9vLiwkiOJqby2KVtGHlpGyqVtyJ+xpjcWaIoI/bHJ/P8/EgWR+8jpIkfn9zTk/aNa3g7LGNMCWCJopRTVWatjmP8t9GkpGXw7FVB3NunJeWtiJ8xxk2WKEqxHYecIn6/bj5Ij5a1efXGEFpZET9jzDmyRFEKpWco036L5c0fYvApJ0y4IZjbezSzIn7GmAKxRFHKbNp3nFFzwvl7x1FCA+sxcVAIjWtW8XZYxpgSzBJFKZGSlsEHP2/h/aWbqVbJh38P7sTATo2tiJ8x5rx5NFGIyADgXcAH+FBVX802/UngPiANOADco6rbPRlTaRQed5RRs8PZsPc4113QmBeva0/d6lbEzxhTODyWKETEB5gEXA7EAatEZKGqRmdp9jfQTVUTReQh4HVgsKdiKm1S0pVXFq3nf79spZ5vJf43tBuXt2/g7bCMMaWMJ/coegCbVXUrgIjMBAYCmYlCVZdlaf87cKcH4ylVft96iLErktiXuJXbejRl9FXt8KtiRfyMMYXPk4miCbAzy3Ac0DOP9vcC3+U0QURGACMA6tWrR1hYWCGFWPIkpSlfxaSwbGcadSsro7pXoX3tw/z9xwpvh+ZVCQkJZfp7kZX1xWnWF4XDk4kip7OommNDkTuBbkDfnKar6hRgCkBgYKCGhoYWUogly9IN+3h5XiT74tO4r09Lulfex5X9+3k7rGIhLCyMsvq9yM764jTri8LhyUQRBzTNMuwP7M7eSET6A2OAvqp60oPxlFiHT6Tw8tdRzF+7m4AG1Zl8x0V0blaLsLD93g7NGFMGeDJRrALaikhLYBcwBLg9awMR6Qz8HzBAVW2rl42q8nX4HsYtjOJ4ciqPX9aWkf3aULG8ld8wxhQdjyUKVU0TkUeAH3Auj52qqlEi8jKwWlUXAm8A1YFZruv9d6jq9Z6KqSTZe8wp4rdk/T4u8PfjtZt7EtTQivgZY4qeR++jUNVFwKJs417I8r6/J9dfEqkqM1ftZOK360nNyGDM1e24p09LfKz8hjHGS+zO7GJk+6ETjJ4Twcqth7iwVW1evbEjLepW83ZYxpgyzhJFMZCeoXy8YhtvLo6hQrlyTBwUwpDuTa2InzGmWLBE4WUxe50ifut2HuWyoPpMGBRMIz8r4meMKT4sUXhJSloGk8M2M2nZZnwrV+C92zpzXcdGVsTPGFPsWKLwgrU7j/LM7HBi9h1nYKfGvHhdB2pXq+jtsIwxJkeWKIpQUko6by2OYeqKbdT3rcxHw7pxWTsr4meMKd4sURSR37YcZPScCHYcTuT2ns0YfVUQNSpbET9jTPFnicLD4pNTeWXRemb8uZPmdaoy4/4L6dW6jrfDMsYYt1mi8KAl0fsYMz+CA8dP8sAlrfhH/wCqVPTxdljGGHNOLFF4wKGEk4z7Opqv1+0mqKEv/xvajY7+Nb0dljHGFIglikKkqixct5txC6NIOJnGk5cH8GDf1lbEzxhTolmiKCS7jybx/PxIlm7YT6emNXn95o4ENPD1dljGGHPeLFGcp4wM5Ys/d/DqdxtIz1DGXtueuy9qYUX8jDGlhiWK87Dt4AlGzwnnj22H6d2mDq8M6kizOlW9HZYxxhQqSxQFkJaewUe/buPtHzdSsXw5XrsphFu7NbXyG8aYUskSxTlavyeeZ+aEEx53jMvbN2DCDcE0qFHZ22GZAkpNTSUuLo7k5GRvh1Jo/Pz8WL9+vbfDKBbKYl9UrlwZf39/KlQovBt6LVG46WRaOpOWbmZy2BZqVq3ApNu7cHVIQ9uLKOHi4uLw9fWlRYsWpeZvefz4cXx97UIKKHt9oaocOnSIuLg4WrZsWWjLtUThhjXbj/DMnHA270/gxs5NGHtte2pZEb9SITk5uVQlCVO2iQh16tThwIEDhbpcSxR5SExJ440fYpj2WyyNalTm4+Hd6RdY39thmUJmScKUJp74PluiyMWvmw4yem44cUeSuOvC5owaEIivFfEzxpRBdstwNseSUhk1ex13fvQHFXzK8eWICxl/Q7AlCeMxPj4+dOrUieDgYK677jqOHj2aOS0qKopLL72UgIAA2rZty/jx41HVzOnfffcd3bp1o127dgQFBfHUU0954yPkKjExkTvuuIOQkBCCg4Pp06cPCQkJhIaG8sMPP5zR9t///jcPP/wwsbGxiAhjx47NnHbw4EEqVKjAI488kuN65s+fz8svv3zGuAsuuIDhw4efMS40NJTVq1dnDsfGxhIcHJw5/Oeff3LJJZcQGBhIUFAQ9913H4mJiQX+/ADbtm2jZ8+etG3blsGDB5OSknJWm9jYWKpUqUKnTp3o1KkTDz74YOa0NWvWEBISQps2bXjssccy//5PPfUUS5cuPa/Y3KaqJeoVEBCgnvJ95B7tPuFHbfXst/rqd+s1KSXNY+sqDMuWLfN2CMVGQfsiOjq6cAMpgGrVqmW+Hzp0qE6YMEFVVRMTE7VVq1b6ww8/qKrqiRMndMCAAfr++++rqmpERIS2atVK169fr6qqqampOmnSJI2Pjy+02FJTU89r/okTJ+oTTzyRObxhwwZNTk7WDz74QO++++4z2vbs2VOXL1+u27Zt01atWmmnTp0yp02ePFkvuOACHTlyZI7r6dWrlx44cCBzODo6WoODg7VRo0aakJCQOb5v3766atWqzOFt27Zphw4dVFV179692qxZM/3tt99UVTUjI0NnzZqle/fuPY8eUL3lllt0xowZqqr6wAMP6OTJk89qkzWO7Lp3766//fabZmRk6IABA3TRokWqqhobG6uXX355jvPk9L0GVmsBt7t26Ak4cPwk4xZG8W3EHto1qsFHw7oT4u/n7bBMEXvp6yiid8cX6jLbN67Bi9d1cLt9r169CA8PB+CLL76gd+/eXHHFFQBUrVqV999/n9DQUEaOHMnrr7/OmDFjCAoKAqB8+fI8/PDDHD9+/IxlJiQk8Oijj7J69WpEhBdffJGbbrqJ6tWrk5CQAMDs2bP55ptvmDZtGnfffTe1a9fm77//plOnTsybN4+1a9dSs6ZT2LJNmzasWLGCcuXK8eCDD7Jjxw7A2SPo3bv3Geves2cPzZs3zxwODAwE4Oabb+b555/n5MmTVKpUidjYWHbv3k2fPn3Yvn07VapUoV27dqxevZpu3brx5Zdfcuutt7J79+6z+mzjxo1UqlSJunXrZo774osvuOuuuwgPD2fhwoXcdttt+fb9pEmTGDZsGL169QKcY/0333xzvvPlRVVZunQpX3zxBQDDhg1j3LhxPPTQQ27Nv2fPHuLj4zNjGjp0KPPnz+eqq66iefPmHDp0iL1799KwYcPzijM/ZTpRqCpz/9rFy99Ek5SSztNXBjLiklZU8LEjcqbopaen89NPP3HvvfcCzmGnrl27ntGmdevWJCQkEB8fT2RkJP/85z/zXe748ePx8/MjIiICgCNHjuQ7z8aNG1myZAk+Pj5kZGQwb948hg8fzh9//EGLFi1o0KABt99+O0888QR9+vRhx44dXHnllWfds3DPPfdwxRVXMHv2bC677DKGDRtG27ZtqVOnDj169OD7779n4MCBzJw5k8GDB59xInbIkCHMnDmThg0b4uPjQ+PGjXNMFCtWrKBLly5njPvyyy/58ccfCQgIYOrUqW4lisjISIYNG5Zvu5iYGAYPHpzjtLCwsMyECnDo0CFq1qxJ+fLOptbf359du3blOO+2bdvo3LkzNWrUYMKECVx88cXs2rULf3//zDbZ5+/SpQsrVqzgpptuyjfu81FmE8Wuo0k8NzeCnzceoEszp4hfm/pl53prc7Zz+eVfmJKSkujUqROxsbF07dqVyy+/HHB+yOR2Bcu5XNmyZMkSZs6cmTlcq1atfOe55ZZb8PFxnp0yePBgXn75ZYYPH565QT+13Ojo6Mx54uPjz7pvoVOnTmzdupXFixezZMkSunfvzsqVK2nXrh233XYbM2fOzEwUU6dOPSOGAQMGMHbsWBo0aJDrhhmcX9316tXLHF61ahX16tWjefPm+Pn58cgjj3DkyBFq1aqVY7+d61VCgYGBrF271q22muV8Ul7ra9SoETt27KBOnTqsWbOGG264gaioqHznr1+/fo7Js7CVuZ/OGRnKJytjueLtn1kVe5hx17Vn1oMXWZIwXlOlShXWrl3L9u3bSUlJYdKkSQB06NDhjBOvAFu3bqV69er4+vrSoUMH1qxZk+/yc0s4WcdlvzO9WrVqme979erF5s2bOXDgAPPnz+fGG28EICMjg5UrV7J27VrWrl3Lrl27cry5rXr16tx4441MnjyZO++8k0WLFgFwww038NNPP/HXX3+RlJR01l5BxYoV6dq1K2+99Vaev5irVKlyRvwzZsxgw4YNtGjRggsuuID4+HjmzJkDQJ06dc7Yozp8+HDmISt3+zMmJibzpHP2V9YLEQDq1q3L0aNHSUtLA5wbPBs3bnzWMitVqkSdOs6TL7t27Urr1q3ZuHEj/v7+xMXFZbbLPn9ycjJVqlTJN+bzVaYSxZYDCQyespIXFkTRpXktfvjHJdzdu6VVejXFgp+fH++99x5vvvkmqamp3HHHHfz6668sWbIEcPY8HnvsMUaNGgXA008/zcSJE9m4cSPgbLjffvvts5Z7xRVX8P7772cOn9pQNmjQgPXr12ceWsqNiDBo0CCefPJJ2rVrl7lBy77cnH5lr1ixInN9KSkpREdHZ56zqF69OqGhodxzzz25Hhr65z//yWuvvZa5zpy0a9eOzZs3Z/bBrFmzCA8PJzY2lsjISBYsWMCMGTMA56qnzz77LPOX+vTp0+nXrx8AjzzyCNOnT+ePP/7IXPZnn33G3r17z1jfqT2KnF5ZDzud6rt+/foxe/bszPUNHDjwrM9w4MAB0tPTAefHwKZNm2jVqhWNGjXC19eX33//HVXlk08+OWP+jRs3nnHVlscU9Cy4t14FueopNS1dJy3bpG3HLNKQF7/Xr1bt0IyMjHNeTnFjVz2dVlquelJVvfbaa/WTTz5RVdXw8HDt27evBgQEaOvWrXXcuHFnfHe//vpr7dKliwYFBWm7du30qaeeOuuqp+PHj+vQoUO1Q4cO2rFjR50zZ46qqs6aNUtbtWqlffv21ZEjR+qwYcNUVXXYsGE6a9asM5axatUqBXTatGmZ4w4cOKC33nqrhoSEaLt27fSBBx4467NNnz5dQ0JCNDg4WNu3b69PP/30GfHPnTtXgcwrt1RzvwLo448/zvGqpxMnTmj79u01IyNDly1bpj179sycFh8fr2lpadqwYUPdvXu3njx5UkeOHKkhISHasWNHveeee/TEiROZ7X/77Tft06ePBgQEaFBQkI4YMeKM6QWxZcsW7d69u7Zu3VpvvvlmTU5OVlXVBQsW6NixY1VVdfbs2dq+fXvt2LGjdu7cWRcuXJg5/6pVq7RDhw7aqlUrHTlyZGb/paSkaFBQUI5XphX2VU+iORwDK84CAwM1JibG7fZRu4/xzJxwInfFM6BDQ16+oQP1fUtHEb+wsDBCQ0O9HUaxUNC+WL9+Pe3atSv8gLyorNU3Anj88ce57rrr6N+//xnjS3NfzJs3j7/++ovx48efNS2n77WIrFHVbgVZV6k9mZ2cms5/lm7ig5+3UqtqRf57RxeuCmnk7bCMMR7w3HPPnXHIqCxIS0tz66q3wlAqE8Xq2MOMmhPO1gMnuKmLP2OvbUfNqlbEz5jSqkGDBlx//fXeDqNI3XLLLUW2rlKVKE6cdIr4TV8ZS2O/Kky/pwd9A+rlO58p2zSPy1CNKWk8cTqh1CSK5RsP8OzcCHYfS2JYrxY8dWUg1SuVmo9nPKRy5cocOnSIOnXqWLIwJZ6q8zyKypUL9zxsid+SHk1MYcK365m9Jo5W9aox64FedGtR29thmRLi1HXqhV2/35uSk5MLfUNRUpXFvjj1hLvCVKITxXcRexi7IIojiSmM7NeaRy9tS+UKPt4Oy5QgFSpUKNQngRUHYWFhdO7c2dthFAvWF4XDo4lCRAYA7wI+wIeq+mq26ZWAT4CuwCFgsKrG5rfc/fHJvLAgiu+j9tKhcQ2m39OdDo2tiJ8xxniCxxKFiPgAk4DLgThglYgsVNXoLM3uBY6oahsRGQK8BuRe1AVISFX6v/0zyWkZjBoQyP0XWxE/Y4zxJE/uUfQANqvqVgARmQkMBLImioHAONf72cD7IiKax2n7g0nKRQ19efWmjrSuV90zkRtjjMnkyUTRBNiZZTgO6JlbG1VNE5FjQB3gYNZGIjICGOEaPDn7od6Rs90r517a1SVbX5Vh1henWV+cZn1xWmBBZ/RkosjpWsPsewrutEFVpwBTAERkdUFvQy9trC9Os744zfriNOuL00Rkdf6tcubJg/txQNMsw/5A9sLpmW1EpDzgBxz2YEzGGGPOkScTxSqgrYi0FJGKwBBgYbY2C4FTj5S6GVia1/kJY4wxRc9jh55c5xweAX7AuTx2qqpGicjLOOVuFwIfAZ+KyGacPYkhbix6iqdiLoGsL06zvjjN+uI064vTCtwXJa7MuDHGmKJlNyAYY4zJkyUKY4wxeSq2iUJEBohIjIhsFpHROUyvJCJfuqb/ISItij7KouFGXzwpItEiEi4iP4lIc2/EWRTy64ss7W4WERWRUntppDt9ISK3ur4bUSLyRVHHWFTc+D/STESWicjfrv8nV3sjTk8Tkakisl9EInOZLiLynqufwkWki1sLLugzVD35wjn5vQVoBVQE1gHts7V5GPjA9X4I8KW34/ZiX/QDqrreP1SW+8LVzhdYDvwOdPN23F78XrQF/gZquYbreztuL/bFFOAh1/v2QKy34/ZQX1wCdAEic5l+NfAdzj1sFwJ/uLPc4rpHkVn+Q1VTgFPlP7IaCEx3vZ8NXCal84EC+faFqi5T1UTX4O8496yURu58LwDGA68DyUUZXBFzpy/uByap6hEAVd1fxDEWFXf6QoEarvd+nH1PV6mgqsvJ+160gcAn6vgdqCki+T4jurgmipzKfzTJrY2qpgGnyn+UNu70RVb34vxiKI3y7QsR6Qw0VdVvijIwL3DnexEABIjIChH53VXNuTRypy/GAXeKSBywCHi0aEIrds51ewIU3+dRFFr5j1LA7c8pIncC3YC+Ho3Ie/LsCxEpB7wD3F1UAXmRO9+L8jiHn0Jx9jJ/EZFgVT3q4diKmjt9cRswTVXfEpFeOPdvBatqhufDK1YKtN0srnsUVv7jNHf6AhHpD4wBrlfVk0UUW1HLry98gWAgTERicY7BLiylJ7Td/T+yQFVTVXUbEIOTOEobd/riXuArAFVdCVTGKRhY1ri1PcmuuCYKK/9xWr594Trc8n84SaK0HoeGfPpCVY+pal1VbaGqLXDO11yvqgUuhlaMufN/ZD7OhQ6ISF2cQ1FbizTKouFOX+wALgMQkXY4iaL0PP/WfQuBoa6rny4EjqnqnvxmKpaHntRz5T9KHDf74g2gOjDLdT5/h6pe77WgPcTNvigT3OyLH4ArRCQaSAeeVtVD3ovaM9zsi38C/xORJ3AOtdxdGn9YisgMnEONdV3nY14EKgCo6gc452euBjYDicBwt5ZbCvvKGGNMISquh56MMcYUE5YojDHG5MkShTHGmDxZojDGGJMnSxTGGGPyZInClFgiUkVEfhYRH9fw9yJyVEQKVL5DRO4RkQhXVc1IEcmpjtT5xPuhiLR3vb9FRNa7Kpp2E5H38pl3kYjUdL0edmNdISIyrZBCN2WcXR5rSiwRGQmUV9V3XcOXAVWBB1T12nNclj/wM9BFVY+JSHWgnuuO5kInIt8Dr6nqsnOcrwXwjaoGu9F2CXCPqu4oUJDGuNgehSnJ7gAWnBpQ1Z+A4wVcVn3XvAmuZSWcShIiEiYi/xaR31x7Gj1c46u56v+vcj3nYKBrvI+IvJll7+TRLMvpJiIvAH2AD0TkDREJPbUXJCLVReTjLPPe5Bof67q7+lWgtYisdc37adY9HxH5XERO3Wz5NaX0RlRTtCxRmBLJVaqhlarGFtIi1wH7gG2uDfV12aZXU9WLcJ6DMtU1bgxO6ZjuOKUy3hCRasAIoCXQWVU7Ap9nXZCqvgysBu5Q1aezrWcsTlmFENe8S7NNHw1sUdVOrnk/xHV3rYj4ARfh3H2Lax0Xn2tHGJOdJQpTUtUFCq0KqqqmAwNw6oZtBN4RkXFZmsxwtVsO1BCRmsAVwGgRWQuE4dQPagb0x3moVpprnnMpVtkfmJQlriP5xP0z0EZE6uNUSJ1zar3AfqDxOazbmBwVy1pPxrghCWfD7DYRaYpzOAacDfkHWae7av/8CfwpIj8CH+M8xwDOLsWsOCWbb1LVmGzrkRzaux1mAeb9FOcw3BDgnizjK+P0kzHnxfYoTInk+qXtIyJuJwtV3ek6ZNMpe5IQkcZy5vODOwHbswwPdrXrg3No6BhOEbpHXYnhVBVfgMXAg+KUv0dEap/DR1sMPJIlrlrZph/HKaee1TTgH67PGJVlfACQ47OTjTkXlihMSbYY56QwACLyCzAL57G4cSJy5TksqwLwpohscB1KGgw8nmX6ERH5DfgA59kG4DxytQIQLs7D7Me7xn+IU9Y6XETWAbefQxwTgFquk+brcJUJP8VV/XWFa/obrnH7gPU4e0BZ9QO+PYd1G5MjuzzWlFiuX/BPqupdHl5PGPBUcX2uhYhUBSJwXdrrGlcJ53LfPlnOWRhTILZHYUosVf0bWHbqhruySJwnG24A/nMqSbg0A0ZbkjCFwfYojDHG5Mn2KIwxxuTJEoUxxpg8WaIwxhiTJ0sUxhhj8mSJwhhjTJ7+H/w59Gd3PM4TAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "svm_pred_prob = SVMClassifier.predict_proba(X_test)[:, 1]\n",
    "fpr, tpr, thresholds = roc_curve(y_test, svm_pred_prob)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "lw = 2\n",
    "plt.plot(fpr, tpr,label='ROC curve SVM (AUC = %0.2f)' % roc_auc)\n",
    "plt.xlim([0.0, 1])\n",
    "plt.ylim([0.0, 1])\n",
    "plt.title('ROC curve for SVM CLASS 1')\n",
    "plt.xlabel('(1 - Specificity)')\n",
    "plt.ylabel('(Sensitivity)')\n",
    "plt.grid(True)\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Score :\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7466666666666667"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "#Random Forest\n",
    "RFClassifier = RandomForestClassifier()\n",
    "RFClassifier.fit(X_train, y_train) \n",
    "print(\"Random Forest Score :\")\n",
    "RFClassifier.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[102,  11],\n",
       "       [ 27,  10]], dtype=int64)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RF_y_predicted = RFClassifier.predict(X_test)\n",
    "class_names = np.unique(np.array(y_test))\n",
    "confusion_matrix(y_test, RF_y_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.90      0.84       113\n",
      "           1       0.48      0.27      0.34        37\n",
      "\n",
      "    accuracy                           0.75       150\n",
      "   macro avg       0.63      0.59      0.59       150\n",
      "weighted avg       0.71      0.75      0.72       150\n",
      "\n",
      "Cross-Validated Accuracy: 0.78\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "report = classification_report(y_test, RF_y_predicted)\n",
    "print(report)\n",
    "\n",
    "scores = cross_val_score(RFClassifier, X, y, cv=10, scoring='accuracy')\n",
    "rf_accuracy = scores.mean()\n",
    "print('Cross-Validated Accuracy: %0.2f' % rf_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "rf_pred_prob = RFClassifier.predict_proba(X_test)[:, 1]\n",
    "fpr, tpr, thresholds = roc_curve(y_test, rf_pred_prob)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "lw = 2\n",
    "plt.plot(fpr, tpr,label='ROC curve  Random Forest (AUC = %0.2f)' % roc_auc)\n",
    "plt.xlim([0.0, 1])\n",
    "plt.ylim([0.0, 1])\n",
    "plt.title('ROC curve for  random forest CLASS 1')\n",
    "plt.xlabel('(1 - Specificity)')\n",
    "plt.ylabel('(Sensitivity)')\n",
    "plt.grid(True)\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Balanced Random Forest Score :\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7866666666666666"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from imblearn.ensemble import BalancedRandomForestClassifier\n",
    "#Balanced Random Forest\n",
    "BRFClassifier=BalancedRandomForestClassifier()\n",
    "BRFClassifier.fit(X_train, y_train) \n",
    "print(\"Balanced Random Forest Score :\")\n",
    "BRFClassifier.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.83      0.85       113\n",
      "           1       0.56      0.65      0.60        37\n",
      "\n",
      "    accuracy                           0.79       150\n",
      "   macro avg       0.72      0.74      0.73       150\n",
      "weighted avg       0.80      0.79      0.79       150\n",
      "\n",
      "Cross-Validated Accuracy: 0.83\n"
     ]
    }
   ],
   "source": [
    "BRF_y_predicted = BRFClassifier.predict(X_test)\n",
    "report = classification_report(y_test, BRF_y_predicted)\n",
    "print(report)\n",
    "\n",
    "scores = cross_val_score(BRFClassifier, X, y, cv=10, scoring='accuracy')\n",
    "brf_accuracy = scores.mean()\n",
    "print('Cross-Validated Accuracy: %0.2f' % brf_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "brf_pred_prob = BRFClassifier.predict_proba(X_test)[:, 1]\n",
    "fpr, tpr, thresholds = roc_curve(y_test, brf_pred_prob)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "lw = 2\n",
    "plt.plot(fpr, tpr,label='ROC curve Balanced Random Forest (AUC = %0.2f)' % roc_auc)\n",
    "plt.xlim([0.0, 1])\n",
    "plt.ylim([0.0, 1])\n",
    "plt.title('ROC curve for Balanced random forest CLASS 1')\n",
    "plt.xlabel('(1 - Specificity)')\n",
    "plt.ylabel('(Sensitivity)')\n",
    "plt.grid(True)\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Discriminant Analysis Score :\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8266666666666667"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#LDA model\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
    "lda = LDA()\n",
    "lda.fit(X_train, y_train) \n",
    "print(\"Linear Discriminant Analysis Score :\")\n",
    "lda.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.89      0.89       113\n",
      "           1       0.66      0.62      0.64        37\n",
      "\n",
      "    accuracy                           0.83       150\n",
      "   macro avg       0.77      0.76      0.76       150\n",
      "weighted avg       0.82      0.83      0.83       150\n",
      "\n",
      "Cross-Validated Accuracy: 0.84\n"
     ]
    }
   ],
   "source": [
    "lda_y_predicted = lda.predict(X_test)\n",
    "report = classification_report(y_test, lda_y_predicted)\n",
    "print(report)\n",
    "\n",
    "scores = cross_val_score(lda, X, y, cv=10, scoring='accuracy')\n",
    "lda_accuracy = scores.mean()\n",
    "print('Cross-Validated Accuracy: %0.2f' % lda_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lda_pred_prob = lda.predict_proba(X_test)[:, 1]\n",
    "fpr, tpr, thresholds = roc_curve(y_test, lda_pred_prob)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "lw = 2\n",
    "plt.plot(fpr, tpr,label='ROC curve LDA (AUC = %0.2f)' % roc_auc)\n",
    "plt.xlim([0.0, 1])\n",
    "plt.ylim([0.0, 1])\n",
    "plt.title('ROC curve for LDA CLASS 1')\n",
    "plt.xlabel('(1 - Specificity)')\n",
    "plt.ylabel('(Sensitivity)')\n",
    "plt.grid(True)\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes Classifier Score :\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import BernoulliNB\n",
    "#Naive Bayes Classifier\n",
    "NBClassifier = BernoulliNB()\n",
    "NBClassifier.fit(X_train, y_train)\n",
    "print(\"Naive Bayes Classifier Score :\")\n",
    "NBClassifier.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.86      0.87       113\n",
      "           1       0.59      0.62      0.61        37\n",
      "\n",
      "    accuracy                           0.80       150\n",
      "   macro avg       0.73      0.74      0.74       150\n",
      "weighted avg       0.80      0.80      0.80       150\n",
      "\n",
      "Cross-Validated Accuracy: 0.82\n"
     ]
    }
   ],
   "source": [
    "nb_y_predicted = NBClassifier.predict(X_test)\n",
    "report = classification_report(y_test, nb_y_predicted)\n",
    "\n",
    "print(report)\n",
    "\n",
    "scores = cross_val_score(NBClassifier, X, y, cv=10, scoring='accuracy')\n",
    "nb_accuracy = scores.mean()\n",
    "print('Cross-Validated Accuracy: %0.2f' % nb_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBClassifier Score :\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8266666666666667"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "#XGBOOST Classifier\n",
    "model_xgb = XGBClassifier()\n",
    "model_xgb.fit(X_train, y_train, verbose=False) \n",
    "print(\"XGBClassifier Score :\")\n",
    "model_xgb.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.89      0.89       113\n",
      "           1       0.66      0.62      0.64        37\n",
      "\n",
      "    accuracy                           0.83       150\n",
      "   macro avg       0.77      0.76      0.76       150\n",
      "weighted avg       0.82      0.83      0.83       150\n",
      "\n",
      "Cross-Validated Accuracy: 0.82\n"
     ]
    }
   ],
   "source": [
    "xgboost_y_predicted = model_xgb.predict(X_test)\n",
    "report = classification_report(y_test, xgboost_y_predicted)\n",
    "\n",
    "print(report)\n",
    "\n",
    "scores = cross_val_score(model_xgb, X, y, cv=10, scoring='accuracy')\n",
    "xgb_accuracy = scores.mean()\n",
    "print('Cross-Validated Accuracy: %0.2f' % xgb_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb_pred_prob = model_xgb.predict_proba(X_test)[:, 1]\n",
    "fpr, tpr, thresholds = roc_curve(y_test, xgb_pred_prob)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "lw = 2\n",
    "plt.plot(fpr, tpr,label='ROC curve XGBOOST (AUC = %0.2f)' % roc_auc)\n",
    "plt.xlim([0.0, 1])\n",
    "plt.ylim([0.0, 1])\n",
    "plt.title('ROC curve for XGBOOST CLASS 1')\n",
    "plt.xlabel('(1 - Specificity)')\n",
    "plt.ylabel('(Sensitivity)')\n",
    "plt.grid(True)\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLPClassifier Score :\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.74"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "#Newral Network Classifier\n",
    "clf_MLP = MLPClassifier(alpha=1e-05, hidden_layer_sizes=(64))\n",
    "clf_MLP.fit(X_train, y_train) \n",
    "print(\"MLPClassifier Score :\")\n",
    "clf_MLP.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.98      0.85       113\n",
      "           1       0.00      0.00      0.00        37\n",
      "\n",
      "    accuracy                           0.74       150\n",
      "   macro avg       0.38      0.49      0.43       150\n",
      "weighted avg       0.56      0.74      0.64       150\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-Validated Accuracy: 0.60\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "mlp_y_predicted = clf_MLP.predict(X_test)\n",
    "report = classification_report(y_test, mlp_y_predicted)\n",
    "\n",
    "print(report)\n",
    "\n",
    "scores = cross_val_score(clf_MLP, X, y, cv=10, scoring='accuracy')\n",
    "mlp_accuracy = scores.mean()\n",
    "print('Cross-Validated Accuracy: %0.2f' % mlp_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The predictive power of each model expressed by ROC curves. For instance, Linear Discriminant Analysis and XGBOOST model has        higher probability of accurate prediction of correct class member, and gaining high level of accuracy prediction probability        as compared to Random Forest, KNN, Naive Bayes, Neural Network and SVM models.\n"
     ]
    }
   ],
   "source": [
    "#Comparing the models\n",
    "fpr9, tpr9, thresholds9 = roc_curve(y_test, nb_y_predicted)\n",
    "roc_auc9 = auc(fpr9, tpr9)\n",
    "\n",
    "fpr8, tpr8, thresholds8 = roc_curve(y_test, mlp_y_predicted)\n",
    "roc_auc8 = auc(fpr8, tpr8)\n",
    "\n",
    "fpr7, tpr7, thresholds7 = roc_curve(y_test, DT_y_predicted)\n",
    "roc_auc7 = auc(fpr7, tpr7)\n",
    "\n",
    "fpr6, tpr6, thresholds6 = roc_curve(y_test, lda_pred_prob)\n",
    "roc_auc6 = auc(fpr6, tpr6)\n",
    "\n",
    "fpr5, tpr5, thresholds5 = roc_curve(y_test, knn_pred_prob)\n",
    "roc_auc5 = auc(fpr5, tpr5)\n",
    "\n",
    "fpr4, tpr4, thresholds4 = roc_curve(y_test, rf_pred_prob)\n",
    "roc_auc4 = auc(fpr4, tpr4)\n",
    "\n",
    "fpr3, tpr3, thresholds3 = roc_curve(y_test, xgb_pred_prob)\n",
    "roc_auc3 = auc(fpr3, tpr3)\n",
    "\n",
    "fpr2, tpr2, thresholds2 = roc_curve(y_test, brf_pred_prob)\n",
    "roc_auc2 = auc(fpr2, tpr2)\n",
    "\n",
    "fpr1, tpr1, thresholds1 = roc_curve(y_test,svm_pred_prob)\n",
    "roc_auc1 = auc(fpr1, tpr1)\n",
    "lw = 2\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.plot(fpr9, tpr9,color='brown',label='ROC NB (AUC = %0.2f, ACC = %0.2f)' % (roc_auc9, nb_accuracy))\n",
    "plt.plot(fpr8, tpr8,color='yellow',label='ROC MLP (AUC = %0.2f, ACC = %0.2f)' % (roc_auc8, mlp_accuracy))\n",
    "plt.plot(fpr7, tpr7,color='cyan',label='ROC Decision Tree (AUC = %0.2f, ACC = %0.2f)' % (roc_auc7, dt_accuracy))\n",
    "plt.plot(fpr6, tpr6,color='orange',label='ROC LDA (AUC = %0.2f, ACC = %0.2f)' % (roc_auc6, lda_accuracy))\n",
    "plt.plot(fpr5, tpr5,color='green',label='ROC KNN (AUC = %0.2f, ACC = %0.2f)' % (roc_auc5, knn_accuracy))\n",
    "plt.plot(fpr4, tpr4,color='red',label='ROC RF (AUC = %0.2f, ACC = %0.2f)' % (roc_auc4, rf_accuracy))\n",
    "plt.plot(fpr3, tpr3,color='blue',label='ROC XGBOOST (AUC = %0.2f, ACC = %0.2f)' % (roc_auc3, xgb_accuracy))\n",
    "plt.plot(fpr2, tpr2,color='black',label='ROC BRF (AUC = %0.2f, ACC = %0.2f)' % (roc_auc2, brf_accuracy))\n",
    "plt.plot(fpr1, tpr1,color='magenta',label='ROC SVM (AUC = %0.2f, ACC = %0.2f)' % (roc_auc1, svm_accuracy))\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.title('ROC curve ')\n",
    "plt.xlabel('(1 - Specificity)')\n",
    "plt.ylabel('(Sensitivity)')\n",
    "plt.grid(True)\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n",
    "\n",
    "print(\"The predictive power of each model expressed by ROC curves. For instance, Linear Discriminant Analysis and XGBOOST model has\\\n",
    "        higher probability of accurate prediction of correct class member, and gaining high level of accuracy prediction probability\\\n",
    "        as compared to Random Forest, KNN, Naive Bayes, Neural Network and SVM models.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>feature</th>\n",
       "      <th>imp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>incident_severity_Major Damage</td>\n",
       "      <td>0.136822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>insured_hobbies_chess</td>\n",
       "      <td>0.072968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>insured_hobbies_cross-fit</td>\n",
       "      <td>0.050191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>insured_occupation_transport-moving</td>\n",
       "      <td>0.022682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>insured_education_level_JD</td>\n",
       "      <td>0.019782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>150</td>\n",
       "      <td>auto_model_Passat</td>\n",
       "      <td>0.018868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>collision_type_Front Collision</td>\n",
       "      <td>0.018670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>116</td>\n",
       "      <td>auto_make_Mercedes</td>\n",
       "      <td>0.018290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>insured_education_level_High School</td>\n",
       "      <td>0.017856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>incident_city_Northbend</td>\n",
       "      <td>0.017273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>incident_state_WV</td>\n",
       "      <td>0.016270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>collision_type_Rear Collision</td>\n",
       "      <td>0.014769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>151</td>\n",
       "      <td>auto_model_Pathfinder</td>\n",
       "      <td>0.014738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>insured_relationship_other-relative</td>\n",
       "      <td>0.014554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>incident_state_VA</td>\n",
       "      <td>0.014317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 feature       imp\n",
       "79        incident_severity_Major Damage  0.136822\n",
       "50                 insured_hobbies_chess  0.072968\n",
       "51             insured_hobbies_cross-fit  0.050191\n",
       "44   insured_occupation_transport-moving  0.022682\n",
       "27            insured_education_level_JD  0.019782\n",
       "150                    auto_model_Passat  0.018868\n",
       "76        collision_type_Front Collision  0.018670\n",
       "116                   auto_make_Mercedes  0.018290\n",
       "26   insured_education_level_High School  0.017856\n",
       "98               incident_city_Northbend  0.017273\n",
       "94                     incident_state_WV  0.016270\n",
       "77         collision_type_Rear Collision  0.014769\n",
       "151                auto_model_Pathfinder  0.014738\n",
       "67   insured_relationship_other-relative  0.014554\n",
       "93                     incident_state_VA  0.014317"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#XGBOOST model has better ROC Curve and Cross-validated accuracy, plot feature importance\n",
    "def rf_feat_importance(m, df):\n",
    "    return pd.DataFrame({'feature':df.columns, 'imp':m.feature_importances_}\n",
    "                       ).sort_values('imp', ascending=False)\n",
    "def plot_fi(fi): \n",
    "    return fi.plot('feature', 'imp', 'barh', figsize=(15,7), legend=False)\n",
    "\n",
    "fi = rf_feat_importance(model_xgb, X_train); fi[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20c3e1169e8>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_fi(fi[:30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#As XGBOOST has the best results with an AUC of 0.82, using this model for scoring\n",
    "test_target = y_test.copy()\n",
    "test_target.reset_index(drop=True, inplace=True)\n",
    "test_target = test_target.replace({1:'Y', 0:'N'})\n",
    "\n",
    "predicted_target = model_xgb.predict(X_test)\n",
    "predicted_target = pd.Series(predicted_target).replace({1:'Y', 0:'N'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>RealClass</th>\n",
       "      <th>PredictedClass</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.942978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.919296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>127</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.914629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.817602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.808937</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    RealClass PredictedClass      rank\n",
       "7           Y              Y  0.942978\n",
       "44          N              Y  0.919296\n",
       "127         Y              Y  0.914629\n",
       "82          N              Y  0.817602\n",
       "76          Y              Y  0.808937"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranks=pd.DataFrame(data=\n",
    "                   { \n",
    "                       'RealClass':test_target, \n",
    "                       'PredictedClass':predicted_target,\n",
    "                       'rank':xgb_pred_prob\n",
    "                   })\n",
    "ranks.sort_values(by=['rank'],ascending=False,inplace=True)\n",
    "ranks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>RealClass</th>\n",
       "      <th>PredictedClass</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.942978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.919296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>127</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.914629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.817602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.808937</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    RealClass PredictedClass      rank\n",
       "7           Y              Y  0.942978\n",
       "44          N              Y  0.919296\n",
       "127         Y              Y  0.914629\n",
       "82          N              Y  0.817602\n",
       "76          Y              Y  0.808937"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top = ranks.where(ranks['rank']>0.5,).dropna()\n",
    "top.head()"
   ]
  }
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